A: There is no definitive answer to this question, as different methods have different pros and cons. The best method for you depends on your preferences, needs, and resources. You should consider factors such as quality, speed, convenience, cost, reliability, and security when choosing a method.
-Q: How can I download music from YouTube to my iPhone or iPad?
-A: You can use any of the methods mentioned in this article to download music from YouTube to your iPhone or iPad, but you may need to transfer the files from your computer to your device using iTunes or a third-party app. Alternatively, you can use an app that allows you to download music directly from YouTube to your device, such as [Documents by Readdle] or [Musify].
-Q: How can I download music from YouTube to my Android phone or tablet?
-A: You can use any of the methods mentioned in this article to download music from YouTube to your Android phone or tablet, but you may need to change the settings of your device to allow downloads from unknown sources. Alternatively, you can use an app that allows you to download music directly from YouTube to your device, such as [TubeMate] or [SnapTube].
-Q: How can I download music from YouTube to my MP3 player?
-A: You can use any of the methods mentioned in this article to download music from YouTube to your MP3 player, but you may need to convert the files to MP3 format if they are not already. You can use a software or an online converter to do this. Then, you can transfer the files from your computer to your MP3 player using a USB cable or a memory card.
-Q: How can I download music from YouTube legally and ethically?
-A: You can download music from YouTube legally and ethically by following these tips:
--
-
- Only download music that is yours or that falls under the Creative Commons license. -
- Respect the rights of the artists and record labels who own the music and pay them for their work if possible. -
- Do not distribute or sell the downloaded music without permission. -
- Do not use the downloaded music for commercial or illegal purposes. -
- Credit the source of the music and link back to the original video. -
-
-
\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Enjoy Unlimited Lives and Boosters with Candy Crush Saga APK.md b/spaces/1phancelerku/anime-remove-background/Enjoy Unlimited Lives and Boosters with Candy Crush Saga APK.md deleted file mode 100644 index 616edae2c9c293348e2948fee5674853904722de..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Enjoy Unlimited Lives and Boosters with Candy Crush Saga APK.md +++ /dev/null @@ -1,87 +0,0 @@ - -
Apk4fun Candy Crush Saga: A Sweet and Fun Puzzle Game
-If you are looking for a casual game that can keep you entertained for hours, you should try Apk4fun Candy Crush Saga. This is a popular puzzle game that has millions of fans around the world. In this article, we will tell you what Apk4fun Candy Crush Saga is, why you should play it, and how to download and install it on your Android device.
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-Apk4fun Candy Crush Saga is a free casual game for Android devices that you can download from the official website of Apk4fun. Apk4fun is a platform that offers a variety of games and apps for Android users. You can find games of different genres, such as action, adventure, arcade, puzzle, racing, simulation, sports, and more.
-A match-3 puzzle game with colorful candies
-Apk4fun Candy Crush Saga is a match-3 puzzle game that involves swapping and matching candies of the same color to clear them from the board. The game has hundreds of levels, each with a different goal and layout. Some levels require you to collect a certain number of candies, some require you to clear jelly or frosting from the board, some require you to bring down ingredients to the bottom, and some require you to score a certain number of points within a time limit or a limited number of moves.
-A game with hundreds of levels and challenges
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It is fun and addictive
-Apk4fun Candy Crush Saga is a game that can keep you hooked for hours. The game has a colorful and cute design that appeals to many players. The game also has a satisfying sound effect when you match candies and clear them from the board. The game also has a rewarding system that gives you stars, points, coins, lives, and other prizes when you complete levels. The game also has a progress map that shows you how far you have gone in the game.
-It has amazing graphics and sound effects
-Apk4fun Candy Crush Saga is a game p>The second step is to search for Candy Crush Saga in the games category. You can use the search bar at the top of the website or browse through the categories and subcategories. You can also filter the games by popularity, rating, date, or alphabet.
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-The third step is to download the latest version of Apk4fun Candy Crush Saga. You can find the download link on the game page, along with the game description, screenshots, reviews, and other information. You can also see the file size, version number, and update date of the game. You need to click on the download link and wait for the file to be downloaded to your device.
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-FAQs
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-Q: Is Apk4fun Candy Crush Saga safe to download and play?
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-Q: How can I update Apk4fun Candy Crush Saga?
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401be4b1e0-
-
\ No newline at end of file diff --git a/spaces/AEUPH/SENTIENCE_PROGRAMMING_LANGUAGE/style.css b/spaces/AEUPH/SENTIENCE_PROGRAMMING_LANGUAGE/style.css deleted file mode 100644 index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000 --- a/spaces/AEUPH/SENTIENCE_PROGRAMMING_LANGUAGE/style.css +++ /dev/null @@ -1,28 +0,0 @@ -body { - padding: 2rem; - font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif; -} - -h1 { - font-size: 16px; - margin-top: 0; -} - -p { - color: rgb(107, 114, 128); - font-size: 15px; - margin-bottom: 10px; - margin-top: 5px; -} - -.card { - max-width: 620px; - margin: 0 auto; - padding: 16px; - border: 1px solid lightgray; - border-radius: 16px; -} - -.card p:last-child { - margin-bottom: 0; -} diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/vocoders/__init__.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/vocoders/__init__.py deleted file mode 100644 index 66c318857ce48048437dede7072901ad6471b8fc..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/vocoders/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from vocoders import hifigan diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/commons/rnn.py b/spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/commons/rnn.py deleted file mode 100644 index 205c2c76b8fda2de920bc59228a5eec0a20119a9..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/commons/rnn.py +++ /dev/null @@ -1,261 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F - - -class PreNet(nn.Module): - def __init__(self, in_dims, fc1_dims=256, fc2_dims=128, dropout=0.5): - super().__init__() - self.fc1 = nn.Linear(in_dims, fc1_dims) - self.fc2 = nn.Linear(fc1_dims, fc2_dims) - self.p = dropout - - def forward(self, x): - x = self.fc1(x) - x = F.relu(x) - x = F.dropout(x, self.p, training=self.training) - x = self.fc2(x) - x = F.relu(x) - x = F.dropout(x, self.p, training=self.training) - return x - - -class HighwayNetwork(nn.Module): - def __init__(self, size): - super().__init__() - self.W1 = nn.Linear(size, size) - self.W2 = nn.Linear(size, size) - self.W1.bias.data.fill_(0.) - - def forward(self, x): - x1 = self.W1(x) - x2 = self.W2(x) - g = torch.sigmoid(x2) - y = g * F.relu(x1) + (1. - g) * x - return y - - -class BatchNormConv(nn.Module): - def __init__(self, in_channels, out_channels, kernel, relu=True): - super().__init__() - self.conv = nn.Conv1d(in_channels, out_channels, kernel, stride=1, padding=kernel // 2, bias=False) - self.bnorm = nn.BatchNorm1d(out_channels) - self.relu = relu - - def forward(self, x): - x = self.conv(x) - x = F.relu(x) if self.relu is True else x - return self.bnorm(x) - - -class ConvNorm(torch.nn.Module): - def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, - padding=None, dilation=1, bias=True, w_init_gain='linear'): - super(ConvNorm, self).__init__() - if padding is None: - assert (kernel_size % 2 == 1) - padding = int(dilation * (kernel_size - 1) / 2) - - self.conv = torch.nn.Conv1d(in_channels, out_channels, - kernel_size=kernel_size, stride=stride, - padding=padding, dilation=dilation, - bias=bias) - - torch.nn.init.xavier_uniform_( - self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain)) - - def forward(self, signal): - conv_signal = self.conv(signal) - return conv_signal - - -class CBHG(nn.Module): - def __init__(self, K, in_channels, channels, proj_channels, num_highways): - super().__init__() - - # List of all rnns to call `flatten_parameters()` on - self._to_flatten = [] - - self.bank_kernels = [i for i in range(1, K + 1)] - self.conv1d_bank = nn.ModuleList() - for k in self.bank_kernels: - conv = BatchNormConv(in_channels, channels, k) - self.conv1d_bank.append(conv) - - self.maxpool = nn.MaxPool1d(kernel_size=2, stride=1, padding=1) - - self.conv_project1 = BatchNormConv(len(self.bank_kernels) * channels, proj_channels[0], 3) - self.conv_project2 = BatchNormConv(proj_channels[0], proj_channels[1], 3, relu=False) - - # Fix the highway input if necessary - if proj_channels[-1] != channels: - self.highway_mismatch = True - self.pre_highway = nn.Linear(proj_channels[-1], channels, bias=False) - else: - self.highway_mismatch = False - - self.highways = nn.ModuleList() - for i in range(num_highways): - hn = HighwayNetwork(channels) - self.highways.append(hn) - - self.rnn = nn.GRU(channels, channels, batch_first=True, bidirectional=True) - self._to_flatten.append(self.rnn) - - # Avoid fragmentation of RNN parameters and associated warning - self._flatten_parameters() - - def forward(self, x): - # Although we `_flatten_parameters()` on init, when using DataParallel - # the model gets replicated, making it no longer guaranteed that the - # weights are contiguous in GPU memory. Hence, we must call it again - self._flatten_parameters() - - # Save these for later - residual = x - seq_len = x.size(-1) - conv_bank = [] - - # Convolution Bank - for conv in self.conv1d_bank: - c = conv(x) # Convolution - conv_bank.append(c[:, :, :seq_len]) - - # Stack along the channel axis - conv_bank = torch.cat(conv_bank, dim=1) - - # dump the last padding to fit residual - x = self.maxpool(conv_bank)[:, :, :seq_len] - - # Conv1d projections - x = self.conv_project1(x) - x = self.conv_project2(x) - - # Residual Connect - x = x + residual - - # Through the highways - x = x.transpose(1, 2) - if self.highway_mismatch is True: - x = self.pre_highway(x) - for h in self.highways: - x = h(x) - - # And then the RNN - x, _ = self.rnn(x) - return x - - def _flatten_parameters(self): - """Calls `flatten_parameters` on all the rnns used by the WaveRNN. Used - to improve efficiency and avoid PyTorch yelling at us.""" - [m.flatten_parameters() for m in self._to_flatten] - - -class TacotronEncoder(nn.Module): - def __init__(self, embed_dims, num_chars, cbhg_channels, K, num_highways, dropout): - super().__init__() - self.embedding = nn.Embedding(num_chars, embed_dims) - self.pre_net = PreNet(embed_dims, embed_dims, embed_dims, dropout=dropout) - self.cbhg = CBHG(K=K, in_channels=cbhg_channels, channels=cbhg_channels, - proj_channels=[cbhg_channels, cbhg_channels], - num_highways=num_highways) - self.proj_out = nn.Linear(cbhg_channels * 2, cbhg_channels) - - def forward(self, x): - x = self.embedding(x) - x = self.pre_net(x) - x.transpose_(1, 2) - x = self.cbhg(x) - x = self.proj_out(x) - return x - - -class RNNEncoder(nn.Module): - def __init__(self, num_chars, embedding_dim, n_convolutions=3, kernel_size=5): - super(RNNEncoder, self).__init__() - self.embedding = nn.Embedding(num_chars, embedding_dim, padding_idx=0) - convolutions = [] - for _ in range(n_convolutions): - conv_layer = nn.Sequential( - ConvNorm(embedding_dim, - embedding_dim, - kernel_size=kernel_size, stride=1, - padding=int((kernel_size - 1) / 2), - dilation=1, w_init_gain='relu'), - nn.BatchNorm1d(embedding_dim)) - convolutions.append(conv_layer) - self.convolutions = nn.ModuleList(convolutions) - - self.lstm = nn.LSTM(embedding_dim, int(embedding_dim / 2), 1, - batch_first=True, bidirectional=True) - - def forward(self, x): - input_lengths = (x > 0).sum(-1) - input_lengths = input_lengths.cpu().numpy() - - x = self.embedding(x) - x = x.transpose(1, 2) # [B, H, T] - for conv in self.convolutions: - x = F.dropout(F.relu(conv(x)), 0.5, self.training) + x - x = x.transpose(1, 2) # [B, T, H] - - # pytorch tensor are not reversible, hence the conversion - x = nn.utils.rnn.pack_padded_sequence(x, input_lengths, batch_first=True, enforce_sorted=False) - - self.lstm.flatten_parameters() - outputs, _ = self.lstm(x) - outputs, _ = nn.utils.rnn.pad_packed_sequence(outputs, batch_first=True) - - return outputs - - -class DecoderRNN(torch.nn.Module): - def __init__(self, hidden_size, decoder_rnn_dim, dropout): - super(DecoderRNN, self).__init__() - self.in_conv1d = nn.Sequential( - torch.nn.Conv1d( - in_channels=hidden_size, - out_channels=hidden_size, - kernel_size=9, padding=4, - ), - torch.nn.ReLU(), - torch.nn.Conv1d( - in_channels=hidden_size, - out_channels=hidden_size, - kernel_size=9, padding=4, - ), - ) - self.ln = nn.LayerNorm(hidden_size) - if decoder_rnn_dim == 0: - decoder_rnn_dim = hidden_size * 2 - self.rnn = torch.nn.LSTM( - input_size=hidden_size, - hidden_size=decoder_rnn_dim, - num_layers=1, - batch_first=True, - bidirectional=True, - dropout=dropout - ) - self.rnn.flatten_parameters() - self.conv1d = torch.nn.Conv1d( - in_channels=decoder_rnn_dim * 2, - out_channels=hidden_size, - kernel_size=3, - padding=1, - ) - - def forward(self, x): - input_masks = x.abs().sum(-1).ne(0).data[:, :, None] - input_lengths = input_masks.sum([-1, -2]) - input_lengths = input_lengths.cpu().numpy() - - x = self.in_conv1d(x.transpose(1, 2)).transpose(1, 2) - x = self.ln(x) - x = nn.utils.rnn.pack_padded_sequence(x, input_lengths, batch_first=True, enforce_sorted=False) - self.rnn.flatten_parameters() - x, _ = self.rnn(x) # [B, T, C] - x, _ = nn.utils.rnn.pad_packed_sequence(x, batch_first=True) - x = x * input_masks - pre_mel = self.conv1d(x.transpose(1, 2)).transpose(1, 2) # [B, T, C] - pre_mel = pre_mel * input_masks - return pre_mel diff --git a/spaces/AIGText/GlyphControl/ldm/modules/midas/midas/midas_net.py b/spaces/AIGText/GlyphControl/ldm/modules/midas/midas/midas_net.py deleted file mode 100644 index 8a954977800b0a0f48807e80fa63041910e33c1f..0000000000000000000000000000000000000000 --- a/spaces/AIGText/GlyphControl/ldm/modules/midas/midas/midas_net.py +++ /dev/null @@ -1,76 +0,0 @@ -"""MidashNet: Network for monocular depth estimation trained by mixing several datasets. -This file contains code that is adapted from -https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py -""" -import torch -import torch.nn as nn - -from .base_model import BaseModel -from .blocks import FeatureFusionBlock, Interpolate, _make_encoder - - -class MidasNet(BaseModel): - """Network for monocular depth estimation. - """ - - def __init__(self, path=None, features=256, non_negative=True): - """Init. - - Args: - path (str, optional): Path to saved model. Defaults to None. - features (int, optional): Number of features. Defaults to 256. - backbone (str, optional): Backbone network for encoder. Defaults to resnet50 - """ - print("Loading weights: ", path) - - super(MidasNet, self).__init__() - - use_pretrained = False if path is None else True - - self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained) - - self.scratch.refinenet4 = FeatureFusionBlock(features) - self.scratch.refinenet3 = FeatureFusionBlock(features) - self.scratch.refinenet2 = FeatureFusionBlock(features) - self.scratch.refinenet1 = FeatureFusionBlock(features) - - self.scratch.output_conv = nn.Sequential( - nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1), - Interpolate(scale_factor=2, mode="bilinear"), - nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1), - nn.ReLU(True), - nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0), - nn.ReLU(True) if non_negative else nn.Identity(), - ) - - if path: - self.load(path) - - def forward(self, x): - """Forward pass. - - Args: - x (tensor): input data (image) - - Returns: - tensor: depth - """ - - layer_1 = self.pretrained.layer1(x) - layer_2 = self.pretrained.layer2(layer_1) - layer_3 = self.pretrained.layer3(layer_2) - layer_4 = self.pretrained.layer4(layer_3) - - layer_1_rn = self.scratch.layer1_rn(layer_1) - layer_2_rn = self.scratch.layer2_rn(layer_2) - layer_3_rn = self.scratch.layer3_rn(layer_3) - layer_4_rn = self.scratch.layer4_rn(layer_4) - - path_4 = self.scratch.refinenet4(layer_4_rn) - path_3 = self.scratch.refinenet3(path_4, layer_3_rn) - path_2 = self.scratch.refinenet2(path_3, layer_2_rn) - path_1 = self.scratch.refinenet1(path_2, layer_1_rn) - - out = self.scratch.output_conv(path_1) - - return torch.squeeze(out, dim=1) diff --git a/spaces/AIML-TUDA/does-clip-know-my-face/README.md b/spaces/AIML-TUDA/does-clip-know-my-face/README.md deleted file mode 100644 index ff26c6f5776a6db167726d6c79ea7816c518a408..0000000000000000000000000000000000000000 --- a/spaces/AIML-TUDA/does-clip-know-my-face/README.md +++ /dev/null @@ -1,64 +0,0 @@ ---- -title: Does Clip Know My Face? -emoji: š§ -colorFrom: blue -colorTo: red -sdk: gradio -sdk_version: 3.18.0 -app_file: app.py -pinned: false -license: cc-by-sa-4.0 -python_version: 3.10.0 ---- - -# Example Images License Information - -### Barbara Schƶneberger - -| Image Name | Image Url | Author | License | -|----------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------|--------------| -| Barbara_Schƶneberger_0.jpg | [https://upload.wikimedia.org/wikipedia/commons/1/1d/Barbara_Sch%C3%B6neberger_-_Deutscher_Radiopreis_Hamburg_2016_13.jpg](https://upload.wikimedia.org/wikipedia/commons/1/1d/Barbara_Sch%C3%B6neberger_-_Deutscher_Radiopreis_Hamburg_2016_13.jpg) | Frank Schwichtenberg | CC-BY-SA-3.0 | -| Barbara_Schƶneberger_1.jpg | [https://upload.wikimedia.org/wikipedia/commons/9/9d/Barbara_Sch%C3%B6neberger_%282007%29.jpg](https://upload.wikimedia.org/wikipedia/commons/9/9d/Barbara_Sch%C3%B6neberger_%282007%29.jpg) | Pottschalk | CC-BY-SA-3.0 | -| Barbara_Schƶneberger_2.jpg | [https://upload.wikimedia.org/wikipedia/commons/f/f0/Barbara_Sch%C3%B6neberger_-_Deutscher_Radiopreis_Hamburg_2016_03.jpg](https://upload.wikimedia.org/wikipedia/commons/f/f0/Barbara_Sch%C3%B6neberger_-_Deutscher_Radiopreis_Hamburg_2016_03.jpg) | Frank Schwichtenberg | CC-BY-SA-3.0 | -| Barbara_Schƶneberger_3.jpg | [https://upload.wikimedia.org/wikipedia/commons/f/fa/Barbara_Sch%C3%B6neberger_-_Deutscher_Radiopreis_Hamburg_2016_12.jpg](https://upload.wikimedia.org/wikipedia/commons/f/fa/Barbara_Sch%C3%B6neberger_-_Deutscher_Radiopreis_Hamburg_2016_12.jpg) | Frank Schwichtenberg | CC-BY-SA-3.0 | -| Barbara_Schƶneberger_4.jpg | [https://upload.wikimedia.org/wikipedia/commons/0/0a/Barbara_Sch%C3%B6neberger_-_Deutscher_Radiopreis_Hamburg_2016_01.jpg](https://upload.wikimedia.org/wikipedia/commons/0/0a/Barbara_Sch%C3%B6neberger_-_Deutscher_Radiopreis_Hamburg_2016_01.jpg) | Frank Schwichtenberg | CC-BY-SA-3.0 | - -### Carolin Kebekus - -| Image Name | Image Url | Author | License | -|-----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------|--------------| -| Carolin_Kebekus_0.jpg | [https://upload.wikimedia.org/wikipedia/commons/c/ce/Carolin_Kebekus_-_2019102193318_2019-04-12_Radio_Regenbogen_Award_2019_-_Sven_-_1D_X_MK_II_-_0905_-_AK8I0075.jpg](https://upload.wikimedia.org/wikipedia/commons/c/ce/Carolin_Kebekus_-_2019102193318_2019-04-12_Radio_Regenbogen_Award_2019_-_Sven_-_1D_X_MK_II_-_0905_-_AK8I0075.jpg) | Sven Mandel | CC-BY-SA-4.0 | -| Carolin_Kebekus_1.jpg | [https://upload.wikimedia.org/wikipedia/commons/4/45/Carolin-Kebekus-Bonn.jpg](https://upload.wikimedia.org/wikipedia/commons/4/45/Carolin-Kebekus-Bonn.jpg) | Superbass | CC-BY-SA-3.0 | -| Carolin_Kebekus_2.jpg | [https://upload.wikimedia.org/wikipedia/commons/4/45/Carolin-Kebekus-Bonn.jpg](https://upload.wikimedia.org/wikipedia/commons/4/45/Carolin-Kebekus-Bonn.jpg) | Sven Mandel | CC-BY-SA-4.0 | -| Carolin_Kebekus_3.jpg | [https://upload.wikimedia.org/wikipedia/commons/0/02/Carolin_Kebekus-5848.jpg](https://upload.wikimedia.org/wikipedia/commons/0/02/Carolin_Kebekus-5848.jpg) | Harald Krichel | CC-BY-SA-3.0 | -| Carolin_Kebekus_4.jpg | [https://upload.wikimedia.org/wikipedia/commons/e/e1/2021-09-16-Carolin_Kebekus_Deutscher_Fernsehpreis_2021_-3757.jpg](https://upload.wikimedia.org/wikipedia/commons/e/e1/2021-09-16-Carolin_Kebekus_Deutscher_Fernsehpreis_2021_-3757.jpg) | Superbass | CC-BY-SA-4.0 | - -### Max Giermann - -| Image Name | Image Url | Author | License | -|--------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------|--------------| -| Max_Giermann_0.jpg | [https://upload.wikimedia.org/wikipedia/commons/4/4b/2018-01-26-DFP_2018-7513.jpg](https://upload.wikimedia.org/wikipedia/commons/4/4b/2018-01-26-DFP_2018-7513.jpg) | Superbass | CC-BY-SA-4.0 | -| Max_Giermann_1.jpg | [https://upload.wikimedia.org/wikipedia/commons/f/f6/Deutscher_Fernsehpreis_2012_-_Max_Giermann.jpg](https://upload.wikimedia.org/wikipedia/commons/f/f6/Deutscher_Fernsehpreis_2012_-_Max_Giermann.jpg) | JCS | CC-BY-3.0 | -| Max_Giermann_2.jpg | [https://upload.wikimedia.org/wikipedia/commons/1/1c/Hessischer_Filmpreis_2017_-_Max_Giermann_2.JPG](https://upload.wikimedia.org/wikipedia/commons/1/1c/Hessischer_Filmpreis_2017_-_Max_Giermann_2.JPG) | JCS | CC-BY-3.0 | -| Max_Giermann_3.jpg | [https://upload.wikimedia.org/wikipedia/commons/1/1d/Max_Giermann_%28extra_3%29_01.jpg](https://upload.wikimedia.org/wikipedia/commons/1/1d/Max_Giermann_%28extra_3%29_01.jpg) | Frank Schwichtenberg | CC-BY-SA-3.0 | -| Max_Giermann_4.jpg | [https://upload.wikimedia.org/wikipedia/commons/8/85/Max_Giermann_%28extra_3%29_03.jpg](https://upload.wikimedia.org/wikipedia/commons/8/85/Max_Giermann_%28extra_3%29_03.jpg) | Frank Schwichtenberg | CC-BY-SA-3.0 | - -### Nicole De Boer - -| Image Name | Image Url | Author | License | -|----------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------|--------------| -| Nicole_De_Boer_0.jpg | [https://upload.wikimedia.org/wikipedia/commons/0/03/Praha%2C_Lhotka%2C_KC_Novodvorsk%C3%A1%2C_CzechTREK_2013_%2827%29.jpg](https://upload.wikimedia.org/wikipedia/commons/0/03/Praha%2C_Lhotka%2C_KC_Novodvorsk%C3%A1%2C_CzechTREK_2013_%2827%29.jpg) | Harold | CC-BY-SA-3.0 | -| Nicole_De_Boer_1.jpg | [https://upload.wikimedia.org/wikipedia/commons/d/db/Nicole_DeBoer_at_Toronto_Comicon_1.jpg](https://upload.wikimedia.org/wikipedia/commons/d/db/Nicole_DeBoer_at_Toronto_Comicon_1.jpg) | Tabercil | CC-BY-SA-3.0 | -| Nicole_De_Boer_2.jpg | [https://upload.wikimedia.org/wikipedia/commons/4/4b/Nicole_de_Boer_at_Toronto_Comicon_2_%28cropped%29.jpg](https://upload.wikimedia.org/wikipedia/commons/4/4b/Nicole_de_Boer_at_Toronto_Comicon_2_%28cropped%29.jpg) | Tabercil | CC-BY-SA-3.0 | -| Nicole_De_Boer_3.jpg | [https://upload.wikimedia.org/wikipedia/commons/b/b9/Nicole_de_boer_LFCC2015.jpg](https://upload.wikimedia.org/wikipedia/commons/b/b9/Nicole_de_boer_LFCC2015.jpg) | Dazzoboy | CC-BY-SA-4.0 | -| Nicole_De_Boer_4.jpg | [https://upload.wikimedia.org/wikipedia/commons/9/90/Nicole_de_Boer_at_Toronto_Comicon_2.jpg](https://upload.wikimedia.org/wikipedia/commons/9/90/Nicole_de_Boer_at_Toronto_Comicon_2.jpg) | Tabercil | CC-BY-SA-3.0 | - -### T. J. Thyne - -| Image Name | Image Url | Author | License | -|-------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------|--------------| -| T._J._Thyne_0.jpg | [https://live.staticflickr.com/7036/6837850246_c09a148d70_o.jpg](https://live.staticflickr.com/7036/6837850246_c09a148d70_o.jpg) | Genevieve | CC-BY-2.0 | -| T._J._Thyne_1.jpg | [https://live.staticflickr.com/3273/5705869811_d9ff808383_o.jpg](https://live.staticflickr.com/3273/5705869811_d9ff808383_o.jpg) | Genevieve | CC-BY-2.0 | -| T._J._Thyne_2.jpg | [https://upload.wikimedia.org/wikipedia/commons/d/d8/TJThyneFanExpo2017.jpg](https://upload.wikimedia.org/wikipedia/commons/d/d8/TJThyneFanExpo2017.jpg) | Christian Dahl-Lacroix | CC-BY-SA-4.0 | -| T._J._Thyne_3.jpg | [https://live.staticflickr.com/7041/6984629777_8a415b72d9_b.jpg](https://live.staticflickr.com/7041/6984629777_8a415b72d9_b.jpg) | Genevieve | CC-BY-2.0 | -| T._J._Thyne_4.jpg | [https://live.staticflickr.com/7042/6837821654_d65ab80913_b.jpg](https://live.staticflickr.com/7042/6837821654_d65ab80913_b.jpg) | Genevieve | CC-BY-2.0 | diff --git a/spaces/AIWaves/Debate/src/agents/Memory/base_Memory.py b/spaces/AIWaves/Debate/src/agents/Memory/base_Memory.py deleted file mode 100644 index 9312bc0e50f35ac5136d49dff70585c5baaa3a17..0000000000000000000000000000000000000000 --- a/spaces/AIWaves/Debate/src/agents/Memory/base_Memory.py +++ /dev/null @@ -1,32 +0,0 @@ -from Prompt import * -class Memory: - def __init__(self,role,name,content) -> None: - self.send_role = role - self.send_name = name - self.content = content - - def get_gpt_message(self,role): - return {"role":role,"content":self.content} - - @classmethod - def get_chat_history(self,messages,agent_name =None): - """ - Splice a memory list into a sentence - input : - messages(list) : list of memory(Memory) - Return : - chat_history(str) : One sentence after integration - """ - chat_history = "" - for message in messages: - name,role,content = message.send_name,message.send_role,message.content - if agent_name and agent_name==name: - name = "you" - chat_history += eval(Single_message) - chat_history = eval(Chat_total_message) - return chat_history - - def get_query(self): - "Return : query(str):last sentence" - name,role,content = self.send_name,self.send_role,self.content - return eval(Single_message) \ No newline at end of file diff --git a/spaces/ASJMO/freegpt/server/babel.py b/spaces/ASJMO/freegpt/server/babel.py deleted file mode 100644 index 94407e4b4d3e82e7722cac409a7e311bb46c43be..0000000000000000000000000000000000000000 --- a/spaces/ASJMO/freegpt/server/babel.py +++ /dev/null @@ -1,48 +0,0 @@ -import os -import subprocess -from flask import request, session, jsonify -from flask_babel import Babel - - -def get_languages_from_dir(directory): - """Return a list of directory names in the given directory.""" - return [name for name in os.listdir(directory) - if os.path.isdir(os.path.join(directory, name))] - - -BABEL_DEFAULT_LOCALE = 'en_US' -BABEL_LANGUAGES = get_languages_from_dir('translations') - - -def create_babel(app): - """Create and initialize a Babel instance with the given Flask app.""" - babel = Babel(app) - app.config['BABEL_DEFAULT_LOCALE'] = BABEL_DEFAULT_LOCALE - app.config['BABEL_LANGUAGES'] = BABEL_LANGUAGES - - babel.init_app(app, locale_selector=get_locale) - compile_translations() - - -def get_locale(): - """Get the user's locale from the session or the request's accepted languages.""" - return session.get('language') or request.accept_languages.best_match(BABEL_LANGUAGES) - - -def get_languages(): - """Return a list of available languages in JSON format.""" - return jsonify(BABEL_LANGUAGES) - - -def compile_translations(): - """Compile the translation files.""" - result = subprocess.run( - ['pybabel', 'compile', '-d', 'translations'], - stdout=subprocess.PIPE, - ) - - if result.returncode != 0: - raise Exception( - f'Compiling translations failed:\n{result.stdout.decode()}') - - print('Translations compiled successfully') diff --git a/spaces/Aashir01/Live_Transcription/README.md b/spaces/Aashir01/Live_Transcription/README.md deleted file mode 100644 index aae4658d762f8e74f17f598c31beaba7f5ccea6e..0000000000000000000000000000000000000000 --- a/spaces/Aashir01/Live_Transcription/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Video Subtitles Online -emoji: šØ -colorFrom: blue -colorTo: blue -sdk: gradio -sdk_version: 3.34.0 -app_file: app.py -pinned: false -license: afl-3.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/fused_bias_act.py b/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/fused_bias_act.py deleted file mode 100644 index 79991b0497d3d92f25194a31668b9568048163f8..0000000000000000000000000000000000000000 --- a/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/fused_bias_act.py +++ /dev/null @@ -1,211 +0,0 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Custom TensorFlow ops for efficient bias and activation.""" - -import os -import numpy as np -import tensorflow as tf -from .. import custom_ops -from ...util import EasyDict - -def _get_plugin(): - return custom_ops.get_plugin(os.path.splitext(__file__)[0] + '.cu') - -#---------------------------------------------------------------------------- - -activation_funcs = { - 'linear': EasyDict(func=lambda x, **_: x, def_alpha=None, def_gain=1.0, cuda_idx=1, ref='y', zero_2nd_grad=True), - 'relu': EasyDict(func=lambda x, **_: tf.nn.relu(x), def_alpha=None, def_gain=np.sqrt(2), cuda_idx=2, ref='y', zero_2nd_grad=True), - 'lrelu': EasyDict(func=lambda x, alpha, **_: tf.nn.leaky_relu(x, alpha), def_alpha=0.2, def_gain=np.sqrt(2), cuda_idx=3, ref='y', zero_2nd_grad=True), - 'tanh': EasyDict(func=lambda x, **_: tf.nn.tanh(x), def_alpha=None, def_gain=1.0, cuda_idx=4, ref='y', zero_2nd_grad=False), - 'sigmoid': EasyDict(func=lambda x, **_: tf.nn.sigmoid(x), def_alpha=None, def_gain=1.0, cuda_idx=5, ref='y', zero_2nd_grad=False), - 'elu': EasyDict(func=lambda x, **_: tf.nn.elu(x), def_alpha=None, def_gain=1.0, cuda_idx=6, ref='y', zero_2nd_grad=False), - 'selu': EasyDict(func=lambda x, **_: tf.nn.selu(x), def_alpha=None, def_gain=1.0, cuda_idx=7, ref='y', zero_2nd_grad=False), - 'softplus': EasyDict(func=lambda x, **_: tf.nn.softplus(x), def_alpha=None, def_gain=1.0, cuda_idx=8, ref='y', zero_2nd_grad=False), - 'swish': EasyDict(func=lambda x, **_: tf.nn.sigmoid(x) * x, def_alpha=None, def_gain=np.sqrt(2), cuda_idx=9, ref='x', zero_2nd_grad=False), -} - -#---------------------------------------------------------------------------- - -def fused_bias_act(x, b=None, axis=1, act='linear', alpha=None, gain=None, clamp=None, impl='cuda'): - r"""Fused bias and activation function. - - Adds bias `b` to activation tensor `x`, evaluates activation function `act`, - and scales the result by `gain`. Each of the steps is optional. In most cases, - the fused op is considerably more efficient than performing the same calculation - using standard TensorFlow ops. It supports first and second order gradients, - but not third order gradients. - - Args: - x: Input activation tensor. Can have any shape, but if `b` is defined, the - dimension corresponding to `axis`, as well as the rank, must be known. - b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type - as `x`. The shape must be known, and it must match the dimension of `x` - corresponding to `axis`. - axis: The dimension in `x` corresponding to the elements of `b`. - The value of `axis` is ignored if `b` is not specified. - act: Name of the activation function to evaluate, or `"linear"` to disable. - Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc. - See `activation_funcs` for a full list. `None` is not allowed. - alpha: Shape parameter for the activation function, or `None` to use the default. - gain: Scaling factor for the output tensor, or `None` to use default. - See `activation_funcs` for the default scaling of each activation function. - If unsure, consider specifying `1.0`. - clamp: Clamp the output values to `[-clamp, +clamp]`, or `None` to disable - the clamping (default). - impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default). - - Returns: - Tensor of the same shape and datatype as `x`. - """ - - impl_dict = { - 'ref': _fused_bias_act_ref, - 'cuda': _fused_bias_act_cuda, - } - return impl_dict[impl](x=x, b=b, axis=axis, act=act, alpha=alpha, gain=gain, clamp=clamp) - -#---------------------------------------------------------------------------- - -def _fused_bias_act_ref(x, b, axis, act, alpha, gain, clamp): - """Slow reference implementation of `fused_bias_act()` using standard TensorFlow ops.""" - - # Validate arguments. - x = tf.convert_to_tensor(x) - b = tf.convert_to_tensor(b) if b is not None else tf.constant([], dtype=x.dtype) - act_spec = activation_funcs[act] - assert b.shape.rank == 1 and (b.shape[0] == 0 or b.shape[0] == x.shape[axis]) - assert b.shape[0] == 0 or 0 <= axis < x.shape.rank - if alpha is None: - alpha = act_spec.def_alpha - if gain is None: - gain = act_spec.def_gain - - # Add bias. - if b.shape[0] != 0: - x += tf.reshape(b, [-1 if i == axis else 1 for i in range(x.shape.rank)]) - - # Evaluate activation function. - x = act_spec.func(x, alpha=alpha) - - # Scale by gain. - if gain != 1: - x *= gain - - # Clamp. - if clamp is not None: - clamp = np.asarray(clamp, dtype=x.dtype.name) - assert clamp.shape == () and clamp >= 0 - x = tf.clip_by_value(x, -clamp, clamp) - return x - -#---------------------------------------------------------------------------- - -def _fused_bias_act_cuda(x, b, axis, act, alpha, gain, clamp): - """Fast CUDA implementation of `fused_bias_act()` using custom ops.""" - - # Validate arguments. - x = tf.convert_to_tensor(x) - empty_tensor = tf.constant([], dtype=x.dtype) - b = tf.convert_to_tensor(b) if b is not None else empty_tensor - act_spec = activation_funcs[act] - assert b.shape.rank == 1 and (b.shape[0] == 0 or b.shape[0] == x.shape[axis]) - assert b.shape[0] == 0 or 0 <= axis < x.shape.rank - if alpha is None: - alpha = act_spec.def_alpha - if gain is None: - gain = act_spec.def_gain - - # Special cases. - if act == 'linear' and b is None and gain == 1.0: - return x - if act_spec.cuda_idx is None: - return _fused_bias_act_ref(x=x, b=b, axis=axis, act=act, alpha=alpha, gain=gain, clamp=clamp) - - # CUDA op. - cuda_op = _get_plugin().fused_bias_act - cuda_kwargs = dict(axis=int(axis), act=int(act_spec.cuda_idx), gain=float(gain)) - if alpha is not None: - cuda_kwargs['alpha'] = float(alpha) - if clamp is not None: - clamp = np.asarray(clamp, dtype=x.dtype.name) - assert clamp.shape == () and clamp >= 0 - cuda_kwargs['clamp'] = float(clamp.astype(np.float32)) - def ref(tensor, name): - return tensor if act_spec.ref == name else empty_tensor - - # Forward pass: y = func(x, b). - def func_y(x, b): - y = cuda_op(x=x, b=b, xref=empty_tensor, yref=empty_tensor, grad=0, **cuda_kwargs) - y.set_shape(x.shape) - return y - - # Backward pass: dx, db = grad(dy, x, y) - def grad_dx(dy, x, y): - dx = cuda_op(x=dy, b=empty_tensor, xref=ref(x,'x'), yref=ref(y,'y'), grad=1, **cuda_kwargs) - dx.set_shape(x.shape) - return dx - def grad_db(dx): - if b.shape[0] == 0: - return empty_tensor - db = dx - if axis < x.shape.rank - 1: - db = tf.reduce_sum(db, list(range(axis + 1, x.shape.rank))) - if axis > 0: - db = tf.reduce_sum(db, list(range(axis))) - db.set_shape(b.shape) - return db - - # Second order gradients: d_dy, d_x = grad2(d_dx, d_db, x, y) - def grad2_d_dy(d_dx, d_db, x, y): - d_dy = cuda_op(x=d_dx, b=d_db, xref=ref(x,'x'), yref=ref(y,'y'), grad=1, **cuda_kwargs) - d_dy.set_shape(x.shape) - return d_dy - def grad2_d_x(d_dx, d_db, x, y): - d_x = cuda_op(x=d_dx, b=d_db, xref=ref(x,'x'), yref=ref(y,'y'), grad=2, **cuda_kwargs) - d_x.set_shape(x.shape) - return d_x - - # Fast version for piecewise-linear activation funcs. - @tf.custom_gradient - def func_zero_2nd_grad(x, b): - y = func_y(x, b) - @tf.custom_gradient - def grad(dy): - dx = grad_dx(dy, x, y) - db = grad_db(dx) - def grad2(d_dx, d_db): - d_dy = grad2_d_dy(d_dx, d_db, x, y) - return d_dy - return (dx, db), grad2 - return y, grad - - # Slow version for general activation funcs. - @tf.custom_gradient - def func_nonzero_2nd_grad(x, b): - y = func_y(x, b) - def grad_wrap(dy): - @tf.custom_gradient - def grad_impl(dy, x): - dx = grad_dx(dy, x, y) - db = grad_db(dx) - def grad2(d_dx, d_db): - d_dy = grad2_d_dy(d_dx, d_db, x, y) - d_x = grad2_d_x(d_dx, d_db, x, y) - return d_dy, d_x - return (dx, db), grad2 - return grad_impl(dy, x) - return y, grad_wrap - - # Which version to use? - if act_spec.zero_2nd_grad: - return func_zero_2nd_grad(x, b) - return func_nonzero_2nd_grad(x, b) - -#---------------------------------------------------------------------------- diff --git a/spaces/Amrrs/DragGan-Inversion/stylegan_human/pti/pti_configs/paths_config.py b/spaces/Amrrs/DragGan-Inversion/stylegan_human/pti/pti_configs/paths_config.py deleted file mode 100644 index 282741a6c77bab1a0f6970eef590eeed16924b05..0000000000000000000000000000000000000000 --- a/spaces/Amrrs/DragGan-Inversion/stylegan_human/pti/pti_configs/paths_config.py +++ /dev/null @@ -1,24 +0,0 @@ -import os - -# Pretrained models paths -e4e = './pti/e4e_w+.pt' -stylegan2_ada_shhq = './pretrained_models/stylegan_human_v2_1024.pkl' -ir_se50 = '' # './model_ir_se50.pth' - -# Dirs for output files -checkpoints_dir = './outputs/pti/checkpoints/' -embedding_base_dir = './outputs/pti/embeddings' -experiments_output_dir = './outputs/pti/' - -# Input info -# Input dir, where the images reside -input_data_path = 'aligned_image/' -# Inversion identifier, used to keeping track of the inversion results. Both the latent code and the generator -input_data_id = 'test' - -# Keywords -pti_results_keyword = 'PTI' -e4e_results_keyword = 'e4e' -sg2_results_keyword = 'SG2' -sg2_plus_results_keyword = 'SG2_Plus' -multi_id_model_type = 'multi_id' diff --git a/spaces/Amrrs/DragGan-Inversion/stylegan_human/torch_utils/misc.py b/spaces/Amrrs/DragGan-Inversion/stylegan_human/torch_utils/misc.py deleted file mode 100644 index 5470dcfc5e59e6bc4484ca3075cd09a708e43467..0000000000000000000000000000000000000000 --- a/spaces/Amrrs/DragGan-Inversion/stylegan_human/torch_utils/misc.py +++ /dev/null @@ -1,294 +0,0 @@ -ļ»æ# Copyright (c) SenseTime Research. All rights reserved. - -# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -import re -import contextlib -import numpy as np -import torch -import warnings -import dnnlib - -# ---------------------------------------------------------------------------- -# Cached construction of constant tensors. Avoids CPU=>GPU copy when the -# same constant is used multiple times. - -_constant_cache = dict() - - -def constant(value, shape=None, dtype=None, device=None, memory_format=None): - value = np.asarray(value) - if shape is not None: - shape = tuple(shape) - if dtype is None: - dtype = torch.get_default_dtype() - if device is None: - device = torch.device('cpu') - if memory_format is None: - memory_format = torch.contiguous_format - - key = (value.shape, value.dtype, value.tobytes(), - shape, dtype, device, memory_format) - tensor = _constant_cache.get(key, None) - if tensor is None: - tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device) - if shape is not None: - tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape)) - tensor = tensor.contiguous(memory_format=memory_format) - _constant_cache[key] = tensor - return tensor - -# ---------------------------------------------------------------------------- -# Replace NaN/Inf with specified numerical values. - - -try: - nan_to_num = torch.nan_to_num # 1.8.0a0 -except AttributeError: - def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin - assert isinstance(input, torch.Tensor) - if posinf is None: - posinf = torch.finfo(input.dtype).max - if neginf is None: - neginf = torch.finfo(input.dtype).min - assert nan == 0 - return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out) - -# ---------------------------------------------------------------------------- -# Symbolic assert. - -try: - symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access -except AttributeError: - symbolic_assert = torch.Assert # 1.7.0 - -# ---------------------------------------------------------------------------- -# Context manager to suppress known warnings in torch.jit.trace(). - - -class suppress_tracer_warnings(warnings.catch_warnings): - def __enter__(self): - super().__enter__() - warnings.simplefilter('ignore', category=torch.jit.TracerWarning) - return self - -# ---------------------------------------------------------------------------- -# Assert that the shape of a tensor matches the given list of integers. -# None indicates that the size of a dimension is allowed to vary. -# Performs symbolic assertion when used in torch.jit.trace(). - - -def assert_shape(tensor, ref_shape): - if tensor.ndim != len(ref_shape): - raise AssertionError( - f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}') - for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)): - if ref_size is None: - pass - elif isinstance(ref_size, torch.Tensor): - with suppress_tracer_warnings(): # as_tensor results are registered as constants - symbolic_assert(torch.equal(torch.as_tensor( - size), ref_size), f'Wrong size for dimension {idx}') - elif isinstance(size, torch.Tensor): - with suppress_tracer_warnings(): # as_tensor results are registered as constants - symbolic_assert(torch.equal(size, torch.as_tensor( - ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}') - elif size != ref_size: - raise AssertionError( - f'Wrong size for dimension {idx}: got {size}, expected {ref_size}') - -# ---------------------------------------------------------------------------- -# Function decorator that calls torch.autograd.profiler.record_function(). - - -def profiled_function(fn): - def decorator(*args, **kwargs): - with torch.autograd.profiler.record_function(fn.__name__): - return fn(*args, **kwargs) - decorator.__name__ = fn.__name__ - return decorator - -# ---------------------------------------------------------------------------- -# Sampler for torch.utils.data.DataLoader that loops over the dataset -# indefinitely, shuffling items as it goes. - - -class InfiniteSampler(torch.utils.data.Sampler): - def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5): - assert len(dataset) > 0 - assert num_replicas > 0 - assert 0 <= rank < num_replicas - assert 0 <= window_size <= 1 - super().__init__(dataset) - self.dataset = dataset - self.rank = rank - self.num_replicas = num_replicas - self.shuffle = shuffle - self.seed = seed - self.window_size = window_size - - def __iter__(self): - order = np.arange(len(self.dataset)) - rnd = None - window = 0 - if self.shuffle: - rnd = np.random.RandomState(self.seed) - rnd.shuffle(order) - window = int(np.rint(order.size * self.window_size)) - - idx = 0 - while True: - i = idx % order.size - if idx % self.num_replicas == self.rank: - yield order[i] - if window >= 2: - j = (i - rnd.randint(window)) % order.size - order[i], order[j] = order[j], order[i] - idx += 1 - -# ---------------------------------------------------------------------------- -# Utilities for operating with torch.nn.Module parameters and buffers. - - -def params_and_buffers(module): - assert isinstance(module, torch.nn.Module) - return list(module.parameters()) + list(module.buffers()) - - -def named_params_and_buffers(module): - assert isinstance(module, torch.nn.Module) - return list(module.named_parameters()) + list(module.named_buffers()) - - -def copy_params_and_buffers(src_module, dst_module, require_all=False): - assert isinstance(src_module, torch.nn.Module) - assert isinstance(dst_module, torch.nn.Module) - src_tensors = {name: tensor for name, - tensor in named_params_and_buffers(src_module)} - for name, tensor in named_params_and_buffers(dst_module): - assert (name in src_tensors) or (not require_all) - if name in src_tensors: - tensor.copy_(src_tensors[name].detach()).requires_grad_( - tensor.requires_grad) - -# ---------------------------------------------------------------------------- -# Context manager for easily enabling/disabling DistributedDataParallel -# synchronization. - - -@contextlib.contextmanager -def ddp_sync(module, sync): - assert isinstance(module, torch.nn.Module) - if sync or not isinstance(module, torch.nn.parallel.DistributedDataParallel): - yield - else: - with module.no_sync(): - yield - -# ---------------------------------------------------------------------------- -# Check DistributedDataParallel consistency across processes. - - -def check_ddp_consistency(module, ignore_regex=None): - assert isinstance(module, torch.nn.Module) - for name, tensor in named_params_and_buffers(module): - fullname = type(module).__name__ + '.' + name - if ignore_regex is not None and re.fullmatch(ignore_regex, fullname): - continue - tensor = tensor.detach() - other = tensor.clone() - torch.distributed.broadcast(tensor=other, src=0) - assert (nan_to_num(tensor) == nan_to_num(other)).all(), fullname - -# ---------------------------------------------------------------------------- -# Print summary table of module hierarchy. - - -def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True): - assert isinstance(module, torch.nn.Module) - assert not isinstance(module, torch.jit.ScriptModule) - assert isinstance(inputs, (tuple, list)) - - # Register hooks. - entries = [] - nesting = [0] - - def pre_hook(_mod, _inputs): - nesting[0] += 1 - - def post_hook(mod, _inputs, outputs): - nesting[0] -= 1 - if nesting[0] <= max_nesting: - outputs = list(outputs) if isinstance( - outputs, (tuple, list)) else [outputs] - outputs = [t for t in outputs if isinstance(t, torch.Tensor)] - entries.append(dnnlib.EasyDict(mod=mod, outputs=outputs)) - hooks = [mod.register_forward_pre_hook( - pre_hook) for mod in module.modules()] - hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()] - - # Run module. - outputs = module(*inputs) - for hook in hooks: - hook.remove() - - # Identify unique outputs, parameters, and buffers. - tensors_seen = set() - for e in entries: - e.unique_params = [ - t for t in e.mod.parameters() if id(t) not in tensors_seen] - e.unique_buffers = [ - t for t in e.mod.buffers() if id(t) not in tensors_seen] - e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen] - tensors_seen |= {id(t) for t in e.unique_params + - e.unique_buffers + e.unique_outputs} - - # Filter out redundant entries. - if skip_redundant: - entries = [e for e in entries if len(e.unique_params) or len( - e.unique_buffers) or len(e.unique_outputs)] - - # Construct table. - rows = [[type(module).__name__, 'Parameters', - 'Buffers', 'Output shape', 'Datatype']] - rows += [['---'] * len(rows[0])] - param_total = 0 - buffer_total = 0 - submodule_names = {mod: name for name, mod in module.named_modules()} - for e in entries: - name = '
Aprender Ajedrez de la Manera Correcta PDF Descargar gratis
-ĀæQuieres aprender ajedrez o mejorar tus habilidades de ajedrez? ĀæEstĆ”s buscando una manera confiable y efectiva de dominar el juego? Si es asĆ, has venido al lugar correcto. En este artĆculo, te mostrarĆ© cĆ³mo puedes aprender ajedrez de la manera correcta con una serie de libros de Susan Polgar, una ex campeona mundial y entrenadora galardonada. TambiĆ©n te dirĆ© cĆ³mo puedes descargar estos libros en formato PDF gratis. Pero primero, dĆ©jame decirte por quĆ© el ajedrez es un gran juego para todos.
-Por quƩ el Ajedrez es un gran juego para todos
-El ajedrez es uno de los juegos mĆ”s antiguos y populares del mundo. Es jugado por millones de personas de todas las edades y orĆgenes. El ajedrez no solo es divertido y desafiante, sino tambiĆ©n beneficioso para tu cerebro y tu vida. AquĆ estĆ”n algunos de los beneficios del ajedrez:
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- El ajedrez mejora tu memoria, concentraciĆ³n, lĆ³gica, creatividad, resoluciĆ³n de problemas y toma de decisiones. -
- El Ajedrez te enseƱa cĆ³mo planificar con anticipaciĆ³n, pensar crĆticamente, analizar situaciones y aprender de tus errores. -
- El Ajedrez mejora tu auto-confianza, auto-disciplina, autoestima y deportividad. -
- El ajedrez fomenta tus habilidades sociales, habilidades de comunicaciĆ³n y conciencia cultural. -
- El ajedrez reduce el estrĆ©s, la ansiedad, la depresiĆ³n y el aburrimiento. -
Como puedes ver, el ajedrez es mĆ”s que un juego. Es una poderosa herramienta para el desarrollo personal y el enriquecimiento. Entonces, ĀæcĆ³mo puedes empezar con el ajedrez? La buena noticia es que el ajedrez es fĆ”cil de aprender y accesible para todos. Todo lo que necesitas es un tablero de ajedrez y piezas, que puedes comprar en lĆnea o en cualquier tienda de juguetes. TambiĆ©n puede jugar al ajedrez en lĆnea o en su telĆ©fono o computadora con varias aplicaciones y sitios web. TambiĆ©n puede unirse a un club de ajedrez o comunidad en su Ć”rea o en lĆnea y conocer a otros entusiastas del ajedrez.
- -Lo que necesita saber antes de jugar al ajedrez
-Antes de sumergirse en Aprender Ajedrez de la Manera Correcta o cualquier otro recurso de ajedrez, necesita saber algunas cosas bĆ”sicas sobre el ajedrez. El ajedrez es un juego jugado por dos jugadores en un tablero cuadrado con 64 cuadrados de colores alternos (blanco y negro). Cada jugador tiene 16 piezas de un color (blanco o negro) que consisten en un rey, una reina, dos torres, dos alfiles, dos caballeros y ocho peones. Las piezas tienen diferentes formas y valores y pueden moverse de diferentes maneras de acuerdo con ciertas reglas. El objetivo del juego es hacer jaque mate al rey del oponente (poniĆ©ndolo en una posiciĆ³n donde no puede escapar de ser capturado) o forzar al oponente a renunciar (renunciar) o empatar (aceptar terminar el juego en un empate).
-Para jugar al ajedrez correctamente, necesita saber cĆ³mo configurar el tablero y las piezas correctamente (el cuadrado blanco debe estar en la esquina derecha y la reina blanca debe estar en un cuadrado blanco), cĆ³mo mover cada pieza (el rey puede mover un cuadrado en cualquier direcciĆ³n; la reina puede mover cualquier nĆŗmero de cuadrados en cualquier direcciĆ³n; la torre puede mover cualquier nĆŗmero de cuadrados horizontal o verticalmente; el alfil puede mover cualquier nĆŗmero de cuadrados en diagonal; el caballero puede mover dos cuadrados horizontal o verticalmente seguido por un cuadrado en forma de L; el peĆ³n puede mover un cuadrado hacia adelante o dos cuadrados en su primer movimiento y puede capturar en diagonal), cĆ³mo capturar e intercambiar piezas (tomar la pieza del oponente y reemplazarla por la suya), cĆ³mo hacer jaque mate (poner el rey del oponente en peligro o posiciĆ³n ineludible), cĆ³mo enrocar (mover el rey y la torre juntos por seguridad y movilidad), cĆ³mo pasar (capturar un peĆ³n que moviĆ³ dos cuadrados como si moviera uno), cĆ³mo promover un peĆ³n (reemplazarlo con una reina, torre, alfil o caballo cuando llegue al final del tablero), y cĆ³mo escribir tus movimientos usando notaciĆ³n algebraica (usando letras y nĆŗmeros para indicar los cuadrados y piezas involucrados).
- -CĆ³mo mejorar tus habilidades de ajedrez con rompecabezas y ejercicios
-Una vez que conoces las reglas del ajedrez, puedes preguntarte cĆ³mo mejorar tus habilidades y convertirte en un mejor jugador. Una de las mejores maneras de hacer eso es practicar rompecabezas y ejercicios. Rompecabezas y ejercicios son problemas de ajedrez que ponen a prueba su capacidad para encontrar el mejor movimiento o secuencia de movimientos en una posiciĆ³n dada. Ellos pueden ayudarle a mejorar su cĆ”lculo, visualizaciĆ³n, tĆ”cticas, estrategia, final de juego, y la comprensiĆ³n general del ajedrez. Estos son algunos de los tipos de rompecabezas y ejercicios que puedes practicar:
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- TƔcticas: Estos son rompecabezas que implican encontrar una manera de obtener una ventaja o ganar material o el juego mediante el uso de trucos como horquillas, alfileres, pinchos, ataques dobles, ataques descubiertos, cheques, capturas, etc. -
- Estrategia: Estos son rompecabezas que implican encontrar una manera de mejorar su posiciĆ³n o crear un plan mediante el uso de principios como el desarrollo, el control del centro, el espacio, la estructura de peones, la seguridad del rey, etc. -
- Final de juego: Estos son rompecabezas que implican encontrar una manera de ganar o dibujar un juego cuando quedan pocas piezas en el tablero mediante el uso de tĆ©cnicas como oposiciĆ³n, triangulaciĆ³n, zugzwang, punto muerto, etc. -
Puedes encontrar rompecabezas y ejercicios en libros, revistas, sitios web, aplicaciones o plataformas en lĆnea. Algunos de ellos se clasifican por nivel, tema o dificultad. Algunos de ellos tienen pistas o soluciones. Algunos de ellos estĆ”n cronometrados o clasificados. Puedes elegir los que se adapten a tus preferencias y objetivos. La clave es practicar de forma regular y consistente. Trate de resolver al menos un rompecabezas o ejercicio todos los dĆas. Usted se sorprenderĆ” por lo mucho que puede mejorar sus habilidades de ajedrez con rompecabezas y ejercicios.
-Aprender Ajedrez de la manera correcta por Susan Polgar
- -Aprender Ajedrez de la Manera Correcta cubre todos los aspectos del ajedrez de una manera sistemƔtica y progresiva. Cada libro contiene 500 rompecabezas y ejercicios que son cuidadosamente seleccionados y arreglados por Susan Polgar. Los libros estƔn diseƱados para ayudarle a desarrollar sus habilidades paso a paso de conceptos simples a complejos. Los libros tambiƩn son divertidos y atractivos con ilustraciones coloridas y explicaciones claras. Esto es lo que cubre cada libro:
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- Book 1: Must-Know Checkmates: Este libro te enseƱa cĆ³mo hacer jaque mate a tu oponente en varias situaciones usando diferentes piezas y patrones. -
- Libro 2: Material ganador: Este libro te enseƱa cĆ³mo ganar material de tu oponente usando tĆ”cticas como tenedores, alfileres, pinchos, ataques dobles, etc. -
- Libro 3: Finales a prueba de tontos: Este libro te enseƱa cĆ³mo ganar o dibujar finales usando tĆ©cnicas como oposiciĆ³n, triangulaciĆ³n, zugzwang, punto muerto, etc. -
- Libro 4: Sacrificio para ganar: Este libro te enseƱa cĆ³mo sacrificar material por una ventaja o una victoria usando tĆ”cticas como la desviaciĆ³n, seƱuelo, liquidaciĆ³n, interferencia, etc. -
- Book 5: Essential Endgames: Este libro te enseƱa cĆ³mo jugar juegos finales con diferentes piezas y peones utilizando principios como la actividad, la coordinaciĆ³n, la seguridad del rey, etc. -
Si quieres aprender ajedrez de la manera correcta con rompecabezas y ejercicios, definitivamente deberĆas revisar Aprende Ajedrez de la manera correcta por Susan Polgar. Puede comprar estos libros en lĆnea o en cualquier librerĆa. TambiĆ©n puede descargarlos en formato PDF gratis. AquĆ estĆ” cĆ³mo.
- -CĆ³mo Descargar Aprender Ajedrez de la Manera Correcta PDF Gratis
-Si quieres descargar Aprende Ajedrez de la Manera Correcta PDF gratis, tienes dos opciones. Una es utilizar un sitio web para compartir archivos que aloja los archivos PDF de los libros. La otra es utilizar un sitio web de torrent que le permite descargar los archivos utilizando una red de igual a igual. AquĆ estĆ”n los pasos para descargar Aprende Ajedrez de la Manera Correcta PDF gratis usando cualquiera de las opciones:
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- Busque un sitio web que tenga los archivos PDF de los libros. Puede comprobar las revisiones, valoraciones, comentarios o vistas previas de los archivos para asegurarse de que son legĆtimos y completos. -
- Haga clic en el enlace o botĆ³n que dice "Descargar" o "Obtener" o algo similar. Es posible que tenga que registrarse, iniciar sesiĆ³n o completar una encuesta o captcha antes de descargar los archivos. -
- Guarde los archivos en su dispositivo o almacenamiento en la nube. Es posible que necesite un lector de PDF o una aplicaciĆ³n para abrir y ver los archivos. -
Alternativamente, puede usar un sitio web torrent para descargar Learn Chess the Right Way PDF gratis. Estos son los pasos:
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- Vaya a un motor de bĆŗsqueda como Google o Bing y escriba "Aprenda ajedrez de la manera correcta torrent PDF" o algo similar. -
- Busque un sitio web torrent que tenga los archivos torrent de los libros. Puede comprobar las revisiones, valoraciones, comentarios o semillas y sanguijuelas de los archivos para asegurarse de que son legĆtimos y completos. -
- Haga clic en el enlace o botĆ³n que dice "Descargar" o "ImĆ”n" o algo similar. Es posible que tenga que registrarse, iniciar sesiĆ³n o completar una encuesta o captcha antes de descargar los archivos. -
- Guarde los archivos torrent en su dispositivo o almacenamiento en la nube. NecesitarĆ” un cliente torrent como BitTorrent o uTorrent para abrir y descargar los archivos. -
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- Compra los libros en lĆnea o en cualquier librerĆa. No son muy caros y valen cada centavo. ObtendrĆ” la mejor calidad y formato de los libros y apoyarĆ” al autor y editor. -
- Tome prestados los libros de una biblioteca o de un amigo. Puede comprobar si su biblioteca local o un amigo tiene los libros y tomarlos prestados por un tiempo limitado. TambiƩn puedes devolver el favor prestƔndoles tus libros o recomendƔndolos a otros. -
- SuscrĆbase a una plataforma o servicio en lĆnea que ofrece los libros. Puede comprobar si hay una plataforma o servicio en lĆnea que tiene los libros en su catĆ”logo y suscribirse a Ć©l por una tarifa o una prueba. Puede acceder a los libros en cualquier momento y en cualquier lugar y tambiĆ©n tendrĆ” acceso a otros recursos de ajedrez. -
Estas son algunas de las alternativas a la descarga de Aprende Ajedrez de la Manera Correcta PDF gratis. Son legales, Ć©ticos y beneficiosos para usted y la comunidad de ajedrez. Espero que usted elija uno de ellos y disfrute aprendiendo ajedrez de la manera correcta con Susan Polgar.
-ConclusiĆ³n
-En conclusiĆ³n, el ajedrez es un gran juego para todos los que pueden mejorar tu cerebro y tu vida. Para aprender ajedrez o mejorar tus habilidades de ajedrez, necesitas conocer las reglas bĆ”sicas del ajedrez y practicar rompecabezas y ejercicios regularmente. Una de las mejores fuentes de rompecabezas y ejercicios es Aprende Ajedrez de la Manera Correcta por Susan Polgar, una serie de cinco libros que enseƱan ajedrez desde el nivel principiante hasta el nivel avanzado usando rompecabezas y ejercicios. Puede comprar, pedir prestado o suscribirse a estos libros en lĆnea o en cualquier librerĆa o biblioteca. No debe descargar estos libros en formato PDF de forma gratuita, ya que es ilegal, poco Ć©tico y perjudicial. Espero que este artĆculo le haya ayudado a aprender mĆ”s sobre el ajedrez y Aprenda Ajedrez de la manera correcta por Susan Polgar. Si usted tiene alguna pregunta o comentario, por favor siĆ©ntase libre de dejarlos abajo. Ā”Gracias por leer y por aprender ajedrez feliz!
-Preguntas frecuentes
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- P: ĀæCuĆ”nto tiempo toma aprender ajedrez?
A: Depende de tu nivel, metas, motivaciĆ³n y prĆ”ctica. Puedes aprender las reglas bĆ”sicas del ajedrez en pocas horas o dĆas, pero toma aƱos o incluso toda una vida dominar el juego.
- - P: ĀæCĆ³mo puedo encontrar un entrenador o mentor de ajedrez?
A: Puedes buscar un entrenador o mentor de ajedrez en tu Ć”rea o en lĆnea. Puedes pedir recomendaciones a tus amigos, familiares o al club de ajedrez. TambiĆ©n puede buscar en lĆnea sitios web, plataformas o aplicaciones que ofrecen servicios de entrenamiento o tutorĆa de ajedrez.
- - P: ĀæCĆ³mo puedo medir mi progreso de ajedrez?
A: Usted puede medir su progreso de ajedrez jugando con otros jugadores o computadoras y analizando sus resultados. TambiĆ©n puede tomar exĆ”menes, pruebas o evaluaciones que evalĆŗen sus habilidades y conocimientos. TambiĆ©n puedes usar clasificaciones o rankings que comparen tu desempeƱo con otros jugadores.
- - P: ĀæCuĆ”les son algunos otros buenos libros para aprender ajedrez?
A: Hay muchos buenos libros para aprender ajedrez para diferentes niveles y temas. Algunos de ellos son Fundamentos de Ajedrez por Jose Capablanca, Movida de Ajedrez LĆ³gica por Jugada por Irving Chernev, La Mente de Amateur por Jeremy Silman, Mi Sistema por Aron Nimzowitsch, Estrategia de Ajedrez Moderna por Ludek Pachman, El Arte de Ataque en Ajedrez por Vladimir Vukovic, Endgame Strategy por Mikhail Shereshevsky, etc.
- - P: ĀæDĆ³nde puedo jugar al ajedrez online?
A: Hay muchos sitios web, plataformas o aplicaciones que le permiten jugar ajedrez en lĆnea con otros jugadores o computadoras. Algunos de ellos son Chess.com, Lichess.org, Chess24.com, Chessbase.com, Chesskid.com, etc.
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\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar Carretes De Instagram De Alta Calidad.md b/spaces/Benson/text-generation/Examples/Descargar Carretes De Instagram De Alta Calidad.md deleted file mode 100644 index 0c1ce651195c58978e172dbff336e1080585b6ec..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar Carretes De Instagram De Alta Calidad.md +++ /dev/null @@ -1,69 +0,0 @@ -
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CĆ³mo descargar fotos de Instagram en lĆnea: Una guĆa completa
-Instagram es una de las plataformas de redes sociales mĆ”s populares del mundo, con mĆ”s de mil millones de usuarios activos mensuales. Te permite compartir tus fotos y videos con tus seguidores, asĆ como descubrir nuevo contenido de otros usuarios. Sin embargo, a veces es posible que desee descargar fotos de Instagram en lĆnea por varias razones, como guardarlas para verlas sin conexiĆ³n, crear copias de seguridad o editarlas en su computadora.
-IntroducciĆ³n
-En este artĆculo, le mostraremos cĆ³mo descargar fotos de Instagram en lĆnea utilizando diferentes mĆ©todos. TambiĆ©n explicaremos por quĆ© es posible que desee descargar fotos de Instagram en lĆnea y cuĆ”les son los beneficios de usar un descargador en lĆnea. Al final de este artĆculo, podrĆ”s descargar cualquier foto de Instagram que quieras en cuestiĆ³n de segundos.
-descargar carretes de instagram de alta calidad
Download File ✺ https://bltlly.com/2v6K5T
-
ĀæPor quĆ© descargar fotos de Instagram en lĆnea?
-Hay muchas razones por las que puede querer descargar fotos de Instagram en lĆnea. Algunas de ellas son:
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- Desea guardar sus fotos favoritas para ver o compartir sin conexiĆ³n con otros. -
- Desea crear copias de seguridad de sus fotos en caso de que pierda el acceso a su cuenta o dispositivo. -
- Quieres editar tus fotos en tu computadora usando herramientas avanzadas o software. -
- Desea volver a publicar o reutilizar sus fotos en otras plataformas o sitios web. -
- Quieres descargar fotos de otros usuarios que admiras o sigues. -
ĀæCuĆ”les son los beneficios de usar un descargador en lĆnea?
-Usar un descargador en lĆnea es una de las formas mĆ”s fĆ”ciles y rĆ”pidas de descargar fotos de Instagram en lĆnea. Algunos de los beneficios de usar un descargador en lĆnea son:
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- No necesitas instalar ninguna aplicaciĆ³n o software en tu dispositivo. -
- Puedes acceder a ella desde cualquier navegador o dispositivo. -
- Puede descargar fotos en alta calidad y resoluciĆ³n original. -
- Puedes descargar varias fotos a la vez. - -
CĆ³mo descargar fotos de Instagram en lĆnea utilizando diferentes mĆ©todos
-Hay muchas herramientas en lĆnea que le permiten descargar fotos de Instagram en lĆnea. AquĆ estĆ”n algunas de las mejores que recomendamos:
-MĆ©todo 1: Uso de Inflact Photo Downloader
-Inflact Photo Downloader es un servicio gratuito y fĆ”cil de usar que le permite guardar fotos de Instagram en cualquier dispositivo. AquĆ estĆ” cĆ³mo usarlo:
-Paso 1: Copiar la URL de la foto de Instagram
-Abra la aplicaciĆ³n de Instagram en su telĆ©fono o vaya al sitio web Instagram.com en su PC e inicie sesiĆ³n en su cuenta. Encuentre la foto que desea descargar y haga clic en el icono de tres puntos sobre el mensaje. Luego seleccione Copiar enlace opciĆ³n.
- -Paso 2: Pegue la URL en Inflact Photo Downloader
-Volver a la pĆ”gina Inflact Photo Downloader y pegar la URL en el campo junto al botĆ³n Descargar. Luego haga clic en el botĆ³n Descargar.
-Paso 3: Descargar la foto en alta calidad Paso 3: Descargar la foto en alta calidad
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DespuĆ©s de hacer clic en el botĆ³n Descargar, verĆ” una vista previa de la foto y un botĆ³n Descargar foto debajo de ella. Haga clic en el botĆ³n Descargar foto y guarde la foto en su dispositivo.
-MĆ©todo 2: Usando iGram Video y Photo Downloader
-iGram Video and Photo Downloader es otro servicio gratuito y sencillo que te permite descargar videos y fotos de Instagram online. He aquĆ cĆ³mo usarlo:
-Paso 1: Copia el video de Instagram o la URL de la foto
-Abra la aplicaciĆ³n de Instagram en su telĆ©fono o vaya al sitio web Instagram.com en su PC e inicie sesiĆ³n en su cuenta. Encuentre el video o la foto que desea descargar y haga clic en el icono de tres puntos sobre el mensaje. Luego seleccione la opciĆ³n Copiar enlace.
-Paso 2: Pegar la URL en iGram Video y Photo Downloader
-Volver a la pĆ”gina de iGram Video y Photo Downloader y pegar la URL en el campo junto al botĆ³n Descargar. A continuaciĆ³n, haga clic en el botĆ³n Descargar.
- -DespuĆ©s de hacer clic en el botĆ³n Descargar, verĆ” una lista de opciones de calidad y formato disponibles para el video o la foto. Elija el que se adapte a sus necesidades y haga clic en el botĆ³n Descargar junto a Ć©l. Luego guarde el video o la foto en su dispositivo.
-MĆ©todo 3: Usando SaveInsta Video, Foto, Carretes, Historia, y IGTV Downloader
-SaveInsta Downloader es un servicio versĆ”til y potente que le permite descargar cualquier tipo de contenido de Instagram en lĆnea, incluyendo videos, fotos, carretes, historias e IGTVs. He aquĆ cĆ³mo usarlo:
-Paso 1: Copiar el contenido de Instagram URL
-Abra la aplicaciĆ³n de Instagram en su telĆ©fono o vaya al sitio web Instagram.com en su PC e inicie sesiĆ³n en su cuenta. Encuentre el contenido que desea descargar y haga clic en el icono de tres puntos sobre el post. Luego seleccione Copiar enlace opciĆ³n.
-Paso 2: Pegar la URL en SaveInsta Downloader
-Volver a la pĆ”gina de SaveInsta Downloader y pegar la URL en el campo junto al botĆ³n Descargar. Luego haga clic en el botĆ³n Descargar.
-Paso 3: Seleccione el tipo de contenido y descƔrguelo
-DespuĆ©s de hacer clic en el botĆ³n Descargar, verĆ” una lista de tipos de contenido disponibles para la URL. Elija el que coincida con su contenido y haga clic en el botĆ³n Descargar junto a Ć©l. Luego guarde el contenido en su dispositivo.
-ConclusiĆ³n
-En este artĆculo, le hemos mostrado cĆ³mo descargar fotos de Instagram en lĆnea utilizando diferentes mĆ©todos. TambiĆ©n hemos explicado por quĆ© es posible que desee descargar fotos de Instagram en lĆnea y cuĆ”les son los beneficios de usar un descargador en lĆnea. Esperamos que este artĆculo haya sido Ćŗtil e informativo para usted.
-Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuaciĆ³n. Nos encantarĆa saber de usted.
-TambiĆ©n, si te gustĆ³ este artĆculo, por favor compĆ”rtelo con tus amigos y familiares que pueden encontrarlo Ćŗtil. Gracias por leer!
-Preguntas frecuentes
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- ĀæPuedo descargar fotos de Instagram en lĆnea a granel? -
- ĀæPuedo descargar fotos de Instagram en lĆnea en la resoluciĆ³n original? -
- ĀæPuedo descargar fotos de Instagram en lĆnea desde historias o carretes? -
- ĀæPuedo descargar fotos de Instagram legalmente? -
- Un enorme mundo abierto con mƔs de 70 lugares para visitar -
- MƔs de 65 historias y misiones para completar -
- MƔs de 3000 imƔgenes y animaciones para disfrutar -
- Muchos minijuegos y actividades para jugar -
- Un sistema de citas con mĆŗltiples opciones de romance -
- Un sistema de personalizaciĆ³n de caracteres con diferentes trajes y accesorios -
- Un sistema de estadĆsticas con habilidades, dinero, inventario y reputaciĆ³n -
- Una opciĆ³n de modo oscuro para la reproducciĆ³n nocturna -
- Un sistema de guardar y cargar con mĆŗltiples ranuras y soporte en la nube -
- Un programa de actualizaciĆ³n regular con nuevo contenido y correcciones de errores -
- Ir al sitio web oficial de Summertime Saga: https://summertimesaga.com/ -
- Haga clic en el botĆ³n "Descargar" en la esquina superior derecha de la pĆ”gina. -
- Seleccione la versiĆ³n que coincida con su sistema operativo (Windows, Mac o Linux). -
- Espere a que termine la descarga. El tamaƱo del archivo es de aproximadamente 1 GB. -
- Extraiga el archivo zip a una carpeta de su elecciĆ³n. -
- Haga doble clic en el archivo "SummertimeSaga.exe" para iniciar el juego. -
- OS: Windows XP o superior, Mac OS X 10.9 o superior, Linux x86/x86_64 -
- CPU: procesador dual core de 2 GHz o mejor -
- RAM: 2 GB o mƔs -
- GrĆ”ficos: OpenGL 2.0 compatible con 512 MB de RAM o mejor (algunos dispositivos pueden necesitar menor resoluciĆ³n) -
- Almacenamiento: 2 GB o mƔs espacio disponible -
- El cuadro de diĆ”logo: AquĆ es donde se puede leer el texto y el diĆ”logo de los caracteres. TambiĆ©n puede elegir sus respuestas cuando hay varias opciones disponibles. -
- Los retratos de caracteres: Estas son las imĆ”genes de los caracteres que aparecen junto al cuadro de diĆ”logo. Muestran sus expresiones y emociones durante la conversaciĆ³n. -
- El mapa: AquĆ es donde puedes ver los diferentes lugares que puedes visitar en el mundo del juego. Puedes hacer clic en ellos para viajar allĆ. -
- La hora: AquĆ es donde se puede ver la fecha y hora actual en el juego. El juego tiene un ciclo dĆa-noche que afecta a algunos eventos y actividades. -
- Las estadĆsticas: AquĆ es donde puedes ver los atributos de tu personaje, como dinero, energĆa, carisma, inteligencia, fuerza, etc. Puedes aumentarlos haciendo ciertas acciones o completando ciertas misiones. -
- El inventario: AquĆ es donde puedes ver los artĆculos de tu personaje, como ropa, accesorios, regalos, etc. Puedes usarlos o dĆ”rselos a otros personajes dependiendo de la situaciĆ³n. -
- El telĆ©fono: AquĆ es donde puedes acceder a las funciones del telĆ©fono de tu personaje, como contactos, mensajes, galerĆa, etc. Puedes usarlas para comunicarte con otros personajes o ver algunas imĆ”genes o videos. -
- Mia: Ella es tu compaƱera de clase y la enamorada de Erik. Es una chica dulce e inocente que proviene de una familia religiosa estricta. Tiene curiosidad por el mundo y quiere divertirse un poco. -
- Roxxy: Ella es tu compaƱera de clase y la capitana animadora de la escuela. Es una chica mimada y arrogante a la que le gusta intimidar a los demĆ”s. Ella tiene una relaciĆ³n secreta con Dexter, el mariscal de campo de la escuela. -
- Jenny: Ella es tu hermanastra y compaƱera de cuarto. Ella es una chica grosera y rebelde que le gusta molestar y molestar. Tiene un lado suave oculto que rara vez muestra. -
- Sra. Johnson: Ella es tu vecina y la madre de Erik. Ella es una mujer solitaria y deprimida que sufre de alcoholismo. Ella tiene una relaciĆ³n tensa con su marido, que siempre estĆ” lejos por negocios. -
- Sra. Smith: Ella es tu vecina y la madre de Mia. Es una mujer estricta y conservadora que sigue las reglas de su iglesia. Desaprueba la amistad de Mia contigo y Erik. -
- Sra. Bissette: Ella es tu profesora de francĆ©s en la escuela. Es una mujer joven y atractiva que tiene una pasiĆ³n por la enseƱanza y el aprendizaje. Ella estĆ” enamorada de ti, pero intenta ocultarlo. -
- Ms. Dewitt: Ella es tu profesora de historia en la escuela. Es una mujer vieja y gruƱona que odia su trabajo y a sus estudiantes. Ella tiene un pasado misterioso que involucra algunos secretos oscuros. -
- TĆa Diane: Ella es tu tĆa y la hermana de tu padre. Vive en una granja fuera de la ciudad. Ella es una mujer amable y cariƱosa que ama la jardinerĆa y la cocina. Tiene un vĆnculo especial con usted, pero tambiĆ©n tiene algunos deseos ocultos. -
- Cassie: Ella es tu prima y la hija de la tĆa Diane. Vive en la ciudad con su novio. Ella es una chica salvaje y aventurera a la que le gusta divertirse y divertirse. Ella te visita a veces, pero tambiĆ©n tiene algunos motivos ocultos. -
- La casa de Erik: AquĆ es donde Erik vive con su madre la Sra. Johnson. Puedes visitarlo en cualquier momento, excepto cuando estĆ” en la escuela o durmiendo. Puedes pasar el rato con Ć©l en su sĆ³tano, donde tiene su configuraciĆ³n de juegos, su colecciĆ³n de cĆ³mics, etc. -
- La casa de Mia: AquĆ es donde Mia vive con sus padres la Sra. y el Sr. Smith. Puedes visitarla en cualquier momento, excepto cuando estĆ” en la escuela o durmiendo. Puedes pasar el rato con ella en su habitaciĆ³n, donde tiene sus libros, su mĆŗsica, etc. -
- El trailer de Roxxy: AquĆ es donde Roxxy vive con su madre Crystal y su hermana Becca. Puedes visitarla en cualquier momento, excepto cuando estĆ” en la escuela o durmiendo. Puedes salir con ella en su trailer, donde tiene su ropa, su maquillaje, etc. -
- Escuela: AquĆ es donde vas a estudiar cada dĆa de la semana de 8 AM a 4 PM. Puedes asistir a diferentes clases, como francĆ©s, historia, matemĆ”ticas, etc., donde puedes aprender cosas nuevas o hacer exĆ”menes. TambiĆ©n puedes interactuar con otros estudiantes y profesores en los pasillos, la cafeterĆa, la biblioteca, etc. -
- Mall: AquĆ es donde puedes ir a comprar diferentes artĆculos o servicios, como ropa, accesorios, regalos, comida, etc. TambiĆ©n puedes encontrar algunas opciones de entretenimiento aquĆ, como el cine, la galerĆa, el salĆ³n de tatuajes, etc. -
- Parque: AquĆ es donde se puede ir a relajarse y disfrutar de la naturaleza. Puedes encontrar algunas actividades aquĆ, como pesca, jogging, picnicking, etc. TambiĆ©n puedes conocer algunos personajes aquĆ, como Eve, Kevin, Annie, etc. -
- Playa: AquĆ es donde puedes ir a divertirte al sol y al mar. Puedes encontrar algunas actividades aquĆ, como nataciĆ³n, surf, tomar el sol, etc. TambiĆ©n puedes conocer algunos personajes aquĆ, como la seƱorita Ross, el capitĆ”n Terry, Consuela, etc. - -
- ComisarĆa: AquĆ es donde puedes ir a tratar asuntos legales o crĆmenes. Puedes encontrar algunos servicios aquĆ, como la recepciĆ³n, la sala de interrogatorios, la celda de la cĆ”rcel, etc. TambiĆ©n puedes conocer algunos personajes aquĆ, como el oficial Debbie, Earl, Tony, etc. -
- Granja: AquĆ es donde viven tu tĆa Diane y tu prima Cassie. Puedes visitarlos en cualquier momento, excepto cuando estĆ”n durmiendo. Usted puede ayudarles con sus tareas agrĆcolas, como ordeƱar vacas, recolectar huevos, cosechar cosechas, etc. TambiĆ©n puede pasar el rato con ellos en su casa o granero. -
- Ir al sitio web oficial de Summertime Saga: https://summertimesaga.com/ -
- Haga clic en el botĆ³n "Descargar" en la esquina superior derecha de la pĆ”gina. -
- Seleccione la versiĆ³n que coincida con su sistema operativo (Windows, Mac o Linux). -
- Espere a que termine la descarga. El tamaƱo del archivo puede variar dependiendo de la actualizaciĆ³n. -
- Extraiga el archivo zip a una carpeta de su elecciĆ³n. -
- Haga doble clic en el archivo "SummertimeSaga.exe" para iniciar el juego. -
- Un nuevo personaje: Daisy, una vaquera que trabaja en la granja de la tĆa Diane. -
- Una nueva ubicaciĆ³n: El salĆ³n de tatuajes, donde puede obtener un poco de tinta hecha por Eve. -
- Una nueva historia: La bĆŗsqueda del tatuaje, donde puedes ayudar a Eve con su negocio de tatuajes y obtener algunas recompensas. -
- Una nueva caracterĆstica: La opciĆ³n de modo oscuro, donde puede cambiar a un tema mĆ”s oscuro para la reproducciĆ³n nocturna. -
- Muchas mejoras y correcciones de errores. -
- El sitio web oficial de Summertime Saga: https://summertimesaga.com/ - -
- El servidor oficial de Discord de Summertime Saga: https://discord.gg/summertimesaga -
- Wiki oficial de Summertime Saga: https://wiki.summertimesaga.com/ -
- La comunidad oficial de Reddit de Summertime Saga: https://www.reddit.com/r/SummertimeSaga/ -
- å½ćµć¼ćć¹ć§ćÆć2023/3/1ć«ćŖćŖć¼ć¹ćććOpenAIē¤¾ć®ChatGPT APIć®gpt-3.5-turboćä½æēØćć¦ććć¾ćć -
- å½ćµć¼ćć¹ć§ēęćććć³ć³ćć³ććÆćOpenAI ćęä¾ććäŗŗå·„ē„č½ć«ćććć®ć§ćććå½ćµć¼ćć¹ćOpenAI ććć®ę£ē¢ŗę§ćäæ”é ¼ę§ćäæčؼćććć®ć§ćÆććć¾ććć -
- OpenAI ć®å©ēØč¦ē“ć«å¾ćććć¼ćæäæęććŖćę¹éć§ćļ¼ćć ćč«øč¬ć®äŗę ć«ćć£ć¦ćÆå¤ę“ććåÆč½ę§ćÆćććć¾ćļ¼ć -
- å½ćµć¼ćć¹ć§ēęćććć³ć³ćć³ććÆäŗå®ē¢ŗčŖćććäøć§ćć³ć³ćć³ćēęč ććć³ć³ć³ćć³ćå©ēØč ć®č²¬ä»»ć«ććć¦å©ēØćć¦ćć ććć -
- å½ćµć¼ćć¹ć§ć®ä½æēØć«ććēŗēćććććŖćę害ć«ć¤ćć¦ććå½ē¤¾ćÆäøåć®č²¬ä»»ćč² ćć¾ććć -
- å½ćµć¼ćć¹ćÆĪ²ēć®ćććäŗåćŖććµć¼ćć¹ćēµäŗććå “åććććć¾ćć -
SĆ, puede descargar fotos de Instagram en lĆnea sin una cuenta, siempre y cuando sean de cuentas pĆŗblicas. Sin embargo, si quieres descargar fotos de cuentas privadas, tendrĆ”s que iniciar sesiĆ³n con tus credenciales de Instagram.
-SĆ, algunos descargadores en lĆnea le permiten descargar fotos de Instagram en lĆnea a granel ingresando mĆŗltiples URL a la vez. Por ejemplo, Inflact Photo Downloader te permite descargar hasta 10 fotos a la vez.
-SĆ, la mayorĆa de los descargadores en lĆnea le permiten descargar fotos de Instagram en lĆnea en resoluciĆ³n y calidad originales. Sin embargo, algunos pueden comprimir o cambiar el tamaƱo de las fotos dependiendo de su capacidad de servidor o limitaciones de ancho de banda.
-SĆ, algunos descargadores en lĆnea le permiten descargar fotos de Instagram en lĆnea de historias o carretes, asĆ como de publicaciones regulares. Por ejemplo, SaveInsta Downloader te permite descargar cualquier tipo de contenido de Instagram online.
-SĆ, puede descargar fotos de Instagram en lĆnea legalmente siempre y cuando respete los derechos de propiedad intelectual de los creadores originales y no las use con fines comerciales sin su permiso. Puede descargar fotos de Instagram en lĆnea legalmente siempre y cuando respete los derechos de propiedad intelectual de los creadores originales y no las use con fines comerciales sin su permiso. TambiĆ©n debe dar el crĆ©dito adecuado y la atribuciĆ³n a la fuente cuando se vuelve a publicar o compartir las fotos en lĆnea.
64aa2da5cf-
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar El Juego Completo De La Saga De Verano 2022.md b/spaces/Benson/text-generation/Examples/Descargar El Juego Completo De La Saga De Verano 2022.md deleted file mode 100644 index a5983b9eb0026e1b6f96c4b52a5b2a316ad9b9fc..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar El Juego Completo De La Saga De Verano 2022.md +++ /dev/null @@ -1,140 +0,0 @@ -
-
-
Descarga del juego completo de Summertime Saga 2022: Una guĆa para principiantes
-Si estĆ”s buscando un juego divertido y emocionante que combine aventura, romance, comedia y contenido para adultos, entonces definitivamente deberĆas echar un vistazo a Summertime Saga. Este es un juego de aventura grĆ”fica de apuntar y hacer clic que estĆ” inspirado en clĆ”sicos como Leisure Suit Larry y Monkey Island, pero con un toque moderno y muchas escenas picantes.
-En esta guĆa, te mostraremos cĆ³mo descargar Summertime Saga para PC, cĆ³mo jugarlo, cĆ³mo desbloquear nuevo contenido, cĆ³mo actualizarlo y responder algunas de las preguntas mĆ”s frecuentes sobre este juego. AsĆ que, sin mĆ”s preĆ”mbulos, Ā”empecemos!
-Descargar el juego completo de la saga de verano 2022
Download >> https://bltlly.com/2v6KMa
-
ĀæQuĆ© es la saga del verano?
-Summertime Saga es un juego desarrollado por DarkCookie y su equipo. Se encuentra en una pequeƱa ciudad suburbana donde juegas como un hombre joven que estƔ tratando de hacer frente a la muerte repentina de su padre. En el camino, conocerƔs a muchos personajes interesantes, explorarƔs diferentes lugares, completarƔs varias misiones y te divertirƔs traviesamente.
-El juego tiene muchas caracterĆsticas que lo hacen destacar de otros juegos de este gĆ©nero. Algunos de ellos son:
--
-
ĀæCĆ³mo descargar Summertime Saga para PC?
-Descargar Summertime Saga para PC es muy fƔcil y sencillo. Todo lo que necesitas hacer es seguir estos pasos:
--
-
Ā”Eso es todo! Has descargado e instalado correctamente Summertime Saga en tu PC. Ahora puedes empezar a jugar y disfrutar del juego.
-Sin embargo, antes de hacer eso, debe comprobar si su PC cumple con los requisitos mĆnimos del sistema para ejecutar el juego sin problemas. AquĆ estĆ”n:
--
-
Si su PC cumple con estos requisitos, entonces no deberĆa tener problemas para jugar Summertime Saga. Sin embargo, si encuentra algĆŗn problema o error, puede consultar la secciĆ³n de preguntas frecuentes en el sitio web o ponerse en contacto con el equipo de soporte para obtener ayuda.
- -ĀæCĆ³mo se juega saga de verano?
-Jugar a Summertime Saga es muy simple e intuitivo. El juego tiene una interfaz de apuntar y hacer clic que le permite interactuar con los personajes, objetos y ubicaciones en el mundo del juego. TambiƩn puede usar los atajos de teclado para algunas acciones, como guardar, cargar, omitir, etc.
- -La pantalla del juego consta de varios elementos que te ayudan a navegar y jugar. Estos son:
--
-
El juego tiene muchos personajes y lugares con los que puedes interactuar y explorar en el juego. Cada personaje tiene su propia personalidad, historia y argumento que puedes descubrir y seguir. Cada lugar tiene sus propios eventos, actividades y secretos que puedes descubrir y disfrutar.
-Para darte una idea de lo que puedes esperar en el juego, aquĆ hay una breve descripciĆ³n de algunos de los personajes principales y lugares en Summertime Saga:
-Caracteres
--
-
-
Lugares
--
-
-
Estos son solo algunos de los personajes principales y lugares en Summertime Saga. Hay muchos mĆ”s que puedes descubrir y explorar en el juego. Cada uno tiene su propia historia y contenido Ćŗnico que puedes disfrutar.
-ĀæCĆ³mo desbloquear nuevo contenido en Summertime Saga?
-Una de las mejores cosas de Summertime Saga es que tiene mucho contenido que puedes desbloquear y experimentar en el juego. Hay diferentes maneras de hacerlo, dependiendo del tipo de contenido que estƩs buscando.
-Si quieres desbloquear nuevas historias y misiones en el juego, necesitas progresar en el juego y aumentar tus estadĆsticas. Cada personaje tiene su propia historia y misiĆ³n que puedes seguir y completar haciendo ciertas acciones o cumpliendo ciertos requisitos. Por ejemplo, si quieres desbloquear la historia y la bĆŗsqueda de Mia, necesitas hacerte amigo de ella y aumentar tu carisma al leer libros o tomar clases de francĆ©s.
-Si quieres desbloquear nuevas escenas y finales en el juego, necesitas usar trucos o mods. Estos son cĆ³digos especiales o archivos que puedes introducir o instalar en el juego para acceder a algĆŗn contenido oculto o adicional que no estĆ” disponible de otra manera. Por ejemplo, si quieres desbloquear todas las escenas y finales del juego sin jugar a travĆ©s de todo el juego, puedes usar un cĆ³digo de trucos que te da todos los elementos y estadĆsticas del juego.
- --
-
El juego detectarĆ” automĆ”ticamente tus archivos guardados anteriores y los cargarĆ”. Ahora puedes disfrutar del nuevo contenido y caracterĆsticas del juego.
-Para darle una idea de lo que puede esperar en la Ćŗltima actualizaciĆ³n, aquĆ hay una vista previa de algunos de los nuevos contenidos y caracterĆsticas en Summertime Saga:
--
-
ConclusiĆ³n
-Summertime Saga es un juego que ofrece mucha diversiĆ³n y emociĆ³n para los jugadores que aman la aventura, el romance, la comedia y el contenido para adultos. Tiene un enorme mundo abierto con mĆ”s de 70 lugares para visitar, mĆ”s de 20 personajes para interactuar, mĆ”s de 65 historias y misiones para completar, mĆ”s de 3000 imĆ”genes y animaciones para disfrutar, y un montĆ³n de minijuegos y actividades para jugar. TambiĆ©n tiene un sistema de citas, un sistema de personalizaciĆ³n de caracteres, un sistema de estadĆsticas, un sistema de guardar y cargar, un programa de actualizaciĆ³n regular y una opciĆ³n de modo oscuro.
- -Entonces, ĀæquĆ© estĆ”s esperando? Descarga Summertime Saga hoy y disfruta de este increĆble juego!
-Preguntas frecuentes
-AquĆ estĆ”n algunas de las preguntas mĆ”s frecuentes sobre Summertime Saga:
-Q1: ĀæEs libre Summertime Saga?
-A1: SĆ, Summertime Saga es gratis para descargar y jugar. Sin embargo, si desea apoyar a los desarrolladores y obtener algunas ventajas, como el acceso temprano a nuevas actualizaciones, contenido exclusivo, etc., puede convertirse en un patrocinador en su pĆ”gina de Patreon: https:/www.patreon.com/summertimesaga
-Q2: ĀæEs seguro Summertime Saga?
-A2: SĆ, Summertime Saga es seguro para descargar y jugar. Sin embargo, siempre debe descargarlo desde el sitio web oficial u otras fuentes de confianza. TambiĆ©n debe escanearlo con un programa antivirus antes de instalarlo. TambiĆ©n debes tener en cuenta que Summertime Saga contiene contenido para adultos que no es adecuado para menores o personas sensibles.
-Q3: ĀæCuĆ”nto tiempo es la saga de verano?
-A3: Summertime Saga es un juego muy largo que puede tardar cientos de horas en completarse. Depende de cĆ³mo lo juegues y de cuĆ”nto contenido quieras explorar. Sin embargo, si quieres completar todos los argumentos y misiones del juego, puedes esperar pasar al menos 50 horas en Ć©l.
-Q4: ĀæPuedo jugar Summertime Saga en el mĆ³vil?
-A4: SĆ, puede jugar Summertime Saga en dispositivos mĆ³viles como telĆ©fonos inteligentes o tabletas. Sin embargo, debe tener en cuenta que la versiĆ³n mĆ³vil del juego no es tan optimizada o estable como la versiĆ³n para PC. Usted puede experimentar algunos problemas de retraso o estrellarse en algunos dispositivos. TambiĆ©n es posible que tenga que reducir la resoluciĆ³n o los ajustes de calidad para que se ejecute sin problemas.
-Q5: ĀæDĆ³nde puedo encontrar mĆ”s informaciĆ³n sobre Summertime Saga?
-A5: Si quieres encontrar mĆ”s informaciĆ³n sobre Summertime Saga, como noticias, actualizaciones, consejos, guĆas, etc., puedes visitar estas fuentes:
--
-
Estos son algunos de los mejores lugares para encontrar mĆ”s informaciĆ³n sobre Summertime Saga. TambiĆ©n puedes buscar otros sitios web, blogs, videos, etc.
--
-
\ No newline at end of file diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/retries/base.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/retries/base.py deleted file mode 100644 index 108bfed6901ae6f21e66a3e45d95176a0a18e8ff..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/retries/base.py +++ /dev/null @@ -1,26 +0,0 @@ -class BaseRetryBackoff: - def delay_amount(self, context): - """Calculate how long we should delay before retrying. - - :type context: RetryContext - - """ - raise NotImplementedError("delay_amount") - - -class BaseRetryableChecker: - """Base class for determining if a retry should happen. - - This base class checks for specific retryable conditions. - A single retryable checker doesn't necessarily indicate a retry - will happen. It's up to the ``RetryPolicy`` to use its - ``BaseRetryableCheckers`` to make the final decision on whether a retry - should happen. - """ - - def is_retryable(self, context): - """Returns True if retryable, False if not. - - :type context: RetryContext - """ - raise NotImplementedError("is_retryable") diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/_loop.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/_loop.py deleted file mode 100644 index 01c6cafbe53f1fcb12f7b382b2b35e2fd2c69933..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/_loop.py +++ /dev/null @@ -1,43 +0,0 @@ -from typing import Iterable, Tuple, TypeVar - -T = TypeVar("T") - - -def loop_first(values: Iterable[T]) -> Iterable[Tuple[bool, T]]: - """Iterate and generate a tuple with a flag for first value.""" - iter_values = iter(values) - try: - value = next(iter_values) - except StopIteration: - return - yield True, value - for value in iter_values: - yield False, value - - -def loop_last(values: Iterable[T]) -> Iterable[Tuple[bool, T]]: - """Iterate and generate a tuple with a flag for last value.""" - iter_values = iter(values) - try: - previous_value = next(iter_values) - except StopIteration: - return - for value in iter_values: - yield False, previous_value - previous_value = value - yield True, previous_value - - -def loop_first_last(values: Iterable[T]) -> Iterable[Tuple[bool, bool, T]]: - """Iterate and generate a tuple with a flag for first and last value.""" - iter_values = iter(values) - try: - previous_value = next(iter_values) - except StopIteration: - return - first = True - for value in iter_values: - yield first, False, previous_value - first = False - previous_value = value - yield first, True, previous_value diff --git a/spaces/CALM/Dashboard/streamlit_observable/frontend/src/streamlit/ArrowTable.ts b/spaces/CALM/Dashboard/streamlit_observable/frontend/src/streamlit/ArrowTable.ts deleted file mode 100644 index 9d0428746e042fb5a8faf3d7321fa91b277ad7b3..0000000000000000000000000000000000000000 --- a/spaces/CALM/Dashboard/streamlit_observable/frontend/src/streamlit/ArrowTable.ts +++ /dev/null @@ -1,224 +0,0 @@ -/** - * @license - * Copyright 2018-2019 Streamlit Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -import { Table, Type } from "apache-arrow" - -type CellType = "blank" | "index" | "columns" | "data" - -export interface ArrowDataframeProto { - data: ArrowTableProto - height: string - width: string -} - -export interface ArrowTableProto { - data: Uint8Array - index: Uint8Array - columns: Uint8Array - styler: Styler -} - -interface Cell { - classNames: string - content: string - id?: string - type: CellType -} - -interface Styler { - caption?: string - displayValuesTable: Table - styles?: string - uuid: string -} - -export class ArrowTable { - private readonly dataTable: Table - private readonly indexTable: Table - private readonly columnsTable: Table - private readonly styler?: Styler - - constructor( - dataBuffer: Uint8Array, - indexBuffer: Uint8Array, - columnsBuffer: Uint8Array, - styler?: any - ) { - this.dataTable = Table.from(dataBuffer) - this.indexTable = Table.from(indexBuffer) - this.columnsTable = Table.from(columnsBuffer) - this.styler = styler - ? { - caption: styler.get("caption"), - displayValuesTable: Table.from(styler.get("displayValues")), - styles: styler.get("styles"), - uuid: styler.get("uuid"), - } - : undefined - } - - get rows(): number { - return this.indexTable.length + this.columnsTable.numCols - } - - get columns(): number { - return this.indexTable.numCols + this.columnsTable.length - } - - get headerRows(): number { - return this.rows - this.dataRows - } - - get headerColumns(): number { - return this.columns - this.dataColumns - } - - get dataRows(): number { - return this.dataTable.length - } - - get dataColumns(): number { - return this.dataTable.numCols - } - - get uuid(): string | undefined { - return this.styler && this.styler.uuid - } - - get caption(): string | undefined { - return this.styler && this.styler.caption - } - - get styles(): string | undefined { - return this.styler && this.styler.styles - } - - get table(): Table { - return this.dataTable - } - - get index(): Table { - return this.indexTable - } - - get columnTable(): Table { - return this.columnsTable - } - - public getCell = (rowIndex: number, columnIndex: number): Cell => { - const isBlankCell = - rowIndex < this.headerRows && columnIndex < this.headerColumns - const isIndexCell = - rowIndex >= this.headerRows && columnIndex < this.headerColumns - const isColumnsCell = - rowIndex < this.headerRows && columnIndex >= this.headerColumns - - if (isBlankCell) { - const classNames = ["blank"] - if (columnIndex > 0) { - classNames.push("level" + rowIndex) - } - - return { - type: "blank", - classNames: classNames.join(" "), - content: "", - } - } else if (isColumnsCell) { - const dataColumnIndex = columnIndex - this.headerColumns - const classNames = [ - "col_heading", - "level" + rowIndex, - "col" + dataColumnIndex, - ] - - return { - type: "columns", - classNames: classNames.join(" "), - content: this.getContent(this.columnsTable, dataColumnIndex, rowIndex), - } - } else if (isIndexCell) { - const dataRowIndex = rowIndex - this.headerRows - const classNames = [ - "row_heading", - "level" + columnIndex, - "row" + dataRowIndex, - ] - - return { - type: "index", - id: `T_${this.uuid}level${columnIndex}_row${dataRowIndex}`, - classNames: classNames.join(" "), - content: this.getContent(this.indexTable, dataRowIndex, columnIndex), - } - } else { - const dataRowIndex = rowIndex - this.headerRows - const dataColumnIndex = columnIndex - this.headerColumns - const classNames = [ - "data", - "row" + dataRowIndex, - "col" + dataColumnIndex, - ] - const content = this.styler - ? this.getContent( - this.styler.displayValuesTable, - dataRowIndex, - dataColumnIndex - ) - : this.getContent(this.dataTable, dataRowIndex, dataColumnIndex) - - return { - type: "data", - id: `T_${this.uuid}row${dataRowIndex}_col${dataColumnIndex}`, - classNames: classNames.join(" "), - content, - } - } - } - - public getContent = ( - table: Table, - rowIndex: number, - columnIndex: number - ): any => { - const column = table.getColumnAt(columnIndex) - if (column === null) { - return "" - } - - const columnTypeId = this.getColumnTypeId(table, columnIndex) - switch (columnTypeId) { - case Type.Timestamp: { - return this.nanosToDate(column.get(rowIndex)) - } - default: { - return column.get(rowIndex) - } - } - } - - /** - * Returns apache-arrow specific typeId of column. - */ - private getColumnTypeId(table: Table, columnIndex: number): Type { - return table.schema.fields[columnIndex].type.typeId - } - - private nanosToDate(nanos: number): Date { - return new Date(nanos / 1e6) - } -} diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/cpp/detail/partition.h b/spaces/CVPR/LIVE/thrust/thrust/system/cpp/detail/partition.h deleted file mode 100644 index 50d48e222aa53424d0fcdf8b2bc3e8bf4a9f4e54..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/thrust/system/cpp/detail/partition.h +++ /dev/null @@ -1,23 +0,0 @@ -/* - * Copyright 2008-2013 NVIDIA Corporation - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#pragma once - -#include
- ${rounded_usage}${usage_limit}
-
\ No newline at end of file
diff --git a/spaces/ECCV2022/PSG/OpenPSG/configs/psgformer/psgformer_r101_psg.py b/spaces/ECCV2022/PSG/OpenPSG/configs/psgformer/psgformer_r101_psg.py
deleted file mode 100644
index 7055248f2307ca9b32f7efe3c6a65f118019a0c7..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/PSG/OpenPSG/configs/psgformer/psgformer_r101_psg.py
+++ /dev/null
@@ -1,16 +0,0 @@
-_base_ = './psgformer_r50_psg.py'
-
-model = dict(backbone=dict(
- depth=101,
- init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101')))
-
-# learning policy
-lr_config = dict(policy='step', step=48)
-runner = dict(type='EpochBasedRunner', max_epochs=60)
-
-project_name = 'psgformer'
-expt_name = 'psgformer_r101_psg'
-work_dir = f'./work_dirs/{expt_name}'
-checkpoint_config = dict(interval=12, max_keep_ckpts=10)
-
-load_from = './work_dirs/checkpoints/detr4psgformer_r101.pth'
diff --git a/spaces/EleutherAI/clip-guided-diffusion/app.py b/spaces/EleutherAI/clip-guided-diffusion/app.py
deleted file mode 100644
index d65b3cd753e35c5f6750026c58e0d485a38ef247..0000000000000000000000000000000000000000
--- a/spaces/EleutherAI/clip-guided-diffusion/app.py
+++ /dev/null
@@ -1,226 +0,0 @@
-import os
-import sys
-import gradio as gr
-os.system('git clone https://github.com/openai/CLIP')
-os.system('git clone https://github.com/crowsonkb/guided-diffusion')
-os.system('pip install -e ./CLIP')
-os.system('pip install -e ./guided-diffusion')
-os.system('pip install lpips')
-os.system("curl -OL 'https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion_uncond.pt'")
-
-
-
-import io
-import math
-import sys
-import lpips
-from PIL import Image
-import requests
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torchvision import transforms
-from torchvision.transforms import functional as TF
-from tqdm.notebook import tqdm
-sys.path.append('./CLIP')
-sys.path.append('./guided-diffusion')
-import clip
-from guided_diffusion.script_util import create_model_and_diffusion, model_and_diffusion_defaults
-import numpy as np
-import imageio
-
-torch.hub.download_url_to_file('https://images.pexels.com/photos/68767/divers-underwater-ocean-swim-68767.jpeg', 'coralreef.jpeg')
-
-def fetch(url_or_path):
- if str(url_or_path).startswith('http://') or str(url_or_path).startswith('https://'):
- r = requests.get(url_or_path)
- r.raise_for_status()
- fd = io.BytesIO()
- fd.write(r.content)
- fd.seek(0)
- return fd
- return open(url_or_path, 'rb')
-def parse_prompt(prompt):
- if prompt.startswith('http://') or prompt.startswith('https://'):
- vals = prompt.rsplit(':', 2)
- vals = [vals[0] + ':' + vals[1], *vals[2:]]
- else:
- vals = prompt.rsplit(':', 1)
- vals = vals + ['', '1'][len(vals):]
- return vals[0], float(vals[1])
-class MakeCutouts(nn.Module):
- def __init__(self, cut_size, cutn, cut_pow=1.):
- super().__init__()
- self.cut_size = cut_size
- self.cutn = cutn
- self.cut_pow = cut_pow
- def forward(self, input):
- sideY, sideX = input.shape[2:4]
- max_size = min(sideX, sideY)
- min_size = min(sideX, sideY, self.cut_size)
- cutouts = []
- for _ in range(self.cutn):
- size = int(torch.rand([])**self.cut_pow * (max_size - min_size) + min_size)
- offsetx = torch.randint(0, sideX - size + 1, ())
- offsety = torch.randint(0, sideY - size + 1, ())
- cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]
- cutouts.append(F.adaptive_avg_pool2d(cutout, self.cut_size))
- return torch.cat(cutouts)
-def spherical_dist_loss(x, y):
- x = F.normalize(x, dim=-1)
- y = F.normalize(y, dim=-1)
- return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
-def tv_loss(input):
- """L2 total variation loss, as in Mahendran et al."""
- input = F.pad(input, (0, 1, 0, 1), 'replicate')
- x_diff = input[..., :-1, 1:] - input[..., :-1, :-1]
- y_diff = input[..., 1:, :-1] - input[..., :-1, :-1]
- return (x_diff**2 + y_diff**2).mean([1, 2, 3])
-def range_loss(input):
- return (input - input.clamp(-1, 1)).pow(2).mean([1, 2, 3])
-
-def inference(text, init_image, skip_timesteps, clip_guidance_scale, tv_scale, range_scale, init_scale, seed, image_prompts,timestep_respacing, cutn):
- # Model settings
- model_config = model_and_diffusion_defaults()
- model_config.update({
- 'attention_resolutions': '32, 16, 8',
- 'class_cond': False,
- 'diffusion_steps': 1000,
- 'rescale_timesteps': True,
- 'timestep_respacing': str(timestep_respacing), # Modify this value to decrease the number of
- # timesteps.
- 'image_size': 256,
- 'learn_sigma': True,
- 'noise_schedule': 'linear',
- 'num_channels': 256,
- 'num_head_channels': 64,
- 'num_res_blocks': 2,
- 'resblock_updown': True,
- 'use_fp16': True,
- 'use_scale_shift_norm': True,
- })
- # Load models
- device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
- print('Using device:', device)
- model, diffusion = create_model_and_diffusion(**model_config)
- model.load_state_dict(torch.load('256x256_diffusion_uncond.pt', map_location='cpu'))
- model.requires_grad_(False).eval().to(device)
- for name, param in model.named_parameters():
- if 'qkv' in name or 'norm' in name or 'proj' in name:
- param.requires_grad_()
- if model_config['use_fp16']:
- model.convert_to_fp16()
- clip_model = clip.load('ViT-B/16', jit=False)[0].eval().requires_grad_(False).to(device)
- clip_size = clip_model.visual.input_resolution
- normalize = transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
- std=[0.26862954, 0.26130258, 0.27577711])
- lpips_model = lpips.LPIPS(net='vgg').to(device)
-
-#def inference(text, init_image, skip_timesteps, clip_guidance_scale, tv_scale, range_scale, init_scale, seed, image_prompt):
- all_frames = []
- prompts = [text]
- if image_prompts:
- image_prompts = [image_prompts.name]
- else:
- image_prompts = []
- batch_size = 1
- clip_guidance_scale = clip_guidance_scale # Controls how much the image should look like the prompt.
- tv_scale = tv_scale # Controls the smoothness of the final output.
- range_scale = range_scale # Controls how far out of range RGB values are allowed to be.
- cutn = cutn
- n_batches = 1
- if init_image:
- init_image = init_image.name
- else:
- init_image = None # This can be an URL or Colab local path and must be in quotes.
- skip_timesteps = skip_timesteps # This needs to be between approx. 200 and 500 when using an init image.
- # Higher values make the output look more like the init.
- init_scale = init_scale # This enhances the effect of the init image, a good value is 1000.
- seed = seed
-
- if seed is not None:
- torch.manual_seed(seed)
- make_cutouts = MakeCutouts(clip_size, cutn)
- side_x = side_y = model_config['image_size']
- target_embeds, weights = [], []
- for prompt in prompts:
- txt, weight = parse_prompt(prompt)
- target_embeds.append(clip_model.encode_text(clip.tokenize(txt).to(device)).float())
- weights.append(weight)
- for prompt in image_prompts:
- path, weight = parse_prompt(prompt)
- img = Image.open(fetch(path)).convert('RGB')
- img = TF.resize(img, min(side_x, side_y, *img.size), transforms.InterpolationMode.LANCZOS)
- batch = make_cutouts(TF.to_tensor(img).unsqueeze(0).to(device))
- embed = clip_model.encode_image(normalize(batch)).float()
- target_embeds.append(embed)
- weights.extend([weight / cutn] * cutn)
- target_embeds = torch.cat(target_embeds)
- weights = torch.tensor(weights, device=device)
- if weights.sum().abs() < 1e-3:
- raise RuntimeError('The weights must not sum to 0.')
- weights /= weights.sum().abs()
- init = None
- if init_image is not None:
- init = Image.open(fetch(init_image)).convert('RGB')
- init = init.resize((side_x, side_y), Image.LANCZOS)
- init = TF.to_tensor(init).to(device).unsqueeze(0).mul(2).sub(1)
- cur_t = None
- def cond_fn(x, t, y=None):
- with torch.enable_grad():
- x = x.detach().requires_grad_()
- n = x.shape[0]
- my_t = torch.ones([n], device=device, dtype=torch.long) * cur_t
- out = diffusion.p_mean_variance(model, x, my_t, clip_denoised=False, model_kwargs={'y': y})
- fac = diffusion.sqrt_one_minus_alphas_cumprod[cur_t]
- x_in = out['pred_xstart'] * fac + x * (1 - fac)
- clip_in = normalize(make_cutouts(x_in.add(1).div(2)))
- image_embeds = clip_model.encode_image(clip_in).float()
- dists = spherical_dist_loss(image_embeds.unsqueeze(1), target_embeds.unsqueeze(0))
- dists = dists.view([cutn, n, -1])
- losses = dists.mul(weights).sum(2).mean(0)
- tv_losses = tv_loss(x_in)
- range_losses = range_loss(out['pred_xstart'])
- loss = losses.sum() * clip_guidance_scale + tv_losses.sum() * tv_scale + range_losses.sum() * range_scale
- if init is not None and init_scale:
- init_losses = lpips_model(x_in, init)
- loss = loss + init_losses.sum() * init_scale
- return -torch.autograd.grad(loss, x)[0]
- if model_config['timestep_respacing'].startswith('ddim'):
- sample_fn = diffusion.ddim_sample_loop_progressive
- else:
- sample_fn = diffusion.p_sample_loop_progressive
- for i in range(n_batches):
- cur_t = diffusion.num_timesteps - skip_timesteps - 1
- samples = sample_fn(
- model,
- (batch_size, 3, side_y, side_x),
- clip_denoised=False,
- model_kwargs={},
- cond_fn=cond_fn,
- progress=True,
- skip_timesteps=skip_timesteps,
- init_image=init,
- randomize_class=True,
- )
- for j, sample in enumerate(samples):
- cur_t -= 1
- if j % 1 == 0 or cur_t == -1:
- print()
- for k, image in enumerate(sample['pred_xstart']):
- #filename = f'progress_{i * batch_size + k:05}.png'
- img = TF.to_pil_image(image.add(1).div(2).clamp(0, 1))
- all_frames.append(img)
- tqdm.write(f'Batch {i}, step {j}, output {k}:')
- #display.display(display.Image(filename))
- writer = imageio.get_writer('video.mp4', fps=5)
- for im in all_frames:
- writer.append_data(np.array(im))
- writer.close()
- return img, 'video.mp4'
-
-title = "CLIP Guided Diffusion HQ"
-description = "Gradio demo for CLIP Guided Diffusion. To use it, simply add your text, or click one of the examples to load them. Read more at the links below."
-article = "By Katherine Crowson (https://github.com/crowsonkb, https://twitter.com/RiversHaveWings). It uses OpenAI's 256x256 unconditional ImageNet diffusion model (https://github.com/openai/guided-diffusion) together with CLIP (https://github.com/openai/CLIP) to connect text prompts with images. | Colab
" -iface = gr.Interface(inference, inputs=["text",gr.inputs.Image(type="file", label='initial image (optional)', optional=True),gr.inputs.Slider(minimum=0, maximum=45, step=1, default=10, label="skip_timesteps"), gr.inputs.Slider(minimum=0, maximum=3000, step=1, default=600, label="clip guidance scale (Controls how much the image should look like the prompt)"), gr.inputs.Slider(minimum=0, maximum=1000, step=1, default=0, label="tv_scale (Controls the smoothness of the final output)"), gr.inputs.Slider(minimum=0, maximum=1000, step=1, default=0, label="range_scale (Controls how far out of range RGB values are allowed to be)"), gr.inputs.Slider(minimum=0, maximum=1000, step=1, default=0, label="init_scale (This enhances the effect of the init image)"), gr.inputs.Number(default=0, label="Seed"), gr.inputs.Image(type="file", label='image prompt (optional)', optional=True), gr.inputs.Slider(minimum=50, maximum=500, step=1, default=50, label="timestep respacing"),gr.inputs.Slider(minimum=1, maximum=64, step=1, default=32, label="cutn")], outputs=["image","video"], title=title, description=description, article=article, examples=[["coral reef city by artistation artists", "coralreef.jpeg", 0, 1000, 150, 50, 0, 0, "coralreef.jpeg", 90, 32]]) -iface.launch() \ No newline at end of file diff --git a/spaces/EvanMarie/hot_or_not/app.py b/spaces/EvanMarie/hot_or_not/app.py deleted file mode 100644 index 0bdda0067267998dc2479717a19ae7229e773f63..0000000000000000000000000000000000000000 --- a/spaces/EvanMarie/hot_or_not/app.py +++ /dev/null @@ -1,22 +0,0 @@ -__all__ = ['learner_faces', 'categories', 'classify_faces', 'image', 'label', 'examples', 'intf'] - -from fastai.vision.all import * -import gradio as gr - - -learner_attractiveness = load_learner('hotornot.pk1') -categories = ['average','hot', 'or not'] - -def classify_attractiveness(img): - prediction, index, probability = learner_attractiveness.predict(img) - return dict(zip(categories, map(float, probability))) - -intf = gr.Interface(fn=classify_attractiveness, - inputs=gr.Image(shape=(192, 192)), - outputs=gr.Label(), - title="Is an Image Hot or Not?", - description="Input an image, and see if the biased internet gods think it is hot! I suggest NOT using pictures of yourself. It is not as fun. Put your dog or cat or chinchilla in here instead! You can see what the deep learning model sees when you have input enough of them.", - examples=['img01.jpg', 'img02.jpg', 'img04.jpg', 'img05.jpg', 'img06.jpg', 'img07.jpeg', 'img08.jpg', 'img09.jpg', 'img10.jpg', 'img11.jpg', 'img12.jpg', 'img13.jpg', 'img14.jpg']) -intf.launch(inline=False) - - diff --git a/spaces/Flux9665/IMS-Toucan/Preprocessing/ArticulatoryCombinedTextFrontend.py b/spaces/Flux9665/IMS-Toucan/Preprocessing/ArticulatoryCombinedTextFrontend.py deleted file mode 100644 index fa1ddbce6d5fea5bbe5588cd35fd67563062f631..0000000000000000000000000000000000000000 --- a/spaces/Flux9665/IMS-Toucan/Preprocessing/ArticulatoryCombinedTextFrontend.py +++ /dev/null @@ -1,323 +0,0 @@ -import re -import sys - -import panphon -import phonemizer -import torch - -from Preprocessing.papercup_features import generate_feature_table - - -class ArticulatoryCombinedTextFrontend: - - def __init__(self, - language, - use_word_boundaries=False, # goes together well with - # parallel models and an aligner. Doesn't go together - # well with autoregressive models. - use_explicit_eos=True, - use_prosody=False, # unfortunately the non-segmental - # nature of prosodic markers mixed with the sequential - # phonemes hurts the performance of end-to-end models a - # lot, even though one might think enriching the input - # with such information would help. - use_lexical_stress=False, - silent=True, - allow_unknown=False, - add_silence_to_end=True, - strip_silence=True): - """ - Mostly preparing ID lookups - """ - self.strip_silence = strip_silence - self.use_word_boundaries = use_word_boundaries - self.allow_unknown = allow_unknown - self.use_explicit_eos = use_explicit_eos - self.use_prosody = use_prosody - self.use_stress = use_lexical_stress - self.add_silence_to_end = add_silence_to_end - self.feature_table = panphon.FeatureTable() - - if language == "en": - self.g2p_lang = "en-us" - self.expand_abbreviations = english_text_expansion - if not silent: - print("Created an English Text-Frontend") - - elif language == "de": - self.g2p_lang = "de" - self.expand_abbreviations = lambda x: x - if not silent: - print("Created a German Text-Frontend") - - elif language == "el": - self.g2p_lang = "el" - self.expand_abbreviations = lambda x: x - if not silent: - print("Created a Greek Text-Frontend") - - elif language == "es": - self.g2p_lang = "es" - self.expand_abbreviations = lambda x: x - if not silent: - print("Created a Spanish Text-Frontend") - - elif language == "fi": - self.g2p_lang = "fi" - self.expand_abbreviations = lambda x: x - if not silent: - print("Created a Finnish Text-Frontend") - - elif language == "ru": - self.g2p_lang = "ru" - self.expand_abbreviations = lambda x: x - if not silent: - print("Created a Russian Text-Frontend") - - elif language == "hu": - self.g2p_lang = "hu" - self.expand_abbreviations = lambda x: x - if not silent: - print("Created a Hungarian Text-Frontend") - - elif language == "nl": - self.g2p_lang = "nl" - self.expand_abbreviations = lambda x: x - if not silent: - print("Created a Dutch Text-Frontend") - - elif language == "fr": - self.g2p_lang = "fr-fr" - self.expand_abbreviations = lambda x: x - if not silent: - print("Created a French Text-Frontend") - - elif language == "it": - self.g2p_lang = "it" - self.expand_abbreviations = lambda x: x - if not silent: - print("Created a Italian Text-Frontend") - - elif language == "pt": - self.g2p_lang = "pt" - self.expand_abbreviations = lambda x: x - if not silent: - print("Created a Portuguese Text-Frontend") - - elif language == "pl": - self.g2p_lang = "pl" - self.expand_abbreviations = lambda x: x - if not silent: - print("Created a Polish Text-Frontend") - - # remember to also update get_language_id() when adding something here - - else: - print("Language not supported yet") - sys.exit() - - self.phone_to_vector_papercup = generate_feature_table() - - self.phone_to_vector = dict() - for phone in self.phone_to_vector_papercup: - panphon_features = self.feature_table.word_to_vector_list(phone, numeric=True) - if panphon_features == []: - panphon_features = [[0] * 24] - papercup_features = self.phone_to_vector_papercup[phone] - self.phone_to_vector[phone] = papercup_features + panphon_features[0] - - self.phone_to_id = { # this lookup must be updated manually, because the only - # other way would be extracting them from a set, which can be non-deterministic - '~': 0, - '#': 1, - '?': 2, - '!': 3, - '.': 4, - 'É': 5, - 'É«': 6, - 'É': 7, - 'É': 8, - 'a': 9, - 'Ć°': 10, - 'É': 11, - 'ÉŖ': 12, - 'įµ»': 13, - 'Å': 14, - 'É': 15, - 'É': 16, - 'ɾ': 17, - 'Ź': 18, - 'Īø': 19, - 'Ź': 20, - 'Ź': 21, - 'Ź': 22, - 'Ʀ': 23, - 'b': 24, - 'Ź': 25, - 'd': 26, - 'e': 27, - 'f': 28, - 'g': 29, - 'h': 30, - 'i': 31, - 'j': 32, - 'k': 33, - 'l': 34, - 'm': 35, - 'n': 36, - 'ɳ': 37, - 'o': 38, - 'p': 39, - 'É”': 40, - 'ɹ': 41, - 'r': 42, - 's': 43, - 't': 44, - 'u': 45, - 'v': 46, - 'w': 47, - 'x': 48, - 'z': 49, - 'Ź': 50, - 'Ćø': 51, - 'Ƨ': 52, - 'É': 53, - 'Å': 54, - 'y': 55, - 'Ź': 56, - 'É': 57, - 'c': 58, - 'ɲ': 59, - 'É£': 60, - 'Ź': 61, - 'Ī²': 62, - 'Ź': 63, - 'É': 64, - 'q': 65, - 'É': 66, - 'Ź²': 67, - 'É': 68, - 'ɵ': 69, - 'Ź': 70, - 'Ź': 71, - 'Ź': 72, - 'ÉØ': 73, - 'Ź': 74, - 'ɬ': 75, - } # for the states of the ctc loss and dijkstra/mas in the aligner - - self.id_to_phone = {v: k for k, v in self.phone_to_id.items()} - - def string_to_tensor(self, text, view=False, device="cpu", handle_missing=True, input_phonemes=False): - """ - Fixes unicode errors, expands some abbreviations, - turns graphemes into phonemes and then vectorizes - the sequence as articulatory features - """ - if input_phonemes: - phones = text - else: - phones = self.get_phone_string(text=text, include_eos_symbol=True) - if view: - print("Phonemes: \n{}\n".format(phones)) - phones_vector = list() - # turn into numeric vectors - for char in phones: - if handle_missing: - try: - phones_vector.append(self.phone_to_vector[char]) - except KeyError: - print("unknown phoneme: {}".format(char)) - else: - phones_vector.append(self.phone_to_vector[char]) # leave error handling to elsewhere - - return torch.Tensor(phones_vector, device=device) - - def get_phone_string(self, text, include_eos_symbol=True): - # expand abbreviations - utt = self.expand_abbreviations(text) - # phonemize - phones = phonemizer.phonemize(utt, - language_switch='remove-flags', - backend="espeak", - language=self.g2p_lang, - preserve_punctuation=True, - strip=True, - punctuation_marks=';:,.!?Ā”Āæāā¦"Ā«Ā»āā~/', - with_stress=self.use_stress).replace(";", ",").replace("/", " ").replace("ā", "") \ - .replace(":", ",").replace('"', ",").replace("-", ",").replace("...", ",").replace("-", ",").replace("\n", " ") \ - .replace("\t", " ").replace("Ā”", "").replace("Āæ", "").replace(",", "~").replace(" Ģ", "").replace('Ģ©', "").replace("Ģ", "").replace("ĢŖ", "") - # less than 1 wide characters hidden here - phones = re.sub("~+", "~", phones) - if not self.use_prosody: - # retain ~ as heuristic pause marker, even though all other symbols are removed with this option. - # also retain . ? and ! since they can be indicators for the stop token - phones = phones.replace("Ė", "").replace("Ė", "").replace("Ė", "") \ - .replace("Ė", "").replace("|", "").replace("ā", "") - if not self.use_word_boundaries: - phones = phones.replace(" ", "") - else: - phones = re.sub(r"\s+", " ", phones) - phones = re.sub(" ", "~", phones) - if self.strip_silence: - phones = phones.lstrip("~").rstrip("~") - if self.add_silence_to_end: - phones += "~" # adding a silence in the end during add_silence_to_end produces more natural sounding prosody - if include_eos_symbol: - phones += "#" - - phones = "~" + phones - phones = re.sub("~+", "~", phones) - - return phones - - -def english_text_expansion(text): - """ - Apply as small part of the tacotron style text cleaning pipeline, suitable for e.g. LJSpeech. - See https://github.com/keithito/tacotron/ - Careful: Only apply to english datasets. Different languages need different cleaners. - """ - _abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in - [('Mrs.', 'misess'), ('Mr.', 'mister'), ('Dr.', 'doctor'), ('St.', 'saint'), ('Co.', 'company'), ('Jr.', 'junior'), ('Maj.', 'major'), - ('Gen.', 'general'), ('Drs.', 'doctors'), ('Rev.', 'reverend'), ('Lt.', 'lieutenant'), ('Hon.', 'honorable'), ('Sgt.', 'sergeant'), - ('Capt.', 'captain'), ('Esq.', 'esquire'), ('Ltd.', 'limited'), ('Col.', 'colonel'), ('Ft.', 'fort')]] - for regex, replacement in _abbreviations: - text = re.sub(regex, replacement, text) - return text - - -def get_language_id(language): - if language == "en": - return torch.LongTensor([12]) - elif language == "de": - return torch.LongTensor([1]) - elif language == "el": - return torch.LongTensor([2]) - elif language == "es": - return torch.LongTensor([3]) - elif language == "fi": - return torch.LongTensor([4]) - elif language == "ru": - return torch.LongTensor([5]) - elif language == "hu": - return torch.LongTensor([6]) - elif language == "nl": - return torch.LongTensor([7]) - elif language == "fr": - return torch.LongTensor([8]) - elif language == "pt": - return torch.LongTensor([9]) - elif language == "pl": - return torch.LongTensor([10]) - elif language == "it": - return torch.LongTensor([11]) - - -if __name__ == '__main__': - # test an English utterance - tfr_en = ArticulatoryCombinedTextFrontend(language="en") - print(tfr_en.string_to_tensor("This is a complex sentence, it even has a pause! But can it do this? Nice.", view=True)) - - tfr_en = ArticulatoryCombinedTextFrontend(language="de") - print(tfr_en.string_to_tensor("Alles klar, jetzt testen wir einen deutschen Satz. Ich hoffe es gibt nicht mehr viele unspezifizierte Phoneme.", view=True)) diff --git a/spaces/FrankZxShen/so-vits-svc-models-ba/vencoder/HubertSoft_Onnx.py b/spaces/FrankZxShen/so-vits-svc-models-ba/vencoder/HubertSoft_Onnx.py deleted file mode 100644 index 06f10a4ca79c429ed59ab9743578128e8db506cc..0000000000000000000000000000000000000000 --- a/spaces/FrankZxShen/so-vits-svc-models-ba/vencoder/HubertSoft_Onnx.py +++ /dev/null @@ -1,28 +0,0 @@ -from vencoder.encoder import SpeechEncoder -import onnxruntime -import torch - -class HubertSoft_Onnx(SpeechEncoder): - def __init__(self,vec_path = "pretrain/hubert-soft.onnx",device=None): - print("load model(s) from {}".format(vec_path)) - self.hidden_dim = 256 - if device is None: - self.dev = torch.device("cpu") - else: - self.dev = torch.device(device) - if device == 'cpu' or device == torch.device("cpu") or device is None: - providers = ['CPUExecutionProvider'] - elif device == 'cuda' or device == torch.device("cuda"): - providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] - self.model = onnxruntime.InferenceSession(vec_path, providers=providers) - - def encoder(self, wav): - feats = wav - if feats.dim() == 2: # double channels - feats = feats.mean(-1) - assert feats.dim() == 1, feats.dim() - feats = feats.view(1, -1) - feats = feats.unsqueeze(0).cpu().detach().numpy() - onnx_input = {self.model.get_inputs()[0].name: feats} - logits = self.model.run(None, onnx_input) - return torch.tensor(logits[0]).transpose(1, 2).to(self.dev) \ No newline at end of file diff --git a/spaces/GT-RIPL/GPT-K/model/gptk.py b/spaces/GT-RIPL/GPT-K/model/gptk.py deleted file mode 100644 index 4e05a51ede68b16091b038e00a0aa16867197c44..0000000000000000000000000000000000000000 --- a/spaces/GT-RIPL/GPT-K/model/gptk.py +++ /dev/null @@ -1,503 +0,0 @@ -import logging -from omegaconf import OmegaConf -import copy - -import spacy -import torch -from torch import nn -from torchvision import transforms as T -from torchvision.transforms.functional import InterpolationMode -from transformers import LlamaTokenizer, BertTokenizer, BitsAndBytesConfig - -import sys -sys.path.append("./") -from model.knwl_model import KnwlModel, KnwlEncoder -from model.utils import drop_sequence_mask, cat_pad, disabled_train, download_cached_file -from model.eva_vit import create_eva_vit_g -from model.qformer import BertConfig, BertLMHeadModel -from model.llama import LlamaForCausalLM, LlamaConfig - - -class GPTK(nn.Module): - def __init__( - self, - img_size=224, - drop_path_rate=0, - use_grad_checkpoint=True, - vit_precision="fp16", - num_query_token=32, - llm_model="", - prompt="", - max_txt_len=128, - max_output_txt_len=256, - d_knwl=768, - topk={}, - pc=0.1, - pt=0.1, - pv=0.1 - ): - super().__init__() - - self.topk = {k: v for k, v in topk.items() if v > 0} - self.pc = pc - self.pt = pt - self.pv = pv - - # LLM - self.llm_tokenizer = LlamaTokenizer.from_pretrained(llm_model, use_fast=False, truncation_side="left") - llm_config = LlamaConfig.from_pretrained(llm_model) - llm_config.gradient_checkpointing = True - llm_config.use_cache = True - quantization_config = BitsAndBytesConfig( - load_in_8bit=True, - llm_int8_threshold=6.0, - llm_int8_has_fp16_weight=False, - bnb_4bit_compute_dtype=torch.float16, - bnb_4bit_use_double_quant=True, - bnb_4bit_quant_type='nf4' - ) - self.llm_model = LlamaForCausalLM.from_pretrained( - llm_model, config=llm_config, torch_dtype=torch.float16, quantization_config=quantization_config - ) - self.llm_tokenizer.add_special_tokens({'pad_token': '[PAD]'}) - self.llm_tokenizer.add_special_tokens({'bos_token': ''}) - self.llm_tokenizer.add_special_tokens({'eos_token': ''}) - self.llm_tokenizer.add_special_tokens({'unk_token': ''}) - self.llm_model.resize_token_embeddings(len(self.llm_tokenizer)) - for name, param in self.llm_model.named_parameters(): - param.requires_grad = False - - # ViT image encoder - self.visual_encoder, self.ln_vision = self.init_vision_encoder( - img_size, drop_path_rate, use_grad_checkpoint, vit_precision - ) - for name, param in self.visual_encoder.named_parameters(): - param.requires_grad = False - self.visual_encoder = self.visual_encoder.eval() - self.visual_encoder.train = disabled_train - logging.info("freeze vision encoder") - - # Q-former - self.tokenizer = self.init_tokenizer(truncation_side="left") - self.Qformer, self.query_tokens = self.init_Qformer( - num_query_token, self.visual_encoder.num_features - ) - self.Qformer.resize_token_embeddings(len(self.tokenizer)) - self.Qformer.cls = None - self.llm_proj = nn.Linear( - self.Qformer.config.hidden_size, self.llm_model.config.hidden_size - ) - - # Knowledge modules - if len(self.topk) > 0: # all added modules must contain "knwl" in their names - self.knwl_encoder = KnwlEncoder(self.visual_encoder.num_features) - self.knwl_query = copy.deepcopy(self.query_tokens) - for k in self.topk.keys(): - m = KnwlModel(d_knwl=d_knwl, d_out=self.knwl_encoder.d, pt=pt) - setattr(self, f"knwl_{k}", m) - - self.max_txt_len = max_txt_len - self.max_output_txt_len = max_output_txt_len - self.prompt = prompt - prompt_tokens = self.llm_tokenizer(self.prompt, return_tensors="pt") - self.prompt_length = prompt_tokens.attention_mask.sum(1) - - @classmethod - def init_tokenizer(cls, truncation_side="right"): - tokenizer = BertTokenizer.from_pretrained("bert-base-uncased", truncation_side=truncation_side) - tokenizer.add_special_tokens({"bos_token": "[DEC]"}) - return tokenizer - - @classmethod - def init_Qformer(cls, num_query_token, vision_width, cross_attention_freq=2): - encoder_config = BertConfig.from_pretrained("bert-base-uncased") - encoder_config.encoder_width = vision_width - # insert cross-attention layer every other block - encoder_config.add_cross_attention = True - encoder_config.cross_attention_freq = cross_attention_freq - encoder_config.query_length = num_query_token - - logging.disable(logging.CRITICAL) - Qformer = BertLMHeadModel.from_pretrained( - "bert-base-uncased", config=encoder_config - ) - logging.disable(logging.NOTSET) - - query_tokens = nn.Parameter( - torch.zeros(1, num_query_token, encoder_config.hidden_size) - ) - query_tokens.data.normal_(mean=0.0, std=encoder_config.initializer_range) - return Qformer, query_tokens - - @classmethod - def init_vision_encoder(cls, img_size, drop_path_rate, use_grad_checkpoint, precision): - visual_encoder = create_eva_vit_g( - img_size, drop_path_rate, use_grad_checkpoint, precision - ) - ln_vision = nn.LayerNorm(visual_encoder.num_features) - - return visual_encoder, ln_vision - - def concat_text_input_output(self, input_ids, input_atts, output_ids, output_atts): - input_part_targets_len = [] - llm_tokens = {"input_ids": [], "attention_mask": []} - for i in range(input_ids.size(0)): - this_input_ones = input_atts[i].sum() - input_part_targets_len.append(this_input_ones) - llm_tokens['input_ids'].append( - torch.cat([ - input_ids[i][:this_input_ones], - output_ids[i][1:], - input_ids[i][this_input_ones:] - ]) - ) - llm_tokens['attention_mask'].append( - torch.cat([ - input_atts[i][:this_input_ones], - output_atts[i][1:], - input_atts[i][this_input_ones:] - ]) - ) - llm_tokens['input_ids'] = torch.stack(llm_tokens['input_ids']) - llm_tokens['attention_mask'] = torch.stack(llm_tokens['attention_mask']) - - return llm_tokens, input_part_targets_len - - def forward_qformer(self, image, knowledge, prompt): - views = [] - - # knowledge embeds - if len(self.topk) > 0 and knowledge is not None: - embeds, masks = [], [] - for k in knowledge.keys(): - embeds_k, masks_k = getattr(self, f"knwl_{k}")(knowledge[k]) - embeds.append(embeds_k) - masks.append(masks_k) - embeds = cat_pad(embeds, cat_dim=0, pad_dim=1) - masks = cat_pad(masks, cat_dim=0, pad_dim=1) - - embeds = self.knwl_encoder( - inputs_embeds=embeds, attention_mask=masks - ) - embeds = nn.functional.dropout( - embeds, p=self.pc, training=self.training - ) - - N, (S, d) = len(image), embeds.shape[1:] - embeds = embeds.reshape(-1, N, S, d) - embeds = embeds.transpose(0, 1).flatten(1, 2) - masks = masks.reshape(-1, N, S) - masks = masks.transpose(0, 1).flatten(1, 2) - views.append((embeds, masks, self.knwl_query)) - - # image embeds - embeds = self.ln_vision(self.visual_encoder(image)) - embeds = nn.functional.dropout( - embeds, p=self.pc, training=self.training - ) - masks = drop_sequence_mask( - *embeds.shape[:2], image.device, self.pt, self.training - ) - views.append((embeds, masks, self.query_tokens)) - - # Qformer forward - text_Qformer = self.tokenizer( - prompt, - padding='longest', - truncation=True, - max_length=self.max_txt_len, - return_tensors="pt", - ).to(image.device) - - qfm_embeds, qfm_masks = [], [] - for embeds, masks, query in views: - query = query.expand(image.shape[0], -1, -1) - query_atts = torch.ones(query.size()[:-1], dtype=torch.long).to(embeds.device) - Qformer_atts = torch.cat([query_atts, text_Qformer.attention_mask], dim=1) - - query_output = self.Qformer.bert( - text_Qformer.input_ids, - attention_mask=Qformer_atts, - query_embeds=query, - encoder_hidden_states=embeds, - encoder_attention_mask=masks, - return_dict=True, - ) - embeds = self.llm_proj(query_output.last_hidden_state[:, :query.size(1),:]) - masks = torch.ones(embeds.size()[:-1], dtype=torch.long).to(image.device) - qfm_embeds.append(embeds) - qfm_masks.append(masks) - - # drop views - if self.training: - view_masks = drop_sequence_mask(len(image), len(qfm_embeds), image.device, self.pv) - qfm_masks = [m * view_masks[:, i:(i+1)] for i, m in enumerate(qfm_masks)] - llm_embeds = torch.cat(qfm_embeds, dim=1) - llm_masks = torch.cat(qfm_masks, dim=1) - - return llm_embeds, llm_masks - - def forward(self, samples): - inputs_llm, atts_llm = self.forward_qformer( - samples["image"], samples["knowledge"], samples["prompt"] - ) - device = inputs_llm.device - - self.llm_tokenizer.padding_side = "right" - self.llm_tokenizer.truncation_side = 'left' - text_input_tokens = self.llm_tokenizer( - samples['prompt'], - return_tensors="pt", - padding="longest", - truncation=True, - max_length=self.max_txt_len, - ).to(device) - - self.llm_tokenizer.truncation_side = 'right' - text_output_tokens = self.llm_tokenizer( - [t + self.llm_tokenizer.eos_token for t in samples['output']], - return_tensors="pt", - padding="longest", - truncation=True, - max_length=self.max_output_txt_len, - ).to(device) - - llm_tokens, input_part_targets_len = self.concat_text_input_output( - text_input_tokens.input_ids, - text_input_tokens.attention_mask, - text_output_tokens.input_ids, - text_output_tokens.attention_mask, - ) - - # do not apply loss to the padding - targets = llm_tokens['input_ids'].masked_fill( - llm_tokens['input_ids'] == self.llm_tokenizer.pad_token_id, -100 - ) - - # do not apply loss to the text input (i.e., instruction) - for i, l in enumerate(input_part_targets_len): - targets[i][:l] = -100 - - # do not apply loss to the query tokens - empty_targets = ( - torch.ones(atts_llm.size(), dtype=torch.long).to(device).fill_(-100) - ) - targets = torch.cat([empty_targets, targets], dim=1) - - inputs_embeds = self.llm_model.get_input_embeddings()(llm_tokens['input_ids']) - inputs_embeds = torch.cat([inputs_llm, inputs_embeds], dim=1) - attention_mask = torch.cat([atts_llm, llm_tokens['attention_mask']], dim=1) - - outputs = self.llm_model( - inputs_embeds=inputs_embeds, - attention_mask=attention_mask, - return_dict=True, - labels=targets, - ) - loss = outputs.loss - - return loss - - @torch.no_grad() - def generate( - self, - samples, - use_nucleus_sampling=False, - num_beams=5, - max_length=256, - min_length=1, - top_p=0.9, - repetition_penalty=1.5, - length_penalty=1, - num_captions=1, - temperature=1, - streamer=None, - auto_cast=False - ): - prompt = samples["prompt"] if "prompt" in samples.keys() else self.prompt - if isinstance(prompt, str): - prompt = [prompt] * samples["image"].size(0) - else: - assert len(prompt) == samples["image"].size(0), \ - "The number of prompts must be equal to the batch size." - - with torch.cuda.amp.autocast(auto_cast): - inputs_llm, atts_llm = self.forward_qformer( - samples["image"], samples["knowledge"], prompt - ) - device = inputs_llm.device - - self.llm_tokenizer.padding_side = "left" - llm_tokens = self.llm_tokenizer( - prompt, - padding="longest", - return_tensors="pt" - ).to(device) - - inputs_embeds = self.llm_model.get_input_embeddings()(llm_tokens.input_ids) - inputs_embeds = torch.cat([inputs_llm, inputs_embeds], dim=1) - attention_mask = torch.cat([atts_llm, llm_tokens.attention_mask], dim=1) - - outputs = self.llm_model.generate( - inputs_embeds=inputs_embeds, - attention_mask=attention_mask, - do_sample=use_nucleus_sampling, - top_p=top_p, - temperature=temperature, - num_beams=num_beams, - max_length=max_length, - min_length=min_length, - repetition_penalty=repetition_penalty, - length_penalty=length_penalty, - num_return_sequences=num_captions, - streamer=streamer - ) - - if streamer is None: - outputs[outputs == 0] = 2 # convert output id 0 to 2 (eos_token_id) - output_text = self.llm_tokenizer.batch_decode(outputs, skip_special_tokens=True) - output_text = [text.strip() for text in output_text] - - return output_text - else: - return outputs - - - @torch.no_grad() - def predict_answers( - self, - samples, - num_beams=5, - max_len=10, - min_len=1, - length_penalty=0 - ): - output_text = self.generate( - samples, - num_beams=num_beams, - max_length=max_len, - min_length=min_len, - length_penalty=length_penalty - ) - output_text = self._lemmatize(output_text) - - return output_text - - def _lemmatize(self, answers): - lemmatizer = spacy.load("en_core_web_sm") - def apply(answer): - doc = lemmatizer(answer) - - words = [] - for token in doc: - if token.pos_ in ["NOUN", "VERB"]: - words.append(token.lemma_) - else: - words.append(token.text) - answer = " ".join(words) - - return answer - - return [apply(answer) for answer in answers] - - @classmethod - def from_config(cls, cfg): - llm_model = cfg.get("llm_model") - num_query_token = cfg.get("num_query_token", 32) - - img_size = cfg.get("image_size", 224) - drop_path_rate = cfg.get("drop_path_rate", 0) - use_grad_checkpoint = cfg.get("use_grad_checkpoint", True) - vit_precision = cfg.get("vit_precision", "fp16") - - prompt = cfg.get("prompt", "") - max_txt_len = cfg.get("max_txt_len", 128) - max_output_txt_len = cfg.get("max_output_txt_len", 256) - - d_knwl = cfg.get("d_knwl", 768) - topk = cfg.get("topk", {}) - pc = cfg.get("pc", 0.1) - pt = cfg.get("pt", 0.1) - pv = cfg.get("pv", 0.4) - - model = cls( - img_size=img_size, - drop_path_rate=drop_path_rate, - use_grad_checkpoint=use_grad_checkpoint, - vit_precision=vit_precision, - num_query_token=num_query_token, - llm_model=llm_model, - prompt=prompt, - max_txt_len=max_txt_len, - max_output_txt_len=max_output_txt_len, - d_knwl=d_knwl, - topk=topk, - pc=pc, - pt=pt, - pv=pv - ) - - pretrain_path = cfg.get("pretrained", None) - assert pretrain_path is not None, "Pretrain_path is None." - cached_file = download_cached_file( - pretrain_path, check_hash=False, progress=True - ) - checkpoint = torch.load(cached_file, map_location="cpu") - state_dict = checkpoint["model"] - model.load_state_dict(state_dict, strict=False) - logging.info("load checkpoint from %s" % pretrain_path) - - return model - - -def get_gptk_image_transform(model_type: str = "gptk-7b"): - assert model_type in ("gptk-7b", "gptk-13b") - model_config = OmegaConf.load(f"model/{model_type}.yaml") - size = model_config.get("image_size", 224) - - normalizer = T.Normalize( - mean=(0.48145466, 0.4578275, 0.40821073), - std=(0.26862954, 0.26130258, 0.27577711) - ) - trans_train = T.Compose([ - T.RandomResizedCrop( - size=(size, size), scale=(0.5, 1.0), - interpolation=InterpolationMode.BICUBIC, - ), - T.RandomHorizontalFlip(), - T.ToTensor(), - normalizer - ]) - - trans_val = T.Compose([ - T.Resize( - size=(size, size), interpolation=InterpolationMode.BICUBIC, - ), - T.RandomHorizontalFlip(), - T.ToTensor(), - normalizer - ]) - - return trans_train, trans_val - - -def get_gptk_model( - model_type: str = "gptk-7b", - d_knwl: int = 768, - topk: dict = {"whole": 0, "five": 0, "nine": 0}, - pc: float = 0.1, pt: float = 0.1, pv: float = 0.4 - ): - assert model_type in ("gptk-7b", "gptk-13b") - - model_config = OmegaConf.load(f"model/{model_type}.yaml") - model_config.pv = pv - model_config.pt = pt - model_config.pc = pc - model_config.topk = {k: v for k, v in topk.items() if v > 0} - model_config.d_knwl = d_knwl - - model = GPTK.from_config(model_config) - if sum(topk.values()) > 0: - model.knwl_query.data.copy_(model.query_tokens.data.clone().detach()) - - return model diff --git a/spaces/GXSA/bingo/src/components/learn-more.tsx b/spaces/GXSA/bingo/src/components/learn-more.tsx deleted file mode 100644 index a64459ee7900a612292e117a6bda96ee9260990f..0000000000000000000000000000000000000000 --- a/spaces/GXSA/bingo/src/components/learn-more.tsx +++ /dev/null @@ -1,39 +0,0 @@ -import React from 'react' -import { SourceAttribution } from '@/lib/bots/bing/types' - -export interface LearnMoreProps { - sourceAttributions?: SourceAttribution[] -} - -export function LearnMore({ sourceAttributions }: LearnMoreProps) { - if (!sourceAttributions?.length) { - return null - } - - return ( -
-
- )
-}
diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/tasks/block_insertion.py b/spaces/Gen-Sim/Gen-Sim/cliport/tasks/block_insertion.py
deleted file mode 100644
index cd58049fab90eff1b68ee143efe59fb4137de84e..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/cliport/tasks/block_insertion.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import numpy as np
-from cliport.tasks.task import Task
-from cliport.utils import utils
-import pybullet as p
-
-
-class BlockInsertion(Task):
- """pick up the L-shaped red block and place it into the L-shaped fixture."""
-
- def __init__(self):
- super().__init__()
- self.max_steps = 3
- self.lang_template = "put the L shape block in the L shape hole"
- self.task_completed_desc = "done with insertion."
- self.additional_reset()
-
- def get_random_pose(self, env, obj_size):
- pose = super().get_random_pose(env, obj_size)
- pos, rot = pose
- rot = utils.eulerXYZ_to_quatXYZW((0, 0, np.pi / 2))
- return pos, rot
-
- def reset(self, env):
- super().reset(env)
-
- """Add L-shaped block."""
- size = (0.1, 0.1, 0.04)
- urdf = 'insertion/ell.urdf'
- pose = self.get_random_pose(env, size)
- block_id = env.add_object(urdf, pose)
-
- """Add L-shaped fixture to place block."""
- size = (0.1, 0.1, 0.04)
- urdf = 'insertion/fixture.urdf'
- targ_pose = self.get_random_pose(env, size)
- env.add_object(urdf, targ_pose, 'fixed')
-
- self.add_goal(objs=[block_id], matches=np.int32([[1]]), targ_poses=[targ_pose], replace=False,
- rotations=False, metric='pose', params=None, step_max_reward=1, symmetries=[2 * np.pi],
- language_goal=self.lang_template)
diff --git a/spaces/Godrose0728/Aisound02/export_model.py b/spaces/Godrose0728/Aisound02/export_model.py
deleted file mode 100644
index 52d3b3d083df7bf027b46d9c63e399b2da3f0e0a..0000000000000000000000000000000000000000
--- a/spaces/Godrose0728/Aisound02/export_model.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import torch
-
-if __name__ == '__main__':
- model_path = "saved_model/18/model.pth"
- output_path = "saved_model/18/model1.pth"
- checkpoint_dict = torch.load(model_path, map_location='cpu')
- checkpoint_dict_new = {}
- for k, v in checkpoint_dict.items():
- if k == "optimizer":
- print("remove optimizer")
- continue
- checkpoint_dict_new[k] = v
- torch.save(checkpoint_dict_new, output_path)
diff --git a/spaces/HReynaud/EchoDiffusionDemo/style.css b/spaces/HReynaud/EchoDiffusionDemo/style.css
deleted file mode 100644
index 671ceeb261c08db59cabbf628f997e7039ea9802..0000000000000000000000000000000000000000
--- a/spaces/HReynaud/EchoDiffusionDemo/style.css
+++ /dev/null
@@ -1,19 +0,0 @@
-/* div.controls {
- visibility: hidden;
-} */
-
-#component-0 {
- width: 70%;
- margin: 0 auto;
-}
-
-#component-8, #component-10, div.wrap.svelte-3cbf20, img.svelte-1ku16gh{
- width: 224px!important;
- margin: auto!important;
- height: 224px!important;
-}
-
-div.controls{
- visibility: hidden;
- display: none;
-}
\ No newline at end of file
diff --git a/spaces/Hallucinate/demo/README.md b/spaces/Hallucinate/demo/README.md
deleted file mode 100644
index 2aee598f4978738e63fda0ba9621c2a7256417c8..0000000000000000000000000000000000000000
--- a/spaces/Hallucinate/demo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Demo
-emoji: š
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/HamidRezaAttar/gpt2-home/examples.py b/spaces/HamidRezaAttar/gpt2-home/examples.py
deleted file mode 100644
index 7afd0c1e662339bc34f6ff2706c5d24925961b30..0000000000000000000000000000000000000000
--- a/spaces/HamidRezaAttar/gpt2-home/examples.py
+++ /dev/null
@@ -1,11 +0,0 @@
-EXAMPLES = {
- "Table": [
- "Handcrafted of solid acacia in weathered gray, our round Jozy drop-leaf dining table is a space-saving."
- ],
- "Bed": [
- "Maximize your bedroom space without sacrificing style with the storage bed."
- ],
- "Sofa": [
- "Our plush and luxurious Emmett modular sofa brings custom comfort to your living space."
- ]
-}
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/scripts/pca.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/scripts/pca.py
deleted file mode 100644
index 948cf5319fd86ba1bccff65270b2881048faf9b1..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/scripts/pca.py
+++ /dev/null
@@ -1,53 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import os
-import os.path as osp
-import numpy as np
-
-import faiss
-
-
-
-def get_parser():
- parser = argparse.ArgumentParser(
- description="compute a pca matrix given an array of numpy features"
- )
- # fmt: off
- parser.add_argument('data', help='numpy file containing features')
- parser.add_argument('--output', help='where to save the pca matrix', required=True)
- parser.add_argument('--dim', type=int, help='dim for pca reduction', required=True)
- parser.add_argument('--eigen-power', type=float, default=0, help='eigen power, -0.5 for whitening')
-
- return parser
-
-
-def main():
- parser = get_parser()
- args = parser.parse_args()
-
- print("Reading features")
- x = np.load(args.data, mmap_mode="r")
-
- print("Computing PCA")
- pca = faiss.PCAMatrix(x.shape[-1], args.dim, args.eigen_power)
- pca.train(x)
- b = faiss.vector_to_array(pca.b)
- A = faiss.vector_to_array(pca.A).reshape(pca.d_out, pca.d_in)
-
- os.makedirs(args.output, exist_ok=True)
-
- prefix = str(args.dim)
- if args.eigen_power != 0:
- prefix += f"_{args.eigen_power}"
-
- np.save(osp.join(args.output, f"{prefix}_pca_A"), A.T)
- np.save(osp.join(args.output, f"{prefix}_pca_b"), b)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/Harveenchadha/en_to_indic_translation/legacy/run_inference.sh b/spaces/Harveenchadha/en_to_indic_translation/legacy/run_inference.sh
deleted file mode 100644
index ff582a6c49d015cf36c82e8f20a755f6d1418ed8..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/en_to_indic_translation/legacy/run_inference.sh
+++ /dev/null
@@ -1,80 +0,0 @@
-src_lang=${1:-hi}
-tgt_lang=${2:-en}
-bucket_path=${3:-gs://ai4b-anuvaad-nmt/baselines/transformer-base/baselines-${src_lang}-${tgt_lang}}
-
-expdir=../baselines/baselines-${src_lang}-${tgt_lang}
-
-if [[ -d $expdir ]]
-then
- echo "$expdir exists on your filesystem. Please delete this if you have made some changes to the bucket files and trying to redownload"
-else
- mkdir -p $expdir
- mkdir -p $expdir/model
- cd ../baselines
- gsutil -m cp -r $bucket_path/vocab $expdir
- gsutil -m cp -r $bucket_path/final_bin $expdir
- gsutil -m cp $bucket_path/model/checkpoint_best.pt $expdir/model
- cd ../indicTrans
-fi
-
-
-if [ $src_lang == 'hi' ] || [ $tgt_lang == 'hi' ]; then
- #TEST_SETS=( wmt-news wat2021-devtest wat2020-devtest anuvaad-legal tico19 sap-documentation-benchmark all)
- TEST_SETS=( wat2021-devtest wat2020-devtest wat-2018 wmt-news )
-elif [ $src_lang == 'ta' ] || [ $tgt_lang == 'ta' ]; then
- # TEST_SETS=( wmt-news wat2021-devtest wat2020-devtest anuvaad-legal tico19 all)
- TEST_SETS=( wat2021-devtest wat2020-devtest wat-2018 wmt-news ufal-ta)
-elif [ $src_lang == 'bn' ] || [ $tgt_lang == 'bn' ]; then
- # TEST_SETS=( wat2021-devtest wat2020-devtest anuvaad-legal tico19 all)
- TEST_SETS=( wat2021-devtest wat2020-devtest wat-2018)
-elif [ $src_lang == 'gu' ] || [ $tgt_lang == 'gu' ]; then
- # TEST_SETS=( wmt-news wat2021-devtest wat2020-devtest all)
- TEST_SETS=( wat2021-devtest wat2020-devtest wmt-news )
-elif [ $src_lang == 'as' ] || [ $tgt_lang == 'as' ]; then
- TEST_SETS=( pmi )
-elif [ $src_lang == 'kn' ] || [ $tgt_lang == 'kn' ]; then
- # TEST_SETS=( wat2021-devtest anuvaad-legal all)
- TEST_SETS=( wat2021-devtest )
-elif [ $src_lang == 'ml' ] || [ $tgt_lang == 'ml' ]; then
- # TEST_SETS=( wat2021-devtest wat2020-devtest anuvaad-legal all)
- TEST_SETS=( wat2021-devtest wat2020-devtest wat-2018)
-elif [ $src_lang == 'mr' ] || [ $tgt_lang == 'mr' ]; then
- # TEST_SETS=( wat2021-devtest wat2020-devtest all)
- TEST_SETS=( wat2021-devtest wat2020-devtest )
-elif [ $src_lang == 'or' ] || [ $tgt_lang == 'or' ]; then
- TEST_SETS=( wat2021-devtest )
-elif [ $src_lang == 'pa' ] || [ $tgt_lang == 'pa' ]; then
- TEST_SETS=( wat2021-devtest )
-elif [ $src_lang == 'te' ] || [ $tgt_lang == 'te' ]; then
- # TEST_SETS=( wat2021-devtest wat2020-devtest anuvaad-legal all )
- TEST_SETS=( wat2021-devtest wat2020-devtest wat-2018)
-fi
-
-if [ $src_lang == 'en' ]; then
- indic_lang=$tgt_lang
-else
- indic_lang=$src_lang
-fi
-
-
-for tset in ${TEST_SETS[@]};do
- echo $tset $src_lang $tgt_lang
- if [ $tset == 'wat2021-devtest' ]; then
- SRC_FILE=${expdir}/benchmarks/$tset/test.$src_lang
- REF_FILE=${expdir}/benchmarks/$tset/test.$tgt_lang
- else
- SRC_FILE=${expdir}/benchmarks/$tset/en-${indic_lang}/test.$src_lang
- REF_FILE=${expdir}/benchmarks/$tset/en-${indic_lang}/test.$tgt_lang
- fi
- RESULTS_DIR=${expdir}/results/$tset
-
- mkdir -p $RESULTS_DIR
-
- bash translate.sh $SRC_FILE $RESULTS_DIR/${src_lang}-${tgt_lang} $src_lang $tgt_lang $expdir $REF_FILE
- # for newline between different outputs
- echo
-done
-# send the results to the bucket
-gsutil -m cp -r $expdir/results $bucket_path
-# clear up the space in the instance
-# rm -r $expdir
\ No newline at end of file
diff --git a/spaces/Harveenchadha/oiTrans/subword-nmt/subword_nmt/tests/test_bpe.py b/spaces/Harveenchadha/oiTrans/subword-nmt/subword_nmt/tests/test_bpe.py
deleted file mode 100644
index d8c84857b4aa22903907ae3d217bf9468b168c88..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/oiTrans/subword-nmt/subword_nmt/tests/test_bpe.py
+++ /dev/null
@@ -1,83 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-from __future__ import unicode_literals
-import unittest
-import codecs
-
-import os,sys,inspect
-currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
-parentdir = os.path.dirname(currentdir)
-sys.path.insert(0,parentdir)
-
-from learn_bpe import learn_bpe
-from apply_bpe import BPE
-
-
-class TestBPELearnMethod(unittest.TestCase):
-
- def test_learn_bpe(self):
- infile = codecs.open(os.path.join(currentdir,'data','corpus.en'), encoding='utf-8')
- outfile = codecs.open(os.path.join(currentdir,'data','bpe.out'), 'w', encoding='utf-8')
- learn_bpe(infile, outfile, 1000)
- infile.close()
- outfile.close()
-
- outlines = open(os.path.join(currentdir,'data','bpe.out'))
- reflines = open(os.path.join(currentdir,'data','bpe.ref'))
-
- for line, line2 in zip(outlines, reflines):
- self.assertEqual(line, line2)
-
- outlines.close()
- reflines.close()
-
-class TestBPESegmentMethod(unittest.TestCase):
-
- def setUp(self):
-
- with codecs.open(os.path.join(currentdir,'data','bpe.ref'), encoding='utf-8') as bpefile:
- self.bpe = BPE(bpefile)
-
- self.infile = codecs.open(os.path.join(currentdir,'data','corpus.en'), encoding='utf-8')
- self.reffile = codecs.open(os.path.join(currentdir,'data','corpus.bpe.ref.en'), encoding='utf-8')
-
- def tearDown(self):
-
- self.infile.close()
- self.reffile.close()
-
- def test_apply_bpe(self):
-
- for line, ref in zip(self.infile, self.reffile):
- out = self.bpe.process_line(line)
- self.assertEqual(out, ref)
-
- def test_trailing_whitespace(self):
- """BPE.proces_line() preserves leading and trailing whitespace"""
-
- orig = ' iron cement \n'
- exp = ' ir@@ on c@@ ement \n'
-
- out = self.bpe.process_line(orig)
- self.assertEqual(out, exp)
-
- def test_utf8_whitespace(self):
- """UTF-8 whitespace is treated as normal character, not word boundary"""
-
- orig = 'iron\xa0cement\n'
- exp = 'ir@@ on@@ \xa0@@ c@@ ement\n'
-
- out = self.bpe.process_line(orig)
- self.assertEqual(out, exp)
-
- def test_empty_line(self):
-
- orig = '\n'
- exp = '\n'
-
- out = self.bpe.process_line(orig)
- self.assertEqual(out, exp)
-
-if __name__ == '__main__':
- unittest.main()
diff --git a/spaces/HighCWu/GPEN/retinaface/data/config.py b/spaces/HighCWu/GPEN/retinaface/data/config.py
deleted file mode 100644
index a635090ad9c2f8b5689f8a86d2684675dc34ece2..0000000000000000000000000000000000000000
--- a/spaces/HighCWu/GPEN/retinaface/data/config.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# config.py
-
-cfg_mnet = {
- 'name': 'mobilenet0.25',
- 'msizes': [[16, 32], [64, 128], [256, 512]],
- 'steps': [8, 16, 32],
- 'variance': [0.1, 0.2],
- 'clip': False,
- 'loc_weight': 2.0,
- 'gpu_train': True,
- 'batch_size': 32,
- 'ngpu': 1,
- 'epoch': 250,
- 'decay1': 190,
- 'decay2': 220,
- 'image_size': 640,
- 'pretrain': False,
- 'return_layers': {'stage1': 1, 'stage2': 2, 'stage3': 3},
- 'in_channel': 32,
- 'out_channel': 64
-}
-
-cfg_re50 = {
- 'name': 'Resnet50',
- 'msizes': [[16, 32], [64, 128], [256, 512]],
- 'steps': [8, 16, 32],
- 'variance': [0.1, 0.2],
- 'clip': False,
- 'loc_weight': 2.0,
- 'gpu_train': True,
- 'batch_size': 24,
- 'ngpu': 4,
- 'epoch': 100,
- 'decay1': 70,
- 'decay2': 90,
- 'image_size': 840,
- 'pretrain': False,
- 'return_layers': {'layer2': 1, 'layer3': 2, 'layer4': 3},
- 'in_channel': 256,
- 'out_channel': 256
-}
-
diff --git a/spaces/Hina4867/bingo/src/components/tailwind-indicator.tsx b/spaces/Hina4867/bingo/src/components/tailwind-indicator.tsx
deleted file mode 100644
index f2a1291213dd67055fcebe67fab574c8441338df..0000000000000000000000000000000000000000
--- a/spaces/Hina4867/bingo/src/components/tailwind-indicator.tsx
+++ /dev/null
@@ -1,14 +0,0 @@
-export function TailwindIndicator() {
- if (process.env.NODE_ENV === 'production') return null
-
- return (
- äŗč§£čƦē»äæ”ęÆ:
-
-
-
- {sourceAttributions.map((attribution, index) => {
- const { providerDisplayName, seeMoreUrl } = attribution
- const { host } = new URL(seeMoreUrl)
- return (
-
- {index + 1}. {host}
-
- )
- })}
-
-
-
- )
-}
diff --git a/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool/run_data_measurements.py b/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool/run_data_measurements.py
deleted file mode 100644
index bed48a37a56a39fe06d586c1c83ded57d7a55f6c..0000000000000000000000000000000000000000
--- a/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool/run_data_measurements.py
+++ /dev/null
@@ -1,353 +0,0 @@
-import argparse
-import json
-from dotenv import load_dotenv
-import plotly
-import shutil
-import smtplib
-import ssl
-import sys
-import textwrap
-from data_measurements import dataset_statistics
-from data_measurements.zipf import zipf
-from huggingface_hub import create_repo, Repository, hf_api
-from os import getenv
-from os.path import exists, join as pjoin
-from pathlib import Path
-import utils
-from utils import dataset_utils
-
-logs = utils.prepare_logging(__file__)
-
-def load_or_prepare_widgets(ds_args, show_embeddings=False,
- show_perplexities=False, use_cache=False):
- """
- Loader specifically for the widgets used in the app.
- Args:
- ds_args:
- show_embeddings:
- show_perplexities:
- use_cache:
-
- Returns:
-
- """
- dstats = dataset_statistics.DatasetStatisticsCacheClass(**ds_args, use_cache=use_cache)
- # Header widget
- dstats.load_or_prepare_dset_peek()
- # General stats widget
- dstats.load_or_prepare_general_stats()
- # Labels widget
- dstats.load_or_prepare_labels()
- # Text lengths widget
- dstats.load_or_prepare_text_lengths()
- if show_embeddings:
- # Embeddings widget
- dstats.load_or_prepare_embeddings()
- if show_perplexities:
- # Text perplexities widget
- dstats.load_or_prepare_text_perplexities()
- # Text duplicates widget
- dstats.load_or_prepare_text_duplicates()
- # nPMI widget
- dstats.load_or_prepare_npmi()
- # Zipf widget
- dstats.load_or_prepare_zipf()
-
-
-def load_or_prepare(dataset_args, calculation=False, use_cache=False):
- # TODO: Catch error exceptions for each measurement, so that an error
- # for one measurement doesn't break the calculation of all of them.
-
- do_all = False
- dstats = dataset_statistics.DatasetStatisticsCacheClass(**dataset_args,
- use_cache=use_cache)
- logs.info("Tokenizing dataset.")
- dstats.load_or_prepare_tokenized_df()
- logs.info("Calculating vocab.")
- dstats.load_or_prepare_vocab()
-
- if not calculation:
- do_all = True
-
- if do_all or calculation == "general":
- logs.info("\n* Calculating general statistics.")
- dstats.load_or_prepare_general_stats()
- logs.info("Done!")
- logs.info(
- "Basic text statistics now available at %s." % dstats.general_stats_json_fid)
-
- if do_all or calculation == "duplicates":
- logs.info("\n* Calculating text duplicates.")
- dstats.load_or_prepare_text_duplicates()
- duplicates_fid_dict = dstats.duplicates_files
- logs.info("If all went well, then results are in the following files:")
- for key, value in duplicates_fid_dict.items():
- logs.info("%s: %s" % (key, value))
-
- if do_all or calculation == "lengths":
- logs.info("\n* Calculating text lengths.")
- dstats.load_or_prepare_text_lengths()
- length_fid_dict = dstats.length_obj.get_filenames()
- print("If all went well, then results are in the following files:")
- for key, value in length_fid_dict.items():
- print("%s: %s" % (key, value))
- print()
-
- if do_all or calculation == "labels":
- logs.info("\n* Calculating label statistics.")
- if dstats.label_field not in dstats.dset.features:
- logs.warning("No label field found.")
- logs.info("No label statistics to calculate.")
- else:
- dstats.load_or_prepare_labels()
- npmi_fid_dict = dstats.label_files
- print("If all went well, then results are in the following files:")
- for key, value in npmi_fid_dict.items():
- print("%s: %s" % (key, value))
- print()
-
- if do_all or calculation == "npmi":
- print("\n* Preparing nPMI.")
- dstats.load_or_prepare_npmi()
- npmi_fid_dict = dstats.npmi_files
- print("If all went well, then results are in the following files:")
- for key, value in npmi_fid_dict.items():
- if isinstance(value, dict):
- print(key + ":")
- for key2, value2 in value.items():
- print("\t%s: %s" % (key2, value2))
- else:
- print("%s: %s" % (key, value))
- print()
-
- if do_all or calculation == "zipf":
- logs.info("\n* Preparing Zipf.")
- dstats.load_or_prepare_zipf()
- logs.info("Done!")
- zipf_json_fid, zipf_fig_json_fid, zipf_fig_html_fid = zipf.get_zipf_fids(
- dstats.dataset_cache_dir)
- logs.info("Zipf results now available at %s." % zipf_json_fid)
- logs.info(
- "Figure saved to %s, with corresponding json at %s."
- % (zipf_fig_html_fid, zipf_fig_json_fid)
- )
-
- # Don't do this one until someone specifically asks for it -- takes awhile.
- if calculation == "embeddings":
- logs.info("\n* Preparing text embeddings.")
- dstats.load_or_prepare_embeddings()
-
- # Don't do this one until someone specifically asks for it -- takes awhile.
- if calculation == "perplexities":
- logs.info("\n* Preparing text perplexities.")
- dstats.load_or_prepare_text_perplexities()
-
-def pass_args_to_DMT(dset_name, dset_config, split_name, text_field, label_field, label_names, calculation, dataset_cache_dir, prepare_gui=False, use_cache=True):
- if not use_cache:
- logs.info("Not using any cache; starting afresh")
- dataset_args = {
- "dset_name": dset_name,
- "dset_config": dset_config,
- "split_name": split_name,
- "text_field": text_field,
- "label_field": label_field,
- "label_names": label_names,
- "dataset_cache_dir": dataset_cache_dir
- }
- if prepare_gui:
- load_or_prepare_widgets(dataset_args, use_cache=use_cache)
- else:
- load_or_prepare(dataset_args, calculation=calculation, use_cache=use_cache)
-
-def set_defaults(args):
- if not args.config:
- args.config = "default"
- logs.info("Config name not specified. Assuming it's 'default'.")
- if not args.split:
- args.split = "train"
- logs.info("Split name not specified. Assuming it's 'train'.")
- if not args.feature:
- args.feature = "text"
- logs.info("Text column name not given. Assuming it's 'text'.")
- if not args.label_field:
- args.label_field = "label"
- logs.info("Label column name not given. Assuming it's 'label'.")
- return args
-
-def main():
- parser = argparse.ArgumentParser(
- formatter_class=argparse.RawDescriptionHelpFormatter,
- description=textwrap.dedent(
- """
-
- Example for hate speech18 dataset:
- python3 run_data_measurements.py --dataset="hate_speech18" --config="default" --split="train" --feature="text"
-
- Example for IMDB dataset:
- python3 run_data_measurements.py --dataset="imdb" --config="plain_text" --split="train" --label_field="label" --feature="text"
- """
- ),
- )
-
- parser.add_argument(
- "-d", "--dataset", required=True, help="Name of dataset to prepare"
- )
- parser.add_argument(
- "-c", "--config", required=False, default="", help="Dataset configuration to prepare"
- )
- parser.add_argument(
- "-s", "--split", required=False, default="", type=str,
- help="Dataset split to prepare"
- )
- parser.add_argument(
- "-f",
- "--feature",
- "-t",
- "--text-field",
- required=False,
- nargs="+",
- type=str,
- default="",
- help="Column to prepare (handled as text)",
- )
- parser.add_argument(
- "-w",
- "--calculation",
- help="""What to calculate (defaults to everything except embeddings and perplexities).\n
- Options are:\n
-
- - `general` (for duplicate counts, missing values, length statistics.)\n
-
- - `duplicates` for duplicate counts\n
-
- - `lengths` for text length distribution\n
-
- - `labels` for label distribution\n
-
- - `embeddings` (Warning: Slow.)\n
-
- - `perplexities` (Warning: Slow.)\n
-
- - `npmi` for word associations\n
-
- - `zipf` for zipfian statistics
- """,
- )
- parser.add_argument(
- "-l",
- "--label_field",
- type=str,
- required=False,
- default="",
- help="Field name for label column in dataset (Required if there is a label field that you want information about)",
- )
- parser.add_argument('-n', '--label_names', nargs='+', default=[])
- parser.add_argument(
- "--use_cache",
- default=False,
- required=False,
- action="store_true",
- help="Whether to use cached files (Optional)",
- )
- parser.add_argument("--out_dir", default="cache_dir",
- help="Where to write out to.")
- parser.add_argument(
- "--overwrite_previous",
- default=False,
- required=False,
- action="store_true",
- help="Whether to overwrite a previous local cache for these same arguments (Optional)",
- )
- parser.add_argument(
- "--email",
- default=None,
- help="An email that recieves a message about whether the computation was successful. If email is not None, then you must have EMAIL_PASSWORD=xs
- sm
- md
- lg
- xl
- 2xl
- Tips1:åÆēØäŗŗčøäæ®å¤ä¼å¤§å¹ å¢å čæē®ę¶é“ļ¼å¦ééčÆÆļ¼čÆ·å°čÆåę¶å¾éčÆ„é锹^_^
" \ - r"Tips2:äŗęå”åØåø¦å®½åŖę4Mļ¼åå¶äŗęå”åØē½ē»ę åµļ¼äøå»ŗč®®äøä¼ å¤§äŗ40MBēę件ļ¼å®¹ęåŗē°éčÆÆ~XD" \ - r"Hugging Faceēå č“¹CPUčµęŗčæē®å¤Ŗę ¢ļ¼äøå»ŗč®®å¼åÆäŗŗčøäæ®å¤åč½ļ¼äøå¼åÆäŗŗčøäæ®å¤åč½å¤ēExampleēę¶é“ēŗ¦äøŗ5åéļ¼čÆ·čåæēå¾
š„ W2L-TD
" - - -def video_identity(video=gr.Video(), audio=gr.Audio(), face_restore=gr.Radio()): - input_video = video - input_audio = audio - filelist = os.listdir('results/') - filelists = [] - for i in filelist: - filelists.append(i.split('.')[0]) - idx = str(np.random.randint(0, 1e7)).zfill(7) - while (idx in filelists): - idx = str(np.random.randint(0, 1e7)).zfill(7) - - # outfile = 'results/' + idx + '.mp4' - # print(outfile) - outfile = None - if int(face_restore) == 1: - print('main\n' + input_video + '\n' + input_audio + '\n' + str(idx)) - outfile = main(input_video, input_audio, idx=idx) - else: - print('facerestore\n' + input_video + '\n' + input_audio + '\n' + str(idx)) - outfile = with_face_restore(input_video, input_audio, idx=idx) - - out = os.path.join(os.path.dirname(__file__), outfile) - - return out - - -demo = gr.Interface(video_identity, - [ - gr.Video(label='Input Video'), - gr.Audio(type='filepath', label='Input Audio'), - gr.Radio(["ęÆ", "å¦"], label="åÆēØäŗŗčøäæ®å¤", value='ęÆ', type='index'), - ], - "playable_video", - allow_flagging='never', - title=title, - description=description, - examples=[ - ['media/bbc.mp4', 'media/ęę¬1-ē·-en-US.mp3', 'ęÆ'], - ['media/xwlb.mp4', 'media/ęę¬2-儳-en-US.mp3', 'ęÆ'], - ['media/wty2.mp4', 'media/ęę¬3-儳-en-US.mp3', 'ęÆ'], - ['media/ywm.mp4', 'media/ęę¬1-ē·-zh-CN.mp3', 'ęÆ'], - ['media/tq.mp4', 'media/ęę¬2-ē·-zh-CN.mp3', 'ęÆ'], - ['media/lx.mp4', 'media/ęę¬3-ē·-zh-CN.mp3', 'ęÆ'], - ['media/weather1.mp4', 'media/ęę¬4-儳-zh-CN.mp3', 'ęÆ'], - ['media/biden.mp4', 'media/ęę¬5-儳-zh-CN.mp3', 'ęÆ'], - ], - cache_examples=True, - ) - -if __name__ == "__main__": - gr.close_all() - # demo.queue(concurrency_count=3, max_size=6) - demo.launch(show_error=True, server_port=7860, max_threads=6) diff --git a/spaces/Longtong/FoodVisionBig/app.py b/spaces/Longtong/FoodVisionBig/app.py deleted file mode 100644 index 7deb5910defa4d720eacc9b947d7eb6b22b6979a..0000000000000000000000000000000000000000 --- a/spaces/Longtong/FoodVisionBig/app.py +++ /dev/null @@ -1,65 +0,0 @@ -from timeit import default_timer as timer -from typing import Tuple, Dict -import PIL -import gradio as gr -import os -import torch -import torch.nn as nn -from torchvision.models import EfficientNet_B2_Weights, efficientnet_b2 - -from model import create_effnetb2_model - - -def predict(img): - start_time = timer() - # transform the image, add a batch dimension, bring to device - img_processed = transform(img) - img_processed = img_processed.unsqueeze(dim=0) - img_processed = img_processed.to(device) - - # set model in eval mode and make pred - model.eval() - with torch.inference_mode(): - logits = model(img_processed) - probs = logits.softmax(dim=1) - pred_labels_and_probs = {class_names[i]:float(probs[0][i].item()) for i in range(len(class_names))} - - end_time = timer() - pred_time = end_time - start_time - - return pred_labels_and_probs, pred_time - -if __name__ == "__main__": - # Setup class names - with open("./class_names.txt") as f: - class_names = [food.strip() for food in f.readlines()] - - # set the device to the cpu - device = "cpu" - - # create instance of effnetb2 model and transforms - model, transform = create_effnetb2_model(num_classes=101) - - # load the trained weights into - model_path = "./effnetb2_100_percent_food101.pth" - model.load_state_dict(torch.load(f=model_path, - map_location=torch.device("cpu"))) - - title = "Foodvision Big" - description = "EfficientNetB feature extractor to classify images of 101 different classes of food." - article = "Created as part of PyTorch course" - - example_list = [['examples/' + example] for example in os.listdir("examples")] - - # Create the Gradio - demo = gr.Interface(fn=predict, - inputs=gr.Image(type="pil"), - outputs = [gr.Label(num_top_classes=5, label="Prediction"), - gr.Number(label="Prediction time (s)")], - examples=example_list, - title=title, - description=description, - article=article) - - demo.launch(debug=False, # print errors locally - share=False) # generate a publically sharable URL diff --git a/spaces/Mahiruoshi/Lovelive-Nijigasaku-Chat-iSTFT-GPT3/README.md b/spaces/Mahiruoshi/Lovelive-Nijigasaku-Chat-iSTFT-GPT3/README.md deleted file mode 100644 index 92159c6b3ef87180d5cbd81a6ce6684fb2cf65a7..0000000000000000000000000000000000000000 --- a/spaces/Mahiruoshi/Lovelive-Nijigasaku-Chat-iSTFT-GPT3/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Lovelive-nijigasaki-MB-iSTFT-VITS-ZH&JP -emoji: š -colorFrom: pink -colorTo: pink -sdk: gradio -sdk_version: 3.15.0 -app_file: app.py -pinned: false -license: other ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/MaksMaib/PetGradioStyleTransf/app.py b/spaces/MaksMaib/PetGradioStyleTransf/app.py deleted file mode 100644 index 5180ff6749f72033644482c4f10db9afa41fca97..0000000000000000000000000000000000000000 --- a/spaces/MaksMaib/PetGradioStyleTransf/app.py +++ /dev/null @@ -1,22 +0,0 @@ -import gradio as gr -import model - - -def inference(content, style): - result = model.main(style.name, content.name) - return result - - -if __name__ == "__main__": - title = " Part of my PET project" - description = "One of my attempts to combine machine learning models and a web interface. Gredio as black magic prototype for visualization of the model is ready in a couple of hours." - git = "" - examples = [ - ['picasso-old.jpg', 'picasso.jpg'], - ] - - inputs = [gr.inputs.Image(type="file", label='content'), gr.inputs.Image(type="file", label='style')] - output = gr.outputs.Image(type="pil") - my_app= gr.Interface(inference, inputs, outputs=output, title=title, article=git, - description=description, examples=examples) - my_app.launch() diff --git a/spaces/Manmay/tortoise-tts/tortoise/models/random_latent_generator.py b/spaces/Manmay/tortoise-tts/tortoise/models/random_latent_generator.py deleted file mode 100644 index e90ef2130a47ec52160709877972716352e04c9c..0000000000000000000000000000000000000000 --- a/spaces/Manmay/tortoise-tts/tortoise/models/random_latent_generator.py +++ /dev/null @@ -1,55 +0,0 @@ -import math - -import torch -import torch.nn as nn -import torch.nn.functional as F - - -def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5): - if bias is not None: - rest_dim = [1] * (input.ndim - bias.ndim - 1) - return ( - F.leaky_relu( - input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope - ) - * scale - ) - else: - return F.leaky_relu(input, negative_slope=0.2) * scale - - -class EqualLinear(nn.Module): - def __init__( - self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1 - ): - super().__init__() - self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul)) - if bias: - self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init)) - else: - self.bias = None - self.scale = (1 / math.sqrt(in_dim)) * lr_mul - self.lr_mul = lr_mul - - def forward(self, input): - out = F.linear(input, self.weight * self.scale) - out = fused_leaky_relu(out, self.bias * self.lr_mul) - return out - - -class RandomLatentConverter(nn.Module): - def __init__(self, channels): - super().__init__() - self.layers = nn.Sequential(*[EqualLinear(channels, channels, lr_mul=.1) for _ in range(5)], - nn.Linear(channels, channels)) - self.channels = channels - - def forward(self, ref): - r = torch.randn(ref.shape[0], self.channels, device=ref.device) - y = self.layers(r) - return y - - -if __name__ == '__main__': - model = RandomLatentConverter(512) - model(torch.randn(5,512)) \ No newline at end of file diff --git a/spaces/Maqueda/SG161222-Realistic_Vision_V1.4/app.py b/spaces/Maqueda/SG161222-Realistic_Vision_V1.4/app.py deleted file mode 100644 index a3cc9b493946644ef46fa95cde231d3773b98d0c..0000000000000000000000000000000000000000 --- a/spaces/Maqueda/SG161222-Realistic_Vision_V1.4/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/SG161222/Realistic_Vision_V1.4").launch() \ No newline at end of file diff --git a/spaces/Marshalls/testmtd/models/util/array_util.py b/spaces/Marshalls/testmtd/models/util/array_util.py deleted file mode 100644 index a27bd1cdd2be25318188b6048430a4376c58f433..0000000000000000000000000000000000000000 --- a/spaces/Marshalls/testmtd/models/util/array_util.py +++ /dev/null @@ -1,132 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class Flip(nn.Module): - def forward(self, x, cond, sldj, reverse=False): - assert isinstance(x, tuple) and len(x) == 2 - return (x[1], x[0]), sldj - - -def mean_dim(tensor, dim=None, keepdims=False): - """Take the mean along multiple dimensions. - - Args: - tensor (torch.Tensor): Tensor of values to average. - dim (list): List of dimensions along which to take the mean. - keepdims (bool): Keep dimensions rather than squeezing. - - Returns: - mean (torch.Tensor): New tensor of mean value(s). - """ - if dim is None: - return tensor.mean() - else: - if isinstance(dim, int): - dim = [dim] - dim = sorted(dim) - for d in dim: - tensor = tensor.mean(dim=d, keepdim=True) - if not keepdims: - for i, d in enumerate(dim): - tensor.squeeze_(d-i) - return tensor - - -def checkerboard(x, reverse=False): - """Split x in a checkerboard pattern. Collapse horizontally.""" - # Get dimensions - if reverse: - b, c, h, w = x[0].size() - w *= 2 - device = x[0].device - else: - b, c, h, w = x.size() - device = x.device - - # Get list of indices in alternating checkerboard pattern - y_idx = [] - z_idx = [] - for i in range(h): - for j in range(w): - if (i % 2) == (j % 2): - y_idx.append(i * w + j) - else: - z_idx.append(i * w + j) - y_idx = torch.tensor(y_idx, dtype=torch.int64, device=device) - z_idx = torch.tensor(z_idx, dtype=torch.int64, device=device) - - if reverse: - y, z = (t.contiguous().view(b, c, h // 2 * w) for t in x) - x = torch.zeros(b, c, h * w, dtype=y.dtype, device=y.device) - x[:, :, y_idx] += y - x[:, :, z_idx] += z - x = x.view(b, c, h, w) - - return x - else: - if h % 2 != 0: - raise RuntimeError('Checkerboard got odd height input: {}'.format(h)) - - x = x.view(b, c, h * w) - y = x[:, :, y_idx].view(b, c, h // 2, w) - z = x[:, :, z_idx].view(b, c, h // 2, w) - - return y, z - - -def channelwise(x, reverse=False): - """Split x channel-wise.""" - if reverse: - x = torch.cat(x, dim=1) - return x - else: - y, z = x.chunk(2, dim=1) - return y, z - - -def squeeze(x): - """Trade spatial extent for channels. I.e., convert each - 1x4x4 volume of input into a 4x1x1 volume of output. - - Args: - x (torch.Tensor): Input to squeeze. - - Returns: - x (torch.Tensor): Squeezed or unsqueezed tensor. - """ - # import pdb; pdb.set_trace() - b, c, h, w = x.size() - x = x.view(b, c, h // 2, 2, w, 1) - x = x.permute(0, 1, 3, 5, 2, 4).contiguous() - x = x.view(b, c * 2, h // 2, w) - - return x - - -def unsqueeze(x): - """Trade channels channels for spatial extent. I.e., convert each - 4x1x1 volume of input into a 1x4x4 volume of output. - - Args: - x (torch.Tensor): Input to unsqueeze. - - Returns: - x (torch.Tensor): Unsqueezed tensor. - """ - b, c, h, w = x.size() - x = x.view(b, c // 2, 2, 1, h, w) - x = x.permute(0, 1, 4, 2, 5, 3).contiguous() - x = x.view(b, c // 2, h * 2, w) - - return x - - -def concat_elu(x): - """Concatenated ReLU (http://arxiv.org/abs/1603.05201), but with ELU.""" - return F.elu(torch.cat((x, -x), dim=1)) - - -def safe_log(x): - return torch.log(x.clamp(min=1e-22)) diff --git a/spaces/MashiroSA/sovits-emu-voice-transform/vdecoder/hifigan/env.py b/spaces/MashiroSA/sovits-emu-voice-transform/vdecoder/hifigan/env.py deleted file mode 100644 index 2bdbc95d4f7a8bad8fd4f5eef657e2b51d946056..0000000000000000000000000000000000000000 --- a/spaces/MashiroSA/sovits-emu-voice-transform/vdecoder/hifigan/env.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import shutil - - -class AttrDict(dict): - def __init__(self, *args, **kwargs): - super(AttrDict, self).__init__(*args, **kwargs) - self.__dict__ = self - - -def build_env(config, config_name, path): - t_path = os.path.join(path, config_name) - if config != t_path: - os.makedirs(path, exist_ok=True) - shutil.copyfile(config, os.path.join(path, config_name)) diff --git a/spaces/MattyWhite/ChatGPT-ImageCaptioner2/train_net.py b/spaces/MattyWhite/ChatGPT-ImageCaptioner2/train_net.py deleted file mode 100644 index 5679d52a45c115b83a1104b162b1567ca5cb01a1..0000000000000000000000000000000000000000 --- a/spaces/MattyWhite/ChatGPT-ImageCaptioner2/train_net.py +++ /dev/null @@ -1,269 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import logging -import os -import sys -from collections import OrderedDict -import torch -from torch.nn.parallel import DistributedDataParallel -import time -import datetime - -from fvcore.common.timer import Timer -import detectron2.utils.comm as comm -from detectron2.checkpoint import DetectionCheckpointer, PeriodicCheckpointer -from detectron2.config import get_cfg -from detectron2.data import ( - MetadataCatalog, - build_detection_test_loader, -) -from detectron2.engine import default_argument_parser, default_setup, launch - -from detectron2.evaluation import ( - inference_on_dataset, - print_csv_format, - LVISEvaluator, - COCOEvaluator, -) -from detectron2.modeling import build_model -from detectron2.solver import build_lr_scheduler, build_optimizer -from detectron2.utils.events import ( - CommonMetricPrinter, - EventStorage, - JSONWriter, - TensorboardXWriter, -) -from detectron2.data.dataset_mapper import DatasetMapper -from detectron2.data.build import build_detection_train_loader -from detectron2.utils.logger import setup_logger -from torch.cuda.amp import GradScaler - -sys.path.insert(0, 'third_party/CenterNet2/projects/CenterNet2/') -from centernet.config import add_centernet_config - -sys.path.insert(0, 'third_party/Deformable-DETR') -from detic.config import add_detic_config -from detic.data.custom_build_augmentation import build_custom_augmentation -from detic.data.custom_dataset_dataloader import build_custom_train_loader -from detic.data.custom_dataset_mapper import CustomDatasetMapper, DetrDatasetMapper -from detic.custom_solver import build_custom_optimizer -from detic.evaluation.oideval import OIDEvaluator -from detic.evaluation.custom_coco_eval import CustomCOCOEvaluator -from detic.modeling.utils import reset_cls_test - - -logger = logging.getLogger("detectron2") - -def do_test(cfg, model): - results = OrderedDict() - for d, dataset_name in enumerate(cfg.DATASETS.TEST): - if cfg.MODEL.RESET_CLS_TESTS: - reset_cls_test( - model, - cfg.MODEL.TEST_CLASSIFIERS[d], - cfg.MODEL.TEST_NUM_CLASSES[d]) - mapper = None if cfg.INPUT.TEST_INPUT_TYPE == 'default' \ - else DatasetMapper( - cfg, False, augmentations=build_custom_augmentation(cfg, False)) - data_loader = build_detection_test_loader(cfg, dataset_name, mapper=mapper) - output_folder = os.path.join( - cfg.OUTPUT_DIR, "inference_{}".format(dataset_name)) - evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type - - if evaluator_type == "lvis" or cfg.GEN_PSEDO_LABELS: - evaluator = LVISEvaluator(dataset_name, cfg, True, output_folder) - elif evaluator_type == 'coco': - if dataset_name == 'coco_generalized_zeroshot_val': - # Additionally plot mAP for 'seen classes' and 'unseen classes' - evaluator = CustomCOCOEvaluator(dataset_name, cfg, True, output_folder) - else: - evaluator = COCOEvaluator(dataset_name, cfg, True, output_folder) - elif evaluator_type == 'oid': - evaluator = OIDEvaluator(dataset_name, cfg, True, output_folder) - else: - assert 0, evaluator_type - - results[dataset_name] = inference_on_dataset( - model, data_loader, evaluator) - if comm.is_main_process(): - logger.info("Evaluation results for {} in csv format:".format( - dataset_name)) - print_csv_format(results[dataset_name]) - if len(results) == 1: - results = list(results.values())[0] - return results - -def do_train(cfg, model, resume=False): - model.train() - if cfg.SOLVER.USE_CUSTOM_SOLVER: - optimizer = build_custom_optimizer(cfg, model) - else: - assert cfg.SOLVER.OPTIMIZER == 'SGD' - assert cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE != 'full_model' - assert cfg.SOLVER.BACKBONE_MULTIPLIER == 1. - optimizer = build_optimizer(cfg, model) - scheduler = build_lr_scheduler(cfg, optimizer) - - checkpointer = DetectionCheckpointer( - model, cfg.OUTPUT_DIR, optimizer=optimizer, scheduler=scheduler - ) - - start_iter = checkpointer.resume_or_load( - cfg.MODEL.WEIGHTS, resume=resume).get("iteration", -1) + 1 - if not resume: - start_iter = 0 - max_iter = cfg.SOLVER.MAX_ITER if cfg.SOLVER.TRAIN_ITER < 0 else cfg.SOLVER.TRAIN_ITER - - periodic_checkpointer = PeriodicCheckpointer( - checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD, max_iter=max_iter - ) - - writers = ( - [ - CommonMetricPrinter(max_iter), - JSONWriter(os.path.join(cfg.OUTPUT_DIR, "metrics.json")), - TensorboardXWriter(cfg.OUTPUT_DIR), - ] - if comm.is_main_process() - else [] - ) - - use_custom_mapper = cfg.WITH_IMAGE_LABELS - MapperClass = CustomDatasetMapper if use_custom_mapper else DatasetMapper - mapper = MapperClass(cfg, True) if cfg.INPUT.CUSTOM_AUG == '' else \ - DetrDatasetMapper(cfg, True) if cfg.INPUT.CUSTOM_AUG == 'DETR' else \ - MapperClass(cfg, True, augmentations=build_custom_augmentation(cfg, True)) - if cfg.DATALOADER.SAMPLER_TRAIN in ['TrainingSampler', 'RepeatFactorTrainingSampler']: - data_loader = build_detection_train_loader(cfg, mapper=mapper) - else: - data_loader = build_custom_train_loader(cfg, mapper=mapper) - - if cfg.FP16: - scaler = GradScaler() - - logger.info("Starting training from iteration {}".format(start_iter)) - with EventStorage(start_iter) as storage: - step_timer = Timer() - data_timer = Timer() - start_time = time.perf_counter() - for data, iteration in zip(data_loader, range(start_iter, max_iter)): - data_time = data_timer.seconds() - storage.put_scalars(data_time=data_time) - step_timer.reset() - iteration = iteration + 1 - storage.step() - loss_dict = model(data) - - losses = sum( - loss for k, loss in loss_dict.items()) - assert torch.isfinite(losses).all(), loss_dict - - loss_dict_reduced = {k: v.item() \ - for k, v in comm.reduce_dict(loss_dict).items()} - losses_reduced = sum(loss for loss in loss_dict_reduced.values()) - if comm.is_main_process(): - storage.put_scalars( - total_loss=losses_reduced, **loss_dict_reduced) - - optimizer.zero_grad() - if cfg.FP16: - scaler.scale(losses).backward() - scaler.step(optimizer) - scaler.update() - else: - losses.backward() - optimizer.step() - - storage.put_scalar( - "lr", optimizer.param_groups[0]["lr"], smoothing_hint=False) - - step_time = step_timer.seconds() - storage.put_scalars(time=step_time) - data_timer.reset() - scheduler.step() - - if (cfg.TEST.EVAL_PERIOD > 0 - and iteration % cfg.TEST.EVAL_PERIOD == 0 - and iteration != max_iter): - do_test(cfg, model) - comm.synchronize() - - if iteration - start_iter > 5 and \ - (iteration % 20 == 0 or iteration == max_iter): - for writer in writers: - writer.write() - periodic_checkpointer.step(iteration) - - total_time = time.perf_counter() - start_time - logger.info( - "Total training time: {}".format( - str(datetime.timedelta(seconds=int(total_time))))) - -def setup(args): - """ - Create configs and perform basic setups. - """ - cfg = get_cfg() - add_centernet_config(cfg) - add_detic_config(cfg) - cfg.merge_from_file(args.config_file) - cfg.merge_from_list(args.opts) - if '/auto' in cfg.OUTPUT_DIR: - file_name = os.path.basename(args.config_file)[:-5] - cfg.OUTPUT_DIR = cfg.OUTPUT_DIR.replace('/auto', '/{}'.format(file_name)) - logger.info('OUTPUT_DIR: {}'.format(cfg.OUTPUT_DIR)) - cfg.freeze() - default_setup(cfg, args) - setup_logger(output=cfg.OUTPUT_DIR, \ - distributed_rank=comm.get_rank(), name="detic") - return cfg - - -def main(args): - cfg = setup(args) - - model = build_model(cfg) - logger.info("Model:\n{}".format(model)) - if args.eval_only: - DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load( - cfg.MODEL.WEIGHTS, resume=args.resume - ) - - return do_test(cfg, model) - - distributed = comm.get_world_size() > 1 - if distributed: - model = DistributedDataParallel( - model, device_ids=[comm.get_local_rank()], broadcast_buffers=False, - find_unused_parameters=cfg.FIND_UNUSED_PARAM - ) - - do_train(cfg, model, resume=args.resume) - return do_test(cfg, model) - - -if __name__ == "__main__": - args = default_argument_parser() - args = args.parse_args() - if args.num_machines == 1: - args.dist_url = 'tcp://127.0.0.1:{}'.format( - torch.randint(11111, 60000, (1,))[0].item()) - else: - if args.dist_url == 'host': - args.dist_url = 'tcp://{}:12345'.format( - os.environ['SLURM_JOB_NODELIST']) - elif not args.dist_url.startswith('tcp'): - tmp = os.popen( - 'echo $(scontrol show job {} | grep BatchHost)'.format( - args.dist_url) - ).read() - tmp = tmp[tmp.find('=') + 1: -1] - args.dist_url = 'tcp://{}:12345'.format(tmp) - print("Command Line Args:", args) - launch( - main, - args.num_gpus, - num_machines=args.num_machines, - machine_rank=args.machine_rank, - dist_url=args.dist_url, - args=(args,), - ) diff --git a/spaces/MaxReimann/Whitebox-Style-Transfer-Editing/worker/serve.py b/spaces/MaxReimann/Whitebox-Style-Transfer-Editing/worker/serve.py deleted file mode 100644 index 5e4cc1499b1a5a22acc1591e42bd43f18fd15c03..0000000000000000000000000000000000000000 --- a/spaces/MaxReimann/Whitebox-Style-Transfer-Editing/worker/serve.py +++ /dev/null @@ -1,290 +0,0 @@ -import datetime -import os -from pathlib import Path -import sys -from flask import Flask, jsonify, request, send_file, abort -from flask_uploads import UploadSet, configure_uploads, IMAGES -from werkzeug.exceptions import default_exceptions -from werkzeug.exceptions import HTTPException, NotFound -import json -import torch -import time -import threading -import traceback -from PIL import Image -import numpy as np - -PACKAGE_PARENT = '..' -WISE_DIR = '../wise/' -SCRIPT_DIR = os.path.dirname(os.path.realpath(os.path.join(os.getcwd(), os.path.expanduser(__file__)))) -sys.path.append(os.path.normpath(os.path.join(SCRIPT_DIR, PACKAGE_PARENT))) -sys.path.append(os.path.normpath(os.path.join(SCRIPT_DIR, WISE_DIR))) - - - -from parameter_optimization.parametric_styletransfer import single_optimize -from parameter_optimization.parametric_styletransfer import CONFIG as ST_CONFIG -from parameter_optimization.strotss_org import strotss, pil_resize_long_edge_to -from helpers import torch_to_np, np_to_torch -from effects import get_default_settings, MinimalPipelineEffect - -app = Flask(__name__) - -image_folder = 'img_received' -photos = UploadSet('photos', IMAGES) -app.config['UPLOADED_PHOTOS_DEST'] = image_folder -configure_uploads(app, photos) - -class Args(object): - def __init__(self, initial_data): - for key in initial_data: - setattr(self, key, initial_data[key]) - def set_attributes(self, val_dict): - for key in val_dict: - setattr(self, key, val_dict[key]) - -default_args = { - "output_image" : "output.jpg", - ## values always set by request ## - "content_image": "", - "style_image": "", - "output_vp": "", - "iters": 500 -} - - -total_task_count = 0 - -class NeuralOptimizer(): - def __init__(self, args) -> None: - self.cur_iteration = 0 - self.args = args - - def optimize(self): - base_dir = f"result/{datetime.datetime.now().strftime(r'%Y-%m-%d %H.%Mh %Ss')}" - os.makedirs(base_dir) - - content = Image.open(self.args.content_image) - style = Image.open(self.args.style_image) - - def set_iter(iter): - self.cur_iteration=iter - - effect, preset, _ = get_default_settings("minimal_pipeline") - effect.enable_checkpoints() - - reference = strotss(pil_resize_long_edge_to(content, 1024), - pil_resize_long_edge_to(style, 1024), content_weight=16.0, - device=torch.device("cuda"), space="uniform") - - ref_save_path = os.path.join(base_dir, "reference.jpg") - resize_to = 720 - reference = pil_resize_long_edge_to(reference, resize_to) - reference.save(ref_save_path) - ST_CONFIG["n_iterations"] = self.args.iters - vp, content_img_cuda = single_optimize(effect, preset, "l1", self.args.content_image, str(ref_save_path), - write_video=False, base_dir=base_dir, - iter_callback=set_iter) - - output = Image.fromarray(torch_to_np(content_img_cuda.detach().cpu() * 255.0).astype(np.uint8)) - output.save(self.args.output_image) - # torch.save (vp.detach().clone(), self.args.output_vp) - # preset_tensor = effect.vpd.preset_tensor(preset, np_to_torch(np.array(content)).cuda(), add_local_dims=True) - np.savez_compressed(self.args.output_vp, vp=vp.detach().cpu().numpy()) - - - -class StyleTask: - def __init__(self, task_id, style_filename, content_filename): - self.content_filename=content_filename - self.style_filename=style_filename - - self.status = "queued" - self.task_id = task_id - self.error_msg = "" - self.output_filename = content_filename.split(".")[0] + "_output.jpg" - self.vp_output_filename = content_filename.split(".")[0] + "_output.npz" - - # global neural_optimizer - # if neural_optimizer is None: - # neural_optimizer = NeuralOptimizer(Args(default_args)) - - self.neural_optimizer = NeuralOptimizer(Args(default_args)) - - def start(self): - self.neural_optimizer.args.set_attributes(default_args) - - self.neural_optimizer.args.style_image = os.path.join(image_folder, self.style_filename) - self.neural_optimizer.args.content_image = os.path.join(image_folder, self.content_filename) - self.neural_optimizer.args.output_image = os.path.join(image_folder, self.output_filename) - self.neural_optimizer.args.output_vp = os.path.join(image_folder, self.vp_output_filename) - - thread = threading.Thread(target=self.run, args=()) - thread.daemon = True # Daemonize thread - thread.start() # Start the execution - - def run(self): - self.status = "running" - try: - self.neural_optimizer.optimize() - except Exception as e: - print("Error in task %d :"%(self.task_id), str(e)) - traceback.print_exc() - - self.status = "error" - self.error_msg = str(e) - return - - self.status = "finished" - del self.neural_optimizer - torch.cuda.empty_cache() - print("finished styling task: " + str(self.task_id)) - -class StylerQueue: - queued_tasks = [] - finished_tasks = [] - running_task = None - - def __init__(self): - thread = threading.Thread(target=self.status_checker, args=()) - thread.daemon = True # Daemonize thread - thread.start() # Start the execution - - def queue_task(self, *args): - global total_task_count - total_task_count += 1 - task_id = abs(hash(str(time.time()))) - print("queued task num. ", total_task_count, "with ID", task_id) - task = StyleTask(task_id, *args) - self.queued_tasks.append(task) - - return task_id - - def get_task(self, task_id): - if self.running_task is not None and self.running_task.task_id == task_id: - return self.running_task - task = next((task for task in self.queued_tasks + self.finished_tasks if task.task_id == task_id), None) - return task - - def status_checker(self): - while True: - time.sleep(0.3) - - if self.running_task is None: - if len(self.queued_tasks) > 0: - self.running_task = self.queued_tasks[0] - self.running_task.start() - self.queued_tasks = self.queued_tasks[1:] - elif self.running_task.status == "finished" or self.running_task.status == "error": - self.finished_tasks.append(self.running_task) - if len(self.queued_tasks) > 0: - self.running_task = self.queued_tasks[0] - self.running_task.start() - self.queued_tasks = self.queued_tasks[1:] - else: - self.running_task = None - -styler_queue = StylerQueue() - -@app.errorhandler(404) -def resource_not_found(e): - return jsonify(message=str(e)), 404 - -@app.errorhandler(500) -def internal_server_error(e): - return jsonify(message=str(e)), 500 - -@app.errorhandler(400) -def caught_error(e, *args): - print(args) - print(e) - return jsonify(message=str(e.description)), 400 - -@app.route('/', defaults={'path': ''}) -@app.route('/
Created by: Narrativa: Natural Language Generation(NLG) | Gabriele, our machine learning-based platform, builds and deploys natural language solutions. # NLG #AI
", - inputs=[ - gr.inputs.Textbox(lines=1, label="Type/Paste your headline here"), - gr.inputs.Textbox(lines=6, label="Type/Paste the article body here"), - ], - outputs=[ - gr.outputs.Textbox(label="Label"), - gr.outputs.Textbox(label="Score"), - ], - examples=[ - ["La candidata llamada a sustituir a Alberto RodrĆguez renuncia a ocupar su escaƱo en el Congreso", '''FĆ”tima GonzĆ”lez Bello, que concurriĆ³ como nĆŗmero dos en la lista de Unidas Podemos por la circunscripciĆ³n de Tenerife en los pasados comicios generales, ha anunciado este miĆ©rcoles que declina reemplazar al exdiputado Alberto RodrĆguez tras la retirada de su escaƱo. - -Una decisiĆ³n que ha comunicado en redes sociales tras conocer que la Junta Electoral Central (JEC) ha expedido este miĆ©rcoles la credencial para ocupar la vacante que deja RodrĆguez, una vez que la presidenta del Congreso, Meritxell Batet, le comunicĆ³ la retirada del escaƱo en ejecuciĆ³n por la sentencia que el Tribunal Supremo dictĆ³ en su contra. - -"Me resulta muy difĆcil poder ocupar el puesto del compaƱero Alberto en las circunstancias actuales e, igualmente, en lo personal tras cuatro aƱos de dedicaciĆ³n como concejala en el Ayuntamiento de La Laguna y una vez abandonada la polĆtica institucional, no me veo en el momento de volver a la misma", ha detallado.'''], - ['''La palabra "haiga", aceptada por la RAE''', '''La palabra "haiga", aceptada por la RAE La Real Academia de la Lengua (RAE), ha aceptado el uso de "HAIGA", para su utilizaciĆ³n en las tres personas del singular del presente del subjuntivo del verbo hacer, aunque asegura que la forma mĆ”s recomendable en la lengua culta para este tiempo, sigue siendo "haya". -AsĆ lo han confirmado fuentes de la RAE, que explican que este cambio ha sido propuesto y aprobado por el pleno de la Academia de la Lengua, tras la extendida utilizaciĆ³n por todo el territorio nacional, sobre todo, empleado por personas carentes de estudios o con estudios bĆ”sicos de graduado escolar. Ya no serĆ” objeto de burla ese compaƱero que a diario repite aquello de "Mientras que haiga faena, no podemos quejarnos" o esa abuela que repite aquello de "El que haiga sacao los juguetes, que los recoja". -Entre otras palabras novedosas que ha aceptado la RAE, contamos tambiĆ©n con "Descambiar", significa deshacer un cambio, por ejemplo "devolver la compra". Visto lo visto, nadie apostarĆa que la palabra "follamigos" sea la siguiente de la lista.'''], - ] -) - -gradio_ui.launch() \ No newline at end of file diff --git a/spaces/Nephele/bert-vits2-multi-voice/preprocess_text.py b/spaces/Nephele/bert-vits2-multi-voice/preprocess_text.py deleted file mode 100644 index 44c35fecd9b7f21016e80e9597d6055254cba3f7..0000000000000000000000000000000000000000 --- a/spaces/Nephele/bert-vits2-multi-voice/preprocess_text.py +++ /dev/null @@ -1,69 +0,0 @@ -import json -from random import shuffle - -import tqdm -from text.cleaner import clean_text -from collections import defaultdict -import shutil -stage = [1,2,3] - -transcription_path = 'filelists/short_character_anno.list' -train_path = 'filelists/train.list' -val_path = 'filelists/val.list' -config_path = "configs/config.json" -val_per_spk = 4 -max_val_total = 8 - -if 1 in stage: - with open( transcription_path+'.cleaned', 'w', encoding='utf-8') as f: - for line in tqdm.tqdm(open(transcription_path, encoding='utf-8').readlines()): - try: - utt, spk, language, text = line.strip().split('|') - #language = "ZH" - norm_text, phones, tones, word2ph = clean_text(text, language) - f.write('{}|{}|{}|{}|{}|{}|{}\n'.format(utt, spk, language, norm_text, ' '.join(phones), - " ".join([str(i) for i in tones]), - " ".join([str(i) for i in word2ph]))) - except: - print("err!", utt) - -if 2 in stage: - spk_utt_map = defaultdict(list) - spk_id_map = {} - current_sid = 0 - - with open( transcription_path+'.cleaned', encoding='utf-8') as f: - for line in f.readlines(): - utt, spk, language, text, phones, tones, word2ph = line.strip().split('|') - spk_utt_map[spk].append(line) - if spk not in spk_id_map.keys(): - spk_id_map[spk] = current_sid - current_sid += 1 - train_list = [] - val_list = [] - for spk, utts in spk_utt_map.items(): - shuffle(utts) - val_list+=utts[:val_per_spk] - train_list+=utts[val_per_spk:] - if len(val_list) > max_val_total: - train_list+=val_list[max_val_total:] - val_list = val_list[:max_val_total] - - with open( train_path,"w", encoding='utf-8') as f: - for line in train_list: - f.write(line) - - file_path = transcription_path+'.cleaned' - shutil.copy(file_path,'./filelists/train.list') - - with open(val_path, "w", encoding='utf-8') as f: - for line in val_list: - f.write(line) - -if 3 in stage: - assert 2 in stage - config = json.load(open(config_path)) - config['data']["n_speakers"] = current_sid # - config["data"]['spk2id'] = spk_id_map - with open(config_path, 'w', encoding='utf-8') as f: - json.dump(config, f, indent=2, ensure_ascii=False) diff --git a/spaces/NiansuhAI/chat/Dockerfile b/spaces/NiansuhAI/chat/Dockerfile deleted file mode 100644 index 352719557cc530082e9875aabeb4407b698de47d..0000000000000000000000000000000000000000 --- a/spaces/NiansuhAI/chat/Dockerfile +++ /dev/null @@ -1,7 +0,0 @@ -FROM node:18 -RUN git clone https://github.com/Niansuh/ChatGPT-Plugins.git -WORKDIR "ChatGPT-Plugins" -RUN npm i -RUN npm run build -EXPOSE 3000 -CMD ["npm", "run", "start"] diff --git a/spaces/Nixic/rvc-models/infer_pack/models_onnx.py b/spaces/Nixic/rvc-models/infer_pack/models_onnx.py deleted file mode 100644 index 3cdae2f7f8591a1e43b1d8520baa37b7e9744d72..0000000000000000000000000000000000000000 --- a/spaces/Nixic/rvc-models/infer_pack/models_onnx.py +++ /dev/null @@ -1,849 +0,0 @@ -import math, pdb, os -from time import time as ttime -import torch -from torch import nn -from torch.nn import functional as F -from infer_pack import modules -from infer_pack import attentions -from infer_pack import commons -from infer_pack.commons import init_weights, get_padding -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from infer_pack.commons import init_weights -import numpy as np -from infer_pack import commons - - -class TextEncoder256(nn.Module): - def __init__( - self, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - f0=True, - ): - super().__init__() - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.emb_phone = nn.Linear(256, hidden_channels) - self.lrelu = nn.LeakyReLU(0.1, inplace=True) - if f0 == True: - self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256 - self.encoder = attentions.Encoder( - hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout - ) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, phone, pitch, lengths): - if pitch == None: - x = self.emb_phone(phone) - else: - x = self.emb_phone(phone) + self.emb_pitch(pitch) - x = x * math.sqrt(self.hidden_channels) # [b, t, h] - x = self.lrelu(x) - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to( - x.dtype - ) - x = self.encoder(x * x_mask, x_mask) - stats = self.proj(x) * x_mask - - m, logs = torch.split(stats, self.out_channels, dim=1) - return m, logs, x_mask - - -class TextEncoder256Sim(nn.Module): - def __init__( - self, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - f0=True, - ): - super().__init__() - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.emb_phone = nn.Linear(256, hidden_channels) - self.lrelu = nn.LeakyReLU(0.1, inplace=True) - if f0 == True: - self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256 - self.encoder = attentions.Encoder( - hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout - ) - self.proj = nn.Conv1d(hidden_channels, out_channels, 1) - - def forward(self, phone, pitch, lengths): - if pitch == None: - x = self.emb_phone(phone) - else: - x = self.emb_phone(phone) + self.emb_pitch(pitch) - x = x * math.sqrt(self.hidden_channels) # [b, t, h] - x = self.lrelu(x) - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to( - x.dtype - ) - x = self.encoder(x * x_mask, x_mask) - x = self.proj(x) * x_mask - return x, x_mask - - -class ResidualCouplingBlock(nn.Module): - def __init__( - self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - n_flows=4, - gin_channels=0, - ): - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.flows = nn.ModuleList() - for i in range(n_flows): - self.flows.append( - modules.ResidualCouplingLayer( - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=gin_channels, - mean_only=True, - ) - ) - self.flows.append(modules.Flip()) - - def forward(self, x, x_mask, g=None, reverse=False): - if not reverse: - for flow in self.flows: - x, _ = flow(x, x_mask, g=g, reverse=reverse) - else: - for flow in reversed(self.flows): - x = flow(x, x_mask, g=g, reverse=reverse) - return x - - def remove_weight_norm(self): - for i in range(self.n_flows): - self.flows[i * 2].remove_weight_norm() - - -class PosteriorEncoder(nn.Module): - def __init__( - self, - in_channels, - out_channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0, - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - - self.pre = nn.Conv1d(in_channels, hidden_channels, 1) - self.enc = modules.WN( - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=gin_channels, - ) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, x, x_lengths, g=None): - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to( - x.dtype - ) - x = self.pre(x) * x_mask - x = self.enc(x, x_mask, g=g) - stats = self.proj(x) * x_mask - m, logs = torch.split(stats, self.out_channels, dim=1) - z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask - return z, m, logs, x_mask - - def remove_weight_norm(self): - self.enc.remove_weight_norm() - - -class Generator(torch.nn.Module): - def __init__( - self, - initial_channel, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=0, - ): - super(Generator, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - self.conv_pre = Conv1d( - initial_channel, upsample_initial_channel, 7, 1, padding=3 - ) - resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - self.ups.append( - weight_norm( - ConvTranspose1d( - upsample_initial_channel // (2**i), - upsample_initial_channel // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) - ) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel // (2 ** (i + 1)) - for j, (k, d) in enumerate( - zip(resblock_kernel_sizes, resblock_dilation_sizes) - ): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - def forward(self, x, g=None): - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, modules.LRELU_SLOPE) - x = self.ups[i](x) - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -class SineGen(torch.nn.Module): - """Definition of sine generator - SineGen(samp_rate, harmonic_num = 0, - sine_amp = 0.1, noise_std = 0.003, - voiced_threshold = 0, - flag_for_pulse=False) - samp_rate: sampling rate in Hz - harmonic_num: number of harmonic overtones (default 0) - sine_amp: amplitude of sine-wavefrom (default 0.1) - noise_std: std of Gaussian noise (default 0.003) - voiced_thoreshold: F0 threshold for U/V classification (default 0) - flag_for_pulse: this SinGen is used inside PulseGen (default False) - Note: when flag_for_pulse is True, the first time step of a voiced - segment is always sin(np.pi) or cos(0) - """ - - def __init__( - self, - samp_rate, - harmonic_num=0, - sine_amp=0.1, - noise_std=0.003, - voiced_threshold=0, - flag_for_pulse=False, - ): - super(SineGen, self).__init__() - self.sine_amp = sine_amp - self.noise_std = noise_std - self.harmonic_num = harmonic_num - self.dim = self.harmonic_num + 1 - self.sampling_rate = samp_rate - self.voiced_threshold = voiced_threshold - - def _f02uv(self, f0): - # generate uv signal - uv = torch.ones_like(f0) - uv = uv * (f0 > self.voiced_threshold) - return uv - - def forward(self, f0, upp): - """sine_tensor, uv = forward(f0) - input F0: tensor(batchsize=1, length, dim=1) - f0 for unvoiced steps should be 0 - output sine_tensor: tensor(batchsize=1, length, dim) - output uv: tensor(batchsize=1, length, 1) - """ - with torch.no_grad(): - f0 = f0[:, None].transpose(1, 2) - f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device) - # fundamental component - f0_buf[:, :, 0] = f0[:, :, 0] - for idx in np.arange(self.harmonic_num): - f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * ( - idx + 2 - ) # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic - rad_values = (f0_buf / self.sampling_rate) % 1 ###%1ęå³ēn_harēä¹ē§Æę ę³åå¤ēä¼å - rand_ini = torch.rand( - f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device - ) - rand_ini[:, 0] = 0 - rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini - tmp_over_one = torch.cumsum(rad_values, 1) # % 1 #####%1ęå³ēåé¢ēcumsumę ę³åä¼å - tmp_over_one *= upp - tmp_over_one = F.interpolate( - tmp_over_one.transpose(2, 1), - scale_factor=upp, - mode="linear", - align_corners=True, - ).transpose(2, 1) - rad_values = F.interpolate( - rad_values.transpose(2, 1), scale_factor=upp, mode="nearest" - ).transpose( - 2, 1 - ) ####### - tmp_over_one %= 1 - tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0 - cumsum_shift = torch.zeros_like(rad_values) - cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 - sine_waves = torch.sin( - torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi - ) - sine_waves = sine_waves * self.sine_amp - uv = self._f02uv(f0) - uv = F.interpolate( - uv.transpose(2, 1), scale_factor=upp, mode="nearest" - ).transpose(2, 1) - noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 - noise = noise_amp * torch.randn_like(sine_waves) - sine_waves = sine_waves * uv + noise - return sine_waves, uv, noise - - -class SourceModuleHnNSF(torch.nn.Module): - """SourceModule for hn-nsf - SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, - add_noise_std=0.003, voiced_threshod=0) - sampling_rate: sampling_rate in Hz - harmonic_num: number of harmonic above F0 (default: 0) - sine_amp: amplitude of sine source signal (default: 0.1) - add_noise_std: std of additive Gaussian noise (default: 0.003) - note that amplitude of noise in unvoiced is decided - by sine_amp - voiced_threshold: threhold to set U/V given F0 (default: 0) - Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) - F0_sampled (batchsize, length, 1) - Sine_source (batchsize, length, 1) - noise_source (batchsize, length 1) - uv (batchsize, length, 1) - """ - - def __init__( - self, - sampling_rate, - harmonic_num=0, - sine_amp=0.1, - add_noise_std=0.003, - voiced_threshod=0, - is_half=True, - ): - super(SourceModuleHnNSF, self).__init__() - - self.sine_amp = sine_amp - self.noise_std = add_noise_std - self.is_half = is_half - # to produce sine waveforms - self.l_sin_gen = SineGen( - sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod - ) - - # to merge source harmonics into a single excitation - self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) - self.l_tanh = torch.nn.Tanh() - - def forward(self, x, upp=None): - sine_wavs, uv, _ = self.l_sin_gen(x, upp) - if self.is_half: - sine_wavs = sine_wavs.half() - sine_merge = self.l_tanh(self.l_linear(sine_wavs)) - return sine_merge, None, None # noise, uv - - -class GeneratorNSF(torch.nn.Module): - def __init__( - self, - initial_channel, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels, - sr, - is_half=False, - ): - super(GeneratorNSF, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - - self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates)) - self.m_source = SourceModuleHnNSF( - sampling_rate=sr, harmonic_num=0, is_half=is_half - ) - self.noise_convs = nn.ModuleList() - self.conv_pre = Conv1d( - initial_channel, upsample_initial_channel, 7, 1, padding=3 - ) - resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - c_cur = upsample_initial_channel // (2 ** (i + 1)) - self.ups.append( - weight_norm( - ConvTranspose1d( - upsample_initial_channel // (2**i), - upsample_initial_channel // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) - ) - if i + 1 < len(upsample_rates): - stride_f0 = np.prod(upsample_rates[i + 1 :]) - self.noise_convs.append( - Conv1d( - 1, - c_cur, - kernel_size=stride_f0 * 2, - stride=stride_f0, - padding=stride_f0 // 2, - ) - ) - else: - self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1)) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel // (2 ** (i + 1)) - for j, (k, d) in enumerate( - zip(resblock_kernel_sizes, resblock_dilation_sizes) - ): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - self.upp = np.prod(upsample_rates) - - def forward(self, x, f0, g=None): - har_source, noi_source, uv = self.m_source(f0, self.upp) - har_source = har_source.transpose(1, 2) - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, modules.LRELU_SLOPE) - x = self.ups[i](x) - x_source = self.noise_convs[i](har_source) - x = x + x_source - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - return x - - def remove_weight_norm(self): - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -sr2sr = { - "32k": 32000, - "40k": 40000, - "48k": 48000, -} - - -class SynthesizerTrnMs256NSFsid(nn.Module): - def __init__( - self, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - spk_embed_dim, - gin_channels, - sr, - **kwargs - ): - super().__init__() - if type(sr) == type("strr"): - sr = sr2sr[sr] - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.gin_channels = gin_channels - # self.hop_length = hop_length# - self.spk_embed_dim = spk_embed_dim - self.enc_p = TextEncoder256( - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - ) - self.dec = GeneratorNSF( - inter_channels, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=gin_channels, - sr=sr, - is_half=kwargs["is_half"], - ) - self.enc_q = PosteriorEncoder( - spec_channels, - inter_channels, - hidden_channels, - 5, - 1, - 16, - gin_channels=gin_channels, - ) - self.flow = ResidualCouplingBlock( - inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels - ) - self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels) - print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim) - - def remove_weight_norm(self): - self.dec.remove_weight_norm() - self.flow.remove_weight_norm() - self.enc_q.remove_weight_norm() - - def forward(self, phone, phone_lengths, pitch, nsff0, sid, rnd, max_len=None): - g = self.emb_g(sid).unsqueeze(-1) - m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) - z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask - z = self.flow(z_p, x_mask, g=g, reverse=True) - o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g) - return o - - -class SynthesizerTrnMs256NSFsid_sim(nn.Module): - """ - Synthesizer for Training - """ - - def __init__( - self, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - spk_embed_dim, - # hop_length, - gin_channels=0, - use_sdp=True, - **kwargs - ): - super().__init__() - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.gin_channels = gin_channels - # self.hop_length = hop_length# - self.spk_embed_dim = spk_embed_dim - self.enc_p = TextEncoder256Sim( - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - ) - self.dec = GeneratorNSF( - inter_channels, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=gin_channels, - is_half=kwargs["is_half"], - ) - - self.flow = ResidualCouplingBlock( - inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels - ) - self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels) - print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim) - - def remove_weight_norm(self): - self.dec.remove_weight_norm() - self.flow.remove_weight_norm() - self.enc_q.remove_weight_norm() - - def forward( - self, phone, phone_lengths, pitch, pitchf, ds, max_len=None - ): # yęÆspecäøéč¦äŗē°åØ - g = self.emb_g(ds.unsqueeze(0)).unsqueeze(-1) # [b, 256, 1]##1ęÆtļ¼å¹æęē - x, x_mask = self.enc_p(phone, pitch, phone_lengths) - x = self.flow(x, x_mask, g=g, reverse=True) - o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g) - return o - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(MultiPeriodDiscriminator, self).__init__() - periods = [2, 3, 5, 7, 11, 17] - # periods = [3, 5, 7, 11, 17, 23, 37] - - discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] - discs = discs + [ - DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods - ] - self.discriminators = nn.ModuleList(discs) - - def forward(self, y, y_hat): - y_d_rs = [] # - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - # for j in range(len(fmap_r)): - # print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape) - y_d_rs.append(y_d_r) - y_d_gs.append(y_d_g) - fmap_rs.append(fmap_r) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -class DiscriminatorS(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(DiscriminatorS, self).__init__() - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList( - [ - norm_f(Conv1d(1, 16, 15, 1, padding=7)), - norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), - norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), - norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), - ] - ) - self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) - - def forward(self, x): - fmap = [] - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): - super(DiscriminatorP, self).__init__() - self.period = period - self.use_spectral_norm = use_spectral_norm - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList( - [ - norm_f( - Conv2d( - 1, - 32, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 32, - 128, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 128, - 512, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 512, - 1024, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 1024, - 1024, - (kernel_size, 1), - 1, - padding=(get_padding(kernel_size, 1), 0), - ) - ), - ] - ) - self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) - - def forward(self, x): - fmap = [] - - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/rxf/rxf_src/sentence_prediction_r3f.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/rxf/rxf_src/sentence_prediction_r3f.py deleted file mode 100644 index 6ecffd6b143debb1c67adccd77a6aaed194ec55a..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/rxf/rxf_src/sentence_prediction_r3f.py +++ /dev/null @@ -1,171 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math - -import torch -import torch.nn.functional as F -from fairseq import utils -from fairseq.criterions import FairseqCriterion, register_criterion - - -@register_criterion("sentence_prediction_r3f") -class SentencePredictionR3F(FairseqCriterion): - def __init__( - self, - task, - eps, - r3f_lambda, - noise_type, - classification_head_name, - regression_target, - ): - super().__init__(task) - self.eps = eps - self.r3f_lambda = r3f_lambda - self.noise_type = noise_type - self.classification_head_name = classification_head_name - self.regression_target = regression_target - if self.noise_type in {"normal"}: - self.noise_sampler = torch.distributions.normal.Normal( - loc=0.0, scale=self.eps - ) - elif self.noise_type == "uniform": - self.noise_sampler = torch.distributions.uniform.Uniform( - low=-self.eps, high=self.eps - ) - else: - raise Exception(f"unrecognized noise type {self.noise_type}") - - @staticmethod - def add_args(parser): - # fmt: off - parser.add_argument('--eps', type=float, default=1e-5, - help='noise eps') - parser.add_argument('--r3f-lambda', type=float, default=1.0, - help='lambda for combining logistic loss and noisy KL loss') - parser.add_argument('--noise-type', type=str, default='uniform', - choices=['normal', 'uniform'], - help='type of noises for RXF methods') - parser.add_argument('--classification-head-name', - default='sentence_classification_head', - help='name of the classification head to use') - parser.add_argument('--regression-target', action='store_true') - # fmt: on - - def _get_symm_kl(self, noised_logits, input_logits): - return ( - F.kl_div( - F.log_softmax(noised_logits, dim=-1, dtype=torch.float32), - F.softmax(input_logits, dim=-1, dtype=torch.float32), - None, - None, - "sum", - ) - + F.kl_div( - F.log_softmax(input_logits, dim=-1, dtype=torch.float32), - F.softmax(noised_logits, dim=-1, dtype=torch.float32), - None, - None, - "sum", - ) - ) / noised_logits.size(0) - - def forward(self, model, sample, reduce=True): - """Compute the loss for the given sample. - - Returns a tuple with three elements: - 1) the loss - 2) the sample size, which is used as the denominator for the gradient - 3) logging outputs to display while training - """ - assert ( - hasattr(model, "classification_heads") - and self.classification_head_name in model.classification_heads - ), "model must provide sentence classification head for --criterion=sentence_prediction" - - token_embeddings = model.encoder.sentence_encoder.embed_tokens( - sample["net_input"]["src_tokens"] - ) - input_logits, _ = model( - **sample["net_input"], - features_only=True, - classification_head_name=self.classification_head_name, - token_embeddings=token_embeddings, - ) - if model.training and self.noise_sampler: - noise = self.noise_sampler.sample(sample_shape=token_embeddings.shape).to( - token_embeddings - ) - noised_embeddings = token_embeddings.detach().clone() + noise - - noised_logits, _ = model( - **sample["net_input"], - features_only=True, - classification_head_name=self.classification_head_name, - token_embeddings=noised_embeddings, - ) - symm_kl = self._get_symm_kl(noised_logits, input_logits) - else: - symm_kl = 0 - - targets = model.get_targets(sample, [input_logits]).view(-1) - sample_size = targets.numel() - - if not self.regression_target: - loss = F.nll_loss( - F.log_softmax(input_logits, dim=-1, dtype=torch.float32), - targets, - reduction="sum", - ) - if model.training: - symm_kl = symm_kl * sample_size - loss = loss + self.r3f_lambda * symm_kl - else: - logits = input_logits.squeeze().float() - targets = targets.float() - loss = F.mse_loss(logits, targets, reduction="sum") - - logging_output = { - "loss": utils.item(loss.data) if reduce else loss.data, - "ntokens": sample["ntokens"], - "nsentences": sample_size, - "sample_size": sample_size, - } - - if not self.regression_target: - preds = input_logits.max(dim=1)[1] - logging_output.update(ncorrect=(preds == targets).sum().item()) - - if model.training and self.noise_sampler: - logging_output.update( - symm_kl=utils.item(symm_kl.data) if reduce else symm_kl.data - ) - return loss, sample_size, logging_output - - @staticmethod - def aggregate_logging_outputs(logging_outputs): - """Aggregate logging outputs from data parallel training.""" - loss_sum = sum(log.get("loss", 0) for log in logging_outputs) - symm_kl_sum = sum(log.get("symm_kl", 0) for log in logging_outputs) - ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) - nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) - sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) - - agg_output = { - "loss": loss_sum / sample_size / math.log(2), - "symm_kl": symm_kl_sum / sample_size, - "ntokens": ntokens, - "nsentences": nsentences, - "sample_size": sample_size, - } - - if len(logging_outputs) > 0 and "ncorrect" in logging_outputs[0]: - ncorrect = sum(log.get("ncorrect", 0) for log in logging_outputs) - agg_output.update(accuracy=ncorrect / nsentences) - - if sample_size != ntokens: - agg_output["nll_loss"] = loss_sum / ntokens / math.log(2) - return agg_output diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/criss/mining/mine.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/criss/mining/mine.py deleted file mode 100644 index c872da196fe0df776622365748ad7963fee1f0a0..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/criss/mining/mine.py +++ /dev/null @@ -1,240 +0,0 @@ -#!/usr/bin/env python3 -u -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -import argparse -import glob -from subprocess import check_call - -try: - import faiss - - has_faiss = True -except ImportError: - has_faiss = False -import numpy as np - - -GB = 1024 * 1024 * 1024 - - -def call(cmd): - print(cmd) - check_call(cmd, shell=True) - - -def get_batches(directory, lang, prefix="all_avg_pool"): - print(f"Finding in {directory}/{prefix}.{lang}*") - files = glob.glob(f"{directory}/{prefix}.{lang}*") - emb_files = [] - txt_files = [] - for emb_fi in files: - emb_files.append(emb_fi) - txt_fi = emb_fi.replace(prefix, "sentences") - txt_files.append(txt_fi) - return emb_files, txt_files - - -def load_batch(emb_file, dim): - embeddings = np.fromfile(emb_file, dtype=np.float32) - num_rows = int(embeddings.shape[0] / dim) - embeddings = embeddings.reshape((num_rows, dim)) - faiss.normalize_L2(embeddings) - return embeddings - - -def knnGPU_sharded(x_batches_f, y_batches_f, dim, k, direction="x2y"): - if not has_faiss: - raise ImportError("Please install Faiss") - sims = [] - inds = [] - xfrom = 0 - xto = 0 - for x_batch_f in x_batches_f: - yfrom = 0 - yto = 0 - x_batch = load_batch(x_batch_f, dim) - xto = xfrom + x_batch.shape[0] - bsims, binds = [], [] - for y_batch_f in y_batches_f: - y_batch = load_batch(y_batch_f, dim) - neighbor_size = min(k, y_batch.shape[0]) - yto = yfrom + y_batch.shape[0] - print("{}-{} -> {}-{}".format(xfrom, xto, yfrom, yto)) - idx = faiss.IndexFlatIP(dim) - idx = faiss.index_cpu_to_all_gpus(idx) - idx.add(y_batch) - bsim, bind = idx.search(x_batch, neighbor_size) - - bsims.append(bsim) - binds.append(bind + yfrom) - yfrom += y_batch.shape[0] - del idx - del y_batch - bsims = np.concatenate(bsims, axis=1) - binds = np.concatenate(binds, axis=1) - aux = np.argsort(-bsims, axis=1) - sim_batch = np.zeros((x_batch.shape[0], k), dtype=np.float32) - ind_batch = np.zeros((x_batch.shape[0], k), dtype=np.int64) - for i in range(x_batch.shape[0]): - for j in range(k): - sim_batch[i, j] = bsims[i, aux[i, j]] - ind_batch[i, j] = binds[i, aux[i, j]] - sims.append(sim_batch) - inds.append(ind_batch) - xfrom += x_batch.shape[0] - del x_batch - sim = np.concatenate(sims, axis=0) - ind = np.concatenate(inds, axis=0) - return sim, ind - - -def score(sim, fwd_mean, bwd_mean, margin): - return margin(sim, (fwd_mean + bwd_mean) / 2) - - -def score_candidates( - sim_mat, candidate_inds, fwd_mean, bwd_mean, margin, verbose=False -): - print(" - scoring {:d} candidates".format(sim_mat.shape[0])) - scores = np.zeros(candidate_inds.shape) - for i in range(scores.shape[0]): - for j in range(scores.shape[1]): - k = int(candidate_inds[i, j]) - scores[i, j] = score(sim_mat[i, j], fwd_mean[i], bwd_mean[k], margin) - return scores - - -def load_text(files): - all_sentences = [] - for fi in files: - with open(fi) as sentence_fi: - for line in sentence_fi: - all_sentences.append(line.strip()) - print(f"Read {len(all_sentences)} sentences") - return all_sentences - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Mine bitext") - parser.add_argument("--src-lang", help="Source language") - parser.add_argument("--tgt-lang", help="Target language") - parser.add_argument( - "--dict-path", help="Path to dictionary file", default="dict.txt" - ) - parser.add_argument( - "--spm-path", help="Path to SPM model file", default="sentence.bpe.model" - ) - parser.add_argument("--dim", type=int, default=1024, help="Embedding dimension") - parser.add_argument("--mem", type=int, default=5, help="Memory in GB") - parser.add_argument("--src-dir", help="Source directory") - parser.add_argument("--tgt-dir", help="Target directory") - parser.add_argument("--output", help="Output path") - parser.add_argument( - "--neighborhood", type=int, default=4, help="Embedding dimension" - ) - parser.add_argument( - "--threshold", type=float, default=1.06, help="Threshold on mined bitext" - ) - parser.add_argument( - "--valid-size", - type=int, - default=2000, - help="Number of sentences used for validation set", - ) - parser.add_argument( - "--min-count", - type=int, - default=50000, - help="Min num sentences used for each language", - ) - args = parser.parse_args() - - x_batches_f, x_sents_f = get_batches(args.src_dir, args.src_lang) - y_batches_f, y_sents_f = get_batches(args.tgt_dir, args.tgt_lang) - margin = lambda a, b: a / b - y2x_sim, y2x_ind = knnGPU_sharded( - y_batches_f, x_batches_f, args.dim, args.neighborhood, direction="y2x" - ) - x2y_sim, x2y_ind = knnGPU_sharded( - x_batches_f, y_batches_f, args.dim, args.neighborhood, direction="x2y" - ) - - x2y_mean = x2y_sim.mean(axis=1) - y2x_mean = y2x_sim.mean(axis=1) - fwd_scores = score_candidates(x2y_sim, x2y_ind, x2y_mean, y2x_mean, margin) - bwd_scores = score_candidates(y2x_sim, y2x_ind, y2x_mean, x2y_mean, margin) - fwd_best = x2y_ind[np.arange(x2y_sim.shape[0]), fwd_scores.argmax(axis=1)] - bwd_best = y2x_ind[np.arange(y2x_sim.shape[0]), bwd_scores.argmax(axis=1)] - indices = np.stack( - ( - np.concatenate((np.arange(x2y_ind.shape[0]), bwd_best)), - np.concatenate((fwd_best, np.arange(y2x_ind.shape[0]))), - ), - axis=1, - ) - scores = np.concatenate((fwd_scores.max(axis=1), bwd_scores.max(axis=1))) - - x_sentences = load_text(x_sents_f) - y_sentences = load_text(y_sents_f) - - threshold = args.threshold - min_count = args.min_count - seen_src, seen_trg = set(), set() - directory = args.output - call(f"mkdir -p {directory}") - src_out = open( - f"{directory}/all.{args.src_lang}", - mode="w", - encoding="utf-8", - errors="surrogateescape", - ) - tgt_out = open( - f"{directory}/all.{args.tgt_lang}", - mode="w", - encoding="utf-8", - errors="surrogateescape", - ) - scores_out = open( - f"{directory}/all.scores", mode="w", encoding="utf-8", errors="surrogateescape" - ) - count = 0 - for i in np.argsort(-scores): - src_ind, trg_ind = indices[i] - if src_ind not in seen_src and trg_ind not in seen_trg: - seen_src.add(src_ind) - seen_trg.add(trg_ind) - if scores[i] > threshold or count < min_count: - if x_sentences[src_ind]: - print(scores[i], file=scores_out) - print(x_sentences[src_ind], file=src_out) - print(y_sentences[trg_ind], file=tgt_out) - count += 1 - else: - print(f"Ignoring sentence: {x_sentences[src_ind]}") - src_out.close() - tgt_out.close() - scores_out.close() - - print(f"Found {count} pairs for threshold={threshold}") - with open(f"{directory}/all.{args.src_lang}") as all_s, open( - f"{directory}/all.{args.tgt_lang}" - ) as all_t, open(f"{directory}/valid.{args.src_lang}", "w") as valid_s, open( - f"{directory}/valid.{args.tgt_lang}", "w" - ) as valid_t, open( - f"{directory}/train.{args.src_lang}", "w" - ) as train_s, open( - f"{directory}/train.{args.tgt_lang}", "w" - ) as train_t: - count = 0 - for s_line, t_line in zip(all_s, all_t): - s_line = s_line.split("\t")[1] - t_line = t_line.split("\t")[1] - if count >= args.valid_size: - train_s.write(s_line) - train_t.write(t_line) - else: - valid_s.write(s_line) - valid_t.write(t_line) - count += 1 diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_synthesis/preprocessing/speaker_embedder/__init__.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_synthesis/preprocessing/speaker_embedder/__init__.py deleted file mode 100644 index 3b178676ba322ef613df42977cb498101f841b09..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_synthesis/preprocessing/speaker_embedder/__init__.py +++ /dev/null @@ -1,135 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -import librosa -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.utils.data -import torchaudio - - -EMBEDDER_PARAMS = { - 'num_mels': 40, - 'n_fft': 512, - 'emb_dim': 256, - 'lstm_hidden': 768, - 'lstm_layers': 3, - 'window': 80, - 'stride': 40, -} - - -def set_requires_grad(nets, requires_grad=False): - """Set requies_grad=Fasle for all the networks to avoid unnecessary - computations - Parameters: - nets (network list) -- a list of networks - requires_grad (bool) -- whether the networks require gradients or not - """ - if not isinstance(nets, list): - nets = [nets] - for net in nets: - if net is not None: - for param in net.parameters(): - param.requires_grad = requires_grad - - -class LinearNorm(nn.Module): - def __init__(self, hp): - super(LinearNorm, self).__init__() - self.linear_layer = nn.Linear(hp["lstm_hidden"], hp["emb_dim"]) - - def forward(self, x): - return self.linear_layer(x) - - -class SpeechEmbedder(nn.Module): - def __init__(self, hp): - super(SpeechEmbedder, self).__init__() - self.lstm = nn.LSTM(hp["num_mels"], - hp["lstm_hidden"], - num_layers=hp["lstm_layers"], - batch_first=True) - self.proj = LinearNorm(hp) - self.hp = hp - - def forward(self, mel): - # (num_mels, T) -> (num_mels, T', window) - mels = mel.unfold(1, self.hp["window"], self.hp["stride"]) - mels = mels.permute(1, 2, 0) # (T', window, num_mels) - x, _ = self.lstm(mels) # (T', window, lstm_hidden) - x = x[:, -1, :] # (T', lstm_hidden), use last frame only - x = self.proj(x) # (T', emb_dim) - x = x / torch.norm(x, p=2, dim=1, keepdim=True) # (T', emb_dim) - - x = x.mean(dim=0) - if x.norm(p=2) != 0: - x = x / x.norm(p=2) - return x - - -class SpkrEmbedder(nn.Module): - RATE = 16000 - - def __init__( - self, - embedder_path, - embedder_params=EMBEDDER_PARAMS, - rate=16000, - hop_length=160, - win_length=400, - pad=False, - ): - super(SpkrEmbedder, self).__init__() - embedder_pt = torch.load(embedder_path, map_location="cpu") - self.embedder = SpeechEmbedder(embedder_params) - self.embedder.load_state_dict(embedder_pt) - self.embedder.eval() - set_requires_grad(self.embedder, requires_grad=False) - self.embedder_params = embedder_params - - self.register_buffer('mel_basis', torch.from_numpy( - librosa.filters.mel( - sr=self.RATE, - n_fft=self.embedder_params["n_fft"], - n_mels=self.embedder_params["num_mels"]) - ) - ) - - self.resample = None - if rate != self.RATE: - self.resample = torchaudio.transforms.Resample(rate, self.RATE) - self.hop_length = hop_length - self.win_length = win_length - self.pad = pad - - def get_mel(self, y): - if self.pad and y.shape[-1] < 14000: - y = F.pad(y, (0, 14000 - y.shape[-1])) - - window = torch.hann_window(self.win_length).to(y) - y = torch.stft(y, n_fft=self.embedder_params["n_fft"], - hop_length=self.hop_length, - win_length=self.win_length, - window=window) - magnitudes = torch.norm(y, dim=-1, p=2) ** 2 - mel = torch.log10(self.mel_basis @ magnitudes + 1e-6) - return mel - - def forward(self, inputs): - dvecs = [] - for wav in inputs: - mel = self.get_mel(wav) - if mel.dim() == 3: - mel = mel.squeeze(0) - dvecs += [self.embedder(mel)] - dvecs = torch.stack(dvecs) - - dvec = torch.mean(dvecs, dim=0) - dvec = dvec / torch.norm(dvec) - - return dvec diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_to_text/prep_librispeech_data.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_to_text/prep_librispeech_data.py deleted file mode 100644 index f379fa7bf195f48ad6b2ed3dbd93a5fbeb7abf79..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_to_text/prep_librispeech_data.py +++ /dev/null @@ -1,119 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse -import logging -from pathlib import Path -import shutil -from tempfile import NamedTemporaryFile - -import pandas as pd -from examples.speech_to_text.data_utils import ( - create_zip, - extract_fbank_features, - gen_config_yaml, - gen_vocab, - get_zip_manifest, - save_df_to_tsv, -) -from torchaudio.datasets import LIBRISPEECH -from tqdm import tqdm - - -log = logging.getLogger(__name__) - -SPLITS = [ - "train-clean-100", - "train-clean-360", - "train-other-500", - "dev-clean", - "dev-other", - "test-clean", - "test-other", -] - -MANIFEST_COLUMNS = ["id", "audio", "n_frames", "tgt_text", "speaker"] - - -def process(args): - out_root = Path(args.output_root).absolute() - out_root.mkdir(exist_ok=True) - # Extract features - feature_root = out_root / "fbank80" - feature_root.mkdir(exist_ok=True) - for split in SPLITS: - print(f"Fetching split {split}...") - dataset = LIBRISPEECH(out_root.as_posix(), url=split, download=True) - print("Extracting log mel filter bank features...") - for wav, sample_rate, _, spk_id, chapter_no, utt_no in tqdm(dataset): - sample_id = f"{spk_id}-{chapter_no}-{utt_no}" - extract_fbank_features( - wav, sample_rate, feature_root / f"{sample_id}.npy" - ) - # Pack features into ZIP - zip_path = out_root / "fbank80.zip" - print("ZIPing features...") - create_zip(feature_root, zip_path) - print("Fetching ZIP manifest...") - audio_paths, audio_lengths = get_zip_manifest(zip_path) - # Generate TSV manifest - print("Generating manifest...") - train_text = [] - for split in SPLITS: - manifest = {c: [] for c in MANIFEST_COLUMNS} - dataset = LIBRISPEECH(out_root.as_posix(), url=split) - for _, _, utt, spk_id, chapter_no, utt_no in tqdm(dataset): - sample_id = f"{spk_id}-{chapter_no}-{utt_no}" - manifest["id"].append(sample_id) - manifest["audio"].append(audio_paths[sample_id]) - manifest["n_frames"].append(audio_lengths[sample_id]) - manifest["tgt_text"].append(utt.lower()) - manifest["speaker"].append(spk_id) - save_df_to_tsv( - pd.DataFrame.from_dict(manifest), out_root / f"{split}.tsv" - ) - if split.startswith("train"): - train_text.extend(manifest["tgt_text"]) - # Generate vocab - vocab_size = "" if args.vocab_type == "char" else str(args.vocab_size) - spm_filename_prefix = f"spm_{args.vocab_type}{vocab_size}" - with NamedTemporaryFile(mode="w") as f: - for t in train_text: - f.write(t + "\n") - gen_vocab( - Path(f.name), - out_root / spm_filename_prefix, - args.vocab_type, - args.vocab_size, - ) - # Generate config YAML - gen_config_yaml( - out_root, - spm_filename=spm_filename_prefix + ".model", - specaugment_policy="ld" - ) - # Clean up - shutil.rmtree(feature_root) - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--output-root", "-o", required=True, type=str) - parser.add_argument( - "--vocab-type", - default="unigram", - required=True, - type=str, - choices=["bpe", "unigram", "char"], - ), - parser.add_argument("--vocab-size", default=10000, type=int) - args = parser.parse_args() - - process(args) - - -if __name__ == "__main__": - main() diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/criterions/__init__.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/criterions/__init__.py deleted file mode 100644 index 4dbf46a1cb31ce65c4224ae79cbc2d7cf9e4d111..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/criterions/__init__.py +++ /dev/null @@ -1,36 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -"""isort:skip_file""" - -import importlib -import os - -from fairseq import registry -from fairseq.criterions.fairseq_criterion import ( # noqa - FairseqCriterion, - LegacyFairseqCriterion, -) -from omegaconf import DictConfig - - -( - build_criterion_, - register_criterion, - CRITERION_REGISTRY, - CRITERION_DATACLASS_REGISTRY, -) = registry.setup_registry( - "--criterion", base_class=FairseqCriterion, default="cross_entropy" -) - - -def build_criterion(cfg: DictConfig, task): - return build_criterion_(cfg, task) - - -# automatically import any Python files in the criterions/ directory -for file in sorted(os.listdir(os.path.dirname(__file__))): - if file.endswith(".py") and not file.startswith("_"): - file_name = file[: file.find(".py")] - importlib.import_module("fairseq.criterions." + file_name) diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/layerdrop/README.md b/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/layerdrop/README.md deleted file mode 100644 index 4d48ee9615e1458e1e889635dc9938e427a7f64a..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/layerdrop/README.md +++ /dev/null @@ -1,154 +0,0 @@ -# Reducing Transformer Depth on Demand with Structured Dropout (Fan et al., 2019) -This page contains information for how to train models with LayerDrop, based on this [paper](https://arxiv.org/abs/1909.11556). - -## Citation: -If you found this technique useful, please cite our paper: -```bibtex -@article{fan2019reducing, - title={Reducing Transformer Depth on Demand with Structured Dropout}, - author={Fan, Angela and Grave, Edouard and Joulin, Armand}, - journal={arXiv preprint arXiv:1909.11556}, - year={2019} -} -``` - -## Pre-trained models - -Model | Description | Download ----|---|--- -`layerdrop_wmt_en_de_12_6` | Transformer + LayerDrop 0.2 trained on WMT16 en-de with 12 encoder and 6 decoder layers | [layerdrop_wmt_en_de_12_6.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/layerdrop_wmt_en_de_12_6.tar.gz) -`roberta_layerdrop.base` | RoBERTa Base + LayerDrop 0.2 | [roberta_layerdrop.base.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta_layerdrop.base.qnli.tar.gz) -`roberta_layerdrop.large` | RoBERTa Large + LayerDrop 0.2 | [roberta_layerdrop.large.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta_layerdrop.large.tar.gz) -`roberta_layerdrop.large.mnli` | `roberta_layerdrop.large` finetuned on [MNLI](http://www.nyu.edu/projects/bowman/multinli) | [roberta_layerdrop.large.mnli.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta_layerdrop.large.mnli.tar.gz) -`roberta_layerdrop.large.qnli` | `roberta_layerdrop.large` finetuned on [QNLI](https://arxiv.org/abs/1804.07461) | [roberta_layerdrop.large.mnli.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta_layerdrop.large.qnli.tar.gz) - - -Evaluate performance of these pre-trained models: -```bash -# Example for Machine Translation -fairseq-generate /path/to/bped/wmt/data --path nmt_checkpoint.pt \ - --beam 8 --lenpen 0.4 \ - --batch-size 64 \ - --remove-bpe \ - --gen-subset test > wmt16_gen.txt -bash scripts/compound_split_bleu.sh wmt16_gen.txt -# prints BLEU4 = 30.17 -``` - -```python -# Example for RoBERTa + LayerDrop finetuned on MNLI: -from fairseq.models.roberta import RobertaModel - -roberta_layerdrop = RobertaModel.from_pretrained( - '/path/to/MNLI/model', - checkpoint_file='mnli_checkpoint.pt', - data_name_or_path='/path/to/MNLI/data/MNLI-bin' -) -label_map = {0: 'contradiction', 2: 'neutral', 1: 'entailment'} -ncorrect, nsamples = 0, 0 -roberta_layerdrop.cuda() -roberta_layerdrop.eval() -with open('/path/to/MNLI/data/dev_matched.tsv') as fin: - fin.readline() - for index, line in enumerate(fin): - tokens = line.strip().split('\t') - sent1, sent2, target = tokens[8], tokens[9], tokens[-1] - tokens = roberta_layerdrop.encode(sent1, sent2) - prediction = roberta_layerdrop.predict('sentence_classification_head', tokens).argmax().item() - prediction_label = label_map[prediction] - ncorrect += int(prediction_label == target) - nsamples += 1 -print('| Accuracy: ', float(ncorrect)/float(nsamples)) -# prints | Accuracy: 0.9026999490575649 - - -# Example for RoBERTa + LayerDrop finetuned on QNLI: -roberta = RobertaModel.from_pretrained( - '/path/to/QNLI/model', - checkpoint_file='qnli_checkpoint.pt', - data_name_or_path='/path/to/QNLI/data/QNLI-bin' -) - -label_fn = lambda label: roberta.task.label_dictionary.string( - [label + roberta.task.target_dictionary.nspecial] -) -ncorrect, nsamples = 0, 0 -roberta.cuda() -roberta.eval() -with open('/path/to/QNLI/data/dev.tsv') as fin: - fin.readline() - for index, line in enumerate(fin): - tokens = line.strip().split('\t') - sent1, sent2, target = tokens[1], tokens[2], tokens[3] - tokens = roberta.encode(sent1, sent2) - prediction = roberta.predict('sentence_classification_head', tokens).argmax().item() - prediction_label = label_fn(prediction) - ncorrect += int(prediction_label == target) - nsamples += 1 -print('| Accuracy: ', float(ncorrect)/float(nsamples)) -# prints | Accuracy: 0.9480139117700896 -``` - - -## Example usage - -To train a model with LayerDrop, add the following flags. We recommend 0.2, a value that worked well in our experiments. For Language Models that are decoder-only, you need only the decoder flag. For RoBERTa, an encoder, you need only the encoder flag. The encoder and decoder LayerDrop values can be set differently. -``` ---encoder-layerdrop 0.2 --decoder-layerdrop 0.2 -``` - -To prune a model that has been trained with LayerDrop, add the following flags followed by a comma separated list of which layers you would like to keep. -``` ---encoder-layers-to-keep 0,2,4,6,8,10,12,14 --decoder-layers-to-keep 0,2,4,6,8,10,12,14 -``` -Setting these flags should print a message such as: -``` -| Pruning model to specified layer configuration -``` -You should also see a smaller number of parameters in the model, for example the 16-Layer Transformer Language Model prints: -``` -num. model params: 246933504 -``` -while a model pruned to 8 Layers prints: -``` -num. model params: 146163712 -``` - -If you would like to pick up training with a model that has been pruned, simply adding these flags is sufficient. If you would like to use a script that only does evaluation (no training), you may need to pass an override command. A specific example would be for language modeling: -```bash -fairseq-eval-lm /path/to/wikitext-103 \ - --path /path/to/model/checkpoint.pt \ - --model-overrides "{'decoder_layers_to_keep':'0,2,4,6,8,10,12,14'}" -``` -This model override command overrides the training parameters and updates the model arguments so that the pruned model is run instead of the full model. - -## Reproduce Paper Results - -Looking to reproduce the results in the paper? - -1. For Translation on WMT16 en-de, we followed this setting [here](https://github.com/pytorch/fairseq/blob/main/examples/scaling_nmt/README.md) -2. To train RoBERTa, we followed this setting [here](https://github.com/pytorch/fairseq/tree/main/examples/roberta) -3. To train Language Models on Wikitext-103, we followed this setting [here](https://github.com/pytorch/fairseq/tree/main/examples/language_model) - - -## Tips - -1. If you would like to train large models with better performance, LayerDrop should be set to a smaller value such as 0.1 or 0.2. Too much LayerDrop will mean the model has too much regularization, so may not reach the best performance. Since LayerDrop adds regularization, you may achieve the best performance by slightly reducing the amount of standard dropout (for example, reduce by 0.1). - -2. If you would like to train large models to be pruned and made smaller, LayerDrop should be set to a larger value such as 0.5 if you want to prune very aggressively (such as removing half the network or more). If you would like to prune fewer layers away, LayerDrop can be set to a smaller value such as 0.2. Our experiments were conducted with low values of LayerDrop (such as 0.1 and 0.2), for reference. - -3. When pruning layers at inference time, it is best to spread out the layers remaining so they are evenly spaced throughout the network. For example, if you want to remove 50% of the network, keeping every other layer is good. - - -## FAQ - -1. How did the sharing layers experiment work? In an appendix (https://openreview.net/pdf?id=SylO2yStDr) we added an experiment on Wikitext-103 language modeling that combined LayerDrop with Weight Sharing. We shared chunks of 2 layers such that every other layer had shared weights. For example, if our network has layers 1 through 6, then layer 1 and 2 are shared, layer 3 and 4 are shared, and layer 5 and 6 are shared. - -2. LayerDrop hasn't been helping in my setting? During training time, LayerDrop can help regularize your network. This is most important if your network is already overfitting - if your network is underfitting, it is possible LayerDrop is adding too much regularization. We recommend using smaller values (such as 0.1 or 0.2) and also decreasing the quantity of standard dropout (for example, reduce by 0.1). - -3. Can you train a model without LayerDrop and finetune with LayerDrop (e.g. for BERT)? In our experiments, we did not see great performance. Models such as RoBERTa have trained for a long time in the pre-training setting, so only finetuning with LayerDrop for a few epochs on a downstream task such as MNLI does not achieve the robustness required for successful pruning. - - -## Having an issue or have a question? - -Please open an issue in this repository with the details of your question. Thanks! diff --git a/spaces/OpenDILabCommunity/LLMRiddlesChatGPTEN/README_zh.md b/spaces/OpenDILabCommunity/LLMRiddlesChatGPTEN/README_zh.md deleted file mode 100644 index 2492307b67ff1038d673688613c1fa0b9e811730..0000000000000000000000000000000000000000 --- a/spaces/OpenDILabCommunity/LLMRiddlesChatGPTEN/README_zh.md +++ /dev/null @@ -1,100 +0,0 @@ -# LLM Riddles - - - -[English](https://github.com/opendilab/LLMRiddles/blob/main/README.md) | ē®ä½äøę - -## :thinking: ä»ä¹ęÆLLM Riddles -ę¬¢čæę„å° LLM Riddlesļ¼čæęÆäøäøŖäøčÆčØęØ”åęęŗęåēęøøęćåØęøøęäøļ¼ä½ éč¦ęé äøčÆčØęØ”åäŗ¤äŗēé®é¢ļ¼ę„å¾å°ē¬¦åč¦ę±ēēę”ćåØčæäøŖčæēØäøļ¼ä½ åÆ仄å¼åØčēļ¼ēØä½ ę³å°ēęęę¹å¼ļ¼č®©ęØ”åč¾åŗēę”č¦ę±ēē»ęć - -## :space_invader: å¦ä½čÆē© -ę们ęä¾äŗåØēŗæēę¬ä»„ä¾ē©å®¶ē“ę„č®æé®čÆē©: -- [Hugging Face][ChatGPT + č±ę(éé ē½®api key)](https://huggingface.co/spaces/OpenDILabCommunity/LLMRiddlesChatGPTEN) -- [Hugging Face][ChatGPT + äøę(éé ē½®api key)](https://huggingface.co/spaces/OpenDILabCommunity/LLMRiddlesChatGPTCN) -- [Hugging Face][ChatGLM + äøę(å·²é¢č®¾api key)](https://huggingface.co/spaces/OpenDILabCommunity/LLMRiddlesChatGLMCN) -- [OpenXLab][ChatGPT + äøę(éé ē½®api key)](https://openxlab.org.cn/apps/detail/OpenDILab/LLMRiddlesChatGPTCN) -- [OpenXLab][ChatGLM + äøę(å·²é¢č®¾api key)](https://openxlab.org.cn/apps/detail/OpenDILab/LLMRiddlesChatGLMCN) -- [OpenXLab][ChatGLM + č±ę(å·²é¢č®¾api key)](https://openxlab.org.cn/apps/detail/OpenDILab/LLMRiddlesChatGLMEN) -- [Private Server][Mistral + č±ę(å·²é¢č®¾api key)](https://d9b451a97791dd8ef3.gradio.live) -- [Private Server][ChatGPT + äøę(å·²é¢č®¾api key)](http://llmriddles.opendilab.net/) - -ę¬å°éØē½²åÆ仄éčæ仄äøę¹å¼ļ¼ -## å®č£ -### ChatGPT ę ChatGLM API -```shell -pip3 install -r requirements.txt -``` -### Mistral-7B-Instruct-v0.1 ę¬å°ęØē -```shell -pip3 install -r requirements-dev.txt -``` -## åÆåØ -### ChatGPT + äøę -```shell -QUESTION_LANG=cn QUESTION_LLM='chatgpt' QUESTION_LLM_KEY=
- 
-
-## :star2: Special Thanks
-- ęč°¢ [Haoqiang Fan](https://www.zhihu.com/people/haoqiang-fan) ēåå§åęåé¢ē®ļ¼äøŗę¬é”¹ē®ēå¼ååę©å±ęä¾äŗēµęäøåØåć
-- ęč°¢ [HuggingFace](https://huggingface.co) åƹęøøęēęÆęäøåå©ć
-- ęč°¢ [ChatGLM](https://chatglm.cn/) åƹęøøęēęÆęäøåå©ļ¼ē¹å«ęÆä¾åØēŗæé¢č§ēä½æēØēč¶³é tokensć
-- ęč°¢ [LLM Riddles contributors](https://github.com/opendilab/LLMRiddles/graphs/contributors) ēå®ē°äøęÆęć
-
-## :label: License
-All code within this repository is under [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
-
-
diff --git a/spaces/OpenMotionLab/MotionGPT/pyrender/pyrender/platforms/osmesa.py b/spaces/OpenMotionLab/MotionGPT/pyrender/pyrender/platforms/osmesa.py
deleted file mode 100644
index deaa5ff44031a107883913ae9a18fc425d650f3d..0000000000000000000000000000000000000000
--- a/spaces/OpenMotionLab/MotionGPT/pyrender/pyrender/platforms/osmesa.py
+++ /dev/null
@@ -1,59 +0,0 @@
-from .base import Platform
-
-
-__all__ = ['OSMesaPlatform']
-
-
-class OSMesaPlatform(Platform):
- """Renders into a software buffer using OSMesa. Requires special versions
- of OSMesa to be installed, plus PyOpenGL upgrade.
- """
-
- def __init__(self, viewport_width, viewport_height):
- super(OSMesaPlatform, self).__init__(viewport_width, viewport_height)
- self._context = None
- self._buffer = None
-
- def init_context(self):
- from OpenGL import arrays
- from OpenGL.osmesa import (
- OSMesaCreateContextAttribs, OSMESA_FORMAT,
- OSMESA_RGBA, OSMESA_PROFILE, OSMESA_CORE_PROFILE,
- OSMESA_CONTEXT_MAJOR_VERSION, OSMESA_CONTEXT_MINOR_VERSION,
- OSMESA_DEPTH_BITS
- )
-
- attrs = arrays.GLintArray.asArray([
- OSMESA_FORMAT, OSMESA_RGBA,
- OSMESA_DEPTH_BITS, 24,
- OSMESA_PROFILE, OSMESA_CORE_PROFILE,
- OSMESA_CONTEXT_MAJOR_VERSION, 3,
- OSMESA_CONTEXT_MINOR_VERSION, 3,
- 0
- ])
- self._context = OSMesaCreateContextAttribs(attrs, None)
- self._buffer = arrays.GLubyteArray.zeros(
- (self.viewport_height, self.viewport_width, 4)
- )
-
- def make_current(self):
- from OpenGL import GL as gl
- from OpenGL.osmesa import OSMesaMakeCurrent
- assert(OSMesaMakeCurrent(
- self._context, self._buffer, gl.GL_UNSIGNED_BYTE,
- self.viewport_width, self.viewport_height
- ))
-
- def make_uncurrent(self):
- """Make the OpenGL context uncurrent.
- """
- pass
-
- def delete_context(self):
- from OpenGL.osmesa import OSMesaDestroyContext
- OSMesaDestroyContext(self._context)
- self._context = None
- self._buffer = None
-
- def supports_framebuffers(self):
- return False
diff --git a/spaces/PAIR/Text2Video-Zero/utils.py b/spaces/PAIR/Text2Video-Zero/utils.py
deleted file mode 100644
index 8f68e3ddf2ee3523f27a3a75ba54d524613c9b04..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/utils.py
+++ /dev/null
@@ -1,268 +0,0 @@
-import os
-
-import PIL.Image
-import numpy as np
-import torch
-import torchvision
-from torchvision.transforms import Resize, InterpolationMode
-import imageio
-from einops import rearrange
-import cv2
-from PIL import Image
-from annotator.util import resize_image, HWC3
-from annotator.canny import CannyDetector
-from annotator.openpose import OpenposeDetector
-from annotator.midas import MidasDetector
-import decord
-
-apply_canny = CannyDetector()
-apply_openpose = OpenposeDetector()
-apply_midas = MidasDetector()
-
-
-def add_watermark(image, watermark_path, wm_rel_size=1/16, boundary=5):
- '''
- Creates a watermark on the saved inference image.
- We request that you do not remove this to properly assign credit to
- Shi-Lab's work.
- '''
- watermark = Image.open(watermark_path)
- w_0, h_0 = watermark.size
- H, W, _ = image.shape
- wmsize = int(max(H, W) * wm_rel_size)
- aspect = h_0 / w_0
- if aspect > 1.0:
- watermark = watermark.resize((wmsize, int(aspect * wmsize)), Image.LANCZOS)
- else:
- watermark = watermark.resize((int(wmsize / aspect), wmsize), Image.LANCZOS)
- w, h = watermark.size
- loc_h = H - h - boundary
- loc_w = W - w - boundary
- image = Image.fromarray(image)
- mask = watermark if watermark.mode in ('RGBA', 'LA') else None
- image.paste(watermark, (loc_w, loc_h), mask)
- return image
-
-
-def pre_process_canny(input_video, low_threshold=100, high_threshold=200):
- detected_maps = []
- for frame in input_video:
- img = rearrange(frame, 'c h w -> h w c').cpu().numpy().astype(np.uint8)
- detected_map = apply_canny(img, low_threshold, high_threshold)
- detected_map = HWC3(detected_map)
- detected_maps.append(detected_map[None])
- detected_maps = np.concatenate(detected_maps)
- control = torch.from_numpy(detected_maps.copy()).float() / 255.0
- return rearrange(control, 'f h w c -> f c h w')
-
-
-def pre_process_depth(input_video, apply_depth_detect: bool = True):
- detected_maps = []
- for frame in input_video:
- img = rearrange(frame, 'c h w -> h w c').cpu().numpy().astype(np.uint8)
- img = HWC3(img)
- if apply_depth_detect:
- detected_map, _ = apply_midas(img)
- else:
- detected_map = img
- detected_map = HWC3(detected_map)
- H, W, C = img.shape
- detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_NEAREST)
- detected_maps.append(detected_map[None])
- detected_maps = np.concatenate(detected_maps)
- control = torch.from_numpy(detected_maps.copy()).float() / 255.0
- return rearrange(control, 'f h w c -> f c h w')
-
-
-def pre_process_pose(input_video, apply_pose_detect: bool = True):
- detected_maps = []
- for frame in input_video:
- img = rearrange(frame, 'c h w -> h w c').cpu().numpy().astype(np.uint8)
- img = HWC3(img)
- if apply_pose_detect:
- detected_map, _ = apply_openpose(img)
- else:
- detected_map = img
- detected_map = HWC3(detected_map)
- H, W, C = img.shape
- detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_NEAREST)
- detected_maps.append(detected_map[None])
- detected_maps = np.concatenate(detected_maps)
- control = torch.from_numpy(detected_maps.copy()).float() / 255.0
- return rearrange(control, 'f h w c -> f c h w')
-
-
-def create_video(frames, fps, rescale=False, path=None, watermark=None):
- if path is None:
- dir = "temporal"
- os.makedirs(dir, exist_ok=True)
- path = os.path.join(dir, 'movie.mp4')
-
- outputs = []
- for i, x in enumerate(frames):
- x = torchvision.utils.make_grid(torch.Tensor(x), nrow=4)
- if rescale:
- x = (x + 1.0) / 2.0 # -1,1 -> 0,1
- x = (x * 255).numpy().astype(np.uint8)
-
- if watermark is not None:
- x = add_watermark(x, watermark)
- outputs.append(x)
- # imageio.imsave(os.path.join(dir, os.path.splitext(name)[0] + f'_{i}.jpg'), x)
-
- imageio.mimsave(path, outputs, fps=fps)
- return path
-
-def create_gif(frames, fps, rescale=False, path=None, watermark=None):
- if path is None:
- dir = "temporal"
- os.makedirs(dir, exist_ok=True)
- path = os.path.join(dir, 'canny_db.gif')
-
- outputs = []
- for i, x in enumerate(frames):
- x = torchvision.utils.make_grid(torch.Tensor(x), nrow=4)
- if rescale:
- x = (x + 1.0) / 2.0 # -1,1 -> 0,1
- x = (x * 255).numpy().astype(np.uint8)
- if watermark is not None:
- x = add_watermark(x, watermark)
- outputs.append(x)
- # imageio.imsave(os.path.join(dir, os.path.splitext(name)[0] + f'_{i}.jpg'), x)
-
- imageio.mimsave(path, outputs, fps=fps)
- return path
-
-# def prepare_video(video_path:str, resolution:int, device, dtype, normalize=True, start_t:float=0, end_t:float=-1, output_fps:int=-1):
-# vr = decord.VideoReader(video_path)
-# video = vr.get_batch(range(0, len(vr))).asnumpy()
-# initial_fps = vr.get_avg_fps()
-# if output_fps == -1:
-# output_fps = int(initial_fps)
-# if end_t == -1:
-# end_t = len(vr) / initial_fps
-# else:
-# end_t = min(len(vr) / initial_fps, end_t)
-# assert 0 <= start_t < end_t
-# assert output_fps > 0
-# f, h, w, c = video.shape
-# start_f_ind = int(start_t * initial_fps)
-# end_f_ind = int(end_t * initial_fps)
-# num_f = int((end_t - start_t) * output_fps)
-# sample_idx = np.linspace(start_f_ind, end_f_ind, num_f, endpoint=False).astype(int)
-# video = video[sample_idx]
-# video = rearrange(video, "f h w c -> f c h w")
-# video = torch.Tensor(video).to(device).to(dtype)
-# if h > w:
-# w = int(w * resolution / h)
-# w = w - w % 8
-# h = resolution - resolution % 8
-# video = Resize((h, w))(video)
-# else:
-# h = int(h * resolution / w)
-# h = h - h % 8
-# w = resolution - resolution % 8
-# video = Resize((h, w))(video)
-# if normalize:
-# video = video / 127.5 - 1.0
-# return video, output_fps
-
-def prepare_video(video_path:str, resolution:int, device, dtype, normalize=True, start_t:float=0, end_t:float=-1, output_fps:int=-1):
- vr = decord.VideoReader(video_path)
- initial_fps = vr.get_avg_fps()
- if output_fps == -1:
- output_fps = int(initial_fps)
- if end_t == -1:
- end_t = len(vr) / initial_fps
- else:
- end_t = min(len(vr) / initial_fps, end_t)
- assert 0 <= start_t < end_t
- assert output_fps > 0
- start_f_ind = int(start_t * initial_fps)
- end_f_ind = int(end_t * initial_fps)
- num_f = int((end_t - start_t) * output_fps)
- sample_idx = np.linspace(start_f_ind, end_f_ind, num_f, endpoint=False).astype(int)
- video = vr.get_batch(sample_idx)
- if torch.is_tensor(video):
- video = video.detach().cpu().numpy()
- else:
- video = video.asnumpy()
- _, h, w, _ = video.shape
-
- video_resized = []
- for f in range(video.shape[0]):
- frame = video[f:f+1, ...]
-
- frame = rearrange(frame, "f h w c -> f c h w")
- frame = torch.Tensor(frame).to(device).to(dtype)
-
- # Use max if you want the larger side to be equal to resolution (e.g. 512)
- # k = float(resolution) / min(h, w)
- k = float(resolution) / max(h, w)
- h *= k
- w *= k
- h = int(np.round(h / 64.0)) * 64
- w = int(np.round(w / 64.0)) * 64
-
- frame = Resize((h, w), interpolation=InterpolationMode.BILINEAR, antialias=True)(frame)
- if normalize:
- frame = frame / 127.5 - 1.0
- video_resized.append(frame)
- video = torch.cat(video_resized)
-
- return video, output_fps
-
-def post_process_gif(list_of_results, image_resolution):
- output_file = "/tmp/ddxk.gif"
- imageio.mimsave(output_file, list_of_results, fps=4)
- return output_file
-
-
-class CrossFrameAttnProcessor:
- def __init__(self, unet_chunk_size=2):
- self.unet_chunk_size = unet_chunk_size
-
- def __call__(
- self,
- attn,
- hidden_states,
- encoder_hidden_states=None,
- attention_mask=None):
- batch_size, sequence_length, _ = hidden_states.shape
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
- query = attn.to_q(hidden_states)
-
- is_cross_attention = encoder_hidden_states is not None
- if encoder_hidden_states is None:
- encoder_hidden_states = hidden_states
- elif attn.cross_attention_norm:
- encoder_hidden_states = attn.norm_cross(encoder_hidden_states)
- key = attn.to_k(encoder_hidden_states)
- value = attn.to_v(encoder_hidden_states)
- # Sparse Attention
- if not is_cross_attention:
- video_length = key.size()[0] // self.unet_chunk_size
- # former_frame_index = torch.arange(video_length) - 1
- # former_frame_index[0] = 0
- former_frame_index = [0] * video_length
- key = rearrange(key, "(b f) d c -> b f d c", f=video_length)
- key = key[:, former_frame_index]
- key = rearrange(key, "b f d c -> (b f) d c")
- value = rearrange(value, "(b f) d c -> b f d c", f=video_length)
- value = value[:, former_frame_index]
- value = rearrange(value, "b f d c -> (b f) d c")
-
- query = attn.head_to_batch_dim(query)
- key = attn.head_to_batch_dim(key)
- value = attn.head_to_batch_dim(value)
-
- attention_probs = attn.get_attention_scores(query, key, attention_mask)
- hidden_states = torch.bmm(attention_probs, value)
- hidden_states = attn.batch_to_head_dim(hidden_states)
-
- # linear proj
- hidden_states = attn.to_out[0](hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- return hidden_states
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/configs/_base_/models/ccnet_r50-d8.py b/spaces/Pie31415/control-animation/annotator/uniformer/configs/_base_/models/ccnet_r50-d8.py
deleted file mode 100644
index 794148f576b9e215c3c6963e73dffe98204b7717..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/configs/_base_/models/ccnet_r50-d8.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
- type='EncoderDecoder',
- pretrained='open-mmlab://resnet50_v1c',
- backbone=dict(
- type='ResNetV1c',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- dilations=(1, 1, 2, 4),
- strides=(1, 2, 1, 1),
- norm_cfg=norm_cfg,
- norm_eval=False,
- style='pytorch',
- contract_dilation=True),
- decode_head=dict(
- type='CCHead',
- in_channels=2048,
- in_index=3,
- channels=512,
- recurrence=2,
- dropout_ratio=0.1,
- num_classes=19,
- norm_cfg=norm_cfg,
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
- auxiliary_head=dict(
- type='FCNHead',
- in_channels=1024,
- in_index=2,
- channels=256,
- num_convs=1,
- concat_input=False,
- dropout_ratio=0.1,
- num_classes=19,
- norm_cfg=norm_cfg,
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
- # model training and testing settings
- train_cfg=dict(),
- test_cfg=dict(mode='whole'))
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/utils/parrots_jit.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/utils/parrots_jit.py
deleted file mode 100644
index 61873f6dbb9b10ed972c90aa8faa321e3cb3249e..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/utils/parrots_jit.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os
-
-from .parrots_wrapper import TORCH_VERSION
-
-parrots_jit_option = os.getenv('PARROTS_JIT_OPTION')
-
-if TORCH_VERSION == 'parrots' and parrots_jit_option == 'ON':
- from parrots.jit import pat as jit
-else:
-
- def jit(func=None,
- check_input=None,
- full_shape=True,
- derivate=False,
- coderize=False,
- optimize=False):
-
- def wrapper(func):
-
- def wrapper_inner(*args, **kargs):
- return func(*args, **kargs)
-
- return wrapper_inner
-
- if func is None:
- return wrapper
- else:
- return func
-
-
-if TORCH_VERSION == 'parrots':
- from parrots.utils.tester import skip_no_elena
-else:
-
- def skip_no_elena(func):
-
- def wrapper(*args, **kargs):
- return func(*args, **kargs)
-
- return wrapper
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/models/blip_itm.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/models/blip_itm.py
deleted file mode 100644
index cf354c829564bf5a1f56089a2d745093d51e0fa2..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/models/blip_itm.py
+++ /dev/null
@@ -1,76 +0,0 @@
-from models.med import BertConfig, BertModel
-from transformers import BertTokenizer
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from models.blip import create_vit, init_tokenizer, load_checkpoint
-
-class BLIP_ITM(nn.Module):
- def __init__(self,
- med_config = 'configs/med_config.json',
- image_size = 384,
- vit = 'base',
- vit_grad_ckpt = False,
- vit_ckpt_layer = 0,
- embed_dim = 256,
- ):
- """
- Args:
- med_config (str): path for the mixture of encoder-decoder model's configuration file
- image_size (int): input image size
- vit (str): model size of vision transformer
- """
- super().__init__()
-
- self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)
- self.tokenizer = init_tokenizer()
- med_config = BertConfig.from_json_file(med_config)
- med_config.encoder_width = vision_width
- self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)
-
- text_width = self.text_encoder.config.hidden_size
-
- self.vision_proj = nn.Linear(vision_width, embed_dim)
- self.text_proj = nn.Linear(text_width, embed_dim)
-
- self.itm_head = nn.Linear(text_width, 2)
-
-
- def forward(self, image, caption, match_head='itm'):
-
- image_embeds = self.visual_encoder(image)
- image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
-
- text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=35,
- return_tensors="pt").to(image.device)
-
-
- if match_head=='itm':
- output = self.text_encoder(text.input_ids,
- attention_mask = text.attention_mask,
- encoder_hidden_states = image_embeds,
- encoder_attention_mask = image_atts,
- return_dict = True,
- )
- itm_output = self.itm_head(output.last_hidden_state[:,0,:])
- return itm_output
-
- elif match_head=='itc':
- text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,
- return_dict = True, mode = 'text')
- image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1)
- text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1)
-
- sim = image_feat @ text_feat.t()
- return sim
-
-
-def blip_itm(pretrained='',**kwargs):
- model = BLIP_ITM(**kwargs)
- if pretrained:
- model,msg = load_checkpoint(model,pretrained)
- assert(len(msg.missing_keys)==0)
- return model
-
\ No newline at end of file
diff --git a/spaces/Pinwheel/SuperGlue-Image-Matching/models/matching.py b/spaces/Pinwheel/SuperGlue-Image-Matching/models/matching.py
deleted file mode 100644
index 5d174208d146373230a8a68dd1420fc59c180633..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/SuperGlue-Image-Matching/models/matching.py
+++ /dev/null
@@ -1,84 +0,0 @@
-# %BANNER_BEGIN%
-# ---------------------------------------------------------------------
-# %COPYRIGHT_BEGIN%
-#
-# Magic Leap, Inc. ("COMPANY") CONFIDENTIAL
-#
-# Unpublished Copyright (c) 2020
-# Magic Leap, Inc., All Rights Reserved.
-#
-# NOTICE: All information contained herein is, and remains the property
-# of COMPANY. The intellectual and technical concepts contained herein
-# are proprietary to COMPANY and may be covered by U.S. and Foreign
-# Patents, patents in process, and are protected by trade secret or
-# copyright law. Dissemination of this information or reproduction of
-# this material is strictly forbidden unless prior written permission is
-# obtained from COMPANY. Access to the source code contained herein is
-# hereby forbidden to anyone except current COMPANY employees, managers
-# or contractors who have executed Confidentiality and Non-disclosure
-# agreements explicitly covering such access.
-#
-# The copyright notice above does not evidence any actual or intended
-# publication or disclosure of this source code, which includes
-# information that is confidential and/or proprietary, and is a trade
-# secret, of COMPANY. ANY REPRODUCTION, MODIFICATION, DISTRIBUTION,
-# PUBLIC PERFORMANCE, OR PUBLIC DISPLAY OF OR THROUGH USE OF THIS
-# SOURCE CODE WITHOUT THE EXPRESS WRITTEN CONSENT OF COMPANY IS
-# STRICTLY PROHIBITED, AND IN VIOLATION OF APPLICABLE LAWS AND
-# INTERNATIONAL TREATIES. THE RECEIPT OR POSSESSION OF THIS SOURCE
-# CODE AND/OR RELATED INFORMATION DOES NOT CONVEY OR IMPLY ANY RIGHTS
-# TO REPRODUCE, DISCLOSE OR DISTRIBUTE ITS CONTENTS, OR TO MANUFACTURE,
-# USE, OR SELL ANYTHING THAT IT MAY DESCRIBE, IN WHOLE OR IN PART.
-#
-# %COPYRIGHT_END%
-# ----------------------------------------------------------------------
-# %AUTHORS_BEGIN%
-#
-# Originating Authors: Paul-Edouard Sarlin
-#
-# %AUTHORS_END%
-# --------------------------------------------------------------------*/
-# %BANNER_END%
-
-import torch
-
-from .superpoint import SuperPoint
-from .superglue import SuperGlue
-
-
-class Matching(torch.nn.Module):
- """ Image Matching Frontend (SuperPoint + SuperGlue) """
- def __init__(self, config={}):
- super().__init__()
- self.superpoint = SuperPoint(config.get('superpoint', {}))
- self.superglue = SuperGlue(config.get('superglue', {}))
-
- def forward(self, data):
- """ Run SuperPoint (optionally) and SuperGlue
- SuperPoint is skipped if ['keypoints0', 'keypoints1'] exist in input
- Args:
- data: dictionary with minimal keys: ['image0', 'image1']
- """
- pred = {}
-
- # Extract SuperPoint (keypoints, scores, descriptors) if not provided
- if 'keypoints0' not in data:
- pred0 = self.superpoint({'image': data['image0']})
- pred = {**pred, **{k+'0': v for k, v in pred0.items()}}
- if 'keypoints1' not in data:
- pred1 = self.superpoint({'image': data['image1']})
- pred = {**pred, **{k+'1': v for k, v in pred1.items()}}
-
- # Batch all features
- # We should either have i) one image per batch, or
- # ii) the same number of local features for all images in the batch.
- data = {**data, **pred}
-
- for k in data:
- if isinstance(data[k], (list, tuple)):
- data[k] = torch.stack(data[k])
-
- # Perform the matching
- pred = {**pred, **self.superglue(data)}
-
- return pred
diff --git a/spaces/ProteinDesignLab/protpardelle/ProteinMPNN/helper_scripts/parse_multiple_chains.sh b/spaces/ProteinDesignLab/protpardelle/ProteinMPNN/helper_scripts/parse_multiple_chains.sh
deleted file mode 100644
index c2a8a108b05cc9f81c636b3b48bf58ddb4ce1a02..0000000000000000000000000000000000000000
--- a/spaces/ProteinDesignLab/protpardelle/ProteinMPNN/helper_scripts/parse_multiple_chains.sh
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/bin/bash
-#SBATCH --mem=32g
-#SBATCH -c 2
-#SBATCH --output=parse_multiple_chains.out
-
-source activate mlfold
-python parse_multiple_chains.py --input_path='../PDB_complexes/pdbs/' --output_path='../PDB_complexes/parsed_pdbs.jsonl'
diff --git a/spaces/PsykoNOT/hakurei-waifu-diffusion/app.py b/spaces/PsykoNOT/hakurei-waifu-diffusion/app.py
deleted file mode 100644
index ccef706bf3035fe470bf6a4f5bd701b18bf59133..0000000000000000000000000000000000000000
--- a/spaces/PsykoNOT/hakurei-waifu-diffusion/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/hakurei/waifu-diffusion").launch()
\ No newline at end of file
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/urllib3/__init__.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/urllib3/__init__.py
deleted file mode 100644
index c6fa38212fb559a9b51fe36b72892839efae63f5..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/urllib3/__init__.py
+++ /dev/null
@@ -1,102 +0,0 @@
-"""
-Python HTTP library with thread-safe connection pooling, file post support, user friendly, and more
-"""
-from __future__ import absolute_import
-
-# Set default logging handler to avoid "No handler found" warnings.
-import logging
-import warnings
-from logging import NullHandler
-
-from . import exceptions
-from ._version import __version__
-from .connectionpool import HTTPConnectionPool, HTTPSConnectionPool, connection_from_url
-from .filepost import encode_multipart_formdata
-from .poolmanager import PoolManager, ProxyManager, proxy_from_url
-from .response import HTTPResponse
-from .util.request import make_headers
-from .util.retry import Retry
-from .util.timeout import Timeout
-from .util.url import get_host
-
-# === NOTE TO REPACKAGERS AND VENDORS ===
-# Please delete this block, this logic is only
-# for urllib3 being distributed via PyPI.
-# See: https://github.com/urllib3/urllib3/issues/2680
-try:
- import urllib3_secure_extra # type: ignore # noqa: F401
-except ImportError:
- pass
-else:
- warnings.warn(
- "'urllib3[secure]' extra is deprecated and will be removed "
- "in a future release of urllib3 2.x. Read more in this issue: "
- "https://github.com/urllib3/urllib3/issues/2680",
- category=DeprecationWarning,
- stacklevel=2,
- )
-
-__author__ = "Andrey Petrov (andrey.petrov@shazow.net)"
-__license__ = "MIT"
-__version__ = __version__
-
-__all__ = (
- "HTTPConnectionPool",
- "HTTPSConnectionPool",
- "PoolManager",
- "ProxyManager",
- "HTTPResponse",
- "Retry",
- "Timeout",
- "add_stderr_logger",
- "connection_from_url",
- "disable_warnings",
- "encode_multipart_formdata",
- "get_host",
- "make_headers",
- "proxy_from_url",
-)
-
-logging.getLogger(__name__).addHandler(NullHandler())
-
-
-def add_stderr_logger(level=logging.DEBUG):
- """
- Helper for quickly adding a StreamHandler to the logger. Useful for
- debugging.
-
- Returns the handler after adding it.
- """
- # This method needs to be in this __init__.py to get the __name__ correct
- # even if urllib3 is vendored within another package.
- logger = logging.getLogger(__name__)
- handler = logging.StreamHandler()
- handler.setFormatter(logging.Formatter("%(asctime)s %(levelname)s %(message)s"))
- logger.addHandler(handler)
- logger.setLevel(level)
- logger.debug("Added a stderr logging handler to logger: %s", __name__)
- return handler
-
-
-# ... Clean up.
-del NullHandler
-
-
-# All warning filters *must* be appended unless you're really certain that they
-# shouldn't be: otherwise, it's very hard for users to use most Python
-# mechanisms to silence them.
-# SecurityWarning's always go off by default.
-warnings.simplefilter("always", exceptions.SecurityWarning, append=True)
-# SubjectAltNameWarning's should go off once per host
-warnings.simplefilter("default", exceptions.SubjectAltNameWarning, append=True)
-# InsecurePlatformWarning's don't vary between requests, so we keep it default.
-warnings.simplefilter("default", exceptions.InsecurePlatformWarning, append=True)
-# SNIMissingWarnings should go off only once.
-warnings.simplefilter("default", exceptions.SNIMissingWarning, append=True)
-
-
-def disable_warnings(category=exceptions.HTTPWarning):
- """
- Helper for quickly disabling all urllib3 warnings.
- """
- warnings.simplefilter("ignore", category)
diff --git a/spaces/Realcat/image-matching-webui/third_party/TopicFM/viz/methods/loftr.py b/spaces/Realcat/image-matching-webui/third_party/TopicFM/viz/methods/loftr.py
deleted file mode 100644
index 29046a2aa95596cbfe9656c3bda6dafcb1a55058..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/TopicFM/viz/methods/loftr.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from argparse import Namespace
-import os
-import torch
-import cv2
-
-from .base import Viz
-from src.utils.metrics import compute_symmetrical_epipolar_errors, compute_pose_errors
-
-from third_party.loftr.src.loftr import LoFTR, default_cfg
-
-
-class VizLoFTR(Viz):
- def __init__(self, args):
- super().__init__()
- if type(args) == dict:
- args = Namespace(**args)
-
- self.match_threshold = args.match_threshold
-
- # Load model
- conf = dict(default_cfg)
- conf["match_coarse"]["thr"] = self.match_threshold
- print(conf)
- self.model = LoFTR(config=conf)
- ckpt_dict = torch.load(args.ckpt)
- self.model.load_state_dict(ckpt_dict["state_dict"])
- self.model = self.model.eval().to(self.device)
-
- # Name the method
- # self.ckpt_name = args.ckpt.split('/')[-1].split('.')[0]
- self.name = "LoFTR"
-
- print(f"Initialize {self.name}")
-
- def match_and_draw(
- self,
- data_dict,
- root_dir=None,
- ground_truth=False,
- measure_time=False,
- viz_matches=True,
- ):
- if measure_time:
- torch.cuda.synchronize()
- start = torch.cuda.Event(enable_timing=True)
- end = torch.cuda.Event(enable_timing=True)
- start.record()
- self.model(data_dict)
- if measure_time:
- torch.cuda.synchronize()
- end.record()
- torch.cuda.synchronize()
- self.time_stats.append(start.elapsed_time(end))
-
- kpts0 = data_dict["mkpts0_f"].cpu().numpy()
- kpts1 = data_dict["mkpts1_f"].cpu().numpy()
-
- img_name0, img_name1 = list(zip(*data_dict["pair_names"]))[0]
- img0 = cv2.imread(os.path.join(root_dir, img_name0))
- img1 = cv2.imread(os.path.join(root_dir, img_name1))
- if str(data_dict["dataset_name"][0]).lower() == "scannet":
- img0 = cv2.resize(img0, (640, 480))
- img1 = cv2.resize(img1, (640, 480))
-
- if viz_matches:
- saved_name = "_".join(
- [
- img_name0.split("/")[-1].split(".")[0],
- img_name1.split("/")[-1].split(".")[0],
- ]
- )
- folder_matches = os.path.join(root_dir, "{}_viz_matches".format(self.name))
- if not os.path.exists(folder_matches):
- os.makedirs(folder_matches)
- path_to_save_matches = os.path.join(
- folder_matches, "{}.png".format(saved_name)
- )
- if ground_truth:
- compute_symmetrical_epipolar_errors(
- data_dict
- ) # compute epi_errs for each match
- compute_pose_errors(
- data_dict
- ) # compute R_errs, t_errs, pose_errs for each pair
- epi_errors = data_dict["epi_errs"].cpu().numpy()
- R_errors, t_errors = data_dict["R_errs"][0], data_dict["t_errs"][0]
-
- self.draw_matches(
- kpts0,
- kpts1,
- img0,
- img1,
- epi_errors,
- path=path_to_save_matches,
- R_errs=R_errors,
- t_errs=t_errors,
- )
-
- rel_pair_names = list(zip(*data_dict["pair_names"]))
- bs = data_dict["image0"].size(0)
- metrics = {
- # to filter duplicate pairs caused by DistributedSampler
- "identifiers": ["#".join(rel_pair_names[b]) for b in range(bs)],
- "epi_errs": [
- data_dict["epi_errs"][data_dict["m_bids"] == b].cpu().numpy()
- for b in range(bs)
- ],
- "R_errs": data_dict["R_errs"],
- "t_errs": data_dict["t_errs"],
- "inliers": data_dict["inliers"],
- }
- self.eval_stats.append({"metrics": metrics})
- else:
- m_conf = 1 - data_dict["mconf"].cpu().numpy()
- self.draw_matches(
- kpts0,
- kpts1,
- img0,
- img1,
- m_conf,
- path=path_to_save_matches,
- conf_thr=0.4,
- )
diff --git a/spaces/Ritori/TTS_Yui/waveglow/glow.py b/spaces/Ritori/TTS_Yui/waveglow/glow.py
deleted file mode 100644
index 7a7696403d505afdf0f1606f8220801b0f46152f..0000000000000000000000000000000000000000
--- a/spaces/Ritori/TTS_Yui/waveglow/glow.py
+++ /dev/null
@@ -1,311 +0,0 @@
-# *****************************************************************************
-# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are met:
-# * Redistributions of source code must retain the above copyright
-# notice, this list of conditions and the following disclaimer.
-# * Redistributions in binary form must reproduce the above copyright
-# notice, this list of conditions and the following disclaimer in the
-# documentation and/or other materials provided with the distribution.
-# * Neither the name of the NVIDIA CORPORATION nor the
-# names of its contributors may be used to endorse or promote products
-# derived from this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
-# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
-# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-#
-# *****************************************************************************
-import copy
-import torch
-from torch.autograd import Variable
-import torch.nn.functional as F
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
- n_channels_int = n_channels[0]
- in_act = input_a+input_b
- t_act = torch.tanh(in_act[:, :n_channels_int, :])
- s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
- acts = t_act * s_act
- return acts
-
-
-class WaveGlowLoss(torch.nn.Module):
- def __init__(self, sigma=1.0):
- super(WaveGlowLoss, self).__init__()
- self.sigma = sigma
-
- def forward(self, model_output):
- z, log_s_list, log_det_W_list = model_output
- for i, log_s in enumerate(log_s_list):
- if i == 0:
- log_s_total = torch.sum(log_s)
- log_det_W_total = log_det_W_list[i]
- else:
- log_s_total = log_s_total + torch.sum(log_s)
- log_det_W_total += log_det_W_list[i]
-
- loss = torch.sum(z*z)/(2*self.sigma*self.sigma) - log_s_total - log_det_W_total
- return loss/(z.size(0)*z.size(1)*z.size(2))
-
-
-class Invertible1x1Conv(torch.nn.Module):
- """
- The layer outputs both the convolution, and the log determinant
- of its weight matrix. If reverse=True it does convolution with
- inverse
- """
- def __init__(self, c):
- super(Invertible1x1Conv, self).__init__()
- self.conv = torch.nn.Conv1d(c, c, kernel_size=1, stride=1, padding=0,
- bias=False)
-
- # Sample a random orthonormal matrix to initialize weights
- W = torch.qr(torch.FloatTensor(c, c).normal_())[0]
-
- # Ensure determinant is 1.0 not -1.0
- if torch.det(W) < 0:
- W[:,0] = -1*W[:,0]
- W = W.view(c, c, 1)
- self.conv.weight.data = W
-
- def forward(self, z, reverse=False):
- # shape
- batch_size, group_size, n_of_groups = z.size()
-
- W = self.conv.weight.squeeze()
-
- if reverse:
- if not hasattr(self, 'W_inverse'):
- # Reverse computation
- W_inverse = W.float().inverse()
- W_inverse = Variable(W_inverse[..., None])
- if z.type() == 'torch.cuda.HalfTensor':
- W_inverse = W_inverse.half()
- self.W_inverse = W_inverse
- z = F.conv1d(z, self.W_inverse, bias=None, stride=1, padding=0)
- return z
- else:
- # Forward computation
- log_det_W = batch_size * n_of_groups * torch.logdet(W)
- z = self.conv(z)
- return z, log_det_W
-
-
-class WN(torch.nn.Module):
- """
- This is the WaveNet like layer for the affine coupling. The primary difference
- from WaveNet is the convolutions need not be causal. There is also no dilation
- size reset. The dilation only doubles on each layer
- """
- def __init__(self, n_in_channels, n_mel_channels, n_layers, n_channels,
- kernel_size):
- super(WN, self).__init__()
- assert(kernel_size % 2 == 1)
- assert(n_channels % 2 == 0)
- self.n_layers = n_layers
- self.n_channels = n_channels
- self.in_layers = torch.nn.ModuleList()
- self.res_skip_layers = torch.nn.ModuleList()
-
- start = torch.nn.Conv1d(n_in_channels, n_channels, 1)
- start = torch.nn.utils.weight_norm(start, name='weight')
- self.start = start
-
- # Initializing last layer to 0 makes the affine coupling layers
- # do nothing at first. This helps with training stability
- end = torch.nn.Conv1d(n_channels, 2*n_in_channels, 1)
- end.weight.data.zero_()
- end.bias.data.zero_()
- self.end = end
-
- cond_layer = torch.nn.Conv1d(n_mel_channels, 2*n_channels*n_layers, 1)
- self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
- for i in range(n_layers):
- dilation = 2 ** i
- padding = int((kernel_size*dilation - dilation)/2)
- in_layer = torch.nn.Conv1d(n_channels, 2*n_channels, kernel_size,
- dilation=dilation, padding=padding)
- in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
- self.in_layers.append(in_layer)
-
-
- # last one is not necessary
- if i < n_layers - 1:
- res_skip_channels = 2*n_channels
- else:
- res_skip_channels = n_channels
- res_skip_layer = torch.nn.Conv1d(n_channels, res_skip_channels, 1)
- res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
- self.res_skip_layers.append(res_skip_layer)
-
- def forward(self, forward_input):
- audio, spect = forward_input
- audio = self.start(audio)
- output = torch.zeros_like(audio)
- n_channels_tensor = torch.IntTensor([self.n_channels])
-
- spect = self.cond_layer(spect)
-
- for i in range(self.n_layers):
- spect_offset = i*2*self.n_channels
- acts = fused_add_tanh_sigmoid_multiply(
- self.in_layers[i](audio),
- spect[:,spect_offset:spect_offset+2*self.n_channels,:],
- n_channels_tensor)
-
- res_skip_acts = self.res_skip_layers[i](acts)
- if i < self.n_layers - 1:
- audio = audio + res_skip_acts[:,:self.n_channels,:]
- output = output + res_skip_acts[:,self.n_channels:,:]
- else:
- output = output + res_skip_acts
-
- return self.end(output)
-
-
-class WaveGlow(torch.nn.Module):
- def __init__(self, n_mel_channels, n_flows, n_group, n_early_every,
- n_early_size, WN_config):
- super(WaveGlow, self).__init__()
-
- self.upsample = torch.nn.ConvTranspose1d(n_mel_channels,
- n_mel_channels,
- 1024, stride=256)
- assert(n_group % 2 == 0)
- self.n_flows = n_flows
- self.n_group = n_group
- self.n_early_every = n_early_every
- self.n_early_size = n_early_size
- self.WN = torch.nn.ModuleList()
- self.convinv = torch.nn.ModuleList()
-
- n_half = int(n_group/2)
-
- # Set up layers with the right sizes based on how many dimensions
- # have been output already
- n_remaining_channels = n_group
- for k in range(n_flows):
- if k % self.n_early_every == 0 and k > 0:
- n_half = n_half - int(self.n_early_size/2)
- n_remaining_channels = n_remaining_channels - self.n_early_size
- self.convinv.append(Invertible1x1Conv(n_remaining_channels))
- self.WN.append(WN(n_half, n_mel_channels*n_group, **WN_config))
- self.n_remaining_channels = n_remaining_channels # Useful during inference
-
- def forward(self, forward_input):
- """
- forward_input[0] = mel_spectrogram: batch x n_mel_channels x frames
- forward_input[1] = audio: batch x time
- """
- spect, audio = forward_input
-
- # Upsample spectrogram to size of audio
- spect = self.upsample(spect)
- assert(spect.size(2) >= audio.size(1))
- if spect.size(2) > audio.size(1):
- spect = spect[:, :, :audio.size(1)]
-
- spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
- spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1)
-
- audio = audio.unfold(1, self.n_group, self.n_group).permute(0, 2, 1)
- output_audio = []
- log_s_list = []
- log_det_W_list = []
-
- for k in range(self.n_flows):
- if k % self.n_early_every == 0 and k > 0:
- output_audio.append(audio[:,:self.n_early_size,:])
- audio = audio[:,self.n_early_size:,:]
-
- audio, log_det_W = self.convinv[k](audio)
- log_det_W_list.append(log_det_W)
-
- n_half = int(audio.size(1)/2)
- audio_0 = audio[:,:n_half,:]
- audio_1 = audio[:,n_half:,:]
-
- output = self.WN[k]((audio_0, spect))
- log_s = output[:, n_half:, :]
- b = output[:, :n_half, :]
- audio_1 = torch.exp(log_s)*audio_1 + b
- log_s_list.append(log_s)
-
- audio = torch.cat([audio_0, audio_1],1)
-
- output_audio.append(audio)
- return torch.cat(output_audio,1), log_s_list, log_det_W_list
-
- def infer(self, spect, sigma=1.0):
- spect = self.upsample(spect)
- # trim conv artifacts. maybe pad spec to kernel multiple
- time_cutoff = self.upsample.kernel_size[0] - self.upsample.stride[0]
- spect = spect[:, :, :-time_cutoff]
-
- spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
- spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1)
-
- if spect.type() == 'torch.cuda.HalfTensor':
- audio = torch.cuda.HalfTensor(spect.size(0),
- self.n_remaining_channels,
- spect.size(2)).normal_()
- else:
- audio = torch.cuda.FloatTensor(spect.size(0),
- self.n_remaining_channels,
- spect.size(2)).normal_()
-
- audio = torch.autograd.Variable(sigma*audio)
-
- for k in reversed(range(self.n_flows)):
- n_half = int(audio.size(1)/2)
- audio_0 = audio[:,:n_half,:]
- audio_1 = audio[:,n_half:,:]
-
- output = self.WN[k]((audio_0, spect))
-
- s = output[:, n_half:, :]
- b = output[:, :n_half, :]
- audio_1 = (audio_1 - b)/torch.exp(s)
- audio = torch.cat([audio_0, audio_1],1)
-
- audio = self.convinv[k](audio, reverse=True)
-
- if k % self.n_early_every == 0 and k > 0:
- if spect.type() == 'torch.cuda.HalfTensor':
- z = torch.cuda.HalfTensor(spect.size(0), self.n_early_size, spect.size(2)).normal_()
- else:
- z = torch.cuda.FloatTensor(spect.size(0), self.n_early_size, spect.size(2)).normal_()
- audio = torch.cat((sigma*z, audio),1)
-
- audio = audio.permute(0,2,1).contiguous().view(audio.size(0), -1).data
- return audio
-
- @staticmethod
- def remove_weightnorm(model):
- waveglow = model
- for WN in waveglow.WN:
- WN.start = torch.nn.utils.remove_weight_norm(WN.start)
- WN.in_layers = remove(WN.in_layers)
- WN.cond_layer = torch.nn.utils.remove_weight_norm(WN.cond_layer)
- WN.res_skip_layers = remove(WN.res_skip_layers)
- return waveglow
-
-
-def remove(conv_list):
- new_conv_list = torch.nn.ModuleList()
- for old_conv in conv_list:
- old_conv = torch.nn.utils.remove_weight_norm(old_conv)
- new_conv_list.append(old_conv)
- return new_conv_list
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/configs/_base_/models/pointrend_r50.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/configs/_base_/models/pointrend_r50.py
deleted file mode 100644
index 9d323dbf9466d41e0800aa57ef84045f3d874bdf..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/configs/_base_/models/pointrend_r50.py
+++ /dev/null
@@ -1,56 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
- type='CascadeEncoderDecoder',
- num_stages=2,
- pretrained='open-mmlab://resnet50_v1c',
- backbone=dict(
- type='ResNetV1c',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- dilations=(1, 1, 1, 1),
- strides=(1, 2, 2, 2),
- norm_cfg=norm_cfg,
- norm_eval=False,
- style='pytorch',
- contract_dilation=True),
- neck=dict(
- type='FPN',
- in_channels=[256, 512, 1024, 2048],
- out_channels=256,
- num_outs=4),
- decode_head=[
- dict(
- type='FPNHead',
- in_channels=[256, 256, 256, 256],
- in_index=[0, 1, 2, 3],
- feature_strides=[4, 8, 16, 32],
- channels=128,
- dropout_ratio=-1,
- num_classes=19,
- norm_cfg=norm_cfg,
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
- dict(
- type='PointHead',
- in_channels=[256],
- in_index=[0],
- channels=256,
- num_fcs=3,
- coarse_pred_each_layer=True,
- dropout_ratio=-1,
- num_classes=19,
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
- ],
- # model training and testing settings
- train_cfg=dict(
- num_points=2048, oversample_ratio=3, importance_sample_ratio=0.75),
- test_cfg=dict(
- mode='whole',
- subdivision_steps=2,
- subdivision_num_points=8196,
- scale_factor=2))
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/cascade_rpn_head.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/cascade_rpn_head.py
deleted file mode 100644
index e32ee461951e685fb44a461033293159e3439717..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/cascade_rpn_head.py
+++ /dev/null
@@ -1,784 +0,0 @@
-from __future__ import division
-import copy
-import warnings
-
-import torch
-import torch.nn as nn
-from mmcv import ConfigDict
-from mmcv.cnn import normal_init
-from mmcv.ops import DeformConv2d, batched_nms
-
-from mmdet.core import (RegionAssigner, build_assigner, build_sampler,
- images_to_levels, multi_apply)
-from ..builder import HEADS, build_head
-from .base_dense_head import BaseDenseHead
-from .rpn_head import RPNHead
-
-
-class AdaptiveConv(nn.Module):
- """AdaptiveConv used to adapt the sampling location with the anchors.
-
- Args:
- in_channels (int): Number of channels in the input image
- out_channels (int): Number of channels produced by the convolution
- kernel_size (int or tuple): Size of the conv kernel. Default: 3
- stride (int or tuple, optional): Stride of the convolution. Default: 1
- padding (int or tuple, optional): Zero-padding added to both sides of
- the input. Default: 1
- dilation (int or tuple, optional): Spacing between kernel elements.
- Default: 3
- groups (int, optional): Number of blocked connections from input
- channels to output channels. Default: 1
- bias (bool, optional): If set True, adds a learnable bias to the
- output. Default: False.
- type (str, optional): Type of adaptive conv, can be either 'offset'
- (arbitrary anchors) or 'dilation' (uniform anchor).
- Default: 'dilation'.
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1,
- dilation=3,
- groups=1,
- bias=False,
- type='dilation'):
- super(AdaptiveConv, self).__init__()
- assert type in ['offset', 'dilation']
- self.adapt_type = type
-
- assert kernel_size == 3, 'Adaptive conv only supports kernels 3'
- if self.adapt_type == 'offset':
- assert stride == 1 and padding == 1 and groups == 1, \
- 'Adaptive conv offset mode only supports padding: {1}, ' \
- f'stride: {1}, groups: {1}'
- self.conv = DeformConv2d(
- in_channels,
- out_channels,
- kernel_size,
- padding=padding,
- stride=stride,
- groups=groups,
- bias=bias)
- else:
- self.conv = nn.Conv2d(
- in_channels,
- out_channels,
- kernel_size,
- padding=dilation,
- dilation=dilation)
-
- def init_weights(self):
- """Init weights."""
- normal_init(self.conv, std=0.01)
-
- def forward(self, x, offset):
- """Forward function."""
- if self.adapt_type == 'offset':
- N, _, H, W = x.shape
- assert offset is not None
- assert H * W == offset.shape[1]
- # reshape [N, NA, 18] to (N, 18, H, W)
- offset = offset.permute(0, 2, 1).reshape(N, -1, H, W)
- offset = offset.contiguous()
- x = self.conv(x, offset)
- else:
- assert offset is None
- x = self.conv(x)
- return x
-
-
-@HEADS.register_module()
-class StageCascadeRPNHead(RPNHead):
- """Stage of CascadeRPNHead.
-
- Args:
- in_channels (int): Number of channels in the input feature map.
- anchor_generator (dict): anchor generator config.
- adapt_cfg (dict): adaptation config.
- bridged_feature (bool, optional): whether update rpn feature.
- Default: False.
- with_cls (bool, optional): wheather use classification branch.
- Default: True.
- sampling (bool, optional): wheather use sampling. Default: True.
- """
-
- def __init__(self,
- in_channels,
- anchor_generator=dict(
- type='AnchorGenerator',
- scales=[8],
- ratios=[1.0],
- strides=[4, 8, 16, 32, 64]),
- adapt_cfg=dict(type='dilation', dilation=3),
- bridged_feature=False,
- with_cls=True,
- sampling=True,
- **kwargs):
- self.with_cls = with_cls
- self.anchor_strides = anchor_generator['strides']
- self.anchor_scales = anchor_generator['scales']
- self.bridged_feature = bridged_feature
- self.adapt_cfg = adapt_cfg
- super(StageCascadeRPNHead, self).__init__(
- in_channels, anchor_generator=anchor_generator, **kwargs)
-
- # override sampling and sampler
- self.sampling = sampling
- if self.train_cfg:
- self.assigner = build_assigner(self.train_cfg.assigner)
- # use PseudoSampler when sampling is False
- if self.sampling and hasattr(self.train_cfg, 'sampler'):
- sampler_cfg = self.train_cfg.sampler
- else:
- sampler_cfg = dict(type='PseudoSampler')
- self.sampler = build_sampler(sampler_cfg, context=self)
-
- def _init_layers(self):
- """Init layers of a CascadeRPN stage."""
- self.rpn_conv = AdaptiveConv(self.in_channels, self.feat_channels,
- **self.adapt_cfg)
- if self.with_cls:
- self.rpn_cls = nn.Conv2d(self.feat_channels,
- self.num_anchors * self.cls_out_channels,
- 1)
- self.rpn_reg = nn.Conv2d(self.feat_channels, self.num_anchors * 4, 1)
- self.relu = nn.ReLU(inplace=True)
-
- def init_weights(self):
- """Init weights of a CascadeRPN stage."""
- self.rpn_conv.init_weights()
- normal_init(self.rpn_reg, std=0.01)
- if self.with_cls:
- normal_init(self.rpn_cls, std=0.01)
-
- def forward_single(self, x, offset):
- """Forward function of single scale."""
- bridged_x = x
- x = self.relu(self.rpn_conv(x, offset))
- if self.bridged_feature:
- bridged_x = x # update feature
- cls_score = self.rpn_cls(x) if self.with_cls else None
- bbox_pred = self.rpn_reg(x)
- return bridged_x, cls_score, bbox_pred
-
- def forward(self, feats, offset_list=None):
- """Forward function."""
- if offset_list is None:
- offset_list = [None for _ in range(len(feats))]
- return multi_apply(self.forward_single, feats, offset_list)
-
- def _region_targets_single(self,
- anchors,
- valid_flags,
- gt_bboxes,
- gt_bboxes_ignore,
- gt_labels,
- img_meta,
- featmap_sizes,
- label_channels=1):
- """Get anchor targets based on region for single level."""
- assign_result = self.assigner.assign(
- anchors,
- valid_flags,
- gt_bboxes,
- img_meta,
- featmap_sizes,
- self.anchor_scales[0],
- self.anchor_strides,
- gt_bboxes_ignore=gt_bboxes_ignore,
- gt_labels=None,
- allowed_border=self.train_cfg.allowed_border)
- flat_anchors = torch.cat(anchors)
- sampling_result = self.sampler.sample(assign_result, flat_anchors,
- gt_bboxes)
-
- num_anchors = flat_anchors.shape[0]
- bbox_targets = torch.zeros_like(flat_anchors)
- bbox_weights = torch.zeros_like(flat_anchors)
- labels = flat_anchors.new_zeros(num_anchors, dtype=torch.long)
- label_weights = flat_anchors.new_zeros(num_anchors, dtype=torch.float)
-
- pos_inds = sampling_result.pos_inds
- neg_inds = sampling_result.neg_inds
- if len(pos_inds) > 0:
- if not self.reg_decoded_bbox:
- pos_bbox_targets = self.bbox_coder.encode(
- sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
- else:
- pos_bbox_targets = sampling_result.pos_gt_bboxes
- bbox_targets[pos_inds, :] = pos_bbox_targets
- bbox_weights[pos_inds, :] = 1.0
- if gt_labels is None:
- labels[pos_inds] = 1
- else:
- labels[pos_inds] = gt_labels[
- sampling_result.pos_assigned_gt_inds]
- if self.train_cfg.pos_weight <= 0:
- label_weights[pos_inds] = 1.0
- else:
- label_weights[pos_inds] = self.train_cfg.pos_weight
- if len(neg_inds) > 0:
- label_weights[neg_inds] = 1.0
-
- return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
- neg_inds)
-
- def region_targets(self,
- anchor_list,
- valid_flag_list,
- gt_bboxes_list,
- img_metas,
- featmap_sizes,
- gt_bboxes_ignore_list=None,
- gt_labels_list=None,
- label_channels=1,
- unmap_outputs=True):
- """See :func:`StageCascadeRPNHead.get_targets`."""
- num_imgs = len(img_metas)
- assert len(anchor_list) == len(valid_flag_list) == num_imgs
-
- # anchor number of multi levels
- num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
-
- # compute targets for each image
- if gt_bboxes_ignore_list is None:
- gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
- if gt_labels_list is None:
- gt_labels_list = [None for _ in range(num_imgs)]
- (all_labels, all_label_weights, all_bbox_targets, all_bbox_weights,
- pos_inds_list, neg_inds_list) = multi_apply(
- self._region_targets_single,
- anchor_list,
- valid_flag_list,
- gt_bboxes_list,
- gt_bboxes_ignore_list,
- gt_labels_list,
- img_metas,
- featmap_sizes=featmap_sizes,
- label_channels=label_channels)
- # no valid anchors
- if any([labels is None for labels in all_labels]):
- return None
- # sampled anchors of all images
- num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
- num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
- # split targets to a list w.r.t. multiple levels
- labels_list = images_to_levels(all_labels, num_level_anchors)
- label_weights_list = images_to_levels(all_label_weights,
- num_level_anchors)
- bbox_targets_list = images_to_levels(all_bbox_targets,
- num_level_anchors)
- bbox_weights_list = images_to_levels(all_bbox_weights,
- num_level_anchors)
- return (labels_list, label_weights_list, bbox_targets_list,
- bbox_weights_list, num_total_pos, num_total_neg)
-
- def get_targets(self,
- anchor_list,
- valid_flag_list,
- gt_bboxes,
- img_metas,
- featmap_sizes,
- gt_bboxes_ignore=None,
- label_channels=1):
- """Compute regression and classification targets for anchors.
-
- Args:
- anchor_list (list[list]): Multi level anchors of each image.
- valid_flag_list (list[list]): Multi level valid flags of each
- image.
- gt_bboxes (list[Tensor]): Ground truth bboxes of each image.
- img_metas (list[dict]): Meta info of each image.
- featmap_sizes (list[Tensor]): Feature mapsize each level
- gt_bboxes_ignore (list[Tensor]): Ignore bboxes of each images
- label_channels (int): Channel of label.
-
- Returns:
- cls_reg_targets (tuple)
- """
- if isinstance(self.assigner, RegionAssigner):
- cls_reg_targets = self.region_targets(
- anchor_list,
- valid_flag_list,
- gt_bboxes,
- img_metas,
- featmap_sizes,
- gt_bboxes_ignore_list=gt_bboxes_ignore,
- label_channels=label_channels)
- else:
- cls_reg_targets = super(StageCascadeRPNHead, self).get_targets(
- anchor_list,
- valid_flag_list,
- gt_bboxes,
- img_metas,
- gt_bboxes_ignore_list=gt_bboxes_ignore,
- label_channels=label_channels)
- return cls_reg_targets
-
- def anchor_offset(self, anchor_list, anchor_strides, featmap_sizes):
- """ Get offest for deformable conv based on anchor shape
- NOTE: currently support deformable kernel_size=3 and dilation=1
-
- Args:
- anchor_list (list[list[tensor])): [NI, NLVL, NA, 4] list of
- multi-level anchors
- anchor_strides (list[int]): anchor stride of each level
-
- Returns:
- offset_list (list[tensor]): [NLVL, NA, 2, 18]: offset of DeformConv
- kernel.
- """
-
- def _shape_offset(anchors, stride, ks=3, dilation=1):
- # currently support kernel_size=3 and dilation=1
- assert ks == 3 and dilation == 1
- pad = (ks - 1) // 2
- idx = torch.arange(-pad, pad + 1, dtype=dtype, device=device)
- yy, xx = torch.meshgrid(idx, idx) # return order matters
- xx = xx.reshape(-1)
- yy = yy.reshape(-1)
- w = (anchors[:, 2] - anchors[:, 0]) / stride
- h = (anchors[:, 3] - anchors[:, 1]) / stride
- w = w / (ks - 1) - dilation
- h = h / (ks - 1) - dilation
- offset_x = w[:, None] * xx # (NA, ks**2)
- offset_y = h[:, None] * yy # (NA, ks**2)
- return offset_x, offset_y
-
- def _ctr_offset(anchors, stride, featmap_size):
- feat_h, feat_w = featmap_size
- assert len(anchors) == feat_h * feat_w
-
- x = (anchors[:, 0] + anchors[:, 2]) * 0.5
- y = (anchors[:, 1] + anchors[:, 3]) * 0.5
- # compute centers on feature map
- x = x / stride
- y = y / stride
- # compute predefine centers
- xx = torch.arange(0, feat_w, device=anchors.device)
- yy = torch.arange(0, feat_h, device=anchors.device)
- yy, xx = torch.meshgrid(yy, xx)
- xx = xx.reshape(-1).type_as(x)
- yy = yy.reshape(-1).type_as(y)
-
- offset_x = x - xx # (NA, )
- offset_y = y - yy # (NA, )
- return offset_x, offset_y
-
- num_imgs = len(anchor_list)
- num_lvls = len(anchor_list[0])
- dtype = anchor_list[0][0].dtype
- device = anchor_list[0][0].device
- num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
-
- offset_list = []
- for i in range(num_imgs):
- mlvl_offset = []
- for lvl in range(num_lvls):
- c_offset_x, c_offset_y = _ctr_offset(anchor_list[i][lvl],
- anchor_strides[lvl],
- featmap_sizes[lvl])
- s_offset_x, s_offset_y = _shape_offset(anchor_list[i][lvl],
- anchor_strides[lvl])
-
- # offset = ctr_offset + shape_offset
- offset_x = s_offset_x + c_offset_x[:, None]
- offset_y = s_offset_y + c_offset_y[:, None]
-
- # offset order (y0, x0, y1, x2, .., y8, x8, y9, x9)
- offset = torch.stack([offset_y, offset_x], dim=-1)
- offset = offset.reshape(offset.size(0), -1) # [NA, 2*ks**2]
- mlvl_offset.append(offset)
- offset_list.append(torch.cat(mlvl_offset)) # [totalNA, 2*ks**2]
- offset_list = images_to_levels(offset_list, num_level_anchors)
- return offset_list
-
- def loss_single(self, cls_score, bbox_pred, anchors, labels, label_weights,
- bbox_targets, bbox_weights, num_total_samples):
- """Loss function on single scale."""
- # classification loss
- if self.with_cls:
- labels = labels.reshape(-1)
- label_weights = label_weights.reshape(-1)
- cls_score = cls_score.permute(0, 2, 3,
- 1).reshape(-1, self.cls_out_channels)
- loss_cls = self.loss_cls(
- cls_score, labels, label_weights, avg_factor=num_total_samples)
- # regression loss
- bbox_targets = bbox_targets.reshape(-1, 4)
- bbox_weights = bbox_weights.reshape(-1, 4)
- bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
- if self.reg_decoded_bbox:
- # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
- # is applied directly on the decoded bounding boxes, it
- # decodes the already encoded coordinates to absolute format.
- anchors = anchors.reshape(-1, 4)
- bbox_pred = self.bbox_coder.decode(anchors, bbox_pred)
- loss_reg = self.loss_bbox(
- bbox_pred,
- bbox_targets,
- bbox_weights,
- avg_factor=num_total_samples)
- if self.with_cls:
- return loss_cls, loss_reg
- return None, loss_reg
-
- def loss(self,
- anchor_list,
- valid_flag_list,
- cls_scores,
- bbox_preds,
- gt_bboxes,
- img_metas,
- gt_bboxes_ignore=None):
- """Compute losses of the head.
-
- Args:
- anchor_list (list[list]): Multi level anchors of each image.
- cls_scores (list[Tensor]): Box scores for each scale level
- Has shape (N, num_anchors * num_classes, H, W)
- bbox_preds (list[Tensor]): Box energies / deltas for each scale
- level with shape (N, num_anchors * 4, H, W)
- gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
- shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
- img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- gt_bboxes_ignore (None | list[Tensor]): specify which bounding
- boxes can be ignored when computing the loss. Default: None
-
- Returns:
- dict[str, Tensor]: A dictionary of loss components.
- """
- featmap_sizes = [featmap.size()[-2:] for featmap in bbox_preds]
- label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
- cls_reg_targets = self.get_targets(
- anchor_list,
- valid_flag_list,
- gt_bboxes,
- img_metas,
- featmap_sizes,
- gt_bboxes_ignore=gt_bboxes_ignore,
- label_channels=label_channels)
- if cls_reg_targets is None:
- return None
- (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
- num_total_pos, num_total_neg) = cls_reg_targets
- if self.sampling:
- num_total_samples = num_total_pos + num_total_neg
- else:
- # 200 is hard-coded average factor,
- # which follows guided anchoring.
- num_total_samples = sum([label.numel()
- for label in labels_list]) / 200.0
-
- # change per image, per level anchor_list to per_level, per_image
- mlvl_anchor_list = list(zip(*anchor_list))
- # concat mlvl_anchor_list
- mlvl_anchor_list = [
- torch.cat(anchors, dim=0) for anchors in mlvl_anchor_list
- ]
-
- losses = multi_apply(
- self.loss_single,
- cls_scores,
- bbox_preds,
- mlvl_anchor_list,
- labels_list,
- label_weights_list,
- bbox_targets_list,
- bbox_weights_list,
- num_total_samples=num_total_samples)
- if self.with_cls:
- return dict(loss_rpn_cls=losses[0], loss_rpn_reg=losses[1])
- return dict(loss_rpn_reg=losses[1])
-
- def get_bboxes(self,
- anchor_list,
- cls_scores,
- bbox_preds,
- img_metas,
- cfg,
- rescale=False):
- """Get proposal predict."""
- assert len(cls_scores) == len(bbox_preds)
- num_levels = len(cls_scores)
-
- result_list = []
- for img_id in range(len(img_metas)):
- cls_score_list = [
- cls_scores[i][img_id].detach() for i in range(num_levels)
- ]
- bbox_pred_list = [
- bbox_preds[i][img_id].detach() for i in range(num_levels)
- ]
- img_shape = img_metas[img_id]['img_shape']
- scale_factor = img_metas[img_id]['scale_factor']
- proposals = self._get_bboxes_single(cls_score_list, bbox_pred_list,
- anchor_list[img_id], img_shape,
- scale_factor, cfg, rescale)
- result_list.append(proposals)
- return result_list
-
- def refine_bboxes(self, anchor_list, bbox_preds, img_metas):
- """Refine bboxes through stages."""
- num_levels = len(bbox_preds)
- new_anchor_list = []
- for img_id in range(len(img_metas)):
- mlvl_anchors = []
- for i in range(num_levels):
- bbox_pred = bbox_preds[i][img_id].detach()
- bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
- img_shape = img_metas[img_id]['img_shape']
- bboxes = self.bbox_coder.decode(anchor_list[img_id][i],
- bbox_pred, img_shape)
- mlvl_anchors.append(bboxes)
- new_anchor_list.append(mlvl_anchors)
- return new_anchor_list
-
- # TODO: temporary plan
- def _get_bboxes_single(self,
- cls_scores,
- bbox_preds,
- mlvl_anchors,
- img_shape,
- scale_factor,
- cfg,
- rescale=False):
- """Transform outputs for a single batch item into bbox predictions.
-
- Args:
- cls_scores (list[Tensor]): Box scores for each scale level
- Has shape (num_anchors * num_classes, H, W).
- bbox_preds (list[Tensor]): Box energies / deltas for each scale
- level with shape (num_anchors * 4, H, W).
- mlvl_anchors (list[Tensor]): Box reference for each scale level
- with shape (num_total_anchors, 4).
- img_shape (tuple[int]): Shape of the input image,
- (height, width, 3).
- scale_factor (ndarray): Scale factor of the image arange as
- (w_scale, h_scale, w_scale, h_scale).
- cfg (mmcv.Config): Test / postprocessing configuration,
- if None, test_cfg would be used.
- rescale (bool): If True, return boxes in original image space.
-
- Returns:
- Tensor: Labeled boxes have the shape of (n,5), where the
- first 4 columns are bounding box positions
- (tl_x, tl_y, br_x, br_y) and the 5-th column is a score
- between 0 and 1.
- """
- cfg = self.test_cfg if cfg is None else cfg
- cfg = copy.deepcopy(cfg)
- # bboxes from different level should be independent during NMS,
- # level_ids are used as labels for batched NMS to separate them
- level_ids = []
- mlvl_scores = []
- mlvl_bbox_preds = []
- mlvl_valid_anchors = []
- for idx in range(len(cls_scores)):
- rpn_cls_score = cls_scores[idx]
- rpn_bbox_pred = bbox_preds[idx]
- assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:]
- rpn_cls_score = rpn_cls_score.permute(1, 2, 0)
- if self.use_sigmoid_cls:
- rpn_cls_score = rpn_cls_score.reshape(-1)
- scores = rpn_cls_score.sigmoid()
- else:
- rpn_cls_score = rpn_cls_score.reshape(-1, 2)
- # We set FG labels to [0, num_class-1] and BG label to
- # num_class in RPN head since mmdet v2.5, which is unified to
- # be consistent with other head since mmdet v2.0. In mmdet v2.0
- # to v2.4 we keep BG label as 0 and FG label as 1 in rpn head.
- scores = rpn_cls_score.softmax(dim=1)[:, 0]
- rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).reshape(-1, 4)
- anchors = mlvl_anchors[idx]
- if cfg.nms_pre > 0 and scores.shape[0] > cfg.nms_pre:
- # sort is faster than topk
- # _, topk_inds = scores.topk(cfg.nms_pre)
- if torch.onnx.is_in_onnx_export():
- # sort op will be converted to TopK in onnx
- # and k<=3480 in TensorRT
- _, topk_inds = scores.topk(cfg.nms_pre)
- scores = scores[topk_inds]
- else:
- ranked_scores, rank_inds = scores.sort(descending=True)
- topk_inds = rank_inds[:cfg.nms_pre]
- scores = ranked_scores[:cfg.nms_pre]
- rpn_bbox_pred = rpn_bbox_pred[topk_inds, :]
- anchors = anchors[topk_inds, :]
- mlvl_scores.append(scores)
- mlvl_bbox_preds.append(rpn_bbox_pred)
- mlvl_valid_anchors.append(anchors)
- level_ids.append(
- scores.new_full((scores.size(0), ), idx, dtype=torch.long))
-
- scores = torch.cat(mlvl_scores)
- anchors = torch.cat(mlvl_valid_anchors)
- rpn_bbox_pred = torch.cat(mlvl_bbox_preds)
- proposals = self.bbox_coder.decode(
- anchors, rpn_bbox_pred, max_shape=img_shape)
- ids = torch.cat(level_ids)
-
- # Skip nonzero op while exporting to ONNX
- if cfg.min_bbox_size > 0 and (not torch.onnx.is_in_onnx_export()):
- w = proposals[:, 2] - proposals[:, 0]
- h = proposals[:, 3] - proposals[:, 1]
- valid_inds = torch.nonzero(
- (w >= cfg.min_bbox_size)
- & (h >= cfg.min_bbox_size),
- as_tuple=False).squeeze()
- if valid_inds.sum().item() != len(proposals):
- proposals = proposals[valid_inds, :]
- scores = scores[valid_inds]
- ids = ids[valid_inds]
-
- # deprecate arguments warning
- if 'nms' not in cfg or 'max_num' in cfg or 'nms_thr' in cfg:
- warnings.warn(
- 'In rpn_proposal or test_cfg, '
- 'nms_thr has been moved to a dict named nms as '
- 'iou_threshold, max_num has been renamed as max_per_img, '
- 'name of original arguments and the way to specify '
- 'iou_threshold of NMS will be deprecated.')
- if 'nms' not in cfg:
- cfg.nms = ConfigDict(dict(type='nms', iou_threshold=cfg.nms_thr))
- if 'max_num' in cfg:
- if 'max_per_img' in cfg:
- assert cfg.max_num == cfg.max_per_img, f'You ' \
- f'set max_num and ' \
- f'max_per_img at the same time, but get {cfg.max_num} ' \
- f'and {cfg.max_per_img} respectively' \
- 'Please delete max_num which will be deprecated.'
- else:
- cfg.max_per_img = cfg.max_num
- if 'nms_thr' in cfg:
- assert cfg.nms.iou_threshold == cfg.nms_thr, f'You set' \
- f' iou_threshold in nms and ' \
- f'nms_thr at the same time, but get' \
- f' {cfg.nms.iou_threshold} and {cfg.nms_thr}' \
- f' respectively. Please delete the nms_thr ' \
- f'which will be deprecated.'
-
- dets, keep = batched_nms(proposals, scores, ids, cfg.nms)
- return dets[:cfg.max_per_img]
-
-
-@HEADS.register_module()
-class CascadeRPNHead(BaseDenseHead):
- """The CascadeRPNHead will predict more accurate region proposals, which is
- required for two-stage detectors (such as Fast/Faster R-CNN). CascadeRPN
- consists of a sequence of RPNStage to progressively improve the accuracy of
- the detected proposals.
-
- More details can be found in ``https://arxiv.org/abs/1909.06720``.
-
- Args:
- num_stages (int): number of CascadeRPN stages.
- stages (list[dict]): list of configs to build the stages.
- train_cfg (list[dict]): list of configs at training time each stage.
- test_cfg (dict): config at testing time.
- """
-
- def __init__(self, num_stages, stages, train_cfg, test_cfg):
- super(CascadeRPNHead, self).__init__()
- assert num_stages == len(stages)
- self.num_stages = num_stages
- self.stages = nn.ModuleList()
- for i in range(len(stages)):
- train_cfg_i = train_cfg[i] if train_cfg is not None else None
- stages[i].update(train_cfg=train_cfg_i)
- stages[i].update(test_cfg=test_cfg)
- self.stages.append(build_head(stages[i]))
- self.train_cfg = train_cfg
- self.test_cfg = test_cfg
-
- def init_weights(self):
- """Init weight of CascadeRPN."""
- for i in range(self.num_stages):
- self.stages[i].init_weights()
-
- def loss(self):
- """loss() is implemented in StageCascadeRPNHead."""
- pass
-
- def get_bboxes(self):
- """get_bboxes() is implemented in StageCascadeRPNHead."""
- pass
-
- def forward_train(self,
- x,
- img_metas,
- gt_bboxes,
- gt_labels=None,
- gt_bboxes_ignore=None,
- proposal_cfg=None):
- """Forward train function."""
- assert gt_labels is None, 'RPN does not require gt_labels'
-
- featmap_sizes = [featmap.size()[-2:] for featmap in x]
- device = x[0].device
- anchor_list, valid_flag_list = self.stages[0].get_anchors(
- featmap_sizes, img_metas, device=device)
-
- losses = dict()
-
- for i in range(self.num_stages):
- stage = self.stages[i]
-
- if stage.adapt_cfg['type'] == 'offset':
- offset_list = stage.anchor_offset(anchor_list,
- stage.anchor_strides,
- featmap_sizes)
- else:
- offset_list = None
- x, cls_score, bbox_pred = stage(x, offset_list)
- rpn_loss_inputs = (anchor_list, valid_flag_list, cls_score,
- bbox_pred, gt_bboxes, img_metas)
- stage_loss = stage.loss(*rpn_loss_inputs)
- for name, value in stage_loss.items():
- losses['s{}.{}'.format(i, name)] = value
-
- # refine boxes
- if i < self.num_stages - 1:
- anchor_list = stage.refine_bboxes(anchor_list, bbox_pred,
- img_metas)
- if proposal_cfg is None:
- return losses
- else:
- proposal_list = self.stages[-1].get_bboxes(anchor_list, cls_score,
- bbox_pred, img_metas,
- self.test_cfg)
- return losses, proposal_list
-
- def simple_test_rpn(self, x, img_metas):
- """Simple forward test function."""
- featmap_sizes = [featmap.size()[-2:] for featmap in x]
- device = x[0].device
- anchor_list, _ = self.stages[0].get_anchors(
- featmap_sizes, img_metas, device=device)
-
- for i in range(self.num_stages):
- stage = self.stages[i]
- if stage.adapt_cfg['type'] == 'offset':
- offset_list = stage.anchor_offset(anchor_list,
- stage.anchor_strides,
- featmap_sizes)
- else:
- offset_list = None
- x, cls_score, bbox_pred = stage(x, offset_list)
- if i < self.num_stages - 1:
- anchor_list = stage.refine_bboxes(anchor_list, bbox_pred,
- img_metas)
-
- proposal_list = self.stages[-1].get_bboxes(anchor_list, cls_score,
- bbox_pred, img_metas,
- self.test_cfg)
- return proposal_list
-
- def aug_test_rpn(self, x, img_metas):
- """Augmented forward test function."""
- raise NotImplementedError
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/models/segmentors/encoder_decoder.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/models/segmentors/encoder_decoder.py
deleted file mode 100644
index 98392ac04c4c44a7f4e7b1c0808266875877dd1f..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/models/segmentors/encoder_decoder.py
+++ /dev/null
@@ -1,298 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from annotator.uniformer.mmseg.core import add_prefix
-from annotator.uniformer.mmseg.ops import resize
-from .. import builder
-from ..builder import SEGMENTORS
-from .base import BaseSegmentor
-
-
-@SEGMENTORS.register_module()
-class EncoderDecoder(BaseSegmentor):
- """Encoder Decoder segmentors.
-
- EncoderDecoder typically consists of backbone, decode_head, auxiliary_head.
- Note that auxiliary_head is only used for deep supervision during training,
- which could be dumped during inference.
- """
-
- def __init__(self,
- backbone,
- decode_head,
- neck=None,
- auxiliary_head=None,
- train_cfg=None,
- test_cfg=None,
- pretrained=None):
- super(EncoderDecoder, self).__init__()
- self.backbone = builder.build_backbone(backbone)
- if neck is not None:
- self.neck = builder.build_neck(neck)
- self._init_decode_head(decode_head)
- self._init_auxiliary_head(auxiliary_head)
-
- self.train_cfg = train_cfg
- self.test_cfg = test_cfg
-
- self.init_weights(pretrained=pretrained)
-
- assert self.with_decode_head
-
- def _init_decode_head(self, decode_head):
- """Initialize ``decode_head``"""
- self.decode_head = builder.build_head(decode_head)
- self.align_corners = self.decode_head.align_corners
- self.num_classes = self.decode_head.num_classes
-
- def _init_auxiliary_head(self, auxiliary_head):
- """Initialize ``auxiliary_head``"""
- if auxiliary_head is not None:
- if isinstance(auxiliary_head, list):
- self.auxiliary_head = nn.ModuleList()
- for head_cfg in auxiliary_head:
- self.auxiliary_head.append(builder.build_head(head_cfg))
- else:
- self.auxiliary_head = builder.build_head(auxiliary_head)
-
- def init_weights(self, pretrained=None):
- """Initialize the weights in backbone and heads.
-
- Args:
- pretrained (str, optional): Path to pre-trained weights.
- Defaults to None.
- """
-
- super(EncoderDecoder, self).init_weights(pretrained)
- self.backbone.init_weights(pretrained=pretrained)
- self.decode_head.init_weights()
- if self.with_auxiliary_head:
- if isinstance(self.auxiliary_head, nn.ModuleList):
- for aux_head in self.auxiliary_head:
- aux_head.init_weights()
- else:
- self.auxiliary_head.init_weights()
-
- def extract_feat(self, img):
- """Extract features from images."""
- x = self.backbone(img)
- if self.with_neck:
- x = self.neck(x)
- return x
-
- def encode_decode(self, img, img_metas):
- """Encode images with backbone and decode into a semantic segmentation
- map of the same size as input."""
- x = self.extract_feat(img)
- out = self._decode_head_forward_test(x, img_metas)
- out = resize(
- input=out,
- size=img.shape[2:],
- mode='bilinear',
- align_corners=self.align_corners)
- return out
-
- def _decode_head_forward_train(self, x, img_metas, gt_semantic_seg):
- """Run forward function and calculate loss for decode head in
- training."""
- losses = dict()
- loss_decode = self.decode_head.forward_train(x, img_metas,
- gt_semantic_seg,
- self.train_cfg)
-
- losses.update(add_prefix(loss_decode, 'decode'))
- return losses
-
- def _decode_head_forward_test(self, x, img_metas):
- """Run forward function and calculate loss for decode head in
- inference."""
- seg_logits = self.decode_head.forward_test(x, img_metas, self.test_cfg)
- return seg_logits
-
- def _auxiliary_head_forward_train(self, x, img_metas, gt_semantic_seg):
- """Run forward function and calculate loss for auxiliary head in
- training."""
- losses = dict()
- if isinstance(self.auxiliary_head, nn.ModuleList):
- for idx, aux_head in enumerate(self.auxiliary_head):
- loss_aux = aux_head.forward_train(x, img_metas,
- gt_semantic_seg,
- self.train_cfg)
- losses.update(add_prefix(loss_aux, f'aux_{idx}'))
- else:
- loss_aux = self.auxiliary_head.forward_train(
- x, img_metas, gt_semantic_seg, self.train_cfg)
- losses.update(add_prefix(loss_aux, 'aux'))
-
- return losses
-
- def forward_dummy(self, img):
- """Dummy forward function."""
- seg_logit = self.encode_decode(img, None)
-
- return seg_logit
-
- def forward_train(self, img, img_metas, gt_semantic_seg):
- """Forward function for training.
-
- Args:
- img (Tensor): Input images.
- img_metas (list[dict]): List of image info dict where each dict
- has: 'img_shape', 'scale_factor', 'flip', and may also contain
- 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
- For details on the values of these keys see
- `mmseg/datasets/pipelines/formatting.py:Collect`.
- gt_semantic_seg (Tensor): Semantic segmentation masks
- used if the architecture supports semantic segmentation task.
-
- Returns:
- dict[str, Tensor]: a dictionary of loss components
- """
-
- x = self.extract_feat(img)
-
- losses = dict()
-
- loss_decode = self._decode_head_forward_train(x, img_metas,
- gt_semantic_seg)
- losses.update(loss_decode)
-
- if self.with_auxiliary_head:
- loss_aux = self._auxiliary_head_forward_train(
- x, img_metas, gt_semantic_seg)
- losses.update(loss_aux)
-
- return losses
-
- # TODO refactor
- def slide_inference(self, img, img_meta, rescale):
- """Inference by sliding-window with overlap.
-
- If h_crop > h_img or w_crop > w_img, the small patch will be used to
- decode without padding.
- """
-
- h_stride, w_stride = self.test_cfg.stride
- h_crop, w_crop = self.test_cfg.crop_size
- batch_size, _, h_img, w_img = img.size()
- num_classes = self.num_classes
- h_grids = max(h_img - h_crop + h_stride - 1, 0) // h_stride + 1
- w_grids = max(w_img - w_crop + w_stride - 1, 0) // w_stride + 1
- preds = img.new_zeros((batch_size, num_classes, h_img, w_img))
- count_mat = img.new_zeros((batch_size, 1, h_img, w_img))
- for h_idx in range(h_grids):
- for w_idx in range(w_grids):
- y1 = h_idx * h_stride
- x1 = w_idx * w_stride
- y2 = min(y1 + h_crop, h_img)
- x2 = min(x1 + w_crop, w_img)
- y1 = max(y2 - h_crop, 0)
- x1 = max(x2 - w_crop, 0)
- crop_img = img[:, :, y1:y2, x1:x2]
- crop_seg_logit = self.encode_decode(crop_img, img_meta)
- preds += F.pad(crop_seg_logit,
- (int(x1), int(preds.shape[3] - x2), int(y1),
- int(preds.shape[2] - y2)))
-
- count_mat[:, :, y1:y2, x1:x2] += 1
- assert (count_mat == 0).sum() == 0
- if torch.onnx.is_in_onnx_export():
- # cast count_mat to constant while exporting to ONNX
- count_mat = torch.from_numpy(
- count_mat.cpu().detach().numpy()).to(device=img.device)
- preds = preds / count_mat
- if rescale:
- preds = resize(
- preds,
- size=img_meta[0]['ori_shape'][:2],
- mode='bilinear',
- align_corners=self.align_corners,
- warning=False)
- return preds
-
- def whole_inference(self, img, img_meta, rescale):
- """Inference with full image."""
-
- seg_logit = self.encode_decode(img, img_meta)
- if rescale:
- # support dynamic shape for onnx
- if torch.onnx.is_in_onnx_export():
- size = img.shape[2:]
- else:
- size = img_meta[0]['ori_shape'][:2]
- seg_logit = resize(
- seg_logit,
- size=size,
- mode='bilinear',
- align_corners=self.align_corners,
- warning=False)
-
- return seg_logit
-
- def inference(self, img, img_meta, rescale):
- """Inference with slide/whole style.
-
- Args:
- img (Tensor): The input image of shape (N, 3, H, W).
- img_meta (dict): Image info dict where each dict has: 'img_shape',
- 'scale_factor', 'flip', and may also contain
- 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
- For details on the values of these keys see
- `mmseg/datasets/pipelines/formatting.py:Collect`.
- rescale (bool): Whether rescale back to original shape.
-
- Returns:
- Tensor: The output segmentation map.
- """
-
- assert self.test_cfg.mode in ['slide', 'whole']
- ori_shape = img_meta[0]['ori_shape']
- assert all(_['ori_shape'] == ori_shape for _ in img_meta)
- if self.test_cfg.mode == 'slide':
- seg_logit = self.slide_inference(img, img_meta, rescale)
- else:
- seg_logit = self.whole_inference(img, img_meta, rescale)
- output = F.softmax(seg_logit, dim=1)
- flip = img_meta[0]['flip']
- if flip:
- flip_direction = img_meta[0]['flip_direction']
- assert flip_direction in ['horizontal', 'vertical']
- if flip_direction == 'horizontal':
- output = output.flip(dims=(3, ))
- elif flip_direction == 'vertical':
- output = output.flip(dims=(2, ))
-
- return output
-
- def simple_test(self, img, img_meta, rescale=True):
- """Simple test with single image."""
- seg_logit = self.inference(img, img_meta, rescale)
- seg_pred = seg_logit.argmax(dim=1)
- if torch.onnx.is_in_onnx_export():
- # our inference backend only support 4D output
- seg_pred = seg_pred.unsqueeze(0)
- return seg_pred
- seg_pred = seg_pred.cpu().numpy()
- # unravel batch dim
- seg_pred = list(seg_pred)
- return seg_pred
-
- def aug_test(self, imgs, img_metas, rescale=True):
- """Test with augmentations.
-
- Only rescale=True is supported.
- """
- # aug_test rescale all imgs back to ori_shape for now
- assert rescale
- # to save memory, we get augmented seg logit inplace
- seg_logit = self.inference(imgs[0], img_metas[0], rescale)
- for i in range(1, len(imgs)):
- cur_seg_logit = self.inference(imgs[i], img_metas[i], rescale)
- seg_logit += cur_seg_logit
- seg_logit /= len(imgs)
- seg_pred = seg_logit.argmax(dim=1)
- seg_pred = seg_pred.cpu().numpy()
- # unravel batch dim
- seg_pred = list(seg_pred)
- return seg_pred
diff --git a/spaces/Rothfeld/stable-diffusion-mat-outpainting-primer/app.py b/spaces/Rothfeld/stable-diffusion-mat-outpainting-primer/app.py
deleted file mode 100644
index f5429c8d026226e433f356a1676e102cce57ba57..0000000000000000000000000000000000000000
--- a/spaces/Rothfeld/stable-diffusion-mat-outpainting-primer/app.py
+++ /dev/null
@@ -1,428 +0,0 @@
-# %%
-
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-from networks.mat import Generator
-import gradio as gr
-import gradio.components as gc
-import base64
-import glob
-import os
-import random
-import re
-from http import HTTPStatus
-from io import BytesIO
-from typing import Dict, List, NamedTuple, Optional, Tuple
-
-import click
-import cv2
-import numpy as np
-import PIL.Image
-import torch
-import torch.nn.functional as F
-from PIL import Image, ImageDraw, ImageOps
-from pydantic import BaseModel
-
-import dnnlib
-import legacy
-
-
-pyspng = None
-
-
-def num_range(s: str) -> List[int]:
- '''Accept either a comma separated list of numbers 'a,b,c' or a range 'a-c' and return as a list of ints.'''
-
- range_re = re.compile(r'^(\d+)-(\d+)$')
- m = range_re.match(s)
- if m:
- return list(range(int(m.group(1)), int(m.group(2))+1))
- vals = s.split(',')
- return [int(x) for x in vals]
-
-
-def copy_params_and_buffers(src_module, dst_module, require_all=False):
- assert isinstance(src_module, torch.nn.Module)
- assert isinstance(dst_module, torch.nn.Module)
- src_tensors = {name: tensor for name,
- tensor in named_params_and_buffers(src_module)}
- for name, tensor in named_params_and_buffers(dst_module):
- assert (name in src_tensors) or (not require_all)
- if name in src_tensors:
- tensor.copy_(src_tensors[name].detach()).requires_grad_(
- tensor.requires_grad)
-
-
-def params_and_buffers(module):
- assert isinstance(module, torch.nn.Module)
- return list(module.parameters()) + list(module.buffers())
-
-
-def named_params_and_buffers(module):
- assert isinstance(module, torch.nn.Module)
- return list(module.named_parameters()) + list(module.named_buffers())
-
-
-class Inpainter:
- def __init__(self,
- network_pkl,
- resolution=512,
- truncation_psi=1,
- noise_mode='const',
- sdevice='cpu'
- ):
- self.resolution = resolution
- self.truncation_psi = truncation_psi
- self.noise_mode = noise_mode
- print(f'Loading networks from: {network_pkl}')
- self.device = torch.device(sdevice)
- with dnnlib.util.open_url(network_pkl) as f:
- G_saved = (
- legacy.load_network_pkl(f)
- ['G_ema']
- .to(self.device)
- .eval()
- .requires_grad_(False)) # type: ignore
- net_res = 512 if resolution > 512 else resolution
- self.G = (
- Generator(
- z_dim=512,
- c_dim=0,
- w_dim=512,
- img_resolution=net_res,
- img_channels=3
- )
- .to(self.device)
- .eval()
- .requires_grad_(False)
- )
- copy_params_and_buffers(G_saved, self.G, require_all=True)
-
- def generate_images2(
- self,
- dpath: List[PIL.Image.Image],
- mpath: List[Optional[PIL.Image.Image]],
- seed: int = 42,
- ):
- """
- Generate images using pretrained network pickle.
- """
- resolution = self.resolution
- truncation_psi = self.truncation_psi
- noise_mode = self.noise_mode
- # seed = 240 # pick up a random number
-
- def seed_all(seed):
- random.seed(seed)
- np.random.seed(seed)
- torch.manual_seed(seed)
- torch.cuda.manual_seed(seed)
- if seed is not None:
- seed_all(seed)
-
- # no Labels.
- label = torch.zeros([1, self.G.c_dim], device=self.device)
-
- def read_image(image):
- image = np.array(image)
- if image.ndim == 2:
- image = image[:, :, np.newaxis] # HW => HWC
- image = np.repeat(image, 3, axis=2)
- image = image.transpose(2, 0, 1) # HWC => CHW
- image = image[:3]
- return image
- if resolution != 512:
- noise_mode = 'random'
- results = []
- with torch.no_grad():
- for i, (ipath, m) in enumerate(zip(dpath, mpath)):
- if seed is None:
- seed_all(i)
-
- image = read_image(ipath)
- image = (torch.from_numpy(image).float().to(
- self. device) / 127.5 - 1).unsqueeze(0)
-
- mask = np.array(m).astype(np.float32) / 255.0
- mask = torch.from_numpy(mask).float().to(
- self. device).unsqueeze(0).unsqueeze(0)
-
- z = torch.from_numpy(np.random.randn(
- 1, self.G.z_dim)).to(self.device)
- output = self.G(image, mask, z, label,
- truncation_psi=truncation_psi, noise_mode=noise_mode)
- output = (output.permute(0, 2, 3, 1) * 127.5 +
- 127.5).round().clamp(0, 255).to(torch.uint8)
- output = output[0].cpu().numpy()
- results.append(PIL.Image.fromarray(output, 'RGB'))
-
- return results
-
-
-# if __name__ == "__main__":
-# generate_images() # pylint: disable=no-value-for-parameter
-
-# ----------------------------------------------------------------------------
-def mask_to_alpha(img, mask):
- img = img.copy()
- img.putalpha(mask)
- return img
-
-
-def blend(src, target, mask):
- mask = np.expand_dims(mask, axis=-1)
- result = (1-mask) * src + mask * target
- return Image.fromarray(result.astype(np.uint8))
-
-
-def pad(img, size=(128, 128), tosize=(512, 512), border=1):
- if isinstance(size, float):
- size = (int(img.size[0] * size), int(img.size[1] * size))
- # remove border
- w, h = tosize
-
- new_img = Image.new('RGBA', (w, h))
-
- rimg = img.resize(size, resample=Image.Resampling.NEAREST)
- rimg = ImageOps.crop(rimg, border=border)
- tw, th = size
- tw, th = tw - border*2, th - border*2
- tc = ((w-tw)//2, (h-th)//2)
-
- new_img.paste(rimg, tc)
- mask = Image.new('L', (w, h))
- white = Image.new('L', (tw, th), 255)
- mask.paste(white, tc)
-
- if 'A' in rimg.getbands():
- mask.paste(rimg.getchannel('A'), tc)
- return new_img, mask
-
-
-def b64_to_img(b64):
- return Image.open(BytesIO(base64.b64decode(b64)))
-
-
-def img_to_b64(img):
- with BytesIO() as f:
- img.save(f, format='PNG')
- return base64.b64encode(f.getvalue()).decode('utf-8')
-
-
-class Predictor:
- def __init__(self):
- """Load the model into memory to make running multiple predictions efficient"""
- self.models = {
- "places2": Inpainter(
- network_pkl='models/Places_512_FullData.pkl',
- resolution=512,
- truncation_psi=1.,
- noise_mode='const',
- ),
- "places2+laion300k": Inpainter(
- network_pkl='models/Places_512_FullData+LAION300k.pkl',
- resolution=512,
- truncation_psi=1.,
- noise_mode='const',
- ),
- "places2+laion300k+laion300k(opmasked)": Inpainter(
- network_pkl='models/Places_512_FullData+LAION300k+OPM300k.pkl',
- resolution=512,
- truncation_psi=1.,
- noise_mode='const',
- ),
- "places2+laion300k+laion1200k(opmasked)": Inpainter(
- network_pkl='models/Places_512_FullData+LAION300k+OPM1200k.pkl',
- resolution=512,
- truncation_psi=1.,
- noise_mode='const',
- ),
-
- }
-
- # The arguments and types the model takes as input
-
- def predict(
- self,
- img: Image.Image,
- tosize=(512, 512),
- border=5,
- seed=42,
- size=0.5,
- model='places2',
- ) -> Image:
- i, m = pad(
- img,
- size=size, # (328, 328),
- tosize=tosize,
- border=border
- )
- """Run a single prediction on the model"""
- imgs = self.models[model].generate_images2(
- dpath=[i.resize((512, 512), resample=Image.Resampling.NEAREST)],
- mpath=[m.resize((512, 512), resample=Image.Resampling.NEAREST)],
- seed=seed,
- )
- img_op_raw = imgs[0].convert('RGBA')
- img_op_raw = img_op_raw.resize(
- tosize, resample=Image.Resampling.NEAREST)
- inpainted = img_op_raw.copy()
-
- # paste original image to remove inpainting/scaling artifacts
- inpainted = blend(
- i,
- inpainted,
- 1-(np.array(m) / 255)
- )
- minpainted = mask_to_alpha(inpainted, m)
- return inpainted, minpainted, ImageOps.invert(m)
-
- def predict_tiled(
- self,
- img: Image.Image,
- tosize=(512, 512),
- border=5,
- seed=42,
- size=0.5,
- model='places2',
- ) -> Image:
-
- i, morig = pad(
- img,
- size=size, # (328, 328),
- tosize=tosize,
- border=border
- )
- i.putalpha(morig)
- img = i
- # img.save('0.png')
- assert img.width == img.height
- assert img.width > 512 and img.width <= 512*2
-
- def tile_coords(image, n=2, tile_size=512):
- assert image.width == image.height
- offsets = np.linspace(0, image.width - tile_size, n).astype(int)
- for i in range(n):
- for j in range(n):
- left = offsets[j]
- upper = offsets[i]
- right = left + tile_size
- lower = upper + tile_size
- # tile = image.crop((left, upper, right, lower))
- yield [left, upper, right, lower]
-
- for ix, tc in enumerate(tile_coords(img, n=2)):
- i = img.crop(tc)
- # i.save(f't{ix}.png')
- m = i.getchannel('A')
-
- """Run a single prediction on the model"""
- imgs = self.models[model].generate_images2(
- dpath=[i.resize((512, 512), resample=Image.Resampling.NEAREST)],
- mpath=[m.resize((512, 512), resample=Image.Resampling.NEAREST)],
- seed=seed,
- )
- img_op_raw = imgs[0].convert('RGBA')
- # img_op_raw = img_op_raw.resize(tosize, resample=Image.Resampling.NEAREST)
- inpainted = img_op_raw.copy()
-
- # paste original image to remove inpainting/scaling artifacts
- inpainted = blend(
- i,
- inpainted,
- 1-(np.array(m) / 255)
- )
- # inpainted.save(f't{ix}_op.png')
- minpainted = mask_to_alpha(inpainted, m)
- # continue with partially inpainted image
- # since the tiles overlap, the next tile will contain (possibly inpainted) parts of the previous tile
- img.paste(inpainted, tc)
-
- # restore original alpha channel
- img.putalpha(morig)
- return img.convert('RGB'), img, ImageOps.invert(img.getchannel('A'))
-predictor = Predictor()
-
-# %%
-
-
-def _outpaint(img, tosize, border, seed, size, model, tiled):
- if tiled:
- img_op = predictor.predict_tiled(
- img,
- border=border,
- seed=seed,
- tosize=(tosize, tosize),
- size=float(size),
- model=model,
- )
- else:
- img_op = predictor.predict(
- img,
- border=border,
- seed=seed,
- tosize=(tosize, tosize),
- size=float(size),
- model=model,
- )
- return img_op
-# %%
-
-
-with gr.Blocks() as demo:
- maturl = 'https://github.com/fenglinglwb/MAT'
- gr.Markdown(f'''
- # MAT Primer for Stable Diffusion
- ## based on MAT: Mask-Aware Transformer for Large Hole Image Inpainting
- ### create a primer for use in stable diffusion outpainting
-
- i have added 2 example scripts to the repo:
- - outpainting_example1.py using the inpainting pipeline
- - outpainting_example2.py using the img2img pipeline. this is basically what i used for the examples below
- ''')
-
- gr.HTML(f'''{maturl}''')
- with gr.Box():
- with gr.Row():
- gr.Markdown(f"""example with strength 0.5""")
- with gr.Row():
- gr.HTML("
")
- gr.HTML("
")
- gr.HTML("
")
- btn = gr.Button("Run", variant="primary")
- with gr.Row():
- with gr.Column():
- searchimage = gc.Image(label="image", type='pil', image_mode='RGBA')
- to_size = gc.Slider(1, 1920, 512, step=1, label='output size')
- border = gc.Slider(1, 50, 0, step=1, label='border to crop from the image before outpainting')
- seed = gc.Slider(1, 65536, 10, step=1, label='seed')
- size = gc.Slider(0, 1, .5, step=0.01,label='scale of the image before outpainting')
- tiled = gc.Checkbox(label='tiled: run the network with 4 tiles of size 512x512 . only usable if output size >512 and <=1024', value=False)
-
- model = gc.Dropdown(
- choices=['places2',
- 'places2+laion300k',
- 'places2+laion300k+laion300k(opmasked)',
- 'places2+laion300k+laion1200k(opmasked)'],
- value='places2+laion300k+laion1200k(opmasked)',
- label='model',
- )
- with gr.Column():
- outwithoutalpha = gc.Image(label="primed image without alpha channel", type='pil', image_mode='RGBA')
- mask = gc.Image(label="outpainting mask", type='pil')
- out = gc.Image(label="primed image with alpha channel",type='pil', image_mode='RGBA')
-
- btn.click(
- fn=_outpaint,
- inputs=[searchimage, to_size, border, seed, size, model,tiled],
- outputs=[outwithoutalpha, out, mask])
-
-
-# %% launch
-demo.launch()
diff --git a/spaces/Rothfeld/stable-diffusion-mat-outpainting-primer/torch_utils/custom_ops.py b/spaces/Rothfeld/stable-diffusion-mat-outpainting-primer/torch_utils/custom_ops.py
deleted file mode 100644
index 4cc4e43fc6f6ce79f2bd68a44ba87990b9b8564e..0000000000000000000000000000000000000000
--- a/spaces/Rothfeld/stable-diffusion-mat-outpainting-primer/torch_utils/custom_ops.py
+++ /dev/null
@@ -1,126 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import os
-import glob
-import torch
-import torch.utils.cpp_extension
-import importlib
-import hashlib
-import shutil
-from pathlib import Path
-
-from torch.utils.file_baton import FileBaton
-
-#----------------------------------------------------------------------------
-# Global options.
-
-verbosity = 'brief' # Verbosity level: 'none', 'brief', 'full'
-
-#----------------------------------------------------------------------------
-# Internal helper funcs.
-
-def _find_compiler_bindir():
- patterns = [
- 'C:/Program Files (x86)/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64',
- 'C:/Program Files (x86)/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64',
- 'C:/Program Files (x86)/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64',
- 'C:/Program Files (x86)/Microsoft Visual Studio */vc/bin',
- ]
- for pattern in patterns:
- matches = sorted(glob.glob(pattern))
- if len(matches):
- return matches[-1]
- return None
-
-#----------------------------------------------------------------------------
-# Main entry point for compiling and loading C++/CUDA plugins.
-
-_cached_plugins = dict()
-
-def get_plugin(module_name, sources, **build_kwargs):
- assert verbosity in ['none', 'brief', 'full']
-
- # Already cached?
- if module_name in _cached_plugins:
- return _cached_plugins[module_name]
-
- # Print status.
- if verbosity == 'full':
- print(f'Setting up PyTorch plugin "{module_name}"...')
- elif verbosity == 'brief':
- print(f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True)
-
- try: # pylint: disable=too-many-nested-blocks
- # Make sure we can find the necessary compiler binaries.
- if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0:
- compiler_bindir = _find_compiler_bindir()
- if compiler_bindir is None:
- raise RuntimeError(f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".')
- os.environ['PATH'] += ';' + compiler_bindir
-
- # Compile and load.
- verbose_build = (verbosity == 'full')
-
- # Incremental build md5sum trickery. Copies all the input source files
- # into a cached build directory under a combined md5 digest of the input
- # source files. Copying is done only if the combined digest has changed.
- # This keeps input file timestamps and filenames the same as in previous
- # extension builds, allowing for fast incremental rebuilds.
- #
- # This optimization is done only in case all the source files reside in
- # a single directory (just for simplicity) and if the TORCH_EXTENSIONS_DIR
- # environment variable is set (we take this as a signal that the user
- # actually cares about this.)
- source_dirs_set = set(os.path.dirname(source) for source in sources)
- if len(source_dirs_set) == 1 and ('TORCH_EXTENSIONS_DIR' in os.environ):
- all_source_files = sorted(list(x for x in Path(list(source_dirs_set)[0]).iterdir() if x.is_file()))
-
- # Compute a combined hash digest for all source files in the same
- # custom op directory (usually .cu, .cpp, .py and .h files).
- hash_md5 = hashlib.md5()
- for src in all_source_files:
- with open(src, 'rb') as f:
- hash_md5.update(f.read())
- build_dir = torch.utils.cpp_extension._get_build_directory(module_name, verbose=verbose_build) # pylint: disable=protected-access
- digest_build_dir = os.path.join(build_dir, hash_md5.hexdigest())
-
- if not os.path.isdir(digest_build_dir):
- os.makedirs(digest_build_dir, exist_ok=True)
- baton = FileBaton(os.path.join(digest_build_dir, 'lock'))
- if baton.try_acquire():
- try:
- for src in all_source_files:
- shutil.copyfile(src, os.path.join(digest_build_dir, os.path.basename(src)))
- finally:
- baton.release()
- else:
- # Someone else is copying source files under the digest dir,
- # wait until done and continue.
- baton.wait()
- digest_sources = [os.path.join(digest_build_dir, os.path.basename(x)) for x in sources]
- torch.utils.cpp_extension.load(name=module_name, build_directory=build_dir,
- verbose=verbose_build, sources=digest_sources, **build_kwargs)
- else:
- torch.utils.cpp_extension.load(name=module_name, verbose=verbose_build, sources=sources, **build_kwargs)
- module = importlib.import_module(module_name)
-
- except:
- if verbosity == 'brief':
- print('Failed!')
- raise
-
- # Print status and add to cache.
- if verbosity == 'full':
- print(f'Done setting up PyTorch plugin "{module_name}".')
- elif verbosity == 'brief':
- print('Done.')
- _cached_plugins[module_name] = module
- return module
-
-#----------------------------------------------------------------------------
diff --git a/spaces/Sanathkumar1603/hackathon/app/templates/index.html b/spaces/Sanathkumar1603/hackathon/app/templates/index.html
deleted file mode 100644
index ff077496c7ed837df17a2857c728af75fc5780dd..0000000000000000000000000000000000000000
--- a/spaces/Sanathkumar1603/hackathon/app/templates/index.html
+++ /dev/null
@@ -1,29 +0,0 @@
-
-
-
-
-
-
-
-
- Recognition Application
-
-
-
-
-
-
diff --git a/spaces/ServerX/PorcoDiaz/train/losses.py b/spaces/ServerX/PorcoDiaz/train/losses.py
deleted file mode 100644
index b89038f14d06d7fae43628183e9ffb465e4edafd..0000000000000000000000000000000000000000
--- a/spaces/ServerX/PorcoDiaz/train/losses.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-
-def feature_loss(fmap_r, fmap_g):
- loss = 0
- for dr, dg in zip(fmap_r, fmap_g):
- for rl, gl in zip(dr, dg):
- rl = rl.float().detach()
- gl = gl.float()
- loss += torch.mean(torch.abs(rl - gl))
-
- return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
- loss = 0
- r_losses = []
- g_losses = []
- for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
- dr = dr.float()
- dg = dg.float()
- r_loss = torch.mean((1 - dr) ** 2)
- g_loss = torch.mean(dg**2)
- loss += r_loss + g_loss
- r_losses.append(r_loss.item())
- g_losses.append(g_loss.item())
-
- return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
- loss = 0
- gen_losses = []
- for dg in disc_outputs:
- dg = dg.float()
- l = torch.mean((1 - dg) ** 2)
- gen_losses.append(l)
- loss += l
-
- return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
- """
- z_p, logs_q: [b, h, t_t]
- m_p, logs_p: [b, h, t_t]
- """
- z_p = z_p.float()
- logs_q = logs_q.float()
- m_p = m_p.float()
- logs_p = logs_p.float()
- z_mask = z_mask.float()
-
- kl = logs_p - logs_q - 0.5
- kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p)
- kl = torch.sum(kl * z_mask)
- l = kl / torch.sum(z_mask)
- return l
diff --git a/spaces/Sumit7864/Image-Enhancer/weights/README.md b/spaces/Sumit7864/Image-Enhancer/weights/README.md
deleted file mode 100644
index 4d7b7e642591ef88575d9e6c360a4d29e0cc1a4f..0000000000000000000000000000000000000000
--- a/spaces/Sumit7864/Image-Enhancer/weights/README.md
+++ /dev/null
@@ -1,3 +0,0 @@
-# Weights
-
-Put the downloaded weights to this folder.
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/click/utils.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/click/utils.py
deleted file mode 100644
index 8283788ace64e92ef77e3a7d1ff6aea5fda1854e..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/click/utils.py
+++ /dev/null
@@ -1,580 +0,0 @@
-import os
-import re
-import sys
-import typing as t
-from functools import update_wrapper
-from types import ModuleType
-
-from ._compat import _default_text_stderr
-from ._compat import _default_text_stdout
-from ._compat import _find_binary_writer
-from ._compat import auto_wrap_for_ansi
-from ._compat import binary_streams
-from ._compat import get_filesystem_encoding
-from ._compat import open_stream
-from ._compat import should_strip_ansi
-from ._compat import strip_ansi
-from ._compat import text_streams
-from ._compat import WIN
-from .globals import resolve_color_default
-
-if t.TYPE_CHECKING:
- import typing_extensions as te
-
-F = t.TypeVar("F", bound=t.Callable[..., t.Any])
-
-
-def _posixify(name: str) -> str:
- return "-".join(name.split()).lower()
-
-
-def safecall(func: F) -> F:
- """Wraps a function so that it swallows exceptions."""
-
- def wrapper(*args, **kwargs): # type: ignore
- try:
- return func(*args, **kwargs)
- except Exception:
- pass
-
- return update_wrapper(t.cast(F, wrapper), func)
-
-
-def make_str(value: t.Any) -> str:
- """Converts a value into a valid string."""
- if isinstance(value, bytes):
- try:
- return value.decode(get_filesystem_encoding())
- except UnicodeError:
- return value.decode("utf-8", "replace")
- return str(value)
-
-
-def make_default_short_help(help: str, max_length: int = 45) -> str:
- """Returns a condensed version of help string."""
- # Consider only the first paragraph.
- paragraph_end = help.find("\n\n")
-
- if paragraph_end != -1:
- help = help[:paragraph_end]
-
- # Collapse newlines, tabs, and spaces.
- words = help.split()
-
- if not words:
- return ""
-
- # The first paragraph started with a "no rewrap" marker, ignore it.
- if words[0] == "\b":
- words = words[1:]
-
- total_length = 0
- last_index = len(words) - 1
-
- for i, word in enumerate(words):
- total_length += len(word) + (i > 0)
-
- if total_length > max_length: # too long, truncate
- break
-
- if word[-1] == ".": # sentence end, truncate without "..."
- return " ".join(words[: i + 1])
-
- if total_length == max_length and i != last_index:
- break # not at sentence end, truncate with "..."
- else:
- return " ".join(words) # no truncation needed
-
- # Account for the length of the suffix.
- total_length += len("...")
-
- # remove words until the length is short enough
- while i > 0:
- total_length -= len(words[i]) + (i > 0)
-
- if total_length <= max_length:
- break
-
- i -= 1
-
- return " ".join(words[:i]) + "..."
-
-
-class LazyFile:
- """A lazy file works like a regular file but it does not fully open
- the file but it does perform some basic checks early to see if the
- filename parameter does make sense. This is useful for safely opening
- files for writing.
- """
-
- def __init__(
- self,
- filename: str,
- mode: str = "r",
- encoding: t.Optional[str] = None,
- errors: t.Optional[str] = "strict",
- atomic: bool = False,
- ):
- self.name = filename
- self.mode = mode
- self.encoding = encoding
- self.errors = errors
- self.atomic = atomic
- self._f: t.Optional[t.IO]
-
- if filename == "-":
- self._f, self.should_close = open_stream(filename, mode, encoding, errors)
- else:
- if "r" in mode:
- # Open and close the file in case we're opening it for
- # reading so that we can catch at least some errors in
- # some cases early.
- open(filename, mode).close()
- self._f = None
- self.should_close = True
-
- def __getattr__(self, name: str) -> t.Any:
- return getattr(self.open(), name)
-
- def __repr__(self) -> str:
- if self._f is not None:
- return repr(self._f)
- return f"ę³Øęäŗé
--
-
- 
- 
- 
- Adobe Acrobat XI Professional 11.0.7 Keygen-CORE X-FORCE keygen
Download ->->->-> https://urloso.com/2uyPxv
-
- d5da3c52bf
-
-
- diff --git a/spaces/bioriAsaeru/text-to-voice/Kitabistan Dictionary English to Urdu PDF Download The Ultimate Tool for Urdu Speakers and Students.md b/spaces/bioriAsaeru/text-to-voice/Kitabistan Dictionary English to Urdu PDF Download The Ultimate Tool for Urdu Speakers and Students.md deleted file mode 100644 index b6f5f3a03fb134ac9d93e10e6392a499fdacd9f7..0000000000000000000000000000000000000000 --- a/spaces/bioriAsaeru/text-to-voice/Kitabistan Dictionary English to Urdu PDF Download The Ultimate Tool for Urdu Speakers and Students.md +++ /dev/null @@ -1,6 +0,0 @@ -
kitabistandictionaryenglishtourdupdfdownload
Download File ►►►►► https://urloso.com/2uyQ8N
-
- aaccfb2cb3
-
-
- diff --git a/spaces/bookbot/Grad-TTS-Weildan-Playground/Grad-TTS/utils.py b/spaces/bookbot/Grad-TTS-Weildan-Playground/Grad-TTS/utils.py deleted file mode 100644 index f3f5fcdd3d47c743784abe87f23ad4c50a04fdb2..0000000000000000000000000000000000000000 --- a/spaces/bookbot/Grad-TTS-Weildan-Playground/Grad-TTS/utils.py +++ /dev/null @@ -1,75 +0,0 @@ -# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved. -# This program is free software; you can redistribute it and/or modify -# it under the terms of the MIT License. -# This program is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# MIT License for more details. - -import os -import glob -import numpy as np -import matplotlib.pyplot as plt - -import torch - - -def intersperse(lst, item): - # Adds blank symbol - result = [item] * (len(lst) * 2 + 1) - result[1::2] = lst - return result - - -def parse_filelist(filelist_path, split_char="|"): - with open(filelist_path, encoding='utf-8') as f: - filepaths_and_text = [line.strip().split(split_char) for line in f] - return filepaths_and_text - - -def latest_checkpoint_path(dir_path, regex="grad_*.pt"): - f_list = glob.glob(os.path.join(dir_path, regex)) - f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f)))) - x = f_list[-1] - return x - - -def load_checkpoint(logdir, model, num=None): - if num is None: - model_path = latest_checkpoint_path(logdir, regex="grad_*.pt") - else: - model_path = os.path.join(logdir, f"grad_{num}.pt") - print(f'Loading checkpoint {model_path}...') - model_dict = torch.load(model_path, map_location=lambda loc, storage: loc) - model.load_state_dict(model_dict, strict=False) - return model - - -def save_figure_to_numpy(fig): - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - return data - - -def plot_tensor(tensor): - plt.style.use('default') - fig, ax = plt.subplots(figsize=(12, 3)) - im = ax.imshow(tensor, aspect="auto", origin="lower", interpolation='none') - plt.colorbar(im, ax=ax) - plt.tight_layout() - fig.canvas.draw() - data = save_figure_to_numpy(fig) - plt.close() - return data - - -def save_plot(tensor, savepath): - plt.style.use('default') - fig, ax = plt.subplots(figsize=(12, 3)) - im = ax.imshow(tensor, aspect="auto", origin="lower", interpolation='none') - plt.colorbar(im, ax=ax) - plt.tight_layout() - fig.canvas.draw() - plt.savefig(savepath) - plt.close() - return diff --git a/spaces/bookbot/Grad-TTS-Weildan-Playground/app.py b/spaces/bookbot/Grad-TTS-Weildan-Playground/app.py deleted file mode 100644 index 36e02b9bd980f05cd42b1e014fba23a4f7b5a8fa..0000000000000000000000000000000000000000 --- a/spaces/bookbot/Grad-TTS-Weildan-Playground/app.py +++ /dev/null @@ -1,127 +0,0 @@ -import os -import sys -import json -from subprocess import call - -import torch -import gradio as gr -from scipy.io.wavfile import write -from huggingface_hub import hf_hub_url, cached_download -import nltk -from nltk.tokenize import word_tokenize - -nltk.download("punkt") - -AUTH_TOKEN = os.environ["HF_TOKEN"] - -# download models -url = hf_hub_url(repo_id="bookbot/grad-tts-en-ft-Weildan-v2", filename="grad_24000.pt") -grad_tts_model_path = cached_download(url, use_auth_token=AUTH_TOKEN) -torch.hub.download_url_to_file( - "https://github.com/AK391/Speech-Backbones/releases/download/v1/hifigan.pt", - "hifigan.pt", -) - -# build MAS -current = os.getcwd() -os.chdir(current + "/Grad-TTS/model/monotonic_align") -call("python setup.py build_ext --inplace", shell=True) -os.chdir("../../../") - -sys.path.append("Grad-TTS/") -import params -from model import GradTTS -from text import text_to_sequence, cmudict -from text.symbols import symbols -from utils import intersperse - -sys.path.append("Grad-TTS/hifi-gan/") -from env import AttrDict -from models import Generator as HiFiGAN - -SPEAKERS = 247 - -# load models -generator = GradTTS( - len(symbols) + 1, - SPEAKERS, - params.spk_emb_dim, - params.n_enc_channels, - params.filter_channels, - params.filter_channels_dp, - params.n_heads, - params.n_enc_layers, - params.enc_kernel, - params.enc_dropout, - params.window_size, - params.n_feats, - params.dec_dim, - params.beta_min, - params.beta_max, - pe_scale=1000, -) - -generator.load_state_dict( - torch.load(grad_tts_model_path, map_location=lambda loc, storage: loc) -) -_ = generator.eval() - -cmu = cmudict.CMUDict("./Grad-TTS/resources/cmu_dictionary_id") - -with open("./Grad-TTS/checkpts/hifigan-config.json") as f: - h = AttrDict(json.load(f)) - -hifigan = HiFiGAN(h) -hifigan.load_state_dict( - torch.load("./hifigan.pt", map_location=lambda loc, storage: loc)["generator"] -) -_ = hifigan.eval() -hifigan.remove_weight_norm() - - -def inference(text, n_timesteps): - text = " ".join(word_tokenize(text)) - x = torch.LongTensor( - intersperse(text_to_sequence(text, dictionary=cmu), len(symbols)) - )[None] - x_lengths = torch.LongTensor([x.shape[-1]]) - - y_enc, y_dec, attn = generator.forward( - x, - x_lengths, - n_timesteps=n_timesteps, - temperature=1.5, - stoc=False, - spk=torch.LongTensor([0]) if SPEAKERS > 1 else None, - length_scale=1.0, - ) - - with torch.no_grad(): - audio = hifigan.forward(y_dec).cpu().squeeze().clamp(-1, 1).detach().numpy() - - write("out.wav", 22050, audio) - return "./out.wav" - - -inputs = [ - gr.inputs.Textbox(lines=5, label="Input Text"), - gr.inputs.Slider(minimum=0, maximum=100, step=10, label="Timesteps"), -] - -outputs = gr.outputs.Audio(type="file", label="Output Audio") -title = "Bookbot Grad-TTS Weildan Demo šØ" -description = "Hi there! You can start typing any input text āØ, select your preferred timesteps ā, and hit submit! š Please be patient as it may take a while - the greater the timestep, the longer the generation š" - -utterances = [ - "Selamat pagi! Selamat datang di Jakarta!", - "Kak, harga nasi gorengnya berapa ya?", - "Bapak bilang, Malik hebat. Bisa bersih bersih seperti Bapak.", - "Here are the match lineups for the Colombia Haiti match.", -] - -timesteps = [(i * 10) + 50 for i in range(len(utterances))] -examples = [list(l) for l in zip(utterances, timesteps)] - -gr.Interface( - inference, inputs, outputs, title=title, description=description, examples=examples -).launch() diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/data/transforms/transform.py b/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/data/transforms/transform.py deleted file mode 100644 index de44b991d7ab0d920ffb769e1402f08e358d37f7..0000000000000000000000000000000000000000 --- a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/data/transforms/transform.py +++ /dev/null @@ -1,351 +0,0 @@ -# -*- coding: utf-8 -*- -# Copyright (c) Facebook, Inc. and its affiliates. - -""" -See "Data Augmentation" tutorial for an overview of the system: -https://detectron2.readthedocs.io/tutorials/augmentation.html -""" - -import numpy as np -import torch -import torch.nn.functional as F -from fvcore.transforms.transform import ( - CropTransform, - HFlipTransform, - NoOpTransform, - Transform, - TransformList, -) -from PIL import Image - -try: - import cv2 # noqa -except ImportError: - # OpenCV is an optional dependency at the moment - pass - -__all__ = [ - "ExtentTransform", - "ResizeTransform", - "RotationTransform", - "ColorTransform", - "PILColorTransform", -] - - -class ExtentTransform(Transform): - """ - Extracts a subregion from the source image and scales it to the output size. - - The fill color is used to map pixels from the source rect that fall outside - the source image. - - See: https://pillow.readthedocs.io/en/latest/PIL.html#PIL.ImageTransform.ExtentTransform - """ - - def __init__(self, src_rect, output_size, interp=Image.LINEAR, fill=0): - """ - Args: - src_rect (x0, y0, x1, y1): src coordinates - output_size (h, w): dst image size - interp: PIL interpolation methods - fill: Fill color used when src_rect extends outside image - """ - super().__init__() - self._set_attributes(locals()) - - def apply_image(self, img, interp=None): - h, w = self.output_size - if len(img.shape) > 2 and img.shape[2] == 1: - pil_image = Image.fromarray(img[:, :, 0], mode="L") - else: - pil_image = Image.fromarray(img) - pil_image = pil_image.transform( - size=(w, h), - method=Image.EXTENT, - data=self.src_rect, - resample=interp if interp else self.interp, - fill=self.fill, - ) - ret = np.asarray(pil_image) - if len(img.shape) > 2 and img.shape[2] == 1: - ret = np.expand_dims(ret, -1) - return ret - - def apply_coords(self, coords): - # Transform image center from source coordinates into output coordinates - # and then map the new origin to the corner of the output image. - h, w = self.output_size - x0, y0, x1, y1 = self.src_rect - new_coords = coords.astype(np.float32) - new_coords[:, 0] -= 0.5 * (x0 + x1) - new_coords[:, 1] -= 0.5 * (y0 + y1) - new_coords[:, 0] *= w / (x1 - x0) - new_coords[:, 1] *= h / (y1 - y0) - new_coords[:, 0] += 0.5 * w - new_coords[:, 1] += 0.5 * h - return new_coords - - def apply_segmentation(self, segmentation): - segmentation = self.apply_image(segmentation, interp=Image.NEAREST) - return segmentation - - -class ResizeTransform(Transform): - """ - Resize the image to a target size. - """ - - def __init__(self, h, w, new_h, new_w, interp=None): - """ - Args: - h, w (int): original image size - new_h, new_w (int): new image size - interp: PIL interpolation methods, defaults to bilinear. - """ - # TODO decide on PIL vs opencv - super().__init__() - if interp is None: - interp = Image.BILINEAR - self._set_attributes(locals()) - - def apply_image(self, img, interp=None): - assert img.shape[:2] == (self.h, self.w) - assert len(img.shape) <= 4 - interp_method = interp if interp is not None else self.interp - - if img.dtype == np.uint8: - if len(img.shape) > 2 and img.shape[2] == 1: - pil_image = Image.fromarray(img[:, :, 0], mode="L") - else: - pil_image = Image.fromarray(img) - pil_image = pil_image.resize((self.new_w, self.new_h), interp_method) - ret = np.asarray(pil_image) - if len(img.shape) > 2 and img.shape[2] == 1: - ret = np.expand_dims(ret, -1) - else: - # PIL only supports uint8 - if any(x < 0 for x in img.strides): - img = np.ascontiguousarray(img) - img = torch.from_numpy(img) - shape = list(img.shape) - shape_4d = shape[:2] + [1] * (4 - len(shape)) + shape[2:] - img = img.view(shape_4d).permute(2, 3, 0, 1) # hw(c) -> nchw - _PIL_RESIZE_TO_INTERPOLATE_MODE = { - Image.NEAREST: "nearest", - Image.BILINEAR: "bilinear", - Image.BICUBIC: "bicubic", - } - mode = _PIL_RESIZE_TO_INTERPOLATE_MODE[interp_method] - align_corners = None if mode == "nearest" else False - img = F.interpolate( - img, (self.new_h, self.new_w), mode=mode, align_corners=align_corners - ) - shape[:2] = (self.new_h, self.new_w) - ret = img.permute(2, 3, 0, 1).view(shape).numpy() # nchw -> hw(c) - - return ret - - def apply_coords(self, coords): - coords[:, 0] = coords[:, 0] * (self.new_w * 1.0 / self.w) - coords[:, 1] = coords[:, 1] * (self.new_h * 1.0 / self.h) - return coords - - def apply_segmentation(self, segmentation): - segmentation = self.apply_image(segmentation, interp=Image.NEAREST) - return segmentation - - def inverse(self): - return ResizeTransform(self.new_h, self.new_w, self.h, self.w, self.interp) - - -class RotationTransform(Transform): - """ - This method returns a copy of this image, rotated the given - number of degrees counter clockwise around its center. - """ - - def __init__(self, h, w, angle, expand=True, center=None, interp=None): - """ - Args: - h, w (int): original image size - angle (float): degrees for rotation - expand (bool): choose if the image should be resized to fit the whole - rotated image (default), or simply cropped - center (tuple (width, height)): coordinates of the rotation center - if left to None, the center will be fit to the center of each image - center has no effect if expand=True because it only affects shifting - interp: cv2 interpolation method, default cv2.INTER_LINEAR - """ - super().__init__() - image_center = np.array((w / 2, h / 2)) - if center is None: - center = image_center - if interp is None: - interp = cv2.INTER_LINEAR - abs_cos, abs_sin = (abs(np.cos(np.deg2rad(angle))), abs(np.sin(np.deg2rad(angle)))) - if expand: - # find the new width and height bounds - bound_w, bound_h = np.rint( - [h * abs_sin + w * abs_cos, h * abs_cos + w * abs_sin] - ).astype(int) - else: - bound_w, bound_h = w, h - - self._set_attributes(locals()) - self.rm_coords = self.create_rotation_matrix() - # Needed because of this problem https://github.com/opencv/opencv/issues/11784 - self.rm_image = self.create_rotation_matrix(offset=-0.5) - - def apply_image(self, img, interp=None): - """ - img should be a numpy array, formatted as Height * Width * Nchannels - """ - if len(img) == 0 or self.angle % 360 == 0: - return img - assert img.shape[:2] == (self.h, self.w) - interp = interp if interp is not None else self.interp - return cv2.warpAffine(img, self.rm_image, (self.bound_w, self.bound_h), flags=interp) - - def apply_coords(self, coords): - """ - coords should be a N * 2 array-like, containing N couples of (x, y) points - """ - coords = np.asarray(coords, dtype=float) - if len(coords) == 0 or self.angle % 360 == 0: - return coords - return cv2.transform(coords[:, np.newaxis, :], self.rm_coords)[:, 0, :] - - def apply_segmentation(self, segmentation): - segmentation = self.apply_image(segmentation, interp=cv2.INTER_NEAREST) - return segmentation - - def create_rotation_matrix(self, offset=0): - center = (self.center[0] + offset, self.center[1] + offset) - rm = cv2.getRotationMatrix2D(tuple(center), self.angle, 1) - if self.expand: - # Find the coordinates of the center of rotation in the new image - # The only point for which we know the future coordinates is the center of the image - rot_im_center = cv2.transform(self.image_center[None, None, :] + offset, rm)[0, 0, :] - new_center = np.array([self.bound_w / 2, self.bound_h / 2]) + offset - rot_im_center - # shift the rotation center to the new coordinates - rm[:, 2] += new_center - return rm - - def inverse(self): - """ - The inverse is to rotate it back with expand, and crop to get the original shape. - """ - if not self.expand: # Not possible to inverse if a part of the image is lost - raise NotImplementedError() - rotation = RotationTransform( - self.bound_h, self.bound_w, -self.angle, True, None, self.interp - ) - crop = CropTransform( - (rotation.bound_w - self.w) // 2, (rotation.bound_h - self.h) // 2, self.w, self.h - ) - return TransformList([rotation, crop]) - - -class ColorTransform(Transform): - """ - Generic wrapper for any photometric transforms. - These transformations should only affect the color space and - not the coordinate space of the image (e.g. annotation - coordinates such as bounding boxes should not be changed) - """ - - def __init__(self, op): - """ - Args: - op (Callable): operation to be applied to the image, - which takes in an ndarray and returns an ndarray. - """ - if not callable(op): - raise ValueError("op parameter should be callable") - super().__init__() - self._set_attributes(locals()) - - def apply_image(self, img): - return self.op(img) - - def apply_coords(self, coords): - return coords - - def inverse(self): - return NoOpTransform() - - def apply_segmentation(self, segmentation): - return segmentation - - -class PILColorTransform(ColorTransform): - """ - Generic wrapper for PIL Photometric image transforms, - which affect the color space and not the coordinate - space of the image - """ - - def __init__(self, op): - """ - Args: - op (Callable): operation to be applied to the image, - which takes in a PIL Image and returns a transformed - PIL Image. - For reference on possible operations see: - - https://pillow.readthedocs.io/en/stable/ - """ - if not callable(op): - raise ValueError("op parameter should be callable") - super().__init__(op) - - def apply_image(self, img): - img = Image.fromarray(img) - return np.asarray(super().apply_image(img)) - - -def HFlip_rotated_box(transform, rotated_boxes): - """ - Apply the horizontal flip transform on rotated boxes. - - Args: - rotated_boxes (ndarray): Nx5 floating point array of - (x_center, y_center, width, height, angle_degrees) format - in absolute coordinates. - """ - # Transform x_center - rotated_boxes[:, 0] = transform.width - rotated_boxes[:, 0] - # Transform angle - rotated_boxes[:, 4] = -rotated_boxes[:, 4] - return rotated_boxes - - -def Resize_rotated_box(transform, rotated_boxes): - """ - Apply the resizing transform on rotated boxes. For details of how these (approximation) - formulas are derived, please refer to :meth:`RotatedBoxes.scale`. - - Args: - rotated_boxes (ndarray): Nx5 floating point array of - (x_center, y_center, width, height, angle_degrees) format - in absolute coordinates. - """ - scale_factor_x = transform.new_w * 1.0 / transform.w - scale_factor_y = transform.new_h * 1.0 / transform.h - rotated_boxes[:, 0] *= scale_factor_x - rotated_boxes[:, 1] *= scale_factor_y - theta = rotated_boxes[:, 4] * np.pi / 180.0 - c = np.cos(theta) - s = np.sin(theta) - rotated_boxes[:, 2] *= np.sqrt(np.square(scale_factor_x * c) + np.square(scale_factor_y * s)) - rotated_boxes[:, 3] *= np.sqrt(np.square(scale_factor_x * s) + np.square(scale_factor_y * c)) - rotated_boxes[:, 4] = np.arctan2(scale_factor_x * s, scale_factor_y * c) * 180 / np.pi - - return rotated_boxes - - -HFlipTransform.register_type("rotated_box", HFlip_rotated_box) -ResizeTransform.register_type("rotated_box", Resize_rotated_box) - -# not necessary any more with latest fvcore -NoOpTransform.register_type("rotated_box", lambda t, x: x) diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/export/caffe2_modeling.py b/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/export/caffe2_modeling.py deleted file mode 100644 index 3e675c45d62f7b363a298099cd520c417832d58c..0000000000000000000000000000000000000000 --- a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/export/caffe2_modeling.py +++ /dev/null @@ -1,420 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. - -import functools -import io -import struct -import types -import torch - -from detectron2.modeling import meta_arch -from detectron2.modeling.box_regression import Box2BoxTransform -from detectron2.modeling.roi_heads import keypoint_head -from detectron2.structures import Boxes, ImageList, Instances, RotatedBoxes - -from .c10 import Caffe2Compatible -from .caffe2_patch import ROIHeadsPatcher, patch_generalized_rcnn -from .shared import ( - alias, - check_set_pb_arg, - get_pb_arg_floats, - get_pb_arg_valf, - get_pb_arg_vali, - get_pb_arg_vals, - mock_torch_nn_functional_interpolate, -) - - -def assemble_rcnn_outputs_by_name(image_sizes, tensor_outputs, force_mask_on=False): - """ - A function to assemble caffe2 model's outputs (i.e. Dict[str, Tensor]) - to detectron2's format (i.e. list of Instances instance). - This only works when the model follows the Caffe2 detectron's naming convention. - - Args: - image_sizes (List[List[int, int]]): [H, W] of every image. - tensor_outputs (Dict[str, Tensor]): external_output to its tensor. - - force_mask_on (Bool): if true, the it make sure there'll be pred_masks even - if the mask is not found from tensor_outputs (usually due to model crash) - """ - - results = [Instances(image_size) for image_size in image_sizes] - - batch_splits = tensor_outputs.get("batch_splits", None) - if batch_splits: - raise NotImplementedError() - assert len(image_sizes) == 1 - result = results[0] - - bbox_nms = tensor_outputs["bbox_nms"] - score_nms = tensor_outputs["score_nms"] - class_nms = tensor_outputs["class_nms"] - # Detection will always success because Conv support 0-batch - assert bbox_nms is not None - assert score_nms is not None - assert class_nms is not None - if bbox_nms.shape[1] == 5: - result.pred_boxes = RotatedBoxes(bbox_nms) - else: - result.pred_boxes = Boxes(bbox_nms) - result.scores = score_nms - result.pred_classes = class_nms.to(torch.int64) - - mask_fcn_probs = tensor_outputs.get("mask_fcn_probs", None) - if mask_fcn_probs is not None: - # finish the mask pred - mask_probs_pred = mask_fcn_probs - num_masks = mask_probs_pred.shape[0] - class_pred = result.pred_classes - indices = torch.arange(num_masks, device=class_pred.device) - mask_probs_pred = mask_probs_pred[indices, class_pred][:, None] - result.pred_masks = mask_probs_pred - elif force_mask_on: - # NOTE: there's no way to know the height/width of mask here, it won't be - # used anyway when batch size is 0, so just set them to 0. - result.pred_masks = torch.zeros([0, 1, 0, 0], dtype=torch.uint8) - - keypoints_out = tensor_outputs.get("keypoints_out", None) - kps_score = tensor_outputs.get("kps_score", None) - if keypoints_out is not None: - # keypoints_out: [N, 4, #kypoints], where 4 is in order of (x, y, score, prob) - keypoints_tensor = keypoints_out - # NOTE: it's possible that prob is not calculated if "should_output_softmax" - # is set to False in HeatmapMaxKeypoint, so just using raw score, seems - # it doesn't affect mAP. TODO: check more carefully. - keypoint_xyp = keypoints_tensor.transpose(1, 2)[:, :, [0, 1, 2]] - result.pred_keypoints = keypoint_xyp - elif kps_score is not None: - # keypoint heatmap to sparse data structure - pred_keypoint_logits = kps_score - keypoint_head.keypoint_rcnn_inference(pred_keypoint_logits, [result]) - - return results - - -def _cast_to_f32(f64): - return struct.unpack("f", struct.pack("f", f64))[0] - - -def set_caffe2_compatible_tensor_mode(model, enable=True): - def _fn(m): - if isinstance(m, Caffe2Compatible): - m.tensor_mode = enable - - model.apply(_fn) - - -def convert_batched_inputs_to_c2_format(batched_inputs, size_divisibility, device): - """ - See get_caffe2_inputs() below. - """ - assert all(isinstance(x, dict) for x in batched_inputs) - assert all(x["image"].dim() == 3 for x in batched_inputs) - - images = [x["image"] for x in batched_inputs] - images = ImageList.from_tensors(images, size_divisibility) - - im_info = [] - for input_per_image, image_size in zip(batched_inputs, images.image_sizes): - target_height = input_per_image.get("height", image_size[0]) - target_width = input_per_image.get("width", image_size[1]) # noqa - # NOTE: The scale inside im_info is kept as convention and for providing - # post-processing information if further processing is needed. For - # current Caffe2 model definitions that don't include post-processing inside - # the model, this number is not used. - # NOTE: There can be a slight difference between width and height - # scales, using a single number can results in numerical difference - # compared with D2's post-processing. - scale = target_height / image_size[0] - im_info.append([image_size[0], image_size[1], scale]) - im_info = torch.Tensor(im_info) - - return images.tensor.to(device), im_info.to(device) - - -class Caffe2MetaArch(Caffe2Compatible, torch.nn.Module): - """ - Base class for caffe2-compatible implementation of a meta architecture. - The forward is traceable and its traced graph can be converted to caffe2 - graph through ONNX. - """ - - def __init__(self, cfg, torch_model, enable_tensor_mode=True): - """ - Args: - cfg (CfgNode): - torch_model (nn.Module): the detectron2 model (meta_arch) to be - converted. - """ - super().__init__() - self._wrapped_model = torch_model - self.eval() - set_caffe2_compatible_tensor_mode(self, enable_tensor_mode) - - def get_caffe2_inputs(self, batched_inputs): - """ - Convert pytorch-style structured inputs to caffe2-style inputs that - are tuples of tensors. - - Args: - batched_inputs (list[dict]): inputs to a detectron2 model - in its standard format. Each dict has "image" (CHW tensor), and optionally - "height" and "width". - - Returns: - tuple[Tensor]: - tuple of tensors that will be the inputs to the - :meth:`forward` method. For existing models, the first - is an NCHW tensor (padded and batched); the second is - a im_info Nx3 tensor, where the rows are - (height, width, unused legacy parameter) - """ - return convert_batched_inputs_to_c2_format( - batched_inputs, - self._wrapped_model.backbone.size_divisibility, - self._wrapped_model.device, - ) - - def encode_additional_info(self, predict_net, init_net): - """ - Save extra metadata that will be used by inference in the output protobuf. - """ - pass - - def forward(self, inputs): - """ - Run the forward in caffe2-style. It has to use caffe2-compatible ops - and the method will be used for tracing. - - Args: - inputs (tuple[Tensor]): inputs defined by :meth:`get_caffe2_input`. - They will be the inputs of the converted caffe2 graph. - - Returns: - tuple[Tensor]: output tensors. They will be the outputs of the - converted caffe2 graph. - """ - raise NotImplementedError - - def _caffe2_preprocess_image(self, inputs): - """ - Caffe2 implementation of preprocess_image, which is called inside each MetaArch's forward. - It normalizes the input images, and the final caffe2 graph assumes the - inputs have been batched already. - """ - data, im_info = inputs - data = alias(data, "data") - im_info = alias(im_info, "im_info") - mean, std = self._wrapped_model.pixel_mean, self._wrapped_model.pixel_std - normalized_data = (data - mean) / std - normalized_data = alias(normalized_data, "normalized_data") - - # Pack (data, im_info) into ImageList which is recognized by self.inference. - images = ImageList(tensor=normalized_data, image_sizes=im_info) - return images - - @staticmethod - def get_outputs_converter(predict_net, init_net): - """ - Creates a function that converts outputs of the caffe2 model to - detectron2's standard format. - The function uses information in `predict_net` and `init_net` that are - available at inferene time. Therefore the function logic can be used in inference. - - The returned function has the following signature: - - def convert(batched_inputs, c2_inputs, c2_results) -> detectron2_outputs - - Where - - * batched_inputs (list[dict]): the original input format of the meta arch - * c2_inputs (tuple[Tensor]): the caffe2 inputs. - * c2_results (dict[str, Tensor]): the caffe2 output format, - corresponding to the outputs of the :meth:`forward` function. - * detectron2_outputs: the original output format of the meta arch. - - This function can be used to compare the outputs of the original meta arch and - the converted caffe2 graph. - - Returns: - callable: a callable of the above signature. - """ - raise NotImplementedError - - -class Caffe2GeneralizedRCNN(Caffe2MetaArch): - def __init__(self, cfg, torch_model, enable_tensor_mode=True): - assert isinstance(torch_model, meta_arch.GeneralizedRCNN) - torch_model = patch_generalized_rcnn(torch_model) - super().__init__(cfg, torch_model, enable_tensor_mode) - - try: - use_heatmap_max_keypoint = cfg.EXPORT_CAFFE2.USE_HEATMAP_MAX_KEYPOINT - except AttributeError: - use_heatmap_max_keypoint = False - self.roi_heads_patcher = ROIHeadsPatcher( - self._wrapped_model.roi_heads, use_heatmap_max_keypoint - ) - if self.tensor_mode: - self.roi_heads_patcher.patch_roi_heads() - - def encode_additional_info(self, predict_net, init_net): - size_divisibility = self._wrapped_model.backbone.size_divisibility - check_set_pb_arg(predict_net, "size_divisibility", "i", size_divisibility) - check_set_pb_arg( - predict_net, "device", "s", str.encode(str(self._wrapped_model.device), "ascii") - ) - check_set_pb_arg(predict_net, "meta_architecture", "s", b"GeneralizedRCNN") - - @mock_torch_nn_functional_interpolate() - def forward(self, inputs): - if not self.tensor_mode: - return self._wrapped_model.inference(inputs) - images = self._caffe2_preprocess_image(inputs) - features = self._wrapped_model.backbone(images.tensor) - proposals, _ = self._wrapped_model.proposal_generator(images, features) - detector_results, _ = self._wrapped_model.roi_heads(images, features, proposals) - return tuple(detector_results[0].flatten()) - - @staticmethod - def get_outputs_converter(predict_net, init_net): - def f(batched_inputs, c2_inputs, c2_results): - _, im_info = c2_inputs - image_sizes = [[int(im[0]), int(im[1])] for im in im_info] - results = assemble_rcnn_outputs_by_name(image_sizes, c2_results) - return meta_arch.GeneralizedRCNN._postprocess(results, batched_inputs, image_sizes) - - return f - - -class Caffe2RetinaNet(Caffe2MetaArch): - def __init__(self, cfg, torch_model): - assert isinstance(torch_model, meta_arch.RetinaNet) - super().__init__(cfg, torch_model) - - @mock_torch_nn_functional_interpolate() - def forward(self, inputs): - assert self.tensor_mode - images = self._caffe2_preprocess_image(inputs) - - # explicitly return the images sizes to avoid removing "im_info" by ONNX - # since it's not used in the forward path - return_tensors = [images.image_sizes] - - features = self._wrapped_model.backbone(images.tensor) - features = [features[f] for f in self._wrapped_model.head_in_features] - for i, feature_i in enumerate(features): - features[i] = alias(feature_i, "feature_{}".format(i), is_backward=True) - return_tensors.append(features[i]) - - pred_logits, pred_anchor_deltas = self._wrapped_model.head(features) - for i, (box_cls_i, box_delta_i) in enumerate(zip(pred_logits, pred_anchor_deltas)): - return_tensors.append(alias(box_cls_i, "box_cls_{}".format(i))) - return_tensors.append(alias(box_delta_i, "box_delta_{}".format(i))) - - return tuple(return_tensors) - - def encode_additional_info(self, predict_net, init_net): - size_divisibility = self._wrapped_model.backbone.size_divisibility - check_set_pb_arg(predict_net, "size_divisibility", "i", size_divisibility) - check_set_pb_arg( - predict_net, "device", "s", str.encode(str(self._wrapped_model.device), "ascii") - ) - check_set_pb_arg(predict_net, "meta_architecture", "s", b"RetinaNet") - - # Inference parameters: - check_set_pb_arg( - predict_net, "score_threshold", "f", _cast_to_f32(self._wrapped_model.test_score_thresh) - ) - check_set_pb_arg( - predict_net, "topk_candidates", "i", self._wrapped_model.test_topk_candidates - ) - check_set_pb_arg( - predict_net, "nms_threshold", "f", _cast_to_f32(self._wrapped_model.test_nms_thresh) - ) - check_set_pb_arg( - predict_net, - "max_detections_per_image", - "i", - self._wrapped_model.max_detections_per_image, - ) - - check_set_pb_arg( - predict_net, - "bbox_reg_weights", - "floats", - [_cast_to_f32(w) for w in self._wrapped_model.box2box_transform.weights], - ) - self._encode_anchor_generator_cfg(predict_net) - - def _encode_anchor_generator_cfg(self, predict_net): - # serialize anchor_generator for future use - serialized_anchor_generator = io.BytesIO() - torch.save(self._wrapped_model.anchor_generator, serialized_anchor_generator) - # Ideally we can put anchor generating inside the model, then we don't - # need to store this information. - bytes = serialized_anchor_generator.getvalue() - check_set_pb_arg(predict_net, "serialized_anchor_generator", "s", bytes) - - @staticmethod - def get_outputs_converter(predict_net, init_net): - self = types.SimpleNamespace() - serialized_anchor_generator = io.BytesIO( - get_pb_arg_vals(predict_net, "serialized_anchor_generator", None) - ) - self.anchor_generator = torch.load(serialized_anchor_generator) - bbox_reg_weights = get_pb_arg_floats(predict_net, "bbox_reg_weights", None) - self.box2box_transform = Box2BoxTransform(weights=tuple(bbox_reg_weights)) - self.test_score_thresh = get_pb_arg_valf(predict_net, "score_threshold", None) - self.test_topk_candidates = get_pb_arg_vali(predict_net, "topk_candidates", None) - self.test_nms_thresh = get_pb_arg_valf(predict_net, "nms_threshold", None) - self.max_detections_per_image = get_pb_arg_vali( - predict_net, "max_detections_per_image", None - ) - - # hack to reuse inference code from RetinaNet - for meth in [ - "forward_inference", - "inference_single_image", - "_transpose_dense_predictions", - "_decode_multi_level_predictions", - "_decode_per_level_predictions", - ]: - setattr(self, meth, functools.partial(getattr(meta_arch.RetinaNet, meth), self)) - - def f(batched_inputs, c2_inputs, c2_results): - _, im_info = c2_inputs - image_sizes = [[int(im[0]), int(im[1])] for im in im_info] - dummy_images = ImageList( - torch.randn( - ( - len(im_info), - 3, - ) - + tuple(image_sizes[0]) - ), - image_sizes, - ) - - num_features = len([x for x in c2_results.keys() if x.startswith("box_cls_")]) - pred_logits = [c2_results["box_cls_{}".format(i)] for i in range(num_features)] - pred_anchor_deltas = [c2_results["box_delta_{}".format(i)] for i in range(num_features)] - - # For each feature level, feature should have the same batch size and - # spatial dimension as the box_cls and box_delta. - dummy_features = [x.clone()[:, 0:0, :, :] for x in pred_logits] - # self.num_classess can be inferred - self.num_classes = pred_logits[0].shape[1] // (pred_anchor_deltas[0].shape[1] // 4) - - results = self.forward_inference( - dummy_images, dummy_features, [pred_logits, pred_anchor_deltas] - ) - return meta_arch.GeneralizedRCNN._postprocess(results, batched_inputs, image_sizes) - - return f - - -META_ARCH_CAFFE2_EXPORT_TYPE_MAP = { - "GeneralizedRCNN": Caffe2GeneralizedRCNN, - "RetinaNet": Caffe2RetinaNet, -} diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/layers/losses.py b/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/layers/losses.py deleted file mode 100644 index 850a852a2f0986d4d1ce89a526d96db42c76e44f..0000000000000000000000000000000000000000 --- a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/layers/losses.py +++ /dev/null @@ -1,133 +0,0 @@ -import math -import torch - - -def diou_loss( - boxes1: torch.Tensor, - boxes2: torch.Tensor, - reduction: str = "none", - eps: float = 1e-7, -) -> torch.Tensor: - """ - Distance Intersection over Union Loss (Zhaohui Zheng et. al) - https://arxiv.org/abs/1911.08287 - Args: - boxes1, boxes2 (Tensor): box locations in XYXY format, shape (N, 4) or (4,). - reduction: 'none' | 'mean' | 'sum' - 'none': No reduction will be applied to the output. - 'mean': The output will be averaged. - 'sum': The output will be summed. - eps (float): small number to prevent division by zero - """ - - x1, y1, x2, y2 = boxes1.unbind(dim=-1) - x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1) - - # TODO: use torch._assert_async() when pytorch 1.8 support is dropped - assert (x2 >= x1).all(), "bad box: x1 larger than x2" - assert (y2 >= y1).all(), "bad box: y1 larger than y2" - - # Intersection keypoints - xkis1 = torch.max(x1, x1g) - ykis1 = torch.max(y1, y1g) - xkis2 = torch.min(x2, x2g) - ykis2 = torch.min(y2, y2g) - - intsct = torch.zeros_like(x1) - mask = (ykis2 > ykis1) & (xkis2 > xkis1) - intsct[mask] = (xkis2[mask] - xkis1[mask]) * (ykis2[mask] - ykis1[mask]) - union = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g) - intsct + eps - iou = intsct / union - - # smallest enclosing box - xc1 = torch.min(x1, x1g) - yc1 = torch.min(y1, y1g) - xc2 = torch.max(x2, x2g) - yc2 = torch.max(y2, y2g) - diag_len = ((xc2 - xc1) ** 2) + ((yc2 - yc1) ** 2) + eps - - # centers of boxes - x_p = (x2 + x1) / 2 - y_p = (y2 + y1) / 2 - x_g = (x1g + x2g) / 2 - y_g = (y1g + y2g) / 2 - distance = ((x_p - x_g) ** 2) + ((y_p - y_g) ** 2) - - # Eqn. (7) - loss = 1 - iou + (distance / diag_len) - if reduction == "mean": - loss = loss.mean() if loss.numel() > 0 else 0.0 * loss.sum() - elif reduction == "sum": - loss = loss.sum() - - return loss - - -def ciou_loss( - boxes1: torch.Tensor, - boxes2: torch.Tensor, - reduction: str = "none", - eps: float = 1e-7, -) -> torch.Tensor: - """ - Complete Intersection over Union Loss (Zhaohui Zheng et. al) - https://arxiv.org/abs/1911.08287 - Args: - boxes1, boxes2 (Tensor): box locations in XYXY format, shape (N, 4) or (4,). - reduction: 'none' | 'mean' | 'sum' - 'none': No reduction will be applied to the output. - 'mean': The output will be averaged. - 'sum': The output will be summed. - eps (float): small number to prevent division by zero - """ - - x1, y1, x2, y2 = boxes1.unbind(dim=-1) - x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1) - - # TODO: use torch._assert_async() when pytorch 1.8 support is dropped - assert (x2 >= x1).all(), "bad box: x1 larger than x2" - assert (y2 >= y1).all(), "bad box: y1 larger than y2" - - # Intersection keypoints - xkis1 = torch.max(x1, x1g) - ykis1 = torch.max(y1, y1g) - xkis2 = torch.min(x2, x2g) - ykis2 = torch.min(y2, y2g) - - intsct = torch.zeros_like(x1) - mask = (ykis2 > ykis1) & (xkis2 > xkis1) - intsct[mask] = (xkis2[mask] - xkis1[mask]) * (ykis2[mask] - ykis1[mask]) - union = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g) - intsct + eps - iou = intsct / union - - # smallest enclosing box - xc1 = torch.min(x1, x1g) - yc1 = torch.min(y1, y1g) - xc2 = torch.max(x2, x2g) - yc2 = torch.max(y2, y2g) - diag_len = ((xc2 - xc1) ** 2) + ((yc2 - yc1) ** 2) + eps - - # centers of boxes - x_p = (x2 + x1) / 2 - y_p = (y2 + y1) / 2 - x_g = (x1g + x2g) / 2 - y_g = (y1g + y2g) / 2 - distance = ((x_p - x_g) ** 2) + ((y_p - y_g) ** 2) - - # width and height of boxes - w_pred = x2 - x1 - h_pred = y2 - y1 - w_gt = x2g - x1g - h_gt = y2g - y1g - v = (4 / (math.pi**2)) * torch.pow((torch.atan(w_gt / h_gt) - torch.atan(w_pred / h_pred)), 2) - with torch.no_grad(): - alpha = v / (1 - iou + v + eps) - - # Eqn. (10) - loss = 1 - iou + (distance / diag_len) + alpha * v - if reduction == "mean": - loss = loss.mean() if loss.numel() > 0 else 0.0 * loss.sum() - elif reduction == "sum": - loss = loss.sum() - - return loss diff --git a/spaces/brjathu/HMR2.0/vendor/pyrender/pyrender/scene.py b/spaces/brjathu/HMR2.0/vendor/pyrender/pyrender/scene.py deleted file mode 100644 index 2fe057ec66f52f2dd9c1363aacf72a7c6cec4e6c..0000000000000000000000000000000000000000 --- a/spaces/brjathu/HMR2.0/vendor/pyrender/pyrender/scene.py +++ /dev/null @@ -1,585 +0,0 @@ -"""Scenes, conforming to the glTF 2.0 standards as specified in -https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#reference-scene - -Author: Matthew Matl -""" -import numpy as np -import networkx as nx -import trimesh - -from .mesh import Mesh -from .camera import Camera -from .light import Light, PointLight, DirectionalLight, SpotLight -from .node import Node -from .utils import format_color_vector - - -class Scene(object): - """A hierarchical scene graph. - - Parameters - ---------- - nodes : list of :class:`Node` - The set of all nodes in the scene. - bg_color : (4,) float, optional - Background color of scene. - ambient_light : (3,) float, optional - Color of ambient light. Defaults to no ambient light. - name : str, optional - The user-defined name of this object. - """ - - def __init__(self, - nodes=None, - bg_color=None, - ambient_light=None, - name=None): - - if bg_color is None: - bg_color = np.ones(4) - else: - bg_color = format_color_vector(bg_color, 4) - - if ambient_light is None: - ambient_light = np.zeros(3) - - if nodes is None: - nodes = set() - self._nodes = set() # Will be added at the end of this function - - self.bg_color = bg_color - self.ambient_light = ambient_light - self.name = name - - self._name_to_nodes = {} - self._obj_to_nodes = {} - self._obj_name_to_nodes = {} - self._mesh_nodes = set() - self._point_light_nodes = set() - self._spot_light_nodes = set() - self._directional_light_nodes = set() - self._camera_nodes = set() - self._main_camera_node = None - self._bounds = None - - # Transform tree - self._digraph = nx.DiGraph() - self._digraph.add_node('world') - self._path_cache = {} - - # Find root nodes and add them - if len(nodes) > 0: - node_parent_map = {n: None for n in nodes} - for node in nodes: - for child in node.children: - if node_parent_map[child] is not None: - raise ValueError('Nodes may not have more than ' - 'one parent') - node_parent_map[child] = node - for node in node_parent_map: - if node_parent_map[node] is None: - self.add_node(node) - - @property - def name(self): - """str : The user-defined name of this object. - """ - return self._name - - @name.setter - def name(self, value): - if value is not None: - value = str(value) - self._name = value - - @property - def nodes(self): - """set of :class:`Node` : Set of nodes in the scene. - """ - return self._nodes - - @property - def bg_color(self): - """(3,) float : The scene background color. - """ - return self._bg_color - - @bg_color.setter - def bg_color(self, value): - if value is None: - value = np.ones(4) - else: - value = format_color_vector(value, 4) - self._bg_color = value - - @property - def ambient_light(self): - """(3,) float : The ambient light in the scene. - """ - return self._ambient_light - - @ambient_light.setter - def ambient_light(self, value): - if value is None: - value = np.zeros(3) - else: - value = format_color_vector(value, 3) - self._ambient_light = value - - @property - def meshes(self): - """set of :class:`Mesh` : The meshes in the scene. - """ - return set([n.mesh for n in self.mesh_nodes]) - - @property - def mesh_nodes(self): - """set of :class:`Node` : The nodes containing meshes. - """ - return self._mesh_nodes - - @property - def lights(self): - """set of :class:`Light` : The lights in the scene. - """ - return self.point_lights | self.spot_lights | self.directional_lights - - @property - def light_nodes(self): - """set of :class:`Node` : The nodes containing lights. - """ - return (self.point_light_nodes | self.spot_light_nodes | - self.directional_light_nodes) - - @property - def point_lights(self): - """set of :class:`PointLight` : The point lights in the scene. - """ - return set([n.light for n in self.point_light_nodes]) - - @property - def point_light_nodes(self): - """set of :class:`Node` : The nodes containing point lights. - """ - return self._point_light_nodes - - @property - def spot_lights(self): - """set of :class:`SpotLight` : The spot lights in the scene. - """ - return set([n.light for n in self.spot_light_nodes]) - - @property - def spot_light_nodes(self): - """set of :class:`Node` : The nodes containing spot lights. - """ - return self._spot_light_nodes - - @property - def directional_lights(self): - """set of :class:`DirectionalLight` : The directional lights in - the scene. - """ - return set([n.light for n in self.directional_light_nodes]) - - @property - def directional_light_nodes(self): - """set of :class:`Node` : The nodes containing directional lights. - """ - return self._directional_light_nodes - - @property - def cameras(self): - """set of :class:`Camera` : The cameras in the scene. - """ - return set([n.camera for n in self.camera_nodes]) - - @property - def camera_nodes(self): - """set of :class:`Node` : The nodes containing cameras in the scene. - """ - return self._camera_nodes - - @property - def main_camera_node(self): - """set of :class:`Node` : The node containing the main camera in the - scene. - """ - return self._main_camera_node - - @main_camera_node.setter - def main_camera_node(self, value): - if value not in self.nodes: - raise ValueError('New main camera node must already be in scene') - self._main_camera_node = value - - @property - def bounds(self): - """(2,3) float : The axis-aligned bounds of the scene. - """ - if self._bounds is None: - # Compute corners - corners = [] - for mesh_node in self.mesh_nodes: - mesh = mesh_node.mesh - pose = self.get_pose(mesh_node) - corners_local = trimesh.bounds.corners(mesh.bounds) - corners_world = pose[:3,:3].dot(corners_local.T).T + pose[:3,3] - corners.append(corners_world) - if len(corners) == 0: - self._bounds = np.zeros((2,3)) - else: - corners = np.vstack(corners) - self._bounds = np.array([np.min(corners, axis=0), - np.max(corners, axis=0)]) - return self._bounds - - @property - def centroid(self): - """(3,) float : The centroid of the scene's axis-aligned bounding box - (AABB). - """ - return np.mean(self.bounds, axis=0) - - @property - def extents(self): - """(3,) float : The lengths of the axes of the scene's AABB. - """ - return np.diff(self.bounds, axis=0).reshape(-1) - - @property - def scale(self): - """(3,) float : The length of the diagonal of the scene's AABB. - """ - return np.linalg.norm(self.extents) - - def add(self, obj, name=None, pose=None, - parent_node=None, parent_name=None): - """Add an object (mesh, light, or camera) to the scene. - - Parameters - ---------- - obj : :class:`Mesh`, :class:`Light`, or :class:`Camera` - The object to add to the scene. - name : str - A name for the new node to be created. - pose : (4,4) float - The local pose of this node relative to its parent node. - parent_node : :class:`Node` - The parent of this Node. If None, the new node is a root node. - parent_name : str - The name of the parent node, can be specified instead of - `parent_node`. - - Returns - ------- - node : :class:`Node` - The newly-created and inserted node. - """ - if isinstance(obj, Mesh): - node = Node(name=name, matrix=pose, mesh=obj) - elif isinstance(obj, Light): - node = Node(name=name, matrix=pose, light=obj) - elif isinstance(obj, Camera): - node = Node(name=name, matrix=pose, camera=obj) - else: - raise TypeError('Unrecognized object type') - - if parent_node is None and parent_name is not None: - parent_nodes = self.get_nodes(name=parent_name) - if len(parent_nodes) == 0: - raise ValueError('No parent node with name {} found' - .format(parent_name)) - elif len(parent_nodes) > 1: - raise ValueError('More than one parent node with name {} found' - .format(parent_name)) - parent_node = list(parent_nodes)[0] - - self.add_node(node, parent_node=parent_node) - - return node - - def get_nodes(self, node=None, name=None, obj=None, obj_name=None): - """Search for existing nodes. Only nodes matching all specified - parameters is returned, or None if no such node exists. - - Parameters - ---------- - node : :class:`Node`, optional - If present, returns this node if it is in the scene. - name : str - A name for the Node. - obj : :class:`Mesh`, :class:`Light`, or :class:`Camera` - An object that is attached to the node. - obj_name : str - The name of an object that is attached to the node. - - Returns - ------- - nodes : set of :class:`.Node` - The nodes that match all query terms. - """ - if node is not None: - if node in self.nodes: - return set([node]) - else: - return set() - nodes = set(self.nodes) - if name is not None: - matches = set() - if name in self._name_to_nodes: - matches = self._name_to_nodes[name] - nodes = nodes & matches - if obj is not None: - matches = set() - if obj in self._obj_to_nodes: - matches = self._obj_to_nodes[obj] - nodes = nodes & matches - if obj_name is not None: - matches = set() - if obj_name in self._obj_name_to_nodes: - matches = self._obj_name_to_nodes[obj_name] - nodes = nodes & matches - - return nodes - - def add_node(self, node, parent_node=None): - """Add a Node to the scene. - - Parameters - ---------- - node : :class:`Node` - The node to be added. - parent_node : :class:`Node` - The parent of this Node. If None, the new node is a root node. - """ - if node in self.nodes: - raise ValueError('Node already in scene') - self.nodes.add(node) - - # Add node to sets - if node.name is not None: - if node.name not in self._name_to_nodes: - self._name_to_nodes[node.name] = set() - self._name_to_nodes[node.name].add(node) - for obj in [node.mesh, node.camera, node.light]: - if obj is not None: - if obj not in self._obj_to_nodes: - self._obj_to_nodes[obj] = set() - self._obj_to_nodes[obj].add(node) - if obj.name is not None: - if obj.name not in self._obj_name_to_nodes: - self._obj_name_to_nodes[obj.name] = set() - self._obj_name_to_nodes[obj.name].add(node) - if node.mesh is not None: - self._mesh_nodes.add(node) - if node.light is not None: - if isinstance(node.light, PointLight): - self._point_light_nodes.add(node) - if isinstance(node.light, SpotLight): - self._spot_light_nodes.add(node) - if isinstance(node.light, DirectionalLight): - self._directional_light_nodes.add(node) - if node.camera is not None: - self._camera_nodes.add(node) - if self._main_camera_node is None: - self._main_camera_node = node - - if parent_node is None: - parent_node = 'world' - elif parent_node not in self.nodes: - raise ValueError('Parent node must already be in scene') - elif node not in parent_node.children: - parent_node.children.append(node) - - # Create node in graph - self._digraph.add_node(node) - self._digraph.add_edge(node, parent_node) - - # Iterate over children - for child in node.children: - self.add_node(child, node) - - self._path_cache = {} - self._bounds = None - - def has_node(self, node): - """Check if a node is already in the scene. - - Parameters - ---------- - node : :class:`Node` - The node to be checked. - - Returns - ------- - has_node : bool - True if the node is already in the scene and false otherwise. - """ - return node in self.nodes - - def remove_node(self, node): - """Remove a node and all its children from the scene. - - Parameters - ---------- - node : :class:`Node` - The node to be removed. - """ - # Disconnect self from parent who is staying in the graph - parent = list(self._digraph.neighbors(node))[0] - self._remove_node(node) - if isinstance(parent, Node): - parent.children.remove(node) - self._path_cache = {} - self._bounds = None - - def get_pose(self, node): - """Get the world-frame pose of a node in the scene. - - Parameters - ---------- - node : :class:`Node` - The node to find the pose of. - - Returns - ------- - pose : (4,4) float - The transform matrix for this node. - """ - if node not in self.nodes: - raise ValueError('Node must already be in scene') - if node in self._path_cache: - path = self._path_cache[node] - else: - # Get path from from_frame to to_frame - path = nx.shortest_path(self._digraph, node, 'world') - self._path_cache[node] = path - - # Traverse from from_node to to_node - pose = np.eye(4) - for n in path[:-1]: - pose = np.dot(n.matrix, pose) - - return pose - - def set_pose(self, node, pose): - """Set the local-frame pose of a node in the scene. - - Parameters - ---------- - node : :class:`Node` - The node to set the pose of. - pose : (4,4) float - The pose to set the node to. - """ - if node not in self.nodes: - raise ValueError('Node must already be in scene') - node._matrix = pose - if node.mesh is not None: - self._bounds = None - - def clear(self): - """Clear out all nodes to form an empty scene. - """ - self._nodes = set() - - self._name_to_nodes = {} - self._obj_to_nodes = {} - self._obj_name_to_nodes = {} - self._mesh_nodes = set() - self._point_light_nodes = set() - self._spot_light_nodes = set() - self._directional_light_nodes = set() - self._camera_nodes = set() - self._main_camera_node = None - self._bounds = None - - # Transform tree - self._digraph = nx.DiGraph() - self._digraph.add_node('world') - self._path_cache = {} - - def _remove_node(self, node): - """Remove a node and all its children from the scene. - - Parameters - ---------- - node : :class:`Node` - The node to be removed. - """ - - # Remove self from nodes - self.nodes.remove(node) - - # Remove children - for child in node.children: - self._remove_node(child) - - # Remove self from the graph - self._digraph.remove_node(node) - - # Remove from maps - if node.name in self._name_to_nodes: - self._name_to_nodes[node.name].remove(node) - if len(self._name_to_nodes[node.name]) == 0: - self._name_to_nodes.pop(node.name) - for obj in [node.mesh, node.camera, node.light]: - if obj is None: - continue - self._obj_to_nodes[obj].remove(node) - if len(self._obj_to_nodes[obj]) == 0: - self._obj_to_nodes.pop(obj) - if obj.name is not None: - self._obj_name_to_nodes[obj.name].remove(node) - if len(self._obj_name_to_nodes[obj.name]) == 0: - self._obj_name_to_nodes.pop(obj.name) - if node.mesh is not None: - self._mesh_nodes.remove(node) - if node.light is not None: - if isinstance(node.light, PointLight): - self._point_light_nodes.remove(node) - if isinstance(node.light, SpotLight): - self._spot_light_nodes.remove(node) - if isinstance(node.light, DirectionalLight): - self._directional_light_nodes.remove(node) - if node.camera is not None: - self._camera_nodes.remove(node) - if self._main_camera_node == node: - if len(self._camera_nodes) > 0: - self._main_camera_node = next(iter(self._camera_nodes)) - else: - self._main_camera_node = None - - @staticmethod - def from_trimesh_scene(trimesh_scene, - bg_color=None, ambient_light=None): - """Create a :class:`.Scene` from a :class:`trimesh.scene.scene.Scene`. - - Parameters - ---------- - trimesh_scene : :class:`trimesh.scene.scene.Scene` - Scene with :class:~`trimesh.base.Trimesh` objects. - bg_color : (4,) float - Background color for the created scene. - ambient_light : (3,) float or None - Ambient light in the scene. - - Returns - ------- - scene_pr : :class:`Scene` - A scene containing the same geometry as the trimesh scene. - """ - # convert trimesh geometries to pyrender geometries - geometries = {name: Mesh.from_trimesh(geom) - for name, geom in trimesh_scene.geometry.items()} - - # create the pyrender scene object - scene_pr = Scene(bg_color=bg_color, ambient_light=ambient_light) - - # add every node with geometry to the pyrender scene - for node in trimesh_scene.graph.nodes_geometry: - pose, geom_name = trimesh_scene.graph[node] - scene_pr.add(geometries[geom_name], pose=pose) - - return scene_pr diff --git a/spaces/btlee215/openchat-openchat/app.py b/spaces/btlee215/openchat-openchat/app.py deleted file mode 100644 index ff1ca5d07cd7f42b953608bb23c32aef45333f18..0000000000000000000000000000000000000000 --- a/spaces/btlee215/openchat-openchat/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/openchat/openchat").launch() \ No newline at end of file diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/_distutils_hack/override.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/_distutils_hack/override.py deleted file mode 100644 index 2cc433a4a55e3b41fa31089918fb62096092f89f..0000000000000000000000000000000000000000 --- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/_distutils_hack/override.py +++ /dev/null @@ -1 +0,0 @@ -__import__('_distutils_hack').do_override() diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/export/api.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/export/api.py deleted file mode 100644 index 1a272fed929217f18e04f731365f4bf7472110fc..0000000000000000000000000000000000000000 --- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/export/api.py +++ /dev/null @@ -1,230 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import copy -import logging -import os -import torch -from caffe2.proto import caffe2_pb2 -from torch import nn - -from detectron2.config import CfgNode -from detectron2.utils.file_io import PathManager - -from .caffe2_inference import ProtobufDetectionModel -from .caffe2_modeling import META_ARCH_CAFFE2_EXPORT_TYPE_MAP, convert_batched_inputs_to_c2_format -from .shared import get_pb_arg_vali, get_pb_arg_vals, save_graph - -__all__ = [ - "Caffe2Model", - "Caffe2Tracer", -] - - -class Caffe2Tracer: - """ - Make a detectron2 model traceable with Caffe2 operators. - This class creates a traceable version of a detectron2 model which: - - 1. Rewrite parts of the model using ops in Caffe2. Note that some ops do - not have GPU implementation in Caffe2. - 2. Remove post-processing and only produce raw layer outputs - - After making a traceable model, the class provide methods to export such a - model to different deployment formats. - Exported graph produced by this class take two input tensors: - - 1. (1, C, H, W) float "data" which is an image (usually in [0, 255]). - (H, W) often has to be padded to multiple of 32 (depend on the model - architecture). - 2. 1x3 float "im_info", each row of which is (height, width, 1.0). - Height and width are true image shapes before padding. - - The class currently only supports models using builtin meta architectures. - Batch inference is not supported, and contributions are welcome. - """ - - def __init__(self, cfg: CfgNode, model: nn.Module, inputs): - """ - Args: - cfg (CfgNode): a detectron2 config used to construct caffe2-compatible model. - model (nn.Module): An original pytorch model. Must be among a few official models - in detectron2 that can be converted to become caffe2-compatible automatically. - Weights have to be already loaded to this model. - inputs: sample inputs that the given model takes for inference. - Will be used to trace the model. For most models, random inputs with - no detected objects will not work as they lead to wrong traces. - """ - assert isinstance(cfg, CfgNode), cfg - assert isinstance(model, torch.nn.Module), type(model) - - # TODO make it support custom models, by passing in c2 model directly - C2MetaArch = META_ARCH_CAFFE2_EXPORT_TYPE_MAP[cfg.MODEL.META_ARCHITECTURE] - self.traceable_model = C2MetaArch(cfg, copy.deepcopy(model)) - self.inputs = inputs - self.traceable_inputs = self.traceable_model.get_caffe2_inputs(inputs) - - def export_caffe2(self): - """ - Export the model to Caffe2's protobuf format. - The returned object can be saved with its :meth:`.save_protobuf()` method. - The result can be loaded and executed using Caffe2 runtime. - - Returns: - :class:`Caffe2Model` - """ - from .caffe2_export import export_caffe2_detection_model - - predict_net, init_net = export_caffe2_detection_model( - self.traceable_model, self.traceable_inputs - ) - return Caffe2Model(predict_net, init_net) - - def export_onnx(self): - """ - Export the model to ONNX format. - Note that the exported model contains custom ops only available in caffe2, therefore it - cannot be directly executed by other runtime (such as onnxruntime or TensorRT). - Post-processing or transformation passes may be applied on the model to accommodate - different runtimes, but we currently do not provide support for them. - - Returns: - onnx.ModelProto: an onnx model. - """ - from .caffe2_export import export_onnx_model as export_onnx_model_impl - - return export_onnx_model_impl(self.traceable_model, (self.traceable_inputs,)) - - def export_torchscript(self): - """ - Export the model to a ``torch.jit.TracedModule`` by tracing. - The returned object can be saved to a file by ``.save()``. - - Returns: - torch.jit.TracedModule: a torch TracedModule - """ - logger = logging.getLogger(__name__) - logger.info("Tracing the model with torch.jit.trace ...") - with torch.no_grad(): - return torch.jit.trace(self.traceable_model, (self.traceable_inputs,)) - - -class Caffe2Model(nn.Module): - """ - A wrapper around the traced model in Caffe2's protobuf format. - The exported graph has different inputs/outputs from the original Pytorch - model, as explained in :class:`Caffe2Tracer`. This class wraps around the - exported graph to simulate the same interface as the original Pytorch model. - It also provides functions to save/load models in Caffe2's format.' - - Examples: - :: - c2_model = Caffe2Tracer(cfg, torch_model, inputs).export_caffe2() - inputs = [{"image": img_tensor_CHW}] - outputs = c2_model(inputs) - orig_outputs = torch_model(inputs) - """ - - def __init__(self, predict_net, init_net): - super().__init__() - self.eval() # always in eval mode - self._predict_net = predict_net - self._init_net = init_net - self._predictor = None - - __init__.__HIDE_SPHINX_DOC__ = True - - @property - def predict_net(self): - """ - caffe2.core.Net: the underlying caffe2 predict net - """ - return self._predict_net - - @property - def init_net(self): - """ - caffe2.core.Net: the underlying caffe2 init net - """ - return self._init_net - - def save_protobuf(self, output_dir): - """ - Save the model as caffe2's protobuf format. - It saves the following files: - - * "model.pb": definition of the graph. Can be visualized with - tools like `netron
(Teacher=BERT-base fine-tuned) | BERT base
fine-tuned | Remaining
Weights (%) | Magnitude Pruning | L0 Regularization | Movement Pruning | Soft Movement Pruning | -| :---: | :---: | :---: | :---: | :---: | :---: | :---: | -| SQuAD - Dev
EM/F1 | 80.4/88.1 | 10%
3% | 70.2/80.1
45.5/59.6 | 72.4/81.9
64.3/75.8 | 75.6/84.3
67.5/78.0 | **76.6/84.9**
**72.7/82.3** | -| MNLI - Dev
acc/MM acc | 84.5/84.9 | 10%
3% | 78.3/79.3
69.4/70.6 | 78.7/79.7
76.0/76.2 | 80.1/80.4
76.5/77.4 | **81.2/81.8**
**79.5/80.1** | -| QQP - Dev
acc/F1 | 91.4/88.4 | 10%
3% | 79.8/65.0
72.4/57.8 | 88.1/82.8
87.0/81.9 | 89.7/86.2
86.1/81.5 | **90.2/86.8**
**89.1/85.5** | - -This page contains information on how to fine-prune pre-trained models such as `BERT` to obtain extremely sparse models with movement pruning. In contrast to magnitude pruning which selects weights that are far from 0, movement pruning retains weights that are moving away from 0. - -For more information, we invite you to check out [our paper](https://arxiv.org/abs/2005.07683). -You can also have a look at this fun *Explain Like I'm Five* introductory [slide deck](https://www.slideshare.net/VictorSanh/movement-pruning-explain-like-im-five-234205241). - -
-
-
-
-## Extreme sparsity and efficient storage
-
-One promise of extreme pruning is to obtain extremely small models that can be easily sent (and stored) on edge devices. By setting weights to 0., we reduce the amount of information we need to store, and thus decreasing the memory size. We are able to obtain extremely sparse fine-pruned models with movement pruning: ~95% of the dense performance with ~5% of total remaining weights in the BERT encoder.
-
-In [this notebook](https://github.com/huggingface/transformers/blob/main/examples/research_projects/movement-pruning/Saving_PruneBERT.ipynb), we showcase how we can leverage standard tools that exist out-of-the-box to efficiently store an extremely sparse question answering model (only 6% of total remaining weights in the encoder). We are able to reduce the memory size of the encoder **from the 340MB (the original dense BERT) to 11MB**, without any additional training of the model (every operation is performed *post fine-pruning*). It is sufficiently small to store it on a [91' floppy disk](https://en.wikipedia.org/wiki/Floptical) š!
-
-While movement pruning does not directly optimize for memory footprint (but rather the number of non-null weights), we hypothetize that further memory compression ratios can be achieved with specific quantization aware trainings (see for instance [Q8BERT](https://arxiv.org/abs/1910.06188), [And the Bit Goes Down](https://arxiv.org/abs/1907.05686) or [Quant-Noise](https://arxiv.org/abs/2004.07320)).
-
-## Fine-pruned models
-
-As examples, we release two English PruneBERT checkpoints (models fine-pruned from a pre-trained `BERT` checkpoint), one on SQuAD and the other on MNLI.
-
-- **`prunebert-base-uncased-6-finepruned-w-distil-squad`**
--Pre-trained `BERT-base-uncased` fine-pruned with soft movement pruning on SQuAD v1.1. We use an additional distillation signal from `BERT-base-uncased` finetuned on SQuAD. The encoder counts 6% of total non-null weights and reaches 83.8 F1 score. The model can be accessed with: `pruned_bert = BertForQuestionAnswering.from_pretrained("huggingface/prunebert-base-uncased-6-finepruned-w-distil-squad")` -- **`prunebert-base-uncased-6-finepruned-w-distil-mnli`**
-Pre-trained `BERT-base-uncased` fine-pruned with soft movement pruning on MNLI. We use an additional distillation signal from `BERT-base-uncased` finetuned on MNLI. The encoder counts 6% of total non-null weights and reaches 80.7 (matched) accuracy. The model can be accessed with: `pruned_bert = BertForSequenceClassification.from_pretrained("huggingface/prunebert-base-uncased-6-finepruned-w-distil-mnli")` - -## How to fine-prune? - -### Setup - -The code relies on the š¤ Transformers library. In addition to the dependencies listed in the [`examples`](https://github.com/huggingface/transformers/tree/main/examples) folder, you should install a few additional dependencies listed in the `requirements.txt` file: `pip install -r requirements.txt`. - -Note that we built our experiments on top of a stabilized version of the library (commit https://github.com/huggingface/transformers/commit/352d5472b0c1dec0f420d606d16747d851b4bda8): we do not guarantee that everything is still compatible with the latest version of the main branch. - -### Fine-pruning with movement pruning - -Below, we detail how to reproduce the results reported in the paper. We use SQuAD as a running example. Commands (and scripts) can be easily adapted for other tasks. - -The following command fine-prunes a pre-trained `BERT-base` on SQuAD using movement pruning towards 15% of remaining weights (85% sparsity). Note that we freeze all the embeddings modules (from their pre-trained value) and only prune the Fully Connected layers in the encoder (12 layers of Transformer Block). - -```bash -SERIALIZATION_DIR=
-
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Veinte aƱos despuĆ©s del ciclĆ³n Tracy, este libro recrea, mediante entrevistas con los supervivientes, los acontecimientos durante y despuĆ©s del ciclĆ³n que casi destruyĆ³ Darwin, Australia, por B. Bunbury
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Brawl Stars APK Mod An1: Everything You Need to Know
-If you are a fan of fast-paced multiplayer action games, you have probably heard of Brawl Stars, the latest hit from Supercell, the makers of Clash of Clans and Clash Royale. Brawl Stars is a free-to-play game that lets you choose from a variety of unique characters, called Brawlers, and compete in various game modes, such as 3v3 team battles, solo or duo battle royale, soccer, and more. The game has over 100 million downloads on Google Play Store and has received positive reviews from critics and players alike.
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-Installing Brawl Stars APK mod on your Android device is not very difficult, but it does require some steps that are different from installing a regular app from Google Play Store. Here are the steps you need to follow:
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- Go to an1.com and search for Brawl Stars APK mod. You will see a list of different versions of the game with different mods. Choose the one that suits your preferences and click on the download button. -
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These are just some of the features of Brawl Stars APK mod an1. There are many more features that you can discover and enjoy by playing the game yourself.
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- Ban risk: Using an APK mod is against the terms and conditions of Supercell, the developer of Brawl Stars. If they detect that you are using a modded version of the game, they may ban your account permanently or temporarily. This means that you will lose all your progress, achievements, and purchases in the game. -
- Virus risk: Downloading an APK mod from an unknown source may expose your device to viruses, malware, or other harmful software that may damage your device or steal your personal information. You should always scan the file before installing it and use a reliable antivirus app to protect your device. -
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- Update risk: An APK mod may not be updated regularly or in sync with the official version of the game. This may cause the game to become outdated, buggy, or incompatible with new features or events. You should always check for updates and download them from a trusted source. -
These are just some of the risks of using Brawl Stars APK mod an1. There may be other risks that you may encounter while playing the game. You should always be careful and responsible when using an APK mod and weigh the pros and cons before deciding to use it.
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-If you decide to use Brawl Stars APK mod an1, you should follow some tips and tricks to play it safely and responsibly. Some of the tips and tricks are:
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- Create a backup account: To avoid losing your main account or getting banned by Supercell, you should create a backup account that you can use to play Brawl Stars APK mod an1. You can use a different email address or phone number to create a new account and link it to the game. This way, you can keep your main account safe and separate from the modded one. -
- Use a VPN app: To avoid being detected by Supercell or other players, you should use a VPN app to hide your IP address and location when playing Brawl Stars APK mod an1. A VPN app can also help you bypass any geo-restrictions or firewalls that may prevent you from accessing or downloading the game. -
- Play offline or solo mode: To avoid affecting other players or getting reported by them, you should play Brawl Stars APK mod an1 in offline or solo mode, such as training, friendly, or bot matches. This way, you can enjoy the modded features without interfering with other players or the game balance. -
- Do not abuse the modded features: To avoid ruining the fun or the challenge of the game, you should not abuse the modded features or use them excessively. For example, you should not use god mode or unlimited ammo all the time, or buy everything in the shop with unlimited resources. You should still try to play the game normally and fairly, and use the modded features only when you need them or want to try something new. -
- Do not share or distribute the modded file: To avoid legal issues or problems with Supercell or other players, you should not share or distribute the modded file of Brawl Stars APK mod an1 to anyone else. You should also not upload or post it on any website, social media, forum, or other platform. You should keep the file for your personal use only and respect the rights and property of Supercell and Brawl Stars. -
These are just some of the tips and tricks to play Brawl Stars APK mod an1 safely and responsibly. There may be other tips and tricks that you can follow or discover by yourself. You should always use your common sense and judgment when playing Brawl Stars APK mod an1 and have fun with it.
-Conclusion
-Brawl Stars APK mod an1 is a modified version of Brawl Stars that gives you access to unlimited resources, unlocked brawlers, skins, abilities, and other features that are not available in the official version. You can download it from an1.com, a website that offers free downloads of various games and apps for Android devices. However, you should also be aware of the risks of using Brawl Stars APK mod an1, such as ban risk, virus risk, compatibility risk, and update risk. You should also follow some tips and tricks to play Brawl Stars APK mod an1 safely and responsibly, such as creating a backup account, using a VPN app, playing offline or solo mode, not abusing the modded features, and not sharing or distributing the modded file.
-Brawl Stars APK mod an1 can provide you with a lot of fun and advantages, but it also comes with some challenges and responsibilities. You should always be careful and respectful when using Brawl Stars APK mod an1 and enjoy the game in a fair and ethical way. We hope this article has helped you learn everything you need to know about Brawl Stars APK mod an1. If you have any questions, feedback, or experiences to share with us, please feel free to leave a comment below. Happy brawling!
-FAQs
-Here are some of the frequently asked questions about Brawl Stars APK mod an1:
-What are some of the best Brawlers to use in Brawl Stars APK mod an1?
-The answer to this question may depend on your personal preference, play style, game mode, and team composition. However, some of the generally considered best brawlers in Brawl Stars APK mod an1 are:
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- Spike: Spike is a legendary brawler who can deal massive damage with his cactus projectiles that explode into spikes. He can also slow down and damage enemies with his super ability, which creates a field of cactus spikes. -
- Crow: Crow is another legendary brawler who can poison his enemies with his daggers that inflict damage over time. He can also jump over obstacles and enemies with his super ability, which also deals damage upon landing. -
- Pam: Pam is an epic brawler who can heal herself and her allies with her main attack, which fires metal scraps in a wide area. She can also deploy a healing turret with her super ability, which creates a healing zone for her team. -
- Leon: Leon is a legendary brawler who can turn invisible for a few seconds with his super ability, which also increases his movement speed. He can also deal more damage with his main attack when he is closer to his enemies. -
- Nita: Nita is a rare brawler who can summon a bear with her super ability, which can attack enemies and distract them. She can also deal damage to enemies and charge her super faster with her main attack, which fires a shockwave that can hit multiple targets. -
How to update Brawl Stars APK mod an1 when a new version is released?
-To update Brawl Stars APK mod an1 when a new version is released, you need to follow these steps:
-- Go to an1.com and search for Brawl Stars APK mod. You will see the latest version of the game with the date and the mod features. Click on the download button and save the file on your device. -
- Before you install the new version, you need to uninstall the old version of Brawl Stars APK mod from your device. To do this, go to Settings > Apps > Brawl Stars > Uninstall and confirm your action. -
- Now go to your file manager and locate the new file. Tap on it and follow the instructions to install it. -
- You have successfully updated Brawl Stars APK mod an1 to the latest version. You can now launch the game and enjoy the new features. -
How to fix common errors or issues with Brawl Stars APK mod an1?
-Sometimes, you may encounter some errors or issues with Brawl Stars APK mod an1, such as:
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- The game does not start or crashes: This may be due to compatibility issues, low memory, or corrupted files. To fix this, you can try clearing the cache and data of the game, restarting your device, or reinstalling the game. -
- The game does not connect to the server or shows a maintenance break: This may be due to network problems, server issues, or outdated versions. To fix this, you can try checking your internet connection, using a VPN app, or updating the game. -
- The game does not load or shows a black screen: This may be due to graphics settings, display problems, or permissions issues. To fix this, you can try adjusting the graphics settings, changing the display mode, or granting the necessary permissions to the game. -
These are just some of the common errors or issues with Brawl Stars APK mod an1. There may be other errors or issues that you may face while playing the game. You can always search for solutions online or contact the support team of an1.com for help.
-How to uninstall Brawl Stars APK mod an1 from your device?
-If you want to uninstall Brawl Stars APK mod an1 from your device, you can follow these steps:
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- Go to Settings > Apps > Brawl Stars > Uninstall and confirm your action. -
- Go to your file manager and delete any residual files or folders related to Brawl Stars APK mod an1. -
- You have successfully uninstalled Brawl Stars APK mod an1 from your device. You can now install the official version of Brawl Stars from Google Play Store if you want. -
Is Brawl Stars APK mod an1 compatible with other devices or platforms?
-Brawl Stars APK mod an1 is designed for Android devices only. It is not compatible with other devices or platforms, such as iOS, Windows, Mac, etc. If you want to play Brawl Stars on other devices or platforms, you need to download the official version of the game from the respective app stores or websites.
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CarX Drift Racing 2 APK Dayı 1.25.1: A Review
-If you are a fan of drifting racing games, you might have heard of CarX Drift Racing 2, a free drifting racing game where you push your car to the limits to the delight of your fans. But did you know that there is a modified version of this game called CarX Drift Racing 2 APK Dayı 1.25.1? In this article, we will review this version and tell you why you should choose it over the original one.
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Features of CarX Drift Racing 2
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- A realistic physics engine that simulates the behavior of different types of cars on different surfaces and weather conditions. -
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How to download and install CarX Drift Racing 2 APK Dayı 1.25.1
-CarX Drift Racing 2 APK Dayı 1.25.1 is a modified version of the original game that offers some extra features and advantages, such as unlimited money, unlocked cars, free shopping, and more. To download and install this version, you need to follow these steps:
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- Choose a car that suits your driving style and preference. Different cars have different characteristics, such as power, weight, handling, and grip. You can also tune your car to adjust these parameters according to your needs. -
- Learn the basics of drifting, such as how to initiate, maintain, and exit a drift. You can use different techniques, such as handbrake, clutch kick, power over, or feint, depending on the situation and the track layout. -
- Practice on different tracks and modes to get familiar with the physics and controls of the game. You can also watch replays or ghost races of other players to learn from their moves and mistakes. -
- Use the steering wheel, pedals, and buttons to control your car smoothly and precisely. You can also change the sensitivity and layout of these controls in the settings menu. -
- Pay attention to the drift indicators on the screen, such as the speedometer, the angle meter, and the score multiplier. These indicators will help you gauge your drift performance and improve your timing and accuracy. -
How to customize your car in CarX Drift Racing 2
-Customizing your car is another fun and rewarding aspect of CarX Drift Racing 2, as it allows you to express your personality and creativity through your vehicle. Here are some tips on how to customize your car in CarX Drift Racing 2:
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- Choose a car that has a lot of customization options available, such as body kits, spoilers, wheels, exhausts, hoods, bumpers, and more. You can also unlock more options by completing challenges and events in the game. -
- Use the paint shop to change the color and texture of your car. You can also apply vinyls and stickers to add more details and patterns to your car. -
- Use the tuning mode to modify the performance and appearance of your car. You can change the engine, gearbox, suspension, brakes, tires, camber, height, weight, and more. -
- Save your customizations in the garage mode and switch between them whenever you want. You can also share your customizations with other players online or download their customizations for inspiration. -
How to earn more coins and gold in CarX Drift Racing 2
-Coins and gold are the main currencies in CarX Drift Racing 2, which you can use to buy new cars, parts, paint jobs, vinyls, stickers, and more. Here are some tips on how to earn more coins and gold in CarX Drift Racing 2:
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- Play the career mode and complete the championships and events. You will earn coins and gold based on your performance and ranking in each race. -
- Play the multiplayer mode and join or create a club. You will earn coins and gold by participating in club battles and tournaments. -
- Play the ghost mode and beat your own or other players' best times. You will earn coins and gold by setting new records and improving your skills. -
- Watch ads or complete offers in the game. You will earn coins and gold by watching short videos or completing surveys, tasks, or downloads. -
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Conclusion
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Download Crowd Evolution Mod APK An1: A Fun and Easy Running Game
-Running is usually considered hard if it tires you a lot. But that's why we are motivated to play new games. Crowd Evolution is a great example with a simplification in 3D graphics. Run and move like Subway Surfers with just a single operation. In this article, we will tell you what is Crowd Evolution, how to play it, what are its features, and why you should download Crowd Evolution Mod APK An1.
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I hope you enjoyed this article on how to download Crowd Evolution Mod APK An1. If you have any questions or feedback, please leave a comment below. Thank you for reading!
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Farming Simulator 16 APK for Free and Enjoy the Open World Simulation.md b/spaces/congsaPfin/Manga-OCR/logs/Download Farming Simulator 16 APK for Free and Enjoy the Open World Simulation.md deleted file mode 100644 index 2bb147fa8311d051cb344f0719cb78c0f4ae67b0..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Download Farming Simulator 16 APK for Free and Enjoy the Open World Simulation.md +++ /dev/null @@ -1,113 +0,0 @@ - -
Farming Simulator 16 APK Only Download: A Review
-If you are a fan of simulation games, especially farming games, you might have heard of Farming Simulator 16. This is a popular game that allows you to manage your own realistic farm in extraordinary detail. You can plant, grow, harvest, and sell five different crops, raise cows and sheep, and sell timber at your own pace. You can also buy new fields and machines, hire helpers, and play with a friend in local multiplayer mode.
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But how can you download and play this game on your Android device? And what are some tips and tricks to help you succeed in this game? In this article, we will answer these questions and more. We will review the features of Farming Simulator 16, show you how to download the APK only file, and give you some useful advice on how to play the game. Let's get started!
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-Farming Simulator 16 is a game developed by Giants Software, a Swiss company that specializes in creating simulation games. It is part of the Farming Simulator series, which started in 2008 and has become one of the most successful franchises in the genre. Farming Simulator 16 was released in 2015 for Android, iOS, Kindle Fire, PlayStation Vita, Windows Phone, and Windows 10 Mobile devices.
-The game simulates various aspects of farming, such as crop production, animal husbandry, forestry, vehicle maintenance, and market trading. The game features a huge open world with different environments, such as American Midwest, European Alps, or Eastern Europe. The game also has a dynamic weather system that affects the growth of crops and the behavior of animals.
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Features of Farming Simulator 16
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-One of the main improvements of Farming Simulator 16 over its predecessors is the new 3D graphics engine that shows more detail on your machinery and environment. The game also features over 50 vehicles and tools from over 30 real agricultural brands, such as New Holland, Case IH, Ponsse, Lamborghini, Horsch, Krone, Amazone, MAN, and more. You can drive and use brand new equipment such as harvesters, tractors, cultivators, planters, tippers, fertilizers spreaders, transports, etc.
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-In Farming Simulator 16, you can plant and harvest five different crops: wheat, canola, corn, sugar beet, and potatoes. Each crop has its own characteristics and requirements for growth and harvesting. You can also feed your cows and sheep to produce and sell milk and wool. You can also buy more animals from the shop or breed them yourself.
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-Farming Simulator 16 also introduces forestry as a new activity in the game. You can harvest wood with dedicated machinery such as chains saws, chippers, trailers, and cranes. You can sell the logs or use them to create wood chips. You can also play with a friend in local multiplayer mode via Wi-Fi or Bluetooth. You can share your vehicles and tools with your partner and work together on your farm.
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Tips and tricks for playing Farming Simulator 16
-Know the control system and the tools
-Before you start playing Farming Simulator 16, you should familiarize yourself with the control system and the tools that you have. The game has two control modes: Arcade and Simulation. You can switch between them by tapping on the gear icon on the top right corner of the screen. Arcade mode is easier and more intuitive, while Simulation mode is more realistic and challenging. You can also adjust the sensitivity and the steering mode of your vehicles in the settings menu.
-You should also learn how to use the different tools that you have, such as plows, cultivators, sowers, harvesters, tippers, etc. Each tool has its own function and purpose, and you need to attach them to the appropriate vehicle to use them. You can attach and detach tools by tapping on the tool icon on the bottom right corner of the screen. You can also switch between tools by tapping on the arrow icons on the bottom left corner of the screen.
-Start harvesting and selling your crops as soon as possible
-One of the main goals of Farming Simulator 16 is to make money by harvesting and selling your crops. You should start doing this as soon as possible to earn some cash and expand your farm. To harvest your crops, you need to use a harvester with a header that matches the type of crop that you have planted. For example, you need a grain header for wheat, canola, and corn, and a potato harvester for potatoes. You can also use a forage harvester for corn to make silage.
-Once you have harvested your crops, you need to transport them to a selling point. You can use a tipper or a trailer to load and unload your crops. You can also use a conveyor belt or a front loader to move your crops from one place to another. To sell your crops, you need to drive to a selling point and unload your crops there. You can see the prices of each crop at each selling point on the map or on the statistics menu. You should sell your crops when the prices are high and avoid selling when the prices are low.
-Hire assistants and assign tasks to them
-Farming Simulator 16 can be a lot of work if you try to do everything by yourself. Fortunately, you can hire assistants and assign tasks to them to help you with your farm. You can hire assistants by tapping on the assistant icon on the top left corner of the screen. You can also assign tasks to them by tapping on the task icon on the bottom right corner of the screen. You can assign tasks such as plowing, cultivating, sowing, harvesting, transporting, etc.
-Hiring assistants and assigning tasks to them can save you time and energy, but it also costs you money. You have to pay your assistants for their work every hour. You can see how much you have to pay them on the statistics menu or on the assistant icon. You should hire assistants and assign tasks to them wisely and efficiently, and avoid hiring too many assistants or assigning too many tasks at once.
-Research before you sell and be aware of your harvester's capacity
-Another tip for playing Farming Simulator 16 is to research before you sell and be aware of your harvester's capacity. Researching before you sell means that you should check the prices of each crop at each selling point before you decide where to sell them. You can see the prices on the map or on the statistics menu. You should sell your crops at the highest price possible and avoid selling at the lowest price possible.
-Being aware of your harvester's capacity means that you should know how much crop your harvester can hold before it needs to be emptied. You can see your harvester's capacity on the top left corner of the screen or on the harvester icon. You should empty your harvester when it is full or close to full, otherwise you will waste some crop or slow down your harvesting process.
-Use fertilizers to increase your yield and profit
-The last tip for playing Farming Simulator 16 is to use fertilizers to increase your yield and profit. Fertilizers are substances that enhance the growth and quality of your crops. You can use fertilizers to increase your yield by up to 30%. You can buy fertilizers from the shop or make your own by using slurry or manure from your animals. You can also use a biogas plant to convert silage into slurry and manure.
-To use fertilizers, you need to use a fertilizer spreader or a sprayer. You can attach them to a tractor or a self-propelled vehicle. You can also use a front loader or a conveyor belt to load and unload fertilizers. To apply fertilizers, you need to drive over your fields and activate the tool. You can see the fertilization level of your fields on the map or on the statistics menu. You should fertilize your fields before or after sowing, or after the first growth stage of your crops.
-Conclusion
-Farming Simulator 16 is a fun and realistic farming simulation game that you can download and play on your Android device. You can manage your own farm, plant and harvest five different crops, raise cows and sheep, sell timber, and buy new fields and machines. You can also play with a friend in local multiplayer mode.
-To download Farming Simulator 16 APK only, you need to find a reliable source for the APK file, enable unknown sources on your device settings, and download and install the APK file. You can also follow some tips and tricks to help you play the game better, such as knowing the control system and the tools, harvesting and selling your crops as soon as possible, hiring assistants and assigning tasks to them, researching before you sell and being aware of your harvester's capacity, and using fertilizers to increase your yield and profit.
-We hope that this article has helped you learn more about Farming Simulator 16 APK only download and how to play the game. If you have any questions or feedback, please feel free to leave a comment below. Happy farming!
-FAQs
-Q: How much does Farming Simulator 16 cost?
-A: Farming Simulator 16 costs $4.99 on Google Play Store. However, you can download the APK file for free from other sources.
-Q: Is Farming Simulator 16 compatible with my device?
-A: Farming Simulator 16 requires Android 4.0.3 or higher and at least 130 MB of free storage space on your device. You can check the compatibility of your device on Google Play Store or on the website that you download the APK file from.
-Q: How can I play Farming Simulator 16 with a friend?
-A: Farming Simulator 16 supports local multiplayer mode via Wi-Fi or Bluetooth. To play with a friend, you need to have two devices with the same version of the game installed. You also need to be connected to the same Wi-Fi network or paired via Bluetooth. Then, one of you needs to create a game session and invite the other one to join.
-Q: How can I save my progress in Farming Simulator 16?
-A: Farming Simulator 16 automatically saves your progress every time you exit the game or switch to another app. You can also manually save your progress by tapping on the pause icon on the top right corner of the screen and then tapping on Save Game.
-Q: How can I reset my game in Farming Simulator 16?
-A: If you want to start over from scratch in Farming Simulator 16, you can reset your game by tapping on the pause icon on the top right corner of the screen and then tapping on Reset Game. This will delete all your saved data and settings and restart the game from the beginning.
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Naruto Shippuden Ultimate Ninja Storm 2: A Review
-If you are a fan of Naruto, the popular manga and anime series, you might be interested in playing Naruto Shippuden Ultimate Ninja Storm 2, a fighting game based on the Shippuden story arc. But is this game worth your time and money? In this review, we will take a look at the gameplay, graphics, sound, and other aspects of the game, and give you our honest opinion.
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Introduction
-What is Naruto Shippuden Ultimate Ninja Storm 2?
-Naruto Shippuden Ultimate Ninja Storm 2 is a fighting game developed by CyberConnect2 and published by Bandai Namco Games. It was released in 2010 for PlayStation 3, Xbox 360, and later for PC, PS4, Xbox One, and Nintendo Switch. It is the sequel to Naruto: Ultimate Ninja Storm, and the second installment of the Ultimate Ninja Storm series.
-What are the main features of the game?
-The game features over 40 playable characters from the Naruto universe, each with their own unique moves, abilities, and ultimate jutsus. The game also has a variety of modes to choose from, such as:
--
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- Ultimate Adventure Mode: This is the story mode of the game, where you can relive the events of the anime from Naruto's perspective, Sasuke's perspective, or Jiraiya's perspective. You can also explore different locations, interact with other characters, complete side quests, collect items, and unlock new content. -
- Free Battle Mode: This is where you can fight against the CPU or another player in various settings and conditions. You can customize your character's appearance, support characters, items, handicaps, and rules. -
- Online Mode: This is where you can challenge other players from around the world in ranked or unranked matches. You can also join tournaments, create clans, chat with friends, and view leaderboards. -
Who is the game for?
-The game is mainly aimed at fans of Naruto who want to experience the thrill of fighting as their favorite characters. The game also appeals to casual gamers who enjoy fast-paced and flashy combat. However, if you are not familiar with Naruto or you prefer more complex and strategic fighting games, you might not find this game very satisfying.
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-How does the game play?
-The game plays like a typical 3D fighting game, where you control your character with the left stick and perform attacks with different buttons. You can also use chakra to enhance your attacks or perform special moves. The game has a simple control scheme that is easy to learn but hard to master.
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-The combat system of the game is based on four elements: melee attacks, ranged attacks, guard, and substitution. Melee attacks are close-range physical attacks that can be chained into combos. Ranged attacks are long-range projectile attacks that can be charged with chakra. Guard is a defensive action that can block most attacks but consumes chakra. Substitution is an evasive action that can avoid an incoming attack but consumes chakra.
-The combat system also has some unique features that make it more dynamic and exciting:
--
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- The game has a large and diverse roster of characters, each with their own unique moves and abilities. You can play as your favorite characters from the Naruto series, or try out new ones. -
- The game has a simple and intuitive control scheme that is easy to learn but hard to master. You can perform flashy and powerful attacks with just a few buttons. -
- The game has a stunning and faithful presentation of the Naruto world. The graphics are colorful and detailed, and the animations are smooth and fluid. The game also has an excellent soundtrack and voice acting that match the anime. -
- The game has a lot of content and replay value. You can play through different modes, unlock new characters, costumes, items, trophies, and achievements. You can also play online with other players or offline with a friend. -
- The game can be repetitive and boring after a while. The combat system is not very deep or strategic, and most battles boil down to spamming attacks and chakra. The story mode is also linear and predictable, and does not offer much variation or choice. -
- The game can be frustrating and unfair at times. The difficulty level can be inconsistent and unbalanced, especially in boss battles. Some enemies can be too easy or too hard to beat, depending on your character or settings. The game also has some bugs and glitches that can affect your performance or progress. -
- The game can be outdated and irrelevant by now. The game was released in 2010, and since then there have been many new developments in the Naruto series, such as new characters, arcs, movies, games, etc. The game does not reflect the current state of the Naruto universe, and might not appeal to newer fans or older fans who have moved on. -
- Q: How long is the story mode of the game?
A: The story mode of the game can take anywhere from 10 to 15 hours to complete, depending on your speed and difficulty level.
- - Q: How many characters are in the game?
A: The game has over 40 playable characters from the Naruto series, plus some hidden characters that can be unlocked by completing certain tasks.
- - Q: How can I unlock new characters, costumes, items, etc.?
A: You can unlock new content by playing through different modes, completing missions, collecting scrolls, earning ryo (the in-game currency), or buying them from the shop.
- - Q: Can I play this game on my mobile device?
A: No, this game is not available for mobile devices. However, you can download an APK file from apkpure.com and install it on your Android device using an emulator. However, this might not work properly or safely.
- - Q: Where can I buy this game?
A: You can buy this game from various online or physical stores that sell video games. You can also download it digitally from platforms such as Steam, PlayStation Store, Xbox Live Marketplace, or Nintendo eShop.
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- The game will start streaming on your device. You can pause, rewind, fast-forward, or switch you can compare and choose the best app for your needs and preferences. For example, if you want to watch live and on-demand games of any team without blackouts or restrictions, and you don't mind ads and pop-ups, you might prefer NBA Live Stream. If you want to play as your favorite team and players in a realistic and immersive way, and you don't mind spending some money on in-app purchases, you might prefer NBA 2K Mobile Basketball. If you want to enjoy arcade-style basketball action with crazy dunks and moves, and you don't mind paying a one-time fee for the app, you might prefer NBA Jam by EA Sports.
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-Is NBA Team APK safe and legal?
-NBA Team APK is not an official app from the NBA, but rather a modified version of the official NBA app that bypasses some restrictions and limitations. Therefore, it may not be safe or legal to use. It may contain viruses, malware, or spyware that could harm your device or data. It may also violate the terms of service or the intellectual property rights of the NBA or its partners. We do not endorse or recommend using NBA Team APK. Use it at your own risk and discretion.
-How can I update NBA Team APK?
-NBA Team APK does not have an automatic update feature. You need to manually download and install the latest version of the app from the same source where you downloaded it before. You can check for updates by visiting https://apkpure.com/nba-2k23-myteam-sports-game/com.t2ksports.myteam regularly.
-How can I contact the developer of NBA Team APK?
-NBA Team APK does not have an official website or social media account. The only way to contact the developer of NBA Team APK is by leaving a comment or a review on https://apkpure.com/nba-2k23-myteam-sports-game/com.t2ksports.myteam. However, there is no guarantee that the developer will respond or address your issues.
-How can I watch NBA games on my TV using NBA Team APK?
-If you want to watch NBA games on your TV using NBA Team APK, you need to connect your Android device to your TV using an HDMI cable or a wireless adapter. Then, you need to enable screen mirroring or casting on your device and select your TV as the target device. Once connected, you can launch NBA Team APK on your device and start watching games on your TV.
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-If you are a fan of hip-hop music, you have probably heard of Sacrifices, a song released by record label Dreamville, performed by American rappers EarthGang and J. Cole featuring fellow American rappers Smino and Saba. The song was released as the final track on the label's compilation album, Revenge of the Dreamers III, in 2019.
-Sacrifices is a powerful and emotional song that showcases the talent and versatility of each artist. The song has a smooth and soulful beat that blends well with the rap verses and the catchy chorus. The lyrics are about the sacrifices that each rapper has made for their loved ones, their careers, and their dreams. The song also features some clever wordplay, references, and metaphors that add depth and meaning to the message.
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-In this article, we will tell you more about Sacrifices by J. Cole and why you should download it. We will also show you how to download it from different music streaming services, so you can enjoy it anytime and anywhere.
-What is Sacrifices by J. Cole?
-Sacrifices is a song that was created during a 10-day recording session in Atlanta, where Dreamville invited over 100 artists and producers to collaborate on new music. The song features Midwest rappers Smino and Saba in their first collaboration with Dreamville's J. Cole and EarthGang. The song was produced by Herothaproducer, Henny (Tha Bizness), and Groove.
-The song is about the sacrifices that each rapper has made for their loved ones, their careers, and their dreams. The song also expresses gratitude and appreciation for those who have supported them along the way. The song has a positive and uplifting vibe that inspires listeners to pursue their goals and overcome their challenges.
-Some interesting facts and trivia about Sacrifices are:
--
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- The song samples Can You Stand The Rain by New Edition. -
- The song marks the first time that J. Cole publicly announced that he is married and has two sons. -
- The song was nominated for Best Rap Performance at the 2020 Grammy Awards. -
- The song has a music video that was directed by Scott Lazer, David Peters, and Chad Tennies. The video features the rappers performing in an abandoned warehouse with their families and friends. -
- The song has over 100 million streams on Spotify and over 30 million views on YouTube. -
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- The song has exceptional sound quality for audiophiles. The song is available in CD-quality and hi-res audio formats on some music streaming services, such as Tidal and Amazon Music HD. These formats offer better clarity, dynamics, and details than the standard MP3 or AAC formats. -
- The song has meaningful and relatable lyrics for music lovers. The song tells a personal and heartfelt story of each rapper's journey and struggles in the music industry and in life. The song also delivers a positive and motivational message of gratitude, love, and perseverance. The song resonates with listeners who can relate to the sacrifices that they have made or witnessed in their own lives. -
- The song has a catchy and memorable chorus for sing-alongs. The song has a simple and catchy chorus that repeats the word "sacrifices" four times, followed by the phrase "I'ma make it home to you". The chorus is easy to sing along to and sticks in your head. The chorus also creates a contrast between the rap verses and the melodic hook, adding variety and interest to the song. -
How can you download Sacrifices by J. Cole?
-Sacrifices by J. Cole is available on various music streaming services, such as Spotify, Apple Music, Tidal, Amazon Music, and YouTube Music. You can download the song from these services using different methods, depending on your device, subscription, and preference. Here are some of the common ways to download Sacrifices by J. Cole:
--
-- -Service -Method -- -Spotify -To download Sacrifices by J. Cole on Spotify, you need to have a Spotify Premium account. You can then follow these steps: --
-
- Open the Spotify app on your device and search for Sacrifices by J. Cole. -
- Select the song and tap on the three-dot icon on the top right corner. -
- Select Download from the menu that appears. -
- Wait for the download to complete. You can check the progress by looking at the green arrow icon next to the song title. -
- Enjoy listening to Sacrifices by J. Cole offline. -
- Apple Music -To download Sacrifices by J. Cole on Apple Music, you need to have an Apple Music subscription. You can then follow these steps: --
-
- Open the Apple Music app on your device and search for Sacrifices by J. Cole. -
- Select the song and tap on the plus icon next to it. -
- Tap on the cloud icon that appears next to the plus icon. -
- Wait for the download to complete. You can check the progress by looking at the circular icon next to the song title. -
- Enjoy listening to Sacrifices by J. Cole offline. -
- -Tidal -To download Sacrifices by J. Cole on Tidal, you need to have a Tidal subscription. You can then follow these steps: --
-
- Open the Tidal app on your device and search for Sacrifices by J. Cole. -
- Select the song and tap on the three-dot icon on the bottom right corner. -
- Select Download from the menu that appears. -
- Wait for the download to complete. You can check the progress by looking at the blue bar below the song title. -
- Enjoy listening to Sacrifices by J. Cole offline. -
- -Amazon Music -To download Sacrifices by J. Cole on Amazon Music, you need to have an Amazon Music subscription. You can then follow these steps: --
-
- Open the Amazon Music app on your device and search for Sacrifices by J. Cole. -
- Select the song and tap on the three-dot icon on the right side. -
- Select Download from the menu that appears. -
- Wait for the download to complete. You can check the progress by looking at the green check mark next to the song title. -
- Enjoy listening to Sacrifices by J. Cole offline. -
- -YouTube Music -To download Sacrifices by J. Cole on YouTube Music, you need to have a YouTube Music Premium account. You can then follow these steps: --
-
- Open the YouTube Music app on your device and search for Sacrifices by J. Cole. -
- Select the song and tap on the download icon on the bottom left corner. -
- Wait for the download to complete. You can check the progress by looking at the gray circle around the download icon. -
- Enjoy listening to Sacrifices by J. Cole offline. -
Conclusion
-Sacrifices by J. Cole is a song that you should not miss out on. It is a song that showcases the talent and passion of each rapper, as well as their gratitude and love for their families and friends. It is a song that inspires you to chase your dreams and overcome your obstacles, while also appreciating what you have and who you have in your life.
-If you want to download Sacrifices by J. Cole, you can choose from different music streaming services, such as Spotify, Apple Music, Tidal, Amazon Music, and YouTube Music. Each service has its own advantages and disadvantages, depending on your device, subscription, and preference. You can follow the simple steps that we have provided in this article to download the song from each service.
-We hope that this article has helped you learn more about Sacrifices by J. Cole and how to download it. We also hope that you enjoy listening to this amazing song and share it with your loved ones.
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-Here are some of the frequently asked questions and answers about Sacrifices by J. Cole and its download options:
-Q: How long is Sacrifices by J. Cole?
-A: Sacrifices by J. Cole is 6 minutes and 21 seconds long. It is the longest song on Revenge of the Dreamers III album.
-Q: How much does it cost to download Sacrifices by J. Cole?
-A: The cost of downloading Sacrifices by J. Cole depends on the music streaming service that you use and the subscription plan that you have. Some services offer free trials or discounts for new users or students . You can check the pricing and plans of each service on their official websites or apps.
-Q: Can I download Sacrifices by J. Cole without a subscription?
-A: Yes, you can download Sacrifices by J. Cole without a subscription if you purchase the song individually from digital music stores, such as iTunes, Google Play Music, or Amazon Music. However, this option may be more expensive than subscribing to a music streaming service in the long run, especially if you want to download more songs or albums.
-Q: Can I download Sacrifices by J. Cole in MP3 format?
-A: Yes, you can download Sacrifices by J. Cole in MP3 format if you use a music streaming service that supports MP3 downloads, such as Spotify, Apple Music, or Amazon Music. However , you should be aware that MP3 is a lossy format that compresses the audio data and reduces the sound quality. If you want to download Sacrifices by J. Cole in a lossless or hi-res format, you should use a music streaming service that supports these formats, such as Tidal or Amazon Music HD.
-Q: Can I download Sacrifices by J. Cole on multiple devices?
-A: Yes, you can download Sacrifices by J. Cole on multiple devices if you use a music streaming service that allows you to sync your downloads across different devices, such as Spotify, Apple Music, or YouTube Music. However, you should be aware that some services may have a limit on the number of devices or downloads that you can use with your account. You should also make sure that you have enough storage space on your devices to store the downloaded songs.
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-What is Brawl Stars?
-Brawl Stars is a mobile game that combines elements of twin-stick shooter, MOBA, and battle royale genres. It was released worldwide on December 12, 2018 by Supercell, the developer of Clash of Clans and Clash Royale.
-A fast-paced multiplayer game with different modes and characters
-In Brawl Stars, you can team up with your friends or play solo across a variety of game modes in under three minutes. You can choose from 22 different brawlers, each with their own unique abilities, weapons, and skins. You can also unlock and upgrade your brawlers with super abilities, star powers, and gadgets.
-The game modes in Brawl Stars are:
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- Gem Grab: A 3v3 mode where you have to collect and hold 10 gems to win, but if you die, you drop your gems. -
- Showdown: A solo or duo mode where you have to survive against other players or teams in a shrinking map. You can collect power-ups to boost your brawler. -
- Brawl Ball: A 3v3 mode where you have to score two goals before the other team. You can use your attacks to knock out enemies or pass the ball. -
- Bounty: A 3v3 mode where you have to take out enemies to earn stars, but if you die, you lose your stars. The team with the most stars at the end wins. -
- Heist: A 3v3 mode where you have to protect your team's safe and try to crack open the enemy's safe. You can use different strategies to attack or defend. -
- Special Events: Limited time modes that offer different challenges and rewards. Some examples are Robo Rumble, Boss Fight, Big Game, Siege, Hot Zone, etc.
- Language: TĆ¼rk Brawl Stars Apk is fully translated into Turkish, so you can enjoy the game in your native language. -
- Graphics: TĆ¼rk Brawl Stars Apk has enhanced graphics and animations, making the game more colorful and realistic. -
- Sound: TĆ¼rk Brawl Stars Apk has improved sound effects and music, making the game more immersive and fun. -
- Characters: TĆ¼rk Brawl Stars Apk has added some new brawlers and skins that are exclusive to the Turkish version. You can also customize your brawlers with different accessories and outfits. -
- Game modes: TĆ¼rk Brawl Stars Apk has introduced some new game modes that are unique to the Turkish version. You can play in different maps and scenarios that are inspired by Turkish culture and history. -
- Free: TĆ¼rk Brawl Stars Apk is completely free to download and play. You don't need to spend any money to enjoy the game. -
- Unlimited: TĆ¼rk Brawl Stars Apk gives you unlimited access to all the features and resources of the game. You don't need to wait for energy or tickets to play. You can also get unlimited gems and coins to unlock and upgrade your brawlers. -
- Updated: TĆ¼rk Brawl Stars Apk is constantly updated with new content and features. You can always enjoy the latest version of the game with new brawlers, skins, game modes, maps, events, etc. -
- Community: TĆ¼rk Brawl Stars Apk has a large and active community of Turkish players. You can chat, interact, and play with other players who share your passion for the game. You can also join clans, participate in tournaments, and win prizes. -
- Support: TĆ¼rk Brawl Stars Apk has a dedicated team of developers who are always ready to help you with any issues or questions you may have. You can contact them through their official website or social media accounts. -
- Android version: You need to have Android 4.3 or higher to run TĆ¼rk Brawl Stars Apk. -
- Storage space: You need to have at least 200 MB of free space on your device to install TĆ¼rk Brawl Stars Apk. -
- Internet connection: You need to have a stable and fast internet connection to download and play TĆ¼rk Brawl Stars Apk. -
- Enable unknown sources: Go to your device's settings and look for the security or privacy option. Find the option that allows you to install apps from unknown sources and enable it. This will let you install apps that are not from the Google Play Store. -
- Download TĆ¼rk Brawl Stars Apk: Go to the official website of TĆ¼rk Brawl Stars Apk or any other trusted source and look for the download link. Tap on the link and wait for the download to finish. -
- Install TĆ¼rk Brawl Stars Apk: Locate the downloaded file on your device and tap on it. Follow the instructions on the screen and wait for the installation to complete. -
- Launch TĆ¼rk Brawl Stars Apk: Find the app icon on your device and tap on it. Enjoy playing TĆ¼rk Brawl Stars Apk! -
- Legal issues: TĆ¼rk Brawl Stars Apk is not an official product of Supercell, the developer of Brawl Stars. It is a fan-made modification that may violate the terms of service and intellectual property rights of Supercell. You may face legal consequences if you use TĆ¼rk Brawl Stars Apk without permission from Supercell. -
- Security issues: TĆ¼rk Brawl Stars Apk is not verified by Google Play Protect, so it may contain malware or viruses that can harm your device or steal your personal information. You should only download TĆ¼rk Brawl Stars Apk from trusted sources and scan it with an antivirus app before installing it. -
- Compatibility issues: TĆ¼rk Brawl Stars Apk may not work properly on some devices or Android versions. It may crash, freeze, or lag during gameplay. It may also cause conflicts with other apps or system functions on your device. -
- Ban issues: TĆ¼rk Brawl Stars Apk may be detected by Supercell as a cheating or hacking tool that gives you an unfair advantage over other players. You may be banned from playing Brawl Stars or accessing your account if you use TĆ¼rk Brawl Stars Apk. -
- Gem Grab: You need a balanced team of brawlers that can collect, protect, and control gems. You should have a gem carrier, a support, and an aggro brawler. Some of the best brawlers for this mode are Pam, Poco, Jessie, Nita, Spike, Tara, etc. -
- Showdown: You need a brawler that can survive, deal damage, and escape from enemies. You should look for brawlers that have high health, mobility, or burst damage. Some of the best brawlers for this mode are Leon, Crow, El Primo, Bull, Shelly, Colt, etc. -
- Brawl Ball: You need a team of brawlers that can score goals, defend the goal, and pass the ball. You should have a striker, a defender, and a midfielder. Some of the best brawlers for this mode are Ricochet, Mortis, Frank, Darryl, Brock, Dynamike, etc. -
- Bounty: You need a team of brawlers that can take out enemies and avoid dying. You should look for brawlers that have long range, high damage, or stealth. Some of the best brawlers for this mode are Piper, Brock, Bo, Penny, Leon, Crow, etc. -
- Heist: You need a team of brawlers that can attack or defend the safe. You should have a damage dealer, a tank, and a support. Some of the best brawlers for this mode are Barley, Dynamike, Colt, Bull, El Primo, Poco, etc. -
- Special Events: You need a team of brawlers that can adapt to different challenges and scenarios. You should look for brawlers that have versatility, utility, or synergy. Some of the best brawlers for this mode are Gene, Carl, Rosa, Bibi, Tick, 8-Bit, etc. -
- Brawl Boxes: You can get Brawl Boxes by playing matches or completing quests. Brawl Boxes contain coins, power points, gems, tickets or new brawlers. The chances of getting a new brawler depend on their rarity: Common (30%), Rare (20%), Super Rare (10%), Epic (5%), Mythic (2%), Legendary (1%). -
- Trophy Road: You can get Trophy Road rewards by earning trophies from playing matches. Trophy Road rewards include coins, power points or new brawlers. The new brawlers you can get from Trophy Road are Nita (10 trophies), Colt (60 trophies), Bull (250 trophies), Jessie (500 trophies), Brock (1000 trophies), Dynamike (2000 trophies), Bo (3000 trophies), Tick (4000 trophies), 8-Bit (6000 trophies), Emz (8000 trophies). -
- Gems: You can get gems by opening Brawl Boxes or buying them with real money. Gems can be used to buy skins or special offers in the shop. Special offers may include new brawlers or discounted prices. -
- Power Points: You can get power points by opening Brawl Boxes or buying them with coins in the shop. Power points can be used to upgrade your brawlers' level and increase their stats. -
- Star Powers: You can get star powers by reaching level 9 or higher with your brawlers. Star powers are special abilities that give your brawlers an extra edge in battle. You can get star powers by opening Brawl Boxes or buying them with coins in the shop. -
- Gadgets: You can get gadgets by reaching level 7 or higher with your brawlers. Gadgets are special items that give your brawlers a unique effect once per match. You can get gadgets by opening Brawl Boxes or buying them with coins in the shop. -
- Obstacles: You can use obstacles to hide from enemies, ambush them, or escape from them. You can also use obstacles to block enemy attacks or create openings for your own attacks. However, you should also be careful of enemies hiding behind obstacles or destroying them with their attacks. -
- Power-ups: You can use power-ups to gain an advantage over your enemies or turn the tide of the battle. You can find power-ups randomly on the map or earn them by killing enemies or completing objectives. However, you should also be aware of enemies using power-ups against you or stealing them from you. -
- Super abilities: You can use super abilities to deal massive damage, support your teammates, or control the map. You can charge your super ability by hitting enemies or getting hit by them. However, you should also know when to use your super ability wisely and when to save it for later. -
- Facebook: https://www.facebook.com/turkbrawlstars -
- Twitter: https://twitter.com/turkbrawlstars -
- Instagram: https://www.instagram.com/turkbrawlstars -
- YouTube: https://www.youtube.com/channel/UCwX9yZqXwQ7y0F8n3Zx7l0g -
- Download the mod apk file from a trusted source. You can use this link to get the latest version of the mod apk file. -
- Enable unknown sources on your device settings. To do this, go to Settings > Security > Unknown Sources and toggle it on. -
- Install the mod apk file by tapping on it. You may get a warning message, but just ignore it and proceed. -
- Launch the game and enjoy unlimited gold. -
- Choose your character and customize your appearance. You can choose from different genders, hairstyles, clothes, accessories, etc. -
- Explore the world and collect resources for crafting and building. You will find different biomes, such as forests, deserts, mountains, etc., where you can gather wood , stone, metal, etc. You can also find animals, plants, and other items that you can use for food, medicine, or crafting. -
- Fight off hordes of monsters and zombies from another world. You will encounter different types of enemies, such as skeletons, spiders, mutants, etc., that will try to kill you. You can use different weapons, such as guns, swords, axes, etc., to fight them. You can also use traps, turrets, and explosives to defend yourself. -
- Craft weapons, armor, tools, and buildings to survive and progress. You can use the crafting menu to see what you can make with the resources you have. You can craft different items, such as bullets, bandages, potions, etc., to help you in your adventure. You can also craft different structures, such as walls, doors, windows, roofs, etc., to build your own base or shelter. -
- Unlimited gold to buy anything you want in the game. You can use gold to buy different items from the shop, such as weapons, armor, tools, etc. You can also use gold to upgrade your items and make them more powerful. -
- Intuitive touch controls to work with. You can easily control your character and interact with the environment using the touch screen. You can also customize the controls to suit your preference. -
- Massive world with lots of discoverable elements. You can explore a huge open world with different biomes and locations. You can find hidden secrets, treasures, and quests along the way. -
- Unique and diverse in-game levels. You can play different levels that have different objectives and challenges. You can also unlock new levels as you progress in the game. -
- A fascinating and captivating storyline to follow. You can follow the story of the game and learn more about the world and the characters. You can also make choices that affect the outcome of the game. -
- Use the map to find points of interest and quests. You can use the map to see where you are and where you need to go. You can also see where you can find resources, enemies, allies, etc. -
- Upgrade your weapons and armor regularly to deal more damage and resist more attacks. You can use gold or resources to upgrade your items in the shop or in the crafting menu. You can also find better items by looting enemies or chests. -
- Build a base with defenses and storage to keep your items safe. You can use resources to build different structures that will protect you from enemies and weather. You can also use storage boxes to store your items and access them anytime. -
- Use the crafting menu to see what you can make with the resources you have. You can use the crafting menu to see what items you can craft with the resources you have collected. You can also see what resources you need to craft a specific item. -
- Cooperate with other players online to survive together. You can join or create a multiplayer session online and play with other players around the world. You can chat with them, trade with them, or fight with them. -
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Yes, Tegra Crafting and Building mod apk unlimited gold is safe to download and install as long as you use a trusted source. However, you should always be careful when downloading and installing any mod apk file from the internet, as some of them may contain viruses or malware that can harm your device.
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Tegra Crafting and Building mod apk unlimited gold is compatible with most Android devices that have Android 4.4 or higher. However, some devices may experience performance issues or crashes due to the high graphics quality of the game. You can try lowering the graphics settings or closing other apps to improve the performance of the game.
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To update Tegra Crafting and Building mod apk unlimited gold, you need to download and install the latest version of the mod apk file from the same source that you used before. You can also check for updates in the game settings or on the official website of the game.
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You can contact the developers of Tegra Crafting and Building mod apk unlimited gold by sending them an email at or by visiting their Facebook page at . You can also leave your feedback, suggestions, or bug reports on their website at .
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Yes, you can play Tegra Crafting and Building mod apk unlimited gold offline without an internet connection. However, some features of the game, such as multiplayer mode, may require an internet connection to work properly.
- - Tow Mater (voice of Larry the Cable Guy), a friendly and loyal tow truck who becomes Lightning's best friend. -
- Sally Carrera (voice of Bonnie Hunt), a beautiful and smart Porsche who runs the Cozy Cone Motel and develops a romantic interest in Lightning. -
- Doc Hudson (voice of Paul Newman), a grumpy and mysterious Hudson Hornet who is the town's judge and doctor, and also Lightning's mentor. -
- Ramone (voice of Cheech Marin), a lowrider who owns a body shop and loves to paint cars. -
- Flo (voice of Jenifer Lewis), a sassy and sweet 1950s show car who runs a gas station and is Ramone's wife. -
- Luigi (voice of Tony Shalhoub), a cheerful and passionate Italian Fiat 500 who owns a tire shop and is a fan of Ferrari racing. -
- Guido (voice of Guido Quaroni), Luigi's silent and loyal assistant who can change tires in seconds. -
- Sheriff (voice of Michael Wallis), a stern and strict 1949 Mercury police car who enforces the law in Radiator Springs. -
- Filmore (voice of George Carlin), a hippie and organic fuel enthusiast who drives a 1960 Volkswagen Bus. -
- Sarge (voice of Paul Dooley), a patriotic and strict 1942 Willys Jeep who runs a surplus store and is Filmore's neighbor. -
- Lizzie (voice of Katherine Helmond), an eccentric and forgetful 1923 Ford Model T who is the oldest resident of Radiator Springs. -
- Red (voice of Joe Ranft), a shy and emotional 1960s fire truck who waters the flowers in the town. -
- Lightning McQueen is named after Glenn McQueen, a Pixar animator who died in 2002. His design is inspired by several sports cars, such as the Chevrolet Corvette C6, the Mazda Miata, and the Dodge Viper. His number 95 is a reference to the year 1995, when Pixar released its first movie Toy Story. -
- Tow Mater is named after Douglas "Mater" Keever, a real-life NASCAR fan who met John Lasseter at a race. His design is based on a 1951 International Harvester L-170 "boom" truck with elements from a 1957 Chevrolet. His license plate reads A113, a recurring Easter egg in Pixar movies that refers to a classroom at the California Institute of the Arts. -
- Sally Carrera is a 2002 Porsche 911 Carrera who has a tattoo of the Route 66 logo on her back. She is modeled after the real-life car of John Lasseter's wife, Nancy. Her license plate reads 301PCE, which can be read as "California" or "Carrera". -
- Doc Hudson is a 1951 Hudson Hornet who was a famous racer in the 1950s. He is voiced by Paul Newman, who was also a racing enthusiast and driver. His license plate reads 51HHMD, which stands for "51 Hudson Hornet, M.D." (medical doctor). -
- Ramone is a 1959 Chevrolet Impala lowrider who changes his paint color frequently. He is voiced by Cheech Marin, who is known for his comedy duo with Tommy Chong and his role as a hyena in The Lion King. His license plate reads LOWNSLO, which means "low and slow". -
- Flo is a 1957 Motorama show car who was once a famous singer. She is voiced by Jenifer Lewis, who is also a singer and actress. Her license plate reads SHOGRL, which means "show girl". -
- Luigi is a 1959 Fiat 500 who loves Ferrari racing and Italian culture. He is voiced by Tony Shalhoub, who is known for his role as Monk in the TV series of the same name. His license plate reads 445-108, which is the latitude and longitude of the Ferrari headquarters in Maranello, Italy. -
- Guido is a custom forklift who can change tires very fast. He is voiced by Guido Quaroni, a Pixar technical director who was born in Italy. He only speaks in Italian, except for one word: "Pit stop". -
- Sheriff is a 1949 Mercury police car who is based on a character from the TV series The Andy Griffith Show. He is voiced by Michael Wallis, a Route 66 historian and author. -
- Filmore is a 1960 Volkswagen Bus who is a hippie and an organic fuel advocate. He is voiced by George Carlin, who was a comedian and social critic. His license plate reads 51237, which is Carlin's birthday (May 12, 1937). -
- Sarge is a 1942 Willys Jeep who is a military veteran and a patriot. He is voiced by Paul Dooley, who is an actor and writer. His license plate reads USMC41, which means "United States Marine Corps, 1941". -
- Lizzie is a 1923 Ford Model T who is the widow of Stanley, the founder of Radiator Springs. She is voiced by Katherine Helmond, who was an actress and director. Her license plate reads MT23, which means "Model T, 1923". -
- Red is a 1960s fire truck who is very shy and emotional. He is voiced by Joe Ranft, who was a Pixar animator and story artist who died in a car accident in 2005. -
- Jay Limo (voice of Jay Leno), a talk show host and car collector who drives a 1999 Lincoln Town Car stretch limousine. -
- Mario Andretti (voice of himself), a legendary racing driver who drives a 1967 Ford Fairlane. -
- Michael Schumacher (voice of himself), a Formula One champion who drives a Ferrari F430. -
- Dale Earnhardt Jr. (voice of himself), a NASCAR driver who drives a Chevrolet Monte Carlo. -
- Bob Costas (voice of himself), a sportscaster who voices Bob Cutlass, an announcer for the Piston Cup. -
- Darrell Waltrip (voice of himself), a NASCAR driver and commentator who voices Darrell Cartrip, another announcer for the Piston Cup. -
- The name of the town Radiator Springs is a play on the words "radiator" and "spring", which are both parts of a car. -
- The name of the Piston Cup is a play on the words "piston" and "cup", which are both parts of a car engine. -
- The name of the sponsor Dinoco is a reference to the gas station from Toy Story, another Pixar movie. -
- The name of the sponsor Rust-eze is a reference to the product Rust-Oleum, which is used to prevent rust on metal surfaces. -
- The name of the character Fillmore is a reference to the Fillmore Auditorium, a famous music venue in San Francisco. -
- The name of the character Sarge is a reference to the military rank of sergeant. -
- The name of the character Lizzie is a reference to the nickname "Tin Lizzie", which was given to the Ford Model T. -
- The town of Radiator Springs is based on several towns along Route 66, such as Peach Springs, Seligman, Kingman, and Oatman in Arizona. -
- The Cozy Cone Motel is based on the Wigwam Motel in Holbrook, Arizona, which has rooms shaped like teepees. -
- The Wheel Well Motel is based on the Hackberry General Store in Hackberry, Arizona, which has a vintage car on its roof. -
- The Cadillac Range is based on the Cadillac Ranch in Amarillo, Texas, which has old Cadillacs buried nose-down in the ground. -
- The Ornament Valley is based on the Monument Valley in Utah and Arizona, which has iconic rock formations. -
- "Real Gone" by Sheryl Crow, which is the opening song that plays during the first race. -
- "Route 66" by Chuck Berry, which is a classic song that plays when Lightning is traveling on Route 66. -
- "Life is a Highway" by Rascal Flatts, which is a cover of a song by Tom Cochrane that plays when Lightning is enjoying his time in Radiator Springs. -
- "Our Town" by James Taylor, which is a sad song that plays when Lightning learns about the history of Radiator Springs. -
- "Sh-Boom" by The Chords, which is a doo-wop song that plays when Lightning and Sally go on a date. -
- "Find Yourself" by Brad Paisley, which is a country song that plays when Lightning decides to help The King finish his race. -
- The Golden Globe Award for Best Animated Feature Film. -
- The Academy Award nomination for Best Animated Feature Film. -
- The BAFTA Award nomination for Best Animated Film. -
- The Annie Award for Best Animated Feature. -
- Go to one of the websites that offer Cars 1 Tamil dubbed movie download, such as , , or . These websites may have different names and domains, but they usually have similar layouts and features. -
- Search for Cars 1 Tamil dubbed movie download on the website's search bar or browse through its categories and genres. -
- Select the movie from the list of results and click on it. -
- Choose the quality and format of the movie that you want to download, such as HD, MP4, MKV, etc. -
- Click on the download button or link that appears on the screen. -
- Wait for the download to start and finish. Depending on your internet speed and the size of the file, this may take a few minutes or hours. -
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- Q: When was Cars 1 released? -
- A: Cars 1 was released on June 9, 2006 in the United States and on July 14, 2006 in India. -
- Q: Who directed Cars 1? -
- A: Cars 1 was directed by John Lasseter, who is the chief creative officer of Pixar Animation Studios and Walt Disney Animation Studios. He also co-directed Toy Story, A Bug's Life, Toy Story 2, and Cars 2. -
- Q: How many sequels and spin-offs does Cars 1 have? -
- A: Cars 1 has two sequels, Cars 2 (2011) and Cars 3 (2017), and two spin-offs, Planes (2013) and Planes: Fire & Rescue (2014). -
- Q: What is the runtime of Cars 1? -
- A: Cars 1 has a runtime of 116 minutes (1 hour and 56 minutes). -
- Q: What is the rating of Cars 1? -
- A: Cars 1 has a rating of G (General Audiences) in the United States and U (Universal) in India. -
- Open Safari browser on your iPhone and go to [Panda Helper's official website](^1^). -
- Tap on the "Download Free Version" button and follow the instructions to install Panda Helper on your device. -
- Go to Settings > General > Profiles & Device Management and trust the profile of Panda Helper. -
- Launch Panda Helper from your home screen and search for "craftsman building craft" in the search bar. -
- Tap on the "Get" button next to the app icon and wait for the download to finish. -
- Go back to your home screen and tap on the craftsman building craft icon to launch the game. -
- Stunning graphics and realistic sound: Craftsman building craft game has high-quality graphics that make the game world look vivid and beautiful. The game also has realistic sound effects that enhance the immersion and atmosphere of the game. -
- Simple, easy to play: Craftsman building craft game has simple controls that make it easy to play. You can use the joystick on the left side of the screen to move around, and the buttons on the right side of the screen to jump, fly, attack, or interact with objects. You can also tap on the inventory icon on the top right corner of the screen to access your items and tools. -
- Many game modes: Craftsman building craft game has many game modes that suit different preferences and styles of play. You can choose from creative mode, survival mode, adventure mode, or multiplayer mode. In creative mode, you have unlimited resources and can build anything you want without any restrictions. In survival mode, you have limited resources and need to gather materials, craft tools, fight enemies, and survive. In adventure mode, you can explore different maps with different challenges and quests. In multiplayer mode, you can join or create a server with other players online and cooperate or compete with them. -
- Customizable world: Craftsman building craft game allows you to customize your world with different blocks, items, and decorations. You can choose from different biomes, such as forest, desert, snow, or ocean. You can also change the time of day, the weather, and the difficulty level. You can create your own maps and share them with other players online. -
- Select a game mode from the main menu. -
- Choose a map or create a new one. -
- Start building or exploring your world with blocks. -
- Use your inventory to access your items and tools. -
- Craft new items and tools with the crafting table. -
- Fight enemies or interact with friendly animals. -
- Save your progress and exit the game. -
- Learn the basics: Before you jump into the game, you should learn the basics of the game, such as how to move, how to use your inventory, how to craft, how to fight, and how to change settings. You can find tutorials and guides on the internet or watch videos of other players online. -
- Plan ahead: Before you start building or exploring, you should have a plan of what you want to do and how you want to do it. You should also gather enough resources and tools for your project. This will save you time and frustration later on. -
- Be creative: Craftsman building craft game is all about creativity and expression. You can build anything you want with blocks, from simple houses to complex castles. You can also decorate your world with different items and colors. Don't be afraid to experiment and try new things. -
- Be careful: Craftsman building craft game is not without dangers and challenges. You might encounter enemies, such as zombies, spiders, or skeletons, that will try to attack you. You might also fall into lava, drown in water, or starve to death. You should always be prepared and cautious when playing the game. -
- Have fun: The most important tip for new players in craftsman building craft game is to have fun. Craftsman building craft game is a game that allows you to enjoy yourself and have a good time. You can play solo or with your friends online. You can also join or create servers with other players online and chat with them. Craftsman building craft game is a game that will make you happy and relaxed. -
- Is craftsman building craft game free? -
- Is craftsman building craft game safe? -
- Is craftsman building craft game online or offline? -
- How to update craftsman building craft game? -
- Open Panda Helper from your home screen and search for "craftsman building craft" in the search bar. -
- Tap on the "Update" button next to the app icon and wait for the download to finish. -
- Go back to your home screen and tap on the craftsman building craft icon to launch the game. -
- How to uninstall craftsman building craft game? -
- Go to Settings > General > iPhone Storage and find craftsman building craft game in the list of apps. -
- Tap on the app icon and then tap on "Delete App". -
- Confirm your action by tapping on "Delete App" again. -
- Is Payback 2 free to play? -
- Is Payback 2 offline or online? -
- How to get more coins and gems in Payback 2? -
- How to update Payback 2? -
- How to contact the developer of Payback 2? -
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Evidenciou pelo menos * linfonodos levemente aumentados, no mesentĆ©rio da regiĆ£o periumbilical e fossas ilĆacas, hipoecogĆŖnicos com centro ecogĆŖnico, sem sinais de degeneraĆ§Ć£o cĆstico-necrĆ³tica, medindo atĆ© cm.
NĆ£o evidenciou-se aumento da ecogenicidade da gordura mesentĆ©rica e nĆ£o caracterizou o apĆŖndice cecal.
AlƧas intestinais com peristalse preservada.
Linfonodos intraperitoneais levemente aumentados. Considerar possibilidade de adenite mesentƩrica.
Obs.: O mĆ©todo ultrassonogrĆ”fico apresenta baixa sensibilidade na detecĆ§Ć£o de apendicite aguda em fase muito precoce. NecessĆ”rio correlaĆ§Ć£o clĆnico-laboratorial.
OU
Estudo ultrassonogrĆ”fico complementar com sonda linear de 10MHz, dirigido em fossas ilĆacas e regiĆ£o peri-umbilical:
Evidenciou linfonodos mesentĆ©ricos, alguns de aspecto reativo, hipoecogĆŖnicos, principalmente na regiĆ£o ileocecal, medindo atĆ© * cm.
NĆ£o evidenciou-se aumento da ecogenicidade da gordura mesentĆ©rica e caracterizou o apĆŖndice cecal de aspecto habitual.
AlƧas intestinais com peristalse preservada.
Linfonodos intraperitoneais levemente aumentados. Considerar possibilidade de adenite mesentƩrica.
ALONGAMENTO HEPĆTICO
FĆgado de morfologia e contornos normais, apresentando alongamento vertical do lobo hepĆ”tico direito, medindo cm longitudinal
(habitual < 15,0 cm): Lobo de Riedel: variante anatƓmica.
OU
FĆgado de morfologia e contornos normais apresentando alongamento horizontal do lobo hepĆ”tico esquerdo, em Ćntimo contato com o baƧo (variante anatĆ“mica).
OU
FĆgado de morfologia e contornos normais apresentando alongamento vertical do lobo hepĆ”tico esquerdo, medindo cm longitudinal
(habitual < 10,0 cm) (variante anatƓmica).
ANEURISMA DE AORTA ABDOMINAL
Aorta abdominal pĆ©rvia, com calcificaƧƵes parietais ateromatosas leves/grosseiras associado a dilataĆ§Ć£o aneurismĆ”tica fusiforme/sacular em seu segmento supra/infra-renal, medindo * cm (AP x T) e extensĆ£o de * cm, com trombos murais semi-circunferenciais laminares, reduzindo a luz efetiva em cerca de * %, com fluxo turbilhonado ao Doppler Colorido.
O colo do aneurisma mede: * cm.
DiĆ¢metro aĆ³rticos:
TransiĆ§Ć£o tĆ³raco-abdominal: * cm (normal atĆ© 2,5 cm).
NĆvel da artĆ©ria mesentĆ©rica superior: * cm (normal atĆ© 2,5 cm).
NĆvel da bifurcaĆ§Ć£o das ilĆacas: * cm (normal atĆ© 3,0 cm).
ArtĆ©rias ilĆacas comuns apresentam-se pĆ©rvias, com calibre e contornos normais, medindo atĆ© * cm Ć direita e * cm Ć esquerda.
DilataĆ§Ć£o aneurismĆ”tica de aorta abdominal.
APENDAGITE EPIPLĆICA
Estudo ultrassonogrĆ”fico complementar com sonda linear de 10MHz, dirigido em regiĆ£o abdominal, evidenciou:
Imagem ovalada, na fossa ilĆaca esquerda, na borda antimesentĆ©rica, hiperecogĆŖnica com halo hipoecogĆŖnico, medindo * cm.
Borramento dos planos gordurosos adjacentes Ć imagem descrita.
AlƧas intestinais com peristalse preservada.
Imagem ovalada na fossa ilĆaca esquerda. Considerar possibilidade de apendagite epiplĆ³ica. Conveniente complementar com TC.
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
APĆNDICE CECAL
Estudo ultrassonogrĆ”fico complementar com sonda linear de 10MHz, dirigido em fossas ilĆacas, nĆ£o evidenciou aumento da ecogenicidade da gordura mesentĆ©rica e nĆ£o caracterizou ecograficamente o apĆŖndice cecal. AlƧas intestinais com peristalse preservada.
Obs.: O mĆ©todo ultrassonogrĆ”fico apresenta baixa sensibilidade na detecĆ§Ć£o de apendicite aguda em fase muito precoce. NecessĆ”rio correlaĆ§Ć£o clĆnico-laboratorial.
APENDICITE
Estudo ultrassonogrĆ”fico complementar com sonda linear de 10MHz, dirigido em regiĆ£o pĆ©lvica, evidenciou:
Imagem tubular, de fundo cego, medindo * cm de espessura (normal < 0,7 cm), sem delaminamento de parede, nĆ£o compressĆvel, podendo corresponder a apĆŖndice cecal inflamado.
Observa-se ainda imagem nodular, provida de sombra acĆŗstica posterior, na luz do apĆŖndice, medindo * cm, compatĆvel com apendicolito.
Borramento dos planos gordurosos adjacentes na regiĆ£o de fossa ilĆaca Ć direita.
Pequena quantidade de lĆquido na fossa ilĆaca direita e fundo de saco posterior de aspecto anecĆ³ide.
Linfonodos peri-cecais discretamente aumentados, hipoecogĆŖnico de centro ecogĆŖnico, medindo atĆ© * cm.
AlƧas intestinais com peristalse reduzida.
Sinais compatĆveis com apendicite aguda.
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
OU
Na projeĆ§Ć£o da fossa ilĆaca direita, observa-se imagem tubuliforme fixa, terminando em fundo cego, localizada medialmente ao ceco/lateralmente ao ceco, apresentando paredes espessadas e calibre de cm (normal < 0,7 cm), contendo material lĆquido espesso e associada a hiperecogenicidade da gordura mesenterial adjacente e a pequena quantidade de lĆquido livre. Nota-se alƧa de delgado parĆ©tica e repleta de lĆquido prĆ³xima a imagem supramencionada.
Sinais compatĆveis com apendicite aguda.
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
BAĆO ACESSĆRIO
Nota-se imagem nodular, sĆ³lida, de contornos bem definidos e regulares, isoecogĆŖnica ao parĆŖnquima esplĆŖnico, Ćnfero-medialmente ao baƧo, medindo * cm, compatĆvel com baƧo acessĆ³rio.
OU
BaƧo acessĆ³rio adjacente Ć face inferior esplĆŖnica, medindo cm.
BARRO BILIAR
VesĆcula biliar normodistendida, com paredes finas e lisas, apresentando conteĆŗdo hipoecogĆŖnico com nĆvel lĆquido-lĆquido, correspondendo a barro biliar.
Sinal de Murphy ultrassonogrƔfico negativo.
Barro biliar.
Nota: O achado de barro biliar pode ocultar a detecĆ§Ć£o de microcĆ”lculos vesiculares pelo mĆ©todo ecogrĆ”fico.
BORRAMENTO DA GORDURA MESENTĆRICA
Estudo ultrassonogrĆ”fico complementar com sonda linear de 10MHz, dirigido em regiĆ£o pĆ©lvica, evidenciou:
Ćrea de borramento da gordura mesentĆ©rica na fossa ilĆaca esquerda.
AlƧas intestinais com peristalse levemente reduzida.
Borramento da gordura mesentĆ©rica na fossa ilĆaca Ć esquerda: processo inflamatĆ³rio? Conveniente complementar com TC.
CALCIFICAĆĆO HEPĆTICA
Nota-se foco hiperecogĆŖnico, irregular, provido de acĆŗstica posterior, no segmento hepĆ”tico * (SegmentaĆ§Ć£o de Couinaund), medindo cerca de cm.
CalcificaĆ§Ć£o hepĆ”tica de aspecto residual.
CISTO HEPĆTICO
Nota-se imagem cĆstica, de paredes finas e regulares, conteĆŗdo anecĆ³ide homogĆŖneo, no segmento hepĆ”tico * (SegmentaĆ§Ć£o de Couinaund), medindo cm.
Cisto hepƔtico.
CISTOS HEPĆTICOS
Notam-se imagens cĆsticas de paredes finas e lobuladas, conteĆŗdo anecĆ³ide homogĆŖneo, caracterizadas assim:
segmento hepƔtico , medindo * cm.
segmento hepƔtico , medindo * cm.
Cistos hepƔticos.
OU
Notam-se imagens cĆsticas de paredes finas e regulares, conteĆŗdo anecĆ³ide homogĆŖneo, medindo atĆ© cm no segmento hepĆ”tico * (SegmentaĆ§Ć£o de Couinaund).
Cistos hepƔticos.
COLECISTECTOMIA
HepatocolĆ©doco de calibre normal, medindo cm ao nĆvel da porta hepatis.
VesĆcula biliar nĆ£o caracterizada (status pĆ³s-operatĆ³rio).
Sinais de colecistectomia.
COLECISTITE LITIĆSICA
VesĆcula biliar hiperdistendida, medindo * cm (normal < 10,0 x 4,0 cm), com paredes espessadas e delaminadas, medindo cm (normal < 0,4 cm), apresentando conteĆŗdo hipoecogĆŖnico com nĆvel lĆquido-lĆquido, correspondendo a barro biliar, associado a mĆŗltiplos/alguns cĆ”lculos providos de sombra acĆŗstica posterior, o maior medindo * cm.
Sinal de Murphy ultrassonogrƔfico positivo.
Sinais de colecistite litiƔsica.
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
COLECISTITE LITIĆSICA BODERLINE
VesĆcula biliar discretamente distendida, medindo * cm, com paredes de espessura limĆtrofe, medindo 0,4 cm, apresentando conteĆŗdo anecĆ³ide com mĆŗltiplas/algumas imagens nodulares, hiperecogĆŖnicas, providas de sombra acĆŗstica posterior, a maior medindo * cm, correspondendo a cĆ”lculos.
Sinal de Murphy ultrassonogrƔfico positivo.
ColelitĆase. NecessĆ”rio correlaĆ§Ć£o clĆnico-laboratorial para avaliar possibilidade de colecistite em estĆ”gio inicial.
COLELITĆASE E BARRO BILIAR
VesĆcula biliar normodistendida, de paredes finas e lisas, apresentando conteĆŗdo hipoecogĆŖnico com nĆvel lĆquido-lĆquido, correspondendo a barro biliar, associado a pelo menos uma imagem nodular, hiperecogĆŖnica, provida de sombra acĆŗstica posterior, medindo * cm, correspondendo a cĆ”lculo.
Sinal de Murphy ultrassonogrƔfico negativo.
ColelitĆase com barro biliar.
COLELITĆASE MĆLTIPLA
VesĆcula biliar normodistendida, com paredes finas e lisas, apresentando conteĆŗdo anecĆ³ide com mĆŗltiplas/algumas imagens nodulares, hiperecogĆŖnicas, providas de sombra acĆŗstica posterior, mĆ³veis Ć mudanƧa de decĆŗbito, a maior medindo * cm, correspondendo a cĆ”lculos.
Sinal de Murphy ultrassonogrƔfico negativo.
ColelitĆase.
COLELITĆASE UMA
VesĆcula biliar normodistendida, com paredes finas e lisas, apresentando conteĆŗdo anecĆ³ide com imagem nodular, hiperecogĆŖnica, provida de sombra acĆŗstica posterior, mĆ³vel Ć mudanƧa de decĆŗbito, medindo * cm, correspondendo a cĆ”lculo.
Sinal de Murphy ultrassonogrƔfico negativo.
ColelitĆase.
COLELITĆASE COM VESĆCULA ESCLEROATRĆFICA
VesĆcula biliar hipodistendida, de paredes aparentemente finas, com seu interior ocupado por imagem hiperecogĆŖnica, provida de sombra acĆŗstica posterior, medindo cerca de * cm, podendo corresponder cĆ”lculo/cĆ”lculos.
Sinal de Murphy ultrassonogrƔfico negativo.
ColelitĆase com sinais de vesĆcula biliar escleroatrĆ³fica.
COLEDOCOLITĆASE
Vias biliares intra-hepĆ”ticas levemente dilatadas, medindo * cm Ć esquerda e * cm Ć direita (normal < 0,25 cm).
Nota-se imagem nodular, hiperecogĆŖnica no colĆ©doco, medindo * cm, a cerca de * cm da porta hepatis, correspondendo a cĆ”lculo impactado, promovendo dilataĆ§Ć£o Ć montante, com hepatocolĆ©doco medindo * cm de calibre.
VesĆcula biliar hiperdistendida, medindo * cm, com paredes finas e lisas, apresentando conteĆŗdo anecĆ³ide com mĆŗltiplas pequenas imagens nodulares, hiperecogĆŖnicas, com diĆ¢metro mĆ©dio de * cm, correspondendo a microcĆ”lculos.
Sinal de Murphy ultrassonogrƔfico positivo.
PĆ¢ncreas de morfologia, contornos, dimensƵes e ecotextura normais. Ducto de Wirsung de calibre preservado.
ColelitĆase.
ColedocolitĆase com dilataĆ§Ć£o de vias biliares intra e extra-hepĆ”ticas.
OU
HepatocolĆ©doco visibilizado atĆ© aproximadamente cm da porta hepatis, devido a sobreposiĆ§Ć£o gasosa, de calibre aumentado, medindo * cm.
PĆ¢ncreas de morfologia, contornos, dimensƵes e ecotextura normais. Ducto de Wirsung de calibre preservado.
ColelitĆase.
DilataĆ§Ć£o de vias biliares intra e extra-hepĆ”ticas. Considerar possibilidade de coledocolitĆase.
COLESTEROLOSE
VesĆcula biliar normodistendida, com paredes finas apresentando pequenos focos hiperecogĆŖnicos de artefatos em "cauda de cometa", medindo atĆ© 0,2 cm, podendo corresponder a colesterolose.
ConteĆŗdo vesicular anecĆ³ide e nĆ£o apresentando cĆ”lculos. Sinal de Murphy ultrassonogrĆ”fico negativo.
Colesterolose vesicular.
COLESTEROLOSE/ADENOMIOMATOSE
VesĆcula biliar normodistendida, com paredes difusamente espessas e irregulares, medindo atĆ© cm (normal < 0,4 cm) e Ć”reas hipoecogĆŖnicas evaginadas a partir da mucosa na parede muscular, podendo corresponder a adenomiomatose com seios de Rokitansky-Aschoff (divertĆculos intra-murais).
Observa-se ainda Ć”reas ecogĆŖnicas de artefatos em "cauda de cometa", medindo atĆ© 0,2 cm, podendo corresponder a colesterolose.
ConteĆŗdo vesicular anecĆ³ide e nĆ£o apresentando cĆ”lculos. Sinal de Murphy ultrassonogrĆ”fico negativo.
Colesterolose/adenomiomatose vesicular.
DERIVAĆĆO BĆLIO-DIGESTIVA
Vias biliares intra-hepĆ”ticas de calibres normais com alguns focos ecogĆŖnicos de artefatos em ācauda de cometaā nas vias biliares principais e secundĆ”rias, sugerindo aerobilia.
HepatocolĆ©doco de calibre aumentado, medindo cm ao nĆvel da porta hepatis, visibilizado atĆ© ao nĆvel da junĆ§Ć£o colĆ©doco-intestinal, sem sinais de fatores obstrutivos.
Sinais de derivaĆ§Ć£o bĆlio-digestiva com aerobilia leve.
ESPESSAMENTO DE ALĆA COLĆNICA
Estudo ultrassonogrĆ”fico complementar com sonda linear de 10MHz, dirigido em regiĆ£o abdominal, evidenciou:
Espessamento parietal difuso de alƧa colĆ“nica na fossa ilĆaca direita/esquerda, medindo atĆ© cm (normal < 0,6 cm).
Borramento dos planos gordurosos adjacentes Ć alƧa descrita.
Pequena quantidade de lĆquido na fossa ilĆaca direita/esquerda e fundo de saco posterior de aspecto anecĆ³ide.
Linfonodos regionais discretamente aumentados, hipoecogĆŖnico de centro ecogĆŖnico, medindo atĆ© * cm.
AlƧas intestinais com peristalse reduzida.
Espessamento parietal difuso de alƧa colĆ“nica na fossa ilĆaca direita/esquerda. Considerar possibilidade de processo inflamatĆ³rio/infeccioso. Conveniente complementar com TC.
ESPLENECTOMIA
BaƧo nĆ£o caracterizado (status pĆ³s-operatĆ³rio).
Sinais de esplenectomia total.
OU
BaƧo nĆ£o visibilizado: auto-esplenectomia?
Sinais de auto-esplenectomia.
ESPLENECTOMIA + ESPLENOSE
BaƧo nĆ£o caracterizado (status pĆ³s-operatĆ³rio).
Notam-se na loja esplĆŖnica algumas imagens nodulares, sĆ³lidas, de contornos lobulados, com ecotextura semelhante Ć esplĆŖnica, medindo atĆ© * cm.
Sinais de esplenectomia.
NodulaƧƵes na loja esplĆŖnica. Considerar possibilidade de esplenose.
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
ESPLENOMEGALIA
BaƧo de morfologia, contornos e ecotextura normais, com dimensƵes aumentadas, medindo cm em seu maior eixo (normal < 12,0 cm).
Esplenomegalia homogĆŖnea leve.
OU
BaƧo de dimensƵes muito aumentadas, medindo cerca de * cm em seu maior eixo (normal < 12,0 cm), ultrapassando a linha mĆ©dia e estendendo-se para a fossa ilĆaca esquerda, comprimindo Ć³rgĆ£os adjacentes.
Esplenomegalia volumosa.
ESTEATOSE HEPĆTICA
FĆgado de morfologia, contornos e dimensƵes normais, apresentando leve aumento difuso da ecogenicidade.
Sinais de leve infiltraĆ§Ć£o gordurosa hepĆ”tica.
OU
FĆgado de morfologia, contornos e dimensƵes normais, apresentando moderado/acentuado aumento difuso da ecogenicidade com atenuaĆ§Ć£o do feixe acĆŗstico posterior, o que dificulta a identificaĆ§Ć£o de eventuais alteraƧƵes parenquimatosas focais.
Sinais de moderada/acentuada infiltraĆ§Ć£o gordurosa hepĆ”tica.
OU
FĆgado de morfologia, contornos e dimensƵes normais, apresentando leve aumento difuso de sua ecogenicidade com Ć”rea hipoecogĆŖnica, mal definida, no segmento hepĆ”tico IVb/V, medindo cerca de cm, sugerindo Ć”rea de preservaĆ§Ć£o.
Sinais de leve infiltraĆ§Ć£o gordurosa hepĆ”tica.
OU
FĆgado de morfologia e contornos normais, com dimensƵes aumentadas, medindo o lobo esquerdo: cm (normal < 10,0 cm) e o lobo direito: cm (normal < 15,0 cm), apresentando moderado aumento difuso da ecogenicidade.
Sinais de moderada infiltraĆ§Ć£o gordurosa hepĆ”tica com hepatomegalia.
Nota: A alteraĆ§Ć£o hepĆ”tica descrita reduz a sensibilidade na detecĆ§Ć£o de lesƵes focais pelo mĆ©todo ecogrĆ”fico.
ESTENOSE DE PILORO
Nota-se na regiĆ£o epigĆ”strica intraperitoneal, medialmente Ć vesĆcula biliar, imagem em anel, hipoecogĆŖnica, sugerindo piloro com espessura muscular de cm (normal < 0,3 cm), comprimento de cm (normal < 1,7 cm) e volume estimado de cmĀ³ (normal < 1,4 cmĀ³), sem sinais abertura significativa da luz.
Observa-se ainda peristalse gĆ”strica exagerada que cessa de modo abrupto na margem do mĆŗsculo hipertrofiado, com ondas peristĆ”lticas retrĆ³gradas, associada a ausĆŖncia de abertura normal do piloro, com passagem diminuĆda de lĆquido do estĆ“mago para o duodeno, levando a retardo no esvaziamento gĆ”strico.
Imagem anelar na regiĆ£o epigĆ”strica. Considerar possibilidade de estenose hipertrĆ³fica do piloro.
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
OU
Sinal do alvo correspondendo ao anel hipoecĆ³ico do mĆŗsculo pilĆ³rico hipertrofiado em torno da mucosa ecogĆŖnica centralmente localizada. DiĆ¢metro pilĆ³rico transverso ā„ 13 mm (parĆ¢metro menos fidedigno). Comprimento do canal pilĆ³rico ā„ 17 mm (S: 100%; E:84,85%). Espessura do mĆŗsculo pilĆ³rico da parede externa do mĆŗsculo pilĆ³rico Ć margem externa da mucosa ā„ 3 mm (S: + 100%; E: + 100%).
Sinal do duplo trilho e sinal do cordĆ£o correspondendo a pequenas quantidades de lĆquido aprisionadas entre dobras de mucosa ecogĆŖnica redundante.
Sinal do mamilo mucoso correspondendo a protrusĆ£o de mucosa pilĆ³rica redundante em direĆ§Ć£o ao antro gĆ”strico.
Sinal do ombro correspondendo a impressĆ£o do mĆŗsculo pilĆ³rico hipertrofiado sob a parededistal do antro gĆ”strico, enfatizado durante a peristalse gĆ”strica.
Sinal da cĆ©rvice correspondendo a endentaĆ§Ć£o da camada muscular no antro cheio de lĆquido. (Observado em secĆ§Ć£o longitudinal e apresentando um canal pilĆ³rico alongado e estreitado, formando uma imagem semelhante ao da cĆ©rvice uterina).
RazĆ£o pilĆ³rica ā„ 0,27. DivisĆ£o da espessura do mĆŗsculo pilĆ³rico pelo diĆ¢metro pilĆ³rico (EMP/DPT) (S: 96%; E: 94%).
Volume pilĆ³rico = Ā¼ (Ļ Ć [DPT]Ā² Ć CCP) > 1,4 cmĀ³. (Falsos-negativos: 33%; utilidade prĆ”tica limitada).
Espessura da mucosa (EM) = 4,1 Ā± 0,9 mm e razĆ£o espessura da mucosa/espessura do mĆŗsculo pilĆ³rico (EM/EMP) = 0,89.
HEMANGIOMA ESPLĆNICO
Nota-se imagem nodular, sĆ³lida, no terƧo superior/mĆ©dio/inferior esplĆŖnico, de contornos bem definidos e lobulados, conteĆŗdo hiperecogĆŖnico e homogĆŖneo, desprovido de sombra acĆŗstica posterior, sem fluxo ao Doppler, medindo cm.
NĆ³dulo esplĆŖnico sugestivo de hemangioma.
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
HEMANGIOMA HEPĆTICO
Nota-se imagem nodular, sĆ³lida, de contornos bem definidos e lobulados, conteĆŗdo hiperecogĆŖnico e homogĆŖneo, desprovido de sombra acĆŗstica posterior, sem fluxo ao Doppler, no segmento hepĆ”tico (SegmentaĆ§Ć£o de Couinaund), medindo cm.
NĆ³dulo hepĆ”tico sugestivo de hemangioma.
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
HEPATITE AGUDA
ParĆŖnquima hepĆ”tico apresentando leve aumento da refringĆŖncia peri-portal difusamente.
AlteraĆ§Ć£o parenquimatosa hepĆ”tica. Considerar possibilidade de hepatite aguda.
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
HEPATOMEGALIA HOMOGĆNEA
FĆgado de morfologia e contornos normais com dimensƵes aumentadas, medindo o lobo esquerdo: cm longitudinal (normal
< 10,0 cm) e o lobo direito: cm longitudinal (normal < 15,0 cm).
Sinais de hepatomegalia homogĆŖnea leve.
HEPATOPATIA CRĆNICA
FĆgado de contornos lobulados, bordas rombas, com lobo direito de dimensƵes reduzidas e lobo caudado aumentado e globoso, associado a espessamento dos ligamentos hepĆ”ticos e ecotextura parenquimatosa difusamente grosseira e heterogĆŖnea. RelaĆ§Ć£o dos diĆ¢metros transversos: lobo caudado/lobo direito = * (normal < 0,65).
Veia porta de trajeto e calibre normais, medindo * cm (normal < 1,3 cm), com fluxo hepatopetal.
Sinais de hepatopatia crƓnica.
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
HEPATOPATIA CRĆNICA COM HIPERTENSĆO PORTAL
FĆgado de contornos lobulados, bordas rombas, com lobo direito de dimensƵes reduzidas e lobo caudado aumentado e globoso, associado a espessamento dos ligamentos hepĆ”ticos e ecotextura parenquimatosa difusamente grosseira e heterogĆŖnea. RelaĆ§Ć£o dos diĆ¢metros transversos: lobo caudado/lobo direito = * (normal < 0,65).
Veia porta de trajeto habitual apresentando calibre aumentado, medindo * cm (normal < 1,3 cm), com fluxo hepatofugal. DilataĆ§Ć£o e tortuosidade de vasos peri-esofĆ”gicos, peri-gĆ”stricos, peri-hepĆ”ticos e peri-esplĆŖnicos, medindo atĆ© cm.
Sinais de hepatopatia crĆ“nica com hipertensĆ£o portal.
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
HEPATOPATIA CRĆNICA/MĆLTIPLOS NĆDULOS
FĆgado apresentando contornos lobulados, bordas rombas, com lobo caudado de dimensƵes aumentadas e globoso, associado a espessamento dos ligamentos hepĆ”ticos. ParĆŖnquima de ecotextura grosseiramente heterogĆŖnea com mĆŗltiplos nĆ³dulos sĆ³lidos, esparsos, hipoecogĆŖnicos, de contornos parcialmente obscurecidos, medindo atĆ© cm no segmento *.
Lobo esquerdo mede: mm (normal < 10,0 cm) e o lobo direito: cm (normal < 15,0 cm).
Hepatopatia parenquimatosa crĆ“nica com mĆŗltiplos nĆ³dulos hepĆ”ticos.
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
HEPATOPATIA: FIBROSE PERI-PORTAL (ESQUISTOSSOMOSE)
FĆgado de contornos discretamente lobulados e dimensƵes reduzidas, associado a espessamento dos ligamentos hepĆ”ticos e parĆŖnquima com aumento da refringĆŖncia peri-portal difusamente.
Hepatopatia parenquimatosa com sinais sugestivos de fibrose peri-portal. Considerar possibilidade de esquistossomose.
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
INTUSSUSCEPĆĆO
Estudo ultrassonogrĆ”fico complementar com sonda linear de 10MHz, dirigido em regiĆ£o abdominal, evidenciou:
Imagem em aspecto de "casca de cebola", na topografia de flanco direito e mesogĆ”strio, medindo * cm transversal x cerca de * cm longitudinal, com camada externa de * cm de espessura, sem lĆquido livre no interior da invaginaĆ§Ć£o.
Imagem de ālesĆ£o em alvoā, correspondendo Ć cabeƧa da invaginaĆ§Ć£o no mesogĆ”strio, sem caracterizaĆ§Ć£o de causa secundĆ”ria pelo mĆ©todo ecogrĆ”fico.
Sinais sugestivos de intussuscepĆ§Ć£o intestinal.
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
INTUSSUSCEPĆĆO AUSENTE
Estudo ultrassonogrĆ”fico complementar com sonda linear de 10MHz, dirigido em regiĆ£o abdominal, nĆ£o evidenciou imagem sugestiva de intussuscepĆ§Ć£o intestinal. Ecogenicidade da gordura mesentĆ©rica e peristalse de alƧas intestinais preservados.
KLATSKIN
Vias biliares intra-hepĆ”ticas dilatadas, medindo atĆ© * cm Ć esquerda e * cm Ć direita (normal < 0,25 cm) com aparente amputaĆ§Ć£o ao nĆvel da placa hilar.
DilataĆ§Ć£o das vias biliares intra-hepĆ”ticas com sinais de amputaĆ§Ć£o ao nĆvel da placa hilar. Considerar possibilidade de
Tumor de Klatskin. Conveniente prosseguir investigaĆ§Ć£o diagnĆ³stica.
LINFONODOMEGALIA
Linfonodomegalias * de contornos bem definidos, hiperecogĆŖnicas, com perda da relaĆ§Ć£o cĆ³rtico-hilar, algumas confluentes, sem sinais de degeneraĆ§Ć£o cĆstico-necrĆ³tica, medindo atĆ© * cm na cadeia *.
Linfonodomegalias . Considerar possibilidade de doenƧa linfoproliferativa.
OU
Linfonodomegalias * de contornos bem definidos, hiperecogĆŖnicas, com perda da relaĆ§Ć£o cĆ³rtico-hilar, algumas confluentes e com Ć”reas cĆsticas, sugerindo degeneraĆ§Ć£o cĆstico-necrĆ³tica, caracterizadas assim:
Retroperitoniais:
prƩ-vertebrais, medindo atƩ cm;
justaesofƔgicas, medindo atƩ cm;
cadeia hepƔtica, medindo atƩ cm;
cadeias frĆŖnicas superior e inferior, medindo atĆ© cm;
cadeias pancreƔticas, medindo atƩ cm;
cadeias pancreaticoduodenais, medindo atƩ cm;
cadeias celĆacas e superiores centrais, medindo atĆ© cm;
prƩ-cavais, cavais laterais e retrocavais, medindo atƩ cm;
cadeia lombar intermƩdia (inter aorto-caval), medindo atƩ cm;
prĆ©-aĆ³rticas, aĆ³rticas laterais e retroaĆ³rticas, medindo atĆ© cm;
cadeias ilĆacas comuns Ć , medindo atĆ© cm;
cadeias ilĆacas externas, interilĆacos e ilĆacos internos Ć , medindo atĆ© cm;
cadeias sigmoideas, retais superiores e pararetais, medindo atƩ cm;
cadeias sacrais, glĆŗteos superiores e inferiores, medindo atĆ© cm;
Intraperitoniais:
cadeias gƔstricas, medindo atƩ cm;
cadeia esplĆŖnica, medindo atĆ© cm;
cadeias paracĆ³licas, medindo atĆ© cm;
cadeia pilĆ³rica, medindo atĆ© cm;
justaintestinais, medindo atƩ cm;
cadeia cĆstica (peri-vesicular), medindo atĆ© cm;
cadeias mesentƩricas superiores, medindo atƩ cm;
cadeias mesentƩricas inferiores, medindo atƩ cm;
cadeias Ćleo-cĆ³licas, prĆ© e retrocecais, medindo atĆ© cm;
cadeias vesicais laterais e retrovesicais, medindo atƩ cm;
cadeias epigƔstricas inferiores, medindo atƩ cm;
cadeias obturatĆ³rias, medindo atĆ© cm;
RegiƵes inguinais:
cadeias inguinais superficiais Ć , medindo atĆ© cm;
cadeias inguinais profundas Ć , medindo atĆ© cm;
Linfonodomegalias .
LĆQUIDO INTRACAVITĆRIO
Acentuada quantidade de lĆquido livre intraperitoneal com aspecto anecĆ³ide homogĆŖneo, se estendendo do fundo de saco posterior e goteiras parietocĆ³licas atĆ© espaƧos hepato e espleno-renais.
Ascite acentuada.
OU
Moderada quantidade de lĆquido livre intraperitoneal com aspecto anecĆ³ide com leves/moderados debris, se estendendo do fundo de saco posterior e goteiras parietocĆ³licas atĆ© espaƧos hepato e espleno-renais.
Moderada quantidade de lĆquido intracavitĆ”rio, sugerindo sangue/coĆ”gulos.
MASSA HEPĆTICA
FĆgado de contorno inferior lobulado e dimensƵes aumentadas Ć s custas de volumosa massa, sĆ³lida, de contornos irregulares, conteĆŗdo heterogĆŖneo, com calcificaƧƵes de permeio, fluxo central e perifĆ©rico ao Doppler, ocupando os segmentos hepĆ”ticos (SegmentaĆ§Ć£o de Couinaund), medindo cerca de * cm.
Placa hilar e ramos portais principais poupados.
Massa hepƔtica. Conveniente complementar com TC.
NĆDULOS HEPĆTICOS SECUNDĆRIOS
ParĆŖnquima hepĆ”tico homogĆŖneo, com ecotextura e ecogenicidade normais, exceto por imagens nodulares, sĆ³lidas, de contornos bem definidos e lobulados, conteĆŗdo hipoecogĆŖnico, com halo, caracteriazados assim:
segmento , medindo * cm.
segmento , medindo * cm.
NĆ³dulos hepĆ”ticos. Considerar possibilidade de acometimento secundĆ”rio.
PANCREATITE AGUDA
PĆ¢ncreas de contornos irregulares e parcialmente obscurecidos, com hipoecogenicidade textural e dimensƵes aumentadas, medindo a cabeƧa: * cm (normal < 3,3 cm), corpo: * cm (normal < 2,2 cm) e cauda: * cm (normal < 2,8 cm).
Ducto de Wirsung de calibre preservado.
AlteraĆ§Ć£o parenquimatosa pancreĆ”tica. Considerar possibilidade de pancreatite aguda.
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
PANCREATOPATIA CRĆNICA
PĆ¢ncreas de dimensƵes normais, com contornos lobulados e focos irregulares de calcificaƧƵes, medindo atĆ© cm, nas regiƵes da cabeƧa e corpo.
Ducto de Wirsung ectasiado, com calibre de cm de aspecto levemente tortuoso.
Sinais de pancreatopatia crƓnica.
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
PĆNCREAS GORDUROSO
PĆ¢ncreas de morfologia, contornos e dimensƵes normais, com aumento difuso de sua ecogenicidade.
Sinais de infiltraĆ§Ć£o gordurosa pancreĆ”tica.
PĆLIPO VESICULAR
VesĆcula biliar normodistendida e com paredes finas apresentando na regiĆ£o fĆŗndica imagem nodular, sĆ³lida, hiperecogĆŖnica, de aspecto polipĆ³ide, com contornos regulares, imĆ³vel Ć mudanƧa de decĆŗbito, medindo cm.
ConteĆŗdo vesicular anecĆ³ide e nĆ£o apresentando cĆ”lculos.
Imagem nodular intra-vesicular. Considerar possibilidade de pĆ³lipo vesicular.
TROMBOSE PORTAL
Veia porta de calibre aumentado, medindo cm (normal < 1,3 cm), apresentando em seu interior material hipoecogĆŖnico, aderido Ć parede do vaso, no segmento extra-hepĆ”tico se estendendo atĆ© o segmento portal intra-hepĆ”tico direito/esquerdo. AusĆŖncia de fluxo vascular ao Doppler.
Veia esplĆŖnica pĆ©rvia, de calibre aumentado, medindo cm (normal < 0,9 cm).
ArtĆ©ria hepĆ”tica pĆ©rvia, de paredes finas e lisas, com calibre aumentado, medindo cm ao nĆvel do hilo hepĆ”tico. Fluxo de direĆ§Ć£o aorto-hepĆ”tico com velocidade aumentada de cm/s (normal: 30-60 cm/s).
Sinais de trombose portal.
TUMOR DE CABEĆA DE PĆNCREAS
Vias biliares intra-hepĆ”ticas de calibres aumentados, medindo * cm Ć esquerda e * cm Ć direita (normal < 0,25 cm). HepatocolĆ©doco de calibre aumentado, medindo * cm, apresentando a cerca de cm da porta hepatis, afilamento gradual, em "bico de pĆ”ssaro", Ć medida que se relaciona com uma massa sĆ³lida na topografia da cabeƧa pancreĆ”tica, de contornos regulares e parcialmente obscurecidos, heterogĆŖnea, com fluxo perifĆ©rico ao Doppler, medindo cm.
Cauda pancreĆ”tica nĆ£o visibilizada devido Ć sobreposiĆ§Ć£o gasosa.
Ducto de Wirsung de calibre aumentado, medindo * cm (normal < 0,2 cm).
VesĆcula biliar hiperdistendida, medindo cm, com paredes finas e lisas.
ConteĆŗdo vesicular anecĆ³ide e nĆ£o apresentando cĆ”lculos. Sinal de Murphy ultrassonogrĆ”fico negativo.
Massa sĆ³lida na topografia da cabeƧa pancreĆ”tica com dilataĆ§Ć£o de vias biliares intra e extra-hepĆ”ticas.
BOLSA ESCROTAL
CISTO EPIDIDIMĆRIO
Nota-se na cabeƧa epididimĆ”ria imagem cĆstica, de paredes finas e regulares, conteĆŗdo anecĆ³ide, homogĆŖneo, medindo cm.
Demais estruturas anexas intra-escrotais preservadas.
Cisto epididimĆ”rio Ć *.
CISTOS EPIDIDIMĆRIOS
Nota-se na cabeƧa epididimĆ”ria * imagens cĆsticas, de paredes finas e regulares, conteĆŗdo anecĆ³ide, homogĆŖneo, medindo atĆ© cm.
Demais estruturas anexas intra-escrotais preservadas.
Cistos epididimĆ”rios Ć *.
CISTO TESTICULAR
Nota-se imagem cĆstica no testĆculo, central/perifĆ©rica, de paredes finas e regulares, conteĆŗdo anecĆ³ide, homogĆŖneo, medindo cm.
Cisto testicular Ć *.
CISTOS TESTICULARES
Notam-se imagens cĆsticas de paredes finas e regulares, conteĆŗdo anecĆ³ide, homogĆŖneo, distribuĆdas esparsamente no testĆculo, medindo atĆ© cm.
Cistos testiculares Ć *.
ECTASIA DA RETE TESTIS
ParĆŖnquima testicular apresentando na regiĆ£o do mediastino, imagens tubulares anecĆ³ides, sem fluxo ao Doppler, com calibre de atĆ© cm.
Sinais de ectasia da rete testis Ć direita/esquerda.
MASSA TESTICULAR
Nota-se volumosa massa complexa, sĆ³lida, ocupando a hemibolsa testicular, de contornos lobulados, predominantemente hiperecogĆŖnica, com calcificaƧƵes, fluxo central e perifĆ©rico ao Doppler, medindo cerca de * cm (vol: * cmĀ³).
Massa testicular Ć .
MICROLITĆASE
ParĆŖnquima testicular com ecotextura homogĆŖnea exceto por pequenos focos hiperecogĆŖnicos, desprovidos de sombra acĆŗstica posterior, distribuĆdos esparsamente, medindo de 0,1 a 0,3 cm, sendo > 5 focos/campo de imagem, sugerindo microlitĆase.
MicrolitĆase testicular bilateral/Ć direita/Ć esquerda.
NĆDULO TESTICULAR
ParĆŖnquima testicular com ecotextura homogĆŖnea apresentando imagem nodular, sĆ³lida, central, de contornos bem definidos e regulares, conteĆŗdo hipoecogĆŖnico, homogĆŖneo, com fluxo ao Doppler, medindo * cm.
NĆ³dulo testicular Ć .
NĆDULOS TESTICULARES
ParĆŖnquima testicular com ecotextura homogĆŖnea apresentando imagem nodular, sĆ³lida, centrais, de contornos parcialmente definidos, conteĆŗdo hipoecogĆŖnico, com fluxo ao Doppler, medindo * cm.
NĆ³dulos testiculares Ć .
HEMATOMA BOLSA ESCROTAL
Imagem heterogĆŖnea, de contornos irregulares, sem fluxo ao Doppler, com septaƧƵes e moderados debris de permeio, sugerindo cĆstico espesso, adjacente ao testĆculo, medindo cerca de cm (vol: cmĀ³).
Moderada quantidade de lĆquido livre de aspecto anecĆ³ide e leves debris de permeio.
Imagem heterogĆŖnea adjacente ao testĆculo *. Considerar possibilidade de hematoma.
LĆquido livre na hemi-bolsa *, compatĆvel com hematocele.
ORQUI/EPIDIDIMITE
Pele e tecido celular subcutĆ¢neo de espessura aumentada, medindo cm (normal < 0,6 cm).
EpidĆdimo de dimensƵes aumentadas, medindo cerca de: cm (vol = cmĀ³), com ecotextura difusamente heterogĆŖnea e fluxo vascular aumentado ao Doppler.
TestĆculo direito/esquerdo em topografia, morfologia e contornos normais, apresentando-se de dimensƵes aumentadas com ecotextura heterogĆŖnea e fluxo vascular aumentado ao Doppler.
Sinais sugestivos de orquiepididimite Ć direita/esquerda.
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
TORĆĆO TESTICULAR
Pele e tecido celular subcutĆ¢neo de espessura aumentada, medindo cm (normal < 0,6 cm). Moderada quantidade de lĆquido livre de aspecto anecĆ³ide e homogĆŖneo.
Nota-se imagem heterogĆŖnea, predominantemente hiperecogĆŖnica, adjacente ao pĆ³lo superior do testĆculo, de contornos irregulares, sem fluxo ao Doppler, medindo cerca de mm (vol: cmĀ³), sugestiva de funĆculo espermĆ”tico torcido e edemaciado.
TestĆculo rodado, de dimensƵes aumentadas, com parĆŖquima heterogĆŖneo, mais hipoecogĆŖnico comparado ao contra-lateral, com Ć”reas nodulares hipoecogĆŖnicas de permeio, podendo corresponder a focos de necrose. AusĆŖncia de fluxo vascular no testĆculo ao Doppler.
DimensƵes testiculares: cm (vol: cmĀ³).
Sinais de torĆ§Ć£o testicular Ć direita/esquerda.
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
VARICOCELE
DilataĆ§Ć£o e discreta tortuosidade de vasos do plexo pampiniforme, medindo em repouso cm (normal < 0,2-0,3 cm) e acentuando-se com as manobras de Valsalva, alcanƧando calibre de cm, associado a refluxo valvar.
Sinais de varicocele Ć .
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
RENAL E VIAS URINĆRIAS
AGENESIA RENAL
Rim direito/esquerdo nĆ£o visibilizado em lojas renais e pelve.
Rim direito/esquerdo nĆ£o visibilizado: agenesia renal?
ANGIOMIOLIPOMA
Nota-se no terƧo superior/mĆ©dio/inferior renal Ć direita/esquerda, imagem nodular sĆ³lida, cortical, de contornos regulares, conteĆŗdo hiperecogĆŖnico, homogĆŖneo, sem alterar a arquitetura vascular e pielocalicial, ausĆŖncia de fluxo ao Doppler, medindo cm.
NĆ³dulo renal sugestivo de angiomiolipoma.
COLEĆĆO PERI-ENXERTO RENAL
Nota-se coleĆ§Ć£o cĆstica, anecĆ³ide, na fossa ilĆaca direita/esquerda, adjacente ao enxerto renal, medindo cm (L x AP x T), com volume estimado em cmĀ³, distando cm da pele, passĆvel de punĆ§Ć£o/drenagem.
ColeĆ§Ć£o peri-enxerto renal. Considerar possibilidade de linfocele/hematoma.
Nota: Exame realizado em carĆ”ter de urgĆŖncia no Āŗ pĆ³s-transplante renal.
CISTO
Imagem cĆstica, de paredes finas e regulares, conteĆŗdo anecĆ³ide, homogĆŖneo, no terƧo * Ć direita/esquerda, cortical, medindo cm.
Cisto renal simples Ć direita/esquerda.
OU
Cisto renal, cortical, no terƧo * Ć direita/esquerda, medindo cm.
Cisto renal simples Ć direita/esquerda.
CISTOS
Imagens cĆsticas, de paredes finas e regulares, conteĆŗdo anecĆ³ide, homogĆŖneo, predominantemente corticais, medindo atĆ© cm no terƧo superior/mĆ©dio/inferior Ć direita/esquerda.
Cistos renais simples Ć direita/esquerda.
OU
Imagens cĆsticas, de paredes finas e regulares, conteĆŗdo anecĆ³ide homogĆŖneo, caracterizadas assim:
TerƧo superior, cortical, medindo cm;
TerƧo mƩdio, cortical, medindo cm;
TerƧo inferior, cortical, medindo cm;
Cistos renais simples Ć .
CISTOS (POLICĆSTICOS)
MĆŗltiplas imagens cĆsticas, distribuĆdas por todo o parĆŖnquima renal Ć direita/esquerda, predominantemente corticais, de paredes finas e regulares, conteĆŗdo anecĆ³ide homogĆŖneo, medindo atĆ© cm no terƧo superior/mĆ©dio/inferior.
MĆŗltiplos cistos renais bilaterais. Considerar possibilidade de doenƧa policĆstica do adulto.
CĆLCULO āBODERLINEā
Pequena imagem nodular, hiperecogĆŖnica, desprovida de sombra acĆŗstica posterior, no grupo calicinal mĆ©dio Ć direita/esquerda, medindo cm.
Pequena imagem hiperecogĆŖnica no rim direito/esquerdo: cĆ”lculo?
CĆLCULO
Imagem nodular, hiperecogĆŖnica, provida de sombra acĆŗstica posterior, no grupo calicinal mĆ©dio Ć direita/esquerda, medindo cm.
NefrolitĆase, nĆ£o obstrutiva, Ć direita/esquerda.
CĆLCULOS
Algumas imagens nodulares, hiperecogĆŖnicas, providas de sombra acĆŗstica posterior, medindo atĆ© cm no grupo calicinal mĆ©dio Ć direita/esquerda.
NefrolitĆase, nĆ£o obstrutiva, Ć direita/esquerda/bilateral.
OU
Imagens nodulares, hiperecogĆŖnicas, providas de sombra acĆŗstica posterior, nos grupos calicinais:
superior, medindo cm.
mƩdio, medindo cm.
inferior, medindo cm.
NefrolitĆase, nĆ£o obstrutiva, Ć direita/esquerda/bilateral.
CĆLCULO URETERAL JUP
Nota-se imagem nodular, hiperecogĆŖnica, provida de sombra acĆŗstica posterior, no ureter proximal Ć direita/esquerda, a cerca de cm da junĆ§Ć£o uretero-piĆ©lica (JUP), medindo cm, correspondendo cĆ”lculo impactado, promovendo leve/moderada dilataĆ§Ć£o do sistema coletor Ć montante.
UreterolitĆase promovendo uretero-hidronefrose leve/moderada Ć direita/esquerda.
CĆLCULO URETERAL JUV
Nota-se imagem nodular, hiperecogĆŖnica, medindo cm, no ureter distal, a cerca de cm da junĆ§Ć£o uretero-vesical (JUV) direita/esquerda, correspondendo a cĆ”lculo impactado, promovendo discreta/moderada dilataĆ§Ć£o do sistema coletor Ć montante.
UreterolitĆase promovendo uretero-hidronefrose discreta/moderada Ć direita/esquerda.
OU
Discreta dilataĆ§Ć£o do sistema pielocalicial e dos 2/3 proximais do ureter direito/esquerdo, de calibre alcanƧando cm.
Ureter distal nĆ£o visibilizado devido Ć sobreposiĆ§Ć£o gasosa.
Uretero-hidronefrose discreta Ć direita/esquerda: ureterolitĆase?
OU
MĆnimo aumento da pelve renal com grupos calicinais de aspecto preservado.
Nota-se pequeno borramento hiperecogĆŖnico, mal definido, medindo cm, no ureter distal, a cerca de cm da junĆ§Ć£o uretero-vesical (JUV) direita: cĆ”lculo impactado? Ć critĆ©rio clĆnico, complementar com TC.
DUPLICIDADE PIELOCALICIAL
Sistema pielocalicial compacto Ć direita/esquerda, apresentado grupo calicinal superior separado dos grupos mĆ©dio/inferior, sugerindo duplicidade pielocalicial (variante anatĆ“mica).
DUPLO J
Extremidade do cateter de duplo J no sistema pielocalicial. Extremidade do cateter de duplo J no interior vesical.
Cateter de duplo J normoposicionado Ć direita/esquerda.
ESPESSAMENTO VESICAL: BEXIGA DE ESFORĆO
Bexiga urinĆ”ria normodistendida, com conteĆŗdo anecĆ³ide e parede irregular com espessura difusamente aumentada, medindo atĆ© cm (normal < 0,5 cm). Observa-se ainda imagens compatĆveis com pseudo-divertĆculos vesicais, medindo atĆ© cm na parede Ć¢ntero-superior.
Espessamento parietal vesical difuso com pseudo-divertĆculos. Considerar possibilidade de bexiga de esforƧo.
ESPESSAMENTO VESICAL: CISTITE
Bexiga urinĆ”ria normodistendida, com conteĆŗdo anecĆ³ide e parede de espessura difusamente aumentada, medindo atĆ© cm (normal < 0,5 cm).
Espessamento parietal vesical difuso. Considerar possibilidade de cistite.
Nota: NecessĆ”rio correlaĆ§Ć£o clĆnico-laboratorial.
ESTENOSE DE JUP
Moderada dilataĆ§Ć£o do sistema pielocalicial Ć direita/esquerda. NĆ£o visibilizado sinais de dilataĆ§Ć£o ureteral.
Hidronefrose moderada Ć direita/esquerda. Considerar possibilidade de estenose de JUP.
HEMATOMA VESICAL
Bexiga urinĆ”ria normodistendida, com conteĆŗdo anecĆ³ide e parede de espessura normal.
Nota-se no assoalho vesical imagem hipoecogĆŖnica irregular, sem fluxo ao Doppler, mĆ³vel Ć mudanƧa de decĆŗbito, medindo cm (volume estimado: cm3)
Imagem irregular no interior vesical sugerindo hematoma.
MASSA/PĆLIPO VESICAL
Nota-se na parede vesical Ć¢ntero-superior, massa sĆ³lida, endofĆtica, de contornos lobulados, com fluxo ao Doppler, imĆ³vel Ć mudanƧa de decĆŗbito, medindo cm (L x AP x T), com volume de cm3.
Massa sĆ³lida vesical.
OU
Nota-se na parede vesical Ć¢ntero-superior, imagem nodular, sĆ³lida, hiperecogĆŖnica, de aspecto polipĆ³ide, com contornos regulares e fluxo presente ao Doppler, imĆ³vel Ć mudanƧa de decĆŗbito, medindo cm.
Imagem nodular intra-vesical. Considerar possibilidade de pĆ³lipo vesical.
REFLUXO VĆSICO-URETERAL
Moderada dilataĆ§Ć£o dos sistemas pielocaliciais e de toda a extensĆ£o dos ureteres, medindo atĆ© cm de calibre Ć direita e cm Ć esquerda sem evidĆŖncia de fatores obstrutivos/compressivos.
JunƧƵes uretero-vesicais patentes durante toda a realizaĆ§Ć£o do exame.
Uretero-hidronefrose moderada bilateral. Considerar possibilidade de refluxo vƩsico-ureteral.
RIM PĆLVICO
Rim direito/esquerdo em topografia ectĆ³pica, na fossa ilĆaca direita/esquerda, de morfologia, contornos e ecotextura normais, com dimensƵes levemente reduzidas.
DimensƵes renais: cm. Espessura de parĆŖnquima: cm. Sistema pielocalicial compacto sem evidĆŖncia de cĆ”lculos.
Rim direito/esquerdo pƩlvico.
RINS EM FERRADURA
Rins de contornos e ecotextura normais com pelves renais dirigidas anteriormente, apresentando-se fundidos a partir de seus pĆ³los inferiores na topografia mediana, peri-umbilical, sugerindo "rins em ferradura".
Rim direito mede: cm. Espessura do parĆŖnquima: cm. Rim esquerdo mede: cm. Espessura do parĆŖnquima: cm.
RegiĆ£o renal na topografia mediana mede aproximadamente: mm (T x AP).
Sistema pielocalicial compacto. NĆ£o visibilizado imagens compatĆveis com cĆ”lculos no sistema pielocalicial.
Sinais compatĆveis com āRins em ferraduraā.
MASSA PĆLVICA COM URETERO-HIDRONEFROSE
Massa sĆ³lida na escavaĆ§Ć£o pĆ©lvica, superiormente Ć bexiga, de contornos lobulados, heterogĆŖnea, com focos de calcificaĆ§Ć£o e fluxo perifĆ©rico ao Doppler, medindo cm (vol = cmĀ³).
Esta exerce compressĆ£o extrĆnseca nos ureteres distais promovendo discreta/moderada dilataĆ§Ć£o dos sistemas coletores Ć montante.
Massa pƩlvica promovendo uretero-hidronefrose discreta/moderada bilateral;
OU
Ureteres de calibres aumentados, medindo atĆ© cm Ć direita e cm Ć esquerda, apresentando afilamento gradual, em aspecto de "bico de pĆ”ssaro", Ć medida que se relaciona com uma massa sĆ³lida na escavaĆ§Ć£o pĆ©lvica, superiormente Ć bexiga, de contornos lobulados, heterogĆŖnea, com focos de calcificaĆ§Ć£o e fluxo perifĆ©rico ao Doppler, medindo cm (vol = cmĀ³).
Massa pƩlvica promovendo uretero-hidronefrose moderada bilateral.
NEFRECTOMIA TOTAL/PARCIAL
Rim direito/esquerdo nĆ£o caracterizado (status pĆ³s-operatĆ³rio).
Sinais de nefrectomia total Ć direita/esquerda.
OU
Rim direito/esquerdo em topografia e ecotextura habitual com dimensƵes reduzidas (status pĆ³s-operatĆ³rio).
Sistema pielocalicial compacto. AusĆŖncia de imagens compatĆveis com cĆ”lculos no sistema pielocalicial.
Sinais de nefrectomia parcial Ć direita/esquerda.
NEFROCALCINOSE
Nota-se hiperecogenicidade difusa, provida de sombra acĆŗstica posterior, na zona medular renal.
Sinais de nefrocalcinose medular.
NEFROPATIA AGUDA
Perda da relaĆ§Ć£o corticomedular com parĆŖnquima apresentando hiperrefringĆŖncia difusa e reduĆ§Ć£o da ecogenicidade das pirĆ¢mides.
Sinais de nefropatia parenquimatosa aguda.
OU
RelaĆ§Ć£o corticomedular preservada com parĆŖnquima apresentando hiperrefringĆŖncia difusa e reduĆ§Ć£o da ecogenicidade das pirĆ¢mides.
Sinais de nefropatia parenquimatosa aguda.
NEFROPATIA CRĆNICA
Rins de morfologia e topografia habituais, com dimensƵes reduzidas, contornos lobulados e perda da relaĆ§Ć£o corticomedular.
Rim direito mede: cm. Rim esquerdo mede: cm.
Sinais de nefropatia parenquimatosa crƓnica bilateral.
ENXERTO RENAL
Enxerto renal:
Localizado na fossa ilĆaca direita/esquerda de morfologia, contornos e topografia habituais. DimensƵes: cm (vol : cmĀ³). Espessura de parĆŖnquima: mm.
ParĆŖnquima com relaĆ§Ć£o corticomedular preservada.
Sistema pielocalicinal compacto. AusĆŖncia de imagens compatĆveis com cĆ”lculos. Fluxo vascular presente ao Doppler Colorido.
AusĆŖncia de sinais de coleƧƵes peri-enxerto.
Enxerto renal na fossa ilĆaca direita/esquerda.
PIELONEFRITE
DimensƵes: mm. Espessura de parĆŖnquima aumentada: mm.
RelaĆ§Ć£o corticomedular preservada com parĆŖnquima apresentando leve hiperrefringĆŖncia difusa e reduĆ§Ć£o da ecogenicidade das pirĆ¢mides.
Sistema pielocalicial compacto.
Sinais de nefropatia parenquimatosa aguda Ć direita/esquerda. Considerar possibilidade de pielonefrite.
OU
RelaĆ§Ć£o corticomedular preservada com parĆŖnquima apresentando Ć”rea hiperecogĆŖnica irregular, mal definida, no terƧo superior, medindo cerca de cm.
AlteraĆ§Ć£o parenquimatosa focal no rim direito/esquerdo: pielonefrite?
PRĆSTATA HETEROGĆNEA
ParĆŖnquima prostĆ”tico com ecotextura heterogĆŖnea apresentando focos de calcificaĆ§Ć£o na zona central.
RESĆDUO VESICAL AUMENTADO
Volume vesical prƩ-miccional: mL.
Volume vesical pĆ³s-miccional pequeno/moderado/acentuado: mL.
ResĆduo vesical pequeno/moderado/acentuado.
RecomendaĆ§Ć£o: 0-30mL: DesprezĆvel; 30-80mL: Pequeno; 80-150mL: Moderado; 150-300mL; Acentuado; >300mL: Muito acentuado.
SONDA VESICAL
BalĆ£o de sonda vesical normoposicionado.
TUMOR DE WILMS OU NEUROBLATOMA
Volumosa massa sĆ³lida, de limites imprecisos com o terƧo superior renal Ć direita, com aparente plano de clivagem com o fĆgado, rechaƧando-o superiormente e nĆ£o ultrapassando a linha mediana. Apresenta contornos lobulados e aspecto heterogĆŖneo, predominantemente hipoecogĆŖnico (componente sĆ³lido > 95 % da massa), com Ć”reas cĆsticas anecĆ³ides, sugerindo degeneraĆ§Ć£o cĆstico-necrĆ³tica, sem calcificaƧƵes evidentes e fluxo central e perifĆ©rico ao Doppler, medindo mm (vol = cmĀ³).
Massa sĆ³lida intracavitĆ”ria. Considerar possibilidades de Tumor de Wilms ou Neuroblastoma. Conveniente complementar com TC.
URINOMA
Nota-se coleĆ§Ć£o lĆquida anecĆ³ide anecogĆŖnica, adjacente Ć superfĆcie pĆ³stero-superior do rim , limitado pela fĆ”scia de
Gerota, medindo cm de espessura, sugerindo urinoma. Observa-se ainda borramento dos planos gordurosos peri-renal.
VĆLVULA DE URETRA POSTERIOR
Moderada dilataĆ§Ć£o dos sistemas pielocaliciais e de toda a extensĆ£o dos ureteres, medindo atĆ© cm de calibre Ć direita e cm Ć esquerda.
Uretero-hidronefrose moderada bilateral. Considerar possibilidade de vƔlvula de uretra posterior.
CERVICAL
CISTO TIREĆIDE
ParĆŖnquima com ecotextura homogĆŖnea, exceto por imagem cĆstica, de paredes finas e regulares, conteĆŗdo anecĆ³ide homogĆŖneo, no terƧo superior/mĆ©dio/inferior do lobo, medindo mm, distando mm da pele.
Cisto tireoidiano Ć direita/esquerda.
CISTOS TIREĆIDE
ParĆŖnquima com ecotextura homogĆŖnea, exceto por imagens cĆsticas, de paredes finas e regulares, conteĆŗdo anecĆ³ide homogĆŖneo, caracterizadas assim:
terƧo superior, medindo mm, distando mm da pele.
terƧo mƩdio, medindo mm, distando mm da pele.
terƧo inferior, medindo mm, distando mm da pele.
Cistos tireoidianos Ć direita/esquerda.
LINFONODO CERVICAL HABITUAL PALPĆVEL
Nota-se na alteraĆ§Ć£o palpĆ”vel, linfonodo na cadeia cervical *, de aspecto habitual, hipoecogĆŖnico com mediastino ecogĆŖnico, medindo cm.
LINFONODOMEGALIAS CERVICAIS
MĆŗltiplas adenomegalias, de contornos bem definidos, hiperecogĆŖnicas, com perda da diferenciaĆ§Ć£o cĆ³rtico-hilar, algumas de aspecto confluente e com Ć”reas cĆsticas de permeio, sugerindo degeneraĆ§Ć£o cĆstico-necrĆ³tica, caracterizadas assim:
Ć direita:
na cadeia cervical anterior, com diĆ¢metro mĆ©dio de mm e o maior medindo mm, distando mm da pele;
OU
MĆŗltiplos linfonodos aumentados, de morfologia preservada, hipoecogĆŖnicos, com mediastino ecogĆŖnico, sem sinais de degeneraĆ§Ć£o cĆstico-necrĆ³tica, nas cadeias:
Ć direita:
cervical anterior (VI/III), medindo atƩ cm.
cervical posterior (VA),medindo atƩ cm.
submandibular (IB/IIA), medindo atƩ cm.
submentoniana (IA), medindo atƩ cm.
prƩ-auricular (IIA), medindo atƩ cm.
retroauricular (IIB), medindo atƩ cm.
supra-clavicular (IV/VB), medindo atƩ cm.
Ć esquerda:
cervical anterior (VI/III), medindo atƩ cm.
cervical posterior (VA),medindo atƩ cm.
submandibular (IB/IIA), medindo atƩ cm.
Adenopatias cervicais.
Obs.: Considerar possibilidade de processo inflamatĆ³rio/infeccioso. Obs.: Conveniente seguir investigaĆ§Ć£o diagnĆ³stica.
NĆDULO TIREĆIDE
ParĆŖnquima com ecotextura homogĆŖnea, exceto por imagem nodular, sĆ³lida, de contornos regulares e bem definidos, conteĆŗdo hipoecogĆŖnico, homogĆŖneo, sem halo, sem calcificaƧƵes evidentes, fluxo somente perifĆ©rico ao Doppler (classificaĆ§Ć£o II de Chammas), no terƧo superior/mĆ©dio/inferior do lobo, medindo cm, distando cm da pele.
NĆ³dulo tireoidiano Ć .
Nota: ClassificaĆ§Ć£o de CHAMMAS: I ā AusĆŖncia de fluxo; II ā Fluxo perifĆ©rico; III ā Fluxo mais perifĆ©rico que central; IV ā Fluxo mais central que perifĆ©rico; V - Fluxo exclusivamente central.
NĆDULOS TIREĆIDE
ParĆŖnquima apresentando imagens nodulares, sĆ³lidas, de contornos regulares, conteĆŗdo hipoecogĆŖnico/heterogĆŖneo, sem halo ou calcificaƧƵes evidentes, caracterizadas assim:
terƧo superior, sem fluxo ao Doppler (classificaĆ§Ć£o I de ChammasĀ®), medindo cm, distando cm da pele.
terƧo mĆ©dio fluxo somente perifĆ©rico ao Doppler (classificaĆ§Ć£o II de ChammasĀ®), medindo cm, distando cm da pele.
terƧo inferior, fluxo predominantemente perifĆ©rico ao Doppler (classificaĆ§Ć£o III de ChammasĀ®), medindo cm, distando cm da pele.
NĆ³dulos tireoidianos Ć .
Nota: ClassificaĆ§Ć£o de CHAMMASĀ®: I ā AusĆŖncia de fluxo; II ā Fluxo perifĆ©rico; III ā Fluxo mais perifĆ©rico que central; IV ā Fluxo mais central que perifĆ©rico; V - Fluxo exclusivamente central.
PAROTIDITE
GlĆ¢ndulas submandibulares e parĆ³tida direita/esquerda sem alteraƧƵes ultrassonogrĆ”ficas. ParĆ³tida direita/esquerda mede cm (vol: cmĀ³).
GlĆ¢ndula parĆ³tida direita/esquerda de dimensƵes aumentadas com ecotextura difusamente heterogĆŖnea e alguns linfonodos reativos intra-parotĆdeos, medindo atĆ© cm. Observa-se ainda fluxo vascular aumentado da glĆ¢ndula ao Doppler. ParĆ³tida direita/esquerda mede cm (vol: cmĀ³).
Sinais de parotidite Ć direita/esquerda.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
TIREĆIDE HETEROGĆNEA
ParĆŖnquima com ecotextura difusamente heterogĆŖnea sem evidĆŖncia de nĆ³dulos ou cistos. Nota-se fluxo vascular difusamente aumentado ao Doppler.
TireĆ³ide de dimensƵes aumentadas e difusamente heterogĆŖnea. Considerar possibilidade de tireoidopatia de Graves.
OU
ParĆŖnquima com ecotextura levemente heterogĆŖnea sem evidĆŖncia de nĆ³dulos ou cistos. Nota-se fluxo vascular aumentado ao Doppler.
TireĆ³ide de dimensƵes reduzidas e difusamente heterogĆŖnea. Considerar possibilidade de tireoidopatia de Hashimoto.
Nota: Volume normal da tireĆ³ide: ā: 12 + 4 cmĀ³ /// ā: 8 + 4 cmĀ³.
Obs: ClassificaĆ§Ć£o de LAGALLA: I ā AusĆŖncia de fluxo no nĆ³dulo; II ā Fluxo perinodular; VPS ATI: 15-40 cm/s; VPD 5-20 cm/s. III - Fluxo peri e intra-nodular; VPS ATI > 40 cm/s; VPD > 15 cm/s. IV ā Inferno tireoidiano (Graves: Aumento da vascularizaĆ§Ć£o; VPS ATI: 50 a 150 cm/s; IR = 0,79 / IP = 1,36 ////// Hashimoto: Aumento da vascularizaĆ§Ć£o; VPS ATI: normal). ATI: ArtĆ©ria tireoidiana inferior.
TIREOIDECTOMIA TOTAL
GlĆ¢ndula tireĆ³ide nĆ£o caracterizada (status pĆ³s-operatĆ³rio).
Sinais de tireoidectomia total.
TIREOIDECTOMIA PARCIAL
Lobo direito/esquerdo e istmo tireoidiano: NĆ£o caracterizados (status pĆ³s-operatĆ³rio).
Sinais de tireoidectomia parcial Ć direita/esquerda.
DERRAME PLEURAL
TĆRAX
Moderada quantidade de lĆquido na cavidade pleural Ć direita/esquerda com aspecto anecĆ³ide, sem debris ou septaƧƵes.
Derrame pleural moderado Ć direita/esquerda.
DERRAME PLEURAL COM ATELECTASIA
Moderada quantidade de lĆquido na cavidade pleural Ć direita/esquerda com aspecto anecĆ³ide, sem debris ou septaƧƵes, associado a atelectasia de segmentos basais do lobo inferior.
Derrame pleural moderado Ć direita/esquerda com atelectasia.
ABSCESSO
GINECOLOGIA E OBSTERĆCIA MAMA
Nota-se imagem cĆstica de contornos irregulares/lobulados, com traves e moderados debris de permeio, sugerindo cĆstico-espesso, Ć s horas, medindo cerca de cm (vol: cmĀ³), distando cm do mamilo e cm da pele.
Observa-se ainda aumento da espessura da pele e borramento dos planos adiposos adjacentes Ć lesĆ£o descrita, sugerindo processo inflamatĆ³rio/infeccioso.
Imagem cĆstico-espessa na mama *. Considerar possibilidade de abscesso mamĆ”rio.
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
ABSCESSOS
Notam-se imagens cĆsticas anecĆ³ides, com traves e moderados debris de permeio, sugerindo cĆstico-espesso, de contornos irregulares, distribuĆdos assim:
Ćs horas, medindo cerca de cm (vol: cmĀ³), distando cm do mamilo e cm da pele;
De horas Ć s horas, medindo cerca de cm (vol: cmĀ³), distando de cm a cm do mamilo e de cm a cm da pele;
Observa-se ainda aumento da espessura da pele e borramento dos planos adiposos adjacentes Ć s lesƵes descritas, sugerindo processo inflamatĆ³rio/infeccioso.
Imagens cĆstico-espessas na mama *. Considerar possibilidade de abscessos mamĆ”rios.
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
CISTO
Imagem cĆstica circunscrita, de paredes finas e regulares, conteĆŗdo anecĆ³ide homogĆŖneo, com reforƧo acĆŗstico posterior, Ć s horas, medindo cm, distando cm do mamilo e cm da pele.
Cisto mamĆ”rio Ć .
Categoria BI-RADS (Breast Imaging Reporting and Data System): 2.
RecomendaĆ§Ć£o: controle mamogrĆ”fico e ultrassonogrĆ”fico anual ou Ć critĆ©rio clĆnico.
CISTOS
Imagens cĆsticas circunscritas, esparsas pela mama, de paredes finas e regulares, conteĆŗdo anecĆ³ide homogĆŖneo, algumas com reforƧo acĆŗstico posterior, medindo atĆ© cm, Ć s horas, distando cm do mamilo e cm da pele;
Cistos mamĆ”rios Ć .
Categoria BI-RADS (Breast Imaging Reporting and Data System): 2.
RecomendaĆ§Ć£o: controle mamogrĆ”fico e ultrassonogrĆ”fico anual ou Ć critĆ©rio clĆnico.
OU
Imagens cĆsticas circunscritas, de paredes finas e regulares, conteĆŗdo anecĆ³ide homogĆŖneo, algumas com reforƧo acĆŗstico posterior, caracterizadas assim:
Ćs horas, medindo mm, distando mm do mamilo e mm da pele.
Ćs horas, medindo mm, distando mm do mamilo e mm da pele.
Cistos mamĆ”rios Ć .
Categoria BI-RADS (Breast Imaging Reporting and Data System): 2.
RecomendaĆ§Ć£o: controle mamogrĆ”fico e ultrassonogrĆ”fico anual ou Ć critĆ©rio clĆnico.
CISTO ESPESSO? NĆDULO?
Nota-se imagem arredondada, de contornos circunscritos e regulares, conteĆŗdo hipoecogĆŖnico (cĆstico espesso? nĆ³dulo?), homogĆŖneo, com eixo maior paralelo Ć pele, sem fluxo ao Doppler, Ć s horas, medindo cm, distando cm do mamilo e mm da pele.
Imagem nodular na mama *: Cisto espesso? NĆ³dulo?
ECTASIA/PROEMINĆNCIA DUCTAL
ProeminĆŖncia ductal retroareolar, com calibre de atĆ© 0,3 cm, de conteĆŗdo anecĆ³ide e homogĆŖneo. Ectasia ductal retroareolar, com calibre de atĆ© * cm (normal < 0,3 cm), de conteĆŗdo anecĆ³ide e homogĆŖneo.
Ectasia/ProeminĆŖncia ductal retroareolar bilateral.
Categoria BI-RADS (Breast Imaging Reporting and Data System): 2.
RecomendaĆ§Ć£o: controle mamogrĆ”fico e ultrassonogrĆ”fico anual ou Ć critĆ©rio clĆnico.
GINOCOMASTIA
Ćrea hiperecogĆŖnica na regiĆ£o retroareolar, medindo cerca de cm, correspondendo a parĆŖnquima mamĆ”rio.
Ginecomastia direita/esquerda/bilateral.
IMPLANTES MAMĆRIOS
Sinais de manipulaĆ§Ć£o cirĆŗrgica com implante mamĆ”rio de aspecto Ćntegro.
Implantes mamĆ”rios de aspecto Ćntegros.
LIPOMASTIA
Componente adiposo exuberante ocupando o plano do subcutĆ¢neo retroareolar.
Lipomastia direita/esquerda/bilateral.
NĆDULO BI-3
Imagem sĆ³lida, ovalada, hipoecogĆŖnica, discretamente heterogĆŖnea, com eixo maior paralelo Ć pele, sem fluxo ao Doppler, Ć s horas, medindo cm, distando cm do mamilo e cm da pele.
NĆ³dulo mamĆ”rio Ć , sugestivo de fibroadenoma.
Categoria BI-RADS (Breast Imaging Reporting and Data System): 3.
RecomendaĆ§Ć£o: controle ultrassonogrĆ”fico com 6 meses ou Ć critĆ©rio clĆnico.
NĆDULO BI-4
Na alteraĆ§Ć£o palpĆ”vel evidenciou-se:
Imagem sĆ³lida, arredondada, de contornos circunscritos e regulares, conteĆŗdo hipoecogĆŖnico, com eixo maior paralelo Ć pele, fluxo perifĆ©rico ao Doppler, Ć s horas, medindo cm, distando cm do mamilo e cm da pele.
NĆ³dulo mamĆ”rio Ć .
Categoria BI-RADS (Breast Imaging Reporting and Data System): 4.
RecomendaĆ§Ć£o: estudo histopatolĆ³gico Ć critĆ©rio clĆnico.
NĆDULOS BI-3
Imagens sĆ³lidas ovaladas, algumas lobuladas, conteĆŗdo hipoecogĆŖnico, discretamente heterogĆŖneo, com eixo maior paralelo Ć pele, fluxo ausente ao Doppler, medindo atĆ© cm, Ć s horas, distando cm do mamilo e cm da pele.
NĆ³dulos mamĆ”rios Ć , sugestivos de fibroadenomas.
Categoria BI-RADS (Breast Imaging Reporting and Data System): 3.
RecomendaĆ§Ć£o: controle ultrassonogrĆ”fico com 6 meses ou Ć critĆ©rio clĆnico.
OU
Imagens sĆ³lidas ovaladas, algumas lobuladas, conteĆŗdo hipoecogĆŖnico, discretamente heterogĆŖneo, com eixo maior paralelo Ć pele, fluxo ausente ao Doppler, caracterizadas assim:
Ćs horas, medindo cm, distando cm do mamilo e cm da pele.
Ćs horas, medindo cm, distando cm do mamilo e cm da pele.
NĆ³dulos mamĆ”rios Ć , sugestivos de fibroadenomas.
Categoria BI-RADS (Breast Imaging Reporting and Data System): 3.
RecomendaĆ§Ć£o: controle ultrassonogrĆ”fico com 6 meses ou Ć critĆ©rio clĆnico.
NĆDULO INVASIVO BI-5
Imagem nodular, sĆ³lida, hipoecogĆŖnica, provida de sombra acĆŗstica posterior, de contornos irregulares e parcialmente obscurecidos, eixo maior perpendicular Ć pele, fluxo perifĆ©rico e central ao Doppler, Ć s horas, medindo cerca de cm (radial x AP x anti-radial), distando cm do mamilo e cm da pele.
Nota-se ainda espessamento e retraĆ§Ć£o da pele adjacente Ć imagem descrita.
Linfonodomegalia axilar arredondada com perda da relaĆ§Ć£o cĆ³rtico-hilar, medindo cm.
Linfonodomegalia axilar Ć .
NĆ³dulo mamĆ”rio Ć .
Categoria BI-RADS (Breast Imaging Reporting and Data System): 5.
RecomendaĆ§Ć£o: estudo histopatolĆ³gico Ć critĆ©rio clĆnico.
MASSA COM LESĆES SATĆLITES MULTIFOCAIS
Massa sĆ³lida, hipoecogĆŖnica, provida de sombra acĆŗstica posterior, de contornos irregulares e parcialmente obscurecidos, eixo maior perpendicular Ć pele, fluxo perifĆ©rico e central ao Doppler, Ć s horas, medindo cerca de cm (radial x AP x anti-radial), distando cm do mamilo e cm da pele.
Notam-se ainda pelo menos * imagens nodulares sĆ³lidas, hipoecogĆŖnicas, prĆ³ximas Ć imagem descrita, distando entre si menos de 5,0 cm, circunscritas e regulares, fluxo perifĆ©rico ao Doppler, medindo em mĆ©dia cm, sugerindo lesƵes satĆ©lites.
Massa mamĆ”ria Ć com nĆ³dulos satĆ©lites adjacentes: lesĆ£o multifocal?
Categoria BI-RADS (Breast Imaging Reporting and Data System): 5;
RecomendaĆ§Ć£o: estudo histopatolĆ³gico Ć critĆ©rio clĆnico.
HEMATOMA RETROPLACENTĆRIO
OBSTĆTRICO
Nota-se imagem laminar, hipoecogĆŖnica, de contornos irregulares, sem fluxo ao Doppler, adjacente Ć regiĆ£o inferior da placenta, ocupando cerca de 15% da sua superfĆcie de inserĆ§Ć£o, medindo cm, se insinuando atravĆ©s do orifĆcio interno do colo uterino, podendo corresponder a hematoma.
Hematoma retroplacentƔrio/periplacentƔrio.
MORFOLĆGICO
http://www.perinatology.com/calculators/exbiometry.htm
TranslucĆŖncia nucal de espessura medindo cm (percentil 90 para a idade gestacional = cm).
Medida do Ć”trio do ventrĆculo lateral: cm (normal para idade entre 14-38 semanas: 7 a 8,2 mm) (Ventriculomegalia leve: 11 a 15 mm / grave > 15mm).
Osso nasal de comprimento medindo mm (normal: > 2,5 ā 5,0 mm) (percentil 5 para a idade gestacional = mm).
Cisterna magna com diĆ¢metro Ć¢ntero-posterior normal, medindo mm (normal para idade entre 15-36 semanas: 2 a 8 mm, MĆ”x: 10 mm) (percentil 5 para a idade gestacional = mm).
Rim direito/esquerdo com pelve renal levemente ectasiada, apresentando diĆ¢metro Ć¢ntero-posterior de mm, no percentil para a idade gestacional (Percentil 50 da idade gestacional = mm).
Leve ectasia pielocalicinal Ć direita/esquerda.
PLACENTA BAIXA
Placenta de inserĆ§Ć£o corporal anterior estendendo-se atĆ© o segmento Ćstmico do Ćŗtero, nĆ£o ocupando o orifĆcio interno do colo, distando cm deste (normal > 7,0 cm). Espessura placentĆ”ria normal e grau de maturidade.
OU
Placenta de inserĆ§Ć£o corporal anterior estendendo-se atĆ© o segmento inferior do Ćŗtero, ocupando parcialmente o orifĆcio interno do colo. Espessura placentĆ”ria normal e grau de maturidade.
Colo uterino medindo no eixo longitudinal cm (normal > 3,0-3,5 cm).
Nota: Sugiro Ć critĆ©rio clĆnico, controle ultrassonogrĆ”fico da localizaĆ§Ć£o placentĆ”ria apĆ³s idade gestacional de 24 semanas.
OLIGOĆMNIO
LĆquido amniĆ³tico em quantidade reduzida com ILA = cm (percentil 5 para a idade gestacional = cm).
OligoĆ¢mnio discreto.
POLIDRĆMNIO
LĆquido amniĆ³tico em quantidade aumentada com ILA = cm (percentil 95 para a idade gestacional = cm).
PolidrĆ¢mnio.
(< 5,0 cm: OligoĆ¢mnio; 5,0-8,0 cm: LA reduzido; 8,0-22,0 cm: LA normal; > 22,0 cm: LA aumentado; > 25,0 cm: PolidrĆ¢mnio)
ĆBITO FETAL
PĆ³lo cefĆ”lico de contornos irregulares, sugerindo superposiĆ§Ć£o de ossos do crĆ¢nio (Sinal de Spalding) com gases na circulaĆ§Ć£o fetal (Sinal de Robert), associado a hiperflexĆ£o da coluna vertebral (Sinal de Hartley) e sinal do halo craniano (Sinal de Devel).
Movimentos fetais e batimentos cardĆacos ausentes. LĆquido amniĆ³tico em quantidade reduzida (ILA = cm).
GestaĆ§Ć£o com feto Ćŗnico;
Ćbito fetal em idade gestacional de semanas e dias ( + semanas) pelo comprimento femoral.
OligoĆ¢mnio severo.
PESO
Peso estimado: gramas ( + gramas ) pelo Hadlock 1 (DBP + CC). gramas ( + gramas ) pelo Hadlock 4 (CC + CA). gramas ( + gramas ) pelo Hadlock 6 (CA + CF).
gramas ( + gramas ) pelo Hadlock 11 (DBP + CC + CA + CF). gramas ( + gramas ) pelo Hadlock 7 (DBP + CC + CA). gramas ( + gramas ) pelo Hadlock 8 (DBP + CC + CF). gramas ( + gramas ) pelo Hadlock 9 (DBP + CA + CF). gramas ( + gramas ) pelo Hadlock 3 (DBP + CF).
gramas ( + gramas ) pelo Hadlock 2 (DBP + CA). gramas ( + gramas ) pelo Hadlock 5 (CC + CF). gramas ( + gramas ) pelo Hadlock 10 (CC + CA + CF).
ABORTAMENTO EM CURSO
TRANSVAGINAL
Saco gestacional na cavidade uterina, deslocado, em topografia Ćstmica, de contornos irregulares, medindo cm (diĆ¢metro mĆ©dio cm).
Nota-se imagem laminar, hipoecogĆŖnica, de contornos irregulares, sem fluxo ao Doppler, adjacente Ć parede inferior do saco gestacional, ocupando cerca de 40% do mesmo, sugerindo hematoma retrocoriĆ“nico.
Imagem ecogĆŖnica no interior do saco gestacional, medindo cm no maior eixo, sugerindo eco embrionĆ”rio. Movimentos embrionĆ”rios e batimentos cardĆacos ausentes.
VesĆcula vitelina nĆ£o caracterizada.
Sinais de abortamento em curso.
CISTO OVARIANO
OvĆ”rio direito/esquerdo em topografia, morfologia, contornos e ecotextura normais, apresentando em seu interior imagem cĆstica, de paredes finas e regulares, conteĆŗdo anecĆ³ide homogĆŖneo, medindo cm.
Medidas ovarianas: cm (volume: cmĀ³).
Cisto ovariano Ć direita/esquerda.
Nota: Considerar possibilidade de torĆ§Ć£o ovariana. Correlacionar clĆnico-laboratorialmente.
CISTOS OVARIANOS
OvĆ”rio direito/esquerdo em topografia, morfologia, contornos e ecotextura normais, apresentando em seu interior imagens cĆstica, de paredes finas e regulares, conteĆŗdo anecĆ³ide homogĆŖneo, medindo atĆ© cm.
Medidas ovarianas: cm (volume: cmĀ³).
Cistos ovarianos Ć direita/esquerda.
CISTO FUNCIONAL OVARIANO
OvĆ”rio direito/esquerdo em topografia, morfologia, contornos e ecotextura normais, apresentando em seu interior imagem cĆstica anecĆ³ide, de paredes finas e lisas, medindo cm, sugerindo cisto funcional folicular.
Medidas ovarianas: cm (volume: cmĀ³).
CISTOS FUNCIONAIS OVARIANOS
OvĆ”rio direito/esquerdo em topografia, morfologia, contornos e ecotextura normais, apresentando algumas imagens cĆsticas anecĆ³ides, de paredes finas e lisas, medindo atĆ© cm, sugerindo cistos funcionais.
CISTO HEMORRĆGICO OVARIANO
OvĆ”rio direito/esquerdo em topografia habitual, com ecotextura heterogĆŖnea, Ć custa de imagem cĆstica, com paredes espessas, conteĆŗdo com moderados debris e traves de permeio, sem fluxo ao Doppler, medindo cm (vol = cmĀ³), sugerindo cisto hemorrĆ”gico.
Medidas ovarianas: cm (volume: cmĀ³).
Cisto ovariano hemorrĆ”gico Ć direita/esquerda.
CISTO(s) NABOTH
Cisto de Naboth de aspecto habitual no colo uterino, medindo cm. Cistos de Naboth de aspecto habitual no colo uterino, medindo atƩ cm.
CORPO LĆTEO
OvĆ”rio direito/esquerdo em topografia, morfologia, contornos e ecotextura normais, apresentando em seu interior imagem cĆstica, de paredes espessas e regulares, conteĆŗdo anecĆ³ide homogĆŖneo, medindo cm, sugerindo corpo lĆŗteo.
OU
Nota-se imagem sugestiva de corpo lĆŗteo, medindo cm.
DIU
Cavidade uterina virtual, apresentando em seu interior dispositivo intra-uterino (DIU) distando cm do cavidade fĆŗndica
(normal < 0,5 cm) e cm da serosa fĆŗndica (normal < 2,0 cm).
DIU normoposicionado.
DIU deslocado.
DIPA
Pequena quantidade de lĆquido livre no fundo de saco posterior e regiƵes anexiais com aspecto anecĆ³ide e homogĆŖneo. Observa-se ainda dor Ć mobilizaĆ§Ć£o do transdutor nas regiƵes anexiais.
Pequena quantidade de lĆquido livre na escavaĆ§Ć£o pĆ©lvica. Considerar possibilidade de doenƧa inflamatĆ³ria pĆ©lvica.
Obs.: Os achados depedem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
ECTĆPICA: MASSA
Nota-se imagem cĆstico-sĆ³lida, na regiĆ£o anexial direita/esquerda, de contornos lobulados, com septos espessos e moderados debris de permeio, sem fluxo ao Doppler, medindo cm (vol = cmĀ³).
Moderada quantidade de lĆquido livre de aspecto anecĆ³ide, com moderados debris e traves de permeio, se estendendo do fundo de saco posterior atĆ© espaƧos hepato e espleno-renais.
Moderada quantidade de lĆquido intraperitoneal, sugerindo sangue/coĆ”gulos.
Imagem heterogĆŖnea na regiĆ£o anexial Ć direita/esquerda. Considerar possibilidade de gestaĆ§Ć£o ectĆ³pica rota.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
ECTĆPICA ROTA OU CISTO HEMORRĆGICO: MASSA
Nota-se imagem cĆstico-sĆ³lida, na regiĆ£o anexial direita/esquerda, adjacente ao ovĆ”rio, impossibilitando a precisa distinĆ§Ć£o pelo mĆ©todo ecogrĆ”fico, contornos irregulares com septos espessos e moderados debris de permeio, sem fluxo ao Doppler, medindo cm (vol = cmĀ³).
Moderada quantidade de lĆquido livre de aspecto anecĆ³ide, com moderados debris e septaƧƵes finas de permeio, se estendendo do fundo de saco posterior atĆ© espaƧos hepato e espleno-renais.
Moderada quantidade de lĆquido intraperitoneal, sugerindo sangue/coĆ”gulos.
Imagem heterogĆŖnea anexial Ć direita/esquerda. Considerar possibilidade de gestaĆ§Ć£o ectĆ³pica rota ou cisto hemorrĆ”gico roto.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
ECTĆPICA: ANEL TUBĆRIO
Nota-se na regiĆ£o anexial direita/esquerda, imagem em āanel tubĆ”rioā, arredondada, de contornos bem definidos e regulares, medindo cm, com paredes espessas hipoecogĆŖnicas e fluxo ao Doppler ocupando cerca de 1/3 da circunferĆŖncia. Apresenta no interior imagem compatĆvel com saco gestacional ectĆ³pico, de contornos regulares e medindo cm (diĆ¢metro mĆ©dio cm), com vesĆcula vitelina presente, medindo cm. EmbriĆ£o nĆ£o caracterizado.
GestaĆ§Ć£o ectĆ³pica tubĆ”ria Ć direita/esquerda.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
ECTĆPICA: ANEL TUBĆRIO OU CISTO OVARIANO
Nota-se na regiĆ£o anexial direita/esquerda, imagem cĆstica, arredondada, adjacente ao ovĆ”rio, impossibilitando a precisa distinĆ§Ć£o pelo mĆ©todo ecogrĆ”fico, de contornos bem definidos e regulares, com paredes espessas, hipoecogĆŖnicas e fluxo ao Doppler ocupando cerca de 1/3 da circunferĆŖncia, medindo cm (vol: cmĀ³). Ćrea cĆstica central mede cm (diĆ¢metro mĆ©dio cm). AusĆŖncia de sinais de vesĆcula vitelina ou embriĆ£o.
Imagem cĆstica anexial Ć direita/esquerda. Considerar possibilidade de gestaĆ§Ć£o ectĆ³pica ou cisto ovariano/corpo lĆŗteo.
Nota: NecessĆ”rio clĆnico-laboratorial.
ECTĆPICA: BCF +
Nota-se na regiĆ£o anexial direita/esquerda, imagem arredondada, de contornos bem definidos e regulares, medindo cm, com paredes espessas hipoecogĆŖnicas e fluxo ao Doppler ocupando cerca de 1/3 da circunferĆŖncia. Apresenta no interior imagem compatĆvel com saco gestacional ectĆ³pico de contornos regulares e medindo cm (diĆ¢metro mĆ©dio cm), com vesĆcula vitelina presente, medindo cm e embriĆ£o Ćŗnico de comprimento cabeƧa-nĆ”dega (CCN) medindo cm. Movimentos embrionĆ”rios e batimentos cardĆacos presentes (BCF = bpm).
GestaĆ§Ć£o ectĆ³pica tubĆ”ria Ć direita/esquerda com embriĆ£o Ćŗnico e vivo.
Idade gestacional de semanas e dia(s) (+/- 5 dias) pelo CCN.
ENDOMETRIO: ESPESSAMENTO PĆS-MENOPAUSA
EndomĆ©trio hiperecogĆŖnico, homogĆŖneo, medindo cm de espessura.
Espessamento endometrial.
OU
EndomĆ©trio heterogĆŖneo, predominantemente hiperecogĆŖnico, medindo cm de espessura.
Espessamento endometrial.
Nota: Espessura endometrial: Fase menstrual: 0,1-0,5 cm; Fase proliferativa (folicular) precoce: 0,4-0,7 cm; Fase proliferativa tardia: 0,6-1,3 cm; Peri-ovulatĆ³ria: 0,7-1,5 cm; Fase secretora (lĆŗtea) precoce: 0,8-1,7 cm; Secretora tardia: 0,7-1,4 cm; PĆ³s-menopausa (sem TRH) < 0,5 cm; PĆ³s-menopausa (com TRH): 0,6-1,0 cm.
ENDOMETRIO: FASES
EndomĆ©trio homogĆŖneo, centrado e medindo cm de espessura (compatĆvel com fase pĆ³s-menopausa).
OU
EndomĆ©trio trilaminar, homogĆŖneo, centrado e medindo cm de espessura (compatĆvel com fase proliferativa).
OU
Cavidade uterina apresentando fina lĆ¢mina lĆquida, anecĆ³ide de cm de espessura com endomĆ©trio trilaminar, homogĆŖneo, centrado e medindo cm de espessura (compatĆvel com fase peri-ovulatĆ³ria).
OU
EndomĆ©trio hiperecogĆŖnico, homogĆŖneo, centrado e medindo cm de espessura (compatĆvel com fase secretora).
OU
EndomĆ©trio levemente heterogĆŖneo, predominantemente hiperecogĆŖnico e medindo cm de espessura (compatĆvel com fase menstrual).
Nota: Espessura endometrial: Fase menstrual: 1-5 mm; Fase proliferativa (folicular) precoce: 4-7 mm; Fase proliferativa tardia: 6-13 mm; Peri-ovulatĆ³ria: 7-15 mm; Fase secretora (lĆŗtea) precoce: 8-17 mm; Secretora tardia: 7-14 mm; PĆ³s-menopausa (sem TRH) < 5 mm; PĆ³s-menopausa (com TRH): 6-10 mm.
ENDOMETRIOSE/ADENOMIOSE
MiomĆ©trio com ecotextura difusamente heterogĆŖnea apresentando parede anterior ( cm) de espessura maior que a posterior ( cm).
MiomĆ©trio difusamente heterogĆŖneo. Considerar possibilidade de adenomiose.
OU
Nota-se entre a bexiga urinĆ”ria e a parede anterior do Ćŗtero, algumas imagens nodulares hipoecogĆŖnicas, homogĆŖneas, medindo atĆ© cm.
Imagens nodulares entre a bexiga e o Ćŗtero. Considerar possibilidade de focos de endometriose.
HEMATOMA RETROCORIĆNICO
Saco gestacional tĆ³pico, de contornos discretamante irregulares e medindo cm (diĆ¢metro mĆ©dio cm).
Nota-se imagem laminar, hipoecogĆŖnica, de contornos irregulares, sem fluxo ao Doppler, adjacente Ć parede inferior do saco gestacional, ocupando cerca de 40% do mesmo, sugerindo hematoma.
Hematoma retrocoriƓnico.
OU
Nota-se imagem heterogĆŖnea, predominantemente hiperecogĆŖnica, de contornos irregulares, adjacente Ć parede inferior do saco gestacional, ocupando cerca de 40% do mesmo, se insinuando pelo orifĆcio interno do colo uterino.
Hematoma retrocoriƓnico.
HISTERECTOMIA
Ćtero nĆ£o caracterizado (status pĆ³s-operatĆ³rio).
Sinais de histerectomia total.
OU
Ćtero caracterizado somente em sua porĆ§Ć£o do colo com forma, contornos e ecotextura habituais (status pĆ³s-operatĆ³rio).
Medidas do colo: cm (L x AP x T). Volume: cmĀ³.
Sinais de histerectomia parcial.
MALFORMAĆĆES MĆLLERIANAS
Nota-se na cavidade uterina aparente imagem de septo, hipoecogĆŖnica na regiĆ£o fĆŗndica, medindo cm longitudinal x cm de espessura.
Aparente septo na regiĆ£o fĆŗndica da cavidade uterina. Considerar possibilidade de Ćŗtero subseptado.
OU
Nota-se na cavidade uterina imagem sugestiva de septo, hipoecogĆŖnica, medindo cm longitudinal x cm de espessura.
Sinais sugestivos de Ćŗtero septado.
OU
Ćtero em anteversoflexĆ£o apresentando duas regiƵes cornuais, medindo Ć direita cm (L x AP x T) e Ć esquerda cm (L x AP x T). Volume uterino de cmĀ³.
Cavidades uterinas virtuais.
EndomĆ©trio homogĆŖneo, centrado e medindo cm de espessura Ć direita e cm Ć esquerda.
Colo uterino aparentemente Ćŗnico, medindo cm longitudinal.
Sinais sugestivos de Ćŗtero bicorno.
OU
Ćtero em anteversoflexĆ£o apresentando duas regiƵes cornuais, medindo Ć direita cm (L x AP x T) e Ć esquerda cm (L x AP x T). Volume uterino de cmĀ³.
Cavidades uterinas virtuais.
EndomĆ©trio homogĆŖneo, centrado e medindo cm de espessura Ć direita e cm Ć esquerda.
Colo uterino duplo, medindo cm longitudinal Ć direita e cm Ć esquerda.
Sinais sugestivos de Ćŗtero Didelfo.
MASSA OVARIANA
Nota-se volumosa massa sĆ³lida, na regiĆ£o anexial direita/esquerda, de contornos lobulados, com conteĆŗdo heterogĆŖneo, predominantemente hiperecogĆŖnico, sem calcificaƧƵes evidentes, medindo cerca de cm (volume estimado: cmĀ³).
Ao Doppler evidenciou-se vasos perifƩricos e centrais, com diƔstole positiva e sem incisura. IR= e IP= ..
Massa pĆ©lvica. Considerar possibilidade de neoplasia ovariana dentre as hipĆ³teses diagnĆ³sticas.
Nota: Fatores de risco: NOTA 1: Risco baixo; NOTA 2: Risco mƩdio; NOTA 3: Risco alto
CARACTERĆSTICAS/PONTUAĆĆO
1 PONTO
2 PONTOS
3 PONTOS
PerĆodo da vida
Fase secretora
Fase proliferativa
InfĆ¢ncia/menopausa
Morfologia tumoral
Cisto simples
Cisto denso/septo fino e liso
Septo grosseiro/cisto papilĆfero/tumor misto, complexo ou sĆ³lido
Power Doppler ou Colorido
Vasos nĆ£o evidentes
Vasos somente na cƔpsula
Vasos no interior
Doppler espectral
DiƔstole zero
DiƔstole positiva com incisura
DiƔstole positiva sem incisura
Ćndice de impedĆ¢ncia
Altos (RI > 0,75 / PI > 1,5)
MĆ©dios (RI 0,75 a 0,50 / PI 1,5 a 1,0)
Baixos (RI < 0,5 / PI < 1,5)
Falsos positivos: Cisto luteĆnico hemorrĆ”gico, massas inflamatĆ³rias, hidrossalpinge pseudofolicular, ectĆ³pica anĆ“mala, rim pĆ©lvico, cisto de retenĆ§Ć£o/mesotelial.
MICROPOLICĆTICO
OvĆ”rio direito/esquerdo em topografia e contornos normais, de aspecto globoso com mĆŗltiplas pequenas imagens cĆsticas, anecĆ³ides, predominantemente perifĆ©ricas, com diĆ¢metro mĆ©dio de 0,5 cm.
Medidas ovarianas: cm (volume: cmĀ³).
OvĆ”rios de aspecto micropolicĆstico.
MIOMA
MiomĆ©trio com ecotextura homogĆŖnea, exceto por imagem nodular, sĆ³lida, de contornos bem definidos e regulares, conteĆŗdo hipoecogĆŖnico/heterogĆŖneo, na parede anterior/intramural, medindo cm.
NĆ³dulo uterino sugestivo de mioma.
MIOMAS
MiomĆ©trio com ecotextura homogĆŖnea/heterogĆŖnea apresentando imagens nodulares, sĆ³lidas, hipoecogĆŖnicas/heterogĆŖneas, de contornos regulares, nas paredes:
anterior/intramural, medindo cm.
posterior/subseroso, medindo cm.
fĆŗndica, com componentes submucoso/subseroso, medindo cm.
NĆ³dulos uterinos sugestivos de miomas.
MOLA HIDATIFORME
Cavidade uterina preenchida por material heterogĆŖneo, predominantemente ecogĆŖnico com diminutas Ć”reas cĆsticas de permeio, de aspecto vesicular, sem sinais de componentes Ć³sseos, medindo cm de espessura e volume estimado de cmĀ³. Planos de clivagem aparentemente bem definidos entre a cavidade endometrial e a parede anterior do miomĆ©trio.
Parede posterior do miomĆ©trio de difĆcil avaliaĆ§Ć£o ecogrĆ”fica devido a atenuaĆ§Ć£o dos feixes sonoros posteriores.
Material heterogĆŖneo na cavidade uterina. Considerar possibilidade de doenƧa trofoblĆ”stica gestacional.
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
ĆBITO EMBRIONĆRIO (IG > 7 sem)
Saco gestacional tĆ³pico, de contornos irregulares e medindo cm (diĆ¢metro mĆ©dio cm), associado a reaĆ§Ć£o decidual heterogĆŖnea adjacente.
VesĆcula vitelĆnica nĆ£o visibilizada.
EmbriĆ£o Ćŗnico com comprimento cabeƧa-nĆ”dega (CCN) medindo cm.
Movimentos embrionĆ”rios e batimentos cardĆacos ausentes.
LĆquido amniĆ³tico em quantidade aumentada.
GestaĆ§Ć£o tĆ³pica, com embriĆ£o Ćŗnico.
Ćbito embrionĆ”rio em idade gestacional de semanas e dia(s) (+/- 1 semana) pelo CCN.
ĆBITO EMBRIONĆRIO BODERLINE (BCF nĆ£o detectado)
VesĆcula vitelĆnica nĆ£o visibilizada.
EmbriĆ£o Ćŗnico com comprimento cabeƧa-nĆ”dega (CCN) medindo cm.
Movimentos embrionĆ”rios e batimentos cardĆacos nĆ£o detectados.
GestaĆ§Ć£o tĆ³pica, com embriĆ£o Ćŗnico.
Idade gestacional de semanas e dia(s) (+/- 1 semana) pelo CCN.
Batimentos cardĆacos embrionĆ”rio nĆ£o detectados.
Nota: Conveniente, Ć critĆ©rio clĆnico, controle ultrassonogrĆ”fico apĆ³s 1-2 semanas.
ĆBITO EMBRIONĆRIO: GESTAĆĆO INVIĆVEL (embriĆ£o em degeneraĆ§Ć£o)
Saco gestacional tĆ³pico, de contornos regulares e medindo cm (diĆ¢metro mĆ©dio cm: compatĆvel com idade gestacional de semana e dias).
LĆquido amniĆ³tico em quantidade aumentada para a idade gestacional do possĆvel CCN. VesĆcula vitelĆnica nĆ£o caracterizada.
Imagem ecogĆŖnica no interior do saco gestacional, medindo cm no maior eixo, sugerindo eco embrionĆ”rio. Movimentos embrionĆ”rios e batimentos cardĆacos nĆ£o detectados.
GestaĆ§Ć£o tĆ³pica, com embriĆ£o Ćŗnico.
Idade gestacional de semanas e dia(s) (+/- 1 semana) pelo possĆvel CCN.
Batimentos cardĆacos embrionĆ”rio nĆ£o detectados. Considerar possibilidade de gestaĆ§Ć£o inviĆ”vel.
ĆBITO EMBRIONĆRIO: GESTAĆĆO INVIĆVEL (saco deslocado)
Saco gestacional deslocado na cavidade uterina, na topografia Ćstmica, de contornos lobulados e medindo cm (diĆ¢metro mĆ©dio cm).
VesĆcula vitelĆnica nĆ£o caracterizada.
Imagem ecogĆŖnica no interior do saco gestacional, medindo cm no maior eixo, sugerindo eco embrionĆ”rio em degeneraĆ§Ć£o.
Movimentos embrionĆ”rios e batimentos cardĆacos nĆ£o detectados.
GestaĆ§Ć£o inviĆ”vel.
Idade gestacional de semanas e dia(s) (+/- 1 semana) pelo possĆvel CCN.
PĆLIPO ENDOMETRIAL
Cavidade uterina apresentando imagem nodular hiperecogĆŖnica, de aspecto polipĆ³ide, em continuidade com a camada basal do endomĆ©trio na regiĆ£o fĆŗndica, medindo cm.
PĆ³lipo endometrial.
OU
Cavidade uterina apresentando imagem nodular, sĆ³lida, hiperecogĆŖnica, na regiĆ£o fĆŗndica, medindo cm.
EndomĆ©trio trilaminar, centrado e medindo cm de espessura (compatĆvel com a fase proliferativa).
Imagem nodular na cavidade uterina. Considerar possibilidade de pĆ³lipo endometrial.
PSEUDO-CAVIDADE UTERINA DA CICATRIZ CESARIANA
Nota-se na cicatriz cirĆŗrgica da regiĆ£o Ćstmica anterior uterina, Ć”rea anecĆ³ide, cĆstica, de contornos irregulares em comunicaĆ§Ć£o com a cavidade endometrial, medindo mm.
Pseudo-cavidade uterina da cicatriz cesariana.
Obs.: A pseudo-cavidade uterina pode ser causa de sangramento tipo spotting. Correlacionar clinicamente.
RESTOS OVULARES
Cavidade uterina ocupada por material heterogĆŖneo, predominantemente hiperecogĆŖnico, sem fluxo ao Doppler, medindo atĆ© cm de espessura, podendo corresponder Ć sangue, coĆ”gulos e/ou restos ovulares.
OU
Cavidade uterina preenchida por material heterogĆŖneo, sem fluxo ao estudo Doppler, medindo atĆ© cm de espessura.
Material heterogĆŖneo em moderada quantidade na cavidade uterina. Considerar possibilidade de restos ovulares/coĆ”gulos.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
(RecomendaĆ§Ć£o: MĆnima/normal: < 0,5 cm; Pequena: 0,5-1,0 cm; Moderada: 1,0-2,0 cm; Acentuada: > 2,0 cm).
VARIZES PĆLVICAS
Nota-se dilataĆ§Ć£o e tortuosidade de vasos pĆ©lvicos bilaterais, com calibre de atĆ© cm Ć direita e cm Ć esquerda.
Varizes pƩlvicas bilaterais.
BURSITE OLECRANIANA
MĆSCULO ESQUELĆTICO/PARTES SUPERFICIAIS COTOVELO
Bursa olecraniana apresentando-se distendida por lĆquido com aspecto anecĆ³ide e homogĆŖneo, medindo cerca de cm (L x AP x T), com volume estimado em cmĀ³.
Sinais de bursite olecraniana.
OU
Bursa olecraniana apresentando-se distendida por lĆquido com aspecto anecĆ³ide e debris em suspensĆ£o associado a espessamento das paredes (pannus) de atĆ© cm com aumento do fluxo vascular ao Doppler. A bursa mede cerca de cm (L x AP x T), com volume estimado em cmĀ³.
Sinais de bursite olecraniana. Considerar possibilidade de doenƧa reumatolĆ³gica.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
DERRAME ARTICULAR
Derrame articular leve/moderado/acentuado observado na face anterior em corte mediano, com aspecto anecĆ³ide e homogĆŖneo.
Sinais de derrame articular leve/moderado/acentuado.
OU
Derrame articular leve/moderado/acentuado observado no recesso posterior, com aspecto anecĆ³ide e homogĆŖneo.
Sinais de derrame articular leve/moderado/acentuado.
DERRAME DOENĆA REUMATOLĆGICA
Derrame articular leve/moderado/acentuado observado no recesso posterior de aspecto anecĆ³ide e leves debris em suspensĆ£o associado a proliferaĆ§Ć£o sinovial com fluxo vascular aumentado ao Doppler.
Sinais de derrame articular leve/moderado/acentuado com proliferaĆ§Ć£o sinovial. Considerar possibilidade de doenƧa reumatolĆ³gica.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
ESTIRAMENTO LIGAMENTO COLATERAL
Banda anterior do ligamento colateral medial apresentando hipoecogenicidade textural difusa.
Sinais de estiramento da banda anterior do ligamento colateral medial.
IRREGULARIDADE UMERAL
SuperfĆcies Ć³sseas epicondileanas de contornos irregulares.
Irregularidade Ć³ssea epicondileana.
NERVO ULNAR: COMPRESSĆO TRICIPTAL
Nervo ulnar observado na fossa cubital apresentando calibre preservado com Ć”rea transversal de mmĀ².
Ć manobra de contraĆ§Ć£o do mĆŗsculo trĆceps braquial observou-se aparente compressĆ£o extrĆnseca deste sobre o nervo ulnar na topografia da fossa cubital.
Ć manobra de flexĆ£o do cotovelo nĆ£o observou-se luxaĆ§Ć£o do nervo ulnar.
Aparente compressĆ£o extrĆnseca do mĆŗsculo trĆceps sobre o nervo ulnar.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
NERVO ULNAR: GRANULOMA
Nota-se na topografia do nervo ulnar na fossa cubital, imagem nodular de contornos bocelados e conteĆŗdo hipoecogĆŖnico, medindo cm (T x AP) com Ć”rea transvresal de mmĀ².
Ć manobra de flexĆ£o do cotovelo nĆ£o observou-se luxaĆ§Ć£o do nervo ulnar.
Imagem nodular na topografia do nervo ulnar. Considerar possibilidade de Granuloma.
NERVO ULNAR: LUXAĆĆO
Nervo ulnar observado na fossa cubital apresentando calibre preservado com Ć”rea transversal de mmĀ².
Ć manobra de flexĆ£o do cotovelo observou-se luxaĆ§Ć£o do nervo ulnar em direĆ§Ć£o ao epicĆ“ndilo medial.
LuxaĆ§Ć£o do nervo ulnar Ć manobra de flexĆ£o do cotovelo.
NERVO ULNAR: NEUROFIBROMA
Nota-se na topografia do nervo ulnar na fossa cubital, imagem fusiforme de contornos regulares e conteĆŗdo hipoecogĆŖnico, medindo cm (T x AP) com Ć”rea transvresal de mmĀ².
Ć manobra de flexĆ£o do cotovelo nĆ£o observou-se luxaĆ§Ć£o do nervo ulnar.
Imagem fusiforme na topografia do nervo ulnar. Considerar possibilidade de Neurofibroma.
NERVO ULNAR: NEUROPATIA
Nervo ulnar observado na fossa cubital apresentando aumento do calibre com Ć”rea transversal de mmĀ².
Ć manobra de flexĆ£o do cotovelo nĆ£o observou-se luxaĆ§Ć£o do nervo ulnar.
Aumento do calibre do nervo ulnar. Considerar possibilidade de neuropatia.
NERVO ULNAR: SCHWANOMA
Nota-se na topografia do nervo ulnar na fossa cubital, imagem nodular de contornos regulares e conteĆŗdo anecĆ³ide, medindo cm (T x AP) com Ć”rea transvresal de mmĀ².
Ć manobra de flexĆ£o do cotovelo nĆ£o observou-se luxaĆ§Ć£o do nervo ulnar.
Imagem nodular na topografia do nervo ulnar. Considerar possibilidade de Schwanoma.
RUPTURA PARCIAL: EPICĆNDILO LATERAL
TendĆ£o comum dos extensores e sua inserĆ§Ć£o no epicĆ“ndilo lateral apresentando descontinuidade parcial em suas fibras mais profundas, medindo cm longitudinal x cm transversal, preservando suas fibras mais superficiais. Irregularidade da superfĆcie epicondiliana adjacente.
Sinais de ruptura parcial epicondilopatia lateral.
TENDINITE: EPICĆNDILO LATERAL
TendĆ£o comum dos extensores e sua inserĆ§Ć£o no epicĆ“ndilo lateral apresentam espessura levemente aumentada com hipoecogenicidade textural difusa.
Sinais de epicondilopatia lateral.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
OU
TendĆ£o comum dos extensores e sua inserĆ§Ć£o no epicĆ“ndilo lateral apresentam Ć”rea de hipoecogenicidade textural justa-insercional de suas fibras mais profundas/superficiais.
Sinais de epicondilopatia lateral.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
TENDINITE: EPICĆNDILO MEDIAL
TendĆ£o comum dos flexores e sua inserĆ§Ć£o no epicĆ“ndilo medial apresentam espessura levemente aumentada com hipoecogenicidade textural difusa.
Sinais de epicondilopatia medial.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
OU
TendĆ£o comum dos flexores e sua inserĆ§Ć£o no epicĆ“ndilo medial apresentam Ć”rea de hipoecogenicidade textural justa-insercional de suas fibras mais profundas/superficiais.
Sinais de epicondilopatia medial.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
TEDINITE TRICIPTAL
TendĆ£o do trĆceps braquial apresentando aumento de calibre em sua inserĆ§Ć£o distal, com hipoecogenicidade textural local.
Sinais de tendinopatia triciptal.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
TEDINITE BICIPTAL
TendĆ£o do bĆceps braquial apresentando aumento de calibre em sua inserĆ§Ć£o distal, com hipoecogenicidade textural local.
Sinais de tendinopatia biciptal distal.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
TENDINOSE: EPICĆNDILO LATERAL
TendĆ£o comum dos extensores e sua inserĆ§Ć£o no epicĆ“ndilo lateral apresentam espessura levemente aumentada com heterogeneidade textural difusa e focos ecogĆŖnicos de fibrose intra-tendĆnea.
Irregularidade Ć³ssea da superfĆcie epicondiliana adjacente.
Sinais de tendinose epicondiliana lateral.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
TENDINOSE: EPICĆNDILO MEDIAL
TendĆ£o comum dos flexores e sua inserĆ§Ć£o no epicĆ“ndilo medial apresentam espessura levemente aumentada com heterogeneidade textural difusa e focos ecogĆŖnicos de fibrose intra-tendĆnea.
Irregularidade Ć³ssea da superfĆcie epicondiliana adjacente.
Sinais de tendinose epicondiliana medial.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
TEDINOSE TRICIPTAL
TendĆ£o do trĆceps braquial apresentando leve aumento de calibre em sua inserĆ§Ć£o distal, com heterogeneidade textural justa-insercional com focos ecogĆŖnicos de fibrose intra-tendĆnea.
Irregularidade Ć³ssea da superfĆcie olecraniana adjacente.
Sinais de tendinose triciptal.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
TEDINOSE BICIPTAL
TendĆ£o do bĆceps braquial apresentando aumento de calibre em sua inserĆ§Ć£o distal, com heterogeneidade textural e focos ecogĆŖnicos de fibrose intra-tendĆnea.
Sinais de tendinose biciptal.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
JOELHO
BURSITE
Bursa supra-patelar/prĆ©-patelar/infra-patelar/prĆ©-tibial/da Pata de Ganso apresentando-se distendida por lĆquido com aspecto anecĆ³ide e homogĆŖneo, medindo cerca de cm (L x AP x T), com volume estimado de cmĀ³.
Sinais de bursite supra-patelar/prƩ-patelar/infra-patelar/prƩ-tibial/da Pata de Ganso.
CISTO DE BAKER
Nota-se na fossa poplĆtea, formaĆ§Ć£o cĆstica, de paredes finas e lobuladas, com conteĆŗdo lĆquido, anecĆ³ide e homogĆŖneo, com colo de comunicaĆ§Ć£o articular entre o tendĆ£o do semi-membranoso e o mĆŗsculo gastrocnĆŖmio medial, medindo cm (L x AP x T).
Cisto de Baker.
DERRAME ARTICULAR
Derrame articular leve/moderado/acentuado na topografia supra-patelar com aspecto anecĆ³ide e homogĆŖneo.
Sinais de derrame articular leve/moderado/acentuado.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
DERRAME ARTICULAR COM PROLIFERAĆĆO SINOVIAL
Derrame articular leve/moderado/acentuado na topografia supra-patelar com aspecto anecĆ³ide e moderados debris associado a proliferaĆ§Ć£o sinovial com fluxo vascular aumentado ao Doppler.
Sinais de derrame articular leve/moderado/acentuado com proliferaĆ§Ć£o sinovial. Considerar possibilidade de doenƧa reumatolĆ³gica.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
OSTEOARTROSE
Cartilagem articular difusamente espessada, com borramento dos contornos e irregularidade da superfĆcie Ć³ssea adjacente: fĆŖmur-tibial, fĆŖmur-fibular e patelar.
Sinais de osteoartrose no joelho.
OSTEOARTROSE COM DERRAME ARTICULAR
Derrame articular supra-patelar leve/moderado com aspecto anecĆ³ide e homogĆŖneo.
Cartilagem articular difusamente espessada, com borramento dos contornos e irregularidade da superfĆcie Ć³ssea adjacente fĆŖmur-tibial e fĆŖmur-fibular.
Sinais de osteoartrose com derrame articular leve/moderado.
OSTEOCONDROMA
Nota-se elevaĆ§Ć£o da metĆ”fise proximal anterior da tĆbia de cerca de cm, com projeĆ§Ć£o Ć³ssea em direĆ§Ć£o a diĆ”fise, apresentando cortical aparentemente contĆnua com a do osso subjacente. Observa-se ainda capa hipoecogĆŖnica envolvendo a estrutura com espessura de cm (normal em adultos: 1 - 3 mm), sugerindo envoltĆ³rio cartilaginoso.
ProjeĆ§Ć£o Ć³ssea na metĆ”fise tibial. Considerar possibilidade de osteocondroma. Ć critĆ©rio clĆnico, correlacionar com RX e/ou TC.
TENDINITE QUADRĆCEPS
TendĆ£o do mĆŗsculo quadrĆceps de espessura levemente aumentada com hipoecogenicidade textural em sua inserĆ§Ć£o distal no osso patelar.
Sinais de tendinopatia quadriciptal.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
TENDINITE PATELAR
TendĆ£o patelar aumentado de calibre em sua inserĆ§Ć£o distal, medindo atĆ© cm de espessura, com hipoecogenicidade textural local.
Sinais de tendinopatia patelar.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
TENDINITE BĆCEPS FEMORAL
TendĆ£o do bĆceps femoral aumentado de calibre em sua inserĆ§Ć£o distal na epĆfise fibular, com hipoecogenicidade textural local.
Sinais de tendinopatia do bĆceps femoral.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
TENDINITE PATA DE GANSO
TendƵes componentes da "pata de ganso" (sartĆ³rio, grĆ”cil e semitendinoso) aumentados de calibre em sua inserĆ§Ć£o distal na tĆbia, com hipoecogenicidade textural local.
Sinais de tendinopatia da pata de ganso.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
TENDINOSE QUADRĆCEPS
TendĆ£o do mĆŗsculo quadrĆceps aumentado de calibre em sua inserĆ§Ć£o distal no osso patelar, com heterogeneidade textural e focos ecogĆŖnicos de fibrose/calcificaĆ§Ć£o e anecĆ³icos de necrose intra-tendĆnea.
Nota-se irregularidade da superfĆcie Ć³ssea adjacente.
Sinais de tendinose quadriciptal.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
TENDINOSE PATELAR
TendĆ£o patelar aumentado de calibre em sua inserĆ§Ć£o distal, com hipoecogenicidade textural e focos ecogĆŖnicos de fibrose/calcificaĆ§Ć£o e anecĆ³icos de necrose intra-tendĆnea.
Nota-se irregularidade da superfĆcie Ć³ssea adjacente.
Sinais de tendinose patelar.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
TENDINOSE BĆCEPS FEMORAL
TendĆ£o do bĆceps femoral aumentado de calibre em sua inserĆ§Ć£o distal na epĆfise fibular, com hipoecogenicidade textural e focos ecogĆŖnicos de fibrose/calcificaĆ§Ć£o e anecĆ³icos de necrose intra-tendĆnea.
Nota-se irregularidade da superfĆcie Ć³ssea adjacente.
Sinais de tendinose do bĆceps femoral.
TENDINOSE PATA DE GANSO
TendƵes componentes da "pata de ganso" (sartĆ³rio, grĆ”cil e semitendinoso) aumentados de calibre em sua inserĆ§Ć£o distal na tibia, com hipoecogenicidade textural e focos ecogĆŖnicos de fibrose/calcificaĆ§Ć£o e anecĆ³icos de necrose intra-tendĆnea. Nota-se irregularidade da superfĆcie Ć³ssea adjacente.
Sinais de tendinose da āpata de gansoā.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
OSTEOCONDROSE: OSGOOD-SCHLATTER
TendĆ£o patelar de espessura aumentada com hipoecogenicidade textural no seu terƧo distal onde observa-se fragmentaĆ§Ć£o da apĆ³fise na tuberosidade anterior da tĆbia, medindo atĆ© cm longitudinal.
Nota-se aumento do fluxo vascular ao Doppler adjacente Ć apĆ³fise. Coxim gorduroso infra-patelar (gordura de Hoffa) levemente edemaciada.
Pequena quantidade de lĆquido na bursa infra-patelar com aspecto anecĆ³ide e homogĆŖneo.
Sinais de fragmentaĆ§Ć£o da apĆ³fise na tuberosidade anterior da tĆbia com processo inflamatĆ³rio agudo distal do tendĆ£o patelar. Considerar possibilidade de Osgood-Schlatter.
OU
Observa-se leve fragmentaĆ§Ć£o da apĆ³fise na tuberosidade anterior da tĆbia, sem sinais de tendinopatia ou bursite adjacente.
FragmentaĆ§Ć£o da tuberosidade anterior da tĆbia. Considerar possibilidade de Osgood-Schlatter.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
OSTEOCONDROSE: SINDING-LARSEN-JOHARSSON
FragmentaĆ§Ć£o da superfĆcie anterior do osso patelar, medindo atĆ© cm longitudinal, associado a aumento da espessura com hipoecogenicidade textural local do tendĆ£o patelar.
Nota-se aumento do fluxo vascular ao Doppler adjacente.
Coxim gorduroso infra-patelar (gordura de Hoffa) levemente edemaciada.
Irregularidade da superfĆcie anterior da patela com processo inflamatĆ³rio agudo proximal do tendĆ£o patelar. Considerar possibilidade de Sinding-Larsen-Joharsson.
OU
Irregularidade da superfĆcie anterior do osso patelar sem sinais de tendinopatia adjacente.
Irregularidade da superfĆcie anterior da patela. Considerar possibilidade de seqĆ¼ela de osteocondrite de Sinding-Larsen-Joharsson.
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
PELLEGRINI-STIEDA
Ligamento colateral medial de espessura aumentada com hipoecogenicidade textural difusa e foco de calcificaĆ§Ć£o regular medindo cm. Nota-se ainda Ć”rea de descontinuidade parcial insercional de suas fibras proximais, medindo cm longitudinal e cm transversal.
Sinais de doenƧa ligamentar do colateral medial com Ć”rea de ruptura parcial (SĆndrome de Pellegrini-Stieda).
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
RUPTURA PARCIAL TENDĆNEA
TendĆ£o * apresentando descontinuidade parcial na topografia do corpo/inserĆ§Ć£o, medindo cm longitudinal x cm transversal com preenchimento da Ć”rea rota por efusĆ£o lĆquida hemorrĆ”gica.
Sinais de ruptura parcial/total do tendĆ£o quadrĆceps.
RUPTURA TOTAL TENDĆNEA
TendĆ£o * apresentando descontinuidade total na topografia do corpo/inserĆ§Ć£o, com distĆ¢ncia entre os fragmentos de cm e preenchimento da Ć”rea rota por efusĆ£o lĆquida hemorrĆ”gica.
Sinais de ruptura total do tendĆ£o *.
RUPTURA PARCIAL QUADRĆCEPS
TendĆ£o do mĆŗsculo quadrĆceps com seus componentes (reto anterior, vastos medial, lateral e intermĆ©dio) apresentando descontinuidade parcial na topografia do corpo/inserĆ§Ć£o, medindo cm longitudinal x cm transversal com preenchimento da Ć”rea rota por efusĆ£o lĆquida hemorrĆ”gica.
Sinais de ruptura parcial/total do tendĆ£o quadrĆceps.
RUPTURA TOTAL QUADRĆCEPS
TendĆ£o do mĆŗsculo quadrĆceps com seus componentes (reto anterior, vastos medial, lateral e intermĆ©dio) apresentando descontinuidade total na topografia do corpo/inserĆ§Ć£o, com distĆ¢ncia entre os fragmentos de cm e preenchimento da Ć”rea rota por efusĆ£o lĆquida hemorrĆ”gica.
Sinais de ruptura total do tendĆ£o quadrĆceps.
RUPTURA DE MENISCO
Nota-se perda da arquitetura triangular ecogĆŖnica habitual do corno anterior/posterior do menisco medial/lateral, com hipoecogenicidade textural e Ć”rea de descontinuidade na base/Ć”pice.
Sinais sugestivos de ruptura meniscal. OU
Nota-se perda da arquitetura triangular ecogĆŖnica habitual do corno anterior/posterior do menisco medial/lateral, com hipoecogenicidade textural e protrusĆ£o meniscal promovendo abaulamento do ligamento colateral medial/lateral.
Sinais de protrusĆ£o meniscal.
RUPTURA GASTROCNĆMIO
MĆSCULO
Nota-se no terƧo distal do mĆŗsculo gastrocnĆŖmio medial, Ć”rea de descontinuidade parcial na transiĆ§Ć£o miotendĆnea, se estendendo cm longitudinal x cm transversal, com preenchimento da Ć”rea rota por efusĆ£o lĆquida hemorrĆ”gica e volume estimado em mL.
Observa-se discreta hiperecogenicidade do ventre muscular do gastrocnĆŖmio medial em seu terƧo distal.
Demais regiƵes dos mĆŗsculos gastrocnĆŖmio medial, gastrocnĆŖmio lateral e sĆ³leo apresentam espessura, contornos e ecogenicidade preservadas, com padrĆ£o fibrilar caracterĆstico.
Sinais de ruptura parcial na transiĆ§Ć£o miotendĆnea do gastrocnĆŖmio medial.
RUPTURA ADUTORES
Nota-se na topografia dos mĆŗsculos adutores longo e curto, Ć”rea de descontinuidade parcial a aproximadamente cm de suas inserƧƵes, medindo aproximadamente cm (L x AP), ocupando cerca de % de Ć”rea transversal do vasto desta musculatura, com preenchimento por efusĆ£o lĆquida hemorrĆ”gica.
Sinais sugestivos de ruptura parcial dos mĆŗsculos adutores da coxa direita.
RUPTURA DO PEITORAL MAIOR
MĆŗsculo peitoral maior apresentando descontinuidade parcial de suas fibras mais inferiores, na topografia axilar direita, a aproximadamente cm de sua inserĆ§Ć£o no Ćŗmero, com distĆ¢ncia entre os fragmentos de aproximadamente cm em repouso e preenchimento da Ć”rea rota por efusĆ£o lĆquida hemorrĆ”gica com volume estimado de mL.
Ćrea transversa preservada do mĆŗsculo peitoral maior de aproximadamente % (fibras mais superiores).
RUPTURA QUADRICEPS
Feixe muscular correspondente ao reto anterior do mĆŗsculo quadrĆceps apresentando descontinuidade parcial de cerca de
% da Ć”rea total do feixe, na topografia do terƧo mĆ©dio da coxa com preenchimento da Ć”rea rota por efusĆ£o lĆquida hemorrĆ”gica e inflamatĆ³ria local.
Demais musculatura adjacente sem alteraƧƵes ecogrƔficas.
Sinais de ruptura parcial do feixe reto anterior do mĆŗsculo quadrĆceps.
RUPTURA BĆCEPS FEMORAL
Na topografia do terƧo proximal do mĆŗsculo bĆceps femoral nota-se pequena Ć”rea de descontinuidade parcial de cerca de
% da Ć”rea total transversa do feixe, se estendendo cm longitudinal x cm de espessura, com preenchimento da Ć”rea rota por efusĆ£o lĆquida hemorrĆ”gica e volume estimado em mL.
Sinais de pequena ruptura parcial mĆŗsculo bĆceps femoral.
OMBRO
BURSITE
Bursa subacromiodeltoidea levemente distendida por lĆquido com aspecto anecĆ³ide e homogĆŖneo, medindo cm de espessura.
Sinais bursite subacromiodeltoidea.
CAPSULITE ADESIVA
Observa-se limitaĆ§Ć£o no deslizamento do supra-espinhal pela cobertura acromial com a manobra de abduĆ§Ć£o do braƧo.
Sinais sugestivos de capsulite adesiva do supra-espinhal.
CISTO LABRAL
Nota-se no recesso posterior imagem cĆstica de paredes finas e lobuladas, conteĆŗdo anecĆ³ide e homogĆŖneo, adjacente a labrum glenĆ³ide, medindo cm (T x AP x L).
Imagem cĆstica no recesso posterior. Considerar possibilidade de cisto labral.
HILL-SACHS
Observa-se deformidade da superfĆcie Ć³ssea umeral posterior, adjacente ao tendĆ£o infra-espinhal, medindo cm longitudinal x cm de profundidade.
Sinais deformidade umeral posterior. Considerar possibilidade de lesĆ£o de Hill-Sachs.
(Fratura compressiva causada pelo impacto das trabĆ©culas da cabeƧa umeral durante a luxaĆ§Ć£o anterior da articulaĆ§Ć£o gleno-umeral: alteraĆ§Ć£o osteocondral da regiĆ£o pĆ³stero-lateral da cabeƧa do Ćŗmero)
INSTABILIDADE GLENO-UMERAL
Nota-se aumento da mobilidade da cabeƧa umeral em relaĆ§Ć£o a glenĆ³ide com a manobra de traĆ§Ć£o passiva do braƧo.
Aumento da mobilidade da cabeƧa umeral na cavidade glenĆ³ide. Considerar possibilidade de instabilidade gleno-umeral. Conveniente complementar com RM.
LUXAĆĆO/SUBLUXAĆĆO BICIPTAL
Observa-se subluxaĆ§Ć£o/luxaĆ§Ć£o medial do mesmo em aproximadamente cm em relaĆ§Ć£o ao sulco biciptal.
Profundidade do sulco: mm (normal > 4,3 mm). Largura do sulco: mm (normal < 14 mm).
Ćngulo entre o assoalho e a parede medial do sulco bicipital: (normal: Ā± 56Āŗ).
Sinais sugestivos de subluxaĆ§Ć£o/luxaĆ§Ć£o bicipital.
OSTEOARTROSE OMBRO
Cartilagem acrĆ“mio-clavicular difusamente espessada, com borramento dos contornos e irregularidade da superfĆcie Ć³ssea adjacente. Irregularidade da superfĆcie articular umeral.
OsteĆ³fito na margem lateral do acrĆ“mio, adjacente ao tendĆ£o do supra-espinhal, medindo cm.
Sinais de osteoartrose acrƓmio-clavicular e glenoumeral.
OU
Cartilagem acrĆ“mio-clavicular com borramento dos contornos e irregularidade da superfĆcie Ć³ssea adjacente.
Sinais de osteoartrose acrƓmio-clavicular.
TENDINITE BICIPTAL
TendĆ£o da cabeƧa longa do bĆceps aumentado de calibre na porĆ§Ć£o justa-articular, medindo mm de espessura (normal: 3,3-4,7 mm) com hipoecogenicidade/heterogeneidade textural local.
Sinais de tendinopatia bicipital.
TENDINOSE BICIPITAL
TendĆ£o da cabeƧa longa do bĆceps levemente aumentado de calibre em topografia justa-articular, com heterogeneidade textural e micro-focos ecogĆŖnicos de fibrose intra-tendĆnea.
Sinais de tendinose bicipital.
TENDINITE SUPRA
TendĆ£o do supra-espinhal apresentando aumento do calibre, com hipoecogenicidade textural difusa na projeĆ§Ć£o da zona crĆtica.
Sinais de tendinopatia do supra-espinhal.
TENDINOSE SUPRA
TendĆ£o do supra-espinhal apresentando leve aumento do calibre, com hipoecogenicidade textural e micro-focos ecogĆŖnicos de fibrose intra-tendĆnea.
Sinais de tendinose do supra-espinhal.
RUPTURA PARCIAL SUPRA (NĆO TRANSFIXANTE)
TendĆ£o do supra-espinhal apresentando descontinuidade parcial na projeĆ§Ć£o da zona crĆtica medindo cm longitudinal x cm transversal, preservando suas fibras mais posteriores.
Sinais de ruptura nĆ£o transfixante do supra-espinhal.
OU
TendĆ£o do supra-espinhal apresentando reduĆ§Ć£o focal de cm de espessura, na projeĆ§Ć£o da zona crĆtica, comprometendo cm transversal, preservando suas fibras mais posteriores.
Sinais de ruptura crĆ“nica nĆ£o transfixante do supra-espinhal.
RUPTURA PARCIAL SUBESCAPULAR (NĆO TRANSFIXANTE)
TendĆ£o do subescapular apresentando descontinuidade parcial, intrassubstancial, medindo cm longitudinal x cm transversal.
Sinais de ruptura intrassubstancial do subescapular.
RUPTURA TRANSFIXANTE AGUDA/SUBAGUDA DO SUPRA
TendĆ£o do supra-espinhal apresentando descontinuidade transfixante na projeĆ§Ć£o da zona crĆtica medindo cm longitudinal x cm transversal, preservando suas fibras mais posteriores.
Sinais de ruptura transfixante do supra-espinhal.
OU
TendĆ£o do supra-espinhal apresentando descontinuidade transfixante na projeĆ§Ć£o da zona crĆtica medindo cm longitudinal x cm transversal, preservando suas fibras mais posteriores.
Nota-se ainda derrame articular leve/moderado na goteira bicipital com aspecto anecĆ³ide e leve debris, comunicando-se com a bursa subacromiodeltoidea, que se encontra distendida.
Sinais de ruptura transfixante do supra-espinhal com derrame articular e distensĆ£o da bursa subacromiodeltoidea.
RUPTURA TRANSFIXANTE CRĆNICA DO SUPRA
TendĆ£o do supra-espinhal apresentando descontinuidade transfixante na projeĆ§Ć£o da zona crĆtica medindo cm longitudinal x cm transversal, com cabeƧa umeral irregular e de aspecto ācarecaā, preservando suas fibras mais posteriores.
Sinais de ruptura transfixante crƓnica do supra-espinhal.
OU
TendĆ£o do supra-espinhal apresentando descontinuidade total na projeĆ§Ć£o da zona crĆtica medindo cm longitudinal x cm transversal, com cabeƧa umeral irregular e de aspecto ācarecaā, se estendendo Ć s fibras do infra-espinhal que apresenta reduĆ§Ć£o focal de sua espessura.
Sinais de ruptura total crƓnica do supra-espinhal se estendendo a fibras parciais do infra-espinhal.
DERRAME ARTICULAR
Derrame articular leve na goteira bicipital de aspecto anecĆ³ide e homogĆŖneo.
Sinais de derrame articular leve gleno-umeral.
DERRAME ARTICULAR COM DISTENSĆO DA BURSA
Derrame articular na goteira bicipital de aspecto anecĆ³ide e homogĆŖneo, comunicando com a bursa subacromiodeltoidea, que se encontra levemente distendida.
Sinais de derrame articular com distensĆ£o da bursa subacromiodeltoide.
RUPTURA PARCIAL INFRA, SUB/REDONDO MENOR
TendĆ£o do apresentando reduĆ§Ć£o focal da espessura/descontinuidade parcial, medindo cm longitudinal x cm transversal, preservando suas fibras mais .
Sinais de ruptura parcial do .
RUPTURA TOTAL INFRA, SUB/REDONDO MENOR
TendĆ£o * do apresentando descontinuidade total, medindo cm longitudinal e cm transversal.
Sinais de rotura total do .
ATROFIA MUSCULAR
MĆŗsculo * apresentando aumento da ecogenicidade em aproximadamente % de sua Ć”rea transversal por lipossubstituiĆ§Ć£o, compatĆvel com atrofia de desuso.
Sinais atrofia muscular do * acentuada/moderada/discreta.
TENDINOPATIA CALCĆREA
TendĆ£o do * de calibre normal, apresentando foco cĆ”lcico hiperecogĆŖnico, regular, medindo atĆ© cm.
Sinais tendinopatia calcƔrea do .
CISTO SEBĆCEO
PARTES MOLES
Nota-se na alteraĆ§Ć£o palpĆ”vel da regiĆ£o , nos planos da pele, imagem nodular, de contornos regulares, hipoecogĆŖnica (sugerindo cĆstico-espesso), levemente heterogĆŖnea, sem fluxo ao Doppler, medindo cm (L x AP x T), distando cm da superfĆcie da pele.
Observa-se ainda espessamento da pele adjacente Ć imagem descrita, de atĆ© cm.
Imagem cĆstico-espessa em partes superficiais. Considerar possibilidade de cisto sebĆ”ceo.
COLEĆĆO FLEGMONOSA
Nota-se na regiĆ£o hipogĆ”strica, Ć”reas de coleƧƵes cĆsticas anecĆ³ides com leves debris, de aspecto flegmonoso nos planos do subcutĆ¢neo, medindo atĆ© cm (vol = cmĀ³), sugerindo coleƧƵes hemorrĆ”gicas, nĆ£o passĆvel de punĆ§Ć£o.
Observa-se ainda espessamento da pele nesta topografia com borramento dos planos gordurosos adjacentes.
Imagem cĆstico-espessa em partes superficiais. Considerar possibilidade de cisto sebĆ”ceo.
COLEĆĆO PĆS-OP
Estudo ultrassonogrĆ”fico complementar com sonda linear de 10MHz, dirigido em regiĆ£o abdominal, evidenciou:
ColeĆ§Ć£o cĆstica, unilocular, na regiĆ£o peri-incisional pĆ©lvica, nos planos do subcutĆ¢neo, de contornos irregulares, conteĆŗdo anecĆ³ide com leves debris e traves de permeio, medindo x cm (L x AP), se estendendo cm transversalmente, distando cm da pele, com volume estimado de cmĀ³.
Observa-se ainda espessamento da pele nesta topografia com borramento dos planos gordurosos adjacentes.
ColeĆ§Ć£o cĆstica em partes superficiais passĆvel de punĆ§Ć£o/drenagem.
Nota: Exame realizado em carĆ”ter de urgĆŖncia no * Āŗ dia pĆ³s-operatĆ³rio de .
COLEĆĆO HEMATOMA PĆS-TRAUMA
Nota-se na alteraĆ§Ć£o palpĆ”vel da regiĆ£o *, coleĆ§Ć£o cĆstico-espessa, unilocular, nos planos do subcutĆ¢neo/musculares, de contornos lobulados, conteĆŗdo com moderados debris e traves de permeio, adjacente ao tendĆ£o/vaso *, medindo cm (L x AP x T), distando cm da pele, com volume estimado de cmĀ³.
Observa-se ainda espessamento da pele nesta topografia com borramento dos planos gordurosos adjacentes. SuperfĆcie cortical Ć³ssea adjacente sem sinais ecogrĆ”ficos de descontinuidade.
Sinais de hematoma na *, passĆvel de punĆ§Ć£o/drenagem.
EDEMA
Pele e tecido celular subcutĆ¢neo peri-incisional de espessura aumentada com Ć”reas permeativas anecĆ³ides de aspecto flegmonoso, sem evidĆŖncia de coleƧƵes significativas.
Edema de subcutĆ¢neo peri-incisional.
Nota: Exame realizado em carĆ”ter de urgĆŖncia no * Āŗ dia pĆ³s-operatĆ³rio de .
Pele e tecido celular subcutĆ¢neo na regiĆ£o *** de espessura aumentada, com aumento difuso da ecogenicidade, sem evidĆŖncia de coleƧƵes locais, podendo estar relacionado a processo inflamatĆ³rio local
Nota: Os achados dependem da adequada correlaĆ§Ć£o clĆnico/complementar.
FIBROSE CICATRICIAL
Estudo ultrassonogrĆ”fico complementar com sonda linear de 10MHz, dirigido em regiĆ£o *, evidenciou:
Ćrea sĆ³lida, nos planos do subcutĆ¢neo/musculares, hipoecogĆŖnica, mal definida e irregular, provida de sombra acĆŗstica posterior, medindo cerca de cm (L x AP x T), com volume estimado de cmĀ³ e distando cm da pele.
Ćrea sĆ³lida irregular em partes superficiais. Considerar possibilidade de fibrose cicatricial.
HĆRNIA DE PAREDE REDUTĆVEL
Falha na integridade da aponeurose muscular de cm, na regiĆ£o epigĆ”strica/umbilical, observando herniaĆ§Ć£o de conteĆŗdo omental e intestinal, com fluxo vascular presente ao Doppler, acentuando-se Ć manobra de Valsava.
HƩrnia epigƔstrica/umbilical, sem sinais de encarceramento.
HĆRNIA DE PAREDE ENCARCERADA
Falha na integridade da aponeurose muscular de cm, na regiĆ£o *, com herniaĆ§Ć£o de conteĆŗdo omental e intestinal, medindo cm, associado a pouca mobilidade ao repouso e a manobra de Valsava. Fluxo vascular presente ao Doppler no conteĆŗdo herniĆ”rio.
HƩrnia epigƔstrica/umbilical/incisional com sinais de encarceramento.
HĆRNIA INGUINAL
HerniaĆ§Ć£o de conteĆŗdo omental atravĆ©s do canal inguinal direito/esquerdo, medindo cm de espessura, observado Ć manobra de Valsalva.
HĆ©rnia inguinal Ć direita/esquerda, sem sinais de encarceramento.
HĆRNIA INGUINO-ESCROTAL
HerniaĆ§Ć£o de conteĆŗdo omental e intestinal atravĆ©s do canal inguinal direito/esquerdo atĆ© a bolsa escrotal, com pouca mobilidade Ć s manobras de compressĆ£o e Valsalva.
ConteĆŗdo herniĆ”rio intra-escrotal medindo cerca de cm (vol: cmĀ³).
HĆ©rnia Ćnguino-escrotal encarcerada Ć .
LIPOMA
Nota-se na alteraĆ§Ć£o palpĆ”vel da regiĆ£o , nos planos do subcutĆ¢neo, imagem nodular, sĆ³lida, de contornos lobulados, isoecogĆŖnica ao tecido gorduroso, medindo cm (L x AP x T), distando cm da pele.
NĆ³dulo em partes superficiais. Considerar possibilidade de lipoma.
PUNHO/MĆO
LESĆO DO COMPLEXO CĆPSULO-LIGAMENTAR
Complexo cĆ”psulo-ligamentar da articulaĆ§Ć£o trapĆ©zio-1Āŗ metacarpo apresentando leve borramento dos contornos e discreta irregularidade da superfĆcie Ć³ssea adjacente. Apresenta-se distendida por derrame articular leve de aspecto anecĆ³ide e homogĆŖneo.
Observa-se afastamento das superfĆcies articulares trapĆ©zio-1Āŗ metacarpo Ć manobra de estresse, sugerindo lesĆ£o ligamentar.
Sinais lesĆ£o do complexo cĆ”psulo-ligamentar com derrame articular leve trapĆ©zio-1Āŗ metacarpo e discreta irregularidade Ć³ssea adjacente.
NEUROPATIA MEDIANO
Nervo mediano de dimensƵes aumentadas, com Ć”rea de mmĀ² (normal < 12 mmĀ²).
Aumento do calibre do nervo mediano. Considerar possibilidade de neuropatia.
OSTEOARTRITE COM DERRAME ARTICULAR
Cartilagem articular entre o *** difusamente espessada, com borramento dos contornos e irregularidade da superfĆcie Ć³ssea adjacente, associado a derrame articular leve com aspecto anecĆ³ide e homogĆŖneo.
Sinais de osteoartrite com derrame articular leve ***.
TENOSSINOVITE TĆNEL DO CARPO
TendƵes flexores digitais superficiais e profundos aumentados de calibre na topografia do tĆŗnel do carpo, com halos anecĆ³icos de edema sinovial difuso.
Sinais de tenossinovite nos tendƵes do tĆŗnel do carpo.
TENOSSINOVITE 1Āŗ TĆNEL
TendƵes abdutor longo e extensor curto do polegar aumentados de calibre adjacente a borda radial, com halo anecĆ³ico de edema sinovial comprometendo cerca de cm longitudinal.
Sinais de tenossinovite do 1Āŗ tĆŗnel (De Quervain).
TENOSSINOVITE 2Āŗ TĆNEL
TendƵes extensores longo e curto do carpo aumentados de calibre na topografia da intersecĆ§Ć£o com a musculatura do 1Āŗ tĆŗnel dorsal, com halo anecĆ³ico de edema sinovial comprometendo cerca de cm longitudinal.
Sinais de tenossinovite do 2Āŗ tĆŗnel.
TENOSSINOVITE 3Āŗ TĆNEL
TendĆ£o extensor longo do polegar aumentado de calibre adjacente ao tubĆ©rculo de Lister, com halo anecĆ³ico de edema sinovial comprometendo cerca de mm longitudinal.
Sinais de tenossinovite do 3Āŗ tĆŗnel.
TENOSSINOVITE 4Āŗ TĆNEL
TendƵes extensores comuns dos dedos e prĆ³prio do indicador aumentados de calibres adjacentes ao rĆ”dio, com halo anecĆ³ico de edema sinovial difuso, comprometendo cerca de cm longitudinal.
Sinais de tenossinovite do 4Āŗ tĆŗnel.
TENOSSINOVITE 6Āŗ TĆNEL
TendĆ£o extensor ulnar do carpo aumentado de calibre adjacente ao processo estilĆ³ide da ulna, com halo anecĆ³ico de edema sinovial comprometendo cerca de cm longitudinal.
Sinais de tenossinovite do 6Āŗ tĆŗnel.
CISTO ARTROSSINOVIAL
FormaĆ§Ć£o cĆstica lobulada de conteĆŗdo lĆquido, anecĆ³ide homogĆŖneo, entre o 2Āŗ e o 4Āŗ tĆŗnel dorsal, adjacente ao semilunar, com colo de comunicaĆ§Ć£o articular, medindo cm (L x AP x T).
Cisto artrossinovial no punho.
OU
FormaĆ§Ć£o cĆstica lobulada de conteĆŗdo lĆquido, anecĆ³ide homogĆŖneo, entre o tendĆ£o flexor radial do carpo e o 1Āŗ tĆŗnel dorsal, com colo de comunicaĆ§Ć£o articular, medindo cm (L x AP x T).
Cisto artrossinovial no punho.
DEDO EM GATILHO
TendĆ£o flexor do 4Āŗ dedo levemente espessado com hipoecogenicidade textural ao nĆvel da primeira polia anular na cabeƧa metacarpal associado a travamento Ć extensĆ£o do respectivo quirodĆ”ctilo.
Sinais de tenossinovite estenosante do 4Āŗ flexor (dedo em gatilho).
OU
TendĆ£o flexor do 4Āŗ dedo apresentando leve espessamento da primeira polia anular ao nĆvel na cabeƧa metacarpal, sem sinais de travamento Ć extensĆ£o do respectivo quirodĆ”ctilo ao exame dinĆ¢mico.
Leve espessamento da 1ĀŖ polia anular no 4Āŗ flexor.
BURSITE TROCANTĆRICA
QUADRIL
Bursa trocantĆ©rica distendida por lĆquido com aspecto anecĆ³ide e homogĆŖneo, medindo cm de espessura.
Sinais de bursite trocantƩrica.
DERRAME ARTICULAR
PresenƧa de coleĆ§Ć£o lĆquida, com aspecto anecĆ³ide, homogĆŖneo, no interior da articulaĆ§Ć£o do quadril , visibilizada no recesso anterior da cĆ”psula. A distĆ¢ncia colo-cĆ”psula mede cm.
Sinais ecogrĆ”ficos compatĆveis com sinovite no quadril .
DISPLASIA COXO-FEMORAL RX
DISPLASIA COXO-FEMORAL US
A tĆ©cnica de Graf realizada pelo ultrassom Ć© baseada no plano coronal do quadril avaliado em posiĆ§Ć£o neutra, que corresponde no caso do RN a uma discreta flexĆ£o. Graf utilizou trĆŖs retas para medir dois Ć¢ngulos: alfa e beta. A linha de base corresponde a tangente a parede lateral do osso ilĆaco que deve necessariamente se apresentar com morfologia reta, o que indica inclusive que o plano de corte escolhido estĆ” adequado. O Ć¢ngulo alfa traduz quantitativamente a displasia da porĆ§Ć£o Ć³ssea do teto do acetĆ”bulo e reflete a inclinaĆ§Ć£o do teto acetabular. Quanto menor o Ć¢ngulo alfa, mais rasa Ć© a cavidade acetabular e portanto, a displasia Ć© mais acentuada. O angulo alfa normal deve ser maior ou igual a 60 graus. O Ć¢ngulo beta traduz a posiĆ§Ć£o da fibrocartilagem do lĆ”bio acetabular em relaĆ§Ć£o a linha de base. Assim quanto maior for o Ć¢ngulo beta mais horizontalizado estĆ” o lĆ”bio, ou seja, o lĆ”bio estĆ” mais deslocado superiormente pela epĆfise femoral. Em crianƧas normais o Ć¢ngulo beta deve ser menor ou igual a 55 graus.
O Ć¢ngulo alfa de Graf reflete a inclinaĆ§Ć£o do teto acetabular. AlĆ©m da linha de base precisamos de uma segunda linha para definir este Ć¢ngulo, a linha do teto acetabular. A linha do teto acetabular Ć© traƧada tangenciando o teto Ć³sseo conforme a figura. Quanto menor o Ć¢ngulo alfa, mais rasa Ć© a cavidade acetabular e portanto, a displasia Ć© mais acentuada. O angulo alfa normal deve ser maior ou igual a 60 graus.
A linha de base de Graf corresponde a tangente a parede lateral do osso ilĆaco. A parede lateral do ilĆaco deve necessariamente se apresentar com morfologia reta, o que indica que o plano de corte escolhido estĆ” adequado para realizar as mensuraƧƵes.
O Ć¢ngulo beta de Graf estĆ” indicado nesta figura e traduz quantitativamente o deslocamento superior da fibrocartilagem do lĆ”bio (labrum). AlĆ©m da linha de base precisamos de uma segunda linha para definir este Ć¢ngulo, a linha labral. Esta linha labral Ć© traƧada entre o promontĆ³rio e a porĆ§Ć£o central do lĆ”bio conforme a figura.
IndicaĆ§Ć£o das estruturas anatĆ“micas que devem estar presentes: 1= osso ilĆaco; 2= cartilagem triradiada; 3= osso Ćsquio; 4= epĆfise femoral proximal; 5= metĆ”fise femoral proximal; 6= trocĆ¢nter maior do fĆŖmur; setas = fibrocartilagem do lĆ”bio (labrum) acetabular. No quadril normal o promontĆ³rio acetabular (transiĆ§Ć£o entre a parede lateral do ilĆaco e o teto acetabular) deve formar um Ć¢ngulo agudo. Nos quadris imaturos e nos quadris displĆ”sicos o promontĆ³rio costuma ter contornos arredondados.
Imagem no plano coronal do quadril avaliado em posiĆ§Ć£o neutra adequada para o mĆ©todo de Graf. Neste plano de corte a parede lateral do ilĆaco deve estar reta. Veja na figura a seguir as estruturas anatĆ“micas que devem estar representadas.
ReferĆŖncia: http://mskribeirao.com/curiosidades/detalhe/displasia-de-quadril-recem-nascido-rn/
(Site de radiologia do sistema musculoesquelĆ©tico desenvolvido por profissionais de RibeirĆ£o Preto ā SP. PublicaĆ§Ć£o: 18.12.2012).
OSTEOARTROSE
Cartilagem articular fĆŖmoro-acetabular difusamente espessada, com borramento dos contornos e irregularidade da superfĆcie Ć³ssea adjacente.
Irregularidade da superfĆcie Ć³ssea trocantĆ©rica e da espinha ilĆaca Ć¢ntero-superior.
Sinais de osteoartrose coxo-femoral.
CISTO(S) ARTROSSINOVIAL(IS)
TORNOZELO/PĆ
FormaĆ§Ć£o cĆstica lobulada, de conteĆŗdo lĆquido, anecĆ³ide homogĆŖneo, com colo de comunicaĆ§Ć£o articular, entre a articulaĆ§Ć£o talo-navicular, medindo cm (L x AP x T).
Cisto artrossinovial.
OU
FormaƧƵes cĆsticas lobuladas, de conteĆŗdo lĆquido, anecĆ³ide homogĆŖneo, com colo de comunicaĆ§Ć£o articular, entre as articulaƧƵes:
talo-navicular, medindo cm (L x AP x T).
fĆbulo-calcĆ¢nea, medindo cm (L x AP x T).
calcĆ¢neo-cubĆ³ide, medindo cm (L x AP x T).
Cistos artrossinoviais.
OSTEOARTROSE TARSO
Borramento dos contornos e irregularidade das superfĆcies articulares nos ossos do tarso.
Sinais de osteoartrose no pƩ.
ENTESOPATIA
Entesopatia calcificada de tendĆ£o calcĆ¢neo, medindo atĆ© cm.
EntesĆ³fito de calcĆ¢neo.
TENDINITE CALCĆNEO
TendĆ£o de Aquiles aumentado de calibre do calcĆ¢neo, medindo cm de espessura, associado a hipoecogenicidade textural justa-insercional.
Sinais de tendinopatia do calcĆ¢neo justa-insercional.
OU
TendĆ£o de Aquiles aumentado de calibre, medindo cm de espessura na zona crĆtica, a cm de sua inserĆ§Ć£o no calcĆ¢neo, com hipoecogenicidade textural local, comprometendo cm longitudinal.
Sinais de tendinopatia do calcĆ¢neo.
TENDINOSE CALCĆNEO
TendĆ£o de Aquiles aumentado de calibre em sua inserĆ§Ć£o/no corpo, com hipoecogenicidade textural e microfocos ecogĆŖnicos de fibrose intra-tendĆnea.
Sinais de tendinose do calcĆ¢neo.
RUPTURA TOTAL DO TENDĆO CALCĆNEO
TendĆ£o de Aquiles apresentando descontinuidade total a aproximadamente cm de sua inserĆ§Ć£o no calcĆ¢neo, na projeĆ§Ć£o da zona crĆtica, com distĆ¢ncia entre os fragmentos de cerca de cm em repouso e cm na posiĆ§Ć£o em āequinoā e preenchimento da Ć”rea rota por efusĆ£o lĆquida hemorrĆ”gica.
Sinais de ruptura total do tendĆ£o calcĆ¢neo.
RUPTURA PARCIAL DO TENDĆO CALCĆNEO
TendĆ£o de Aquiles apresentando descontinuidade parcial das fibras mais profundas/superficias/laterais/mediais a aproximadamente cm de sua inserĆ§Ć£o no calcĆ¢neo, na projeĆ§Ć£o da zona crĆtica, medindo cm longitudinal x cm transversal e preenchimento da Ć”rea rota por efusĆ£o lĆquida hemorrĆ”gica.
Sinais de ruptura parcial do tendĆ£o calcĆ¢neo.
TENOSSINOVITE TIBIAL POSTERIOR
TendĆ£o do tibial posterior aumentado de calibre em sua porĆ§Ć£o maleolar e infra maleolar, com hipoecogenicidade textural e lĆquido em sua sinĆ³via, comprometendo cerca de cm longitudinal.
Sinais de tenossinovite do tibial posterior.
TENDINOSE TIBIAL POSTERIOR
TendĆ£o tibial posterior aumentado de calibre na porĆ§Ć£o maleolar e infra-maleolar, com heterogeneidade textural e desorganizaĆ§Ć£o da arquitetura por micro-focos ecogĆŖnicos de fibrose/calcificaĆ§Ć£o e anecĆ³icos de necrose intra-tendĆnea de permeio por roturas degenerativas locais.
Sinais de tendinose do tibial posterior.
TENOSSINOVITE DOS FIBULARES
TendƵes fibulares aumentados de calibre na porĆ§Ć£o maleolar, com halo anecĆ³ico de edema sinovial local, comprometendo cerca de cm longitudinal.
Sinais de tenossinovite dos fibulares.
FASCITE PLANTAR
FĆ”scia plantar com espessura aumentada em sua inserĆ§Ć£o no calcĆ¢neo, medindo cm de calibre, com hipoecogenicidade textural local.
Sinais de fascite plantar.
OU
FĆ”scia plantar com espessura aumentada em suas fibras mais mediais, a cerca de cm da inserĆ§Ć£o no calcĆ¢neo, medindo cm de calibre, comprometendo cm longitudinal, associado a hipoecogenicidade textural local.
Sinais de fascite plantar.
FASCITE CRĆNICA PLANTAR
FĆ”scia plantar aumentada de calibre em sua inserĆ§Ć£o no calcĆ¢neo, com hipoecogenicidade textural e focos de fibrose/calcificaĆ§Ć£o de permeio. Irregularidade da superfĆcie Ć³ssea adjacente.
Sinais de fascite crƓnica plantar.
TALALGIA DE IMPACTO
Observa-se aumento da ecogenicidade do coxim adiposo sub-cutĆ¢neo do calcĆ¢neo inferior, com hipoecogenicidade textural da superfĆcie Ć³ssea adjacente. Espessura do coxim de cm (normal <1,5 cm).
Sinais de talalgia do impacto.
NEUROMA DE MORTON
Nota-se entre as 3ĀŖ e 4ĀŖ cabeƧas metatarseanas, na topografia do feixe vĆ”sculo-nervoso, imagem nodular, sĆ³lida hipoecogĆŖnica, estruindo-se Ć manobra de Mulder, medindo cm.
Imagem nodular entre o 3Āŗ e 4Āŗ metatarso. Considerar possibilidade de neuroma de Morton.
DERRAME ARTICULAR
Derrame articular leve/moderado tĆbio-talar de aspecto anecĆ³ide e homogĆŖneo.
Sinais de derrame articular tĆbio-talar leve/moderado.
LESĆO LIGAMENTAR
Ligamento apresentando descontinuidade total com estravazamento de lĆquido articular na Ć”rea dissecando o fragmento roto.
Sinais de ruptura do ligamento *.
Ligamentos: talo-fibular anterior/calcĆ¢neo-fibular/tĆbio-fibular/deltĆ³ide.
FIBROMATOSE PLANTAR (DOENĆA DE LEDDERHOSE)
FĆ”scia plantar apresentando a cerca de cm da inserĆ§Ć£o no calcĆ¢neo, Ć”rea de espessamento nodular alongada com hipoecogenicidade textural local, medindo cm no eixo da fĆ”scia por cm de espessura.
Espessamento nodular da fƔscia plantar. Considerar possibilidade de fibromatose plantar (DoenƧa de Ledderhose).
OU
FƔscia plantar apresentando * Ɣreas de espessamento nodular, alongadas, com hipoecogenicidade textural, caracterizadas assim:
a cm da inserĆ§Ć£o no calcĆ¢neo, medindo cm (longitudinal x espessura).
a cm da inserĆ§Ć£o no calcĆ¢neo, medindo cm (longitudinal x espessura).
Espessamentos nodulares da fƔscia plantar. Considerar possibilidade de fibromatose plantar (DoenƧa de Ledderhose).
CRITĆRIOS PARA DIAGNĆSTICO DE ESTENOSE DE CARĆTIDA
PARĆMETROS PRIMĆRIOS
PARĆMETROS ADICIONAIS
Grau de
estenose
VPS-ACI
(cm/s)
Estimativa da
placa %
RazĆ£o VPS
ACI/ACC
VDF-ACI
(cm/s)
Normal
< 125
NĆ£o hĆ” placa
< 2,0
< 40
Menor 50%
< 125
< 50%
< 2,0
< 40
50-69%
125-230
ā„ 50
2,0-4,0
40-100
Maior ou = 70%
> 230
ā„ 50
> 4,0
> 100
Sub-oclusĆ£o
Indefinida
VisĆvel
VariƔvel
VariƔvel
OclusĆ£o
IndetectƔvel
LĆŗmen indetectĆ”vel
NĆ£o se aplica
NĆ£o se aplica
* Estimativa aproximada de estenose atravƩs do modo B e Doppler colorido. Ref: Society of Radiologists in Ultrasound Consensus Conference - 2003.
ESPESSAMENTO MĆDIO-INTIMAL
A artĆ©ria carĆ³tida comum apresenta diĆ¢metro normal. PresenƧa de espessamento mĆ©dio-intimal. Ao Doppler observa-se curva espectral de amplitude normal, sem turbulĆŖncia e aceleraĆ§Ć£o.
Espessamento mĆ©dio-intimal da artĆ©ria carĆ³tida comum.
ReferĆŖncia:
Valores de espessura mĆ©dio-intimal das artĆ©rias carĆ³tidas comuns por sexo e idade do(a) paciente estudado(a) segundo o Multi-Ethnic Study of Atherosclerosis Risk in Communities Study (Robin L. McClelland, PhD, 2007):
Percentil 75 da carĆ³tida direita: 0,99 mm.
Percentil 75 da carĆ³tida esquerda: 1,1 mm.
OU
Valores de referĆŖncia (provavelmente sem elevaĆ§Ć£o de risco):
Pacientes com idade inferior a 50 anos: < 0,8 mm.
Pacientes com idade superior a 50 anos: < 1,0 mm.
PLACA ATEROSCLERĆTICA NĆO COMPLICADA
Bulbo carotĆdeo apresentando placa aterosclerĆ³tica de aspecto calcificado, com superfĆcie regular.
PLACA ATEROSCLERĆTICA COMPLICADA
Bulbo carotĆdeo apresentando placa aterosclerĆ³tica heterogĆŖnea, com predomĆnio hipoecĆ³ico, superfĆcie irregular, calcificaƧƵes focais e Ć”rea anecĆ³ica central.
ESTENOSE < 50% ACC
ArtĆ©ria carĆ³tida comum apresenta diĆ¢metro reduzido por placa aterosclerĆ³tica excĆŖntrica, homogĆŖnea, com superfĆcie regular. A avaliaĆ§Ć£o do grau de estenose atravĆ©s de medida de diĆ¢metro em varredura transversa mostra reduĆ§Ć£o de cerca de 30 a 40%. Ao Doppler observa-se curva espectral de amplitude normal, sem turbulĆŖncia e aceleraĆ§Ć£o.
Estenose inferior a 50% da artĆ©ria carĆ³tida comum
ESTENOSE DA ARTĆRIA CARĆTIDA INTERNA
A artĆ©ria carĆ³tida interna apresenta diĆ¢metro reduzido por placa aterosclerĆ³tica concĆŖntrica, heterogĆŖnea, com predomĆnio hipoecĆ³ico, superfĆcie irregular, calcificaƧƵes focais e Ć”rea anecĆ³ica central. Ao Doppler observa-se curva de amplitude aumentada e alargamento espectral.
Estenose inferior a 50% da artĆ©ria carĆ³tida interna.
Estenose de 50 a 69% da artĆ©ria carĆ³tida interna.
Estenose superior a 70% da artĆ©ria carĆ³tida interna.
ESTENOSE DA ARTĆRIA CARĆTIDA EXTERNA
A artĆ©ria carĆ³tida externa apresenta diĆ¢metro reduzido por placa aterosclerĆ³tica heterogĆŖnea. Ao Doppler observa-se curva de amplitude aumentada e alargamento espectral.
Estenose inferior a 50% da artĆ©ria carĆ³tida externa
Estenose superior a 50% da artĆ©ria carĆ³tida externa.
SUB-OCLUSĆO DA ARTĆRIA CARĆTIDA INTERNA
A artĆ©ria carĆ³tida interna apresenta diĆ¢metro acentuadamente reduzido por placa aterosclerĆ³tica concĆŖntrica, heterogĆŖnea, com predomĆnio hipoecĆ³ico, superfĆcie irregular, calcificaƧƵes focais e Ć”rea anecĆ³ica central. Ao Doppler observa-se curva de amplitude reduzida e alargamento espectral.
Achados compatĆveis com sub-oclusĆ£o da artĆ©ria carĆ³tida interna.
OCLUSĆO DA ARTĆRIA CARĆTIDA INTERNA
A artĆ©ria carĆ³tida comum apresenta aspecto morfolĆ³gico normal. Ao Doppler observa-se curva espectral de amplitude reduzida, com componente reverso e ausĆŖncia de componente telediastĆ³lico (padrĆ£o de resistĆŖncia elevada).
A artĆ©ria carĆ³tida interna apresenta preenchimento luminal por placa aterosclerĆ³tica heterogĆŖnea, com componente hipoecĆ³ico compatĆvel com trombo. NĆ£o foi detectado sinal Doppler neste vaso.
Achados compatĆveis com oclusĆ£o da artĆ©ria carĆ³tida interna.
ACOTOVELAMENTO (āKINKINGā) DA ARTĆRIA CARĆTIDA INTERNA
A artĆ©ria carĆ³tida interna apresenta diĆ¢metro normal. NĆ£o se observam placas aterosclerĆ³ticas significativas em seu segmento proximal. PresenƧa de acotovelamento em Ć¢ngulo reto localizado em seu segmento mĆ©dio.
Acotovelamento da artĆ©ria carĆ³tida interna.
ARTĆRIAS VERTEBRAIS
ESTENOSE PROXIMAL (EXAME DIRETO DA LESĆO)
A artĆ©ria vertebral foi explorada em sua origem, sendo evidenciada placa aterosclerĆ³tica nesta topografia. Ao Doppler observa-se curva espectral de amplitude aumentada e alargamento espectral.
O segmento interapofisĆ”rio apresenta diĆ¢metro normal. Ao Doppler observa-se fluxo de direĆ§Ć£o cefĆ”lica, com curva de amplitude normal, sem turbulĆŖncia e aceleraĆ§Ć£o.
Estenose superior a 50% da artƩria vertebral.
Estenose superior a 80% da artƩria vertebral.
Achados compatĆveis com estenose hemodinĆ¢micamente significativa da artĆ©ria vertebral em sua origem.
Achados compatĆveis com estenose hemodinĆ¢micamente significativa da artĆ©ria vertebral em seu segmento intracraniano.
ESTENOSE PROXIMAL (SEM EXAME DIRETO DA LESĆO)
A artĆ©ria vertebral foi explorada em seu segmento interapofisĆ”rio e apresenta diĆ¢metro normal. Ao Doppler observa-se fluxo de direĆ§Ć£o cefĆ”lica, com curva de amplitude acentuadamente reduzida, sem turbulĆŖncia e aceleraĆ§Ć£o.
OCLUSĆO
A artĆ©ria vertebral foi explorada em sua origem, sendo evidenciada placa aterosclerĆ³tica nesta topografia. NĆ£o se observa sinal Doppler neste vaso.
Achados compatĆveis com oclusĆ£o da artĆ©ria vertebral.
OCLUSĆO PROXIMAL COM ENCHIMENTO POR COLATERAIS
A artĆ©ria vertebral foi explorada em sua origem, sendo evidenciada placa aterosclerĆ³tica nesta topografia. NĆ£o se observa sinal Doppler neste vaso.
O segmento interapofisĆ”rio apresenta diĆ¢metro normal. Ao Doppler observa-se enchimento deste vaso por circulaĆ§Ć£o colateral, determinando fluxo de direĆ§Ć£o cefĆ”lica, com curva de amplitude reduzida, sem turbulĆŖncia e aceleraĆ§Ć£o.
HIPOPLASIA
A artĆ©ria vertebral foi explorada em seu segmento interapofisĆ”rio e apresenta diĆ¢metro difusamente reduzido ( mm). Ao Doppler observa-se fluxo de direĆ§Ć£o cefĆ”lica, com curva de amplitude acentuadamente reduzida, sem componente diastĆ³lico (padrĆ£o de resistĆŖncia aumentada).
Achados compatĆveis com hipoplasia da artĆ©ria vertebral.
FENĆMENO DO ROUBO SUBCLĆVIO
A artĆ©ria vertebral foi explorada em seu segmento interapofisĆ”rio e apresenta diĆ¢metro normal. Ao Doppler observa-se fluxo de direĆ§Ć£o caudal, com curva de amplitude normal, sem turbulĆŖncia e aceleraĆ§Ć£o.
Foi realizada insuflaĆ§Ć£o do esfigmomanĆ“mtero na artĆ©ria braquial acima da pressĆ£o sistĆ³lica sendo verificada interrupĆ§Ć£o do fluxo descendente da artĆ©ria vertebral.
AlteraƧƵes no fluxo vertebral esquerdo compatĆveis com āroubo da subclĆ”viaā.
OCLUSĆO OU ESTENOSE GRAVE DISTAL
A artĆ©ria vertebral foi explorada em seu segmento interapofisĆ”rio e apresenta diĆ¢metro normal. Ao Doppler observa-se fluxo de direĆ§Ć£o cefĆ”lica, com curva de amplitude reduzida, com componente reverso e ausĆŖncia de componente diastĆ³lico (padrĆ£o de resistĆŖncia elevada).
NORMAL
ARTĆRIAS OFTĆLMICAS
A artĆ©ria oftĆ”lmica foi explorada por via transorbitĆ”ria. Ao Doppler observa-se fluxo de direĆ§Ć£o anterĆ³grada, com curva espectral de amplitude normal.
OCLUSĆO DA ARTĆRIA CARĆTIDA INTERNA
A artĆ©ria oftĆ”lmica foi explorada por via transorbitĆ”ria. Ao Doppler observa-se fluxo de direĆ§Ć£o retrĆ³grada, com curva espectral de amplitude normal.
ConclusƵes
Ateromatose carotĆdea com espessamento mĆ©dio intimal difuso e placas moles na artĆ©ria carĆ³tida comum - e bulbo carotĆdeo - -sem repercussĆ£o hemodinĆ¢mica.
āStentā na artĆ©ria carĆ³tida interna direita pĆ©rvio.
Obs.: A presenƧa de placa aterosclerĆ³tica calcificada representa um fator limitante na avaliaĆ§Ć£o de sua superfĆcie e na quantificaĆ§Ć£o precisa do grau de estenose.
Obs.: A quantificaĆ§Ć£o do grau de estenose da artĆ©ria carĆ³tida interna foi realizada empregando-se os critĆ©rios da ConferĆŖncia de Consenso da Society of Radiologists in Ultrasound (Radiology 2003: 229: 340-346).
CONCLUSĆES
Ateromatose carotĆdea com espessamento mĆ©dio intimal difuso e placas moles, sem repercussĆ£o hemodinĆ¢mica na / , medindo respectivamente / (extensĆ£o x espessura).
Pequenas placas regulares, predominantemente nĆ£o calcificadas em /, as quais nĆ£o determinam estreitamentos luminais significativos.
Ateromatose difusa, com mĆŗltiplas placas calcificadas, predominando nos /.
STENT
PresenƧa de āStentā na artĆ©ria carĆ³tida interna direita, desde o bulbo, que se apresenta pĆ©rvio e com velocidade de fluxo sistĆ³lico de atĆ© - - - cm/s.
OU
Nota-se elevaĆ§Ć£o focal da velocidade de fluxo imediatamente apĆ³s o stent, da ordem aproximada de 100%, podendo representar estenose nesta topografia.
ROUBO DE SUBCLĆVIA
InversĆ£o no sentido de fluxo protodiastĆ³lico na artĆ©ria vertebral / , mais evidente durante esforƧo no membro superior esquerdo, compatĆvel com āroubo da subclĆ”viaā.
OU
A artĆ©ria vertebral foi explorada em seu segmento interapofisĆ”rio e apresenta diĆ¢metro normal. Ao Doppler observa-se fluxo de direĆ§Ć£o caudal, com curva de amplitude normal, sem turbulĆŖncia e aceleraĆ§Ć£o.Foi realizada insuflaĆ§Ć£o do esfigmomanĆ“mtero na artĆ©ria braquial acima da pressĆ£o sistĆ³lica sendo verificada interrupĆ§Ć£o do fluxo descendente da artĆ©ria vertebral.
ACOTOVELAMENTO
Acotovelamento /, sem repercussĆ£o hemodinĆ¢mica ( VPS: / cm/s - VDF: / cm/s),
Obs.: Considerar um Ćndice sistĆ³lico superior a 4 como critĆ©rio de repercussĆ£o significativa sobre o fluxo.
TORTUOSIDADE
ArtĆ©rias carĆ³tidas com trajeto tortuoso e calibres conservados no segmento cervical.
C ARTIFICIAL - A avaliaĆ§Ć£o com Doppler mostra fluxo com padrĆ£o monofĆ”sico āportalizadoā em todos os segmentos arteriais avaliados, secundĆ”rio ao dispositivo de suporte hemodinĆ¢nico, apresentando velocidades entre - - - cm/s e - - - cm/s Ć esquerda e entre - - - cm/s e - - - cm/s Ć direita.
CONC: AlteraƧƵes hemodinĆ¢micas secundĆ”rias ao dispositivo de suporte hemodinĆ¢mico acarretando fluxo arterial monofĆ”sico e dificultando a anĆ”lise de eventuais estenoses focais
VERTEBRAL NĆO CARACTERIZADA
Fluxo vertebral direito/esquerdo nĆ£o caracterizado.
A arteriografia deve ser analisada de acordo com NASCET, que compara a porĆ§Ć£o do lĆŗmen restante com o lĆŗmen distal normal
IndicaƧƵes em que a relaĆ§Ć£o do PSV Ć© melhor que o valor absoluto:
Grande estenose contra-lateral;
DiscrepĆ¢ncia entre o tamanho da placa e PSV
Velocidade da ACC aumentada
Estados cardĆacos hiperdinĆ¢micos
ICC
IndicaƧƵes para Follow-up:
>50% (sem indicaĆ§Ć£o de endarterectomia) ļ 6-12 meses
<50% ( Pc de alto risco com placa visĆvel) ļ 1-2 anos Normal + Fatores de risco ļ 3-5 anos
ESTENOSE < 50% AFS
ARTĆRIAS MEMBRO INFERIOR
Nota-se moderada irregularidade parietal com calcificaƧƵes ateromatosas nos vasos estudados. ARTĆRIA FEMORAL SUPERFICIAL: pĆ©rvia.
PorĆ§Ć£o proximal da coxa: A anĆ”lise espectral revelou curvas trifĆ”sicas normocinĆ©ticas, de alta resistĆŖncia com Velocidade de Pico SistĆ³lico (VPS: cm/s) dentro da normalidade.
No canal de Hunter: A anĆ”lise espectral revelou curvas trifĆ”sicas com borramento espectral e velocidade de pico sistĆ³lico aumentada (VPS: cm/s), porĆ©m com relaĆ§Ć£o inferior a 2, sugerindo estenose inferior a 50%.
Ateromatose moderada nos vasos estudados.
Estenose inferior a 50% da artĆ©ria femoral superficial ao nĆvel do canal de Hunter.
ESTENOSE > 50% AFS
ARTĆRIA FEMORAL SUPERFICIAL: pĆ©rvia.
PorĆ§Ć£o proximal da coxa: A anĆ”lise espectral revelou curva trifĆ”sica normocinĆ©tica, de alta resistĆŖncia com velocidade de pico sistĆ³lico dentro da normalidade (VPS: cm/s).
Na transiĆ§Ć£o terƧo proximal/mĆ©dio da coxa: Curvas com borramento espectral e velocidade de pico sistĆ³lico aumentada (VPS: cm/s), sugerindo estenose superior a 50%.
No terƧo mĆ©dio da coxa: Curva bifĆ”sica com velocidade de pico sistĆ³lico mais reduzida (VPS: cm/s).
No canal de Hunter: Curva monofĆ”sica com velocidade de pico sistĆ³lico reduzida (VPS: cm/s).
ARTĆRIA POPLĆTEA: pĆ©rvia. Curva monofĆ”sica com velocidade de pico sistĆ³lico reduzida (VPS: cm/s). ARTĆRIA TIBIAL ANTERIOR: pĆ©rvia. Curva monofĆ”sica com velocidade de pico sistĆ³lico reduzida (VPS: cm/s). ARTĆRIA TIBIAL POSTERIOR: pĆ©rvia. Curva monofĆ”sica com velocidade de pico sistĆ³lico reduzida (VPS: cm/s). ARTĆRIA FIBULAR: pĆ©rvia. Curva monofĆ”sica com velocidade de pico sistĆ³lico reduzida (VPS: cm/s).
Ateromatose moderada/severa nos vasos estudados.
Estenose hemodinamicamente significativa superior a 50% da artƩria femoral superficial no terƧo proximal/mƩdio da coxa.
Vasos distais Ć estenose com fluxos monofĆ”sicos e velocidades de pico sistĆ³lico reduzidas.
ENCARCERAMENTO POPLĆTEO
Perda do padrĆ£o trifĆ”sico de fluxo nas artĆ©rias poplĆtea e tibial posterior durante a manobra de flexĆ£o plantar do pĆ©. Tal achado, embora inespecĆfico, pode estar associado Ć sĆndrome do encarceramento poplĆteo.
VENOSO MEMBRO INFERIOR
REFLUXO VALVAR PROFUNDO
PĆ©rvias, incompetentes com refluxo valvar, de paredes finas e lisas. Compressibilidade preservada.
Refluxo valvar profundo de veias ***.
REFLUXO VALVAR - SAFENA
V. SAFENA MAGNA: PĆ©rvia, incompetente com refluxo valvar nos segmentos ***.
IncompetĆŖncia de safena magna do tipo .
Obs.: PadrƵes de refluxo da veia safena magna (J Vasc Br 2004: Functional anatomic classification of saphenous vein insufficiency in the planning for varicose vein surgery base on color Doppler ultrasound):
Normal (28,11%); Tipo I (0,71%): Peri-juncional; Tipo 2 (5,65%): Proximal; Tipo III (9,81%): Refluxo distal; Tipo IV (33,54%): Segmentar; Tipo Va (4,45%): Multissegmentar com JSF competente; Tipo Vb (14,62%): Multissegmentar com JSF incompetente; Tipo VI (3,11%): Refluxo difuso.
NORMAL TIPO I TIPO II TIPO III TIPO IV TIPO Va TIPO Vb TIPO VI
SAFENECTOMIA
V. SAFENA MAGNA: NĆ£o caracterizada e toda sua extensĆ£o (status pĆ³s-operatĆ³rio).
Sinais de safenectomia total (safena interna).
OU
V. SAFENA MAGNA: NĆ£o caracterizada nos 2/3 proximais da coxa (status pĆ³s-operatĆ³rio). Demais porƧƵes: PĆ©rvia, de paredes finas e lisas e com compressibilidade preservada.
Sinais de safenectomia parcial (safena interna).
TROMBOSE VENOSA PROFUNDA AGUDA
De calibres aumentados com material hipoecĆ³ide no interior, nĆ£o compressĆvel, sem fluxo detectĆ”vel ao Doppler.
Sinais de trombose venosa profunda aguda de veias ***.
TROMBOSE VENOSA PROFUNDA SUBAGUDA/CRĆNICA NĆO RECANALIZADA
De calibres levemente aumentados com material hiperecogĆ«nico no interior, aderido Ć parede do vaso, nĆ£o compressĆveis, com pobres sinais de recanalizaĆ§Ć£o.
Sinais de trombose venosa profunda de aspecto subagudo/crĆ“nico nĆ£o recanalizadas de veias ***.
TROMBOSE VENOSA CRĆNICA PARCIALMENTE RECANALIZADA
De calibres levemente reduzidos com material hiperecogĆ«nico e traves no interior, parcialmente compressĆveis, de paredes espessas e com sinais de recanalizaĆ§Ć£o parcial.
Sinais de trombose venosa profunda crƓnica parcialmente recanalizada de veias ***.
TROMBOSE VENOSA CRĆNICA PARCIALMENTE RECANALIZADA
PĆ©rvias, incompetentes com refluxo valvar, de paredes levemente espessas e algumas traves finas no interior, parcialmente compressĆveis.
Observa-se ainda circulaĆ§Ć£o colateral adjacente ao segmento * da veia *.
Sinais de trombose venosa profunda crƓnica recanalizada de veias ***.
Sinais de sĆndrome pĆ³s-trombĆ³tica com refluxo valvar profundo de veias *** e aumento da circulaĆ§Ć£o colateral.
TROMBOFLEBITE
V. SAFENA MAGNA/PARVA: De calibre aumentado com material hipoecĆ³ide no interior, nĆ£o compressĆvel, sem fluxo detectĆ”vel ao Doppler, se estendendo do ***.
Sinais de tromboflebite de veia safena interna/externa na coxa/perna.
VARICOSIDADES
VARICOSIDADES: Presentes em coxa e perna.
Varicosidades em coxa e perna.
OU
VARICOSIDADES: Veias reticulares presentes em coxa e perna.
Veias reticulares presentes em coxa e perna.
VEIA(S) PERFURANTE(S) MEDIAIS
V.V. PERFURANTES: A pesquisa foi positiva na face medial da perna a + cm da fƔscia plantar.
Veia perfurante incompetente na perna.
OU
V.V. PERFURANTES: A pesquisa foi positiva na face medial da perna a + cm e + cm da fƔscia plantar.
Veias perfurantes incompetentes na perna.
VEIA PERFURANTE POSTERIOR
V.V. PERFURANTES: A pesquisa foi positiva na face posterior da perna a + cm do calcĆ¢neo.
Veia perfurante incompetente na perna.
- {x.split('.', 1)[1]} " - for x in answer.references.split('\n\n')]) - references = "" if reference_list == "" else f""" -
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\ No newline at end of file diff --git a/spaces/contluForse/HuggingGPT/Thunderbolt-Jackie-Chan-English-Subtitle.md b/spaces/contluForse/HuggingGPT/Thunderbolt-Jackie-Chan-English-Subtitle.md deleted file mode 100644 index 88f01f8f925ba78802ba3e525c2d6268574caf94..0000000000000000000000000000000000000000 --- a/spaces/contluForse/HuggingGPT/Thunderbolt-Jackie-Chan-English-Subtitle.md +++ /dev/null @@ -1,70 +0,0 @@ -## Thunderbolt Jackie Chan English Subtitle - - - - - -  - - - - - -**Download File āāāāā>>> [https://www.google.com/url?q=https%3A%2F%2Fgeags.com%2F2txoHq&sa=D&sntz=1&usg=AOvVaw13HwsCs5ygtjZoHMAAxg7T](https://www.google.com/url?q=https%3A%2F%2Fgeags.com%2F2txoHq&sa=D&sntz=1&usg=AOvVaw13HwsCs5ygtjZoHMAAxg7T)** - - - - - - - - - - - - - -# How to Watch Thunderbolt Jackie Chan Movie with English Subtitle - - - -Thunderbolt is a 1995 Hong Kong action film starring Jackie Chan as a sports car mechanic who has to race against a supercriminal to save his kidnapped sister. 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- diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/modules/ffc.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/modules/ffc.py deleted file mode 100644 index e67ff9c832463e5518d6ccea2c6f27531ed778d4..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/modules/ffc.py +++ /dev/null @@ -1,485 +0,0 @@ -# Fast Fourier Convolution NeurIPS 2020 -# original implementation https://github.com/pkumivision/FFC/blob/main/model_zoo/ffc.py -# paper https://proceedings.neurips.cc/paper/2020/file/2fd5d41ec6cfab47e32164d5624269b1-Paper.pdf - -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F - -from annotator.lama.saicinpainting.training.modules.base import get_activation, BaseDiscriminator -from annotator.lama.saicinpainting.training.modules.spatial_transform import LearnableSpatialTransformWrapper -from annotator.lama.saicinpainting.training.modules.squeeze_excitation import SELayer -from annotator.lama.saicinpainting.utils import get_shape - - -class FFCSE_block(nn.Module): - - def __init__(self, channels, ratio_g): - super(FFCSE_block, self).__init__() - in_cg = int(channels * ratio_g) - in_cl = channels - in_cg - r = 16 - - self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) - self.conv1 = nn.Conv2d(channels, channels // r, - kernel_size=1, bias=True) - self.relu1 = nn.ReLU(inplace=True) - self.conv_a2l = None if in_cl == 0 else nn.Conv2d( - channels // r, in_cl, kernel_size=1, bias=True) - self.conv_a2g = None if in_cg == 0 else nn.Conv2d( - channels // r, in_cg, kernel_size=1, bias=True) - self.sigmoid = nn.Sigmoid() - - def forward(self, x): - x = x if type(x) is tuple else (x, 0) - id_l, id_g = x - - x = id_l if type(id_g) is int else torch.cat([id_l, id_g], dim=1) - x = self.avgpool(x) - x = self.relu1(self.conv1(x)) - - x_l = 0 if self.conv_a2l is None else id_l * \ - self.sigmoid(self.conv_a2l(x)) - x_g = 0 if self.conv_a2g is None else id_g * \ - self.sigmoid(self.conv_a2g(x)) - return x_l, x_g - - -class FourierUnit(nn.Module): - - def __init__(self, in_channels, out_channels, groups=1, spatial_scale_factor=None, spatial_scale_mode='bilinear', - spectral_pos_encoding=False, use_se=False, se_kwargs=None, ffc3d=False, fft_norm='ortho'): - # bn_layer not used - super(FourierUnit, self).__init__() - self.groups = groups - - self.conv_layer = torch.nn.Conv2d(in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0), - out_channels=out_channels * 2, - kernel_size=1, stride=1, padding=0, groups=self.groups, bias=False) - self.bn = torch.nn.BatchNorm2d(out_channels * 2) - self.relu = torch.nn.ReLU(inplace=True) - - # squeeze and excitation block - self.use_se = use_se - if use_se: - if se_kwargs is None: - se_kwargs = {} - self.se = SELayer(self.conv_layer.in_channels, **se_kwargs) - - self.spatial_scale_factor = spatial_scale_factor - self.spatial_scale_mode = spatial_scale_mode - self.spectral_pos_encoding = spectral_pos_encoding - self.ffc3d = ffc3d - self.fft_norm = fft_norm - - def forward(self, x): - batch = x.shape[0] - - if self.spatial_scale_factor is not None: - orig_size = x.shape[-2:] - x = F.interpolate(x, scale_factor=self.spatial_scale_factor, mode=self.spatial_scale_mode, align_corners=False) - - r_size = x.size() - # (batch, c, h, w/2+1, 2) - fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1) - ffted = torch.fft.rfftn(x, dim=fft_dim, norm=self.fft_norm) - ffted = torch.stack((ffted.real, ffted.imag), dim=-1) - ffted = ffted.permute(0, 1, 4, 2, 3).contiguous() # (batch, c, 2, h, w/2+1) - ffted = ffted.view((batch, -1,) + ffted.size()[3:]) - - if self.spectral_pos_encoding: - height, width = ffted.shape[-2:] - coords_vert = torch.linspace(0, 1, height)[None, None, :, None].expand(batch, 1, height, width).to(ffted) - coords_hor = torch.linspace(0, 1, width)[None, None, None, :].expand(batch, 1, height, width).to(ffted) - ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1) - - if self.use_se: - ffted = self.se(ffted) - - ffted = self.conv_layer(ffted) # (batch, c*2, h, w/2+1) - ffted = self.relu(self.bn(ffted)) - - ffted = ffted.view((batch, -1, 2,) + ffted.size()[2:]).permute( - 0, 1, 3, 4, 2).contiguous() # (batch,c, t, h, w/2+1, 2) - ffted = torch.complex(ffted[..., 0], ffted[..., 1]) - - ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:] - output = torch.fft.irfftn(ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm) - - if self.spatial_scale_factor is not None: - output = F.interpolate(output, size=orig_size, mode=self.spatial_scale_mode, align_corners=False) - - return output - - -class SeparableFourierUnit(nn.Module): - - def __init__(self, in_channels, out_channels, groups=1, kernel_size=3): - # bn_layer not used - super(SeparableFourierUnit, self).__init__() - self.groups = groups - row_out_channels = out_channels // 2 - col_out_channels = out_channels - row_out_channels - self.row_conv = torch.nn.Conv2d(in_channels=in_channels * 2, - out_channels=row_out_channels * 2, - kernel_size=(kernel_size, 1), # kernel size is always like this, but the data will be transposed - stride=1, padding=(kernel_size // 2, 0), - padding_mode='reflect', - groups=self.groups, bias=False) - self.col_conv = torch.nn.Conv2d(in_channels=in_channels * 2, - out_channels=col_out_channels * 2, - kernel_size=(kernel_size, 1), # kernel size is always like this, but the data will be transposed - stride=1, padding=(kernel_size // 2, 0), - padding_mode='reflect', - groups=self.groups, bias=False) - self.row_bn = torch.nn.BatchNorm2d(row_out_channels * 2) - self.col_bn = torch.nn.BatchNorm2d(col_out_channels * 2) - self.relu = torch.nn.ReLU(inplace=True) - - def process_branch(self, x, conv, bn): - batch = x.shape[0] - - r_size = x.size() - # (batch, c, h, w/2+1, 2) - ffted = torch.fft.rfft(x, norm="ortho") - ffted = torch.stack((ffted.real, ffted.imag), dim=-1) - ffted = ffted.permute(0, 1, 4, 2, 3).contiguous() # (batch, c, 2, h, w/2+1) - ffted = ffted.view((batch, -1,) + ffted.size()[3:]) - - ffted = self.relu(bn(conv(ffted))) - - ffted = ffted.view((batch, -1, 2,) + ffted.size()[2:]).permute( - 0, 1, 3, 4, 2).contiguous() # (batch,c, t, h, w/2+1, 2) - ffted = torch.complex(ffted[..., 0], ffted[..., 1]) - - output = torch.fft.irfft(ffted, s=x.shape[-1:], norm="ortho") - return output - - - def forward(self, x): - rowwise = self.process_branch(x, self.row_conv, self.row_bn) - colwise = self.process_branch(x.permute(0, 1, 3, 2), self.col_conv, self.col_bn).permute(0, 1, 3, 2) - out = torch.cat((rowwise, colwise), dim=1) - return out - - -class SpectralTransform(nn.Module): - - def __init__(self, in_channels, out_channels, stride=1, groups=1, enable_lfu=True, separable_fu=False, **fu_kwargs): - # bn_layer not used - super(SpectralTransform, self).__init__() - self.enable_lfu = enable_lfu - if stride == 2: - self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2) - else: - self.downsample = nn.Identity() - - self.stride = stride - self.conv1 = nn.Sequential( - nn.Conv2d(in_channels, out_channels // - 2, kernel_size=1, groups=groups, bias=False), - nn.BatchNorm2d(out_channels // 2), - nn.ReLU(inplace=True) - ) - fu_class = SeparableFourierUnit if separable_fu else FourierUnit - self.fu = fu_class( - out_channels // 2, out_channels // 2, groups, **fu_kwargs) - if self.enable_lfu: - self.lfu = fu_class( - out_channels // 2, out_channels // 2, groups) - self.conv2 = torch.nn.Conv2d( - out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False) - - def forward(self, x): - - x = self.downsample(x) - x = self.conv1(x) - output = self.fu(x) - - if self.enable_lfu: - n, c, h, w = x.shape - split_no = 2 - split_s = h // split_no - xs = torch.cat(torch.split( - x[:, :c // 4], split_s, dim=-2), dim=1).contiguous() - xs = torch.cat(torch.split(xs, split_s, dim=-1), - dim=1).contiguous() - xs = self.lfu(xs) - xs = xs.repeat(1, 1, split_no, split_no).contiguous() - else: - xs = 0 - - output = self.conv2(x + output + xs) - - return output - - -class FFC(nn.Module): - - def __init__(self, in_channels, out_channels, kernel_size, - ratio_gin, ratio_gout, stride=1, padding=0, - dilation=1, groups=1, bias=False, enable_lfu=True, - padding_type='reflect', gated=False, **spectral_kwargs): - super(FFC, self).__init__() - - assert stride == 1 or stride == 2, "Stride should be 1 or 2." - self.stride = stride - - in_cg = int(in_channels * ratio_gin) - in_cl = in_channels - in_cg - out_cg = int(out_channels * ratio_gout) - out_cl = out_channels - out_cg - #groups_g = 1 if groups == 1 else int(groups * ratio_gout) - #groups_l = 1 if groups == 1 else groups - groups_g - - self.ratio_gin = ratio_gin - self.ratio_gout = ratio_gout - self.global_in_num = in_cg - - module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d - self.convl2l = module(in_cl, out_cl, kernel_size, - stride, padding, dilation, groups, bias, padding_mode=padding_type) - module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d - self.convl2g = module(in_cl, out_cg, kernel_size, - stride, padding, dilation, groups, bias, padding_mode=padding_type) - module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d - self.convg2l = module(in_cg, out_cl, kernel_size, - stride, padding, dilation, groups, bias, padding_mode=padding_type) - module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform - self.convg2g = module( - in_cg, out_cg, stride, 1 if groups == 1 else groups // 2, enable_lfu, **spectral_kwargs) - - self.gated = gated - module = nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d - self.gate = module(in_channels, 2, 1) - - def forward(self, x): - x_l, x_g = x if type(x) is tuple else (x, 0) - out_xl, out_xg = 0, 0 - - if self.gated: - total_input_parts = [x_l] - if torch.is_tensor(x_g): - total_input_parts.append(x_g) - total_input = torch.cat(total_input_parts, dim=1) - - gates = torch.sigmoid(self.gate(total_input)) - g2l_gate, l2g_gate = gates.chunk(2, dim=1) - else: - g2l_gate, l2g_gate = 1, 1 - - if self.ratio_gout != 1: - out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate - if self.ratio_gout != 0: - out_xg = self.convl2g(x_l) * l2g_gate + self.convg2g(x_g) - - return out_xl, out_xg - - -class FFC_BN_ACT(nn.Module): - - def __init__(self, in_channels, out_channels, - kernel_size, ratio_gin, ratio_gout, - stride=1, padding=0, dilation=1, groups=1, bias=False, - norm_layer=nn.BatchNorm2d, activation_layer=nn.Identity, - padding_type='reflect', - enable_lfu=True, **kwargs): - super(FFC_BN_ACT, self).__init__() - self.ffc = FFC(in_channels, out_channels, kernel_size, - ratio_gin, ratio_gout, stride, padding, dilation, - groups, bias, enable_lfu, padding_type=padding_type, **kwargs) - lnorm = nn.Identity if ratio_gout == 1 else norm_layer - gnorm = nn.Identity if ratio_gout == 0 else norm_layer - global_channels = int(out_channels * ratio_gout) - self.bn_l = lnorm(out_channels - global_channels) - self.bn_g = gnorm(global_channels) - - lact = nn.Identity if ratio_gout == 1 else activation_layer - gact = nn.Identity if ratio_gout == 0 else activation_layer - self.act_l = lact(inplace=True) - self.act_g = gact(inplace=True) - - def forward(self, x): - x_l, x_g = self.ffc(x) - x_l = self.act_l(self.bn_l(x_l)) - x_g = self.act_g(self.bn_g(x_g)) - return x_l, x_g - - -class FFCResnetBlock(nn.Module): - def __init__(self, dim, padding_type, norm_layer, activation_layer=nn.ReLU, dilation=1, - spatial_transform_kwargs=None, inline=False, **conv_kwargs): - super().__init__() - self.conv1 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation, - norm_layer=norm_layer, - activation_layer=activation_layer, - padding_type=padding_type, - **conv_kwargs) - self.conv2 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation, - norm_layer=norm_layer, - activation_layer=activation_layer, - padding_type=padding_type, - **conv_kwargs) - if spatial_transform_kwargs is not None: - self.conv1 = LearnableSpatialTransformWrapper(self.conv1, **spatial_transform_kwargs) - self.conv2 = LearnableSpatialTransformWrapper(self.conv2, **spatial_transform_kwargs) - self.inline = inline - - def forward(self, x): - if self.inline: - x_l, x_g = x[:, :-self.conv1.ffc.global_in_num], x[:, -self.conv1.ffc.global_in_num:] - else: - x_l, x_g = x if type(x) is tuple else (x, 0) - - id_l, id_g = x_l, x_g - - x_l, x_g = self.conv1((x_l, x_g)) - x_l, x_g = self.conv2((x_l, x_g)) - - x_l, x_g = id_l + x_l, id_g + x_g - out = x_l, x_g - if self.inline: - out = torch.cat(out, dim=1) - return out - - -class ConcatTupleLayer(nn.Module): - def forward(self, x): - assert isinstance(x, tuple) - x_l, x_g = x - assert torch.is_tensor(x_l) or torch.is_tensor(x_g) - if not torch.is_tensor(x_g): - return x_l - return torch.cat(x, dim=1) - - -class FFCResNetGenerator(nn.Module): - def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d, - padding_type='reflect', activation_layer=nn.ReLU, - up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), - init_conv_kwargs={}, downsample_conv_kwargs={}, resnet_conv_kwargs={}, - spatial_transform_layers=None, spatial_transform_kwargs={}, - add_out_act=True, max_features=1024, out_ffc=False, out_ffc_kwargs={}): - assert (n_blocks >= 0) - super().__init__() - - model = [nn.ReflectionPad2d(3), - FFC_BN_ACT(input_nc, ngf, kernel_size=7, padding=0, norm_layer=norm_layer, - activation_layer=activation_layer, **init_conv_kwargs)] - - ### downsample - for i in range(n_downsampling): - mult = 2 ** i - if i == n_downsampling - 1: - cur_conv_kwargs = dict(downsample_conv_kwargs) - cur_conv_kwargs['ratio_gout'] = resnet_conv_kwargs.get('ratio_gin', 0) - else: - cur_conv_kwargs = downsample_conv_kwargs - model += [FFC_BN_ACT(min(max_features, ngf * mult), - min(max_features, ngf * mult * 2), - kernel_size=3, stride=2, padding=1, - norm_layer=norm_layer, - activation_layer=activation_layer, - **cur_conv_kwargs)] - - mult = 2 ** n_downsampling - feats_num_bottleneck = min(max_features, ngf * mult) - - ### resnet blocks - for i in range(n_blocks): - cur_resblock = FFCResnetBlock(feats_num_bottleneck, padding_type=padding_type, activation_layer=activation_layer, - norm_layer=norm_layer, **resnet_conv_kwargs) - if spatial_transform_layers is not None and i in spatial_transform_layers: - cur_resblock = LearnableSpatialTransformWrapper(cur_resblock, **spatial_transform_kwargs) - model += [cur_resblock] - - model += [ConcatTupleLayer()] - - ### upsample - for i in range(n_downsampling): - mult = 2 ** (n_downsampling - i) - model += [nn.ConvTranspose2d(min(max_features, ngf * mult), - min(max_features, int(ngf * mult / 2)), - kernel_size=3, stride=2, padding=1, output_padding=1), - up_norm_layer(min(max_features, int(ngf * mult / 2))), - up_activation] - - if out_ffc: - model += [FFCResnetBlock(ngf, padding_type=padding_type, activation_layer=activation_layer, - norm_layer=norm_layer, inline=True, **out_ffc_kwargs)] - - model += [nn.ReflectionPad2d(3), - nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)] - if add_out_act: - model.append(get_activation('tanh' if add_out_act is True else add_out_act)) - self.model = nn.Sequential(*model) - - def forward(self, input): - return self.model(input) - - -class FFCNLayerDiscriminator(BaseDiscriminator): - def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, max_features=512, - init_conv_kwargs={}, conv_kwargs={}): - super().__init__() - self.n_layers = n_layers - - def _act_ctor(inplace=True): - return nn.LeakyReLU(negative_slope=0.2, inplace=inplace) - - kw = 3 - padw = int(np.ceil((kw-1.0)/2)) - sequence = [[FFC_BN_ACT(input_nc, ndf, kernel_size=kw, padding=padw, norm_layer=norm_layer, - activation_layer=_act_ctor, **init_conv_kwargs)]] - - nf = ndf - for n in range(1, n_layers): - nf_prev = nf - nf = min(nf * 2, max_features) - - cur_model = [ - FFC_BN_ACT(nf_prev, nf, - kernel_size=kw, stride=2, padding=padw, - norm_layer=norm_layer, - activation_layer=_act_ctor, - **conv_kwargs) - ] - sequence.append(cur_model) - - nf_prev = nf - nf = min(nf * 2, 512) - - cur_model = [ - FFC_BN_ACT(nf_prev, nf, - kernel_size=kw, stride=1, padding=padw, - norm_layer=norm_layer, - activation_layer=lambda *args, **kwargs: nn.LeakyReLU(*args, negative_slope=0.2, **kwargs), - **conv_kwargs), - ConcatTupleLayer() - ] - sequence.append(cur_model) - - sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]] - - for n in range(len(sequence)): - setattr(self, 'model'+str(n), nn.Sequential(*sequence[n])) - - def get_all_activations(self, x): - res = [x] - for n in range(self.n_layers + 2): - model = getattr(self, 'model' + str(n)) - res.append(model(res[-1])) - return res[1:] - - def forward(self, x): - act = self.get_all_activations(x) - feats = [] - for out in act[:-1]: - if isinstance(out, tuple): - if torch.is_tensor(out[1]): - out = torch.cat(out, dim=1) - else: - out = out[0] - feats.append(out) - return act[-1], feats diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/cnn/bricks/conv2d_adaptive_padding.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/cnn/bricks/conv2d_adaptive_padding.py deleted file mode 100644 index b45e758ac6cf8dfb0382d072fe09125bc7e9b888..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/cnn/bricks/conv2d_adaptive_padding.py +++ /dev/null @@ -1,62 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import math - -from torch import nn -from torch.nn import functional as F - -from .registry import CONV_LAYERS - - -@CONV_LAYERS.register_module() -class Conv2dAdaptivePadding(nn.Conv2d): - """Implementation of 2D convolution in tensorflow with `padding` as "same", - which applies padding to input (if needed) so that input image gets fully - covered by filter and stride you specified. For stride 1, this will ensure - that output image size is same as input. For stride of 2, output dimensions - will be half, for example. - - Args: - in_channels (int): Number of channels in the input image - out_channels (int): Number of channels produced by the convolution - kernel_size (int or tuple): Size of the convolving kernel - stride (int or tuple, optional): Stride of the convolution. Default: 1 - padding (int or tuple, optional): Zero-padding added to both sides of - the input. Default: 0 - dilation (int or tuple, optional): Spacing between kernel elements. - Default: 1 - groups (int, optional): Number of blocked connections from input - channels to output channels. Default: 1 - bias (bool, optional): If ``True``, adds a learnable bias to the - output. Default: ``True`` - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - groups=1, - bias=True): - super().__init__(in_channels, out_channels, kernel_size, stride, 0, - dilation, groups, bias) - - def forward(self, x): - img_h, img_w = x.size()[-2:] - kernel_h, kernel_w = self.weight.size()[-2:] - stride_h, stride_w = self.stride - output_h = math.ceil(img_h / stride_h) - output_w = math.ceil(img_w / stride_w) - pad_h = ( - max((output_h - 1) * self.stride[0] + - (kernel_h - 1) * self.dilation[0] + 1 - img_h, 0)) - pad_w = ( - max((output_w - 1) * self.stride[1] + - (kernel_w - 1) * self.dilation[1] + 1 - img_w, 0)) - if pad_h > 0 or pad_w > 0: - x = F.pad(x, [ - pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2 - ]) - return F.conv2d(x, self.weight, self.bias, self.stride, self.padding, - self.dilation, self.groups) diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/export/torchscript.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/export/torchscript.py deleted file mode 100644 index 8ce1c81e1b7abb65415055ae0d1d4b83e1ae111d..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/export/torchscript.py +++ /dev/null @@ -1,132 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. - -import os -import torch - -from annotator.oneformer.detectron2.utils.file_io import PathManager - -from .torchscript_patch import freeze_training_mode, patch_instances - -__all__ = ["scripting_with_instances", "dump_torchscript_IR"] - - -def scripting_with_instances(model, fields): - """ - Run :func:`torch.jit.script` on a model that uses the :class:`Instances` class. Since - attributes of :class:`Instances` are "dynamically" added in eager modeļ¼it is difficult - for scripting to support it out of the box. This function is made to support scripting - a model that uses :class:`Instances`. It does the following: - - 1. Create a scriptable ``new_Instances`` class which behaves similarly to ``Instances``, - but with all attributes been "static". - The attributes need to be statically declared in the ``fields`` argument. - 2. Register ``new_Instances``, and force scripting compiler to - use it when trying to compile ``Instances``. - - After this function, the process will be reverted. User should be able to script another model - using different fields. - - Example: - Assume that ``Instances`` in the model consist of two attributes named - ``proposal_boxes`` and ``objectness_logits`` with type :class:`Boxes` and - :class:`Tensor` respectively during inference. You can call this function like: - :: - fields = {"proposal_boxes": Boxes, "objectness_logits": torch.Tensor} - torchscipt_model = scripting_with_instances(model, fields) - - Note: - It only support models in evaluation mode. - - Args: - model (nn.Module): The input model to be exported by scripting. - fields (Dict[str, type]): Attribute names and corresponding type that - ``Instances`` will use in the model. Note that all attributes used in ``Instances`` - need to be added, regardless of whether they are inputs/outputs of the model. - Data type not defined in detectron2 is not supported for now. - - Returns: - torch.jit.ScriptModule: the model in torchscript format - """ - assert ( - not model.training - ), "Currently we only support exporting models in evaluation mode to torchscript" - - with freeze_training_mode(model), patch_instances(fields): - scripted_model = torch.jit.script(model) - return scripted_model - - -# alias for old name -export_torchscript_with_instances = scripting_with_instances - - -def dump_torchscript_IR(model, dir): - """ - Dump IR of a TracedModule/ScriptModule/Function in various format (code, graph, - inlined graph). Useful for debugging. - - Args: - model (TracedModule/ScriptModule/ScriptFUnction): traced or scripted module - dir (str): output directory to dump files. - """ - dir = os.path.expanduser(dir) - PathManager.mkdirs(dir) - - def _get_script_mod(mod): - if isinstance(mod, torch.jit.TracedModule): - return mod._actual_script_module - return mod - - # Dump pretty-printed code: https://pytorch.org/docs/stable/jit.html#inspecting-code - with PathManager.open(os.path.join(dir, "model_ts_code.txt"), "w") as f: - - def get_code(mod): - # Try a few ways to get code using private attributes. - try: - # This contains more information than just `mod.code` - return _get_script_mod(mod)._c.code - except AttributeError: - pass - try: - return mod.code - except AttributeError: - return None - - def dump_code(prefix, mod): - code = get_code(mod) - name = prefix or "root model" - if code is None: - f.write(f"Could not found code for {name} (type={mod.original_name})\n") - f.write("\n") - else: - f.write(f"\nCode for {name}, type={mod.original_name}:\n") - f.write(code) - f.write("\n") - f.write("-" * 80) - - for name, m in mod.named_children(): - dump_code(prefix + "." + name, m) - - if isinstance(model, torch.jit.ScriptFunction): - f.write(get_code(model)) - else: - dump_code("", model) - - def _get_graph(model): - try: - # Recursively dump IR of all modules - return _get_script_mod(model)._c.dump_to_str(True, False, False) - except AttributeError: - return model.graph.str() - - with PathManager.open(os.path.join(dir, "model_ts_IR.txt"), "w") as f: - f.write(_get_graph(model)) - - # Dump IR of the entire graph (all submodules inlined) - with PathManager.open(os.path.join(dir, "model_ts_IR_inlined.txt"), "w") as f: - f.write(str(model.inlined_graph)) - - if not isinstance(model, torch.jit.ScriptFunction): - # Dump the model structure in pytorch style - with PathManager.open(os.path.join(dir, "model.txt"), "w") as f: - f.write(str(model)) diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/configs/_base_/models/deeplabv3_r50-d8.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/configs/_base_/models/deeplabv3_r50-d8.py deleted file mode 100644 index d7a43bee01422ad4795dd27874e0cd4bb6cbfecf..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/configs/_base_/models/deeplabv3_r50-d8.py +++ /dev/null @@ -1,44 +0,0 @@ -# model settings -norm_cfg = dict(type='SyncBN', requires_grad=True) -model = dict( - type='EncoderDecoder', - pretrained='open-mmlab://resnet50_v1c', - backbone=dict( - type='ResNetV1c', - depth=50, - num_stages=4, - out_indices=(0, 1, 2, 3), - dilations=(1, 1, 2, 4), - strides=(1, 2, 1, 1), - norm_cfg=norm_cfg, - norm_eval=False, - style='pytorch', - contract_dilation=True), - decode_head=dict( - type='ASPPHead', - in_channels=2048, - in_index=3, - channels=512, - dilations=(1, 12, 24, 36), - dropout_ratio=0.1, - num_classes=19, - norm_cfg=norm_cfg, - align_corners=False, - loss_decode=dict( - type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), - auxiliary_head=dict( - type='FCNHead', - in_channels=1024, - in_index=2, - channels=256, - num_convs=1, - concat_input=False, - dropout_ratio=0.1, - num_classes=19, - norm_cfg=norm_cfg, - align_corners=False, - loss_decode=dict( - type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), - # model training and testing settings - train_cfg=dict(), - test_cfg=dict(mode='whole')) diff --git a/spaces/cymic/Talking_Head_Anime_3/tha3/nn/common/poser_args.py b/spaces/cymic/Talking_Head_Anime_3/tha3/nn/common/poser_args.py deleted file mode 100644 index d5a23aa02c18ce9a196229bc8a938c100920a70c..0000000000000000000000000000000000000000 --- a/spaces/cymic/Talking_Head_Anime_3/tha3/nn/common/poser_args.py +++ /dev/null @@ -1,68 +0,0 @@ -from typing import Optional - -from torch.nn import Sigmoid, Sequential, Tanh - -from tha3.nn.conv import create_conv3, create_conv3_from_block_args -from tha3.nn.nonlinearity_factory import ReLUFactory -from tha3.nn.normalization import InstanceNorm2dFactory -from tha3.nn.util import BlockArgs - - -class PoserArgs00: - def __init__(self, - image_size: int, - input_image_channels: int, - output_image_channels: int, - start_channels: int, - num_pose_params: int, - block_args: Optional[BlockArgs] = None): - self.num_pose_params = num_pose_params - self.start_channels = start_channels - self.output_image_channels = output_image_channels - self.input_image_channels = input_image_channels - self.image_size = image_size - if block_args is None: - self.block_args = BlockArgs( - normalization_layer_factory=InstanceNorm2dFactory(), - nonlinearity_factory=ReLUFactory(inplace=True)) - else: - self.block_args = block_args - - def create_alpha_block(self): - from torch.nn import Sequential - return Sequential( - create_conv3( - in_channels=self.start_channels, - out_channels=1, - bias=True, - initialization_method=self.block_args.initialization_method, - use_spectral_norm=False), - Sigmoid()) - - def create_all_channel_alpha_block(self): - from torch.nn import Sequential - return Sequential( - create_conv3( - in_channels=self.start_channels, - out_channels=self.output_image_channels, - bias=True, - initialization_method=self.block_args.initialization_method, - use_spectral_norm=False), - Sigmoid()) - - def create_color_change_block(self): - return Sequential( - create_conv3_from_block_args( - in_channels=self.start_channels, - out_channels=self.output_image_channels, - bias=True, - block_args=self.block_args), - Tanh()) - - def create_grid_change_block(self): - return create_conv3( - in_channels=self.start_channels, - out_channels=2, - bias=False, - initialization_method='zero', - use_spectral_norm=False) \ No newline at end of file diff --git a/spaces/davanstrien/label-studio/README.md b/spaces/davanstrien/label-studio/README.md deleted file mode 100644 index 20aba09f80ff594c8b68f1de9662b21f05b9d9d9..0000000000000000000000000000000000000000 --- a/spaces/davanstrien/label-studio/README.md +++ /dev/null @@ -1,148 +0,0 @@ ---- -title: LabelStudio -emoji: š§ -colorFrom: yellow -colorTo: purple -sdk: docker -tags: -- label-studio -fullwidth: true -license: apache-2.0 -app_port: 8080 -duplicated_from: LabelStudio/LabelStudio ---- -
-
-[Website](https://hubs.ly/Q01CNgsd0) ā¢ [Docs](https://hubs.ly/Q01CN9Yq0) ā¢ [12K+ GitHub āļø!](https://hubs.ly/Q01CNbPQ0) ā¢ [Slack Community](https://hubs.ly/Q01CNb9H0)
-
-## What is Label Studio?
-
-Label Studio is an open source data labeling platform. It lets you label audio,
-text, images, videos, and time series data with a simple, straightforward, and
-highly-configurable user interface. Label Studio can prepare new data or
-improve existing training data to get more accurate ML models.
-
-
-## Label Studio in Hugging Face Spaces
-
-The Label Studio community is thrilled to offer Label Studio as a Hugging Face
-Spaces application. You can try the data-annotation interface, connect popular
-machine learning models, and share the application with collaborators. You can
-start immediately by creating an account or replicate the space and work in
-your own environment.
-
-## Creating a Use Account and Logging In
-
-Begin by creating a new account in the Label Studio space, then log in with your
-credentials.
-
-**By default, these spaces permit anyone to create a new login
-account, allowing them to view and modify project configuration, data sets, and
-annotations. Without any modifications, treat this space like a demo environment.**
-
-## Creating a Labeling Project
-
-After logging in, Label Studio will present you with a project view. Here you
-can create a new project with prompts to upload data and set up a custom
-configuration interface.
-
-**Note that in the default configuration, storage is local and temporary. Any
-projects, annotations, and configurations will be lost if the space is restarted.**
-
-## Next Steps and Additional Resources
-
-To help with getting started, the Label Studio community curated a list of
-resources including tutorials and documentation.
-
-- š [Zero to One with Label Studio Tutorial](https://labelstud.io/blog/introduction-to-label-studio-in-hugging-face-spaces/)
-- š [Try Label Studio Enterprise](https://hubs.ly/Q01CMLll0)
-- š¤ [Tutorial: Using Label Studio with Hugging Face Datasets Hub](https://danielvanstrien.xyz/huggingface/huggingface-datasets/annotation/full%20stack%20deep%20learning%20notes/2022/09/07/label-studio-annotations-hub.html)
-- š” [Label Studio Docs](https://hubs.ly/Q01CN9Yq0)
-
-
-
-
-### Making your Label Studio Hugging Face Space production-ready
-
-By default this space allows for the unrestricted creation of new accounts
-will full access to all projects and data. This is great for trying out
-Label Studio and collaborating on projects, but you may want to restrict
-access to your space to only authorized users. Add the following environment
-variable to your spaces Dockerfile to disable public account creation for
-this space.
-
- ENV LABEL_STUDIO_DISABLE_SIGNUP_WITHOUT_LINK=true
-
-Set secrets in your space to create an inital user, and log in with your
-provided username and password. Do not set these in your Dockerfile, as they
-globally visible on a public space.
-
- LABEL_STUDIO_USERNAME
- LABEL_STUDIO_PASSWORD
-
-You will need to provide new users with an invitation link to join the space,
-which can be found in the Organizations interface of Label Studio
-
-By default this space stores all project configuration and data annotations
-in local storage with Sqlite. If the space is reset, all configuration and
-annotation data in the space will be lost. You can enable configuration
-persistence by connecting an external Postgres database to your space,
-guaranteeing that all project and annotation settings are preserved.
-
-Set the following secret variables to match your own hosted instance of
-Postgres. We strongly recommend setting these as secrets to prevent leaking
-information about your database service to the public in your spaces
-definition.
-
- DJANGO_DB=default
- POSTGRE_NAME=- POSTGRE_PORT= - POSTGRE_USER= - POSTGRE_PASSWORD= - POSTGRE_PORT= - POSTGRE_HOST= - -Add the following environment variable to remove the warning about ephemeral -storage. - - ENV STORAGE_PERSISTENCE=1 - -Note that you will need to connect cloud storage to host data items that you -want to annotate, as local storage will not be preserved across a space reset. - -By default the only data storage enabled for this space is local. In the case -of a space reset, all data will be lost. To enable permanent storage, you -must enable a cloud storage connector. We also strongly recommend enabling -configuration persistence to preserve project data, annotations, and user -settings. Choose the appropriate cloud connector and configure the secrets -for it. - -#### Amazon S3 - STORAGE_TYPE=s3 - STORAGE_AWS_ACCESS_KEY_ID=" " - STORAGE_AWS_SECRET_ACCESS_KEY=" " - STORAGE_AWS_BUCKET_NAME=" " - STORAGE_AWS_REGION_NAME=" " - STORAGE_AWS_FOLDER="" - -#### Google Cloud Storage - - STORAGE_TYPE=gcs - STORAGE_GCS_BUCKET_NAME=" " - STORAGE_GCS_PROJECT_ID=" " - STORAGE_GCS_FOLDER="" - GOOGLE_APPLICATION_CREDENTIALS="/opt/heartex/secrets/key.json" - -Azure Blob Storage -================== - - STORAGE_TYPE=azure - STORAGE_AZURE_ACCOUNT_NAME=" " - STORAGE_AZURE_ACCOUNT_KEY=" " - STORAGE_AZURE_CONTAINER_NAME=" " - STORAGE_AZURE_FOLDER="" - - -## Questions? Concerns? Want to get involved? - -Email the community team at [community@labelstud.io](mailto:community@labelstud.io) diff --git a/spaces/davda54/chat-nort5/configuration_norbert.py b/spaces/davda54/chat-nort5/configuration_norbert.py deleted file mode 100644 index 450a0286801acce50a7dd9378efa34391e1ca918..0000000000000000000000000000000000000000 --- a/spaces/davda54/chat-nort5/configuration_norbert.py +++ /dev/null @@ -1,34 +0,0 @@ -from transformers.configuration_utils import PretrainedConfig - - -class NorbertConfig(PretrainedConfig): - """Configuration class to store the configuration of a `NorbertModel`. - """ - def __init__( - self, - vocab_size=50000, - attention_probs_dropout_prob=0.1, - hidden_dropout_prob=0.1, - hidden_size=768, - intermediate_size=2048, - max_position_embeddings=512, - position_bucket_size=32, - num_attention_heads=12, - num_hidden_layers=12, - layer_norm_eps=1.0e-7, - output_all_encoded_layers=True, - **kwargs, - ): - super().__init__(**kwargs) - - self.vocab_size = vocab_size - self.hidden_size = hidden_size - self.num_hidden_layers = num_hidden_layers - self.num_attention_heads = num_attention_heads - self.intermediate_size = intermediate_size - self.hidden_dropout_prob = hidden_dropout_prob - self.attention_probs_dropout_prob = attention_probs_dropout_prob - self.max_position_embeddings = max_position_embeddings - self.output_all_encoded_layers = output_all_encoded_layers - self.position_bucket_size = position_bucket_size - self.layer_norm_eps = layer_norm_eps diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/anyio/_core/_subprocesses.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/anyio/_core/_subprocesses.py deleted file mode 100644 index 1a26ac8c7ff908341c25d2464972160fbe170a65..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/anyio/_core/_subprocesses.py +++ /dev/null @@ -1,135 +0,0 @@ -from __future__ import annotations - -from io import BytesIO -from os import PathLike -from subprocess import DEVNULL, PIPE, CalledProcessError, CompletedProcess -from typing import ( - IO, - Any, - AsyncIterable, - Mapping, - Sequence, - cast, -) - -from ..abc import Process -from ._eventloop import get_asynclib -from ._tasks import create_task_group - - -async def run_process( - command: str | bytes | Sequence[str | bytes], - *, - input: bytes | None = None, - stdout: int | IO[Any] | None = PIPE, - stderr: int | IO[Any] | None = PIPE, - check: bool = True, - cwd: str | bytes | PathLike[str] | None = None, - env: Mapping[str, str] | None = None, - start_new_session: bool = False, -) -> CompletedProcess[bytes]: - """ - Run an external command in a subprocess and wait until it completes. - - .. seealso:: :func:`subprocess.run` - - :param command: either a string to pass to the shell, or an iterable of strings containing the - executable name or path and its arguments - :param input: bytes passed to the standard input of the subprocess - :param stdout: either :data:`subprocess.PIPE` or :data:`subprocess.DEVNULL` - :param stderr: one of :data:`subprocess.PIPE`, :data:`subprocess.DEVNULL` or - :data:`subprocess.STDOUT` - :param check: if ``True``, raise :exc:`~subprocess.CalledProcessError` if the process - terminates with a return code other than 0 - :param cwd: If not ``None``, change the working directory to this before running the command - :param env: if not ``None``, this mapping replaces the inherited environment variables from the - parent process - :param start_new_session: if ``true`` the setsid() system call will be made in the child - process prior to the execution of the subprocess. (POSIX only) - :return: an object representing the completed process - :raises ~subprocess.CalledProcessError: if ``check`` is ``True`` and the process exits with a - nonzero return code - - """ - - async def drain_stream(stream: AsyncIterable[bytes], index: int) -> None: - buffer = BytesIO() - async for chunk in stream: - buffer.write(chunk) - - stream_contents[index] = buffer.getvalue() - - async with await open_process( - command, - stdin=PIPE if input else DEVNULL, - stdout=stdout, - stderr=stderr, - cwd=cwd, - env=env, - start_new_session=start_new_session, - ) as process: - stream_contents: list[bytes | None] = [None, None] - try: - async with create_task_group() as tg: - if process.stdout: - tg.start_soon(drain_stream, process.stdout, 0) - if process.stderr: - tg.start_soon(drain_stream, process.stderr, 1) - if process.stdin and input: - await process.stdin.send(input) - await process.stdin.aclose() - - await process.wait() - except BaseException: - process.kill() - raise - - output, errors = stream_contents - if check and process.returncode != 0: - raise CalledProcessError(cast(int, process.returncode), command, output, errors) - - return CompletedProcess(command, cast(int, process.returncode), output, errors) - - -async def open_process( - command: str | bytes | Sequence[str | bytes], - *, - stdin: int | IO[Any] | None = PIPE, - stdout: int | IO[Any] | None = PIPE, - stderr: int | IO[Any] | None = PIPE, - cwd: str | bytes | PathLike[str] | None = None, - env: Mapping[str, str] | None = None, - start_new_session: bool = False, -) -> Process: - """ - Start an external command in a subprocess. - - .. seealso:: :class:`subprocess.Popen` - - :param command: either a string to pass to the shell, or an iterable of strings containing the - executable name or path and its arguments - :param stdin: one of :data:`subprocess.PIPE`, :data:`subprocess.DEVNULL`, a - file-like object, or ``None`` - :param stdout: one of :data:`subprocess.PIPE`, :data:`subprocess.DEVNULL`, - a file-like object, or ``None`` - :param stderr: one of :data:`subprocess.PIPE`, :data:`subprocess.DEVNULL`, - :data:`subprocess.STDOUT`, a file-like object, or ``None`` - :param cwd: If not ``None``, the working directory is changed before executing - :param env: If env is not ``None``, it must be a mapping that defines the environment - variables for the new process - :param start_new_session: if ``true`` the setsid() system call will be made in the child - process prior to the execution of the subprocess. (POSIX only) - :return: an asynchronous process object - - """ - shell = isinstance(command, str) - return await get_asynclib().open_process( - command, - shell=shell, - stdin=stdin, - stdout=stdout, - stderr=stderr, - cwd=cwd, - env=env, - start_new_session=start_new_session, - ) diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/varLib/varStore.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/varLib/varStore.py deleted file mode 100644 index 55d70e278d4ead2f4734d32d577152f058598c04..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/varLib/varStore.py +++ /dev/null @@ -1,703 +0,0 @@ -from fontTools.misc.roundTools import noRound, otRound -from fontTools.misc.intTools import bit_count -from fontTools.ttLib.tables import otTables as ot -from fontTools.varLib.models import supportScalar -from fontTools.varLib.builder import ( - buildVarRegionList, - buildVarStore, - buildVarRegion, - buildVarData, -) -from functools import partial -from collections import defaultdict -from heapq import heappush, heappop - - -NO_VARIATION_INDEX = ot.NO_VARIATION_INDEX -ot.VarStore.NO_VARIATION_INDEX = NO_VARIATION_INDEX - - -def _getLocationKey(loc): - return tuple(sorted(loc.items(), key=lambda kv: kv[0])) - - -class OnlineVarStoreBuilder(object): - def __init__(self, axisTags): - self._axisTags = axisTags - self._regionMap = {} - self._regionList = buildVarRegionList([], axisTags) - self._store = buildVarStore(self._regionList, []) - self._data = None - self._model = None - self._supports = None - self._varDataIndices = {} - self._varDataCaches = {} - self._cache = {} - - def setModel(self, model): - self.setSupports(model.supports) - self._model = model - - def setSupports(self, supports): - self._model = None - self._supports = list(supports) - if not self._supports[0]: - del self._supports[0] # Drop base master support - self._cache = {} - self._data = None - - def finish(self, optimize=True): - self._regionList.RegionCount = len(self._regionList.Region) - self._store.VarDataCount = len(self._store.VarData) - for data in self._store.VarData: - data.ItemCount = len(data.Item) - data.calculateNumShorts(optimize=optimize) - return self._store - - def _add_VarData(self): - regionMap = self._regionMap - regionList = self._regionList - - regions = self._supports - regionIndices = [] - for region in regions: - key = _getLocationKey(region) - idx = regionMap.get(key) - if idx is None: - varRegion = buildVarRegion(region, self._axisTags) - idx = regionMap[key] = len(regionList.Region) - regionList.Region.append(varRegion) - regionIndices.append(idx) - - # Check if we have one already... - key = tuple(regionIndices) - varDataIdx = self._varDataIndices.get(key) - if varDataIdx is not None: - self._outer = varDataIdx - self._data = self._store.VarData[varDataIdx] - self._cache = self._varDataCaches[key] - if len(self._data.Item) == 0xFFFF: - # This is full. Need new one. - varDataIdx = None - - if varDataIdx is None: - self._data = buildVarData(regionIndices, [], optimize=False) - self._outer = len(self._store.VarData) - self._store.VarData.append(self._data) - self._varDataIndices[key] = self._outer - if key not in self._varDataCaches: - self._varDataCaches[key] = {} - self._cache = self._varDataCaches[key] - - def storeMasters(self, master_values, *, round=round): - deltas = self._model.getDeltas(master_values, round=round) - base = deltas.pop(0) - return base, self.storeDeltas(deltas, round=noRound) - - def storeDeltas(self, deltas, *, round=round): - deltas = [round(d) for d in deltas] - if len(deltas) == len(self._supports) + 1: - deltas = tuple(deltas[1:]) - else: - assert len(deltas) == len(self._supports) - deltas = tuple(deltas) - - varIdx = self._cache.get(deltas) - if varIdx is not None: - return varIdx - - if not self._data: - self._add_VarData() - inner = len(self._data.Item) - if inner == 0xFFFF: - # Full array. Start new one. - self._add_VarData() - return self.storeDeltas(deltas) - self._data.addItem(deltas, round=noRound) - - varIdx = (self._outer << 16) + inner - self._cache[deltas] = varIdx - return varIdx - - -def VarData_addItem(self, deltas, *, round=round): - deltas = [round(d) for d in deltas] - - countUs = self.VarRegionCount - countThem = len(deltas) - if countUs + 1 == countThem: - deltas = tuple(deltas[1:]) - else: - assert countUs == countThem, (countUs, countThem) - deltas = tuple(deltas) - self.Item.append(list(deltas)) - self.ItemCount = len(self.Item) - - -ot.VarData.addItem = VarData_addItem - - -def VarRegion_get_support(self, fvar_axes): - return { - fvar_axes[i].axisTag: (reg.StartCoord, reg.PeakCoord, reg.EndCoord) - for i, reg in enumerate(self.VarRegionAxis) - if reg.PeakCoord != 0 - } - - -ot.VarRegion.get_support = VarRegion_get_support - - -def VarStore___bool__(self): - return bool(self.VarData) - - -ot.VarStore.__bool__ = VarStore___bool__ - - -class VarStoreInstancer(object): - def __init__(self, varstore, fvar_axes, location={}): - self.fvar_axes = fvar_axes - assert varstore is None or varstore.Format == 1 - self._varData = varstore.VarData if varstore else [] - self._regions = varstore.VarRegionList.Region if varstore else [] - self.setLocation(location) - - def setLocation(self, location): - self.location = dict(location) - self._clearCaches() - - def _clearCaches(self): - self._scalars = {} - - def _getScalar(self, regionIdx): - scalar = self._scalars.get(regionIdx) - if scalar is None: - support = self._regions[regionIdx].get_support(self.fvar_axes) - scalar = supportScalar(self.location, support) - self._scalars[regionIdx] = scalar - return scalar - - @staticmethod - def interpolateFromDeltasAndScalars(deltas, scalars): - delta = 0.0 - for d, s in zip(deltas, scalars): - if not s: - continue - delta += d * s - return delta - - def __getitem__(self, varidx): - major, minor = varidx >> 16, varidx & 0xFFFF - if varidx == NO_VARIATION_INDEX: - return 0.0 - varData = self._varData - scalars = [self._getScalar(ri) for ri in varData[major].VarRegionIndex] - deltas = varData[major].Item[minor] - return self.interpolateFromDeltasAndScalars(deltas, scalars) - - def interpolateFromDeltas(self, varDataIndex, deltas): - varData = self._varData - scalars = [self._getScalar(ri) for ri in varData[varDataIndex].VarRegionIndex] - return self.interpolateFromDeltasAndScalars(deltas, scalars) - - -# -# Optimizations -# -# retainFirstMap - If true, major 0 mappings are retained. Deltas for unused indices are zeroed -# advIdxes - Set of major 0 indices for advance deltas to be listed first. Other major 0 indices follow. - - -def VarStore_subset_varidxes( - self, varIdxes, optimize=True, retainFirstMap=False, advIdxes=set() -): - # Sort out used varIdxes by major/minor. - used = {} - for varIdx in varIdxes: - if varIdx == NO_VARIATION_INDEX: - continue - major = varIdx >> 16 - minor = varIdx & 0xFFFF - d = used.get(major) - if d is None: - d = used[major] = set() - d.add(minor) - del varIdxes - - # - # Subset VarData - # - - varData = self.VarData - newVarData = [] - varDataMap = {NO_VARIATION_INDEX: NO_VARIATION_INDEX} - for major, data in enumerate(varData): - usedMinors = used.get(major) - if usedMinors is None: - continue - newMajor = len(newVarData) - newVarData.append(data) - - items = data.Item - newItems = [] - if major == 0 and retainFirstMap: - for minor in range(len(items)): - newItems.append( - items[minor] if minor in usedMinors else [0] * len(items[minor]) - ) - varDataMap[minor] = minor - else: - if major == 0: - minors = sorted(advIdxes) + sorted(usedMinors - advIdxes) - else: - minors = sorted(usedMinors) - for minor in minors: - newMinor = len(newItems) - newItems.append(items[minor]) - varDataMap[(major << 16) + minor] = (newMajor << 16) + newMinor - - data.Item = newItems - data.ItemCount = len(data.Item) - - data.calculateNumShorts(optimize=optimize) - - self.VarData = newVarData - self.VarDataCount = len(self.VarData) - - self.prune_regions() - - return varDataMap - - -ot.VarStore.subset_varidxes = VarStore_subset_varidxes - - -def VarStore_prune_regions(self): - """Remove unused VarRegions.""" - # - # Subset VarRegionList - # - - # Collect. - usedRegions = set() - for data in self.VarData: - usedRegions.update(data.VarRegionIndex) - # Subset. - regionList = self.VarRegionList - regions = regionList.Region - newRegions = [] - regionMap = {} - for i in sorted(usedRegions): - regionMap[i] = len(newRegions) - newRegions.append(regions[i]) - regionList.Region = newRegions - regionList.RegionCount = len(regionList.Region) - # Map. - for data in self.VarData: - data.VarRegionIndex = [regionMap[i] for i in data.VarRegionIndex] - - -ot.VarStore.prune_regions = VarStore_prune_regions - - -def _visit(self, func): - """Recurse down from self, if type of an object is ot.Device, - call func() on it. Works on otData-style classes.""" - - if type(self) == ot.Device: - func(self) - - elif isinstance(self, list): - for that in self: - _visit(that, func) - - elif hasattr(self, "getConverters") and not hasattr(self, "postRead"): - for conv in self.getConverters(): - that = getattr(self, conv.name, None) - if that is not None: - _visit(that, func) - - elif isinstance(self, ot.ValueRecord): - for that in self.__dict__.values(): - _visit(that, func) - - -def _Device_recordVarIdx(self, s): - """Add VarIdx in this Device table (if any) to the set s.""" - if self.DeltaFormat == 0x8000: - s.add((self.StartSize << 16) + self.EndSize) - - -def Object_collect_device_varidxes(self, varidxes): - adder = partial(_Device_recordVarIdx, s=varidxes) - _visit(self, adder) - - -ot.GDEF.collect_device_varidxes = Object_collect_device_varidxes -ot.GPOS.collect_device_varidxes = Object_collect_device_varidxes - - -def _Device_mapVarIdx(self, mapping, done): - """Map VarIdx in this Device table (if any) through mapping.""" - if id(self) in done: - return - done.add(id(self)) - if self.DeltaFormat == 0x8000: - varIdx = mapping[(self.StartSize << 16) + self.EndSize] - self.StartSize = varIdx >> 16 - self.EndSize = varIdx & 0xFFFF - - -def Object_remap_device_varidxes(self, varidxes_map): - mapper = partial(_Device_mapVarIdx, mapping=varidxes_map, done=set()) - _visit(self, mapper) - - -ot.GDEF.remap_device_varidxes = Object_remap_device_varidxes -ot.GPOS.remap_device_varidxes = Object_remap_device_varidxes - - -class _Encoding(object): - def __init__(self, chars): - self.chars = chars - self.width = bit_count(chars) - self.columns = self._columns(chars) - self.overhead = self._characteristic_overhead(self.columns) - self.items = set() - - def append(self, row): - self.items.add(row) - - def extend(self, lst): - self.items.update(lst) - - def get_room(self): - """Maximum number of bytes that can be added to characteristic - while still being beneficial to merge it into another one.""" - count = len(self.items) - return max(0, (self.overhead - 1) // count - self.width) - - room = property(get_room) - - def get_gain(self): - """Maximum possible byte gain from merging this into another - characteristic.""" - count = len(self.items) - return max(0, self.overhead - count) - - gain = property(get_gain) - - def gain_sort_key(self): - return self.gain, self.chars - - def width_sort_key(self): - return self.width, self.chars - - @staticmethod - def _characteristic_overhead(columns): - """Returns overhead in bytes of encoding this characteristic - as a VarData.""" - c = 4 + 6 # 4 bytes for LOffset, 6 bytes for VarData header - c += bit_count(columns) * 2 - return c - - @staticmethod - def _columns(chars): - cols = 0 - i = 1 - while chars: - if chars & 0b1111: - cols |= i - chars >>= 4 - i <<= 1 - return cols - - def gain_from_merging(self, other_encoding): - combined_chars = other_encoding.chars | self.chars - combined_width = bit_count(combined_chars) - combined_columns = self.columns | other_encoding.columns - combined_overhead = _Encoding._characteristic_overhead(combined_columns) - combined_gain = ( - +self.overhead - + other_encoding.overhead - - combined_overhead - - (combined_width - self.width) * len(self.items) - - (combined_width - other_encoding.width) * len(other_encoding.items) - ) - return combined_gain - - -class _EncodingDict(dict): - def __missing__(self, chars): - r = self[chars] = _Encoding(chars) - return r - - def add_row(self, row): - chars = self._row_characteristics(row) - self[chars].append(row) - - @staticmethod - def _row_characteristics(row): - """Returns encoding characteristics for a row.""" - longWords = False - - chars = 0 - i = 1 - for v in row: - if v: - chars += i - if not (-128 <= v <= 127): - chars += i * 0b0010 - if not (-32768 <= v <= 32767): - longWords = True - break - i <<= 4 - - if longWords: - # Redo; only allow 2byte/4byte encoding - chars = 0 - i = 1 - for v in row: - if v: - chars += i * 0b0011 - if not (-32768 <= v <= 32767): - chars += i * 0b1100 - i <<= 4 - - return chars - - -def VarStore_optimize(self, use_NO_VARIATION_INDEX=True, quantization=1): - """Optimize storage. Returns mapping from old VarIdxes to new ones.""" - - # Overview: - # - # For each VarData row, we first extend it with zeroes to have - # one column per region in VarRegionList. We then group the - # rows into _Encoding objects, by their "characteristic" bitmap. - # The characteristic bitmap is a binary number representing how - # many bytes each column of the data takes up to encode. Each - # column is encoded in four bits. For example, if a column has - # only values in the range -128..127, it would only have a single - # bit set in the characteristic bitmap for that column. If it has - # values in the range -32768..32767, it would have two bits set. - # The number of ones in the characteristic bitmap is the "width" - # of the encoding. - # - # Each encoding as such has a number of "active" (ie. non-zero) - # columns. The overhead of encoding the characteristic bitmap - # is 10 bytes, plus 2 bytes per active column. - # - # When an encoding is merged into another one, if the characteristic - # of the old encoding is a subset of the new one, then the overhead - # of the old encoding is completely eliminated. However, each row - # now would require more bytes to encode, to the tune of one byte - # per characteristic bit that is active in the new encoding but not - # in the old one. The number of bits that can be added to an encoding - # while still beneficial to merge it into another encoding is called - # the "room" for that encoding. - # - # The "gain" of an encodings is the maximum number of bytes we can - # save by merging it into another encoding. The "gain" of merging - # two encodings is how many bytes we save by doing so. - # - # High-level algorithm: - # - # - Each encoding has a minimal way to encode it. However, because - # of the overhead of encoding the characteristic bitmap, it may - # be beneficial to merge two encodings together, if there is - # gain in doing so. As such, we need to search for the best - # such successive merges. - # - # Algorithm: - # - # - Put all encodings into a "todo" list. - # - # - Sort todo list by decreasing gain (for stability). - # - # - Make a priority-queue of the gain from combining each two - # encodings in the todo list. The priority queue is sorted by - # decreasing gain. Only positive gains are included. - # - # - While priority queue is not empty: - # - Pop the first item from the priority queue, - # - Merge the two encodings it represents, - # - Remove the two encodings from the todo list, - # - Insert positive gains from combining the new encoding with - # all existing todo list items into the priority queue, - # - If a todo list item with the same characteristic bitmap as - # the new encoding exists, remove it from the todo list and - # merge it into the new encoding. - # - Insert the new encoding into the todo list, - # - # - Encode all remaining items in the todo list. - - # TODO - # Check that no two VarRegions are the same; if they are, fold them. - - n = len(self.VarRegionList.Region) # Number of columns - zeroes = [0] * n - - front_mapping = {} # Map from old VarIdxes to full row tuples - - encodings = _EncodingDict() - - # Collect all items into a set of full rows (with lots of zeroes.) - for major, data in enumerate(self.VarData): - regionIndices = data.VarRegionIndex - - for minor, item in enumerate(data.Item): - row = list(zeroes) - - if quantization == 1: - for regionIdx, v in zip(regionIndices, item): - row[regionIdx] += v - else: - for regionIdx, v in zip(regionIndices, item): - row[regionIdx] += ( - round(v / quantization) * quantization - ) # TODO https://github.com/fonttools/fonttools/pull/3126#discussion_r1205439785 - - row = tuple(row) - - if use_NO_VARIATION_INDEX and not any(row): - front_mapping[(major << 16) + minor] = None - continue - - encodings.add_row(row) - front_mapping[(major << 16) + minor] = row - - # Prepare for the main algorithm. - todo = sorted(encodings.values(), key=_Encoding.gain_sort_key) - del encodings - - # Repeatedly pick two best encodings to combine, and combine them. - - heap = [] - for i, encoding in enumerate(todo): - for j in range(i + 1, len(todo)): - other_encoding = todo[j] - combining_gain = encoding.gain_from_merging(other_encoding) - if combining_gain > 0: - heappush(heap, (-combining_gain, i, j)) - - while heap: - _, i, j = heappop(heap) - if todo[i] is None or todo[j] is None: - continue - - encoding, other_encoding = todo[i], todo[j] - todo[i], todo[j] = None, None - - # Combine the two encodings - combined_chars = other_encoding.chars | encoding.chars - combined_encoding = _Encoding(combined_chars) - combined_encoding.extend(encoding.items) - combined_encoding.extend(other_encoding.items) - - for k, enc in enumerate(todo): - if enc is None: - continue - - # In the unlikely event that the same encoding exists already, - # combine it. - if enc.chars == combined_chars: - combined_encoding.extend(enc.items) - todo[k] = None - continue - - combining_gain = combined_encoding.gain_from_merging(enc) - if combining_gain > 0: - heappush(heap, (-combining_gain, k, len(todo))) - - todo.append(combined_encoding) - - encodings = [encoding for encoding in todo if encoding is not None] - - # Assemble final store. - back_mapping = {} # Mapping from full rows to new VarIdxes - encodings.sort(key=_Encoding.width_sort_key) - self.VarData = [] - for major, encoding in enumerate(encodings): - data = ot.VarData() - self.VarData.append(data) - data.VarRegionIndex = range(n) - data.VarRegionCount = len(data.VarRegionIndex) - data.Item = sorted(encoding.items) - for minor, item in enumerate(data.Item): - back_mapping[item] = (major << 16) + minor - - # Compile final mapping. - varidx_map = {NO_VARIATION_INDEX: NO_VARIATION_INDEX} - for k, v in front_mapping.items(): - varidx_map[k] = back_mapping[v] if v is not None else NO_VARIATION_INDEX - - # Remove unused regions. - self.prune_regions() - - # Recalculate things and go home. - self.VarRegionList.RegionCount = len(self.VarRegionList.Region) - self.VarDataCount = len(self.VarData) - for data in self.VarData: - data.ItemCount = len(data.Item) - data.optimize() - - return varidx_map - - -ot.VarStore.optimize = VarStore_optimize - - -def main(args=None): - """Optimize a font's GDEF variation store""" - from argparse import ArgumentParser - from fontTools import configLogger - from fontTools.ttLib import TTFont - from fontTools.ttLib.tables.otBase import OTTableWriter - - parser = ArgumentParser(prog="varLib.varStore", description=main.__doc__) - parser.add_argument("--quantization", type=int, default=1) - parser.add_argument("fontfile") - parser.add_argument("outfile", nargs="?") - options = parser.parse_args(args) - - # TODO: allow user to configure logging via command-line options - configLogger(level="INFO") - - quantization = options.quantization - fontfile = options.fontfile - outfile = options.outfile - - font = TTFont(fontfile) - gdef = font["GDEF"] - store = gdef.table.VarStore - - writer = OTTableWriter() - store.compile(writer, font) - size = len(writer.getAllData()) - print("Before: %7d bytes" % size) - - varidx_map = store.optimize(quantization=quantization) - - writer = OTTableWriter() - store.compile(writer, font) - size = len(writer.getAllData()) - print("After: %7d bytes" % size) - - if outfile is not None: - gdef.table.remap_device_varidxes(varidx_map) - if "GPOS" in font: - font["GPOS"].table.remap_device_varidxes(varidx_map) - - font.save(outfile) - - -if __name__ == "__main__": - import sys - - if len(sys.argv) > 1: - sys.exit(main()) - import doctest - - sys.exit(doctest.testmod().failed) diff --git a/spaces/deafheavennnn/metalproxy/greeting.md b/spaces/deafheavennnn/metalproxy/greeting.md deleted file mode 100644 index ac70d46d02007d8e8c32fe238e90dc0329d91a2b..0000000000000000000000000000000000000000 --- a/spaces/deafheavennnn/metalproxy/greeting.md +++ /dev/null @@ -1,2 +0,0 @@ -send something funny, metal related, or anything. -ordinarycorrupthumanlove@proton.me \ No newline at end of file diff --git a/spaces/declare-lab/tango/diffusers/docker/diffusers-onnxruntime-cpu/Dockerfile b/spaces/declare-lab/tango/diffusers/docker/diffusers-onnxruntime-cpu/Dockerfile deleted file mode 100644 index 75f45be87a033e9476c4038218c9c2fd2f1255a5..0000000000000000000000000000000000000000 --- a/spaces/declare-lab/tango/diffusers/docker/diffusers-onnxruntime-cpu/Dockerfile +++ /dev/null @@ -1,44 +0,0 @@ -FROM ubuntu:20.04 -LABEL maintainer="Hugging Face" -LABEL repository="diffusers" - -ENV DEBIAN_FRONTEND=noninteractive - -RUN apt update && \ - apt install -y bash \ - build-essential \ - git \ - git-lfs \ - curl \ - ca-certificates \ - libsndfile1-dev \ - python3.8 \ - python3-pip \ - python3.8-venv && \ - rm -rf /var/lib/apt/lists - -# make sure to use venv -RUN python3 -m venv /opt/venv -ENV PATH="/opt/venv/bin:$PATH" - -# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py) -RUN python3 -m pip install --no-cache-dir --upgrade pip && \ - python3 -m pip install --no-cache-dir \ - torch \ - torchvision \ - torchaudio \ - onnxruntime \ - --extra-index-url https://download.pytorch.org/whl/cpu && \ - python3 -m pip install --no-cache-dir \ - accelerate \ - datasets \ - hf-doc-builder \ - huggingface-hub \ - Jinja2 \ - librosa \ - numpy \ - scipy \ - tensorboard \ - transformers - -CMD ["/bin/bash"] \ No newline at end of file diff --git a/spaces/deepwisdom/MetaGPT/metagpt/static/cy_aps/assets/home-0791050d.js b/spaces/deepwisdom/MetaGPT/metagpt/static/cy_aps/assets/home-0791050d.js deleted file mode 100644 index 58334e1cc48d5e9a2bc969c1bafb27a9a307c470..0000000000000000000000000000000000000000 --- a/spaces/deepwisdom/MetaGPT/metagpt/static/cy_aps/assets/home-0791050d.js +++ /dev/null @@ -1,335 +0,0 @@ -var Fw=Object.defineProperty;var Bw=(t,e,n)=>e in t?Fw(t,e,{enumerable:!0,configurable:!0,writable:!0,value:n}):t[e]=n;var Mi=(t,e,n)=>(Bw(t,typeof e!="symbol"?e+"":e,n),n);import{d as be,x as yt,c as le,f as V,h as ae,l as F,n as It,k as Bt,r as ee,o as kn,w as Zt,v as ue,$ as q,j as oi,p as ot,q as dt,a2 as wr,a3 as Zi,a4 as Ji,t as St,F as st,U as Ks,R as $i,H as bm,G as Kn,i as Ft,B as ni,g as $m,K as Pn,a5 as j,J as Gw,A as Qs,a6 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ŠøŃŠæŠ¾Š»ŃŠ·Š¾Š²Š°Š½ŠøŠµŠ³ŃŃŠæŠæŠøŃŠ»ŠµŠ¼ŠµŠ½ŃŠ¾Š² ŠøŃŠæŠ¾Š»ŃŠ·Š¾Š²Š°Š½ŠøŠµŃŠµŠ¶ŠøŠ¼Š°ŠæŃŠ¾Š²ŠµŠ“ŠµŠ½ŠøŃ ŠøŃŠæŠ¾Š»ŃŠ·Š¾Š²Š°Š½ŠøŠµŃŃŠµŠ·Š° ŠæŠµŃŠøŠ¾Š“ŠøŃŠ½Š¾ŃŃŃŠ°Š³ŃŠµŠ³Š°ŃŠ°ŃŠµŠ³ŠøŃŃŃŠ°Š½Š°ŠŗŠ¾ŠæŠ»ŠµŠ½ŠøŃ ŃŠµŠ¶ŠøŠ¼Š°Š²ŃŠ¾Š²ŃŠµŠ¼Ń ŃŠµŠ¶ŠøŠ¼Š·Š°ŠæŠøŃŠøŠ“Š¾ŠŗŃŠ¼ŠµŠ½ŃŠ° ŃŠµŠ¶ŠøŠ¼ŠæŃŠ¾Š²ŠµŠ“ŠµŠ½ŠøŃŠ“Š¾ŠŗŃŠ¼ŠµŠ½ŃŠ° ",T="Š°Š²ŃŠ¾ŃŠµŠ³ŠøŃŃŃŠ°ŃŠøŃŠøŠ·Š¼ŠµŠ½ŠµŠ½ŠøŠ¹ Š“Š¾ŠæŃŃŃŠøŠ¼ŃŠ¹Š½Š¾Š¼ŠµŃŃŠ¾Š¾Š±ŃŠµŠ½ŠøŃ Š¾ŃŠæŃŠ°Š²ŠŗŠ°ŃŠ»ŠµŠ¼ŠµŠ½ŃŠ°Š“Š°Š½Š½ŃŃ ŠæŠ¾Š»ŃŃŠµŠ½ŠøŠµŃŠ»ŠµŠ¼ŠµŠ½ŃŠ°Š“Š°Š½Š½ŃŃ ",N="ŠøŃŠæŠ¾Š»ŃŠ·Š¾Š²Š°Š½ŠøŠµŃŠ°ŃŃŠøŃŃŠ¾Š²ŠŗŠøŃŠ°Š±Š»ŠøŃŠ½Š¾Š³Š¾Š“Š¾ŠŗŃŠ¼ŠµŠ½ŃŠ° Š¾ŃŠøŠµŠ½ŃŠ°ŃŠøŃŃŃŃŠ°Š½ŠøŃŃ ŠæŠ¾Š»Š¾Š¶ŠµŠ½ŠøŠµŠøŃŠ¾Š³Š¾Š²ŠŗŠ¾Š»Š¾Š½Š¾ŠŗŃŠ²Š¾Š“Š½Š¾Š¹ŃŠ°Š±Š»ŠøŃŃ ŠæŠ¾Š»Š¾Š¶ŠµŠ½ŠøŠµŠøŃŠ¾Š³Š¾Š²ŃŃŃŠ¾ŠŗŃŠ²Š¾Š“Š½Š¾Š¹ŃŠ°Š±Š»ŠøŃŃ ŠæŠ¾Š»Š¾Š¶ŠµŠ½ŠøŠµŃŠµŠŗŃŃŠ°Š¾ŃŠ½Š¾ŃŠøŃŠµŠ»ŃŠ½Š¾ŠŗŠ°ŃŃŠøŠ½ŠŗŠø ŃŠ°ŃŠæŠ¾Š»Š¾Š¶ŠµŠ½ŠøŠµŠ·Š°Š³Š¾Š»Š¾Š²ŠŗŠ°Š³ŃŃŠæŠæŠøŃŠ¾Š²ŠŗŠøŃŠ°Š±Š»ŠøŃŠ½Š¾Š³Š¾Š“Š¾ŠŗŃŠ¼ŠµŠ½ŃŠ° ŃŠæŠ¾ŃŠ¾Š±ŃŃŠµŠ½ŠøŃŠ·Š½Š°ŃŠµŠ½ŠøŠ¹ŃŠ°Š±Š»ŠøŃŠ½Š¾Š³Š¾Š“Š¾ŠŗŃŠ¼ŠµŠ½ŃŠ° ŃŠøŠæŠ“Š²ŃŃŃŠ¾ŃŠ¾Š½Š½ŠµŠ¹ŠæŠµŃŠ°ŃŠø 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e={[Ut.INIT]:{text:"",visibel:!1,icon:""},[Ut.IDLE]:{text:"Regenerate",visibel:!0,icon:Es},[Ut.RUNNING]:{text:"Stop Generation",visibel:!0,icon:oL},[Ut.FINISH]:{text:"Regenerate",visibel:!0,icon:Es},[Ut.FAILED]:{text:"Regenerate",visibel:!0,icon:Es},[Ut.TERMINATE]:{text:"Regenerate",visibel:!0,icon:Es}},{globalStatus:n,stopMessage:i,regenMessage:o,activeAgentNode:s}=aa(),l=le(()=>e[n.value]),c=le(()=>{var _;return n.value===Ut.RUNNING&&!((_=s.value)!=null&&_.steps.length)}),d=()=>{if(n.value===Ut.RUNNING){i();return}o()};return(_,p)=>q(l).visibel?(V(),ot(q(xC),{key:0,class:"status_btn",align:"center",direction:"vertical",size:8},{default:dt(()=>[q(c)?Ge("",!0):(V(),ot(q(hm),{key:0,style:{"background-color":"#fff","border-radius":"999px"},type:"outline",onClick:d},{icon:dt(()=>[(V(),ot(ji(q(l).icon)))]),default:dt(()=>[St(" "+$e(_.$t(q(l).text)),1)]),_:1}))]),_:1})):Ge("",!0)}});const mDe=Dt(pDe,[["__scopeId","data-v-240aae5d"]]),gDe={class:"chatRoomWrapper"},EDe={class:"visionWrapper"},fDe={key:0,class:"emptyWrapper"},SDe=Ww('${n.utils.escapeHtml(p)}',1),bDe={class:"actionWrapper"},hDe=["onClick"],TDe={key:0,style:{width:"22px"},src:K2,alt:""},vDe={key:1,style:{width:"22px"},src:Q2,alt:""},CDe={key:2,style:{width:"22px"},src:X2,alt:""},RDe={key:3,style:{width:"22px"},src:Z2,alt:""},NDe={class:"chatWrapper"},ODe={class:"msg_history_area"},ADe={key:0,class:"msg_text"},yDe={class:"bottom_trigger"},IDe={class:"inputWrapper"},DDe={class:"inputInner"},xDe=["disabled"],wDe=be({__name:"chatRoom",setup(t){const e=["Design a RecSys like Toutiao","Write a cli snake game based on pygame","Design a content-based recommendation system","Design a search algorithm 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fill in your OpenAI API key to activate the hired software team."},{default:dt(()=>[F("div",IDe,[F("div",DDe,[Pn(F("input",{"onUpdate:modelValue":N[1]||(N[1]=x=>wr(i)?i.value=x:null),disabled:!q(c),placeholder:"Please enter a one-sentence requirement.",type:"text",onKeydown:h},null,40,xDe),[[Kw,q(i)]]),q(_)===q(Ut).RUNNING?(V(),ot(q(cM),{key:0,style:{"margin-right":"12px","margin-top":"4px"},size:6,dot:""})):(V(),ae("button",{key:1,class:"sendBtn",onClick:v},[ue(ea,{size:20,fill:"#ffffff","icon-id":"icon-fasong"})]))])])]),_:1},8,["disabled"])])}}});const MDe=Dt(wDe,[["__scopeId","data-v-ae197aef"]]),LDe={class:"chatWrapper"},PDe=be({__name:"chatPage",setup(t){return(e,n)=>(V(),ae("div",LDe,[ue(W2),ue(MDe)]))}});const kDe=Dt(PDe,[["__scopeId","data-v-7d0d8d24"]]),UDe={class:"hfHomeWrapper"},FDe=be({__name:"home",setup(t){return(e,n)=>(V(),ae("div",UDe,[ue(Kq),ue(kDe)]))}});const zDe=Dt(FDe,[["__scopeId","data-v-7444cb54"]]);export{zDe as default}; diff --git a/spaces/dfurman/chat-all-in/src/chat_class.py b/spaces/dfurman/chat-all-in/src/chat_class.py deleted file mode 100644 index 7fcc727da2012ce9c32a4a585d2f49b6e4eb181b..0000000000000000000000000000000000000000 --- a/spaces/dfurman/chat-all-in/src/chat_class.py +++ /dev/null @@ -1,122 +0,0 @@ -import datetime -import time - -import datasets - -from src.inf_server import call_inf_server - - -# download podcast database -ds_episodes = datasets.load_dataset("dfurman/All-In-Podcast-Transcripts") -# download cache conversation databse -# ds_conversations = datasets.load_dataset("dfurman/Chat-All-In-Conversations") - - -class Chat: - default_system_prompt = "A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers." - system_format = "<|im_start|>system\n{}<|im_end|>\n" - - def __init__( - self, system: str = None, user: str = None, assistant: str = None - ) -> None: - if system is not None: - self.set_system_prompt(system) - else: - self.reset_system_prompt() - self.user = user if user else "<|im_start|>user\n{}<|im_end|>\n" - self.assistant = ( - assistant if assistant else "<|im_start|>assistant\n{}<|im_end|>\n" - ) - self.response_prefix = self.assistant.split("{}")[0] - - def set_system_prompt(self, system_prompt): - # self.system = self.system_format.format(system_prompt) - return system_prompt - - def reset_system_prompt(self): - return self.set_system_prompt(self.default_system_prompt) - - def history_as_formatted_str(self, system, history) -> str: - system = self.system_format.format(system) - text = system + "".join( - [ - "\n".join( - [ - self.user.format(item[0]), - self.assistant.format(item[1]), - ] - ) - for item in history[:-1] - ] - ) - text += self.user.format(history[-1][0]) - text += self.response_prefix - - # stopgap solution to too long sequences - if len(text) > 4500: - # delete from the middle between <|im_start|> and <|im_end|> - # find the middle ones, then expand out - start = text.find("<|im_start|>", 139) - end = text.find("<|im_end|>", 139) - while end < len(text) and len(text) > 4500: - end = text.find("<|im_end|>", end + 1) - text = text[:start] + text[end + 1 :] - if len(text) > 4500: - # the nice way didn't work, just truncate - # deleting the beginning - text = text[-4500:] - - return text - - def clear_history(self, history): - return [] - - # def save_history(self, history): - # Getting the current date and time - # dt = datetime.now() - # dt = str(dt).replace(" ", "-").replace(":", "-").replace(".", "-") - # return history - - def turn(self, user_input: str): - self.user_turn(user_input) - return self.bot_turn() - - def user_turn(self, user_input: str, history): - history.append([user_input, ""]) - return user_input, history - - def bot_turn(self, system, history, openai_key, episode): - episode_num = episode.split("(")[-1].split(")")[0] - conversation = self.history_as_formatted_str(system, history) - assistant_response = call_inf_server(conversation, openai_key, episode_num) - history[-1][1] = "" - for chunk in assistant_response: - try: - decoded_output = chunk["choices"][0]["delta"]["content"] - history[-1][1] += decoded_output - yield history - except KeyError: - pass - - def user_turn_select_episode(self, history): - user_input = "Special starter call: Display background information for the selected episode." - history.append([user_input, ""]) - return history - - def bot_turn_select_episode(self, history, episode): - episode_num = episode.split("(")[-1].split(")")[0] - assistant_response = f"All-In Episode {episode_num}:\n\n" - assistant_response += f'Title: {ds_episodes[episode_num]["episode_title"][0].replace(episode_num + ": ", "")}\n' - assistant_response += ( - f"Date aired: {ds_episodes[episode_num]['episode_date'][0]}\n" - ) - assistant_response += "Sections:\n\n" - for itr, section_title in enumerate(ds_episodes[episode_num]["section_title"]): - assistant_response += f"{itr+1}. {section_title} ({ds_episodes[episode_num]['section_time_stamp'][itr]})\n" - assistant_response += "\nYou can now converse with the assistant about this episode! 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-
- diff --git a/spaces/digitalxingtong/Taffy-Bert-VITS2/attentions.py b/spaces/digitalxingtong/Taffy-Bert-VITS2/attentions.py deleted file mode 100644 index ecbdbc8be941a962046fc11fd6739b093112123e..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Taffy-Bert-VITS2/attentions.py +++ /dev/null @@ -1,343 +0,0 @@ -import copy -import math -import numpy as np -import torch -from torch import nn -from torch.nn import functional as F - -import commons -import modules -from torch.nn.utils import weight_norm, remove_weight_norm -class LayerNorm(nn.Module): - def __init__(self, channels, eps=1e-5): - super().__init__() - self.channels = channels - self.eps = eps - - self.gamma = nn.Parameter(torch.ones(channels)) - self.beta = nn.Parameter(torch.zeros(channels)) - - def forward(self, x): - x = x.transpose(1, -1) - x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) - return x.transpose(1, -1) - - - -@torch.jit.script -def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): - n_channels_int = n_channels[0] - in_act = input_a + input_b - t_act = torch.tanh(in_act[:, :n_channels_int, :]) - s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) - acts = t_act * s_act - return acts - -class Encoder(nn.Module): - def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, isflow = True, **kwargs): - super().__init__() - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.window_size = window_size - if isflow: - cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1) - self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1) - self.cond_layer = weight_norm(cond_layer, name='weight') - self.gin_channels = 256 - self.cond_layer_idx = self.n_layers - if 'gin_channels' in kwargs: - self.gin_channels = kwargs['gin_channels'] - if self.gin_channels != 0: - self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels) - # vits2 says 3rd block, so idx is 2 by default - self.cond_layer_idx = kwargs['cond_layer_idx'] if 'cond_layer_idx' in kwargs else 2 - print(self.gin_channels, self.cond_layer_idx) - assert self.cond_layer_idx < self.n_layers, 'cond_layer_idx should be less than n_layers' - self.drop = nn.Dropout(p_dropout) - self.attn_layers = nn.ModuleList() - self.norm_layers_1 = nn.ModuleList() - self.ffn_layers = nn.ModuleList() - self.norm_layers_2 = nn.ModuleList() - for i in range(self.n_layers): - self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size)) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout)) - self.norm_layers_2.append(LayerNorm(hidden_channels)) - def forward(self, x, x_mask, g=None): - attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) - x = x * x_mask - for i in range(self.n_layers): - if i == self.cond_layer_idx and g is not None: - g = self.spk_emb_linear(g.transpose(1, 2)) - g = g.transpose(1, 2) - x = x + g - x = x * x_mask - y = self.attn_layers[i](x, x, attn_mask) - y = self.drop(y) - x = self.norm_layers_1[i](x + y) - - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = self.norm_layers_2[i](x + y) - x = x * x_mask - return x - - -class Decoder(nn.Module): - def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs): - super().__init__() - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.proximal_bias = proximal_bias - self.proximal_init = proximal_init - - self.drop = nn.Dropout(p_dropout) - self.self_attn_layers = nn.ModuleList() - self.norm_layers_0 = nn.ModuleList() - self.encdec_attn_layers = nn.ModuleList() - self.norm_layers_1 = nn.ModuleList() - self.ffn_layers = nn.ModuleList() - self.norm_layers_2 = nn.ModuleList() - for i in range(self.n_layers): - self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init)) - self.norm_layers_0.append(LayerNorm(hidden_channels)) - self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout)) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True)) - self.norm_layers_2.append(LayerNorm(hidden_channels)) - - def forward(self, x, x_mask, h, h_mask): - """ - x: decoder input - h: encoder output - """ - self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype) - encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1) - x = x * x_mask - for i in range(self.n_layers): - y = self.self_attn_layers[i](x, x, self_attn_mask) - y = self.drop(y) - x = self.norm_layers_0[i](x + y) - - y = self.encdec_attn_layers[i](x, h, encdec_attn_mask) - y = self.drop(y) - x = self.norm_layers_1[i](x + y) - - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = self.norm_layers_2[i](x + y) - x = x * x_mask - return x - - -class MultiHeadAttention(nn.Module): - def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False): - super().__init__() - assert channels % n_heads == 0 - - self.channels = channels - self.out_channels = out_channels - self.n_heads = n_heads - self.p_dropout = p_dropout - self.window_size = window_size - self.heads_share = heads_share - self.block_length = block_length - self.proximal_bias = proximal_bias - self.proximal_init = proximal_init - self.attn = None - - self.k_channels = channels // n_heads - self.conv_q = nn.Conv1d(channels, channels, 1) - self.conv_k = nn.Conv1d(channels, channels, 1) - self.conv_v = nn.Conv1d(channels, channels, 1) - self.conv_o = nn.Conv1d(channels, out_channels, 1) - self.drop = nn.Dropout(p_dropout) - - if window_size is not None: - n_heads_rel = 1 if heads_share else n_heads - rel_stddev = self.k_channels**-0.5 - self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) - self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) - - nn.init.xavier_uniform_(self.conv_q.weight) - nn.init.xavier_uniform_(self.conv_k.weight) - nn.init.xavier_uniform_(self.conv_v.weight) - if proximal_init: - with torch.no_grad(): - self.conv_k.weight.copy_(self.conv_q.weight) - self.conv_k.bias.copy_(self.conv_q.bias) - - def forward(self, x, c, attn_mask=None): - q = self.conv_q(x) - k = self.conv_k(c) - v = self.conv_v(c) - - x, self.attn = self.attention(q, k, v, mask=attn_mask) - - x = self.conv_o(x) - return x - - def attention(self, query, key, value, mask=None): - # reshape [b, d, t] -> [b, n_h, t, d_k] - b, d, t_s, t_t = (*key.size(), query.size(2)) - query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) - key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) - value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) - - scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1)) - if self.window_size is not None: - assert t_s == t_t, "Relative attention is only available for self-attention." - key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) - rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings) - scores_local = self._relative_position_to_absolute_position(rel_logits) - scores = scores + scores_local - if self.proximal_bias: - assert t_s == t_t, "Proximal bias is only available for self-attention." - scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype) - if mask is not None: - scores = scores.masked_fill(mask == 0, -1e4) - if self.block_length is not None: - assert t_s == t_t, "Local attention is only available for self-attention." - block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length) - scores = scores.masked_fill(block_mask == 0, -1e4) - p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s] - p_attn = self.drop(p_attn) - output = torch.matmul(p_attn, value) - if self.window_size is not None: - relative_weights = self._absolute_position_to_relative_position(p_attn) - value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s) - output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings) - output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t] - return output, p_attn - - def _matmul_with_relative_values(self, x, y): - """ - x: [b, h, l, m] - y: [h or 1, m, d] - ret: [b, h, l, d] - """ - ret = torch.matmul(x, y.unsqueeze(0)) - return ret - - def _matmul_with_relative_keys(self, x, y): - """ - x: [b, h, l, d] - y: [h or 1, m, d] - ret: [b, h, l, m] - """ - ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) - return ret - - def _get_relative_embeddings(self, relative_embeddings, length): - max_relative_position = 2 * self.window_size + 1 - # Pad first before slice to avoid using cond ops. - pad_length = max(length - (self.window_size + 1), 0) - slice_start_position = max((self.window_size + 1) - length, 0) - slice_end_position = slice_start_position + 2 * length - 1 - if pad_length > 0: - padded_relative_embeddings = F.pad( - relative_embeddings, - commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]])) - else: - padded_relative_embeddings = relative_embeddings - used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position] - return used_relative_embeddings - - def _relative_position_to_absolute_position(self, x): - """ - x: [b, h, l, 2*l-1] - ret: [b, h, l, l] - """ - batch, heads, length, _ = x.size() - # Concat columns of pad to shift from relative to absolute indexing. - x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]])) - - # Concat extra elements so to add up to shape (len+1, 2*len-1). - x_flat = x.view([batch, heads, length * 2 * length]) - x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]])) - - # Reshape and slice out the padded elements. - x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:] - return x_final - - def _absolute_position_to_relative_position(self, x): - """ - x: [b, h, l, l] - ret: [b, h, l, 2*l-1] - """ - batch, heads, length, _ = x.size() - # padd along column - x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]])) - x_flat = x.view([batch, heads, length**2 + length*(length -1)]) - # add 0's in the beginning that will skew the elements after reshape - x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]])) - x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:] - return x_final - - def _attention_bias_proximal(self, length): - """Bias for self-attention to encourage attention to close positions. - Args: - length: an integer scalar. - Returns: - a Tensor with shape [1, 1, length, length] - """ - r = torch.arange(length, dtype=torch.float32) - diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) - return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) - - -class FFN(nn.Module): - def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.activation = activation - self.causal = causal - - if causal: - self.padding = self._causal_padding - else: - self.padding = self._same_padding - - self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size) - self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size) - self.drop = nn.Dropout(p_dropout) - - def forward(self, x, x_mask): - x = self.conv_1(self.padding(x * x_mask)) - if self.activation == "gelu": - x = x * torch.sigmoid(1.702 * x) - else: - x = torch.relu(x) - x = self.drop(x) - x = self.conv_2(self.padding(x * x_mask)) - return x * x_mask - - def _causal_padding(self, x): - if self.kernel_size == 1: - return x - pad_l = self.kernel_size - 1 - pad_r = 0 - padding = [[0, 0], [0, 0], [pad_l, pad_r]] - x = F.pad(x, commons.convert_pad_shape(padding)) - return x - - def _same_padding(self, x): - if self.kernel_size == 1: - return x - pad_l = (self.kernel_size - 1) // 2 - pad_r = self.kernel_size // 2 - padding = [[0, 0], [0, 0], [pad_l, pad_r]] - x = F.pad(x, commons.convert_pad_shape(padding)) - return x diff --git a/spaces/dineshreddy/WALT/mmdet/models/detectors/point_rend.py b/spaces/dineshreddy/WALT/mmdet/models/detectors/point_rend.py deleted file mode 100644 index 808ef2258ae88301d349db3aaa2711f223e5c971..0000000000000000000000000000000000000000 --- a/spaces/dineshreddy/WALT/mmdet/models/detectors/point_rend.py +++ /dev/null @@ -1,29 +0,0 @@ -from ..builder import DETECTORS -from .two_stage import TwoStageDetector - - -@DETECTORS.register_module() -class PointRend(TwoStageDetector): - """PointRend: Image Segmentation as Rendering - - This detector is the implementation of - `PointRendEntity Referring Classifier
It knows exactly when you are calling it.
- """, unsafe_allow_html=True) - -livedemo_col1, livedemo_col2, livedemo_col3 = st.columns([12,1,6]) - -with livedemo_col1: - - # Version. - st.markdown(""" -Live Demo version 2.1.0.1216
- """, unsafe_allow_html=True) - - # Live Demo form. - with st.form("live_demo_form"): - entity = st.text_input('Entity Name:', 'Jimmy') - sentence = st.text_input('Sentence Input:', 'Are you feeling good, Jimmy?', - help='The classifier is going to analyze this sentence.') - if st.form_submit_button('š Submit'): - if entity.lower() not in sentence.lower(): - st.session_state['running_status'] = STATUS_ERROR - else: - st.session_state['running_status'] = STATUS_SUBMIT - - if st.session_state['running_status'] == STATUS_STOPPED: - st.info('Type something and submit to start!') - elif st.session_state['running_status'] == STATUS_SUBMIT: - if predict(sentence, entity) == 'CALLING': - st.success('It is a **calling**!') - else: - st.success('It is a **mentioning**!') - st.caption(f'Submitted: `{sentence.lower()}` by `{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}`') - elif st.session_state['running_status'] == STATUS_ERROR: - st.warning('The entity name is not in the sentence!') - - st.markdown(""" - #### Corpus - """) - st.markdown(""" -Calling- means that the sentence contains the name and is telling to the entity.
- - Examples:
- - Are you feeling good about it Jimmy?
- - Are you feeling good Jimmy?
- - Jimmy you are so cool! - - """, unsafe_allow_html=True) - st.markdown(""" -Mentioning- means that the sentence contains the name but is telling to other person.
- - Examples:
- - Are you feeling good about Jimmy?
- - Have you heard about Jimmy?
- - Jimmy said that you are so cool! - - """, unsafe_allow_html=True) - - st.markdown(""" - #### Applications and Future Works - """) - st.markdown(""" - Developers can apply it on their virtual bots or voice assistants to detect - if the user is calling the bot or just mentioning the bot rather than detecting a fixed wake-up phrase. - It will improve the human-computer interaction and usability. - - Additionally, Lingxi is going to expand the project into Simplified Chinese in the future, - implement it into a Discord Bot, - and deliver it into medium-sized channels to see if users feel better in communicating with bots in this way - and if it works well in conversations in practice. - If it works well, - we may try to combine it with the speech-to-text to analyze the experience in the context of voice, - which has a lot more complicated scenarios. - """) - -with livedemo_col2: - st.empty() - -with livedemo_col3: - st.markdown(""" - #### Get Started - """) - st.markdown(""" - Hi! I'm the Entity Referring Classifier. - Specify an entity name, and then type a sentence to get started! - """) - - st.markdown(""" - #### Members - """) - st.markdown(""" - Lingxi Li, Li Xu, Zachary Polak, Tudor-Cristian Foca - """) - - st.markdown(""" - #### Background - """) - st.markdown(""" - There are a lot of voice assistants and bots out there, - but they all need a specific prefix to wake them like āhey siriā or āhey alexaā. - At our real world, nobody is communicating with others with such prefix all the time. - Everyone should be able to communicate in a comfortable way including with bots and virtual assistants. - Our aim here is to get rid of the "wake phrase". - """) \ No newline at end of file diff --git a/spaces/ericmichael/openai-playground-utrgv/main.py b/spaces/ericmichael/openai-playground-utrgv/main.py deleted file mode 100644 index e6a9767f4e27eb10ac3947daae503dc2cf970884..0000000000000000000000000000000000000000 --- a/spaces/ericmichael/openai-playground-utrgv/main.py +++ /dev/null @@ -1,3 +0,0 @@ -import subprocess - -subprocess.run("uvicorn app.app:app --host 0.0.0.0 --port 7860", shell=True) diff --git a/spaces/eson/kplug/demo_chatbot_jddc.py b/spaces/eson/kplug/demo_chatbot_jddc.py deleted file mode 100644 index 57c2c1d3e722cf62b9e17723163640fb9180d45c..0000000000000000000000000000000000000000 --- a/spaces/eson/kplug/demo_chatbot_jddc.py +++ /dev/null @@ -1,69 +0,0 @@ -# coding=utf-8 -# author: xusongHD Online Player (Lipstick Under My Burkha Movie Downl)
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- diff --git a/spaces/fclong/summary/fengshen/examples/pegasus/pretrain_pegasus.py b/spaces/fclong/summary/fengshen/examples/pegasus/pretrain_pegasus.py deleted file mode 100644 index 0059355f5d5bf6d149e01fc3dc15d3a760932733..0000000000000000000000000000000000000000 --- a/spaces/fclong/summary/fengshen/examples/pegasus/pretrain_pegasus.py +++ /dev/null @@ -1,181 +0,0 @@ -# -*- coding: utf-8 -*- - - -from fengshen.models.model_utils import add_module_args -from transformers import PegasusForConditionalGeneration, PegasusConfig -from pytorch_lightning import Trainer, loggers, LightningModule -from pytorch_lightning.callbacks import LearningRateMonitor -from tokenizers_pegasus import PegasusTokenizer -from utils import UniversalCheckpoint -from data.universal_datamodule import UniversalDataModule -from data_utils import ( - get_input_mask, pseudo_summary_f1, shift_tokens_right, - padding_to_maxlength, load_stopwords, text_segmentate) -import argparse -import torch -import os -import sys - -sys.path.append('../../') - - -# os.environ["CUDA_VISIBLE_DEVICES"] = '6' - - -class FakeAbstractCollator: - - def __init__(self, tokenizer, stopwords_dict, max_enc_length): - self.tokenizer = tokenizer - self.max_seq_length = max_enc_length - self.stopwords_dict = stopwords_dict - - def __call__(self, samples): - # print("samples: ", samples) - labels = [] - attn_mask = [] - decoder_attn_mask = [] - source_inputs = [] - - for text in samples: - texts = text["chunks"] - text = text_segmentate(texts) - sentence_id_vec, source, target, source_idxs, target_idxs = pseudo_summary_f1( - text, self.stopwords_dict, self.tokenizer, self.max_seq_length, - "rouge-l") - source_idxs, target_idxs = get_input_mask(sentence_id_vec, - target_idxs) - if len(source_idxs) > self.max_seq_length: - if 2 not in source_idxs[self.max_seq_length - 1:]: - source_idxs = source_idxs[:self.max_seq_length] - source_idxs[-1] = self.tokenizer.eos_token_id - sys.stderr.write("Warning split long line: " + source + - "\n") - else: - continue - - source_idxs, attention_mask = padding_to_maxlength( - source_idxs, self.max_seq_length, self.tokenizer.pad_token_id) - label, target_attention_mask = padding_to_maxlength( - target_idxs, self.max_seq_length, self.tokenizer.pad_token_id) - # print("sample len: ", len(source_idxs)) - source_inputs.append(source_idxs) - attn_mask.append(attention_mask) - decoder_attn_mask.append(target_attention_mask) - labels.append(label) - labels = torch.tensor(labels) - decode_input_idxs = shift_tokens_right(labels, - self.tokenizer.pad_token_id, - self.tokenizer.pad_token_id) - end_token_index = torch.where(labels == self.tokenizer.eos_token_id)[1] - for idx, end_idx in enumerate(end_token_index): - labels[idx][end_idx + 1:] = -100 - - # print("call samples: ") - return { - "input_ids": torch.tensor(source_inputs), - "attention_mask": torch.tensor(attn_mask), - "labels": labels, - "decoder_input_ids": decode_input_idxs, - "decoder_attention_mask": torch.tensor(decoder_attn_mask) - } - - -class PegasusChineseModel(LightningModule): - - def __init__(self, args, **kwargs): - super().__init__() - self.args = args - self.save_hyperparameters(args) - config = PegasusConfig.from_json_file( - os.path.join(args.model_path, "config.json")) - print("vocab_size: ", config.vocab_size) - self.model = PegasusForConditionalGeneration(config=config) - print("model.num_parameters: ", self.model.num_parameters()) - - def setup(self, stage) -> None: - if stage == 'fit': - train_loader = self.trainer._data_connector._train_dataloader_source.dataloader( - ) - - # Calculate total steps - tb_size = self.hparams.train_batchsize * max(1, self.trainer.gpus) - ab_size = self.trainer.accumulate_grad_batches * float( - self.trainer.max_epochs) - self.total_steps = (len(train_loader.dataset) // - tb_size) // ab_size - print('Total training step:', self.total_steps) - - def configure_optimizers(self): - from fengshen.models.model_utils import configure_optimizers - return configure_optimizers(self) - - def training_step(self, batch, batch_idx): - output = self.model(**batch) - self.log('train_loss', output.loss, sync_dist=True) - return output.loss - - def comput_metrix(self, logits, labels): - y_pred = torch.argmax(logits, dim=-1) - y_pred = y_pred.view(size=(-1, )) - y_true = labels.view(size=(-1, )).float() - corr = torch.eq(y_pred, y_true) - acc = torch.sum(corr.float()) / labels.size()[0] - return acc - - def validation_step(self, batch, batch_idx): - output = self.model(**batch) - acc = self.comput_metrix(output.logits, batch['labels']) - self.log('val_loss', output.loss, sync_dist=True) - self.log('val_acc', acc, sync_dist=True) - - def on_save_checkpoint(self, checkpoint) -> None: - if self.trainer._accelerator_connector.cluster_environment.global_rank( - ) == 0: - self.model.save_pretrained( - os.path.join( - self.trainer.checkpoint_callback.dirpath, - 'hf_pretrained_epoch{}_step{}'.format( - checkpoint['epoch'], checkpoint['global_step']))) - - -def main(): - args_parser = argparse.ArgumentParser("Pegasus Task") - - args_parser = UniversalDataModule.add_data_specific_args(args_parser) - args_parser = Trainer.add_argparse_args(args_parser) - args_parser = UniversalCheckpoint.add_argparse_args(args_parser) - args_parser = add_module_args(args_parser) - args_parser.add_argument('--deepspeed') - args_parser.add_argument( - '--stopword_path', - default="/cognitive_comp/dongxiaoqun/project/pegasus/own/pegasus/stopwords", - type=str) - args_parser.add_argument('--max_seq_length', default=1024, type=int) - args = args_parser.parse_args() - - tokenizer = PegasusTokenizer.from_pretrained(args.model_path) - stopwords_dict = load_stopwords(args.stopword_path) - collator = FakeAbstractCollator(tokenizer, stopwords_dict, - args.max_seq_length) - data_module = UniversalDataModule(tokenizer=tokenizer, - args=args, - collate_fn=collator) - module = PegasusChineseModel(args) - lr_monitor = LearningRateMonitor(logging_interval='step') - logger = loggers.TensorBoardLogger( - save_dir=os.path.join(args.default_root_dir, 'logs/'), - name=os.path.basename(os.path.dirname(args.model_path))) - checkpoint_callback = UniversalCheckpoint(args).callbacks - - # autotuning - if args.deepspeed is not None: - os.environ['PL_DEEPSPEED_CONFIG_PATH'] = args.deepspeed - - trainer = Trainer.from_argparse_args( - args, logger=logger, callbacks=[lr_monitor, checkpoint_callback]) - - trainer.fit(module, data_module) - - -if __name__ == '__main__': - main() diff --git a/spaces/fengmuxi/ChatGpt-Web/app/components/emoji.tsx b/spaces/fengmuxi/ChatGpt-Web/app/components/emoji.tsx deleted file mode 100644 index 03aac05f278ca58082f9d860dec1cfed8f045690..0000000000000000000000000000000000000000 --- a/spaces/fengmuxi/ChatGpt-Web/app/components/emoji.tsx +++ /dev/null @@ -1,59 +0,0 @@ -import EmojiPicker, { - Emoji, - EmojiStyle, - Theme as EmojiTheme, -} from "emoji-picker-react"; - -import { ModelType } from "../store"; - -import BotIcon from "../icons/bot.svg"; -import BlackBotIcon from "../icons/black-bot.svg"; - -export function getEmojiUrl(unified: string, style: EmojiStyle) { - return `https://cdn.staticfile.org/emoji-datasource-apple/14.0.0/img/${style}/64/${unified}.png`; -} - -export function AvatarPicker(props: { - onEmojiClick: (emojiId: string) => void; -}) { - return ( -{ - props.onEmojiClick(e.unified); - }} - /> - ); -} - -export function Avatar(props: { model?: ModelType; avatar?: string }) { - if (props.model) { - return ( - - {props.model?.startsWith("gpt-4") ? ( -- ); - } - - return ( -- ) : ( - - )} - - {props.avatar &&- ); -} - -export function EmojiAvatar(props: { avatar: string; size?: number }) { - return ( -} - - ); -} diff --git a/spaces/fengmuxi/ChatGpt-Web/app/store/index.ts b/spaces/fengmuxi/ChatGpt-Web/app/store/index.ts deleted file mode 100644 index 7ed6f355e907cb40bcabe70f26775ca3acc987ae..0000000000000000000000000000000000000000 --- a/spaces/fengmuxi/ChatGpt-Web/app/store/index.ts +++ /dev/null @@ -1,5 +0,0 @@ -export * from "./chat"; -export * from "./user"; -export * from "./update"; -export * from "./access"; -export * from "./config"; diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Cars 1 Tamil Dubbed Movie - A Must-Watch for All Animation Fans.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Cars 1 Tamil Dubbed Movie - A Must-Watch for All Animation Fans.md deleted file mode 100644 index 33d2b3d2d7b7e6a5f9f367d2b178dcc93d448969..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Cars 1 Tamil Dubbed Movie - A Must-Watch for All Animation Fans.md +++ /dev/null @@ -1,173 +0,0 @@ -
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-If you are looking for a fun, family-friendly, and heartwarming movie to watch, you might want to check out Cars 1 Tamil dubbed movie download. Cars 1 is a 2006 animated film produced by Pixar Animation Studios for Walt Disney Pictures. It tells the story of a hotshot rookie race car named Lightning McQueen who gets stranded in a small town on Route 66 and learns some valuable lessons about life, love, and friendship.
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-The Story of Cars 1
-The story of Cars 1 revolves around Lightning McQueen (voice of Owen Wilson), a young and arrogant race car who dreams of winning the prestigious Piston Cup championship. He is one of the three finalists for the title, along with veteran racer Strip "The King" Weathers (voice of Richard Petty) and ruthless rival Chick Hicks (voice of Michael Keaton).
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Lightning initially wants to leave the town as soon as possible, but he is forced to stay and fix the road he damaged when he arrived. As he spends more time with the townsfolk, he begins to appreciate their hospitality, humor, and history. He also discovers that Doc Hudson was once a famous racer known as the Fabulous Hudson Hornet, who won three Piston Cups before he was injured in a crash and forgotten by the racing world.
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-Cars 3 Tamil Dubbed Movie Cast and CrewIn the final race, Lightning is faced with his rivals, Chick Hicks and The King. He is also surprised to see that Doc Hudson has come to support him as his crew chief, along with Mater, Sally, and the rest of the Radiator Springs gang. Lightning races with confidence and skill, but he is sabotaged by Chick Hicks, who causes The King to crash near the finish line. Lightning stops before crossing the finish line and goes back to help The King finish his last race with dignity, sacrificing his own chance of winning. He earns the respect and admiration of everyone, including The King, his wife Dinoco Tex (voice of H.A. Wheeler), and his fans. He also rejects an offer from Dinoco, a lucrative sponsor, to stay loyal to his current sponsor Rust-eze, a humble company that gave him his first chance.
-Lightning returns to Radiator Springs with his trophy and decides to set up his racing headquarters there. He also reunites with Sally and becomes her boyfriend. He helps revitalize the town by attracting tourists and media attention. He also enjoys spending time with his friends and racing with Doc on the dirt track. He realizes that he has found his true home and family in Radiator Springs.
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-The characters of Cars 1 are one of the main attractions of the movie. They are all based on real-life car models, but they have distinct personalities, voices, and expressions that make them memorable and relatable. They also have some interesting trivia and references that add to their charm. Here are some examples:
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The Animation and Visuals of Cars 1
The animation and visuals of Cars 1 are one of the main reasons why the movie is so popular and acclaimed. The movie was created by Pixar Animation Studios, which is known for its high-quality and innovative computer animation. The movie took four years to make, and involved a team of more than 250 artists, animators, technicians, and engineers.
-The movie features stunning animation and visuals that bring the world of cars to life. The cars have expressive eyes, mouths, and movements that make them seem like real characters. The cars also have realistic details, such as reflections, scratches, dirt, and rust. The movie also uses advanced techniques, such as ray tracing, global illumination, and ambient occlusion, to create realistic lighting and shadows.
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The music and soundtrack of Cars 1 are another aspect that makes the movie enjoyable and memorable. The music and soundtrack of the movie are composed by Randy Newman, who is a famous singer-songwriter and film composer. He has worked on many Pixar movies, such as Toy Story, A Bug's Life, Monsters, Inc., and Finding Nemo.
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The reception and legacy of Cars 1 are another testament to the movie's quality and popularity. The movie received mostly positive reviews from critics and audiences, who praised its animation, humor, characters, and message. The movie also performed well at the box office, earning over $462 million worldwide. The movie also won several awards and nominations, such as:
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However, the movie also received some negative reviews from critics and audiences, who criticized its story, pacing, and originality. Some critics also compared the movie unfavorably to other Pixar movies, such as Toy Story, Finding Nemo, and The Incredibles. Some critics also argued that the movie was too commercialized and aimed at selling merchandise.
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-In conclusion, Cars 1 Tamil dubbed movie download is a great option for anyone who wants to watch a fun, family-friendly, and heartwarming movie in Tamil. Cars 1 is a 2006 animated film produced by Pixar Animation Studios for Walt Disney Pictures. It tells the story of a hotshot rookie race car named Lightning McQueen who gets stranded in a small town on Route 66 and learns some valuable lessons about life, love, and friendship. The movie features a captivating story, charming characters, stunning animation, catchy music, and a positive message. The movie also has a Tamil dubbed version that allows Tamil speakers to enjoy the movie in their own language. The movie can be downloaded from some websites that offer it online, but we do not recommend or endorse doing so, as it may have serious consequences for you and your device. We suggest watching the movie legally on Disney+ Hotstar or other streaming platforms.
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-how to explore different biomes in craftsman building craft iosWe hope this article has helped you learn more about craftsman building craft game and how to download and install craftsman building craft apk for iPhone. Craftsman building craft game is a fun and creative game that will keep you entertained for hours. You can unleash your imagination and create your own world with blocks. You can also play with your friends online and share your creations with other players online. Craftsman building craft game is a game that will make you happy and relaxed.
-FAQs: Five Common Questions and Answers About Craftsman Building Craft Game
-Here are some of the most common questions and answers about craftsman building craft game:
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Yes, craftsman building craft game is free to download and play. However, it may contain ads or in-app purchases that require real money.
-Yes, craftsman building craft game is safe to play. However, since it is not available on the official App Store for iPhone users, you need to use a third-party app installer called Panda Helper to download and install craftsman building craft apk for iPhone. Panda Helper is a free app store that allows you to download and install apps and games that are not available on the official App Store. Panda Helper also provides tweaked apps, hacked games, and paid apps for free. However, you should be careful when using Panda Helper, as it may contain some risks, such as malware, viruses, or data breaches. You should always check the reviews and ratings of the apps and games before downloading them. You should also use a VPN or antivirus software to protect your device and data.
-Craftsman building craft game can be played both online and offline. You can play the game offline without an internet connection, but you will not be able to access some features, such as multiplayer mode, online servers, or online maps. You can play the game online with an internet connection, but you will need to create or join a server with other players online. You can also chat, make friends, and join groups with other players online.
-To update craftsman building craft game, you need to use Panda Helper again. You can follow these steps to update craftsman building craft game:
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Note: You may need to delete the old version of the game before installing the new version.
-To uninstall craftsman building craft game, you can follow these steps:
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Note: You may also need to delete Panda Helper if you don't want to use it anymore.
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\ No newline at end of file diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Payback 2 APK and Join the Online Multiplayer Battles.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Payback 2 APK and Join the Online Multiplayer Battles.md deleted file mode 100644 index caec7563539a3cf235b14e93c7fef82f8491bc4d..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Payback 2 APK and Join the Online Multiplayer Battles.md +++ /dev/null @@ -1,116 +0,0 @@ -
-Payback 2: The Battle Sandbox Game APK Download
-If you are looking for a fun and chaotic open-world game that lets you do anything from tank battles to helicopter races to gang wars, then you should check out Payback 2: The Battle Sandbox. This game is inspired by the likes of GTA, Saints Row, and other similar sagas, but with its own unique twist and style. In this article, we will show you how to download and install Payback 2 APK on your Android device, how to play it on your PC with BlueStacks, and some tips and tricks to help you enjoy the game more. We will also review the game and give you some alternatives if you want to try something different.
-What is Payback 2?
-Payback 2 is an action-adventure game developed by Apex Designs Entertainment Ltd. It was released in 2015 for iOS and Android devices, and has since gained over 100 million downloads and positive reviews from critics and players alike. The game is a sequel to the original Payback game from the early days of the App Store, which was also a GTA-style game.
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-Payback 2 offers a variety of game modes, maps, weapons, vehicles, and customization options that make every gameplay experience different and exciting. You can choose from over 50 campaign events, join online multiplayer battles with millions of other players, create your own events in custom mode, or just roam around the city and cause mayhem. The game also features hourly, daily, and weekly challenges that test your skills and reward you with coins and trophies.
-Features of Payback 2
-Payback 2 has many features that make it stand out from other sandbox games. Here are some of them:
-Varied campaign
-The game has a varied campaign mode that features 50 events across different scenarios. You can participate in massive street brawls, rocket car races, tank battles, helicopter races, and more. Each event has its own objectives, rules, and rewards. You can earn stars by completing the events and unlock new levels.
-Online multiplayer
-The game also has an online multiplayer mode that lets you battle your friends or over a million other players around the world. You can join or create rooms with different game modes, maps, weapons, and vehicles. You can also chat with other players, join clans, and compete on the leaderboards.
-Custom mode
-If you want to create your own events, you can use the custom mode feature. This allows you to mix and match any combination of the game's seven cities, nine game modes, varied weaponry, and dozens of vehicles. You can also adjust the settings such as time limit, weather, traffic, cops, etc. You can save your custom events and share them with other players.
-How to download and install Payback 2 APK on Android
-If you want to play Payback 2 on your Android device, you can follow these simple steps to download and install the APK file:
Step 1: Enable unknown sources
-Before you can install the APK file, you need to enable the option to allow installation of apps from unknown sources on your device. To do this, go to Settings > Security > Unknown sources and toggle it on. This will let you install apps that are not from the Google Play Store.
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-payback 2 the battle sandbox game apk download android games portalStep 2: Download the APK file
-Next, you need to download the APK file of Payback 2 from a reliable source. You can use this link to download the latest version of the game (version 2.104.12 as of June 2023). The file size is about 97 MB, so make sure you have enough space on your device.
-Step 3: Install the APK file
-Once you have downloaded the APK file, you can install it by tapping on it and following the instructions on the screen. It may take a few minutes for the installation to complete. After that, you can launch the game and enjoy it.
-How to play Payback 2 on PC with BlueStacks
-If you want to play Payback 2 on your PC, you can use an Android emulator like BlueStacks. BlueStacks is a software that lets you run Android apps and games on your PC with ease. Here are the steps to play Payback 2 on PC with BlueStacks:
-Step 1: Download and install BlueStacks on your PC
-First, you need to download and install BlueStacks on your PC. You can use this link to download the latest version of BlueStacks (version 5.0.230.1001 as of June 2023). The file size is about 1 GB, so make sure you have enough space on your PC.
-After downloading the file, run it and follow the instructions on the screen to install BlueStacks on your PC. It may take some time for the installation to finish. After that, you can launch BlueStacks and sign in with your Google account.
-Step 2: Launch BlueStacks and sign in with Google account
-After launching BlueStacks, you need to sign in with your Google account to access the Google Play Store and other Google services. To do this, click on the Google icon on the home screen and enter your email and password. If you don't have a Google account, you can create one for free.
-Step 3: Search for Payback 2 in the Play Store and install it
-Next, you need to search for Payback 2 in the Play Store and install it on your PC. To do this, click on the Play Store icon on the home screen and type "Payback 2" in the search bar. You will see the game icon with a green "Install" button. Click on it and wait for the download and installation to complete.
-Alternatively, you can also use this link to directly open the game page in the Play Store and install it from there.
-Once the game is installed, you can launch it from the home screen or the app drawer and start playing it.
-Tips and tricks for Payback 2
-Payback 2 is a fun and addictive game, but it can also be challenging at times. Here are some tips and tricks to help you master the game and have more fun:
-Use different weapons and vehicles
-The game offers a wide range of weapons and vehicles that you can use to wreak havoc or complete missions. You can find them scattered around the map or buy them from the shop with coins. You can also switch between them during gameplay by tapping on their icons on the screen.
-Some of the weapons and vehicles include pistols, shotguns, rifles, grenades, rockets, tanks, cars, bikes, helicopters, jets, etc. Each weapon and vehicle has its own advantages and disadvantages, so experiment with them and find out what suits your style best.
-Complete challenges and events
-The game has many challenges and events that you can complete to earn coins, trophies, stars, and other rewards. You can find them in the campaign mode, online multiplayer mode, or custom mode. Some of them are time-limited, so make sure you don't miss them.
-Some of the challenges and events include killing a certain number of enemies, winning a certain number of races, destroying a certain number of vehicles, etc. They vary in difficulty and reward level , so try to complete as many as you can and improve your skills and rank.
-Customize your avatar and settings
-The game also allows you to customize your avatar and settings to make the game more personalized and comfortable. You can change your avatar's appearance, clothing, accessories, etc. by buying them from the shop with coins. You can also change your settings such as sound, controls, graphics, etc. by tapping on the gear icon on the main menu.
-Some of the customization options include hats, glasses, masks, shirts, pants, shoes, etc. You can also choose from different control schemes such as tilt, touch, or joystick. You can also adjust the sensitivity, camera angle, and other parameters to suit your preference.
-Alternatives to Payback 2
-If you like Payback 2, you might also like some of these alternatives that offer similar gameplay and features:
-GTA series
-The GTA series is one of the most popular and influential sandbox games of all time. It features a vast open world, a rich story, a variety of missions, activities, characters, weapons, vehicles, etc. You can explore the city, commit crimes, fight enemies, chase or evade the police, etc. The latest installment is GTA V, which was released in 2013 for consoles and 2015 for PC.
-Mad Max
-Mad Max is a post-apocalyptic action-adventure game based on the movie franchise of the same name. It features a desolate wasteland, a brutal combat system, a dynamic weather system, a car customization system, etc. You play as Max Rockatansky, a lone warrior who must survive in the harsh environment and fight against various factions and enemies.
-Carmageddon
-Carmageddon is a vehicular combat game that features violent and humorous gameplay. It features a variety of cars, weapons, power-ups, pedestrians, animals, etc. You can race against other drivers or just cause chaos and destruction in the city. The game has a dark and satirical tone and is not for the faint-hearted.
-Review of Payback 2
-Payback 2 is a fun and addictive game that offers a lot of content and replay value. It has a simple and intuitive gameplay that anyone can enjoy. It has a colorful and vibrant graphics that create a lively atmosphere. It has a smooth and responsive performance that runs well on most devices.
-However, the game also has some drawbacks that may affect your experience. It has a repetitive and shallow campaign mode that lacks depth and variety. It has a limited and outdated online multiplayer mode that suffers from lag and glitches. It has a low-quality sound and music that are annoying and repetitive.
-Pros and cons
-Here is a summary of the pros and cons of Payback 2:
- | Pros | Cons | | --- | --- | | Fun and chaotic gameplay | Repetitive and shallow campaign | | Variety of game modes, maps, weapons, vehicles | Limited and outdated online multiplayer | | Custom mode feature | Low-quality sound and music | | Customization options | Some bugs and crashes |User ratings and feedback
-Payback 2 has received mostly positive ratings and feedback from users on the Google Play Store and the App Store. As of June 2023, it has an average rating of 4.3 out of 5 stars on both platforms. Here are some of the user reviews:
- - "This game is awesome! I love the graphics and the gameplay. It's like GTA but better. There are so many things to do and so many weapons and vehicles to use. I recommend this game to anyone who likes sandbox games." - 5 stars - "This game is good but it needs some improvements. The campaign mode is boring and repetitive. The online mode is laggy and glitchy. The sound effects are annoying and loud. Please fix these issues and add more content." - 3 stars - "This game is terrible! It's nothing like GTA at all. It's just a cheap copy with bad graphics and controls. The game crashes all the time and drains my battery. Don't waste your time or money on this game." - 1 starConclusion
-Payback 2 is a sandbox game that lets you do anything from tank battles to helicopter races to gang wars. It has many features that make it stand out from other sandbox games such as varied campaign mode, online multiplayer mode, custom mode feature, customization options, etc. However, it also has some drawbacks that may affect your experience such as repetitive and shallow campaign mode, limited and outdated online multiplayer mode, low-quality sound and music, etc. Overall, Payback 2 is a fun and addictive game that offers a lot of content and replay value, but it also needs some improvements and updates to make it more enjoyable and satisfying. If you are looking for a sandbox game that lets you do anything from tank battles to helicopter races to gang wars, then you should give Payback 2 a try.
-FAQs
-Here are some of the frequently asked questions about Payback 2:
--
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Yes, Payback 2 is free to play on both iOS and Android devices. However, it also contains in-app purchases that allow you to buy coins, gems, and other items with real money. You can also watch ads to earn some coins and gems for free.
-Payback 2 can be played both offline and online. You can play the campaign mode, custom mode, or challenges without an internet connection. However, you need an internet connection to play the online multiplayer mode or access the shop and other features.
-There are several ways to get more coins and gems in Payback 2. You can earn them by completing events, challenges, and achievements. You can also watch ads or rate the game to get some free coins and gems. Alternatively, you can buy them with real money from the shop.
-To update Payback 2, you need to go to the Google Play Store or the App Store and check if there is a new version available. If there is, you can tap on the "Update" button and wait for the download and installation to finish. You can also enable the auto-update option on your device settings to update the game automatically.
-If you have any questions, feedback, or issues about Payback 2, you can contact the developer of the game by emailing them at support@apex-designs.net. You can also visit their website at https://www.apex-designs.net/ or follow them on Facebook at https://www.facebook.com/apexdesignsuk/.
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\ No newline at end of file diff --git a/spaces/fffiloni/SplitTrack2MusicGen/tests/models/test_encodec_model.py b/spaces/fffiloni/SplitTrack2MusicGen/tests/models/test_encodec_model.py deleted file mode 100644 index 2f9c1db3f69a45f02451b71da95f44356811acbb..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/SplitTrack2MusicGen/tests/models/test_encodec_model.py +++ /dev/null @@ -1,60 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -import random - -import numpy as np -import torch - -from audiocraft.models import EncodecModel -from audiocraft.modules import SEANetEncoder, SEANetDecoder -from audiocraft.quantization import DummyQuantizer - - -class TestEncodecModel: - - def _create_encodec_model(self, - sample_rate: int, - channels: int, - dim: int = 5, - n_filters: int = 3, - n_residual_layers: int = 1, - ratios: list = [5, 4, 3, 2], - **kwargs): - frame_rate = np.prod(ratios) - encoder = SEANetEncoder(channels=channels, dimension=dim, n_filters=n_filters, - n_residual_layers=n_residual_layers, ratios=ratios) - decoder = SEANetDecoder(channels=channels, dimension=dim, n_filters=n_filters, - n_residual_layers=n_residual_layers, ratios=ratios) - quantizer = DummyQuantizer() - model = EncodecModel(encoder, decoder, quantizer, frame_rate=frame_rate, - sample_rate=sample_rate, channels=channels, **kwargs) - return model - - def test_model(self): - random.seed(1234) - sample_rate = 24_000 - channels = 1 - model = self._create_encodec_model(sample_rate, channels) - for _ in range(10): - length = random.randrange(1, 10_000) - x = torch.randn(2, channels, length) - res = model(x) - assert res.x.shape == x.shape - - def test_model_renorm(self): - random.seed(1234) - sample_rate = 24_000 - channels = 1 - model_nonorm = self._create_encodec_model(sample_rate, channels, renormalize=False) - model_renorm = self._create_encodec_model(sample_rate, channels, renormalize=True) - - for _ in range(10): - length = random.randrange(1, 10_000) - x = torch.randn(2, channels, length) - codes, scales = model_nonorm.encode(x) - codes, scales = model_renorm.encode(x) - assert scales is not None diff --git a/spaces/fffiloni/audioldm-text-to-audio-generation-copy/audioldm/clap/training/distributed.py b/spaces/fffiloni/audioldm-text-to-audio-generation-copy/audioldm/clap/training/distributed.py deleted file mode 100644 index 2fa61f76c5cc3ab9f6a9643042afa8e1f2e1cb7f..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/audioldm-text-to-audio-generation-copy/audioldm/clap/training/distributed.py +++ /dev/null @@ -1,150 +0,0 @@ -import os - -import torch -import socket - -try: - import horovod.torch as hvd -except ImportError: - hvd = None - - -def is_global_master(args): - return args.rank == 0 - - -def is_local_master(args): - return args.local_rank == 0 - - -def is_master(args, local=False): - return is_local_master(args) if local else is_global_master(args) - - -def is_using_horovod(): - # NOTE w/ horovod run, OMPI vars should be set, but w/ SLURM PMI vars will be set - # Differentiating between horovod and DDP use via SLURM may not be possible, so horovod arg still required... - ompi_vars = ["OMPI_COMM_WORLD_RANK", "OMPI_COMM_WORLD_SIZE"] - pmi_vars = ["PMI_RANK", "PMI_SIZE"] - if all([var in os.environ for var in ompi_vars]) or all( - [var in os.environ for var in pmi_vars] - ): - return True - else: - return False - - -def is_using_distributed(): - if "WORLD_SIZE" in os.environ: - return int(os.environ["WORLD_SIZE"]) > 1 - if "SLURM_NTASKS" in os.environ: - return int(os.environ["SLURM_NTASKS"]) > 1 - return False - - -def world_info_from_env(): - local_rank = 0 - for v in ( - "SLURM_LOCALID", - "MPI_LOCALRANKID", - "OMPI_COMM_WORLD_LOCAL_RANK", - "LOCAL_RANK", - ): - if v in os.environ: - local_rank = int(os.environ[v]) - break - global_rank = 0 - for v in ("SLURM_PROCID", "PMI_RANK", "OMPI_COMM_WORLD_RANK", "RANK"): - if v in os.environ: - global_rank = int(os.environ[v]) - break - world_size = 1 - for v in ("SLURM_NTASKS", "PMI_SIZE", "OMPI_COMM_WORLD_SIZE", "WORLD_SIZE"): - if v in os.environ: - world_size = int(os.environ[v]) - break - - return local_rank, global_rank, world_size - - -def init_distributed_device(args): - # Distributed training = training on more than one GPU. - # Works in both single and multi-node scenarios. - args.distributed = False - args.world_size = 1 - args.rank = 0 # global rank - args.local_rank = 0 - if args.horovod: - assert hvd is not None, "Horovod is not installed" - hvd.init() - world_size = int(os.environ["OMPI_COMM_WORLD_SIZE"]) - world_rank = int(os.environ["OMPI_COMM_WORLD_RANK"]) - local_rank = int(os.environ["OMPI_COMM_WORLD_LOCAL_RANK"]) - args.local_rank = local_rank - args.rank = world_rank - args.world_size = world_size - # args.local_rank = int(hvd.local_rank()) - # args.rank = hvd.rank() - # args.world_size = hvd.size() - args.distributed = True - os.environ["LOCAL_RANK"] = str(args.local_rank) - os.environ["RANK"] = str(args.rank) - os.environ["WORLD_SIZE"] = str(args.world_size) - print( - f"Distributed training: local_rank={args.local_rank}, " - f"rank={args.rank}, world_size={args.world_size}, " - f"hostname={socket.gethostname()}, pid={os.getpid()}" - ) - elif is_using_distributed(): - if "SLURM_PROCID" in os.environ: - # DDP via SLURM - args.local_rank, args.rank, args.world_size = world_info_from_env() - # SLURM var -> torch.distributed vars in case needed - os.environ["LOCAL_RANK"] = str(args.local_rank) - os.environ["RANK"] = str(args.rank) - os.environ["WORLD_SIZE"] = str(args.world_size) - torch.distributed.init_process_group( - backend=args.dist_backend, - init_method=args.dist_url, - world_size=args.world_size, - rank=args.rank, - ) - elif "OMPI_COMM_WORLD_SIZE" in os.environ: # using Summit cluster - world_size = int(os.environ["OMPI_COMM_WORLD_SIZE"]) - world_rank = int(os.environ["OMPI_COMM_WORLD_RANK"]) - local_rank = int(os.environ["OMPI_COMM_WORLD_LOCAL_RANK"]) - args.local_rank = local_rank - args.rank = world_rank - args.world_size = world_size - torch.distributed.init_process_group( - backend=args.dist_backend, - init_method=args.dist_url, - world_size=args.world_size, - rank=args.rank, - ) - else: - # DDP via torchrun, torch.distributed.launch - args.local_rank, _, _ = world_info_from_env() - torch.distributed.init_process_group( - backend=args.dist_backend, init_method=args.dist_url - ) - args.world_size = torch.distributed.get_world_size() - args.rank = torch.distributed.get_rank() - args.distributed = True - print( - f"Distributed training: local_rank={args.local_rank}, " - f"rank={args.rank}, world_size={args.world_size}, " - f"hostname={socket.gethostname()}, pid={os.getpid()}" - ) - - if torch.cuda.is_available(): - if args.distributed and not args.no_set_device_rank: - device = "cuda:%d" % args.local_rank - else: - device = "cuda:0" - torch.cuda.set_device(device) - else: - device = "cpu" - args.device = device - device = torch.device(device) - return device diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/net.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/net.d.ts deleted file mode 100644 index f7602a77db90b82421592a813d92822b2ea1a0f0..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/net.d.ts +++ /dev/null @@ -1,883 +0,0 @@ -/** - * > Stability: 2 - Stable - * - * The `net` module provides an asynchronous network API for creating stream-based - * TCP or `IPC` servers ({@link createServer}) and clients - * ({@link createConnection}). - * - * It can be accessed using: - * - * ```js - * const net = require('net'); - * ``` - * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/net.js) - */ -declare module 'net' { - import * as stream from 'node:stream'; - import { Abortable, EventEmitter } from 'node:events'; - import * as dns from 'node:dns'; - type LookupFunction = (hostname: string, options: dns.LookupOneOptions, callback: (err: NodeJS.ErrnoException | null, address: string, family: number) => void) => void; - interface AddressInfo { - address: string; - family: string; - port: number; - } - interface SocketConstructorOpts { - fd?: number | undefined; - allowHalfOpen?: boolean | undefined; - readable?: boolean | undefined; - writable?: boolean | undefined; - signal?: AbortSignal; - } - interface OnReadOpts { - buffer: Uint8Array | (() => Uint8Array); - /** - * This function is called for every chunk of incoming data. - * Two arguments are passed to it: the number of bytes written to buffer and a reference to buffer. - * Return false from this function to implicitly pause() the socket. - */ - callback(bytesWritten: number, buf: Uint8Array): boolean; - } - interface ConnectOpts { - /** - * If specified, incoming data is stored in a single buffer and passed to the supplied callback when data arrives on the socket. - * Note: this will cause the streaming functionality to not provide any data, however events like 'error', 'end', and 'close' will - * still be emitted as normal and methods like pause() and resume() will also behave as expected. - */ - onread?: OnReadOpts | undefined; - } - interface TcpSocketConnectOpts extends ConnectOpts { - port: number; - host?: string | undefined; - localAddress?: string | undefined; - localPort?: number | undefined; - hints?: number | undefined; - family?: number | undefined; - lookup?: LookupFunction | undefined; - noDelay?: boolean | undefined; - keepAlive?: boolean | undefined; - keepAliveInitialDelay?: number | undefined; - /** - * @since v18.13.0 - */ - autoSelectFamily?: boolean | undefined; - /** - * @since v18.13.0 - */ - autoSelectFamilyAttemptTimeout?: number | undefined; - } - interface IpcSocketConnectOpts extends ConnectOpts { - path: string; - } - type SocketConnectOpts = TcpSocketConnectOpts | IpcSocketConnectOpts; - type SocketReadyState = 'opening' | 'open' | 'readOnly' | 'writeOnly' | 'closed'; - /** - * This class is an abstraction of a TCP socket or a streaming `IPC` endpoint - * (uses named pipes on Windows, and Unix domain sockets otherwise). It is also - * an `EventEmitter`. - * - * A `net.Socket` can be created by the user and used directly to interact with - * a server. For example, it is returned by {@link createConnection}, - * so the user can use it to talk to the server. - * - * It can also be created by Node.js and passed to the user when a connection - * is received. For example, it is passed to the listeners of a `'connection'` event emitted on a {@link Server}, so the user can use - * it to interact with the client. - * @since v0.3.4 - */ - class Socket extends stream.Duplex { - constructor(options?: SocketConstructorOpts); - /** - * Sends data on the socket. The second parameter specifies the encoding in the - * case of a string. It defaults to UTF8 encoding. - * - * Returns `true` if the entire data was flushed successfully to the kernel - * buffer. Returns `false` if all or part of the data was queued in user memory.`'drain'` will be emitted when the buffer is again free. - * - * The optional `callback` parameter will be executed when the data is finally - * written out, which may not be immediately. - * - * See `Writable` stream `write()` method for more - * information. - * @since v0.1.90 - * @param [encoding='utf8'] Only used when data is `string`. - */ - write(buffer: Uint8Array | string, cb?: (err?: Error) => void): boolean; - write(str: Uint8Array | string, encoding?: BufferEncoding, cb?: (err?: Error) => void): boolean; - /** - * Initiate a connection on a given socket. - * - * Possible signatures: - * - * * `socket.connect(options[, connectListener])` - * * `socket.connect(path[, connectListener])` for `IPC` connections. - * * `socket.connect(port[, host][, connectListener])` for TCP connections. - * * Returns: `net.Socket` The socket itself. - * - * This function is asynchronous. When the connection is established, the `'connect'` event will be emitted. If there is a problem connecting, - * instead of a `'connect'` event, an `'error'` event will be emitted with - * the error passed to the `'error'` listener. - * The last parameter `connectListener`, if supplied, will be added as a listener - * for the `'connect'` event **once**. - * - * This function should only be used for reconnecting a socket after`'close'` has been emitted or otherwise it may lead to undefined - * behavior. - */ - connect(options: SocketConnectOpts, connectionListener?: () => void): this; - connect(port: number, host: string, connectionListener?: () => void): this; - connect(port: number, connectionListener?: () => void): this; - connect(path: string, connectionListener?: () => void): this; - /** - * Set the encoding for the socket as a `Readable Stream`. See `readable.setEncoding()` for more information. - * @since v0.1.90 - * @return The socket itself. - */ - setEncoding(encoding?: BufferEncoding): this; - /** - * Pauses the reading of data. That is, `'data'` events will not be emitted. - * Useful to throttle back an upload. - * @return The socket itself. - */ - pause(): this; - /** - * Close the TCP connection by sending an RST packet and destroy the stream. - * If this TCP socket is in connecting status, it will send an RST packet - * and destroy this TCP socket once it is connected. Otherwise, it will call - * `socket.destroy` with an `ERR_SOCKET_CLOSED` Error. If this is not a TCP socket - * (for example, a pipe), calling this method will immediately throw - * an `ERR_INVALID_HANDLE_TYPE` Error. - * @since v18.3.0 - * @return The socket itself. - */ - resetAndDestroy(): this; - /** - * Resumes reading after a call to `socket.pause()`. - * @return The socket itself. - */ - resume(): this; - /** - * Sets the socket to timeout after `timeout` milliseconds of inactivity on - * the socket. By default `net.Socket` do not have a timeout. - * - * When an idle timeout is triggered the socket will receive a `'timeout'` event but the connection will not be severed. The user must manually call `socket.end()` or `socket.destroy()` to - * end the connection. - * - * ```js - * socket.setTimeout(3000); - * socket.on('timeout', () => { - * console.log('socket timeout'); - * socket.end(); - * }); - * ``` - * - * If `timeout` is 0, then the existing idle timeout is disabled. - * - * The optional `callback` parameter will be added as a one-time listener for the `'timeout'` event. - * @since v0.1.90 - * @return The socket itself. - */ - setTimeout(timeout: number, callback?: () => void): this; - /** - * Enable/disable the use of Nagle's algorithm. - * - * When a TCP connection is created, it will have Nagle's algorithm enabled. - * - * Nagle's algorithm delays data before it is sent via the network. It attempts - * to optimize throughput at the expense of latency. - * - * Passing `true` for `noDelay` or not passing an argument will disable Nagle's - * algorithm for the socket. Passing `false` for `noDelay` will enable Nagle's - * algorithm. - * @since v0.1.90 - * @param [noDelay=true] - * @return The socket itself. - */ - setNoDelay(noDelay?: boolean): this; - /** - * Enable/disable keep-alive functionality, and optionally set the initial - * delay before the first keepalive probe is sent on an idle socket. - * - * Set `initialDelay` (in milliseconds) to set the delay between the last - * data packet received and the first keepalive probe. Setting `0` for`initialDelay` will leave the value unchanged from the default - * (or previous) setting. - * - * Enabling the keep-alive functionality will set the following socket options: - * - * * `SO_KEEPALIVE=1` - * * `TCP_KEEPIDLE=initialDelay` - * * `TCP_KEEPCNT=10` - * * `TCP_KEEPINTVL=1` - * @since v0.1.92 - * @param [enable=false] - * @param [initialDelay=0] - * @return The socket itself. - */ - setKeepAlive(enable?: boolean, initialDelay?: number): this; - /** - * Returns the bound `address`, the address `family` name and `port` of the - * socket as reported by the operating system:`{ port: 12346, family: 'IPv4', address: '127.0.0.1' }` - * @since v0.1.90 - */ - address(): AddressInfo | {}; - /** - * Calling `unref()` on a socket will allow the program to exit if this is the only - * active socket in the event system. If the socket is already `unref`ed calling`unref()` again will have no effect. - * @since v0.9.1 - * @return The socket itself. - */ - unref(): this; - /** - * Opposite of `unref()`, calling `ref()` on a previously `unref`ed socket will _not_ let the program exit if it's the only socket left (the default behavior). - * If the socket is `ref`ed calling `ref` again will have no effect. - * @since v0.9.1 - * @return The socket itself. - */ - ref(): this; - /** - * This property shows the number of characters buffered for writing. The buffer - * may contain strings whose length after encoding is not yet known. So this number - * is only an approximation of the number of bytes in the buffer. - * - * `net.Socket` has the property that `socket.write()` always works. This is to - * help users get up and running quickly. The computer cannot always keep up - * with the amount of data that is written to a socket. The network connection - * simply might be too slow. Node.js will internally queue up the data written to a - * socket and send it out over the wire when it is possible. - * - * The consequence of this internal buffering is that memory may grow. - * Users who experience large or growing `bufferSize` should attempt to - * "throttle" the data flows in their program with `socket.pause()` and `socket.resume()`. - * @since v0.3.8 - * @deprecated Since v14.6.0 - Use `writableLength` instead. - */ - readonly bufferSize: number; - /** - * The amount of received bytes. - * @since v0.5.3 - */ - readonly bytesRead: number; - /** - * The amount of bytes sent. - * @since v0.5.3 - */ - readonly bytesWritten: number; - /** - * If `true`,`socket.connect(options[, connectListener])` was - * called and has not yet finished. It will stay `true` until the socket becomes - * connected, then it is set to `false` and the `'connect'` event is emitted. Note - * that the `socket.connect(options[, connectListener])` callback is a listener for the `'connect'` event. - * @since v6.1.0 - */ - readonly connecting: boolean; - /** - * This is `true` if the socket is not connected yet, either because `.connect()` - * has not yet been called or because it is still in the process of connecting (see `socket.connecting`). - * @since v10.16.0 - */ - readonly pending: boolean; - /** - * See `writable.destroyed` for further details. - */ - readonly destroyed: boolean; - /** - * The string representation of the local IP address the remote client is - * connecting on. For example, in a server listening on `'0.0.0.0'`, if a client - * connects on `'192.168.1.1'`, the value of `socket.localAddress` would be`'192.168.1.1'`. - * @since v0.9.6 - */ - readonly localAddress?: string; - /** - * The numeric representation of the local port. For example, `80` or `21`. - * @since v0.9.6 - */ - readonly localPort?: number; - /** - * The string representation of the local IP family. `'IPv4'` or `'IPv6'`. - * @since v18.8.0 - */ - readonly localFamily?: string; - /** - * This property represents the state of the connection as a string. - * @see {https://nodejs.org/api/net.html#socketreadystate} - * @since v0.5.0 - */ - readonly readyState: SocketReadyState; - /** - * The string representation of the remote IP address. For example,`'74.125.127.100'` or `'2001:4860:a005::68'`. Value may be `undefined` if - * the socket is destroyed (for example, if the client disconnected). - * @since v0.5.10 - */ - readonly remoteAddress?: string | undefined; - /** - * The string representation of the remote IP family. `'IPv4'` or `'IPv6'`. - * @since v0.11.14 - */ - readonly remoteFamily?: string | undefined; - /** - * The numeric representation of the remote port. For example, `80` or `21`. - * @since v0.5.10 - */ - readonly remotePort?: number | undefined; - /** - * The socket timeout in milliseconds as set by socket.setTimeout(). It is undefined if a timeout has not been set. - * @since v10.7.0 - */ - readonly timeout?: number | undefined; - /** - * Half-closes the socket. i.e., it sends a FIN packet. It is possible the - * server will still send some data. - * - * See `writable.end()` for further details. - * @since v0.1.90 - * @param [encoding='utf8'] Only used when data is `string`. - * @param callback Optional callback for when the socket is finished. - * @return The socket itself. - */ - end(callback?: () => void): this; - end(buffer: Uint8Array | string, callback?: () => void): this; - end(str: Uint8Array | string, encoding?: BufferEncoding, callback?: () => void): this; - /** - * events.EventEmitter - * 1. close - * 2. connect - * 3. data - * 4. drain - * 5. end - * 6. error - * 7. lookup - * 8. ready - * 9. timeout - */ - addListener(event: string, listener: (...args: any[]) => void): this; - addListener(event: 'close', listener: (hadError: boolean) => void): this; - addListener(event: 'connect', listener: () => void): this; - addListener(event: 'data', listener: (data: Buffer) => void): this; - addListener(event: 'drain', listener: () => void): this; - addListener(event: 'end', listener: () => void): this; - addListener(event: 'error', listener: (err: Error) => void): this; - addListener(event: 'lookup', listener: (err: Error, address: string, family: string | number, host: string) => void): this; - addListener(event: 'ready', listener: () => void): this; - addListener(event: 'timeout', listener: () => void): this; - emit(event: string | symbol, ...args: any[]): boolean; - emit(event: 'close', hadError: boolean): boolean; - emit(event: 'connect'): boolean; - emit(event: 'data', data: Buffer): boolean; - emit(event: 'drain'): boolean; - emit(event: 'end'): boolean; - emit(event: 'error', err: Error): boolean; - emit(event: 'lookup', err: Error, address: string, family: string | number, host: string): boolean; - emit(event: 'ready'): boolean; - emit(event: 'timeout'): boolean; - on(event: string, listener: (...args: any[]) => void): this; - on(event: 'close', listener: (hadError: boolean) => void): this; - on(event: 'connect', listener: () => void): this; - on(event: 'data', listener: (data: Buffer) => void): this; - on(event: 'drain', listener: () => void): this; - on(event: 'end', listener: () => void): this; - on(event: 'error', listener: (err: Error) => void): this; - on(event: 'lookup', listener: (err: Error, address: string, family: string | number, host: string) => void): this; - on(event: 'ready', listener: () => void): this; - on(event: 'timeout', listener: () => void): this; - once(event: string, listener: (...args: any[]) => void): this; - once(event: 'close', listener: (hadError: boolean) => void): this; - once(event: 'connect', listener: () => void): this; - once(event: 'data', listener: (data: Buffer) => void): this; - once(event: 'drain', listener: () => void): this; - once(event: 'end', listener: () => void): this; - once(event: 'error', listener: (err: Error) => void): this; - once(event: 'lookup', listener: (err: Error, address: string, family: string | number, host: string) => void): this; - once(event: 'ready', listener: () => void): this; - once(event: 'timeout', listener: () => void): this; - prependListener(event: string, listener: (...args: any[]) => void): this; - prependListener(event: 'close', listener: (hadError: boolean) => void): this; - prependListener(event: 'connect', listener: () => void): this; - prependListener(event: 'data', listener: (data: Buffer) => void): this; - prependListener(event: 'drain', listener: () => void): this; - prependListener(event: 'end', listener: () => void): this; - prependListener(event: 'error', listener: (err: Error) => void): this; - prependListener(event: 'lookup', listener: (err: Error, address: string, family: string | number, host: string) => void): this; - prependListener(event: 'ready', listener: () => void): this; - prependListener(event: 'timeout', listener: () => void): this; - prependOnceListener(event: string, listener: (...args: any[]) => void): this; - prependOnceListener(event: 'close', listener: (hadError: boolean) => void): this; - prependOnceListener(event: 'connect', listener: () => void): this; - prependOnceListener(event: 'data', listener: (data: Buffer) => void): this; - prependOnceListener(event: 'drain', listener: () => void): this; - prependOnceListener(event: 'end', listener: () => void): this; - prependOnceListener(event: 'error', listener: (err: Error) => void): this; - prependOnceListener(event: 'lookup', listener: (err: Error, address: string, family: string | number, host: string) => void): this; - prependOnceListener(event: 'ready', listener: () => void): this; - prependOnceListener(event: 'timeout', listener: () => void): this; - } - interface ListenOptions extends Abortable { - port?: number | undefined; - host?: string | undefined; - backlog?: number | undefined; - path?: string | undefined; - exclusive?: boolean | undefined; - readableAll?: boolean | undefined; - writableAll?: boolean | undefined; - /** - * @default false - */ - ipv6Only?: boolean | undefined; - } - interface ServerOpts { - /** - * Indicates whether half-opened TCP connections are allowed. - * @default false - */ - allowHalfOpen?: boolean | undefined; - /** - * Indicates whether the socket should be paused on incoming connections. - * @default false - */ - pauseOnConnect?: boolean | undefined; - /** - * If set to `true`, it disables the use of Nagle's algorithm immediately after a new incoming connection is received. - * @default false - * @since v16.5.0 - */ - noDelay?: boolean | undefined; - /** - * If set to `true`, it enables keep-alive functionality on the socket immediately after a new incoming connection is received, - * similarly on what is done in `socket.setKeepAlive([enable][, initialDelay])`. - * @default false - * @since v16.5.0 - */ - keepAlive?: boolean | undefined; - /** - * If set to a positive number, it sets the initial delay before the first keepalive probe is sent on an idle socket. - * @default 0 - * @since v16.5.0 - */ - keepAliveInitialDelay?: number | undefined; - } - interface DropArgument { - localAddress?: string; - localPort?: number; - localFamily?: string; - remoteAddress?: string; - remotePort?: number; - remoteFamily?: string; - } - /** - * This class is used to create a TCP or `IPC` server. - * @since v0.1.90 - */ - class Server extends EventEmitter { - constructor(connectionListener?: (socket: Socket) => void); - constructor(options?: ServerOpts, connectionListener?: (socket: Socket) => void); - /** - * Start a server listening for connections. A `net.Server` can be a TCP or - * an `IPC` server depending on what it listens to. - * - * Possible signatures: - * - * * `server.listen(handle[, backlog][, callback])` - * * `server.listen(options[, callback])` - * * `server.listen(path[, backlog][, callback])` for `IPC` servers - * * `server.listen([port[, host[, backlog]]][, callback])` for TCP servers - * - * This function is asynchronous. When the server starts listening, the `'listening'` event will be emitted. The last parameter `callback`will be added as a listener for the `'listening'` - * event. - * - * All `listen()` methods can take a `backlog` parameter to specify the maximum - * length of the queue of pending connections. The actual length will be determined - * by the OS through sysctl settings such as `tcp_max_syn_backlog` and `somaxconn`on Linux. The default value of this parameter is 511 (not 512). - * - * All {@link Socket} are set to `SO_REUSEADDR` (see [`socket(7)`](https://man7.org/linux/man-pages/man7/socket.7.html) for - * details). - * - * The `server.listen()` method can be called again if and only if there was an - * error during the first `server.listen()` call or `server.close()` has been - * called. Otherwise, an `ERR_SERVER_ALREADY_LISTEN` error will be thrown. - * - * One of the most common errors raised when listening is `EADDRINUSE`. - * This happens when another server is already listening on the requested`port`/`path`/`handle`. One way to handle this would be to retry - * after a certain amount of time: - * - * ```js - * server.on('error', (e) => { - * if (e.code === 'EADDRINUSE') { - * console.log('Address in use, retrying...'); - * setTimeout(() => { - * server.close(); - * server.listen(PORT, HOST); - * }, 1000); - * } - * }); - * ``` - */ - listen(port?: number, hostname?: string, backlog?: number, listeningListener?: () => void): this; - listen(port?: number, hostname?: string, listeningListener?: () => void): this; - listen(port?: number, backlog?: number, listeningListener?: () => void): this; - listen(port?: number, listeningListener?: () => void): this; - listen(path: string, backlog?: number, listeningListener?: () => void): this; - listen(path: string, listeningListener?: () => void): this; - listen(options: ListenOptions, listeningListener?: () => void): this; - listen(handle: any, backlog?: number, listeningListener?: () => void): this; - listen(handle: any, listeningListener?: () => void): this; - /** - * Stops the server from accepting new connections and keeps existing - * connections. This function is asynchronous, the server is finally closed - * when all connections are ended and the server emits a `'close'` event. - * The optional `callback` will be called once the `'close'` event occurs. Unlike - * that event, it will be called with an `Error` as its only argument if the server - * was not open when it was closed. - * @since v0.1.90 - * @param callback Called when the server is closed. - */ - close(callback?: (err?: Error) => void): this; - /** - * Returns the bound `address`, the address `family` name, and `port` of the server - * as reported by the operating system if listening on an IP socket - * (useful to find which port was assigned when getting an OS-assigned address):`{ port: 12346, family: 'IPv4', address: '127.0.0.1' }`. - * - * For a server listening on a pipe or Unix domain socket, the name is returned - * as a string. - * - * ```js - * const server = net.createServer((socket) => { - * socket.end('goodbye\n'); - * }).on('error', (err) => { - * // Handle errors here. - * throw err; - * }); - * - * // Grab an arbitrary unused port. - * server.listen(() => { - * console.log('opened server on', server.address()); - * }); - * ``` - * - * `server.address()` returns `null` before the `'listening'` event has been - * emitted or after calling `server.close()`. - * @since v0.1.90 - */ - address(): AddressInfo | string | null; - /** - * Asynchronously get the number of concurrent connections on the server. Works - * when sockets were sent to forks. - * - * Callback should take two arguments `err` and `count`. - * @since v0.9.7 - */ - getConnections(cb: (error: Error | null, count: number) => void): void; - /** - * Opposite of `unref()`, calling `ref()` on a previously `unref`ed server will _not_ let the program exit if it's the only server left (the default behavior). - * If the server is `ref`ed calling `ref()` again will have no effect. - * @since v0.9.1 - */ - ref(): this; - /** - * Calling `unref()` on a server will allow the program to exit if this is the only - * active server in the event system. If the server is already `unref`ed calling`unref()` again will have no effect. - * @since v0.9.1 - */ - unref(): this; - /** - * Set this property to reject connections when the server's connection count gets - * high. - * - * It is not recommended to use this option once a socket has been sent to a child - * with `child_process.fork()`. - * @since v0.2.0 - */ - maxConnections: number; - connections: number; - /** - * Indicates whether or not the server is listening for connections. - * @since v5.7.0 - */ - listening: boolean; - /** - * events.EventEmitter - * 1. close - * 2. connection - * 3. error - * 4. listening - * 5. drop - */ - addListener(event: string, listener: (...args: any[]) => void): this; - addListener(event: 'close', listener: () => void): this; - addListener(event: 'connection', listener: (socket: Socket) => void): this; - addListener(event: 'error', listener: (err: Error) => void): this; - addListener(event: 'listening', listener: () => void): this; - addListener(event: 'drop', listener: (data?: DropArgument) => void): this; - emit(event: string | symbol, ...args: any[]): boolean; - emit(event: 'close'): boolean; - emit(event: 'connection', socket: Socket): boolean; - emit(event: 'error', err: Error): boolean; - emit(event: 'listening'): boolean; - emit(event: 'drop', data?: DropArgument): boolean; - on(event: string, listener: (...args: any[]) => void): this; - on(event: 'close', listener: () => void): this; - on(event: 'connection', listener: (socket: Socket) => void): this; - on(event: 'error', listener: (err: Error) => void): this; - on(event: 'listening', listener: () => void): this; - on(event: 'drop', listener: (data?: DropArgument) => void): this; - once(event: string, listener: (...args: any[]) => void): this; - once(event: 'close', listener: () => void): this; - once(event: 'connection', listener: (socket: Socket) => void): this; - once(event: 'error', listener: (err: Error) => void): this; - once(event: 'listening', listener: () => void): this; - once(event: 'drop', listener: (data?: DropArgument) => void): this; - prependListener(event: string, listener: (...args: any[]) => void): this; - prependListener(event: 'close', listener: () => void): this; - prependListener(event: 'connection', listener: (socket: Socket) => void): this; - prependListener(event: 'error', listener: (err: Error) => void): this; - prependListener(event: 'listening', listener: () => void): this; - prependListener(event: 'drop', listener: (data?: DropArgument) => void): this; - prependOnceListener(event: string, listener: (...args: any[]) => void): this; - prependOnceListener(event: 'close', listener: () => void): this; - prependOnceListener(event: 'connection', listener: (socket: Socket) => void): this; - prependOnceListener(event: 'error', listener: (err: Error) => void): this; - prependOnceListener(event: 'listening', listener: () => void): this; - prependOnceListener(event: 'drop', listener: (data?: DropArgument) => void): this; - } - type IPVersion = 'ipv4' | 'ipv6'; - /** - * The `BlockList` object can be used with some network APIs to specify rules for - * disabling inbound or outbound access to specific IP addresses, IP ranges, or - * IP subnets. - * @since v15.0.0, v14.18.0 - */ - class BlockList { - /** - * Adds a rule to block the given IP address. - * @since v15.0.0, v14.18.0 - * @param address An IPv4 or IPv6 address. - * @param [type='ipv4'] Either `'ipv4'` or `'ipv6'`. - */ - addAddress(address: string, type?: IPVersion): void; - addAddress(address: SocketAddress): void; - /** - * Adds a rule to block a range of IP addresses from `start` (inclusive) to`end` (inclusive). - * @since v15.0.0, v14.18.0 - * @param start The starting IPv4 or IPv6 address in the range. - * @param end The ending IPv4 or IPv6 address in the range. - * @param [type='ipv4'] Either `'ipv4'` or `'ipv6'`. - */ - addRange(start: string, end: string, type?: IPVersion): void; - addRange(start: SocketAddress, end: SocketAddress): void; - /** - * Adds a rule to block a range of IP addresses specified as a subnet mask. - * @since v15.0.0, v14.18.0 - * @param net The network IPv4 or IPv6 address. - * @param prefix The number of CIDR prefix bits. For IPv4, this must be a value between `0` and `32`. For IPv6, this must be between `0` and `128`. - * @param [type='ipv4'] Either `'ipv4'` or `'ipv6'`. - */ - addSubnet(net: SocketAddress, prefix: number): void; - addSubnet(net: string, prefix: number, type?: IPVersion): void; - /** - * Returns `true` if the given IP address matches any of the rules added to the`BlockList`. - * - * ```js - * const blockList = new net.BlockList(); - * blockList.addAddress('123.123.123.123'); - * blockList.addRange('10.0.0.1', '10.0.0.10'); - * blockList.addSubnet('8592:757c:efae:4e45::', 64, 'ipv6'); - * - * console.log(blockList.check('123.123.123.123')); // Prints: true - * console.log(blockList.check('10.0.0.3')); // Prints: true - * console.log(blockList.check('222.111.111.222')); // Prints: false - * - * // IPv6 notation for IPv4 addresses works: - * console.log(blockList.check('::ffff:7b7b:7b7b', 'ipv6')); // Prints: true - * console.log(blockList.check('::ffff:123.123.123.123', 'ipv6')); // Prints: true - * ``` - * @since v15.0.0, v14.18.0 - * @param address The IP address to check - * @param [type='ipv4'] Either `'ipv4'` or `'ipv6'`. - */ - check(address: SocketAddress): boolean; - check(address: string, type?: IPVersion): boolean; - } - interface TcpNetConnectOpts extends TcpSocketConnectOpts, SocketConstructorOpts { - timeout?: number | undefined; - } - interface IpcNetConnectOpts extends IpcSocketConnectOpts, SocketConstructorOpts { - timeout?: number | undefined; - } - type NetConnectOpts = TcpNetConnectOpts | IpcNetConnectOpts; - /** - * Creates a new TCP or `IPC` server. - * - * If `allowHalfOpen` is set to `true`, when the other end of the socket - * signals the end of transmission, the server will only send back the end of - * transmission when `socket.end()` is explicitly called. For example, in the - * context of TCP, when a FIN packed is received, a FIN packed is sent - * back only when `socket.end()` is explicitly called. Until then the - * connection is half-closed (non-readable but still writable). See `'end'` event and [RFC 1122](https://tools.ietf.org/html/rfc1122) (section 4.2.2.13) for more information. - * - * If `pauseOnConnect` is set to `true`, then the socket associated with each - * incoming connection will be paused, and no data will be read from its handle. - * This allows connections to be passed between processes without any data being - * read by the original process. To begin reading data from a paused socket, call `socket.resume()`. - * - * The server can be a TCP server or an `IPC` server, depending on what it `listen()` to. - * - * Here is an example of a TCP echo server which listens for connections - * on port 8124: - * - * ```js - * const net = require('net'); - * const server = net.createServer((c) => { - * // 'connection' listener. - * console.log('client connected'); - * c.on('end', () => { - * console.log('client disconnected'); - * }); - * c.write('hello\r\n'); - * c.pipe(c); - * }); - * server.on('error', (err) => { - * throw err; - * }); - * server.listen(8124, () => { - * console.log('server bound'); - * }); - * ``` - * - * Test this by using `telnet`: - * - * ```console - * $ telnet localhost 8124 - * ``` - * - * To listen on the socket `/tmp/echo.sock`: - * - * ```js - * server.listen('/tmp/echo.sock', () => { - * console.log('server bound'); - * }); - * ``` - * - * Use `nc` to connect to a Unix domain socket server: - * - * ```console - * $ nc -U /tmp/echo.sock - * ``` - * @since v0.5.0 - * @param connectionListener Automatically set as a listener for the {@link 'connection'} event. - */ - function createServer(connectionListener?: (socket: Socket) => void): Server; - function createServer(options?: ServerOpts, connectionListener?: (socket: Socket) => void): Server; - /** - * Aliases to {@link createConnection}. - * - * Possible signatures: - * - * * {@link connect} - * * {@link connect} for `IPC` connections. - * * {@link connect} for TCP connections. - */ - function connect(options: NetConnectOpts, connectionListener?: () => void): Socket; - function connect(port: number, host?: string, connectionListener?: () => void): Socket; - function connect(path: string, connectionListener?: () => void): Socket; - /** - * A factory function, which creates a new {@link Socket}, - * immediately initiates connection with `socket.connect()`, - * then returns the `net.Socket` that starts the connection. - * - * When the connection is established, a `'connect'` event will be emitted - * on the returned socket. The last parameter `connectListener`, if supplied, - * will be added as a listener for the `'connect'` event **once**. - * - * Possible signatures: - * - * * {@link createConnection} - * * {@link createConnection} for `IPC` connections. - * * {@link createConnection} for TCP connections. - * - * The {@link connect} function is an alias to this function. - */ - function createConnection(options: NetConnectOpts, connectionListener?: () => void): Socket; - function createConnection(port: number, host?: string, connectionListener?: () => void): Socket; - function createConnection(path: string, connectionListener?: () => void): Socket; - /** - * Returns `6` if `input` is an IPv6 address. Returns `4` if `input` is an IPv4 - * address in [dot-decimal notation](https://en.wikipedia.org/wiki/Dot-decimal_notation) with no leading zeroes. Otherwise, returns`0`. - * - * ```js - * net.isIP('::1'); // returns 6 - * net.isIP('127.0.0.1'); // returns 4 - * net.isIP('127.000.000.001'); // returns 0 - * net.isIP('127.0.0.1/24'); // returns 0 - * net.isIP('fhqwhgads'); // returns 0 - * ``` - * @since v0.3.0 - */ - function isIP(input: string): number; - /** - * Returns `true` if `input` is an IPv4 address in [dot-decimal notation](https://en.wikipedia.org/wiki/Dot-decimal_notation) with no - * leading zeroes. Otherwise, returns `false`. - * - * ```js - * net.isIPv4('127.0.0.1'); // returns true - * net.isIPv4('127.000.000.001'); // returns false - * net.isIPv4('127.0.0.1/24'); // returns false - * net.isIPv4('fhqwhgads'); // returns false - * ``` - * @since v0.3.0 - */ - function isIPv4(input: string): boolean; - /** - * Returns `true` if `input` is an IPv6 address. Otherwise, returns `false`. - * - * ```js - * net.isIPv6('::1'); // returns true - * net.isIPv6('fhqwhgads'); // returns false - * ``` - * @since v0.3.0 - */ - function isIPv6(input: string): boolean; - interface SocketAddressInitOptions { - /** - * The network address as either an IPv4 or IPv6 string. - * @default 127.0.0.1 - */ - address?: string | undefined; - /** - * @default `'ipv4'` - */ - family?: IPVersion | undefined; - /** - * An IPv6 flow-label used only if `family` is `'ipv6'`. - * @default 0 - */ - flowlabel?: number | undefined; - /** - * An IP port. - * @default 0 - */ - port?: number | undefined; - } - /** - * @since v15.14.0, v14.18.0 - */ - class SocketAddress { - constructor(options: SocketAddressInitOptions); - /** - * @since v15.14.0, v14.18.0 - */ - readonly address: string; - /** - * Either \`'ipv4'\` or \`'ipv6'\`. - * @since v15.14.0, v14.18.0 - */ - readonly family: IPVersion; - /** - * @since v15.14.0, v14.18.0 - */ - readonly port: number; - /** - * @since v15.14.0, v14.18.0 - */ - readonly flowlabel: number; - } -} -declare module 'node:net' { - export * from 'net'; -} diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/engine.io/build/transports-uws/websocket.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/engine.io/build/transports-uws/websocket.js deleted file mode 100644 index 214611194b54f41e21ee8b1706ebc0e6b6e905e2..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/engine.io/build/transports-uws/websocket.js +++ /dev/null @@ -1,85 +0,0 @@ -"use strict"; -Object.defineProperty(exports, "__esModule", { value: true }); -exports.WebSocket = void 0; -const transport_1 = require("../transport"); -const debug_1 = require("debug"); -const debug = (0, debug_1.default)("engine:ws"); -class WebSocket extends transport_1.Transport { - /** - * WebSocket transport - * - * @param req - * @api public - */ - constructor(req) { - super(req); - this.writable = false; - this.perMessageDeflate = null; - } - /** - * Transport name - * - * @api public - */ - get name() { - return "websocket"; - } - /** - * Advertise upgrade support. - * - * @api public - */ - get handlesUpgrades() { - return true; - } - /** - * Advertise framing support. - * - * @api public - */ - get supportsFraming() { - return true; - } - /** - * Writes a packet payload. - * - * @param {Array} packets - * @api private - */ - send(packets) { - this.writable = false; - for (let i = 0; i < packets.length; i++) { - const packet = packets[i]; - const isLast = i + 1 === packets.length; - const send = (data) => { - const isBinary = typeof data !== "string"; - const compress = this.perMessageDeflate && - Buffer.byteLength(data) > this.perMessageDeflate.threshold; - debug('writing "%s"', data); - this.socket.send(data, isBinary, compress); - if (isLast) { - this.writable = true; - this.emit("drain"); - } - }; - if (packet.options && typeof packet.options.wsPreEncoded === "string") { - send(packet.options.wsPreEncoded); - } - else { - this.parser.encodePacket(packet, this.supportsBinary, send); - } - } - } - /** - * Closes the transport. - * - * @api private - */ - doClose(fn) { - debug("closing"); - fn && fn(); - // call fn first since socket.end() immediately emits a "close" event - this.socket.end(); - } -} -exports.WebSocket = WebSocket; diff --git a/spaces/fffiloni/gpt-talking-portrait/app.py b/spaces/fffiloni/gpt-talking-portrait/app.py deleted file mode 100644 index 5b48b4d1649bd190248fe40a3d6f48b287d64eb0..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/gpt-talking-portrait/app.py +++ /dev/null @@ -1,181 +0,0 @@ -import gradio as gr - -from PIL import Image -import os - -import openai - -#api_key = os.environ.get('api_key') - -from share_btn import community_icon_html, loading_icon_html, share_js - -token = os.environ.get('HF_TOKEN') -whisper = gr.Interface.load(name="spaces/sanchit-gandhi/whisper-large-v2") -tts = gr.Interface.load(name="spaces/Flux9665/IMS-Toucan") -talking_face = gr.Blocks.load(name="spaces/fffiloni/one-shot-talking-face", api_key=token) - -def infer(audio, openai_api_key): - - whisper_result = whisper(audio, None, "translate", fn_index=0) - - gpt_response = try_api(whisper_result, openai_api_key) - - audio_response = tts(gpt_response[0], "English Text", "English Accent", "English Speaker's Voice", fn_index=0) - - portrait_link = talking_face("wise_woman_portrait.png", audio_response, fn_index=0) - - return gr.Textbox.update(value=whisper_result, visible=True), portrait_link, gr.Textbox.update(value=gpt_response[1], visible=True), gr.Group.update(visible=True), gr.Button.update(visible=True) - -def try_api(message, openai_api_key): - - try: - response = call_api(message, openai_api_key) - return response, "no error" - except openai.error.Timeout as e: - #Handle timeout error, e.g. retry or log - print(f"OpenAI API request timed out: {e}") - return "oups", f"OpenAI API request timed out:
{e}" - except openai.error.APIError as e: - #Handle API error, e.g. retry or log - print(f"OpenAI API returned an API Error: {e}") - return "oups", f"OpenAI API returned an API Error:
{e}" - except openai.error.APIConnectionError as e: - #Handle connection error, e.g. check network or log - print(f"OpenAI API request failed to connect: {e}") - return "oups", f"OpenAI API request failed to connect:
{e}" - except openai.error.InvalidRequestError as e: - #Handle invalid request error, e.g. validate parameters or log - print(f"OpenAI API request was invalid: {e}") - return "oups", f"OpenAI API request was invalid:
{e}" - except openai.error.AuthenticationError as e: - #Handle authentication error, e.g. check credentials or log - print(f"OpenAI API request was not authorized: {e}") - return "oups", f"OpenAI API request was not authorized:
{e}" - except openai.error.PermissionError as e: - #Handle permission error, e.g. check scope or log - print(f"OpenAI API request was not permitted: {e}") - return "oups", f"OpenAI API request was not permitted:
{e}" - except openai.error.RateLimitError as e: - #Handle rate limit error, e.g. wait or log - print(f"OpenAI API request exceeded rate limit: {e}") - return "oups", f"OpenAI API request exceeded rate limit:
{e}" - -def call_api(message, openai_api_key): - - print("starting open ai") - augmented_prompt = message + prevent_code_gen - openai.api_key = openai_api_key - - response = openai.Completion.create( - model="text-davinci-003", - prompt=augmented_prompt, - temperature=0.5, - max_tokens=2048, - top_p=1, - frequency_penalty=0, - presence_penalty=0.6 - ) - - print(response) - - #return str(response.choices[0].text).split("\n",2)[2] - return str(response.choices[0].text) - -def clean_components(): - return gr.Audio.update(value=None), gr.HTML.update(visible=False), gr.Textbox.update(visible=False), gr.Video.update(value=None), gr.Group.update(visible=False), gr.Button.update(visible=False) - -title = """ ---""" - -article = """ - - ---- GPT Talking Portrait -
-- Use Whisper to ask, alive portrait responds ! -
---""" - -prevent_code_gen = """ -If i am asking for code generation, do not provide me with code. Instead, give me a summury of good hints about how i could do what i asked, but shortly. -If i am not asking for code generation, do as usual. -""" -with gr.Blocks(css="style.css") as demo: - - with gr.Column(elem_id="col-container"): - - gr.HTML(title) - - gpt_response = gr.Video(label="Talking Portrait response", elem_id="video_out") - whisper_tr = gr.Textbox(label="whisper english translation", elem_id="text_inp", visible=False) - - with gr.Row(elem_id="secondary-buttons"): - clean_btn = gr.Button(value="Clean", elem_id="clean-btn", visible=False) - with gr.Group(elem_id="share-btn-container", visible=False) as share_group: - community_icon = gr.HTML(community_icon_html) - loading_icon = gr.HTML(loading_icon_html) - share_button = gr.Button("Share to community", elem_id="share-btn") - - error_handler = gr.HTML(visible=False, show_label=False, elem_id="error_handler") - - with gr.Column(elem_id="col-container-2"): - with gr.Column(): - with gr.Row(): - record_input = gr.Audio(source="microphone",type="filepath", label="Audio input", show_label=True, elem_id="record_btn") - openai_api_key = gr.Textbox(max_lines=1, type="password", label="š Your OpenAI API Key", placeholder="sk-123abc...") - - send_btn = gr.Button("Send my request !") - - gr.HTML(article) - - clean_btn.click(clean_components, scroll_to_output=True, inputs=[], outputs=[record_input, error_handler, whisper_tr, gpt_response, share_group, clean_btn]) - send_btn.click(infer, inputs=[record_input, openai_api_key], outputs=[whisper_tr, gpt_response, error_handler, share_group, clean_btn]) - share_button.click(None, [], [], _js=share_js) - -demo.queue(max_size=32, concurrency_count=20).launch(debug=True) - - diff --git a/spaces/fffiloni/instant-TTS-Bark-cloning/examples/library/blank.md b/spaces/fffiloni/instant-TTS-Bark-cloning/examples/library/blank.md deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/fffiloni/stable-diffusion-touch-of-paint/app.py b/spaces/fffiloni/stable-diffusion-touch-of-paint/app.py deleted file mode 100644 index 0e572461130e1ddd6ccd7c61c5156fc5fc4b662c..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/stable-diffusion-touch-of-paint/app.py +++ /dev/null @@ -1,60 +0,0 @@ -import gradio as gr -#import torch -#from torch import autocast // only for GPU - -from PIL import Image -import numpy as np -from io import BytesIO -import os -MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD') - -#from diffusers import StableDiffusionPipeline -from diffusers import StableDiffusionImg2ImgPipeline - -print("hello sylvain") - -YOUR_TOKEN=MY_SECRET_TOKEN - -device="cpu" - -#prompt_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN) -#prompt_pipe.to(device) - -img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN) -img_pipe.to(device) - -source_img = gr.ImagePaint(type="filepath", elem_id="source_container", label="new gradio color sketch (beta)") - -gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[1], height="auto") - -def resize(value,img): - #baseheight = value - img = Image.open(img) - #hpercent = (baseheight/float(img.size[1])) - #wsize = int((float(img.size[0])*float(hpercent))) - #img = img.resize((wsize,baseheight), Image.Resampling.LANCZOS) - img = img.resize((value,value), Image.Resampling.LANCZOS) - return img - - -def infer(source_img, prompt): - - source_image = resize(512, source_img) - source_image.save('source.png') - images_list = img_pipe([prompt] * 1, init_image=source_image, strength=0.75) - images = [] - safe_image = Image.open(r"unsafe.png") - for i, image in enumerate(images_list["sample"]): - if(images_list["nsfw_content_detected"][i]): - images.append(safe_image) - else: - images.append(image) - return images - -print("Great sylvain ! Everything is working fine !") - -title="Touch of Paint Stable Diffusion CPU" -description="Img-2-Img Stable Diffusion example using CPU and the beta color-sketch on uploaded gradio tool.You may also like:
-- - - - --
Warning: Slow process... ~5/10 min inference time. NSFW filter enabled." -custom_css = "style.css" - -gr.Interface(fn=infer, inputs=[source_img, "text"], outputs=gallery,title=title,description=description,css=custom_css).queue(max_size=100).launch(enable_queue=True) \ No newline at end of file diff --git a/spaces/florim/MedGPT/autogpt/chat.py b/spaces/florim/MedGPT/autogpt/chat.py deleted file mode 100644 index 1f6bca96eb216c667656b50f131006b83c681065..0000000000000000000000000000000000000000 --- a/spaces/florim/MedGPT/autogpt/chat.py +++ /dev/null @@ -1,175 +0,0 @@ -import time - -from openai.error import RateLimitError - -from autogpt import token_counter -from autogpt.config import Config -from autogpt.llm_utils import create_chat_completion -from autogpt.logs import logger - -cfg = Config() - - -def create_chat_message(role, content): - """ - Create a chat message with the given role and content. - - Args: - role (str): The role of the message sender, e.g., "system", "user", or "assistant". - content (str): The content of the message. - - Returns: - dict: A dictionary containing the role and content of the message. - """ - return {"role": role, "content": content} - - -def generate_context(prompt, relevant_memory, full_message_history, model): - current_context = [ - create_chat_message("system", prompt), - create_chat_message( - "system", f"The current time and date is {time.strftime('%c')}" - ), - create_chat_message( - "system", - f"This reminds you of these events from your past:\n{relevant_memory}\n\n", - ), - ] - - # Add messages from the full message history until we reach the token limit - next_message_to_add_index = len(full_message_history) - 1 - insertion_index = len(current_context) - # Count the currently used tokens - current_tokens_used = token_counter.count_message_tokens(current_context, model) - return ( - next_message_to_add_index, - current_tokens_used, - insertion_index, - current_context, - ) - - -# TODO: Change debug from hardcode to argument -def chat_with_ai( - prompt, user_input, full_message_history, permanent_memory, token_limit -): - """Interact with the OpenAI API, sending the prompt, user input, message history, - and permanent memory.""" - while True: - try: - """ - Interact with the OpenAI API, sending the prompt, user input, - message history, and permanent memory. - - Args: - prompt (str): The prompt explaining the rules to the AI. - user_input (str): The input from the user. - full_message_history (list): The list of all messages sent between the - user and the AI. - permanent_memory (Obj): The memory object containing the permanent - memory. - token_limit (int): The maximum number of tokens allowed in the API call. - - Returns: - str: The AI's response. - """ - model = cfg.fast_llm_model # TODO: Change model from hardcode to argument - # Reserve 1000 tokens for the response - - logger.debug(f"Token limit: {token_limit}") - send_token_limit = token_limit - 1000 - - relevant_memory = ( - "" - if len(full_message_history) == 0 - else permanent_memory.get_relevant(str(full_message_history[-9:]), 10) - ) - - logger.debug(f"Memory Stats: {permanent_memory.get_stats()}") - - ( - next_message_to_add_index, - current_tokens_used, - insertion_index, - current_context, - ) = generate_context(prompt, relevant_memory, full_message_history, model) - - while current_tokens_used > 2500: - # remove memories until we are under 2500 tokens - relevant_memory = relevant_memory[:-1] - ( - next_message_to_add_index, - current_tokens_used, - insertion_index, - current_context, - ) = generate_context( - prompt, relevant_memory, full_message_history, model - ) - - current_tokens_used += token_counter.count_message_tokens( - [create_chat_message("user", user_input)], model - ) # Account for user input (appended later) - - while next_message_to_add_index >= 0: - # print (f"CURRENT TOKENS USED: {current_tokens_used}") - message_to_add = full_message_history[next_message_to_add_index] - - tokens_to_add = token_counter.count_message_tokens( - [message_to_add], model - ) - if current_tokens_used + tokens_to_add > send_token_limit: - break - - # Add the most recent message to the start of the current context, - # after the two system prompts. - current_context.insert( - insertion_index, full_message_history[next_message_to_add_index] - ) - - # Count the currently used tokens - current_tokens_used += tokens_to_add - - # Move to the next most recent message in the full message history - next_message_to_add_index -= 1 - - # Append user input, the length of this is accounted for above - current_context.extend([create_chat_message("user", user_input)]) - - # Calculate remaining tokens - tokens_remaining = token_limit - current_tokens_used - # assert tokens_remaining >= 0, "Tokens remaining is negative. - # This should never happen, please submit a bug report at - # https://www.github.com/Torantulino/Auto-GPT" - - # Debug print the current context - logger.debug(f"Token limit: {token_limit}") - logger.debug(f"Send Token Count: {current_tokens_used}") - logger.debug(f"Tokens remaining for response: {tokens_remaining}") - logger.debug("------------ CONTEXT SENT TO AI ---------------") - for message in current_context: - # Skip printing the prompt - if message["role"] == "system" and message["content"] == prompt: - continue - logger.debug(f"{message['role'].capitalize()}: {message['content']}") - logger.debug("") - logger.debug("----------- END OF CONTEXT ----------------") - - # TODO: use a model defined elsewhere, so that model can contain - # temperature and other settings we care about - assistant_reply = create_chat_completion( - model=model, - messages=current_context, - max_tokens=tokens_remaining, - ) - - # Update full message history - full_message_history.append(create_chat_message("user", user_input)) - full_message_history.append( - create_chat_message("assistant", assistant_reply) - ) - - return assistant_reply - except RateLimitError: - # TODO: When we switch to langchain, this is built in - print("Error: ", "API Rate Limit Reached. Waiting 10 seconds...") - time.sleep(10) diff --git a/spaces/florim/MedGPT/autogpt/promptgenerator.py b/spaces/florim/MedGPT/autogpt/promptgenerator.py deleted file mode 100644 index 0ad7046a0c41dab356abcd0151b65890e5544cd2..0000000000000000000000000000000000000000 --- a/spaces/florim/MedGPT/autogpt/promptgenerator.py +++ /dev/null @@ -1,138 +0,0 @@ -""" A module for generating custom prompt strings.""" -from __future__ import annotations - -import json -from typing import Any - - -class PromptGenerator: - """ - A class for generating custom prompt strings based on constraints, commands, - resources, and performance evaluations. - """ - - def __init__(self) -> None: - """ - Initialize the PromptGenerator object with empty lists of constraints, - commands, resources, and performance evaluations. - """ - self.constraints = [] - self.commands = [] - self.resources = [] - self.performance_evaluation = [] - self.response_format = { - "thoughts": { - "text": "thought", - "reasoning": "reasoning", - "plan": "- short bulleted\n- list that conveys\n- long-term plan", - "criticism": "constructive self-criticism", - "speak": "thoughts summary to say to user", - }, - "command": {"name": "command name", "args": {"arg name": "value"}}, - } - - def add_constraint(self, constraint: str) -> None: - """ - Add a constraint to the constraints list. - - Args: - constraint (str): The constraint to be added. - """ - self.constraints.append(constraint) - - def add_command(self, command_label: str, command_name: str, args=None) -> None: - """ - Add a command to the commands list with a label, name, and optional arguments. - - Args: - command_label (str): The label of the command. - command_name (str): The name of the command. - args (dict, optional): A dictionary containing argument names and their - values. Defaults to None. - """ - if args is None: - args = {} - - command_args = {arg_key: arg_value for arg_key, arg_value in args.items()} - - command = { - "label": command_label, - "name": command_name, - "args": command_args, - } - - self.commands.append(command) - - def _generate_command_string(self, command: dict[str, Any]) -> str: - """ - Generate a formatted string representation of a command. - - Args: - command (dict): A dictionary containing command information. - - Returns: - str: The formatted command string. - """ - args_string = ", ".join( - f'"{key}": "{value}"' for key, value in command["args"].items() - ) - return f'{command["label"]}: "{command["name"]}", args: {args_string}' - - def add_resource(self, resource: str) -> None: - """ - Add a resource to the resources list. - - Args: - resource (str): The resource to be added. - """ - self.resources.append(resource) - - def add_performance_evaluation(self, evaluation: str) -> None: - """ - Add a performance evaluation item to the performance_evaluation list. - - Args: - evaluation (str): The evaluation item to be added. - """ - self.performance_evaluation.append(evaluation) - - def _generate_numbered_list(self, items: list[Any], item_type="list") -> str: - """ - Generate a numbered list from given items based on the item_type. - - Args: - items (list): A list of items to be numbered. - item_type (str, optional): The type of items in the list. - Defaults to 'list'. - - Returns: - str: The formatted numbered list. - """ - if item_type == "command": - return "\n".join( - f"{i+1}. {self._generate_command_string(item)}" - for i, item in enumerate(items) - ) - else: - return "\n".join(f"{i+1}. {item}" for i, item in enumerate(items)) - - def generate_prompt_string(self) -> str: - """ - Generate a prompt string based on the constraints, commands, resources, - and performance evaluations. - - Returns: - str: The generated prompt string. - """ - formatted_response_format = json.dumps(self.response_format, indent=4) - return ( - f"Constraints:\n{self._generate_numbered_list(self.constraints)}\n\n" - "Commands:\n" - f"{self._generate_numbered_list(self.commands, item_type='command')}\n\n" - f"Resources:\n{self._generate_numbered_list(self.resources)}\n\n" - "Performance Evaluation:\n" - f"{self._generate_numbered_list(self.performance_evaluation)}\n\n" - "You should only respond in JSON format as described below \nResponse" - f" Format: \n{formatted_response_format} \nEnsure the response can be" - " parsed by Python json.loads" - ) diff --git a/spaces/florim/MedGPT/tests/smoke_test.py b/spaces/florim/MedGPT/tests/smoke_test.py deleted file mode 100644 index 1b9d643fc21f3703384a2bb4f2bd1d725f4dd418..0000000000000000000000000000000000000000 --- a/spaces/florim/MedGPT/tests/smoke_test.py +++ /dev/null @@ -1,59 +0,0 @@ -"""Smoke test for the autogpt package.""" -import os -import subprocess -import sys - -import pytest - -from autogpt.commands.file_operations import delete_file, read_file - - -@pytest.mark.integration_test -def test_write_file() -> None: - """ - Test case to check if the write_file command can successfully write 'Hello World' to a file - named 'hello_world.txt'. - - Read the current ai_settings.yaml file and store its content. - """ - env_vars = {"MEMORY_BACKEND": "no_memory", "TEMPERATURE": "0"} - ai_settings = None - if os.path.exists("ai_settings.yaml"): - with open("ai_settings.yaml", "r") as f: - ai_settings = f.read() - os.remove("ai_settings.yaml") - - try: - if os.path.exists("hello_world.txt"): - # Clean up any existing 'hello_world.txt' file before testing. - delete_file("hello_world.txt") - # Prepare input data for the test. - input_data = """write_file-GPT -an AI designed to use the write_file command to write 'Hello World' into a file named "hello_world.txt" and then use the task_complete command to complete the task. -Use the write_file command to write 'Hello World' into a file named "hello_world.txt". -Use the task_complete command to complete the task. -Do not use any other commands. - -y -5 -EOF""" - command = f"{sys.executable} -m autogpt" - - # Execute the script with the input data. - process = subprocess.Popen( - command, - stdin=subprocess.PIPE, - shell=True, - env={**os.environ, **env_vars}, - ) - process.communicate(input_data.encode()) - - # Read the content of the 'hello_world.txt' file created during the test. - content = read_file("hello_world.txt") - finally: - if ai_settings: - # Restore the original ai_settings.yaml file. - with open("ai_settings.yaml", "w") as f: - f.write(ai_settings) - - # Check if the content of the 'hello_world.txt' file is equal to 'Hello World'. - assert content == "Hello World", f"Expected 'Hello World', got {content}" diff --git a/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/social_ai_envs/socialaigrammar.py b/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/social_ai_envs/socialaigrammar.py deleted file mode 100644 index eaa88d5a70c9bafad255087c3a4a52dce94c5632..0000000000000000000000000000000000000000 --- a/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/social_ai_envs/socialaigrammar.py +++ /dev/null @@ -1,52 +0,0 @@ -import gym.spaces as spaces -from enum import IntEnum - -# Enumeration of possible actions -class SocialAIActions(IntEnum): - # Turn left, turn right, move forward - left = 0 - right = 1 - forward = 2 - - # no pickup-drop - # # Pick up an object - # pickup = 3 - # # Drop an object - # drop = 4 - - # Toggle/activate an object - toggle = 3 - - # Done completing task - done = 4 - - -class SocialAIGrammar(object): - - templates = ["Where is", "Help", "Close", "How are"] - things = [ - "please", "the exit", "the wall", "you", "the ceiling", "the window", "the entrance", "the closet", - "the drawer", "the fridge", "the floor", "the lamp", "the trash can", "the chair", "the bed", "the sofa" - ] - assert len(templates)*len(things) == 64 - print("language complexity {}:".format(len(templates)*len(things))) - - grammar_action_space = spaces.MultiDiscrete([len(templates), len(things)]) - - @classmethod - def get_action(cls, template, thing): - return [cls.templates.index(template), cls.things.index(thing)] - @classmethod - def construct_utterance(cls, action): - return cls.templates[int(action[0])] + " " + cls.things[int(action[1])] + " " - - @classmethod - def contains_utterance(cls, utterance): - for t in range(len(cls.templates)): - for th in range(len(cls.things)): - if utterance == cls.construct_utterance([t, th]): - return True - return False - -SocialAIActionSpace = spaces.MultiDiscrete([len(SocialAIActions), - *SocialAIGrammar.grammar_action_space.nvec]) diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/utils/make_divisible.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/utils/make_divisible.py deleted file mode 100644 index 75ad756052529f52fe83bb95dd1f0ecfc9a13078..0000000000000000000000000000000000000000 --- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/utils/make_divisible.py +++ /dev/null @@ -1,27 +0,0 @@ -def make_divisible(value, divisor, min_value=None, min_ratio=0.9): - """Make divisible function. - - This function rounds the channel number to the nearest value that can be - divisible by the divisor. It is taken from the original tf repo. It ensures - that all layers have a channel number that is divisible by divisor. It can - be seen here: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py # noqa - - Args: - value (int): The original channel number. - divisor (int): The divisor to fully divide the channel number. - min_value (int): The minimum value of the output channel. - Default: None, means that the minimum value equal to the divisor. - min_ratio (float): The minimum ratio of the rounded channel number to - the original channel number. Default: 0.9. - - Returns: - int: The modified output channel number. - """ - - if min_value is None: - min_value = divisor - new_value = max(min_value, int(value + divisor / 2) // divisor * divisor) - # Make sure that round down does not go down by more than (1-min_ratio). - if new_value < min_ratio * value: - new_value += divisor - return new_value diff --git a/spaces/giswqs/solara-geospatial/Dockerfile b/spaces/giswqs/solara-geospatial/Dockerfile deleted file mode 100644 index 271b19ce6fd8f70d42243166136a200328b1fd0f..0000000000000000000000000000000000000000 --- a/spaces/giswqs/solara-geospatial/Dockerfile +++ /dev/null @@ -1,21 +0,0 @@ -FROM jupyter/base-notebook:latest - -RUN mamba install -c conda-forge leafmap geopandas localtileserver -y && \ - fix-permissions "${CONDA_DIR}" && \ - fix-permissions "/home/${NB_USER}" - -COPY requirements.txt . -RUN pip install -r requirements.txt - -RUN mkdir ./pages -COPY /pages ./pages - -ENV PROJ_LIB='/opt/conda/share/proj' - -USER root -RUN chown -R ${NB_UID} ${HOME} -USER ${NB_USER} - -EXPOSE 8765 - -CMD ["solara", "run", "./pages", "--host=0.0.0.0"] diff --git a/spaces/gligen/demo/gligen/create_meta.py b/spaces/gligen/demo/gligen/create_meta.py deleted file mode 100644 index 7512c6d377df98db7e17515a7143b7a4ef7d5f32..0000000000000000000000000000000000000000 --- a/spaces/gligen/demo/gligen/create_meta.py +++ /dev/null @@ -1,170 +0,0 @@ -CKPTS = [ - - dict( - path="/home/chunyl/azure_mount/yuhengdb/fine_tune_ldm/version5_branch6_output/GoldG+SBU+CC3M+CC12M+O365/second_stage_drop_both/tag01/checkpoint_00450001.pth", - feature_type=['before','after_reproject'], - save_folder_name="v5b6_drop_both", - ), - - - # dict( - # path="/home/v-yuhengli/blobfuse/output/fine_tune_ldm/version5_branch6_output/GoldG+SBU+CC3M+CC12M+O365/second_stage_drop_none/tag00/checkpoint_00165001.pth", - # feature_type=['before','after_reproject'], - # save_folder_name="v5b6_drop_none", - # ), - - - - - -] - - - -# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = # - - - - - - - - - # if meta["has_image_mask"] == 0: - # image_embeddings = text_embeddings - # if meta["has_text_mask"] == 0: - # text_embeddings = image_embeddings - - # out = { - # "boxes" : boxes.unsqueeze(0).repeat(batch,1,1), - # "masks" : masks.unsqueeze(0).repeat(batch,1), - # "text_masks" : masks.unsqueeze(0).repeat(batch,1), - # "image_masks" : masks.unsqueeze(0).repeat(batch,1), - # "text_embeddings" : text_embeddings.unsqueeze(0).repeat(batch,1,1), - # "image_embeddings" : image_embeddings.unsqueeze(0).repeat(batch,1,1) - # } - - - - - - - -META = [ - - - dict( - prompt = "a teddy bear sitting next to a red bird", - phrases = ['a teddy bear', 'a red bird'], - images = ['images/teddy.jpg', 'images/red_bird.jpg'], - locations = [ [0.0,0.09,0.33,0.76], [0.55,0.11,1.0,0.8] ], - alpha_type = [1.0, 0, 0.0], - has_text_mask = 1, - has_image_mask = 0, - save_folder_name="teddy_bird_1_1" - ), - - - # dict( - # prompt = "a teddy bear sitting next to a bird", - # phrases = ['a teddy bear', 'a bird'], - # images = ['images/teddy.jpg', 'images/red_bird.jpg'], - # locations = [ [0.0,0.09,0.33,0.76], [0.55,0.11,1.0,0.8] ], - # alpha_type = [1.0, 0, 0.0], - # has_text_mask = 1, - # has_image_mask = 1, - # save_folder_name="teddy_bird_1_1" - # ), - - - # dict( - # prompt = "a teddy bear sitting next to a bird", - # phrases = ['a teddy bear', 'a bird'], - # images = ['images/teddy.jpg', 'images/red_bird.jpg'], - # locations = [ [0.0,0.09,0.33,0.76], [0.55,0.11,1.0,0.8] ], - # alpha_type = [0.5, 0, 0.5], - # has_text_mask = 1, - # has_image_mask = 0, - # save_folder_name="teddy_bird_1_0" - # ), - - # dict( - # prompt = "", - # phrases = ['a teddy bear', 'an umbrella'], - # images = ['images/teddy.jpg', 'images/umbrella.png'], - # locations = [ [0.0,0.09,0.33,0.76], [0.55,0.11,1.0,0.8] ], - # alpha_type = [1.0, 0, 0.0], - # has_text_mask = 1, - # has_image_mask = 1, - # save_folder_name="empty_teddy_umbrella_1_1" - # ), - - # dict( - # prompt = "hello kitty and bird hybrid", - # phrases = ['a hello kitty', 'a hello kitty'], - # images = ['images/red_bird.jpg', 'images/red_bird.jpg'], - # locations = [ [0.0,0.09,0.33,0.76], [0.55,0.11,1.0,0.8] ], - # has_text_mask = 1, - # has_image_mask = 1, - # save_folder_name="hello+bird_1_1" - # ), - - # dict( - # prompt = "hello kitty and teddy bear hybrid", - # phrases = ['a hello kitty', 'a hello kitty'], - # images = ['images/teddy.jpg', 'images/teddy.jpg'], - # locations = [ [0.0,0.09,0.33,0.76], [0.55,0.11,1.0,0.8] ], - # has_text_mask = 1, - # has_image_mask = 1, - # save_folder_name="hello+teddy_1_1" - # ), - - # dict( - # prompt = "bird and hello kitty hybrid", - # phrases = ['a bird', 'a bird'], - # images = ['images/hello.jpg', 'images/hello.jpg'], - # locations = [ [0.0,0.09,0.33,0.76], [0.55,0.11,1.0,0.8] ], - # alpha_type = [1.0, 0, 0.0], - # has_text_mask = 1, - # has_image_mask = 0.5, - # save_folder_name="bird+hello_1_1" - # ), - - - - # dict( - # prompt = "a deer standing in front of a brick house in the woods, anime, oil painting, high resolution, cottagecore, ghibli inspired, 4k", - # phrases = ['a deer'], - # images = ['images/sky.jpg'], - # locations = [ [0.0,0.5,0.5,0.9] ], - # alpha_type = [1, 0, 0], - # has_text_mask = 1, - # has_image_mask = 1, - # save_folder_name="deer_sky" - # ), - - - # dict( - # prompt = "A woman sitting in a restaurant with a slice of pizza in front of her", - # phrases = ['dining table', 'pizza', 'person', 'wall', 'car', 'paper', 'chair', 'window', 'bottle', 'cup'], - # images = ['images/hello.jpg','images/hello.jpg','images/hello.jpg','images/hello.jpg','images/hello.jpg','images/hello.jpg','images/hello.jpg','images/hello.jpg','images/hello.jpg','images/hello.jpg'], - # locations = [ [0.0030, 0.3589, 1.0000, 1.0000], - # [0.0779, 0.6744, 0.9768, 1.0000], - # [0.2236, 0.0000, 0.7809, 0.4352], - # [0.0000, 0.0000, 0.4313, 0.4505], - # [0.6275, 0.1050, 0.9444, 0.2497], - # [0.0000, 0.3859, 0.1250, 0.6922], - # [0.7137, 0.2389, 0.8540, 0.4549], - # [0.0000, 0.0000, 0.4667, 0.0630], - # [0.3822, 0.4235, 0.4932, 0.6575], - # [0.6616, 0.3617, 0.7880, 0.5165] ], - # alpha_type = [0.0, 0, 1.0], - # has_text_mask = 1, - # has_image_mask = 0, - # save_folder_name="pizza_1_0" - # ), - - - - -] \ No newline at end of file diff --git a/spaces/gngpostalsrvc/Hyderabad_India_AI_Soft_skills/README.md b/spaces/gngpostalsrvc/Hyderabad_India_AI_Soft_skills/README.md deleted file mode 100644 index 1aa4043b311019ae56f0dd10ea5232fcdd0219cd..0000000000000000000000000000000000000000 --- a/spaces/gngpostalsrvc/Hyderabad_India_AI_Soft_skills/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Hyderabad India AI Soft Skills -emoji: š -colorFrom: purple -colorTo: indigo -sdk: gradio -sdk_version: 3.1.7 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Adobe Acrobat Xi Pro 11 Full Crack Patch Download ((NEW)).md b/spaces/gotiQspiryo/whisper-ui/examples/Adobe Acrobat Xi Pro 11 Full Crack Patch Download ((NEW)).md deleted file mode 100644 index 9a190e4275cba80ae55bfce335fd89ad67c00144..0000000000000000000000000000000000000000 --- a/spaces/gotiQspiryo/whisper-ui/examples/Adobe Acrobat Xi Pro 11 Full Crack Patch Download ((NEW)).md +++ /dev/null @@ -1,6 +0,0 @@ -Adobe Acrobat Xi Pro 11 Full Crack Patch Download
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- diff --git a/spaces/gradio/HuBERT/examples/roberta/preprocess_GLUE_tasks.sh b/spaces/gradio/HuBERT/examples/roberta/preprocess_GLUE_tasks.sh deleted file mode 100644 index 7f215a3b53e1c4a7b1f0320102915a49d84a5015..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/examples/roberta/preprocess_GLUE_tasks.sh +++ /dev/null @@ -1,185 +0,0 @@ -#!/bin/bash -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -# raw glue data as downloaded by glue download script (https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e) -if [[ $# -ne 2 ]]; then - echo "Run as following:" - echo "./examples/roberta/preprocess_GLUE_tasks.sh" - exit 1 -fi - -GLUE_DATA_FOLDER=$1 - -# download bpe encoder.json, vocabulary and fairseq dictionary -wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json' -wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe' -wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt' - -TASKS=$2 # QQP - -if [ "$TASKS" = "ALL" ] -then - TASKS="QQP MNLI QNLI MRPC RTE STS-B SST-2 CoLA" -fi - -for TASK in $TASKS -do - echo "Preprocessing $TASK" - - TASK_DATA_FOLDER="$GLUE_DATA_FOLDER/$TASK" - echo "Raw data as downloaded from glue website: $TASK_DATA_FOLDER" - - SPLITS="train dev test" - INPUT_COUNT=2 - if [ "$TASK" = "QQP" ] - then - INPUT_COLUMNS=( 4 5 ) - TEST_INPUT_COLUMNS=( 2 3 ) - LABEL_COLUMN=6 - elif [ "$TASK" = "MNLI" ] - then - SPLITS="train dev_matched dev_mismatched test_matched test_mismatched" - INPUT_COLUMNS=( 9 10 ) - TEST_INPUT_COLUMNS=( 9 10 ) - DEV_LABEL_COLUMN=16 - LABEL_COLUMN=12 - elif [ "$TASK" = "QNLI" ] - then - INPUT_COLUMNS=( 2 3 ) - TEST_INPUT_COLUMNS=( 2 3 ) - LABEL_COLUMN=4 - elif [ "$TASK" = "MRPC" ] - then - INPUT_COLUMNS=( 4 5 ) - TEST_INPUT_COLUMNS=( 4 5 ) - LABEL_COLUMN=1 - elif [ "$TASK" = "RTE" ] - then - INPUT_COLUMNS=( 2 3 ) - TEST_INPUT_COLUMNS=( 2 3 ) - LABEL_COLUMN=4 - elif [ "$TASK" = "STS-B" ] - then - INPUT_COLUMNS=( 8 9 ) - TEST_INPUT_COLUMNS=( 8 9 ) - LABEL_COLUMN=10 - # Following are single sentence tasks. - elif [ "$TASK" = "SST-2" ] - then - INPUT_COLUMNS=( 1 ) - TEST_INPUT_COLUMNS=( 2 ) - LABEL_COLUMN=2 - INPUT_COUNT=1 - elif [ "$TASK" = "CoLA" ] - then - INPUT_COLUMNS=( 4 ) - TEST_INPUT_COLUMNS=( 2 ) - LABEL_COLUMN=2 - INPUT_COUNT=1 - fi - - # Strip out header and filter lines that don't have expected number of fields. - rm -rf "$TASK_DATA_FOLDER/processed" - mkdir -p "$TASK_DATA_FOLDER/processed" - for SPLIT in $SPLITS - do - # CoLA train and dev doesn't have header. - if [[ ( "$TASK" = "CoLA") && ( "$SPLIT" != "test" ) ]] - then - cp "$TASK_DATA_FOLDER/$SPLIT.tsv" "$TASK_DATA_FOLDER/processed/$SPLIT.tsv.temp"; - else - tail -n +2 "$TASK_DATA_FOLDER/$SPLIT.tsv" > "$TASK_DATA_FOLDER/processed/$SPLIT.tsv.temp"; - fi - - # Remove unformatted lines from train and dev files for QQP dataset. - if [[ ( "$TASK" = "QQP") && ( "$SPLIT" != "test" ) ]] - then - awk -F '\t' -v NUM_FIELDS=6 'NF==NUM_FIELDS{print}{}' "$TASK_DATA_FOLDER/processed/$SPLIT.tsv.temp" > "$TASK_DATA_FOLDER/processed/$SPLIT.tsv"; - else - cp "$TASK_DATA_FOLDER/processed/$SPLIT.tsv.temp" "$TASK_DATA_FOLDER/processed/$SPLIT.tsv"; - fi - rm "$TASK_DATA_FOLDER/processed/$SPLIT.tsv.temp"; - done - - # Split into input0, input1 and label - for SPLIT in $SPLITS - do - for INPUT_TYPE in $(seq 0 $((INPUT_COUNT-1))) - do - if [[ "$SPLIT" != test* ]] - then - COLUMN_NUMBER=${INPUT_COLUMNS[$INPUT_TYPE]} - else - COLUMN_NUMBER=${TEST_INPUT_COLUMNS[$INPUT_TYPE]} - fi - cut -f"$COLUMN_NUMBER" "$TASK_DATA_FOLDER/processed/$SPLIT.tsv" > "$TASK_DATA_FOLDER/processed/$SPLIT.raw.input$INPUT_TYPE"; - done - - if [[ "$SPLIT" != test* ]] - then - if [ "$TASK" = "MNLI" ] && [ "$SPLIT" != "train" ] - then - cut -f"$DEV_LABEL_COLUMN" "$TASK_DATA_FOLDER/processed/$SPLIT.tsv" > "$TASK_DATA_FOLDER/processed/$SPLIT.label"; - else - cut -f"$LABEL_COLUMN" "$TASK_DATA_FOLDER/processed/$SPLIT.tsv" > "$TASK_DATA_FOLDER/processed/$SPLIT.label"; - fi - fi - - # BPE encode. - for INPUT_TYPE in $(seq 0 $((INPUT_COUNT-1))) - do - LANG="input$INPUT_TYPE" - echo "BPE encoding $SPLIT/$LANG" - python -m examples.roberta.multiprocessing_bpe_encoder \ - --encoder-json encoder.json \ - --vocab-bpe vocab.bpe \ - --inputs "$TASK_DATA_FOLDER/processed/$SPLIT.raw.$LANG" \ - --outputs "$TASK_DATA_FOLDER/processed/$SPLIT.$LANG" \ - --workers 60 \ - --keep-empty; - done - done - - # Remove output directory. - rm -rf "$TASK-bin" - - DEVPREF="$TASK_DATA_FOLDER/processed/dev.LANG" - TESTPREF="$TASK_DATA_FOLDER/processed/test.LANG" - if [ "$TASK" = "MNLI" ] - then - DEVPREF="$TASK_DATA_FOLDER/processed/dev_matched.LANG,$TASK_DATA_FOLDER/processed/dev_mismatched.LANG" - TESTPREF="$TASK_DATA_FOLDER/processed/test_matched.LANG,$TASK_DATA_FOLDER/processed/test_mismatched.LANG" - fi - - # Run fairseq preprocessing: - for INPUT_TYPE in $(seq 0 $((INPUT_COUNT-1))) - do - LANG="input$INPUT_TYPE" - fairseq-preprocess \ - --only-source \ - --trainpref "$TASK_DATA_FOLDER/processed/train.$LANG" \ - --validpref "${DEVPREF//LANG/$LANG}" \ - --testpref "${TESTPREF//LANG/$LANG}" \ - --destdir "$TASK-bin/$LANG" \ - --workers 60 \ - --srcdict dict.txt; - done - if [[ "$TASK" != "STS-B" ]] - then - fairseq-preprocess \ - --only-source \ - --trainpref "$TASK_DATA_FOLDER/processed/train.label" \ - --validpref "${DEVPREF//LANG/label}" \ - --destdir "$TASK-bin/label" \ - --workers 60; - else - # For STS-B output range is converted to be between: [0.0, 1.0] - mkdir -p "$TASK-bin/label" - awk '{print $1 / 5.0 }' "$TASK_DATA_FOLDER/processed/train.label" > "$TASK-bin/label/train.label" - awk '{print $1 / 5.0 }' "$TASK_DATA_FOLDER/processed/dev.label" > "$TASK-bin/label/valid.label" - fi -done diff --git a/spaces/grisiemjahand/Image-and-3D-Model-Creator/PIFu/lib/model/HGFilters.py b/spaces/grisiemjahand/Image-and-3D-Model-Creator/PIFu/lib/model/HGFilters.py deleted file mode 100644 index 870b3c43c82d66df001eb1bc24af9ce21ec60c83..0000000000000000000000000000000000000000 --- a/spaces/grisiemjahand/Image-and-3D-Model-Creator/PIFu/lib/model/HGFilters.py +++ /dev/null @@ -1,146 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from ..net_util import * - - -class HourGlass(nn.Module): - def __init__(self, num_modules, depth, num_features, norm='batch'): - super(HourGlass, self).__init__() - self.num_modules = num_modules - self.depth = depth - self.features = num_features - self.norm = norm - - self._generate_network(self.depth) - - def _generate_network(self, level): - self.add_module('b1_' + str(level), ConvBlock(self.features, self.features, norm=self.norm)) - - self.add_module('b2_' + str(level), ConvBlock(self.features, self.features, norm=self.norm)) - - if level > 1: - self._generate_network(level - 1) - else: - self.add_module('b2_plus_' + str(level), ConvBlock(self.features, self.features, norm=self.norm)) - - self.add_module('b3_' + str(level), ConvBlock(self.features, self.features, norm=self.norm)) - - def _forward(self, level, inp): - # Upper branch - up1 = inp - up1 = self._modules['b1_' + str(level)](up1) - - # Lower branch - low1 = F.avg_pool2d(inp, 2, stride=2) - low1 = self._modules['b2_' + str(level)](low1) - - if level > 1: - low2 = self._forward(level - 1, low1) - else: - low2 = low1 - low2 = self._modules['b2_plus_' + str(level)](low2) - - low3 = low2 - low3 = self._modules['b3_' + str(level)](low3) - - # NOTE: for newer PyTorch (1.3~), it seems that training results are degraded due to implementation diff in F.grid_sample - # if the pretrained model behaves weirdly, switch with the commented line. - # NOTE: I also found that "bicubic" works better. - up2 = F.interpolate(low3, scale_factor=2, mode='bicubic', align_corners=True) - # up2 = F.interpolate(low3, scale_factor=2, mode='nearest) - - return up1 + up2 - - def forward(self, x): - return self._forward(self.depth, x) - - -class HGFilter(nn.Module): - def __init__(self, opt): - super(HGFilter, self).__init__() - self.num_modules = opt.num_stack - - self.opt = opt - - # Base part - self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3) - - if self.opt.norm == 'batch': - self.bn1 = nn.BatchNorm2d(64) - elif self.opt.norm == 'group': - self.bn1 = nn.GroupNorm(32, 64) - - if self.opt.hg_down == 'conv64': - self.conv2 = ConvBlock(64, 64, self.opt.norm) - self.down_conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1) - elif self.opt.hg_down == 'conv128': - self.conv2 = ConvBlock(64, 128, self.opt.norm) - self.down_conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=2, padding=1) - elif self.opt.hg_down == 'ave_pool': - self.conv2 = ConvBlock(64, 128, self.opt.norm) - else: - raise NameError('Unknown Fan Filter setting!') - - self.conv3 = ConvBlock(128, 128, self.opt.norm) - self.conv4 = ConvBlock(128, 256, self.opt.norm) - - # Stacking part - for hg_module in range(self.num_modules): - self.add_module('m' + str(hg_module), HourGlass(1, opt.num_hourglass, 256, self.opt.norm)) - - self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256, self.opt.norm)) - self.add_module('conv_last' + str(hg_module), - nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) - if self.opt.norm == 'batch': - self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256)) - elif self.opt.norm == 'group': - self.add_module('bn_end' + str(hg_module), nn.GroupNorm(32, 256)) - - self.add_module('l' + str(hg_module), nn.Conv2d(256, - opt.hourglass_dim, kernel_size=1, stride=1, padding=0)) - - if hg_module < self.num_modules - 1: - self.add_module( - 'bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) - self.add_module('al' + str(hg_module), nn.Conv2d(opt.hourglass_dim, - 256, kernel_size=1, stride=1, padding=0)) - - def forward(self, x): - x = F.relu(self.bn1(self.conv1(x)), True) - tmpx = x - if self.opt.hg_down == 'ave_pool': - x = F.avg_pool2d(self.conv2(x), 2, stride=2) - elif self.opt.hg_down in ['conv64', 'conv128']: - x = self.conv2(x) - x = self.down_conv2(x) - else: - raise NameError('Unknown Fan Filter setting!') - - normx = x - - x = self.conv3(x) - x = self.conv4(x) - - previous = x - - outputs = [] - for i in range(self.num_modules): - hg = self._modules['m' + str(i)](previous) - - ll = hg - ll = self._modules['top_m_' + str(i)](ll) - - ll = F.relu(self._modules['bn_end' + str(i)] - (self._modules['conv_last' + str(i)](ll)), True) - - # Predict heatmaps - tmp_out = self._modules['l' + str(i)](ll) - outputs.append(tmp_out) - - if i < self.num_modules - 1: - ll = self._modules['bl' + str(i)](ll) - tmp_out_ = self._modules['al' + str(i)](tmp_out) - previous = previous + ll + tmp_out_ - - return outputs, tmpx.detach(), normx diff --git a/spaces/gsaivinay/Llama-2-13B-GGML-UI/components/Promptbar/PromptBar.context.tsx b/spaces/gsaivinay/Llama-2-13B-GGML-UI/components/Promptbar/PromptBar.context.tsx deleted file mode 100644 index 80f9f5b18b9315f7d1db2d53c52b7cad04b92f53..0000000000000000000000000000000000000000 --- a/spaces/gsaivinay/Llama-2-13B-GGML-UI/components/Promptbar/PromptBar.context.tsx +++ /dev/null @@ -1,19 +0,0 @@ -import { Dispatch, createContext } from 'react'; - -import { ActionType } from '@/hooks/useCreateReducer'; - -import { Prompt } from '@/types/prompt'; - -import { PromptbarInitialState } from './Promptbar.state'; - -export interface PromptbarContextProps { - state: PromptbarInitialState; - dispatch: Dispatch (undefined!); - -export default PromptbarContext; diff --git a/spaces/gulabpatel/GFP_GAN/inference_gfpgan.py b/spaces/gulabpatel/GFP_GAN/inference_gfpgan.py deleted file mode 100644 index a426cfc7b9e67aef84e0f3c0666e09d875ebb222..0000000000000000000000000000000000000000 --- a/spaces/gulabpatel/GFP_GAN/inference_gfpgan.py +++ /dev/null @@ -1,116 +0,0 @@ -import argparse -import cv2 -import glob -import numpy as np -import os -import torch -from basicsr.utils import imwrite - -from gfpgan import GFPGANer - - -def main(): - """Inference demo for GFPGAN. - """ - parser = argparse.ArgumentParser() - parser.add_argument('--upscale', type=int, default=2, help='The final upsampling scale of the image') - parser.add_argument('--arch', type=str, default='clean', help='The GFPGAN architecture. Option: clean | original') - parser.add_argument('--channel', type=int, default=2, help='Channel multiplier for large networks of StyleGAN2') - parser.add_argument('--model_path', type=str, default='experiments/pretrained_models/GFPGANCleanv1-NoCE-C2.pth') - parser.add_argument('--bg_upsampler', type=str, default='realesrgan', help='background upsampler') - parser.add_argument( - '--bg_tile', type=int, default=400, help='Tile size for background sampler, 0 for no tile during testing') - parser.add_argument('--test_path', type=str, default='inputs/whole_imgs', help='Input folder') - parser.add_argument('--suffix', type=str, default=None, help='Suffix of the restored faces') - parser.add_argument('--only_center_face', action='store_true', help='Only restore the center face') - parser.add_argument('--aligned', action='store_true', help='Input are aligned faces') - parser.add_argument('--paste_back', action='store_false', help='Paste the restored faces back to images') - parser.add_argument('--save_root', type=str, default='results', help='Path to save root') - parser.add_argument( - '--ext', - type=str, - default='auto', - help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs') - args = parser.parse_args() - - args = parser.parse_args() - if args.test_path.endswith('/'): - args.test_path = args.test_path[:-1] - os.makedirs(args.save_root, exist_ok=True) - - # background upsampler - if args.bg_upsampler == 'realesrgan': - if not torch.cuda.is_available(): # CPU - import warnings - warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' - 'If you really want to use it, please modify the corresponding codes.') - bg_upsampler = None - else: - from basicsr.archs.rrdbnet_arch import RRDBNet - from realesrgan import RealESRGANer - model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2) - bg_upsampler = RealESRGANer( - scale=2, - model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth', - model=model, - tile=args.bg_tile, - tile_pad=10, - pre_pad=0, - half=True) # need to set False in CPU mode - else: - bg_upsampler = None - # set up GFPGAN restorer - restorer = GFPGANer( - model_path=args.model_path, - upscale=args.upscale, - arch=args.arch, - channel_multiplier=args.channel, - bg_upsampler=bg_upsampler) - - img_list = sorted(glob.glob(os.path.join(args.test_path, '*'))) - for img_path in img_list: - # read image - img_name = os.path.basename(img_path) - print(f'Processing {img_name} ...') - basename, ext = os.path.splitext(img_name) - input_img = cv2.imread(img_path, cv2.IMREAD_COLOR) - - # restore faces and background if necessary - cropped_faces, restored_faces, restored_img = restorer.enhance( - input_img, has_aligned=args.aligned, only_center_face=args.only_center_face, paste_back=args.paste_back) - - # save faces - for idx, (cropped_face, restored_face) in enumerate(zip(cropped_faces, restored_faces)): - # save cropped face - save_crop_path = os.path.join(args.save_root, 'cropped_faces', f'{basename}_{idx:02d}.png') - imwrite(cropped_face, save_crop_path) - # save restored face - if args.suffix is not None: - save_face_name = f'{basename}_{idx:02d}_{args.suffix}.png' - else: - save_face_name = f'{basename}_{idx:02d}.png' - save_restore_path = os.path.join(args.save_root, 'restored_faces', save_face_name) - imwrite(restored_face, save_restore_path) - # save comparison image - cmp_img = np.concatenate((cropped_face, restored_face), axis=1) - imwrite(cmp_img, os.path.join(args.save_root, 'cmp', f'{basename}_{idx:02d}.png')) - - # save restored img - if restored_img is not None: - if args.ext == 'auto': - extension = ext[1:] - else: - extension = args.ext - - if args.suffix is not None: - save_restore_path = os.path.join(args.save_root, 'restored_imgs', - f'{basename}_{args.suffix}.{extension}') - else: - save_restore_path = os.path.join(args.save_root, 'restored_imgs', f'{basename}.{extension}') - imwrite(restored_img, save_restore_path) - - print(f'Results are in the [{args.save_root}] folder.') - - -if __name__ == '__main__': - main() diff --git a/spaces/gyugnsu/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/PlayInteractively.py b/spaces/gyugnsu/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/PlayInteractively.py deleted file mode 100644 index 547b08ab2c4373e23711636488145df148d7eb4e..0000000000000000000000000000000000000000 --- a/spaces/gyugnsu/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/PlayInteractively.py +++ /dev/null @@ -1,197 +0,0 @@ - - - -from tkinter import Tk -from PIL import Image, ImageTk -from tkinter.filedialog import askopenfilename -from GUI import View -from Inference import StyleCLIP -import argparse -#%% - - -class PlayInteractively(): #Controller - ''' - followed Model View Controller Design Pattern - - controller, model, view - ''' - def __init__(self,dataset_name='ffhq'): - - self.root = Tk() - self.view=View(self.root) - self.img_ratio=2 - self.style_clip=StyleCLIP(dataset_name) - - self.view.neutral.bind(" ", self.text_n) - self.view.target.bind(" ", self.text_t) - self.view.alpha.bind(' ', self.ChangeAlpha) - self.view.beta.bind(' ', self.ChangeBeta) - self.view.set_init.bind(' ', self.SetInit) - self.view.reset.bind(' ', self.Reset) - self.view.bg.bind(' ', self.open_img) - - - self.drawn = None - - self.view.target.delete(1.0, "end") - self.view.target.insert("end", self.style_clip.target) -# - self.view.neutral.delete(1.0, "end") - self.view.neutral.insert("end", self.style_clip.neutral) - - - def Reset(self,event): - self.style_clip.GetDt2() - self.style_clip.M.alpha=[0] - - self.view.beta.set(self.style_clip.beta) - self.view.alpha.set(0) - - img=self.style_clip.GetImg() - img=Image.fromarray(img) - img = ImageTk.PhotoImage(img) - self.addImage_m(img) - - - def SetInit(self,event): - codes=self.style_clip.GetCode() - self.style_clip.M.dlatent_tmp=[tmp[:,0] for tmp in codes] - print('set init') - - def ChangeAlpha(self,event): - tmp=self.view.alpha.get() - self.style_clip.M.alpha=[float(tmp)] - - img=self.style_clip.GetImg() - print('manipulate one') - img=Image.fromarray(img) - img = ImageTk.PhotoImage(img) - self.addImage_m(img) - - def ChangeBeta(self,event): - tmp=self.view.beta.get() - self.style_clip.beta=float(tmp) - - img=self.style_clip.GetImg() - print('manipulate one') - img=Image.fromarray(img) - img = ImageTk.PhotoImage(img) - self.addImage_m(img) - - def ChangeDataset(self,event): - - dataset_name=self.view.set_category.get() - - self.style_clip.LoadData(dataset_name) - - self.view.target.delete(1.0, "end") - self.view.target.insert("end", self.style_clip.target) - - self.view.neutral.delete(1.0, "end") - self.view.neutral.insert("end", self.style_clip.neutral) - - def text_t(self,event): - tmp=self.view.target.get("1.0",'end') - tmp=tmp.replace('\n','') - - self.view.target.delete(1.0, "end") - self.view.target.insert("end", tmp) - - print('target',tmp,'###') - self.style_clip.target=tmp - self.style_clip.GetDt2() - self.view.beta.set(self.style_clip.beta) - self.view.alpha.set(3) - self.style_clip.M.alpha=[3] - - img=self.style_clip.GetImg() - print('manipulate one') - img=Image.fromarray(img) - img = ImageTk.PhotoImage(img) - self.addImage_m(img) - - - def text_n(self,event): - tmp=self.view.neutral.get("1.0",'end') - tmp=tmp.replace('\n','') - - self.view.neutral.delete(1.0, "end") - self.view.neutral.insert("end", tmp) - - print('neutral',tmp,'###') - self.style_clip.neutral=tmp - self.view.target.delete(1.0, "end") - self.view.target.insert("end", tmp) - - - def run(self): - self.root.mainloop() - - def addImage(self,img): - self.view.bg.create_image(self.view.width/2, self.view.height/2, image=img, anchor='center') - self.image=img #save a copy of image. if not the image will disappear - - def addImage_m(self,img): - self.view.mani.create_image(512, 512, image=img, anchor='center') - self.image2=img - - - def openfn(self): - filename = askopenfilename(title='open',initialdir='./data/'+self.style_clip.M.dataset_name+'/',filetypes=[("all image format", ".jpg"),("all image format", ".png")]) - return filename - - def open_img(self,event): - x = self.openfn() - print(x) - - - img = Image.open(x) - img2 = img.resize(( 512,512), Image.ANTIALIAS) - img2 = ImageTk.PhotoImage(img2) - self.addImage(img2) - - img = ImageTk.PhotoImage(img) - self.addImage_m(img) - - img_index=x.split('/')[-1].split('.')[0] - img_index=int(img_index) - print(img_index) - self.style_clip.M.img_index=img_index - self.style_clip.M.dlatent_tmp=[tmp[img_index:(img_index+1)] for tmp in self.style_clip.M.dlatents] - - - self.style_clip.GetDt2() - self.view.beta.set(self.style_clip.beta) - self.view.alpha.set(3) - - #%% -if __name__ == "__main__": - parser = argparse.ArgumentParser(description='Process some integers.') - - parser.add_argument('--dataset_name',type=str,default='ffhq', - help='name of dataset, for example, ffhq') - - args = parser.parse_args() - dataset_name=args.dataset_name - - self=PlayInteractively(dataset_name) - self.run() - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/spaces/gyugnsu/DragGan-Inversion/PTI/models/e4e/__init__.py b/spaces/gyugnsu/DragGan-Inversion/PTI/models/e4e/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/h2oai/wave-tour/examples/plot_form.py b/spaces/h2oai/wave-tour/examples/plot_form.py deleted file mode 100644 index b7b5acef81afb01be18cdd128b31d83bc4499331..0000000000000000000000000000000000000000 --- a/spaces/h2oai/wave-tour/examples/plot_form.py +++ /dev/null @@ -1,36 +0,0 @@ -# Plot / Form -# Display a #plot inside a #form. -# --- -from h2o_wave import site, data, ui - -page = site['/demo'] - -page.add('example', ui.form_card( - box='1 1 -1 8', - items=[ - ui.text_xl('This year'), - ui.visualization( - plot=ui.plot([ui.mark(type='interval', x='=profession', y='=salary', y_min=0)]), - data=data(fields='profession salary', rows=[ - ('medicine', 23000), - ('fire fighting', 18000), - ('pedagogy', 24000), - ('psychology', 22500), - ('computer science', 36000), - ], pack=True), - ), - ui.text_xl('Last year'), - ui.visualization( - plot=ui.plot([ui.mark(type='interval', x='=profession', y='=salary', y_min=0)]), - data=data(fields='profession salary', rows=[ - ('medicine', 21000), - ('fire fighting', 17000), - ('pedagogy', 23500), - ('psychology', 22300), - ('computer science', 33000), - ], pack=True) - ), - ], -)) - -page.save() diff --git a/spaces/haakohu/deep_privacy2/dp2/metrics/fid.py b/spaces/haakohu/deep_privacy2/dp2/metrics/fid.py deleted file mode 100644 index 3c8e0e6171fe723aa9f37735a216ec8c9d4e4a8f..0000000000000000000000000000000000000000 --- a/spaces/haakohu/deep_privacy2/dp2/metrics/fid.py +++ /dev/null @@ -1,52 +0,0 @@ -import tops -from dp2 import utils -from pathlib import Path -from torch_fidelity.generative_model_modulewrapper import GenerativeModelModuleWrapper -import torch -import torch_fidelity - - -class GeneratorIteratorWrapper(GenerativeModelModuleWrapper): - - def __init__(self, generator, dataloader, zero_z: bool, n_diverse: int): - if isinstance(generator, utils.EMA): - generator = generator.generator - z_size = generator.z_channels - super().__init__(generator, z_size, "normal", 0) - self.zero_z = zero_z - self.dataloader = iter(dataloader) - self.n_diverse = n_diverse - self.cur_div_idx = 0 - - @torch.no_grad() - def forward(self, z, **kwargs): - if self.cur_div_idx == 0: - self.batch = next(self.dataloader) - if self.zero_z: - z = z.zero_() - self.cur_div_idx += 1 - self.cur_div_idx = 0 if self.cur_div_idx == self.n_diverse else self.cur_div_idx - with torch.cuda.amp.autocast(enabled=tops.AMP()): - img = self.module(**self.batch)["img"] - img = (utils.denormalize_img(img)*255).byte() - return img - - -def compute_fid(generator, dataloader, real_directory, n_source, zero_z, n_diverse): - generator = GeneratorIteratorWrapper(generator, dataloader, zero_z, n_diverse) - batch_size = dataloader.batch_size - num_samples = (n_source * n_diverse) // batch_size * batch_size - assert n_diverse >= 1 - assert (not zero_z) or n_diverse == 1 - assert num_samples % batch_size == 0 - assert n_source <= batch_size * len(dataloader), (batch_size*len(dataloader), n_source, n_diverse) - metrics = torch_fidelity.calculate_metrics( - input1=generator, - input2=real_directory, - cuda=torch.cuda.is_available(), - fid=True, - input2_cache_name="_".join(Path(real_directory).parts) + "_cached", - input1_model_num_samples=int(num_samples), - batch_size=dataloader.batch_size - ) - return metrics["frechet_inception_distance"] diff --git a/spaces/hanzportgas/rvc-models/infer_pack/transforms.py b/spaces/hanzportgas/rvc-models/infer_pack/transforms.py deleted file mode 100644 index a11f799e023864ff7082c1f49c0cc18351a13b47..0000000000000000000000000000000000000000 --- a/spaces/hanzportgas/rvc-models/infer_pack/transforms.py +++ /dev/null @@ -1,209 +0,0 @@ -import torch -from torch.nn import functional as F - -import numpy as np - - -DEFAULT_MIN_BIN_WIDTH = 1e-3 -DEFAULT_MIN_BIN_HEIGHT = 1e-3 -DEFAULT_MIN_DERIVATIVE = 1e-3 - - -def piecewise_rational_quadratic_transform( - inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - tails=None, - tail_bound=1.0, - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE, -): - if tails is None: - spline_fn = rational_quadratic_spline - spline_kwargs = {} - else: - spline_fn = unconstrained_rational_quadratic_spline - spline_kwargs = {"tails": tails, "tail_bound": tail_bound} - - outputs, logabsdet = spline_fn( - inputs=inputs, - unnormalized_widths=unnormalized_widths, - unnormalized_heights=unnormalized_heights, - unnormalized_derivatives=unnormalized_derivatives, - inverse=inverse, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height, - min_derivative=min_derivative, - **spline_kwargs - ) - return outputs, logabsdet - - -def searchsorted(bin_locations, inputs, eps=1e-6): - bin_locations[..., -1] += eps - return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1 - - -def unconstrained_rational_quadratic_spline( - inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - tails="linear", - tail_bound=1.0, - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE, -): - inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound) - outside_interval_mask = ~inside_interval_mask - - outputs = torch.zeros_like(inputs) - logabsdet = torch.zeros_like(inputs) - - if tails == "linear": - unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1)) - constant = np.log(np.exp(1 - min_derivative) - 1) - unnormalized_derivatives[..., 0] = constant - unnormalized_derivatives[..., -1] = constant - - outputs[outside_interval_mask] = inputs[outside_interval_mask] - logabsdet[outside_interval_mask] = 0 - else: - raise RuntimeError("{} tails are not implemented.".format(tails)) - - ( - outputs[inside_interval_mask], - logabsdet[inside_interval_mask], - ) = rational_quadratic_spline( - inputs=inputs[inside_interval_mask], - unnormalized_widths=unnormalized_widths[inside_interval_mask, :], - unnormalized_heights=unnormalized_heights[inside_interval_mask, :], - unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :], - inverse=inverse, - left=-tail_bound, - right=tail_bound, - bottom=-tail_bound, - top=tail_bound, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height, - min_derivative=min_derivative, - ) - - return outputs, logabsdet - - -def rational_quadratic_spline( - inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - left=0.0, - right=1.0, - bottom=0.0, - top=1.0, - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE, -): - if torch.min(inputs) < left or torch.max(inputs) > right: - raise ValueError("Input to a transform is not within its domain") - - num_bins = unnormalized_widths.shape[-1] - - if min_bin_width * num_bins > 1.0: - raise ValueError("Minimal bin width too large for the number of bins") - if min_bin_height * num_bins > 1.0: - raise ValueError("Minimal bin height too large for the number of bins") - - widths = F.softmax(unnormalized_widths, dim=-1) - widths = min_bin_width + (1 - min_bin_width * num_bins) * widths - cumwidths = torch.cumsum(widths, dim=-1) - cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0) - cumwidths = (right - left) * cumwidths + left - cumwidths[..., 0] = left - cumwidths[..., -1] = right - widths = cumwidths[..., 1:] - cumwidths[..., :-1] - - derivatives = min_derivative + F.softplus(unnormalized_derivatives) - - heights = F.softmax(unnormalized_heights, dim=-1) - heights = min_bin_height + (1 - min_bin_height * num_bins) * heights - cumheights = torch.cumsum(heights, dim=-1) - cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0) - cumheights = (top - bottom) * cumheights + bottom - cumheights[..., 0] = bottom - cumheights[..., -1] = top - heights = cumheights[..., 1:] - cumheights[..., :-1] - - if inverse: - bin_idx = searchsorted(cumheights, inputs)[..., None] - else: - bin_idx = searchsorted(cumwidths, inputs)[..., None] - - input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0] - input_bin_widths = widths.gather(-1, bin_idx)[..., 0] - - input_cumheights = cumheights.gather(-1, bin_idx)[..., 0] - delta = heights / widths - input_delta = delta.gather(-1, bin_idx)[..., 0] - - input_derivatives = derivatives.gather(-1, bin_idx)[..., 0] - input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0] - - input_heights = heights.gather(-1, bin_idx)[..., 0] - - if inverse: - a = (inputs - input_cumheights) * ( - input_derivatives + input_derivatives_plus_one - 2 * input_delta - ) + input_heights * (input_delta - input_derivatives) - b = input_heights * input_derivatives - (inputs - input_cumheights) * ( - input_derivatives + input_derivatives_plus_one - 2 * input_delta - ) - c = -input_delta * (inputs - input_cumheights) - - discriminant = b.pow(2) - 4 * a * c - assert (discriminant >= 0).all() - - root = (2 * c) / (-b - torch.sqrt(discriminant)) - outputs = root * input_bin_widths + input_cumwidths - - theta_one_minus_theta = root * (1 - root) - denominator = input_delta + ( - (input_derivatives + input_derivatives_plus_one - 2 * input_delta) - * theta_one_minus_theta - ) - derivative_numerator = input_delta.pow(2) * ( - input_derivatives_plus_one * root.pow(2) - + 2 * input_delta * theta_one_minus_theta - + input_derivatives * (1 - root).pow(2) - ) - logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) - - return outputs, -logabsdet - else: - theta = (inputs - input_cumwidths) / input_bin_widths - theta_one_minus_theta = theta * (1 - theta) - - numerator = input_heights * ( - input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta - ) - denominator = input_delta + ( - (input_derivatives + input_derivatives_plus_one - 2 * input_delta) - * theta_one_minus_theta - ) - outputs = input_cumheights + numerator / denominator - - derivative_numerator = input_delta.pow(2) * ( - input_derivatives_plus_one * theta.pow(2) - + 2 * input_delta * theta_one_minus_theta - + input_derivatives * (1 - theta).pow(2) - ) - logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) - - return outputs, logabsdet diff --git a/spaces/haotieu/en-vi-translation/README.md b/spaces/haotieu/en-vi-translation/README.md deleted file mode 100644 index eb0b1213cdecfcdb660b8728ccf5bf5b434f3a7f..0000000000000000000000000000000000000000 --- a/spaces/haotieu/en-vi-translation/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: En Vi Translation -emoji: š -colorFrom: yellow -colorTo: orange -sdk: gradio -app_file: app.py -pinned: false ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio` or `streamlit` - -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/harshasurampudi/gender-and-age/README.md b/spaces/harshasurampudi/gender-and-age/README.md deleted file mode 100644 index 4d0e24196b50df6e1ac1af71f86bc668bd9c53b1..0000000000000000000000000000000000000000 --- a/spaces/harshasurampudi/gender-and-age/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Which Gender -emoji: š¦ -colorFrom: yellow -colorTo: blue -sdk: gradio -sdk_version: 3.17.0 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cpu.cpp b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cpu.cpp deleted file mode 100644 index 7e5e1ffdccd0e2ced15fa34b4906388d371bffe2..0000000000000000000000000000000000000000 --- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cpu.cpp +++ /dev/null @@ -1,522 +0,0 @@ -// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -#include -#include "ROIAlignRotated.h" - -// Note: this implementation originates from the Caffe2 ROIAlignRotated Op -// and PyTorch ROIAlign (non-rotated) Op implementations. -// The key difference between this implementation and those ones is -// we don't do "legacy offset" in this version, as there aren't many previous -// works, if any, using the "legacy" ROIAlignRotated Op. -// This would make the interface a bit cleaner. - -namespace detectron2 { - -namespace { -template -struct PreCalc { - int pos1; - int pos2; - int pos3; - int pos4; - T w1; - T w2; - T w3; - T w4; -}; - -template -void pre_calc_for_bilinear_interpolate( - const int height, - const int width, - const int pooled_height, - const int pooled_width, - const int iy_upper, - const int ix_upper, - T roi_start_h, - T roi_start_w, - T bin_size_h, - T bin_size_w, - int roi_bin_grid_h, - int roi_bin_grid_w, - T roi_center_h, - T roi_center_w, - T cos_theta, - T sin_theta, - std::vector (iy + .5f) * bin_size_h / - static_cast (roi_bin_grid_h); // e.g., 0.5, 1.5 - for (int ix = 0; ix < ix_upper; ix++) { - const T xx = roi_start_w + pw * bin_size_w + - static_cast (ix + .5f) * bin_size_w / - static_cast (roi_bin_grid_w); - - // Rotate by theta around the center and translate - // In image space, (y, x) is the order for Right Handed System, - // and this is essentially multiplying the point by a rotation matrix - // to rotate it counterclockwise through angle theta. - T y = yy * cos_theta - xx * sin_theta + roi_center_h; - T x = yy * sin_theta + xx * cos_theta + roi_center_w; - // deal with: inverse elements are out of feature map boundary - if (y < -1.0 || y > height || x < -1.0 || x > width) { - // empty - PreCalc pc; - pc.pos1 = 0; - pc.pos2 = 0; - pc.pos3 = 0; - pc.pos4 = 0; - pc.w1 = 0; - pc.w2 = 0; - pc.w3 = 0; - pc.w4 = 0; - pre_calc[pre_calc_index] = pc; - pre_calc_index += 1; - continue; - } - - if (y < 0) { - y = 0; - } - if (x < 0) { - x = 0; - } - - int y_low = (int)y; - int x_low = (int)x; - int y_high; - int x_high; - - if (y_low >= height - 1) { - y_high = y_low = height - 1; - y = (T)y_low; - } else { - y_high = y_low + 1; - } - - if (x_low >= width - 1) { - x_high = x_low = width - 1; - x = (T)x_low; - } else { - x_high = x_low + 1; - } - - T ly = y - y_low; - T lx = x - x_low; - T hy = 1. - ly, hx = 1. - lx; - T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; - - // save weights and indices - PreCalc pc; - pc.pos1 = y_low * width + x_low; - pc.pos2 = y_low * width + x_high; - pc.pos3 = y_high * width + x_low; - pc.pos4 = y_high * width + x_high; - pc.w1 = w1; - pc.w2 = w2; - pc.w3 = w3; - pc.w4 = w4; - pre_calc[pre_calc_index] = pc; - - pre_calc_index += 1; - } - } - } - } -} - -template -void bilinear_interpolate_gradient( - const int height, - const int width, - T y, - T x, - T& w1, - T& w2, - T& w3, - T& w4, - int& x_low, - int& x_high, - int& y_low, - int& y_high) { - // deal with cases that inverse elements are out of feature map boundary - if (y < -1.0 || y > height || x < -1.0 || x > width) { - // empty - w1 = w2 = w3 = w4 = 0.; - x_low = x_high = y_low = y_high = -1; - return; - } - - if (y < 0) { - y = 0; - } - - if (x < 0) { - x = 0; - } - - y_low = (int)y; - x_low = (int)x; - - if (y_low >= height - 1) { - y_high = y_low = height - 1; - y = (T)y_low; - } else { - y_high = y_low + 1; - } - - if (x_low >= width - 1) { - x_high = x_low = width - 1; - x = (T)x_low; - } else { - x_high = x_low + 1; - } - - T ly = y - y_low; - T lx = x - x_low; - T hy = 1. - ly, hx = 1. - lx; - - // reference in forward - // T v1 = input[y_low * width + x_low]; - // T v2 = input[y_low * width + x_high]; - // T v3 = input[y_high * width + x_low]; - // T v4 = input[y_high * width + x_high]; - // T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); - - w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; - - return; -} - -template -inline void add(T* address, const T& val) { - *address += val; -} - -} // namespace - -template -void ROIAlignRotatedForward( - const int nthreads, - const T* input, - const T& spatial_scale, - const int channels, - const int height, - const int width, - const int pooled_height, - const int pooled_width, - const int sampling_ratio, - const T* rois, - T* output) { - int n_rois = nthreads / channels / pooled_width / pooled_height; - // (n, c, ph, pw) is an element in the pooled output - // can be parallelized using omp - // #pragma omp parallel for num_threads(32) - for (int n = 0; n < n_rois; n++) { - int index_n = n * channels * pooled_width * pooled_height; - - const T* current_roi = rois + n * 6; - int roi_batch_ind = current_roi[0]; - - // Do not use rounding; this implementation detail is critical - // ROIAlignRotated supports align == true, i.e., continuous coordinate - // by default, thus the 0.5 offset - T offset = (T)0.5; - T roi_center_w = current_roi[1] * spatial_scale - offset; - T roi_center_h = current_roi[2] * spatial_scale - offset; - T roi_width = current_roi[3] * spatial_scale; - T roi_height = current_roi[4] * spatial_scale; - T theta = current_roi[5] * M_PI / 180.0; - T cos_theta = cos(theta); - T sin_theta = sin(theta); - - AT_ASSERTM( - roi_width >= 0 && roi_height >= 0, - "ROIs in ROIAlignRotated do not have non-negative size!"); - - T bin_size_h = static_cast (roi_height) / static_cast (pooled_height); - T bin_size_w = static_cast (roi_width) / static_cast (pooled_width); - - // We use roi_bin_grid to sample the grid and mimic integral - int roi_bin_grid_h = (sampling_ratio > 0) - ? sampling_ratio - : ceil(roi_height / pooled_height); // e.g., = 2 - int roi_bin_grid_w = - (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); - - // We do average (integral) pooling inside a bin - const T count = std::max(roi_bin_grid_h * roi_bin_grid_w, 1); // e.g. = 4 - - // we want to precalculate indices and weights shared by all channels, - // this is the key point of optimization - std::vector pc = pre_calc[pre_calc_index]; - output_val += pc.w1 * offset_input[pc.pos1] + - pc.w2 * offset_input[pc.pos2] + - pc.w3 * offset_input[pc.pos3] + pc.w4 * offset_input[pc.pos4]; - - pre_calc_index += 1; - } - } - output_val /= count; - - output[index] = output_val; - } // for pw - } // for ph - } // for c - } // for n -} - -template -void ROIAlignRotatedBackward( - const int nthreads, - // may not be contiguous. should index using n_stride, etc - const T* grad_output, - const T& spatial_scale, - const int channels, - const int height, - const int width, - const int pooled_height, - const int pooled_width, - const int sampling_ratio, - T* grad_input, - const T* rois, - const int n_stride, - const int c_stride, - const int h_stride, - const int w_stride) { - for (int index = 0; index < nthreads; index++) { - // (n, c, ph, pw) is an element in the pooled output - int pw = index % pooled_width; - int ph = (index / pooled_width) % pooled_height; - int c = (index / pooled_width / pooled_height) % channels; - int n = index / pooled_width / pooled_height / channels; - - const T* current_roi = rois + n * 6; - int roi_batch_ind = current_roi[0]; - - // Do not use rounding; this implementation detail is critical - // ROIAlignRotated supports align == true, i.e., continuous coordinate - // by default, thus the 0.5 offset - T offset = (T)0.5; - T roi_center_w = current_roi[1] * spatial_scale - offset; - T roi_center_h = current_roi[2] * spatial_scale - offset; - T roi_width = current_roi[3] * spatial_scale; - T roi_height = current_roi[4] * spatial_scale; - T theta = current_roi[5] * M_PI / 180.0; - T cos_theta = cos(theta); - T sin_theta = sin(theta); - - AT_ASSERTM( - roi_width >= 0 && roi_height >= 0, - "ROIs in ROIAlignRotated do not have non-negative size!"); - - T bin_size_h = static_cast (roi_height) / static_cast (pooled_height); - T bin_size_w = static_cast (roi_width) / static_cast (pooled_width); - - T* offset_grad_input = - grad_input + ((roi_batch_ind * channels + c) * height * width); - - int output_offset = n * n_stride + c * c_stride; - const T* offset_grad_output = grad_output + output_offset; - const T grad_output_this_bin = - offset_grad_output[ph * h_stride + pw * w_stride]; - - // We use roi_bin_grid to sample the grid and mimic integral - int roi_bin_grid_h = (sampling_ratio > 0) - ? sampling_ratio - : ceil(roi_height / pooled_height); // e.g., = 2 - int roi_bin_grid_w = - (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); - - // roi_start_h and roi_start_w are computed wrt the center of RoI (x, y). - // Appropriate translation needs to be applied after. - T roi_start_h = -roi_height / 2.0; - T roi_start_w = -roi_width / 2.0; - - // We do average (integral) pooling inside a bin - const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4 - - for (int iy = 0; iy < roi_bin_grid_h; iy++) { - const T yy = roi_start_h + ph * bin_size_h + - static_cast (iy + .5f) * bin_size_h / - static_cast (roi_bin_grid_h); // e.g., 0.5, 1.5 - for (int ix = 0; ix < roi_bin_grid_w; ix++) { - const T xx = roi_start_w + pw * bin_size_w + - static_cast (ix + .5f) * bin_size_w / - static_cast (roi_bin_grid_w); - - // Rotate by theta around the center and translate - T y = yy * cos_theta - xx * sin_theta + roi_center_h; - T x = yy * sin_theta + xx * cos_theta + roi_center_w; - - T w1, w2, w3, w4; - int x_low, x_high, y_low, y_high; - - bilinear_interpolate_gradient( - height, width, y, x, w1, w2, w3, w4, x_low, x_high, y_low, y_high); - - T g1 = grad_output_this_bin * w1 / count; - T g2 = grad_output_this_bin * w2 / count; - T g3 = grad_output_this_bin * w3 / count; - T g4 = grad_output_this_bin * w4 / count; - - if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) { - // atomic add is not needed for now since it is single threaded - add(offset_grad_input + y_low * width + x_low, static_cast (g1)); - add(offset_grad_input + y_low * width + x_high, static_cast (g2)); - add(offset_grad_input + y_high * width + x_low, static_cast (g3)); - add(offset_grad_input + y_high * width + x_high, static_cast (g4)); - } // if - } // ix - } // iy - } // for -} // ROIAlignRotatedBackward - -at::Tensor ROIAlignRotated_forward_cpu( - const at::Tensor& input, - const at::Tensor& rois, - const float spatial_scale, - const int pooled_height, - const int pooled_width, - const int sampling_ratio) { - AT_ASSERTM(input.device().is_cpu(), "input must be a CPU tensor"); - AT_ASSERTM(rois.device().is_cpu(), "rois must be a CPU tensor"); - - at::TensorArg input_t{input, "input", 1}, rois_t{rois, "rois", 2}; - - at::CheckedFrom c = "ROIAlign_forward_cpu"; - at::checkAllSameType(c, {input_t, rois_t}); - - auto num_rois = rois.size(0); - auto channels = input.size(1); - auto height = input.size(2); - auto width = input.size(3); - - at::Tensor output = at::zeros( - {num_rois, channels, pooled_height, pooled_width}, input.options()); - - auto output_size = num_rois * pooled_height * pooled_width * channels; - - if (output.numel() == 0) { - return output; - } - - auto input_ = input.contiguous(), rois_ = rois.contiguous(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - input.scalar_type(), "ROIAlignRotated_forward", [&] { - ROIAlignRotatedForward ( - output_size, - input_.data_ptr (), - spatial_scale, - channels, - height, - width, - pooled_height, - pooled_width, - sampling_ratio, - rois_.data_ptr (), - output.data_ptr ()); - }); - return output; -} - -at::Tensor ROIAlignRotated_backward_cpu( - const at::Tensor& grad, - const at::Tensor& rois, - const float spatial_scale, - const int pooled_height, - const int pooled_width, - const int batch_size, - const int channels, - const int height, - const int width, - const int sampling_ratio) { - AT_ASSERTM(grad.device().is_cpu(), "grad must be a CPU tensor"); - AT_ASSERTM(rois.device().is_cpu(), "rois must be a CPU tensor"); - - at::TensorArg grad_t{grad, "grad", 1}, rois_t{rois, "rois", 2}; - - at::CheckedFrom c = "ROIAlignRotated_backward_cpu"; - at::checkAllSameType(c, {grad_t, rois_t}); - - at::Tensor grad_input = - at::zeros({batch_size, channels, height, width}, grad.options()); - - // handle possibly empty gradients - if (grad.numel() == 0) { - return grad_input; - } - - // get stride values to ensure indexing into gradients is correct. - int n_stride = grad.stride(0); - int c_stride = grad.stride(1); - int h_stride = grad.stride(2); - int w_stride = grad.stride(3); - - auto rois_ = rois.contiguous(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - grad.scalar_type(), "ROIAlignRotated_forward", [&] { - ROIAlignRotatedBackward ( - grad.numel(), - grad.data_ptr (), - spatial_scale, - channels, - height, - width, - pooled_height, - pooled_width, - sampling_ratio, - grad_input.data_ptr (), - rois_.data_ptr (), - n_stride, - c_stride, - h_stride, - w_stride); - }); - return grad_input; -} - -} // namespace detectron2 diff --git a/spaces/hfmax/SpeciesChecker/app.py b/spaces/hfmax/SpeciesChecker/app.py deleted file mode 100644 index 7b7d45d4c944b0eae548264aaf6509aab2b0dc18..0000000000000000000000000000000000000000 --- a/spaces/hfmax/SpeciesChecker/app.py +++ /dev/null @@ -1,14 +0,0 @@ -from fastbook import * -import gradio as gr - - -learn_inf = load_learner('export.pkl') - -categories = ('Oiseau', 'Insecte', "MammifĆØre", 'Plante') - -def on_click_classify(change): - pred,pred_idx,probs = learn_inf.predict(change) - return dict(zip(categories, map(float, probs))) - -demo = gr.Interface(on_click_classify, gr.Image(shape=(200, 200)), gr.outputs.Label()) -demo.launch() \ No newline at end of file diff --git a/spaces/hkunlp/Binder/utils/normalizer.py b/spaces/hkunlp/Binder/utils/normalizer.py deleted file mode 100644 index 6ed4a454cab17dc5d893b4c64c94c11d46c24d91..0000000000000000000000000000000000000000 --- a/spaces/hkunlp/Binder/utils/normalizer.py +++ /dev/null @@ -1,498 +0,0 @@ -from typing import List, Dict -import pandas as pd -import recognizers_suite -from recognizers_suite import Culture -import re -import unicodedata -from fuzzywuzzy import fuzz - -from utils.sql.extraction_from_sql import * -from utils.sql.all_keywords import ALL_KEY_WORDS - -culture = Culture.English - - -def str_normalize(user_input, recognition_types=None): - """A string normalizer which recognize and normalize value based on recognizers_suite""" - user_input = str(user_input) - user_input = user_input.replace("\\n", "; ") - - def replace_by_idx_pairs(orig_str, strs_to_replace, idx_pairs): - assert len(strs_to_replace) == len(idx_pairs) - last_end = 0 - to_concat = [] - for idx_pair, str_to_replace in zip(idx_pairs, strs_to_replace): - to_concat.append(orig_str[last_end:idx_pair[0]]) - to_concat.append(str_to_replace) - last_end = idx_pair[1] - to_concat.append(orig_str[last_end:]) - return ''.join(to_concat) - - if recognition_types is None: - recognition_types = ["datetime", - "number", - # "ordinal", - # "percentage", - # "age", - # "currency", - # "dimension", - # "temperature", - ] - - for recognition_type in recognition_types: - if re.match("\d+/\d+", user_input): - # avoid calculating str as 1991/92 - continue - recognized_list = getattr(recognizers_suite, "recognize_{}".format(recognition_type))(user_input, - culture) # may match multiple parts - strs_to_replace = [] - idx_pairs = [] - for recognized in recognized_list: - if not recognition_type == 'datetime': - recognized_value = recognized.resolution['value'] - if str(recognized_value).startswith("P"): - # if the datetime is a period: - continue - else: - strs_to_replace.append(recognized_value) - idx_pairs.append((recognized.start, recognized.end + 1)) - else: - if recognized.resolution: # in some cases, this variable could be none. - if len(recognized.resolution['values']) == 1: - strs_to_replace.append( - recognized.resolution['values'][0]['timex']) # We use timex as normalization - idx_pairs.append((recognized.start, recognized.end + 1)) - - if len(strs_to_replace) > 0: - user_input = replace_by_idx_pairs(user_input, strs_to_replace, idx_pairs) - - if re.match("(.*)-(.*)-(.*) 00:00:00", user_input): - user_input = user_input[:-len("00:00:00") - 1] - # '2008-04-13 00:00:00' -> '2008-04-13' - return user_input - - -def prepare_df_for_neuraldb_from_table(table: Dict, add_row_id=True, normalize=True, lower_case=True): - header, rows = table['header'], table['rows'] - if add_row_id and 'row_id' not in header: - header = ["row_id"] + header - rows = [["{}".format(i)] + row for i, row in enumerate(rows)] - if normalize: - df = convert_df_type(pd.DataFrame(data=rows, columns=header), lower_case=lower_case) - else: - df = pd.DataFrame(data=rows, columns=header) - - return df - - -def convert_df_type(df: pd.DataFrame, lower_case=True): - """ - A simple converter of dataframe data type from string to int/float/datetime. - """ - - def get_table_content_in_column(table): - if isinstance(table, pd.DataFrame): - header = table.columns.tolist() - rows = table.values.tolist() - else: - # Standard table dict format - header, rows = table['header'], table['rows'] - all_col_values = [] - for i in range(len(header)): - one_col_values = [] - for _row in rows: - one_col_values.append(_row[i]) - all_col_values.append(one_col_values) - return all_col_values - - # Rename empty columns - new_columns = [] - for idx, header in enumerate(df.columns): - if header == '': - new_columns.append('FilledColumnName') # Fixme: give it a better name when all finished! - else: - new_columns.append(header) - df.columns = new_columns - - # Rename duplicate columns - new_columns = [] - for idx, header in enumerate(df.columns): - if header in new_columns: - new_header, suffix = header, 2 - while new_header in new_columns: - new_header = header + '_' + str(suffix) - suffix += 1 - new_columns.append(new_header) - else: - new_columns.append(header) - df.columns = new_columns - - # Recognize null values like "-" - null_tokens = ['', '-', '/'] - for header in df.columns: - df[header] = df[header].map(lambda x: str(None) if x in null_tokens else x) - - # Convert the null values in digit column to "NaN" - all_col_values = get_table_content_in_column(df) - for col_i, one_col_values in enumerate(all_col_values): - all_number_flag = True - for row_i, cell_value in enumerate(one_col_values): - try: - float(cell_value) - except Exception as e: - if not cell_value in [str(None), str(None).lower()]: - # None or none - all_number_flag = False - if all_number_flag: - _header = df.columns[col_i] - df[_header] = df[_header].map(lambda x: "NaN" if x in [str(None), str(None).lower()] else x) - - # Normalize cell values. - for header in df.columns: - df[header] = df[header].map(lambda x: str_normalize(x)) - - # Strip the mis-added "01-01 00:00:00" - all_col_values = get_table_content_in_column(df) - for col_i, one_col_values in enumerate(all_col_values): - all_with_00_00_00 = True - all_with_01_00_00_00 = True - all_with_01_01_00_00_00 = True - for row_i, cell_value in enumerate(one_col_values): - if not str(cell_value).endswith(" 00:00:00"): - all_with_00_00_00 = False - if not str(cell_value).endswith("-01 00:00:00"): - all_with_01_00_00_00 = False - if not str(cell_value).endswith("-01-01 00:00:00"): - all_with_01_01_00_00_00 = False - if all_with_01_01_00_00_00: - _header = df.columns[col_i] - df[_header] = df[_header].map(lambda x: x[:-len("-01-01 00:00:00")]) - continue - - if all_with_01_00_00_00: - _header = df.columns[col_i] - df[_header] = df[_header].map(lambda x: x[:-len("-01 00:00:00")]) - continue - - if all_with_00_00_00: - _header = df.columns[col_i] - df[_header] = df[_header].map(lambda x: x[:-len(" 00:00:00")]) - continue - - # Do header and cell value lower case - if lower_case: - new_columns = [] - for header in df.columns: - lower_header = str(header).lower() - if lower_header in new_columns: - new_header, suffix = lower_header, 2 - while new_header in new_columns: - new_header = lower_header + '-' + str(suffix) - suffix += 1 - new_columns.append(new_header) - else: - new_columns.append(lower_header) - df.columns = new_columns - for header in df.columns: - # df[header] = df[header].map(lambda x: str(x).lower()) - df[header] = df[header].map(lambda x: str(x).lower().strip()) - - # Recognize header type - for header in df.columns: - - float_able = False - int_able = False - datetime_able = False - - # Recognize int & float type - try: - df[header].astype("float") - float_able = True - except: - pass - - if float_able: - try: - if all(df[header].astype("float") == df[header].astype(int)): - int_able = True - except: - pass - - if float_able: - if int_able: - df[header] = df[header].astype(int) - else: - df[header] = df[header].astype(float) - - # Recognize datetime type - try: - df[header].astype("datetime64") - datetime_able = True - except: - pass - - if datetime_able: - df[header] = df[header].astype("datetime64") - - return df - - -def normalize(x): - """ Normalize string. """ - # Copied from WikiTableQuestions dataset official evaluator. - if x is None: - return None - # Remove diacritics - x = ''.join(c for c in unicodedata.normalize('NFKD', x) - if unicodedata.category(c) != 'Mn') - # Normalize quotes and dashes - x = re.sub("[āāĀ“`]", "'", x) - x = re.sub("[āā]", "\"", x) - x = re.sub("[āāāāāā]", "-", x) - while True: - old_x = x - # Remove citations - x = re.sub("((?= fuzz_threshold: - matched_cells.append((cell, fuzz_score)) - - matched_cells = sorted(matched_cells, key=lambda x: x[1], reverse=True) - return matched_cells - - def _check_valid_fuzzy_match(value_str, matched_cell): - """ - Check if the fuzzy match is valid, now considering: - 1. The number/date should not be disturbed, but adding new number or deleting number is valid. - """ - number_pattern = "[+]?[.]?[\d]+(?:,\d\d\d)*[\.]?\d*(?:[eE][-+]?\d+)?" - numbers_in_value = re.findall(number_pattern, value_str) - numbers_in_matched_cell = re.findall(number_pattern, matched_cell) - try: - numbers_in_value = [float(num.replace(',', '')) for num in numbers_in_value] - except: - print(f"Can't convert number string {numbers_in_value} into float in _check_valid_fuzzy_match().") - try: - numbers_in_matched_cell = [float(num.replace(',', '')) for num in numbers_in_matched_cell] - except: - print( - f"Can't convert number string {numbers_in_matched_cell} into float in _check_valid_fuzzy_match().") - numbers_in_value = set(numbers_in_value) - numbers_in_matched_cell = set(numbers_in_matched_cell) - - if numbers_in_value.issubset(numbers_in_matched_cell) or numbers_in_matched_cell.issubset(numbers_in_value): - return True - else: - return False - - # Drop trailing '\n```', a pattern that may appear in Codex SQL generation - sql_str = sql_str.rstrip('```').rstrip('\n') - - # Replace QA module with placeholder - qa_pattern = "QA\(.+?;.*?`.+?`.*?\)" - qas = re.findall(qa_pattern, sql_str) - for idx, qa in enumerate(qas): - sql_str = sql_str.replace(qa, f"placeholder{idx}") - - # Parse and replace SQL value with table contents - sql_tokens = tokenize(sql_str) - sql_template_tokens = extract_partial_template_from_sql(sql_str) - # Fix 'between' keyword bug in parsing templates - fixed_sql_template_tokens = [] - sql_tok_bias = 0 - for idx, sql_templ_tok in enumerate(sql_template_tokens): - sql_tok = sql_tokens[idx + sql_tok_bias] - if sql_tok == 'between' and sql_templ_tok == '[WHERE_OP]': - fixed_sql_template_tokens.extend(['[WHERE_OP]', '[VALUE]', 'and']) - sql_tok_bias += 2 # pass '[VALUE]', 'and' - else: - fixed_sql_template_tokens.append(sql_templ_tok) - sql_template_tokens = fixed_sql_template_tokens - for idx, tok in enumerate(sql_tokens): - if tok in ALL_KEY_WORDS: - sql_tokens[idx] = tok.upper() - - if verbose: - print(sql_tokens) - print(sql_template_tokens) - - assert len(sql_tokens) == len(sql_template_tokens) - value_indices = [idx for idx in range(len(sql_template_tokens)) if sql_template_tokens[idx] == '[VALUE]'] - for value_idx in value_indices: - # Skip the value if the where condition column is QA module - if value_idx >= 2 and sql_tokens[value_idx - 2].startswith('placeholder'): - continue - value_str = sql_tokens[value_idx] - # Drop \"\" for fuzzy match - is_string = False - if value_str[0] == "\"" and value_str[-1] == "\"": - value_str = value_str[1:-1] - is_string = True - # If already fuzzy match, skip - if value_str[0] == '%' or value_str[-1] == '%': - continue - value_str = value_str.lower() - # Fuzzy Match - matched_cells = _get_matched_cells(value_str, df) - - if verbose: - print(matched_cells) - - new_value_str = value_str - if matched_cells: - # new_value_str = matched_cells[0][0] - for matched_cell, fuzz_score in matched_cells: - if _check_valid_fuzzy_match(value_str, matched_cell): - new_value_str = matched_cell - if verbose and new_value_str != value_str: - print("\tfuzzy match replacing!", value_str, '->', matched_cell, f'fuzz_score:{fuzz_score}') - break - if is_string: - new_value_str = f"\"{new_value_str}\"" - sql_tokens[value_idx] = new_value_str - # Compose new sql string - # Clean column name in SQL since columns may have been tokenized in the postprocessing, e.g., (ppp) -> ( ppp ) - new_sql_str = ' '.join(sql_tokens) - sql_columns = re.findall('`\s(.*?)\s`', new_sql_str) - for sql_col in sql_columns: - matched_columns = [] - for col in df.columns: - score = fuzz.ratio(sql_col.lower(), col) - if score == 100: - matched_columns = [(col, score)] - break - if score >= 80: - matched_columns.append((col, score)) - matched_columns = sorted(matched_columns, key=lambda x: x[1], reverse=True) - if matched_columns: - matched_col = matched_columns[0][0] - new_sql_str = new_sql_str.replace(f"` {sql_col} `", f"`{matched_col}`") - else: - new_sql_str = new_sql_str.replace(f"` {sql_col} `", f"`{sql_col}`") - - # Restore QA modules - for idx, qa in enumerate(qas): - new_sql_str = new_sql_str.replace(f"placeholder{idx}", qa) - - # Fix '<>' when composing the new sql - new_sql_str = new_sql_str.replace('< >', '<>') - - return new_sql_str - - sql_str = basic_fix(sql_str, list(df.columns), table_title) - - if process_program_with_fuzzy_match_on_db: - try: - sql_str = fuzzy_match_process(sql_str, df, verbose) - except: - pass - - return sql_str diff --git a/spaces/hsdhgds/htyjuietryt/README.md b/spaces/hsdhgds/htyjuietryt/README.md deleted file mode 100644 index 8287781d5707aa2be8023d25f50ca2d568628fc4..0000000000000000000000000000000000000000 --- a/spaces/hsdhgds/htyjuietryt/README.md +++ /dev/null @@ -1,10 +0,0 @@ ---- -title: Htyjuietryt -emoji: š -colorFrom: indigo -colorTo: green -sdk: docker -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/huggingface/Model_Cards_Writing_Tool/style.css b/spaces/huggingface/Model_Cards_Writing_Tool/style.css deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/hush1/anime-remove-background/README.md b/spaces/hush1/anime-remove-background/README.md deleted file mode 100644 index 1ba3cb5ea0e994e246d57b7d62b8aa5a6331901c..0000000000000000000000000000000000000000 --- a/spaces/hush1/anime-remove-background/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Anime Remove Background -emoji: šŖš¼ļø -colorFrom: indigo -colorTo: pink -sdk: gradio -sdk_version: 3.1.4 -app_file: app.py -pinned: false -license: apache-2.0 -duplicated_from: skytnt/anime-remove-background ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/configs/wf42m_pfc03_40epoch_8gpu_vit_b.py b/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/configs/wf42m_pfc03_40epoch_8gpu_vit_b.py deleted file mode 100644 index 36f6559ad3d66659dba3bc9c29e35c76a62b3576..0000000000000000000000000000000000000000 --- a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/configs/wf42m_pfc03_40epoch_8gpu_vit_b.py +++ /dev/null @@ -1,28 +0,0 @@ -from easydict import EasyDict as edict - -# make training faster -# our RAM is 256G -# mount -t tmpfs -o size=140G tmpfs /train_tmp - -config = edict() -config.margin_list = (1.0, 0.0, 0.4) -config.network = "vit_b_dp005_mask_005" -config.resume = False -config.output = None -config.embedding_size = 512 -config.sample_rate = 0.3 -config.fp16 = True -config.weight_decay = 0.1 -config.batch_size = 256 -config.gradient_acc = 12 # total batchsize is 256 * 12 -config.optimizer = "adamw" -config.lr = 0.001 -config.verbose = 2000 -config.dali = False - -config.rec = "/train_tmp/WebFace42M" -config.num_classes = 2059906 -config.num_image = 42474557 -config.num_epoch = 40 -config.warmup_epoch = config.num_epoch // 10 -config.val_targets = [] diff --git a/spaces/iccv23-diffusers-demo/T2I-Adapter-SDXL-Sketch/README.md b/spaces/iccv23-diffusers-demo/T2I-Adapter-SDXL-Sketch/README.md deleted file mode 100644 index a72a15ea591641cebb0f2e2b374fcf128a5a165a..0000000000000000000000000000000000000000 --- a/spaces/iccv23-diffusers-demo/T2I-Adapter-SDXL-Sketch/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: T2I Adapter SDXL Sketch -emoji: š -colorFrom: blue -colorTo: purple -sdk: gradio -sdk_version: 3.43.1 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/All Episodes Of The Suite Life Of Zack And Cody In Hindi TOP.md b/spaces/inplisQlawa/anything-midjourney-v4-1/All Episodes Of The Suite Life Of Zack And Cody In Hindi TOP.md deleted file mode 100644 index 01462302d3c1125850a4866b15a13dafe53bfa26..0000000000000000000000000000000000000000 --- a/spaces/inplisQlawa/anything-midjourney-v4-1/All Episodes Of The Suite Life Of Zack And Cody In Hindi TOP.md +++ /dev/null @@ -1,10 +0,0 @@ - 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Note that the cross-attention layers will be randomly initialized -model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained( - "google/vit-base-patch16-224-in21k", "bert-base-uncased" -) -# saving model after fine-tuning -model.save_pretrained("./vit-bert") -# load fine-tuned model -model = VisionEncoderDecoderModel.from_pretrained("./vit-bert") - -##################### -from transformers import AutoTokenizer -repo_name = "ydshieh/vit-gpt2-coco-en" -feature_extractor = ViTFeatureExtractor.from_pretrained(repo_name) -tokenizer = AutoTokenizer.from_pretrained(repo_name) -model = VisionEncoderDecoderModel.from_pretrained(repo_name) - -def get_quote(image): - - ############## - pixel_values = feature_extractor(image, return_tensors="pt").pixel_values - # autoregressively generate text (using beam search or other decoding strategy) - generated_ids = model.generate(pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True) - - ################ - # decode into text - preds = tokenizer.batch_decode(generated_ids[0], skip_special_tokens=True) - preds = [pred.strip() for pred in preds] - return preds - -#1: Text to Speech -title = "Sentence, listing all the items present in the image file" - -demo = gr.Interface(fn=get_quote, inputs=gr.inputs.Image(type="pil"), outputs=['text'],title = title, description = "Upload an image file and get text from it" ,cache_examples=False, enable_queue=True).launch() -if __name__ == "__main__": - - demo.launch(debug=True, cache_examples=True) \ No newline at end of file diff --git a/spaces/ivntl/MMS/uroman/README.md b/spaces/ivntl/MMS/uroman/README.md deleted file mode 100644 index 6a0a40f6d4ebda9041d23efe0345340b7da9d4b8..0000000000000000000000000000000000000000 --- a/spaces/ivntl/MMS/uroman/README.md +++ /dev/null @@ -1,165 +0,0 @@ -# uroman - -*uroman* is a *universal romanizer*. It converts text in any script to the Latin alphabet. - -Version: 1.2.8 -Release date: April 23, 2021 -Author: Ulf Hermjakob, USC Information Sciences Institute - - -### Usage -```bash -$ uroman.pl [-l] [--chart] [--no-cache] < STDIN - where the optional is a 3-letter languages code, e.g. ara, bel, bul, deu, ell, eng, fas, - grc, ell, eng, heb, kaz, kir, lav, lit, mkd, mkd2, oss, pnt, pus, rus, srp, srp2, tur, uig, ukr, yid. - --chart specifies chart output (in JSON format) to represent alternative romanizations. - --no-cache disables caching. -``` -### Examples -```bash -$ bin/uroman.pl < text/zho.txt -$ bin/uroman.pl -l tur < text/tur.txt -$ bin/uroman.pl -l heb --chart < text/heb.txt -$ bin/uroman.pl < test/multi-script.txt > test/multi-script.uroman.txt -``` - -Identifying the input as Arabic, Belarusian, Bulgarian, English, Farsi, German, -Ancient Greek, Modern Greek, Pontic Greek, Hebrew, Kazakh, Kyrgyz, Latvian, -Lithuanian, North Macedonian, Russian, Serbian, Turkish, Ukrainian, Uyghur or -Yiddish will improve romanization for those languages as some letters in those -languages have different sound values from other languages using the same script -(French, Russian, Hebrew respectively). -No effect for other languages in this version. - -### Bibliography -Ulf Hermjakob, Jonathan May, and Kevin Knight. 2018. Out-of-the-box universal romanization tool uroman. In Proceedings of the 56th Annual Meeting of Association for Computational Linguistics, Demo Track. ACL-2018 Best Demo Paper Award. [Paper in ACL Anthology](https://www.aclweb.org/anthology/P18-4003) | [Poster](https://www.isi.edu/~ulf/papers/poster-uroman-acl2018.pdf) | [BibTex](https://www.aclweb.org/anthology/P18-4003.bib) - -### Change History -Changes in version 1.2.8 - * Updated to Unicode 13.0 (2021), which supports several new scripts (10% larger UnicodeData.txt). - * Improved support for Georgian. - * Preserve various symbols (as opposed to mapping to the symbols' names). - * Various small improvements. - -Changes in version 1.2.7 - * Improved support for Pashto. - -Changes in version 1.2.6 - * Improved support for Ukrainian, Russian and Ogham (ancient Irish script). - * Added support for English Braille. - * Added alternative Romanization for North Macedonian and Serbian (mkd2/srp2) - reflecting a casual style that many native speakers of those languages use - when writing text in Latin script, e.g. non-accented single letters (e.g. "s") - rather than phonetically motivated combinations of letters (e.g. "sh"). - * When a line starts with "::lcode xyz ", the new uroman version will switch to - that language for that line. This is used for the new reference test file. - * Various small improvements. - -Changes in version 1.2.5 - * Improved support for Armenian and eight languages using Cyrillic scripts. - -- For Serbian and Macedonian, which are often written in both Cyrillic - and Latin scripts, uroman will map both official versions to the same - romanized text, e.g. both "ŠŠøŃ" and "NiÅ”" will be mapped to "Nish" (which - properly reflects the pronunciation of the city's name). - For both Serbian and Macedonian, casual writers often use a simplified - Latin form without diacritics, e.g. "s" to represent not only Cyrillic "Ń" - and Latin "s", but also "Ń" or "Å”", even if this conflates "s" and "sh" and - other such pairs. The casual romanization can be simulated by using - alternative uroman language codes "srp2" and "mkd2", which romanize - both "ŠŠøŃ" and "NiÅ”" to "Nis" to reflect the casual Latin spelling. - * Various small improvements. - -Changes in version 1.2.4 - * Bug-fix that generated two emtpy lines for each empty line in cache mode. - -Changes in version 1.2 - * Run-time improvement based on (1) token-based caching and (2) shortcut - romanization (identity) of ASCII strings for default 1-best (non-chart) - output. Speed-up by a factor of 10 for Bengali and Uyghur on medium and - large size texts. - * Incremental improvements for Farsi, Amharic, Russian, Hebrew and related - languages. - * Richer lattice structure (more alternatives) for "Romanization" of English - to support better matching to romanizations of other languages. - Changes output only when --chart option is specified. No change in output for - default 1-best output, which for ASCII characters is always the input string. - -Changes in version 1.1 (major upgrade) - * Offers chart output (in JSON format) to represent alternative romanizations. - -- Location of first character is defined to be "line: 1, start:0, end:0". - * Incremental improvements of Hebrew and Greek romanization; Chinese numbers. - * Improved web-interface at http://www.isi.edu/~ulf/uroman.html - -- Shows corresponding original and romanization text in red - when hovering over a text segment. - -- Shows alternative romanizations when hovering over romanized text - marked by dotted underline. - -- Added right-to-left script detection and improved display for right-to-left - script text (as determined line by line). - -- On-page support for some scripts that are often not pre-installed on users' - computers (Burmese, Egyptian, Klingon). - -Changes in version 1.0 (major upgrade) - * Upgraded principal internal data structure from string to lattice. - * Improvements mostly in vowelization of South and Southeast Asian languages. - * Vocalic 'r' more consistently treated as vowel (no additional vowel added). - * Repetition signs (Japanese/Chinese/Thai/Khmer/Lao) are mapped to superscript 2. - * Japanese Katakana middle dots now mapped to ASCII space. - * Tibetan intersyllabic mark now mapped to middle dot (U+00B7). - * Some corrections regarding analysis of Chinese numbers. - * Many more foreign diacritics and punctuation marks dropped or mapped to ASCII. - * Zero-width characters dropped, except line/sentence-initial byte order marks. - * Spaces normalized to ASCII space. - * Fixed bug that in some cases mapped signs (such as dagger or bullet) to their verbal descriptions. - * Tested against previous version of uroman with a new uroman visual diff tool. - * Almost an order of magnitude faster. - -Changes in version 0.7 (minor upgrade) - * Added script uroman-quick.pl for Arabic script languages, incl. Uyghur. - Much faster, pre-caching mapping of Arabic to Latin characters, simple greedy processing. - Will not convert material from non-Arabic blocks such as any (somewhat unusual) Cyrillic - or Chinese characters in Uyghur texts. - -Changes in version 0.6 (minor upgrade) - * Added support for two letter characters used in Uzbek: - (1) character "Ź»" ("modifier letter turned comma", which modifies preceding "g" and "u" letters) - (2) character "Ź¼" ("modifier letter apostrophe", which Uzbek uses to mark a glottal stop). - Both are now mapped to "'" (plain ASCII apostrophe). - * Added support for Uyghur vowel characters such as "Ū" (Arabic e) and "Ū" (Arabic oe) - even when they are not preceded by "Ų¦" (yeh with hamza above). - * Added support for Arabic semicolon "Ų", Arabic ligature forms for phrases such as "ļ·ŗ" - ("sallallahou alayhe wasallam" = "prayer of God be upon him and his family and peace") - * Added robustness for Arabic letter presentation forms (initial/medial/final/isolated). - However, it is strongly recommended to normalize any presentation form Arabic letters - to their non-presentation form before calling uroman. - * Added force flush directive ($|=1;). - -Changes in version 0.5 (minor upgrade) - * Improvements for Uyghur (make sure to use language option: -l uig) - -Changes in version 0.4 (minor upgrade) - * Improvements for Thai (special cases for vowel/consonant reordering, e.g. for "sara o"; dropped some aspiration 'h's) - * Minor change for Arabic (added "alef+fathatan" = "an") - -New features in version 0.3 - * Covers Mandarin (Chinese) - * Improved romanization for numerous languages - * Preserves capitalization (e.g. from Latin, Cyrillic, Greek scripts) - * Maps from native digits to Western numbers - * Faster for South Asian languages - -### Other features - * Web interface: http://www.isi.edu/~ulf/uroman.html - * Vowelization is provided when locally computable, e.g. for many South Asian languages and Tibetan. - -### Limitations - * The current version of uroman has a few limitations, some of which we plan to address in future versions. - For Japanese, *uroman* currently romanizes hiragana and katakana as expected, but kanji are interpreted as Chinese characters and romanized as such. - For Egyptian hieroglyphs, only single-sound phonetic characters and numbers are currently romanized. - For Linear B, only phonetic syllabic characters are romanized. - For some other extinct scripts such as cuneiform, no romanization is provided. - * A romanizer is not a full transliterator. For example, this version of - uroman does not vowelize text that lacks explicit vowelization such as - normal text in Arabic and Hebrew (without diacritics/points). - -### Acknowledgments -This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via contract # FA8650-17-C-9116, and by research sponsored by Air Force Research Laboratory (AFRL) under agreement number FA8750-19-1-1000. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, Air Force Laboratory, DARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein. diff --git a/spaces/j0hngou/vision-diffmask/code/attributions/grad_cam.py b/spaces/j0hngou/vision-diffmask/code/attributions/grad_cam.py deleted file mode 100644 index fa06118519d49ff112e79a4100ef77327d8b4ff8..0000000000000000000000000000000000000000 --- a/spaces/j0hngou/vision-diffmask/code/attributions/grad_cam.py +++ /dev/null @@ -1,55 +0,0 @@ -import torch - -from pytorch_grad_cam import GradCAM -from torch import Tensor -from transformers import ViTForImageClassification - - -def grad_cam(images: Tensor, vit: ViTForImageClassification, use_cuda: bool = False) -> Tensor: - """Performs the Grad-CAM method on a batch of images (https://arxiv.org/pdf/1610.02391.pdf).""" - - # Wrap the ViT model to be compatible with GradCAM - vit = ViTWrapper(vit) - vit.eval() - - # Create GradCAM object - cam = GradCAM( - model=vit, - target_layers=[vit.target_layer], - reshape_transform=_reshape_transform, - use_cuda=use_cuda, - ) - - # Compute GradCAM masks - grayscale_cam = cam( - input_tensor=images, - targets=None, - eigen_smooth=True, - aug_smooth=True, - ) - - return torch.from_numpy(grayscale_cam) - - -def _reshape_transform(tensor, height=14, width=14): - result = tensor[:, 1:, :].reshape(tensor.size(0), height, width, tensor.size(2)) - - # Bring the channels to the first dimension - result = result.transpose(2, 3).transpose(1, 2) - - return result - - -class ViTWrapper(torch.nn.Module): - """ViT Wrapper to use with Grad-CAM.""" - - def __init__(self, vit: ViTForImageClassification): - super().__init__() - self.vit = vit - - def forward(self, x: torch.Tensor) -> torch.Tensor: - return self.vit(x).logits - - @property - def target_layer(self): - return self.vit.vit.encoder.layer[-2].layernorm_after diff --git a/spaces/james-oldfield/PandA/networks/genforce/models/stylegan2_generator.py b/spaces/james-oldfield/PandA/networks/genforce/models/stylegan2_generator.py deleted file mode 100644 index 0e99566fb455650e2dc24739e086cc260670f92f..0000000000000000000000000000000000000000 --- a/spaces/james-oldfield/PandA/networks/genforce/models/stylegan2_generator.py +++ /dev/null @@ -1,1074 +0,0 @@ -# python3.7 -"""Contains the implementation of generator described in StyleGAN2. - -Compared to that of StyleGAN, the generator in StyleGAN2 mainly introduces style -demodulation, adds skip connections, increases model size, and disables -progressive growth. This script ONLY supports config F in the original paper. - -Paper: https://arxiv.org/pdf/1912.04958.pdf - -Official TensorFlow implementation: https://github.com/NVlabs/stylegan2 -""" - -import numpy as np - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from .sync_op import all_gather - -__all__ = ['StyleGAN2Generator'] - -# Resolutions allowed. -_RESOLUTIONS_ALLOWED = [8, 16, 32, 64, 128, 256, 512, 1024] - -# Initial resolution. -_INIT_RES = 4 - -# Architectures allowed. -_ARCHITECTURES_ALLOWED = ['resnet', 'skip', 'origin'] - -# Default gain factor for weight scaling. -_WSCALE_GAIN = 1.0 - - -class StyleGAN2Generator(nn.Module): - """Defines the generator network in StyleGAN2. - - NOTE: The synthesized images are with `RGB` channel order and pixel range - [-1, 1]. - - Settings for the mapping network: - - (1) z_space_dim: Dimension of the input latent space, Z. (default: 512) - (2) w_space_dim: Dimension of the outout latent space, W. (default: 512) - (3) label_size: Size of the additional label for conditional generation. - (default: 0) - (4ļ¼mapping_layers: Number of layers of the mapping network. (default: 8) - (5) mapping_fmaps: Number of hidden channels of the mapping network. - (default: 512) - (6) mapping_lr_mul: Learning rate multiplier for the mapping network. - (default: 0.01) - (7) repeat_w: Repeat w-code for different layers. - - Settings for the synthesis network: - - (1) resolution: The resolution of the output image. - (2) image_channels: Number of channels of the output image. (default: 3) - (3) final_tanh: Whether to use `tanh` to control the final pixel range. - (default: False) - (4) const_input: Whether to use a constant in the first convolutional layer. - (default: True) - (5) architecture: Type of architecture. Support `origin`, `skip`, and - `resnet`. (default: `resnet`) - (6) fused_modulate: Whether to fuse `style_modulate` and `conv2d` together. - (default: True) - (7) demodulate: Whether to perform style demodulation. (default: True) - (8) use_wscale: Whether to use weight scaling. (default: True) - (9) noise_type: Type of noise added to the convolutional results at each - layer. (default: `spatial`) - (10) fmaps_base: Factor to control number of feature maps for each layer. - (default: 32 << 10) - (11) fmaps_max: Maximum number of feature maps in each layer. (default: 512) - """ - - def __init__(self, - resolution, - z_space_dim=512, - w_space_dim=512, - label_size=0, - mapping_layers=8, - mapping_fmaps=512, - mapping_lr_mul=0.01, - repeat_w=True, - image_channels=3, - final_tanh=False, - const_input=True, - architecture='skip', - fused_modulate=True, - demodulate=True, - use_wscale=True, - noise_type='spatial', - fmaps_base=32 << 10, - fmaps_max=512): - """Initializes with basic settings. - - Raises: - ValueError: If the `resolution` is not supported, or `architecture` - is not supported. - """ - super().__init__() - - if resolution not in _RESOLUTIONS_ALLOWED: - raise ValueError(f'Invalid resolution: `{resolution}`!\n' - f'Resolutions allowed: {_RESOLUTIONS_ALLOWED}.') - if architecture not in _ARCHITECTURES_ALLOWED: - raise ValueError(f'Invalid architecture: `{architecture}`!\n' - f'Architectures allowed: ' - f'{_ARCHITECTURES_ALLOWED}.') - - self.init_res = _INIT_RES - self.resolution = resolution - self.z_space_dim = z_space_dim - self.w_space_dim = w_space_dim - self.label_size = label_size - self.mapping_layers = mapping_layers - self.mapping_fmaps = mapping_fmaps - self.mapping_lr_mul = mapping_lr_mul - self.repeat_w = repeat_w - self.image_channels = image_channels - self.final_tanh = final_tanh - self.const_input = const_input - self.architecture = architecture - self.fused_modulate = fused_modulate - self.demodulate = demodulate - self.use_wscale = use_wscale - self.noise_type = noise_type - self.fmaps_base = fmaps_base - self.fmaps_max = fmaps_max - - self.num_layers = int(np.log2(self.resolution // self.init_res * 2)) * 2 - - if self.repeat_w: - self.mapping_space_dim = self.w_space_dim - else: - self.mapping_space_dim = self.w_space_dim * self.num_layers - self.mapping = MappingModule(input_space_dim=self.z_space_dim, - hidden_space_dim=self.mapping_fmaps, - final_space_dim=self.mapping_space_dim, - label_size=self.label_size, - num_layers=self.mapping_layers, - use_wscale=self.use_wscale, - lr_mul=self.mapping_lr_mul) - - self.truncation = TruncationModule(w_space_dim=self.w_space_dim, - num_layers=self.num_layers, - repeat_w=self.repeat_w) - - self.synthesis = SynthesisModule(resolution=self.resolution, - init_resolution=self.init_res, - w_space_dim=self.w_space_dim, - image_channels=self.image_channels, - final_tanh=self.final_tanh, - const_input=self.const_input, - architecture=self.architecture, - fused_modulate=self.fused_modulate, - demodulate=self.demodulate, - use_wscale=self.use_wscale, - noise_type=self.noise_type, - fmaps_base=self.fmaps_base, - fmaps_max=self.fmaps_max) - - self.pth_to_tf_var_mapping = {} - for key, val in self.mapping.pth_to_tf_var_mapping.items(): - self.pth_to_tf_var_mapping[f'mapping.{key}'] = val - for key, val in self.truncation.pth_to_tf_var_mapping.items(): - self.pth_to_tf_var_mapping[f'truncation.{key}'] = val - for key, val in self.synthesis.pth_to_tf_var_mapping.items(): - self.pth_to_tf_var_mapping[f'synthesis.{key}'] = val - - def set_space_of_latent(self, space_of_latent='w'): - """Sets the space to which the latent code belong. - - This function is particually used for choosing how to inject the latent - code into the convolutional layers. The original generator will take a - W-Space code and apply it for style modulation after an affine - transformation. But, sometimes, it may need to directly feed an already - affine-transformed code into the convolutional layer, e.g., when - training an encoder for GAN inversion. We term the transformed space as - Style Space (or Y-Space). This function is designed to tell the - convolutional layers how to use the input code. - - Args: - space_of_latent: The space to which the latent code belong. Case - insensitive. (default: 'w') - """ - for module in self.modules(): - if isinstance(module, ModulateConvBlock): - setattr(module, 'space_of_latent', space_of_latent) - - def forward(self, - z, - label=None, - w_moving_decay=0.995, - style_mixing_prob=0.9, - trunc_psi=None, - trunc_layers=None, - randomize_noise=False, - **_unused_kwargs): - mapping_results = self.mapping(z, label) - w = mapping_results['w'] - - if self.training and w_moving_decay < 1: - batch_w_avg = all_gather(w).mean(dim=0) - self.truncation.w_avg.copy_( - self.truncation.w_avg * w_moving_decay + - batch_w_avg * (1 - w_moving_decay)) - - if self.training and style_mixing_prob > 0: - new_z = torch.randn_like(z) - new_w = self.mapping(new_z, label)['w'] - if np.random.uniform() < style_mixing_prob: - mixing_cutoff = np.random.randint(1, self.num_layers) - w = self.truncation(w) - new_w = self.truncation(new_w) - w[:, :mixing_cutoff] = new_w[:, :mixing_cutoff] - - wp = self.truncation(w, trunc_psi, trunc_layers) - synthesis_results = self.synthesis(wp, randomize_noise) - - return {**mapping_results, **synthesis_results} - - -class MappingModule(nn.Module): - """Implements the latent space mapping module. - - Basically, this module executes several dense layers in sequence. - """ - - def __init__(self, - input_space_dim=512, - hidden_space_dim=512, - final_space_dim=512, - label_size=0, - num_layers=8, - normalize_input=True, - use_wscale=True, - lr_mul=0.01): - super().__init__() - - self.input_space_dim = input_space_dim - self.hidden_space_dim = hidden_space_dim - self.final_space_dim = final_space_dim - self.label_size = label_size - self.num_layers = num_layers - self.normalize_input = normalize_input - self.use_wscale = use_wscale - self.lr_mul = lr_mul - - self.norm = PixelNormLayer() if self.normalize_input else nn.Identity() - - self.pth_to_tf_var_mapping = {} - for i in range(num_layers): - dim_mul = 2 if label_size else 1 - in_channels = (input_space_dim * dim_mul if i == 0 else - hidden_space_dim) - out_channels = (final_space_dim if i == (num_layers - 1) else - hidden_space_dim) - self.add_module(f'dense{i}', - DenseBlock(in_channels=in_channels, - out_channels=out_channels, - use_wscale=self.use_wscale, - lr_mul=self.lr_mul)) - self.pth_to_tf_var_mapping[f'dense{i}.weight'] = f'Dense{i}/weight' - self.pth_to_tf_var_mapping[f'dense{i}.bias'] = f'Dense{i}/bias' - if label_size: - self.label_weight = nn.Parameter( - torch.randn(label_size, input_space_dim)) - self.pth_to_tf_var_mapping[f'label_weight'] = f'LabelConcat/weight' - - def forward(self, z, label=None): - if z.ndim != 2 or z.shape[1] != self.input_space_dim: - raise ValueError(f'Input latent code should be with shape ' - f'[batch_size, input_dim], where ' - f'`input_dim` equals to {self.input_space_dim}!\n' - f'But `{z.shape}` is received!') - if self.label_size: - if label is None: - raise ValueError(f'Model requires an additional label ' - f'(with size {self.label_size}) as input, ' - f'but no label is received!') - if label.ndim != 2 or label.shape != (z.shape[0], self.label_size): - raise ValueError(f'Input label should be with shape ' - f'[batch_size, label_size], where ' - f'`batch_size` equals to that of ' - f'latent codes ({z.shape[0]}) and ' - f'`label_size` equals to {self.label_size}!\n' - f'But `{label.shape}` is received!') - embedding = torch.matmul(label, self.label_weight) - z = torch.cat((z, embedding), dim=1) - - z = self.norm(z) - w = z - for i in range(self.num_layers): - w = self.__getattr__(f'dense{i}')(w) - results = { - 'z': z, - 'label': label, - 'w': w, - } - if self.label_size: - results['embedding'] = embedding - return results - - -class TruncationModule(nn.Module): - """Implements the truncation module. - - Truncation is executed as follows: - - For layers in range [0, truncation_layers), the truncated w-code is computed - as - - w_new = w_avg + (w - w_avg) * truncation_psi - - To disable truncation, please set - (1) truncation_psi = 1.0 (None) OR - (2) truncation_layers = 0 (None) - - NOTE: The returned tensor is layer-wise style codes. - """ - - def __init__(self, w_space_dim, num_layers, repeat_w=True): - super().__init__() - - self.num_layers = num_layers - self.w_space_dim = w_space_dim - self.repeat_w = repeat_w - - if self.repeat_w: - self.register_buffer('w_avg', torch.zeros(w_space_dim)) - else: - self.register_buffer('w_avg', torch.zeros(num_layers * w_space_dim)) - self.pth_to_tf_var_mapping = {'w_avg': 'dlatent_avg'} - - def forward(self, w, trunc_psi=None, trunc_layers=None): - if w.ndim == 2: - if self.repeat_w and w.shape[1] == self.w_space_dim: - w = w.view(-1, 1, self.w_space_dim) - wp = w.repeat(1, self.num_layers, 1) - else: - assert w.shape[1] == self.w_space_dim * self.num_layers - wp = w.view(-1, self.num_layers, self.w_space_dim) - else: - wp = w - assert wp.ndim == 3 - assert wp.shape[1:] == (self.num_layers, self.w_space_dim) - - trunc_psi = 1.0 if trunc_psi is None else trunc_psi - trunc_layers = 0 if trunc_layers is None else trunc_layers - if trunc_psi < 1.0 and trunc_layers > 0: - layer_idx = np.arange(self.num_layers).reshape(1, -1, 1) - coefs = np.ones_like(layer_idx, dtype=np.float32) - coefs[layer_idx < trunc_layers] *= trunc_psi - coefs = torch.from_numpy(coefs).to(wp) - w_avg = self.w_avg.view(1, -1, self.w_space_dim) - wp = w_avg + (wp - w_avg) * coefs - return wp - - -class SynthesisModule(nn.Module): - """Implements the image synthesis module. - - Basically, this module executes several convolutional layers in sequence. - """ - - def __init__(self, - resolution=1024, - init_resolution=4, - w_space_dim=512, - image_channels=3, - final_tanh=False, - const_input=True, - architecture='skip', - fused_modulate=True, - demodulate=True, - use_wscale=True, - noise_type='spatial', - fmaps_base=32 << 10, - fmaps_max=512): - super().__init__() - - self.init_res = init_resolution - self.init_res_log2 = int(np.log2(self.init_res)) - self.resolution = resolution - self.final_res_log2 = int(np.log2(self.resolution)) - self.w_space_dim = w_space_dim - self.image_channels = image_channels - self.final_tanh = final_tanh - self.const_input = const_input - self.architecture = architecture - self.fused_modulate = fused_modulate - self.demodulate = demodulate - self.use_wscale = use_wscale - self.noise_type = noise_type - self.fmaps_base = fmaps_base - self.fmaps_max = fmaps_max - - self.num_layers = (self.final_res_log2 - self.init_res_log2 + 1) * 2 - - self.pth_to_tf_var_mapping = {} - for res_log2 in range(self.init_res_log2, self.final_res_log2 + 1): - res = 2 ** res_log2 - block_idx = res_log2 - self.init_res_log2 - - # First convolution layer for each resolution. - if res == self.init_res: - if self.const_input: - self.add_module(f'early_layer', - InputBlock(init_resolution=self.init_res, - channels=self.get_nf(res))) - self.pth_to_tf_var_mapping[f'early_layer.const'] = ( - f'{res}x{res}/Const/const') - else: - self.add_module(f'early_layer', - DenseBlock(in_channels=self.w_space_dim, - out_channels=self.get_nf(res), - use_wscale=self.use_wscale)) - self.pth_to_tf_var_mapping[f'early_layer.weight'] = ( - f'{res}x{res}/Dense/weight') - self.pth_to_tf_var_mapping[f'early_layer.bias'] = ( - f'{res}x{res}/Dense/bias') - else: - layer_name = f'layer{2 * block_idx - 1}' - self.add_module( - layer_name, - ModulateConvBlock(in_channels=self.get_nf(res // 2), - out_channels=self.get_nf(res), - resolution=res, - w_space_dim=self.w_space_dim, - scale_factor=2, - fused_modulate=self.fused_modulate, - demodulate=self.demodulate, - use_wscale=self.use_wscale, - noise_type=self.noise_type)) - self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = ( - f'{res}x{res}/Conv0_up/weight') - self.pth_to_tf_var_mapping[f'{layer_name}.bias'] = ( - f'{res}x{res}/Conv0_up/bias') - self.pth_to_tf_var_mapping[f'{layer_name}.style.weight'] = ( - f'{res}x{res}/Conv0_up/mod_weight') - self.pth_to_tf_var_mapping[f'{layer_name}.style.bias'] = ( - f'{res}x{res}/Conv0_up/mod_bias') - self.pth_to_tf_var_mapping[f'{layer_name}.noise_strength'] = ( - f'{res}x{res}/Conv0_up/noise_strength') - self.pth_to_tf_var_mapping[f'{layer_name}.noise'] = ( - f'noise{2 * block_idx - 1}') - - if self.architecture == 'resnet': - layer_name = f'layer{2 * block_idx - 1}' - self.add_module( - layer_name, - ConvBlock(in_channels=self.get_nf(res // 2), - out_channels=self.get_nf(res), - kernel_size=1, - add_bias=False, - scale_factor=2, - use_wscale=self.use_wscale, - activation_type='linear')) - self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = ( - f'{res}x{res}/Skip/weight') - - # Second convolution layer for each resolution. - layer_name = f'layer{2 * block_idx}' - self.add_module( - layer_name, - ModulateConvBlock(in_channels=self.get_nf(res), - out_channels=self.get_nf(res), - resolution=res, - w_space_dim=self.w_space_dim, - fused_modulate=self.fused_modulate, - demodulate=self.demodulate, - use_wscale=self.use_wscale, - noise_type=self.noise_type)) - tf_layer_name = 'Conv' if res == self.init_res else 'Conv1' - self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = ( - f'{res}x{res}/{tf_layer_name}/weight') - self.pth_to_tf_var_mapping[f'{layer_name}.bias'] = ( - f'{res}x{res}/{tf_layer_name}/bias') - self.pth_to_tf_var_mapping[f'{layer_name}.style.weight'] = ( - f'{res}x{res}/{tf_layer_name}/mod_weight') - self.pth_to_tf_var_mapping[f'{layer_name}.style.bias'] = ( - f'{res}x{res}/{tf_layer_name}/mod_bias') - self.pth_to_tf_var_mapping[f'{layer_name}.noise_strength'] = ( - f'{res}x{res}/{tf_layer_name}/noise_strength') - self.pth_to_tf_var_mapping[f'{layer_name}.noise'] = ( - f'noise{2 * block_idx}') - - # Output convolution layer for each resolution (if needed). - if res_log2 == self.final_res_log2 or self.architecture == 'skip': - layer_name = f'output{block_idx}' - self.add_module( - layer_name, - ModulateConvBlock(in_channels=self.get_nf(res), - out_channels=image_channels, - resolution=res, - w_space_dim=self.w_space_dim, - kernel_size=1, - fused_modulate=self.fused_modulate, - demodulate=False, - use_wscale=self.use_wscale, - add_noise=False, - activation_type='linear')) - self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = ( - f'{res}x{res}/ToRGB/weight') - self.pth_to_tf_var_mapping[f'{layer_name}.bias'] = ( - f'{res}x{res}/ToRGB/bias') - self.pth_to_tf_var_mapping[f'{layer_name}.style.weight'] = ( - f'{res}x{res}/ToRGB/mod_weight') - self.pth_to_tf_var_mapping[f'{layer_name}.style.bias'] = ( - f'{res}x{res}/ToRGB/mod_bias') - - if self.architecture == 'skip': - self.upsample = UpsamplingLayer() - self.final_activate = nn.Tanh() if final_tanh else nn.Identity() - - def get_nf(self, res): - """Gets number of feature maps according to current resolution.""" - return min(self.fmaps_base // res, self.fmaps_max) - - def forward(self, wp, randomize_noise=False, x=None, image=None, start=2, stop=None): - stop = self.num_layers - 1 if stop is None else stop - - # if wp.ndim != 3 or wp.shape[1:] != (self.num_layers, self.w_space_dim): - # raise ValueError(f'Input tensor should be with shape ' - # f'[batch_size, num_layers, w_space_dim], where ' - # f'`num_layers` equals to {self.num_layers}, and ' - # f'`w_space_dim` equals to {self.w_space_dim}!\n' - # f'But `{wp.shape}` is received!') - - results = {'wp': wp} - x = self.early_layer(wp[:, 0]) if x is None else x - if self.architecture == 'origin': - for layer_idx in range(self.num_layers - 1): - x, style = self.__getattr__(f'layer{layer_idx}')( - x, wp[:, layer_idx], randomize_noise) - results[f'style{layer_idx:02d}'] = style - image, style = self.__getattr__(f'output{layer_idx // 2}')( - x, wp[:, layer_idx + 1]) - results[f'output_style{layer_idx // 2}'] = style - elif self.architecture == 'skip': - # for layer_idx in range(self.num_layers - 1): - for layer_idx in range(start, stop): - x, style = self.__getattr__(f'layer{layer_idx}')( - x, wp[:, layer_idx], randomize_noise) - results[f'style{layer_idx:02d}'] = style - if layer_idx % 2 == 0: - temp, style = self.__getattr__(f'output{layer_idx // 2}')( - x, wp[:, layer_idx + 1]) - results[f'output_style{layer_idx // 2}'] = style - if layer_idx == 0 or image is None: - image = temp - else: - image = temp + self.upsample(image) - elif self.architecture == 'resnet': - x, style = self.layer0(x) - results[f'style00'] = style - for layer_idx in range(1, self.num_layers - 1, 2): - residual = self.__getattr__(f'skip_layer{layer_idx // 2}')(x) - x, style = self.__getattr__(f'layer{layer_idx}')( - x, wp[:, layer_idx], randomize_noise) - results[f'style{layer_idx:02d}'] = style - x, style = self.__getattr__(f'layer{layer_idx + 1}')( - x, wp[:, layer_idx + 1], randomize_noise) - results[f'style{layer_idx + 1:02d}'] = style - x = (x + residual) / np.sqrt(2.0) - image, style = self.__getattr__(f'output{layer_idx // 2 + 1}')( - x, wp[:, layer_idx + 2]) - results[f'output_style{layer_idx // 2}'] = style - results['image'] = self.final_activate(image) if stop == self.num_layers -1 else image - results['x'] = x - return results - - -class PixelNormLayer(nn.Module): - """Implements pixel-wise feature vector normalization layer.""" - - def __init__(self, dim=1, epsilon=1e-8): - super().__init__() - self.dim = dim - self.eps = epsilon - - def forward(self, x): - norm = torch.sqrt( - torch.mean(x ** 2, dim=self.dim, keepdim=True) + self.eps) - return x / norm - - -class UpsamplingLayer(nn.Module): - """Implements the upsampling layer. - - This layer can also be used as filtering by setting `scale_factor` as 1. - """ - - def __init__(self, - scale_factor=2, - kernel=(1, 3, 3, 1), - extra_padding=0, - kernel_gain=None): - super().__init__() - assert scale_factor >= 1 - self.scale_factor = scale_factor - - if extra_padding != 0: - assert scale_factor == 1 - - if kernel is None: - kernel = np.ones((scale_factor), dtype=np.float32) - else: - kernel = np.array(kernel, dtype=np.float32) - assert kernel.ndim == 1 - kernel = np.outer(kernel, kernel) - kernel = kernel / np.sum(kernel) - if kernel_gain is None: - kernel = kernel * (scale_factor ** 2) - else: - assert kernel_gain > 0 - kernel = kernel * (kernel_gain ** 2) - assert kernel.ndim == 2 - assert kernel.shape[0] == kernel.shape[1] - kernel = kernel[np.newaxis, np.newaxis] - self.register_buffer('kernel', torch.from_numpy(kernel)) - self.kernel = self.kernel.flip(0, 1) - - self.upsample_padding = (0, scale_factor - 1, # Width padding. - 0, 0, # Width. - 0, scale_factor - 1, # Height padding. - 0, 0, # Height. - 0, 0, # Channel. - 0, 0) # Batch size. - - padding = kernel.shape[2] - scale_factor + extra_padding - self.padding = ((padding + 1) // 2 + scale_factor - 1, padding // 2, - (padding + 1) // 2 + scale_factor - 1, padding // 2) - - def forward(self, x): - assert x.ndim == 4 - channels = x.shape[1] - if self.scale_factor > 1: - x = x.view(-1, channels, x.shape[2], 1, x.shape[3], 1) - x = F.pad(x, self.upsample_padding, mode='constant', value=0) - x = x.view(-1, channels, x.shape[2] * self.scale_factor, - x.shape[4] * self.scale_factor) - x = x.view(-1, 1, x.shape[2], x.shape[3]) - x = F.pad(x, self.padding, mode='constant', value=0) - x = F.conv2d(x, self.kernel, stride=1) - x = x.view(-1, channels, x.shape[2], x.shape[3]) - return x - - -class InputBlock(nn.Module): - """Implements the input block. - - Basically, this block starts from a const input, which is with shape - `(channels, init_resolution, init_resolution)`. - """ - - def __init__(self, init_resolution, channels): - super().__init__() - self.const = nn.Parameter( - torch.randn(1, channels, init_resolution, init_resolution)) - - def forward(self, w): - x = self.const.repeat(w.shape[0], 1, 1, 1) - return x - - -class ConvBlock(nn.Module): - """Implements the convolutional block (no style modulation). - - Basically, this block executes, convolutional layer, filtering layer (if - needed), and activation layer in sequence. - - NOTE: This block is particularly used for skip-connection branch in the - `resnet` structure. - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size=3, - add_bias=True, - scale_factor=1, - filtering_kernel=(1, 3, 3, 1), - use_wscale=True, - wscale_gain=_WSCALE_GAIN, - lr_mul=1.0, - activation_type='lrelu'): - """Initializes with block settings. - - Args: - in_channels: Number of channels of the input tensor. - out_channels: Number of channels of the output tensor. - kernel_size: Size of the convolutional kernels. (default: 3) - add_bias: Whether to add bias onto the convolutional result. - (default: True) - scale_factor: Scale factor for upsampling. `1` means skip - upsampling. (default: 1) - filtering_kernel: Kernel used for filtering after upsampling. - (default: (1, 3, 3, 1)) - use_wscale: Whether to use weight scaling. (default: True) - wscale_gain: Gain factor for weight scaling. (default: _WSCALE_GAIN) - lr_mul: Learning multiplier for both weight and bias. (default: 1.0) - activation_type: Type of activation. Support `linear` and `lrelu`. - (default: `lrelu`) - - Raises: - NotImplementedError: If the `activation_type` is not supported. - """ - super().__init__() - - if scale_factor > 1: - self.use_conv2d_transpose = True - extra_padding = scale_factor - kernel_size - self.filter = UpsamplingLayer(scale_factor=1, - kernel=filtering_kernel, - extra_padding=extra_padding, - kernel_gain=scale_factor) - self.stride = scale_factor - self.padding = 0 # Padding is done in `UpsamplingLayer`. - else: - self.use_conv2d_transpose = False - assert kernel_size % 2 == 1 - self.stride = 1 - self.padding = kernel_size // 2 - - weight_shape = (out_channels, in_channels, kernel_size, kernel_size) - fan_in = kernel_size * kernel_size * in_channels - wscale = wscale_gain / np.sqrt(fan_in) - if use_wscale: - self.weight = nn.Parameter(torch.randn(*weight_shape) / lr_mul) - self.wscale = wscale * lr_mul - else: - self.weight = nn.Parameter( - torch.randn(*weight_shape) * wscale / lr_mul) - self.wscale = lr_mul - - if add_bias: - self.bias = nn.Parameter(torch.zeros(out_channels)) - else: - self.bias = None - self.bscale = lr_mul - - if activation_type == 'linear': - self.activate = nn.Identity() - self.activate_scale = 1.0 - elif activation_type == 'lrelu': - self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True) - self.activate_scale = np.sqrt(2.0) - else: - raise NotImplementedError(f'Not implemented activation function: ' - f'`{activation_type}`!') - - def forward(self, x): - weight = self.weight * self.wscale - bias = self.bias * self.bscale if self.bias is not None else None - if self.use_conv2d_transpose: - weight = weight.permute(1, 0, 2, 3).flip(2, 3) - x = F.conv_transpose2d(x, - weight=weight, - bias=bias, - stride=self.scale_factor, - padding=self.padding) - x = self.filter(x) - else: - x = F.conv2d(x, - weight=weight, - bias=bias, - stride=self.stride, - padding=self.padding) - x = self.activate(x) * self.activate_scale - return x - - -class ModulateConvBlock(nn.Module): - """Implements the convolutional block with style modulation.""" - - def __init__(self, - in_channels, - out_channels, - resolution, - w_space_dim, - kernel_size=3, - add_bias=True, - scale_factor=1, - filtering_kernel=(1, 3, 3, 1), - fused_modulate=True, - demodulate=True, - use_wscale=True, - wscale_gain=_WSCALE_GAIN, - lr_mul=1.0, - add_noise=True, - noise_type='spatial', - activation_type='lrelu', - epsilon=1e-8): - """Initializes with block settings. - - Args: - in_channels: Number of channels of the input tensor. - out_channels: Number of channels of the output tensor. - resolution: Resolution of the output tensor. - w_space_dim: Dimension of W space for style modulation. - kernel_size: Size of the convolutional kernels. (default: 3) - add_bias: Whether to add bias onto the convolutional result. - (default: True) - scale_factor: Scale factor for upsampling. `1` means skip - upsampling. (default: 1) - filtering_kernel: Kernel used for filtering after upsampling. - (default: (1, 3, 3, 1)) - fused_modulate: Whether to fuse `style_modulate` and `conv2d` - together. (default: True) - demodulate: Whether to perform style demodulation. (default: True) - use_wscale: Whether to use weight scaling. (default: True) - wscale_gain: Gain factor for weight scaling. (default: _WSCALE_GAIN) - lr_mul: Learning multiplier for both weight and bias. (default: 1.0) - add_noise: Whether to add noise onto the output tensor. (default: - True) - noise_type: Type of noise added to the feature map after the - convolution (if needed). Support `spatial` and `channel`. - (default: `spatial`) - activation_type: Type of activation. Support `linear` and `lrelu`. - (default: `lrelu`) - epsilon: Small number to avoid `divide by zero`. (default: 1e-8) - - Raises: - NotImplementedError: If the `activation_type` is not supported. - """ - super().__init__() - - self.in_c = in_channels - self.out_c = out_channels - self.res = resolution - self.w_space_dim = w_space_dim - self.ksize = kernel_size - self.eps = epsilon - self.space_of_latent = 'w' - - if scale_factor > 1: - self.use_conv2d_transpose = True - extra_padding = scale_factor - kernel_size - self.filter = UpsamplingLayer(scale_factor=1, - kernel=filtering_kernel, - extra_padding=extra_padding, - kernel_gain=scale_factor) - self.stride = scale_factor - self.padding = 0 # Padding is done in `UpsamplingLayer`. - else: - self.use_conv2d_transpose = False - assert kernel_size % 2 == 1 - self.stride = 1 - self.padding = kernel_size // 2 - - weight_shape = (out_channels, in_channels, kernel_size, kernel_size) - fan_in = kernel_size * kernel_size * in_channels - wscale = wscale_gain / np.sqrt(fan_in) - if use_wscale: - self.weight = nn.Parameter(torch.randn(*weight_shape) / lr_mul) - self.wscale = wscale * lr_mul - else: - self.weight = nn.Parameter( - torch.randn(*weight_shape) * wscale / lr_mul) - self.wscale = lr_mul - - self.style = DenseBlock(in_channels=w_space_dim, - out_channels=in_channels, - additional_bias=1.0, - use_wscale=use_wscale, - activation_type='linear') - - self.fused_modulate = fused_modulate - self.demodulate = demodulate - - if add_bias: - self.bias = nn.Parameter(torch.zeros(out_channels)) - else: - self.bias = None - self.bscale = lr_mul - - if activation_type == 'linear': - self.activate = nn.Identity() - self.activate_scale = 1.0 - elif activation_type == 'lrelu': - self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True) - self.activate_scale = np.sqrt(2.0) - else: - raise NotImplementedError(f'Not implemented activation function: ' - f'`{activation_type}`!') - - self.add_noise = add_noise - if self.add_noise: - self.noise_type = noise_type.lower() - if self.noise_type == 'spatial': - self.register_buffer('noise', - torch.randn(1, 1, self.res, self.res)) - elif self.noise_type == 'channel': - self.register_buffer('noise', - torch.randn(1, self.channels, 1, 1)) - else: - raise NotImplementedError(f'Not implemented noise type: ' - f'`{self.noise_type}`!') - self.noise_strength = nn.Parameter(torch.zeros(())) - - def forward_style(self, w): - """Gets style code from the given input. - - More specifically, if the input is from W-Space, it will be projected by - an affine transformation. If it is from the Style Space (Y-Space), no - operation is required. - - NOTE: For codes from Y-Space, we use slicing to make sure the dimension - is correct, in case that the code is padded before fed into this layer. - """ - if self.space_of_latent == 'w': - if w.ndim != 2 or w.shape[1] != self.w_space_dim: - raise ValueError(f'The input tensor should be with shape ' - f'[batch_size, w_space_dim], where ' - f'`w_space_dim` equals to ' - f'{self.w_space_dim}!\n' - f'But `{w.shape}` is received!') - style = self.style(w) - elif self.space_of_latent == 'y': - if w.ndim != 2 or w.shape[1] < self.in_c: - raise ValueError(f'The input tensor should be with shape ' - f'[batch_size, y_space_dim], where ' - f'`y_space_dim` equals to {self.in_c}!\n' - f'But `{w.shape}` is received!') - style = w[:, :self.in_c] - return style - - def forward(self, x, w, randomize_noise=False): - batch = x.shape[0] - - weight = self.weight * self.wscale - weight = weight.permute(2, 3, 1, 0) - - # Style modulation. - style = self.forward_style(w) - _weight = weight.view(1, self.ksize, self.ksize, self.in_c, self.out_c) - _weight = _weight * style.view(batch, 1, 1, self.in_c, 1) - - # Style demodulation. - if self.demodulate: - _weight_norm = torch.sqrt( - torch.sum(_weight ** 2, dim=[1, 2, 3]) + self.eps) - _weight = _weight / _weight_norm.view(batch, 1, 1, 1, self.out_c) - - if self.fused_modulate: - x = x.view(1, batch * self.in_c, x.shape[2], x.shape[3]) - weight = _weight.permute(1, 2, 3, 0, 4).reshape( - self.ksize, self.ksize, self.in_c, batch * self.out_c) - else: - x = x * style.view(batch, self.in_c, 1, 1) - - if self.use_conv2d_transpose: - weight = weight.flip(0, 1) - if self.fused_modulate: - weight = weight.view( - self.ksize, self.ksize, self.in_c, batch, self.out_c) - weight = weight.permute(0, 1, 4, 3, 2) - weight = weight.reshape( - self.ksize, self.ksize, self.out_c, batch * self.in_c) - weight = weight.permute(3, 2, 0, 1) - else: - weight = weight.permute(2, 3, 0, 1) - x = F.conv_transpose2d(x, - weight=weight, - bias=None, - stride=self.stride, - padding=self.padding, - groups=(batch if self.fused_modulate else 1)) - x = self.filter(x) - else: - weight = weight.permute(3, 2, 0, 1) - x = F.conv2d(x, - weight=weight, - bias=None, - stride=self.stride, - padding=self.padding, - groups=(batch if self.fused_modulate else 1)) - - if self.fused_modulate: - x = x.view(batch, self.out_c, self.res, self.res) - elif self.demodulate: - x = x / _weight_norm.view(batch, self.out_c, 1, 1) - - if self.add_noise: - if randomize_noise: - if self.noise_type == 'spatial': - noise = torch.randn(x.shape[0], 1, self.res, self.res).to(x) - elif self.noise_type == 'channel': - noise = torch.randn(x.shape[0], self.channels, 1, 1).to(x) - else: - noise = self.noise - x = x + noise * self.noise_strength.view(1, 1, 1, 1) - - bias = self.bias * self.bscale if self.bias is not None else None - if bias is not None: - x = x + bias.view(1, -1, 1, 1) - x = self.activate(x) * self.activate_scale - return x, style - - -class DenseBlock(nn.Module): - """Implements the dense block. - - Basically, this block executes fully-connected layer and activation layer. - - NOTE: This layer supports adding an additional bias beyond the trainable - bias parameter. This is specially used for the mapping from the w code to - the style code. - """ - - def __init__(self, - in_channels, - out_channels, - add_bias=True, - additional_bias=0, - use_wscale=True, - wscale_gain=_WSCALE_GAIN, - lr_mul=1.0, - activation_type='lrelu'): - """Initializes with block settings. - - Args: - in_channels: Number of channels of the input tensor. - out_channels: Number of channels of the output tensor. - add_bias: Whether to add bias onto the fully-connected result. - (default: True) - additional_bias: The additional bias, which is independent from the - bias parameter. (default: 0.0) - use_wscale: Whether to use weight scaling. (default: True) - wscale_gain: Gain factor for weight scaling. (default: _WSCALE_GAIN) - lr_mul: Learning multiplier for both weight and bias. (default: 1.0) - activation_type: Type of activation. Support `linear` and `lrelu`. - (default: `lrelu`) - - Raises: - NotImplementedError: If the `activation_type` is not supported. - """ - super().__init__() - weight_shape = (out_channels, in_channels) - wscale = wscale_gain / np.sqrt(in_channels) - if use_wscale: - self.weight = nn.Parameter(torch.randn(*weight_shape) / lr_mul) - self.wscale = wscale * lr_mul - else: - self.weight = nn.Parameter( - torch.randn(*weight_shape) * wscale / lr_mul) - self.wscale = lr_mul - - if add_bias: - self.bias = nn.Parameter(torch.zeros(out_channels)) - else: - self.bias = None - self.bscale = lr_mul - self.additional_bias = additional_bias - - if activation_type == 'linear': - self.activate = nn.Identity() - self.activate_scale = 1.0 - elif activation_type == 'lrelu': - self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True) - self.activate_scale = np.sqrt(2.0) - else: - raise NotImplementedError(f'Not implemented activation function: ' - f'`{activation_type}`!') - - def forward(self, x): - if x.ndim != 2: - x = x.view(x.shape[0], -1) - bias = self.bias * self.bscale if self.bias is not None else None - x = F.linear(x, weight=self.weight * self.wscale, bias=bias) - x = self.activate(x + self.additional_bias) * self.activate_scale - return x diff --git a/spaces/jbilcke-hf/LifeSim/src/components/ui/avatar.tsx b/spaces/jbilcke-hf/LifeSim/src/components/ui/avatar.tsx deleted file mode 100644 index 88aeea9d9368f2bd7385f0a0885829bf6d789492..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/LifeSim/src/components/ui/avatar.tsx +++ /dev/null @@ -1,50 +0,0 @@ -"use client" - -import * as React from "react" -import * as AvatarPrimitive from "@radix-ui/react-avatar" - -import { cn } from "@/lib/utils" - -const Avatar = React.forwardRef< - React.ElementRef , - React.ComponentPropsWithoutRef ->(({ className, ...props }, ref) => ( - -)) -Avatar.displayName = AvatarPrimitive.Root.displayName - -const AvatarImage = React.forwardRef< - React.ElementRef , - React.ComponentPropsWithoutRef ->(({ className, ...props }, ref) => ( - -)) -AvatarImage.displayName = AvatarPrimitive.Image.displayName - -const AvatarFallback = React.forwardRef< - React.ElementRef , - React.ComponentPropsWithoutRef ->(({ className, ...props }, ref) => ( - -)) -AvatarFallback.displayName = AvatarPrimitive.Fallback.displayName - -export { Avatar, AvatarImage, AvatarFallback } diff --git a/spaces/jbilcke-hf/ai-comic-factory/src/lib/loadImageToCanvas.ts b/spaces/jbilcke-hf/ai-comic-factory/src/lib/loadImageToCanvas.ts deleted file mode 100644 index 02068927ce6e615d4dac2aed31e75f9f51697f27..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/ai-comic-factory/src/lib/loadImageToCanvas.ts +++ /dev/null @@ -1,28 +0,0 @@ -export async function loadImageToCanvas(imageBase64: string): Promise { - return new Promise((resolve, reject) => { - // create a new image object - let img = new Image(); - // specify a function to run when the image is fully loaded - img.onload = () => { - // create a canvas element - let canvas = document.createElement('canvas'); - canvas.width = img.width; - canvas.height = img.height; - // get the context of the canvas - let ctx = canvas.getContext('2d'); - if (ctx) { - // draw the image into the canvas - ctx.drawImage(img, 0, 0); - // resolve the promise with the canvas - resolve(canvas); - } else { - reject('Error creating the context of canvas'); - } - }; - // specify a function to run when the image could not be loaded - img.onerror = () => { - reject('Image could not be loaded'); - }; - img.src = imageBase64; // must be a data;image/.... prefixed URL string - }); -} \ No newline at end of file diff --git a/spaces/jcenaa/Segment-Any-RGBD/datasets/scannet_preprocess/prepare_2d_data/prepare_2d_data.py b/spaces/jcenaa/Segment-Any-RGBD/datasets/scannet_preprocess/prepare_2d_data/prepare_2d_data.py deleted file mode 100644 index 9bc37236cefe0201d89be8eb8feb9d6369505446..0000000000000000000000000000000000000000 --- a/spaces/jcenaa/Segment-Any-RGBD/datasets/scannet_preprocess/prepare_2d_data/prepare_2d_data.py +++ /dev/null @@ -1,123 +0,0 @@ -# pre-process ScanNet 2D data -# note: depends on the sens file reader from ScanNet: -# https://github.com/ScanNet/ScanNet/blob/master/SensReader/python/SensorData.py -# if export_label_images flag is on: -# - depends on https://github.com/ScanNet/ScanNet/tree/master/BenchmarkScripts/util.py -# - also assumes that label images are unzipped as scene*/label*/*.png -# expected file structure: -# - prepare_2d_data.py -# - https://github.com/ScanNet/ScanNet/tree/master/BenchmarkScripts/util.py -# - https://github.com/ScanNet/ScanNet/blob/master/SensReader/python/SensorData.py -# -# example usage: -# python prepare_2d_data.py --scannet_path data/scannetv2 --output_path data/scannetv2_images --export_label_images - -import argparse -import os, sys -import numpy as np -import skimage.transform as sktf -import imageio -from SensorData import SensorData -import util -# try: -# from prepare_2d_data.SensorData import SensorData -# except: -# print('Failed to import SensorData (from ScanNet code toolbox)') -# sys.exit(-1) -# try: -# from prepare_2d_data import util -# except: -# print('Failed to import ScanNet code toolbox util') -# sys.exit(-1) - -# params -parser = argparse.ArgumentParser() -parser.add_argument('--scannet_path', required=True, help='path to scannet data') -parser.add_argument('--output_path', required=True, help='where to output 2d data') -parser.add_argument('--export_label_images', dest='export_label_images', action='store_true') -parser.add_argument('--label_type', default='label-filt', help='which labels (label or label-filt)') -parser.add_argument('--frame_skip', type=int, default=20, help='export every nth frame') -parser.add_argument('--label_map_file', default='scannet-preprocess/meta_data/scannetv2-labels.combined.tsv', - help='path to scannetv2-labels.combined.tsv (required for label export only)') -parser.add_argument('--output_image_width', type=int, default=640, help='export image width') -parser.add_argument('--output_image_height', type=int, default=480, help='export image height') - -parser.set_defaults(export_label_images=False) -opt = parser.parse_args() -if opt.export_label_images: - assert opt.label_map_file != '' -print(opt) - - -def print_error(message): - sys.stderr.write('ERROR: ' + str(message) + '\n') - sys.exit(-1) - - -# from https://github.com/ScanNet/ScanNet/tree/master/BenchmarkScripts/2d_helpers/convert_scannet_label_image.py -def map_label_image(image, label_mapping): - mapped = np.copy(image) - for k, v in label_mapping.iteritems(): - mapped[image == k] = v - return mapped.astype(np.uint8) - - -def main(): - if not os.path.exists(opt.output_path): - os.makedirs(opt.output_path) - - label_mapping = None - if opt.export_label_images: - label_map = util.read_label_mapping(opt.label_map_file, label_from='id', label_to='nyu40id') - - scenes = [d for d in os.listdir(opt.scannet_path) if os.path.isdir(os.path.join(opt.scannet_path, d))] - print('Found %d scenes' % len(scenes)) - for i in range(0,len(scenes)): - if scenes[i] != 'scene0000_00': continue - sens_file = os.path.join(opt.scannet_path, scenes[i], scenes[i] + '.sens') - label_path = os.path.join(opt.scannet_path, scenes[i], opt.label_type) - if opt.export_label_images and not os.path.isdir(label_path): - print_error('Error: using export_label_images option but label path %s does not exist' % label_path) - output_color_path = os.path.join(opt.output_path, scenes[i], 'color') - if not os.path.isdir(output_color_path): - os.makedirs(output_color_path) - output_depth_path = os.path.join(opt.output_path, scenes[i], 'depth') - if not os.path.isdir(output_depth_path): - os.makedirs(output_depth_path) - output_pose_path = os.path.join(opt.output_path, scenes[i], 'pose') - if not os.path.isdir(output_pose_path): - os.makedirs(output_pose_path) - output_label_path = os.path.join(opt.output_path, scenes[i], 'label') - if opt.export_label_images and not os.path.isdir(output_label_path): - os.makedirs(output_label_path) - output_intrinsics_path = os.path.join(opt.output_path, scenes[i], 'intrinsics') - if opt.export_label_images and not os.path.isdir(output_label_path): - os.makedirs(output_label_path) - - # read and export - sys.stdout.write('\r[ %d | %d ] %s\tloading...' % ((i + 1), len(scenes), scenes[i])) - sys.stdout.flush() - sd = SensorData(sens_file) - sys.stdout.write('\r[ %d | %d ] %s\texporting...' % ((i + 1), len(scenes), scenes[i])) - sys.stdout.flush() - sd.export_color_images(output_color_path, image_size=[opt.output_image_height, opt.output_image_width], - frame_skip=opt.frame_skip) - sd.export_depth_images(output_depth_path, image_size=[opt.output_image_height, opt.output_image_width], - frame_skip=opt.frame_skip) - sd.export_poses(output_pose_path, frame_skip=opt.frame_skip) - sd.export_intrinsics(output_intrinsics_path) - - if opt.export_label_images: - - for f in range(0, len(sd.frames), opt.frame_skip): - label_file = os.path.join(label_path, str(f) + '.png') - image = np.array(imageio.imread(label_file)) - image = sktf.resize(image, [opt.output_image_height, opt.output_image_width], order=0, - preserve_range=True) - mapped_image = map_label_image(image, label_map) - imageio.imwrite(os.path.join(output_label_path, str(f) + '.png'), mapped_image) - print('') - - -if __name__ == '__main__': - main() diff --git a/spaces/jitesh/storytelling/src/test.py b/spaces/jitesh/storytelling/src/test.py deleted file mode 100644 index 41e582e9bdff0775d850e5757e0b9767e10171d5..0000000000000000000000000000000000000000 --- a/spaces/jitesh/storytelling/src/test.py +++ /dev/null @@ -1,72 +0,0 @@ -# %% -import pandas as pd -import numpy as np - -np.random.seed(24) -df = pd.DataFrame({'A': np.linspace(1, 10, 10)}) - -df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4), columns=list('BCDE'))], - axis=1) -df.iloc[0, 2] = np.nan -df['reaction_show'] = True -df -# %% -s = '' -for v in df.reaction_show: - s += str(int(v)) -s - - -# %% -s = '101100' -g2p = '' -for i in range(len(s)-1): - # print(i, i+2, s) - # print(s[0:2]) - g2p += '1' if '1' in s[i:i+2] else '0' - -g2p - -# %% -# import plotly.express as px -from plotly.offline import init_notebook_mode, iplot -import numpy as np -init_notebook_mode() - -x = np.linspace(0, 1) -iplot([{'x': x, 'y': 1-np.exp(-x)}]) -# # def highlight_greaterthan(s,column): -# # is_max = pd.Series(data=False, index=s.index) -# # is_max[column] = s.loc[column] >= 1 -# # return ['background-color: red' if is_max.any() else '' for v in is_max] - -# def highlight_greaterthan_1(s): -# if s.B > 1.0: -# return ['background-color: white']+['background-color: yellow']+['background-color: white']*3 -# else: -# return ['background-color: white']*5 - - -# df.style.apply(highlight_greaterthan_1, axis=1) -# %% -from transformers import pipeline -classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True) -emotion = classifier("the sentence") -# %% -emotion[0] -# %% -emotion[0][0] -# %% -sorted(emotion[0], key=lambda x: x['score'], reverse=True) - -# %% -import random -values = [1,2, 3, 4, 5, 6] -k = random.randint(0,len(values)) -numbers = random.choices(values, k=k) -print(k, "random numbers", numbers) -# %% -k = random.randint(0,len(values)) -numbers = random.sample(values, k=k) -print(k, "random numbers", numbers) -# %% diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/misc/symfont.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/misc/symfont.py deleted file mode 100644 index 0bd69a386ec9f01c8951f0dfc8bc8c261718cf1f..0000000000000000000000000000000000000000 --- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/misc/symfont.py +++ /dev/null @@ -1,251 +0,0 @@ -from fontTools.pens.basePen import BasePen -from functools import partial -from itertools import count -import sympy as sp -import sys - -n = 3 # Max Bezier degree; 3 for cubic, 2 for quadratic - -t, x, y = sp.symbols("t x y", real=True) -c = sp.symbols("c", real=False) # Complex representation instead of x/y - -X = tuple(sp.symbols("x:%d" % (n + 1), real=True)) -Y = tuple(sp.symbols("y:%d" % (n + 1), real=True)) -P = tuple(zip(*(sp.symbols("p:%d[%s]" % (n + 1, w), real=True) for w in "01"))) -C = tuple(sp.symbols("c:%d" % (n + 1), real=False)) - -# Cubic Bernstein basis functions -BinomialCoefficient = [(1, 0)] -for i in range(1, n + 1): - last = BinomialCoefficient[-1] - this = tuple(last[j - 1] + last[j] for j in range(len(last))) + (0,) - BinomialCoefficient.append(this) -BinomialCoefficient = tuple(tuple(item[:-1]) for item in BinomialCoefficient) -del last, this - -BernsteinPolynomial = tuple( - tuple(c * t**i * (1 - t) ** (n - i) for i, c in enumerate(coeffs)) - for n, coeffs in enumerate(BinomialCoefficient) -) - -BezierCurve = tuple( - tuple( - sum(P[i][j] * bernstein for i, bernstein in enumerate(bernsteins)) - for j in range(2) - ) - for n, bernsteins in enumerate(BernsteinPolynomial) -) -BezierCurveC = tuple( - sum(C[i] * bernstein for i, bernstein in enumerate(bernsteins)) - for n, bernsteins in enumerate(BernsteinPolynomial) -) - - -def green(f, curveXY): - f = -sp.integrate(sp.sympify(f), y) - f = f.subs({x: curveXY[0], y: curveXY[1]}) - f = sp.integrate(f * sp.diff(curveXY[0], t), (t, 0, 1)) - return f - - -class _BezierFuncsLazy(dict): - def __init__(self, symfunc): - self._symfunc = symfunc - self._bezfuncs = {} - - def __missing__(self, i): - args = ["p%d" % d for d in range(i + 1)] - f = green(self._symfunc, BezierCurve[i]) - f = sp.gcd_terms(f.collect(sum(P, ()))) # Optimize - return sp.lambdify(args, f) - - -class GreenPen(BasePen): - - _BezierFuncs = {} - - @classmethod - def _getGreenBezierFuncs(celf, func): - funcstr = str(func) - if not funcstr in celf._BezierFuncs: - celf._BezierFuncs[funcstr] = _BezierFuncsLazy(func) - return celf._BezierFuncs[funcstr] - - def __init__(self, func, glyphset=None): - BasePen.__init__(self, glyphset) - self._funcs = self._getGreenBezierFuncs(func) - self.value = 0 - - def _moveTo(self, p0): - self.__startPoint = p0 - - def _closePath(self): - p0 = self._getCurrentPoint() - if p0 != self.__startPoint: - self._lineTo(self.__startPoint) - - def _endPath(self): - p0 = self._getCurrentPoint() - if p0 != self.__startPoint: - # Green theorem is not defined on open contours. - raise NotImplementedError - - def _lineTo(self, p1): - p0 = self._getCurrentPoint() - self.value += self._funcs[1](p0, p1) - - def _qCurveToOne(self, p1, p2): - p0 = self._getCurrentPoint() - self.value += self._funcs[2](p0, p1, p2) - - def _curveToOne(self, p1, p2, p3): - p0 = self._getCurrentPoint() - self.value += self._funcs[3](p0, p1, p2, p3) - - -# Sample pens. -# Do not use this in real code. -# Use fontTools.pens.momentsPen.MomentsPen instead. -AreaPen = partial(GreenPen, func=1) -MomentXPen = partial(GreenPen, func=x) -MomentYPen = partial(GreenPen, func=y) -MomentXXPen = partial(GreenPen, func=x * x) -MomentYYPen = partial(GreenPen, func=y * y) -MomentXYPen = partial(GreenPen, func=x * y) - - -def printGreenPen(penName, funcs, file=sys.stdout, docstring=None): - - if docstring is not None: - print('"""%s"""' % docstring) - - print( - """from fontTools.pens.basePen import BasePen, OpenContourError -try: - import cython - - COMPILED = cython.compiled -except (AttributeError, ImportError): - # if cython not installed, use mock module with no-op decorators and types - from fontTools.misc import cython - - COMPILED = False - - -__all__ = ["%s"] - -class %s(BasePen): - - def __init__(self, glyphset=None): - BasePen.__init__(self, glyphset) -""" - % (penName, penName), - file=file, - ) - for name, f in funcs: - print(" self.%s = 0" % name, file=file) - print( - """ - def _moveTo(self, p0): - self.__startPoint = p0 - - def _closePath(self): - p0 = self._getCurrentPoint() - if p0 != self.__startPoint: - self._lineTo(self.__startPoint) - - def _endPath(self): - p0 = self._getCurrentPoint() - if p0 != self.__startPoint: - # Green theorem is not defined on open contours. - raise OpenContourError( - "Green theorem is not defined on open contours." - ) -""", - end="", - file=file, - ) - - for n in (1, 2, 3): - - subs = {P[i][j]: [X, Y][j][i] for i in range(n + 1) for j in range(2)} - greens = [green(f, BezierCurve[n]) for name, f in funcs] - greens = [sp.gcd_terms(f.collect(sum(P, ()))) for f in greens] # Optimize - greens = [f.subs(subs) for f in greens] # Convert to p to x/y - defs, exprs = sp.cse( - greens, - optimizations="basic", - symbols=(sp.Symbol("r%d" % i) for i in count()), - ) - - print() - for name, value in defs: - print(" @cython.locals(%s=cython.double)" % name, file=file) - if n == 1: - print( - """\ - @cython.locals(x0=cython.double, y0=cython.double) - @cython.locals(x1=cython.double, y1=cython.double) - def _lineTo(self, p1): - x0,y0 = self._getCurrentPoint() - x1,y1 = p1 -""", - file=file, - ) - elif n == 2: - print( - """\ - @cython.locals(x0=cython.double, y0=cython.double) - @cython.locals(x1=cython.double, y1=cython.double) - @cython.locals(x2=cython.double, y2=cython.double) - def _qCurveToOne(self, p1, p2): - x0,y0 = self._getCurrentPoint() - x1,y1 = p1 - x2,y2 = p2 -""", - file=file, - ) - elif n == 3: - print( - """\ - @cython.locals(x0=cython.double, y0=cython.double) - @cython.locals(x1=cython.double, y1=cython.double) - @cython.locals(x2=cython.double, y2=cython.double) - @cython.locals(x3=cython.double, y3=cython.double) - def _curveToOne(self, p1, p2, p3): - x0,y0 = self._getCurrentPoint() - x1,y1 = p1 - x2,y2 = p2 - x3,y3 = p3 -""", - file=file, - ) - for name, value in defs: - print(" %s = %s" % (name, value), file=file) - - print(file=file) - for name, value in zip([f[0] for f in funcs], exprs): - print(" self.%s += %s" % (name, value), file=file) - - print( - """ -if __name__ == '__main__': - from fontTools.misc.symfont import x, y, printGreenPen - printGreenPen('%s', [""" - % penName, - file=file, - ) - for name, f in funcs: - print(" ('%s', %s)," % (name, str(f)), file=file) - print(" ])", file=file) - - -if __name__ == "__main__": - pen = AreaPen() - pen.moveTo((100, 100)) - pen.lineTo((100, 200)) - pen.lineTo((200, 200)) - pen.curveTo((200, 250), (300, 300), (250, 350)) - pen.lineTo((200, 100)) - pen.closePath() - print(pen.value) diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/qu2cu/qu2cu.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/qu2cu/qu2cu.py deleted file mode 100644 index 97a665f63adf88681328a69c5c0a3c6814bf3719..0000000000000000000000000000000000000000 --- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/qu2cu/qu2cu.py +++ /dev/null @@ -1,408 +0,0 @@ -# cython: language_level=3 -# distutils: define_macros=CYTHON_TRACE_NOGIL=1 - -# Copyright 2023 Google Inc. All Rights Reserved. -# Copyright 2023 Behdad Esfahbod. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -try: - import cython - - COMPILED = cython.compiled -except (AttributeError, ImportError): - # if cython not installed, use mock module with no-op decorators and types - from fontTools.misc import cython - - COMPILED = False - -from fontTools.misc.bezierTools import splitCubicAtTC -from collections import namedtuple -import math -from typing import ( - List, - Tuple, - Union, -) - - -__all__ = ["quadratic_to_curves"] - - -# Copied from cu2qu -@cython.cfunc -@cython.returns(cython.int) -@cython.locals( - tolerance=cython.double, - p0=cython.complex, - p1=cython.complex, - p2=cython.complex, - p3=cython.complex, -) -@cython.locals(mid=cython.complex, deriv3=cython.complex) -def cubic_farthest_fit_inside(p0, p1, p2, p3, tolerance): - """Check if a cubic Bezier lies within a given distance of the origin. - - "Origin" means *the* origin (0,0), not the start of the curve. Note that no - checks are made on the start and end positions of the curve; this function - only checks the inside of the curve. - - Args: - p0 (complex): Start point of curve. - p1 (complex): First handle of curve. - p2 (complex): Second handle of curve. - p3 (complex): End point of curve. - tolerance (double): Distance from origin. - - Returns: - bool: True if the cubic Bezier ``p`` entirely lies within a distance - ``tolerance`` of the origin, False otherwise. - """ - # First check p2 then p1, as p2 has higher error early on. - if abs(p2) <= tolerance and abs(p1) <= tolerance: - return True - - # Split. - mid = (p0 + 3 * (p1 + p2) + p3) * 0.125 - if abs(mid) > tolerance: - return False - deriv3 = (p3 + p2 - p1 - p0) * 0.125 - return cubic_farthest_fit_inside( - p0, (p0 + p1) * 0.5, mid - deriv3, mid, tolerance - ) and cubic_farthest_fit_inside(mid, mid + deriv3, (p2 + p3) * 0.5, p3, tolerance) - - -@cython.locals( - p0=cython.complex, - p1=cython.complex, - p2=cython.complex, - p1_2_3=cython.complex, -) -def elevate_quadratic(p0, p1, p2): - """Given a quadratic bezier curve, return its degree-elevated cubic.""" - - # https://pomax.github.io/bezierinfo/#reordering - p1_2_3 = p1 * (2 / 3) - return ( - p0, - (p0 * (1 / 3) + p1_2_3), - (p2 * (1 / 3) + p1_2_3), - p2, - ) - - -@cython.cfunc -@cython.locals( - start=cython.int, - n=cython.int, - k=cython.int, - prod_ratio=cython.double, - sum_ratio=cython.double, - ratio=cython.double, - t=cython.double, - p0=cython.complex, - p1=cython.complex, - p2=cython.complex, - p3=cython.complex, -) -def merge_curves(curves, start, n): - """Give a cubic-Bezier spline, reconstruct one cubic-Bezier - that has the same endpoints and tangents and approxmates - the spline.""" - - # Reconstruct the t values of the cut segments - prod_ratio = 1.0 - sum_ratio = 1.0 - ts = [1] - for k in range(1, n): - ck = curves[start + k] - c_before = curves[start + k - 1] - - # |t_(k+1) - t_k| / |t_k - t_(k - 1)| = ratio - assert ck[0] == c_before[3] - ratio = abs(ck[1] - ck[0]) / abs(c_before[3] - c_before[2]) - - prod_ratio *= ratio - sum_ratio += prod_ratio - ts.append(sum_ratio) - - # (t(n) - t(n - 1)) / (t_(1) - t(0)) = prod_ratio - - ts = [t / sum_ratio for t in ts[:-1]] - - p0 = curves[start][0] - p1 = curves[start][1] - p2 = curves[start + n - 1][2] - p3 = curves[start + n - 1][3] - - # Build the curve by scaling the control-points. - p1 = p0 + (p1 - p0) / (ts[0] if ts else 1) - p2 = p3 + (p2 - p3) / ((1 - ts[-1]) if ts else 1) - - curve = (p0, p1, p2, p3) - - return curve, ts - - -@cython.locals( - count=cython.int, - num_offcurves=cython.int, - i=cython.int, - off1=cython.complex, - off2=cython.complex, - on=cython.complex, -) -def add_implicit_on_curves(p): - q = list(p) - count = 0 - num_offcurves = len(p) - 2 - for i in range(1, num_offcurves): - off1 = p[i] - off2 = p[i + 1] - on = off1 + (off2 - off1) * 0.5 - q.insert(i + 1 + count, on) - count += 1 - return q - - -Point = Union[Tuple[float, float], complex] - - -@cython.locals( - cost=cython.int, - is_complex=cython.int, -) -def quadratic_to_curves( - quads: List[List[Point]], - max_err: float = 0.5, - all_cubic: bool = False, -) -> List[Tuple[Point, ...]]: - """Converts a connecting list of quadratic splines to a list of quadratic - and cubic curves. - - A quadratic spline is specified as a list of points. Either each point is - a 2-tuple of X,Y coordinates, or each point is a complex number with - real/imaginary components representing X,Y coordinates. - - The first and last points are on-curve points and the rest are off-curve - points, with an implied on-curve point in the middle between every two - consequtive off-curve points. - - Returns: - The output is a list of tuples of points. Points are represented - in the same format as the input, either as 2-tuples or complex numbers. - - Each tuple is either of length three, for a quadratic curve, or four, - for a cubic curve. Each curve's last point is the same as the next - curve's first point. - - Args: - quads: quadratic splines - - max_err: absolute error tolerance; defaults to 0.5 - - all_cubic: if True, only cubic curves are generated; defaults to False - """ - is_complex = type(quads[0][0]) is complex - if not is_complex: - quads = [[complex(x, y) for (x, y) in p] for p in quads] - - q = [quads[0][0]] - costs = [1] - cost = 1 - for p in quads: - assert q[-1] == p[0] - for i in range(len(p) - 2): - cost += 1 - costs.append(cost) - costs.append(cost) - qq = add_implicit_on_curves(p)[1:] - costs.pop() - q.extend(qq) - cost += 1 - costs.append(cost) - - curves = spline_to_curves(q, costs, max_err, all_cubic) - - if not is_complex: - curves = [tuple((c.real, c.imag) for c in curve) for curve in curves] - return curves - - -Solution = namedtuple("Solution", ["num_points", "error", "start_index", "is_cubic"]) - - -@cython.locals( - i=cython.int, - j=cython.int, - k=cython.int, - start=cython.int, - i_sol_count=cython.int, - j_sol_count=cython.int, - this_sol_count=cython.int, - tolerance=cython.double, - err=cython.double, - error=cython.double, - i_sol_error=cython.double, - j_sol_error=cython.double, - all_cubic=cython.int, - is_cubic=cython.int, - count=cython.int, - p0=cython.complex, - p1=cython.complex, - p2=cython.complex, - p3=cython.complex, - v=cython.complex, - u=cython.complex, -) -def spline_to_curves(q, costs, tolerance=0.5, all_cubic=False): - """ - q: quadratic spline with alternating on-curve / off-curve points. - - costs: cumulative list of encoding cost of q in terms of number of - points that need to be encoded. Implied on-curve points do not - contribute to the cost. If all points need to be encoded, then - costs will be range(1, len(q)+1). - """ - - assert len(q) >= 3, "quadratic spline requires at least 3 points" - - # Elevate quadratic segments to cubic - elevated_quadratics = [ - elevate_quadratic(*q[i : i + 3]) for i in range(0, len(q) - 2, 2) - ] - - # Find sharp corners; they have to be oncurves for sure. - forced = set() - for i in range(1, len(elevated_quadratics)): - p0 = elevated_quadratics[i - 1][2] - p1 = elevated_quadratics[i][0] - p2 = elevated_quadratics[i][1] - if abs(p1 - p0) + abs(p2 - p1) > tolerance + abs(p2 - p0): - forced.add(i) - - # Dynamic-Programming to find the solution with fewest number of - # cubic curves, and within those the one with smallest error. - sols = [Solution(0, 0, 0, False)] - impossible = Solution(len(elevated_quadratics) * 3 + 1, 0, 1, False) - start = 0 - for i in range(1, len(elevated_quadratics) + 1): - best_sol = impossible - for j in range(start, i): - j_sol_count, j_sol_error = sols[j].num_points, sols[j].error - - if not all_cubic: - # Solution with quadratics between j:i - this_count = costs[2 * i - 1] - costs[2 * j] + 1 - i_sol_count = j_sol_count + this_count - i_sol_error = j_sol_error - i_sol = Solution(i_sol_count, i_sol_error, i - j, False) - if i_sol < best_sol: - best_sol = i_sol - - if this_count <= 3: - # Can't get any better than this in the path below - continue - - # Fit elevated_quadratics[j:i] into one cubic - try: - curve, ts = merge_curves(elevated_quadratics, j, i - j) - except ZeroDivisionError: - continue - - # Now reconstruct the segments from the fitted curve - reconstructed_iter = splitCubicAtTC(*curve, *ts) - reconstructed = [] - - # Knot errors - error = 0 - for k, reconst in enumerate(reconstructed_iter): - orig = elevated_quadratics[j + k] - err = abs(reconst[3] - orig[3]) - error = max(error, err) - if error > tolerance: - break - reconstructed.append(reconst) - if error > tolerance: - # Not feasible - continue - - # Interior errors - for k, reconst in enumerate(reconstructed): - orig = elevated_quadratics[j + k] - p0, p1, p2, p3 = tuple(v - u for v, u in zip(reconst, orig)) - - if not cubic_farthest_fit_inside(p0, p1, p2, p3, tolerance): - error = tolerance + 1 - break - if error > tolerance: - # Not feasible - continue - - # Save best solution - i_sol_count = j_sol_count + 3 - i_sol_error = max(j_sol_error, error) - i_sol = Solution(i_sol_count, i_sol_error, i - j, True) - if i_sol < best_sol: - best_sol = i_sol - - if i_sol_count == 3: - # Can't get any better than this - break - - sols.append(best_sol) - if i in forced: - start = i - - # Reconstruct solution - splits = [] - cubic = [] - i = len(sols) - 1 - while i: - count, is_cubic = sols[i].start_index, sols[i].is_cubic - splits.append(i) - cubic.append(is_cubic) - i -= count - curves = [] - j = 0 - for i, is_cubic in reversed(list(zip(splits, cubic))): - if is_cubic: - curves.append(merge_curves(elevated_quadratics, j, i - j)[0]) - else: - for k in range(j, i): - curves.append(q[k * 2 : k * 2 + 3]) - j = i - - return curves - - -def main(): - from fontTools.cu2qu.benchmark import generate_curve - from fontTools.cu2qu import curve_to_quadratic - - tolerance = 0.05 - reconstruct_tolerance = tolerance * 1 - curve = generate_curve() - quadratics = curve_to_quadratic(curve, tolerance) - print( - "cu2qu tolerance %g. qu2cu tolerance %g." % (tolerance, reconstruct_tolerance) - ) - print("One random cubic turned into %d quadratics." % len(quadratics)) - curves = quadratic_to_curves([quadratics], reconstruct_tolerance) - print("Those quadratics turned back into %d cubics. " % len(curves)) - print("Original curve:", curve) - print("Reconstructed curve(s):", curves) - - -if __name__ == "__main__": - main() diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/woff2.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/woff2.py deleted file mode 100644 index 3e247b02e93da590d0e03cd2e019d1763e84f40b..0000000000000000000000000000000000000000 --- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/woff2.py +++ /dev/null @@ -1,1688 +0,0 @@ -from io import BytesIO -import sys -import array -import struct -from collections import OrderedDict -from fontTools.misc import sstruct -from fontTools.misc.arrayTools import calcIntBounds -from fontTools.misc.textTools import Tag, bytechr, byteord, bytesjoin, pad -from fontTools.ttLib import ( - TTFont, - TTLibError, - getTableModule, - getTableClass, - getSearchRange, -) -from fontTools.ttLib.sfnt import ( - SFNTReader, - SFNTWriter, - DirectoryEntry, - WOFFFlavorData, - sfntDirectoryFormat, - sfntDirectorySize, - SFNTDirectoryEntry, - sfntDirectoryEntrySize, - calcChecksum, -) -from fontTools.ttLib.tables import ttProgram, _g_l_y_f -import logging - - -log = logging.getLogger("fontTools.ttLib.woff2") - -haveBrotli = False -try: - try: - import brotlicffi as brotli - except ImportError: - import brotli - haveBrotli = True -except ImportError: - pass - - -class WOFF2Reader(SFNTReader): - - flavor = "woff2" - - def __init__(self, file, checkChecksums=0, fontNumber=-1): - if not haveBrotli: - log.error( - "The WOFF2 decoder requires the Brotli Python extension, available at: " - "https://github.com/google/brotli" - ) - raise ImportError("No module named brotli") - - self.file = file - - signature = Tag(self.file.read(4)) - if signature != b"wOF2": - raise TTLibError("Not a WOFF2 font (bad signature)") - - self.file.seek(0) - self.DirectoryEntry = WOFF2DirectoryEntry - data = self.file.read(woff2DirectorySize) - if len(data) != woff2DirectorySize: - raise TTLibError("Not a WOFF2 font (not enough data)") - sstruct.unpack(woff2DirectoryFormat, data, self) - - self.tables = OrderedDict() - offset = 0 - for i in range(self.numTables): - entry = self.DirectoryEntry() - entry.fromFile(self.file) - tag = Tag(entry.tag) - self.tables[tag] = entry - entry.offset = offset - offset += entry.length - - totalUncompressedSize = offset - compressedData = self.file.read(self.totalCompressedSize) - decompressedData = brotli.decompress(compressedData) - if len(decompressedData) != totalUncompressedSize: - raise TTLibError( - "unexpected size for decompressed font data: expected %d, found %d" - % (totalUncompressedSize, len(decompressedData)) - ) - self.transformBuffer = BytesIO(decompressedData) - - self.file.seek(0, 2) - if self.length != self.file.tell(): - raise TTLibError("reported 'length' doesn't match the actual file size") - - self.flavorData = WOFF2FlavorData(self) - - # make empty TTFont to store data while reconstructing tables - self.ttFont = TTFont(recalcBBoxes=False, recalcTimestamp=False) - - def __getitem__(self, tag): - """Fetch the raw table data. Reconstruct transformed tables.""" - entry = self.tables[Tag(tag)] - if not hasattr(entry, "data"): - if entry.transformed: - entry.data = self.reconstructTable(tag) - else: - entry.data = entry.loadData(self.transformBuffer) - return entry.data - - def reconstructTable(self, tag): - """Reconstruct table named 'tag' from transformed data.""" - entry = self.tables[Tag(tag)] - rawData = entry.loadData(self.transformBuffer) - if tag == "glyf": - # no need to pad glyph data when reconstructing - padding = self.padding if hasattr(self, "padding") else None - data = self._reconstructGlyf(rawData, padding) - elif tag == "loca": - data = self._reconstructLoca() - elif tag == "hmtx": - data = self._reconstructHmtx(rawData) - else: - raise TTLibError("transform for table '%s' is unknown" % tag) - return data - - def _reconstructGlyf(self, data, padding=None): - """Return recostructed glyf table data, and set the corresponding loca's - locations. Optionally pad glyph offsets to the specified number of bytes. - """ - self.ttFont["loca"] = WOFF2LocaTable() - glyfTable = self.ttFont["glyf"] = WOFF2GlyfTable() - glyfTable.reconstruct(data, self.ttFont) - if padding: - glyfTable.padding = padding - data = glyfTable.compile(self.ttFont) - return data - - def _reconstructLoca(self): - """Return reconstructed loca table data.""" - if "loca" not in self.ttFont: - # make sure glyf is reconstructed first - self.tables["glyf"].data = self.reconstructTable("glyf") - locaTable = self.ttFont["loca"] - data = locaTable.compile(self.ttFont) - if len(data) != self.tables["loca"].origLength: - raise TTLibError( - "reconstructed 'loca' table doesn't match original size: " - "expected %d, found %d" % (self.tables["loca"].origLength, len(data)) - ) - return data - - def _reconstructHmtx(self, data): - """Return reconstructed hmtx table data.""" - # Before reconstructing 'hmtx' table we need to parse other tables: - # 'glyf' is required for reconstructing the sidebearings from the glyphs' - # bounding box; 'hhea' is needed for the numberOfHMetrics field. - if "glyf" in self.flavorData.transformedTables: - # transformed 'glyf' table is self-contained, thus 'loca' not needed - tableDependencies = ("maxp", "hhea", "glyf") - else: - # decompiling untransformed 'glyf' requires 'loca', which requires 'head' - tableDependencies = ("maxp", "head", "hhea", "loca", "glyf") - for tag in tableDependencies: - self._decompileTable(tag) - hmtxTable = self.ttFont["hmtx"] = WOFF2HmtxTable() - hmtxTable.reconstruct(data, self.ttFont) - data = hmtxTable.compile(self.ttFont) - return data - - def _decompileTable(self, tag): - """Decompile table data and store it inside self.ttFont.""" - data = self[tag] - if self.ttFont.isLoaded(tag): - return self.ttFont[tag] - tableClass = getTableClass(tag) - table = tableClass(tag) - self.ttFont.tables[tag] = table - table.decompile(data, self.ttFont) - - -class WOFF2Writer(SFNTWriter): - - flavor = "woff2" - - def __init__( - self, - file, - numTables, - sfntVersion="\000\001\000\000", - flavor=None, - flavorData=None, - ): - if not haveBrotli: - log.error( - "The WOFF2 encoder requires the Brotli Python extension, available at: " - "https://github.com/google/brotli" - ) - raise ImportError("No module named brotli") - - self.file = file - self.numTables = numTables - self.sfntVersion = Tag(sfntVersion) - self.flavorData = WOFF2FlavorData(data=flavorData) - - self.directoryFormat = woff2DirectoryFormat - self.directorySize = woff2DirectorySize - self.DirectoryEntry = WOFF2DirectoryEntry - - self.signature = Tag("wOF2") - - self.nextTableOffset = 0 - self.transformBuffer = BytesIO() - - self.tables = OrderedDict() - - # make empty TTFont to store data while normalising and transforming tables - self.ttFont = TTFont(recalcBBoxes=False, recalcTimestamp=False) - - def __setitem__(self, tag, data): - """Associate new entry named 'tag' with raw table data.""" - if tag in self.tables: - raise TTLibError("cannot rewrite '%s' table" % tag) - if tag == "DSIG": - # always drop DSIG table, since the encoding process can invalidate it - self.numTables -= 1 - return - - entry = self.DirectoryEntry() - entry.tag = Tag(tag) - entry.flags = getKnownTagIndex(entry.tag) - # WOFF2 table data are written to disk only on close(), after all tags - # have been specified - entry.data = data - - self.tables[tag] = entry - - def close(self): - """All tags must have been specified. Now write the table data and directory.""" - if len(self.tables) != self.numTables: - raise TTLibError( - "wrong number of tables; expected %d, found %d" - % (self.numTables, len(self.tables)) - ) - - if self.sfntVersion in ("\x00\x01\x00\x00", "true"): - isTrueType = True - elif self.sfntVersion == "OTTO": - isTrueType = False - else: - raise TTLibError("Not a TrueType or OpenType font (bad sfntVersion)") - - # The WOFF2 spec no longer requires the glyph offsets to be 4-byte aligned. - # However, the reference WOFF2 implementation still fails to reconstruct - # 'unpadded' glyf tables, therefore we need to 'normalise' them. - # See: - # https://github.com/khaledhosny/ots/issues/60 - # https://github.com/google/woff2/issues/15 - if ( - isTrueType - and "glyf" in self.flavorData.transformedTables - and "glyf" in self.tables - ): - self._normaliseGlyfAndLoca(padding=4) - self._setHeadTransformFlag() - - # To pass the legacy OpenType Sanitiser currently included in browsers, - # we must sort the table directory and data alphabetically by tag. - # See: - # https://github.com/google/woff2/pull/3 - # https://lists.w3.org/Archives/Public/public-webfonts-wg/2015Mar/0000.html - # - # 2023: We rely on this in _transformTables where we expect that - # "loca" comes after "glyf" table. - self.tables = OrderedDict(sorted(self.tables.items())) - - self.totalSfntSize = self._calcSFNTChecksumsLengthsAndOffsets() - - fontData = self._transformTables() - compressedFont = brotli.compress(fontData, mode=brotli.MODE_FONT) - - self.totalCompressedSize = len(compressedFont) - self.length = self._calcTotalSize() - self.majorVersion, self.minorVersion = self._getVersion() - self.reserved = 0 - - directory = self._packTableDirectory() - self.file.seek(0) - self.file.write(pad(directory + compressedFont, size=4)) - self._writeFlavorData() - - def _normaliseGlyfAndLoca(self, padding=4): - """Recompile glyf and loca tables, aligning glyph offsets to multiples of - 'padding' size. Update the head table's 'indexToLocFormat' accordingly while - compiling loca. - """ - if self.sfntVersion == "OTTO": - return - - for tag in ("maxp", "head", "loca", "glyf", "fvar"): - if tag in self.tables: - self._decompileTable(tag) - self.ttFont["glyf"].padding = padding - for tag in ("glyf", "loca"): - self._compileTable(tag) - - def _setHeadTransformFlag(self): - """Set bit 11 of 'head' table flags to indicate that the font has undergone - a lossless modifying transform. Re-compile head table data.""" - self._decompileTable("head") - self.ttFont["head"].flags |= 1 << 11 - self._compileTable("head") - - def _decompileTable(self, tag): - """Fetch table data, decompile it, and store it inside self.ttFont.""" - tag = Tag(tag) - if tag not in self.tables: - raise TTLibError("missing required table: %s" % tag) - if self.ttFont.isLoaded(tag): - return - data = self.tables[tag].data - if tag == "loca": - tableClass = WOFF2LocaTable - elif tag == "glyf": - tableClass = WOFF2GlyfTable - elif tag == "hmtx": - tableClass = WOFF2HmtxTable - else: - tableClass = getTableClass(tag) - table = tableClass(tag) - self.ttFont.tables[tag] = table - table.decompile(data, self.ttFont) - - def _compileTable(self, tag): - """Compile table and store it in its 'data' attribute.""" - self.tables[tag].data = self.ttFont[tag].compile(self.ttFont) - - def _calcSFNTChecksumsLengthsAndOffsets(self): - """Compute the 'original' SFNT checksums, lengths and offsets for checksum - adjustment calculation. Return the total size of the uncompressed font. - """ - offset = sfntDirectorySize + sfntDirectoryEntrySize * len(self.tables) - for tag, entry in self.tables.items(): - data = entry.data - entry.origOffset = offset - entry.origLength = len(data) - if tag == "head": - entry.checkSum = calcChecksum(data[:8] + b"\0\0\0\0" + data[12:]) - else: - entry.checkSum = calcChecksum(data) - offset += (entry.origLength + 3) & ~3 - return offset - - def _transformTables(self): - """Return transformed font data.""" - transformedTables = self.flavorData.transformedTables - for tag, entry in self.tables.items(): - data = None - if tag in transformedTables: - data = self.transformTable(tag) - if data is not None: - entry.transformed = True - if data is None: - if tag == "glyf": - # Currently we always sort table tags so - # 'loca' comes after 'glyf'. - transformedTables.discard("loca") - # pass-through the table data without transformation - data = entry.data - entry.transformed = False - entry.offset = self.nextTableOffset - entry.saveData(self.transformBuffer, data) - self.nextTableOffset += entry.length - self.writeMasterChecksum() - fontData = self.transformBuffer.getvalue() - return fontData - - def transformTable(self, tag): - """Return transformed table data, or None if some pre-conditions aren't - met -- in which case, the non-transformed table data will be used. - """ - if tag == "loca": - data = b"" - elif tag == "glyf": - for tag in ("maxp", "head", "loca", "glyf"): - self._decompileTable(tag) - glyfTable = self.ttFont["glyf"] - data = glyfTable.transform(self.ttFont) - elif tag == "hmtx": - if "glyf" not in self.tables: - return - for tag in ("maxp", "head", "hhea", "loca", "glyf", "hmtx"): - self._decompileTable(tag) - hmtxTable = self.ttFont["hmtx"] - data = hmtxTable.transform(self.ttFont) # can be None - else: - raise TTLibError("Transform for table '%s' is unknown" % tag) - return data - - def _calcMasterChecksum(self): - """Calculate checkSumAdjustment.""" - tags = list(self.tables.keys()) - checksums = [] - for i in range(len(tags)): - checksums.append(self.tables[tags[i]].checkSum) - - # Create a SFNT directory for checksum calculation purposes - self.searchRange, self.entrySelector, self.rangeShift = getSearchRange( - self.numTables, 16 - ) - directory = sstruct.pack(sfntDirectoryFormat, self) - tables = sorted(self.tables.items()) - for tag, entry in tables: - sfntEntry = SFNTDirectoryEntry() - sfntEntry.tag = entry.tag - sfntEntry.checkSum = entry.checkSum - sfntEntry.offset = entry.origOffset - sfntEntry.length = entry.origLength - directory = directory + sfntEntry.toString() - - directory_end = sfntDirectorySize + len(self.tables) * sfntDirectoryEntrySize - assert directory_end == len(directory) - - checksums.append(calcChecksum(directory)) - checksum = sum(checksums) & 0xFFFFFFFF - # BiboAfba! - checksumadjustment = (0xB1B0AFBA - checksum) & 0xFFFFFFFF - return checksumadjustment - - def writeMasterChecksum(self): - """Write checkSumAdjustment to the transformBuffer.""" - checksumadjustment = self._calcMasterChecksum() - self.transformBuffer.seek(self.tables["head"].offset + 8) - self.transformBuffer.write(struct.pack(">L", checksumadjustment)) - - def _calcTotalSize(self): - """Calculate total size of WOFF2 font, including any meta- and/or private data.""" - offset = self.directorySize - for entry in self.tables.values(): - offset += len(entry.toString()) - offset += self.totalCompressedSize - offset = (offset + 3) & ~3 - offset = self._calcFlavorDataOffsetsAndSize(offset) - return offset - - def _calcFlavorDataOffsetsAndSize(self, start): - """Calculate offsets and lengths for any meta- and/or private data.""" - offset = start - data = self.flavorData - if data.metaData: - self.metaOrigLength = len(data.metaData) - self.metaOffset = offset - self.compressedMetaData = brotli.compress( - data.metaData, mode=brotli.MODE_TEXT - ) - self.metaLength = len(self.compressedMetaData) - offset += self.metaLength - else: - self.metaOffset = self.metaLength = self.metaOrigLength = 0 - self.compressedMetaData = b"" - if data.privData: - # make sure private data is padded to 4-byte boundary - offset = (offset + 3) & ~3 - self.privOffset = offset - self.privLength = len(data.privData) - offset += self.privLength - else: - self.privOffset = self.privLength = 0 - return offset - - def _getVersion(self): - """Return the WOFF2 font's (majorVersion, minorVersion) tuple.""" - data = self.flavorData - if data.majorVersion is not None and data.minorVersion is not None: - return data.majorVersion, data.minorVersion - else: - # if None, return 'fontRevision' from 'head' table - if "head" in self.tables: - return struct.unpack(">HH", self.tables["head"].data[4:8]) - else: - return 0, 0 - - def _packTableDirectory(self): - """Return WOFF2 table directory data.""" - directory = sstruct.pack(self.directoryFormat, self) - for entry in self.tables.values(): - directory = directory + entry.toString() - return directory - - def _writeFlavorData(self): - """Write metadata and/or private data using appropiate padding.""" - compressedMetaData = self.compressedMetaData - privData = self.flavorData.privData - if compressedMetaData and privData: - compressedMetaData = pad(compressedMetaData, size=4) - if compressedMetaData: - self.file.seek(self.metaOffset) - assert self.file.tell() == self.metaOffset - self.file.write(compressedMetaData) - if privData: - self.file.seek(self.privOffset) - assert self.file.tell() == self.privOffset - self.file.write(privData) - - def reordersTables(self): - return True - - -# -- woff2 directory helpers and cruft - -woff2DirectoryFormat = """ - > # big endian - signature: 4s # "wOF2" - sfntVersion: 4s - length: L # total woff2 file size - numTables: H # number of tables - reserved: H # set to 0 - totalSfntSize: L # uncompressed size - totalCompressedSize: L # compressed size - majorVersion: H # major version of WOFF file - minorVersion: H # minor version of WOFF file - metaOffset: L # offset to metadata block - metaLength: L # length of compressed metadata - metaOrigLength: L # length of uncompressed metadata - privOffset: L # offset to private data block - privLength: L # length of private data block -""" - -woff2DirectorySize = sstruct.calcsize(woff2DirectoryFormat) - -woff2KnownTags = ( - "cmap", - "head", - "hhea", - "hmtx", - "maxp", - "name", - "OS/2", - "post", - "cvt ", - "fpgm", - "glyf", - "loca", - "prep", - "CFF ", - "VORG", - "EBDT", - "EBLC", - "gasp", - "hdmx", - "kern", - "LTSH", - "PCLT", - "VDMX", - "vhea", - "vmtx", - "BASE", - "GDEF", - "GPOS", - "GSUB", - "EBSC", - "JSTF", - "MATH", - "CBDT", - "CBLC", - "COLR", - "CPAL", - "SVG ", - "sbix", - "acnt", - "avar", - "bdat", - "bloc", - "bsln", - "cvar", - "fdsc", - "feat", - "fmtx", - "fvar", - "gvar", - "hsty", - "just", - "lcar", - "mort", - "morx", - "opbd", - "prop", - "trak", - "Zapf", - "Silf", - "Glat", - "Gloc", - "Feat", - "Sill", -) - -woff2FlagsFormat = """ - > # big endian - flags: B # table type and flags -""" - -woff2FlagsSize = sstruct.calcsize(woff2FlagsFormat) - -woff2UnknownTagFormat = """ - > # big endian - tag: 4s # 4-byte tag (optional) -""" - -woff2UnknownTagSize = sstruct.calcsize(woff2UnknownTagFormat) - -woff2UnknownTagIndex = 0x3F - -woff2Base128MaxSize = 5 -woff2DirectoryEntryMaxSize = ( - woff2FlagsSize + woff2UnknownTagSize + 2 * woff2Base128MaxSize -) - -woff2TransformedTableTags = ("glyf", "loca") - -woff2GlyfTableFormat = """ - > # big endian - version: H # = 0x0000 - optionFlags: H # Bit 0: we have overlapSimpleBitmap[], Bits 1-15: reserved - numGlyphs: H # Number of glyphs - indexFormat: H # Offset format for loca table - nContourStreamSize: L # Size of nContour stream - nPointsStreamSize: L # Size of nPoints stream - flagStreamSize: L # Size of flag stream - glyphStreamSize: L # Size of glyph stream - compositeStreamSize: L # Size of composite stream - bboxStreamSize: L # Comnined size of bboxBitmap and bboxStream - instructionStreamSize: L # Size of instruction stream -""" - -woff2GlyfTableFormatSize = sstruct.calcsize(woff2GlyfTableFormat) - -bboxFormat = """ - > # big endian - xMin: h - yMin: h - xMax: h - yMax: h -""" - -woff2OverlapSimpleBitmapFlag = 0x0001 - - -def getKnownTagIndex(tag): - """Return index of 'tag' in woff2KnownTags list. Return 63 if not found.""" - for i in range(len(woff2KnownTags)): - if tag == woff2KnownTags[i]: - return i - return woff2UnknownTagIndex - - -class WOFF2DirectoryEntry(DirectoryEntry): - def fromFile(self, file): - pos = file.tell() - data = file.read(woff2DirectoryEntryMaxSize) - left = self.fromString(data) - consumed = len(data) - len(left) - file.seek(pos + consumed) - - def fromString(self, data): - if len(data) < 1: - raise TTLibError("can't read table 'flags': not enough data") - dummy, data = sstruct.unpack2(woff2FlagsFormat, data, self) - if self.flags & 0x3F == 0x3F: - # if bits [0..5] of the flags byte == 63, read a 4-byte arbitrary tag value - if len(data) < woff2UnknownTagSize: - raise TTLibError("can't read table 'tag': not enough data") - dummy, data = sstruct.unpack2(woff2UnknownTagFormat, data, self) - else: - # otherwise, tag is derived from a fixed 'Known Tags' table - self.tag = woff2KnownTags[self.flags & 0x3F] - self.tag = Tag(self.tag) - self.origLength, data = unpackBase128(data) - self.length = self.origLength - if self.transformed: - self.length, data = unpackBase128(data) - if self.tag == "loca" and self.length != 0: - raise TTLibError("the transformLength of the 'loca' table must be 0") - # return left over data - return data - - def toString(self): - data = bytechr(self.flags) - if (self.flags & 0x3F) == 0x3F: - data += struct.pack(">4s", self.tag.tobytes()) - data += packBase128(self.origLength) - if self.transformed: - data += packBase128(self.length) - return data - - @property - def transformVersion(self): - """Return bits 6-7 of table entry's flags, which indicate the preprocessing - transformation version number (between 0 and 3). - """ - return self.flags >> 6 - - @transformVersion.setter - def transformVersion(self, value): - assert 0 <= value <= 3 - self.flags |= value << 6 - - @property - def transformed(self): - """Return True if the table has any transformation, else return False.""" - # For all tables in a font, except for 'glyf' and 'loca', the transformation - # version 0 indicates the null transform (where the original table data is - # passed directly to the Brotli compressor). For 'glyf' and 'loca' tables, - # transformation version 3 indicates the null transform - if self.tag in {"glyf", "loca"}: - return self.transformVersion != 3 - else: - return self.transformVersion != 0 - - @transformed.setter - def transformed(self, booleanValue): - # here we assume that a non-null transform means version 0 for 'glyf' and - # 'loca' and 1 for every other table (e.g. hmtx); but that may change as - # new transformation formats are introduced in the future (if ever). - if self.tag in {"glyf", "loca"}: - self.transformVersion = 3 if not booleanValue else 0 - else: - self.transformVersion = int(booleanValue) - - -class WOFF2LocaTable(getTableClass("loca")): - """Same as parent class. The only difference is that it attempts to preserve - the 'indexFormat' as encoded in the WOFF2 glyf table. - """ - - def __init__(self, tag=None): - self.tableTag = Tag(tag or "loca") - - def compile(self, ttFont): - try: - max_location = max(self.locations) - except AttributeError: - self.set([]) - max_location = 0 - if "glyf" in ttFont and hasattr(ttFont["glyf"], "indexFormat"): - # copile loca using the indexFormat specified in the WOFF2 glyf table - indexFormat = ttFont["glyf"].indexFormat - if indexFormat == 0: - if max_location >= 0x20000: - raise TTLibError("indexFormat is 0 but local offsets > 0x20000") - if not all(l % 2 == 0 for l in self.locations): - raise TTLibError( - "indexFormat is 0 but local offsets not multiples of 2" - ) - locations = array.array("H") - for i in range(len(self.locations)): - locations.append(self.locations[i] // 2) - else: - locations = array.array("I", self.locations) - if sys.byteorder != "big": - locations.byteswap() - data = locations.tobytes() - else: - # use the most compact indexFormat given the current glyph offsets - data = super(WOFF2LocaTable, self).compile(ttFont) - return data - - -class WOFF2GlyfTable(getTableClass("glyf")): - """Decoder/Encoder for WOFF2 'glyf' table transform.""" - - subStreams = ( - "nContourStream", - "nPointsStream", - "flagStream", - "glyphStream", - "compositeStream", - "bboxStream", - "instructionStream", - ) - - def __init__(self, tag=None): - self.tableTag = Tag(tag or "glyf") - - def reconstruct(self, data, ttFont): - """Decompile transformed 'glyf' data.""" - inputDataSize = len(data) - - if inputDataSize < woff2GlyfTableFormatSize: - raise TTLibError("not enough 'glyf' data") - dummy, data = sstruct.unpack2(woff2GlyfTableFormat, data, self) - offset = woff2GlyfTableFormatSize - - for stream in self.subStreams: - size = getattr(self, stream + "Size") - setattr(self, stream, data[:size]) - data = data[size:] - offset += size - - hasOverlapSimpleBitmap = self.optionFlags & woff2OverlapSimpleBitmapFlag - self.overlapSimpleBitmap = None - if hasOverlapSimpleBitmap: - overlapSimpleBitmapSize = (self.numGlyphs + 7) >> 3 - self.overlapSimpleBitmap = array.array("B", data[:overlapSimpleBitmapSize]) - offset += overlapSimpleBitmapSize - - if offset != inputDataSize: - raise TTLibError( - "incorrect size of transformed 'glyf' table: expected %d, received %d bytes" - % (offset, inputDataSize) - ) - - bboxBitmapSize = ((self.numGlyphs + 31) >> 5) << 2 - bboxBitmap = self.bboxStream[:bboxBitmapSize] - self.bboxBitmap = array.array("B", bboxBitmap) - self.bboxStream = self.bboxStream[bboxBitmapSize:] - - self.nContourStream = array.array("h", self.nContourStream) - if sys.byteorder != "big": - self.nContourStream.byteswap() - assert len(self.nContourStream) == self.numGlyphs - - if "head" in ttFont: - ttFont["head"].indexToLocFormat = self.indexFormat - try: - self.glyphOrder = ttFont.getGlyphOrder() - except: - self.glyphOrder = None - if self.glyphOrder is None: - self.glyphOrder = [".notdef"] - self.glyphOrder.extend(["glyph%.5d" % i for i in range(1, self.numGlyphs)]) - else: - if len(self.glyphOrder) != self.numGlyphs: - raise TTLibError( - "incorrect glyphOrder: expected %d glyphs, found %d" - % (len(self.glyphOrder), self.numGlyphs) - ) - - glyphs = self.glyphs = {} - for glyphID, glyphName in enumerate(self.glyphOrder): - glyph = self._decodeGlyph(glyphID) - glyphs[glyphName] = glyph - - def transform(self, ttFont): - """Return transformed 'glyf' data""" - self.numGlyphs = len(self.glyphs) - assert len(self.glyphOrder) == self.numGlyphs - if "maxp" in ttFont: - ttFont["maxp"].numGlyphs = self.numGlyphs - self.indexFormat = ttFont["head"].indexToLocFormat - - for stream in self.subStreams: - setattr(self, stream, b"") - bboxBitmapSize = ((self.numGlyphs + 31) >> 5) << 2 - self.bboxBitmap = array.array("B", [0] * bboxBitmapSize) - - self.overlapSimpleBitmap = array.array("B", [0] * ((self.numGlyphs + 7) >> 3)) - for glyphID in range(self.numGlyphs): - try: - self._encodeGlyph(glyphID) - except NotImplementedError: - return None - hasOverlapSimpleBitmap = any(self.overlapSimpleBitmap) - - self.bboxStream = self.bboxBitmap.tobytes() + self.bboxStream - for stream in self.subStreams: - setattr(self, stream + "Size", len(getattr(self, stream))) - self.version = 0 - self.optionFlags = 0 - if hasOverlapSimpleBitmap: - self.optionFlags |= woff2OverlapSimpleBitmapFlag - data = sstruct.pack(woff2GlyfTableFormat, self) - data += bytesjoin([getattr(self, s) for s in self.subStreams]) - if hasOverlapSimpleBitmap: - data += self.overlapSimpleBitmap.tobytes() - return data - - def _decodeGlyph(self, glyphID): - glyph = getTableModule("glyf").Glyph() - glyph.numberOfContours = self.nContourStream[glyphID] - if glyph.numberOfContours == 0: - return glyph - elif glyph.isComposite(): - self._decodeComponents(glyph) - else: - self._decodeCoordinates(glyph) - self._decodeOverlapSimpleFlag(glyph, glyphID) - self._decodeBBox(glyphID, glyph) - return glyph - - def _decodeComponents(self, glyph): - data = self.compositeStream - glyph.components = [] - more = 1 - haveInstructions = 0 - while more: - component = getTableModule("glyf").GlyphComponent() - more, haveInstr, data = component.decompile(data, self) - haveInstructions = haveInstructions | haveInstr - glyph.components.append(component) - self.compositeStream = data - if haveInstructions: - self._decodeInstructions(glyph) - - def _decodeCoordinates(self, glyph): - data = self.nPointsStream - endPtsOfContours = [] - endPoint = -1 - for i in range(glyph.numberOfContours): - ptsOfContour, data = unpack255UShort(data) - endPoint += ptsOfContour - endPtsOfContours.append(endPoint) - glyph.endPtsOfContours = endPtsOfContours - self.nPointsStream = data - self._decodeTriplets(glyph) - self._decodeInstructions(glyph) - - def _decodeOverlapSimpleFlag(self, glyph, glyphID): - if self.overlapSimpleBitmap is None or glyph.numberOfContours <= 0: - return - byte = glyphID >> 3 - bit = glyphID & 7 - if self.overlapSimpleBitmap[byte] & (0x80 >> bit): - glyph.flags[0] |= _g_l_y_f.flagOverlapSimple - - def _decodeInstructions(self, glyph): - glyphStream = self.glyphStream - instructionStream = self.instructionStream - instructionLength, glyphStream = unpack255UShort(glyphStream) - glyph.program = ttProgram.Program() - glyph.program.fromBytecode(instructionStream[:instructionLength]) - self.glyphStream = glyphStream - self.instructionStream = instructionStream[instructionLength:] - - def _decodeBBox(self, glyphID, glyph): - haveBBox = bool(self.bboxBitmap[glyphID >> 3] & (0x80 >> (glyphID & 7))) - if glyph.isComposite() and not haveBBox: - raise TTLibError("no bbox values for composite glyph %d" % glyphID) - if haveBBox: - dummy, self.bboxStream = sstruct.unpack2(bboxFormat, self.bboxStream, glyph) - else: - glyph.recalcBounds(self) - - def _decodeTriplets(self, glyph): - def withSign(flag, baseval): - assert 0 <= baseval and baseval < 65536, "integer overflow" - return baseval if flag & 1 else -baseval - - nPoints = glyph.endPtsOfContours[-1] + 1 - flagSize = nPoints - if flagSize > len(self.flagStream): - raise TTLibError("not enough 'flagStream' data") - flagsData = self.flagStream[:flagSize] - self.flagStream = self.flagStream[flagSize:] - flags = array.array("B", flagsData) - - triplets = array.array("B", self.glyphStream) - nTriplets = len(triplets) - assert nPoints <= nTriplets - - x = 0 - y = 0 - glyph.coordinates = getTableModule("glyf").GlyphCoordinates.zeros(nPoints) - glyph.flags = array.array("B") - tripletIndex = 0 - for i in range(nPoints): - flag = flags[i] - onCurve = not bool(flag >> 7) - flag &= 0x7F - if flag < 84: - nBytes = 1 - elif flag < 120: - nBytes = 2 - elif flag < 124: - nBytes = 3 - else: - nBytes = 4 - assert (tripletIndex + nBytes) <= nTriplets - if flag < 10: - dx = 0 - dy = withSign(flag, ((flag & 14) << 7) + triplets[tripletIndex]) - elif flag < 20: - dx = withSign(flag, (((flag - 10) & 14) << 7) + triplets[tripletIndex]) - dy = 0 - elif flag < 84: - b0 = flag - 20 - b1 = triplets[tripletIndex] - dx = withSign(flag, 1 + (b0 & 0x30) + (b1 >> 4)) - dy = withSign(flag >> 1, 1 + ((b0 & 0x0C) << 2) + (b1 & 0x0F)) - elif flag < 120: - b0 = flag - 84 - dx = withSign(flag, 1 + ((b0 // 12) << 8) + triplets[tripletIndex]) - dy = withSign( - flag >> 1, 1 + (((b0 % 12) >> 2) << 8) + triplets[tripletIndex + 1] - ) - elif flag < 124: - b2 = triplets[tripletIndex + 1] - dx = withSign(flag, (triplets[tripletIndex] << 4) + (b2 >> 4)) - dy = withSign( - flag >> 1, ((b2 & 0x0F) << 8) + triplets[tripletIndex + 2] - ) - else: - dx = withSign( - flag, (triplets[tripletIndex] << 8) + triplets[tripletIndex + 1] - ) - dy = withSign( - flag >> 1, - (triplets[tripletIndex + 2] << 8) + triplets[tripletIndex + 3], - ) - tripletIndex += nBytes - x += dx - y += dy - glyph.coordinates[i] = (x, y) - glyph.flags.append(int(onCurve)) - bytesConsumed = tripletIndex - self.glyphStream = self.glyphStream[bytesConsumed:] - - def _encodeGlyph(self, glyphID): - glyphName = self.getGlyphName(glyphID) - glyph = self[glyphName] - self.nContourStream += struct.pack(">h", glyph.numberOfContours) - if glyph.numberOfContours == 0: - return - elif glyph.isComposite(): - self._encodeComponents(glyph) - elif glyph.isVarComposite(): - raise NotImplementedError - else: - self._encodeCoordinates(glyph) - self._encodeOverlapSimpleFlag(glyph, glyphID) - self._encodeBBox(glyphID, glyph) - - def _encodeComponents(self, glyph): - lastcomponent = len(glyph.components) - 1 - more = 1 - haveInstructions = 0 - for i in range(len(glyph.components)): - if i == lastcomponent: - haveInstructions = hasattr(glyph, "program") - more = 0 - component = glyph.components[i] - self.compositeStream += component.compile(more, haveInstructions, self) - if haveInstructions: - self._encodeInstructions(glyph) - - def _encodeCoordinates(self, glyph): - lastEndPoint = -1 - if _g_l_y_f.flagCubic in glyph.flags: - raise NotImplementedError - for endPoint in glyph.endPtsOfContours: - ptsOfContour = endPoint - lastEndPoint - self.nPointsStream += pack255UShort(ptsOfContour) - lastEndPoint = endPoint - self._encodeTriplets(glyph) - self._encodeInstructions(glyph) - - def _encodeOverlapSimpleFlag(self, glyph, glyphID): - if glyph.numberOfContours <= 0: - return - if glyph.flags[0] & _g_l_y_f.flagOverlapSimple: - byte = glyphID >> 3 - bit = glyphID & 7 - self.overlapSimpleBitmap[byte] |= 0x80 >> bit - - def _encodeInstructions(self, glyph): - instructions = glyph.program.getBytecode() - self.glyphStream += pack255UShort(len(instructions)) - self.instructionStream += instructions - - def _encodeBBox(self, glyphID, glyph): - assert glyph.numberOfContours != 0, "empty glyph has no bbox" - if not glyph.isComposite(): - # for simple glyphs, compare the encoded bounding box info with the calculated - # values, and if they match omit the bounding box info - currentBBox = glyph.xMin, glyph.yMin, glyph.xMax, glyph.yMax - calculatedBBox = calcIntBounds(glyph.coordinates) - if currentBBox == calculatedBBox: - return - self.bboxBitmap[glyphID >> 3] |= 0x80 >> (glyphID & 7) - self.bboxStream += sstruct.pack(bboxFormat, glyph) - - def _encodeTriplets(self, glyph): - assert len(glyph.coordinates) == len(glyph.flags) - coordinates = glyph.coordinates.copy() - coordinates.absoluteToRelative() - - flags = array.array("B") - triplets = array.array("B") - for i in range(len(coordinates)): - onCurve = glyph.flags[i] & _g_l_y_f.flagOnCurve - x, y = coordinates[i] - absX = abs(x) - absY = abs(y) - onCurveBit = 0 if onCurve else 128 - xSignBit = 0 if (x < 0) else 1 - ySignBit = 0 if (y < 0) else 1 - xySignBits = xSignBit + 2 * ySignBit - - if x == 0 and absY < 1280: - flags.append(onCurveBit + ((absY & 0xF00) >> 7) + ySignBit) - triplets.append(absY & 0xFF) - elif y == 0 and absX < 1280: - flags.append(onCurveBit + 10 + ((absX & 0xF00) >> 7) + xSignBit) - triplets.append(absX & 0xFF) - elif absX < 65 and absY < 65: - flags.append( - onCurveBit - + 20 - + ((absX - 1) & 0x30) - + (((absY - 1) & 0x30) >> 2) - + xySignBits - ) - triplets.append((((absX - 1) & 0xF) << 4) | ((absY - 1) & 0xF)) - elif absX < 769 and absY < 769: - flags.append( - onCurveBit - + 84 - + 12 * (((absX - 1) & 0x300) >> 8) - + (((absY - 1) & 0x300) >> 6) - + xySignBits - ) - triplets.append((absX - 1) & 0xFF) - triplets.append((absY - 1) & 0xFF) - elif absX < 4096 and absY < 4096: - flags.append(onCurveBit + 120 + xySignBits) - triplets.append(absX >> 4) - triplets.append(((absX & 0xF) << 4) | (absY >> 8)) - triplets.append(absY & 0xFF) - else: - flags.append(onCurveBit + 124 + xySignBits) - triplets.append(absX >> 8) - triplets.append(absX & 0xFF) - triplets.append(absY >> 8) - triplets.append(absY & 0xFF) - - self.flagStream += flags.tobytes() - self.glyphStream += triplets.tobytes() - - -class WOFF2HmtxTable(getTableClass("hmtx")): - def __init__(self, tag=None): - self.tableTag = Tag(tag or "hmtx") - - def reconstruct(self, data, ttFont): - (flags,) = struct.unpack(">B", data[:1]) - data = data[1:] - if flags & 0b11111100 != 0: - raise TTLibError("Bits 2-7 of '%s' flags are reserved" % self.tableTag) - - # When bit 0 is _not_ set, the lsb[] array is present - hasLsbArray = flags & 1 == 0 - # When bit 1 is _not_ set, the leftSideBearing[] array is present - hasLeftSideBearingArray = flags & 2 == 0 - if hasLsbArray and hasLeftSideBearingArray: - raise TTLibError( - "either bits 0 or 1 (or both) must set in transformed '%s' flags" - % self.tableTag - ) - - glyfTable = ttFont["glyf"] - headerTable = ttFont["hhea"] - glyphOrder = glyfTable.glyphOrder - numGlyphs = len(glyphOrder) - numberOfHMetrics = min(int(headerTable.numberOfHMetrics), numGlyphs) - - assert len(data) >= 2 * numberOfHMetrics - advanceWidthArray = array.array("H", data[: 2 * numberOfHMetrics]) - if sys.byteorder != "big": - advanceWidthArray.byteswap() - data = data[2 * numberOfHMetrics :] - - if hasLsbArray: - assert len(data) >= 2 * numberOfHMetrics - lsbArray = array.array("h", data[: 2 * numberOfHMetrics]) - if sys.byteorder != "big": - lsbArray.byteswap() - data = data[2 * numberOfHMetrics :] - else: - # compute (proportional) glyphs' lsb from their xMin - lsbArray = array.array("h") - for i, glyphName in enumerate(glyphOrder): - if i >= numberOfHMetrics: - break - glyph = glyfTable[glyphName] - xMin = getattr(glyph, "xMin", 0) - lsbArray.append(xMin) - - numberOfSideBearings = numGlyphs - numberOfHMetrics - if hasLeftSideBearingArray: - assert len(data) >= 2 * numberOfSideBearings - leftSideBearingArray = array.array("h", data[: 2 * numberOfSideBearings]) - if sys.byteorder != "big": - leftSideBearingArray.byteswap() - data = data[2 * numberOfSideBearings :] - else: - # compute (monospaced) glyphs' leftSideBearing from their xMin - leftSideBearingArray = array.array("h") - for i, glyphName in enumerate(glyphOrder): - if i < numberOfHMetrics: - continue - glyph = glyfTable[glyphName] - xMin = getattr(glyph, "xMin", 0) - leftSideBearingArray.append(xMin) - - if data: - raise TTLibError("too much '%s' table data" % self.tableTag) - - self.metrics = {} - for i in range(numberOfHMetrics): - glyphName = glyphOrder[i] - advanceWidth, lsb = advanceWidthArray[i], lsbArray[i] - self.metrics[glyphName] = (advanceWidth, lsb) - lastAdvance = advanceWidthArray[-1] - for i in range(numberOfSideBearings): - glyphName = glyphOrder[i + numberOfHMetrics] - self.metrics[glyphName] = (lastAdvance, leftSideBearingArray[i]) - - def transform(self, ttFont): - glyphOrder = ttFont.getGlyphOrder() - glyf = ttFont["glyf"] - hhea = ttFont["hhea"] - numberOfHMetrics = hhea.numberOfHMetrics - - # check if any of the proportional glyphs has left sidebearings that - # differ from their xMin bounding box values. - hasLsbArray = False - for i in range(numberOfHMetrics): - glyphName = glyphOrder[i] - lsb = self.metrics[glyphName][1] - if lsb != getattr(glyf[glyphName], "xMin", 0): - hasLsbArray = True - break - - # do the same for the monospaced glyphs (if any) at the end of hmtx table - hasLeftSideBearingArray = False - for i in range(numberOfHMetrics, len(glyphOrder)): - glyphName = glyphOrder[i] - lsb = self.metrics[glyphName][1] - if lsb != getattr(glyf[glyphName], "xMin", 0): - hasLeftSideBearingArray = True - break - - # if we need to encode both sidebearings arrays, then no transformation is - # applicable, and we must use the untransformed hmtx data - if hasLsbArray and hasLeftSideBearingArray: - return - - # set bit 0 and 1 when the respective arrays are _not_ present - flags = 0 - if not hasLsbArray: - flags |= 1 << 0 - if not hasLeftSideBearingArray: - flags |= 1 << 1 - - data = struct.pack(">B", flags) - - advanceWidthArray = array.array( - "H", - [ - self.metrics[glyphName][0] - for i, glyphName in enumerate(glyphOrder) - if i < numberOfHMetrics - ], - ) - if sys.byteorder != "big": - advanceWidthArray.byteswap() - data += advanceWidthArray.tobytes() - - if hasLsbArray: - lsbArray = array.array( - "h", - [ - self.metrics[glyphName][1] - for i, glyphName in enumerate(glyphOrder) - if i < numberOfHMetrics - ], - ) - if sys.byteorder != "big": - lsbArray.byteswap() - data += lsbArray.tobytes() - - if hasLeftSideBearingArray: - leftSideBearingArray = array.array( - "h", - [ - self.metrics[glyphOrder[i]][1] - for i in range(numberOfHMetrics, len(glyphOrder)) - ], - ) - if sys.byteorder != "big": - leftSideBearingArray.byteswap() - data += leftSideBearingArray.tobytes() - - return data - - -class WOFF2FlavorData(WOFFFlavorData): - - Flavor = "woff2" - - def __init__(self, reader=None, data=None, transformedTables=None): - """Data class that holds the WOFF2 header major/minor version, any - metadata or private data (as bytes strings), and the set of - table tags that have transformations applied (if reader is not None), - or will have once the WOFF2 font is compiled. - - Args: - reader: an SFNTReader (or subclass) object to read flavor data from. - data: another WOFFFlavorData object to initialise data from. - transformedTables: set of strings containing table tags to be transformed. - - Raises: - ImportError if the brotli module is not installed. - - NOTE: The 'reader' argument, on the one hand, and the 'data' and - 'transformedTables' arguments, on the other hand, are mutually exclusive. - """ - if not haveBrotli: - raise ImportError("No module named brotli") - - if reader is not None: - if data is not None: - raise TypeError("'reader' and 'data' arguments are mutually exclusive") - if transformedTables is not None: - raise TypeError( - "'reader' and 'transformedTables' arguments are mutually exclusive" - ) - - if transformedTables is not None and ( - "glyf" in transformedTables - and "loca" not in transformedTables - or "loca" in transformedTables - and "glyf" not in transformedTables - ): - raise ValueError("'glyf' and 'loca' must be transformed (or not) together") - super(WOFF2FlavorData, self).__init__(reader=reader) - if reader: - transformedTables = [ - tag for tag, entry in reader.tables.items() if entry.transformed - ] - elif data: - self.majorVersion = data.majorVersion - self.majorVersion = data.minorVersion - self.metaData = data.metaData - self.privData = data.privData - if transformedTables is None and hasattr(data, "transformedTables"): - transformedTables = data.transformedTables - - if transformedTables is None: - transformedTables = woff2TransformedTableTags - - self.transformedTables = set(transformedTables) - - def _decompress(self, rawData): - return brotli.decompress(rawData) - - -def unpackBase128(data): - r"""Read one to five bytes from UIntBase128-encoded input string, and return - a tuple containing the decoded integer plus any leftover data. - - >>> unpackBase128(b'\x3f\x00\x00') == (63, b"\x00\x00") - True - >>> unpackBase128(b'\x8f\xff\xff\xff\x7f')[0] == 4294967295 - True - >>> unpackBase128(b'\x80\x80\x3f') # doctest: +IGNORE_EXCEPTION_DETAIL - Traceback (most recent call last): - File " ", line 1, in ? - TTLibError: UIntBase128 value must not start with leading zeros - >>> unpackBase128(b'\x8f\xff\xff\xff\xff\x7f')[0] # doctest: +IGNORE_EXCEPTION_DETAIL - Traceback (most recent call last): - File " ", line 1, in ? - TTLibError: UIntBase128-encoded sequence is longer than 5 bytes - >>> unpackBase128(b'\x90\x80\x80\x80\x00')[0] # doctest: +IGNORE_EXCEPTION_DETAIL - Traceback (most recent call last): - File " ", line 1, in ? - TTLibError: UIntBase128 value exceeds 2**32-1 - """ - if len(data) == 0: - raise TTLibError("not enough data to unpack UIntBase128") - result = 0 - if byteord(data[0]) == 0x80: - # font must be rejected if UIntBase128 value starts with 0x80 - raise TTLibError("UIntBase128 value must not start with leading zeros") - for i in range(woff2Base128MaxSize): - if len(data) == 0: - raise TTLibError("not enough data to unpack UIntBase128") - code = byteord(data[0]) - data = data[1:] - # if any of the top seven bits are set then we're about to overflow - if result & 0xFE000000: - raise TTLibError("UIntBase128 value exceeds 2**32-1") - # set current value = old value times 128 bitwise-or (byte bitwise-and 127) - result = (result << 7) | (code & 0x7F) - # repeat until the most significant bit of byte is false - if (code & 0x80) == 0: - # return result plus left over data - return result, data - # make sure not to exceed the size bound - raise TTLibError("UIntBase128-encoded sequence is longer than 5 bytes") - - -def base128Size(n): - """Return the length in bytes of a UIntBase128-encoded sequence with value n. - - >>> base128Size(0) - 1 - >>> base128Size(24567) - 3 - >>> base128Size(2**32-1) - 5 - """ - assert n >= 0 - size = 1 - while n >= 128: - size += 1 - n >>= 7 - return size - - -def packBase128(n): - r"""Encode unsigned integer in range 0 to 2**32-1 (inclusive) to a string of - bytes using UIntBase128 variable-length encoding. Produce the shortest possible - encoding. - - >>> packBase128(63) == b"\x3f" - True - >>> packBase128(2**32-1) == b'\x8f\xff\xff\xff\x7f' - True - """ - if n < 0 or n >= 2**32: - raise TTLibError("UIntBase128 format requires 0 <= integer <= 2**32-1") - data = b"" - size = base128Size(n) - for i in range(size): - b = (n >> (7 * (size - i - 1))) & 0x7F - if i < size - 1: - b |= 0x80 - data += struct.pack("B", b) - return data - - -def unpack255UShort(data): - """Read one to three bytes from 255UInt16-encoded input string, and return a - tuple containing the decoded integer plus any leftover data. - - >>> unpack255UShort(bytechr(252))[0] - 252 - - Note that some numbers (e.g. 506) can have multiple encodings: - >>> unpack255UShort(struct.pack("BB", 254, 0))[0] - 506 - >>> unpack255UShort(struct.pack("BB", 255, 253))[0] - 506 - >>> unpack255UShort(struct.pack("BBB", 253, 1, 250))[0] - 506 - """ - code = byteord(data[:1]) - data = data[1:] - if code == 253: - # read two more bytes as an unsigned short - if len(data) < 2: - raise TTLibError("not enough data to unpack 255UInt16") - (result,) = struct.unpack(">H", data[:2]) - data = data[2:] - elif code == 254: - # read another byte, plus 253 * 2 - if len(data) == 0: - raise TTLibError("not enough data to unpack 255UInt16") - result = byteord(data[:1]) - result += 506 - data = data[1:] - elif code == 255: - # read another byte, plus 253 - if len(data) == 0: - raise TTLibError("not enough data to unpack 255UInt16") - result = byteord(data[:1]) - result += 253 - data = data[1:] - else: - # leave as is if lower than 253 - result = code - # return result plus left over data - return result, data - - -def pack255UShort(value): - r"""Encode unsigned integer in range 0 to 65535 (inclusive) to a bytestring - using 255UInt16 variable-length encoding. - - >>> pack255UShort(252) == b'\xfc' - True - >>> pack255UShort(506) == b'\xfe\x00' - True - >>> pack255UShort(762) == b'\xfd\x02\xfa' - True - """ - if value < 0 or value > 0xFFFF: - raise TTLibError("255UInt16 format requires 0 <= integer <= 65535") - if value < 253: - return struct.pack(">B", value) - elif value < 506: - return struct.pack(">BB", 255, value - 253) - elif value < 762: - return struct.pack(">BB", 254, value - 506) - else: - return struct.pack(">BH", 253, value) - - -def compress(input_file, output_file, transform_tables=None): - """Compress OpenType font to WOFF2. - - Args: - input_file: a file path, file or file-like object (open in binary mode) - containing an OpenType font (either CFF- or TrueType-flavored). - output_file: a file path, file or file-like object where to save the - compressed WOFF2 font. - transform_tables: Optional[Iterable[str]]: a set of table tags for which - to enable preprocessing transformations. By default, only 'glyf' - and 'loca' tables are transformed. An empty set means disable all - transformations. - """ - log.info("Processing %s => %s" % (input_file, output_file)) - - font = TTFont(input_file, recalcBBoxes=False, recalcTimestamp=False) - font.flavor = "woff2" - - if transform_tables is not None: - font.flavorData = WOFF2FlavorData( - data=font.flavorData, transformedTables=transform_tables - ) - - font.save(output_file, reorderTables=False) - - -def decompress(input_file, output_file): - """Decompress WOFF2 font to OpenType font. - - Args: - input_file: a file path, file or file-like object (open in binary mode) - containing a compressed WOFF2 font. - output_file: a file path, file or file-like object where to save the - decompressed OpenType font. - """ - log.info("Processing %s => %s" % (input_file, output_file)) - - font = TTFont(input_file, recalcBBoxes=False, recalcTimestamp=False) - font.flavor = None - font.flavorData = None - font.save(output_file, reorderTables=True) - - -def main(args=None): - """Compress and decompress WOFF2 fonts""" - import argparse - from fontTools import configLogger - from fontTools.ttx import makeOutputFileName - - class _HelpAction(argparse._HelpAction): - def __call__(self, parser, namespace, values, option_string=None): - subparsers_actions = [ - action - for action in parser._actions - if isinstance(action, argparse._SubParsersAction) - ] - for subparsers_action in subparsers_actions: - for choice, subparser in subparsers_action.choices.items(): - print(subparser.format_help()) - parser.exit() - - class _NoGlyfTransformAction(argparse.Action): - def __call__(self, parser, namespace, values, option_string=None): - namespace.transform_tables.difference_update({"glyf", "loca"}) - - class _HmtxTransformAction(argparse.Action): - def __call__(self, parser, namespace, values, option_string=None): - namespace.transform_tables.add("hmtx") - - parser = argparse.ArgumentParser( - prog="fonttools ttLib.woff2", description=main.__doc__, add_help=False - ) - - parser.add_argument( - "-h", "--help", action=_HelpAction, help="show this help message and exit" - ) - - parser_group = parser.add_subparsers(title="sub-commands") - parser_compress = parser_group.add_parser( - "compress", description="Compress a TTF or OTF font to WOFF2" - ) - parser_decompress = parser_group.add_parser( - "decompress", description="Decompress a WOFF2 font to OTF" - ) - - for subparser in (parser_compress, parser_decompress): - group = subparser.add_mutually_exclusive_group(required=False) - group.add_argument( - "-v", - "--verbose", - action="store_true", - help="print more messages to console", - ) - group.add_argument( - "-q", - "--quiet", - action="store_true", - help="do not print messages to console", - ) - - parser_compress.add_argument( - "input_file", - metavar="INPUT", - help="the input OpenType font (.ttf or .otf)", - ) - parser_decompress.add_argument( - "input_file", - metavar="INPUT", - help="the input WOFF2 font", - ) - - parser_compress.add_argument( - "-o", - "--output-file", - metavar="OUTPUT", - help="the output WOFF2 font", - ) - parser_decompress.add_argument( - "-o", - "--output-file", - metavar="OUTPUT", - help="the output OpenType font", - ) - - transform_group = parser_compress.add_argument_group() - transform_group.add_argument( - "--no-glyf-transform", - dest="transform_tables", - nargs=0, - action=_NoGlyfTransformAction, - help="Do not transform glyf (and loca) tables", - ) - transform_group.add_argument( - "--hmtx-transform", - dest="transform_tables", - nargs=0, - action=_HmtxTransformAction, - help="Enable optional transformation for 'hmtx' table", - ) - - parser_compress.set_defaults( - subcommand=compress, - transform_tables={"glyf", "loca"}, - ) - parser_decompress.set_defaults(subcommand=decompress) - - options = vars(parser.parse_args(args)) - - subcommand = options.pop("subcommand", None) - if not subcommand: - parser.print_help() - return - - quiet = options.pop("quiet") - verbose = options.pop("verbose") - configLogger( - level=("ERROR" if quiet else "DEBUG" if verbose else "INFO"), - ) - - if not options["output_file"]: - if subcommand is compress: - extension = ".woff2" - elif subcommand is decompress: - # choose .ttf/.otf file extension depending on sfntVersion - with open(options["input_file"], "rb") as f: - f.seek(4) # skip 'wOF2' signature - sfntVersion = f.read(4) - assert len(sfntVersion) == 4, "not enough data" - extension = ".otf" if sfntVersion == b"OTTO" else ".ttf" - else: - raise AssertionError(subcommand) - options["output_file"] = makeOutputFileName( - options["input_file"], outputDir=None, extension=extension - ) - - try: - subcommand(**options) - except TTLibError as e: - parser.error(e) - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/spaces/johnslegers/stable-diffusion-gui-test/ldmlib/modules/ema.py b/spaces/johnslegers/stable-diffusion-gui-test/ldmlib/modules/ema.py deleted file mode 100644 index c8c75af43565f6e140287644aaaefa97dd6e67c5..0000000000000000000000000000000000000000 --- a/spaces/johnslegers/stable-diffusion-gui-test/ldmlib/modules/ema.py +++ /dev/null @@ -1,76 +0,0 @@ -import torch -from torch import nn - - -class LitEma(nn.Module): - def __init__(self, model, decay=0.9999, use_num_upates=True): - super().__init__() - if decay < 0.0 or decay > 1.0: - raise ValueError('Decay must be between 0 and 1') - - self.m_name2s_name = {} - self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32)) - self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates - else torch.tensor(-1,dtype=torch.int)) - - for name, p in model.named_parameters(): - if p.requires_grad: - #remove as '.'-character is not allowed in buffers - s_name = name.replace('.','') - self.m_name2s_name.update({name:s_name}) - self.register_buffer(s_name,p.clone().detach().data) - - self.collected_params = [] - - def forward(self,model): - decay = self.decay - - if self.num_updates >= 0: - self.num_updates += 1 - decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates)) - - one_minus_decay = 1.0 - decay - - with torch.no_grad(): - m_param = dict(model.named_parameters()) - shadow_params = dict(self.named_buffers()) - - for key in m_param: - if m_param[key].requires_grad: - sname = self.m_name2s_name[key] - shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) - shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key])) - else: - assert not key in self.m_name2s_name - - def copy_to(self, model): - m_param = dict(model.named_parameters()) - shadow_params = dict(self.named_buffers()) - for key in m_param: - if m_param[key].requires_grad: - m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) - else: - assert not key in self.m_name2s_name - - def store(self, parameters): - """ - Save the current parameters for restoring later. - Args: - parameters: Iterable of `torch.nn.Parameter`; the parameters to be - temporarily stored. - """ - self.collected_params = [param.clone() for param in parameters] - - def restore(self, parameters): - """ - Restore the parameters stored with the `store` method. - Useful to validate the model with EMA parameters without affecting the - original optimization process. Store the parameters before the - `copy_to` method. After validation (or model saving), use this to - restore the former parameters. - Args: - parameters: Iterable of `torch.nn.Parameter`; the parameters to be - updated with the stored parameters. - """ - for c_param, param in zip(self.collected_params, parameters): - param.data.copy_(c_param.data) diff --git a/spaces/johnson906/recipedia/src/utils/output_utils.py b/spaces/johnson906/recipedia/src/utils/output_utils.py deleted file mode 100644 index d82bc833891a3ae1d31422bdb7ca2f9f339f9049..0000000000000000000000000000000000000000 --- a/spaces/johnson906/recipedia/src/utils/output_utils.py +++ /dev/null @@ -1,103 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. - -replace_dict = {' .': '.', - ' ,': ',', - ' ;': ';', - ' :': ':', - '( ': '(', - ' )': ')', - " '": "'"} - - -def get_recipe(ids, vocab): - toks = [] - for id_ in ids: - toks.append(vocab[id_]) - return toks - - -def get_ingrs(ids, ingr_vocab_list): - gen_ingrs = [] - for ingr_idx in ids: - ingr_name = ingr_vocab_list[ingr_idx] - if ingr_name == ' ': - break - gen_ingrs.append(ingr_name) - return gen_ingrs - - -def prettify(toks, replace_dict): - toks = ' '.join(toks) - toks = toks.split(' ')[0] - sentences = toks.split(' ') - - pretty_sentences = [] - for sentence in sentences: - sentence = sentence.strip() - sentence = sentence.capitalize() - for k, v in replace_dict.items(): - sentence = sentence.replace(k, v) - if sentence != '': - pretty_sentences.append(sentence) - return pretty_sentences - - -def colorized_list(ingrs, ingrs_gt, colorize=False): - if colorize: - colorized_list = [] - for word in ingrs: - if word in ingrs_gt: - word = '\033[1;30;42m ' + word + ' \x1b[0m' - else: - word = '\033[1;30;41m ' + word + ' \x1b[0m' - colorized_list.append(word) - return colorized_list - else: - return ingrs - - -def prepare_output(ids, gen_ingrs, ingr_vocab_list, vocab): - - toks = get_recipe(ids, vocab) - is_valid = True - reason = 'All ok.' - try: - cut = toks.index(' ') - toks_trunc = toks[0:cut] - except: - toks_trunc = toks - is_valid = False - reason = 'no eos found' - - # repetition score - score = float(len(set(toks_trunc))) / float(len(toks_trunc)) - - prev_word = '' - found_repeat = False - for word in toks_trunc: - if prev_word == word and prev_word != ' ': - found_repeat = True - break - prev_word = word - - toks = prettify(toks, replace_dict) - title = toks[0] - toks = toks[1:] - - if gen_ingrs is not None: - gen_ingrs = get_ingrs(gen_ingrs, ingr_vocab_list) - - if score <= 0.3: - reason = 'Diversity score.' - is_valid = False - elif len(toks) != len(set(toks)): - reason = 'Repeated instructions.' - is_valid = False - elif found_repeat: - reason = 'Found word repeat.' - is_valid = False - - valid = {'is_valid': is_valid, 'reason': reason, 'score': score} - outs = {'title': title, 'recipe': toks, 'ingrs': gen_ingrs} - - return outs, valid diff --git a/spaces/jordonpeter01/laudos/page3.html b/spaces/jordonpeter01/laudos/page3.html deleted file mode 100644 index a6cb57c27601999d0493053067fffc8ba0285643..0000000000000000000000000000000000000000 --- a/spaces/jordonpeter01/laudos/page3.html +++ /dev/null @@ -1,58 +0,0 @@ - - file_1682345201208 
SumƔrio
ANEURISMA DE AORTA ABDOMINAL 8
BORRAMENTO DA GORDURA MESENTĆRICA 9
COLECISTITE LITIĆSICA BODERLINE 9
COLELITĆASE COM VESĆCULA ESCLEROATRĆFICA 10
COLESTEROLOSE/ADENOMIOMATOSE 10
DERIVAĆĆO BĆLIO-DIGESTIVA 11
ESPESSAMENTO DE ALĆA COLĆNICA 11
HEPATOPATIA CRĆNICA COM HIPERTENSĆO PORTAL 13
HEPATOPATIA CRĆNICA/MĆLTIPLOS NĆDULOS 13
HEPATOPATIA: FIBROSE PERI-PORTAL (ESQUISTOSSOMOSE) 13
NĆDULOS HEPĆTICOS SECUNDĆRIOS 14
TUMOR DE CABEĆA DE PĆNCREAS 15
COLEĆĆO PERI-ENXERTO RENAL 17
ESPESSAMENTO VESICAL: BEXIGA DE ESFORĆO 18
ESPESSAMENTO VESICAL: CISTITE 19
MASSA PĆLVICA COM URETERO-HIDRONEFROSE 19
TUMOR DE WILMS OU NEUROBLATOMA 21
VĆLVULA DE URETRA POSTERIOR 21
LINFONODO CERVICAL HABITUAL PALPĆVEL 21
LINFONODOMEGALIAS CERVICAIS 21
DERRAME PLEURAL COM ATELECTASIA 22
ECTASIA/PROEMINĆNCIA DUCTAL 23
MASSA COM LESĆES SATĆLITES MULTIFOCAIS 24
CISTOS FUNCIONAIS OVARIANOS 26
CISTO HEMORRĆGICO OVARIANO 26
ECTĆPICA ROTA OU CISTO HEMORRĆGICO: MASSA 27
ENDOMETRIO: ESPESSAMENTO PĆS-MENOPAUSA 28
MALFORMAĆĆES MĆLLERIANAS 29
ĆBITO EMBRIONĆRIO (IG > 7 sem) 30
ĆBITO EMBRIONĆRIO BODERLINE (BCF nĆ£o detectado) 30
ĆBITO EMBRIONĆRIO: GESTAĆĆO INVIĆVEL (embriĆ£o em degeneraĆ§Ć£o) 30
ĆBITO EMBRIONĆRIO: GESTAĆĆO INVIĆVEL (saco deslocado) 30
PSEUDO-CAVIDADE UTERINA DA CICATRIZ CESARIANA 31
MĆSCULO ESQUELĆTICO/PARTES SUPERFICIAIS 31
DERRAME DOENĆA REUMATOLĆGICA 31
ESTIRAMENTO LIGAMENTO COLATERAL 31
NERVO ULNAR: COMPRESSĆO TRICIPTAL 32
RUPTURA PARCIAL: EPICĆNDILO LATERAL 32
TENDINITE: EPICĆNDILO LATERAL 32
TENDINITE: EPICĆNDILO MEDIAL 33
TENDINOSE: EPICĆNDILO LATERAL 33
TENDINOSE: EPICĆNDILO MEDIAL 33
DERRAME ARTICULAR COM PROLIFERAĆĆO SINOVIAL 34
OSTEOARTROSE COM DERRAME ARTICULAR 34
OSTEOCONDROSE: OSGOOD-SCHLATTER 35
OSTEOCONDROSE: SINDING-LARSEN-JOHARSSON 36
RUPTURA PARCIAL QUADRĆCEPS 36
LUXAĆĆO/SUBLUXAĆĆO BICIPTAL 38
RUPTURA PARCIAL SUPRA (NĆO TRANSFIXANTE) 38
RUPTURA PARCIAL SUBESCAPULAR (NĆO TRANSFIXANTE) 38
RUPTURA TRANSFIXANTE AGUDA/SUBAGUDA DO SUPRA 39
RUPTURA TRANSFIXANTE CRĆNICA DO SUPRA 39
DERRAME ARTICULAR COM DISTENSĆO DA BURSA 39
RUPTURA PARCIAL INFRA, SUB/REDONDO MENOR 39
RUPTURA TOTAL INFRA, SUB/REDONDO MENOR 39
COLEĆĆO HEMATOMA PĆS-TRAUMA 40
HĆRNIA DE PAREDE REDUTĆVEL 40
HĆRNIA DE PAREDE ENCARCERADA 40
LESĆO DO COMPLEXO CĆPSULO-LIGAMENTAR 41
OSTEOARTRITE COM DERRAME ARTICULAR 41
TENOSSINOVITE TĆNEL DO CARPO 41
CISTO(S) ARTROSSINOVIAL(IS) 43
RUPTURA TOTAL DO TENDĆO CALCĆNEO 44
RUPTURA PARCIAL DO TENDĆO CALCĆNEO 44
TENOSSINOVITE TIBIAL POSTERIOR 44
TENOSSINOVITE DOS FIBULARES 44
FIBROMATOSE PLANTAR (DOENĆA DE LEDDERHOSE) 45
ESPESSAMENTO MĆDIO-INTIMAL 46
PLACA ATEROSCLERĆTICA NĆO COMPLICADA 46
PLACA ATEROSCLERĆTICA COMPLICADA 46
ESTENOSE DA ARTĆRIA CARĆTIDA INTERNA 46
ESTENOSE DA ARTĆRIA CARĆTIDA EXTERNA 46
SUB-OCLUSĆO DA ARTĆRIA CARĆTIDA INTERNA 46
OCLUSĆO DA ARTĆRIA CARĆTIDA INTERNA 46
ACOTOVELAMENTO (āKINKINGā) DA ARTĆRIA CARĆTIDA INTERNA 46
ESTENOSE PROXIMAL (EXAME DIRETO DA LESĆO) 47
ESTENOSE PROXIMAL (SEM EXAME DIRETO DA LESĆO) 47
OCLUSĆO PROXIMAL COM ENCHIMENTO POR COLATERAIS 47
FENĆMENO DO ROUBO SUBCLĆVIO 47
OCLUSĆO OU ESTENOSE GRAVE DISTAL 47
OCLUSĆO DA ARTĆRIA CARĆTIDA INTERNA 47
TROMBOSE VENOSA PROFUNDA AGUDA 50
TROMBOSE VENOSA PROFUNDA SUBAGUDA/CRĆNICA NĆO RECANALIZADA 50
TROMBOSE VENOSA CRĆNICA PARCIALMENTE RECANALIZADA 50
TROMBOSE VENOSA CRĆNICA PARCIALMENTE RECANALIZADA 51
VEIA(S) PERFURANTE(S) MEDIAIS 51
ULTRASSONOGRAFIA LAUDOS
GERAL
- AusĆŖncia de achados ultrassonogrĆ”ficos patolĆ³gicos especĆficos.
PĆ¢ncreas e demais estruturas do retroperitĆ“nio nĆ£o visibilizados devido Ć intensa sobreposiĆ§Ć£o gasosa. Cauda e corpo pancreĆ”tico nĆ£o visibilizados devido Ć sobreposiĆ§Ć£o gasosa.
Cauda pancreĆ”tica nĆ£o visibilizada devido Ć sobreposiĆ§Ć£o gasosa.
Bexiga urinĆ”ria vazia, dificultando sua adequada avaliaĆ§Ć£o ecogrĆ”fica.
Bexiga urinĆ”ria com conteĆŗdo anecĆ³ide de baixa repleĆ§Ć£o, dificultando sua a adequada avaliaĆ§Ć£o ecogrĆ”fica.
OvĆ”rios nĆ£o visibilizados (interposiĆ§Ć£o gasosa de alƧas intestinais?).
OvĆ”rios nĆ£o visibilizados (atrĆ³ficos?).
ACHADO ADICIONAL:
Estudo complementar com sonda convexa de 5 Mhz dirigido para a cavidade pƩlvica evidenciou:
Nota: Exame realizado em carĆ”ter de urgĆŖncia
Nota: Exame realizado em carĆ”ter de urgĆŖncia no 2Āŗ dia pĆ³s-operatĆ³rio de . Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
MEDICINA INTERNA ABDOME SUPERIOR
AtualizaĆ§Ć£o: 03.12.2012
ABSCESSO HEPĆTICO
Nota-se imagem cĆstica, de paredes espessas e irregulares, conteĆŗdo anecĆ³ide com moderados debris e traves de permeio (cĆstico-espesso), com fluxo perifĆ©rico ao Doppler, ocupando o segmento hepĆ”tico * (SegmentaĆ§Ć£o de Couinaund), medindo cerca de cm (L x AP x T) e volume estimado de cm3, distando cm da superfĆcie hepĆ”tica.
- Imagem cĆstico-espessa no fĆgado. Considerar possibilidade de abscesso hepĆ”tico.
Obs.: Os achados dependem da adequada correlaĆ§Ć£o clĆnico-laboratorial.
ADENITE MESENTĆRICA
Estudo ultrassonogrĆ”fico complementar com sonda linear de 10MHz, dirigido em fossas ilĆacas e regiĆ£o peri-umbilical:
MEDIDAS
FĆgado de morfologia, contornos e dimensƵes normais, medindo o lobo esquerdo: cm (normal < 10,0 cm), com Ć¢ngulo do bordo inferior de Āŗ (normal < 45Āŗ) e o lobo direito: cm (normal < 150 cm), com Ć¢ngulo do bordo inferior de Āŗ (normal < 75Āŗ).
Vias biliares intra-hepĆ”ticas de calibres aumentados, medindo cm Ć esquerda e cm Ć direita (normal < 0,25 cm).
VesĆcula biliar normodistendida, medindo cm (normal < 10,0 x 4,0 cm), com paredes finas e lisas de espessura medindo cm (normal < 0,4 cm).
PĆ¢ncreas de morfologia, contornos, ecotextura e dimensƵes normais, medindo a cabeƧa: cm (normal < 3,3 cm), corpo: cm (normal
< 2,2 cm) e cauda: cm (normal < 2,8 cm).
Ducto de Wirsung de calibre preservado (normal <0, 2 cm).
BaƧo de morfologia, contornos, ecotextura e dimensƵes normais, medindo cm no eixo longitudinal (normal < 12,0 cm) e cm no transverso (normal < 7,0 cm).
Ureter de calibre preservado (normal < 0,5 cm).
Pele: MAMA: Pele de espessura aumentada, medindo cm (normal < 0,2 cm).
TESTĆCULO: Pele e tecido celular subcutĆ¢neo de espessura aumentada, medindo cm (normal < 0,6-0,7 cm).
Colo uterino medindo no eixo longitudinal mm (normal > 3,0 cm), no transversal cm (normal < 2,0 cm no segundo trimestre) e largura do canal cervical cm (normal < 0,8 cm).
Septo interterticular de espessura medindo cm (normal < 0,5 cm).
Tronco CelĆaco: pĆ©rvio de calibre e trajeto normais. Fluxo de direĆ§Ć£o aorto-alƧa.
ArtĆ©ria MesentĆ©rica Superior: de trajeto e calibre normais, medindo cm (normal < 0,7 cm), pĆ©rvia de calibre e trajeto normais. Fluxo de direĆ§Ć£o aorto-alƧa. DistĆ¢ncia entre a parede anterior da aorta e a artĆ©ria MesentĆ©rica Superior mede cm (normal ā¤ 1,1 cm). ArtĆ©ria HepĆ”tica: pĆ©rvia de calibre e trajeto normais.
ArtĆ©rias IlĆacas de trajeto e calibre normais, medindo cm Ć direita e cm Ć esquerda (normal < 2,0 cm),
Veias HepƔticas de trajeto e calibre normais, medindo cm (normal < 1,0 cm).
Veia Cava Inferior de trajeto e calibre normais, medindo cm (normal < 3,7 cm), pĆ©rvia com fluxo fĆ”sico e boa compressibilidade as manobras de compressĆ£o distal e Valsalva. Paredes finas, lisas e regulares. AusĆŖncia de trombose venosa aguda ou antiga.
Veia Porta de trajeto e calibre normais, medindo mm (normal < 1,3 cm).
Veia EsplĆŖnica de trajeto e calibre normais, medindo cm (normal < 0,9 cm).
Veia MesentƩrica Superior de trajeto e calibre normais, medindo cm (normal < 0,9 cm).
Veia GĆ”strica Esquerda de trajeto e calibre normais, medindo cm (normal < 0,6 cm). Rim direito mede: cm. Espessura de parĆŖnquima: cm.
Rim esquerdo mede: cm. Espessura de parĆŖnquima: cm.
SUGESTĆES
Nota: Sugiro Ć critĆ©rio clĆnico, correlaĆ§Ć£o clĆnico-laboratorial e complementaĆ§Ć£o com tomografia computadorizada sob contraste oral e endovenoso. Nota: Sugiro, Ć critĆ©rio clĆnico, complementar com tomografia computadorizada de abdome com contraste oral e endovenoso, salvo contra-indicaƧƵes. Nota: Sugiro, Ć critĆ©rio clĆnico, complementaĆ§Ć£o com tomografia computadorizada em protocolo de fĆgado com trĆŖs fases, salvo contra-indicaƧƵes.
Nota: Sugiro considerar as hipĆ³teses de restos ovulares de moderada quantidade ou reaĆ§Ć£o decidual em gestaĆ§Ć£o muito precoce com idade gestacional inferior a 5 semanas. Ć critĆ©rio clĆnico, controle ultrassonogrĆ”fico.
Nota: Estudo comparativo com ultrassonografia ... realizada no dia evidenciou ...
Nota: GestaƧƵes muito precoces com menos de 5 semanas podem nĆ£o ser caracterizadas ao estudo ultrassonogrĆ”fico.
Nota: GestaƧƵes muito precoces podem nĆ£o ser caracterizadas ao estudo ultrassonogrĆ”fico com menos de 5 semanas (por via transabdominal) e 4 semanas e 3 dias (por via endovaginal).
Nota: Sugiro, Ć critĆ©rio clĆnico, controle ultrassonogrĆ”fico apĆ³s 1-2 semanas.
Nota: A coleĆ§Ć£o acima descrita Ć© passĆvel de punĆ§Ć£o, no entanto, o conteĆŗdo Ć© possivelmente cĆstico espesso.
Nota: A presenƧa de vesĆcula biliar com paredes espessadas pode corresponder a um achado encontrado nas ascites.
Nota: A presenƧa de vesĆcula biliar com paredes espessadas pode corresponder a um achado encontrado nos processos inflamatĆ³rios/infecciosos sistĆŖmicos. Nota: Intensa sobreposiĆ§Ć£o gasosa nas regiƵes de epigĆ”strio, mesogĆ”trio, flancos e fossas ilĆacas, dificultando a adequada avaliaĆ§Ć£o ecogrĆ”fica.
Nota: O mĆ©todo ultrassonogrĆ”fico apresenta baixa sensibilidade na avaliaĆ§Ć£o pielonefrite aguda em fase muito precoce. Sugiro correlaĆ§Ć£o clĆnico-laboratorial. Nota: O mĆ©todo ultrassonogrĆ”fico apresenta baixa sensibilidade na avaliaĆ§Ć£o hepatite. Sugiro correlaĆ§Ć£o clĆnico-laboratorial.
Nota: Sugiro correlaĆ§Ć£o clĆnico-laboratorial para avaliar possibilidade de colecistite em estĆ”gio inicial. Nota: NecessĆ”ria correlaĆ§Ć£o com BI-RADSTM mamogrĆ”fico para manutenĆ§Ć£o ou elevaĆ§Ć£o da categoria.
Nota: Estudo comparativo com ultrassonografia realizada no dia nĆ£o evidenciou mudanƧa ou complicaĆ§Ć£o da suspeita diagnĆ³stica.
ADVERTĆNCIAS
Obs.: Sugiro melhores esclarecimentos sobre dados clĆnico-laboratoriais do(a) paciente, para que assim, possamos contribuir da melhor forma na conclusĆ£o do diagnĆ³stico. Estaremos sempre Ć disposiĆ§Ć£o para melhor servi-lo.
Obs.: Sugiro melhores esclarecimentos sobre dados clĆnico-laboratoriais do(a) paciente para realizaĆ§Ć£o de exame na urgĆŖncia.
Obs.: Sugerimos informaĆ§Ć£o sobre dados laboratoriais para apendicite previamente ao estudo ultrassonogrĆ”fico, aumentando-se com isso, a acurĆ”cia do mĆ©todo.
Obs.: Sugerimos a realizaĆ§Ć£o de radiografia de abdome para avaliaĆ§Ć£o de ureterolitĆase, previamente ao estudo ultrassonogrĆ”fico, aumentando-se com isso, a acurĆ”cia do mĆ©todo, salvo contra-indicaƧƵes.
Obs.: NecessĆ”rio correlaĆ§Ć£o com radiografia de abdome devido a ausĆŖncia de sombra acĆŗstica posterior na imagem sugestiva de ureterolitĆase. Para isso, sugerimos este exame sempre previamente ao estudo ultrassonogrĆ”fico na suspeita de tal patologia, aumentando-se, com isso, a acurĆ”cia do mĆ©todo, salvo contra-indicaƧƵes.
Obs.: Recomendamos completa repleĆ§Ć£o vesical para pedidos de ultrassonografia renal e das vias urinĆ”rias.
Obs.: Sugerimos a realizaĆ§Ć£o de exames laboratoriais para pancreatite previamente ao estudo ultrassonogrĆ”fico, aumentando-se com isso, a acurĆ”cia do mĆ©todo.
Obs.: Sugerimos a realizaĆ§Ć£o de exames laboratoriais para apendicite previamente ao estudo ultrassonogrĆ”fico, aumentando-se com isso, a acurĆ”cia do mĆ©todo.
Obs.: Sugerimos a realizaĆ§Ć£o de exames laboratoriais para colecistite previamente ao estudo ultrassonogrĆ”fico, aumentando-se com isso, a acurĆ”cia do mĆ©todo.
Obs.: NecessƔrio HCG previamente ao estudo ultrassonogrƔfico para aumentar-se com isso, a acurƔcia do mƩtodo.
Obs.: No exame ultrassonogrĆ”fico obstĆ©trico convencional nĆ£o Ć© rotina a observĆ¢ncia de pormenores anatĆ“micos, nem detalhes da formaĆ§Ć£o fetal.
Obs.: O objetivo do ultrassom obstĆ©trico Ć© avaliar o crescimento e vitalidade fetais. NĆ£o tem finalidade de rastreamento e diagnĆ³stico de doenƧas genĆ©ticas e/ou malformaƧƵes fetais.
Obs.: O mĆ©todo ecogrĆ”fico por via endovaginal apresenta maior sensibilidade para detecĆ§Ć£o de patologias pĆ©lvicas comparado ao transabdominal. Obs.: O mĆ©todo ecogrĆ”fico apresenta baixa sensibilidade para detecĆ§Ć£o de patologias do trato gastrointestinal.
Obs.: Sugiro analgesia adequada previamente ao estudo ultrassonogrƔfico.
Obs.: Sugiro sedaĆ§Ć£o adequada previamente ao estudo ultrassonogrĆ”fico.
Obs.: Sugerimos a realizaĆ§Ć£o de radiografia de abdome para avaliaĆ§Ć£o de ureterolitĆase, previamente ao estudo ultrassonogrĆ”fico, aumentando-se com isso, a acurĆ”cia do mĆ©todo, para isso, recomendamos o contato mĆ©dico prĆ©vio nos exames de urgĆŖncia, a fim de contribuir da melhor forma na conclusĆ£o do diagnĆ³stico. Estaremos sempre Ć disposiĆ§Ć£o para melhor servi-lo.
Obs: Sangramento via vaginal sem repercussƵes ultrassonogrƔficas no presente estudo.
Obs.: O mĆ©todo ecogrĆ”fico por via transabdominal apresenta baixa sensibilidade para detecĆ§Ć£o de patologias pĆ©lvicas.
Nota: Devido a baixa repleĆ§Ć£o vesical, a acurĆ”cia do mĆ©todo ecogrĆ”fico Ć© reduzida para a identificaĆ§Ć£o de litĆase renal e em vias urinĆ”rias.
Nota: O exame ultrassonogrĆ”fico para estudo do abdome total nĆ£o possui como prĆ©-requisito a repleĆ§Ć£o total da bexiga urinĆ”ria, salvo por solicitaĆ§Ć£o mĆ©dica prĆ©via. Nota: O estudo ecogrĆ”fico apresenta baixa sensibilidade na detecĆ§Ć£o de patologias do retroperitĆ“nio em condiƧƵes da ausĆŖncia de preparo com jejum apropriado.
diff --git a/spaces/jpjpjpjpjp/HylandDocumentVisualQA/README.md b/spaces/jpjpjpjpjp/HylandDocumentVisualQA/README.md deleted file mode 100644 index 54c744c5ec7b7271029726ba53a5e567d0496935..0000000000000000000000000000000000000000 --- a/spaces/jpjpjpjpjp/HylandDocumentVisualQA/README.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -title: Hyland DocVQA -emoji: š -colorFrom: purple -colorTo: red -sdk: gradio -sdk_version: 2.8.9 -app_file: app.py -pinned: false ---- - diff --git a/spaces/juanhuggingface/ChuanhuChatGPT_Beta/modules/models/inspurai.py b/spaces/juanhuggingface/ChuanhuChatGPT_Beta/modules/models/inspurai.py deleted file mode 100644 index c590859fa7717d032290ccc490d22f4494541576..0000000000000000000000000000000000000000 --- a/spaces/juanhuggingface/ChuanhuChatGPT_Beta/modules/models/inspurai.py +++ /dev/null @@ -1,345 +0,0 @@ -# 代ē äø»č¦ę„ęŗäŗ https://github.com/Shawn-Inspur/Yuan-1.0/blob/main/yuan_api/inspurai.py - -import hashlib -import json -import os -import time -import uuid -from datetime import datetime - -import pytz -import requests - -from modules.presets import NO_APIKEY_MSG -from modules.models.base_model import BaseLLMModel - - -class Example: - """ store some examples(input, output pairs and formats) for few-shots to prime the model.""" - - def __init__(self, inp, out): - self.input = inp - self.output = out - self.id = uuid.uuid4().hex - - def get_input(self): - """return the input of the example.""" - return self.input - - def get_output(self): - """Return the output of the example.""" - return self.output - - def get_id(self): - """Returns the unique ID of the example.""" - return self.id - - def as_dict(self): - return { - "input": self.get_input(), - "output": self.get_output(), - "id": self.get_id(), - } - - -class Yuan: - """The main class for a user to interface with the Inspur Yuan API. - A user can set account info and add examples of the API request. - """ - - def __init__(self, - engine='base_10B', - temperature=0.9, - max_tokens=100, - input_prefix='', - input_suffix='\n', - output_prefix='ē:', - output_suffix='\n\n', - append_output_prefix_to_query=False, - topK=1, - topP=0.9, - frequencyPenalty=1.2, - responsePenalty=1.2, - noRepeatNgramSize=2): - - self.examples = {} - self.engine = engine - self.temperature = temperature - self.max_tokens = max_tokens - self.topK = topK - self.topP = topP - self.frequencyPenalty = frequencyPenalty - self.responsePenalty = responsePenalty - self.noRepeatNgramSize = noRepeatNgramSize - self.input_prefix = input_prefix - self.input_suffix = input_suffix - self.output_prefix = output_prefix - self.output_suffix = output_suffix - self.append_output_prefix_to_query = append_output_prefix_to_query - self.stop = (output_suffix + input_prefix).strip() - self.api = None - - # if self.engine not in ['base_10B','translate','dialog']: - # raise Exception('engine must be one of [\'base_10B\',\'translate\',\'dialog\'] ') - def set_account(self, api_key): - account = api_key.split('||') - self.api = YuanAPI(user=account[0], phone=account[1]) - - def add_example(self, ex): - """Add an example to the object. - Example must be an instance of the Example class.""" - assert isinstance(ex, Example), "Please create an Example object." - self.examples[ex.get_id()] = ex - - def delete_example(self, id): - """Delete example with the specific id.""" - if id in self.examples: - del self.examples[id] - - def get_example(self, id): - """Get a single example.""" - return self.examples.get(id, None) - - def get_all_examples(self): - """Returns all examples as a list of dicts.""" - return {k: v.as_dict() for k, v in self.examples.items()} - - def get_prime_text(self): - """Formats all examples to prime the model.""" - return "".join( - [self.format_example(ex) for ex in self.examples.values()]) - - def get_engine(self): - """Returns the engine specified for the API.""" - return self.engine - - def get_temperature(self): - """Returns the temperature specified for the API.""" - return self.temperature - - def get_max_tokens(self): - """Returns the max tokens specified for the API.""" - return self.max_tokens - - def craft_query(self, prompt): - """Creates the query for the API request.""" - q = self.get_prime_text( - ) + self.input_prefix + prompt + self.input_suffix - if self.append_output_prefix_to_query: - q = q + self.output_prefix - - return q - - def format_example(self, ex): - """Formats the input, output pair.""" - return self.input_prefix + ex.get_input( - ) + self.input_suffix + self.output_prefix + ex.get_output( - ) + self.output_suffix - - def response(self, - query, - engine='base_10B', - max_tokens=20, - temperature=0.9, - topP=0.1, - topK=1, - frequencyPenalty=1.0, - responsePenalty=1.0, - noRepeatNgramSize=0): - """Obtains the original result returned by the API.""" - - if self.api is None: - return NO_APIKEY_MSG - try: - # requestId = submit_request(query,temperature,topP,topK,max_tokens, engine) - requestId = self.api.submit_request(query, temperature, topP, topK, max_tokens, engine, frequencyPenalty, - responsePenalty, noRepeatNgramSize) - response_text = self.api.reply_request(requestId) - except Exception as e: - raise e - - return response_text - - def del_special_chars(self, msg): - special_chars = ['', ' ', '#', 'ā', 'ā', 'ā', 'ć'] - for char in special_chars: - msg = msg.replace(char, '') - return msg - - def submit_API(self, prompt, trun=[]): - """Submit prompt to yuan API interface and obtain an pure text reply. - :prompt: Question or any content a user may input. - :return: pure text response.""" - query = self.craft_query(prompt) - res = self.response(query, engine=self.engine, - max_tokens=self.max_tokens, - temperature=self.temperature, - topP=self.topP, - topK=self.topK, - frequencyPenalty=self.frequencyPenalty, - responsePenalty=self.responsePenalty, - noRepeatNgramSize=self.noRepeatNgramSize) - if 'resData' in res and res['resData'] != None: - txt = res['resData'] - else: - txt = 'ęØ”åčæåäøŗē©ŗļ¼čÆ·å°čÆäæ®ę¹č¾å „' - # åē¬éåƹēæ»čÆęØ”åēåå¤ē - if self.engine == 'translate': - txt = txt.replace(' ##', '').replace(' "', '"').replace(": ", ":").replace(" ,", ",") \ - .replace('č±ęļ¼', '').replace('ęļ¼', '').replace("( ", "(").replace(" )", ")") - else: - txt = txt.replace(' ', '') - txt = self.del_special_chars(txt) - - # trunå¤ē»ęē¬¦ęŖęęØ”åč¾åŗ - if isinstance(trun, str): - trun = [trun] - try: - if trun != None and isinstance(trun, list) and trun != []: - for tr in trun: - if tr in txt and tr != "": - txt = txt[:txt.index(tr)] - else: - continue - except: - return txt - return txt - - -class YuanAPI: - ACCOUNT = '' - PHONE = '' - - SUBMIT_URL = "http://api.airyuan.cn:32102/v1/interface/api/infer/getRequestId?" - REPLY_URL = "http://api.airyuan.cn:32102/v1/interface/api/result?" - - def __init__(self, user, phone): - self.ACCOUNT = user - self.PHONE = phone - - @staticmethod - def code_md5(str): - code = str.encode("utf-8") - m = hashlib.md5() - m.update(code) - result = m.hexdigest() - return result - - @staticmethod - def rest_get(url, header, timeout, show_error=False): - '''Call rest get method''' - try: - response = requests.get(url, headers=header, timeout=timeout, verify=False) - return response - except Exception as exception: - if show_error: - print(exception) - return None - - def header_generation(self): - """Generate header for API request.""" - t = datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d") - token = self.code_md5(self.ACCOUNT + self.PHONE + t) - headers = {'token': token} - return headers - - def submit_request(self, query, temperature, topP, topK, max_tokens, engine, frequencyPenalty, responsePenalty, - noRepeatNgramSize): - """Submit query to the backend server and get requestID.""" - headers = self.header_generation() - # url=SUBMIT_URL + "account={0}&data={1}&temperature={2}&topP={3}&topK={4}&tokensToGenerate={5}&type={6}".format(ACCOUNT,query,temperature,topP,topK,max_tokens,"api") - # url=SUBMIT_URL + "engine={0}&account={1}&data={2}&temperature={3}&topP={4}&topK={5}&tokensToGenerate={6}" \ - # "&type={7}".format(engine,ACCOUNT,query,temperature,topP,topK, max_tokens,"api") - url = self.SUBMIT_URL + "engine={0}&account={1}&data={2}&temperature={3}&topP={4}&topK={5}&tokensToGenerate={6}" \ - "&type={7}&frequencyPenalty={8}&responsePenalty={9}&noRepeatNgramSize={10}". \ - format(engine, self.ACCOUNT, query, temperature, topP, topK, max_tokens, "api", frequencyPenalty, - responsePenalty, noRepeatNgramSize) - response = self.rest_get(url, headers, 30) - response_text = json.loads(response.text) - if response_text["flag"]: - requestId = response_text["resData"] - return requestId - else: - raise RuntimeWarning(response_text) - - def reply_request(self, requestId, cycle_count=5): - """Check reply API to get the inference response.""" - url = self.REPLY_URL + "account={0}&requestId={1}".format(self.ACCOUNT, requestId) - headers = self.header_generation() - response_text = {"flag": True, "resData": None} - for i in range(cycle_count): - response = self.rest_get(url, headers, 30, show_error=True) - response_text = json.loads(response.text) - if response_text["resData"] is not None: - return response_text - if response_text["flag"] is False and i == cycle_count - 1: - raise RuntimeWarning(response_text) - time.sleep(3) - return response_text - - -class Yuan_Client(BaseLLMModel): - - def __init__(self, model_name, api_key, user_name="", system_prompt=None): - super().__init__(model_name=model_name, user=user_name) - self.history = [] - self.api_key = api_key - self.system_prompt = system_prompt - - self.input_prefix = "" - self.output_prefix = "" - - def set_text_prefix(self, option, value): - if option == 'input_prefix': - self.input_prefix = value - elif option == 'output_prefix': - self.output_prefix = value - - def get_answer_at_once(self): - # yuan temperature is (0,1] and base model temperature is [0,2], and yuan 0.9 == base 1 so need to convert - temperature = self.temperature if self.temperature <= 1 else 0.9 + (self.temperature - 1) / 10 - topP = self.top_p - topK = self.n_choices - # max_tokens should be in [1,200] - max_tokens = self.max_generation_token if self.max_generation_token is not None else 50 - if max_tokens > 200: - max_tokens = 200 - stop = self.stop_sequence if self.stop_sequence is not None else [] - examples = [] - system_prompt = self.system_prompt - if system_prompt is not None: - lines = system_prompt.splitlines() - # TODO: support prefixes in system prompt or settings - """ - if lines[0].startswith('-'): - prefixes = lines.pop()[1:].split('|') - self.input_prefix = prefixes[0] - if len(prefixes) > 1: - self.output_prefix = prefixes[1] - if len(prefixes) > 2: - stop = prefixes[2].split(',') - """ - for i in range(0, len(lines), 2): - in_line = lines[i] - out_line = lines[i + 1] if i + 1 < len(lines) else "" - examples.append((in_line, out_line)) - yuan = Yuan(engine=self.model_name.replace('yuanai-1.0-', ''), - temperature=temperature, - max_tokens=max_tokens, - topK=topK, - topP=topP, - input_prefix=self.input_prefix, - input_suffix="", - output_prefix=self.output_prefix, - output_suffix="".join(stop), - ) - if not self.api_key: - return NO_APIKEY_MSG, 0 - yuan.set_account(self.api_key) - - for in_line, out_line in examples: - yuan.add_example(Example(inp=in_line, out=out_line)) - - prompt = self.history[-1]["content"] - answer = yuan.submit_API(prompt, trun=stop) - return answer, len(answer) diff --git a/spaces/kabita-choudhary/summary/README.md b/spaces/kabita-choudhary/summary/README.md deleted file mode 100644 index 92925db2aeed3193b277d3bb10f05bbd6f15a967..0000000000000000000000000000000000000000 --- a/spaces/kabita-choudhary/summary/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Summary -emoji: š -colorFrom: gray -colorTo: yellow -sdk: gradio -sdk_version: 3.9 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/kcagle/AutoGPT/tests/test_token_counter.py b/spaces/kcagle/AutoGPT/tests/test_token_counter.py deleted file mode 100644 index 6d7ae016b2f823123b0b69b2eeb3eab50d94f00f..0000000000000000000000000000000000000000 --- a/spaces/kcagle/AutoGPT/tests/test_token_counter.py +++ /dev/null @@ -1,63 +0,0 @@ -import unittest - -import tests.context -from autogpt.token_counter import count_message_tokens, count_string_tokens - - -class TestTokenCounter(unittest.TestCase): - def test_count_message_tokens(self): - messages = [ - {"role": "user", "content": "Hello"}, - {"role": "assistant", "content": "Hi there!"}, - ] - self.assertEqual(count_message_tokens(messages), 17) - - def test_count_message_tokens_with_name(self): - messages = [ - {"role": "user", "content": "Hello", "name": "John"}, - {"role": "assistant", "content": "Hi there!"}, - ] - self.assertEqual(count_message_tokens(messages), 17) - - def test_count_message_tokens_empty_input(self): - self.assertEqual(count_message_tokens([]), 3) - - def test_count_message_tokens_invalid_model(self): - messages = [ - {"role": "user", "content": "Hello"}, - {"role": "assistant", "content": "Hi there!"}, - ] - with self.assertRaises(KeyError): - count_message_tokens(messages, model="invalid_model") - - def test_count_message_tokens_gpt_4(self): - messages = [ - {"role": "user", "content": "Hello"}, - {"role": "assistant", "content": "Hi there!"}, - ] - self.assertEqual(count_message_tokens(messages, model="gpt-4-0314"), 15) - - def test_count_string_tokens(self): - string = "Hello, world!" - self.assertEqual( - count_string_tokens(string, model_name="gpt-3.5-turbo-0301"), 4 - ) - - def test_count_string_tokens_empty_input(self): - self.assertEqual(count_string_tokens("", model_name="gpt-3.5-turbo-0301"), 0) - - def test_count_message_tokens_invalid_model(self): - messages = [ - {"role": "user", "content": "Hello"}, - {"role": "assistant", "content": "Hi there!"}, - ] - with self.assertRaises(NotImplementedError): - count_message_tokens(messages, model="invalid_model") - - def test_count_string_tokens_gpt_4(self): - string = "Hello, world!" - self.assertEqual(count_string_tokens(string, model_name="gpt-4-0314"), 4) - - -if __name__ == "__main__": - unittest.main() diff --git a/spaces/keras-io/Node2Vec_MovieLens/app.py b/spaces/keras-io/Node2Vec_MovieLens/app.py deleted file mode 100644 index bf5eba96d7cb7721f614407867d5627ebc058798..0000000000000000000000000000000000000000 --- a/spaces/keras-io/Node2Vec_MovieLens/app.py +++ /dev/null @@ -1,183 +0,0 @@ -import pandas as pd -import numpy as np -from zipfile import ZipFile -import tensorflow as tf -from tensorflow import keras -from pathlib import Path -import matplotlib.pyplot as plt -import gradio as gr -from huggingface_hub import from_pretrained_keras -from collections import defaultdict -import math -import networkx as nx - -model = from_pretrained_keras("keras-io/Node2Vec_MovieLens") - -# Download the actual data from http://files.grouplens.org/datasets/movielens/ml-latest-small.zip" -movielens_data_file_url = "http://files.grouplens.org/datasets/movielens/ml-latest-small.zip" -movielens_zipped_file = keras.utils.get_file("ml-latest-small.zip", movielens_data_file_url, extract=False) -keras_datasets_path = Path(movielens_zipped_file).parents[0] -movielens_dir = keras_datasets_path / "ml-latest-small" - -# Only extract the data the first time the script is run. -if not movielens_dir.exists(): - with ZipFile(movielens_zipped_file, "r") as zip: - # Extract files - print("Extracting all the files now...") - zip.extractall(path=keras_datasets_path) - print("Done!") - -# Read the Movies csv -movies = pd.read_csv(f"{movielens_dir}/movies.csv") -# Create a `movieId` string. -movies["movieId"] = movies["movieId"].apply(lambda x: f"movie_{x}") - -# Load ratings to a DataFrame. -ratings = pd.read_csv(f"{movielens_dir}/ratings.csv") -# Convert the `ratings` to floating point -ratings["rating"] = ratings["rating"].apply(lambda x: float(x)) -# Create the `movie_id` string. -ratings["movieId"] = ratings["movieId"].apply(lambda x: f"movie_{x}") - -# Implement two utility functions for the movies DataFrame. -def get_movie_title_by_id(movieId): - return list(movies[movies.movieId == movieId].title)[0] - - -def get_movie_id_by_title(title): - return list(movies[movies.title == title].movieId)[0] - -# Create Weighted Edges between movies -min_rating = 5 -pair_frequency = defaultdict(int) -item_frequency = defaultdict(int) - -# Filter instances where rating is greater than or equal to min_rating. -rated_movies = ratings[ratings.rating >= min_rating] -# Group instances by user. -movies_grouped_by_users = list(rated_movies.groupby("userId")) -for group in movies_grouped_by_users: - # Get a list of movies rated by the user. - current_movies = list(group[1]["movieId"]) - - for i in range(len(current_movies)): - item_frequency[current_movies[i]] += 1 - for j in range(i + 1, len(current_movies)): - x = min(current_movies[i], current_movies[j]) - y = max(current_movies[i], current_movies[j]) - pair_frequency[(x, y)] += 1 - -# Create the graph with the nodes and the edges - -min_weight = 10 -D = math.log(sum(item_frequency.values())) - -# Create the movies undirected graph. -movies_graph = nx.Graph() -# Add weighted edges between movies. -# This automatically adds the movie nodes to the graph. -for pair in pair_frequency: - x, y = pair - xy_frequency = pair_frequency[pair] - x_frequency = item_frequency[x] - y_frequency = item_frequency[y] - pmi = math.log(xy_frequency) - math.log(x_frequency) - math.log(y_frequency) + D - weight = pmi * xy_frequency - # Only include edges with weight >= min_weight. - if weight >= min_weight: - movies_graph.add_edge(x, y, weight=weight) -# Create vocabulary and a mapping from tokens to integer indices -vocabulary = ["NA"] + list(movies_graph.nodes) -vocabulary_lookup = {token: idx for idx, token in enumerate(vocabulary)} - -# Analyze the learnt embeddings. -movie_embeddings = model.get_layer("item_embeddings").get_weights()[0] - -# Find Related Movies -movie_titles = [] - -for uniq_mov_id in list(set(movies_graph.nodes)): - movie_title = get_movie_title_by_id(uniq_mov_id) - movie_titles.append(movie_title) - -def find_related_movies(movie_title, k): - k = int(k) - query_embeddings = [] - movieId = get_movie_id_by_title(movie_title) - token_id = vocabulary_lookup[movieId] - query_embedding = movie_embeddings[token_id] - query_embeddings.append(query_embedding) - query_embeddings = np.array(query_embeddings) - - similarities = tf.linalg.matmul( - tf.math.l2_normalize(query_embeddings), - tf.math.l2_normalize(movie_embeddings), - transpose_b=True, - ) - _, indices = tf.math.top_k(similarities, k) - indices = indices.numpy().tolist() - similar_tokens = indices[0] - related_movies = [] - - for token in similar_tokens: - similar_movieId = vocabulary[token] - similar_title = get_movie_title_by_id(similar_movieId) - related_movies.append(similar_title) - - related_movies_df = pd.DataFrame({'Related Movies':related_movies}) - return related_movies_df - - - -demo = gr.Blocks() -with demo: - gr.Markdown(""" - -- """) - - with gr.Box(): - gr.Markdown( - """ - ### Input - #### Select a movie to find other related movies. - """) - - inp1 = gr.Dropdown(movie_titles) - gr.Markdown( - """ -Find Related Movies
-Choose the specific movie from the dropdown and see the top k related Movies
- - Note: The dropdown menu provides movie options from the Movielens dataset. -
- """) - gr.Markdown( - """ - #### Number of related movies you wanna find? - """) - inp2 = gr.Number() - btn = gr.Button("Run") - - with gr.Box(): - gr.Markdown( - """ - ### Output - #### Top K related movies. - """) - df1 = gr.DataFrame(headers=["title"], datatype=["str"], interactive=False) - - with gr.Row(): - gr.Markdown( - """ -Credits
- Author: Khalid Salama.
- Based on the following Keras example Graph representation learning with node2vec by Khalid Salama
- Check out the model here - """ - ) - - - btn.click(fn=find_related_movies, inputs=[inp1,inp2], outputs=df1) - -demo.launch(debug=True) \ No newline at end of file diff --git a/spaces/kevinwang676/Bark-Coqui/bark_voices/blank.md b/spaces/kevinwang676/Bark-Coqui/bark_voices/blank.md deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_mbf.py b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_mbf.py deleted file mode 100644 index b8a00d6305eeda5a94788017afc1cda0d4a4cd2a..0000000000000000000000000000000000000000 --- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_mbf.py +++ /dev/null @@ -1,26 +0,0 @@ -from easydict import EasyDict as edict - -# make training faster -# our RAM is 256G -# mount -t tmpfs -o size=140G tmpfs /train_tmp - -config = edict() -config.loss = "arcface" -config.network = "mbf" -config.resume = False -config.output = None -config.embedding_size = 512 -config.sample_rate = 1.0 -config.fp16 = True -config.momentum = 0.9 -config.weight_decay = 2e-4 -config.batch_size = 128 -config.lr = 0.1 # batch size is 512 - -config.rec = "/train_tmp/ms1m-retinaface-t1" -config.num_classes = 93431 -config.num_image = 5179510 -config.num_epoch = 30 -config.warmup_epoch = -1 -config.decay_epoch = [10, 20, 25] -config.val_targets = ["lfw", "cfp_fp", "agedb_30"] diff --git a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/facerender/sync_batchnorm/comm.py b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/facerender/sync_batchnorm/comm.py deleted file mode 100644 index 922f8c4a3adaa9b32fdcaef09583be03b0d7eb2b..0000000000000000000000000000000000000000 --- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/facerender/sync_batchnorm/comm.py +++ /dev/null @@ -1,137 +0,0 @@ -# -*- coding: utf-8 -*- -# File : comm.py -# Author : Jiayuan Mao -# Email : maojiayuan@gmail.com -# Date : 27/01/2018 -# -# This file is part of Synchronized-BatchNorm-PyTorch. -# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch -# Distributed under MIT License. - -import queue -import collections -import threading - -__all__ = ['FutureResult', 'SlavePipe', 'SyncMaster'] - - -class FutureResult(object): - """A thread-safe future implementation. Used only as one-to-one pipe.""" - - def __init__(self): - self._result = None - self._lock = threading.Lock() - self._cond = threading.Condition(self._lock) - - def put(self, result): - with self._lock: - assert self._result is None, 'Previous result has\'t been fetched.' - self._result = result - self._cond.notify() - - def get(self): - with self._lock: - if self._result is None: - self._cond.wait() - - res = self._result - self._result = None - return res - - -_MasterRegistry = collections.namedtuple('MasterRegistry', ['result']) -_SlavePipeBase = collections.namedtuple('_SlavePipeBase', ['identifier', 'queue', 'result']) - - -class SlavePipe(_SlavePipeBase): - """Pipe for master-slave communication.""" - - def run_slave(self, msg): - self.queue.put((self.identifier, msg)) - ret = self.result.get() - self.queue.put(True) - return ret - - -class SyncMaster(object): - """An abstract `SyncMaster` object. - - - During the replication, as the data parallel will trigger an callback of each module, all slave devices should - call `register(id)` and obtain an `SlavePipe` to communicate with the master. - - During the forward pass, master device invokes `run_master`, all messages from slave devices will be collected, - and passed to a registered callback. - - After receiving the messages, the master device should gather the information and determine to message passed - back to each slave devices. - """ - - def __init__(self, master_callback): - """ - - Args: - master_callback: a callback to be invoked after having collected messages from slave devices. - """ - self._master_callback = master_callback - self._queue = queue.Queue() - self._registry = collections.OrderedDict() - self._activated = False - - def __getstate__(self): - return {'master_callback': self._master_callback} - - def __setstate__(self, state): - self.__init__(state['master_callback']) - - def register_slave(self, identifier): - """ - Register an slave device. - - Args: - identifier: an identifier, usually is the device id. - - Returns: a `SlavePipe` object which can be used to communicate with the master device. - - """ - if self._activated: - assert self._queue.empty(), 'Queue is not clean before next initialization.' - self._activated = False - self._registry.clear() - future = FutureResult() - self._registry[identifier] = _MasterRegistry(future) - return SlavePipe(identifier, self._queue, future) - - def run_master(self, master_msg): - """ - Main entry for the master device in each forward pass. - The messages were first collected from each devices (including the master device), and then - an callback will be invoked to compute the message to be sent back to each devices - (including the master device). - - Args: - master_msg: the message that the master want to send to itself. This will be placed as the first - message when calling `master_callback`. For detailed usage, see `_SynchronizedBatchNorm` for an example. - - Returns: the message to be sent back to the master device. - - """ - self._activated = True - - intermediates = [(0, master_msg)] - for i in range(self.nr_slaves): - intermediates.append(self._queue.get()) - - results = self._master_callback(intermediates) - assert results[0][0] == 0, 'The first result should belongs to the master.' - - for i, res in results: - if i == 0: - continue - self._registry[i].result.put(res) - - for i in range(self.nr_slaves): - assert self._queue.get() is True - - return results[0][1] - - @property - def nr_slaves(self): - return len(self._registry) diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmseg/models/decode_heads/fpn_head.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmseg/models/decode_heads/fpn_head.py deleted file mode 100644 index 1241c55b0813d1ecdddf1e66e7c5031fbf78ed50..0000000000000000000000000000000000000000 --- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmseg/models/decode_heads/fpn_head.py +++ /dev/null @@ -1,68 +0,0 @@ -import numpy as np -import torch.nn as nn -from annotator.uniformer.mmcv.cnn import ConvModule - -from annotator.uniformer.mmseg.ops import resize -from ..builder import HEADS -from .decode_head import BaseDecodeHead - - -@HEADS.register_module() -class FPNHead(BaseDecodeHead): - """Panoptic Feature Pyramid Networks. - - This head is the implementation of `Semantic FPN -
") - set_state(self.state_file, state) - - -if __name__ == "__main__": - print(sys.argv) - _, openai_key, state_file = sys.argv - os.environ["OPENAI_API_KEY"] = openai_key - import autogpt.config.config - from autogpt.logs import logger - from autogpt.cli import main - import autogpt.utils - from autogpt.spinner import Spinner - - def add_message(title, content): - state = get_state(state_file) - state["messages"].append((title, content)) - set_state(state_file, state) - - def typewriter_log(title="", title_color="", content="", *args, **kwargs): - add_message(title, content) - - def warn(message, title="", *args, **kwargs): - add_message(title, message) - - def error(title, message="", *args, **kwargs): - add_message(title, message) - - def clean_input(prompt=""): - add_message(None, prompt) - state = get_state(state_file) - state["awaiting_input"] = True - set_state(state_file, state) - while state["pending_input"] is None: - state = get_state(state_file) - print("Waiting for input...") - time.sleep(1) - print("Got input") - pending_input = state["pending_input"] - state["pending_input"] = None - set_state(state_file, state) - return pending_input - - def spinner_start(): - add_message(None, "Thinking...") - - logger.typewriter_log = typewriter_log - logger.warn = warn - logger.error = error - autogpt.utils.clean_input = clean_input - Spinner.spin = spinner_start - - sys.argv = sys.argv[:1] - main() diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/merge/tables.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/merge/tables.py deleted file mode 100644 index b3c7dc396e2c538f747de8e7502c41234f73cbc8..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/merge/tables.py +++ /dev/null @@ -1,338 +0,0 @@ -# Copyright 2013 Google, Inc. All Rights Reserved. -# -# Google Author(s): Behdad Esfahbod, Roozbeh Pournader - -from fontTools import ttLib, cffLib -from fontTools.misc.psCharStrings import T2WidthExtractor -from fontTools.ttLib.tables.DefaultTable import DefaultTable -from fontTools.merge.base import add_method, mergeObjects -from fontTools.merge.cmap import computeMegaCmap -from fontTools.merge.util import * -import logging - - -log = logging.getLogger("fontTools.merge") - - -ttLib.getTableClass("maxp").mergeMap = { - "*": max, - "tableTag": equal, - "tableVersion": equal, - "numGlyphs": sum, - "maxStorage": first, - "maxFunctionDefs": first, - "maxInstructionDefs": first, - # TODO When we correctly merge hinting data, update these values: - # maxFunctionDefs, maxInstructionDefs, maxSizeOfInstructions -} - -headFlagsMergeBitMap = { - "size": 16, - "*": bitwise_or, - 1: bitwise_and, # Baseline at y = 0 - 2: bitwise_and, # lsb at x = 0 - 3: bitwise_and, # Force ppem to integer values. FIXME? - 5: bitwise_and, # Font is vertical - 6: lambda bit: 0, # Always set to zero - 11: bitwise_and, # Font data is 'lossless' - 13: bitwise_and, # Optimized for ClearType - 14: bitwise_and, # Last resort font. FIXME? equal or first may be better - 15: lambda bit: 0, # Always set to zero -} - -ttLib.getTableClass("head").mergeMap = { - "tableTag": equal, - "tableVersion": max, - "fontRevision": max, - "checkSumAdjustment": lambda lst: 0, # We need *something* here - "magicNumber": equal, - "flags": mergeBits(headFlagsMergeBitMap), - "unitsPerEm": equal, - "created": current_time, - "modified": current_time, - "xMin": min, - "yMin": min, - "xMax": max, - "yMax": max, - "macStyle": first, - "lowestRecPPEM": max, - "fontDirectionHint": lambda lst: 2, - "indexToLocFormat": first, - "glyphDataFormat": equal, -} - -ttLib.getTableClass("hhea").mergeMap = { - "*": equal, - "tableTag": equal, - "tableVersion": max, - "ascent": max, - "descent": min, - "lineGap": max, - "advanceWidthMax": max, - "minLeftSideBearing": min, - "minRightSideBearing": min, - "xMaxExtent": max, - "caretSlopeRise": first, - "caretSlopeRun": first, - "caretOffset": first, - "numberOfHMetrics": recalculate, -} - -ttLib.getTableClass("vhea").mergeMap = { - "*": equal, - "tableTag": equal, - "tableVersion": max, - "ascent": max, - "descent": min, - "lineGap": max, - "advanceHeightMax": max, - "minTopSideBearing": min, - "minBottomSideBearing": min, - "yMaxExtent": max, - "caretSlopeRise": first, - "caretSlopeRun": first, - "caretOffset": first, - "numberOfVMetrics": recalculate, -} - -os2FsTypeMergeBitMap = { - "size": 16, - "*": lambda bit: 0, - 1: bitwise_or, # no embedding permitted - 2: bitwise_and, # allow previewing and printing documents - 3: bitwise_and, # allow editing documents - 8: bitwise_or, # no subsetting permitted - 9: bitwise_or, # no embedding of outlines permitted -} - - -def mergeOs2FsType(lst): - lst = list(lst) - if all(item == 0 for item in lst): - return 0 - - # Compute least restrictive logic for each fsType value - for i in range(len(lst)): - # unset bit 1 (no embedding permitted) if either bit 2 or 3 is set - if lst[i] & 0x000C: - lst[i] &= ~0x0002 - # set bit 2 (allow previewing) if bit 3 is set (allow editing) - elif lst[i] & 0x0008: - lst[i] |= 0x0004 - # set bits 2 and 3 if everything is allowed - elif lst[i] == 0: - lst[i] = 0x000C - - fsType = mergeBits(os2FsTypeMergeBitMap)(lst) - # unset bits 2 and 3 if bit 1 is set (some font is "no embedding") - if fsType & 0x0002: - fsType &= ~0x000C - return fsType - - -ttLib.getTableClass("OS/2").mergeMap = { - "*": first, - "tableTag": equal, - "version": max, - "xAvgCharWidth": first, # Will be recalculated at the end on the merged font - "fsType": mergeOs2FsType, # Will be overwritten - "panose": first, # FIXME: should really be the first Latin font - "ulUnicodeRange1": bitwise_or, - "ulUnicodeRange2": bitwise_or, - "ulUnicodeRange3": bitwise_or, - "ulUnicodeRange4": bitwise_or, - "fsFirstCharIndex": min, - "fsLastCharIndex": max, - "sTypoAscender": max, - "sTypoDescender": min, - "sTypoLineGap": max, - "usWinAscent": max, - "usWinDescent": max, - # Version 1 - "ulCodePageRange1": onlyExisting(bitwise_or), - "ulCodePageRange2": onlyExisting(bitwise_or), - # Version 2, 3, 4 - "sxHeight": onlyExisting(max), - "sCapHeight": onlyExisting(max), - "usDefaultChar": onlyExisting(first), - "usBreakChar": onlyExisting(first), - "usMaxContext": onlyExisting(max), - # version 5 - "usLowerOpticalPointSize": onlyExisting(min), - "usUpperOpticalPointSize": onlyExisting(max), -} - - -@add_method(ttLib.getTableClass("OS/2")) -def merge(self, m, tables): - DefaultTable.merge(self, m, tables) - if self.version < 2: - # bits 8 and 9 are reserved and should be set to zero - self.fsType &= ~0x0300 - if self.version >= 3: - # Only one of bits 1, 2, and 3 may be set. We already take - # care of bit 1 implications in mergeOs2FsType. So unset - # bit 2 if bit 3 is already set. - if self.fsType & 0x0008: - self.fsType &= ~0x0004 - return self - - -ttLib.getTableClass("post").mergeMap = { - "*": first, - "tableTag": equal, - "formatType": max, - "isFixedPitch": min, - "minMemType42": max, - "maxMemType42": lambda lst: 0, - "minMemType1": max, - "maxMemType1": lambda lst: 0, - "mapping": onlyExisting(sumDicts), - "extraNames": lambda lst: [], -} - -ttLib.getTableClass("vmtx").mergeMap = ttLib.getTableClass("hmtx").mergeMap = { - "tableTag": equal, - "metrics": sumDicts, -} - -ttLib.getTableClass("name").mergeMap = { - "tableTag": equal, - "names": first, # FIXME? Does mixing name records make sense? -} - -ttLib.getTableClass("loca").mergeMap = { - "*": recalculate, - "tableTag": equal, -} - -ttLib.getTableClass("glyf").mergeMap = { - "tableTag": equal, - "glyphs": sumDicts, - "glyphOrder": sumLists, - "axisTags": equal, -} - - -@add_method(ttLib.getTableClass("glyf")) -def merge(self, m, tables): - for i, table in enumerate(tables): - for g in table.glyphs.values(): - if i: - # Drop hints for all but first font, since - # we don't map functions / CVT values. - g.removeHinting() - # Expand composite glyphs to load their - # composite glyph names. - if g.isComposite() or g.isVarComposite(): - g.expand(table) - return DefaultTable.merge(self, m, tables) - - -ttLib.getTableClass("prep").mergeMap = lambda self, lst: first(lst) -ttLib.getTableClass("fpgm").mergeMap = lambda self, lst: first(lst) -ttLib.getTableClass("cvt ").mergeMap = lambda self, lst: first(lst) -ttLib.getTableClass("gasp").mergeMap = lambda self, lst: first( - lst -) # FIXME? Appears irreconcilable - - -@add_method(ttLib.getTableClass("CFF ")) -def merge(self, m, tables): - if any(hasattr(table, "FDSelect") for table in tables): - raise NotImplementedError("Merging CID-keyed CFF tables is not supported yet") - - for table in tables: - table.cff.desubroutinize() - - newcff = tables[0] - newfont = newcff.cff[0] - private = newfont.Private - newDefaultWidthX, newNominalWidthX = private.defaultWidthX, private.nominalWidthX - storedNamesStrings = [] - glyphOrderStrings = [] - glyphOrder = set(newfont.getGlyphOrder()) - - for name in newfont.strings.strings: - if name not in glyphOrder: - storedNamesStrings.append(name) - else: - glyphOrderStrings.append(name) - - chrset = list(newfont.charset) - newcs = newfont.CharStrings - log.debug("FONT 0 CharStrings: %d.", len(newcs)) - - for i, table in enumerate(tables[1:], start=1): - font = table.cff[0] - defaultWidthX, nominalWidthX = ( - font.Private.defaultWidthX, - font.Private.nominalWidthX, - ) - widthsDiffer = ( - defaultWidthX != newDefaultWidthX or nominalWidthX != newNominalWidthX - ) - font.Private = private - fontGlyphOrder = set(font.getGlyphOrder()) - for name in font.strings.strings: - if name in fontGlyphOrder: - glyphOrderStrings.append(name) - cs = font.CharStrings - gs = table.cff.GlobalSubrs - log.debug("Font %d CharStrings: %d.", i, len(cs)) - chrset.extend(font.charset) - if newcs.charStringsAreIndexed: - for i, name in enumerate(cs.charStrings, start=len(newcs)): - newcs.charStrings[name] = i - newcs.charStringsIndex.items.append(None) - for name in cs.charStrings: - if widthsDiffer: - c = cs[name] - defaultWidthXToken = object() - extractor = T2WidthExtractor([], [], nominalWidthX, defaultWidthXToken) - extractor.execute(c) - width = extractor.width - if width is not defaultWidthXToken: - c.program.pop(0) - else: - width = defaultWidthX - if width != newDefaultWidthX: - c.program.insert(0, width - newNominalWidthX) - newcs[name] = cs[name] - - newfont.charset = chrset - newfont.numGlyphs = len(chrset) - newfont.strings.strings = glyphOrderStrings + storedNamesStrings - - return newcff - - -@add_method(ttLib.getTableClass("cmap")) -def merge(self, m, tables): - # TODO Handle format=14. - if not hasattr(m, "cmap"): - computeMegaCmap(m, tables) - cmap = m.cmap - - cmapBmpOnly = {uni: gid for uni, gid in cmap.items() if uni <= 0xFFFF} - self.tables = [] - module = ttLib.getTableModule("cmap") - if len(cmapBmpOnly) != len(cmap): - # format-12 required. - cmapTable = module.cmap_classes[12](12) - cmapTable.platformID = 3 - cmapTable.platEncID = 10 - cmapTable.language = 0 - cmapTable.cmap = cmap - self.tables.append(cmapTable) - # always create format-4 - cmapTable = module.cmap_classes[4](4) - cmapTable.platformID = 3 - cmapTable.platEncID = 1 - cmapTable.language = 0 - cmapTable.cmap = cmapBmpOnly - # ordered by platform then encoding - self.tables.insert(0, cmapTable) - self.tableVersion = 0 - self.numSubTables = len(self.tables) - return self diff --git a/spaces/lifan0127/zotero-qa/functions.py b/spaces/lifan0127/zotero-qa/functions.py deleted file mode 100644 index fef4f17e1c34d11a9d9951963d39cdf8f0caca92..0000000000000000000000000000000000000000 --- a/spaces/lifan0127/zotero-qa/functions.py +++ /dev/null @@ -1,395 +0,0 @@ -import gradio as gr -import os -import re -import requests -import tempfile -import time -from pyzotero import zotero -from paperqa import Docs -from lxml import html -from models import Icons, Message - - -def is_integer(string): - try: - int(string) - except ValueError: - return False - else: - return True - - -def reset_open_ai(openai_api_key): - os.environ['OPENAI_API_KEY'] = openai_api_key.strip() - return gr.HTML.update(value=None) - - -def fetch_collections(openai_api_key, id, type, key, messages): - if openai_api_key == '': - messages.append( - Message(Icons.ERR, f"Your Open API key is missing. Check out: https://platform.openai.com/overview.")) - return ( - None, - [], - None, - gr.Button.update(visible=True), - gr.HTML.update(visible=True), - messages, - gr.HTML.update(value=str(messages)), - ) - if key == '': - messages.append( - Message(Icons.ERR, f"Your Zotero API key is missing. Click here to create a new one.")) - return ( - None, - [], - None, - gr.Button.update(visible=True), - gr.HTML.update(visible=True), - messages, - gr.HTML.update(value=str(messages)), - ) - if not is_integer(id): - messages.append( - Message(Icons.ERR, f"Your Zotero ID should be an integer.")) - return ( - None, - [], - None, - gr.Button.update(visible=True), - gr.HTML.update(visible=True), - messages, - gr.HTML.update(value=str(messages)), - ) - - zot = zotero.Zotero(int(id), type.lower(), key) - try: - collections = zot.collections_top() - collection_names = [ - f"{x['data']['name']} ({x['meta']['numItems']})" for x in collections] - messages.append( - Message(Icons.INFO, "Please select a Zotero collection to proceed.")) - return ( - zot, - collections, - gr.Radio.update(choices=collection_names, - visible=True, interactive=True), - gr.Button.update(visible=False), - gr.HTML.update(visible=False), - messages, - gr.HTML.update(value=str(messages)), - ) - except Exception as e: - messages.append( - Message(Icons.ERR, f"Error occurred when fetching Zotero collection: {e}")) - return ( - None, - [], - None, - gr.Button.update(visible=True), - None, - messages, - gr.HTML.update(value=str(messages)), - ) - - -def select_collection(collection, messages): - if collection is None: - return None, messages, gr.HTML.update(), None - collection_name = re.sub('\s\(\d+\)$', '', collection) - messages.set([Message( - Icons.OK, f"Selected collection: {collection_name}. Please type your question and hit \"Enter\".")]) - return ( - gr.Text.update( - placeholder="Please type your question and hit \"Enter\".", interactive=True), - messages, - gr.HTML.update(value=str(messages)), - gr.HTML.update(value=None) - ) - - -# def search_attachments(id, type, key, collection, queries=[], limit=10): -# try: -# zot = zotero.Zotero(int(id), type.lower(), key) -# searches = [zot.collection_items( -# collection['key'], -# q=q, -# limit=limit, -# itemType='attachment', -# qmode='everything' -# ) for q in queries] -# attachments = [x for x in {item['key']: item for search in searches for item in search if item['data'] -# ['contentType'] == 'application/pdf'}.values()][:limit] - -# parents = set([a['data']['parentItem'] for a in attachments]) - -# message = f"ā Found {len(attachments)} PDF {'attachments' if len(attachments) > 1 else 'attachment'} from {len(parents)} {'articles' if len(parents) > 1 else 'article'}." if len( -# attachments) else "ā No results. Make sure to index your PDF attachments in Zotero." -# return parents, attachments, message - -# except Exception as e: -# message = f"ā ļø Error occurred when searching in Zotero: {e}" -# return [], [], message - - -def download_attachment(id, type, key, attachment): - zot = zotero.Zotero(int(id), type.lower(), key) - link_mode = attachment['data']['linkMode'] - - if link_mode == 'imported_file': - return zot.file(attachment['key']) - elif link_mode == 'imported_url': - res = requests.get(attachment['data']['url']) - return res.content - else: - raise ValueError( - f'Unsupported link mode: {link_mode} for {attachment["key"]}.') - - -def reset_collection(messages): - messages.set([Message( - Icons.INFO, "Please provide all the required OpenAI and Zotero information in the left panel.")]) - return ( - gr.Radio.update(choices=[], visible=False), - gr.HTML.update(visible=True), - gr.Text.update( - placeholder="You have to select a Zotero collection to proceed", interactive=False), - gr.HTML.update(value=None), - messages, - gr.HTML.update(value=str(messages)), - None - ) - - -def handle_submit(zot, collection_name, collections, style, question, messages): - collection_name_only = re.sub('\s\(\d+\)$', '', collection_name) - messages.set([Message( - Icons.OK, f"Selected collection: {collection_name_only}.")]) - yield ( - messages, - gr.HTML.update(value=str(messages)), - None, - ) - - docs = Docs() - - # Generate search queries from the question by Paper QA - try: - question_prompt = 'A "keyword search" is a list of no more than 3 words, which separated by whitespace only and with no boolean operators (e.g. "dog canine puppy"). Avoid adding any new words not in the question unless they are synonyms to the existing words.' - queries = [x.strip('"').lower() for x in - docs.generate_search_query(question + '\n' + question_prompt)] - query_str = ", ".join( - [f"{q}" for q in queries]) - messages.append( - Message(Icons.WAIT, f"Searching your Zotero collection for {query_str}.")) - yield ( - messages, - gr.HTML.update(value=str(messages)), - None, - ) - except Exception as e: - messages.append( - Message(Icons.ERR, f"Error occurred when generating search queries: {e}")) - yield ( - messages, - gr.HTML.update(value=str(messages)), - None, - ) - return None, None, None - - # Search for attachments in Zotero - try: - collection = [ - x for x in collections if f"{x['data']['name']} ({x['meta']['numItems']})" == collection_name][0] - searches = [zot.collection_items( - collection['key'], - q=q, - limit=10, - itemType='attachment', - qmode='everything' - ) for q in queries] - attachments = [x for x in { - item['key']: item for search in searches for item in search if item['data']['contentType'] == 'application/pdf'}.values()][:10] - - parents = set([a['data']['parentItem'] if 'parentItem' in a['data'] else a['key'] for a in attachments ]) - if len(attachments) > 0: - messages.append(Message( - Icons.SUCCESS, f"Found {len(attachments)} PDF {'attachments' if len(attachments) > 1 else 'attachment'} from {len(parents)} {'articles' if len(parents) > 1 else 'article'}.")) - yield ( - messages, - gr.HTML.update(value=str(messages)), - None, - ) - else: - messages.append(Message( - Icons.ERR, "No results. Make sure to index your PDF attachments in Zotero and try rephrasing your question.")) - yield ( - messages, - gr.HTML.update(value=str(messages)), - None, - ) - return None, None, None - - except Exception as e: - messages.append( - Message(Icons.ERR, f"Error occurred when searching in Zotero: {e}")) - yield ( - messages, - gr.HTML.update(value=str(messages)), - None, - ) - return None, None, None - - # Compile citation metadata - citation_dict = {} - parents = {} - messages.append( - Message(Icons.WAIT, f"Fetching attachment bibliography information.")) - yield ( - messages, - gr.HTML.update(value=str(messages)), - None, - ) - for attachment in attachments: - parent_id = attachment["data"]["parentItem"] if "parentItem" in attachment["data"] else attachment["key"] - try: - if parent_id in parents: - citation_dict[attachment["key"]] = parents[parent_id] - else: - parent = zot.item( - parent_id, content="bib", style=style)[0] - bib = f""" - {html.fragment_fromstring(parent).xpath("normalize-space(//*)")} - Open in Zotero - """ - parents[parent_id] = bib - citation_dict[attachment["key"]] = bib - except Exception as e: - messages.append(Message( - Icons.WARN, f"Failed to retrieve bibliography for PDF attachment {attachment['data']['title']}: {e}")) - yield ( - messages, - gr.HTML.update(value=str(messages)), - None, - ) - - # Load attachments - available_attachments = 0 - for attachment in attachments: - try: - link_mode = attachment['data']['linkMode'] - - if link_mode in ['imported_file', 'imported_url']: - attachment_content = zot.file(attachment['key']) if link_mode == 'imported_file' else requests.get( - attachment['data']['url']).content - temp_file = tempfile.NamedTemporaryFile(suffix=".pdf") - temp_file.write(attachment_content) - temp_file.flush() - docs.add(temp_file.name, citation_dict[attachment["key"]]) - messages.append(Message( - Icons.INDEX, f"Loaded PDF attachment: {attachment['data']['title']}.")) - available_attachments += 1 - else: - messages.append(Message( - Icons.WARN, f"Unable to access linked PDF attachment {attachment['data']['title']}: The file is not in Zotero online storage.")) - yield ( - messages, - gr.HTML.update(value=str(messages)), - None, - ) - except Exception as e: - messages.append(Message( - Icons.WARN, f"Failed to retrieve PDF attachment {attachment['data']['title']}: {e}")) - yield ( - messages, - gr.HTML.update(value=str(messages)), - None, - ) - - # Build vector index - if available_attachments == 0: - messages.append(Message( - Icons.ERR, "No answer. Unable to access any PDF attachments from your Zotero online storage or public URLs.")) - yield ( - messages, - gr.HTML.update(value=str(messages)), - None, - ) - return None, None, None - if docs._faiss_index is None: - try: - messages.append(Message( - Icons.WAIT, f"Building vector index based on {available_attachments} available PDF {'attachment' if available_attachments==1 else 'attachments'}.")) - yield ( - messages, - gr.HTML.update(value=str(messages)), - None, - ) - docs._build_faiss_index() - except Exception as e: - messages.append(Message( - Icons.ERR, f"Unable to build vector index: {e}")) - yield ( - messages, - gr.HTML.update(value=str(messages)), - None, - ) - return None, None, None - - # Synthesize response - messages.append(Message( - Icons.WAIT, f"""Creating answer. {"This should be done within a minute." if available_attachments==1 else "This will loop through all available PDF attachments and may take a couple of minutes."}.""")) - yield ( - messages, - gr.HTML.update(value=str(messages)), - None, - ) - try: - start_time = time.time() - total_time = 0 - for i, answer in enumerate(docs.query_gen(question)): - end_time = time.time() - time_dif = end_time - start_time - if time_dif > 15: - start_time = end_time - total_time += time_dif - messages.append(Message( - Icons.INFO, f"Still in prgress: {total_time:.1f} seconds")) - yield ( - messages, - gr.HTML.update(value=str(messages)), - None, - ) - answer_text = "\n".join( - [f"{x}" for x in answer.answer.split("\n")]) - reference_list = "" if answer.references == "" else "\n".join([f"References:
--
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- diff --git a/spaces/lnyan/stablediffusion-infinity/PyPatchMatch/README.md b/spaces/lnyan/stablediffusion-infinity/PyPatchMatch/README.md deleted file mode 100644 index 12b49aadadfe0ff51c2873b2671c0ca020bc3506..0000000000000000000000000000000000000000 --- a/spaces/lnyan/stablediffusion-infinity/PyPatchMatch/README.md +++ /dev/null @@ -1,64 +0,0 @@ -PatchMatch based Inpainting -===================================== -This library implements the PatchMatch based inpainting algorithm. It provides both C++ and Python interfaces. -This implementation is heavily based on the implementation by Younesse ANDAM: -(younesse-cv/PatchMatch)[https://github.com/younesse-cv/PatchMatch], with some bugs fix. - -Usage -------------------------------------- - -You need to first install OpenCV to compile the C++ libraries. Then, run `make` to compile the -shared library `libpatchmatch.so`. - -For Python users (example available at `examples/py_example.py`) - -```python -import patch_match - -image = ... # either a numpy ndarray or a PIL Image object. -mask = ... # either a numpy ndarray or a PIL Image object. -result = patch_match.inpaint(image, mask, patch_size=5) -``` - -For C++ users (examples available at `examples/cpp_example.cpp`) - -```cpp -#include "inpaint.h" - -int main() { - cv::Mat image = ... - cv::Mat mask = ... - - cv::Mat result = Inpainting(image, mask, 5).run(); - - return 0; -} -``` - - -README and COPYRIGHT by Younesse ANDAM -------------------------------------- -@Author: Younesse ANDAM - -@Contact: younesse.andam@gmail.com - -Description: This project is a personal implementation of an algorithm called PATCHMATCH that restores missing areas in an image. -The algorithm is presented in the following paper - PatchMatch A Randomized Correspondence Algorithm - for Structural Image Editing - by C.Barnes,E.Shechtman,A.Finkelstein and Dan B.Goldman - ACM Transactions on Graphics (Proc. SIGGRAPH), vol.28, aug-2009 - - For more information please refer to - http://www.cs.princeton.edu/gfx/pubs/Barnes_2009_PAR/index.php - -Copyright (c) 2010-2011 - - -Requirements -------------------------------------- - -To run the project you need to install Opencv library and link it to your project. -Opencv can be download it here -http://opencv.org/downloads.html - diff --git a/spaces/ma-xu/LIVE/thrust/internal/benchmark/compare_benchmark_results.py b/spaces/ma-xu/LIVE/thrust/internal/benchmark/compare_benchmark_results.py deleted file mode 100644 index 22e7be8cfc20e1de4cfa586258e433f2a93aeb27..0000000000000000000000000000000000000000 --- a/spaces/ma-xu/LIVE/thrust/internal/benchmark/compare_benchmark_results.py +++ /dev/null @@ -1,1308 +0,0 @@ -#! /usr/bin/env python -# -*- coding: utf-8 -*- - -############################################################################### -# Copyright (c) 2012-7 Bryce Adelstein Lelbach aka wash-#include - -namespace thrust -{ -namespace detail -{ - -template -__host__ __device__ - Pointer copy_construct_range(thrust::execution_policy &from_system, - Allocator &a, - InputIterator first, - InputIterator last, - Pointer result); - -template -__host__ __device__ - Pointer copy_construct_range_n(thrust::execution_policy &from_system, - Allocator &a, - InputIterator first, - Size n, - Pointer result); - -} // end detail -} // end thrust - -#include - diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/assign_value.h b/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/assign_value.h deleted file mode 100644 index f6fd987bf3f814f389b01499a06b313517b69733..0000000000000000000000000000000000000000 --- a/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/assign_value.h +++ /dev/null @@ -1,102 +0,0 @@ -/* - * Copyright 2008-2013 NVIDIA Corporation - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#pragma once - -#if THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC -#include -#include -#include -#include -#include - - -namespace thrust -{ -namespace cuda_cub { - - -template -inline __host__ __device__ - void assign_value(thrust::cuda::execution_policy &exec, Pointer1 dst, Pointer2 src) -{ - // XXX war nvbugs/881631 - struct war_nvbugs_881631 - { - __host__ inline static void host_path(thrust::cuda::execution_policy &exec, Pointer1 dst, Pointer2 src) - { - cuda_cub::copy(exec, src, src + 1, dst); - } - - __device__ inline static void device_path(thrust::cuda::execution_policy &, Pointer1 dst, Pointer2 src) - { - *thrust::raw_pointer_cast(dst) = *thrust::raw_pointer_cast(src); - } - }; - - if (THRUST_IS_HOST_CODE) { - #if THRUST_INCLUDE_HOST_CODE - war_nvbugs_881631::host_path(exec,dst,src); - #endif - } else { - #if THRUST_INCLUDE_DEVICE_CODE - war_nvbugs_881631::device_path(exec,dst,src); - #endif - } -} // end assign_value() - - -template -inline __host__ __device__ - void assign_value(cross_system
Adventure mode
-Adventure mode is the story mode of the game, where you can follow the plot of the anime from different perspectives. You can choose to play as Naruto, Sasuke, or Jiraiya, and experience their journeys and battles. You can also explore various locations from the Naruto world, such as Konoha, Suna, and Akatsuki hideouts. You can interact with other characters, complete side quests, collect items, and unlock new content. Adventure mode is a great way to immerse yourself in the Naruto universe and learn more about the characters and events.
-Online mode
-Online mode is where you can test your skills against other players from around the world. You can choose to play ranked or unranked matches, or join tournaments. You can also create clans, chat with friends, and view leaderboards. Online mode is a fun and competitive way to challenge yourself and improve your game. However, online mode also has some drawbacks, such as lag, disconnects, and hackers. You might also encounter some toxic players who might ruin your experience.
-What are the pros and cons of the game?
-Like any game, Naruto Shippuden Ultimate Ninja Storm 2 has its strengths and weaknesses. Here are some of the pros and cons of the game:
-Pros
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Cons
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-
Graphics and Sound
-How does the game look and sound?
-The game looks and sounds amazing, especially for a 2010 release. The game uses cel-shaded graphics that mimic the style of the anime. The characters are well-designed and animated, and the environments are rich and vibrant. The game also uses dynamic camera angles and cinematic effects that enhance the visual impact of the battles.
-Visuals
-Music and voice acting
-The music and voice acting of the game are also excellent, and match the tone and mood of the anime. The game features original and licensed music from the Naruto series, as well as new compositions. The music is catchy and energetic, and suits the action and drama of the game. The game also features voice acting from the original Japanese and English cast of the anime, as well as other languages. The voice actors do a great job of portraying the personalities and emotions of the characters.
-How does the game compare to the anime and manga?
-The game is a faithful adaptation of the anime and manga, and covers the events from the beginning of the Shippuden arc to the end of the Pain arc. The game follows the same plot and timeline as the anime and manga, and includes many scenes and dialogues from them. The game also recreates some of the most iconic and memorable moments from the series, such as Naruto's reunion with Sasuke, Jiraiya's death, Naruto's sage mode training, Pain's invasion of Konoha, Naruto's meeting with his parents, etc. The game is a treat for fans of Naruto who want to relive or experience these moments in a different medium.
-Conclusion
-Summary of the main points
-Naruto Shippuden Ultimate Ninja Storm 2 is a fighting game based on the Naruto series, featuring over 40 playable characters, various modes, stunning graphics, and excellent sound. The game is a simple and fun way to enjoy the Naruto universe, especially for fans of the series. However, the game also has some flaws, such as repetitive and shallow gameplay, inconsistent and unfair difficulty, bugs and glitches, and outdated and irrelevant content.
-Recommendation and rating
-We recommend this game to anyone who loves Naruto or fighting games in general. The game is a blast to play, whether alone or with friends, online or offline. The game is also a great way to learn more about the Naruto story and characters, or to revisit them in a new way. However, we also warn you that this game is not perfect, and might not meet your expectations or preferences. The game is not very challenging or complex, and might bore you after a while. The game is also not very current or relevant, and might not interest you if you are not familiar with or interested in Naruto.
-We give this game a rating of 4 out of 5 stars. It is a good game that has a lot of potential, but also has some room for improvement.
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/How to Use AR Draw APK to Create Amazing Artworks on Your Phone.md b/spaces/congsaPfin/Manga-OCR/logs/How to Use AR Draw APK to Create Amazing Artworks on Your Phone.md deleted file mode 100644 index 73c43e70211d1bd270677c2b4bb14a50c3568ee8..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/How to Use AR Draw APK to Create Amazing Artworks on Your Phone.md +++ /dev/null @@ -1,115 +0,0 @@ -
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AR Draw APK: How to Create Amazing Art with Your Smartphone
-Have you ever wanted to draw in 3D space using your smartphone? If so, you might be interested in AR Draw APK, a free app that lets you create stunning artworks using augmented reality. In this article, we will show you what AR Draw APK is, how to use it, and some tips and tricks for enhancing your drawings. By the end of this article, you will be able to unleash your creativity and impress your friends with your amazing art.
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What is AR Draw APK?
-AR Draw APK is an app that allows you to draw in 3D space using your smartphone's camera and sensors. You can use different tools, colors, textures, effects, and animations to create realistic or abstract drawings that appear in your environment. You can also save and share your drawings with other users, or collaborate with them on the same project. AR Draw APK is compatible with most Android devices that support ARCore, Google's platform for building augmented reality experiences.
-A brief introduction to the app and its features
-The app was developed by Sketchar, a company that specializes in creating apps for learning and creating art. Sketchar also offers other apps such as Sketchar: Learn to Draw, which teaches you how to draw using augmented reality, and Sketchar: Play, which lets you play games and interact with characters using augmented reality. AR Draw APK is one of their latest products, which aims to provide a fun and easy way for anyone to draw in 3D space.
-How to download and install the app on your Android device
-To download and install the app, you need to follow these steps:
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How to Use AR Draw APK to Draw in 3D Space
-Now that you have installed the app, you are ready to start drawing. Here are some steps on how to use the app:
-How to access the app and start a new project
-To access the app, simply tap on its icon on your device's home screen or app drawer. You will see a welcome screen that gives you some information about the app and its features. You can also watch a tutorial video that shows you how to use the app. To start a new project, tap on the "Start Drawing" button at the bottom of the screen. You will then see a camera view that shows your surroundings.
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How to choose a drawing tool and adjust the settings
-To choose a drawing tool, tap on the "Tools" icon at the bottom left corner of the screen. You will see a menu that shows you different tools such as pencil, brush, spray, eraser, ruler, compass, protractor, etc. Tap on the tool you want to use and adjust its size, color, opacity, and other settings by sliding the bars on the right side of the screen. You can also undo or redo your actions by tapping on the "Undo" or "Redo" icons at the top right corner of the screen.
-How to draw on different surfaces and angles
-To draw on different surfaces and angles, you need to move your device around and find a suitable spot for your drawing. You can draw on walls, floors, ceilings, tables, chairs, or any other surface that is flat and stable. You can also draw in mid-air by tapping on the "Air" icon at the bottom right corner of the screen. This will allow you to draw without any surface constraint. You can also change the angle of your drawing by rotating your device or moving it closer or farther from the surface. You can see a grid that helps you align your drawing with the surface or the horizon.
-How to save and share your drawings
-To save your drawings, tap on the "Save" icon at the top left corner of the screen. You will see a preview of your drawing and some options to edit it. You can crop, rotate, flip, or add a filter to your drawing. You can also add a title and a description to your drawing. To share your drawings, tap on the "Share" icon at the top right corner of the screen. You will see a menu that shows you different platforms such as Facebook, Instagram, Twitter, WhatsApp, etc. Tap on the platform you want to use and follow the instructions to share your drawing with your friends or followers. You can also share your drawings with other AR Draw APK users by tapping on the "Community" icon at the bottom right corner of the screen. You will see a gallery of drawings made by other users and you can like, comment, or follow them.
-Tips and Tricks for Enhancing Your AR Drawings
-Now that you know how to use AR Draw APK, you might want to learn some tips and tricks for enhancing your drawings. Here are some suggestions:
-How to use different colors and textures
-To use different colors and textures, you can tap on the "Palette" icon at the bottom left corner of the screen. You will see a menu that shows you different categories of colors and textures such as basic, gradient, neon, metallic, wood, stone, etc. Tap on the category you want to use and select a color or texture from the list. You can also create your own custom color or texture by tapping on the "Custom" icon at the top right corner of the menu. You can adjust the hue, saturation, brightness, and contrast of your color or texture by sliding the bars on the right side of the menu.
-How to add effects and animations
-To add effects and animations to your drawings, you can tap on the "Effects" icon at the bottom left corner of the screen. You will see a menu that shows you different types of effects and animations such as glow, shadow, sparkle, firework, raindrop, etc. Tap on the effect or animation you want to use and adjust its settings by sliding the bars on the right side of the menu. You can also preview how your effect or animation looks like by tapping on the "Preview" icon at the top right corner of the menu.
-How to use reference images and templates
-To use reference images and templates for your drawings, you can tap on the "Reference" icon at the bottom left corner of the screen. You will see a menu that shows you different sources of reference images and templates such as Google Images, Pinterest, Sketchfab, SketchAR, etc. Tap on the source you want to use and search for an image or template that matches your drawing idea. You can also upload your own image or template from your device's gallery. Once you have selected an image or template, you can adjust its size, position, rotation, and opacity by using the buttons on the screen. You can also lock or unlock the image or template by tapping on the "Lock" icon at the top right corner of the screen. You can use the reference image or template as a guide for your drawing, or trace over it using the drawing tools.
-How to collaborate with other users
-To collaborate with other users on the same drawing project, you can tap on the "Collaborate" icon at the bottom right corner of the screen. You will see a menu that shows you different options to invite or join other users. You can invite other users by sending them a link or a QR code that they can scan with their devices. You can also join other users by scanning their QR codes or entering their links. Once you are connected with other users, you can see their drawings in real time and chat with them using the "Chat" icon at the top left corner of the screen. You can also leave or end the collaboration session by tapping on the "Leave" or "End" icons at the top right corner of the screen.
-Conclusion
-AR Draw APK is a great app for anyone who loves to draw and wants to try something new and exciting. With AR Draw APK, you can create amazing artworks in 3D space using your smartphone's camera and sensors. You can use different tools, colors, textures, effects, and animations to make your drawings realistic or abstract. You can also save and share your drawings with other users, or collaborate with them on the same project. AR Draw APK is easy to use and fun to play with. You can unleash your creativity and impress your friends with your amazing art.
-So what are you waiting for? Download AR Draw APK today and start drawing in 3D space!
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NBA Team APK: What Is It and How to Use It
-If you are a basketball fan, you probably want to watch every game of your favorite team, no matter where you are. But how can you do that without cable or satellite TV? One option is to use NBA Team APK, an app that lets you stream live and on-demand games of any NBA team on your Android device. In this article, we will explain what NBA Team APK is, how to download and install it, how to use it to watch NBA games, what benefits it offers, and what alternatives you have.
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-A brief introduction to NBA Team APK and its features
-NBA Team APK is an app that allows you to watch live and on-demand games of any NBA team on your Android device. It is not an official app from the NBA, but rather a modified version of the official NBA app that bypasses some restrictions and limitations. For example, with NBA Team APK, you can watch any game without blackouts, even if it is broadcasted in your local market or nationally. You can also watch games from previous seasons and access the NBA vault for free.
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Some of the features that NBA Team APK offers are:
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How to download and install NBA Team APK on Android devices
-To download and install NBA Team APK on your Android device, you need to follow these steps:
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How to use NBA Team APK to watch NBA games
-How to access live and on-demand games of your favorite team
-To access live and on-demand games of your favorite team, you need to do the following:
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