-
-## GPT 3.5/4
-
-NOT REQUIRE ANY API KEY ❌🔑
-
-This project features a WebUI utilizing the [G4F API](https://github.com/xtekky/gpt4free). - -## Star History -[](https://star-history.com/#VadimBoev/freegpt-webui-v2&Timeline) - -
- -## Legal Notice -This repository is _not_ associated with or endorsed by providers of the APIs contained in this GitHub repository. This -project is intended **for educational purposes only**. This is just a little personal project. Sites may contact me to -improve their security or request the removal of their site from this repository. - -Please note the following: - -1. **Disclaimer**: The APIs, services, and trademarks mentioned in this repository belong to their respective owners. - This project is _not_ claiming any right over them nor is it affiliated with or endorsed by any of the providers - mentioned. - -2. **Responsibility**: The author of this repository is _not_ responsible for any consequences, damages, or losses - arising from the use or misuse of this repository or the content provided by the third-party APIs. Users are solely - responsible for their actions and any repercussions that may follow. We strongly recommend the users to follow the - TOS of the each Website. - -3. **Educational Purposes Only**: This repository and its content are provided strictly for educational purposes. By - using the information and code provided, users acknowledge that they are using the APIs and models at their own risk - and agree to comply with any applicable laws and regulations. - -4. **Copyright**: All content in this repository, including but not limited to code, images, and documentation, is the - intellectual property of the repository author, unless otherwise stated. Unauthorized copying, distribution, or use - of any content in this repository is strictly prohibited without the express written consent of the repository - author. - -5. **Indemnification**: Users agree to indemnify, defend, and hold harmless the author of this repository from and - against any and all claims, liabilities, damages, losses, or expenses, including legal fees and costs, arising out of - or in any way connected with their use or misuse of this repository, its content, or related third-party APIs. - -6. **Updates and Changes**: The author reserves the right to modify, update, or remove any content, information, or - features in this repository at any time without prior notice. Users are responsible for regularly reviewing the - content and any changes made to this repository. - -By using this repository or any code related to it, you agree to these terms. The author is not responsible for any -copies, forks, or reuploads made by other users. This is the author's only account and repository. To prevent -impersonation or irresponsible actions, you may comply with the GNU GPL license this Repository uses. \ No newline at end of file diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/GetChildrenHeight.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/GetChildrenHeight.js deleted file mode 100644 index 8f60f6dc8620faf62af9192a494ca9b948adef3f..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/GetChildrenHeight.js +++ /dev/null @@ -1,58 +0,0 @@ -var GetChildrenHeight = function (minimumMode) { - if (this.rexSizer.hidden) { - return 0; - } - - if (minimumMode === undefined) { - minimumMode = true; - } - - var result = 0; - var children = this.sizerChildren; - var child, padding, childHeight; - if (this.orientation === 0) { // x - // Get maximun height - for (var i = 0, cnt = children.length; i < cnt; i++) { - child = children[i]; - if (child.rexSizer.hidden) { - continue; - } - - padding = child.rexSizer.padding; - childHeight = this.getChildHeight(child) + padding.top + padding.bottom; - result = Math.max(childHeight, result); - } - } else { - // Get summation of minimum height - var itemSpace = this.space.item; - var isFirstChild = true; - for (var i = 0, cnt = children.length; i < cnt; i++) { - child = children[i]; - if (!child.hasOwnProperty('rexSizer')) { - continue; - } - if (child.rexSizer.hidden) { - continue; - } - - if ((child.rexSizer.proportion === 0) || minimumMode) { - childHeight = this.getChildHeight(child); - } else { - childHeight = 0; - } - padding = child.rexSizer.padding; - childHeight += (padding.top + padding.bottom); - - if (isFirstChild) { - isFirstChild = false; - } else { - childHeight += itemSpace; - } - - result += childHeight; - } - } - return result + this.space.top + this.space.bottom; -} - -export default GetChildrenHeight; \ No newline at end of file diff --git a/spaces/Akmyradov/TurkmenTTSweSTT/vits/attentions.py b/spaces/Akmyradov/TurkmenTTSweSTT/vits/attentions.py deleted file mode 100644 index 4e0b0c1fd48c962e21e1fbe60b23fc574927435c..0000000000000000000000000000000000000000 --- a/spaces/Akmyradov/TurkmenTTSweSTT/vits/attentions.py +++ /dev/null @@ -1,303 +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 modules import LayerNorm - - -class Encoder(nn.Module): - def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **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 - - 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): - attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) - x = x * x_mask - for i in range(self.n_layers): - 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/Amrrs/DragGan-Inversion/PTI/utils/log_utils.py b/spaces/Amrrs/DragGan-Inversion/PTI/utils/log_utils.py deleted file mode 100644 index b29eae05f1c3ba34df60c074373b417c5420e836..0000000000000000000000000000000000000000 --- a/spaces/Amrrs/DragGan-Inversion/PTI/utils/log_utils.py +++ /dev/null @@ -1,79 +0,0 @@ -import numpy as np -from PIL import Image -import wandb -from PTI.configs import global_config -import torch -import matplotlib.pyplot as plt - - -def log_image_from_w(w, G, name): - img = get_image_from_w(w, G) - pillow_image = Image.fromarray(img) - wandb.log( - {f"{name}": [ - wandb.Image(pillow_image, caption=f"current inversion {name}")]}, - step=global_config.training_step) - - -def log_images_from_w(ws, G, names): - for name, w in zip(names, ws): - w = w.to(global_config.device) - log_image_from_w(w, G, name) - - -def plot_image_from_w(w, G): - img = get_image_from_w(w, G) - pillow_image = Image.fromarray(img) - plt.imshow(pillow_image) - plt.show() - - -def plot_image(img): - img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).detach().cpu().numpy() - pillow_image = Image.fromarray(img[0]) - plt.imshow(pillow_image) - plt.show() - - -def save_image(name, method_type, results_dir, image, run_id): - image.save(f'{results_dir}/{method_type}_{name}_{run_id}.jpg') - - -def save_w(w, G, name, method_type, results_dir): - im = get_image_from_w(w, G) - im = Image.fromarray(im, mode='RGB') - save_image(name, method_type, results_dir, im) - - -def save_concat_image(base_dir, image_latents, new_inv_image_latent, new_G, - old_G, - file_name, - extra_image=None): - images_to_save = [] - if extra_image is not None: - images_to_save.append(extra_image) - for latent in image_latents: - images_to_save.append(get_image_from_w(latent, old_G)) - images_to_save.append(get_image_from_w(new_inv_image_latent, new_G)) - result_image = create_alongside_images(images_to_save) - result_image.save(f'{base_dir}/{file_name}.jpg') - - -def save_single_image(base_dir, image_latent, G, file_name): - image_to_save = get_image_from_w(image_latent, G) - image_to_save = Image.fromarray(image_to_save, mode='RGB') - image_to_save.save(f'{base_dir}/{file_name}.jpg') - - -def create_alongside_images(images): - res = np.concatenate([np.array(image) for image in images], axis=1) - return Image.fromarray(res, mode='RGB') - - -def get_image_from_w(w, G): - if len(w.size()) <= 2: - w = w.unsqueeze(0) - with torch.no_grad(): - img = G.synthesis(w, noise_mode='const') - img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).detach().cpu().numpy() - return img[0] diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/using-diffusers/depth2img.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/using-diffusers/depth2img.md deleted file mode 100644 index b5602e3081daa6089265e002cc4df1cd8473a1e3..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/using-diffusers/depth2img.md +++ /dev/null @@ -1,57 +0,0 @@ - - -# Text-guided depth-to-image 생성 - -[[open-in-colab]] - -[`StableDiffusionDepth2ImgPipeline`]을 사용하면 텍스트 프롬프트와 초기 이미지를 전달하여 새 이미지의 생성을 조절할 수 있습니다. 또한 이미지 구조를 보존하기 위해 `depth_map`을 전달할 수도 있습니다. `depth_map`이 제공되지 않으면 파이프라인은 통합된 [depth-estimation model](https://github.com/isl-org/MiDaS)을 통해 자동으로 깊이를 예측합니다. - - -먼저 [`StableDiffusionDepth2ImgPipeline`]의 인스턴스를 생성합니다: - -```python -import torch -import requests -from PIL import Image - -from diffusers import StableDiffusionDepth2ImgPipeline - -pipe = StableDiffusionDepth2ImgPipeline.from_pretrained( - "stabilityai/stable-diffusion-2-depth", - torch_dtype=torch.float16, -).to("cuda") -``` - -이제 프롬프트를 파이프라인에 전달합니다. 특정 단어가 이미지 생성을 가이드 하는것을 방지하기 위해 `negative_prompt`를 전달할 수도 있습니다: - -```python -url = "http://images.cocodataset.org/val2017/000000039769.jpg" -init_image = Image.open(requests.get(url, stream=True).raw) -prompt = "two tigers" -n_prompt = "bad, deformed, ugly, bad anatomy" -image = pipe(prompt=prompt, image=init_image, negative_prompt=n_prompt, strength=0.7).images[0] -image -``` - -| Input | Output | -|---------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------| -|
| 
|
-
-아래의 Spaces를 가지고 놀며 depth map이 있는 이미지와 없는 이미지의 차이가 있는지 확인해 보세요!
-
-
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/deepfloyd_if/test_if_img2img_superresolution.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/deepfloyd_if/test_if_img2img_superresolution.py
deleted file mode 100644
index 500557108aed05b9b01020964f13b15fdb9abed0..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/deepfloyd_if/test_if_img2img_superresolution.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace 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 random
-import unittest
-
-import torch
-
-from diffusers import IFImg2ImgSuperResolutionPipeline
-from diffusers.utils import floats_tensor
-from diffusers.utils.import_utils import is_xformers_available
-from diffusers.utils.testing_utils import skip_mps, torch_device
-
-from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
-from ..test_pipelines_common import PipelineTesterMixin
-from . import IFPipelineTesterMixin
-
-
-@skip_mps
-class IFImg2ImgSuperResolutionPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMixin, unittest.TestCase):
- pipeline_class = IFImg2ImgSuperResolutionPipeline
- params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
- batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"original_image"})
- required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
-
- def get_dummy_components(self):
- return self._get_superresolution_dummy_components()
-
- def get_dummy_inputs(self, device, seed=0):
- if str(device).startswith("mps"):
- generator = torch.manual_seed(seed)
- else:
- generator = torch.Generator(device=device).manual_seed(seed)
-
- original_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
- image = floats_tensor((1, 3, 16, 16), rng=random.Random(seed)).to(device)
-
- inputs = {
- "prompt": "A painting of a squirrel eating a burger",
- "image": image,
- "original_image": original_image,
- "generator": generator,
- "num_inference_steps": 2,
- "output_type": "numpy",
- }
-
- return inputs
-
- @unittest.skipIf(
- torch_device != "cuda" or not is_xformers_available(),
- reason="XFormers attention is only available with CUDA and `xformers` installed",
- )
- def test_xformers_attention_forwardGenerator_pass(self):
- self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)
-
- def test_save_load_optional_components(self):
- self._test_save_load_optional_components()
-
- @unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA")
- def test_save_load_float16(self):
- # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
- super().test_save_load_float16(expected_max_diff=1e-1)
-
- def test_attention_slicing_forward_pass(self):
- self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)
-
- def test_save_load_local(self):
- self._test_save_load_local()
-
- def test_inference_batch_single_identical(self):
- self._test_inference_batch_single_identical(
- expected_max_diff=1e-2,
- )
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_unclip/__init__.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_unclip/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/fsaf/README.md b/spaces/Andy1621/uniformer_image_detection/configs/fsaf/README.md
deleted file mode 100644
index 42468c8bf596d675d74e0c1d453e0641c5dc3b9c..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/fsaf/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
-# Feature Selective Anchor-Free Module for Single-Shot Object Detection
-
-[ALGORITHM]
-
-FSAF is an anchor-free method published in CVPR2019 ([https://arxiv.org/pdf/1903.00621.pdf](https://arxiv.org/pdf/1903.00621.pdf)).
-Actually it is equivalent to the anchor-based method with only one anchor at each feature map position in each FPN level.
-And this is how we implemented it.
-Only the anchor-free branch is released for its better compatibility with the current framework and less computational budget.
-
-In the original paper, feature maps within the central 0.2-0.5 area of a gt box are tagged as ignored. However,
-it is empirically found that a hard threshold (0.2-0.2) gives a further gain on the performance. (see the table below)
-
-## Main Results
-
-### Results on R50/R101/X101-FPN
-
-| Backbone | ignore range | ms-train| Lr schd |Train Mem (GB)| Train time (s/iter) | Inf time (fps) | box AP | Config | Download |
-|:----------:| :-------: |:-------:|:-------:|:------------:|:---------------:|:--------------:|:-------------:|:------:|:--------:|
-| R-50 | 0.2-0.5 | N | 1x | 3.15 | 0.43 | 12.3 | 36.0 (35.9) | | [model](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco_20200715-b555b0e0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco_20200715_094657.log.json) |
-| R-50 | 0.2-0.2 | N | 1x | 3.15 | 0.43 | 13.0 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco-94ccc51f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco_20200428_072327.log.json)|
-| R-101 | 0.2-0.2 | N | 1x | 5.08 | 0.58 | 10.8 | 39.3 (37.9) | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco-9e71098f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco_20200428_160348.log.json)|
-| X-101 | 0.2-0.2 | N | 1x | 9.38 | 1.23 | 5.6 | 42.4 (41.0) | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco-e3f6e6fd.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco_20200428_160424.log.json)|
-
-**Notes:**
-
-- *1x means the model is trained for 12 epochs.*
-- *AP values in the brackets represent those reported in the original paper.*
-- *All results are obtained with a single model and single-scale test.*
-- *X-101 backbone represents ResNext-101-64x4d.*
-- *All pretrained backbones use pytorch style.*
-- *All models are trained on 8 Titan-XP gpus and tested on a single gpu.*
-
-## Citations
-
-BibTeX reference is as follows.
-
-```latex
-@inproceedings{zhu2019feature,
- title={Feature Selective Anchor-Free Module for Single-Shot Object Detection},
- author={Zhu, Chenchen and He, Yihui and Savvides, Marios},
- booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
- pages={840--849},
- year={2019}
-}
-```
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/iou_calculators/__init__.py b/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/iou_calculators/__init__.py
deleted file mode 100644
index e71369a58a05fa25e6a754300875fdbb87cb26a5..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/iou_calculators/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .builder import build_iou_calculator
-from .iou2d_calculator import BboxOverlaps2D, bbox_overlaps
-
-__all__ = ['build_iou_calculator', 'BboxOverlaps2D', 'bbox_overlaps']
diff --git a/spaces/ArkanDash/rvc-models/vc_infer_pipeline.py b/spaces/ArkanDash/rvc-models/vc_infer_pipeline.py
deleted file mode 100644
index c26d45068f9b6bf2b194b13c3c89f8a06347c124..0000000000000000000000000000000000000000
--- a/spaces/ArkanDash/rvc-models/vc_infer_pipeline.py
+++ /dev/null
@@ -1,306 +0,0 @@
-import numpy as np, parselmouth, torch, pdb
-from time import time as ttime
-import torch.nn.functional as F
-from config import x_pad, x_query, x_center, x_max
-import scipy.signal as signal
-import pyworld, os, traceback, faiss
-from scipy import signal
-
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-
-class VC(object):
- def __init__(self, tgt_sr, device, is_half):
- self.sr = 16000 # hubert输入采样率
- self.window = 160 # 每帧点数
- self.t_pad = self.sr * x_pad # 每条前后pad时间
- self.t_pad_tgt = tgt_sr * x_pad
- self.t_pad2 = self.t_pad * 2
- self.t_query = self.sr * x_query # 查询切点前后查询时间
- self.t_center = self.sr * x_center # 查询切点位置
- self.t_max = self.sr * x_max # 免查询时长阈值
- self.device = device
- self.is_half = is_half
-
- def get_f0(self, x, p_len, f0_up_key, f0_method, inp_f0=None):
- time_step = self.window / self.sr * 1000
- f0_min = 50
- f0_max = 1100
- f0_mel_min = 1127 * np.log(1 + f0_min / 700)
- f0_mel_max = 1127 * np.log(1 + f0_max / 700)
- if f0_method == "pm":
- f0 = (
- parselmouth.Sound(x, self.sr)
- .to_pitch_ac(
- time_step=time_step / 1000,
- voicing_threshold=0.6,
- pitch_floor=f0_min,
- pitch_ceiling=f0_max,
- )
- .selected_array["frequency"]
- )
- pad_size = (p_len - len(f0) + 1) // 2
- if pad_size > 0 or p_len - len(f0) - pad_size > 0:
- f0 = np.pad(
- f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
- )
- elif f0_method == "harvest":
- f0, t = pyworld.harvest(
- x.astype(np.double),
- fs=self.sr,
- f0_ceil=f0_max,
- f0_floor=f0_min,
- frame_period=10,
- )
- f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
- f0 = signal.medfilt(f0, 3)
- f0 *= pow(2, f0_up_key / 12)
- # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- tf0 = self.sr // self.window # 每秒f0点数
- if inp_f0 is not None:
- delta_t = np.round(
- (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
- ).astype("int16")
- replace_f0 = np.interp(
- list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
- )
- shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
- f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
- # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- f0bak = f0.copy()
- f0_mel = 1127 * np.log(1 + f0 / 700)
- f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
- f0_mel_max - f0_mel_min
- ) + 1
- f0_mel[f0_mel <= 1] = 1
- f0_mel[f0_mel > 255] = 255
- f0_coarse = np.rint(f0_mel).astype(np.int)
- return f0_coarse, f0bak # 1-0
-
- def vc(
- self,
- model,
- net_g,
- sid,
- audio0,
- pitch,
- pitchf,
- times,
- index,
- big_npy,
- index_rate,
- ): # ,file_index,file_big_npy
- feats = torch.from_numpy(audio0)
- if self.is_half:
- feats = feats.half()
- else:
- feats = feats.float()
- if feats.dim() == 2: # double channels
- feats = feats.mean(-1)
- assert feats.dim() == 1, feats.dim()
- feats = feats.view(1, -1)
- padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-
- inputs = {
- "source": feats.to(self.device),
- "padding_mask": padding_mask,
- "output_layer": 9, # layer 9
- }
- t0 = ttime()
- with torch.no_grad():
- logits = model.extract_features(**inputs)
- feats = model.final_proj(logits[0])
-
- if (
- isinstance(index, type(None)) == False
- and isinstance(big_npy, type(None)) == False
- and index_rate != 0
- ):
- npy = feats[0].cpu().numpy()
- if self.is_half:
- npy = npy.astype("float32")
- _, I = index.search(npy, 1)
- npy = big_npy[I.squeeze()]
- if self.is_half:
- npy = npy.astype("float16")
- feats = (
- torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
- + (1 - index_rate) * feats
- )
-
- feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
- t1 = ttime()
- p_len = audio0.shape[0] // self.window
- if feats.shape[1] < p_len:
- p_len = feats.shape[1]
- if pitch != None and pitchf != None:
- pitch = pitch[:, :p_len]
- pitchf = pitchf[:, :p_len]
- p_len = torch.tensor([p_len], device=self.device).long()
- with torch.no_grad():
- if pitch != None and pitchf != None:
- audio1 = (
- (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768)
- .data.cpu()
- .float()
- .numpy()
- .astype(np.int16)
- )
- else:
- audio1 = (
- (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768)
- .data.cpu()
- .float()
- .numpy()
- .astype(np.int16)
- )
- del feats, p_len, padding_mask
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
- t2 = ttime()
- times[0] += t1 - t0
- times[2] += t2 - t1
- return audio1
-
- def pipeline(
- self,
- model,
- net_g,
- sid,
- audio,
- times,
- f0_up_key,
- f0_method,
- file_index,
- file_big_npy,
- index_rate,
- if_f0,
- f0_file=None,
- ):
- if (
- file_big_npy != ""
- and file_index != ""
- and os.path.exists(file_big_npy) == True
- and os.path.exists(file_index) == True
- and index_rate != 0
- ):
- try:
- index = faiss.read_index(file_index)
- big_npy = np.load(file_big_npy)
- except:
- traceback.print_exc()
- index = big_npy = None
- else:
- index = big_npy = None
- print("Feature retrieval library doesn't exist or ratio is 0")
- audio = signal.filtfilt(bh, ah, audio)
- audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
- opt_ts = []
- if audio_pad.shape[0] > self.t_max:
- audio_sum = np.zeros_like(audio)
- for i in range(self.window):
- audio_sum += audio_pad[i : i - self.window]
- for t in range(self.t_center, audio.shape[0], self.t_center):
- opt_ts.append(
- t
- - self.t_query
- + np.where(
- np.abs(audio_sum[t - self.t_query : t + self.t_query])
- == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
- )[0][0]
- )
- s = 0
- audio_opt = []
- t = None
- t1 = ttime()
- audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
- p_len = audio_pad.shape[0] // self.window
- inp_f0 = None
- if hasattr(f0_file, "name") == True:
- try:
- with open(f0_file.name, "r") as f:
- lines = f.read().strip("\n").split("\n")
- inp_f0 = []
- for line in lines:
- inp_f0.append([float(i) for i in line.split(",")])
- inp_f0 = np.array(inp_f0, dtype="float32")
- except:
- traceback.print_exc()
- sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
- pitch, pitchf = None, None
- if if_f0 == 1:
- pitch, pitchf = self.get_f0(audio_pad, p_len, f0_up_key, f0_method, inp_f0)
- pitch = pitch[:p_len]
- pitchf = pitchf[:p_len]
- pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
- pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
- t2 = ttime()
- times[1] += t2 - t1
- for t in opt_ts:
- t = t // self.window * self.window
- if if_f0 == 1:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[s : t + self.t_pad2 + self.window],
- pitch[:, s // self.window : (t + self.t_pad2) // self.window],
- pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
- times,
- index,
- big_npy,
- index_rate,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- else:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[s : t + self.t_pad2 + self.window],
- None,
- None,
- times,
- index,
- big_npy,
- index_rate,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- s = t
- if if_f0 == 1:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[t:],
- pitch[:, t // self.window :] if t is not None else pitch,
- pitchf[:, t // self.window :] if t is not None else pitchf,
- times,
- index,
- big_npy,
- index_rate,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- else:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[t:],
- None,
- None,
- times,
- index,
- big_npy,
- index_rate,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- audio_opt = np.concatenate(audio_opt)
- del pitch, pitchf, sid
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
- return audio_opt
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/models/installation_report.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/models/installation_report.py
deleted file mode 100644
index fef3757f222b67fc1f4de52d260c49d64b6a4e16..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/models/installation_report.py
+++ /dev/null
@@ -1,53 +0,0 @@
-from typing import Any, Dict, Sequence
-
-from pip._vendor.packaging.markers import default_environment
-
-from pip import __version__
-from pip._internal.req.req_install import InstallRequirement
-
-
-class InstallationReport:
- def __init__(self, install_requirements: Sequence[InstallRequirement]):
- self._install_requirements = install_requirements
-
- @classmethod
- def _install_req_to_dict(cls, ireq: InstallRequirement) -> Dict[str, Any]:
- assert ireq.download_info, f"No download_info for {ireq}"
- res = {
- # PEP 610 json for the download URL. download_info.archive_info.hashes may
- # be absent when the requirement was installed from the wheel cache
- # and the cache entry was populated by an older pip version that did not
- # record origin.json.
- "download_info": ireq.download_info.to_dict(),
- # is_direct is true if the requirement was a direct URL reference (which
- # includes editable requirements), and false if the requirement was
- # downloaded from a PEP 503 index or --find-links.
- "is_direct": bool(ireq.original_link),
- # requested is true if the requirement was specified by the user (aka
- # top level requirement), and false if it was installed as a dependency of a
- # requirement. https://peps.python.org/pep-0376/#requested
- "requested": ireq.user_supplied,
- # PEP 566 json encoding for metadata
- # https://www.python.org/dev/peps/pep-0566/#json-compatible-metadata
- "metadata": ireq.get_dist().metadata_dict,
- }
- if ireq.user_supplied and ireq.extras:
- # For top level requirements, the list of requested extras, if any.
- res["requested_extras"] = list(sorted(ireq.extras))
- return res
-
- def to_dict(self) -> Dict[str, Any]:
- return {
- "version": "1",
- "pip_version": __version__,
- "install": [
- self._install_req_to_dict(ireq) for ireq in self._install_requirements
- ],
- # https://peps.python.org/pep-0508/#environment-markers
- # TODO: currently, the resolver uses the default environment to evaluate
- # environment markers, so that is what we report here. In the future, it
- # should also take into account options such as --python-version or
- # --platform, perhaps under the form of an environment_override field?
- # https://github.com/pypa/pip/issues/11198
- "environment": default_environment(),
- }
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/sysconfig.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/sysconfig.py
deleted file mode 100644
index 6a979f8c91fce3c8239b36ddb8764dc85dea41f2..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/sysconfig.py
+++ /dev/null
@@ -1,558 +0,0 @@
-"""Provide access to Python's configuration information. The specific
-configuration variables available depend heavily on the platform and
-configuration. The values may be retrieved using
-get_config_var(name), and the list of variables is available via
-get_config_vars().keys(). Additional convenience functions are also
-available.
-
-Written by: Fred L. Drake, Jr.
-Email: 100 ? `100`}`
- }}
- >
-
- )
-}
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar Apk X Aire Behringer.md b/spaces/Benson/text-generation/Examples/Descargar Apk X Aire Behringer.md
deleted file mode 100644
index 3f3545766742f899fbddc5b5af47bd1e516ee6ec..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Apk X Aire Behringer.md
+++ /dev/null
@@ -1,61 +0,0 @@
-
-Cómo descargar APK X AIR Behringer para dispositivos Android
-Si usted es un músico, ingeniero de sonido, o intérprete en vivo que utiliza un mezclador digital BEHRINGER X AIR, es posible que desee descargar APK X AIR Behringer para su dispositivo Android. Esta aplicación le permite controlar todas las funciones de mezcla, procesamiento y efectos de su mezclador desde su tableta o teléfono inteligente. En este artículo, te mostraré cómo descargar, instalar y usar esta aplicación, así como algunos consejos y trucos para sacarle el máximo partido.
-Beneficios de APK X AIR Behringer
-APK X AIR Behringer es una aplicación gratuita que ofrece un control completo para los mezcladores X18, XR18, XR16 y XR12. La interfaz de usuario es configurable para acceso simplificado o edición de nivel experto (S/E), para mezclar 18 canales de entrada a 12 buses. Control también se proporciona para los cuatro procesadores de efectos estéreo internos - todos los cuales cuentan con el aclamado motor de procesamiento de audio BEHRINGER X32.
-descargar apk x aire behringer
Download ——— https://bltlly.com/2v6LG8
-
La aplicación proporciona la movilidad para ir donde usted necesita para obtener el máximo provecho de su sistema, lo que le permite ajustar la mezcla de la casa desde cualquier asiento o mezclas de monitor de ajuste fino desde el escenario. Dado que todos los mezcladores BEHRINGER X AIR cuentan con puntos de acceso internos, la configuración de la aplicación no podría ser más simple - solo seleccione la red X AIR y conecte su dispositivo Android a ella. Al abrir la aplicación, su mezclador X AIR se mostrará como un dispositivo controlable, e incluso le permitirá bloquear su dispositivo Android a ese mezclador X AIR específico. También puede ejecutar la aplicación en modo de demostración sin conectarse a su mezclador.
-No se requiere hardware adicional, por lo que la aplicación es la solución ideal para aplicaciones de mezcla remota sin problemas. Ya sea que lo utilice para espectáculos en vivo, grabaciones de estudio, ensayos, podcasts o seminarios web, APK X AIR Behringer puede ayudarle a lograr una calidad de sonido profesional con facilidad y comodidad.
-Requisitos para APK X AIR Behringer
-Para usar APK X AIR Behringer, necesita lo siguiente:
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-
-
- Un mezclador digital BEHRINGER X AIR (X18, XR18, XR16 o XR12) con firmware versión 1.15 o superior -
- Una red Wi-Fi que conecta tu dispositivo y tu mezclador -
- Una conexión a Internet para descargar la aplicación -
Pasos para descargar APK X AIR Behringer
-Aquí están los pasos para descargar APK X AIR Behringer para su dispositivo Android:
-Paso 1: Encontrar el enlace de descarga oficial para APK X AIR Behringer
-La aplicación no está disponible en la Google Play Store, por lo que necesita encontrar el enlace oficial de descarga desde el sitio web de BEHRINGER. Puede escanear el código QR en la página del producto o ir a esta URL: [https://www.behringer.com/behringer/product?modelCode=P0BI8]
-Paso 2: Habilitar fuentes desconocidas en la configuración del dispositivo
-Dado que está descargando la aplicación desde una fuente de terceros, debe habilitar fuentes desconocidas en la configuración de su dispositivo. Esto le permitirá instalar aplicaciones que no son de Google Play Store. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. Es posible que vea un mensaje de advertencia que dice que instalar desde fuentes desconocidas puede dañar su dispositivo, pero puede ignorarlo siempre y cuando confíe en el origen de la aplicación.
-Paso 3: Descargar e instalar el archivo APK
-Una vez que haya habilitado fuentes desconocidas, puede descargar el archivo APK desde el enlace que encontró en el paso 1. El tamaño del archivo es de aproximadamente 5.6 MB y debería tomar unos segundos para descargar dependiendo de su velocidad de Internet. Una vez finalizada la descarga, abre el archivo y sigue las instrucciones para instalar la aplicación en tu dispositivo. Es posible que necesite conceder algunos permisos a la aplicación, como el acceso a su red Wi-Fi y almacenamiento.
-Paso 4: Conecte su dispositivo a su mezclador X AIR a través de Wi-Fi
- -Paso 5: Iniciar la aplicación y disfrutar de sus características
-Ahora que ha instalado y conectado la aplicación, puede iniciarla y comenzar a controlar su mezclador de forma remota. Verá una lista de dispositivos disponibles en la pantalla de inicio de la aplicación. Toque en el que coincida con el nombre de la red del mezclador y el número de modelo. A continuación, verá un mensaje de confirmación que dice "Conectado". Ahora puede acceder a todas las funciones de mezcla, procesamiento y efectos de su mezclador desde su dispositivo. También puede cambiar entre el modo S/E, la superposición RTA, el modo de envío de bus único, la función AutoMixing y las instantáneas internas desde el menú de la aplicación.
- -Consejos y trucos para el uso de APK X AIR Behringer
-Para optimizar tu experiencia con APK X AIR Behringer, aquí hay algunos consejos y trucos que puedes probar:
-Consejo 1: Utilice el modo S/ E para cambiar entre la edición de nivel simplificado y experto
-La aplicación tiene dos modos de operación: simplificado (S) y experto (E). El modo S proporciona una interfaz optimizada que le permite ajustar solo los parámetros más esenciales de cada canal, tales como ganancia, silenciar, solo, pan, EQ, dinámica y enviar niveles. El modo E proporciona una interfaz con todas las funciones que le permite acceder a todos los parámetros de cada canal, tales como configuración de preamplificador, configuración de puerta, configuración de compresor, configuración de limitador, configuración de retardo, etc. Puede cambiar entre los modos S y E pulsando el botón S/ E en la esquina superior izquierda de la aplicación.
-Consejo 2: Utilice la superposición RTA para ajustar la configuración de EQ
- -Consejo 3: Utilice el modo de envío de bus único para el monitoreo personal
-La aplicación tiene un solo modo de envío de bus que le permite controlar solo un nivel de envío de bus por canal a la vez. Esto es útil para aplicaciones de monitoreo personal donde cada músico o intérprete quiere ajustar su propia mezcla de monitor sin afectar a los demás. Para usar este modo, toque en el botón de envío de bus único en la esquina superior derecha de la aplicación y seleccione un bus de la lista. A continuación, verá un fader azul que representa el nivel de envío de ese bus para cada canal. Puede arrastrarlo hacia arriba o hacia abajo para ajustar el nivel de envío. También puede tocar en los botones de silencio o solo para silenciar o solo el bus.
-Consejo 4: Utilice la función de Auto-Mixing para conferencias o discusiones de panel
-La aplicación tiene una función AutoMixing que ajusta automáticamente la ganancia de varios micrófonos en tiempo real para reducir el ruido de fondo y la retroalimentación. Esto es útil para conferencias o mesas redondas donde varios oradores están hablando al mismo tiempo. Para utilizar esta función, toque en el botón de Auto Mixing en la esquina superior derecha de la aplicación y seleccione un canal de entrada de la lista. A continuación, verá un indicador verde que muestra el estado de AutoMixing de ese canal. También puede ajustar el umbral, el peso y los parámetros de destino del algoritmo AutoMixing.
-Consejo 5: Utilice las instantáneas internas para guardar y recuperar la configuración
-La aplicación tiene una característica de instantánea interna que le permite guardar y recuperar la configuración del mezclador en cualquier momento. Esto es útil para cambiar entre diferentes escenas o ajustes preestablecidos rápida y fácilmente. Para utilizar esta función, toque en el botón de instantánea en la esquina superior derecha de la aplicación y seleccione una ranura de instantánea de la lista. A continuación, puede nombrar, guardar, cargar o eliminar su instantánea. También puede usar la función de bloqueo para evitar cambios accidentales en su instantánea.
-Conclusión
- -Si tiene alguna pregunta o comentario sobre APK X AIR Behringer, no dude en ponerse en contacto con el equipo de atención al cliente de BEHRINGER o visitar su sitio web para obtener más información. También puede consultar su canal de YouTube para obtener tutoriales y demostraciones de sus productos.
-Gracias por leer este artículo y espero que te haya sido útil. Si lo hizo, por favor compartirlo con sus amigos y colegas que podrían estar interesados en APK X AIR Behringer. Y no te olvides de descargar la aplicación y probarlo por ti mismo!
-Preguntas frecuentes
-Aquí hay algunas preguntas y respuestas comunes sobre APK X AIR Behringer:
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-
- ¿Es APK X AIR Behringer compatible con otros productos BEHRINGER? -
- ¿Puedo usar APK X AIR Behringer con múltiples dispositivos al mismo tiempo? -
- ¿Puedo usar APK X AIR Behringer sin conexión? -
- ¿Cómo puedo actualizar APK X AIR Behringer? -
- ¿Cómo puedo desinstalar APK X AIR Behringer? -
APK X AIR Behringer está diseñado específicamente para los mezcladores X18, XR18, XR16 y XR12. No es compatible con otros productos BEHRINGER, como el X32 o X AIR EDIT.
-Sí, puede usar APK X AIR Behringer con varios dispositivos al mismo tiempo, siempre y cuando estén conectados a la misma red Wi-Fi que su mezclador. Sin embargo, debe tener cuidado de no hacer cambios conflictivos en la configuración del mezclador desde diferentes dispositivos, ya que esto puede causar resultados inesperados.
-No, no se puede utilizar APK X AIR Behringer fuera de línea. Necesita una conexión a Internet para descargar la aplicación y una conexión Wi-Fi para conectarse a su mezclador.
-Para actualizar APK X AIR Behringer, es necesario comprobar si hay nuevas versiones en el sitio web de BEHRINGER y descargarlos manualmente. La aplicación no tiene una función de actualización automática.
-Para desinstalar APK X AIR Behringer, es necesario ir a Configuración > Aplicaciones en el dispositivo y encontrar la aplicación de la lista. A continuación, toque en él y seleccione desinstalar.
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-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar Azulejos De Piano 2 Apk Mod.md b/spaces/Benson/text-generation/Examples/Descargar Azulejos De Piano 2 Apk Mod.md deleted file mode 100644 index 7962394020c1ee673035918e15831c8d87570abb..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar Azulejos De Piano 2 Apk Mod.md +++ /dev/null @@ -1,50 +0,0 @@ - -
Descargar Piano Tiles 2 APK Mod: Un divertido y desafiante juego de música
-¿Te gusta la música y los juegos de ritmo? ¿Quieres probar tus reflejos y habilidades de coordinación? Si es así, entonces deberías probar Piano Tiles 2, uno de los juegos de música más populares y adictivos del mundo. Y si quieres disfrutar del juego con más características y beneficios, entonces usted debe descargar Piano Tiles 2 APK Mod, una versión modificada del juego que le da acceso ilimitado a todas las canciones, monedas, diamantes, y más. En este artículo, le diremos todo lo que necesita saber sobre Piano Tiles 2 y cómo descargar e instalar Piano Tiles 2 APK Mod en su dispositivo Android.
-¿Qué es Piano Tiles 2?
-Piano Tiles 2 es una secuela del juego original Piano Tiles, también conocido como Don’t Tap the White Tile. Es un juego simple pero desafiante donde tienes que tocar las fichas negras que aparecen en la pantalla en sincronía con la música. El juego tiene cientos de canciones de diferentes géneros, como clásica, pop, rock, jazz y más. También puedes competir con otros jugadores de todo el mundo y ver quién puede puntuar más alto en la clasificación.
-descargar azulejos de piano 2 apk mod
DOWNLOAD ✪ https://bltlly.com/2v6LLo
-
Características de las baldosas de piano 2
-Piano Tiles 2 tiene muchas características que lo convierten en un juego divertido y emocionante para jugar. Algunas de ellas son:
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-
- Sonido y gráficos de alta calidad: El juego tiene gráficos impresionantes y animaciones suaves que crean una experiencia realista de tocar el piano. La calidad de sonido también es excelente, con notas claras y nítidas que coinciden perfectamente con las canciones. -
- Varias canciones y niveles: El juego tiene una gran colección de canciones de diferentes géneros y épocas, como Mozart, Beethoven, Chopin, Taylor Swift, Ed Sheeran, Bruno Mars y más. Puede elegir entre diferentes niveles de dificultad, que van desde fácil de dominar. - -
- Logros y recompensas: El juego tiene muchos logros que puedes desbloquear completando ciertas tareas o alcanzando ciertos hitos. También puedes ganar monedas y diamantes jugando o viendo anuncios. Puedes usar estas monedas para comprar nuevas canciones, skins, boosters y más. -
Cómo jugar Piano Tiles 2
-La jugabilidad de Piano Tiles 2 es muy simple e intuitiva. Todo lo que tienes que hacer es tocar las fichas negras que aparecen en la pantalla en sincronía con la música. Tienes que evitar tocar las fichas blancas o perder las fichas negras, de lo contrario perderás el juego. Cuanto más rápido toque, mayor será su puntuación será. También puedes usar boosters como monedas dobles, auto-play o revive para ayudarte en situaciones difíciles.
-¿Por qué descargar Piano Tiles 2 APK Mod?
-Piano Tiles 2 APK Mod es una versión modificada del juego original que le da acceso ilimitado a todas las características y beneficios del juego. Algunas de las ventajas de descargar Piano Tiles 2 APK Mod son:
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-
- Todas las canciones desbloqueadas: Puedes reproducir cualquier canción que quieras sin tener que gastar monedas o diamantes o esperar a que se desbloqueen. -
- Todas las monedas y diamantes ilimitados: y están familiarizados con, ya que esto le ayudará a tocar las fichas con mayor precisión y disfrutar de la música más.
-
Usa los amplificadores sabiamente
-El juego tiene varios potenciadores que pueden ayudarte de diferentes maneras. Algunos de ellos son:
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-
- Monedas dobles: Este booster duplicará la cantidad de monedas que ganes en un juego. Puedes usarlo para comprar más canciones, skins u otros boosters. -
- Auto-play: Este booster hará que el juego se juegue solo por unos segundos. Puedes usarlo para descansar tus dedos o evitar fichas difíciles. -
- Revive: Este refuerzo te permitirá continuar el juego después de cometer un error. Puedes usarlo para guardar tu progreso o mejorar tu puntuación. -
Practica y mejora tus habilidades
-La mejor manera de mejorar en Piano Tiles 2 es practicar y mejorar tus habilidades. Usted debe jugar el juego con regularidad y probar diferentes canciones y niveles. También debe prestar atención al ritmo y el tiempo de las fichas, así como la velocidad y la dirección de las fichas deslizantes. También debes tratar de tocar las fichas con ambas manos, ya que esto aumentará tu eficiencia y coordinación. Cuanto más juegues, más aprenderás y dominarás el juego.
- -Conclusión
-Piano Tiles 2 es un divertido y desafiante juego de música que pondrá a prueba tus reflejos y habilidades de coordinación. Tiene cientos de canciones de diferentes géneros y niveles de dificultad, así como sonido de alta calidad y gráficos. También puedes competir con otros jugadores de todo el mundo y ver quién puede jugar más rápido y mejor. Si quieres disfrutar del juego con más características y beneficios, usted debe descargar Piano Tiles 2 APK Mod, una versión modificada del juego que le da acceso ilimitado a todas las canciones, monedas, diamantes, y más. Puede descargar e instalar Piano Tiles 2 APK Mod en su dispositivo Android siguiendo los sencillos pasos que hemos proporcionado en este artículo. Esperamos que te diviertas jugando Piano Tiles 2!
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre Piano Tiles 2 y Piano Tiles 2 APK Mod:
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-
- Es Piano Tiles 2 libre para jugar?
-Sí, Piano Tiles 2 es gratis, pero tiene algunas compras en la aplicación que requieren dinero real. También puedes ver anuncios para ganar monedas o diamantes.
- - ¿Es seguro usar Piano Tiles 2 APK Mod?
-Sí, Piano Tiles 2 APK Mod es seguro de usar, siempre y cuando se descarga de una fuente confiable. Lo hemos probado en nuestros dispositivos y no encontramos virus o malware.
- - ¿Puedo tocar Piano Tiles 2 sin conexión?
- - - ¿Puedo actualizar Piano Tiles 2 APK Mod?
-Sí, puede actualizar Piano Tiles 2 APK Mod, pero puede perder algunas de las características modded si lo hace. Te recomendamos que busques actualizaciones de la misma fuente donde descargaste el archivo APK.
- - ¿Puedo sincronizar mi progreso entre dispositivos?
-Sí, puedes sincronizar tu progreso entre dispositivos iniciando sesión con tu cuenta de Facebook. Sin embargo, esto puede no funcionar para Piano Tiles 2 APK Mod, ya que puede entrar en conflicto con los datos originales del juego.
-
-
-
\ No newline at end of file diff --git a/spaces/BetterAPI/BetterChat_new/src/app.html b/spaces/BetterAPI/BetterChat_new/src/app.html deleted file mode 100644 index cbee75a1325edc1e113cb99a35bf491d216bb8a1..0000000000000000000000000000000000000000 --- a/spaces/BetterAPI/BetterChat_new/src/app.html +++ /dev/null @@ -1,45 +0,0 @@ - - - - - - -HuggingChat - - %sveltekit.head% - - -%sveltekit.body%- - - - - diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/resources/params.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/resources/params.py deleted file mode 100644 index 3c5c74b3c7c0521a12ce2911635985fe0ed64798..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/resources/params.py +++ /dev/null @@ -1,167 +0,0 @@ -# Copyright 2014 Amazon.com, Inc. or its affiliates. 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. A copy of -# the License is located at -# -# https://aws.amazon.com/apache2.0/ -# -# or in the "license" file accompanying this file. This file 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 re - -import jmespath -from botocore import xform_name - -from ..exceptions import ResourceLoadException - -INDEX_RE = re.compile(r'\[(.*)\]$') - - -def get_data_member(parent, path): - """ - Get a data member from a parent using a JMESPath search query, - loading the parent if required. If the parent cannot be loaded - and no data is present then an exception is raised. - - :type parent: ServiceResource - :param parent: The resource instance to which contains data we - are interested in. - :type path: string - :param path: The JMESPath expression to query - :raises ResourceLoadException: When no data is present and the - resource cannot be loaded. - :returns: The queried data or ``None``. - """ - # Ensure the parent has its data loaded, if possible. - if parent.meta.data is None: - if hasattr(parent, 'load'): - parent.load() - else: - raise ResourceLoadException( - f'{parent.__class__.__name__} has no load method!' - ) - - return jmespath.search(path, parent.meta.data) - - -def create_request_parameters(parent, request_model, params=None, index=None): - """ - Handle request parameters that can be filled in from identifiers, - resource data members or constants. - - By passing ``params``, you can invoke this method multiple times and - build up a parameter dict over time, which is particularly useful - for reverse JMESPath expressions that append to lists. - - :type parent: ServiceResource - :param parent: The resource instance to which this action is attached. - :type request_model: :py:class:`~boto3.resources.model.Request` - :param request_model: The action request model. - :type params: dict - :param params: If set, then add to this existing dict. It is both - edited in-place and returned. - :type index: int - :param index: The position of an item within a list - :rtype: dict - :return: Pre-filled parameters to be sent to the request operation. - """ - if params is None: - params = {} - - for param in request_model.params: - source = param.source - target = param.target - - if source == 'identifier': - # Resource identifier, e.g. queue.url - value = getattr(parent, xform_name(param.name)) - elif source == 'data': - # If this is a data member then it may incur a load - # action before returning the value. - value = get_data_member(parent, param.path) - elif source in ['string', 'integer', 'boolean']: - # These are hard-coded values in the definition - value = param.value - elif source == 'input': - # This is provided by the user, so ignore it here - continue - else: - raise NotImplementedError(f'Unsupported source type: {source}') - - build_param_structure(params, target, value, index) - - return params - - -def build_param_structure(params, target, value, index=None): - """ - This method provides a basic reverse JMESPath implementation that - lets you go from a JMESPath-like string to a possibly deeply nested - object. The ``params`` are mutated in-place, so subsequent calls - can modify the same element by its index. - - >>> build_param_structure(params, 'test[0]', 1) - >>> print(params) - {'test': [1]} - - >>> build_param_structure(params, 'foo.bar[0].baz', 'hello world') - >>> print(params) - {'test': [1], 'foo': {'bar': [{'baz': 'hello, world'}]}} - - """ - pos = params - parts = target.split('.') - - # First, split into parts like 'foo', 'bar[0]', 'baz' and process - # each piece. It can either be a list or a dict, depending on if - # an index like `[0]` is present. We detect this via a regular - # expression, and keep track of where we are in params via the - # pos variable, walking down to the last item. Once there, we - # set the value. - for i, part in enumerate(parts): - # Is it indexing an array? - result = INDEX_RE.search(part) - if result: - if result.group(1): - if result.group(1) == '*': - part = part[:-3] - else: - # We have an explicit index - index = int(result.group(1)) - part = part[: -len(str(index) + '[]')] - else: - # Index will be set after we know the proper part - # name and that it's a list instance. - index = None - part = part[:-2] - - if part not in pos or not isinstance(pos[part], list): - pos[part] = [] - - # This means we should append, e.g. 'foo[]' - if index is None: - index = len(pos[part]) - - while len(pos[part]) <= index: - # Assume it's a dict until we set the final value below - pos[part].append({}) - - # Last item? Set the value, otherwise set the new position - if i == len(parts) - 1: - pos[part][index] = value - else: - # The new pos is the *item* in the array, not the array! - pos = pos[part][index] - else: - if part not in pos: - pos[part] = {} - - # Last item? Set the value, otherwise set the new position - if i == len(parts) - 1: - pos[part] = value - else: - pos = pos[part] diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/jpcntx.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/jpcntx.py deleted file mode 100644 index 2f53bdda09e92da38e31cac1a6d415f4670137f7..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/jpcntx.py +++ /dev/null @@ -1,238 +0,0 @@ -######################## BEGIN LICENSE BLOCK ######################## -# The Original Code is Mozilla Communicator client code. -# -# The Initial Developer of the Original Code is -# Netscape Communications Corporation. -# Portions created by the Initial Developer are Copyright (C) 1998 -# the Initial Developer. All Rights Reserved. -# -# Contributor(s): -# Mark Pilgrim - port to Python -# -# This library is free software; you can redistribute it and/or -# modify it under the terms of the GNU Lesser General Public -# License as published by the Free Software Foundation; either -# version 2.1 of the License, or (at your option) any later version. -# -# This library 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 GNU -# Lesser General Public License for more details. -# -# You should have received a copy of the GNU Lesser General Public -# License along with this library; if not, write to the Free Software -# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA -# 02110-1301 USA -######################### END LICENSE BLOCK ######################### - -from typing import List, Tuple, Union - -# This is hiragana 2-char sequence table, the number in each cell represents its frequency category -# fmt: off -jp2_char_context = ( - (0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1), - (2, 4, 0, 4, 0, 3, 0, 4, 0, 3, 4, 4, 4, 2, 4, 3, 3, 4, 3, 2, 3, 3, 4, 2, 3, 3, 3, 2, 4, 1, 4, 3, 3, 1, 5, 4, 3, 4, 3, 4, 3, 5, 3, 0, 3, 5, 4, 2, 0, 3, 1, 0, 3, 3, 0, 3, 3, 0, 1, 1, 0, 4, 3, 0, 3, 3, 0, 4, 0, 2, 0, 3, 5, 5, 5, 5, 4, 0, 4, 1, 0, 3, 4), - (0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2), - (0, 4, 0, 5, 0, 5, 0, 4, 0, 4, 5, 4, 4, 3, 5, 3, 5, 1, 5, 3, 4, 3, 4, 4, 3, 4, 3, 3, 4, 3, 5, 4, 4, 3, 5, 5, 3, 5, 5, 5, 3, 5, 5, 3, 4, 5, 5, 3, 1, 3, 2, 0, 3, 4, 0, 4, 2, 0, 4, 2, 1, 5, 3, 2, 3, 5, 0, 4, 0, 2, 0, 5, 4, 4, 5, 4, 5, 0, 4, 0, 0, 4, 4), - (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), - (0, 3, 0, 4, 0, 3, 0, 3, 0, 4, 5, 4, 3, 3, 3, 3, 4, 3, 5, 4, 4, 3, 5, 4, 4, 3, 4, 3, 4, 4, 4, 4, 5, 3, 4, 4, 3, 4, 5, 5, 4, 5, 5, 1, 4, 5, 4, 3, 0, 3, 3, 1, 3, 3, 0, 4, 4, 0, 3, 3, 1, 5, 3, 3, 3, 5, 0, 4, 0, 3, 0, 4, 4, 3, 4, 3, 3, 0, 4, 1, 1, 3, 4), - (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), - (0, 4, 0, 3, 0, 3, 0, 4, 0, 3, 4, 4, 3, 2, 2, 1, 2, 1, 3, 1, 3, 3, 3, 3, 3, 4, 3, 1, 3, 3, 5, 3, 3, 0, 4, 3, 0, 5, 4, 3, 3, 5, 4, 4, 3, 4, 4, 5, 0, 1, 2, 0, 1, 2, 0, 2, 2, 0, 1, 0, 0, 5, 2, 2, 1, 4, 0, 3, 0, 1, 0, 4, 4, 3, 5, 4, 3, 0, 2, 1, 0, 4, 3), - (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), - (0, 3, 0, 5, 0, 4, 0, 2, 1, 4, 4, 2, 4, 1, 4, 2, 4, 2, 4, 3, 3, 3, 4, 3, 3, 3, 3, 1, 4, 2, 3, 3, 3, 1, 4, 4, 1, 1, 1, 4, 3, 3, 2, 0, 2, 4, 3, 2, 0, 3, 3, 0, 3, 1, 1, 0, 0, 0, 3, 3, 0, 4, 2, 2, 3, 4, 0, 4, 0, 3, 0, 4, 4, 5, 3, 4, 4, 0, 3, 0, 0, 1, 4), - (1, 4, 0, 4, 0, 4, 0, 4, 0, 3, 5, 4, 4, 3, 4, 3, 5, 4, 3, 3, 4, 3, 5, 4, 4, 4, 4, 3, 4, 2, 4, 3, 3, 1, 5, 4, 3, 2, 4, 5, 4, 5, 5, 4, 4, 5, 4, 4, 0, 3, 2, 2, 3, 3, 0, 4, 3, 1, 3, 2, 1, 4, 3, 3, 4, 5, 0, 3, 0, 2, 0, 4, 5, 5, 4, 5, 4, 0, 4, 0, 0, 5, 4), - (0, 5, 0, 5, 0, 4, 0, 3, 0, 4, 4, 3, 4, 3, 3, 3, 4, 0, 4, 4, 4, 3, 4, 3, 4, 3, 3, 1, 4, 2, 4, 3, 4, 0, 5, 4, 1, 4, 5, 4, 4, 5, 3, 2, 4, 3, 4, 3, 2, 4, 1, 3, 3, 3, 2, 3, 2, 0, 4, 3, 3, 4, 3, 3, 3, 4, 0, 4, 0, 3, 0, 4, 5, 4, 4, 4, 3, 0, 4, 1, 0, 1, 3), - (0, 3, 1, 4, 0, 3, 0, 2, 0, 3, 4, 4, 3, 1, 4, 2, 3, 3, 4, 3, 4, 3, 4, 3, 4, 4, 3, 2, 3, 1, 5, 4, 4, 1, 4, 4, 3, 5, 4, 4, 3, 5, 5, 4, 3, 4, 4, 3, 1, 2, 3, 1, 2, 2, 0, 3, 2, 0, 3, 1, 0, 5, 3, 3, 3, 4, 3, 3, 3, 3, 4, 4, 4, 4, 5, 4, 2, 0, 3, 3, 2, 4, 3), - (0, 2, 0, 3, 0, 1, 0, 1, 0, 0, 3, 2, 0, 0, 2, 0, 1, 0, 2, 1, 3, 3, 3, 1, 2, 3, 1, 0, 1, 0, 4, 2, 1, 1, 3, 3, 0, 4, 3, 3, 1, 4, 3, 3, 0, 3, 3, 2, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 4, 1, 0, 2, 3, 2, 2, 2, 1, 3, 3, 3, 4, 4, 3, 2, 0, 3, 1, 0, 3, 3), - (0, 4, 0, 4, 0, 3, 0, 3, 0, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 3, 4, 2, 4, 3, 4, 3, 3, 2, 4, 3, 4, 5, 4, 1, 4, 5, 3, 5, 4, 5, 3, 5, 4, 0, 3, 5, 5, 3, 1, 3, 3, 2, 2, 3, 0, 3, 4, 1, 3, 3, 2, 4, 3, 3, 3, 4, 0, 4, 0, 3, 0, 4, 5, 4, 4, 5, 3, 0, 4, 1, 0, 3, 4), - (0, 2, 0, 3, 0, 3, 0, 0, 0, 2, 2, 2, 1, 0, 1, 0, 0, 0, 3, 0, 3, 0, 3, 0, 1, 3, 1, 0, 3, 1, 3, 3, 3, 1, 3, 3, 3, 0, 1, 3, 1, 3, 4, 0, 0, 3, 1, 1, 0, 3, 2, 0, 0, 0, 0, 1, 3, 0, 1, 0, 0, 3, 3, 2, 0, 3, 0, 0, 0, 0, 0, 3, 4, 3, 4, 3, 3, 0, 3, 0, 0, 2, 3), - (2, 3, 0, 3, 0, 2, 0, 1, 0, 3, 3, 4, 3, 1, 3, 1, 1, 1, 3, 1, 4, 3, 4, 3, 3, 3, 0, 0, 3, 1, 5, 4, 3, 1, 4, 3, 2, 5, 5, 4, 4, 4, 4, 3, 3, 4, 4, 4, 0, 2, 1, 1, 3, 2, 0, 1, 2, 0, 0, 1, 0, 4, 1, 3, 3, 3, 0, 3, 0, 1, 0, 4, 4, 4, 5, 5, 3, 0, 2, 0, 0, 4, 4), - (0, 2, 0, 1, 0, 3, 1, 3, 0, 2, 3, 3, 3, 0, 3, 1, 0, 0, 3, 0, 3, 2, 3, 1, 3, 2, 1, 1, 0, 0, 4, 2, 1, 0, 2, 3, 1, 4, 3, 2, 0, 4, 4, 3, 1, 3, 1, 3, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 4, 1, 1, 1, 2, 0, 3, 0, 0, 0, 3, 4, 2, 4, 3, 2, 0, 1, 0, 0, 3, 3), - (0, 1, 0, 4, 0, 5, 0, 4, 0, 2, 4, 4, 2, 3, 3, 2, 3, 3, 5, 3, 3, 3, 4, 3, 4, 2, 3, 0, 4, 3, 3, 3, 4, 1, 4, 3, 2, 1, 5, 5, 3, 4, 5, 1, 3, 5, 4, 2, 0, 3, 3, 0, 1, 3, 0, 4, 2, 0, 1, 3, 1, 4, 3, 3, 3, 3, 0, 3, 0, 1, 0, 3, 4, 4, 4, 5, 5, 0, 3, 0, 1, 4, 5), - (0, 2, 0, 3, 0, 3, 0, 0, 0, 2, 3, 1, 3, 0, 4, 0, 1, 1, 3, 0, 3, 4, 3, 2, 3, 1, 0, 3, 3, 2, 3, 1, 3, 0, 2, 3, 0, 2, 1, 4, 1, 2, 2, 0, 0, 3, 3, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 2, 2, 0, 3, 2, 1, 3, 3, 0, 2, 0, 2, 0, 0, 3, 3, 1, 2, 4, 0, 3, 0, 2, 2, 3), - (2, 4, 0, 5, 0, 4, 0, 4, 0, 2, 4, 4, 4, 3, 4, 3, 3, 3, 1, 2, 4, 3, 4, 3, 4, 4, 5, 0, 3, 3, 3, 3, 2, 0, 4, 3, 1, 4, 3, 4, 1, 4, 4, 3, 3, 4, 4, 3, 1, 2, 3, 0, 4, 2, 0, 4, 1, 0, 3, 3, 0, 4, 3, 3, 3, 4, 0, 4, 0, 2, 0, 3, 5, 3, 4, 5, 2, 0, 3, 0, 0, 4, 5), - (0, 3, 0, 4, 0, 1, 0, 1, 0, 1, 3, 2, 2, 1, 3, 0, 3, 0, 2, 0, 2, 0, 3, 0, 2, 0, 0, 0, 1, 0, 1, 1, 0, 0, 3, 1, 0, 0, 0, 4, 0, 3, 1, 0, 2, 1, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 4, 2, 2, 3, 1, 0, 3, 0, 0, 0, 1, 4, 4, 4, 3, 0, 0, 4, 0, 0, 1, 4), - (1, 4, 1, 5, 0, 3, 0, 3, 0, 4, 5, 4, 4, 3, 5, 3, 3, 4, 4, 3, 4, 1, 3, 3, 3, 3, 2, 1, 4, 1, 5, 4, 3, 1, 4, 4, 3, 5, 4, 4, 3, 5, 4, 3, 3, 4, 4, 4, 0, 3, 3, 1, 2, 3, 0, 3, 1, 0, 3, 3, 0, 5, 4, 4, 4, 4, 4, 4, 3, 3, 5, 4, 4, 3, 3, 5, 4, 0, 3, 2, 0, 4, 4), - (0, 2, 0, 3, 0, 1, 0, 0, 0, 1, 3, 3, 3, 2, 4, 1, 3, 0, 3, 1, 3, 0, 2, 2, 1, 1, 0, 0, 2, 0, 4, 3, 1, 0, 4, 3, 0, 4, 4, 4, 1, 4, 3, 1, 1, 3, 3, 1, 0, 2, 0, 0, 1, 3, 0, 0, 0, 0, 2, 0, 0, 4, 3, 2, 4, 3, 5, 4, 3, 3, 3, 4, 3, 3, 4, 3, 3, 0, 2, 1, 0, 3, 3), - (0, 2, 0, 4, 0, 3, 0, 2, 0, 2, 5, 5, 3, 4, 4, 4, 4, 1, 4, 3, 3, 0, 4, 3, 4, 3, 1, 3, 3, 2, 4, 3, 0, 3, 4, 3, 0, 3, 4, 4, 2, 4, 4, 0, 4, 5, 3, 3, 2, 2, 1, 1, 1, 2, 0, 1, 5, 0, 3, 3, 2, 4, 3, 3, 3, 4, 0, 3, 0, 2, 0, 4, 4, 3, 5, 5, 0, 0, 3, 0, 2, 3, 3), - (0, 3, 0, 4, 0, 3, 0, 1, 0, 3, 4, 3, 3, 1, 3, 3, 3, 0, 3, 1, 3, 0, 4, 3, 3, 1, 1, 0, 3, 0, 3, 3, 0, 0, 4, 4, 0, 1, 5, 4, 3, 3, 5, 0, 3, 3, 4, 3, 0, 2, 0, 1, 1, 1, 0, 1, 3, 0, 1, 2, 1, 3, 3, 2, 3, 3, 0, 3, 0, 1, 0, 1, 3, 3, 4, 4, 1, 0, 1, 2, 2, 1, 3), - (0, 1, 0, 4, 0, 4, 0, 3, 0, 1, 3, 3, 3, 2, 3, 1, 1, 0, 3, 0, 3, 3, 4, 3, 2, 4, 2, 0, 1, 0, 4, 3, 2, 0, 4, 3, 0, 5, 3, 3, 2, 4, 4, 4, 3, 3, 3, 4, 0, 1, 3, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 4, 2, 3, 3, 3, 0, 3, 0, 0, 0, 4, 4, 4, 5, 3, 2, 0, 3, 3, 0, 3, 5), - (0, 2, 0, 3, 0, 0, 0, 3, 0, 1, 3, 0, 2, 0, 0, 0, 1, 0, 3, 1, 1, 3, 3, 0, 0, 3, 0, 0, 3, 0, 2, 3, 1, 0, 3, 1, 0, 3, 3, 2, 0, 4, 2, 2, 0, 2, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 2, 0, 1, 0, 1, 0, 0, 0, 1, 3, 1, 2, 0, 0, 0, 1, 0, 0, 1, 4), - (0, 3, 0, 3, 0, 5, 0, 1, 0, 2, 4, 3, 1, 3, 3, 2, 1, 1, 5, 2, 1, 0, 5, 1, 2, 0, 0, 0, 3, 3, 2, 2, 3, 2, 4, 3, 0, 0, 3, 3, 1, 3, 3, 0, 2, 5, 3, 4, 0, 3, 3, 0, 1, 2, 0, 2, 2, 0, 3, 2, 0, 2, 2, 3, 3, 3, 0, 2, 0, 1, 0, 3, 4, 4, 2, 5, 4, 0, 3, 0, 0, 3, 5), - (0, 3, 0, 3, 0, 3, 0, 1, 0, 3, 3, 3, 3, 0, 3, 0, 2, 0, 2, 1, 1, 0, 2, 0, 1, 0, 0, 0, 2, 1, 0, 0, 1, 0, 3, 2, 0, 0, 3, 3, 1, 2, 3, 1, 0, 3, 3, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 3, 1, 2, 3, 0, 3, 0, 1, 0, 3, 2, 1, 0, 4, 3, 0, 1, 1, 0, 3, 3), - (0, 4, 0, 5, 0, 3, 0, 3, 0, 4, 5, 5, 4, 3, 5, 3, 4, 3, 5, 3, 3, 2, 5, 3, 4, 4, 4, 3, 4, 3, 4, 5, 5, 3, 4, 4, 3, 4, 4, 5, 4, 4, 4, 3, 4, 5, 5, 4, 2, 3, 4, 2, 3, 4, 0, 3, 3, 1, 4, 3, 2, 4, 3, 3, 5, 5, 0, 3, 0, 3, 0, 5, 5, 5, 5, 4, 4, 0, 4, 0, 1, 4, 4), - (0, 4, 0, 4, 0, 3, 0, 3, 0, 3, 5, 4, 4, 2, 3, 2, 5, 1, 3, 2, 5, 1, 4, 2, 3, 2, 3, 3, 4, 3, 3, 3, 3, 2, 5, 4, 1, 3, 3, 5, 3, 4, 4, 0, 4, 4, 3, 1, 1, 3, 1, 0, 2, 3, 0, 2, 3, 0, 3, 0, 0, 4, 3, 1, 3, 4, 0, 3, 0, 2, 0, 4, 4, 4, 3, 4, 5, 0, 4, 0, 0, 3, 4), - (0, 3, 0, 3, 0, 3, 1, 2, 0, 3, 4, 4, 3, 3, 3, 0, 2, 2, 4, 3, 3, 1, 3, 3, 3, 1, 1, 0, 3, 1, 4, 3, 2, 3, 4, 4, 2, 4, 4, 4, 3, 4, 4, 3, 2, 4, 4, 3, 1, 3, 3, 1, 3, 3, 0, 4, 1, 0, 2, 2, 1, 4, 3, 2, 3, 3, 5, 4, 3, 3, 5, 4, 4, 3, 3, 0, 4, 0, 3, 2, 2, 4, 4), - (0, 2, 0, 1, 0, 0, 0, 0, 0, 1, 2, 1, 3, 0, 0, 0, 0, 0, 2, 0, 1, 2, 1, 0, 0, 1, 0, 0, 0, 0, 3, 0, 0, 1, 0, 1, 1, 3, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 2, 0, 3, 4, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1), - (0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 4, 0, 4, 1, 4, 0, 3, 0, 4, 0, 3, 0, 4, 0, 3, 0, 3, 0, 4, 1, 5, 1, 4, 0, 0, 3, 0, 5, 0, 5, 2, 0, 1, 0, 0, 0, 2, 1, 4, 0, 1, 3, 0, 0, 3, 0, 0, 3, 1, 1, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0), - (1, 4, 0, 5, 0, 3, 0, 2, 0, 3, 5, 4, 4, 3, 4, 3, 5, 3, 4, 3, 3, 0, 4, 3, 3, 3, 3, 3, 3, 2, 4, 4, 3, 1, 3, 4, 4, 5, 4, 4, 3, 4, 4, 1, 3, 5, 4, 3, 3, 3, 1, 2, 2, 3, 3, 1, 3, 1, 3, 3, 3, 5, 3, 3, 4, 5, 0, 3, 0, 3, 0, 3, 4, 3, 4, 4, 3, 0, 3, 0, 2, 4, 3), - (0, 1, 0, 4, 0, 0, 0, 0, 0, 1, 4, 0, 4, 1, 4, 2, 4, 0, 3, 0, 1, 0, 1, 0, 0, 0, 0, 0, 2, 0, 3, 1, 1, 1, 0, 3, 0, 0, 0, 1, 2, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 3, 0, 0, 0, 0, 3, 2, 0, 2, 2, 0, 1, 0, 0, 0, 2, 3, 2, 3, 3, 0, 0, 0, 0, 2, 1, 0), - (0, 5, 1, 5, 0, 3, 0, 3, 0, 5, 4, 4, 5, 1, 5, 3, 3, 0, 4, 3, 4, 3, 5, 3, 4, 3, 3, 2, 4, 3, 4, 3, 3, 0, 3, 3, 1, 4, 4, 3, 4, 4, 4, 3, 4, 5, 5, 3, 2, 3, 1, 1, 3, 3, 1, 3, 1, 1, 3, 3, 2, 4, 5, 3, 3, 5, 0, 4, 0, 3, 0, 4, 4, 3, 5, 3, 3, 0, 3, 4, 0, 4, 3), - (0, 5, 0, 5, 0, 3, 0, 2, 0, 4, 4, 3, 5, 2, 4, 3, 3, 3, 4, 4, 4, 3, 5, 3, 5, 3, 3, 1, 4, 0, 4, 3, 3, 0, 3, 3, 0, 4, 4, 4, 4, 5, 4, 3, 3, 5, 5, 3, 2, 3, 1, 2, 3, 2, 0, 1, 0, 0, 3, 2, 2, 4, 4, 3, 1, 5, 0, 4, 0, 3, 0, 4, 3, 1, 3, 2, 1, 0, 3, 3, 0, 3, 3), - (0, 4, 0, 5, 0, 5, 0, 4, 0, 4, 5, 5, 5, 3, 4, 3, 3, 2, 5, 4, 4, 3, 5, 3, 5, 3, 4, 0, 4, 3, 4, 4, 3, 2, 4, 4, 3, 4, 5, 4, 4, 5, 5, 0, 3, 5, 5, 4, 1, 3, 3, 2, 3, 3, 1, 3, 1, 0, 4, 3, 1, 4, 4, 3, 4, 5, 0, 4, 0, 2, 0, 4, 3, 4, 4, 3, 3, 0, 4, 0, 0, 5, 5), - (0, 4, 0, 4, 0, 5, 0, 1, 1, 3, 3, 4, 4, 3, 4, 1, 3, 0, 5, 1, 3, 0, 3, 1, 3, 1, 1, 0, 3, 0, 3, 3, 4, 0, 4, 3, 0, 4, 4, 4, 3, 4, 4, 0, 3, 5, 4, 1, 0, 3, 0, 0, 2, 3, 0, 3, 1, 0, 3, 1, 0, 3, 2, 1, 3, 5, 0, 3, 0, 1, 0, 3, 2, 3, 3, 4, 4, 0, 2, 2, 0, 4, 4), - (2, 4, 0, 5, 0, 4, 0, 3, 0, 4, 5, 5, 4, 3, 5, 3, 5, 3, 5, 3, 5, 2, 5, 3, 4, 3, 3, 4, 3, 4, 5, 3, 2, 1, 5, 4, 3, 2, 3, 4, 5, 3, 4, 1, 2, 5, 4, 3, 0, 3, 3, 0, 3, 2, 0, 2, 3, 0, 4, 1, 0, 3, 4, 3, 3, 5, 0, 3, 0, 1, 0, 4, 5, 5, 5, 4, 3, 0, 4, 2, 0, 3, 5), - (0, 5, 0, 4, 0, 4, 0, 2, 0, 5, 4, 3, 4, 3, 4, 3, 3, 3, 4, 3, 4, 2, 5, 3, 5, 3, 4, 1, 4, 3, 4, 4, 4, 0, 3, 5, 0, 4, 4, 4, 4, 5, 3, 1, 3, 4, 5, 3, 3, 3, 3, 3, 3, 3, 0, 2, 2, 0, 3, 3, 2, 4, 3, 3, 3, 5, 3, 4, 1, 3, 3, 5, 3, 2, 0, 0, 0, 0, 4, 3, 1, 3, 3), - (0, 1, 0, 3, 0, 3, 0, 1, 0, 1, 3, 3, 3, 2, 3, 3, 3, 0, 3, 0, 0, 0, 3, 1, 3, 0, 0, 0, 2, 2, 2, 3, 0, 0, 3, 2, 0, 1, 2, 4, 1, 3, 3, 0, 0, 3, 3, 3, 0, 1, 0, 0, 2, 1, 0, 0, 3, 0, 3, 1, 0, 3, 0, 0, 1, 3, 0, 2, 0, 1, 0, 3, 3, 1, 3, 3, 0, 0, 1, 1, 0, 3, 3), - (0, 2, 0, 3, 0, 2, 1, 4, 0, 2, 2, 3, 1, 1, 3, 1, 1, 0, 2, 0, 3, 1, 2, 3, 1, 3, 0, 0, 1, 0, 4, 3, 2, 3, 3, 3, 1, 4, 2, 3, 3, 3, 3, 1, 0, 3, 1, 4, 0, 1, 1, 0, 1, 2, 0, 1, 1, 0, 1, 1, 0, 3, 1, 3, 2, 2, 0, 1, 0, 0, 0, 2, 3, 3, 3, 1, 0, 0, 0, 0, 0, 2, 3), - (0, 5, 0, 4, 0, 5, 0, 2, 0, 4, 5, 5, 3, 3, 4, 3, 3, 1, 5, 4, 4, 2, 4, 4, 4, 3, 4, 2, 4, 3, 5, 5, 4, 3, 3, 4, 3, 3, 5, 5, 4, 5, 5, 1, 3, 4, 5, 3, 1, 4, 3, 1, 3, 3, 0, 3, 3, 1, 4, 3, 1, 4, 5, 3, 3, 5, 0, 4, 0, 3, 0, 5, 3, 3, 1, 4, 3, 0, 4, 0, 1, 5, 3), - 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(0, 3, 0, 3, 0, 1, 0, 3, 0, 2, 3, 3, 3, 0, 3, 1, 2, 0, 3, 3, 2, 3, 3, 2, 3, 2, 3, 1, 3, 0, 4, 3, 2, 0, 3, 3, 1, 4, 3, 3, 2, 3, 4, 3, 1, 3, 3, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 4, 1, 1, 0, 3, 0, 3, 1, 0, 2, 3, 3, 3, 3, 3, 1, 0, 0, 2, 0, 3, 3), - (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 2, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 3, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 2, 0, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3), - (0, 2, 0, 3, 1, 3, 0, 3, 0, 2, 3, 3, 3, 1, 3, 1, 3, 1, 3, 1, 3, 3, 3, 1, 3, 0, 2, 3, 1, 1, 4, 3, 3, 2, 3, 3, 1, 2, 2, 4, 1, 3, 3, 0, 1, 4, 2, 3, 0, 1, 3, 0, 3, 0, 0, 1, 3, 0, 2, 0, 0, 3, 3, 2, 1, 3, 0, 3, 0, 2, 0, 3, 4, 4, 4, 3, 1, 0, 3, 0, 0, 3, 3), - (0, 2, 0, 1, 0, 2, 0, 0, 0, 1, 3, 2, 2, 1, 3, 0, 1, 1, 3, 0, 3, 2, 3, 1, 2, 0, 2, 0, 1, 1, 3, 3, 3, 0, 3, 3, 1, 1, 2, 3, 2, 3, 3, 1, 2, 3, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 0, 1, 0, 0, 2, 1, 2, 1, 3, 0, 3, 0, 0, 0, 3, 4, 4, 4, 3, 2, 0, 2, 0, 0, 2, 4), - (0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 3, 1, 0, 0, 0, 0, 0, 0, 0, 3), - (0, 3, 0, 3, 0, 2, 0, 3, 0, 3, 3, 3, 2, 3, 2, 2, 2, 0, 3, 1, 3, 3, 3, 2, 3, 3, 0, 0, 3, 0, 3, 2, 2, 0, 2, 3, 1, 4, 3, 4, 3, 3, 2, 3, 1, 5, 4, 4, 0, 3, 1, 2, 1, 3, 0, 3, 1, 1, 2, 0, 2, 3, 1, 3, 1, 3, 0, 3, 0, 1, 0, 3, 3, 4, 4, 2, 1, 0, 2, 1, 0, 2, 4), - (0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 4, 2, 5, 1, 4, 0, 2, 0, 2, 1, 3, 1, 4, 0, 2, 1, 0, 0, 2, 1, 4, 1, 1, 0, 3, 3, 0, 5, 1, 3, 2, 3, 3, 1, 0, 3, 2, 3, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 4, 0, 1, 0, 3, 0, 2, 0, 1, 0, 3, 3, 3, 4, 3, 3, 0, 0, 0, 0, 2, 3), - (0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 1, 0, 0, 0, 0, 0, 3), - (0, 1, 0, 3, 0, 4, 0, 3, 0, 2, 4, 3, 1, 0, 3, 2, 2, 1, 3, 1, 2, 2, 3, 1, 1, 1, 2, 1, 3, 0, 1, 2, 0, 1, 3, 2, 1, 3, 0, 5, 5, 1, 0, 0, 1, 3, 2, 1, 0, 3, 0, 0, 1, 0, 0, 0, 0, 0, 3, 4, 0, 1, 1, 1, 3, 2, 0, 2, 0, 1, 0, 2, 3, 3, 1, 2, 3, 0, 1, 0, 1, 0, 4), - (0, 0, 0, 1, 0, 3, 0, 3, 0, 2, 2, 1, 0, 0, 4, 0, 3, 0, 3, 1, 3, 0, 3, 0, 3, 0, 1, 0, 3, 0, 3, 1, 3, 0, 3, 3, 0, 0, 1, 2, 1, 1, 1, 0, 1, 2, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 1, 2, 0, 0, 2, 0, 0, 0, 0, 2, 3, 3, 3, 3, 0, 0, 0, 0, 1, 4), - (0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 3, 1, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 0, 2, 0, 2, 3, 0, 0, 2, 2, 3, 1, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 2, 0, 0, 0, 0, 2, 3), - (2, 4, 0, 5, 0, 5, 0, 4, 0, 3, 4, 3, 3, 3, 4, 3, 3, 3, 4, 3, 4, 4, 5, 4, 5, 5, 5, 2, 3, 0, 5, 5, 4, 1, 5, 4, 3, 1, 5, 4, 3, 4, 4, 3, 3, 4, 3, 3, 0, 3, 2, 0, 2, 3, 0, 3, 0, 0, 3, 3, 0, 5, 3, 2, 3, 3, 0, 3, 0, 3, 0, 3, 4, 5, 4, 5, 3, 0, 4, 3, 0, 3, 4), - 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(1, 4, 0, 4, 0, 3, 0, 1, 0, 3, 3, 3, 1, 1, 3, 3, 2, 2, 3, 3, 1, 0, 3, 2, 2, 1, 2, 0, 3, 1, 2, 1, 2, 0, 3, 2, 0, 2, 2, 3, 3, 4, 3, 0, 3, 3, 1, 2, 0, 1, 1, 3, 1, 2, 0, 0, 3, 0, 1, 1, 0, 3, 2, 2, 3, 3, 0, 3, 0, 0, 0, 2, 3, 3, 4, 3, 3, 0, 1, 0, 0, 1, 4), - (0, 4, 0, 4, 0, 4, 0, 0, 0, 3, 4, 4, 3, 1, 4, 2, 3, 2, 3, 3, 3, 1, 4, 3, 4, 0, 3, 0, 4, 2, 3, 3, 2, 2, 5, 4, 2, 1, 3, 4, 3, 4, 3, 1, 3, 3, 4, 2, 0, 2, 1, 0, 3, 3, 0, 0, 2, 0, 3, 1, 0, 4, 4, 3, 4, 3, 0, 4, 0, 1, 0, 2, 4, 4, 4, 4, 4, 0, 3, 2, 0, 3, 3), - (0, 0, 0, 1, 0, 4, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 2, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2), - (0, 2, 0, 3, 0, 4, 0, 4, 0, 1, 3, 3, 3, 0, 4, 0, 2, 1, 2, 1, 1, 1, 2, 0, 3, 1, 1, 0, 1, 0, 3, 1, 0, 0, 3, 3, 2, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 2, 0, 2, 2, 0, 3, 1, 0, 0, 1, 0, 1, 1, 0, 1, 2, 0, 3, 0, 0, 0, 0, 1, 0, 0, 3, 3, 4, 3, 1, 0, 1, 0, 3, 0, 2), - (0, 0, 0, 3, 0, 5, 0, 0, 0, 0, 1, 0, 2, 0, 3, 1, 0, 1, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 4, 0, 0, 0, 2, 3, 0, 1, 4, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 3), - (0, 2, 0, 5, 0, 5, 0, 1, 0, 2, 4, 3, 3, 2, 5, 1, 3, 2, 3, 3, 3, 0, 4, 1, 2, 0, 3, 0, 4, 0, 2, 2, 1, 1, 5, 3, 0, 0, 1, 4, 2, 3, 2, 0, 3, 3, 3, 2, 0, 2, 4, 1, 1, 2, 0, 1, 1, 0, 3, 1, 0, 1, 3, 1, 2, 3, 0, 2, 0, 0, 0, 1, 3, 5, 4, 4, 4, 0, 3, 0, 0, 1, 3), - (0, 4, 0, 5, 0, 4, 0, 4, 0, 4, 5, 4, 3, 3, 4, 3, 3, 3, 4, 3, 4, 4, 5, 3, 4, 5, 4, 2, 4, 2, 3, 4, 3, 1, 4, 4, 1, 3, 5, 4, 4, 5, 5, 4, 4, 5, 5, 5, 2, 3, 3, 1, 4, 3, 1, 3, 3, 0, 3, 3, 1, 4, 3, 4, 4, 4, 0, 3, 0, 4, 0, 3, 3, 4, 4, 5, 0, 0, 4, 3, 0, 4, 5), - (0, 4, 0, 4, 0, 3, 0, 3, 0, 3, 4, 4, 4, 3, 3, 2, 4, 3, 4, 3, 4, 3, 5, 3, 4, 3, 2, 1, 4, 2, 4, 4, 3, 1, 3, 4, 2, 4, 5, 5, 3, 4, 5, 4, 1, 5, 4, 3, 0, 3, 2, 2, 3, 2, 1, 3, 1, 0, 3, 3, 3, 5, 3, 3, 3, 5, 4, 4, 2, 3, 3, 4, 3, 3, 3, 2, 1, 0, 3, 2, 1, 4, 3), - (0, 4, 0, 5, 0, 4, 0, 3, 0, 3, 5, 5, 3, 2, 4, 3, 4, 0, 5, 4, 4, 1, 4, 4, 4, 3, 3, 3, 4, 3, 5, 5, 2, 3, 3, 4, 1, 2, 5, 5, 3, 5, 5, 2, 3, 5, 5, 4, 0, 3, 2, 0, 3, 3, 1, 1, 5, 1, 4, 1, 0, 4, 3, 2, 3, 5, 0, 4, 0, 3, 0, 5, 4, 3, 4, 3, 0, 0, 4, 1, 0, 4, 4), - (1, 3, 0, 4, 0, 2, 0, 2, 0, 2, 5, 5, 3, 3, 3, 3, 3, 0, 4, 2, 3, 4, 4, 4, 3, 4, 0, 0, 3, 4, 5, 4, 3, 3, 3, 3, 2, 5, 5, 4, 5, 5, 5, 4, 3, 5, 5, 5, 1, 3, 1, 0, 1, 0, 0, 3, 2, 0, 4, 2, 0, 5, 2, 3, 2, 4, 1, 3, 0, 3, 0, 4, 5, 4, 5, 4, 3, 0, 4, 2, 0, 5, 4), - (0, 3, 0, 4, 0, 5, 0, 3, 0, 3, 4, 4, 3, 2, 3, 2, 3, 3, 3, 3, 3, 2, 4, 3, 3, 2, 2, 0, 3, 3, 3, 3, 3, 1, 3, 3, 3, 0, 4, 4, 3, 4, 4, 1, 1, 4, 4, 2, 0, 3, 1, 0, 1, 1, 0, 4, 1, 0, 2, 3, 1, 3, 3, 1, 3, 4, 0, 3, 0, 1, 0, 3, 1, 3, 0, 0, 1, 0, 2, 0, 0, 4, 4), - (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), - (0, 3, 0, 3, 0, 2, 0, 3, 0, 1, 5, 4, 3, 3, 3, 1, 4, 2, 1, 2, 3, 4, 4, 2, 4, 4, 5, 0, 3, 1, 4, 3, 4, 0, 4, 3, 3, 3, 2, 3, 2, 5, 3, 4, 3, 2, 2, 3, 0, 0, 3, 0, 2, 1, 0, 1, 2, 0, 0, 0, 0, 2, 1, 1, 3, 1, 0, 2, 0, 4, 0, 3, 4, 4, 4, 5, 2, 0, 2, 0, 0, 1, 3), - (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 4, 2, 1, 1, 0, 1, 0, 3, 2, 0, 0, 3, 1, 1, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0, 2, 0, 0, 0, 1, 4, 0, 4, 2, 1, 0, 0, 0, 0, 0, 1), - (0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 3, 1, 0, 0, 0, 2, 0, 2, 1, 0, 0, 1, 2, 1, 0, 1, 1, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 1, 0, 0, 0, 0, 0, 1, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2), - (0, 4, 0, 4, 0, 4, 0, 3, 0, 4, 4, 3, 4, 2, 4, 3, 2, 0, 4, 4, 4, 3, 5, 3, 5, 3, 3, 2, 4, 2, 4, 3, 4, 3, 1, 4, 0, 2, 3, 4, 4, 4, 3, 3, 3, 4, 4, 4, 3, 4, 1, 3, 4, 3, 2, 1, 2, 1, 3, 3, 3, 4, 4, 3, 3, 5, 0, 4, 0, 3, 0, 4, 3, 3, 3, 2, 1, 0, 3, 0, 0, 3, 3), - (0, 4, 0, 3, 0, 3, 0, 3, 0, 3, 5, 5, 3, 3, 3, 3, 4, 3, 4, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 4, 3, 5, 3, 3, 1, 3, 2, 4, 5, 5, 5, 5, 4, 3, 4, 5, 5, 3, 2, 2, 3, 3, 3, 3, 2, 3, 3, 1, 2, 3, 2, 4, 3, 3, 3, 4, 0, 4, 0, 2, 0, 4, 3, 2, 2, 1, 2, 0, 3, 0, 0, 4, 1), -) -# fmt: on - - -class JapaneseContextAnalysis: - NUM_OF_CATEGORY = 6 - DONT_KNOW = -1 - ENOUGH_REL_THRESHOLD = 100 - MAX_REL_THRESHOLD = 1000 - MINIMUM_DATA_THRESHOLD = 4 - - def __init__(self) -> None: - self._total_rel = 0 - self._rel_sample: List[int] = [] - self._need_to_skip_char_num = 0 - self._last_char_order = -1 - self._done = False - self.reset() - - def reset(self) -> None: - self._total_rel = 0 # total sequence received - # category counters, each integer counts sequence in its category - self._rel_sample = [0] * self.NUM_OF_CATEGORY - # if last byte in current buffer is not the last byte of a character, - # we need to know how many bytes to skip in next buffer - self._need_to_skip_char_num = 0 - self._last_char_order = -1 # The order of previous char - # If this flag is set to True, detection is done and conclusion has - # been made - self._done = False - - def feed(self, byte_str: Union[bytes, bytearray], num_bytes: int) -> None: - if self._done: - return - - # The buffer we got is byte oriented, and a character may span in more than one - # buffers. In case the last one or two byte in last buffer is not - # complete, we record how many byte needed to complete that character - # and skip these bytes here. We can choose to record those bytes as - # well and analyse the character once it is complete, but since a - # character will not make much difference, by simply skipping - # this character will simply our logic and improve performance. - i = self._need_to_skip_char_num - while i < num_bytes: - order, char_len = self.get_order(byte_str[i : i + 2]) - i += char_len - if i > num_bytes: - self._need_to_skip_char_num = i - num_bytes - self._last_char_order = -1 - else: - if (order != -1) and (self._last_char_order != -1): - self._total_rel += 1 - if self._total_rel > self.MAX_REL_THRESHOLD: - self._done = True - break - self._rel_sample[ - jp2_char_context[self._last_char_order][order] - ] += 1 - self._last_char_order = order - - def got_enough_data(self) -> bool: - return self._total_rel > self.ENOUGH_REL_THRESHOLD - - def get_confidence(self) -> float: - # This is just one way to calculate confidence. It works well for me. - if self._total_rel > self.MINIMUM_DATA_THRESHOLD: - return (self._total_rel - self._rel_sample[0]) / self._total_rel - return self.DONT_KNOW - - def get_order(self, _: Union[bytes, bytearray]) -> Tuple[int, int]: - return -1, 1 - - -class SJISContextAnalysis(JapaneseContextAnalysis): - def __init__(self) -> None: - super().__init__() - self._charset_name = "SHIFT_JIS" - - @property - def charset_name(self) -> str: - return self._charset_name - - def get_order(self, byte_str: Union[bytes, bytearray]) -> Tuple[int, int]: - if not byte_str: - return -1, 1 - # find out current char's byte length - first_char = byte_str[0] - if (0x81 <= first_char <= 0x9F) or (0xE0 <= first_char <= 0xFC): - char_len = 2 - if (first_char == 0x87) or (0xFA <= first_char <= 0xFC): - self._charset_name = "CP932" - else: - char_len = 1 - - # return its order if it is hiragana - if len(byte_str) > 1: - second_char = byte_str[1] - if (first_char == 202) and (0x9F <= second_char <= 0xF1): - return second_char - 0x9F, char_len - - return -1, char_len - - -class EUCJPContextAnalysis(JapaneseContextAnalysis): - def get_order(self, byte_str: Union[bytes, bytearray]) -> Tuple[int, int]: - if not byte_str: - return -1, 1 - # find out current char's byte length - first_char = byte_str[0] - if (first_char == 0x8E) or (0xA1 <= first_char <= 0xFE): - char_len = 2 - elif first_char == 0x8F: - char_len = 3 - else: - char_len = 1 - - # return its order if it is hiragana - if len(byte_str) > 1: - second_char = byte_str[1] - if (first_char == 0xA4) and (0xA1 <= second_char <= 0xF3): - return second_char - 0xA1, char_len - - return -1, char_len diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/TensorMask/README.md b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/TensorMask/README.md deleted file mode 100644 index ffc5282094aa71b112974587e57e24c0cf9922a7..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/TensorMask/README.md +++ /dev/null @@ -1,63 +0,0 @@ - -# TensorMask in Detectron2 -**A Foundation for Dense Object Segmentation** - -Xinlei Chen, Ross Girshick, Kaiming He, Piotr Dollár - -[[`arXiv`](https://arxiv.org/abs/1903.12174)] [[`BibTeX`](#CitingTensorMask)] - --- -In this repository, we release code for TensorMask in Detectron2. -TensorMask is a dense sliding-window instance segmentation framework that, for the first time, achieves results close to the well-developed Mask R-CNN framework -- both qualitatively and quantitatively. It establishes a conceptually complementary direction for object instance segmentation research. - -## Installation -First install Detectron 2 following [INSTALL.md](https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md). Then compile the TensorMask-specific op (`swap_align2nat`): -```bash -cd /path/to/detectron2/projects/TensorMask -python setup.py build develop -``` - -## Training - -To train a model, run: -```bash -python /path/to/detectron2/projects/TensorMask/train_net.py --config-file
--``` - -For example, to launch TensorMask BiPyramid training (1x schedule) with ResNet-50 backbone on 8 GPUs, -one should execute: -```bash -python /path/to/detectron2/projects/TensorMask/train_net.py --config-file configs/tensormask_R_50_FPN_1x.yaml --num-gpus 8 -``` - -## Evaluation - -Model evaluation can be done similarly (6x schedule with scale augmentation): -```bash -python /path/to/detectron2/projects/TensorMask/train_net.py --config-file configs/tensormask_R_50_FPN_6x.yaml --eval-only MODEL.WEIGHTS /path/to/model_checkpoint -``` - -# Pretrained Models - -| Backbone | lr sched | AP box | AP mask | download | -| -------- | -------- | -- | --- | -------- | -| R50 | 1x | 37.6 | 32.4 | model \| metrics | -| R50 | 6x | 41.4 | 35.8 | model \| metrics | - - -## Citing TensorMask - -If you use TensorMask, please use the following BibTeX entry. - -``` -@InProceedings{chen2019tensormask, - title={Tensormask: A Foundation for Dense Object Segmentation}, - author={Chen, Xinlei and Girshick, Ross and He, Kaiming and Doll{\'a}r, Piotr}, - journal={The International Conference on Computer Vision (ICCV)}, - year={2019} -} -``` - diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/docs/_source/conf.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/docs/_source/conf.py deleted file mode 100644 index 4ec8c847cf1e74fc312952617bb7c42c6d757b7e..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/docs/_source/conf.py +++ /dev/null @@ -1,108 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -# -# Configuration file for the Sphinx documentation builder. -# -# This file only contains a selection of the most common options. For a full -# list see the documentation: -# http://www.sphinx-doc.org/en/master/config - -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# -import os -import sys -sys.path.insert(0, os.path.abspath('../..')) - -RELEASE = os.environ.get('RELEASE', False) - -# -- Project information ----------------------------------------------------- - -project = u'OpenVQA' -copyright = u'2019, MILVLG' -author = u'MILVLG' - -# The version info for the project you're documenting, acts as replacement for -# |version| and |release|, also used in various other places throughout the -# built documents. -# -# The short X.Y version. -# version = '1.0' -# The full version, including alpha/beta/rc tags. -# release = '0.0' - - -# -- General configuration --------------------------------------------------- - -master_doc = 'index' - -# The suffix(es) of source filenames. -# You can specify multiple suffix as a list of string: -# -source_suffix = { - '.rst': 'restructuredtext', - '.txt': 'markdown', - '.md': 'markdown', -} - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - 'sphinx.ext.autodoc', - 'sphinx.ext.autosummary', - 'sphinx.ext.doctest', - 'sphinx.ext.intersphinx', - 'sphinx.ext.todo', - 'sphinx.ext.coverage', - 'sphinx.ext.napoleon', - 'sphinx.ext.viewcode', - 'sphinx_markdown_tables', - 'recommonmark', -] - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] - - -# -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -html_theme = 'sphinx_rtd_theme' - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] - -# Add cusotm css overrides -def setup(app): - app.add_stylesheet( "custom.css" ) - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] -if RELEASE: - templates_path = ['_templates-stable'] - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' - -# Disable docstring inheritance -autodoc_inherit_docstrings = False - - -# -- Other Options ------------------------------------------------------------ - -# intersphinx_mapping = { -# 'python': ('https://docs.python.org/3', None) -# } diff --git a/spaces/CVPR/LIVE/pybind11/tests/cross_module_gil_utils.cpp b/spaces/CVPR/LIVE/pybind11/tests/cross_module_gil_utils.cpp deleted file mode 100644 index 07db9f6e48a10dfd2d4370c3daff6e793d6675d2..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/pybind11/tests/cross_module_gil_utils.cpp +++ /dev/null @@ -1,73 +0,0 @@ -/* - tests/cross_module_gil_utils.cpp -- tools for acquiring GIL from a different module - - Copyright (c) 2019 Google LLC - - All rights reserved. Use of this source code is governed by a - BSD-style license that can be found in the LICENSE file. -*/ -#include -#include - -// This file mimics a DSO that makes pybind11 calls but does not define a -// PYBIND11_MODULE. The purpose is to test that such a DSO can create a -// py::gil_scoped_acquire when the running thread is in a GIL-released state. -// -// Note that we define a Python module here for convenience, but in general -// this need not be the case. The typical scenario would be a DSO that implements -// shared logic used internally by multiple pybind11 modules. - -namespace { - -namespace py = pybind11; -void gil_acquire() { py::gil_scoped_acquire gil; } - -constexpr char kModuleName[] = "cross_module_gil_utils"; - -#if PY_MAJOR_VERSION >= 3 -struct PyModuleDef moduledef = { - PyModuleDef_HEAD_INIT, - kModuleName, - NULL, - 0, - NULL, - NULL, - NULL, - NULL, - NULL -}; -#else -PyMethodDef module_methods[] = { - {NULL, NULL, 0, NULL} -}; -#endif - -} // namespace - -extern "C" PYBIND11_EXPORT -#if PY_MAJOR_VERSION >= 3 -PyObject* PyInit_cross_module_gil_utils() -#else -void initcross_module_gil_utils() -#endif -{ - - PyObject* m = -#if PY_MAJOR_VERSION >= 3 - PyModule_Create(&moduledef); -#else - Py_InitModule(kModuleName, module_methods); -#endif - - if (m != NULL) { - static_assert( - sizeof(&gil_acquire) == sizeof(void*), - "Function pointer must have the same size as void*"); - PyModule_AddObject(m, "gil_acquire_funcaddr", - PyLong_FromVoidPtr(reinterpret_cast - -// this system inherits count -#include - diff --git a/spaces/CikeyQI/Yunzai/Yunzai/plugins/system/add.js b/spaces/CikeyQI/Yunzai/Yunzai/plugins/system/add.js deleted file mode 100644 index eacf1ac98268bd8dc9e89ddd044047dfe21c4121..0000000000000000000000000000000000000000 --- a/spaces/CikeyQI/Yunzai/Yunzai/plugins/system/add.js +++ /dev/null @@ -1,446 +0,0 @@ -import cfg from "../../lib/config/config.js" -import plugin from "../../lib/plugins/plugin.js" -import common from "../../lib/common/common.js" -import fs from "node:fs" -import path from "node:path" -import lodash from "lodash" -import fetch from "node-fetch" -import { fileTypeFromBuffer } from "file-type" - -let messageMap = {} - -export class add extends plugin { - constructor() { - super({ - name: "添加消息", - dsc: "添加消息", - event: "message", - priority: 50000, - rule: [ - { - reg: "^#(全局)?添加", - fnc: "add" - }, - { - reg: "^#(全局)?删除", - fnc: "del" - }, - { - reg: "", - fnc: "getMessage", - log: false - }, - { - reg: "^#(全局)?(消息|词条)", - fnc: "list" - } - ] - }) - - this.path = "data/messageJson/" - } - - async init() { - common.mkdirs(this.path) - } - - /** 群号key */ - get grpKey() { - return `Yz:group_id:${this.e.user_id}` - } - - /** #添加 */ - async add() { - this.isGlobal = Boolean(this.e.msg.match(/^#全局/)) - await this.getGroupId() - - if (!this.group_id) { - await this.reply("请先在群内触发消息，确定添加的群") - return - } - - this.initMessageMap() - - if (!this.checkAuth()) return false - /** 获取关键词 */ - this.getKeyWord() - if (!this.e.keyWord) { - await this.reply("添加错误：没有关键词") - return - } - - this.e.message = [] - this.setContext("addContext") - - return this.reply("请发送添加内容，完成后发送#结束添加", true, { at: true }) - } - - /** 获取群号 */ - async getGroupId() { - /** 添加全局消息，存入到机器人文件中 */ - if (this.isGlobal) { - this.group_id = "global" - return this.group_id - } - - if (this.e.isGroup) { - this.group_id = this.e.group_id - redis.setEx(this.grpKey, 3600 * 24 * 30, String(this.group_id)) - return this.group_id - } - - // redis获取 - let groupId = await redis.get(this.grpKey) - if (groupId) { - this.group_id = groupId - return this.group_id - } - - return false - } - - checkAuth() { - if (this.e.isMaster) return true - - const groupCfg = cfg.getGroup(this.e.self_id, this.group_id) - if (groupCfg.addLimit == 2) { - this.reply("暂无权限，只有主人才能操作") - return false - } - if (groupCfg.addLimit == 1) { - if (!this.e.member.is_admin) { - this.reply("暂无权限，只有管理员才能操作") - return false - } - } - - if (groupCfg.addPrivate != 1 && !this.e.isGroup) { - this.reply("禁止私聊添加") - return false - } - - return true - } - - /** 获取添加关键词 */ - getKeyWord() { - this.e.isGlobal = Boolean(this.e.msg.match(/^#全局/)) - this.keyWord = this.e.raw_message.replace(/#(全局)?(添加|删除)/, "").trim() - this.e.keyWord = this.trimAlias(this.keyWord) - } - - /** 过滤别名 */ - trimAlias(msg) { - const groupCfg = cfg.getGroup(this.e.self_id, this.group_id) - let alias = groupCfg.botAlias - if (!Array.isArray(alias)) - alias = [alias] - - for (const name of alias) - if (msg.startsWith(name)) - msg = lodash.trimStart(msg, name).trim() - - return msg - } - - /** 添加内容 */ - async addContext() { - const context = this.getContext()?.addContext - this.isGlobal = context.isGlobal - await this.getGroupId() - /** 关键词 */ - this.keyWord = context.keyWord - - if (!this.e.msg?.includes("#结束添加")) { - /** 添加内容 */ - for (const i of this.e.message) { - if (i.url) i.file = await this.saveFile(i) - if (i.type == "at" && i.qq == this.e.self_id) continue - context.message.push(i) - } - return - } - - this.finish("addContext") - if (!context.message?.length) { - this.reply("添加错误：没有添加内容") - return - } - - if (!messageMap[this.group_id]) - messageMap[this.group_id] = new Map() - - /** 支持单个关键词添加多个 */ - let message = messageMap[this.group_id].get(this.keyWord) - if (Array.isArray(message)) - message.push(context.message) - else - message = [context.message] - messageMap[this.group_id].set(this.keyWord, message) - - if (message.length > 1) - this.keyWord += String(message.length) - - this.saveJson() - return this.reply(`添加成功：${this.keyWord}`) - } - - saveJson() { - let obj = {} - for (let [k, v] of messageMap[this.group_id]) - obj[k] = v - - fs.writeFileSync(`${this.path}${this.group_id}.json`, JSON.stringify(obj, "", "\t")) - } - - async makeBuffer(file) { - if (file.match(/^base64:\/\//)) - return Buffer.from(file.replace(/^base64:\/\//, ""), "base64") - else if (file.match(/^https?:\/\//)) - return Buffer.from(await (await fetch(file)).arrayBuffer()) - else if (fs.existsSync(file)) - return Buffer.from(fs.readFileSync(file)) - return file - } - - async fileType(data) { - const file = { name: `${this.group_id}/${data.type}/${Date.now()}` } - try { - file.url = data.url.replace(/^base64:\/\/.*/, "base64://...") - file.buffer = await this.makeBuffer(data.url) - file.type = await fileTypeFromBuffer(file.buffer) - file.name = `${file.name}.${file.type.ext}` - } catch (err) { - logger.error(`文件类型检测错误：${logger.red(err)}`) - file.name = `${file.name}-${path.basename(data.file || data.url)}` - } - return file - } - - async saveFile(data) { - const file = await this.fileType(data) - if (file.name && Buffer.isBuffer(file.buffer) && common.mkdirs(path.dirname(`${this.path}${file.name}`))) { - fs.writeFileSync(`${this.path}${file.name}`, file.buffer) - return file.name - } - return data.url - } - - async getMessage() { - if (!this.e.raw_message) return false - this.isGlobal = false - - await this.getGroupId() - if (!this.group_id) return false - - this.initMessageMap() - this.initGlobalMessageMap() - - this.keyWord = this.trimAlias(this.e.raw_message.trim()) - let keyWord = this.keyWord - - let num = 0 - if (isNaN(keyWord)) { - num = keyWord.charAt(keyWord.length-1) - - if (!isNaN(num) && !messageMap[this.group_id].has(keyWord) && !messageMap.global.has(keyWord)) { - keyWord = lodash.trimEnd(keyWord, num).trim() - num-- - } - } - - let msg = [ - ...messageMap[this.group_id].get(keyWord) || [], - ...messageMap.global.get(keyWord) || [], - ] - if (lodash.isEmpty(msg)) return false - - if (!msg[num]) - num = lodash.random(0, msg.length-1) - - msg = [...msg[num]] - for (const i in msg) - if (msg[i].file && fs.existsSync(`${this.path}${msg[i].file}`)) - msg[i] = { ...msg[i], file: `base64://${fs.readFileSync(`${this.path}${msg[i].file}`).toString("base64")}` } - - logger.mark(`[发送消息]${this.e.logText} ${this.keyWord}`) - const groupCfg = cfg.getGroup(this.e.self_id, this.group_id) - return this.reply(msg, Boolean(groupCfg.addReply), { - at: Boolean(groupCfg.addAt), - recallMsg: groupCfg.addRecall, - }) - } - - /** 初始化已添加内容 */ - initMessageMap() { - if (messageMap[this.group_id]) return - messageMap[this.group_id] = new Map() - - const path = `${this.path}${this.group_id}.json` - if (!fs.existsSync(path)) return - - try { - const message = JSON.parse(fs.readFileSync(path, "utf8")) - for (const i in message) - messageMap[this.group_id].set(i, message[i]) - } catch (err) { - logger.error(`JSON 格式错误：${path} ${err}`) - } - } - - /** 初始化全局已添加内容 */ - initGlobalMessageMap() { - if (messageMap.global) return - messageMap.global = new Map() - - const globalPath = `${this.path}global.json` - if (!fs.existsSync(globalPath)) return - - try { - const message = JSON.parse(fs.readFileSync(globalPath, "utf8")) - for (const i in message) - messageMap.global.set(i, message[i]) - } catch (err) { - logger.error(`JSON 格式错误：${globalPath} ${err}`) - } - } - - async del() { - this.isGlobal = this.e.msg.includes("全局") - await this.getGroupId() - if (!(this.group_id && this.checkAuth())) return false - - this.initMessageMap() - - this.getKeyWord() - if (!this.keyWord) { - await this.reply("删除错误：没有关键词") - return false - } - - this.keyWord = this.trimAlias(this.keyWord) - let keyWord = this.keyWord - - let num = false - let index = 0 - if (isNaN(keyWord)) { - num = keyWord.charAt(keyWord.length-1) - - if (!isNaN(num) && !messageMap[this.group_id].has(keyWord)) { - keyWord = lodash.trimEnd(keyWord, num).trim() - index = num-1 - } else { - num = false - } - } - - let arr = messageMap[this.group_id].get(keyWord) - if (!arr) { - // await this.reply(`暂无此消息：${keyWord}`) - return false - } - - let tmp = [] - if (num) { - if (!arr[index]) { - // await this.reply(`暂无此消息：${keyWord}${num}`) - return false - } - - tmp = arr[index] - arr.splice(index, 1) - - if (arr.length <= 0) { - messageMap[this.group_id].delete(keyWord) - } else { - messageMap[this.group_id].set(keyWord, arr) - } - } else { - if (this.e.msg.includes("删除全部")) { - tmp = arr - arr = [] - } else { - tmp = arr.pop() - } - - if (arr.length <= 0) { - messageMap[this.group_id].delete(keyWord) - } else { - messageMap[this.group_id].set(keyWord, arr) - } - } - - this.saveJson() - return this.reply(`删除成功：${this.keyWord}`) - } - - async list() { - this.isGlobal = Boolean(this.e.msg.match(/^#全局/)) - - let page = 1 - let pageSize = 100 - let type = "list" - - await this.getGroupId() - if (!this.group_id) return false - - this.initMessageMap() - - const search = this.e.msg.replace(/^#(全局)?(消息|词条)/, "").trim() - if (search.match(/^列表/)) - page = search.replace(/^列表/, "") || 1 - else - type = "search" - - let list = messageMap[this.group_id] - - if (lodash.isEmpty(list)) { - await this.reply("暂无消息") - return - } - - let arr = [] - if (type == "list") - for (let [k, v] of messageMap[this.group_id]) - arr.push({ key: k, val: v, num: arr.length+1 }) - else - for (let [k, v] of messageMap[this.group_id]) - if (k.includes(search)) - arr.push({ key: k, val: v, num: arr.length+1 }) - - let count = arr.length - arr = arr.reverse() - - if (type == "list") - arr = this.pagination(page, pageSize, arr) - if (lodash.isEmpty(arr)) return false - - let msg = [] - let num = 0 - for (const i of arr) { - if (num >= page * pageSize) break - - let keyWord = i.key - if (!keyWord) continue - - msg.push(`${i.num}. ${keyWord}(${i.val.length})`) - num++ - } - msg = [msg.join("\n")] - - if (type == "list" && count > 100) - msg.push(`更多内容请翻页查看\n如：#消息列表${Number(page)+1}`) - - let title = `消息列表：第${page}页，共${count}条` - if (type == "search") - title = `消息${search}：共${count}条` - - return this.reply(await common.makeForwardMsg(this.e, msg, title)) - } - - /** 分页 */ - pagination(pageNo, pageSize, array) { - let offset = (pageNo-1) * pageSize - return offset+pageSize >= array.length ? array.slice(offset, array.length) : array.slice(offset, offset+pageSize) - } -} \ No newline at end of file diff --git a/spaces/Cyril666/my_abi/dataset.py b/spaces/Cyril666/my_abi/dataset.py deleted file mode 100644 index e424cb2134ba0d992515b2446302e1a758a3db66..0000000000000000000000000000000000000000 --- a/spaces/Cyril666/my_abi/dataset.py +++ /dev/null @@ -1,278 +0,0 @@ -import logging -import re - -import cv2 -import lmdb -import six -from fastai.vision import * -from torchvision import transforms - -from transforms import CVColorJitter, CVDeterioration, CVGeometry -from utils import CharsetMapper, onehot - - -class ImageDataset(Dataset): - "`ImageDataset` read data from LMDB database." - - def __init__(self, - path:PathOrStr, - is_training:bool=True, - img_h:int=32, - img_w:int=100, - max_length:int=25, - check_length:bool=True, - case_sensitive:bool=False, - charset_path:str='data/charset_36.txt', - convert_mode:str='RGB', - data_aug:bool=True, - deteriorate_ratio:float=0., - multiscales:bool=True, - one_hot_y:bool=True, - return_idx:bool=False, - return_raw:bool=False, - **kwargs): - self.path, self.name = Path(path), Path(path).name - assert self.path.is_dir() and self.path.exists(), f"{path} is not a valid directory." - self.convert_mode, self.check_length = convert_mode, check_length - self.img_h, self.img_w = img_h, img_w - self.max_length, self.one_hot_y = max_length, one_hot_y - self.return_idx, self.return_raw = return_idx, return_raw - self.case_sensitive, self.is_training = case_sensitive, is_training - self.data_aug, self.multiscales = data_aug, multiscales - self.charset = CharsetMapper(charset_path, max_length=max_length+1) - self.c = self.charset.num_classes - - self.env = lmdb.open(str(path), readonly=True, lock=False, readahead=False, meminit=False) - assert self.env, f'Cannot open LMDB dataset from {path}.' - with self.env.begin(write=False) as txn: - self.length = int(txn.get('num-samples'.encode())) - - if self.is_training and self.data_aug: - self.augment_tfs = transforms.Compose([ - CVGeometry(degrees=45, translate=(0.0, 0.0), scale=(0.5, 2.), shear=(45, 15), distortion=0.5, p=0.5), - CVDeterioration(var=20, degrees=6, factor=4, p=0.25), - CVColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1, p=0.25) - ]) - self.totensor = transforms.ToTensor() - - def __len__(self): return self.length - - def _next_image(self, index): - next_index = random.randint(0, len(self) - 1) - return self.get(next_index) - - def _check_image(self, x, pixels=6): - if x.size[0] <= pixels or x.size[1] <= pixels: return False - else: return True - - def resize_multiscales(self, img, borderType=cv2.BORDER_CONSTANT): - def _resize_ratio(img, ratio, fix_h=True): - if ratio * self.img_w < self.img_h: - if fix_h: trg_h = self.img_h - else: trg_h = int(ratio * self.img_w) - trg_w = self.img_w - else: trg_h, trg_w = self.img_h, int(self.img_h / ratio) - img = cv2.resize(img, (trg_w, trg_h)) - pad_h, pad_w = (self.img_h - trg_h) / 2, (self.img_w - trg_w) / 2 - top, bottom = math.ceil(pad_h), math.floor(pad_h) - left, right = math.ceil(pad_w), math.floor(pad_w) - img = cv2.copyMakeBorder(img, top, bottom, left, right, borderType) - return img - - if self.is_training: - if random.random() < 0.5: - base, maxh, maxw = self.img_h, self.img_h, self.img_w - h, w = random.randint(base, maxh), random.randint(base, maxw) - return _resize_ratio(img, h/w) - else: return _resize_ratio(img, img.shape[0] / img.shape[1]) # keep aspect ratio - else: return _resize_ratio(img, img.shape[0] / img.shape[1]) # keep aspect ratio - - def resize(self, img): - if self.multiscales: return self.resize_multiscales(img, cv2.BORDER_REPLICATE) - else: return cv2.resize(img, (self.img_w, self.img_h)) - - def get(self, idx): - with self.env.begin(write=False) as txn: - image_key, label_key = f'image-{idx+1:09d}', f'label-{idx+1:09d}' - try: - label = str(txn.get(label_key.encode()), 'utf-8') # label - label = re.sub('[^0-9a-zA-Z]+', '', label) - if self.check_length and self.max_length > 0: - if len(label) > self.max_length or len(label) <= 0: - #logging.info(f'Long or short text image is found: {self.name}, {idx}, {label}, {len(label)}') - return self._next_image(idx) - label = label[:self.max_length] - - imgbuf = txn.get(image_key.encode()) # image - buf = six.BytesIO() - buf.write(imgbuf) - buf.seek(0) - with warnings.catch_warnings(): - warnings.simplefilter("ignore", UserWarning) # EXIF warning from TiffPlugin - image = PIL.Image.open(buf).convert(self.convert_mode) - if self.is_training and not self._check_image(image): - #logging.info(f'Invalid image is found: {self.name}, {idx}, {label}, {len(label)}') - return self._next_image(idx) - except: - import traceback - traceback.print_exc() - logging.info(f'Corrupted image is found: {self.name}, {idx}, {label}, {len(label)}') - return self._next_image(idx) - return image, label, idx - - def _process_training(self, image): - if self.data_aug: image = self.augment_tfs(image) - image = self.resize(np.array(image)) - return image - - def _process_test(self, image): - return self.resize(np.array(image)) # TODO:move is_training to here - - def __getitem__(self, idx): - image, text, idx_new = self.get(idx) - if not self.is_training: assert idx == idx_new, f'idx {idx} != idx_new {idx_new} during testing.' - - if self.is_training: image = self._process_training(image) - else: image = self._process_test(image) - if self.return_raw: return image, text - image = self.totensor(image) - - length = tensor(len(text) + 1).to(dtype=torch.long) # one for end token - label = self.charset.get_labels(text, case_sensitive=self.case_sensitive) - label = tensor(label).to(dtype=torch.long) - if self.one_hot_y: label = onehot(label, self.charset.num_classes) - - if self.return_idx: y = [label, length, idx_new] - else: y = [label, length] - return image, y - - -class TextDataset(Dataset): - def __init__(self, - path:PathOrStr, - delimiter:str='\t', - max_length:int=25, - charset_path:str='data/charset_36.txt', - case_sensitive=False, - one_hot_x=True, - one_hot_y=True, - is_training=True, - smooth_label=False, - smooth_factor=0.2, - use_sm=False, - **kwargs): - self.path = Path(path) - self.case_sensitive, self.use_sm = case_sensitive, use_sm - self.smooth_factor, self.smooth_label = smooth_factor, smooth_label - self.charset = CharsetMapper(charset_path, max_length=max_length+1) - self.one_hot_x, self.one_hot_y, self.is_training = one_hot_x, one_hot_y, is_training - if self.is_training and self.use_sm: self.sm = SpellingMutation(charset=self.charset) - - dtype = {'inp': str, 'gt': str} - self.df = pd.read_csv(self.path, dtype=dtype, delimiter=delimiter, na_filter=False) - self.inp_col, self.gt_col = 0, 1 - - def __len__(self): return len(self.df) - - def __getitem__(self, idx): - text_x = self.df.iloc[idx, self.inp_col] - text_x = re.sub('[^0-9a-zA-Z]+', '', text_x) - if not self.case_sensitive: text_x = text_x.lower() - if self.is_training and self.use_sm: text_x = self.sm(text_x) - - length_x = tensor(len(text_x) + 1).to(dtype=torch.long) # one for end token - label_x = self.charset.get_labels(text_x, case_sensitive=self.case_sensitive) - label_x = tensor(label_x) - if self.one_hot_x: - label_x = onehot(label_x, self.charset.num_classes) - if self.is_training and self.smooth_label: - label_x = torch.stack([self.prob_smooth_label(l) for l in label_x]) - x = [label_x, length_x] - - text_y = self.df.iloc[idx, self.gt_col] - text_y = re.sub('[^0-9a-zA-Z]+', '', text_y) - if not self.case_sensitive: text_y = text_y.lower() - length_y = tensor(len(text_y) + 1).to(dtype=torch.long) # one for end token - label_y = self.charset.get_labels(text_y, case_sensitive=self.case_sensitive) - label_y = tensor(label_y) - if self.one_hot_y: label_y = onehot(label_y, self.charset.num_classes) - y = [label_y, length_y] - - return x, y - - def prob_smooth_label(self, one_hot): - one_hot = one_hot.float() - delta = torch.rand([]) * self.smooth_factor - num_classes = len(one_hot) - noise = torch.rand(num_classes) - noise = noise / noise.sum() * delta - one_hot = one_hot * (1 - delta) + noise - return one_hot - - -class SpellingMutation(object): - def __init__(self, pn0=0.7, pn1=0.85, pn2=0.95, pt0=0.7, pt1=0.85, charset=None): - """ - Args: - pn0: the prob of not modifying characters is (pn0) - pn1: the prob of modifying one characters is (pn1 - pn0) - pn2: the prob of modifying two characters is (pn2 - pn1), - and three (1 - pn2) - pt0: the prob of replacing operation is pt0. - pt1: the prob of inserting operation is (pt1 - pt0), - and deleting operation is (1 - pt1) - """ - super().__init__() - self.pn0, self.pn1, self.pn2 = pn0, pn1, pn2 - self.pt0, self.pt1 = pt0, pt1 - self.charset = charset - logging.info(f'the probs: pn0={self.pn0}, pn1={self.pn1} ' + - f'pn2={self.pn2}, pt0={self.pt0}, pt1={self.pt1}') - - def is_digit(self, text, ratio=0.5): - length = max(len(text), 1) - digit_num = sum([t in self.charset.digits for t in text]) - if digit_num / length < ratio: return False - return True - - def is_unk_char(self, char): - # return char == self.charset.unk_char - return (char not in self.charset.digits) and (char not in self.charset.alphabets) - - def get_num_to_modify(self, length): - prob = random.random() - if prob < self.pn0: num_to_modify = 0 - elif prob < self.pn1: num_to_modify = 1 - elif prob < self.pn2: num_to_modify = 2 - else: num_to_modify = 3 - - if length <= 1: num_to_modify = 0 - elif length >= 2 and length <= 4: num_to_modify = min(num_to_modify, 1) - else: num_to_modify = min(num_to_modify, length // 2) # smaller than length // 2 - return num_to_modify - - def __call__(self, text, debug=False): - if self.is_digit(text): return text - length = len(text) - num_to_modify = self.get_num_to_modify(length) - if num_to_modify <= 0: return text - - chars = [] - index = np.arange(0, length) - random.shuffle(index) - index = index[: num_to_modify] - if debug: self.index = index - for i, t in enumerate(text): - if i not in index: chars.append(t) - elif self.is_unk_char(t): chars.append(t) - else: - prob = random.random() - if prob < self.pt0: # replace - chars.append(random.choice(self.charset.alphabets)) - elif prob < self.pt1: # insert - chars.append(random.choice(self.charset.alphabets)) - chars.append(t) - else: # delete - continue - new_text = ''.join(chars[: self.charset.max_length-1]) - return new_text if len(new_text) >= 1 else text \ No newline at end of file diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/mix.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/mix.py deleted file mode 100644 index caf2c68b835101c4f3d18d3d53fbb1b8494b3dba..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/mix.py +++ /dev/null @@ -1,129 +0,0 @@ -""" -Ways to transform interfaces to produce new interfaces -""" -import asyncio -import warnings - -from gradio_client.documentation import document, set_documentation_group - -import gradio - -set_documentation_group("mix_interface") - - -@document() -class Parallel(gradio.Interface): - """ - Creates a new Interface consisting of multiple Interfaces in parallel (comparing their outputs). - The Interfaces to put in Parallel must share the same input components (but can have different output components). - - Demos: interface_parallel, interface_parallel_load - Guides: advanced-interface-features - """ - - def __init__(self, *interfaces: gradio.Interface, **options): - """ - Parameters: - interfaces: any number of Interface objects that are to be compared in parallel - options: additional kwargs that are passed into the new Interface object to customize it - Returns: - an Interface object comparing the given models - """ - outputs = [] - - for interface in interfaces: - if not (isinstance(interface, gradio.Interface)): - warnings.warn( - "Parallel requires all inputs to be of type Interface. " - "May not work as expected." - ) - outputs.extend(interface.output_components) - - async def parallel_fn(*args): - return_values_with_durations = await asyncio.gather( - *[interface.call_function(0, list(args)) for interface in interfaces] - ) - return_values = [rv["prediction"] for rv in return_values_with_durations] - combined_list = [] - for interface, return_value in zip(interfaces, return_values): - if len(interface.output_components) == 1: - combined_list.append(return_value) - else: - combined_list.extend(return_value) - if len(outputs) == 1: - return combined_list[0] - return combined_list - - parallel_fn.__name__ = " | ".join([io.__name__ for io in interfaces]) - - kwargs = { - "fn": parallel_fn, - "inputs": interfaces[0].input_components, - "outputs": outputs, - } - kwargs.update(options) - super().__init__(**kwargs) - - -@document() -class Series(gradio.Interface): - """ - Creates a new Interface from multiple Interfaces in series (the output of one is fed as the input to the next, - and so the input and output components must agree between the interfaces). - - Demos: interface_series, interface_series_load - Guides: advanced-interface-features - """ - - def __init__(self, *interfaces: gradio.Interface, **options): - """ - Parameters: - interfaces: any number of Interface objects that are to be connected in series - options: additional kwargs that are passed into the new Interface object to customize it - Returns: - an Interface object connecting the given models - """ - - async def connected_fn(*data): - for idx, interface in enumerate(interfaces): - # skip preprocessing for first interface since the Series interface will include it - if idx > 0 and not (interface.api_mode): - data = [ - input_component.preprocess(data[i]) - for i, input_component in enumerate(interface.input_components) - ] - - # run all of predictions sequentially - data = (await interface.call_function(0, list(data)))["prediction"] - if len(interface.output_components) == 1: - data = [data] - - # skip postprocessing for final interface since the Series interface will include it - if idx < len(interfaces) - 1 and not (interface.api_mode): - data = [ - output_component.postprocess(data[i]) - for i, output_component in enumerate( - interface.output_components - ) - ] - - if len(interface.output_components) == 1: # type: ignore - return data[0] - return data - - for interface in interfaces: - if not (isinstance(interface, gradio.Interface)): - warnings.warn( - "Series requires all inputs to be of type Interface. May " - "not work as expected." - ) - connected_fn.__name__ = " => ".join([io.__name__ for io in interfaces]) - - kwargs = { - "fn": connected_fn, - "inputs": interfaces[0].input_components, - "outputs": interfaces[-1].output_components, - "_api_mode": interfaces[0].api_mode, # TODO: set api_mode per-interface - } - kwargs.update(options) - super().__init__(**kwargs) diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-1e03cd90.css b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-1e03cd90.css deleted file mode 100644 index 6692555db405e6eb83d0671b1ef9922ee30770d3..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-1e03cd90.css +++ /dev/null @@ -1 +0,0 @@ -.preview.svelte-w0jac3.svelte-w0jac3{display:flex;position:absolute;inset:0;flex-direction:column;z-index:var(--layer-2);backdrop-filter:blur(8px);background:var(--background-fill-primary);height:var(--size-full)}.fixed-height.svelte-w0jac3.svelte-w0jac3{min-height:var(--size-80);max-height:55vh}@media (min-width: 1280px){.fixed-height.svelte-w0jac3.svelte-w0jac3{min-height:450px}}.preview.svelte-w0jac3 img.svelte-w0jac3{width:var(--size-full);height:calc(var(--size-full) - 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-from torch.cuda.amp import autocast -from ..utils import load_class_freq, get_fed_loss_inds -from .zero_shot_classifier import ZeroShotClassifier - -__all__ = ["DeticFastRCNNOutputLayers"] - - -class DeticFastRCNNOutputLayers(FastRCNNOutputLayers): - @configurable - def __init__( - self, - input_shape: ShapeSpec, - *, - mult_proposal_score=False, - cls_score=None, - sync_caption_batch = False, - use_sigmoid_ce = False, - use_fed_loss = False, - ignore_zero_cats = False, - fed_loss_num_cat = 50, - dynamic_classifier = False, - image_label_loss = '', - use_zeroshot_cls = False, - image_loss_weight = 0.1, - with_softmax_prop = False, - caption_weight = 1.0, - neg_cap_weight = 1.0, - add_image_box = False, - debug = False, - prior_prob = 0.01, - cat_freq_path = '', - fed_loss_freq_weight = 0.5, - softmax_weak_loss = False, - **kwargs, - ): - super().__init__( - input_shape=input_shape, - **kwargs, - ) - self.mult_proposal_score = mult_proposal_score - self.sync_caption_batch = sync_caption_batch - self.use_sigmoid_ce = use_sigmoid_ce - self.use_fed_loss = use_fed_loss - self.ignore_zero_cats = ignore_zero_cats - self.fed_loss_num_cat = fed_loss_num_cat - self.dynamic_classifier = dynamic_classifier - self.image_label_loss = image_label_loss - self.use_zeroshot_cls = use_zeroshot_cls - self.image_loss_weight = image_loss_weight - self.with_softmax_prop = with_softmax_prop - self.caption_weight = caption_weight - self.neg_cap_weight = neg_cap_weight - self.add_image_box = add_image_box - self.softmax_weak_loss = softmax_weak_loss - self.debug = debug - - if softmax_weak_loss: - assert image_label_loss in ['max_size'] - - if self.use_sigmoid_ce: - bias_value = -math.log((1 - prior_prob) / prior_prob) - nn.init.constant_(self.cls_score.bias, bias_value) - - if self.use_fed_loss or self.ignore_zero_cats: - freq_weight = load_class_freq(cat_freq_path, fed_loss_freq_weight) - self.register_buffer('freq_weight', freq_weight) - else: - self.freq_weight = None - - if self.use_fed_loss and len(self.freq_weight) < self.num_classes: - # assert self.num_classes == 11493 - print('Extending federated loss weight') - self.freq_weight = torch.cat( - [self.freq_weight, - self.freq_weight.new_zeros( - self.num_classes - len(self.freq_weight))] - ) - - assert (not self.dynamic_classifier) or (not self.use_fed_loss) - input_size = input_shape.channels * \ - (input_shape.width or 1) * (input_shape.height or 1) - - if self.use_zeroshot_cls: - del self.cls_score - del self.bbox_pred - assert cls_score is not None - self.cls_score = cls_score - self.bbox_pred = nn.Sequential( - nn.Linear(input_size, input_size), - nn.ReLU(inplace=True), - nn.Linear(input_size, 4) - ) - weight_init.c2_xavier_fill(self.bbox_pred[0]) - nn.init.normal_(self.bbox_pred[-1].weight, std=0.001) - nn.init.constant_(self.bbox_pred[-1].bias, 0) - - if self.with_softmax_prop: - self.prop_score = nn.Sequential( - nn.Linear(input_size, input_size), - nn.ReLU(inplace=True), - nn.Linear(input_size, self.num_classes + 1), - ) - weight_init.c2_xavier_fill(self.prop_score[0]) - nn.init.normal_(self.prop_score[-1].weight, mean=0, std=0.001) - nn.init.constant_(self.prop_score[-1].bias, 0) - - - @classmethod - def from_config(cls, cfg, input_shape): - ret = super().from_config(cfg, input_shape) - ret.update({ - 'mult_proposal_score': cfg.MODEL.ROI_BOX_HEAD.MULT_PROPOSAL_SCORE, - 'sync_caption_batch': cfg.MODEL.SYNC_CAPTION_BATCH, - 'use_sigmoid_ce': cfg.MODEL.ROI_BOX_HEAD.USE_SIGMOID_CE, - 'use_fed_loss': cfg.MODEL.ROI_BOX_HEAD.USE_FED_LOSS, - 'ignore_zero_cats': cfg.MODEL.ROI_BOX_HEAD.IGNORE_ZERO_CATS, - 'fed_loss_num_cat': cfg.MODEL.ROI_BOX_HEAD.FED_LOSS_NUM_CAT, - 'dynamic_classifier': cfg.MODEL.DYNAMIC_CLASSIFIER, - 'image_label_loss': cfg.MODEL.ROI_BOX_HEAD.IMAGE_LABEL_LOSS, - 'use_zeroshot_cls': cfg.MODEL.ROI_BOX_HEAD.USE_ZEROSHOT_CLS, - 'image_loss_weight': cfg.MODEL.ROI_BOX_HEAD.IMAGE_LOSS_WEIGHT, - 'with_softmax_prop': cfg.MODEL.ROI_BOX_HEAD.WITH_SOFTMAX_PROP, - 'caption_weight': cfg.MODEL.ROI_BOX_HEAD.CAPTION_WEIGHT, - 'neg_cap_weight': cfg.MODEL.ROI_BOX_HEAD.NEG_CAP_WEIGHT, - 'add_image_box': cfg.MODEL.ROI_BOX_HEAD.ADD_IMAGE_BOX, - 'debug': cfg.DEBUG or cfg.SAVE_DEBUG or cfg.IS_DEBUG, - 'prior_prob': cfg.MODEL.ROI_BOX_HEAD.PRIOR_PROB, - 'cat_freq_path': cfg.MODEL.ROI_BOX_HEAD.CAT_FREQ_PATH, - 'fed_loss_freq_weight': cfg.MODEL.ROI_BOX_HEAD.FED_LOSS_FREQ_WEIGHT, - 'softmax_weak_loss': cfg.MODEL.ROI_BOX_HEAD.SOFTMAX_WEAK_LOSS, - }) - if ret['use_zeroshot_cls']: - ret['cls_score'] = ZeroShotClassifier(cfg, input_shape) - return ret - - def losses(self, predictions, proposals, \ - use_advanced_loss=True, - classifier_info=(None,None,None)): - """ - enable advanced loss - """ - scores, proposal_deltas = predictions - gt_classes = ( - cat([p.gt_classes for p in proposals], dim=0) if len(proposals) else torch.empty(0) - ) - num_classes = self.num_classes - if self.dynamic_classifier: - _, cls_id_map = classifier_info[1] - gt_classes = cls_id_map[gt_classes] - num_classes = scores.shape[1] - 1 - assert cls_id_map[self.num_classes] == num_classes - _log_classification_stats(scores, gt_classes) - - if len(proposals): - proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0) # Nx4 - assert not proposal_boxes.requires_grad, "Proposals should not require gradients!" - gt_boxes = cat( - [(p.gt_boxes if p.has("gt_boxes") else p.proposal_boxes).tensor for p in proposals], - dim=0, - ) - else: - proposal_boxes = gt_boxes = torch.empty((0, 4), device=proposal_deltas.device) - - if self.use_sigmoid_ce: - loss_cls = self.sigmoid_cross_entropy_loss(scores, gt_classes) - else: - loss_cls = self.softmax_cross_entropy_loss(scores, gt_classes) - return { - "loss_cls": loss_cls, - "loss_box_reg": self.box_reg_loss( - proposal_boxes, gt_boxes, proposal_deltas, gt_classes, - num_classes=num_classes) - } - - - def sigmoid_cross_entropy_loss(self, pred_class_logits, gt_classes): - if pred_class_logits.numel() == 0: - return pred_class_logits.new_zeros([1])[0] # This is more robust than .sum() * 0. - - B = pred_class_logits.shape[0] - C = pred_class_logits.shape[1] - 1 - - target = pred_class_logits.new_zeros(B, C + 1) - target[range(len(gt_classes)), gt_classes] = 1 # B x (C + 1) - target = target[:, :C] # B x C - - weight = 1 - - if self.use_fed_loss and (self.freq_weight is not None): # fedloss - appeared = get_fed_loss_inds( - gt_classes, - num_sample_cats=self.fed_loss_num_cat, - C=C, - weight=self.freq_weight) - appeared_mask = appeared.new_zeros(C + 1) - appeared_mask[appeared] = 1 # C + 1 - appeared_mask = appeared_mask[:C] - fed_w = appeared_mask.view(1, C).expand(B, C) - weight = weight * fed_w.float() - if self.ignore_zero_cats and (self.freq_weight is not None): - w = (self.freq_weight.view(-1) > 1e-4).float() - weight = weight * w.view(1, C).expand(B, C) - # import pdb; pdb.set_trace() - - cls_loss = F.binary_cross_entropy_with_logits( - pred_class_logits[:, :-1], target, reduction='none') # B x C - loss = torch.sum(cls_loss * weight) / B - return loss - - - def softmax_cross_entropy_loss(self, pred_class_logits, gt_classes): - """ - change _no_instance handling - """ - if pred_class_logits.numel() == 0: - return pred_class_logits.new_zeros([1])[0] - - if self.ignore_zero_cats and (self.freq_weight is not None): - zero_weight = torch.cat([ - (self.freq_weight.view(-1) > 1e-4).float(), - self.freq_weight.new_ones(1)]) # C + 1 - loss = F.cross_entropy( - pred_class_logits, gt_classes, - weight=zero_weight, reduction="mean") - elif self.use_fed_loss and (self.freq_weight is not None): # fedloss - C = pred_class_logits.shape[1] - 1 - appeared = get_fed_loss_inds( - gt_classes, - num_sample_cats=self.fed_loss_num_cat, - C=C, - weight=self.freq_weight) - appeared_mask = appeared.new_zeros(C + 1).float() - appeared_mask[appeared] = 1. # C + 1 - appeared_mask[C] = 1. - loss = F.cross_entropy( - pred_class_logits, gt_classes, - weight=appeared_mask, reduction="mean") - else: - loss = F.cross_entropy( - pred_class_logits, gt_classes, reduction="mean") - return loss - - - def box_reg_loss( - self, proposal_boxes, gt_boxes, pred_deltas, gt_classes, - num_classes=-1): - """ - Allow custom background index - """ - num_classes = num_classes if num_classes > 0 else self.num_classes - box_dim = proposal_boxes.shape[1] # 4 or 5 - fg_inds = nonzero_tuple((gt_classes >= 0) & (gt_classes < num_classes))[0] - if pred_deltas.shape[1] == box_dim: # cls-agnostic regression - fg_pred_deltas = pred_deltas[fg_inds] - else: - fg_pred_deltas = pred_deltas.view(-1, self.num_classes, box_dim)[ - fg_inds, gt_classes[fg_inds] - ] - - if self.box_reg_loss_type == "smooth_l1": - gt_pred_deltas = self.box2box_transform.get_deltas( - proposal_boxes[fg_inds], - gt_boxes[fg_inds], - ) - loss_box_reg = smooth_l1_loss( - fg_pred_deltas, gt_pred_deltas, self.smooth_l1_beta, reduction="sum" - ) - elif self.box_reg_loss_type == "giou": - fg_pred_boxes = self.box2box_transform.apply_deltas( - fg_pred_deltas, proposal_boxes[fg_inds] - ) - loss_box_reg = giou_loss(fg_pred_boxes, gt_boxes[fg_inds], reduction="sum") - else: - raise ValueError(f"Invalid bbox reg loss type '{self.box_reg_loss_type}'") - return loss_box_reg / max(gt_classes.numel(), 1.0) - - def inference(self, predictions, proposals): - """ - enable use proposal boxes - """ - predictions = (predictions[0], predictions[1]) - boxes = self.predict_boxes(predictions, proposals) - scores = self.predict_probs(predictions, proposals) - if self.mult_proposal_score: - proposal_scores = [p.get('objectness_logits') for p in proposals] - scores = [(s * ps[:, None]) ** 0.5 \ - for s, ps in zip(scores, proposal_scores)] - image_shapes = [x.image_size for x in proposals] - return fast_rcnn_inference( - boxes, - scores, - image_shapes, - self.test_score_thresh, - self.test_nms_thresh, - self.test_topk_per_image, - ) - - - def predict_probs(self, predictions, proposals): - """ - support sigmoid - """ - # scores, _ = predictions - scores = predictions[0] - num_inst_per_image = [len(p) for p in proposals] - if self.use_sigmoid_ce: - probs = scores.sigmoid() - else: - probs = F.softmax(scores, dim=-1) - return probs.split(num_inst_per_image, dim=0) - - - def image_label_losses(self, predictions, proposals, image_labels, \ - classifier_info=(None,None,None), ann_type='image'): - ''' - Inputs: - scores: N x (C + 1) - image_labels B x 1 - ''' - num_inst_per_image = [len(p) for p in proposals] - scores = predictions[0] - scores = scores.split(num_inst_per_image, dim=0) # B x n x (C + 1) - if self.with_softmax_prop: - prop_scores = predictions[2].split(num_inst_per_image, dim=0) - else: - prop_scores = [None for _ in num_inst_per_image] - B = len(scores) - img_box_count = 0 - select_size_count = 0 - select_x_count = 0 - select_y_count = 0 - max_score_count = 0 - storage = get_event_storage() - loss = scores[0].new_zeros([1])[0] - caption_loss = scores[0].new_zeros([1])[0] - for idx, (score, labels, prop_score, p) in enumerate(zip( - scores, image_labels, prop_scores, proposals)): - if score.shape[0] == 0: - loss += score.new_zeros([1])[0] - continue - if 'caption' in ann_type: - score, caption_loss_img = self._caption_loss( - score, classifier_info, idx, B) - caption_loss += self.caption_weight * caption_loss_img - if ann_type == 'caption': - continue - - if self.debug: - p.selected = score.new_zeros( - (len(p),), dtype=torch.long) - 1 - for i_l, label in enumerate(labels): - if self.dynamic_classifier: - if idx == 0 and i_l == 0 and comm.is_main_process(): - storage.put_scalar('stats_label', label) - label = classifier_info[1][1][label] - assert label < score.shape[1] - if self.image_label_loss in ['wsod', 'wsddn']: - loss_i, ind = self._wsddn_loss(score, prop_score, label) - elif self.image_label_loss == 'max_score': - loss_i, ind = self._max_score_loss(score, label) - elif self.image_label_loss == 'max_size': - loss_i, ind = self._max_size_loss(score, label, p) - elif self.image_label_loss == 'first': - loss_i, ind = self._first_loss(score, label) - elif self.image_label_loss == 'image': - loss_i, ind = self._image_loss(score, label) - elif self.image_label_loss == 'min_loss': - loss_i, ind = self._min_loss_loss(score, label) - else: - assert 0 - loss += loss_i / len(labels) - if type(ind) == type([]): - img_box_count = sum(ind) / len(ind) - if self.debug: - for ind_i in ind: - p.selected[ind_i] = label - else: - img_box_count = ind - select_size_count = p[ind].proposal_boxes.area() / \ - (p.image_size[0] * p.image_size[1]) - max_score_count = score[ind, label].sigmoid() - select_x_count = (p.proposal_boxes.tensor[ind, 0] + \ - p.proposal_boxes.tensor[ind, 2]) / 2 / p.image_size[1] - select_y_count = (p.proposal_boxes.tensor[ind, 1] + \ - p.proposal_boxes.tensor[ind, 3]) / 2 / p.image_size[0] - if self.debug: - p.selected[ind] = label - - loss = loss / B - storage.put_scalar('stats_l_image', loss.item()) - if 'caption' in ann_type: - caption_loss = caption_loss / B - loss = loss + caption_loss - storage.put_scalar('stats_l_caption', caption_loss.item()) - if comm.is_main_process(): - storage.put_scalar('pool_stats', img_box_count) - storage.put_scalar('stats_select_size', select_size_count) - storage.put_scalar('stats_select_x', select_x_count) - storage.put_scalar('stats_select_y', select_y_count) - storage.put_scalar('stats_max_label_score', max_score_count) - - return { - 'image_loss': loss * self.image_loss_weight, - 'loss_cls': score.new_zeros([1])[0], - 'loss_box_reg': score.new_zeros([1])[0]} - - - def forward(self, x, classifier_info=(None,None,None)): - """ - enable classifier_info - """ - if x.dim() > 2: - x = torch.flatten(x, start_dim=1) - scores = [] - - if classifier_info[0] is not None: - cls_scores = self.cls_score(x, classifier=classifier_info[0]) - scores.append(cls_scores) - else: - cls_scores = self.cls_score(x) - scores.append(cls_scores) - - if classifier_info[2] is not None: - cap_cls = classifier_info[2] - if self.sync_caption_batch: - caption_scores = self.cls_score(x, classifier=cap_cls[:, :-1]) - else: - caption_scores = self.cls_score(x, classifier=cap_cls) - scores.append(caption_scores) - scores = torch.cat(scores, dim=1) # B x C' or B x N or B x (C'+N) - - proposal_deltas = self.bbox_pred(x) - if self.with_softmax_prop: - prop_score = self.prop_score(x) - return scores, proposal_deltas, prop_score - else: - return scores, proposal_deltas - - - def _caption_loss(self, score, classifier_info, idx, B): - assert (classifier_info[2] is not None) - assert self.add_image_box - cls_and_cap_num = score.shape[1] - cap_num = classifier_info[2].shape[0] - score, caption_score = score.split( - [cls_and_cap_num - cap_num, cap_num], dim=1) - # n x (C + 1), n x B - caption_score = caption_score[-1:] # 1 x B # -1: image level box - caption_target = caption_score.new_zeros( - caption_score.shape) # 1 x B or 1 x MB, M: num machines - if self.sync_caption_batch: - # caption_target: 1 x MB - rank = comm.get_rank() - global_idx = B * rank + idx - assert (classifier_info[2][ - global_idx, -1] - rank) ** 2 < 1e-8, \ - '{} {} {} {} {}'.format( - rank, global_idx, - classifier_info[2][global_idx, -1], - classifier_info[2].shape, - classifier_info[2][:, -1]) - caption_target[:, global_idx] = 1. - else: - assert caption_score.shape[1] == B - caption_target[:, idx] = 1. - caption_loss_img = F.binary_cross_entropy_with_logits( - caption_score, caption_target, reduction='none') - if self.sync_caption_batch: - fg_mask = (caption_target > 0.5).float() - assert (fg_mask.sum().item() - 1.) ** 2 < 1e-8, '{} {}'.format( - fg_mask.shape, fg_mask) - pos_loss = (caption_loss_img * fg_mask).sum() - neg_loss = (caption_loss_img * (1. - fg_mask)).sum() - caption_loss_img = pos_loss + self.neg_cap_weight * neg_loss - else: - caption_loss_img = caption_loss_img.sum() - return score, caption_loss_img - - - def _wsddn_loss(self, score, prop_score, label): - assert prop_score is not None - loss = 0 - final_score = score.sigmoid() * \ - F.softmax(prop_score, dim=0) # B x (C + 1) - img_score = torch.clamp( - torch.sum(final_score, dim=0), - min=1e-10, max=1-1e-10) # (C + 1) - target = img_score.new_zeros(img_score.shape) # (C + 1) - target[label] = 1. - loss += F.binary_cross_entropy(img_score, target) - ind = final_score[:, label].argmax() - return loss, ind - - - def _max_score_loss(self, score, label): - loss = 0 - target = score.new_zeros(score.shape[1]) - target[label] = 1. - ind = score[:, label].argmax().item() - loss += F.binary_cross_entropy_with_logits( - score[ind], target, reduction='sum') - return loss, ind - - - def _min_loss_loss(self, score, label): - loss = 0 - target = score.new_zeros(score.shape) - target[:, label] = 1. - with torch.no_grad(): - x = F.binary_cross_entropy_with_logits( - score, target, reduction='none').sum(dim=1) # n - ind = x.argmin().item() - loss += F.binary_cross_entropy_with_logits( - score[ind], target[0], reduction='sum') - return loss, ind - - - def _first_loss(self, score, label): - loss = 0 - target = score.new_zeros(score.shape[1]) - target[label] = 1. - ind = 0 - loss += F.binary_cross_entropy_with_logits( - score[ind], target, reduction='sum') - return loss, ind - - - def _image_loss(self, score, label): - assert self.add_image_box - target = score.new_zeros(score.shape[1]) - target[label] = 1. - ind = score.shape[0] - 1 - loss = F.binary_cross_entropy_with_logits( - score[ind], target, reduction='sum') - return loss, ind - - - def _max_size_loss(self, score, label, p): - loss = 0 - target = score.new_zeros(score.shape[1]) - target[label] = 1. - sizes = p.proposal_boxes.area() - ind = sizes[:-1].argmax().item() if len(sizes) > 1 else 0 - if self.softmax_weak_loss: - loss += F.cross_entropy( - score[ind:ind+1], - score.new_tensor(label, dtype=torch.long).view(1), - reduction='sum') - else: - loss += F.binary_cross_entropy_with_logits( - score[ind], target, reduction='sum') - return loss, ind - - - -def put_label_distribution(storage, hist_name, hist_counts, num_classes): - """ - """ - ht_min, ht_max = 0, num_classes - hist_edges = torch.linspace( - start=ht_min, end=ht_max, steps=num_classes + 1, dtype=torch.float32) - - hist_params = dict( - tag=hist_name, - min=ht_min, - max=ht_max, - num=float(hist_counts.sum()), - sum=float((hist_counts * torch.arange(len(hist_counts))).sum()), - sum_squares=float(((hist_counts * torch.arange(len(hist_counts))) ** 2).sum()), - bucket_limits=hist_edges[1:].tolist(), - bucket_counts=hist_counts.tolist(), - global_step=storage._iter, - ) - storage._histograms.append(hist_params) \ No newline at end of file diff --git a/spaces/Datasculptor/StyleGAN-NADA/e4e/models/encoders/__init__.py b/spaces/Datasculptor/StyleGAN-NADA/e4e/models/encoders/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Datasculptor/StyleGAN-NADA/styleclip/styleclip_global.py b/spaces/Datasculptor/StyleGAN-NADA/styleclip/styleclip_global.py deleted file mode 100644 index 96fa7569ebd51a5e6c2deddb57ccceb4f4376904..0000000000000000000000000000000000000000 --- a/spaces/Datasculptor/StyleGAN-NADA/styleclip/styleclip_global.py +++ /dev/null @@ -1,181 +0,0 @@ -''' -Code adapted from Stitch it in Time by Tzaban et al. -https://github.com/rotemtzaban/STIT -''' - - -import numpy as np -import torch -from tqdm import tqdm -from pathlib import Path -import os - -import clip - -imagenet_templates = [ - 'a bad photo of a {}.', - 'a photo of many {}.', - 'a sculpture of a {}.', - 'a photo of the hard to see {}.', - 'a low resolution photo of the {}.', - 'a rendering of a {}.', - 'graffiti of a {}.', - 'a bad photo of the {}.', - 'a cropped photo of the {}.', - 'a tattoo of a {}.', - 'the embroidered {}.', - 'a photo of a hard to see {}.', - 'a bright photo of a {}.', - 'a photo of a clean {}.', - 'a photo of a dirty {}.', - 'a dark photo of the {}.', - 'a drawing of a {}.', - 'a photo of my {}.', - 'the plastic {}.', - 'a photo of the cool {}.', - 'a close-up photo of a {}.', - 'a black and white photo of the {}.', - 'a painting of the {}.', - 'a painting of a {}.', - 'a pixelated photo of the {}.', - 'a sculpture of the {}.', - 'a bright photo of the {}.', - 'a cropped photo of a {}.', - 'a plastic {}.', - 'a photo of the dirty {}.', - 'a jpeg corrupted photo of a {}.', - 'a blurry photo of the {}.', - 'a photo of the {}.', - 'a good photo of the {}.', - 'a rendering of the {}.', - 'a {} in a video game.', - 'a photo of one {}.', - 'a doodle of a {}.', - 'a close-up photo of the {}.', - 'a photo of a {}.', - 'the origami {}.', - 'the {} in a video game.', - 'a sketch of a {}.', - 'a doodle of the {}.', - 'a origami {}.', - 'a low resolution photo of a {}.', - 'the toy {}.', - 'a rendition of the {}.', - 'a photo of the clean {}.', - 'a photo of a large {}.', - 'a rendition of a {}.', - 'a photo of a nice {}.', - 'a photo of a weird {}.', - 'a blurry photo of a {}.', - 'a cartoon {}.', - 'art of a {}.', - 'a sketch of the {}.', - 'a embroidered {}.', - 'a pixelated photo of a {}.', - 'itap of the {}.', - 'a jpeg corrupted photo of the {}.', - 'a good photo of a {}.', - 'a plushie {}.', - 'a photo of the nice {}.', - 'a photo of the small {}.', - 'a photo of the weird {}.', - 'the cartoon {}.', - 'art of the {}.', - 'a drawing of the {}.', - 'a photo of the large {}.', - 'a black and white photo of a {}.', - 'the plushie {}.', - 'a dark photo of a {}.', - 'itap of a {}.', - 'graffiti of the {}.', - 'a toy {}.', - 'itap of my {}.', - 'a photo of a cool {}.', - 'a photo of a small {}.', - 'a tattoo of the {}.', -] - -CONV_CODE_INDICES = [(0, 512), (1024, 1536), (1536, 2048), (2560, 3072), (3072, 3584), (4096, 4608), (4608, 5120), (5632, 6144), (6144, 6656), (7168, 7680), (7680, 7936), (8192, 8448), (8448, 8576), (8704, 8832), (8832, 8896), (8960, 9024), (9024, 9056)] -FFHQ_CODE_INDICES = [(0, 512), (512, 1024), (1024, 1536), (1536, 2048), (2560, 3072), (3072, 3584), (4096, 4608), (4608, 5120), (5632, 6144), (6144, 6656), (7168, 7680), (7680, 7936), (8192, 8448), (8448, 8576), (8704, 8832), (8832, 8896), (8960, 9024), (9024, 9056)] + \ - [(2048, 2560), (3584, 4096), (5120, 5632), (6656, 7168), (7936, 8192), (8576, 8704), (8896, 8960), (9056, 9088)] - -def zeroshot_classifier(model, classnames, templates, device): - - with torch.no_grad(): - zeroshot_weights = [] - for classname in tqdm(classnames): - texts = [template.format(classname) for template in templates] # format with class - texts = clip.tokenize(texts).to(device) # tokenize - class_embeddings = model.encode_text(texts) # embed with text encoder - class_embeddings /= class_embeddings.norm(dim=-1, keepdim=True) - class_embedding = class_embeddings.mean(dim=0) - class_embedding /= class_embedding.norm() - zeroshot_weights.append(class_embedding) - zeroshot_weights = torch.stack(zeroshot_weights, dim=1).to(device) - return zeroshot_weights - -def expand_to_full_dim(partial_tensor): - full_dim_tensor = torch.zeros(size=(1, 9088)) - - start_idx = 0 - for conv_start, conv_end in CONV_CODE_INDICES: - length = conv_end - conv_start - full_dim_tensor[:, conv_start:conv_end] = partial_tensor[start_idx:start_idx + length] - start_idx += length - - return full_dim_tensor - -def get_direction(neutral_class, target_class, beta, di, clip_model=None): - - device = "cuda" if torch.cuda.is_available() else "cpu" - - if clip_model is None: - clip_model, _ = clip.load("ViT-B/32", device=device) - - class_names = [neutral_class, target_class] - class_weights = zeroshot_classifier(clip_model, class_names, imagenet_templates, device) - - dt = class_weights[:, 1] - class_weights[:, 0] - dt = dt / dt.norm() - - dt = dt.float() - di = di.float() - - relevance = di @ dt - mask = relevance.abs() > beta - direction = relevance * mask - direction_max = direction.abs().max() - if direction_max > 0: - direction = direction / direction_max - else: - raise ValueError(f'Beta value {beta} is too high for mapping from {neutral_class} to {target_class},' - f' try setting it to a lower value') - return direction - -def style_tensor_to_style_dict(style_tensor, refernce_generator): - style_layers = refernce_generator.modulation_layers - - style_dict = {} - for layer_idx, layer in enumerate(style_layers): - style_dict[layer] = style_tensor[:, FFHQ_CODE_INDICES[layer_idx][0]:FFHQ_CODE_INDICES[layer_idx][1]] - - return style_dict - -def style_dict_to_style_tensor(style_dict, reference_generator): - style_layers = reference_generator.modulation_layers - - style_tensor = torch.zeros(size=(1, 9088)) - for layer in style_dict: - layer_idx = style_layers.index(layer) - style_tensor[:, FFHQ_CODE_INDICES[layer_idx][0]:FFHQ_CODE_INDICES[layer_idx][1]] = style_dict[layer] - - return style_tensor - -def project_code_with_styleclip(source_latent, source_class, target_class, alpha, beta, reference_generator, di, clip_model=None): - edit_direction = get_direction(source_class, target_class, beta, di, clip_model) - - edit_full_dim = expand_to_full_dim(edit_direction) - - source_s = style_dict_to_style_tensor(source_latent, reference_generator) - - return source_s + alpha * edit_full_dim \ No newline at end of file diff --git a/spaces/Detomo/ai-comic-generation/src/components/icons/full-screen.tsx b/spaces/Detomo/ai-comic-generation/src/components/icons/full-screen.tsx deleted file mode 100644 index 34ec93bbab4b8359868737dbab9c6f7f6d594e03..0000000000000000000000000000000000000000 --- a/spaces/Detomo/ai-comic-generation/src/components/icons/full-screen.tsx +++ /dev/null @@ -1,16 +0,0 @@ -export function FullScreenIcon() { - return ( - - ) -} \ No newline at end of file diff --git a/spaces/Dhrushreddy/profile1/README.md b/spaces/Dhrushreddy/profile1/README.md deleted file mode 100644 index 4d6a8835a84f11a82edf37df2d653b976224d5a0..0000000000000000000000000000000000000000 --- a/spaces/Dhrushreddy/profile1/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Profile1 -emoji: 📊 -colorFrom: pink -colorTo: indigo -sdk: streamlit -sdk_version: 1.21.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/DiamondYin/Voice-ChatGPT-Streamlit-12/app.py b/spaces/DiamondYin/Voice-ChatGPT-Streamlit-12/app.py deleted file mode 100644 index c14ec429648632e650cb293f45324b272bd752a7..0000000000000000000000000000000000000000 --- a/spaces/DiamondYin/Voice-ChatGPT-Streamlit-12/app.py +++ /dev/null @@ -1,293 +0,0 @@ -import streamlit as st -import openai -import os -import base64 -import glob -import json -import mistune -import pytz -import math -import requests -import time - -from datetime import datetime -from openai import ChatCompletion -from xml.etree import ElementTree as ET -from bs4 import BeautifulSoup -from collections import deque -from audio_recorder_streamlit import audio_recorder - -def generate_filename(prompt, file_type): - central = pytz.timezone('US/Central') - safe_date_time = datetime.now(central).strftime("%m%d_%I%M") - safe_prompt = "".join(x for x in prompt if x.isalnum())[:45] - return f"{safe_date_time}_{safe_prompt}.{file_type}" - -def transcribe_audio(openai_key, file_path, model): - OPENAI_API_URL = "https://api.openai.com/v1/audio/transcriptions" - headers = { - "Authorization": f"Bearer {openai_key}", - } - with open(file_path, 'rb') as f: - data = {'file': f} - response = requests.post(OPENAI_API_URL, headers=headers, files=data, data={'model': model}) - if response.status_code == 200: - st.write(response.json()) - - response2 = chat_with_model(response.json().get('text'), '') # ************************************* - st.write('Responses:') - #st.write(response) - st.write(response2) - return response.json().get('text') - else: - st.write(response.json()) - st.error("Error in API call.") - return None - -def save_and_play_audio(audio_recorder): - audio_bytes = audio_recorder() - if audio_bytes: - filename = generate_filename("Recording", "wav") - with open(filename, 'wb') as f: - f.write(audio_bytes) - st.audio(audio_bytes, format="audio/wav") - return filename - return None - -def create_file(filename, prompt, response): - if filename.endswith(".txt"): - with open(filename, 'w') as file: - file.write(f"{prompt}\n{response}") - elif filename.endswith(".htm"): - with open(filename, 'w') as file: - file.write(f"{prompt} {response}") - elif filename.endswith(".md"): - with open(filename, 'w') as file: - file.write(f"{prompt}\n\n{response}") - -def truncate_document(document, length): - return document[:length] -def divide_document(document, max_length): - return [document[i:i+max_length] for i in range(0, len(document), max_length)] - -def get_table_download_link(file_path): - with open(file_path, 'r') as file: - data = file.read() - b64 = base64.b64encode(data.encode()).decode() - file_name = os.path.basename(file_path) - ext = os.path.splitext(file_name)[1] # get the file extension - if ext == '.txt': - mime_type = 'text/plain' - elif ext == '.py': - mime_type = 'text/plain' - elif ext == '.xlsx': - mime_type = 'text/plain' - elif ext == '.csv': - mime_type = 'text/plain' - elif ext == '.htm': - mime_type = 'text/html' - elif ext == '.md': - mime_type = 'text/markdown' - else: - mime_type = 'application/octet-stream' # general binary data type - href = f'{file_name}' - return href - -def CompressXML(xml_text): - root = ET.fromstring(xml_text) - for elem in list(root.iter()): - if isinstance(elem.tag, str) and 'Comment' in elem.tag: - elem.parent.remove(elem) - return ET.tostring(root, encoding='unicode', method="xml") - -def read_file_content(file,max_length): - if file.type == "application/json": - content = json.load(file) - return str(content) - elif file.type == "text/html" or file.type == "text/htm": - content = BeautifulSoup(file, "html.parser") - return content.text - elif file.type == "application/xml" or file.type == "text/xml": - tree = ET.parse(file) - root = tree.getroot() - xml = CompressXML(ET.tostring(root, encoding='unicode')) - return xml - elif file.type == "text/markdown" or file.type == "text/md": - md = mistune.create_markdown() - content = md(file.read().decode()) - return content - elif file.type == "text/plain": - return file.getvalue().decode() - else: - return "" - -def chat_with_model(prompt, document_section, model_choice='gpt-3.5-turbo'): - model = model_choice - conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}] - conversation.append({'role': 'user', 'content': prompt}) - if len(document_section)>0: - conversation.append({'role': 'assistant', 'content': document_section}) - - # iterate through the stream of events - start_time = time.time() - - - report = [] - res_box = st.empty() - - collected_chunks = [] - collected_messages = [] - - for chunk in openai.ChatCompletion.create( - model='gpt-3.5-turbo', - messages=conversation, - temperature=0.5, - stream=True - ): - - collected_chunks.append(chunk) # save the event response - chunk_message = chunk['choices'][0]['delta'] # extract the message - collected_messages.append(chunk_message) # save the message - - content=chunk["choices"][0].get("delta",{}).get("content") - - try: - report.append(content) - if len(content) > 0: - result = "".join(report).strip() - #result = result.replace("\n", "") - res_box.markdown(f'*{result}*') - except: - st.write('.') - - full_reply_content = ''.join([m.get('content', '') for m in collected_messages]) - #st.write(f"Full conversation received: {full_reply_content}") - st.write("Elapsed time:") - st.write(time.time() - start_time) - return full_reply_content - -def chat_with_file_contents(prompt, file_content, model_choice='gpt-3.5-turbo'): - conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}] - conversation.append({'role': 'user', 'content': prompt}) - if len(file_content)>0: - conversation.append({'role': 'assistant', 'content': file_content}) - response = openai.ChatCompletion.create(model=model_choice, messages=conversation) - return response['choices'][0]['message']['content'] - - -def main(): - # Sidebar and global - openai.api_key = os.getenv('OPENAI_KEY') - st.set_page_config(page_title="GPT Streamlit Document Reasoner",layout="wide") - menu = ["htm", "txt", "xlsx", "csv", "md", "py"] #619 - choice = st.sidebar.selectbox("Output File Type:", menu) - model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301')) - - # Audio, transcribe, GPT: - filename = save_and_play_audio(audio_recorder) - if filename is not None: - transcription = transcribe_audio(openai.api_key, filename, "whisper-1") - st.write(transcription) - gptOutput = chat_with_model(transcription, '', model_choice) # ************************************* - filename = generate_filename(transcription, choice) - create_file(filename, transcription, gptOutput) - st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True) - - - user_prompt = st.text_area("Enter prompts, instructions & questions:", '', height=100) - - collength, colupload = st.columns([2,3]) # adjust the ratio as needed - with collength: - #max_length = 12000 - optimal for gpt35 turbo. 2x=24000 for gpt4. 8x=96000 for gpt4-32k. - max_length = st.slider("File section length for large files", min_value=1000, max_value=128000, value=12000, step=1000) - with colupload: - uploaded_file = st.file_uploader("Add a file for context:", type=["xml", "json", "xlsx","csv","html", "htm", "md", "txt"]) - - document_sections = deque() - document_responses = {} - - if uploaded_file is not None: - file_content = read_file_content(uploaded_file, max_length) - document_sections.extend(divide_document(file_content, max_length)) - - if len(document_sections) > 0: - - if st.button("👁️ View Upload"): - st.markdown("**Sections of the uploaded file:**") - for i, section in enumerate(list(document_sections)): - st.markdown(f"**Section {i+1}**\n{section}") - - st.markdown("**Chat with the model:**") - for i, section in enumerate(list(document_sections)): - if i in document_responses: - st.markdown(f"**Section {i+1}**\n{document_responses[i]}") - else: - if st.button(f"Chat about Section {i+1}"): - st.write('Reasoning with your inputs...') - response = chat_with_model(user_prompt, section, model_choice) # ************************************* - st.write('Response:') - st.write(response) - document_responses[i] = response - filename = generate_filename(f"{user_prompt}_section_{i+1}", choice) - create_file(filename, user_prompt, response) - st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True) - - if st.button('💬 Chat'): - st.write('Reasoning with your inputs...') - response = chat_with_model(user_prompt, ''.join(list(document_sections,)), model_choice) # ************************************* - st.write('Response:') - st.write(response) - - filename = generate_filename(user_prompt, choice) - create_file(filename, user_prompt, response) - st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True) - - all_files = glob.glob("*.*") - all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 20] # exclude files with short names - all_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True) # sort by file type and file name in descending order - - # sidebar of files - file_contents='' - next_action='' - for file in all_files: - col1, col2, col3, col4, col5 = st.sidebar.columns([1,6,1,1,1]) # adjust the ratio as needed - with col1: - if st.button("🌐", key="md_"+file): # md emoji button - with open(file, 'r') as f: - file_contents = f.read() - next_action='md' - with col2: - st.markdown(get_table_download_link(file), unsafe_allow_html=True) - with col3: - if st.button("📂", key="open_"+file): # open emoji button - with open(file, 'r') as f: - file_contents = f.read() - next_action='open' - with col4: - if st.button("🔍", key="read_"+file): # search emoji button - with open(file, 'r') as f: - file_contents = f.read() - next_action='search' - with col5: - if st.button("🗑", key="delete_"+file): - os.remove(file) - st.experimental_rerun() - - if len(file_contents) > 0: - if next_action=='open': - file_content_area = st.text_area("File Contents:", file_contents, height=500) - if next_action=='md': - st.markdown(file_contents) - if next_action=='search': - file_content_area = st.text_area("File Contents:", file_contents, height=500) - st.write('Reasoning with your inputs...') - #response = chat_with_file_contents(user_prompt, file_contents) - response = chat_with_model(user_prompt, file_contents, model_choice) - st.write('Response:') - st.write(response) - filename = generate_filename(file_content_area, choice) - create_file(filename, file_content_area, response) - st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True) - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/spaces/EPFL-VILAB/MultiMAE/mask2former/data/datasets/register_ade20k_instance.py b/spaces/EPFL-VILAB/MultiMAE/mask2former/data/datasets/register_ade20k_instance.py deleted file mode 100644 index 1ded7095cde756dfa1d94c25b2f7d1d2e5da6313..0000000000000000000000000000000000000000 --- a/spaces/EPFL-VILAB/MultiMAE/mask2former/data/datasets/register_ade20k_instance.py +++ /dev/null @@ -1,53 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import json -import logging -import numpy as np -import os -from PIL import Image - -from detectron2.data import DatasetCatalog, MetadataCatalog -from detectron2.data.datasets.coco import load_coco_json, register_coco_instances -from detectron2.utils.file_io import PathManager - -ADE_CATEGORIES = [{'id': 7, 'name': 'bed'}, {'id': 8, 'name': 'windowpane'}, {'id': 10, 'name': 'cabinet'}, {'id': 12, 'name': 'person'}, {'id': 14, 'name': 'door'}, {'id': 15, 'name': 'table'}, {'id': 18, 'name': 'curtain'}, {'id': 19, 'name': 'chair'}, {'id': 20, 'name': 'car'}, {'id': 22, 'name': 'painting'}, {'id': 23, 'name': 'sofa'}, {'id': 24, 'name': 'shelf'}, {'id': 27, 'name': 'mirror'}, {'id': 30, 'name': 'armchair'}, {'id': 31, 'name': 'seat'}, {'id': 32, 'name': 'fence'}, {'id': 33, 'name': 'desk'}, {'id': 35, 'name': 'wardrobe'}, {'id': 36, 'name': 'lamp'}, {'id': 37, 'name': 'bathtub'}, {'id': 38, 'name': 'railing'}, {'id': 39, 'name': 'cushion'}, {'id': 41, 'name': 'box'}, {'id': 42, 'name': 'column'}, {'id': 43, 'name': 'signboard'}, {'id': 44, 'name': 'chest of drawers'}, {'id': 45, 'name': 'counter'}, {'id': 47, 'name': 'sink'}, {'id': 49, 'name': 'fireplace'}, {'id': 50, 'name': 'refrigerator'}, {'id': 53, 'name': 'stairs'}, {'id': 55, 'name': 'case'}, {'id': 56, 'name': 'pool table'}, {'id': 57, 'name': 'pillow'}, {'id': 58, 'name': 'screen door'}, {'id': 62, 'name': 'bookcase'}, {'id': 64, 'name': 'coffee table'}, {'id': 65, 'name': 'toilet'}, {'id': 66, 'name': 'flower'}, {'id': 67, 'name': 'book'}, {'id': 69, 'name': 'bench'}, {'id': 70, 'name': 'countertop'}, {'id': 71, 'name': 'stove'}, {'id': 72, 'name': 'palm'}, {'id': 73, 'name': 'kitchen island'}, {'id': 74, 'name': 'computer'}, {'id': 75, 'name': 'swivel chair'}, {'id': 76, 'name': 'boat'}, {'id': 78, 'name': 'arcade machine'}, {'id': 80, 'name': 'bus'}, {'id': 81, 'name': 'towel'}, {'id': 82, 'name': 'light'}, {'id': 83, 'name': 'truck'}, {'id': 85, 'name': 'chandelier'}, {'id': 86, 'name': 'awning'}, {'id': 87, 'name': 'streetlight'}, {'id': 88, 'name': 'booth'}, {'id': 89, 'name': 'television receiver'}, {'id': 90, 'name': 'airplane'}, {'id': 92, 'name': 'apparel'}, {'id': 93, 'name': 'pole'}, {'id': 95, 'name': 'bannister'}, {'id': 97, 'name': 'ottoman'}, {'id': 98, 'name': 'bottle'}, {'id': 102, 'name': 'van'}, {'id': 103, 'name': 'ship'}, {'id': 104, 'name': 'fountain'}, {'id': 107, 'name': 'washer'}, {'id': 108, 'name': 'plaything'}, {'id': 110, 'name': 'stool'}, {'id': 111, 'name': 'barrel'}, {'id': 112, 'name': 'basket'}, {'id': 115, 'name': 'bag'}, {'id': 116, 'name': 'minibike'}, {'id': 118, 'name': 'oven'}, {'id': 119, 'name': 'ball'}, {'id': 120, 'name': 'food'}, {'id': 121, 'name': 'step'}, {'id': 123, 'name': 'trade name'}, {'id': 124, 'name': 'microwave'}, {'id': 125, 'name': 'pot'}, {'id': 126, 'name': 'animal'}, {'id': 127, 'name': 'bicycle'}, {'id': 129, 'name': 'dishwasher'}, {'id': 130, 'name': 'screen'}, {'id': 132, 'name': 'sculpture'}, {'id': 133, 'name': 'hood'}, {'id': 134, 'name': 'sconce'}, {'id': 135, 'name': 'vase'}, {'id': 136, 'name': 'traffic light'}, {'id': 137, 'name': 'tray'}, {'id': 138, 'name': 'ashcan'}, {'id': 139, 'name': 'fan'}, {'id': 142, 'name': 'plate'}, {'id': 143, 'name': 'monitor'}, {'id': 144, 'name': 'bulletin board'}, {'id': 146, 'name': 'radiator'}, {'id': 147, 'name': 'glass'}, {'id': 148, 'name': 'clock'}, {'id': 149, 'name': 'flag'}] - - -_PREDEFINED_SPLITS = { - # point annotations without masks - "ade20k_instance_train": ( - "ADEChallengeData2016/images/training", - "ADEChallengeData2016/ade20k_instance_train.json", - ), - "ade20k_instance_val": ( - "ADEChallengeData2016/images/validation", - "ADEChallengeData2016/ade20k_instance_val.json", - ), -} - - -def _get_ade_instances_meta(): - thing_ids = [k["id"] for k in ADE_CATEGORIES] - assert len(thing_ids) == 100, len(thing_ids) - # Mapping from the incontiguous ADE category id to an id in [0, 99] - thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)} - thing_classes = [k["name"] for k in ADE_CATEGORIES] - ret = { - "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, - "thing_classes": thing_classes, - } - return ret - - -def register_all_ade20k_instance(root): - for key, (image_root, json_file) in _PREDEFINED_SPLITS.items(): - # Assume pre-defined datasets live in `./datasets`. - register_coco_instances( - key, - _get_ade_instances_meta(), - os.path.join(root, json_file) if "://" not in json_file else json_file, - os.path.join(root, image_root), - ) - - -_root = os.getenv("DETECTRON2_DATASETS", "datasets") -register_all_ade20k_instance(_root) diff --git a/spaces/Egrt/GCycleGAN/utils/callbacks.py b/spaces/Egrt/GCycleGAN/utils/callbacks.py deleted file mode 100644 index d70115ead91c64f2f7aaa3b559cb0351642ee65d..0000000000000000000000000000000000000000 --- a/spaces/Egrt/GCycleGAN/utils/callbacks.py +++ /dev/null @@ -1,65 +0,0 @@ -import os - -import torch -import matplotlib -matplotlib.use('Agg') -import scipy.signal -from matplotlib import pyplot as plt -from torch.utils.tensorboard import SummaryWriter - - -class LossHistory(): - def __init__(self, log_dir, model, input_shape): - self.log_dir = log_dir - - os.makedirs(self.log_dir) - self.writer = SummaryWriter(self.log_dir) - try: - for m in model: - dummy_input = torch.randn(2, 3, input_shape[0], input_shape[1]) - self.writer.add_graph(m, dummy_input) - except: - pass - - def append_loss(self, epoch, **kwargs): - if not os.path.exists(self.log_dir): - os.makedirs(self.log_dir) - - for key, value in kwargs.items(): - if not hasattr(self, key): - setattr(self, key, []) - #---------------------------------# - # 为列表添加数值 - #---------------------------------# - getattr(self, key).append(value) - - #---------------------------------# - # 写入txt - #---------------------------------# - with open(os.path.join(self.log_dir, key + ".txt"), 'a') as f: - f.write(str(value)) - f.write("\n") - - #---------------------------------# - # 写入tensorboard - #---------------------------------# - self.writer.add_scalar(key, value, epoch) - - self.loss_plot(**kwargs) - - def loss_plot(self, **kwargs): - plt.figure() - - for key, value in kwargs.items(): - losses = getattr(self, key) - plt.plot(range(len(losses)), losses, linewidth = 2, label = key) - - plt.grid(True) - plt.xlabel('Epoch') - plt.ylabel('Loss') - plt.legend(loc="upper right") - - plt.savefig(os.path.join(self.log_dir, "epoch_loss.png")) - - plt.cla() - plt.close("all") \ No newline at end of file diff --git a/spaces/ElainaFanBoy/IRONY-Real-ESRGAN/realesrgan/data/realesrgan_dataset.py b/spaces/ElainaFanBoy/IRONY-Real-ESRGAN/realesrgan/data/realesrgan_dataset.py deleted file mode 100644 index 4cf2d9e6583a6789b771679734ce55bb8a22e628..0000000000000000000000000000000000000000 --- a/spaces/ElainaFanBoy/IRONY-Real-ESRGAN/realesrgan/data/realesrgan_dataset.py +++ /dev/null @@ -1,192 +0,0 @@ -import cv2 -import math -import numpy as np -import os -import os.path as osp -import random -import time -import torch -from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels -from basicsr.data.transforms import augment -from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor -from basicsr.utils.registry import DATASET_REGISTRY -from torch.utils import data as data - - -@DATASET_REGISTRY.register() -class RealESRGANDataset(data.Dataset): - """Dataset used for Real-ESRGAN model: - Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. - - It loads gt (Ground-Truth) images, and augments them. - It also generates blur kernels and sinc kernels for generating low-quality images. - Note that the low-quality images are processed in tensors on GPUS for faster processing. - - Args: - opt (dict): Config for train datasets. It contains the following keys: - dataroot_gt (str): Data root path for gt. - meta_info (str): Path for meta information file. - io_backend (dict): IO backend type and other kwarg. - use_hflip (bool): Use horizontal flips. - use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation). - Please see more options in the codes. - """ - - def __init__(self, opt): - super(RealESRGANDataset, self).__init__() - self.opt = opt - self.file_client = None - self.io_backend_opt = opt['io_backend'] - self.gt_folder = opt['dataroot_gt'] - - # file client (lmdb io backend) - if self.io_backend_opt['type'] == 'lmdb': - self.io_backend_opt['db_paths'] = [self.gt_folder] - self.io_backend_opt['client_keys'] = ['gt'] - if not self.gt_folder.endswith('.lmdb'): - raise ValueError(f"'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}") - with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin: - self.paths = [line.split('.')[0] for line in fin] - else: - # disk backend with meta_info - # Each line in the meta_info describes the relative path to an image - with open(self.opt['meta_info']) as fin: - paths = [line.strip().split(' ')[0] for line in fin] - self.paths = [os.path.join(self.gt_folder, v) for v in paths] - - # blur settings for the first degradation - self.blur_kernel_size = opt['blur_kernel_size'] - self.kernel_list = opt['kernel_list'] - self.kernel_prob = opt['kernel_prob'] # a list for each kernel probability - self.blur_sigma = opt['blur_sigma'] - self.betag_range = opt['betag_range'] # betag used in generalized Gaussian blur kernels - self.betap_range = opt['betap_range'] # betap used in plateau blur kernels - self.sinc_prob = opt['sinc_prob'] # the probability for sinc filters - - # blur settings for the second degradation - self.blur_kernel_size2 = opt['blur_kernel_size2'] - self.kernel_list2 = opt['kernel_list2'] - self.kernel_prob2 = opt['kernel_prob2'] - self.blur_sigma2 = opt['blur_sigma2'] - self.betag_range2 = opt['betag_range2'] - self.betap_range2 = opt['betap_range2'] - self.sinc_prob2 = opt['sinc_prob2'] - - # a final sinc filter - self.final_sinc_prob = opt['final_sinc_prob'] - - self.kernel_range = [2 * v + 1 for v in range(3, 11)] # kernel size ranges from 7 to 21 - # TODO: kernel range is now hard-coded, should be in the configure file - self.pulse_tensor = torch.zeros(21, 21).float() # convolving with pulse tensor brings no blurry effect - self.pulse_tensor[10, 10] = 1 - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - # -------------------------------- Load gt images -------------------------------- # - # Shape: (h, w, c); channel order: BGR; image range: [0, 1], float32. - gt_path = self.paths[index] - # avoid errors caused by high latency in reading files - retry = 3 - while retry > 0: - try: - img_bytes = self.file_client.get(gt_path, 'gt') - except (IOError, OSError) as e: - logger = get_root_logger() - logger.warn(f'File client error: {e}, remaining retry times: {retry - 1}') - # change another file to read - index = random.randint(0, self.__len__()) - gt_path = self.paths[index] - time.sleep(1) # sleep 1s for occasional server congestion - else: - break - finally: - retry -= 1 - img_gt = imfrombytes(img_bytes, float32=True) - - # -------------------- Do augmentation for training: flip, rotation -------------------- # - img_gt = augment(img_gt, self.opt['use_hflip'], self.opt['use_rot']) - - # crop or pad to 400 - # TODO: 400 is hard-coded. You may change it accordingly - h, w = img_gt.shape[0:2] - crop_pad_size = 400 - # pad - if h < crop_pad_size or w < crop_pad_size: - pad_h = max(0, crop_pad_size - h) - pad_w = max(0, crop_pad_size - w) - img_gt = cv2.copyMakeBorder(img_gt, 0, pad_h, 0, pad_w, cv2.BORDER_REFLECT_101) - # crop - if img_gt.shape[0] > crop_pad_size or img_gt.shape[1] > crop_pad_size: - h, w = img_gt.shape[0:2] - # randomly choose top and left coordinates - top = random.randint(0, h - crop_pad_size) - left = random.randint(0, w - crop_pad_size) - img_gt = img_gt[top:top + crop_pad_size, left:left + crop_pad_size, ...] - - # ------------------------ Generate kernels (used in the first degradation) ------------------------ # - kernel_size = random.choice(self.kernel_range) - if np.random.uniform() < self.opt['sinc_prob']: - # this sinc filter setting is for kernels ranging from [7, 21] - if kernel_size < 13: - omega_c = np.random.uniform(np.pi / 3, np.pi) - else: - omega_c = np.random.uniform(np.pi / 5, np.pi) - kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False) - else: - kernel = random_mixed_kernels( - self.kernel_list, - self.kernel_prob, - kernel_size, - self.blur_sigma, - self.blur_sigma, [-math.pi, math.pi], - self.betag_range, - self.betap_range, - noise_range=None) - # pad kernel - pad_size = (21 - kernel_size) // 2 - kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size))) - - # ------------------------ Generate kernels (used in the second degradation) ------------------------ # - kernel_size = random.choice(self.kernel_range) - if np.random.uniform() < self.opt['sinc_prob2']: - if kernel_size < 13: - omega_c = np.random.uniform(np.pi / 3, np.pi) - else: - omega_c = np.random.uniform(np.pi / 5, np.pi) - kernel2 = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False) - else: - kernel2 = random_mixed_kernels( - self.kernel_list2, - self.kernel_prob2, - kernel_size, - self.blur_sigma2, - self.blur_sigma2, [-math.pi, math.pi], - self.betag_range2, - self.betap_range2, - noise_range=None) - - # pad kernel - pad_size = (21 - kernel_size) // 2 - kernel2 = np.pad(kernel2, ((pad_size, pad_size), (pad_size, pad_size))) - - # ------------------------------------- the final sinc kernel ------------------------------------- # - if np.random.uniform() < self.opt['final_sinc_prob']: - kernel_size = random.choice(self.kernel_range) - omega_c = np.random.uniform(np.pi / 3, np.pi) - sinc_kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=21) - sinc_kernel = torch.FloatTensor(sinc_kernel) - else: - sinc_kernel = self.pulse_tensor - - # BGR to RGB, HWC to CHW, numpy to tensor - img_gt = img2tensor([img_gt], bgr2rgb=True, float32=True)[0] - kernel = torch.FloatTensor(kernel) - kernel2 = torch.FloatTensor(kernel2) - - return_d = {'gt': img_gt, 'kernel1': kernel, 'kernel2': kernel2, 'sinc_kernel': sinc_kernel, 'gt_path': gt_path} - return return_d - - def __len__(self): - return len(self.paths) diff --git a/spaces/ElainaFanBoy/MusicGen/CHANGELOG.md b/spaces/ElainaFanBoy/MusicGen/CHANGELOG.md deleted file mode 100644 index 6aaad6b5ee31e4685ead54c1a46d7f57b225912d..0000000000000000000000000000000000000000 --- a/spaces/ElainaFanBoy/MusicGen/CHANGELOG.md +++ /dev/null @@ -1,20 +0,0 @@ -# Changelog - -All notable changes to this project will be documented in this file. - -The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/). - -## [0.0.2a] - TBD - -Improved demo, fixed top p (thanks @jnordberg). - -Compressor tanh on output to avoid clipping with some style (especially piano). -Now repeating the conditioning periodically if it is too short. - -More options when launching Gradio app locally (thanks @ashleykleynhans). - -Testing out PyTorch 2.0 memory efficient attention. - -## [0.0.1] - 2023-06-09 - -Initial release, with model evaluation only. diff --git a/spaces/EronSamez/RVC_HFmeu/infer/modules/train/extract_feature_print.py b/spaces/EronSamez/RVC_HFmeu/infer/modules/train/extract_feature_print.py deleted file mode 100644 index f771dd9b8ba92262e6844e7b5781de43c342833a..0000000000000000000000000000000000000000 --- a/spaces/EronSamez/RVC_HFmeu/infer/modules/train/extract_feature_print.py +++ /dev/null @@ -1,137 +0,0 @@ -import os -import sys -import traceback - -os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" -os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0" - -device = sys.argv[1] -n_part = int(sys.argv[2]) -i_part = int(sys.argv[3]) -if len(sys.argv) == 6: - exp_dir = sys.argv[4] - version = sys.argv[5] -else: - i_gpu = sys.argv[4] - exp_dir = sys.argv[5] - os.environ["CUDA_VISIBLE_DEVICES"] = str(i_gpu) - version = sys.argv[6] -import fairseq -import numpy as np -import soundfile as sf -import torch -import torch.nn.functional as F - -if "privateuseone" not in device: - device = "cpu" - if torch.cuda.is_available(): - device = "cuda" - elif torch.backends.mps.is_available(): - device = "mps" -else: - import torch_directml - - device = torch_directml.device(torch_directml.default_device()) - - def forward_dml(ctx, x, scale): - ctx.scale = scale - res = x.clone().detach() - return res - - fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml - -f = open("%s/extract_f0_feature.log" % exp_dir, "a+") - - -def printt(strr): - print(strr) - f.write("%s\n" % strr) - f.flush() - - -printt(sys.argv) -model_path = "assets/hubert/hubert_base.pt" - -printt(exp_dir) -wavPath = "%s/1_16k_wavs" % exp_dir -outPath = ( - "%s/3_feature256" % exp_dir if version == "v1" else "%s/3_feature768" % exp_dir -) -os.makedirs(outPath, exist_ok=True) - - -# wave must be 16k, hop_size=320 -def readwave(wav_path, normalize=False): - wav, sr = sf.read(wav_path) - assert sr == 16000 - feats = torch.from_numpy(wav).float() - if feats.dim() == 2: # double channels - feats = feats.mean(-1) - assert feats.dim() == 1, feats.dim() - if normalize: - with torch.no_grad(): - feats = F.layer_norm(feats, feats.shape) - feats = feats.view(1, -1) - return feats - - -# HuBERT model -printt("load model(s) from {}".format(model_path)) -# if hubert model is exist -if os.access(model_path, os.F_OK) == False: - printt( - "Error: Extracting is shut down because %s does not exist, you may download it from https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main" - % model_path - ) - exit(0) -models, saved_cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task( - [model_path], - suffix="", -) -model = models[0] -model = model.to(device) -printt("move model to %s" % device) -if device not in ["mps", "cpu"]: - model = model.half() -model.eval() - -todo = sorted(list(os.listdir(wavPath)))[i_part::n_part] -n = max(1, len(todo) // 10) # 最多打印十条 -if len(todo) == 0: - printt("no-feature-todo") -else: - printt("all-feature-%s" % len(todo)) - for idx, file in enumerate(todo): - try: - if file.endswith(".wav"): - wav_path = "%s/%s" % (wavPath, file) - out_path = "%s/%s" % (outPath, file.replace("wav", "npy")) - - if os.path.exists(out_path): - continue - - feats = readwave(wav_path, normalize=saved_cfg.task.normalize) - padding_mask = torch.BoolTensor(feats.shape).fill_(False) - inputs = { - "source": feats.half().to(device) - if device not in ["mps", "cpu"] - else feats.to(device), - "padding_mask": padding_mask.to(device), - "output_layer": 9 if version == "v1" else 12, # layer 9 - } - with torch.no_grad(): - logits = model.extract_features(**inputs) - feats = ( - model.final_proj(logits[0]) if version == "v1" else logits[0] - ) - - feats = feats.squeeze(0).float().cpu().numpy() - if np.isnan(feats).sum() == 0: - np.save(out_path, feats, allow_pickle=False) - else: - printt("%s-contains nan" % file) - if idx % n == 0: - printt("now-%s,all-%s,%s,%s" % (len(todo), idx, file, feats.shape)) - except: - printt(traceback.format_exc()) - printt("all-feature-done") diff --git a/spaces/EronSamez/RVC_HFmeu/lib/uvr5_pack/lib_v5/model_param_init.py b/spaces/EronSamez/RVC_HFmeu/lib/uvr5_pack/lib_v5/model_param_init.py deleted file mode 100644 index b995c0bfb1194746187692e2ab1c2a6dbaaaec6c..0000000000000000000000000000000000000000 --- a/spaces/EronSamez/RVC_HFmeu/lib/uvr5_pack/lib_v5/model_param_init.py +++ /dev/null @@ -1,69 +0,0 @@ -import json -import os -import pathlib - -default_param = {} -default_param["bins"] = 768 -default_param["unstable_bins"] = 9 # training only -default_param["reduction_bins"] = 762 # training only -default_param["sr"] = 44100 -default_param["pre_filter_start"] = 757 -default_param["pre_filter_stop"] = 768 -default_param["band"] = {} - - -default_param["band"][1] = { - "sr": 11025, - "hl": 128, - "n_fft": 960, - "crop_start": 0, - "crop_stop": 245, - "lpf_start": 61, # inference only - "res_type": "polyphase", -} - -default_param["band"][2] = { - "sr": 44100, - "hl": 512, - "n_fft": 1536, - "crop_start": 24, - "crop_stop": 547, - "hpf_start": 81, # inference only - "res_type": "sinc_best", -} - - -def int_keys(d): - r = {} - for k, v in d: - if k.isdigit(): - k = int(k) - r[k] = v - return r - - -class ModelParameters(object): - def __init__(self, config_path=""): - if ".pth" == pathlib.Path(config_path).suffix: - import zipfile - - with zipfile.ZipFile(config_path, "r") as zip: - self.param = json.loads( - zip.read("param.json"), object_pairs_hook=int_keys - ) - elif ".json" == pathlib.Path(config_path).suffix: - with open(config_path, "r") as f: - self.param = json.loads(f.read(), object_pairs_hook=int_keys) - else: - self.param = default_param - - for k in [ - "mid_side", - "mid_side_b", - "mid_side_b2", - "stereo_w", - "stereo_n", - "reverse", - ]: - if not k in self.param: - self.param[k] = False diff --git a/spaces/EsoCode/text-generation-webui/extensions/multimodal/DOCS.md b/spaces/EsoCode/text-generation-webui/extensions/multimodal/DOCS.md deleted file mode 100644 index eaa4365e9a304a14ebbdb1d4d435f3a2a1f7a7d2..0000000000000000000000000000000000000000 --- a/spaces/EsoCode/text-generation-webui/extensions/multimodal/DOCS.md +++ /dev/null @@ -1,85 +0,0 @@ -# Technical description of multimodal extension - -## Working principle -Multimodality extension does most of the stuff which is required for any image input: - -- adds the UI -- saves the images as base64 JPEGs to history -- provides the hooks to the UI -- if there are images in the prompt, it: - - splits the prompt to text and image parts - - adds image start/end markers to text parts, then encodes and embeds the text parts - - calls the vision pipeline to embed the images - - stitches the embeddings together, and returns them to text generation -- loads the appropriate vision pipeline, selected either from model name, or by specifying --multimodal-pipeline parameter - -Now, for the pipelines, they: - -- load the required vision models -- return some consts, for example the number of tokens taken up by image -- and most importantly: return the embeddings for LLM, given a list of images - -## Prompts/history - -To save images in prompt/history, this extension is using a base64 JPEG, wrapped in a HTML tag, like so: -``` - 
-```
-where `{img_str}` is the actual image data. This format makes displaying them in the UI for free. Do note, that this format is required to be exactly the same, the regex used to find the images is: `)
`.
-
-## LLM input
-To describe the input, let's see it on an example prompt:
-```
-text1text2 text3 -``` -where `textN` is N-th text, ` ` is N-th image, in HTML format specified above. - -**The first step is to split the prompt into image/text parts**, so we get: -``` -['text1', ' ', 'text2', ' ', 'text3'] -``` -this is done in `MultimodalEmbedder._split_prompt(...)` function, which returns a list of `PromptPart`s - dataclasses wrapping the separate parts. - -This function also appends the image start/end markers to text, which are provided by `AbstractMultimodalPipeline.image_start()` / `AbstractMultimodalPipeline.image_end()` functions. If image start is ` `, and end is ``, this function will return: -``` -['text1
', '
', 'text2 ', '
', 'text3'] -``` - -**The returned prompt parts are then turned into token embeddings.** - -First, they are modified to token IDs, for the text it is done using standard `modules.text_generation.encode()` function, and for the images the returned token IDs are changed to placeholders. The placeholder is a list of `N` times `placeholder token id`, where `N` is specified using `AbstractMultimodalPipeline.num_image_embeds()`, and placeholder token IDs using `AbstractMultimodalPipeline.placeholder_token_id()`. - -Now, based on the token IDs, the prompt might get truncated, especially if `max_new_tokens` are unreasonably high. Unfortunately, it can't be done simply, just by trimming the prompt to be short enough. This way will lead to sometimes splitting the prompt in the middle of an image embedding, which usually breaks the generation. Therefore, in this case, the entire image needs to be removed from input. This is done inside `MultimodalEmbedder._encode_text(...)` function. - -**After the tokenization, the tokens need to get embedded**, the text and images are once again treated separately. - -The text parts are turned to embeddings, using `AbstractMultimodalPipeline.embed_tokens(...)` function. It uses standard embedding function from the model, but to support many LLMs, the actual function is returned by the pipeline (as it might be different for different LLMs), for LLaMA it is `shared.model.model.embed_tokens(...)`. - -The image parts are turned to embeddings, using `AbstractMultimodalPipeline.embed_images(...)` function. This function is specific for a given pipeline, it takes the images as input, forwards them through vision model/projector, and returns the embeddings. - -**Now, the returned embeddings are stitched together**, using `torch.cat()`, this is creating the final input to the LLM. - -## Pipelines - -All of the pipelines should subclass `AbstractMultimodalPipeline` class. The idea is to allow for new pipelines to be added in the same way as user extensions - git clone into `extensions/multimodal/pipelines`. - -The pipelines are the description of the vision part, containing vision model/multimodal projector. All of the pipelines should have an unique `name()`, which is then selected by user, in `--multimodal-pipeline` CLI argument. For an example, see `pipelines/llava/llava.py`. - -## Pipeline modules - -Pipelines are organized into "pipeline modules" - subdirectories in `pipelines` directory. The pipeline modules should contain a file called `pipelines.py`, that should contain the following fields: -- `available_pipelines: List[str]` - list of pipelines provided by this module, shown as the list of available pipelines to the user -- `def get_pipeline(name: str, params: dict) -> Optional[AbstractMultimodalPipeline]`: - a function to get a concrete pipeline by `name`, if `name` doesn't match any, should return `None`. `params` is the user settings for multimodal extension -- `def get_pipeline_from_model_name(model_name: str, params: dict) -> Optional[AbstractMultimodalPipeline]`: - a function to get a pipeline from `model_name`, should be eager to return `None`, unless the determination can be done clearly (for example: minigpt-4 bases on vicuna - it should never return the pipeline, but llava can, as it has its own specific LLM finetune) - -**NOTE**: A pipeline module should lazy-import the pipelines only when necessary, and it should keep its imports to minimum - -## Pipeline params - -The pipelines will get the extension `params` in the constructor. They should honor the following fields: -- `vision_device` - string, specifying `torch.device` to run the vision model (CLIP/ViT) on -- `vision_bits` - int, number of fp bits to load the vision model(s) in -- `projector_device` - string, specifying `torch.device` to run the projector models (Linear layers, QFormer, etc.) on -- `projector_bits` - int, number of fp bits to load the projector models in - -As a helper, `AbstractMultimodalPipeline` has `_get_device(self, setting_name: str, params: dict)` and `_get_dtype(self, setting_name: str, params: dict)` helper functions, which parse string/int and return `torch.device` / `torch.dtype`. diff --git a/spaces/FelixLuoX/codeformer/CodeFormer/basicsr/utils/logger.py b/spaces/FelixLuoX/codeformer/CodeFormer/basicsr/utils/logger.py deleted file mode 100644 index 9714bf59c30fc82de24c1ee58d9118d0864b3572..0000000000000000000000000000000000000000 --- a/spaces/FelixLuoX/codeformer/CodeFormer/basicsr/utils/logger.py +++ /dev/null @@ -1,169 +0,0 @@ -import datetime -import logging -import time - -from .dist_util import get_dist_info, master_only - -initialized_logger = {} - - -class MessageLogger(): - """Message logger for printing. - Args: - opt (dict): Config. It contains the following keys: - name (str): Exp name. - logger (dict): Contains 'print_freq' (str) for logger interval. - train (dict): Contains 'total_iter' (int) for total iters. - use_tb_logger (bool): Use tensorboard logger. - start_iter (int): Start iter. Default: 1. - tb_logger (obj:`tb_logger`): Tensorboard logger. Default： None. - """ - - def __init__(self, opt, start_iter=1, tb_logger=None): - self.exp_name = opt['name'] - self.interval = opt['logger']['print_freq'] - self.start_iter = start_iter - self.max_iters = opt['train']['total_iter'] - self.use_tb_logger = opt['logger']['use_tb_logger'] - self.tb_logger = tb_logger - self.start_time = time.time() - self.logger = get_root_logger() - - @master_only - def __call__(self, log_vars): - """Format logging message. - Args: - log_vars (dict): It contains the following keys: - epoch (int): Epoch number. - iter (int): Current iter. - lrs (list): List for learning rates. - time (float): Iter time. - data_time (float): Data time for each iter. - """ - # epoch, iter, learning rates - epoch = log_vars.pop('epoch') - current_iter = log_vars.pop('iter') - lrs = log_vars.pop('lrs') - - message = (f'[{self.exp_name[:5]}..][epoch:{epoch:3d}, ' f'iter:{current_iter:8,d}, lr:(') - for v in lrs: - message += f'{v:.3e},' - message += ')] ' - - # time and estimated time - if 'time' in log_vars.keys(): - iter_time = log_vars.pop('time') - data_time = log_vars.pop('data_time') - - total_time = time.time() - self.start_time - time_sec_avg = total_time / (current_iter - self.start_iter + 1) - eta_sec = time_sec_avg * (self.max_iters - current_iter - 1) - eta_str = str(datetime.timedelta(seconds=int(eta_sec))) - message += f'[eta: {eta_str}, ' - message += f'time (data): {iter_time:.3f} ({data_time:.3f})] ' - - # other items, especially losses - for k, v in log_vars.items(): - message += f'{k}: {v:.4e} ' - # tensorboard logger - if self.use_tb_logger: - if k.startswith('l_'): - self.tb_logger.add_scalar(f'losses/{k}', v, current_iter) - else: - self.tb_logger.add_scalar(k, v, current_iter) - self.logger.info(message) - - -@master_only -def init_tb_logger(log_dir): - from torch.utils.tensorboard import SummaryWriter - tb_logger = SummaryWriter(log_dir=log_dir) - return tb_logger - - -@master_only -def init_wandb_logger(opt): - """We now only use wandb to sync tensorboard log.""" - import wandb - logger = logging.getLogger('basicsr') - - project = opt['logger']['wandb']['project'] - resume_id = opt['logger']['wandb'].get('resume_id') - if resume_id: - wandb_id = resume_id - resume = 'allow' - logger.warning(f'Resume wandb logger with id={wandb_id}.') - else: - wandb_id = wandb.util.generate_id() - resume = 'never' - - wandb.init(id=wandb_id, resume=resume, name=opt['name'], config=opt, project=project, sync_tensorboard=True) - - logger.info(f'Use wandb logger with id={wandb_id}; project={project}.') - - -def get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=None): - """Get the root logger. - The logger will be initialized if it has not been initialized. By default a - StreamHandler will be added. If `log_file` is specified, a FileHandler will - also be added. - Args: - logger_name (str): root logger name. Default: 'basicsr'. - log_file (str | None): The log filename. If specified, a FileHandler - will be added to the root logger. - log_level (int): The root logger level. Note that only the process of - rank 0 is affected, while other processes will set the level to - "Error" and be silent most of the time. - Returns: - logging.Logger: The root logger. - """ - logger = logging.getLogger(logger_name) - # if the logger has been initialized, just return it - if logger_name in initialized_logger: - return logger - - format_str = '%(asctime)s %(levelname)s: %(message)s' - stream_handler = logging.StreamHandler() - stream_handler.setFormatter(logging.Formatter(format_str)) - logger.addHandler(stream_handler) - logger.propagate = False - rank, _ = get_dist_info() - if rank != 0: - logger.setLevel('ERROR') - elif log_file is not None: - logger.setLevel(log_level) - # add file handler - # file_handler = logging.FileHandler(log_file, 'w') - file_handler = logging.FileHandler(log_file, 'a') #Shangchen: keep the previous log - file_handler.setFormatter(logging.Formatter(format_str)) - file_handler.setLevel(log_level) - logger.addHandler(file_handler) - initialized_logger[logger_name] = True - return logger - - -def get_env_info(): - """Get environment information. - Currently, only log the software version. - """ - import torch - import torchvision - - from basicsr.version import __version__ - msg = r""" - ____ _ _____ ____ - / __ ) ____ _ _____ (_)_____/ ___/ / __ \ - / __ |/ __ `// ___// // ___/\__ \ / /_/ / - / /_/ // /_/ /(__ )/ // /__ ___/ // _, _/ - /_____/ \__,_//____//_/ \___//____//_/ |_| - ______ __ __ __ __ - / ____/____ ____ ____/ / / / __ __ _____ / /__ / / - / / __ / __ \ / __ \ / __ / / / / / / // ___// //_/ / / - / /_/ // /_/ // /_/ // /_/ / / /___/ /_/ // /__ / /< /_/ - \____/ \____/ \____/ \____/ /_____/\____/ \___//_/|_| (_) - """ - msg += ('\nVersion Information: ' - f'\n\tBasicSR: {__version__}' - f'\n\tPyTorch: {torch.__version__}' - f'\n\tTorchVision: {torchvision.__version__}') - return msg \ No newline at end of file diff --git a/spaces/FridaZuley/RVC_HFKawaii/Applio-RVC-Fork/utils/README.md b/spaces/FridaZuley/RVC_HFKawaii/Applio-RVC-Fork/utils/README.md deleted file mode 100644 index fb45a36b5909585aa964f2033762ee59b55526b0..0000000000000000000000000000000000000000 --- a/spaces/FridaZuley/RVC_HFKawaii/Applio-RVC-Fork/utils/README.md +++ /dev/null @@ -1,6 +0,0 @@ -# External Colab Code -Code used to make Google Colab work correctly -- Repo link: https://github.com/IAHispano/Applio-RVC-Fork/ - -Thanks to https://github.com/kalomaze/externalcolabcode - diff --git a/spaces/GAIR/Factool/factool/knowledge_qa/google_serper.py b/spaces/GAIR/Factool/factool/knowledge_qa/google_serper.py deleted file mode 100644 index 7e7884f8392ab69e6ece7d3a448fb656d33994dd..0000000000000000000000000000000000000000 --- a/spaces/GAIR/Factool/factool/knowledge_qa/google_serper.py +++ /dev/null @@ -1,118 +0,0 @@ -# The following code was adapted from https://github.com/hwchase17/langchain/blob/master/langchain/utilities/google_serper.py - -"""Util that calls Google Search using the Serper.dev API.""" -import pdb -import requests -import asyncio -import aiohttp -import yaml -import os - -from factool.env_config import factool_env_config - -# env -# serper_api_key = factool_env_config.serper_api_key - - -class GoogleSerperAPIWrapper(): - """Wrapper around the Serper.dev Google Search API. - You can create a free API key at https://serper.dev. - To use, you should have the environment variable ``SERPER_API_KEY`` - set with your API key, or pass `serper_api_key` as a named parameter - to the constructor. - Example: - .. code-block:: python - from langchain import GoogleSerperAPIWrapper - google_serper = GoogleSerperAPIWrapper() - """ - def __init__(self, snippet_cnt = 10) -> None: - self.k = snippet_cnt - self.gl = "us" - self.hl = "en" - self.serper_api_key = os.environ.get("SERPER_API_KEY", None) - assert self.serper_api_key is not None, "Please set the SERPER_API_KEY environment variable." - - async def _google_serper_search_results(self, session, search_term: str, gl: str, hl: str) -> dict: - headers = { - "X-API-KEY": self.serper_api_key or "", - "Content-Type": "application/json", - } - params = {"q": search_term, "gl": gl, "hl": hl} - async with session.post( - "https://google.serper.dev/search", headers=headers, params=params, raise_for_status=True - ) as response: - return await response.json() - - def _parse_results(self, results): - snippets = [] - - if results.get("answerBox"): - answer_box = results.get("answerBox", {}) - if answer_box.get("answer"): - element = {"content":answer_box.get("answer"),"source":"None"} - return [element] - elif answer_box.get("snippet"): - element = {"content":answer_box.get("snippet").replace("\n", " "),"source":"None"} - return [element] - elif answer_box.get("snippetHighlighted"): - element = {"content":answer_box.get("snippetHighlighted"),"source":"None"} - return [element] - - if results.get("knowledgeGraph"): - kg = results.get("knowledgeGraph", {}) - title = kg.get("title") - entity_type = kg.get("type") - if entity_type: - element = {"content":f"{title}: {entity_type}","source":"None"} - snippets.append(element) - description = kg.get("description") - if description: - element = {"content":description,"source":"None"} - snippets.append(element) - for attribute, value in kg.get("attributes", {}).items(): - element = {"content":f"{attribute}: {value}","source":"None"} - snippets.append(element) - - for result in results["organic"][: self.k]: - if "snippet" in result: - element = {"content":result["snippet"],"source":result["link"]} - snippets.append(element) - for attribute, value in result.get("attributes", {}).items(): - element = {"content":f"{attribute}: {value}","source":result["link"]} - snippets.append(element) - - if len(snippets) == 0: - element = {"content":"No good Google Search Result was found","source":"None"} - return [element] - - # keep only the first k snippets - snippets = snippets[:int(self.k / 2)] - - return snippets - - async def parallel_searches(self, search_queries, gl, hl): - async with aiohttp.ClientSession() as session: - tasks = [self._google_serper_search_results(session, query, gl, hl) for query in search_queries] - search_results = await asyncio.gather(*tasks, return_exceptions=True) - return search_results - - async def run(self, queries): - """Run query through GoogleSearch and parse result.""" - flattened_queries = [] - - for sublist in queries: - if sublist is None: - sublist = ['None', 'None'] - for item in sublist: - flattened_queries.append(item) - - results = await self.parallel_searches(flattened_queries, gl=self.gl, hl=self.hl) - snippets_list = [] - for i in range(len(results)): - snippets_list.append(self._parse_results(results[i])) - snippets_split = [snippets_list[i] + snippets_list[i+1] for i in range(0, len(snippets_list), 2)] - return snippets_split - -if __name__ == "__main__": - search = GoogleSerperAPIWrapper() - print(asyncio.run(search.run("What is the capital of the United States?"))) \ No newline at end of file diff --git a/spaces/GIZ/SDSN-demo/utils/sdg_classifier.py b/spaces/GIZ/SDSN-demo/utils/sdg_classifier.py deleted file mode 100644 index 57e633f689b18ec4512730ebb32429c8ea8b7b06..0000000000000000000000000000000000000000 --- a/spaces/GIZ/SDSN-demo/utils/sdg_classifier.py +++ /dev/null @@ -1,177 +0,0 @@ -from haystack.nodes import TransformersDocumentClassifier -from haystack.schema import Document -from typing import List, Tuple -from typing_extensions import Literal -import logging -import pandas as pd -from pandas import DataFrame, Series -from utils.checkconfig import getconfig -from utils.streamlitcheck import check_streamlit -from utils.preprocessing import processingpipeline -try: - import streamlit as st -except ImportError: - logging.info("Streamlit not installed") - -## Labels dictionary ### -_lab_dict = {0: 'no_cat', - 1:'SDG 1 - No poverty', - 2:'SDG 2 - Zero hunger', - 3:'SDG 3 - Good health and well-being', - 4:'SDG 4 - Quality education', - 5:'SDG 5 - Gender equality', - 6:'SDG 6 - Clean water and sanitation', - 7:'SDG 7 - Affordable and clean energy', - 8:'SDG 8 - Decent work and economic growth', - 9:'SDG 9 - Industry, Innovation and Infrastructure', - 10:'SDG 10 - Reduced inequality', - 11:'SDG 11 - Sustainable cities and communities', - 12:'SDG 12 - Responsible consumption and production', - 13:'SDG 13 - Climate action', - 14:'SDG 14 - Life below water', - 15:'SDG 15 - Life on land', - 16:'SDG 16 - Peace, justice and strong institutions', - 17:'SDG 17 - Partnership for the goals',} - -@st.cache(allow_output_mutation=True) -def load_sdgClassifier(config_file:str = None, classifier_name:str = None): - """ - loads the document classifier using haystack, where the name/path of model - in HF-hub as string is used to fetch the model object.Either configfile or - model should be passed. - 1. https://docs.haystack.deepset.ai/reference/document-classifier-api - 2. https://docs.haystack.deepset.ai/docs/document_classifier - - Params - -------- - config_file: config file path from which to read the model name - classifier_name: if modelname is passed, it takes a priority if not \ - found then will look for configfile, else raise error. - - - Return: document classifier model - """ - if not classifier_name: - if not config_file: - logging.warning("Pass either model name or config file") - return - else: - config = getconfig(config_file) - classifier_name = config.get('sdg','MODEL') - - logging.info("Loading classifier") - doc_classifier = TransformersDocumentClassifier( - model_name_or_path=classifier_name, - task="text-classification") - - return doc_classifier - - -@st.cache(allow_output_mutation=True) -def sdg_classification(haystack_doc:List[Document], - threshold:float = 0.8, - classifier_model:TransformersDocumentClassifier= None - )->Tuple[DataFrame,Series]: - """ - Text-Classification on the list of texts provided. Classifier provides the - most appropriate label for each text. these labels are in terms of if text - belongs to which particular Sustainable Devleopment Goal (SDG). - - Params - --------- - haystack_doc: List of haystack Documents. The output of Preprocessing Pipeline - contains the list of paragraphs in different format,here the list of - Haystack Documents is used. - threshold: threshold value for the model to keep the results from classifier - classifiermodel: you can pass the classifier model directly,which takes priority - however if not then looks for model in streamlit session. - In case of streamlit avoid passing the model directly. - - - Returns - ---------- - df: Dataframe with two columns['SDG:int', 'text'] - x: Series object with the unique SDG covered in the document uploaded and - the number of times it is covered/discussed/count_of_paragraphs. - - """ - logging.info("Working on SDG Classification") - if not classifier_model: - if check_streamlit(): - classifier_model = st.session_state['sdg_classifier'] - else: - logging.warning("No streamlit envinornment found, Pass the classifier") - return - - results = classifier_model.predict(haystack_doc) - - - labels_= [(l.meta['classification']['label'], - l.meta['classification']['score'],l.content,) for l in results] - - df = DataFrame(labels_, columns=["SDG","Relevancy","text"]) - - df = df.sort_values(by="Relevancy", ascending=False).reset_index(drop=True) - df.index += 1 - df =df[df['Relevancy']>threshold] - - # creating the dataframe for value counts of SDG, along with 'title' of SDGs - x = df['SDG'].value_counts() - x = x.rename('count') - x = x.rename_axis('SDG').reset_index() - x["SDG"] = pd.to_numeric(x["SDG"]) - x = x.sort_values(by=['count'], ascending=False) - x['SDG_name'] = x['SDG'].apply(lambda x: _lab_dict[x]) - x['SDG_Num'] = x['SDG'].apply(lambda x: "SDG "+str(x)) - - df['SDG'] = pd.to_numeric(df['SDG']) - df = df.sort_values('SDG') - - return df, x - -def runSDGPreprocessingPipeline(file_name:str, file_path:str, - split_by: Literal["sentence", "word"] = 'sentence', - split_length:int = 2, split_respect_sentence_boundary:bool = False, - split_overlap:int = 0,remove_punc:bool = False)->List[Document]: - """ - creates the pipeline and runs the preprocessing pipeline, - the params for pipeline are fetched from paramconfig - - Params - ------------ - - file_name: filename, in case of streamlit application use - st.session_state['filename'] - file_path: filepath, in case of streamlit application use st.session_state['filepath'] - split_by: document splitting strategy either as word or sentence - split_length: when synthetically creating the paragrpahs from document, - it defines the length of paragraph. - split_respect_sentence_boundary: Used when using 'word' strategy for - splititng of text. - split_overlap: Number of words or sentences that overlap when creating - the paragraphs. This is done as one sentence or 'some words' make sense - when read in together with others. Therefore the overlap is used. - remove_punc: to remove all Punctuation including ',' and '.' or not - - - Return - -------------- - List[Document]: When preprocessing pipeline is run, the output dictionary - has four objects. For the Haysatck implementation of SDG classification we, - need to use the List of Haystack Document, which can be fetched by - key = 'documents' on output. - - """ - - sdg_processing_pipeline = processingpipeline() - - output_sdg_pre = sdg_processing_pipeline.run(file_paths = file_path, - params= {"FileConverter": {"file_path": file_path, \ - "file_name": file_name}, - "UdfPreProcessor": {"remove_punc": remove_punc, \ - "split_by": split_by, \ - "split_length":split_length,\ - "split_overlap": split_overlap, \ - "split_respect_sentence_boundary":split_respect_sentence_boundary}}) - - return output_sdg_pre diff --git a/spaces/GMFTBY/PandaGPT/scripts/train.sh b/spaces/GMFTBY/PandaGPT/scripts/train.sh deleted file mode 100644 index e071d72afdd773e803e1ce316538594c31d7d41d..0000000000000000000000000000000000000000 --- a/spaces/GMFTBY/PandaGPT/scripts/train.sh +++ /dev/null @@ -1,12 +0,0 @@ -#!/bin/bash - -deepspeed --include localhost:0,1,2,3,4,5,6,7 --master_addr 127.0.0.1 --master_port 28457 train_sft.py \ - --model openllama_peft \ - --stage 1\ - --data_path ../data/pandagpt4_visual_instruction_data.json\ - --image_root_path ../data/images/\ - --imagebind_ckpt_path ../pretrained_ckpt/imagebind_ckpt/\ - --vicuna_ckpt_path ../pretrained_ckpt/vicuna_ckpt/13b_v0/\ - --max_tgt_len 400\ - --save_path ./ckpt/pandagpt_13b_v0_peft/\ - --log_path ./ckpt/pandagpt_13b_v0_peft/log_rest/ diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/agents/transporter.py b/spaces/Gen-Sim/Gen-Sim/cliport/agents/transporter.py deleted file mode 100644 index d24967d1e7f2684176732a06bb9271676f43bbc3..0000000000000000000000000000000000000000 --- a/spaces/Gen-Sim/Gen-Sim/cliport/agents/transporter.py +++ /dev/null @@ -1,539 +0,0 @@ -import os -import numpy as np - -import torch -import torch.nn.functional as F -from pytorch_lightning import LightningModule - -from cliport.tasks import cameras -from cliport.utils import utils -from cliport.models.core.attention import Attention -from cliport.models.core.transport import Transport -from cliport.models.streams.two_stream_attention import TwoStreamAttention -from cliport.models.streams.two_stream_transport import TwoStreamTransport - -from cliport.models.streams.two_stream_attention import TwoStreamAttentionLat -from cliport.models.streams.two_stream_transport import TwoStreamTransportLat -import time -import IPython - -class TransporterAgent(LightningModule): - def __init__(self, name, cfg, train_ds, test_ds): - super().__init__() - utils.set_seed(0) - self.automatic_optimization=False - self.device_type = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # this is bad for PL :( - self.name = name - self.cfg = cfg - self.train_loader = train_ds - self.test_loader = test_ds - - self.train_ds = train_ds.dataset - self.test_ds = test_ds.dataset - - self.name = name - self.task = cfg['train']['task'] - self.total_steps = 0 - self.crop_size = 64 - self.n_rotations = cfg['train']['n_rotations'] - - self.pix_size = 0.003125 - self.in_shape = (320, 160, 6) - self.cam_config = cameras.RealSenseD415.CONFIG - self.bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.28]]) - - self.val_repeats = cfg['train']['val_repeats'] - self.save_steps = cfg['train']['save_steps'] - - self._build_model() - ## - # reduce the number of parameters here - ## - self._optimizers = { - 'attn': torch.optim.Adam(self.attention.parameters(), lr=self.cfg['train']['lr']), - 'trans': torch.optim.Adam(self.transport.parameters(), lr=self.cfg['train']['lr']) - } - print("Agent: {}, Logging: {}".format(name, cfg['train']['log'])) - - def configure_optimizers(self): - return self._optimizers - - def _build_model(self): - self.attention = None - self.transport = None - raise NotImplementedError() - - def forward(self, x): - raise NotImplementedError() - - def cross_entropy_with_logits(self, pred, labels, reduction='mean'): - # Lucas found that both sum and mean work equally well - x = (-labels.view(len(labels), -1) * F.log_softmax(pred.view(len(labels), -1), -1)) - if reduction == 'sum': - return x.sum() - elif reduction == 'mean': - return x.mean() - else: - raise NotImplementedError() - - def attn_forward(self, inp, softmax=True): - inp_img = inp['inp_img'] - output = self.attention.forward(inp_img, softmax=softmax) - return output - - def attn_training_step(self, frame, backprop=True, compute_err=False): - inp_img = frame['img'] - p0, p0_theta = frame['p0'], frame['p0_theta'] - - inp = {'inp_img': inp_img} - out = self.attn_forward(inp, softmax=False) - return self.attn_criterion(backprop, compute_err, inp, out, p0, p0_theta) - - def attn_criterion(self, backprop, compute_err, inp, out, p, theta): - # Get label. - if type(theta) is torch.Tensor: - theta = theta.detach().cpu().numpy() - - theta_i = theta / (2 * np.pi / self.attention.n_rotations) - theta_i = np.int32(np.round(theta_i)) % self.attention.n_rotations - inp_img = inp['inp_img'].float() - - label_size = inp_img.shape[:3] + (self.attention.n_rotations,) - label = torch.zeros(label_size, dtype=torch.float, device=out.device) - - # remove this for-loop laters - for idx, p_i in enumerate(p): - label[idx, int(p_i[0]), int(p_i[1]), theta_i[idx]] = 1 - label = label.permute((0, 3, 1, 2)).contiguous() - - # Get loss. - loss = self.cross_entropy_with_logits(out, label) - - # Backpropagate. - if backprop: - attn_optim = self._optimizers['attn'] - self.manual_backward(loss) - attn_optim.step() - attn_optim.zero_grad() - - # Pixel and Rotation error (not used anywhere). - err = {} - if compute_err: - with torch.no_grad(): - pick_conf = self.attn_forward(inp) - pick_conf = pick_conf[0].permute(1,2,0) - pick_conf = pick_conf.detach().cpu().numpy() - p = p[0] - theta = theta[0] - - # single batch - argmax = np.argmax(pick_conf) - argmax = np.unravel_index(argmax, shape=pick_conf.shape) - p0_pix = argmax[:2] - p0_theta = argmax[2] * (2 * np.pi / pick_conf.shape[2]) - - err = { - 'dist': np.linalg.norm(np.array(p.detach().cpu().numpy()) - p0_pix, ord=1), - 'theta': np.absolute((theta - p0_theta) % np.pi) - } - return loss, err - - def trans_forward(self, inp, softmax=True): - inp_img = inp['inp_img'] - p0 = inp['p0'] - - output = self.transport.forward(inp_img, p0, softmax=softmax) - return output - - def transport_training_step(self, frame, backprop=True, compute_err=False): - inp_img = frame['img'].float() - p0 = frame['p0'] - p1, p1_theta = frame['p1'], frame['p1_theta'] - - inp = {'inp_img': inp_img, 'p0': p0} - output = self.trans_forward(inp, softmax=False) - err, loss = self.transport_criterion(backprop, compute_err, inp, output, p0, p1, p1_theta) - return loss, err - - def transport_criterion(self, backprop, compute_err, inp, output, p, q, theta): - s = time.time() - if type(theta) is torch.Tensor: - theta = theta.detach().cpu().numpy() - - itheta = theta / (2 * np.pi / self.transport.n_rotations) - itheta = np.int32(np.round(itheta)) % self.transport.n_rotations - - # Get one-hot pixel label map. - inp_img = inp['inp_img'] - - # label_size = inp_img.shape[:2] + (self.transport.n_rotations,) - label_size = inp_img.shape[:3] + (self.transport.n_rotations,) - label = torch.zeros(label_size, dtype=torch.float, device=output.device) - - # remove this for-loop laters - q[:,0] = torch.clamp(q[:,0], 0, label.shape[1]-1) - q[:,1] = torch.clamp(q[:,1], 0, label.shape[2]-1) - - for idx, q_i in enumerate(q): - label[idx, int(q_i[0]), int(q_i[1]), itheta[idx]] = 1 - label = label.permute((0, 3, 1, 2)).contiguous() - - # Get loss. - loss = self.cross_entropy_with_logits(output, label) - - if backprop: - transport_optim = self._optimizers['trans'] - transport_optim.zero_grad() - self.manual_backward(loss) - transport_optim.step() - - # Pixel and Rotation error (not used anywhere). - err = {} - if compute_err: - with torch.no_grad(): - place_conf = self.trans_forward(inp) - # pick the first batch - place_conf = place_conf[0] - q = q[0] - theta = theta[0] - place_conf = place_conf.permute(1, 2, 0) - place_conf = place_conf.detach().cpu().numpy() - argmax = np.argmax(place_conf) - argmax = np.unravel_index(argmax, shape=place_conf.shape) - p1_pix = argmax[:2] - p1_theta = argmax[2] * (2 * np.pi / place_conf.shape[2]) - - err = { - 'dist': np.linalg.norm(np.array(q.detach().cpu().numpy()) - p1_pix, ord=1), - 'theta': np.absolute((theta - p1_theta) % np.pi) - } - - self.transport.iters += 1 - return err, loss - - def training_step(self, batch, batch_idx): - - self.attention.train() - self.transport.train() - - frame, _ = batch - self.start_time = time.time() - - # Get training losses. - step = self.total_steps + 1 - loss0, err0 = self.attn_training_step(frame) - - self.start_time = time.time() - - if isinstance(self.transport, Attention): - loss1, err1 = self.attn_training_step(frame) - else: - loss1, err1 = self.transport_training_step(frame) - - total_loss = loss0 + loss1 - self.total_steps = step - self.start_time = time.time() - self.log('tr/attn/loss', loss0) - self.log('tr/trans/loss', loss1) - self.log('tr/loss', total_loss) - self.check_save_iteration() - - return dict( - loss=total_loss, - ) - - def check_save_iteration(self): - global_step = self.total_steps - - if (global_step + 1) % 100 == 0: - # save lastest checkpoint - print(f"Saving last.ckpt Epoch: {self.trainer.current_epoch} | Global Step: {self.trainer.global_step}") - self.save_last_checkpoint() - - def save_last_checkpoint(self): - checkpoint_path = os.path.join(self.cfg['train']['train_dir'], 'checkpoints') - ckpt_path = os.path.join(checkpoint_path, 'last.ckpt') - self.trainer.save_checkpoint(ckpt_path) - - def validation_step(self, batch, batch_idx): - self.attention.eval() - self.transport.eval() - - loss0, loss1 = 0, 0 - assert self.val_repeats >= 1 - for i in range(self.val_repeats): - frame, _ = batch - l0, err0 = self.attn_training_step(frame, backprop=False, compute_err=True) - loss0 += l0 - if isinstance(self.transport, Attention): - l1, err1 = self.attn_training_step(frame, backprop=False, compute_err=True) - loss1 += l1 - else: - l1, err1 = self.transport_training_step(frame, backprop=False, compute_err=True) - loss1 += l1 - loss0 /= self.val_repeats - loss1 /= self.val_repeats - val_total_loss = loss0 + loss1 - - return dict( - val_loss=val_total_loss, - val_loss0=loss0, - val_loss1=loss1, - val_attn_dist_err=err0['dist'], - val_attn_theta_err=err0['theta'], - val_trans_dist_err=err1['dist'], - val_trans_theta_err=err1['theta'], - ) - - def training_epoch_end(self, all_outputs): - super().training_epoch_end(all_outputs) - utils.set_seed(self.trainer.current_epoch+1) - - def validation_epoch_end(self, all_outputs): - mean_val_total_loss = np.mean([v['val_loss'].item() for v in all_outputs]) - mean_val_loss0 = np.mean([v['val_loss0'].item() for v in all_outputs]) - mean_val_loss1 = np.mean([v['val_loss1'].item() for v in all_outputs]) - total_attn_dist_err = np.sum([v['val_attn_dist_err'].sum() for v in all_outputs]) - total_attn_theta_err = np.sum([v['val_attn_theta_err'].sum() for v in all_outputs]) - total_trans_dist_err = np.sum([v['val_trans_dist_err'].sum() for v in all_outputs]) - total_trans_theta_err = np.sum([v['val_trans_theta_err'].sum() for v in all_outputs]) - - - self.log('vl/attn/loss', mean_val_loss0) - self.log('vl/trans/loss', mean_val_loss1) - self.log('vl/loss', mean_val_total_loss) - self.log('vl/total_attn_dist_err', total_attn_dist_err) - self.log('vl/total_attn_theta_err', total_attn_theta_err) - self.log('vl/total_trans_dist_err', total_trans_dist_err) - self.log('vl/total_trans_theta_err', total_trans_theta_err) - - print("\nAttn Err - Dist: {:.2f}, Theta: {:.2f}".format(total_attn_dist_err, total_attn_theta_err)) - print("Transport Err - Dist: {:.2f}, Theta: {:.2f}".format(total_trans_dist_err, total_trans_theta_err)) - - return dict( - val_loss=mean_val_total_loss, - val_loss0=mean_val_loss0, - mean_val_loss1=mean_val_loss1, - total_attn_dist_err=total_attn_dist_err, - total_attn_theta_err=total_attn_theta_err, - total_trans_dist_err=total_trans_dist_err, - total_trans_theta_err=total_trans_theta_err, - ) - - def act(self, obs, info=None, goal=None): # pylint: disable=unused-argument - """Run inference and return best action given visual observations.""" - # Get heightmap from RGB-D images. - img = self.test_ds.get_image(obs) - - # Attention model forward pass. - pick_inp = {'inp_img': img} - pick_conf = self.attn_forward(pick_inp) - - - pick_conf = pick_conf.detach().cpu().numpy() - argmax = np.argmax(pick_conf) - argmax = np.unravel_index(argmax, shape=pick_conf.shape) - p0_pix = argmax[:2] - p0_theta = argmax[2] * (2 * np.pi / pick_conf.shape[2]) - - # Transport model forward pass. - place_inp = {'inp_img': img, 'p0': p0_pix} - place_conf = self.trans_forward(place_inp) - place_conf = place_conf.permute(1, 2, 0) - place_conf = place_conf.detach().cpu().numpy() - argmax = np.argmax(place_conf) - argmax = np.unravel_index(argmax, shape=place_conf.shape) - p1_pix = argmax[:2] - p1_theta = argmax[2] * (2 * np.pi / place_conf.shape[2]) - - # Pixels to end effector poses. - hmap = img[:, :, 3] - p0_xyz = utils.pix_to_xyz(p0_pix, hmap, self.bounds, self.pix_size) - p1_xyz = utils.pix_to_xyz(p1_pix, hmap, self.bounds, self.pix_size) - p0_xyzw = utils.eulerXYZ_to_quatXYZW((0, 0, -p0_theta)) - p1_xyzw = utils.eulerXYZ_to_quatXYZW((0, 0, -p1_theta)) - - return { - 'pose0': (np.asarray(p0_xyz), np.asarray(p0_xyzw)), - 'pose1': (np.asarray(p1_xyz), np.asarray(p1_xyzw)), - 'pick': p0_pix, - 'place': p1_pix, - } - - def optimizer_step(self, current_epoch, batch_nb, optimizer, optimizer_i, second_order_closure, on_tpu, using_native_amp, using_lbfgs): - pass - - def configure_optimizers(self): - pass - - def train_dataloader(self): - return self.train_loader - - def val_dataloader(self): - return self.test_loader - - def load(self, model_path): - self.load_state_dict(torch.load(model_path)['state_dict']) - self.to(device=self.device_type) - - -class OriginalTransporterAgent(TransporterAgent): - - def __init__(self, name, cfg, train_ds, test_ds): - super().__init__(name, cfg, train_ds, test_ds) - - def _build_model(self): - stream_fcn = 'plain_resnet' - self.attention = Attention( - stream_fcn=(stream_fcn, None), - in_shape=self.in_shape, - n_rotations=1, - preprocess=utils.preprocess, - cfg=self.cfg, - device=self.device_type, - ) - self.transport = Transport( - stream_fcn=(stream_fcn, None), - in_shape=self.in_shape, - n_rotations=self.n_rotations, - crop_size=self.crop_size, - preprocess=utils.preprocess, - cfg=self.cfg, - device=self.device_type, - ) - - -class ClipUNetTransporterAgent(TransporterAgent): - - def __init__(self, name, cfg, train_ds, test_ds): - super().__init__(name, cfg, train_ds, test_ds) - - def _build_model(self): - stream_fcn = 'clip_unet' - self.attention = Attention( - stream_fcn=(stream_fcn, None), - in_shape=self.in_shape, - n_rotations=1, - preprocess=utils.preprocess, - cfg=self.cfg, - device=self.device_type, - ) - self.transport = Transport( - stream_fcn=(stream_fcn, None), - in_shape=self.in_shape, - n_rotations=self.n_rotations, - crop_size=self.crop_size, - preprocess=utils.preprocess, - cfg=self.cfg, - device=self.device_type, - ) - - -class TwoStreamClipUNetTransporterAgent(TransporterAgent): - - def __init__(self, name, cfg, train_ds, test_ds): - super().__init__(name, cfg, train_ds, test_ds) - - def _build_model(self): - stream_one_fcn = 'plain_resnet' - stream_two_fcn = 'clip_unet' - self.attention = TwoStreamAttention( - stream_fcn=(stream_one_fcn, stream_two_fcn), - in_shape=self.in_shape, - n_rotations=1, - preprocess=utils.preprocess, - cfg=self.cfg, - device=self.device_type, - ) - self.transport = TwoStreamTransport( - stream_fcn=(stream_one_fcn, stream_two_fcn), - in_shape=self.in_shape, - n_rotations=self.n_rotations, - crop_size=self.crop_size, - preprocess=utils.preprocess, - cfg=self.cfg, - device=self.device_type, - ) - - -class TwoStreamClipUNetLatTransporterAgent(TransporterAgent): - - def __init__(self, name, cfg, train_ds, test_ds): - super().__init__(name, cfg, train_ds, test_ds) - - def _build_model(self): - stream_one_fcn = 'plain_resnet_lat' - stream_two_fcn = 'clip_unet_lat' - self.attention = TwoStreamAttentionLat( - stream_fcn=(stream_one_fcn, stream_two_fcn), - in_shape=self.in_shape, - n_rotations=1, - preprocess=utils.preprocess, - cfg=self.cfg, - device=self.device_type, - ) - self.transport = TwoStreamTransportLat( - stream_fcn=(stream_one_fcn, stream_two_fcn), - in_shape=self.in_shape, - n_rotations=self.n_rotations, - crop_size=self.crop_size, - preprocess=utils.preprocess, - cfg=self.cfg, - device=self.device_type, - ) - - -class TwoStreamClipWithoutSkipsTransporterAgent(TransporterAgent): - - def __init__(self, name, cfg, train_ds, test_ds): - super().__init__(name, cfg, train_ds, test_ds) - - def _build_model(self): - # TODO: lateral version - stream_one_fcn = 'plain_resnet' - stream_two_fcn = 'clip_woskip' - self.attention = TwoStreamAttention( - stream_fcn=(stream_one_fcn, stream_two_fcn), - in_shape=self.in_shape, - n_rotations=1, - preprocess=utils.preprocess, - cfg=self.cfg, - device=self.device_type, - ) - self.transport = TwoStreamTransport( - stream_fcn=(stream_one_fcn, stream_two_fcn), - in_shape=self.in_shape, - n_rotations=self.n_rotations, - crop_size=self.crop_size, - preprocess=utils.preprocess, - cfg=self.cfg, - device=self.device_type, - ) - - -class TwoStreamRN50BertUNetTransporterAgent(TransporterAgent): - - def __init__(self, name, cfg, train_ds, test_ds): - super().__init__(name, cfg, train_ds, test_ds) - - def _build_model(self): - # TODO: lateral version - stream_one_fcn = 'plain_resnet' - stream_two_fcn = 'rn50_bert_unet' - self.attention = TwoStreamAttention( - stream_fcn=(stream_one_fcn, stream_two_fcn), - in_shape=self.in_shape, - n_rotations=1, - preprocess=utils.preprocess, - cfg=self.cfg, - device=self.device_type, - ) - self.transport = TwoStreamTransport( - stream_fcn=(stream_one_fcn, stream_two_fcn), - in_shape=self.in_shape, - n_rotations=self.n_rotations, - crop_size=self.crop_size, - preprocess=utils.preprocess, - cfg=self.cfg, - device=self.device_type, - ) diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/tasks/primitives.py b/spaces/Gen-Sim/Gen-Sim/cliport/tasks/primitives.py deleted file mode 100644 index de6d4da015622d54c160f65dc9a1682bab649267..0000000000000000000000000000000000000000 --- a/spaces/Gen-Sim/Gen-Sim/cliport/tasks/primitives.py +++ /dev/null @@ -1,113 +0,0 @@ -"""Motion primitives.""" - -import numpy as np -from cliport.utils import utils - - -class PickPlace(): - """Pick and place primitive.""" - - def __init__(self, height=0.32, speed=0.01): - self.height, self.speed = height, speed - - def __call__(self, movej, movep, ee, pose0, pose1): - """Execute pick and place primitive. - - Args: - movej: function to move robot joints. - movep: function to move robot end effector pose. - ee: robot end effector. - pose0: SE(3) picking pose. - pose1: SE(3) placing pose. - - Returns: - timeout: robot movement timed out if True. - """ - - pick_pose, place_pose = pose0, pose1 - - # Execute picking primitive. - prepick_to_pick = ((0, 0, 0.32), (0, 0, 0, 1)) - postpick_to_pick = ((0, 0, self.height), (0, 0, 0, 1)) - prepick_pose = utils.multiply(pick_pose, prepick_to_pick) - postpick_pose = utils.multiply(pick_pose, postpick_to_pick) - timeout = movep(prepick_pose) - - # Move towards pick pose until contact is detected. - delta = (np.float32([0, 0, -0.001]), - utils.eulerXYZ_to_quatXYZW((0, 0, 0))) - targ_pose = prepick_pose - while not ee.detect_contact(): # and target_pose[2] > 0: - targ_pose = utils.multiply(targ_pose, delta) - timeout |= movep(targ_pose) - if timeout: - return True - - # Activate end effector, move up, and check picking success. - ee.activate() - timeout |= movep(postpick_pose, self.speed) - pick_success = ee.check_grasp() - - # Execute placing primitive if pick is successful. - if pick_success: - preplace_to_place = ((0, 0, self.height), (0, 0, 0, 1)) - postplace_to_place = ((0, 0, 0.32), (0, 0, 0, 1)) - preplace_pose = utils.multiply(place_pose, preplace_to_place) - postplace_pose = utils.multiply(place_pose, postplace_to_place) - targ_pose = preplace_pose - while not ee.detect_contact(): - targ_pose = utils.multiply(targ_pose, delta) - timeout |= movep(targ_pose, self.speed) - if timeout: - return True - ee.release() - timeout |= movep(postplace_pose) - - # Move to prepick pose if pick is not successful. - else: - ee.release() - timeout |= movep(prepick_pose) - - return timeout - - -def push(movej, movep, ee, pose0, pose1): # pylint: disable=unused-argument - """Execute pushing primitive. - - Args: - movej: function to move robot joints. - movep: function to move robot end effector pose. - ee: robot end effector. - pose0: SE(3) starting pose. - pose1: SE(3) ending pose. - - Returns: - timeout: robot movement timed out if True. - """ - - # Adjust push start and end positions. - pos0 = np.float32((pose0[0][0], pose0[0][1], 0.005)) - pos1 = np.float32((pose1[0][0], pose1[0][1], 0.005)) - vec = np.float32(pos1) - np.float32(pos0) - length = np.linalg.norm(vec) - vec = vec / length - pos0 -= vec * 0.02 - pos1 -= vec * 0.05 - - # Align spatula against push direction. - theta = np.arctan2(vec[1], vec[0]) - rot = utils.eulerXYZ_to_quatXYZW((0, 0, theta)) - - over0 = (pos0[0], pos0[1], 0.31) - over1 = (pos1[0], pos1[1], 0.31) - - # Execute push. - timeout = movep((over0, rot)) - timeout |= movep((pos0, rot)) - n_push = np.int32(np.floor(np.linalg.norm(pos1 - pos0) / 0.01)) - for _ in range(n_push): - target = pos0 + vec * n_push * 0.01 - timeout |= movep((target, rot), speed=0.003) - timeout |= movep((pos1, rot), speed=0.003) - timeout |= movep((over1, rot)) - return timeout diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/utils/utils.py b/spaces/Gen-Sim/Gen-Sim/cliport/utils/utils.py deleted file mode 100644 index 8d1ecf6a5925b7a4e7ac254b8bdbf5d3f1ed1ee4..0000000000000000000000000000000000000000 --- a/spaces/Gen-Sim/Gen-Sim/cliport/utils/utils.py +++ /dev/null @@ -1,1257 +0,0 @@ -"""Miscellaneous utilities.""" - -import cv2 -import random -import matplotlib -import matplotlib.pyplot as plt -import meshcat -import meshcat.geometry as g -import meshcat.transformations as mtf - -import PIL -import yaml -import numpy as np -from transforms3d import euler - -import pybullet as p -import kornia -from omegaconf import OmegaConf - -import os -import torch -import torchvision - - -# ----------------------------------------------------------------------------- -# HEIGHTMAP UTILS -# ----------------------------------------------------------------------------- - -def get_heightmap(points, colors, bounds, pixel_size): - """Get top-down (z-axis) orthographic heightmap image from 3D pointcloud. - - Args: - points: HxWx3 float array of 3D points in world coordinates. - colors: HxWx3 uint8 array of values in range 0-255 aligned with points. - bounds: 3x2 float array of values (rows: X,Y,Z; columns: min,max) defining - region in 3D space to generate heightmap in world coordinates. - pixel_size: float defining size of each pixel in meters. - - Returns: - heightmap: HxW float array of height (from lower z-bound) in meters. - colormap: HxWx3 uint8 array of backprojected color aligned with heightmap. - """ - width = int(np.round((bounds[0, 1] - bounds[0, 0]) / pixel_size)) - height = int(np.round((bounds[1, 1] - bounds[1, 0]) / pixel_size)) - heightmap = np.zeros((height, width), dtype=np.float32) - colormap = np.zeros((height, width, colors.shape[-1]), dtype=np.uint8) - - # Filter out 3D points that are outside of the predefined bounds. - ix = (points[Ellipsis, 0] >= bounds[0, 0]) & (points[Ellipsis, 0] < bounds[0, 1]) - iy = (points[Ellipsis, 1] >= bounds[1, 0]) & (points[Ellipsis, 1] < bounds[1, 1]) - iz = (points[Ellipsis, 2] >= bounds[2, 0]) & (points[Ellipsis, 2] < bounds[2, 1]) - valid = ix & iy & iz - points = points[valid] - colors = colors[valid] - - # Sort 3D points by z-value, which works with array assignment to simulate - # z-buffering for rendering the heightmap image. - iz = np.argsort(points[:, -1]) - points, colors = points[iz], colors[iz] - px = np.int32(np.floor((points[:, 0] - bounds[0, 0]) / pixel_size)) - py = np.int32(np.floor((points[:, 1] - bounds[1, 0]) / pixel_size)) - px = np.clip(px, 0, width - 1) - py = np.clip(py, 0, height - 1) - heightmap[py, px] = points[:, 2] - bounds[2, 0] - for c in range(colors.shape[-1]): - colormap[py, px, c] = colors[:, c] - return heightmap, colormap - - -def get_pointcloud(depth, intrinsics): - """Get 3D pointcloud from perspective depth image. - - Args: - depth: HxW float array of perspective depth in meters. - intrinsics: 3x3 float array of camera intrinsics matrix. - - Returns: - points: HxWx3 float array of 3D points in camera coordinates. - """ - height, width = depth.shape - xlin = np.linspace(0, width - 1, width) - ylin = np.linspace(0, height - 1, height) - px, py = np.meshgrid(xlin, ylin) - px = (px - intrinsics[0, 2]) * (depth / intrinsics[0, 0]) - py = (py - intrinsics[1, 2]) * (depth / intrinsics[1, 1]) - points = np.float32([px, py, depth]).transpose(1, 2, 0) - return points - - -def transform_pointcloud(points, transform): - """Apply rigid transformation to 3D pointcloud. - - Args: - points: HxWx3 float array of 3D points in camera coordinates. - transform: 4x4 float array representing a rigid transformation matrix. - - Returns: - points: HxWx3 float array of transformed 3D points. - """ - padding = ((0, 0), (0, 0), (0, 1)) - homogen_points = np.pad(points.copy(), padding, - 'constant', constant_values=1) - for i in range(3): - points[Ellipsis, i] = np.sum(transform[i, :] * homogen_points, axis=-1) - return points - - -def reconstruct_heightmaps(color, depth, configs, bounds, pixel_size): - """Reconstruct top-down heightmap views from multiple 3D pointclouds.""" - heightmaps, colormaps = [], [] - for color, depth, config in zip(color, depth, configs): - intrinsics = np.array(config['intrinsics']).reshape(3, 3) - xyz = get_pointcloud(depth, intrinsics) - position = np.array(config['position']).reshape(3, 1) - rotation = p.getMatrixFromQuaternion(config['rotation']) - rotation = np.array(rotation).reshape(3, 3) - transform = np.eye(4) - transform[:3, :] = np.hstack((rotation, position)) - xyz = transform_pointcloud(xyz, transform) - heightmap, colormap = get_heightmap(xyz, color, bounds, pixel_size) - heightmaps.append(heightmap) - colormaps.append(colormap) - return heightmaps, colormaps - - -def pix_to_xyz(pixel, height, bounds, pixel_size, skip_height=False): - """Convert from pixel location on heightmap to 3D position.""" - u, v = pixel - x = bounds[0, 0] + v * pixel_size - y = bounds[1, 0] + u * pixel_size - if not skip_height: - z = bounds[2, 0] + height[u, v] - else: - z = 0.0 - return (x, y, z) - - -def xyz_to_pix(position, bounds, pixel_size): - """Convert from 3D position to pixel location on heightmap.""" - u = int(np.round((position[1] - bounds[1, 0]) / pixel_size)) - v = int(np.round((position[0] - bounds[0, 0]) / pixel_size)) - return (u, v) - - -def unproject_vectorized(uv_coordinates, depth_values, - intrinsic, - distortion): - """Vectorized version of unproject(), for N points. - - Args: - uv_coordinates: pixel coordinates to unproject of shape (n, 2). - depth_values: depth values corresponding index-wise to the uv_coordinates of - shape (n). - intrinsic: array of shape (3, 3). This is typically the return value of - intrinsics_to_matrix. - distortion: camera distortion parameters of shape (5,). - - Returns: - xyz coordinates in camera frame of shape (n, 3). - """ - cam_mtx = intrinsic # shape [3, 3] - cam_dist = np.array(distortion) # shape [5] - - # shape of points_undistorted is [N, 2] after the squeeze(). - points_undistorted = cv2.undistortPoints( - uv_coordinates.reshape((-1, 1, 2)), cam_mtx, cam_dist).squeeze() - - x = points_undistorted[:, 0] * depth_values - y = points_undistorted[:, 1] * depth_values - - xyz = np.vstack((x, y, depth_values)).T - return xyz - - -def unproject_depth_vectorized(im_depth, depth_dist, - camera_mtx, - camera_dist): - """Unproject depth image into 3D point cloud, using calibration. - - Args: - im_depth: raw depth image, pre-calibration of shape (height, width). - depth_dist: depth distortion parameters of shape (8,) - camera_mtx: intrinsics matrix of shape (3, 3). This is typically the return - value of intrinsics_to_matrix. - camera_dist: camera distortion parameters shape (5,). - - Returns: - numpy array of shape [3, H*W]. each column is xyz coordinates - """ - h, w = im_depth.shape - - # shape of each u_map, v_map is [H, W]. - u_map, v_map = np.meshgrid(np.linspace( - 0, w - 1, w), np.linspace(0, h - 1, h)) - - adjusted_depth = depth_dist[0] + im_depth * depth_dist[1] - - # shape after stack is [N, 2], where N = H * W. - uv_coordinates = np.stack((u_map.reshape(-1), v_map.reshape(-1)), axis=-1) - - return unproject_vectorized(uv_coordinates, adjusted_depth.reshape(-1), - camera_mtx, camera_dist) - - -# ----------------------------------------------------------------------------- -# MATH UTILS -# ----------------------------------------------------------------------------- - - -def sample_distribution(prob, n_samples=1): - """Sample data point from a custom distribution.""" - flat_prob = prob.flatten() / np.sum(prob) - rand_ind = np.random.choice( - np.arange(len(flat_prob)), n_samples, p=flat_prob, replace=False) - rand_ind_coords = np.array(np.unravel_index(rand_ind, prob.shape)).T - return np.int32(rand_ind_coords.squeeze()) - - -# ------------------------------------------------------------------------- -# Transformation Helper Functions -# ------------------------------------------------------------------------- - - -def invert(pose): - return p.invertTransform(pose[0], pose[1]) - - -def multiply(pose0, pose1): - return p.multiplyTransforms(pose0[0], pose0[1], pose1[0], pose1[1]) - - -def apply(pose, position): - position = np.float32(position) - position_shape = position.shape - position = np.float32(position).reshape(3, -1) - rotation = np.float32(p.getMatrixFromQuaternion(pose[1])).reshape(3, 3) - translation = np.float32(pose[0]).reshape(3, 1) - position = rotation @ position + translation - return tuple(position.reshape(position_shape)) - - -def eulerXYZ_to_quatXYZW(rotation): # pylint: disable=invalid-name - """Abstraction for converting from a 3-parameter rotation to quaterion. - - This will help us easily switch which rotation parameterization we use. - Quaternion should be in xyzw order for pybullet. - - Args: - rotation: a 3-parameter rotation, in xyz order tuple of 3 floats - - Returns: - quaternion, in xyzw order, tuple of 4 floats - """ - euler_zxy = (rotation[2], rotation[0], rotation[1]) - quaternion_wxyz = euler.euler2quat(*euler_zxy, axes='szxy') - q = quaternion_wxyz - quaternion_xyzw = (q[1], q[2], q[3], q[0]) - return quaternion_xyzw - - -def quatXYZW_to_eulerXYZ(quaternion_xyzw): # pylint: disable=invalid-name - """Abstraction for converting from quaternion to a 3-parameter toation. - - This will help us easily switch which rotation parameterization we use. - Quaternion should be in xyzw order for pybullet. - - Args: - quaternion_xyzw: in xyzw order, tuple of 4 floats - - Returns: - rotation: a 3-parameter rotation, in xyz order, tuple of 3 floats - """ - q = quaternion_xyzw - quaternion_wxyz = np.array([q[3], q[0], q[1], q[2]]) - euler_zxy = euler.quat2euler(quaternion_wxyz, axes='szxy') - euler_xyz = (euler_zxy[1], euler_zxy[2], euler_zxy[0]) - return euler_xyz - - -def apply_transform(transform_to_from, points_from): - r"""Transforms points (3D) into new frame. - - Using transform_to_from notation. - - Args: - transform_to_from: numpy.ndarray of shape [B,4,4], SE3 - points_from: numpy.ndarray of shape [B,3,N] - - Returns: - points_to: numpy.ndarray of shape [B,3,N] - """ - num_points = points_from.shape[-1] - - # non-batched - if len(transform_to_from.shape) == 2: - ones = np.ones((1, num_points)) - - # makes these each into homogenous vectors - points_from = np.vstack((points_from, ones)) # [4,N] - points_to = transform_to_from @ points_from # [4,N] - return points_to[0:3, :] # [3,N] - - # batched - else: - assert len(transform_to_from.shape) == 3 - batch_size = transform_to_from.shape[0] - zeros = np.ones((batch_size, 1, num_points)) - points_from = np.concatenate((points_from, zeros), axis=1) - assert points_from.shape[1] == 4 - points_to = transform_to_from @ points_from - return points_to[:, 0:3, :] - - -# ----------------------------------------------------------------------------- -# IMAGE UTILS -# ----------------------------------------------------------------------------- - - -def preprocess(img, dist='transporter'): - """Pre-process input (subtract mean, divide by std).""" - - transporter_color_mean = [0.18877631, 0.18877631, 0.18877631] - transporter_color_std = [0.07276466, 0.07276466, 0.07276466] - transporter_depth_mean = 0.00509261 - transporter_depth_std = 0.00903967 - - franka_color_mean = [0.622291933, 0.628313992, 0.623031488] - franka_color_std = [0.168154213, 0.17626014, 0.184527364] - franka_depth_mean = 0.872146842 - franka_depth_std = 0.195743116 - - clip_color_mean = [0.48145466, 0.4578275, 0.40821073] - clip_color_std = [0.26862954, 0.26130258, 0.27577711] - - # choose distribution - if dist == 'clip': - color_mean = clip_color_mean - color_std = clip_color_std - elif dist == 'mdetr': - color_mean = [0.485, 0.456, 0.406] - color_std = [0.229, 0.224, 0.225] - elif dist == 'franka': - color_mean = franka_color_mean - color_std = franka_color_std - else: - color_mean = transporter_color_mean - color_std = transporter_color_std - - if dist == 'franka': - depth_mean = franka_depth_mean - depth_std = franka_depth_std - else: - depth_mean = transporter_depth_mean - depth_std = transporter_depth_std - - # convert to pytorch tensor (if required) - if type(img) == torch.Tensor: - def cast_shape(stat, img): - tensor = torch.from_numpy(np.array(stat)).to(device=img.device, dtype=img.dtype) - tensor = tensor.unsqueeze(0).unsqueeze(-1).unsqueeze(-1) - tensor = tensor.repeat(img.shape[0], 1, img.shape[-2], img.shape[-1]) - return tensor - - color_mean = cast_shape(color_mean, img) - color_std = cast_shape(color_std, img) - depth_mean = cast_shape(depth_mean, img) - depth_std = cast_shape(depth_std, img) - - # normalize - img = img.clone() - img[:, :3, :, :] = ((img[:, :3, :, :] / 255 - color_mean) / color_std) - img[:, 3:, :, :] = ((img[:, 3:, :, :] - depth_mean) / depth_std) - else: - # normalize - img[:, :, :3] = (img[:, :, :3] / 255 - color_mean) / color_std - img[:, :, 3:] = (img[:, :, 3:] - depth_mean) / depth_std - - # if dist == 'franka' or dist == 'transporter': - # print(np.mean(img[:,:3,:,:].detach().cpu().numpy(), axis=(0,2,3)), - # np.mean(img[:,3,:,:].detach().cpu().numpy())) - - return img - -def map_kit_scale(scale): - return (scale[0] / 10, scale[1] / 10, scale[2] / 10) - -def deprocess(img): - color_mean = 0.18877631 - depth_mean = 0.00509261 - color_std = 0.07276466 - depth_std = 0.00903967 - - img[:, :, :3] = np.uint8(((img[:, :, :3] * color_std) + color_mean) * 255) - img[:, :, 3:] = np.uint8(((img[:, :, 3:] * depth_std) + depth_mean) * 255) - return img - - -def get_fused_heightmap(obs, configs, bounds, pix_size): - """Reconstruct orthographic heightmaps with segmentation masks.""" - heightmaps, colormaps = reconstruct_heightmaps( - obs['color'], obs['depth'], configs, bounds, pix_size) - colormaps = np.float32(colormaps) - heightmaps = np.float32(heightmaps) - - # Fuse maps from different views. - valid = np.sum(colormaps, axis=3) > 0 - repeat = np.sum(valid, axis=0) - repeat[repeat == 0] = 1 - cmap = np.sum(colormaps, axis=0) / repeat[Ellipsis, None] - cmap = np.uint8(np.round(cmap)) - hmap = np.max(heightmaps, axis=0) # Max to handle occlusions. - return cmap, hmap - - -def get_image_transform(theta, trans, pivot=(0, 0)): - """Compute composite 2D rigid transformation matrix.""" - # Get 2D rigid transformation matrix that rotates an image by theta (in - # radians) around pivot (in pixels) and translates by trans vector (in - # pixels) - pivot_t_image = np.array([[1., 0., -pivot[0]], [0., 1., -pivot[1]], - [0., 0., 1.]]) - image_t_pivot = np.array([[1., 0., pivot[0]], [0., 1., pivot[1]], - [0., 0., 1.]]) - transform = np.array([[np.cos(theta), -np.sin(theta), trans[0]], - [np.sin(theta), np.cos(theta), trans[1]], [0., 0., 1.]]) - return np.dot(image_t_pivot, np.dot(transform, pivot_t_image)) - - -def check_transform(image, pixel, transform): - """Valid transform only if pixel locations are still in FoV after transform.""" - new_pixel = np.flip( - np.int32( - np.round( - np.dot(transform, - np.float32([pixel[1], pixel[0], - 1.]).reshape(3, 1))))[:2].squeeze()) - valid = np.all( - new_pixel >= 0 - ) and new_pixel[0] < image.shape[0] and new_pixel[1] < image.shape[1] - return valid, new_pixel - - -def get_se3_from_image_transform(theta, trans, pivot, heightmap, bounds, - pixel_size): - """Calculate SE3 from image transform.""" - position_center = pix_to_xyz( - np.flip(np.int32(np.round(pivot))), - heightmap, - bounds, - pixel_size, - skip_height=False) - new_position_center = pix_to_xyz( - np.flip(np.int32(np.round(pivot + trans))), - heightmap, - bounds, - pixel_size, - skip_height=True) - # Don't look up the z height, it might get augmented out of frame - new_position_center = (new_position_center[0], new_position_center[1], - position_center[2]) - - delta_position = np.array(new_position_center) - np.array(position_center) - - t_world_center = np.eye(4) - t_world_center[0:3, 3] = np.array(position_center) - - t_centernew_center = np.eye(4) - euler_zxy = (-theta, 0, 0) - t_centernew_center[0:3, 0:3] = euler.euler2mat( - *euler_zxy, axes='szxy')[0:3, 0:3] - - t_centernew_center_tonly = np.eye(4) - t_centernew_center_tonly[0:3, 3] = -delta_position - t_centernew_center = t_centernew_center @ t_centernew_center_tonly - - t_world_centernew = t_world_center @ np.linalg.inv(t_centernew_center) - return t_world_center, t_world_centernew - - -def get_random_image_transform_params(image_size, theta_sigma=60): - theta = np.random.normal(0, np.deg2rad(theta_sigma)) - - trans_sigma = np.min(image_size) / 6 - trans = np.random.normal(0, trans_sigma, size=2) # [x, y] - pivot = (image_size[1] / 2, image_size[0] / 2) - return theta, trans, pivot - - -def q_mult(q1, q2): - w1, x1, y1, z1 = q1 - w2, x2, y2, z2 = q2 - w = w1 * w2 - x1 * x2 - y1 * y2 - z1 * z2 - x = w1 * x2 + x1 * w2 + y1 * z2 - z1 * y2 - y = w1 * y2 + y1 * w2 + z1 * x2 - x1 * z2 - z = w1 * z2 + z1 * w2 + x1 * y2 - y1 * x2 - return (w, x, y, z) - -def perturb(input_image, pixels, theta_sigma=60, add_noise=False): - """Data augmentation on images.""" - image_size = input_image.shape[:2] - - # Compute random rigid transform. - while True: - theta, trans, pivot = get_random_image_transform_params(image_size, theta_sigma=theta_sigma) - transform = get_image_transform(theta, trans, pivot) - transform_params = theta, trans, pivot - - # Ensure pixels remain in the image after transform. - is_valid = True - new_pixels = [] - new_rounded_pixels = [] - for pixel in pixels: - pixel = np.float32([pixel[1], pixel[0], 1.]).reshape(3, 1) - - rounded_pixel = np.int32(np.round(transform @ pixel))[:2].squeeze() - rounded_pixel = np.flip(rounded_pixel) - - pixel = (transform @ pixel)[:2].squeeze() - pixel = np.flip(pixel) - - in_fov_rounded = rounded_pixel[0] < image_size[0] and rounded_pixel[ - 1] < image_size[1] - in_fov = pixel[0] < image_size[0] and pixel[1] < image_size[1] - - is_valid = is_valid and np.all(rounded_pixel >= 0) and np.all( - pixel >= 0) and in_fov_rounded and in_fov - - new_pixels.append(pixel) - new_rounded_pixels.append(rounded_pixel) - if is_valid: - break - - # Apply rigid transform to image and pixel labels. - input_image = cv2.warpAffine( - input_image, - transform[:2, :], (image_size[1], image_size[0]), - flags=cv2.INTER_LINEAR) - - # Apply noise - color = np.int32(input_image[:,:,:3]) - depth = np.float32(input_image[:,:,3:]) - - if add_noise: - color += np.int32(np.random.normal(0, 3, image_size + (3,))) - color = np.uint8(np.clip(color, 0, 255)) - - depth += np.float32(np.random.normal(0, 0.003, image_size + (3,))) - - input_image = np.concatenate((color, depth), axis=2) - - # length of 5 - transform_params = np.array([theta, trans[0], trans[1], pivot[0], pivot[1]]) - return input_image, new_pixels, new_rounded_pixels, transform_params - - -def apply_perturbation(input_image, transform_params): - '''Apply data augmentation with specific transform params''' - image_size = input_image.shape[:2] - - # Apply rigid transform to image and pixel labels. - theta, trans, pivot = transform_params[0], transform_params[1:3], transform_params[3:5] - transform = get_image_transform(theta, trans, pivot) - - input_image = cv2.warpAffine( - input_image, - transform[:2, :], (image_size[1], image_size[0]), - flags=cv2.INTER_LINEAR) - return input_image - - -class ImageRotator: - """Rotate for n rotations.""" - # Reference: https://kornia.readthedocs.io/en/latest/tutorials/warp_affine.html?highlight=rotate - - def __init__(self, n_rotations): - self.angles = [] - for i in range(n_rotations): - theta = i * 2 * 180 / n_rotations - self.angles.append(theta) - - def __call__(self, x_list, pivot, reverse=False): - rot_x_list = [] - for i, angle in enumerate(self.angles): - x = x_list[i]# .unsqueeze(0) - # create transformation (rotation) - size = len(x) - alpha = angle if not reverse else (-1.0 * angle) # in degrees - angle = torch.ones(size) * alpha - - # define the rotation center - if type(pivot) is not torch.Tensor: - center = torch.FloatTensor(pivot)[...,[1,0]] - center = center.view(1,-1).repeat((size,1)) - else: - center = pivot[...,[1,0]].view(1,-1).clone().to(angle.device) - # center: torch.tensor = torch.ones(size, 2) - # center[..., 0] = int(pivot[1]) - # center[..., 1] = int(pivot[0]) - - # define the scale factor - scale = torch.ones(size, 2) - - # # compute the transformation matrix - M = kornia.geometry.get_rotation_matrix2d(center, angle, scale) - # x_warped = torchvision.transforms.functional.affine(x.float(), scale=1., - # center=[int(pivot[1]),int(pivot[0])], - # angle=alpha, translate=[0,0], shear=0, - # interpolation= torchvision.transforms.InterpolationMode.BILINEAR) - - - # apply the transformation to original image - # M = M.repeat(len(x), 1, 1) - _, _, h, w = x.shape - x_warped = kornia.geometry.transform.warp_affine(x.float(), M.to(x.device), dsize=(h, w)) - x_warped = x_warped - rot_x_list.append(x_warped) - - return rot_x_list - -# KD Tree Utils -# Construct K-D Tree to roughly estimate how many objects can fit inside the box. -class TreeNode: - - def __init__(self, parent, children, bbox): - self.parent = parent - self.children = children - self.bbox = bbox # min x, min y, min z, max x, max y, max z - -def KDTree(node, min_object_dim, margin, bboxes): - size = node.bbox[3:] - node.bbox[:3] - - # Choose which axis to split. - split = size > 2 * min_object_dim - if np.sum(split) == 0: - bboxes.append(node.bbox) - return - split = np.float32(split) / np.sum(split) - split_axis = np.random.choice(range(len(split)), 1, p=split)[0] - - # Split along chosen axis and create 2 children - cut_ind = np.random.rand() * \ - (size[split_axis] - 2 * min_object_dim) + \ - node.bbox[split_axis] + min_object_dim - child1_bbox = node.bbox.copy() - child1_bbox[3 + split_axis] = cut_ind - margin / 2. - child2_bbox = node.bbox.copy() - child2_bbox[split_axis] = cut_ind + margin / 2. - node.children = [ - TreeNode(node, [], bbox=child1_bbox), - TreeNode(node, [], bbox=child2_bbox) - ] - KDTree(node.children[0], min_object_dim, margin, bboxes) - KDTree(node.children[1], min_object_dim, margin, bboxes) - -# ----------------------------------------------------------------------------- -# Shape Name UTILS -# ----------------------------------------------------------------------------- -google_seen_obj_shapes = { - 'train': [ - 'alarm clock', - 'android toy', - 'black boot with leopard print', - 'black fedora', - 'black razer mouse', - 'black sandal', - 'black shoe with orange stripes', - 'bull figure', - 'butterfinger chocolate', - 'c clamp', - 'can opener', - 'crayon box', - 'dog statue', - 'frypan', - 'green and white striped towel', - 'grey soccer shoe with cleats', - 'hard drive', - 'honey dipper', - 'magnifying glass', - 'mario figure', - 'nintendo 3ds', - 'nintendo cartridge', - 'office depot box', - 'orca plush toy', - 'pepsi gold caffeine free box', - 'pepsi wild cherry box', - 'porcelain cup', - 'purple tape', - 'red and white flashlight', - 'rhino figure', - 'rocket racoon figure', - 'scissors', - 'silver tape', - 'spatula with purple head', - 'spiderman figure', - 'tablet', - 'toy school bus', - ], - 'val': [ - 'ball puzzle', - 'black and blue sneakers', - 'black shoe with green stripes', - 'brown fedora', - 'dinosaur figure', - 'hammer', - 'light brown boot with golden laces', - 'lion figure', - 'pepsi max box', - 'pepsi next box', - 'porcelain salad plate', - 'porcelain spoon', - 'red and white striped towel', - 'red cup', - 'screwdriver', - 'toy train', - 'unicorn toy', - 'white razer mouse', - 'yoshi figure' - ], - 'test': [ - 'ball puzzle', - 'black and blue sneakers', - 'black shoe with green stripes', - 'brown fedora', - 'dinosaur figure', - 'hammer', - 'light brown boot with golden laces', - 'lion figure', - 'pepsi max box', - 'pepsi next box', - 'porcelain salad plate', - 'porcelain spoon', - 'red and white striped towel', - 'red cup', - 'screwdriver', - 'toy train', - 'unicorn toy', - 'white razer mouse', - 'yoshi figure' - ], - } - -google_unseen_obj_shapes = { - 'train': [ - 'alarm clock', - 'android toy', - 'black boot with leopard print', - 'black fedora', - 'black razer mouse', - 'black sandal', - 'black shoe with orange stripes', - 'bull figure', - 'butterfinger chocolate', - 'c clamp', - 'can opener', - 'crayon box', - 'dog statue', - 'frypan', - 'green and white striped towel', - 'grey soccer shoe with cleats', - 'hard drive', - 'honey dipper', - 'magnifying glass', - 'mario figure', - 'nintendo 3ds', - 'nintendo cartridge', - 'office depot box', - 'orca plush toy', - 'pepsi gold caffeine free box', - 'pepsi wild cherry box', - 'porcelain cup', - 'purple tape', - 'red and white flashlight', - 'rhino figure', - 'rocket racoon figure', - 'scissors', - 'silver tape', - 'spatula with purple head', - 'spiderman figure', - 'tablet', - 'toy school bus', - ], - 'val': [ - 'ball puzzle', - 'black and blue sneakers', - 'black shoe with green stripes', - 'brown fedora', - 'dinosaur figure', - 'hammer', - 'light brown boot with golden laces', - 'lion figure', - 'pepsi max box', - 'pepsi next box', - 'porcelain salad plate', - 'porcelain spoon', - 'red and white striped towel', - 'red cup', - 'screwdriver', - 'toy train', - 'unicorn toy', - 'white razer mouse', - 'yoshi figure' - ], - 'test': [ - 'ball puzzle', - 'black and blue sneakers', - 'black shoe with green stripes', - 'brown fedora', - 'dinosaur figure', - 'hammer', - 'light brown boot with golden laces', - 'lion figure', - 'pepsi max box', - 'pepsi next box', - 'porcelain salad plate', - 'porcelain spoon', - 'red and white striped towel', - 'red cup', - 'screwdriver', - 'toy train', - 'unicorn toy', - 'white razer mouse', - 'yoshi figure' - ], - } - -google_all_shapes = { - 'train': [ - 'alarm clock', - 'android toy', - 'ball puzzle', - 'black and blue sneakers', - 'black boot with leopard print', - 'black fedora', - 'black razer mouse', - 'black sandal', - 'black shoe with green stripes', - 'black shoe with orange stripes', - 'brown fedora', - 'bull figure', - 'butterfinger chocolate', - 'c clamp', - 'can opener', - 'crayon box', - 'dinosaur figure', - 'dog statue', - 'frypan', - 'green and white striped towel', - 'grey soccer shoe with cleats', - 'hammer', - 'hard drive', - 'honey dipper', - 'light brown boot with golden laces', - 'lion figure', - 'magnifying glass', - 'mario figure', - 'nintendo 3ds', - 'nintendo cartridge', - 'office depot box', - 'orca plush toy', - 'pepsi gold caffeine free box', - 'pepsi max box', - 'pepsi next box', - 'pepsi wild cherry box', - 'porcelain cup', - 'porcelain salad plate', - 'porcelain spoon', - 'purple tape', - 'red and white flashlight', - 'red and white striped towel', - 'red cup', - 'rhino figure', - 'rocket racoon figure', - 'scissors', - 'screwdriver', - 'silver tape', - 'spatula with purple head', - 'spiderman figure', - 'tablet', - 'toy school bus', - 'toy train', - 'unicorn toy', - 'white razer mouse', - 'yoshi figure', - ], - 'val': [ - 'alarm clock', - 'android toy', - 'ball puzzle', - 'black and blue sneakers', - 'black boot with leopard print', - 'black fedora', - 'black razer mouse', - 'black sandal', - 'black shoe with green stripes', - 'black shoe with orange stripes', - 'brown fedora', - 'bull figure', - 'butterfinger chocolate', - 'c clamp', - 'can opener', - 'crayon box', - 'dinosaur figure', - 'dog statue', - 'frypan', - 'green and white striped towel', - 'grey soccer shoe with cleats', - 'hammer', - 'hard drive', - 'honey dipper', - 'light brown boot with golden laces', - 'lion figure', - 'magnifying glass', - 'mario figure', - 'nintendo 3ds', - 'nintendo cartridge', - 'office depot box', - 'orca plush toy', - 'pepsi gold caffeine free box', - 'pepsi max box', - 'pepsi next box', - 'pepsi wild cherry box', - 'porcelain cup', - 'porcelain salad plate', - 'porcelain spoon', - 'purple tape', - 'red and white flashlight', - 'red and white striped towel', - 'red cup', - 'rhino figure', - 'rocket racoon figure', - 'scissors', - 'screwdriver', - 'silver tape', - 'spatula with purple head', - 'spiderman figure', - 'tablet', - 'toy school bus', - 'toy train', - 'unicorn toy', - 'white razer mouse', - 'yoshi figure', - ], - 'test': [ - 'alarm clock', - 'android toy', - 'ball puzzle', - 'black and blue sneakers', - 'black boot with leopard print', - 'black fedora', - 'black razer mouse', - 'black sandal', - 'black shoe with green stripes', - 'black shoe with orange stripes', - 'brown fedora', - 'bull figure', - 'butterfinger chocolate', - 'c clamp', - 'can opener', - 'crayon box', - 'dinosaur figure', - 'dog statue', - 'frypan', - 'green and white striped towel', - 'grey soccer shoe with cleats', - 'hammer', - 'hard drive', - 'honey dipper', - 'light brown boot with golden laces', - 'lion figure', - 'magnifying glass', - 'mario figure', - 'nintendo 3ds', - 'nintendo cartridge', - 'office depot box', - 'orca plush toy', - 'pepsi gold caffeine free box', - 'pepsi max box', - 'pepsi next box', - 'pepsi wild cherry box', - 'porcelain cup', - 'porcelain salad plate', - 'porcelain spoon', - 'purple tape', - 'red and white flashlight', - 'red and white striped towel', - 'red cup', - 'rhino figure', - 'rocket racoon figure', - 'scissors', - 'screwdriver', - 'silver tape', - 'spatula with purple head', - 'spiderman figure', - 'tablet', - 'toy school bus', - 'toy train', - 'unicorn toy', - 'white razer mouse', - 'yoshi figure', - ], - } -assembling_kit_shapes = { - 0: "letter R shape", - 1: "letter A shape", - 2: "triangle", - 3: "square", - 4: "plus", - 5: "letter T shape", - 6: "diamond", - 7: "pentagon", - 8: "rectangle", - 9: "flower", - 10: "star", - 11: "circle", - 12: "letter G shape", - 13: "letter V shape", - 14: "letter E shape", - 15: "letter L shape", - 16: "ring", - 17: "hexagon", - 18: "heart", - 19: "letter M shape", - } - -# ----------------------------------------------------------------------------- -# COLOR AND PLOT UTILS -# ----------------------------------------------------------------------------- - - -# Colors (Tableau palette). -COLORS = { - 'blue': [78.0 / 255.0, 121.0 / 255.0, 167.0 / 255.0], - 'red': [255.0 / 255.0, 087.0 / 255.0, 089.0 / 255.0], - 'green': [089.0 / 255.0, 169.0 / 255.0, 078.0 / 255.0], - 'orange': [242.0 / 255.0, 142.0 / 255.0, 043.0 / 255.0], - 'yellow': [237.0 / 255.0, 201.0 / 255.0, 072.0 / 255.0], - 'purple': [176.0 / 255.0, 122.0 / 255.0, 161.0 / 255.0], - 'pink': [255.0 / 255.0, 157.0 / 255.0, 167.0 / 255.0], - 'cyan': [118.0 / 255.0, 183.0 / 255.0, 178.0 / 255.0], - 'brown': [156.0 / 255.0, 117.0 / 255.0, 095.0 / 255.0], - 'white': [255.0 / 255.0, 255.0 / 255.0, 255.0 / 255.0], - 'gray': [186.0 / 255.0, 176.0 / 255.0, 172.0 / 255.0], - 'indigo': [75.0 / 255.0, 0.0 / 255.0, 130.0 / 255.0], - 'violet': [143.0 / 255.0, 0.0 / 255.0, 255.0 / 255.0], - 'black': [0.0 / 255.0, 0.0 / 255.0, 0.0 / 255.0], - 'silver': [192.0 / 255.0, 192.0 / 255.0, 192.0 / 255.0], - 'gold': [255.0 / 255.0, 215.0 / 255.0, 0.0 / 255.0], - -} - -COLORS_NAMES = list(COLORS.keys()) -TRAIN_COLORS = ['blue', 'red', 'green', 'yellow', 'brown', 'gray', 'cyan'] -EVAL_COLORS = ['blue', 'red', 'green', 'orange', 'purple', 'pink', 'white'] - - -def get_colors(mode, n_colors=-1, **kwargs): - all_color_names = get_colors_names(mode) - - if n_colors == -1: - all_color_names = all_color_names - else: - all_color_names = random.sample(all_color_names, n_colors) - return [COLORS[cn] for cn in all_color_names], all_color_names - -def get_colors_names(mode): - if mode == 'train': - return TRAIN_COLORS - elif mode == 'full': - return TRAIN_COLORS - else: - return TRAIN_COLORS - -def get_random_color(): - return get_colors(mode='train', n_colors=1) - -def solve_hanoi_all(n_disks): - # Solve Hanoi sequence with dynamic programming. - hanoi_steps = [] # [[object index, from rod, to rod], ...] - - def solve_hanoi(n, t0, t1, t2): - if n == 0: - hanoi_steps.append([n, t0, t1]) - return - solve_hanoi(n - 1, t0, t2, t1) - hanoi_steps.append([n, t0, t1]) - solve_hanoi(n - 1, t2, t1, t0) - - solve_hanoi(n_disks - 1, 0, 2, 1) - return hanoi_steps - -def plot(fname, # pylint: disable=dangerous-default-value - title, - ylabel, - xlabel, - data, - xlim=[-np.inf, 0], - xticks=None, - ylim=[np.inf, -np.inf], - show_std=True): - """Plot frame data.""" - # Data is a dictionary that maps experiment names to tuples with 3 - # elements: x (size N array) and y (size N array) and y_std (size N array) - - # Get data limits. - for name, (x, y, _) in data.items(): - del name - y = np.array(y) - xlim[0] = max(xlim[0], np.min(x)) - xlim[1] = max(xlim[1], np.max(x)) - ylim[0] = min(ylim[0], np.min(y)) - ylim[1] = max(ylim[1], np.max(y)) - - # Draw background. - plt.title(title, fontsize=14) - plt.ylim(ylim) - plt.ylabel(ylabel, fontsize=14) - plt.yticks(fontsize=14) - plt.xlim(xlim) - plt.xlabel(xlabel, fontsize=14) - plt.grid(True, linestyle='-', color=[0.8, 0.8, 0.8]) - ax = plt.gca() - for axis in ['top', 'bottom', 'left', 'right']: - ax.spines[axis].set_color('#000000') - plt.rcParams.update({'font.size': 14}) - plt.rcParams['mathtext.default'] = 'regular' - matplotlib.rcParams['pdf.fonttype'] = 42 - matplotlib.rcParams['ps.fonttype'] = 42 - - # Draw data. - color_iter = 0 - for name, (x, y, std) in data.items(): - del name - x, y, std = np.float32(x), np.float32(y), np.float32(std) - upper = np.clip(y + std, ylim[0], ylim[1]) - lower = np.clip(y - std, ylim[0], ylim[1]) - color = COLORS[list(COLORS.keys())[color_iter]] - if show_std: - plt.fill_between(x, upper, lower, color=color, linewidth=0, alpha=0.3) - plt.plot(x, y, color=color, linewidth=2, marker='o', alpha=1.) - color_iter += 1 - - if xticks: - plt.xticks(ticks=range(len(xticks)), labels=xticks, fontsize=14) - else: - plt.xticks(fontsize=14) - plt.legend([name for name, _ in data.items()], - loc='lower right', fontsize=14) - plt.tight_layout() - plt.savefig(fname) - plt.clf() - - -# ----------------------------------------------------------------------------- -# MESHCAT UTILS -# ----------------------------------------------------------------------------- - -def create_visualizer(clear=True): - print('Waiting for meshcat server... have you started a server?') - vis = meshcat.Visualizer(zmq_url='tcp://127.0.0.1:6000') - if clear: - vis.delete() - return vis - - -def make_frame(vis, name, h, radius, o=1.0): - """Add a red-green-blue triad to the Meschat visualizer. - - Args: - vis (MeshCat Visualizer): the visualizer - name (string): name for this frame (should be unique) - h (float): height of frame visualization - radius (float): radius of frame visualization - o (float): opacity - """ - vis[name]['x'].set_object( - g.Cylinder(height=h, radius=radius), - g.MeshLambertMaterial(color=0xff0000, reflectivity=0.8, opacity=o)) - rotate_x = mtf.rotation_matrix(np.pi / 2.0, [0, 0, 1]) - rotate_x[0, 3] = h / 2 - vis[name]['x'].set_transform(rotate_x) - - vis[name]['y'].set_object( - g.Cylinder(height=h, radius=radius), - g.MeshLambertMaterial(color=0x00ff00, reflectivity=0.8, opacity=o)) - rotate_y = mtf.rotation_matrix(np.pi / 2.0, [0, 1, 0]) - rotate_y[1, 3] = h / 2 - vis[name]['y'].set_transform(rotate_y) - - vis[name]['z'].set_object( - g.Cylinder(height=h, radius=radius), - g.MeshLambertMaterial(color=0x0000ff, reflectivity=0.8, opacity=o)) - rotate_z = mtf.rotation_matrix(np.pi / 2.0, [1, 0, 0]) - rotate_z[2, 3] = h / 2 - vis[name]['z'].set_transform(rotate_z) - - -def meshcat_visualize(vis, obs, act, info): - """Visualize data using meshcat.""" - - for key in sorted(info.keys()): - pose = info[key] - pick_transform = np.eye(4) - pick_transform[0:3, 3] = pose[0] - quaternion_wxyz = np.asarray( - [pose[1][3], pose[1][0], pose[1][1], pose[1][2]]) - pick_transform[0:3, 0:3] = mtf.quaternion_matrix(quaternion_wxyz)[0:3, 0:3] - label = 'obj_' + str(key) - make_frame(vis, label, h=0.05, radius=0.0012, o=1.0) - vis[label].set_transform(pick_transform) - - for cam_index in range(len(act['camera_config'])): - verts = unproject_depth_vectorized( - obs['depth'][cam_index], np.array([0, 1]), - np.array(act['camera_config'][cam_index]['intrinsics']).reshape(3, 3), - np.zeros(5)) - - # switch from [N,3] to [3,N] - verts = verts.T - - cam_transform = np.eye(4) - cam_transform[0:3, 3] = act['camera_config'][cam_index]['position'] - quaternion_xyzw = act['camera_config'][cam_index]['rotation'] - quaternion_wxyz = np.asarray([ - quaternion_xyzw[3], quaternion_xyzw[0], quaternion_xyzw[1], - quaternion_xyzw[2] - ]) - cam_transform[0:3, 0:3] = mtf.quaternion_matrix(quaternion_wxyz)[0:3, 0:3] - verts = apply_transform(cam_transform, verts) - - colors = obs['color'][cam_index].reshape(-1, 3).T / 255.0 - - vis['pointclouds/' + str(cam_index)].set_object( - g.PointCloud(position=verts, color=colors)) - - -# ----------------------------------------------------------------------------- -# CONFIG UTILS -# ----------------------------------------------------------------------------- - -def set_seed(seed, torch=False): - random.seed(seed) - os.environ['PYTHONHASHSEED'] = str(seed) - np.random.seed(seed) - - if torch: - import torch - torch.manual_seed(seed) - - -def load_cfg(yaml_path): - with open(yaml_path, 'r') as f: - data = yaml.safe_load(f) - return data - - -def load_hydra_config(config_path): - return OmegaConf.load(config_path) diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/detr/README.md b/spaces/Gradio-Blocks/uniformer_image_detection/configs/detr/README.md deleted file mode 100644 index 711a308a5549b28c36515405feabf2ca0f7c7c1f..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/detr/README.md +++ /dev/null @@ -1,27 +0,0 @@ -# DETR - -## Introduction - -[ALGORITHM] - -We provide the config files for DETR: [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872). - -```BibTeX -@inproceedings{detr, - author = {Nicolas Carion and - Francisco Massa and - Gabriel Synnaeve and - Nicolas Usunier and - Alexander Kirillov and - Sergey Zagoruyko}, - title = {End-to-End Object Detection with Transformers}, - booktitle = {ECCV}, - year = {2020} -} -``` - -## Results and Models - -| Backbone | Model | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | -|:------:|:--------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| -| R-50 | DETR |150e |7.9| | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detr/detr_r50_8x2_150e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835.log.json) | diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/vfnet/vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/vfnet/vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py deleted file mode 100644 index 8da3122657adc2785129c28a84473c25777abba3..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/vfnet/vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py +++ /dev/null @@ -1,16 +0,0 @@ -_base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py' -model = dict( - pretrained='open-mmlab://res2net101_v1d_26w_4s', - backbone=dict( - type='Res2Net', - depth=101, - scales=4, - base_width=26, - num_stages=4, - out_indices=(0, 1, 2, 3), - frozen_stages=1, - norm_cfg=dict(type='BN', requires_grad=True), - norm_eval=True, - style='pytorch', - dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), - stage_with_dcn=(False, True, True, True))) diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/fcn/fcn_r50-d8_480x480_40k_pascal_context.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/fcn/fcn_r50-d8_480x480_40k_pascal_context.py deleted file mode 100644 index 7c57a6f8ff0a7dbb18666c1b9c882da10e586aa3..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/fcn/fcn_r50-d8_480x480_40k_pascal_context.py +++ /dev/null @@ -1,9 +0,0 @@ -_base_ = [ - '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/pascal_context.py', - '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' -] -model = dict( - decode_head=dict(num_classes=60), - auxiliary_head=dict(num_classes=60), - test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320))) -optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001) diff --git a/spaces/GroveStreet/GTA_SOVITS/vencoder/dphubert/__init__.py b/spaces/GroveStreet/GTA_SOVITS/vencoder/dphubert/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/GuXiaoBei/wechat-chatbot/scripts/shutdown.sh b/spaces/GuXiaoBei/wechat-chatbot/scripts/shutdown.sh deleted file mode 100644 index c2bf6b14adcafd46e7278ab3730ab7f78b82c593..0000000000000000000000000000000000000000 --- a/spaces/GuXiaoBei/wechat-chatbot/scripts/shutdown.sh +++ /dev/null @@ -1,16 +0,0 @@ -#!/bin/bash - -#关闭服务 -cd `dirname $0`/.. -export BASE_DIR=`pwd` -pid=`ps ax | grep -i app.py | grep "${BASE_DIR}" | grep python3 | grep -v grep | awk '{print $1}'` -if [ -z "$pid" ] ; then - echo "No chatgpt-on-wechat running." - exit -1; -fi - -echo "The chatgpt-on-wechat(${pid}) is running..." - -kill ${pid} - -echo "Send shutdown request to chatgpt-on-wechat(${pid}) OK" diff --git a/spaces/HaoFeng2019/DocGeoNet/extractor.py b/spaces/HaoFeng2019/DocGeoNet/extractor.py deleted file mode 100644 index 2e242193b8e14be6c74f89afd20b7d11cd8b6d62..0000000000000000000000000000000000000000 --- a/spaces/HaoFeng2019/DocGeoNet/extractor.py +++ /dev/null @@ -1,115 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class ResidualBlock(nn.Module): - def __init__(self, in_planes, planes, norm_fn='group', stride=1): - super(ResidualBlock, self).__init__() - - self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, stride=stride) - self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1) - self.relu = nn.ReLU(inplace=True) - - num_groups = planes // 8 - - if norm_fn == 'group': - self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) - self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) - if not stride == 1: - self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) - - elif norm_fn == 'batch': - self.norm1 = nn.BatchNorm2d(planes) - self.norm2 = nn.BatchNorm2d(planes) - if not stride == 1: - self.norm3 = nn.BatchNorm2d(planes) - - elif norm_fn == 'instance': - self.norm1 = nn.InstanceNorm2d(planes) - self.norm2 = nn.InstanceNorm2d(planes) - if not stride == 1: - self.norm3 = nn.InstanceNorm2d(planes) - - elif norm_fn == 'none': - self.norm1 = nn.Sequential() - self.norm2 = nn.Sequential() - if not stride == 1: - self.norm3 = nn.Sequential() - - if stride == 1: - self.downsample = None - - else: - self.downsample = nn.Sequential( - nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3) - - - def forward(self, x): - y = x - y = self.relu(self.norm1(self.conv1(y))) - y = self.relu(self.norm2(self.conv2(y))) - - if self.downsample is not None: - x = self.downsample(x) - - return self.relu(x+y) - - -class BasicEncoder(nn.Module): - def __init__(self, input_dim=128, output_dim=128, norm_fn='batch'): - super(BasicEncoder, self).__init__() - self.norm_fn = norm_fn - - if self.norm_fn == 'group': - self.norm1 = nn.GroupNorm(num_groups=8, num_channels=64) - - elif self.norm_fn == 'batch': - self.norm1 = nn.BatchNorm2d(64) - - elif self.norm_fn == 'instance': - self.norm1 = nn.InstanceNorm2d(64) - - elif self.norm_fn == 'none': - self.norm1 = nn.Sequential() - - self.conv1 = nn.Conv2d(input_dim, 64, kernel_size=7, stride=2, padding=3) - self.relu1 = nn.ReLU(inplace=True) - - self.in_planes = 64 - self.layer1 = self._make_layer(64, stride=1) - self.layer2 = self._make_layer(128, stride=2) - self.layer3 = self._make_layer(192, stride=2) - - # output convolution - self.conv2 = nn.Conv2d(192, output_dim, kernel_size=1) - - for m in self.modules(): - if isinstance(m, nn.Conv2d): - nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') - elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)): - if m.weight is not None: - nn.init.constant_(m.weight, 1) - if m.bias is not None: - nn.init.constant_(m.bias, 0) - - def _make_layer(self, dim, stride=1): - layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride) - layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1) - layers = (layer1, layer2) - - self.in_planes = dim - return nn.Sequential(*layers) - - def forward(self, x): - x = self.conv1(x) - x = self.norm1(x) - x = self.relu1(x) - - x = self.layer1(x) - x = self.layer2(x) - x = self.layer3(x) - - x = self.conv2(x) - - return x diff --git a/spaces/HarryLee/eCommerceImageCaptioning/run_scripts/caption/train_caption_stage1_el.sh b/spaces/HarryLee/eCommerceImageCaptioning/run_scripts/caption/train_caption_stage1_el.sh deleted file mode 100644 index f12ee52c9d24fe296410da30b67e0ef5e9e76254..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/run_scripts/caption/train_caption_stage1_el.sh +++ /dev/null @@ -1,109 +0,0 @@ -#!/usr/bin/env - -# The port for communication. Note that if you want to run multiple tasks on the same machine, -# you need to specify different port numbers. -export MASTER_PORT=1051 - -log_dir=./stage1_logs -save_dir=./stage1_checkpoints -mkdir -p $log_dir $save_dir - -bpe_dir=../../utils/BPE -user_dir=../../ofa_module - -data_dir=../../dataset/caption_data -data=${data_dir}/caption_stage1_train.tsv,${data_dir}/caption_val.tsv -restore_file=../../checkpoints/ofa_large.pt -selected_cols=0,4,2 - -task=caption -arch=ofa_large -criterion=adjust_label_smoothed_encouraging_loss # for el -label_smoothing=0.1 -lr=1e-5 -max_epoch=5 -warmup_ratio=0.06 -batch_size=8 -update_freq=4 -resnet_drop_path_rate=0.0 -encoder_drop_path_rate=0.1 -decoder_drop_path_rate=0.1 -dropout=0.1 -attention_dropout=0.0 -max_src_length=80 -max_tgt_length=20 -num_bins=1000 -patch_image_size=480 -eval_cider_cached=${data_dir}/cider_cached_tokens/coco-valid-words.p -drop_worst_ratio=0.05 # modified from 0.2 for el -log_end=0.75 # for el -for max_epoch in {2,}; do - echo "max_epoch "${max_epoch} - for warmup_ratio in {0.06,}; do - echo "warmup_ratio "${warmup_ratio} - for drop_worst_after in {2500,}; do - echo "drop_worst_after "${drop_worst_after} - - log_file=${log_dir}/${max_epoch}"_"${warmup_ratio}"_"${drop_worst_after}_el${log_end}_".log" - save_path=${save_dir}/${max_epoch}"_"${warmup_ratio}"_"${drop_worst_after}_el${log_end}_ - mkdir -p $save_path - - CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m torch.distributed.launch --nproc_per_node=4 --master_port=${MASTER_PORT} ../../train.py \ - $data \ - --selected-cols=${selected_cols} \ - --bpe-dir=${bpe_dir} \ - --user-dir=${user_dir} \ - --restore-file=${restore_file} \ - --reset-optimizer --reset-dataloader --reset-meters \ - --save-dir=${save_path} \ - --task=${task} \ - --arch=${arch} \ - --criterion=${criterion} \ - --label-smoothing=${label_smoothing} \ - --batch-size=${batch_size} \ - --update-freq=${update_freq} \ - --encoder-normalize-before \ - --decoder-normalize-before \ - --share-decoder-input-output-embed \ - --share-all-embeddings \ - --layernorm-embedding \ - --patch-layernorm-embedding \ - --code-layernorm-embedding \ - --resnet-drop-path-rate=${resnet_drop_path_rate} \ - --encoder-drop-path-rate=${encoder_drop_path_rate} \ - --decoder-drop-path-rate=${decoder_drop_path_rate} \ - --dropout=${dropout} \ - --attention-dropout=${attention_dropout} \ - --weight-decay=0.01 --optimizer=adam --adam-betas="(0.9,0.999)" --adam-eps=1e-08 --clip-norm=1.0 \ - --lr-scheduler=polynomial_decay --lr=${lr} \ - --max-epoch=${max_epoch} --warmup-ratio=${warmup_ratio} \ - --log-format=simple --log-interval=10 \ - --fixed-validation-seed=7 \ - --no-epoch-checkpoints --keep-best-checkpoints=1 \ - --save-interval=1 --validate-interval=1 \ - --save-interval-updates=500 --validate-interval-updates=500 \ - --eval-cider \ - --eval-cider-cached-tokens=${eval_cider_cached} \ - --eval-args='{"beam":5,"max_len_b":16,"no_repeat_ngram_size":3}' \ - --best-checkpoint-metric=cider --maximize-best-checkpoint-metric \ - --max-src-length=${max_src_length} \ - --max-tgt-length=${max_tgt_length} \ - --find-unused-parameters \ - --freeze-encoder-embedding \ - --freeze-decoder-embedding \ - --add-type-embedding \ - --scale-attn \ - --scale-fc \ - --scale-heads \ - --disable-entangle \ - --num-bins=${num_bins} \ - --patch-image-size=${patch_image_size} \ - --drop-worst-ratio=${drop_worst_ratio} \ - --drop-worst-after=${drop_worst_after} \ - --log-end ${log_end} \ - --fp16 \ - --fp16-scale-window=512 \ - --num-workers=0 > ${log_file} 2>&1 - done - done -done diff --git a/spaces/Harveenchadha/Hindi_TTS/vakyansh_tts/scripts/train_hifi.sh b/spaces/Harveenchadha/Hindi_TTS/vakyansh_tts/scripts/train_hifi.sh deleted file mode 100644 index 6955a6e0f07777c1db68eae0e25bb48900adb70d..0000000000000000000000000000000000000000 --- a/spaces/Harveenchadha/Hindi_TTS/vakyansh_tts/scripts/train_hifi.sh +++ /dev/null @@ -1,10 +0,0 @@ -python ../src/hifi_gan/train.py \ - --config '' \ - --input_wavs_dir '' \ - --input_mels_dir '' \ - --input_training_file '' \ - --input_validation_file '' \ - --checkpoint_path '' \ - --logs_path '' \ - --checkpoint_interval 10000 \ - --stdout_interval 50 diff --git a/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/src/glow_tts/utils.py b/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/src/glow_tts/utils.py deleted file mode 100644 index a591aa319ccb264110111cda55c4a232b41aae74..0000000000000000000000000000000000000000 --- a/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/src/glow_tts/utils.py +++ /dev/null @@ -1,282 +0,0 @@ -import os -import glob -import sys -import argparse -import logging -import json -import subprocess -import numpy as np -from scipy.io.wavfile import read -import torch - -MATPLOTLIB_FLAG = False - -logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) -logger = logging - - -def load_checkpoint(checkpoint_path, model, optimizer=None): - assert os.path.isfile(checkpoint_path) - checkpoint_dict = torch.load(checkpoint_path, map_location="cpu") - iteration = 1 - if "iteration" in checkpoint_dict.keys(): - iteration = checkpoint_dict["iteration"] - if "learning_rate" in checkpoint_dict.keys(): - learning_rate = checkpoint_dict["learning_rate"] - if optimizer is not None and "optimizer" in checkpoint_dict.keys(): - optimizer.load_state_dict(checkpoint_dict["optimizer"]) - saved_state_dict = checkpoint_dict["model"] - if hasattr(model, "module"): - state_dict = model.module.state_dict() - else: - state_dict = model.state_dict() - new_state_dict = {} - for k, v in state_dict.items(): - try: - new_state_dict[k] = saved_state_dict[k] - except: - logger.info("%s is not in the checkpoint" % k) - new_state_dict[k] = v - if hasattr(model, "module"): - model.module.load_state_dict(new_state_dict) - else: - model.load_state_dict(new_state_dict) - logger.info( - "Loaded checkpoint '{}' (iteration {})".format(checkpoint_path, iteration) - ) - return model, optimizer, learning_rate, iteration - - -def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path): - logger.info( - "Saving model and optimizer state at iteration {} to {}".format( - iteration, checkpoint_path - ) - ) - if hasattr(model, "module"): - state_dict = model.module.state_dict() - else: - state_dict = model.state_dict() - torch.save( - { - "model": state_dict, - "iteration": iteration, - "optimizer": optimizer.state_dict(), - "learning_rate": learning_rate, - }, - checkpoint_path, - ) - - -def summarize(writer, global_step, scalars={}, histograms={}, images={}): - for k, v in scalars.items(): - writer.add_scalar(k, v, global_step) - for k, v in histograms.items(): - writer.add_histogram(k, v, global_step) - for k, v in images.items(): - writer.add_image(k, v, global_step, dataformats="HWC") - - -def latest_checkpoint_path(dir_path, regex="G_*.pth"): - 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] - print(x) - return x - - -def plot_spectrogram_to_numpy(spectrogram): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger("matplotlib") - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots() - im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none") - plt.colorbar(im, ax=ax) - plt.xlabel("Frames") - plt.ylabel("Channels") - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="") - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def plot_alignment_to_numpy(alignment, info=None): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger("matplotlib") - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(6, 4)) - im = ax.imshow(alignment, aspect="auto", origin="lower", interpolation="none") - fig.colorbar(im, ax=ax) - xlabel = "Decoder timestep" - if info is not None: - xlabel += "\n\n" + info - plt.xlabel(xlabel) - plt.ylabel("Encoder timestep") - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="") - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def load_wav_to_torch(full_path): - sampling_rate, data = read(full_path) - return torch.FloatTensor(data.astype(np.float32)), sampling_rate - - -def load_filepaths_and_text(filename, split="|"): - with open(filename, encoding="utf-8") as f: - filepaths_and_text = [line.strip().split(split) for line in f] - return filepaths_and_text - - -def get_hparams(init=True): - parser = argparse.ArgumentParser() - parser.add_argument("-c", "--config", type=str, help="JSON file for configuration") - parser.add_argument("-m", "--model", type=str, help="Model name") - # parser.add_argument('-g', '--gan', type=str, - # help='Model name') - parser.add_argument("-l", "--logs", type=str, help="logs name") - # parser.add_argument('-s', '--mels', type=str, - # help='logs name') - - args = parser.parse_args() - # model_dir = os.path.join("./logs", args.model) - model_dir = args.model - if not os.path.exists(model_dir): - os.makedirs(model_dir) - - config_path = args.config - config_save_path = os.path.join(model_dir, "config.json") - - # if not config_path : config_path = config_save_path - - if init: - with open(config_path, "r") as f: - data = f.read() - with open(config_save_path, "w") as f: - f.write(data) - else: - with open(config_save_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - hparams.model_dir = model_dir - hparams.log_dir = args.logs - # hparams.mels_dir = args.mels - # hparams.gan_dir = args.gan - return hparams - - -def get_hparams_from_dir(model_dir): - config_save_path = os.path.join(model_dir, "config.json") - with open(config_save_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - hparams.model_dir = model_dir - return hparams - - -def get_hparams_from_file(config_path): - with open(config_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - return hparams - - -def check_git_hash(model_dir): - source_dir = os.path.dirname(os.path.realpath(__file__)) - if not os.path.exists(os.path.join(source_dir, ".git")): - logger.warn( - "{} is not a git repository, therefore hash value comparison will be ignored.".format( - source_dir - ) - ) - return - - cur_hash = subprocess.getoutput("git rev-parse HEAD") - - path = os.path.join(model_dir, "githash") - if os.path.exists(path): - saved_hash = open(path).read() - if saved_hash != cur_hash: - logger.warn( - "git hash values are different. {}(saved) != {}(current)".format( - saved_hash[:8], cur_hash[:8] - ) - ) - else: - open(path, "w").write(cur_hash) - - -def get_logger(model_dir, filename="train.log"): - global logger - logger = logging.getLogger(os.path.basename(model_dir)) - logger.setLevel(logging.DEBUG) - - formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s") - if not os.path.exists(model_dir): - os.makedirs(model_dir) - h = logging.FileHandler(os.path.join(model_dir, filename)) - h.setLevel(logging.DEBUG) - h.setFormatter(formatter) - logger.addHandler(h) - return logger - - -class HParams: - def __init__(self, **kwargs): - for k, v in kwargs.items(): - if type(v) == dict: - v = HParams(**v) - self[k] = v - - def keys(self): - return self.__dict__.keys() - - def items(self): - return self.__dict__.items() - - def values(self): - return self.__dict__.values() - - def __len__(self): - return len(self.__dict__) - - def __getitem__(self, key): - return getattr(self, key) - - def __setitem__(self, key, value): - return setattr(self, key, value) - - def __contains__(self, key): - return key in self.__dict__ - - def __repr__(self): - return self.__dict__.__repr__() diff --git a/spaces/Harveenchadha/oiTrans/scripts/concat_joint_data.py b/spaces/Harveenchadha/oiTrans/scripts/concat_joint_data.py deleted file mode 100644 index f1496177b0f47869e8e58ebdb0395c2c457e300a..0000000000000000000000000000000000000000 --- a/spaces/Harveenchadha/oiTrans/scripts/concat_joint_data.py +++ /dev/null @@ -1,130 +0,0 @@ -import os -from tqdm import tqdm -import sys - -LANGS = [ - "as", - "bn", - "gu", - "hi", - "kn", - "ml", - "mr", - "or", - "pa", - "ta", - "te", - #"ur" -] - - -def add_token(sent, tag_infos): - """ add special tokens specified by tag_infos to each element in list - - tag_infos: list of tuples (tag_type,tag) - - each tag_info results in a token of the form: __{tag_type}__{tag}__ - - """ - - tokens = [] - for tag_type, tag in tag_infos: - token = '__' + tag_type + '__' + tag + '__' - tokens.append(token) - - return ' '.join(tokens) + ' ' + sent - - -def concat_data(data_dir, outdir, lang_pair_list, - out_src_lang='SRC', out_trg_lang='TGT', split='train'): - """ - data_dir: input dir, contains directories for language pairs named l1-l2 - """ - os.makedirs(outdir, exist_ok=True) - - out_src_fname = '{}/{}.{}'.format(outdir, split, out_src_lang) - out_trg_fname = '{}/{}.{}'.format(outdir, split, out_trg_lang) -# out_meta_fname='{}/metadata.txt'.format(outdir) - - print() - print(out_src_fname) - print(out_trg_fname) -# print(out_meta_fname) - - # concatenate train data - if os.path.isfile(out_src_fname): - os.unlink(out_src_fname) - if os.path.isfile(out_trg_fname): - os.unlink(out_trg_fname) -# if os.path.isfile(out_meta_fname): -# os.unlink(out_meta_fname) - - for src_lang, trg_lang in tqdm(lang_pair_list): - print('src: {}, tgt:{}'.format(src_lang, trg_lang)) - - in_src_fname = '{}/{}-{}/{}.{}'.format( - data_dir, src_lang, trg_lang, split, src_lang) - in_trg_fname = '{}/{}-{}/{}.{}'.format( - data_dir, src_lang, trg_lang, split, trg_lang) - - if not os.path.exists(in_src_fname): - continue - if not os.path.exists(in_trg_fname): - continue - - print(in_src_fname) - os.system('cat {} >> {}'.format(in_src_fname, out_src_fname)) - - print(in_trg_fname) - os.system('cat {} >> {}'.format(in_trg_fname, out_trg_fname)) - - -# with open('{}/lang_pairs.txt'.format(outdir),'w',encoding='utf-8') as lpfile: -# lpfile.write('\n'.join( [ '-'.join(x) for x in lang_pair_list ] )) - - corpus_stats(data_dir, outdir, lang_pair_list, split) - - -def corpus_stats(data_dir, outdir, lang_pair_list, split): - """ - data_dir: input dir, contains directories for language pairs named l1-l2 - """ - - with open('{}/{}_lang_pairs.txt'.format(outdir, split), 'w', encoding='utf-8') as lpfile: - - for src_lang, trg_lang in tqdm(lang_pair_list): - print('src: {}, tgt:{}'.format(src_lang, trg_lang)) - - in_src_fname = '{}/{}-{}/{}.{}'.format( - data_dir, src_lang, trg_lang, split, src_lang) - # in_trg_fname='{}/{}-{}/train.{}'.format(data_dir,src_lang,trg_lang,trg_lang) - if not os.path.exists(in_src_fname): - continue - - print(in_src_fname) - corpus_size = 0 - with open(in_src_fname, 'r', encoding='utf-8') as infile: - corpus_size = sum(map(lambda x: 1, infile)) - - lpfile.write('{}\t{}\t{}\n'.format( - src_lang, trg_lang, corpus_size)) - - -if __name__ == '__main__': - - in_dir = sys.argv[1] - out_dir = sys.argv[2] - src_lang = sys.argv[3] - tgt_lang = sys.argv[4] - split = sys.argv[5] - lang_pair_list = [] - - if src_lang == 'en': - for lang in LANGS: - lang_pair_list.append(['en', lang]) - else: - for lang in LANGS: - lang_pair_list.append([lang, 'en']) - - concat_data(in_dir, out_dir, lang_pair_list, split=split) - diff --git a/spaces/Hellisotherpeople/HF-SHAP/app.py b/spaces/Hellisotherpeople/HF-SHAP/app.py deleted file mode 100644 index 157f6498f33f927a3e81c095104daf7c7d0050d4..0000000000000000000000000000000000000000 --- a/spaces/Hellisotherpeople/HF-SHAP/app.py +++ /dev/null @@ -1,157 +0,0 @@ -import subprocess -import sys - - -##Lines 1-8 are necessary because the normal requirements.txt path for installing a package from disk doesn't work on HF spaces, thank you to Omar Sanseviero for the help! - -import numpy as np -import pandas as pd -import shap -import streamlit as st -import streamlit.components.v1 as components -from datasets import load_dataset -from transformers import (AutoModelForCausalLM, AutoModelForQuestionAnswering, - AutoModelForSeq2SeqLM, - AutoModelForSequenceClassification, AutoTokenizer, - pipeline) - - -st.set_page_config(page_title="HF-SHAP") -st.title("HF-SHAP: A front end for SHAP") -st.caption("By Allen Roush") -st.caption("github: https://github.com/Hellisotherpeople") -st.caption("Linkedin: https://www.linkedin.com/in/allen-roush-27721011b/") -st.title("SHAP (SHapley Additive exPlanations)") -st.image("https://shap.readthedocs.io/en/latest/_images/shap_header.png", width = 700) -st.caption("By Lundberg, Scott M and Lee, Su-In") -st.caption("Slightly modified by Allen Roush to fix a bug with text plotting not working outside of Jupyter Notebooks") -st.caption("Full Citation: https://raw.githubusercontent.com/slundberg/shap/master/docs/references/shap_nips.bib") -st.caption("See on github:: https://github.com/slundberg/shap") -st.caption("More details of how SHAP works: https://christophm.github.io/interpretable-ml-book/shap.html") - - -form = st.sidebar.form("Main Settings") - -form.header("Main Settings") - - - -task_done = form.selectbox("Which NLP task do you want to solve?", ["Text Generation", "Sentiment Analysis", "Translation", "Summarization"]) - - - - - -custom_doc = form.checkbox("Use a document from an existing dataset?", value = False) -if custom_doc: - dataset_name = form.text_area("Enter the name of the huggingface Dataset to do analysis of:", value = "Hellisotherpeople/DebateSum") - dataset_name_2 = form.text_area("Enter the name of the config for the dataset if it has one", value = "") - split_name = form.text_area("Enter the name of the split of the dataset that you want to use", value = "train") - number_of_records = form.number_input("Enter the number of documents that you want to analyze from the dataset", value = 200) - column_name = form.text_area("Enter the name of the column that we are doing analysis on (the X value)", value = "Full-Document") - index_to_analyze_start = form.number_input("Enter the index start of the document that you want to analyze of the dataset", value = 1) - index_to_analyze_end = form.number_input("Enter the index end of the document that you want to analyze of the dataset", value = 2) - form.caption("Multiple documents may not work on certain tasks") -else: - doc = st.text_area("Enter a custom document", value = "This is an example custom document") - - - -if task_done == "Text Generation": - model_name = form.text_area("Enter the name of the pre-trained model from transformers that we are using for Text Generation", value = "gpt2") - form.caption("This will download a new model, so it may take awhile or even break if the model is too large") - decoder = form.checkbox("Is this a decoder model?", value = True) - form.caption("This should be true for models like GPT-2, and false for models like BERT") - max_length = form.number_input("What's the max length of the text?", value = 50) - min_length = form.number_input("What's the min length of the text?", value = 20, max_value = max_length) - penalize_repetion = form.number_input("How strongly do we want to penalize repetition in the text generation?", value = 2) - sample = form.checkbox("Shall we use top-k and top-p decoding?", value = True) - form.caption("Setting this to false makes it greedy") - if sample: - top_k = form.number_input("What value of K should we use for Top-K sampling? Set to zero to disable", value = 50) - form.caption("In Top-K sampling, the K most likely next words are filtered and the probability mass is redistributed among only those K next words. ") - top_p = form.number_input("What value of P should we use for Top-p sampling? Set to zero to disable", value = 0.95, max_value = 1.0, min_value = 0.0) - form.caption("Top-p sampling chooses from the smallest possible set of words whose cumulative probability exceeds the probability p. The probability mass is then redistributed among this set of words.") - temperature = form.number_input("How spicy/interesting do we want our models output to be", value = 1.05, min_value = 0.0) - form.caption("Setting this higher decreases the likelihood of high probability words and increases the likelihood of low probability (and presumably more interesting) words") - form.caption("For more details on what these settings mean, see here: https://huggingface.co/blog/how-to-generate") - - -elif task_done == "Sentiment Analysis": - model_name = form.text_area("Enter the name of the pre-trained model from transformers that we are using for Sentiment Analysis", value = "nateraw/bert-base-uncased-emotion") - rescale_logits = form.checkbox("Do we rescale the probabilities in terms of log odds?", value = False) -elif task_done == "Translation": - model_name = form.text_area("Enter the name of the pre-trained model from transformers that we are using for Translation", value = "Helsinki-NLP/opus-mt-en-es") -elif task_done == "Summarization": - model_name = form.text_area("Enter the name of the pre-trained model from transformers that we are using for Translation", value = "sshleifer/distilbart-xsum-12-1") -else: - model_name = form.text_area("Enter the name of the pre-trained model from transformers that we are using for Question Answering", value = "deepset/roberta-base-squad2") - -form.header("Model Explanation Display Settings") -output_width = form.number_input("Enter the number of pixels for width of model explanation html display", value = 800) -output_height = form.number_input("Enter the number of pixels for height of model explanation html display", value = 1000) -form.form_submit_button("Submit") - -@st.cache -def load_and_process_data(path, name, streaming, split_name, number_of_records): - dataset = load_dataset(path = path, name = name, streaming=streaming) - #return list(dataset) - dataset_head = dataset[split_name].take(number_of_records) - df = pd.DataFrame.from_dict(dataset_head) - return df[column_name] - - - -@st.cache(allow_output_mutation=True) -def load_model(model_name): - tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True) - if task_done == "Text Generation": - model = AutoModelForCausalLM.from_pretrained(model_name) - model.config.is_decoder=decoder - if sample == True: - model.config.task_specific_params["text-generation"] = {"do_sample": sample, "max_length": max_length, "min_length": min_length, "temperature": temperature, "top_k": top_k, "top_p" : top_p, "no_repeat_ngram_size": penalize_repetion} - else: - model.config.task_specific_params["text-generation"] = {"do_sample": sample, "max_length": max_length, "min_length": min_length, "no_repeat_ngram_size": penalize_repetion} - - elif task_done == "Sentiment Analysis": - model = AutoModelForSequenceClassification.from_pretrained(model_name) - elif task_done == "Translation": - model = AutoModelForSeq2SeqLM.from_pretrained(model_name) - elif task_done == "Summarization": - model = AutoModelForSeq2SeqLM.from_pretrained(model_name) - elif task_done == "Question Answering": - #TODO: This one is going to be harder... - # https://shap.readthedocs.io/en/latest/example_notebooks/text_examples/question_answering/Explaining%20a%20Question%20Answering%20Transformers%20Model.html - model = AutoModelForQuestionAnswering.from_pretrained(model_name) - - return tokenizer, model - -tokenizer, model = load_model(model_name) - - - - - - -if custom_doc: - df = load_and_process_data(dataset_name, dataset_name_2, True, split_name, number_of_records) - doc = list(df[index_to_analyze_start:index_to_analyze_end]) - st.write(doc) - -if task_done == "Sentiment Analysis": - pred = pipeline("text-classification", model=model, tokenizer=tokenizer, return_all_scores=True) - explainer = shap.Explainer(pred, rescale_to_logits = rescale_logits) -else: - explainer = shap.Explainer(model, tokenizer) - -if custom_doc: - shap_values = explainer(doc) -else: - shap_values = explainer([doc]) - - - -the_plot = shap.plots.text(shap_values, display = False) -st.caption("The plot is interactive! Try Hovering over or clicking on the input or output text") -components.html(the_plot, height = output_height, width = output_width, scrolling = True) - diff --git a/spaces/HighCWu/GPEN/face_model/op/fused_bias_act.cpp b/spaces/HighCWu/GPEN/face_model/op/fused_bias_act.cpp deleted file mode 100644 index 02be898f970bcc8ea297867fcaa4e71b24b3d949..0000000000000000000000000000000000000000 --- a/spaces/HighCWu/GPEN/face_model/op/fused_bias_act.cpp +++ /dev/null @@ -1,21 +0,0 @@ -#include - - -torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, - int act, int grad, float alpha, float scale); - -#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") -#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) - -torch::Tensor fused_bias_act(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, - int act, int grad, float alpha, float scale) { - CHECK_CUDA(input); - CHECK_CUDA(bias); - - return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale); -} - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)"); -} \ No newline at end of file diff --git a/spaces/HuggingFaceH4/open_llm_leaderboard/src/get_model_info/hardocded_metadata/flags.py b/spaces/HuggingFaceH4/open_llm_leaderboard/src/get_model_info/hardocded_metadata/flags.py deleted file mode 100644 index cbd47b2608a0e6e07681b0ee1391af8e364ad00b..0000000000000000000000000000000000000000 --- a/spaces/HuggingFaceH4/open_llm_leaderboard/src/get_model_info/hardocded_metadata/flags.py +++ /dev/null @@ -1,18 +0,0 @@ -# Models which have been flagged by users as being problematic for a reason or another -# (Model name to forum discussion link) -FLAGGED_MODELS = { - "Voicelab/trurl-2-13b": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/202", - "deepnight-research/llama-2-70B-inst": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/207", - "Aspik101/trurl-2-13b-pl-instruct_unload": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/213", - "Fredithefish/ReasonixPajama-3B-HF": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/236", - "TigerResearch/tigerbot-7b-sft-v1": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/237", - "gaodrew/gaodrew-gorgonzola-13b": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/215", - "AIDC-ai-business/Marcoroni-70B": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/287", - "AIDC-ai-business/Marcoroni-13B": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/287", - "AIDC-ai-business/Marcoroni-7B": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/287", -} - -# Models which have been requested by orgs to not be submitted on the leaderboard -DO_NOT_SUBMIT_MODELS = [ - "Voicelab/trurl-2-13b", # trained on MMLU -] diff --git a/spaces/HuguesdeF/moulinette/README.md b/spaces/HuguesdeF/moulinette/README.md deleted file mode 100644 index fb946ece8788e8e39ad92576774ced43520b7ad4..0000000000000000000000000000000000000000 --- a/spaces/HuguesdeF/moulinette/README.md +++ /dev/null @@ -1,39 +0,0 @@ ---- -title: Moulinette -emoji: ⚙️ -colorFrom: indigo -colorTo: indigo -sdk: docker -pinned: false -license: apache-2.0 ---- -# Moulinette Seguin Moreau - -## Installation Windows - -L'installation Windows de la Moulinette à logos passe par la création d'un conteneur virtuel, appelé "image Docker". -Il fonctionne comme une petite machine virtuelle. Afin de s'en servir, il faut donc 1/Construire cette image virtuelle et 2/L'exécuter. - -Pour installer la Moulinette sur Windows, voici la procédure: -* Installer docker desktop: https://www.docker.com/products/docker-desktop/ -* Ouvrir Docker Desktop. Se rendre dans les paramètres/général, puis cliquer sur "Start Docker Desktop when you log in", ce qui ouvrira automatiquement Docker au démarrage de l'ordinateur. -* Ouvrir une invite de commande Windows, en cherchant "Run" dans la barre de recherche, puis tapper "cmd" dans la fenêtre Run. -* Se rendre, depuis l'invite de commande dans le dossier contenant le code (Et donc ce fichier readme !). Pour aller dans un dossier utiliser la commande "cd". -* Vérifier que docker est maintenant accessible, après l'installation précédente, en tapant: ```docker -v``` qui doit donner la version de Docker installée. -* Entrer la commande: -``` -docker build . -t moulinette -``` -S'assurer que le build s'est bien passé en tapant ```docker images``` qui liste toutes les images Docker présentes sur l'ordinateur. -Une image doit s'appeller "moulinette". - -* Puis, une fois le "build" realisé, entrer la commande: -``` -docker run -d --restart unless-stopped -p 8501:8501 moulinette -``` -Cette commande exécute (docker run) l'image docker "moulinette", aiguille le port du docker 8501 vers le port de la machine hôte 8501 (avec le -p), - et relance l'image docker au démarrage de l'ordinateur (--restart). Enfin le -d indique de lancer l'exécution en mode "détaché", c'est-à-dire en tâche de fond. - -* Se rendre dans son navigateur web et rentrer l'url: localhost:8501 -* Ajouter cette page aux favoris. - diff --git a/spaces/ICML2022/OFA/fairseq/examples/criss/download_and_preprocess_flores_test.sh b/spaces/ICML2022/OFA/fairseq/examples/criss/download_and_preprocess_flores_test.sh deleted file mode 100644 index ed4b390fbdee3991efeb298050e12065d7fe605b..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/examples/criss/download_and_preprocess_flores_test.sh +++ /dev/null @@ -1,64 +0,0 @@ -#!/bin/bash -# Copyright (c) Facebook, Inc. and its 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. - -SPM_ENCODE=flores/scripts/spm_encode.py -DATA=data_tmp -SPM_MODEL=criss_checkpoints/sentence.bpe.model -DICT=criss_checkpoints/dict.txt - -download_data() { - CORPORA=$1 - URL=$2 - - if [ -f $CORPORA ]; then - echo "$CORPORA already exists, skipping download" - else - echo "Downloading $URL" - wget $URL -O $CORPORA --no-check-certificate || rm -f $CORPORA - if [ -f $CORPORA ]; then - echo "$URL successfully downloaded." - else - echo "$URL not successfully downloaded." - rm -f $CORPORA - fi - fi -} - -if [[ -f flores ]]; then - echo "flores already cloned" -else - git clone https://github.com/facebookresearch/flores -fi - -mkdir -p $DATA -download_data $DATA/wikipedia_en_ne_si_test_sets.tgz "https://github.com/facebookresearch/flores/raw/master/data/wikipedia_en_ne_si_test_sets.tgz" -pushd $DATA -pwd -tar -vxf wikipedia_en_ne_si_test_sets.tgz -popd - - -for lang in ne_NP si_LK; do - datadir=$DATA/${lang}-en_XX-flores - rm -rf $datadir - mkdir -p $datadir - TEST_PREFIX=$DATA/wikipedia_en_ne_si_test_sets/wikipedia.test - python $SPM_ENCODE \ - --model ${SPM_MODEL} \ - --output_format=piece \ - --inputs ${TEST_PREFIX}.${lang:0:2}-en.${lang:0:2} ${TEST_PREFIX}.${lang:0:2}-en.en \ - --outputs $datadir/test.bpe.${lang}-en_XX.${lang} $datadir/test.bpe.${lang}-en_XX.en_XX - - # binarize data - fairseq-preprocess \ - --source-lang ${lang} --target-lang en_XX \ - --testpref $datadir/test.bpe.${lang}-en_XX \ - --destdir $datadir \ - --srcdict ${DICT} \ - --joined-dictionary \ - --workers 4 -done diff --git a/spaces/ICML2022/OFA/fairseq/examples/noisychannel/__init__.py b/spaces/ICML2022/OFA/fairseq/examples/noisychannel/__init__.py deleted file mode 100644 index 89f1aef4f6328d25425e0bcabb42dfffd2ed35f0..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/examples/noisychannel/__init__.py +++ /dev/null @@ -1,6 +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. - -from .rerank_options import * # noqa diff --git a/spaces/ICML2022/resefa/utils/loggers/__init__.py b/spaces/ICML2022/resefa/utils/loggers/__init__.py deleted file mode 100644 index 665fd01dc34ae7a520dadfe4581c97e59dd6affe..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/resefa/utils/loggers/__init__.py +++ /dev/null @@ -1,32 +0,0 @@ -# python3.7 -"""Collects all loggers.""" - -from .normal_logger import NormalLogger -from .rich_logger import RichLogger -from .dummy_logger import DummyLogger - -__all__ = ['build_logger'] - -_LOGGERS = { - 'normal': NormalLogger, - 'rich': RichLogger, - 'dummy': DummyLogger -} - - -def build_logger(logger_type='normal', **kwargs): - """Builds a logger. - - Args: - logger_type: Type of logger, which is case insensitive. - (default: `normal`) - **kwargs: Additional arguments to build the logger. - - Raises: - ValueError: If the `logger_type` is not supported. - """ - logger_type = logger_type.lower() - if logger_type not in _LOGGERS: - raise ValueError(f'Invalid logger type: `{logger_type}`!\n' - f'Types allowed: {list(_LOGGERS)}.') - return _LOGGERS[logger_type](**kwargs) diff --git a/spaces/Iceclear/StableSR/StableSR/ldm/util.py b/spaces/Iceclear/StableSR/StableSR/ldm/util.py deleted file mode 100644 index 1b1301a55396c445ecdb28cc444fa10fcbd06391..0000000000000000000000000000000000000000 --- a/spaces/Iceclear/StableSR/StableSR/ldm/util.py +++ /dev/null @@ -1,211 +0,0 @@ -import importlib - -import torch -import numpy as np -from collections import abc -from einops import rearrange -from functools import partial - -import multiprocessing as mp -from threading import Thread -from queue import Queue - -from inspect import isfunction -from PIL import Image, ImageDraw, ImageFont - - -def log_txt_as_img(wh, xc, size=10): - # wh a tuple of (width, height) - # xc a list of captions to plot - b = len(xc) - txts = list() - for bi in range(b): - txt = Image.new("RGB", wh, color="white") - draw = ImageDraw.Draw(txt) - font = ImageFont.truetype('data/DejaVuSans.ttf', size=size) - nc = int(40 * (wh[0] / 256)) - lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) - - try: - draw.text((0, 0), lines, fill="black", font=font) - except UnicodeEncodeError: - print("Cant encode string for logging. Skipping.") - - txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 - txts.append(txt) - txts = np.stack(txts) - txts = torch.tensor(txts) - return txts - - -def ismap(x): - if not isinstance(x, torch.Tensor): - return False - return (len(x.shape) == 4) and (x.shape[1] > 3) - - -def isimage(x): - if not isinstance(x, torch.Tensor): - return False - return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) - - -def exists(x): - return x is not None - - -def default(val, d): - if exists(val): - return val - return d() if isfunction(d) else d - - -def mean_flat(tensor): - """ - https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 - Take the mean over all non-batch dimensions. - """ - return tensor.mean(dim=list(range(1, len(tensor.shape)))) - - -def count_params(model, verbose=False): - total_params = sum(p.numel() for p in model.parameters()) - if verbose: - print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.") - return total_params - - -def instantiate_from_config(config): - if not "target" in config: - if config == '__is_first_stage__': - return None - elif config == "__is_unconditional__": - return None - raise KeyError("Expected key `target` to instantiate.") - return get_obj_from_str(config["target"])(**config.get("params", dict())) - -def instantiate_from_config_sr(config): - if not "target" in config: - if config == '__is_first_stage__': - return None - elif config == "__is_unconditional__": - return None - raise KeyError("Expected key `target` to instantiate.") - return get_obj_from_str(config["target"])(config.get("params", dict())) - -def get_obj_from_str(string, reload=False): - module, cls = string.rsplit(".", 1) - if reload: - module_imp = importlib.import_module(module) - importlib.reload(module_imp) - return getattr(importlib.import_module(module, package=None), cls) - - -def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False): - # create dummy dataset instance - - # run prefetching - if idx_to_fn: - res = func(data, worker_id=idx) - else: - res = func(data) - Q.put([idx, res]) - Q.put("Done") - - -def parallel_data_prefetch( - func: callable, data, n_proc, target_data_type="ndarray", cpu_intensive=True, use_worker_id=False -): - # if target_data_type not in ["ndarray", "list"]: - # raise ValueError( - # "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray." - # ) - if isinstance(data, np.ndarray) and target_data_type == "list": - raise ValueError("list expected but function got ndarray.") - elif isinstance(data, abc.Iterable): - if isinstance(data, dict): - print( - f'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.' - ) - data = list(data.values()) - if target_data_type == "ndarray": - data = np.asarray(data) - else: - data = list(data) - else: - raise TypeError( - f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}." - ) - - if cpu_intensive: - Q = mp.Queue(1000) - proc = mp.Process - else: - Q = Queue(1000) - proc = Thread - # spawn processes - if target_data_type == "ndarray": - arguments = [ - [func, Q, part, i, use_worker_id] - for i, part in enumerate(np.array_split(data, n_proc)) - ] - else: - step = ( - int(len(data) / n_proc + 1) - if len(data) % n_proc != 0 - else int(len(data) / n_proc) - ) - arguments = [ - [func, Q, part, i, use_worker_id] - for i, part in enumerate( - [data[i: i + step] for i in range(0, len(data), step)] - ) - ] - processes = [] - for i in range(n_proc): - p = proc(target=_do_parallel_data_prefetch, args=arguments[i]) - processes += [p] - - # start processes - print(f"Start prefetching...") - import time - - start = time.time() - gather_res = [[] for _ in range(n_proc)] - try: - for p in processes: - p.start() - - k = 0 - while k < n_proc: - # get result - res = Q.get() - if res == "Done": - k += 1 - else: - gather_res[res[0]] = res[1] - - except Exception as e: - print("Exception: ", e) - for p in processes: - p.terminate() - - raise e - finally: - for p in processes: - p.join() - print(f"Prefetching complete. [{time.time() - start} sec.]") - - if target_data_type == 'ndarray': - if not isinstance(gather_res[0], np.ndarray): - return np.concatenate([np.asarray(r) for r in gather_res], axis=0) - - # order outputs - return np.concatenate(gather_res, axis=0) - elif target_data_type == 'list': - out = [] - for r in gather_res: - out.extend(r) - return out - else: - return gather_res diff --git a/spaces/Intel/NeuralChat-ICX-INT4/fastchat/serve/gradio_block_arena_named.py b/spaces/Intel/NeuralChat-ICX-INT4/fastchat/serve/gradio_block_arena_named.py deleted file mode 100644 index 0db977bad8887f7b7a653b835bac508efd65aba6..0000000000000000000000000000000000000000 --- a/spaces/Intel/NeuralChat-ICX-INT4/fastchat/serve/gradio_block_arena_named.py +++ /dev/null @@ -1,395 +0,0 @@ -import json -import time - -import gradio as gr -import numpy as np - -from fastchat.conversation import get_default_conv_template -from fastchat.utils import ( - build_logger, - violates_moderation, - moderation_msg, -) -from fastchat.serve.gradio_patch import Chatbot as grChatbot -from fastchat.serve.gradio_web_server import ( - http_bot, - get_conv_log_filename, - no_change_btn, - enable_btn, - disable_btn, -) - - -logger = build_logger("gradio_web_server_multi", "gradio_web_server_multi.log") - -num_models = 2 -enable_moderation = False - - -def set_global_vars_named(enable_moderation_): - global enable_moderation - enable_moderation = enable_moderation_ - - -def load_demo_side_by_side_named(models, url_params): - states = (None,) * num_models - - model_left = models[0] - if len(models) > 1: - weights = ([8, 4, 2, 1] + [1] * 32)[:len(models) - 1] - weights = weights / np.sum(weights) - model_right = np.random.choice(models[1:], p=weights) - else: - model_right = model_left - - selector_updates = ( - gr.Dropdown.update(model_left, visible=True), - gr.Dropdown.update(model_right, visible=True), - ) - - return ( - states - + selector_updates - + (gr.Chatbot.update(visible=True),) * num_models - + ( - gr.Textbox.update(visible=True), - gr.Box.update(visible=True), - gr.Row.update(visible=True), - gr.Row.update(visible=True), - gr.Accordion.update(visible=True), - ) - ) - - -def vote_last_response(states, vote_type, model_selectors, request: gr.Request): - with open(get_conv_log_filename(), "a") as fout: - data = { - "tstamp": round(time.time(), 4), - "type": vote_type, - "models": [x for x in model_selectors], - "states": [x.dict() for x in states], - "ip": request.client.host, - } - fout.write(json.dumps(data) + "\n") - - -def leftvote_last_response( - state0, state1, model_selector0, model_selector1, request: gr.Request -): - logger.info(f"leftvote (named). ip: {request.client.host}") - vote_last_response( - [state0, state1], "leftvote", [model_selector0, model_selector1], request - ) - return ("",) + (disable_btn,) * 3 - - -def rightvote_last_response( - state0, state1, model_selector0, model_selector1, request: gr.Request -): - logger.info(f"rightvote (named). ip: {request.client.host}") - vote_last_response( - [state0, state1], "rightvote", [model_selector0, model_selector1], request - ) - return ("",) + (disable_btn,) * 3 - - -def tievote_last_response( - state0, state1, model_selector0, model_selector1, request: gr.Request -): - logger.info(f"tievote (named). ip: {request.client.host}") - vote_last_response( - [state0, state1], "tievote", [model_selector0, model_selector1], request - ) - return ("",) + (disable_btn,) * 3 - - -def regenerate(state0, state1, request: gr.Request): - logger.info(f"regenerate (named). ip: {request.client.host}") - states = [state0, state1] - for i in range(num_models): - states[i].messages[-1][-1] = None - states[i].skip_next = False - return states + [x.to_gradio_chatbot() for x in states] + [""] + [disable_btn] * 5 - - -def clear_history(request: gr.Request): - logger.info(f"clear_history (named). ip: {request.client.host}") - return [None] * num_models + [None] * num_models + [""] + [disable_btn] * 5 - - -def share_click(state0, state1, model_selector0, model_selector1, - request: gr.Request): - logger.info(f"share (named). ip: {request.client.host}") - if state0 is not None and state1 is not None: - vote_last_response( - [state0, state1], "share", [model_selector0, model_selector1], request - ) - - -def add_text(state0, state1, text, request: gr.Request): - logger.info(f"add_text (named). ip: {request.client.host}. len: {len(text)}") - states = [state0, state1] - - for i in range(num_models): - if states[i] is None: - states[i] = get_default_conv_template("vicuna").copy() - - if len(text) <= 0: - for i in range(num_models): - states[i].skip_next = True - return ( - states - + [x.to_gradio_chatbot() for x in states] - + [""] - + [ - no_change_btn, - ] - * 5 - ) - - if enable_moderation: - flagged = violates_moderation(text) - if flagged: - logger.info(f"violate moderation (named). ip: {request.client.host}. text: {text}") - for i in range(num_models): - states[i].skip_next = True - return ( - states - + [x.to_gradio_chatbot() for x in states] - + [moderation_msg] - + [ - no_change_btn, - ] - * 5 - ) - - text = text[:1536] # Hard cut-off - for i in range(num_models): - states[i].append_message(states[i].roles[0], text) - states[i].append_message(states[i].roles[1], None) - states[i].skip_next = False - - return ( - states - + [x.to_gradio_chatbot() for x in states] - + [""] - + [ - disable_btn, - ] - * 5 - ) - - -def http_bot_all( - state0, - state1, - model_selector0, - model_selector1, - temperature, - max_new_tokens, - request: gr.Request, -): - logger.info(f"http_bot_all (named). ip: {request.client.host}") - states = [state0, state1] - model_selector = [model_selector0, model_selector1] - gen = [] - for i in range(num_models): - gen.append( - http_bot(states[i], model_selector[i], temperature, max_new_tokens, request) - ) - - chatbots = [None] * num_models - while True: - stop = True - for i in range(num_models): - try: - ret = next(gen[i]) - states[i], chatbots[i] = ret[0], ret[1] - buttons = ret[2:] - stop = False - except StopIteration: - pass - yield states + chatbots + list(buttons) - if stop: - break - - for i in range(10): - if i % 2 == 0: - yield states + chatbots + [disable_btn] * 3 + list(buttons)[3:] - else: - yield states + chatbots + list(buttons) - time.sleep(0.2) - - -def build_side_by_side_ui_named(models): - notice_markdown = """ -# ⚔️ Chatbot Arena ⚔️ -Rules: -- Chat with two models side-by-side and vote for which one is better! -- You pick the models you want to chat with. -- You can continue chating and voting or click "Clear history" to start a new round. -- A leaderboard will be available soon. -- [[GitHub]](https://github.com/lm-sys/FastChat) [[Twitter]](https://twitter.com/lmsysorg) [[Discord]](https://discord.gg/h6kCZb72G7) - -### Terms of use -By using this service, users are required to agree to the following terms: The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. **The service collects user dialogue data for future research.** -The demo works better on desktop devices with a wide screen. - -### Choose two models to chat with -| | | -| ---- | ---- | -| [Vicuna](https://vicuna.lmsys.org): a chat assistant fine-tuned from LLaMA on user-shared conversations by LMSYS. | [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/): a dialogue model for academic research by BAIR | -| [OpenAssistant (oasst)](https://open-assistant.io/): a chat-based assistant for everyone by LAION. | [Dolly](https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm): an instruction-tuned open large language model by Databricks. | -| [ChatGLM](https://chatglm.cn/blog): an open bilingual dialogue language model by Tsinghua University | [StableLM](https://github.com/stability-AI/stableLM/): Stability AI language models. | -| [Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html): a model fine-tuned from LLaMA on instruction-following demonstrations by Stanford. | [LLaMA](https://arxiv.org/abs/2302.13971): open and efficient foundation language models by Meta. | -""" - - learn_more_markdown = """ -### License -The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation. -""" - - states = [gr.State() for _ in range(num_models)] - model_selectors = [None] * num_models - chatbots = [None] * num_models - - notice = gr.Markdown(notice_markdown, elem_id="notice_markdown") - - with gr.Box(elem_id="share-region"): - with gr.Row(): - for i in range(num_models): - with gr.Column(): - model_selectors[i] = gr.Dropdown( - choices=models, - value=models[i] if len(models) > i else "", - interactive=True, - show_label=False, - ).style(container=False) - - with gr.Row(): - for i in range(num_models): - label = "Model A" if i == 0 else "Model B" - with gr.Column(): - chatbots[i] = grChatbot(label=label, elem_id=f"chatbot{i}", - visible=False).style(height=550) - - with gr.Box() as button_row: - with gr.Row(): - leftvote_btn = gr.Button(value="👈 A is better", interactive=False) - tie_btn = gr.Button(value="🤝 Tie", interactive=False) - rightvote_btn = gr.Button(value="👉 B is better", interactive=False) - - with gr.Row(): - with gr.Column(scale=20): - textbox = gr.Textbox( - show_label=False, - placeholder="Enter text and press ENTER", - visible=False, - ).style(container=False) - with gr.Column(scale=1, min_width=50): - send_btn = gr.Button(value="Send", visible=False) - - with gr.Row() as button_row2: - regenerate_btn = gr.Button(value="🔄 Regenerate", interactive=False) - clear_btn = gr.Button(value="🗑️ Clear history", interactive=False) - share_btn = gr.Button(value="📷 Share") - - with gr.Accordion("Parameters", open=False, visible=True) as parameter_row: - temperature = gr.Slider( - minimum=0.0, - maximum=1.0, - value=0.7, - step=0.1, - interactive=True, - label="Temperature", - ) - max_output_tokens = gr.Slider( - minimum=0, - maximum=1024, - value=512, - step=64, - interactive=True, - label="Max output tokens", - ) - - gr.Markdown(learn_more_markdown) - - # Register listeners - btn_list = [leftvote_btn, rightvote_btn, tie_btn, regenerate_btn, clear_btn] - leftvote_btn.click( - leftvote_last_response, - states + model_selectors, - [textbox, leftvote_btn, rightvote_btn, tie_btn], - ) - rightvote_btn.click( - rightvote_last_response, - states + model_selectors, - [textbox, leftvote_btn, rightvote_btn, tie_btn], - ) - tie_btn.click( - tievote_last_response, - states + model_selectors, - [textbox, leftvote_btn, rightvote_btn, tie_btn], - ) - regenerate_btn.click( - regenerate, states, states + chatbots + [textbox] + btn_list - ).then( - http_bot_all, - states + model_selectors + [temperature, max_output_tokens], - states + chatbots + btn_list, - ) - clear_btn.click(clear_history, None, states + chatbots + [textbox] + btn_list) - - share_js=""" -function (a, b, c, d) { - const captureElement = document.querySelector('#share-region'); - html2canvas(captureElement) - .then(canvas => { - canvas.style.display = 'none' - document.body.appendChild(canvas) - return canvas - }) - .then(canvas => { - const image = canvas.toDataURL('image/png') - const a = document.createElement('a') - a.setAttribute('download', 'chatbot-arena.png') - a.setAttribute('href', image) - a.click() - canvas.remove() - }); - return [a, b, c, d]; -} -""" - share_btn.click(share_click, states + model_selectors, [], _js=share_js) - - for i in range(num_models): - model_selectors[i].change( - clear_history, None, states + chatbots + [textbox] + btn_list - ) - - textbox.submit( - add_text, states + [textbox], states + chatbots + [textbox] + btn_list - ).then( - http_bot_all, - states + model_selectors + [temperature, max_output_tokens], - states + chatbots + btn_list, - ) - send_btn.click( - add_text, states + [textbox], states + chatbots + [textbox] + btn_list - ).then( - http_bot_all, - states + model_selectors + [temperature, max_output_tokens], - states + chatbots + btn_list, - ) - - return ( - states, - model_selectors, - chatbots, - textbox, - send_btn, - button_row, - button_row2, - parameter_row, - ) - diff --git a/spaces/Juno360219/cloudqi-cqi_text_to_image_pt_v0/README.md b/spaces/Juno360219/cloudqi-cqi_text_to_image_pt_v0/README.md deleted file mode 100644 index c6cc054cd7fea45bcfdb0c3d0a0c4590c62656d9..0000000000000000000000000000000000000000 --- a/spaces/Juno360219/cloudqi-cqi_text_to_image_pt_v0/README.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -title: Shiny for Python template -emoji: 🌍 -colorFrom: yellow -colorTo: indigo -sdk: docker -pinned: false -license: mit -duplicated_from: posit/shiny-for-python-template ---- - -This is a templated Space for [Shiny for Python](https://shiny.rstudio.com/py/). - -To get started with a new app do the following: - -1) Install Shiny with `pip install shiny` -2) Create a new app with `shiny create .` -3) Then run the app with `shiny run --reload` - -To learn more about this framework please see the [Documentation](https://shiny.rstudio.com/py/docs/overview.html). diff --git a/spaces/Kayson/InstructDiffusion/dataset/pose/pose.py b/spaces/Kayson/InstructDiffusion/dataset/pose/pose.py deleted file mode 100644 index beb9ec8afc0ff60f8e431bc27005cb271af495c6..0000000000000000000000000000000000000000 --- a/spaces/Kayson/InstructDiffusion/dataset/pose/pose.py +++ /dev/null @@ -1,760 +0,0 @@ -# ------------------------------------------------------------------------------ -# Copyright (c) Microsoft -# Licensed under the MIT License. -# Written by Bin Xiao (Bin.Xiao@microsoft.com) -# Modified by Zigang Geng (zigang@mail.ustc.edu.cn) -# ------------------------------------------------------------------------------ - -from __future__ import annotations - -import logging -import os -import json -import copy -import math -import random -from pathlib import Path -from typing import Any - -import cv2 -import numpy as np -import torch -import torchvision -from einops import rearrange -from PIL import Image -from torch.utils.data import Dataset -import torchvision.transforms as transforms -from pycocotools.coco import COCO - - -logger = logging.getLogger(__name__) - - -colors = { - 'red': (255, 0, 0), - 'green': (0, 255, 0), - 'blue': (0, 0, 255), - 'yellow': (255, 255, 0), - 'cyan': (0, 255, 255), - 'magenta': (255, 0, 255), - 'gray': (128, 128, 128), - 'white': (255, 255, 255), - 'black': (0, 0, 0)} - - -def readTXT(txt_path): - with open(txt_path, 'r') as f: - listInTXT = [line.strip() for line in f] - - return listInTXT - - -class PoseDataset(Dataset): - def __init__(self, root, image_set, is_train, max_prompt_num=5, min_prompt_num=1, - radius=10, size=256, transparency=0.0, sample_weight=1.0, transform=None): - - self.sample_weight = sample_weight - self.max_prompt_num = max_prompt_num - self.min_prompt_num = min_prompt_num - self.radius = radius - self.transparency = transparency - self.num_joints = 0 - self.pixel_std = 200 - self.flip_pairs = [] - self.parent_ids = [] - - self.keypoints_type = {} - - self.is_train = is_train - self.image_set = image_set - self.root = root - - self.scale_factor = 0.35 - self.rotation_factor = 45 - self.flip = True - self.num_joints_half_body = 8 - self.prob_half_body = 0.3 - - self.image_size = np.array((size, size)) - self.heatmap_size = np.array((size, size)) - - self.transform = transform - self.db = [] - - pose_diverse_prompt_path = 'dataset/prompt/prompt_pose.txt' - self.pose_diverse_prompt_list = [] - with open(pose_diverse_prompt_path) as f: - line = f.readline() - while line: - line = line.strip('\n') - self.pose_diverse_prompt_list.append(line) - line = f.readline() - - def _get_db(self): - raise NotImplementedError - - def evaluate(self, preds, output_dir, *args, **kwargs): - raise NotImplementedError - - def half_body_transform(self, joints, joints_vis): - upper_joints = [] - lower_joints = [] - for joint_id in range(self.num_joints): - if joints_vis[joint_id][0] > 0: - if joint_id in self.upper_body_ids: - upper_joints.append(joints[joint_id]) - else: - lower_joints.append(joints[joint_id]) - - if np.random.randn() < 0.5 and len(upper_joints) > 2: - selected_joints = upper_joints - else: - selected_joints = lower_joints \ - if len(lower_joints) > 2 else upper_joints - - if len(selected_joints) < 2: - return None, None - - selected_joints = np.array(selected_joints, dtype=np.float32) - center = selected_joints.mean(axis=0)[:2] - - left_top = np.amin(selected_joints, axis=0) - right_bottom = np.amax(selected_joints, axis=0) - - w = right_bottom[0] - left_top[0] - h = right_bottom[1] - left_top[1] - - if w > self.aspect_ratio * h: - h = w * 1.0 / self.aspect_ratio - elif w < self.aspect_ratio * h: - w = h * self.aspect_ratio - - scale = np.array( - [ - w * 1.0 / self.pixel_std, - h * 1.0 / self.pixel_std - ], - dtype=np.float32 - ) - - scale = scale * 1.5 - - return center, scale - - def __len__(self,): - return int(len(self.db) * self.sample_weight) - - def __getitem__(self, idx): - if self.sample_weight >= 1: - idx = idx % len(self.db) - else: - idx = int(idx / self.sample_weight) + random.randint(0, int(1 / self.sample_weight) - 1) - - db_rec = copy.deepcopy(self.db[idx]) - - image_file = db_rec['image'] - filename = db_rec['filename'] if 'filename' in db_rec else '' - imgnum = db_rec['imgnum'] if 'imgnum' in db_rec else '' - - data_numpy = cv2.imread( - image_file, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION - ) - data_numpy = cv2.cvtColor(data_numpy, cv2.COLOR_BGR2RGB) - - if data_numpy is None: - logger.error('=> fail to read {}'.format(image_file)) - raise ValueError('Fail to read {}'.format(image_file)) - - joints = db_rec['joints_3d'] - joints_vis = db_rec['joints_3d_vis'] - - c = db_rec['center'] - s = db_rec['scale'] - score = db_rec['score'] if 'score' in db_rec else 1 - r = 0 - - if self.is_train: - if (np.sum(joints_vis[:, 0]) > self.num_joints_half_body - and np.random.rand() < self.prob_half_body): - c_half_body, s_half_body = self.half_body_transform( - joints, joints_vis - ) - - if c_half_body is not None and s_half_body is not None: - c, s = c_half_body, s_half_body - - sf = self.scale_factor - rf = self.rotation_factor - s = s * np.clip(np.random.randn()*sf + 1, 1 - sf, 1 + sf) - r = np.clip(np.random.randn()*rf, -rf*2, rf*2) \ - if random.random() <= 0.6 else 0 - - if self.flip and random.random() <= 0.5: - data_numpy = data_numpy[:, ::-1, :] - joints, joints_vis = fliplr_joints( - joints, joints_vis, data_numpy.shape[1], self.flip_pairs) - c[0] = data_numpy.shape[1] - c[0] - 1 - - trans = get_affine_transform(c, s, r, self.image_size) - input = cv2.warpAffine( - data_numpy, - trans, - (int(self.image_size[0]), int(self.image_size[1])), - flags=cv2.INTER_LINEAR) - - if self.transform: - input = self.transform(input) - - for i in range(self.num_joints): - if joints_vis[i, 0] > 0.0: - joints[i, 0:2] = affine_transform(joints[i, 0:2], trans) - - target, prompt = self.generate_target(input, joints, joints_vis) - - # return Image.fromarray(input), Image.fromarray(target), prompt - - image_0 = rearrange(2 * torch.tensor(np.array(input)).float() / 255 - 1, "h w c -> c h w") - image_1 = rearrange(2 * torch.tensor(np.array(target)).float() / 255 - 1, "h w c -> c h w") - - return dict(edited=image_1, edit=dict(c_concat=image_0, c_crossattn=prompt)) - - def generate_target(self, input, joints, joints_vis): - ''' - :param input: [height, width, 3] - :param joints: [num_joints, 3] - :param joints_vis: [num_joints, 3] - :return: target - ''' - radius = self.radius - target = copy.deepcopy(input) - - joint_num = random.randint(self.min_prompt_num, self.max_prompt_num) - joint_ids = np.random.choice([i for i in range(self.num_joints)], joint_num, replace=False) - random_color_names = random.sample(list(colors.keys()), len(joint_ids)) - random_marker_names = ['circle' for i in range(len(joint_ids))] - - prompt = "" - - for color_idx, joint_id in enumerate(joint_ids): - feat_stride = self.image_size / self.heatmap_size - mu_x = int(joints[joint_id][0] / feat_stride[0] + 0.5) - mu_y = int(joints[joint_id][1] / feat_stride[1] + 0.5) - # Check that any part of the gaussian is in-bounds - ul = [int(mu_x - radius), int(mu_y - radius)] - br = [int(mu_x + radius + 1), int(mu_y + radius + 1)] - if ul[0] >= self.heatmap_size[0] or ul[1] >= self.heatmap_size[1] \ - or br[0] < 0 or br[1] < 0: - # If not, just return the image as is - joints_vis[joint_id][0] = 0 - continue - - marker_size = 2 * radius + 1 - g = np.zeros((marker_size, marker_size)) - x, y = np.indices((marker_size, marker_size)) - interval = int((marker_size - marker_size / math.sqrt(2)) // 2) - mask = (x - radius) ** 2 + (y - radius) ** 2 <= radius ** 2 + 1 - g[mask] = 1 - - # Usable gaussian range - g_x = max(0, -ul[0]), min(br[0], self.heatmap_size[0]) - ul[0] - g_y = max(0, -ul[1]), min(br[1], self.heatmap_size[1]) - ul[1] - # Image range - img_x = max(0, ul[0]), min(br[0], self.heatmap_size[0]) - img_y = max(0, ul[1]), min(br[1], self.heatmap_size[1]) - - v = joints_vis[joint_id][0] - random_color_name = random_color_names[color_idx] - random_color = colors[random_color_name] - - prompt += random.choice(self.pose_diverse_prompt_list).format( - color=random_color_name, - joint=self.keypoints_type[joint_id]) - - if v > 0.5: - target[img_y[0]:img_y[1], img_x[0]:img_x[1]][g[g_y[0]:g_y[1], g_x[0]:g_x[1]]>0] \ - = self.transparency*target[img_y[0]:img_y[1], img_x[0]:img_x[1]][g[g_y[0]:g_y[1], g_x[0]:g_x[1]]>0] \ - + (1-self.transparency)*np.array(random_color) - - return target, prompt - - -class COCODataset(PoseDataset): - def __init__(self, root, image_set, is_train, max_prompt_num=5, min_prompt_num=1, - radius=10, size=256, transparency=0.0, sample_weight=1.0, transform=None): - - super().__init__(root, image_set, is_train, max_prompt_num, min_prompt_num, - radius, size, transparency, sample_weight, transform) - - self.keypoints_type = { - 0: "nose", - 1: "left eye", - 2: "right eye", - 3: "left ear", - 4: "right ear", - 5: "left shoulder", - 6: "right shoulder", - 7: "left elbow", - 8: "right elbow", - 9: "left wrist", - 10: "right wrist", - 11: "left hip", - 12: "right hip", - 13: "left knee", - 14: "right knee", - 15: "left ankle", - 16: "right ankle" - } - - self.image_width = size - self.image_height = size - self.aspect_ratio = self.image_width * 1.0 / self.image_height - self.pixel_std = 200 - - self.coco = COCO(self._get_ann_file_keypoint()) - - # deal with class names - cats = [cat['name'] - for cat in self.coco.loadCats(self.coco.getCatIds())] - self.classes = ['__background__'] + cats - logger.info('=> classes: {}'.format(self.classes)) - self.num_classes = len(self.classes) - self._class_to_ind = dict(zip(self.classes, range(self.num_classes))) - self._class_to_coco_ind = dict(zip(cats, self.coco.getCatIds())) - self._coco_ind_to_class_ind = dict( - [ - (self._class_to_coco_ind[cls], self._class_to_ind[cls]) - for cls in self.classes[1:] - ] - ) - - # load image file names - self.image_set_index = self._load_image_set_index() - self.num_images = len(self.image_set_index) - logger.info('=> num_images: {}'.format(self.num_images)) - - self.num_joints = 17 - self.flip_pairs = [[1, 2], [3, 4], [5, 6], [7, 8], - [9, 10], [11, 12], [13, 14], [15, 16]] - self.parent_ids = None - self.upper_body_ids = (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10) - self.lower_body_ids = (11, 12, 13, 14, 15, 16) - - if 'coco' in self.root: - self.db = self._get_db() - - logger.info('=> load {} samples'.format(len(self.db))) - - def _get_ann_file_keypoint(self): - """ self.root / annotations / person_keypoints_train2017.json """ - if 'coco' in self.root: - prefix = 'person_keypoints' \ - if 'test' not in self.image_set else 'image_info' - return os.path.join( - self.root, - 'annotations', - prefix + '_' + self.image_set + '.json' - ) - elif 'crowdpose' in self.root: - prefix = 'crowdpose' - return os.path.join( - self.root, - 'json', - prefix + '_' + self.image_set + '.json' - ) - elif 'aic' in self.root: - prefix = 'aic' - return os.path.join( - self.root, - 'annotations', - prefix + '_' + self.image_set + '.json' - ) - else: - raise ValueError('Please write the path for this new dataset.') - - def _load_image_set_index(self): - """ image id: int """ - image_ids = self.coco.getImgIds() - return image_ids - - def _get_db(self): - gt_db = self._load_coco_keypoint_annotations() - return gt_db - - def _load_coco_keypoint_annotations(self): - """ ground truth bbox and keypoints """ - gt_db = [] - for index in self.image_set_index: - gt_db.extend(self._load_coco_keypoint_annotation_kernal(index)) - return gt_db - - def _load_coco_keypoint_annotation_kernal(self, index): - """ - coco ann: [u'segmentation', u'area', u'iscrowd', u'image_id', u'bbox', u'category_id', u'id'] - iscrowd: - crowd instances are handled by marking their overlaps with all categories to -1 - and later excluded in training - bbox: - [x1, y1, w, h] - :param index: coco image id - :return: db entry - """ - im_ann = self.coco.loadImgs(index)[0] - width = im_ann['width'] - height = im_ann['height'] - - annIds = self.coco.getAnnIds(imgIds=index, iscrowd=False) - objs = self.coco.loadAnns(annIds) - - # sanitize bboxes - valid_objs = [] - for obj in objs: - x, y, w, h = obj['bbox'] - x1 = np.max((0, x)) - y1 = np.max((0, y)) - x2 = np.min((width - 1, x1 + np.max((0, w - 1)))) - y2 = np.min((height - 1, y1 + np.max((0, h - 1)))) - if 'crowdpose' in self.root: - obj['area'] = 1 - if obj['area'] > 0 and x2 >= x1 and y2 >= y1: - obj['clean_bbox'] = [x1, y1, x2-x1, y2-y1] - valid_objs.append(obj) - objs = valid_objs - - rec = [] - for obj in objs: - cls = self._coco_ind_to_class_ind[obj['category_id']] - if cls != 1: - continue - - # ignore objs without keypoints annotation - if max(obj['keypoints']) == 0: - continue - - joints_3d = np.zeros((self.num_joints, 3), dtype=np.float32) - joints_3d_vis = np.zeros((self.num_joints, 3), dtype=np.float32) - for ipt in range(self.num_joints): - joints_3d[ipt, 0] = obj['keypoints'][ipt * 3 + 0] - joints_3d[ipt, 1] = obj['keypoints'][ipt * 3 + 1] - joints_3d[ipt, 2] = 0 - t_vis = obj['keypoints'][ipt * 3 + 2] - if t_vis > 1: - t_vis = 1 - joints_3d_vis[ipt, 0] = t_vis - joints_3d_vis[ipt, 1] = t_vis - joints_3d_vis[ipt, 2] = 0 - - center, scale = self._box2cs(obj['clean_bbox'][:4]) - rec.append({ - 'image': self.image_path_from_index(index, im_ann), - 'center': center, - 'scale': scale, - 'joints_3d': joints_3d, - 'joints_3d_vis': joints_3d_vis, - 'filename': '', - 'imgnum': 0, - }) - - return rec - - def _box2cs(self, box): - x, y, w, h = box[:4] - return self._xywh2cs(x, y, w, h) - - def _xywh2cs(self, x, y, w, h): - center = np.zeros((2), dtype=np.float32) - center[0] = x + w * 0.5 - center[1] = y + h * 0.5 - - if w > self.aspect_ratio * h: - h = w * 1.0 / self.aspect_ratio - elif w < self.aspect_ratio * h: - w = h * self.aspect_ratio - scale = np.array( - [w * 1.0 / self.pixel_std, h * 1.0 / self.pixel_std], - dtype=np.float32) - if center[0] != -1: - scale = scale * 1.25 - - return center, scale - - def image_path_from_index(self, index, im_ann): - """ example: images / train2017 / 000000119993.jpg """ - if 'coco' in self.root: - file_name = '%012d.jpg' % index - if '2014' in self.image_set: - file_name = 'COCO_%s_' % self.image_set + file_name - - prefix = 'test2017' if 'test' in self.image_set else self.image_set - - data_name = prefix - - image_path = os.path.join( - self.root, 'images', data_name, file_name) - - return image_path - elif 'crowdpose' in self.root: - file_name = f'{index}.jpg' - - image_path = os.path.join( - self.root, 'images', file_name) - - return image_path - elif 'aic' in self.root: - file_name = im_ann["file_name"] - - image_path = os.path.join( - self.root, 'ai_challenger_keypoint_train_20170902', 'keypoint_train_images_20170902', file_name) - - return image_path - - -def flip_back(output_flipped, matched_parts): - ''' - ouput_flipped: numpy.ndarray(batch_size, num_joints, height, width) - ''' - assert output_flipped.ndim == 4,\ - 'output_flipped should be [batch_size, num_joints, height, width]' - - output_flipped = output_flipped[:, :, :, ::-1] - - for pair in matched_parts: - tmp = output_flipped[:, pair[0], :, :].copy() - output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :] - output_flipped[:, pair[1], :, :] = tmp - - return output_flipped - - -def fliplr_joints(joints, joints_vis, width, matched_parts): - """ - flip coords - """ - # Flip horizontal - joints[:, 0] = width - joints[:, 0] - 1 - - # Change left-right parts - for pair in matched_parts: - joints[pair[0], :], joints[pair[1], :] = \ - joints[pair[1], :], joints[pair[0], :].copy() - joints_vis[pair[0], :], joints_vis[pair[1], :] = \ - joints_vis[pair[1], :], joints_vis[pair[0], :].copy() - - return joints*joints_vis, joints_vis - - -def get_affine_transform( - center, scale, rot, output_size, - shift=np.array([0, 0], dtype=np.float32), inv=0 -): - if not isinstance(scale, np.ndarray) and not isinstance(scale, list): - print(scale) - scale = np.array([scale, scale]) - - scale_tmp = scale * 200.0 - src_w = scale_tmp[0] - dst_w = output_size[0] - dst_h = output_size[1] - - rot_rad = np.pi * rot / 180 - src_dir = get_dir([0, src_w * -0.5], rot_rad) - dst_dir = np.array([0, dst_w * -0.5], np.float32) - - src = np.zeros((3, 2), dtype=np.float32) - dst = np.zeros((3, 2), dtype=np.float32) - src[0, :] = center + scale_tmp * shift - src[1, :] = center + src_dir + scale_tmp * shift - dst[0, :] = [dst_w * 0.5, dst_h * 0.5] - dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir - - src[2:, :] = get_3rd_point(src[0, :], src[1, :]) - dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :]) - - if inv: - trans = cv2.getAffineTransform(np.float32(dst), np.float32(src)) - else: - trans = cv2.getAffineTransform(np.float32(src), np.float32(dst)) - - return trans - - -def affine_transform(pt, t): - new_pt = np.array([pt[0], pt[1], 1.]).T - new_pt = np.dot(t, new_pt) - return new_pt[:2] - - -def get_3rd_point(a, b): - direct = a - b - return b + np.array([-direct[1], direct[0]], dtype=np.float32) - - -def get_dir(src_point, rot_rad): - sn, cs = np.sin(rot_rad), np.cos(rot_rad) - - src_result = [0, 0] - src_result[0] = src_point[0] * cs - src_point[1] * sn - src_result[1] = src_point[0] * sn + src_point[1] * cs - - return src_result - - -class CrowdPoseDataset(COCODataset): - def __init__(self, root, image_set, is_train, max_prompt_num=5, min_prompt_num=1, - radius=10, size=256, transparency=0.0, sample_weight=1.0, transform=None): - - super().__init__(root, image_set, is_train, max_prompt_num, min_prompt_num, - radius, size, transparency, sample_weight, transform) - - self.keypoints_type = { - 0: 'left_shoulder', - 1: 'right_shoulder', - 2: 'left_elbow', - 3: 'right_elbow', - 4: 'left_wrist', - 5: 'right_wrist', - 6: 'left_hip', - 7: 'right_hip', - 8: 'left_knee', - 9: 'right_knee', - 10: 'left_ankle', - 11: 'right_ankle', - 12: 'top_head', - 13: 'neck' - } - - self.num_joints = 14 - self.prob_half_body = -1 - self.flip_pairs = [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11]] - self.parent_ids = None - self.upper_body_ids = (0, 1, 2, 3, 4, 5, 12, 13) - self.lower_body_ids = (6, 7, 8, 9, 10, 11) - - self.db = self._get_db() - - logger.info('=> load {} samples'.format(len(self.db))) - - -class AICDataset(COCODataset): - def __init__(self, root, image_set, is_train, max_prompt_num=5, min_prompt_num=1, - radius=10, size=256, transparency=0.0, sample_weight=1.0, transform=None): - super().__init__(root, image_set, is_train, max_prompt_num, min_prompt_num, - radius, size, transparency, sample_weight, transform) - - self.keypoints_type = { - 0: "right_shoulder", - 1: "right_elbow", - 2: "right_wrist", - 3: "left_shoulder", - 4: "left_elbow", - 5: "left_wrist", - 6: "right_hip", - 7: "right_knee", - 8: "right_ankle", - 9: "left_hip", - 10: "left_knee", - 11: "left_ankle", - 12: "head_top", - 13: "neck" - } - - self.num_joints = 14 - self.prob_half_body = -1 - self.flip_pairs = [[0, 3], [1, 4], [2, 5], [6, 9], [7, 10], [8, 11]] - self.parent_ids = None - self.upper_body_ids = (0, 1, 2, 3, 4, 5, 12, 13) - self.lower_body_ids = (6, 7, 8, 9, 10, 11) - - self.db = self._get_db() - - logger.info('=> load {} samples'.format(len(self.db))) - - -class MPIIDataset(PoseDataset): - def __init__(self, root, image_set, is_train, max_prompt_num=5, min_prompt_num=1, - radius=10, size=256, transparency=0.0, sample_weight=1.0, transform=None): - super().__init__(root, image_set, is_train, max_prompt_num, min_prompt_num, - radius, size, transparency, sample_weight, transform) - - self.keypoints_type = { - 0: 'right_ankle', - 1: 'right_knee', - 2: 'right_hip', - 3: 'left_hip', - 4: 'left_knee', - 5: 'left_ankle', - 6: 'pelvis', - 7: 'thorax', - 8: 'upper_neck', - 9: 'head_top', - 10: 'right_wrist', - 11: 'right_elbow', - 12: 'right_shoulder', - 13: 'left_shoulder', - 14: 'left_elbow', - 15: 'left_wrist' - } - - self.data_format = 'jpg' - self.num_joints = 16 - self.prob_half_body = -1 - self.flip_pairs = [[0, 5], [1, 4], [2, 3], [10, 15], [11, 14], [12, 13]] - self.parent_ids = None - self.upper_body_ids = (7, 8, 9, 10, 11, 12, 13, 14, 15) - self.lower_body_ids = (0, 1, 2, 3, 4, 5, 6) - - self.db = self._get_db() - - logger.info('=> load {} samples'.format(len(self.db))) - - def _get_db(self): - # create train/val split - file_name = os.path.join( - self.root, 'annot', self.image_set+'.json' - ) - with open(file_name) as anno_file: - anno = json.load(anno_file) - - gt_db = [] - for a in anno: - image_name = a['image'] - - c = np.array(a['center'], dtype=np.float32) - s = np.array([a['scale'], a['scale']], dtype=np.float32) - - # Adjust center/scale slightly to avoid cropping limbs - if c[0] != -1: - c[1] = c[1] + 15 * s[1] - s = s * 1.25 - - # MPII uses matlab format, index is based 1, - # we should first convert to 0-based index - c = c - 1 - - joints_3d = np.zeros((self.num_joints, 3), dtype=np.float32) - joints_3d_vis = np.zeros((self.num_joints, 3), dtype=np.float32) - if self.image_set != 'test': - joints = np.array(a['joints']) - joints[:, 0:2] = joints[:, 0:2] - 1 - joints_vis = np.array(a['joints_vis']) - assert len(joints) == self.num_joints, \ - 'joint num diff: {} vs {}'.format(len(joints), - self.num_joints) - - joints_3d[:, 0:2] = joints[:, 0:2] - joints_3d_vis[:, 0] = joints_vis[:] - joints_3d_vis[:, 1] = joints_vis[:] - - image_dir = 'images.zip@' if self.data_format == 'zip' else 'images' - gt_db.append( - { - 'image': os.path.join(self.root, image_dir, image_name), - 'center': c, - 'scale': s, - 'joints_3d': joints_3d, - 'joints_3d_vis': joints_3d_vis, - 'filename': '', - 'imgnum': 0, - } - ) - - return gt_db diff --git a/spaces/Kevin676/AutoGPT/autogpt/agent/__init__.py b/spaces/Kevin676/AutoGPT/autogpt/agent/__init__.py deleted file mode 100644 index e928af2205b1c52d19dc89ec4246e8c1d2c20e3f..0000000000000000000000000000000000000000 --- a/spaces/Kevin676/AutoGPT/autogpt/agent/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from autogpt.agent.agent import Agent -from autogpt.agent.agent_manager import AgentManager - -__all__ = ["Agent", "AgentManager"] diff --git a/spaces/Kevin676/AutoGPT/autogpt/memory/weaviate.py b/spaces/Kevin676/AutoGPT/autogpt/memory/weaviate.py deleted file mode 100644 index 5408e9a97aa3594ad443448cfc31f2546a01eb09..0000000000000000000000000000000000000000 --- a/spaces/Kevin676/AutoGPT/autogpt/memory/weaviate.py +++ /dev/null @@ -1,127 +0,0 @@ -import uuid - -import weaviate -from weaviate import Client -from weaviate.embedded import EmbeddedOptions -from weaviate.util import generate_uuid5 - -from autogpt.config import Config -from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding - - -def default_schema(weaviate_index): - return { - "class": weaviate_index, - "properties": [ - { - "name": "raw_text", - "dataType": ["text"], - "description": "original text for the embedding", - } - ], - } - - -class WeaviateMemory(MemoryProviderSingleton): - def __init__(self, cfg): - auth_credentials = self._build_auth_credentials(cfg) - - url = f"{cfg.weaviate_protocol}://{cfg.weaviate_host}:{cfg.weaviate_port}" - - if cfg.use_weaviate_embedded: - self.client = Client( - embedded_options=EmbeddedOptions( - hostname=cfg.weaviate_host, - port=int(cfg.weaviate_port), - persistence_data_path=cfg.weaviate_embedded_path, - ) - ) - - print( - f"Weaviate Embedded running on: {url} with persistence path: {cfg.weaviate_embedded_path}" - ) - else: - self.client = Client(url, auth_client_secret=auth_credentials) - - self.index = WeaviateMemory.format_classname(cfg.memory_index) - self._create_schema() - - @staticmethod - def format_classname(index): - # weaviate uses capitalised index names - # The python client uses the following code to format - # index names before the corresponding class is created - if len(index) == 1: - return index.capitalize() - return index[0].capitalize() + index[1:] - - def _create_schema(self): - schema = default_schema(self.index) - if not self.client.schema.contains(schema): - self.client.schema.create_class(schema) - - def _build_auth_credentials(self, cfg): - if cfg.weaviate_username and cfg.weaviate_password: - return weaviate.AuthClientPassword( - cfg.weaviate_username, cfg.weaviate_password - ) - if cfg.weaviate_api_key: - return weaviate.AuthApiKey(api_key=cfg.weaviate_api_key) - else: - return None - - def add(self, data): - vector = get_ada_embedding(data) - - doc_uuid = generate_uuid5(data, self.index) - data_object = {"raw_text": data} - - with self.client.batch as batch: - batch.add_data_object( - uuid=doc_uuid, - data_object=data_object, - class_name=self.index, - vector=vector, - ) - - return f"Inserting data into memory at uuid: {doc_uuid}:\n data: {data}" - - def get(self, data): - return self.get_relevant(data, 1) - - def clear(self): - self.client.schema.delete_all() - - # weaviate does not yet have a neat way to just remove the items in an index - # without removing the entire schema, therefore we need to re-create it - # after a call to delete_all - self._create_schema() - - return "Obliterated" - - def get_relevant(self, data, num_relevant=5): - query_embedding = get_ada_embedding(data) - try: - results = ( - self.client.query.get(self.index, ["raw_text"]) - .with_near_vector({"vector": query_embedding, "certainty": 0.7}) - .with_limit(num_relevant) - .do() - ) - - if len(results["data"]["Get"][self.index]) > 0: - return [ - str(item["raw_text"]) for item in results["data"]["Get"][self.index] - ] - else: - return [] - - except Exception as err: - print(f"Unexpected error {err=}, {type(err)=}") - return [] - - def get_stats(self): - result = self.client.query.aggregate(self.index).with_meta_count().do() - class_data = result["data"]["Aggregate"][self.index] - - return class_data[0]["meta"] if class_data else {} diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/ppg_extractor/encoder/subsampling.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/ppg_extractor/encoder/subsampling.py deleted file mode 100644 index e754126b2ec1f2d914206ec35ec026c7b6add17f..0000000000000000000000000000000000000000 --- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/ppg_extractor/encoder/subsampling.py +++ /dev/null @@ -1,218 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -# Copyright 2019 Shigeki Karita -# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) - -"""Subsampling layer definition.""" -import logging -import torch - -from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding - - -class Conv2dSubsampling(torch.nn.Module): - """Convolutional 2D subsampling (to 1/4 length or 1/2 length). - - :param int idim: input dim - :param int odim: output dim - :param flaot dropout_rate: dropout rate - :param torch.nn.Module pos_enc: custom position encoding layer - - """ - - def __init__(self, idim, odim, dropout_rate, pos_enc=None, - subsample_by_2=False, - ): - """Construct an Conv2dSubsampling object.""" - super(Conv2dSubsampling, self).__init__() - self.subsample_by_2 = subsample_by_2 - if subsample_by_2: - self.conv = torch.nn.Sequential( - torch.nn.Conv2d(1, odim, kernel_size=5, stride=1, padding=2), - torch.nn.ReLU(), - torch.nn.Conv2d(odim, odim, kernel_size=4, stride=2, padding=1), - torch.nn.ReLU(), - ) - self.out = torch.nn.Sequential( - torch.nn.Linear(odim * (idim // 2), odim), - pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate), - ) - else: - self.conv = torch.nn.Sequential( - torch.nn.Conv2d(1, odim, kernel_size=4, stride=2, padding=1), - torch.nn.ReLU(), - torch.nn.Conv2d(odim, odim, kernel_size=4, stride=2, padding=1), - torch.nn.ReLU(), - ) - self.out = torch.nn.Sequential( - torch.nn.Linear(odim * (idim // 4), odim), - pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate), - ) - - def forward(self, x, x_mask): - """Subsample x. - - :param torch.Tensor x: input tensor - :param torch.Tensor x_mask: input mask - :return: subsampled x and mask - :rtype Tuple[torch.Tensor, torch.Tensor] - - """ - x = x.unsqueeze(1) # (b, c, t, f) - x = self.conv(x) - b, c, t, f = x.size() - x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) - if x_mask is None: - return x, None - if self.subsample_by_2: - return x, x_mask[:, :, ::2] - else: - return x, x_mask[:, :, ::2][:, :, ::2] - - def __getitem__(self, key): - """Subsample x. - - When reset_parameters() is called, if use_scaled_pos_enc is used, - return the positioning encoding. - - """ - if key != -1: - raise NotImplementedError("Support only `-1` (for `reset_parameters`).") - return self.out[key] - - -class Conv2dNoSubsampling(torch.nn.Module): - """Convolutional 2D without subsampling. - - :param int idim: input dim - :param int odim: output dim - :param flaot dropout_rate: dropout rate - :param torch.nn.Module pos_enc: custom position encoding layer - - """ - - def __init__(self, idim, odim, dropout_rate, pos_enc=None): - """Construct an Conv2dSubsampling object.""" - super().__init__() - logging.info("Encoder does not do down-sample on mel-spectrogram.") - self.conv = torch.nn.Sequential( - torch.nn.Conv2d(1, odim, kernel_size=5, stride=1, padding=2), - torch.nn.ReLU(), - torch.nn.Conv2d(odim, odim, kernel_size=5, stride=1, padding=2), - torch.nn.ReLU(), - ) - self.out = torch.nn.Sequential( - torch.nn.Linear(odim * idim, odim), - pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate), - ) - - def forward(self, x, x_mask): - """Subsample x. - - :param torch.Tensor x: input tensor - :param torch.Tensor x_mask: input mask - :return: subsampled x and mask - :rtype Tuple[torch.Tensor, torch.Tensor] - - """ - x = x.unsqueeze(1) # (b, c, t, f) - x = self.conv(x) - b, c, t, f = x.size() - x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) - if x_mask is None: - return x, None - return x, x_mask - - def __getitem__(self, key): - """Subsample x. - - When reset_parameters() is called, if use_scaled_pos_enc is used, - return the positioning encoding. - - """ - if key != -1: - raise NotImplementedError("Support only `-1` (for `reset_parameters`).") - return self.out[key] - - -class Conv2dSubsampling6(torch.nn.Module): - """Convolutional 2D subsampling (to 1/6 length). - - :param int idim: input dim - :param int odim: output dim - :param flaot dropout_rate: dropout rate - - """ - - def __init__(self, idim, odim, dropout_rate): - """Construct an Conv2dSubsampling object.""" - super(Conv2dSubsampling6, self).__init__() - self.conv = torch.nn.Sequential( - torch.nn.Conv2d(1, odim, 3, 2), - torch.nn.ReLU(), - torch.nn.Conv2d(odim, odim, 5, 3), - torch.nn.ReLU(), - ) - self.out = torch.nn.Sequential( - torch.nn.Linear(odim * (((idim - 1) // 2 - 2) // 3), odim), - PositionalEncoding(odim, dropout_rate), - ) - - def forward(self, x, x_mask): - """Subsample x. - - :param torch.Tensor x: input tensor - :param torch.Tensor x_mask: input mask - :return: subsampled x and mask - :rtype Tuple[torch.Tensor, torch.Tensor] - """ - x = x.unsqueeze(1) # (b, c, t, f) - x = self.conv(x) - b, c, t, f = x.size() - x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) - if x_mask is None: - return x, None - return x, x_mask[:, :, :-2:2][:, :, :-4:3] - - -class Conv2dSubsampling8(torch.nn.Module): - """Convolutional 2D subsampling (to 1/8 length). - - :param int idim: input dim - :param int odim: output dim - :param flaot dropout_rate: dropout rate - - """ - - def __init__(self, idim, odim, dropout_rate): - """Construct an Conv2dSubsampling object.""" - super(Conv2dSubsampling8, self).__init__() - self.conv = torch.nn.Sequential( - torch.nn.Conv2d(1, odim, 3, 2), - torch.nn.ReLU(), - torch.nn.Conv2d(odim, odim, 3, 2), - torch.nn.ReLU(), - torch.nn.Conv2d(odim, odim, 3, 2), - torch.nn.ReLU(), - ) - self.out = torch.nn.Sequential( - torch.nn.Linear(odim * ((((idim - 1) // 2 - 1) // 2 - 1) // 2), odim), - PositionalEncoding(odim, dropout_rate), - ) - - def forward(self, x, x_mask): - """Subsample x. - - :param torch.Tensor x: input tensor - :param torch.Tensor x_mask: input mask - :return: subsampled x and mask - :rtype Tuple[torch.Tensor, torch.Tensor] - """ - x = x.unsqueeze(1) # (b, c, t, f) - x = self.conv(x) - b, c, t, f = x.size() - x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) - if x_mask is None: - return x, None - return x, x_mask[:, :, :-2:2][:, :, :-2:2][:, :, :-2:2] diff --git a/spaces/KingBlaze1227/PC-PICKERS/README.md b/spaces/KingBlaze1227/PC-PICKERS/README.md deleted file mode 100644 index 1453b7a93e22cf243f0d811f768c39c53d44ff6e..0000000000000000000000000000000000000000 --- a/spaces/KingBlaze1227/PC-PICKERS/README.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -title: PC PICKERS -emoji: 🐠 -colorFrom: pink -colorTo: yellow -sdk: static -pinned: false -license: cc ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Kreaols/ChuanhuChatGPT/assets/custom.css b/spaces/Kreaols/ChuanhuChatGPT/assets/custom.css deleted file mode 100644 index 22108488886cfc8d7772214dd9b83727b3fca6a3..0000000000000000000000000000000000000000 --- a/spaces/Kreaols/ChuanhuChatGPT/assets/custom.css +++ /dev/null @@ -1,468 +0,0 @@ -:root { - --chatbot-color-light: #000000; - --chatbot-color-dark: #FFFFFF; - --chatbot-background-color-light: #F3F3F3; - --chatbot-background-color-dark: #121111; - --message-user-background-color-light: #95EC69; - --message-user-background-color-dark: #26B561; - --message-bot-background-color-light: #FFFFFF; - --message-bot-background-color-dark: #2C2C2C; -} - -#app_title { - font-weight: var(--prose-header-text-weight); - font-size: var(--text-xxl); - line-height: 1.3; - text-align: left; - margin-top: 6px; - white-space: nowrap; -} -#description { - text-align: center; - margin: 32px 0 4px 0; -} - -/* gradio的页脚信息 */ -footer { - /* display: none !important; */ - margin-top: .2em !important; - font-size: 85%; -} -#footer { - text-align: center; -} -#footer div { - display: inline-block; -} -#footer .versions{ - font-size: 85%; - opacity: 0.60; -} - -#float_display { - position: absolute; - max-height: 30px; -} -/* user_info */ -#user_info { - white-space: nowrap; - position: absolute; left: 8em; top: .2em; - z-index: var(--layer-2); - box-shadow: var(--block-shadow); - border: none; border-radius: var(--block-label-radius); - background: var(--color-accent); - padding: var(--block-label-padding); - font-size: var(--block-label-text-size); line-height: var(--line-sm); - width: auto; min-height: 30px!important; - opacity: 1; - transition: opacity 0.3s ease-in-out; -} -#user_info .wrap { - opacity: 0; -} -#user_info p { - color: white; - font-weight: var(--block-label-text-weight); -} -#user_info.hideK { - opacity: 0; - transition: opacity 1s ease-in-out; -} - -/* status_display */ -#status_display { - display: flex; - min-height: 2em; - align-items: flex-end; - justify-content: flex-end; -} -#status_display p { - font-size: .85em; - font-family: ui-monospace, "SF Mono", "SFMono-Regular", "Menlo", "Consolas", "Liberation Mono", "Microsoft Yahei UI", "Microsoft Yahei", monospace; - /* Windows下中文的monospace会fallback为新宋体，实在太丑，这里折中使用微软雅黑 */ - color: var(--body-text-color-subdued); -} - -#status_display { - transition: all 0.6s; -} -#chuanhu_chatbot { - transition: height 0.3s ease; -} - -/* usage_display */ -.insert_block { - position: relative; - margin: 0; - padding: .5em 1em; - box-shadow: var(--block-shadow); - border-width: var(--block-border-width); - border-color: var(--block-border-color); - border-radius: var(--block-radius); - background: var(--block-background-fill); - width: 100%; - line-height: var(--line-sm); - min-height: 2em; -} -#usage_display p, #usage_display span { - margin: 0; - font-size: .85em; - color: var(--body-text-color-subdued); -} -.progress-bar { - background-color: var(--input-background-fill);; - margin: .5em 0 !important; - height: 20px; - border-radius: 10px; - overflow: hidden; -} -.progress { - background-color: var(--block-title-background-fill); - height: 100%; - border-radius: 10px; - text-align: right; - transition: width 0.5s ease-in-out; -} -.progress-text { - /* color: white; */ - color: var(--color-accent) !important; - font-size: 1em !important; - font-weight: bold; - padding-right: 10px; - line-height: 20px; -} - -.apSwitch { - top: 2px; - display: inline-block; - height: 24px; - position: relative; - width: 48px; - border-radius: 12px; -} -.apSwitch input { - display: none !important; -} -.apSlider { - background-color: var(--neutral-200); - bottom: 0; - cursor: pointer; - left: 0; - position: absolute; - right: 0; - top: 0; - transition: .4s; - font-size: 18px; - border-radius: 12px; -} -.apSlider::before { - bottom: -1.5px; - left: 1px; - position: absolute; - transition: .4s; - content: "🌞"; -} -input:checked + .apSlider { - background-color: var(--primary-600); -} -input:checked + .apSlider::before { - transform: translateX(23px); - content:"🌚"; -} - -/* Override Slider Styles (for webkit browsers like Safari and Chrome) - * 好希望这份提案能早日实现 https://github.com/w3c/csswg-drafts/issues/4410 - * 进度滑块在各个平台还是太不统一了 - */ -input[type="range"] { - -webkit-appearance: none; - height: 4px; - background: var(--input-background-fill); - border-radius: 5px; - background-image: linear-gradient(var(--primary-500),var(--primary-500)); - background-size: 0% 100%; - background-repeat: no-repeat; -} -input[type="range"]::-webkit-slider-thumb { - -webkit-appearance: none; - height: 20px; - width: 20px; - border-radius: 50%; - border: solid 0.5px #ddd; - background-color: white; - cursor: ew-resize; - box-shadow: var(--input-shadow); - transition: background-color .1s ease; -} -input[type="range"]::-webkit-slider-thumb:hover { - background: var(--neutral-50); -} -input[type=range]::-webkit-slider-runnable-track { - -webkit-appearance: none; - box-shadow: none; - border: none; - background: transparent; -} - -#submit_btn, #cancel_btn { - 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height: 21px; -} -/* list */ -ol:not(.options), ul:not(.options) { - padding-inline-start: 2em !important; -} - -/* 亮色（默认） */ -#chuanhu_chatbot { - background-color: var(--chatbot-background-color-light) !important; - color: var(--chatbot-color-light) !important; -} -[data-testid = "bot"] { - background-color: var(--message-bot-background-color-light) !important; -} -[data-testid = "user"] { - background-color: var(--message-user-background-color-light) !important; -} -/* 暗色 */ -.dark #chuanhu_chatbot { - background-color: var(--chatbot-background-color-dark) !important; - color: var(--chatbot-color-dark) !important; -} -.dark [data-testid = "bot"] { - background-color: var(--message-bot-background-color-dark) !important; -} -.dark [data-testid = "user"] { - background-color: var(--message-user-background-color-dark) !important; -} - -/* 屏幕宽度大于等于500px的设备 */ -/* update on 2023.4.8: 高度的细致调整已写入JavaScript */ -@media screen and (min-width: 500px) { - #chuanhu_chatbot { - height: calc(100vh - 200px); - } - #chuanhu_chatbot .wrap { - max-height: calc(100vh - 200px - var(--line-sm)*1rem - 2*var(--block-label-margin) ); - } -} -/* 屏幕宽度小于500px的设备 */ -@media screen and (max-width: 499px) { - #chuanhu_chatbot { - height: calc(100vh - 140px); - } - #chuanhu_chatbot .wrap { - max-height: calc(100vh - 140px - var(--line-sm)*1rem - 2*var(--block-label-margin) ); - } - [data-testid = "bot"] { - max-width: 95% !important; - } - #app_title h1{ - letter-spacing: -1px; font-size: 22px; - } -} -#chuanhu_chatbot .wrap { - overflow-x: hidden; -} -/* 对话气泡 */ -.message { - border-radius: var(--radius-xl) !important; - border: none; - padding: var(--spacing-xl) !important; - font-size: var(--text-md) !important; - line-height: var(--line-md) !important; - min-height: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl)); - min-width: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl)); -} -[data-testid = "bot"] { - max-width: 85%; - border-bottom-left-radius: 0 !important; -} -[data-testid = "user"] { - max-width: 85%; - width: auto !important; - border-bottom-right-radius: 0 !important; -} - -.message.user p { - white-space: pre-wrap; -} -.message .user-message { - display: block; - padding: 0 !important; - white-space: pre-wrap; -} - -.message .md-message p { - margin-top: 0.6em !important; - margin-bottom: 0.6em !important; -} -.message .md-message p:first-child { margin-top: 0 !important; } -.message .md-message p:last-of-type { margin-bottom: 0 !important; } - -.message .md-message { - display: block; - padding: 0 !important; -} -.message .raw-message p { - margin:0 !important; -} -.message .raw-message { - display: block; - padding: 0 !important; - white-space: pre-wrap; -} -.raw-message.hideM, .md-message.hideM { - display: none; -} - -/* custom buttons */ -.chuanhu-btn { - border-radius: 5px; - /* background-color: #E6E6E6 !important; */ - color: rgba(120, 120, 120, 0.64) !important; - padding: 4px !important; - position: absolute; - right: -22px; - cursor: pointer !important; - transition: color .2s ease, background-color .2s ease; -} -.chuanhu-btn:hover { - background-color: rgba(167, 167, 167, 0.25) !important; - color: unset !important; -} -.chuanhu-btn:active { - background-color: rgba(167, 167, 167, 0.5) !important; -} -.chuanhu-btn:focus { - outline: none; -} -.copy-bot-btn { - /* top: 18px; */ - bottom: 0; -} -.toggle-md-btn { - /* top: 0; */ - bottom: 20px; -} -.copy-code-btn { - position: relative; - float: right; - font-size: 1em; - cursor: pointer; -} - -.message-wrap>div img{ - border-radius: 10px !important; -} - -/* history message */ -.wrap>.history-message { - padding: 10px !important; -} -.history-message { - /* padding: 0 !important; */ - opacity: 80%; - display: flex; - flex-direction: column; -} -.history-message>.history-message { - padding: 0 !important; -} -.history-message>.message-wrap { - padding: 0 !important; - margin-bottom: 16px; -} -.history-message>.message { - margin-bottom: 16px; -} -.wrap>.history-message::after { - content: ""; - display: block; - height: 2px; - background-color: var(--body-text-color-subdued); - margin-bottom: 10px; - margin-top: -10px; - clear: both; -} -.wrap>.history-message>:last-child::after { - content: "仅供查看"; - display: block; - text-align: center; - color: var(--body-text-color-subdued); - font-size: 0.8em; -} - -/* 表格 */ -table { - margin: 1em 0; - border-collapse: collapse; - empty-cells: show; -} -td,th { - border: 1.2px solid var(--border-color-primary) !important; - padding: 0.2em; -} -thead { - background-color: rgba(175,184,193,0.2); -} -thead th { - padding: .5em .2em; -} -/* 行内代码 */ -.message :not(pre) code { - display: inline; - white-space: break-spaces; - font-family: var(--font-mono); - border-radius: 6px; - margin: 0 2px 0 2px; - padding: .2em .4em .1em .4em; - background-color: rgba(175,184,193,0.2); -} -/* 代码块 */ -.message pre, -.message pre[class*=language-] { - color: #fff; - overflow-x: auto; - overflow-y: hidden; - margin: .8em 1em 1em 0em !important; - padding: var(--spacing-xl) 1.2em !important; - border-radius: var(--radius-lg) !important; -} -.message pre code, -.message pre code[class*=language-] { - color: #fff; - padding: 0; - margin: 0; - background-color: unset; - text-shadow: none; - font-family: var(--font-mono); -} -/* 覆盖 gradio 丑陋的复制按钮样式 */ -pre button[title="copy"] { - border-radius: 5px; - transition: background-color .2s ease; -} -pre button[title="copy"]:hover { - background-color: #333232; -} -pre button .check { - color: #fff !important; - background: var(--neutral-950) !important; -} - -/* 覆盖prism.css */ -.language-css .token.string, -.style .token.string, -.token.entity, -.token.operator, -.token.url { - background: none !important; -} diff --git a/spaces/KyanChen/RSPrompter/mmdet/__init__.py b/spaces/KyanChen/RSPrompter/mmdet/__init__.py deleted file mode 100644 index 9f6140e121bc140896a7f432465651bfb1111575..0000000000000000000000000000000000000000 --- a/spaces/KyanChen/RSPrompter/mmdet/__init__.py +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import mmcv -import mmengine -from mmengine.utils import digit_version - -from .version import __version__, version_info - -mmcv_minimum_version = '2.0' -mmcv_maximum_version = '2.5.0' -mmcv_version = digit_version(mmcv.__version__) - -mmengine_minimum_version = '0.7.0' -mmengine_maximum_version = '1.5.0' -mmengine_version = digit_version(mmengine.__version__) - -assert (mmcv_version >= digit_version(mmcv_minimum_version) - and mmcv_version < digit_version(mmcv_maximum_version)), \ - f'MMCV=={mmcv.__version__} is used but incompatible. ' \ - f'Please install mmcv>={mmcv_minimum_version}, <{mmcv_maximum_version}.' - -assert (mmengine_version >= digit_version(mmengine_minimum_version) - and mmengine_version < digit_version(mmengine_maximum_version)), \ - f'MMEngine=={mmengine.__version__} is used but incompatible. ' \ - f'Please install mmengine>={mmengine_minimum_version}, ' \ - f'<{mmengine_maximum_version}.' - -__all__ = ['__version__', 'version_info', 'digit_version'] diff --git a/spaces/LabelStudio/LabelStudio/Dockerfile b/spaces/LabelStudio/LabelStudio/Dockerfile deleted file mode 100644 index 9ba913c6937a5238dd32d654197330a4bbf6f63e..0000000000000000000000000000000000000000 --- a/spaces/LabelStudio/LabelStudio/Dockerfile +++ /dev/null @@ -1,127 +0,0 @@ -FROM heartexlabs/label-studio:hf-latest - -################################################################################ -# -# How to Disable Public Account Creation -# -------------------------------------- -# 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. Uncomment the following line -# 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. -# -################################################################################ - -################################################################################ -# -# How to Enable Persistent Storage for Label Studio in Hugging Face Spaces -# ------------------------------------------------------------------------ -# -# 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 through one of two methods: -# -# 1) Enabling Hugging Face Persistent Storage for saving project and annotation -# settings, as well as local task storage. -# 2) Connecting an external Postgres database for saving project and annotation -# settings, and cloud by connecting cloud storage for tasks. -# -################################################################################ - -################################################################################ -# -# How to Enable Hugging Face Persistent Storage for Label Studio -# -------------------------------------------------------------- -# -# In the Hugging Face Label Studio Space settings, select the appropriate -# Persistent Storage tier. Note that Persistent Storage is a paid add-on. -# By default, persistent storage is mounted to /data. In your Space settings, -# set the following variables: -# -# LABEL_STUDIO_BASE_DATA_DIR=/data -# ENV STORAGE_PERSISTENCE=1 -# -# Your space will restart. NOTE: if you have existing settings and data, -# they will be lost in this first restart. Data and setting will only be -# preserved on subsequent restarts of the space. -# -################################################################################ - -################################################################################ -# -# How to Enable Configuration Persistence with Postgres -# ----------------------------------------------------- -# -# 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. -# -# ENV DJANGO_DB=default -# ENV POSTGRE_NAME= -# ENV POSTGRE_PORT= -# ENV POSTGRE_USER= -# ENV POSTGRE_PASSWORD= -# ENV POSTGRE_PORT= -# ENV POSTGRE_HOST= -# -# Uncomment the following line or set the following Space 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. -# -# -# How to Enable Cloud Storage -# --------------------------- -# 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="" -# -################################################################################ - -CMD exec label-studio --host=$SPACE_HOST diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_pack/models.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_pack/models.py deleted file mode 100644 index d898604960f129fc37f464ee3669bb61cfa8f614..0000000000000000000000000000000000000000 --- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_pack/models.py +++ /dev/null @@ -1,1142 +0,0 @@ -import math -import torch -from torch import nn -from torch.nn import functional as F -from lib.infer.infer_pack import modules -from lib.infer.infer_pack import attentions -from lib.infer.infer_pack.commons import get_padding -from torch.nn import Conv1d, ConvTranspose1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from lib.infer.infer_pack.commons import init_weights -import numpy as np -from lib.infer.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 TextEncoder768(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(768, 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 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) - if uv.device.type == "privateuseone": # for DirectML - uv = uv.float() - 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 ###%1意味着n_har的乘积无法后处理优化 - 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 #####%1意味着后面的cumsum无法再优化 - 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, pitchf, y, y_lengths, ds - ): # 这里ds是id，[bs,1] - # print(1,pitch.shape)#[bs,t] - g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的 - m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) - z_p = self.flow(z, y_mask, g=g) - z_slice, ids_slice = commons.rand_slice_segments( - z, y_lengths, self.segment_size - ) - # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length) - pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size) - # print(-2,pitchf.shape,z_slice.shape) - o = self.dec(z_slice, pitchf, g=g) - return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) - - def infer(self, phone, phone_lengths, pitch, nsff0, sid, rate=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) * torch.randn_like(m_p) * 0.66666) * x_mask - if rate: - head = int(z_p.shape[2] * rate) - z_p = z_p[:, :, -head:] - x_mask = x_mask[:, :, -head:] - nsff0 = nsff0[:, -head:] - z = self.flow(z_p, x_mask, g=g, reverse=True) - o = self.dec(z * x_mask, nsff0, g=g) - return o, x_mask, (z, z_p, m_p, logs_p) - - -class SynthesizerTrnMs768NSFsid(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 = TextEncoder768( - 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, pitchf, y, y_lengths, ds - ): # 这里ds是id，[bs,1] - # print(1,pitch.shape)#[bs,t] - g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的 - m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) - z_p = self.flow(z, y_mask, g=g) - z_slice, ids_slice = commons.rand_slice_segments( - z, y_lengths, self.segment_size - ) - # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length) - pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size) - # print(-2,pitchf.shape,z_slice.shape) - o = self.dec(z_slice, pitchf, g=g) - return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) - - def infer(self, phone, phone_lengths, pitch, nsff0, sid, rate=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) * torch.randn_like(m_p) * 0.66666) * x_mask - if rate: - head = int(z_p.shape[2] * rate) - z_p = z_p[:, :, -head:] - x_mask = x_mask[:, :, -head:] - nsff0 = nsff0[:, -head:] - z = self.flow(z_p, x_mask, g=g, reverse=True) - o = self.dec(z * x_mask, nsff0, g=g) - return o, x_mask, (z, z_p, m_p, logs_p) - - -class SynthesizerTrnMs256NSFsid_nono(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=None, - **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 = TextEncoder256( - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - f0=False, - ) - self.dec = Generator( - inter_channels, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=gin_channels, - ) - 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, y, y_lengths, ds): # 这里ds是id，[bs,1] - g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的 - m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) - z_p = self.flow(z, y_mask, g=g) - z_slice, ids_slice = commons.rand_slice_segments( - z, y_lengths, self.segment_size - ) - o = self.dec(z_slice, g=g) - return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) - - def infer(self, phone, phone_lengths, sid, rate=None): - g = self.emb_g(sid).unsqueeze(-1) - m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) - z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask - if rate: - head = int(z_p.shape[2] * rate) - z_p = z_p[:, :, -head:] - x_mask = x_mask[:, :, -head:] - z = self.flow(z_p, x_mask, g=g, reverse=True) - o = self.dec(z * x_mask, g=g) - return o, x_mask, (z, z_p, m_p, logs_p) - - -class SynthesizerTrnMs768NSFsid_nono(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=None, - **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 = TextEncoder768( - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - f0=False, - ) - self.dec = Generator( - inter_channels, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=gin_channels, - ) - 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, y, y_lengths, ds): # 这里ds是id，[bs,1] - g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的 - m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) - z_p = self.flow(z, y_mask, g=g) - z_slice, ids_slice = commons.rand_slice_segments( - z, y_lengths, self.segment_size - ) - o = self.dec(z_slice, g=g) - return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) - - def infer(self, phone, phone_lengths, sid, rate=None): - g = self.emb_g(sid).unsqueeze(-1) - m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) - z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask - if rate: - head = int(z_p.shape[2] * rate) - z_p = z_p[:, :, -head:] - x_mask = x_mask[:, :, -head:] - z = self.flow(z_p, x_mask, g=g, reverse=True) - o = self.dec(z * x_mask, g=g) - return o, x_mask, (z, z_p, m_p, logs_p) - - -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 MultiPeriodDiscriminatorV2(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(MultiPeriodDiscriminatorV2, self).__init__() - # periods = [2, 3, 5, 7, 11, 17] - periods = [2, 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 \ No newline at end of file diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/tools/server.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/tools/server.py deleted file mode 100644 index 0b6110f0779f2f0e6c1804abca6c3990975732ce..0000000000000000000000000000000000000000 --- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/tools/server.py +++ /dev/null @@ -1,373 +0,0 @@ -from flask import Flask, request, Response -import os -import sys -import requests -import shutil -import subprocess -import wget -import signal -from bs4 import BeautifulSoup -import logging -import click - - -app = Flask(__name__) - -# Disable flask starting message -log = logging.getLogger('werkzeug') -log.setLevel(logging.ERROR) - -def secho(text, file=None, nl=None, err=None, color=None, **styles): - pass - -def echo(text, file=None, nl=None, err=None, color=None, **styles): - pass - -click.echo = echo -click.secho = secho - -# Get the current directory path -now_dir = os.path.dirname(os.path.abspath(__file__)) - -# Go up two levels in the directory hierarchy -for _ in range(2): - now_dir = os.path.dirname(now_dir) - -# Add now_dir to sys.path so Python can find modules in that location -sys.path.append(now_dir) - -from assets.i18n.i18n import I18nAuto -i18n = I18nAuto() - -# Use the code from the resources module but with some changes -def find_folder_parent(search_dir, folder_name): - for dirpath, dirnames, filenames in os.walk(search_dir): - if folder_name in dirnames: - return os.path.abspath(dirpath) - return None - -def get_mediafire_download_link(url): - response = requests.get(url) - response.raise_for_status() - soup = BeautifulSoup(response.text, 'html.parser') - download_button = soup.find('a', {'class': 'input popsok', 'aria-label': 'Download file'}) - if download_button: - download_link = download_button.get('href') - return download_link - else: - return None - -def download_from_url(url): - file_path = find_folder_parent(now_dir, "assets") - print(file_path) - zips_path = os.path.join(file_path, "assets", "zips") - print(zips_path) - os.makedirs(zips_path, exist_ok=True) - if url != "": - print(i18n("Downloading the file: ") + f"{url}") - if "drive.google.com" in url: - if "file/d/" in url: - file_id = url.split("file/d/")[1].split("/")[0] - elif "id=" in url: - file_id = url.split("id=")[1].split("&")[0] - else: - return None - - if file_id: - os.chdir(zips_path) - result = subprocess.run( - ["gdown", f"https://drive.google.com/uc?id={file_id}", "--fuzzy"], - capture_output=True, - text=True, - encoding="utf-8", - ) - if ( - "Too many users have viewed or downloaded this file recently" - in str(result.stderr) - ): - return "too much use" - if "Cannot retrieve the public link of the file." in str(result.stderr): - return "private link" - print(result.stderr) - - elif "/blob/" in url or "/resolve/" in url: - os.chdir(zips_path) - if "/blob/" in url: - url = url.replace("/blob/", "/resolve/") - - response = requests.get(url, stream=True) - if response.status_code == 200: - file_name = url.split("/")[-1] - file_name = file_name.replace("%20", "_") - total_size_in_bytes = int(response.headers.get('content-length', 0)) - block_size = 1024 # 1 Kibibyte - progress_bar_length = 50 - progress = 0 - with open(os.path.join(zips_path, file_name), 'wb') as file: - for data in response.iter_content(block_size): - file.write(data) - progress += len(data) - progress_percent = int((progress / total_size_in_bytes) * 100) - num_dots = int((progress / total_size_in_bytes) * progress_bar_length) - progress_bar = "[" + "." * num_dots + " " * (progress_bar_length - num_dots) + "]" - print(f"{progress_percent}% {progress_bar} {progress}/{total_size_in_bytes} ", end="\r") - if progress_percent == 100: - print("\n") - else: - os.chdir(file_path) - return None - elif "mega.nz" in url: - if "#!" in url: - file_id = url.split("#!")[1].split("!")[0] - elif "file/" in url: - file_id = url.split("file/")[1].split("/")[0] - else: - return None - if file_id: - print("Mega.nz is unsupported due mega.py deprecation") - elif "/tree/main" in url: - response = requests.get(url) - soup = BeautifulSoup(response.content, "html.parser") - temp_url = "" - for link in soup.find_all("a", href=True): - if link["href"].endswith(".zip"): - temp_url = link["href"] - break - if temp_url: - url = temp_url - url = url.replace("blob", "resolve") - if "huggingface.co" not in url: - url = "https://huggingface.co" + url - - wget.download(url) - else: - print("No .zip file found on the page.") - elif "cdn.discordapp.com" in url: - file = requests.get(url) - os.chdir("./assets/zips") - if file.status_code == 200: - name = url.split("/") - with open( - os.path.join(name[-1]), "wb" - ) as newfile: - newfile.write(file.content) - else: - return None - elif "pixeldrain.com" in url: - try: - file_id = url.split("pixeldrain.com/u/")[1] - os.chdir(zips_path) - print(file_id) - response = requests.get(f"https://pixeldrain.com/api/file/{file_id}") - if response.status_code == 200: - file_name = ( - response.headers.get("Content-Disposition") - .split("filename=")[-1] - .strip('";') - ) - os.makedirs(zips_path, exist_ok=True) - with open(os.path.join(zips_path, file_name), "wb") as newfile: - newfile.write(response.content) - os.chdir(file_path) - return "downloaded" - else: - os.chdir(file_path) - return None - except Exception as e: - print(e) - os.chdir(file_path) - return None - elif "mediafire.com" in url: - download_link = get_mediafire_download_link(url) - if download_link: - os.chdir(zips_path) - wget.download(download_link) - else: - return None - elif "www.weights.gg" in url: - #Pls weights creator dont fix this because yes. c: - url_parts = url.split("/") - weights_gg_index = url_parts.index("www.weights.gg") - if weights_gg_index != -1 and weights_gg_index < len(url_parts) - 1: - model_part = "/".join(url_parts[weights_gg_index + 1:]) - if "models" in model_part: - model_part = model_part.split("models/")[-1] - print(model_part) - if model_part: - download_url = f"https://www.weights.gg/es/models/{model_part}" - response = requests.get(download_url) - if response.status_code == 200: - soup = BeautifulSoup(response.text, "html.parser") - button_link = soup.find("a", class_="bg-black text-white px-3 py-2 rounded-lg flex items-center gap-1") - if button_link: - download_link = button_link["href"] - result = download_from_url(download_link) - if result == "downloaded": - return "downloaded" - else: - return None - else: - return None - else: - return None - else: - return None - else: - return None - else: - return None - else: - os.chdir(zips_path) - wget.download(url) - - # Fix points in the zips - for currentPath, _, zipFiles in os.walk(zips_path): - for Files in zipFiles: - filePart = Files.split(".") - extensionFile = filePart[len(filePart) - 1] - filePart.pop() - nameFile = "_".join(filePart) - realPath = os.path.join(currentPath, Files) - os.rename(realPath, nameFile + "." + extensionFile) - - os.chdir(file_path) - print(i18n("Full download")) - return "downloaded" - else: - return None - -def extract_and_show_progress(zipfile_path, unzips_path): - try: - # Use shutil because zipfile not working - shutil.unpack_archive(zipfile_path, unzips_path) - return True - except Exception as e: - print(f"Error al descomprimir {zipfile_path}: {e}") - return False - - -@app.route('/download/ (false) - - const copyToClipboard = (value: string) => { - if (typeof window === 'undefined' || !navigator.clipboard?.writeText) { - return - } - - if (!value) { - return - } - - navigator.clipboard.writeText(value).then(() => { - setIsCopied(true) - - setTimeout(() => { - setIsCopied(false) - }, timeout) - }) - } - - return { isCopied, copyToClipboard } -} diff --git a/spaces/NoorAzam/model4/app.py b/spaces/NoorAzam/model4/app.py deleted file mode 100644 index 93416c9454473157cb6838da3f4771fb04d89c81..0000000000000000000000000000000000000000 --- a/spaces/NoorAzam/model4/app.py +++ /dev/null @@ -1,8 +0,0 @@ -import gradio as gr - -def greet(name): - return "Hello " + name + "!" - -demo = gr.Interface(fn=greet, inputs="text", outputs="text") - -demo.launch() \ No newline at end of file diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/textless_nlp/gslm/unit2speech/glow.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/textless_nlp/gslm/unit2speech/glow.py deleted file mode 100644 index 7a7696403d505afdf0f1606f8220801b0f46152f..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/textless_nlp/gslm/unit2speech/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/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/benchmark/dummy_model.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/benchmark/dummy_model.py deleted file mode 100644 index ff26e4fe655d8e8d7f9942c4bd3df7cd267405fb..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/benchmark/dummy_model.py +++ /dev/null @@ -1,96 +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 torch.nn as nn -import torch.nn.functional as F -from fairseq.data import Dictionary -from fairseq.models import ( - FairseqDecoder, - FairseqLanguageModel, - register_model, - register_model_architecture, -) - - -@register_model("dummy_model") -class DummyModel(FairseqLanguageModel): - def __init__(self, args, encoder): - super().__init__(encoder) - self.args = args - - @staticmethod - def add_args(parser): - parser.add_argument("--num-layers", type=int, default=24) - parser.add_argument("--embed-dim", type=int, default=1024) - - @classmethod - def build_model(cls, args, task): - encoder = DummyEncoder( - num_embed=len(task.target_dictionary), - embed_dim=args.embed_dim, - num_layers=args.num_layers, - ) - return cls(args, encoder) - - def forward(self, src_tokens, masked_tokens=None, **kwargs): - return self.decoder(src_tokens, masked_tokens=masked_tokens) - - -class DummyEncoder(FairseqDecoder): - def __init__(self, num_embed=50000, embed_dim=1024, num_layers=24): - super().__init__(Dictionary()) - self.embed = nn.Embedding( - num_embeddings=num_embed, embedding_dim=embed_dim, padding_idx=0 - ) - self.layers_a = nn.ModuleList( - [ - nn.Sequential( - nn.LayerNorm(embed_dim), - nn.Linear(embed_dim, 3 * embed_dim), # q, k, v input projection - nn.Linear(3 * embed_dim, embed_dim), # skip self-attention - nn.Linear(embed_dim, embed_dim), # output projection - nn.Dropout(), - ) - for i in range(num_layers) - ] - ) - self.layers_b = nn.ModuleList( - [ - nn.Sequential( - nn.LayerNorm(embed_dim), - nn.Linear(embed_dim, 4 * embed_dim), # FFN - nn.ReLU(), - nn.Linear(4 * embed_dim, embed_dim), # FFN - nn.Dropout(0.1), - ) - for i in range(num_layers) - ] - ) - self.out_proj = nn.Linear(embed_dim, num_embed) - - def forward(self, tokens, masked_tokens=None): - x = self.embed(tokens) - for layer_a, layer_b in zip(self.layers_a, self.layers_b): - x = x + layer_a(x) - x = x + layer_b(x) - x = self.out_proj(x) - if masked_tokens is not None: - x = x[masked_tokens] - return (x,) - - def max_positions(self): - return 1024 - - def get_normalized_probs(self, net_output, log_probs, sample=None): - logits = net_output[0].float() - if log_probs: - return F.log_softmax(logits, dim=-1) - else: - return F.softmax(logits, dim=-1) - - -@register_model_architecture("dummy_model", "dummy_model") -def base_architecture(args): - pass diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/linformer/linformer_src/models/__init__.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/linformer/linformer_src/models/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_text_joint_to_text/__init__.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_text_joint_to_text/__init__.py deleted file mode 100644 index 239d2e69f9a235095dee1ea7b3a94164a77273f5..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_text_joint_to_text/__init__.py +++ /dev/null @@ -1,6 +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. - -from . import tasks, criterions, models # noqa diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/textless_nlp/gslm/ulm/sample.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/textless_nlp/gslm/ulm/sample.py deleted file mode 100644 index 77302a6894cacf07588cf34fb1e695dc519d7df5..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/textless_nlp/gslm/ulm/sample.py +++ /dev/null @@ -1,174 +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. -""" -Sample from a trained LM; hacked fairseq-interactive -""" -from collections import namedtuple -import os -import ast -import numpy as np - -from fairseq import checkpoint_utils, options, tasks, utils - -import tqdm - -Batch = namedtuple('Batch', 'ids src_tokens src_lengths') -Translation = namedtuple('Translation', 'src_str hypos pos_scores alignments') - - -def make_batches(lines, args, task, max_positions): - tokens = [ - task.source_dictionary.encode_line( - src_str, add_if_not_exist=False - ).long() - for src_str in lines - ] - lengths = [t.numel() for t in tokens] - itr = task.get_batch_iterator( - dataset=task.build_dataset_for_inference(tokens, lengths), - max_tokens=args.dataset.max_tokens, - max_sentences=args.dataset.batch_size, - max_positions=max_positions, - ignore_invalid_inputs=args.dataset.skip_invalid_size_inputs_valid_test - ).next_epoch_itr(shuffle=False) - for batch in itr: - yield Batch( - ids=batch['id'], - src_tokens=batch['net_input']['src_tokens'], src_lengths=batch['net_input']['src_lengths'], - ) - - -def main(args): - arg_prompts = args.prompts - arg_output = args.output - arg_debug = args.debug - arg_sample_size = args.samples_per_prompt - - try: - from fairseq.dataclass.utils import convert_namespace_to_omegaconf - args = convert_namespace_to_omegaconf(args) - except: - pass - - # if args.max_tokens is None and args.max_sentences is None: - if args.common.seed is not None: - np.random.seed(args.common.seed) - utils.set_torch_seed(args.common.seed) - - if args.generation.sampling: - args.generation.nbest = args.generation.beam = arg_sample_size - - task = tasks.setup_task(args.task) - - overrides = ast.literal_eval(args.common_eval.model_overrides) - - models, _model_args = checkpoint_utils.load_model_ensemble( - args.common_eval.path.split(os.pathsep), - arg_overrides=overrides, - task=task, - suffix=getattr(args, "checkpoint_suffix", ""), - ) - - # Set dictionaries - src_dict = task.source_dictionary - tgt_dict = task.target_dictionary - - # Optimize ensemble for generation - for model in models: - model.prepare_for_inference_(args) - model.cuda() - - # Load alignment dictionary for unknown word replacement - # (None if no unknown word replacement, empty if no path to align dictionary) - align_dict = utils.load_align_dict(args.generation.replace_unk) - - max_positions = utils.resolve_max_positions( - task.max_positions(), - *[model.max_positions() for model in models] - ) - - output_file = open(arg_output, 'w') - - with open(arg_prompts, 'r') as fin: - lines = fin.readlines() - - split = [x.split('|', 1) for x in lines] - seq_id = [x[0] for x in split] - prompts = [x[1] for x in split] - - if args.generation.prefix_size >= 0: - prompts = [' '.join(l.split()[:args.generation.prefix_size]) - for l in prompts] - - if arg_debug: - prompts = prompts[:10] - - generator = task.build_generator(models, args.generation) - - start_id = 0 - pbar = tqdm.tqdm(total=len(prompts)) - for batch in make_batches(prompts, args, task, max_positions): - src_tokens = batch.src_tokens - src_lengths = batch.src_lengths - src_tokens = src_tokens.cuda() - src_lengths = src_lengths.cuda() - - sample = { - 'net_input': { - 'src_tokens': src_tokens, - 'src_lengths': src_lengths, - }, - } - - results = [] - translations = task.inference_step(generator, models, sample) - for i, (id, hypos) in enumerate(zip(batch.ids.tolist(), translations)): - src_tokens_i = utils.strip_pad(src_tokens[i], tgt_dict.pad()) - results.append((i + start_id, src_tokens_i, hypos)) - - # sort output to match input order - for id, src_tokens, hypos in sorted(results, key=lambda x: x[0]): - if src_dict is not None: - src_str = src_dict.string( - src_tokens, args.common_eval.post_process) - - # Process top predictions - for hypo_id, hypo in enumerate(hypos): - _hypo_tokens, hypo_str, _alignment = utils.post_process_prediction( - hypo_tokens=hypo['tokens'].int().cpu(), - src_str=src_str, - alignment=hypo['alignment'], - align_dict=align_dict, - tgt_dict=tgt_dict, - remove_bpe=args.common_eval.post_process, - ) - - detok_hypo_str = hypo_str - utterance = detok_hypo_str - print(f'{seq_id[id]}__{hypo_id}|{utterance}', file=output_file) - pbar.update(1) - start_id += len(results) - - # output_file.close() - - -def cli_main(): - parser = options.get_interactive_generation_parser() - parser.add_argument('--prompts', type=str, default=None, required=True) - parser.add_argument('--output', type=str, default=None, required=True) - parser.add_argument('--debug', action='store_true') - parser.add_argument('--samples-per-prompt', type=int, default=1) - - args = options.parse_args_and_arch(parser) - - np.random.seed(args.seed) - utils.set_torch_seed(args.seed) - - main(args) - - -if __name__ == '__main__': - cli_main() diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/nat/nonautoregressive_ensembles.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/nat/nonautoregressive_ensembles.py deleted file mode 100644 index 705a04fb49658c91114a26efd411b4653c65b943..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/nat/nonautoregressive_ensembles.py +++ /dev/null @@ -1,253 +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.models.nat import ( - _apply_del_words, - _apply_ins_masks, - _apply_ins_words, - _fill, - _skip, - _skip_encoder_out, -) - - -class _EnsembleModelEncoder(object): - def __init__(self, models): - self.models = models - - def reorder_encoder_out(self, encoder_outs, new_order): - encoder_outs = [ - model.encoder.reorder_encoder_out(encoder_out, new_order) - for model, encoder_out in zip(self.models, encoder_outs) - ] - return encoder_outs - - -class BasicEnsembleModel(torch.nn.Module): - """A wrapper around an ensemble of models.""" - - def __init__(self, models): - super().__init__() - self.models = torch.nn.ModuleList(models) - self.bos = self.models[0].decoder.dictionary.bos() - self.eos = self.models[0].decoder.dictionary.eos() - self.pad = self.models[0].decoder.dictionary.pad() - self.unk = self.models[0].decoder.dictionary.unk() - self.encoder = _EnsembleModelEncoder(self.models) - - def has_encoder(self): - return hasattr(self.models[0], "encoder") - - def max_decoder_positions(self): - return min(m.max_decoder_positions() for m in self.models) - - @torch.no_grad() - def forward_encoder(self, encoder_input): - if not self.has_encoder(): - return None - return [model.forward_encoder(encoder_input) for model in self.models] - - @torch.no_grad() - def forward_decoder(self, *inputs): - raise NotImplementedError - - def initialize_output_tokens(self, *inputs): - raise NotImplementedError - - -class EnsembleLevT(BasicEnsembleModel): - """A wrapper around an ensemble of models.""" - - def __init__(self, models): - super().__init__(models) - - @torch.no_grad() - def forward_decoder( - self, decoder_out, encoder_outs, eos_penalty=0.0, max_ratio=None, **kwargs - ): - # LevT ensembling - # A pipeline of three steps: deletion, placeholder, and word insertion. - # We need to average scores in each step in a pipeline way because of dependence. - # deletion - output_tokens = decoder_out.output_tokens - output_scores = decoder_out.output_scores - attn = decoder_out.attn - - bsz = output_tokens.size(0) - if max_ratio is None: - max_lens = output_tokens.new().fill_(255) - else: - if not encoder_outs[0]["encoder_padding_mask"]: - src_lens = ( - encoder_outs[0]["encoder_out"][0].new(bsz) - .fill_(encoder_outs[0]["encoder_out"][0].size(1)) - ) - else: - src_lens = (~encoder_outs[0]["encoder_padding_mask"][0]).sum(1) - max_lens = (src_lens * max_ratio).clamp(min=10).long() - - # delete words - # do not delete tokens if it is ' if e.mod is module else submodule_names[e.mod] - param_size = sum(t.numel() for t in e.unique_params) - buffer_size = sum(t.numel() for t in e.unique_buffers) - output_shapes = [str(list(e.outputs[0].shape)) for t in e.outputs] - output_dtypes = [str(t.dtype).split('.')[-1] for t in e.outputs] - rows += [[ - name + (':0' if len(e.outputs) >= 2 else ''), - str(param_size) if param_size else '-', - str(buffer_size) if buffer_size else '-', - (output_shapes + ['-'])[0], - (output_dtypes + ['-'])[0], - ]] - for idx in range(1, len(e.outputs)): - rows += [[name + f':{idx}', '-', '-', output_shapes[idx], output_dtypes[idx]]] - param_total += param_size - buffer_total += buffer_size - rows += [['---'] * len(rows[0])] - rows += [['Total', str(param_total), str(buffer_total), '-', '-']] - - # Print table. - widths = [max(len(cell) for cell in column) for column in zip(*rows)] - print() - for row in rows: - print(' '.join(cell + ' ' * (width - len(cell)) for cell, width in zip(row, widths))) - print() - return outputs - -#---------------------------------------------------------------------------- diff --git a/spaces/Ritori/TTS_Yui/text/numbers.py b/spaces/Ritori/TTS_Yui/text/numbers.py deleted file mode 100644 index 0d5f7fa818a45ecf132627d240afac653e148070..0000000000000000000000000000000000000000 --- a/spaces/Ritori/TTS_Yui/text/numbers.py +++ /dev/null @@ -1,71 +0,0 @@ -""" from https://github.com/keithito/tacotron """ - -import inflect -import re - - -_inflect = inflect.engine() -_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])') -_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)') -_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)') -_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)') -_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)') -_number_re = re.compile(r'[0-9]+') - - -def _remove_commas(m): - return m.group(1).replace(',', '') - - -def _expand_decimal_point(m): - return m.group(1).replace('.', ' point ') - - -def _expand_dollars(m): - match = m.group(1) - parts = match.split('.') - if len(parts) > 2: - return match + ' dollars' # Unexpected format - dollars = int(parts[0]) if parts[0] else 0 - cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0 - if dollars and cents: - dollar_unit = 'dollar' if dollars == 1 else 'dollars' - cent_unit = 'cent' if cents == 1 else 'cents' - return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit) - elif dollars: - dollar_unit = 'dollar' if dollars == 1 else 'dollars' - return '%s %s' % (dollars, dollar_unit) - elif cents: - cent_unit = 'cent' if cents == 1 else 'cents' - return '%s %s' % (cents, cent_unit) - else: - return 'zero dollars' - - -def _expand_ordinal(m): - return _inflect.number_to_words(m.group(0)) - - -def _expand_number(m): - num = int(m.group(0)) - if num > 1000 and num < 3000: - if num == 2000: - return 'two thousand' - elif num > 2000 and num < 2010: - return 'two thousand ' + _inflect.number_to_words(num % 100) - elif num % 100 == 0: - return _inflect.number_to_words(num // 100) + ' hundred' - else: - return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ') - else: - return _inflect.number_to_words(num, andword='') - - -def normalize_numbers(text): - text = re.sub(_comma_number_re, _remove_commas, text) - text = re.sub(_pounds_re, r'\1 pounds', text) - text = re.sub(_dollars_re, _expand_dollars, text) - text = re.sub(_decimal_number_re, _expand_decimal_point, text) - text = re.sub(_ordinal_re, _expand_ordinal, text) - text = re.sub(_number_re, _expand_number, text) - return text diff --git a/spaces/RobPruzan/automaticlitassesment/README.md b/spaces/RobPruzan/automaticlitassesment/README.md deleted file mode 100644 index 8ea880bbb57834038f35cf6efd152e41fff88736..0000000000000000000000000000000000000000 --- a/spaces/RobPruzan/automaticlitassesment/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Automaticlitassesment -emoji: ⚡ -colorFrom: indigo -colorTo: green -sdk: gradio -sdk_version: 3.1.4 -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/Robert001/UniControl-Demo/annotator/uniformer/mmcv/runner/hooks/logger/neptune.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/runner/hooks/logger/neptune.py deleted file mode 100644 index 7a38772b0c93a8608f32c6357b8616e77c139dc9..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/runner/hooks/logger/neptune.py +++ /dev/null @@ -1,82 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from ...dist_utils import master_only -from ..hook import HOOKS -from .base import LoggerHook - - -@HOOKS.register_module() -class NeptuneLoggerHook(LoggerHook): - """Class to log metrics to NeptuneAI. - - It requires `neptune-client` to be installed. - - Args: - init_kwargs (dict): a dict contains the initialization keys as below: - - project (str): Name of a project in a form of - namespace/project_name. If None, the value of - NEPTUNE_PROJECT environment variable will be taken. - - api_token (str): User’s API token. - If None, the value of NEPTUNE_API_TOKEN environment - variable will be taken. Note: It is strongly recommended - to use NEPTUNE_API_TOKEN environment variable rather than - placing your API token in plain text in your source code. - - name (str, optional, default is 'Untitled'): Editable name of - the run. Name is displayed in the run's Details and in - Runs table as a column. - Check https://docs.neptune.ai/api-reference/neptune#init for - more init arguments. - interval (int): Logging interval (every k iterations). - ignore_last (bool): Ignore the log of last iterations in each epoch - if less than `interval`. - reset_flag (bool): Whether to clear the output buffer after logging - by_epoch (bool): Whether EpochBasedRunner is used. - - .. _NeptuneAI: - https://docs.neptune.ai/you-should-know/logging-metadata - """ - - def __init__(self, - init_kwargs=None, - interval=10, - ignore_last=True, - reset_flag=True, - with_step=True, - by_epoch=True): - - super(NeptuneLoggerHook, self).__init__(interval, ignore_last, - reset_flag, by_epoch) - self.import_neptune() - self.init_kwargs = init_kwargs - self.with_step = with_step - - def import_neptune(self): - try: - import neptune.new as neptune - except ImportError: - raise ImportError( - 'Please run "pip install neptune-client" to install neptune') - self.neptune = neptune - self.run = None - - @master_only - def before_run(self, runner): - if self.init_kwargs: - self.run = self.neptune.init(**self.init_kwargs) - else: - self.run = self.neptune.init() - - @master_only - def log(self, runner): - tags = self.get_loggable_tags(runner) - if tags: - for tag_name, tag_value in tags.items(): - if self.with_step: - self.run[tag_name].log( - tag_value, step=self.get_iter(runner)) - else: - tags['global_step'] = self.get_iter(runner) - self.run[tag_name].log(tags) - - @master_only - def after_run(self, runner): - self.run.stop() diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/backbones/resnet.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/backbones/resnet.py deleted file mode 100644 index 3826815a6d94fdc4c54001d4c186d10ca3380e80..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/backbones/resnet.py +++ /dev/null @@ -1,663 +0,0 @@ -import torch.nn as nn -import torch.utils.checkpoint as cp -from mmcv.cnn import (build_conv_layer, build_norm_layer, build_plugin_layer, - constant_init, kaiming_init) -from mmcv.runner import load_checkpoint -from torch.nn.modules.batchnorm import _BatchNorm - -from mmdet.utils import get_root_logger -from ..builder import BACKBONES -from ..utils import ResLayer - - -class BasicBlock(nn.Module): - expansion = 1 - - def __init__(self, - inplanes, - planes, - stride=1, - dilation=1, - downsample=None, - style='pytorch', - with_cp=False, - conv_cfg=None, - norm_cfg=dict(type='BN'), - dcn=None, - plugins=None): - super(BasicBlock, self).__init__() - assert dcn is None, 'Not implemented yet.' - assert plugins is None, 'Not implemented yet.' - - self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1) - self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2) - - self.conv1 = build_conv_layer( - conv_cfg, - inplanes, - planes, - 3, - stride=stride, - padding=dilation, - dilation=dilation, - bias=False) - self.add_module(self.norm1_name, norm1) - self.conv2 = build_conv_layer( - conv_cfg, planes, planes, 3, padding=1, bias=False) - self.add_module(self.norm2_name, norm2) - - self.relu = nn.ReLU(inplace=True) - self.downsample = downsample - self.stride = stride - self.dilation = dilation - self.with_cp = with_cp - - @property - def norm1(self): - """nn.Module: normalization layer after the first convolution layer""" - return getattr(self, self.norm1_name) - - @property - def norm2(self): - """nn.Module: normalization layer after the second convolution layer""" - return getattr(self, self.norm2_name) - - def forward(self, x): - """Forward function.""" - - def _inner_forward(x): - identity = x - - out = self.conv1(x) - out = self.norm1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.norm2(out) - - if self.downsample is not None: - identity = self.downsample(x) - - out += identity - - return out - - if self.with_cp and x.requires_grad: - out = cp.checkpoint(_inner_forward, x) - else: - out = _inner_forward(x) - - out = self.relu(out) - - return out - - -class Bottleneck(nn.Module): - expansion = 4 - - def __init__(self, - inplanes, - planes, - stride=1, - dilation=1, - downsample=None, - style='pytorch', - with_cp=False, - conv_cfg=None, - norm_cfg=dict(type='BN'), - dcn=None, - plugins=None): - """Bottleneck block for ResNet. - - If style is "pytorch", the stride-two layer is the 3x3 conv layer, if - it is "caffe", the stride-two layer is the first 1x1 conv layer. - """ - super(Bottleneck, self).__init__() - assert style in ['pytorch', 'caffe'] - assert dcn is None or isinstance(dcn, dict) - assert plugins is None or isinstance(plugins, list) - if plugins is not None: - allowed_position = ['after_conv1', 'after_conv2', 'after_conv3'] - assert all(p['position'] in allowed_position for p in plugins) - - self.inplanes = inplanes - self.planes = planes - self.stride = stride - self.dilation = dilation - self.style = style - self.with_cp = with_cp - self.conv_cfg = conv_cfg - self.norm_cfg = norm_cfg - self.dcn = dcn - self.with_dcn = dcn is not None - self.plugins = plugins - self.with_plugins = plugins is not None - - if self.with_plugins: - # collect plugins for conv1/conv2/conv3 - self.after_conv1_plugins = [ - plugin['cfg'] for plugin in plugins - if plugin['position'] == 'after_conv1' - ] - self.after_conv2_plugins = [ - plugin['cfg'] for plugin in plugins - if plugin['position'] == 'after_conv2' - ] - self.after_conv3_plugins = [ - plugin['cfg'] for plugin in plugins - if plugin['position'] == 'after_conv3' - ] - - if self.style == 'pytorch': - self.conv1_stride = 1 - self.conv2_stride = stride - else: - self.conv1_stride = stride - self.conv2_stride = 1 - - self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1) - self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2) - self.norm3_name, norm3 = build_norm_layer( - norm_cfg, planes * self.expansion, postfix=3) - - self.conv1 = build_conv_layer( - conv_cfg, - inplanes, - planes, - kernel_size=1, - stride=self.conv1_stride, - bias=False) - self.add_module(self.norm1_name, norm1) - fallback_on_stride = False - if self.with_dcn: - fallback_on_stride = dcn.pop('fallback_on_stride', False) - if not self.with_dcn or fallback_on_stride: - self.conv2 = build_conv_layer( - conv_cfg, - planes, - planes, - kernel_size=3, - stride=self.conv2_stride, - padding=dilation, - dilation=dilation, - bias=False) - else: - assert self.conv_cfg is None, 'conv_cfg must be None for DCN' - self.conv2 = build_conv_layer( - dcn, - planes, - planes, - kernel_size=3, - stride=self.conv2_stride, - padding=dilation, - dilation=dilation, - bias=False) - - self.add_module(self.norm2_name, norm2) - self.conv3 = build_conv_layer( - conv_cfg, - planes, - planes * self.expansion, - kernel_size=1, - bias=False) - self.add_module(self.norm3_name, norm3) - - self.relu = nn.ReLU(inplace=True) - self.downsample = downsample - - if self.with_plugins: - self.after_conv1_plugin_names = self.make_block_plugins( - planes, self.after_conv1_plugins) - self.after_conv2_plugin_names = self.make_block_plugins( - planes, self.after_conv2_plugins) - self.after_conv3_plugin_names = self.make_block_plugins( - planes * self.expansion, self.after_conv3_plugins) - - def make_block_plugins(self, in_channels, plugins): - """make plugins for block. - - Args: - in_channels (int): Input channels of plugin. - plugins (list[dict]): List of plugins cfg to build. - - Returns: - list[str]: List of the names of plugin. - """ - assert isinstance(plugins, list) - plugin_names = [] - for plugin in plugins: - plugin = plugin.copy() - name, layer = build_plugin_layer( - plugin, - in_channels=in_channels, - postfix=plugin.pop('postfix', '')) - assert not hasattr(self, name), f'duplicate plugin {name}' - self.add_module(name, layer) - plugin_names.append(name) - return plugin_names - - def forward_plugin(self, x, plugin_names): - out = x - for name in plugin_names: - out = getattr(self, name)(x) - return out - - @property - def norm1(self): - """nn.Module: normalization layer after the first convolution layer""" - return getattr(self, self.norm1_name) - - @property - def norm2(self): - """nn.Module: normalization layer after the second convolution layer""" - return getattr(self, self.norm2_name) - - @property - def norm3(self): - """nn.Module: normalization layer after the third convolution layer""" - return getattr(self, self.norm3_name) - - def forward(self, x): - """Forward function.""" - - def _inner_forward(x): - identity = x - out = self.conv1(x) - out = self.norm1(out) - out = self.relu(out) - - if self.with_plugins: - out = self.forward_plugin(out, self.after_conv1_plugin_names) - - out = self.conv2(out) - out = self.norm2(out) - out = self.relu(out) - - if self.with_plugins: - out = self.forward_plugin(out, self.after_conv2_plugin_names) - - out = self.conv3(out) - out = self.norm3(out) - - if self.with_plugins: - out = self.forward_plugin(out, self.after_conv3_plugin_names) - - if self.downsample is not None: - identity = self.downsample(x) - - out += identity - - return out - - if self.with_cp and x.requires_grad: - out = cp.checkpoint(_inner_forward, x) - else: - out = _inner_forward(x) - - out = self.relu(out) - - return out - - -@BACKBONES.register_module() -class ResNet(nn.Module): - """ResNet backbone. - - Args: - depth (int): Depth of resnet, from {18, 34, 50, 101, 152}. - stem_channels (int | None): Number of stem channels. If not specified, - it will be the same as `base_channels`. Default: None. - base_channels (int): Number of base channels of res layer. Default: 64. - in_channels (int): Number of input image channels. Default: 3. - num_stages (int): Resnet stages. Default: 4. - strides (Sequence[int]): Strides of the first block of each stage. - dilations (Sequence[int]): Dilation of each stage. - out_indices (Sequence[int]): Output from which stages. - style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two - layer is the 3x3 conv layer, otherwise the stride-two layer is - the first 1x1 conv layer. - deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv - avg_down (bool): Use AvgPool instead of stride conv when - downsampling in the bottleneck. - frozen_stages (int): Stages to be frozen (stop grad and set eval mode). - -1 means not freezing any parameters. - norm_cfg (dict): Dictionary to construct and config norm layer. - norm_eval (bool): Whether to set norm layers to eval mode, namely, - freeze running stats (mean and var). Note: Effect on Batch Norm - and its variants only. - plugins (list[dict]): List of plugins for stages, each dict contains: - - - cfg (dict, required): Cfg dict to build plugin. - - position (str, required): Position inside block to insert - plugin, options are 'after_conv1', 'after_conv2', 'after_conv3'. - - stages (tuple[bool], optional): Stages to apply plugin, length - should be same as 'num_stages'. - with_cp (bool): Use checkpoint or not. Using checkpoint will save some - memory while slowing down the training speed. - zero_init_residual (bool): Whether to use zero init for last norm layer - in resblocks to let them behave as identity. - - Example: - >>> from mmdet.models import ResNet - >>> import torch - >>> self = ResNet(depth=18) - >>> self.eval() - >>> inputs = torch.rand(1, 3, 32, 32) - >>> level_outputs = self.forward(inputs) - >>> for level_out in level_outputs: - ... print(tuple(level_out.shape)) - (1, 64, 8, 8) - (1, 128, 4, 4) - (1, 256, 2, 2) - (1, 512, 1, 1) - """ - - arch_settings = { - 18: (BasicBlock, (2, 2, 2, 2)), - 34: (BasicBlock, (3, 4, 6, 3)), - 50: (Bottleneck, (3, 4, 6, 3)), - 101: (Bottleneck, (3, 4, 23, 3)), - 152: (Bottleneck, (3, 8, 36, 3)) - } - - def __init__(self, - depth, - in_channels=3, - stem_channels=None, - base_channels=64, - num_stages=4, - strides=(1, 2, 2, 2), - dilations=(1, 1, 1, 1), - out_indices=(0, 1, 2, 3), - style='pytorch', - deep_stem=False, - avg_down=False, - frozen_stages=-1, - conv_cfg=None, - norm_cfg=dict(type='BN', requires_grad=True), - norm_eval=True, - dcn=None, - stage_with_dcn=(False, False, False, False), - plugins=None, - with_cp=False, - zero_init_residual=True): - super(ResNet, self).__init__() - if depth not in self.arch_settings: - raise KeyError(f'invalid depth {depth} for resnet') - self.depth = depth - if stem_channels is None: - stem_channels = base_channels - self.stem_channels = stem_channels - self.base_channels = base_channels - self.num_stages = num_stages - assert num_stages >= 1 and num_stages <= 4 - self.strides = strides - self.dilations = dilations - assert len(strides) == len(dilations) == num_stages - self.out_indices = out_indices - assert max(out_indices) < num_stages - self.style = style - self.deep_stem = deep_stem - self.avg_down = avg_down - self.frozen_stages = frozen_stages - self.conv_cfg = conv_cfg - self.norm_cfg = norm_cfg - self.with_cp = with_cp - self.norm_eval = norm_eval - self.dcn = dcn - self.stage_with_dcn = stage_with_dcn - if dcn is not None: - assert len(stage_with_dcn) == num_stages - self.plugins = plugins - self.zero_init_residual = zero_init_residual - self.block, stage_blocks = self.arch_settings[depth] - self.stage_blocks = stage_blocks[:num_stages] - self.inplanes = stem_channels - - self._make_stem_layer(in_channels, stem_channels) - - self.res_layers = [] - for i, num_blocks in enumerate(self.stage_blocks): - stride = strides[i] - dilation = dilations[i] - dcn = self.dcn if self.stage_with_dcn[i] else None - if plugins is not None: - stage_plugins = self.make_stage_plugins(plugins, i) - else: - stage_plugins = None - planes = base_channels * 2**i - res_layer = self.make_res_layer( - block=self.block, - inplanes=self.inplanes, - planes=planes, - num_blocks=num_blocks, - stride=stride, - dilation=dilation, - style=self.style, - avg_down=self.avg_down, - with_cp=with_cp, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - dcn=dcn, - plugins=stage_plugins) - self.inplanes = planes * self.block.expansion - layer_name = f'layer{i + 1}' - self.add_module(layer_name, res_layer) - self.res_layers.append(layer_name) - - self._freeze_stages() - - self.feat_dim = self.block.expansion * base_channels * 2**( - len(self.stage_blocks) - 1) - - def make_stage_plugins(self, plugins, stage_idx): - """Make plugins for ResNet ``stage_idx`` th stage. - - Currently we support to insert ``context_block``, - ``empirical_attention_block``, ``nonlocal_block`` into the backbone - like ResNet/ResNeXt. They could be inserted after conv1/conv2/conv3 of - Bottleneck. - - An example of plugins format could be: - - Examples: - >>> plugins=[ - ... dict(cfg=dict(type='xxx', arg1='xxx'), - ... stages=(False, True, True, True), - ... position='after_conv2'), - ... dict(cfg=dict(type='yyy'), - ... stages=(True, True, True, True), - ... position='after_conv3'), - ... dict(cfg=dict(type='zzz', postfix='1'), - ... stages=(True, True, True, True), - ... position='after_conv3'), - ... dict(cfg=dict(type='zzz', postfix='2'), - ... stages=(True, True, True, True), - ... position='after_conv3') - ... ] - >>> self = ResNet(depth=18) - >>> stage_plugins = self.make_stage_plugins(plugins, 0) - >>> assert len(stage_plugins) == 3 - - Suppose ``stage_idx=0``, the structure of blocks in the stage would be: - - .. code-block:: none - - conv1-> conv2->conv3->yyy->zzz1->zzz2 - - Suppose 'stage_idx=1', the structure of blocks in the stage would be: - - .. code-block:: none - - conv1-> conv2->xxx->conv3->yyy->zzz1->zzz2 - - If stages is missing, the plugin would be applied to all stages. - - Args: - plugins (list[dict]): List of plugins cfg to build. The postfix is - required if multiple same type plugins are inserted. - stage_idx (int): Index of stage to build - - Returns: - list[dict]: Plugins for current stage - """ - stage_plugins = [] - for plugin in plugins: - plugin = plugin.copy() - stages = plugin.pop('stages', None) - assert stages is None or len(stages) == self.num_stages - # whether to insert plugin into current stage - if stages is None or stages[stage_idx]: - stage_plugins.append(plugin) - - return stage_plugins - - def make_res_layer(self, **kwargs): - """Pack all blocks in a stage into a ``ResLayer``.""" - return ResLayer(**kwargs) - - @property - def norm1(self): - """nn.Module: the normalization layer named "norm1" """ - return getattr(self, self.norm1_name) - - def _make_stem_layer(self, in_channels, stem_channels): - if self.deep_stem: - self.stem = nn.Sequential( - build_conv_layer( - self.conv_cfg, - in_channels, - stem_channels // 2, - kernel_size=3, - stride=2, - padding=1, - bias=False), - build_norm_layer(self.norm_cfg, stem_channels // 2)[1], - nn.ReLU(inplace=True), - build_conv_layer( - self.conv_cfg, - stem_channels // 2, - stem_channels // 2, - kernel_size=3, - stride=1, - padding=1, - bias=False), - build_norm_layer(self.norm_cfg, stem_channels // 2)[1], - nn.ReLU(inplace=True), - build_conv_layer( - self.conv_cfg, - stem_channels // 2, - stem_channels, - kernel_size=3, - stride=1, - padding=1, - bias=False), - build_norm_layer(self.norm_cfg, stem_channels)[1], - nn.ReLU(inplace=True)) - else: - self.conv1 = build_conv_layer( - self.conv_cfg, - in_channels, - stem_channels, - kernel_size=7, - stride=2, - padding=3, - bias=False) - self.norm1_name, norm1 = build_norm_layer( - self.norm_cfg, stem_channels, postfix=1) - self.add_module(self.norm1_name, norm1) - self.relu = nn.ReLU(inplace=True) - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) - - def _freeze_stages(self): - if self.frozen_stages >= 0: - if self.deep_stem: - self.stem.eval() - for param in self.stem.parameters(): - param.requires_grad = False - else: - self.norm1.eval() - for m in [self.conv1, self.norm1]: - for param in m.parameters(): - param.requires_grad = False - - for i in range(1, self.frozen_stages + 1): - m = getattr(self, f'layer{i}') - m.eval() - for param in m.parameters(): - param.requires_grad = False - - def init_weights(self, pretrained=None): - """Initialize the weights in backbone. - - Args: - pretrained (str, optional): Path to pre-trained weights. - Defaults to None. - """ - if isinstance(pretrained, str): - logger = get_root_logger() - load_checkpoint(self, pretrained, strict=False, logger=logger) - elif pretrained is None: - for m in self.modules(): - if isinstance(m, nn.Conv2d): - kaiming_init(m) - elif isinstance(m, (_BatchNorm, nn.GroupNorm)): - constant_init(m, 1) - - if self.dcn is not None: - for m in self.modules(): - if isinstance(m, Bottleneck) and hasattr( - m.conv2, 'conv_offset'): - constant_init(m.conv2.conv_offset, 0) - - if self.zero_init_residual: - for m in self.modules(): - if isinstance(m, Bottleneck): - constant_init(m.norm3, 0) - elif isinstance(m, BasicBlock): - constant_init(m.norm2, 0) - else: - raise TypeError('pretrained must be a str or None') - - def forward(self, x): - """Forward function.""" - if self.deep_stem: - x = self.stem(x) - else: - x = self.conv1(x) - x = self.norm1(x) - x = self.relu(x) - x = self.maxpool(x) - outs = [] - for i, layer_name in enumerate(self.res_layers): - res_layer = getattr(self, layer_name) - x = res_layer(x) - if i in self.out_indices: - outs.append(x) - return tuple(outs) - - def train(self, mode=True): - """Convert the model into training mode while keep normalization layer - freezed.""" - super(ResNet, self).train(mode) - self._freeze_stages() - if mode and self.norm_eval: - for m in self.modules(): - # trick: eval have effect on BatchNorm only - if isinstance(m, _BatchNorm): - m.eval() - - -@BACKBONES.register_module() -class ResNetV1d(ResNet): - r"""ResNetV1d variant described in `Bag of Tricks - ') - item['others'] = item.get('others', None) - phone_list += item['ph'].split(" ") - word_list += item['word'].split(" ") - spk_names.add(item['spk_name']) - items.append(item) - - # add encoded tokens - ph_encoder, word_encoder = self._phone_encoder(phone_list), self._word_encoder(word_list) - spk_map = self.build_spk_map(spk_names) - args = [{ - 'ph': item['ph'], 'word': item['word'], 'spk_name': item['spk_name'], - 'word_encoder': word_encoder, 'ph_encoder': ph_encoder, 'spk_map': spk_map - } for item in items] - for idx, item_new_kv in multiprocess_run_tqdm(self.preprocess_second_pass, args, desc='Add encoded tokens'): - items[idx].update(item_new_kv) - - # build mfa data - if self.preprocess_args['use_mfa']: - mfa_dict = set() - mfa_input_dir = f'{processed_dir}/mfa_inputs' - remove_file(mfa_input_dir) - # group MFA inputs for better parallelism - mfa_groups = [i // self.preprocess_args['nsample_per_mfa_group'] for i in range(len(items))] - if self.preprocess_args['mfa_group_shuffle']: - random.seed(hparams['seed']) - random.shuffle(mfa_groups) - args = [{ - 'item': item, 'mfa_input_dir': mfa_input_dir, - 'mfa_group': mfa_group, 'wav_processed_tmp': wav_processed_tmp_dir, - 'preprocess_args': self.preprocess_args - } for item, mfa_group in zip(items, mfa_groups)] - for i, (ph_gb_word_nosil, new_wav_align_fn) in multiprocess_run_tqdm( - self.build_mfa_inputs, args, desc='Build MFA data'): - items[i]['wav_align_fn'] = new_wav_align_fn - for w in ph_gb_word_nosil.split(" "): - mfa_dict.add(f"{w} {w.replace('_', ' ')}") - mfa_dict = sorted(mfa_dict) - with open(f'{processed_dir}/mfa_dict.txt', 'w') as f: - f.writelines([f'{l}\n' for l in mfa_dict]) - with open(f"{processed_dir}/{self.meta_csv_filename}.json", 'w') as f: - f.write(re.sub(r'\n\s+([\d+\]])', r'\1', json.dumps(items, ensure_ascii=False, sort_keys=False, indent=1))) - remove_file(wav_processed_tmp_dir) - - @classmethod - def preprocess_first_pass(cls, item_name, txt_raw, txt_processor, - wav_fn, wav_processed_dir, wav_processed_tmp, - preprocess_args, txt_loader=None, others=None): - try: - if txt_loader is not None: - txt_raw = txt_loader(txt_raw) - ph, txt, word, ph2word, ph_gb_word = cls.txt_to_ph(txt_processor, txt_raw, preprocess_args) - wav_fn, wav_align_fn = cls.process_wav( - item_name, wav_fn, - hparams['processed_data_dir'], - wav_processed_tmp, preprocess_args) - - # wav for binarization - ext = os.path.splitext(wav_fn)[1] - os.makedirs(wav_processed_dir, exist_ok=True) - new_wav_fn = f"{wav_processed_dir}/{item_name}{ext}" - move_link_func = move_file if os.path.dirname(wav_fn) == wav_processed_tmp else link_file - move_link_func(wav_fn, new_wav_fn) - return { - 'txt': txt, 'txt_raw': txt_raw, 'ph': ph, - 'word': word, 'ph2word': ph2word, 'ph_gb_word': ph_gb_word, - 'wav_fn': new_wav_fn, 'wav_align_fn': wav_align_fn, - 'others': others - } - except: - traceback.print_exc() - print(f"| Error is caught. item_name: {item_name}.") - return None - - @staticmethod - def txt_to_ph(txt_processor, txt_raw, preprocess_args): - txt_struct, txt = txt_processor.process(txt_raw, preprocess_args) - ph = [p for w in txt_struct for p in w[1]] - return " ".join(ph), txt - - @staticmethod - def process_wav(item_name, wav_fn, processed_dir, wav_processed_tmp, preprocess_args): - processors = [get_wav_processor_cls(v) for v in preprocess_args['wav_processors']] - processors = [k() for k in processors if k is not None] - if len(processors) >= 1: - sr_file = librosa.core.get_samplerate(wav_fn) - output_fn_for_align = None - ext = os.path.splitext(wav_fn)[1] - input_fn = f"{wav_processed_tmp}/{item_name}{ext}" - link_file(wav_fn, input_fn) - for p in processors: - outputs = p.process(input_fn, sr_file, wav_processed_tmp, processed_dir, item_name, preprocess_args) - if len(outputs) == 3: - input_fn, sr, output_fn_for_align = outputs - else: - input_fn, sr = outputs - return input_fn, output_fn_for_align - else: - return wav_fn, wav_fn - - def _phone_encoder(self, ph_set): - ph_set_fn = f"{self.processed_dir}/phone_set.json" - if self.preprocess_args['reset_phone_dict'] or not os.path.exists(ph_set_fn): - ph_set = sorted(set(ph_set)) - json.dump(ph_set, open(ph_set_fn, 'w'), ensure_ascii=False) - print("| Build phone set: ", ph_set) - else: - ph_set = json.load(open(ph_set_fn, 'r')) - print("| Load phone set: ", ph_set) - return build_token_encoder(ph_set_fn) - - def _word_encoder(self, word_set): - word_set_fn = f"{self.processed_dir}/word_set.json" - if self.preprocess_args['reset_word_dict']: - word_set = Counter(word_set) - total_words = sum(word_set.values()) - word_set = word_set.most_common(hparams['word_dict_size']) - num_unk_words = total_words - sum([x[1] for x in word_set]) - word_set = [' ', ' '] + [x[0] for x in word_set] - word_set = sorted(set(word_set)) - json.dump(word_set, open(word_set_fn, 'w'), ensure_ascii=False) - print(f"| Build word set. Size: {len(word_set)}, #total words: {total_words}," - f" #unk_words: {num_unk_words}, word_set[:10]:, {word_set[:10]}.") - else: - word_set = json.load(open(word_set_fn, 'r')) - print("| Load word set. Size: ", len(word_set), word_set[:10]) - return build_token_encoder(word_set_fn) - - @classmethod - def preprocess_second_pass(cls, word, ph, spk_name, word_encoder, ph_encoder, spk_map): - word_token = word_encoder.encode(word) - ph_token = ph_encoder.encode(ph) - spk_id = spk_map[spk_name] - return {'word_token': word_token, 'ph_token': ph_token, 'spk_id': spk_id} - - def build_spk_map(self, spk_names): - spk_map = {x: i for i, x in enumerate(sorted(list(spk_names)))} - assert len(spk_map) == 0 or len(spk_map) <= hparams['num_spk'], len(spk_map) - print(f"| Number of spks: {len(spk_map)}, spk_map: {spk_map}") - json.dump(spk_map, open(self.spk_map_fn, 'w'), ensure_ascii=False) - return spk_map - - @classmethod - def build_mfa_inputs(cls, item, mfa_input_dir, mfa_group, wav_processed_tmp, preprocess_args): - item_name = item['item_name'] - wav_align_fn = item['wav_align_fn'] - ph_gb_word = item['ph_gb_word'] - ext = os.path.splitext(wav_align_fn)[1] - mfa_input_group_dir = f'{mfa_input_dir}/{mfa_group}' - os.makedirs(mfa_input_group_dir, exist_ok=True) - new_wav_align_fn = f"{mfa_input_group_dir}/{item_name}{ext}" - move_link_func = move_file if os.path.dirname(wav_align_fn) == wav_processed_tmp else link_file - move_link_func(wav_align_fn, new_wav_align_fn) - ph_gb_word_nosil = " ".join(["_".join([p for p in w.split("_") if not is_sil_phoneme(p)]) - for w in ph_gb_word.split(" ") if not is_sil_phoneme(w)]) - with open(f'{mfa_input_group_dir}/{item_name}.lab', 'w') as f_txt: - f_txt.write(ph_gb_word_nosil) - return ph_gb_word_nosil, new_wav_align_fn - - def load_spk_map(self, base_dir): - spk_map_fn = f"{base_dir}/spk_map.json" - spk_map = json.load(open(spk_map_fn, 'r')) - return spk_map - - def load_dict(self, base_dir): - ph_encoder = build_token_encoder(f'{base_dir}/phone_set.json') - return ph_encoder - - @property - def meta_csv_filename(self): - return 'metadata' - - @property - def wav_processed_dirname(self): - return 'wav_processed' \ No newline at end of file diff --git a/spaces/Rongjiehuang/ProDiff/usr/diffspeech_task.py b/spaces/Rongjiehuang/ProDiff/usr/diffspeech_task.py deleted file mode 100644 index e4fca7e9e46fc378468188d58fc42bc989df824c..0000000000000000000000000000000000000000 --- a/spaces/Rongjiehuang/ProDiff/usr/diffspeech_task.py +++ /dev/null @@ -1,122 +0,0 @@ -import torch - -import utils -from utils.hparams import hparams -from .diff.net import DiffNet -from .diff.shallow_diffusion_tts import GaussianDiffusion -from .task import DiffFsTask -from vocoders.base_vocoder import get_vocoder_cls, BaseVocoder -from utils.pitch_utils import denorm_f0 -from tasks.tts.fs2_utils import FastSpeechDataset - -DIFF_DECODERS = { - 'wavenet': lambda hp: DiffNet(hp['audio_num_mel_bins']), -} - - -class DiffSpeechTask(DiffFsTask): - def __init__(self): - super(DiffSpeechTask, self).__init__() - self.dataset_cls = FastSpeechDataset - self.vocoder: BaseVocoder = get_vocoder_cls(hparams)() - - def build_tts_model(self): - mel_bins = hparams['audio_num_mel_bins'] - self.model = GaussianDiffusion( - phone_encoder=self.phone_encoder, - out_dims=mel_bins, denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams), - timesteps=hparams['timesteps'], - K_step=hparams['K_step'], - loss_type=hparams['diff_loss_type'], - spec_min=hparams['spec_min'], spec_max=hparams['spec_max'], - ) - if hparams['fs2_ckpt'] != '': - utils.load_ckpt(self.model.fs2, hparams['fs2_ckpt'], 'model', strict=True) - # self.model.fs2.decoder = None - for k, v in self.model.fs2.named_parameters(): - if not 'predictor' in k: - v.requires_grad = False - - def build_optimizer(self, model): - self.optimizer = optimizer = torch.optim.AdamW( - filter(lambda p: p.requires_grad, model.parameters()), - lr=hparams['lr'], - betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), - weight_decay=hparams['weight_decay']) - return optimizer - - def run_model(self, model, sample, return_output=False, infer=False): - txt_tokens = sample['txt_tokens'] # [B, T_t] - target = sample['mels'] # [B, T_s, 80] - # mel2ph = sample['mel2ph'] if hparams['use_gt_dur'] else None # [B, T_s] - mel2ph = sample['mel2ph'] - f0 = sample['f0'] - uv = sample['uv'] - energy = sample['energy'] - # fs2_mel = sample['fs2_mels'] - spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') - if hparams['pitch_type'] == 'cwt': - cwt_spec = sample[f'cwt_spec'] - f0_mean = sample['f0_mean'] - f0_std = sample['f0_std'] - sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph) - - output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed, - ref_mels=target, f0=f0, uv=uv, energy=energy, infer=infer) - - losses = {} - if 'diff_loss' in output: - losses['mel'] = output['diff_loss'] - self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses) - if hparams['use_pitch_embed']: - self.add_pitch_loss(output, sample, losses) - if hparams['use_energy_embed']: - self.add_energy_loss(output['energy_pred'], energy, losses) - if not return_output: - return losses - else: - return losses, output - - def validation_step(self, sample, batch_idx): - outputs = {} - txt_tokens = sample['txt_tokens'] # [B, T_t] - - energy = sample['energy'] - spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') - mel2ph = sample['mel2ph'] - f0 = sample['f0'] - uv = sample['uv'] - - outputs['losses'] = {} - - outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False) - - - outputs['total_loss'] = sum(outputs['losses'].values()) - outputs['nsamples'] = sample['nsamples'] - outputs = utils.tensors_to_scalars(outputs) - if batch_idx < hparams['num_valid_plots']: - # model_out = self.model( - # txt_tokens, spk_embed=spk_embed, mel2ph=None, f0=None, uv=None, energy=None, ref_mels=None, inference=True) - # self.plot_mel(batch_idx, model_out['mel_out'], model_out['fs2_mel'], name=f'diffspeech_vs_fs2_{batch_idx}') - model_out = self.model( - txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, energy=energy, ref_mels=None, infer=True) - gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams) - self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=model_out.get('f0_denorm')) - self.plot_mel(batch_idx, sample['mels'], model_out['mel_out']) - return outputs - - ############ - # validation plots - ############ - def plot_wav(self, batch_idx, gt_wav, wav_out, is_mel=False, gt_f0=None, f0=None, name=None): - gt_wav = gt_wav[0].cpu().numpy() - wav_out = wav_out[0].cpu().numpy() - gt_f0 = gt_f0[0].cpu().numpy() - f0 = f0[0].cpu().numpy() - if is_mel: - gt_wav = self.vocoder.spec2wav(gt_wav, f0=gt_f0) - wav_out = self.vocoder.spec2wav(wav_out, f0=f0) - self.logger.experiment.add_audio(f'gt_{batch_idx}', gt_wav, sample_rate=hparams['audio_sample_rate'], global_step=self.global_step) - self.logger.experiment.add_audio(f'wav_{batch_idx}', wav_out, sample_rate=hparams['audio_sample_rate'], global_step=self.global_step) - diff --git a/spaces/Rothfeld/stable-diffusion-mat-outpainting-primer/datasets/dataset_256_val.py b/spaces/Rothfeld/stable-diffusion-mat-outpainting-primer/datasets/dataset_256_val.py deleted file mode 100644 index 26b619986ce380a88da88ff5792cb11166cf7e6d..0000000000000000000000000000000000000000 --- a/spaces/Rothfeld/stable-diffusion-mat-outpainting-primer/datasets/dataset_256_val.py +++ /dev/null @@ -1,282 +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 numpy as np -import zipfile -import PIL.Image -import cv2 -import json -import torch -import dnnlib -import glob - -try: - import pyspng -except ImportError: - pyspng = None - -from datasets.mask_generator_256 import RandomMask - -#---------------------------------------------------------------------------- - -class Dataset(torch.utils.data.Dataset): - def __init__(self, - name, # Name of the dataset. - raw_shape, # Shape of the raw image data (NCHW). - max_size = None, # Artificially limit the size of the dataset. None = no limit. Applied before xflip. - use_labels = False, # Enable conditioning labels? False = label dimension is zero. - xflip = False, # Artificially double the size of the dataset via x-flips. Applied after max_size. - random_seed = 0, # Random seed to use when applying max_size. - ): - self._name = name - self._raw_shape = list(raw_shape) - self._use_labels = use_labels - self._raw_labels = None - self._label_shape = None - - # Apply max_size. - self._raw_idx = np.arange(self._raw_shape[0], dtype=np.int64) - if (max_size is not None) and (self._raw_idx.size > max_size): - np.random.RandomState(random_seed).shuffle(self._raw_idx) - self._raw_idx = np.sort(self._raw_idx[:max_size]) - - # Apply xflip. - self._xflip = np.zeros(self._raw_idx.size, dtype=np.uint8) - if xflip: - self._raw_idx = np.tile(self._raw_idx, 2) - self._xflip = np.concatenate([self._xflip, np.ones_like(self._xflip)]) - - def _get_raw_labels(self): - if self._raw_labels is None: - self._raw_labels = self._load_raw_labels() if self._use_labels else None - if self._raw_labels is None: - self._raw_labels = np.zeros([self._raw_shape[0], 0], dtype=np.float32) - assert isinstance(self._raw_labels, np.ndarray) - assert self._raw_labels.shape[0] == self._raw_shape[0] - assert self._raw_labels.dtype in [np.float32, np.int64] - if self._raw_labels.dtype == np.int64: - assert self._raw_labels.ndim == 1 - assert np.all(self._raw_labels >= 0) - return self._raw_labels - - def close(self): # to be overridden by subclass - pass - - def _load_raw_image(self, raw_idx): # to be overridden by subclass - raise NotImplementedError - - def _load_raw_labels(self): # to be overridden by subclass - raise NotImplementedError - - def __getstate__(self): - return dict(self.__dict__, _raw_labels=None) - - def __del__(self): - try: - self.close() - except: - pass - - def __len__(self): - return self._raw_idx.size - - def __getitem__(self, idx): - image = self._load_raw_image(self._raw_idx[idx]) - assert isinstance(image, np.ndarray) - assert list(image.shape) == self.image_shape - assert image.dtype == np.uint8 - if self._xflip[idx]: - assert image.ndim == 3 # CHW - image = image[:, :, ::-1] - return image.copy(), self.get_label(idx) - - def get_label(self, idx): - label = self._get_raw_labels()[self._raw_idx[idx]] - if label.dtype == np.int64: - onehot = np.zeros(self.label_shape, dtype=np.float32) - onehot[label] = 1 - label = onehot - return label.copy() - - def get_details(self, idx): - d = dnnlib.EasyDict() - d.raw_idx = int(self._raw_idx[idx]) - d.xflip = (int(self._xflip[idx]) != 0) - d.raw_label = self._get_raw_labels()[d.raw_idx].copy() - return d - - @property - def name(self): - return self._name - - @property - def image_shape(self): - return list(self._raw_shape[1:]) - - @property - def num_channels(self): - assert len(self.image_shape) == 3 # CHW - return self.image_shape[0] - - @property - def resolution(self): - assert len(self.image_shape) == 3 # CHW - assert self.image_shape[1] == self.image_shape[2] - return self.image_shape[1] - - @property - def label_shape(self): - if self._label_shape is None: - raw_labels = self._get_raw_labels() - if raw_labels.dtype == np.int64: - self._label_shape = [int(np.max(raw_labels)) + 1] - else: - self._label_shape = raw_labels.shape[1:] - return list(self._label_shape) - - @property - def label_dim(self): - assert len(self.label_shape) == 1 - return self.label_shape[0] - - @property - def has_labels(self): - return any(x != 0 for x in self.label_shape) - - @property - def has_onehot_labels(self): - return self._get_raw_labels().dtype == np.int64 - - -#---------------------------------------------------------------------------- - - -class ImageFolderMaskDataset(Dataset): - def __init__(self, - path, # Path to directory or zip. - resolution = None, # Ensure specific resolution, None = highest available. - hole_range=[0,1], - **super_kwargs, # Additional arguments for the Dataset base class. - ): - self._path = path - self._zipfile = None - self._hole_range = hole_range - - if os.path.isdir(self._path): - self._type = 'dir' - self._all_fnames = {os.path.relpath(os.path.join(root, fname), start=self._path) for root, _dirs, files in os.walk(self._path) for fname in files} - elif self._file_ext(self._path) == '.zip': - self._type = 'zip' - self._all_fnames = set(self._get_zipfile().namelist()) - else: - raise IOError('Path must point to a directory or zip') - - PIL.Image.init() - self._image_fnames = sorted(fname for fname in self._all_fnames if self._file_ext(fname) in PIL.Image.EXTENSION) - if len(self._image_fnames) == 0: - raise IOError('No image files found in the specified path') - - name = os.path.splitext(os.path.basename(self._path))[0] - raw_shape = [len(self._image_fnames)] + list(self._load_raw_image(0).shape) - if resolution is not None and (raw_shape[2] != resolution or raw_shape[3] != resolution): - raise IOError('Image files do not match the specified resolution') - self._load_mask() - super().__init__(name=name, raw_shape=raw_shape, **super_kwargs) - - def _load_mask(self, mpath='/data/liwenbo/datasets/Places365/standard/masks_val_256_eval'): - self.masks = sorted(glob.glob(mpath + '/*.png')) - - @staticmethod - def _file_ext(fname): - return os.path.splitext(fname)[1].lower() - - def _get_zipfile(self): - assert self._type == 'zip' - if self._zipfile is None: - self._zipfile = zipfile.ZipFile(self._path) - return self._zipfile - - def _open_file(self, fname): - if self._type == 'dir': - return open(os.path.join(self._path, fname), 'rb') - if self._type == 'zip': - return self._get_zipfile().open(fname, 'r') - return None - - def close(self): - try: - if self._zipfile is not None: - self._zipfile.close() - finally: - self._zipfile = None - - def __getstate__(self): - return dict(super().__getstate__(), _zipfile=None) - - def _load_raw_image(self, raw_idx): - fname = self._image_fnames[raw_idx] - with self._open_file(fname) as f: - if pyspng is not None and self._file_ext(fname) == '.png': - image = pyspng.load(f.read()) - else: - image = np.array(PIL.Image.open(f)) - if image.ndim == 2: - image = image[:, :, np.newaxis] # HW => HWC - - # for grayscale image - if image.shape[2] == 1: - image = np.repeat(image, 3, axis=2) - - # restricted to 256x256 - res = 256 - H, W, C = image.shape - if H < res or W < res: - top = 0 - bottom = max(0, res - H) - left = 0 - right = max(0, res - W) - image = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_REFLECT) - H, W, C = image.shape - h = (H - res) // 2 - w = (W - res) // 2 - image = image[h:h+res, w:w+res, :] - - image = np.ascontiguousarray(image.transpose(2, 0, 1)) # HWC => CHW - return image - - def _load_raw_labels(self): - fname = 'labels.json' - if fname not in self._all_fnames: - return None - with self._open_file(fname) as f: - labels = json.load(f)['labels'] - if labels is None: - return None - labels = dict(labels) - labels = [labels[fname.replace('\\', '/')] for fname in self._image_fnames] - labels = np.array(labels) - labels = labels.astype({1: np.int64, 2: np.float32}[labels.ndim]) - return labels - - def __getitem__(self, idx): - image = self._load_raw_image(self._raw_idx[idx]) - - # for grayscale image - if image.shape[0] == 1: - image = np.repeat(image, 3, axis=0) - - assert isinstance(image, np.ndarray) - assert list(image.shape) == self.image_shape - assert image.dtype == np.uint8 - if self._xflip[idx]: - assert image.ndim == 3 # CHW - image = image[:, :, ::-1] - # mask = RandomMask(image.shape[-1], hole_range=self._hole_range) # hole as 0, reserved as 1 - mask = cv2.imread(self.masks[idx], cv2.IMREAD_GRAYSCALE).astype(np.float32)[np.newaxis, :, :] / 255.0 - return image.copy(), mask, self.get_label(idx) diff --git a/spaces/SAAZIZI/SummarizeAV/query_service/__init__.py b/spaces/SAAZIZI/SummarizeAV/query_service/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/SeViLA/SeViLA/lavis/models/clip_models/__init__.py b/spaces/SeViLA/SeViLA/lavis/models/clip_models/__init__.py deleted file mode 100644 index 325e25255550a00fdd082deb82a8a0da567cadb0..0000000000000000000000000000000000000000 --- a/spaces/SeViLA/SeViLA/lavis/models/clip_models/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -""" - Copyright (c) 2022, salesforce.com, inc. - All rights reserved. - SPDX-License-Identifier: BSD-3-Clause - For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause - - Based on https://github.com/mlfoundations/open_clip -""" - -""" OpenAI pretrained model functions -Adapted from https://github.com/mlfoundations/open_clip and https://github.com/openai/CLIP. - -Originally MIT License, Copyright (c) 2021 OpenAI. -""" diff --git a/spaces/Shredder/CONBERT-3/fin_readability_sustainability.py b/spaces/Shredder/CONBERT-3/fin_readability_sustainability.py deleted file mode 100644 index 53ea0c60eab0dd27868f9bdc6d4652ea0ddc71b9..0000000000000000000000000000000000000000 --- a/spaces/Shredder/CONBERT-3/fin_readability_sustainability.py +++ /dev/null @@ -1,110 +0,0 @@ -import torch -import transformers -from torch.utils.data import Dataset, DataLoader -from transformers import RobertaModel, RobertaTokenizer, BertModel, BertTokenizer -import pandas as pd - -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - -MAX_LEN = 128 -BATCH_SIZE = 20 -text_col_name = 'sentence' - -def scoring_data_prep(dataset): - out = [] - target = [] - mask = [] - - for i in range(len(dataset)): - rec = dataset[i] - out.append(rec['ids'].reshape(-1,MAX_LEN)) - mask.append(rec['mask'].reshape(-1,MAX_LEN)) - - out_stack = torch.cat(out, dim = 0) - mask_stack = torch.cat(mask, dim =0 ) - out_stack = out_stack.to(device, dtype = torch.long) - mask_stack = mask_stack.to(device, dtype = torch.long) - - return out_stack, mask_stack - -class Triage(Dataset): - """ - This is a subclass of torch packages Dataset class. It processes input to create ids, masks and targets required for model training. - """ - - def __init__(self, dataframe, tokenizer, max_len, text_col_name): - self.len = len(dataframe) - self.data = dataframe - self.tokenizer = tokenizer - self.max_len = max_len - self.text_col_name = text_col_name - - - def __getitem__(self, index): - title = str(self.data[self.text_col_name][index]) - title = " ".join(title.split()) - inputs = self.tokenizer.encode_plus( - title, - None, - add_special_tokens=True, - max_length=self.max_len, - pad_to_max_length=True, #padding='max_length' #For future version use `padding='max_length'` - return_token_type_ids=True, - truncation=True, - ) - ids = inputs["input_ids"] - mask = inputs["attention_mask"] - - return { - "ids": torch.tensor(ids, dtype=torch.long), - "mask": torch.tensor(mask, dtype=torch.long), - - } - - def __len__(self): - return self.len - -class BERTClass(torch.nn.Module): - def __init__(self, num_class, task): - super(BERTClass, self).__init__() - self.num_class = num_class - if task =="sustanability": - self.l1 = RobertaModel.from_pretrained("roberta-base") - else: - self.l1 = BertModel.from_pretrained("ProsusAI/finbert") - self.pre_classifier = torch.nn.Linear(768, 768) - self.dropout = torch.nn.Dropout(0.3) - self.classifier = torch.nn.Linear(768, self.num_class) - self.history = dict() - - def forward(self, input_ids, attention_mask): - output_1 = self.l1(input_ids=input_ids, attention_mask=attention_mask) - hidden_state = output_1[0] - pooler = hidden_state[:, 0] - pooler = self.pre_classifier(pooler) - pooler = torch.nn.ReLU()(pooler) - pooler = self.dropout(pooler) - output = self.classifier(pooler) - return output - -def do_predict(model, tokenizer, test_df): - test_set = Triage(test_df, tokenizer, MAX_LEN, text_col_name) - test_params = {'batch_size' : BATCH_SIZE, 'shuffle': False, 'num_workers':0} - test_loader = DataLoader(test_set, **test_params) - out_stack, mask_stack = scoring_data_prep(dataset = test_set) - n = 0 - combined_output = [] - model.eval() - with torch.no_grad(): - while n < test_df.shape[0]: - output = model(out_stack[n:n+BATCH_SIZE,:],mask_stack[n:n+BATCH_SIZE,:]) - n = n + BATCH_SIZE - combined_output.append(output) - combined_output = torch.cat(combined_output, dim = 0) - preds = torch.argsort(combined_output, axis = 1, descending = True) - preds = preds.to('cpu') - actual_predictions = [i[0] for i in preds.tolist()] - combined_output = combined_output.to('cpu') - prob_predictions= [i[1] for i in combined_output.tolist()] - return (actual_predictions, prob_predictions) - \ No newline at end of file diff --git a/spaces/Singularity666/RadiXGPT_/app.py b/spaces/Singularity666/RadiXGPT_/app.py deleted file mode 100644 index f67a828cddf75e6d1cca19e109a16fbef2a8f855..0000000000000000000000000000000000000000 --- a/spaces/Singularity666/RadiXGPT_/app.py +++ /dev/null @@ -1,149 +0,0 @@ -import torch -from PIL import Image -import streamlit as st -import numpy as np -import pandas as pd -from main import predict_caption, CLIPModel, get_text_embeddings -import openai -import base64 -from docx import Document -from docx.enum.text import WD_PARAGRAPH_ALIGNMENT -from io import BytesIO -import re - -openai.api_key = "sk-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" - -st.markdown( - """ - - """, - unsafe_allow_html=True, -) - - - -device = torch.device("cpu") - -testing_df = pd.read_csv("testing_df.csv") -model = CLIPModel() # Create an instance of CLIPModel -# Load the model -state_dict = torch.load("weights.pt", map_location=torch.device('cpu')) -print("Loaded State Dict Keys:", state_dict.keys()) - -# Create an instance of CLIPModel -model = CLIPModel().to(device) -print("Model Keys:", model.state_dict().keys()) - -# Load the state_dict into the model -model.load_state_dict(state_dict, strict=False) # Set strict=False to ignore unexpected keys - -text_embeddings = torch.load('saved_text_embeddings.pt', map_location=device) - -def download_link(content, filename, link_text): - b64 = base64.b64encode(content).decode() - href = f'{link_text}' - return href - -def show_predicted_caption(image, top_k=8): - matches = predict_caption( - image, model, text_embeddings, testing_df["caption"] - )[:top_k] - cleaned_matches = [re.sub(r'\s\(ROCO_\d+\)', '', match) for match in matches] # Add this line to clean the matches - return cleaned_matches # Return the cleaned_matches instead of matches - -def generate_radiology_report(prompt): - response = openai.Completion.create( - engine="text-davinci-003", - prompt=prompt, - max_tokens=800, - n=1, - stop=None, - temperature=1, - ) - report = response.choices[0].text.strip() - # Remove reference string from the report - report = re.sub(r'\(ROCO_\d+\)', '', report).strip() - return report - - -def save_as_docx(text, filename): - document = Document() - document.add_paragraph(text) - with BytesIO() as output: - document.save(output) - output.seek(0) - return output.getvalue() - -st.title("RadiXGPT: An Evolution of machine doctors towards Radiology") - - -# Collect user's personal information -st.subheader("Personal Information") -first_name = st.text_input("First Name") -last_name = st.text_input("Last Name") -age = st.number_input("Age", min_value=0, max_value=120, value=25, step=1) -gender = st.selectbox("Gender", ["Male", "Female", "Other"]) - -st.write("Upload Scan to get Radiological Report:") -uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"]) -if uploaded_file is not None: - image = Image.open(uploaded_file) - if st.button("Generate Caption"): - with st.spinner("Generating caption..."): - image_np = np.array(image) - caption = show_predicted_caption(image_np)[0] - - st.success(f"Caption: {caption}") - - # Generate the radiology report - radiology_report = generate_radiology_report(f"Write Complete Radiology Report for this with clinical info, subjective, Assessment, Finding, Impressions, Conclusion and more in proper order : {caption}") - - # Add personal information to the radiology report - radiology_report_with_personal_info = f"Patient Name: {first_name} {last_name}\nAge: {age}\nGender: {gender}\n\n{radiology_report}" - - st.header("Radiology Report") - st.write(radiology_report_with_personal_info) - st.markdown(download_link(save_as_docx(radiology_report_with_personal_info, "radiology_report.docx"), "radiology_report.docx", "Download Report as DOCX"), unsafe_allow_html=True) - - feedback_options = ["Satisfied", "Not Satisfied"] - selected_feedback = st.radio("Please provide feedback on the generated report:", feedback_options) - - if selected_feedback == "Not Satisfied": - if st.button("Regenerate Report"): - with st.spinner("Regenerating report..."): - alternative_caption = get_alternative_caption(image_np, model, text_embeddings, testing_df["caption"]) - regenerated_radiology_report = generate_radiology_report(f"Write Complete Radiology Report for this with clinical info, subjective, Assessment, Finding, Impressions, Conclusion and more in proper order : {alternative_caption}") - - regenerated_radiology_report_with_personal_info = f"Patient Name: {first_name} {last_name}\nAge: {age}\nGender: {gender}\n\n{regenerated_radiology_report}" - - st.header("Regenerated Radiology Report") - st.write(regenerated_radiology_report_with_personal_info) - st.markdown(download_link(save_as_docx(regenerated_radiology_report_with_personal_info, "regenerated_radiology_report.docx"), "regenerated_radiology_report.docx", "Download Regenerated Report as DOCX"), unsafe_allow_html=True) \ No newline at end of file diff --git a/spaces/Smotto/Vocal-Isolator/src/constants.py b/spaces/Smotto/Vocal-Isolator/src/constants.py deleted file mode 100644 index 6845f8a7ababcb635ae7fb4f1a6ab68d2d16810a..0000000000000000000000000000000000000000 --- a/spaces/Smotto/Vocal-Isolator/src/constants.py +++ /dev/null @@ -1,9 +0,0 @@ -# Third-party -import torch - -# Global Variables -COMPUTATION_DEVICE = "cuda" if torch.cuda.is_available() else "cpu" -EXECUTION_PROVIDER_LIST = ["CUDAExecutionProvider", "CPUExecutionProvider"] -ONNX_MODEL_PATH = "./pretrained_models/MDX_net/Kim_Vocal.onnx" -INPUT_FOLDER = "./datasets/input" -OUTPUT_FOLDER = "./datasets/output" diff --git a/spaces/Sultannn/YOLOX-Demo/README.md b/spaces/Sultannn/YOLOX-Demo/README.md deleted file mode 100644 index c2554d23cda278a5563ab897aa06a12e5e2e5573..0000000000000000000000000000000000000000 --- a/spaces/Sultannn/YOLOX-Demo/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: YOLOX Demo -emoji: 🖼 -colorFrom: blue -colorTo: yellow -sdk: gradio -sdk_version: 2.8.10 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/_distutils_hack/override.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/_distutils_hack/override.py deleted file mode 100644 index 2cc433a4a55e3b41fa31089918fb62096092f89f..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/_distutils_hack/override.py +++ /dev/null @@ -1 +0,0 @@ -__import__('_distutils_hack').do_override() diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/aiohttp/web_fileresponse.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/aiohttp/web_fileresponse.py deleted file mode 100644 index f41ed3fd0a9c1e0d5e45ce1e97b99bfef8361cac..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/aiohttp/web_fileresponse.py +++ /dev/null @@ -1,288 +0,0 @@ -import asyncio -import mimetypes -import os -import pathlib -import sys -from typing import ( # noqa - IO, - TYPE_CHECKING, - Any, - Awaitable, - Callable, - Iterator, - List, - Optional, - Tuple, - Union, - cast, -) - -from . import hdrs -from .abc import AbstractStreamWriter -from .helpers import ETAG_ANY, ETag -from .typedefs import Final, LooseHeaders -from .web_exceptions import ( - HTTPNotModified, - HTTPPartialContent, - HTTPPreconditionFailed, - HTTPRequestRangeNotSatisfiable, -) -from .web_response import StreamResponse - -__all__ = ("FileResponse",) - -if TYPE_CHECKING: # pragma: no cover - from .web_request import BaseRequest - - -_T_OnChunkSent = Optional[Callable[[bytes], Awaitable[None]]] - - -NOSENDFILE: Final[bool] = bool(os.environ.get("AIOHTTP_NOSENDFILE")) - - -class FileResponse(StreamResponse): - """A response object can be used to send files.""" - - def __init__( - self, - path: Union[str, pathlib.Path], - chunk_size: int = 256 * 1024, - status: int = 200, - reason: Optional[str] = None, - headers: Optional[LooseHeaders] = None, - ) -> None: - super().__init__(status=status, reason=reason, headers=headers) - - if isinstance(path, str): - path = pathlib.Path(path) - - self._path = path - self._chunk_size = chunk_size - - async def _sendfile_fallback( - self, writer: AbstractStreamWriter, fobj: IO[Any], offset: int, count: int - ) -> AbstractStreamWriter: - # To keep memory usage low,fobj is transferred in chunks - # controlled by the constructor's chunk_size argument. - - chunk_size = self._chunk_size - loop = asyncio.get_event_loop() - - await loop.run_in_executor(None, fobj.seek, offset) - - chunk = await loop.run_in_executor(None, fobj.read, chunk_size) - while chunk: - await writer.write(chunk) - count = count - chunk_size - if count <= 0: - break - chunk = await loop.run_in_executor(None, fobj.read, min(chunk_size, count)) - - await writer.drain() - return writer - - async def _sendfile( - self, request: "BaseRequest", fobj: IO[Any], offset: int, count: int - ) -> AbstractStreamWriter: - writer = await super().prepare(request) - assert writer is not None - - if NOSENDFILE or sys.version_info < (3, 7) or self.compression: - return await self._sendfile_fallback(writer, fobj, offset, count) - - loop = request._loop - transport = request.transport - assert transport is not None - - try: - await loop.sendfile(transport, fobj, offset, count) - except NotImplementedError: - return await self._sendfile_fallback(writer, fobj, offset, count) - - await super().write_eof() - return writer - - @staticmethod - def _strong_etag_match(etag_value: str, etags: Tuple[ETag, ...]) -> bool: - if len(etags) == 1 and etags[0].value == ETAG_ANY: - return True - return any(etag.value == etag_value for etag in etags if not etag.is_weak) - - async def _not_modified( - self, request: "BaseRequest", etag_value: str, last_modified: float - ) -> Optional[AbstractStreamWriter]: - self.set_status(HTTPNotModified.status_code) - self._length_check = False - self.etag = etag_value # type: ignore[assignment] - self.last_modified = last_modified # type: ignore[assignment] - # Delete any Content-Length headers provided by user. HTTP 304 - # should always have empty response body - return await super().prepare(request) - - async def _precondition_failed( - self, request: "BaseRequest" - ) -> Optional[AbstractStreamWriter]: - self.set_status(HTTPPreconditionFailed.status_code) - self.content_length = 0 - return await super().prepare(request) - - async def prepare(self, request: "BaseRequest") -> Optional[AbstractStreamWriter]: - filepath = self._path - - gzip = False - if "gzip" in request.headers.get(hdrs.ACCEPT_ENCODING, ""): - gzip_path = filepath.with_name(filepath.name + ".gz") - - if gzip_path.is_file(): - filepath = gzip_path - gzip = True - - loop = asyncio.get_event_loop() - st: os.stat_result = await loop.run_in_executor(None, filepath.stat) - - etag_value = f"{st.st_mtime_ns:x}-{st.st_size:x}" - last_modified = st.st_mtime - - # https://tools.ietf.org/html/rfc7232#section-6 - ifmatch = request.if_match - if ifmatch is not None and not self._strong_etag_match(etag_value, ifmatch): - return await self._precondition_failed(request) - - unmodsince = request.if_unmodified_since - if ( - unmodsince is not None - and ifmatch is None - and st.st_mtime > unmodsince.timestamp() - ): - return await self._precondition_failed(request) - - ifnonematch = request.if_none_match - if ifnonematch is not None and self._strong_etag_match(etag_value, ifnonematch): - return await self._not_modified(request, etag_value, last_modified) - - modsince = request.if_modified_since - if ( - modsince is not None - and ifnonematch is None - and st.st_mtime <= modsince.timestamp() - ): - return await self._not_modified(request, etag_value, last_modified) - - if hdrs.CONTENT_TYPE not in self.headers: - ct, encoding = mimetypes.guess_type(str(filepath)) - if not ct: - ct = "application/octet-stream" - should_set_ct = True - else: - encoding = "gzip" if gzip else None - should_set_ct = False - - status = self._status - file_size = st.st_size - count = file_size - - start = None - - ifrange = request.if_range - if ifrange is None or st.st_mtime <= ifrange.timestamp(): - # If-Range header check: - # condition = cached date >= last modification date - # return 206 if True else 200. - # if False: - # Range header would not be processed, return 200 - # if True but Range header missing - # return 200 - try: - rng = request.http_range - start = rng.start - end = rng.stop - except ValueError: - # https://tools.ietf.org/html/rfc7233: - # A server generating a 416 (Range Not Satisfiable) response to - # a byte-range request SHOULD send a Content-Range header field - # with an unsatisfied-range value. - # The complete-length in a 416 response indicates the current - # length of the selected representation. - # - # Will do the same below. Many servers ignore this and do not - # send a Content-Range header with HTTP 416 - self.headers[hdrs.CONTENT_RANGE] = f"bytes */{file_size}" - self.set_status(HTTPRequestRangeNotSatisfiable.status_code) - return await super().prepare(request) - - # If a range request has been made, convert start, end slice - # notation into file pointer offset and count - if start is not None or end is not None: - if start < 0 and end is None: # return tail of file - start += file_size - if start < 0: - # if Range:bytes=-1000 in request header but file size - # is only 200, there would be trouble without this - start = 0 - count = file_size - start - else: - # rfc7233:If the last-byte-pos value is - # absent, or if the value is greater than or equal to - # the current length of the representation data, - # the byte range is interpreted as the remainder - # of the representation (i.e., the server replaces the - # value of last-byte-pos with a value that is one less than - # the current length of the selected representation). - count = ( - min(end if end is not None else file_size, file_size) - start - ) - - if start >= file_size: - # HTTP 416 should be returned in this case. - # - # According to https://tools.ietf.org/html/rfc7233: - # If a valid byte-range-set includes at least one - # byte-range-spec with a first-byte-pos that is less than - # the current length of the representation, or at least one - # suffix-byte-range-spec with a non-zero suffix-length, - # then the byte-range-set is satisfiable. Otherwise, the - # byte-range-set is unsatisfiable. - self.headers[hdrs.CONTENT_RANGE] = f"bytes */{file_size}" - self.set_status(HTTPRequestRangeNotSatisfiable.status_code) - return await super().prepare(request) - - status = HTTPPartialContent.status_code - # Even though you are sending the whole file, you should still - # return a HTTP 206 for a Range request. - self.set_status(status) - - if should_set_ct: - self.content_type = ct # type: ignore[assignment] - if encoding: - self.headers[hdrs.CONTENT_ENCODING] = encoding - if gzip: - self.headers[hdrs.VARY] = hdrs.ACCEPT_ENCODING - - self.etag = etag_value # type: ignore[assignment] - self.last_modified = st.st_mtime # type: ignore[assignment] - self.content_length = count - - self.headers[hdrs.ACCEPT_RANGES] = "bytes" - - real_start = cast(int, start) - - if status == HTTPPartialContent.status_code: - self.headers[hdrs.CONTENT_RANGE] = "bytes {}-{}/{}".format( - real_start, real_start + count - 1, file_size - ) - - # If we are sending 0 bytes calling sendfile() will throw a ValueError - if count == 0 or request.method == hdrs.METH_HEAD or self.status in [204, 304]: - return await super().prepare(request) - - fobj = await loop.run_in_executor(None, filepath.open, "rb") - if start: # be aware that start could be None or int=0 here. - offset = start - else: - offset = 0 - - try: - return await self._sendfile(request, fobj, offset, count) - finally: - await loop.run_in_executor(None, fobj.close) diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/abc/_sockets.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/abc/_sockets.py deleted file mode 100644 index 6aac5f7c22395759ebe3d5633d2adcf1f4ff1fe5..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/abc/_sockets.py +++ /dev/null @@ -1,160 +0,0 @@ -from __future__ import annotations - -import socket -from abc import abstractmethod -from contextlib import AsyncExitStack -from io import IOBase -from ipaddress import IPv4Address, IPv6Address -from socket import AddressFamily -from typing import ( - Any, - Callable, - Collection, - Mapping, - Tuple, - TypeVar, - Union, -) - -from .._core._tasks import create_task_group -from .._core._typedattr import ( - TypedAttributeProvider, - TypedAttributeSet, - typed_attribute, -) -from ._streams import ByteStream, Listener, UnreliableObjectStream -from ._tasks import TaskGroup - -IPAddressType = Union[str, IPv4Address, IPv6Address] -IPSockAddrType = Tuple[str, int] -SockAddrType = Union[IPSockAddrType, str] -UDPPacketType = Tuple[bytes, IPSockAddrType] -T_Retval = TypeVar("T_Retval") - - -class SocketAttribute(TypedAttributeSet): - #: the address family of the underlying socket - family: AddressFamily = typed_attribute() - #: the local socket address of the underlying socket - local_address: SockAddrType = typed_attribute() - #: for IP addresses, the local port the underlying socket is bound to - local_port: int = typed_attribute() - #: the underlying stdlib socket object - raw_socket: socket.socket = typed_attribute() - #: the remote address the underlying socket is connected to - remote_address: SockAddrType = typed_attribute() - #: for IP addresses, the remote port the underlying socket is connected to - remote_port: int = typed_attribute() - - -class _SocketProvider(TypedAttributeProvider): - @property - def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]: - from .._core._sockets import convert_ipv6_sockaddr as convert - - attributes: dict[Any, Callable[[], Any]] = { - SocketAttribute.family: lambda: self._raw_socket.family, - SocketAttribute.local_address: lambda: convert( - self._raw_socket.getsockname() - ), - SocketAttribute.raw_socket: lambda: self._raw_socket, - } - try: - peername: tuple[str, int] | None = convert(self._raw_socket.getpeername()) - except OSError: - peername = None - - # Provide the remote address for connected sockets - if peername is not None: - attributes[SocketAttribute.remote_address] = lambda: peername - - # Provide local and remote ports for IP based sockets - if self._raw_socket.family in (AddressFamily.AF_INET, AddressFamily.AF_INET6): - attributes[ - SocketAttribute.local_port - ] = lambda: self._raw_socket.getsockname()[1] - if peername is not None: - remote_port = peername[1] - attributes[SocketAttribute.remote_port] = lambda: remote_port - - return attributes - - @property - @abstractmethod - def _raw_socket(self) -> socket.socket: - pass - - -class SocketStream(ByteStream, _SocketProvider): - """ - Transports bytes over a socket. - - Supports all relevant extra attributes from :class:`~SocketAttribute`. - """ - - -class UNIXSocketStream(SocketStream): - @abstractmethod - async def send_fds(self, message: bytes, fds: Collection[int | IOBase]) -> None: - """ - Send file descriptors along with a message to the peer. - - :param message: a non-empty bytestring - :param fds: a collection of files (either numeric file descriptors or open file or socket - objects) - """ - - @abstractmethod - async def receive_fds(self, msglen: int, maxfds: int) -> tuple[bytes, list[int]]: - """ - Receive file descriptors along with a message from the peer. - - :param msglen: length of the message to expect from the peer - :param maxfds: maximum number of file descriptors to expect from the peer - :return: a tuple of (message, file descriptors) - """ - - -class SocketListener(Listener[SocketStream], _SocketProvider): - """ - Listens to incoming socket connections. - - Supports all relevant extra attributes from :class:`~SocketAttribute`. - """ - - @abstractmethod - async def accept(self) -> SocketStream: - """Accept an incoming connection.""" - - async def serve( - self, - handler: Callable[[SocketStream], Any], - task_group: TaskGroup | None = None, - ) -> None: - async with AsyncExitStack() as exit_stack: - if task_group is None: - task_group = await exit_stack.enter_async_context(create_task_group()) - - while True: - stream = await self.accept() - task_group.start_soon(handler, stream) - - -class UDPSocket(UnreliableObjectStream[UDPPacketType], _SocketProvider): - """ - Represents an unconnected UDP socket. - - Supports all relevant extra attributes from :class:`~SocketAttribute`. - """ - - async def sendto(self, data: bytes, host: str, port: int) -> None: - """Alias for :meth:`~.UnreliableObjectSendStream.send` ((data, (host, port))).""" - return await self.send((data, (host, port))) - - -class ConnectedUDPSocket(UnreliableObjectStream[bytes], _SocketProvider): - """ - Represents an connected UDP socket. - - Supports all relevant extra attributes from :class:`~SocketAttribute`. - """ diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_plugins/extensions/types/__init__.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_plugins/extensions/types/__init__.py deleted file mode 100644 index 274d7bc4fcf5f780858b55a14c6c4ac85f2a7f0d..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_plugins/extensions/types/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -import warnings -with warnings.catch_warnings(): - try: - __import__('pkg_resources').declare_namespace(__name__) - except ImportError: - import pkgutil - __path__ = pkgutil.extend_path(__path__, __name__) diff --git a/spaces/Superlang/ImageProcessor/annotator/openpose/face.py b/spaces/Superlang/ImageProcessor/annotator/openpose/face.py deleted file mode 100644 index f3c46d77664aa9fa91c63785a1485a396f05cacc..0000000000000000000000000000000000000000 --- a/spaces/Superlang/ImageProcessor/annotator/openpose/face.py +++ /dev/null @@ -1,362 +0,0 @@ -import logging -import numpy as np -from torchvision.transforms import ToTensor, ToPILImage -import torch -import torch.nn.functional as F -import cv2 - -from . import util -from torch.nn import Conv2d, Module, ReLU, MaxPool2d, init - - -class FaceNet(Module): - """Model the cascading heatmaps. """ - def __init__(self): - super(FaceNet, self).__init__() - # cnn to make feature map - self.relu = ReLU() - self.max_pooling_2d = MaxPool2d(kernel_size=2, stride=2) - self.conv1_1 = Conv2d(in_channels=3, out_channels=64, - kernel_size=3, stride=1, padding=1) - self.conv1_2 = Conv2d( - in_channels=64, out_channels=64, kernel_size=3, stride=1, - padding=1) - self.conv2_1 = Conv2d( - in_channels=64, out_channels=128, kernel_size=3, stride=1, - padding=1) - self.conv2_2 = Conv2d( - in_channels=128, out_channels=128, kernel_size=3, stride=1, - padding=1) - self.conv3_1 = Conv2d( - in_channels=128, out_channels=256, kernel_size=3, stride=1, - padding=1) - self.conv3_2 = Conv2d( - in_channels=256, out_channels=256, kernel_size=3, stride=1, - padding=1) - self.conv3_3 = Conv2d( - in_channels=256, out_channels=256, kernel_size=3, stride=1, - padding=1) - self.conv3_4 = Conv2d( - in_channels=256, out_channels=256, kernel_size=3, stride=1, - padding=1) - self.conv4_1 = Conv2d( - in_channels=256, out_channels=512, kernel_size=3, stride=1, - padding=1) - self.conv4_2 = Conv2d( - in_channels=512, out_channels=512, kernel_size=3, stride=1, - padding=1) - self.conv4_3 = Conv2d( - in_channels=512, out_channels=512, kernel_size=3, stride=1, - padding=1) - self.conv4_4 = Conv2d( - in_channels=512, out_channels=512, kernel_size=3, stride=1, - padding=1) - self.conv5_1 = Conv2d( - in_channels=512, out_channels=512, kernel_size=3, stride=1, - padding=1) - self.conv5_2 = Conv2d( - in_channels=512, out_channels=512, kernel_size=3, stride=1, - padding=1) - self.conv5_3_CPM = Conv2d( - in_channels=512, out_channels=128, kernel_size=3, stride=1, - padding=1) - - # stage1 - self.conv6_1_CPM = Conv2d( - in_channels=128, out_channels=512, kernel_size=1, stride=1, - padding=0) - self.conv6_2_CPM = Conv2d( - in_channels=512, out_channels=71, kernel_size=1, stride=1, - padding=0) - - # stage2 - self.Mconv1_stage2 = Conv2d( - in_channels=199, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv2_stage2 = Conv2d( - in_channels=128, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv3_stage2 = Conv2d( - in_channels=128, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv4_stage2 = Conv2d( - in_channels=128, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv5_stage2 = Conv2d( - in_channels=128, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv6_stage2 = Conv2d( - in_channels=128, out_channels=128, kernel_size=1, stride=1, - padding=0) - self.Mconv7_stage2 = Conv2d( - in_channels=128, out_channels=71, kernel_size=1, stride=1, - padding=0) - - # stage3 - self.Mconv1_stage3 = Conv2d( - in_channels=199, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv2_stage3 = Conv2d( - in_channels=128, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv3_stage3 = Conv2d( - in_channels=128, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv4_stage3 = Conv2d( - in_channels=128, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv5_stage3 = Conv2d( - in_channels=128, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv6_stage3 = Conv2d( - in_channels=128, out_channels=128, kernel_size=1, stride=1, - padding=0) - self.Mconv7_stage3 = Conv2d( - in_channels=128, out_channels=71, kernel_size=1, stride=1, - padding=0) - - # stage4 - self.Mconv1_stage4 = Conv2d( - in_channels=199, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv2_stage4 = Conv2d( - in_channels=128, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv3_stage4 = Conv2d( - in_channels=128, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv4_stage4 = Conv2d( - in_channels=128, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv5_stage4 = Conv2d( - in_channels=128, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv6_stage4 = Conv2d( - in_channels=128, out_channels=128, kernel_size=1, stride=1, - padding=0) - self.Mconv7_stage4 = Conv2d( - in_channels=128, out_channels=71, kernel_size=1, stride=1, - padding=0) - - # stage5 - self.Mconv1_stage5 = Conv2d( - in_channels=199, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv2_stage5 = Conv2d( - in_channels=128, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv3_stage5 = Conv2d( - in_channels=128, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv4_stage5 = Conv2d( - in_channels=128, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv5_stage5 = Conv2d( - in_channels=128, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv6_stage5 = Conv2d( - in_channels=128, out_channels=128, kernel_size=1, stride=1, - padding=0) - self.Mconv7_stage5 = Conv2d( - in_channels=128, out_channels=71, kernel_size=1, stride=1, - padding=0) - - # stage6 - self.Mconv1_stage6 = Conv2d( - in_channels=199, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv2_stage6 = Conv2d( - in_channels=128, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv3_stage6 = Conv2d( - in_channels=128, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv4_stage6 = Conv2d( - in_channels=128, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv5_stage6 = Conv2d( - in_channels=128, out_channels=128, kernel_size=7, stride=1, - padding=3) - self.Mconv6_stage6 = Conv2d( - in_channels=128, out_channels=128, kernel_size=1, stride=1, - padding=0) - self.Mconv7_stage6 = Conv2d( - in_channels=128, out_channels=71, kernel_size=1, stride=1, - padding=0) - - for m in self.modules(): - if isinstance(m, Conv2d): - init.constant_(m.bias, 0) - - def forward(self, x): - """Return a list of heatmaps.""" - heatmaps = [] - - h = self.relu(self.conv1_1(x)) - h = self.relu(self.conv1_2(h)) - h = self.max_pooling_2d(h) - h = self.relu(self.conv2_1(h)) - h = self.relu(self.conv2_2(h)) - h = self.max_pooling_2d(h) - h = self.relu(self.conv3_1(h)) - h = self.relu(self.conv3_2(h)) - h = self.relu(self.conv3_3(h)) - h = self.relu(self.conv3_4(h)) - h = self.max_pooling_2d(h) - h = self.relu(self.conv4_1(h)) - h = self.relu(self.conv4_2(h)) - h = self.relu(self.conv4_3(h)) - h = self.relu(self.conv4_4(h)) - h = self.relu(self.conv5_1(h)) - h = self.relu(self.conv5_2(h)) - h = self.relu(self.conv5_3_CPM(h)) - feature_map = h - - # stage1 - h = self.relu(self.conv6_1_CPM(h)) - h = self.conv6_2_CPM(h) - heatmaps.append(h) - - # stage2 - h = torch.cat([h, feature_map], dim=1) # channel concat - h = self.relu(self.Mconv1_stage2(h)) - h = self.relu(self.Mconv2_stage2(h)) - h = self.relu(self.Mconv3_stage2(h)) - h = self.relu(self.Mconv4_stage2(h)) - h = self.relu(self.Mconv5_stage2(h)) - h = self.relu(self.Mconv6_stage2(h)) - h = self.Mconv7_stage2(h) - heatmaps.append(h) - - # stage3 - h = torch.cat([h, feature_map], dim=1) # channel concat - h = self.relu(self.Mconv1_stage3(h)) - h = self.relu(self.Mconv2_stage3(h)) - h = self.relu(self.Mconv3_stage3(h)) - h = self.relu(self.Mconv4_stage3(h)) - h = self.relu(self.Mconv5_stage3(h)) - h = self.relu(self.Mconv6_stage3(h)) - h = self.Mconv7_stage3(h) - heatmaps.append(h) - - # stage4 - h = torch.cat([h, feature_map], dim=1) # channel concat - h = self.relu(self.Mconv1_stage4(h)) - h = self.relu(self.Mconv2_stage4(h)) - h = self.relu(self.Mconv3_stage4(h)) - h = self.relu(self.Mconv4_stage4(h)) - h = self.relu(self.Mconv5_stage4(h)) - h = self.relu(self.Mconv6_stage4(h)) - h = self.Mconv7_stage4(h) - heatmaps.append(h) - - # stage5 - h = torch.cat([h, feature_map], dim=1) # channel concat - h = self.relu(self.Mconv1_stage5(h)) - h = self.relu(self.Mconv2_stage5(h)) - h = self.relu(self.Mconv3_stage5(h)) - h = self.relu(self.Mconv4_stage5(h)) - h = self.relu(self.Mconv5_stage5(h)) - h = self.relu(self.Mconv6_stage5(h)) - h = self.Mconv7_stage5(h) - heatmaps.append(h) - - # stage6 - h = torch.cat([h, feature_map], dim=1) # channel concat - h = self.relu(self.Mconv1_stage6(h)) - h = self.relu(self.Mconv2_stage6(h)) - h = self.relu(self.Mconv3_stage6(h)) - h = self.relu(self.Mconv4_stage6(h)) - h = self.relu(self.Mconv5_stage6(h)) - h = self.relu(self.Mconv6_stage6(h)) - h = self.Mconv7_stage6(h) - heatmaps.append(h) - - return heatmaps - - -LOG = logging.getLogger(__name__) -TOTEN = ToTensor() -TOPIL = ToPILImage() - - -params = { - 'gaussian_sigma': 2.5, - 'inference_img_size': 736, # 368, 736, 1312 - 'heatmap_peak_thresh': 0.1, - 'crop_scale': 1.5, - 'line_indices': [ - [0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], - [6, 7], [7, 8], [8, 9], [9, 10], [10, 11], [11, 12], [12, 13], - [13, 14], [14, 15], [15, 16], - [17, 18], [18, 19], [19, 20], [20, 21], - [22, 23], [23, 24], [24, 25], [25, 26], - [27, 28], [28, 29], [29, 30], - [31, 32], [32, 33], [33, 34], [34, 35], - [36, 37], [37, 38], [38, 39], [39, 40], [40, 41], [41, 36], - [42, 43], [43, 44], [44, 45], [45, 46], [46, 47], [47, 42], - [48, 49], [49, 50], [50, 51], [51, 52], [52, 53], [53, 54], - [54, 55], [55, 56], [56, 57], [57, 58], [58, 59], [59, 48], - [60, 61], [61, 62], [62, 63], [63, 64], [64, 65], [65, 66], - [66, 67], [67, 60] - ], -} - - -class Face(object): - """ - The OpenPose face landmark detector model. - - Args: - inference_size: set the size of the inference image size, suggested: - 368, 736, 1312, default 736 - gaussian_sigma: blur the heatmaps, default 2.5 - heatmap_peak_thresh: return landmark if over threshold, default 0.1 - - """ - def __init__(self, face_model_path, - inference_size=None, - gaussian_sigma=None, - heatmap_peak_thresh=None): - self.inference_size = inference_size or params["inference_img_size"] - self.sigma = gaussian_sigma or params['gaussian_sigma'] - self.threshold = heatmap_peak_thresh or params["heatmap_peak_thresh"] - self.model = FaceNet() - self.model.load_state_dict(torch.load(face_model_path)) - # if torch.cuda.is_available(): - # self.model = self.model.cuda() - # print('cuda') - self.model.eval() - - def __call__(self, face_img): - H, W, C = face_img.shape - - w_size = 384 - x_data = torch.from_numpy(util.smart_resize(face_img, (w_size, w_size))).permute([2, 0, 1]) / 256.0 - 0.5 - - x_data = x_data.to(self.cn_device) - - with torch.no_grad(): - hs = self.model(x_data[None, ...]) - heatmaps = F.interpolate( - hs[-1], - (H, W), - mode='bilinear', align_corners=True).cpu().numpy()[0] - return heatmaps - - def compute_peaks_from_heatmaps(self, heatmaps): - all_peaks = [] - for part in range(heatmaps.shape[0]): - map_ori = heatmaps[part].copy() - binary = np.ascontiguousarray(map_ori > 0.05, dtype=np.uint8) - - if np.sum(binary) == 0: - continue - - positions = np.where(binary > 0.5) - intensities = map_ori[positions] - mi = np.argmax(intensities) - y, x = positions[0][mi], positions[1][mi] - all_peaks.append([x, y]) - - return np.array(all_peaks) \ No newline at end of file diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/ops/roi_align_rotated.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/ops/roi_align_rotated.py deleted file mode 100644 index 0ce4961a3555d4da8bc3e32f1f7d5ad50036587d..0000000000000000000000000000000000000000 --- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/ops/roi_align_rotated.py +++ /dev/null @@ -1,177 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import torch.nn as nn -from torch.autograd import Function - -from ..utils import ext_loader - -ext_module = ext_loader.load_ext( - '_ext', ['roi_align_rotated_forward', 'roi_align_rotated_backward']) - - -class RoIAlignRotatedFunction(Function): - - @staticmethod - def symbolic(g, features, rois, out_size, spatial_scale, sample_num, - aligned, clockwise): - if isinstance(out_size, int): - out_h = out_size - out_w = out_size - elif isinstance(out_size, tuple): - assert len(out_size) == 2 - assert isinstance(out_size[0], int) - assert isinstance(out_size[1], int) - out_h, out_w = out_size - else: - raise TypeError( - '"out_size" must be an integer or tuple of integers') - return g.op( - 'mmcv::MMCVRoIAlignRotated', - features, - rois, - output_height_i=out_h, - output_width_i=out_h, - spatial_scale_f=spatial_scale, - sampling_ratio_i=sample_num, - aligned_i=aligned, - clockwise_i=clockwise) - - @staticmethod - def forward(ctx, - features, - rois, - out_size, - spatial_scale, - sample_num=0, - aligned=True, - clockwise=False): - if isinstance(out_size, int): - out_h = out_size - out_w = out_size - elif isinstance(out_size, tuple): - assert len(out_size) == 2 - assert isinstance(out_size[0], int) - assert isinstance(out_size[1], int) - out_h, out_w = out_size - else: - raise TypeError( - '"out_size" must be an integer or tuple of integers') - ctx.spatial_scale = spatial_scale - ctx.sample_num = sample_num - ctx.aligned = aligned - ctx.clockwise = clockwise - ctx.save_for_backward(rois) - ctx.feature_size = features.size() - - batch_size, num_channels, data_height, data_width = features.size() - num_rois = rois.size(0) - - output = features.new_zeros(num_rois, num_channels, out_h, out_w) - ext_module.roi_align_rotated_forward( - features, - rois, - output, - pooled_height=out_h, - pooled_width=out_w, - spatial_scale=spatial_scale, - sample_num=sample_num, - aligned=aligned, - clockwise=clockwise) - return output - - @staticmethod - def backward(ctx, grad_output): - feature_size = ctx.feature_size - spatial_scale = ctx.spatial_scale - aligned = ctx.aligned - clockwise = ctx.clockwise - sample_num = ctx.sample_num - rois = ctx.saved_tensors[0] - assert feature_size is not None - batch_size, num_channels, data_height, data_width = feature_size - - out_w = grad_output.size(3) - out_h = grad_output.size(2) - - grad_input = grad_rois = None - - if ctx.needs_input_grad[0]: - grad_input = rois.new_zeros(batch_size, num_channels, data_height, - data_width) - ext_module.roi_align_rotated_backward( - grad_output.contiguous(), - rois, - grad_input, - pooled_height=out_h, - pooled_width=out_w, - spatial_scale=spatial_scale, - sample_num=sample_num, - aligned=aligned, - clockwise=clockwise) - return grad_input, grad_rois, None, None, None, None, None - - -roi_align_rotated = RoIAlignRotatedFunction.apply - - -class RoIAlignRotated(nn.Module): - """RoI align pooling layer for rotated proposals. - - It accepts a feature map of shape (N, C, H, W) and rois with shape - (n, 6) with each roi decoded as (batch_index, center_x, center_y, - w, h, angle). The angle is in radian. - - Args: - out_size (tuple): h, w - spatial_scale (float): scale the input boxes by this number - sample_num (int): number of inputs samples to take for each - output sample. 0 to take samples densely for current models. - aligned (bool): if False, use the legacy implementation in - MMDetection. If True, align the results more perfectly. - Default: True. - clockwise (bool): If True, the angle in each proposal follows a - clockwise fashion in image space, otherwise, the angle is - counterclockwise. Default: False. - - Note: - The implementation of RoIAlign when aligned=True is modified from - https://github.com/facebookresearch/detectron2/ - - The meaning of aligned=True: - - Given a continuous coordinate c, its two neighboring pixel - indices (in our pixel model) are computed by floor(c - 0.5) and - ceil(c - 0.5). For example, c=1.3 has pixel neighbors with discrete - indices [0] and [1] (which are sampled from the underlying signal - at continuous coordinates 0.5 and 1.5). But the original roi_align - (aligned=False) does not subtract the 0.5 when computing - neighboring pixel indices and therefore it uses pixels with a - slightly incorrect alignment (relative to our pixel model) when - performing bilinear interpolation. - - With `aligned=True`, - we first appropriately scale the ROI and then shift it by -0.5 - prior to calling roi_align. This produces the correct neighbors; - - The difference does not make a difference to the model's - performance if ROIAlign is used together with conv layers. - """ - - def __init__(self, - out_size, - spatial_scale, - sample_num=0, - aligned=True, - clockwise=False): - super(RoIAlignRotated, self).__init__() - - self.out_size = out_size - self.spatial_scale = float(spatial_scale) - self.sample_num = int(sample_num) - self.aligned = aligned - self.clockwise = clockwise - - def forward(self, features, rois): - return RoIAlignRotatedFunction.apply(features, rois, self.out_size, - self.spatial_scale, - self.sample_num, self.aligned, - self.clockwise) diff --git a/spaces/TabbyML/tabby-template-space/README.md b/spaces/TabbyML/tabby-template-space/README.md deleted file mode 100644 index 9346d32f16fd9787bbb1a957171223be775bf3a5..0000000000000000000000000000000000000000 --- a/spaces/TabbyML/tabby-template-space/README.md +++ /dev/null @@ -1,15 +0,0 @@ ---- -title: Tabby Template Space -emoji: 🏷️ -colorFrom: gray -colorTo: purple -sdk: docker -app_port: 8080 -fullWidth: true -suggested_storage: small -suggested_hardware: t4-medium -tags: - - tabby ---- - -This is the Tabby Space Template you can use to deploy and run your own instance of Tabby on the Hugging Face Hub. diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/packaging/_elffile.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/packaging/_elffile.py deleted file mode 100644 index 6fb19b30bb53c18f38a9ef02dd7c4478670fb962..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/packaging/_elffile.py +++ /dev/null @@ -1,108 +0,0 @@ -""" -ELF file parser. - -This provides a class ``ELFFile`` that parses an ELF executable in a similar -interface to ``ZipFile``. Only the read interface is implemented. - -Based on: https://gist.github.com/lyssdod/f51579ae8d93c8657a5564aefc2ffbca -ELF header: https://refspecs.linuxfoundation.org/elf/gabi4+/ch4.eheader.html -""" - -import enum -import os -import struct -from typing import IO, Optional, Tuple - - -class ELFInvalid(ValueError): - pass - - -class EIClass(enum.IntEnum): - C32 = 1 - C64 = 2 - - -class EIData(enum.IntEnum): - Lsb = 1 - Msb = 2 - - -class EMachine(enum.IntEnum): - I386 = 3 - S390 = 22 - Arm = 40 - X8664 = 62 - AArc64 = 183 - - -class ELFFile: - """ - Representation of an ELF executable. - """ - - def __init__(self, f: IO[bytes]) -> None: - self._f = f - - try: - ident = self._read("16B") - except struct.error: - raise ELFInvalid("unable to parse identification") - magic = bytes(ident[:4]) - if magic != b"\x7fELF": - raise ELFInvalid(f"invalid magic: {magic!r}") - - self.capacity = ident[4] # Format for program header (bitness). - self.encoding = ident[5] # Data structure encoding (endianness). - - try: - # e_fmt: Format for program header. - # p_fmt: Format for section header. - # p_idx: Indexes to find p_type, p_offset, and p_filesz. - e_fmt, self._p_fmt, self._p_idx = { - (1, 1): (" -#include -#include -#include "upfirdn2d.h" - -//------------------------------------------------------------------------ - -static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx, int upy, int downx, int downy, int padx0, int padx1, int pady0, int pady1, bool flip, float gain) -{ - // Validate arguments. - TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device"); - TORCH_CHECK(f.device() == x.device(), "f must reside on the same device as x"); - TORCH_CHECK(f.dtype() == torch::kFloat, "f must be float32"); - TORCH_CHECK(x.numel() <= INT_MAX, "x is too large"); - TORCH_CHECK(f.numel() <= INT_MAX, "f is too large"); - TORCH_CHECK(x.dim() == 4, "x must be rank 4"); - TORCH_CHECK(f.dim() == 2, "f must be rank 2"); - TORCH_CHECK(f.size(0) >= 1 && f.size(1) >= 1, "f must be at least 1x1"); - TORCH_CHECK(upx >= 1 && upy >= 1, "upsampling factor must be at least 1"); - TORCH_CHECK(downx >= 1 && downy >= 1, "downsampling factor must be at least 1"); - - // Create output tensor. - const at::cuda::OptionalCUDAGuard device_guard(device_of(x)); - int outW = ((int)x.size(3) * upx + padx0 + padx1 - (int)f.size(1) + downx) / downx; - int outH = ((int)x.size(2) * upy + pady0 + pady1 - (int)f.size(0) + downy) / downy; - TORCH_CHECK(outW >= 1 && outH >= 1, "output must be at least 1x1"); - torch::Tensor y = torch::empty({x.size(0), x.size(1), outH, outW}, x.options(), x.suggest_memory_format()); - TORCH_CHECK(y.numel() <= INT_MAX, "output is too large"); - - // Initialize CUDA kernel parameters. - upfirdn2d_kernel_params p; - p.x = x.data_ptr(); - p.f = f.data_ptr (); - p.y = y.data_ptr(); - p.up = make_int2(upx, upy); - p.down = make_int2(downx, downy); - p.pad0 = make_int2(padx0, pady0); - p.flip = (flip) ? 1 : 0; - p.gain = gain; - p.inSize = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0)); - p.inStride = make_int4((int)x.stride(3), (int)x.stride(2), (int)x.stride(1), (int)x.stride(0)); - p.filterSize = make_int2((int)f.size(1), (int)f.size(0)); - p.filterStride = make_int2((int)f.stride(1), (int)f.stride(0)); - p.outSize = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0)); - p.outStride = make_int4((int)y.stride(3), (int)y.stride(2), (int)y.stride(1), (int)y.stride(0)); - p.sizeMajor = (p.inStride.z == 1) ? p.inSize.w : p.inSize.w * p.inSize.z; - p.sizeMinor = (p.inStride.z == 1) ? p.inSize.z : 1; - - // Choose CUDA kernel. - upfirdn2d_kernel_spec spec; - AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] - { - spec = choose_upfirdn2d_kernel (p); - }); - - // Set looping options. - p.loopMajor = (p.sizeMajor - 1) / 16384 + 1; - p.loopMinor = spec.loopMinor; - p.loopX = spec.loopX; - p.launchMinor = (p.sizeMinor - 1) / p.loopMinor + 1; - p.launchMajor = (p.sizeMajor - 1) / p.loopMajor + 1; - - // Compute grid size. - dim3 blockSize, gridSize; - if (spec.tileOutW < 0) // large - { - blockSize = dim3(4, 32, 1); - gridSize = dim3( - ((p.outSize.y - 1) / blockSize.x + 1) * p.launchMinor, - (p.outSize.x - 1) / (blockSize.y * p.loopX) + 1, - p.launchMajor); - } - else // small - { - blockSize = dim3(256, 1, 1); - gridSize = dim3( - ((p.outSize.y - 1) / spec.tileOutH + 1) * p.launchMinor, - (p.outSize.x - 1) / (spec.tileOutW * p.loopX) + 1, - p.launchMajor); - } - - // Launch CUDA kernel. - void* args[] = {&p}; - AT_CUDA_CHECK(cudaLaunchKernel(spec.kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream())); - return y; -} - -//------------------------------------------------------------------------ - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) -{ - m.def("upfirdn2d", &upfirdn2d); -} - -//------------------------------------------------------------------------ diff --git a/spaces/afffffdf/QSign/unidbg-fetch-qsign/bin/unidbg-fetch-qsign.bat b/spaces/afffffdf/QSign/unidbg-fetch-qsign/bin/unidbg-fetch-qsign.bat deleted file mode 100644 index 4e44bab8aa65d16e35e935f1273de2e98ce80cf9..0000000000000000000000000000000000000000 --- a/spaces/afffffdf/QSign/unidbg-fetch-qsign/bin/unidbg-fetch-qsign.bat +++ /dev/null @@ -1,89 +0,0 @@ -@rem -@rem Copyright 2015 the original author or authors. -@rem -@rem Licensed under the Apache License, Version 2.0 (the "License"); -@rem you may not use this file except in compliance with the License. -@rem You may obtain a copy of the License at -@rem -@rem https://www.apache.org/licenses/LICENSE-2.0 -@rem -@rem Unless required by applicable law or agreed to in writing, software -@rem distributed under the License is distributed on an "AS IS" BASIS, -@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -@rem See the License for the specific language governing permissions and -@rem limitations under the License. -@rem - -@if "%DEBUG%" == "" @echo off -@rem ########################################################################## -@rem -@rem unidbg-fetch-qsign startup script for Windows -@rem -@rem ########################################################################## - -@rem Set local scope for the variables with windows NT shell -if "%OS%"=="Windows_NT" setlocal - -set DIRNAME=%~dp0 -if "%DIRNAME%" == "" set DIRNAME=. -set APP_BASE_NAME=%~n0 -set APP_HOME=%DIRNAME%.. - -@rem Resolve any "." and ".." in APP_HOME to make it shorter. -for %%i in ("%APP_HOME%") do set APP_HOME=%%~fi - -@rem Add default JVM options here. You can also use JAVA_OPTS and UNIDBG_FETCH_QSIGN_OPTS to pass JVM options to this script. -set DEFAULT_JVM_OPTS= - -@rem Find java.exe -if defined JAVA_HOME goto findJavaFromJavaHome - -set JAVA_EXE=java.exe -%JAVA_EXE% -version >NUL 2>&1 -if "%ERRORLEVEL%" == "0" goto execute - -echo. -echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH. -echo. -echo Please set the JAVA_HOME variable in your environment to match the -echo location of your Java installation. - -goto fail - -:findJavaFromJavaHome -set JAVA_HOME=%JAVA_HOME:"=% -set JAVA_EXE=%JAVA_HOME%/bin/java.exe - -if exist "%JAVA_EXE%" goto execute - -echo. -echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME% -echo. -echo Please set the JAVA_HOME variable in your environment to match the -echo location of your Java installation. - -goto fail - -:execute -@rem Setup the command line - -set CLASSPATH=%APP_HOME%\lib\unidbg-fetch-qsign-1.1.0.jar;%APP_HOME%\lib\unidbg-fix.jar;%APP_HOME%\lib\ktor-server-content-negotiation-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-serialization-kotlinx-json-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-server-netty-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-server-host-common-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-server-core-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-serialization-kotlinx-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-serialization-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-events-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-websockets-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-http-cio-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-http-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-network-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-utils-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-io-jvm-2.3.1.jar;%APP_HOME%\lib\kotlin-stdlib-jdk8-1.8.22.jar;%APP_HOME%\lib\kotlinx-serialization-json-jvm-1.5.1.jar;%APP_HOME%\lib\kotlinx-serialization-protobuf-jvm-1.5.1.jar;%APP_HOME%\lib\kotlinx-serialization-core-jvm-1.5.1.jar;%APP_HOME%\lib\logback-classic-1.2.11.jar;%APP_HOME%\lib\kotlinx-coroutines-jdk8-1.7.1.jar;%APP_HOME%\lib\kotlinx-coroutines-core-jvm-1.7.1.jar;%APP_HOME%\lib\kotlin-stdlib-jdk7-1.8.22.jar;%APP_HOME%\lib\kotlin-reflect-1.8.10.jar;%APP_HOME%\lib\kotlin-stdlib-1.8.22.jar;%APP_HOME%\lib\slf4j-api-1.7.36.jar;%APP_HOME%\lib\kotlin-stdlib-common-1.8.22.jar;%APP_HOME%\lib\config-1.4.2.jar;%APP_HOME%\lib\jansi-2.4.0.jar;%APP_HOME%\lib\netty-codec-http2-4.1.92.Final.jar;%APP_HOME%\lib\alpn-api-1.1.3.v20160715.jar;%APP_HOME%\lib\netty-transport-native-kqueue-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-native-epoll-4.1.92.Final.jar;%APP_HOME%\lib\logback-core-1.2.11.jar;%APP_HOME%\lib\annotations-23.0.0.jar;%APP_HOME%\lib\netty-codec-http-4.1.92.Final.jar;%APP_HOME%\lib\netty-handler-4.1.92.Final.jar;%APP_HOME%\lib\netty-codec-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-classes-kqueue-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-classes-epoll-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-native-unix-common-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-4.1.92.Final.jar;%APP_HOME%\lib\netty-buffer-4.1.92.Final.jar;%APP_HOME%\lib\netty-resolver-4.1.92.Final.jar;%APP_HOME%\lib\netty-common-4.1.92.Final.jar - - -@rem Execute unidbg-fetch-qsign -"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %UNIDBG_FETCH_QSIGN_OPTS% -classpath "%CLASSPATH%" MainKt %* - -:end -@rem End local scope for the variables with windows NT shell -if "%ERRORLEVEL%"=="0" goto mainEnd - -:fail -rem Set variable UNIDBG_FETCH_QSIGN_EXIT_CONSOLE if you need the _script_ return code instead of -rem the _cmd.exe /c_ return code! -if not "" == "%UNIDBG_FETCH_QSIGN_EXIT_CONSOLE%" exit 1 -exit /b 1 - -:mainEnd -if "%OS%"=="Windows_NT" endlocal - -:omega diff --git a/spaces/agunes/ChatGPT4/app.py b/spaces/agunes/ChatGPT4/app.py deleted file mode 100644 index 7e09e57ef928fd2451fd0ed1295d0994ca75d026..0000000000000000000000000000000000000000 --- a/spaces/agunes/ChatGPT4/app.py +++ /dev/null @@ -1,193 +0,0 @@ -import gradio as gr -import os -import json -import requests - -#Streaming endpoint -API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream" - -#Huggingface provided GPT4 OpenAI API Key -OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") - -#Inferenec function -def predict(system_msg, inputs, top_p, temperature, chat_counter, chatbot=[], history=[]): - - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {OPENAI_API_KEY}" - } - print(f"system message is ^^ {system_msg}") - if system_msg.strip() == '': - initial_message = [{"role": "user", "content": f"{inputs}"},] - multi_turn_message = [] - else: - initial_message= [{"role": "system", "content": system_msg}, - {"role": "user", "content": f"{inputs}"},] - multi_turn_message = [{"role": "system", "content": system_msg},] - - if chat_counter == 0 : - payload = { - "model": "gpt-4", - "messages": initial_message , - "temperature" : 1.0, - "top_p":1.0, - "n" : 1, - "stream": True, - "presence_penalty":0, - "frequency_penalty":0, - } - print(f"chat_counter - {chat_counter}") - else: #if chat_counter != 0 : - messages=multi_turn_message # Of the type of - [{"role": "system", "content": system_msg},] - for data in chatbot: - user = {} - user["role"] = "user" - user["content"] = data[0] - assistant = {} - assistant["role"] = "assistant" - assistant["content"] = data[1] - messages.append(user) - messages.append(assistant) - temp = {} - temp["role"] = "user" - temp["content"] = inputs - messages.append(temp) - #messages - payload = { - "model": "gpt-4", - "messages": messages, # Of the type of [{"role": "user", "content": f"{inputs}"}], - "temperature" : temperature, #1.0, - "top_p": top_p, #1.0, - "n" : 1, - "stream": True, - "presence_penalty":0, - "frequency_penalty":0,} - - chat_counter+=1 - - history.append(inputs) - print(f"Logging : payload is - {payload}") - # make a POST request to the API endpoint using the requests.post method, passing in stream=True - response = requests.post(API_URL, headers=headers, json=payload, stream=True) - print(f"Logging : response code - {response}") - token_counter = 0 - partial_words = "" - - counter=0 - for chunk in response.iter_lines(): - #Skipping first chunk - if counter == 0: - counter+=1 - continue - # check whether each line is non-empty - if chunk.decode() : - chunk = chunk.decode() - # decode each line as response data is in bytes - if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']: - partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"] - if token_counter == 0: - history.append(" " + partial_words) - else: - history[-1] = partial_words - chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ] # convert to tuples of list - token_counter+=1 - yield chat, history, chat_counter, response # resembles {chatbot: chat, state: history} - -#Resetting to blank -def reset_textbox(): - return gr.update(value='') - -#to set a component as visible=False -def set_visible_false(): - return gr.update(visible=False) - -#to set a component as visible=True -def set_visible_true(): - return gr.update(visible=True) - -title = """ 🔥GPT4 with ChatCompletions API +🚀Gradio-Streaming
""" - -#display message for themes feature -theme_addon_msg = """🌟 Discover Gradio Themes with this Demo, featuring v3.22.0! Gradio v3.23.0 also enables seamless Theme sharing. You can develop or modify a theme, and send it to the hub using simple -""" - -#Using info to add additional information about System message in GPT4 -system_msg_info = """A conversation could begin with a system message to gently instruct the assistant. -System message helps set the behavior of the AI Assistant. For example, the assistant could be instructed with 'You are a helpful assistant.'""" - -#Modifying existing Gradio Theme -theme = gr.themes.Soft(primary_hue="zinc", secondary_hue="green", neutral_hue="green", - text_size=gr.themes.sizes.text_lg) - -with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;} #chatbot {height: 520px; overflow: auto;}""", - theme=theme) as demo: - gr.HTML(title) - gr.HTML("""theme.push_to_hub(). -
🏆Participate in Gradio's Theme Building Hackathon to exhibit your creative flair and win fabulous rewards! Join here - Gradio-Themes-Party🎨 🏆🔥This Huggingface Gradio Demo provides you full access to GPT4 API (4096 token limit). 🎉🥳🎉You don't need any OPENAI API key🙌""") - gr.HTML(theme_addon_msg) - gr.HTML('''
Duplicate the Space and run securely with your OpenAI API Key
'} - - def __init__(self, *args, **kwds): - super(ConditionalMarkPropFieldDefTypeForShape, self).__init__(*args, **kwds) - - -class ConditionalNumberValueDef(VegaLiteSchema): - """ConditionalNumberValueDef schema wrapper - - anyOf(:class:`ConditionalPredicateNumberValueDef`, - :class:`ConditionalSelectionNumberValueDef`) - """ - _schema = {'$ref': '#/definitions/ConditionalNumberValueDef'} - - def __init__(self, *args, **kwds): - super(ConditionalNumberValueDef, self).__init__(*args, **kwds) - - -class ConditionalPredicateMarkPropFieldDef(ConditionalMarkPropFieldDef): - """ConditionalPredicateMarkPropFieldDef schema wrapper - - Mapping(required=[test, type]) - - Attributes - ---------- - - test : :class:`LogicalOperandPredicate` - Predicate for triggering the condition - type : :class:`StandardType` - The encoded field's type of measurement ( ``"quantitative"``, ``"temporal"``, - ``"ordinal"``, or ``"nominal"`` ). - It can also be a ``"geojson"`` type for encoding `'geoshape' - '} - - def __init__(self, test=Undefined, type=Undefined, aggregate=Undefined, bin=Undefined, - field=Undefined, legend=Undefined, scale=Undefined, sort=Undefined, timeUnit=Undefined, - title=Undefined, **kwds): - super(ConditionalPredicateMarkPropFieldDef, self).__init__(test=test, type=type, - aggregate=aggregate, bin=bin, - field=field, legend=legend, - scale=scale, sort=sort, - timeUnit=timeUnit, title=title, - **kwds) - - -class ConditionalPredicateMarkPropFieldDefTypeForShape(ConditionalMarkPropFieldDefTypeForShape): - """ConditionalPredicateMarkPropFieldDefTypeForShape schema wrapper - - Mapping(required=[test, type]) - - Attributes - ---------- - - test : :class:`LogicalOperandPredicate` - Predicate for triggering the condition - type : :class:`TypeForShape` - The encoded field's type of measurement ( ``"quantitative"``, ``"temporal"``, - ``"ordinal"``, or ``"nominal"`` ). - It can also be a ``"geojson"`` type for encoding `'geoshape' - '} - - def __init__(self, test=Undefined, value=Undefined, **kwds): - super(ConditionalPredicateNumberValueDef, self).__init__(test=test, value=value, **kwds) - - -class ConditionalSelectionMarkPropFieldDef(ConditionalMarkPropFieldDef): - """ConditionalSelectionMarkPropFieldDef schema wrapper - - Mapping(required=[selection, type]) - - Attributes - ---------- - - selection : :class:`SelectionOperand` - A `selection name '} - - def __init__(self, selection=Undefined, type=Undefined, aggregate=Undefined, bin=Undefined, - field=Undefined, legend=Undefined, scale=Undefined, sort=Undefined, timeUnit=Undefined, - title=Undefined, **kwds): - super(ConditionalSelectionMarkPropFieldDef, self).__init__(selection=selection, type=type, - aggregate=aggregate, bin=bin, - field=field, legend=legend, - scale=scale, sort=sort, - timeUnit=timeUnit, title=title, - **kwds) - - -class ConditionalSelectionMarkPropFieldDefTypeForShape(ConditionalMarkPropFieldDefTypeForShape): - """ConditionalSelectionMarkPropFieldDefTypeForShape schema wrapper - - Mapping(required=[selection, type]) - - Attributes - ---------- - - selection : :class:`SelectionOperand` - A `selection name '} - - def __init__(self, selection=Undefined, value=Undefined, **kwds): - super(ConditionalSelectionNumberValueDef, self).__init__(selection=selection, value=value, - **kwds) - - -class ConditionalStringValueDef(VegaLiteSchema): - """ConditionalStringValueDef schema wrapper - - anyOf(:class:`ConditionalPredicateStringValueDef`, - :class:`ConditionalSelectionStringValueDef`) - """ - _schema = {'$ref': '#/definitions/ConditionalStringValueDef'} - - def __init__(self, *args, **kwds): - super(ConditionalStringValueDef, self).__init__(*args, **kwds) - - -class ConditionalPredicateStringValueDef(ConditionalStringValueDef): - """ConditionalPredicateStringValueDef schema wrapper - - Mapping(required=[test, value]) - - Attributes - ---------- - - test : :class:`LogicalOperandPredicate` - Predicate for triggering the condition - value : anyOf(string, None) - A constant value in visual domain (e.g., ``"red"`` / "#0099ff" for color, values - between ``0`` to ``1`` for opacity). - """ - _schema = {'$ref': '#/definitions/ConditionalPredicate '} - - def __init__(self, test=Undefined, value=Undefined, **kwds): - super(ConditionalPredicateStringValueDef, self).__init__(test=test, value=value, **kwds) - - -class ConditionalSelectionStringValueDef(ConditionalStringValueDef): - """ConditionalSelectionStringValueDef schema wrapper - - Mapping(required=[selection, value]) - - Attributes - ---------- - - selection : :class:`SelectionOperand` - A `selection name '} - - def __init__(self, selection=Undefined, value=Undefined, **kwds): - super(ConditionalSelectionStringValueDef, self).__init__(selection=selection, value=value, - **kwds) - - -class ConditionalTextFieldDef(VegaLiteSchema): - """ConditionalTextFieldDef schema wrapper - - anyOf(:class:`ConditionalPredicateTextFieldDef`, :class:`ConditionalSelectionTextFieldDef`) - """ - _schema = {'$ref': '#/definitions/ConditionalTextFieldDef'} - - def __init__(self, *args, **kwds): - super(ConditionalTextFieldDef, self).__init__(*args, **kwds) - - -class ConditionalPredicateTextFieldDef(ConditionalTextFieldDef): - """ConditionalPredicateTextFieldDef schema wrapper - - Mapping(required=[test, type]) - - Attributes - ---------- - - test : :class:`LogicalOperandPredicate` - Predicate for triggering the condition - type : :class:`StandardType` - The encoded field's type of measurement ( ``"quantitative"``, ``"temporal"``, - ``"ordinal"``, or ``"nominal"`` ). - It can also be a ``"geojson"`` type for encoding `'geoshape' - '} - - def __init__(self, test=Undefined, type=Undefined, aggregate=Undefined, bin=Undefined, - field=Undefined, format=Undefined, formatType=Undefined, timeUnit=Undefined, - title=Undefined, **kwds): - super(ConditionalPredicateTextFieldDef, self).__init__(test=test, type=type, - aggregate=aggregate, bin=bin, - field=field, format=format, - formatType=formatType, timeUnit=timeUnit, - title=title, **kwds) - - -class ConditionalSelectionTextFieldDef(ConditionalTextFieldDef): - """ConditionalSelectionTextFieldDef schema wrapper - - Mapping(required=[selection, type]) - - Attributes - ---------- - - selection : :class:`SelectionOperand` - A `selection name