-
-
\ No newline at end of file diff --git a/spaces/4Taps/SadTalker/src/face3d/util/__init__.py b/spaces/4Taps/SadTalker/src/face3d/util/__init__.py deleted file mode 100644 index 04eecb58b62f8c9d11d17606c6241d278a48b9b9..0000000000000000000000000000000000000000 --- a/spaces/4Taps/SadTalker/src/face3d/util/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -"""This package includes a miscellaneous collection of useful helper functions.""" -from src.face3d.util import * - diff --git a/spaces/801artistry/RVC801/lib/uvr5_pack/lib_v5/layers_33966KB.py b/spaces/801artistry/RVC801/lib/uvr5_pack/lib_v5/layers_33966KB.py deleted file mode 100644 index a38b7bb3ae3136b07eadfc2db445fef4c2de186b..0000000000000000000000000000000000000000 --- a/spaces/801artistry/RVC801/lib/uvr5_pack/lib_v5/layers_33966KB.py +++ /dev/null @@ -1,126 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F - -from . import spec_utils - - -class Conv2DBNActiv(nn.Module): - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(Conv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, - nout, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - bias=False, - ), - nn.BatchNorm2d(nout), - activ(), - ) - - def __call__(self, x): - return self.conv(x) - - -class SeperableConv2DBNActiv(nn.Module): - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(SeperableConv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, - nin, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - groups=nin, - bias=False, - ), - nn.Conv2d(nin, nout, kernel_size=1, bias=False), - nn.BatchNorm2d(nout), - activ(), - ) - - def __call__(self, x): - return self.conv(x) - - -class Encoder(nn.Module): - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): - super(Encoder, self).__init__() - self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ) - - def __call__(self, x): - skip = self.conv1(x) - h = self.conv2(skip) - - return h, skip - - -class Decoder(nn.Module): - def __init__( - self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False - ): - super(Decoder, self).__init__() - self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.dropout = nn.Dropout2d(0.1) if dropout else None - - def __call__(self, x, skip=None): - x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True) - if skip is not None: - skip = spec_utils.crop_center(skip, x) - x = torch.cat([x, skip], dim=1) - h = self.conv(x) - - if self.dropout is not None: - h = self.dropout(h) - - return h - - -class ASPPModule(nn.Module): - def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU): - super(ASPPModule, self).__init__() - self.conv1 = nn.Sequential( - nn.AdaptiveAvgPool2d((1, None)), - Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ), - ) - self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - self.conv3 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[0], dilations[0], activ=activ - ) - self.conv4 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[1], dilations[1], activ=activ - ) - self.conv5 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.conv6 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.conv7 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.bottleneck = nn.Sequential( - Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1) - ) - - def forward(self, x): - _, _, h, w = x.size() - feat1 = F.interpolate( - self.conv1(x), size=(h, w), mode="bilinear", align_corners=True - ) - feat2 = self.conv2(x) - feat3 = self.conv3(x) - feat4 = self.conv4(x) - feat5 = self.conv5(x) - feat6 = self.conv6(x) - feat7 = self.conv7(x) - out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1) - bottle = self.bottleneck(out) - return bottle diff --git a/spaces/AIConsultant/MusicGen/audiocraft/modules/chroma.py b/spaces/AIConsultant/MusicGen/audiocraft/modules/chroma.py deleted file mode 100644 index e84fb66b4a4aaefb0b3ccac8a9a44c3b20e48f61..0000000000000000000000000000000000000000 --- a/spaces/AIConsultant/MusicGen/audiocraft/modules/chroma.py +++ /dev/null @@ -1,66 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -import typing as tp - -from einops import rearrange -from librosa import filters -import torch -from torch import nn -import torch.nn.functional as F -import torchaudio - - -class ChromaExtractor(nn.Module): - """Chroma extraction and quantization. - - Args: - sample_rate (int): Sample rate for the chroma extraction. - n_chroma (int): Number of chroma bins for the chroma extraction. - radix2_exp (int): Size of stft window for the chroma extraction (power of 2, e.g. 12 -> 2^12). - nfft (int, optional): Number of FFT. - winlen (int, optional): Window length. - winhop (int, optional): Window hop size. - argmax (bool, optional): Whether to use argmax. Defaults to False. - norm (float, optional): Norm for chroma normalization. Defaults to inf. - """ - def __init__(self, sample_rate: int, n_chroma: int = 12, radix2_exp: int = 12, nfft: tp.Optional[int] = None, - winlen: tp.Optional[int] = None, winhop: tp.Optional[int] = None, argmax: bool = False, - norm: float = torch.inf): - super().__init__() - self.winlen = winlen or 2 ** radix2_exp - self.nfft = nfft or self.winlen - self.winhop = winhop or (self.winlen // 4) - self.sample_rate = sample_rate - self.n_chroma = n_chroma - self.norm = norm - self.argmax = argmax - self.register_buffer('fbanks', torch.from_numpy(filters.chroma(sr=sample_rate, n_fft=self.nfft, tuning=0, - n_chroma=self.n_chroma)), persistent=False) - self.spec = torchaudio.transforms.Spectrogram(n_fft=self.nfft, win_length=self.winlen, - hop_length=self.winhop, power=2, center=True, - pad=0, normalized=True) - - def forward(self, wav: torch.Tensor) -> torch.Tensor: - T = wav.shape[-1] - # in case we are getting a wav that was dropped out (nullified) - # from the conditioner, make sure wav length is no less that nfft - if T < self.nfft: - pad = self.nfft - T - r = 0 if pad % 2 == 0 else 1 - wav = F.pad(wav, (pad // 2, pad // 2 + r), 'constant', 0) - assert wav.shape[-1] == self.nfft, f"expected len {self.nfft} but got {wav.shape[-1]}" - - spec = self.spec(wav).squeeze(1) - raw_chroma = torch.einsum('cf,...ft->...ct', self.fbanks, spec) - norm_chroma = torch.nn.functional.normalize(raw_chroma, p=self.norm, dim=-2, eps=1e-6) - norm_chroma = rearrange(norm_chroma, 'b d t -> b t d') - - if self.argmax: - idx = norm_chroma.argmax(-1, keepdim=True) - norm_chroma[:] = 0 - norm_chroma.scatter_(dim=-1, index=idx, value=1) - - return norm_chroma diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/discriminator/model.py b/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/discriminator/model.py deleted file mode 100644 index 5263368a5e74d9d07840399469ca12a54e7fecbc..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/discriminator/model.py +++ /dev/null @@ -1,295 +0,0 @@ -import functools -import torch.nn as nn - - -class ActNorm(nn.Module): - def __init__(self, num_features, logdet=False, affine=True, - allow_reverse_init=False): - assert affine - super().__init__() - self.logdet = logdet - self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1)) - self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1)) - self.allow_reverse_init = allow_reverse_init - - self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8)) - - def initialize(self, input): - with torch.no_grad(): - flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1) - mean = ( - flatten.mean(1) - .unsqueeze(1) - .unsqueeze(2) - .unsqueeze(3) - .permute(1, 0, 2, 3) - ) - std = ( - flatten.std(1) - .unsqueeze(1) - .unsqueeze(2) - .unsqueeze(3) - .permute(1, 0, 2, 3) - ) - - self.loc.data.copy_(-mean) - self.scale.data.copy_(1 / (std + 1e-6)) - - def forward(self, input, reverse=False): - if reverse: - return self.reverse(input) - if len(input.shape) == 2: - input = input[:, :, None, None] - squeeze = True - else: - squeeze = False - - _, _, height, width = input.shape - - if self.training and self.initialized.item() == 0: - self.initialize(input) - self.initialized.fill_(1) - - h = self.scale * (input + self.loc) - - if squeeze: - h = h.squeeze(-1).squeeze(-1) - - if self.logdet: - log_abs = torch.log(torch.abs(self.scale)) - logdet = height * width * torch.sum(log_abs) - logdet = logdet * torch.ones(input.shape[0]).to(input) - return h, logdet - - return h - - def reverse(self, output): - if self.training and self.initialized.item() == 0: - if not self.allow_reverse_init: - raise RuntimeError( - "Initializing ActNorm in reverse direction is " - "disabled by default. Use allow_reverse_init=True to enable." - ) - else: - self.initialize(output) - self.initialized.fill_(1) - - if len(output.shape) == 2: - output = output[:, :, None, None] - squeeze = True - else: - squeeze = False - - h = output / self.scale - self.loc - - if squeeze: - h = h.squeeze(-1).squeeze(-1) - return h - -def weights_init(m): - classname = m.__class__.__name__ - if classname.find('Conv') != -1: - nn.init.normal_(m.weight.data, 0.0, 0.02) - elif classname.find('BatchNorm') != -1: - nn.init.normal_(m.weight.data, 1.0, 0.02) - nn.init.constant_(m.bias.data, 0) - - -class NLayerDiscriminator(nn.Module): - """Defines a PatchGAN discriminator as in Pix2Pix - --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py - """ - def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False): - """Construct a PatchGAN discriminator - Parameters: - input_nc (int) -- the number of channels in input images - ndf (int) -- the number of filters in the last conv layer - n_layers (int) -- the number of conv layers in the discriminator - norm_layer -- normalization layer - """ - super(NLayerDiscriminator, self).__init__() - if not use_actnorm: - norm_layer = nn.BatchNorm2d - else: - norm_layer = ActNorm - if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters - use_bias = norm_layer.func != nn.BatchNorm2d - else: - use_bias = norm_layer != nn.BatchNorm2d - - kw = 4 - padw = 1 - sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)] - nf_mult = 1 - nf_mult_prev = 1 - for n in range(1, n_layers): # gradually increase the number of filters - nf_mult_prev = nf_mult - nf_mult = min(2 ** n, 8) - sequence += [ - nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), - norm_layer(ndf * nf_mult), - nn.LeakyReLU(0.2, True) - ] - - nf_mult_prev = nf_mult - nf_mult = min(2 ** n_layers, 8) - sequence += [ - nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), - norm_layer(ndf * nf_mult), - nn.LeakyReLU(0.2, True) - ] - # output 1 channel prediction map - sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] - self.main = nn.Sequential(*sequence) - - def forward(self, input): - """Standard forward.""" - return self.main(input) - -class NLayerDiscriminator1dFeats(NLayerDiscriminator): - """Defines a PatchGAN discriminator as in Pix2Pix - --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py - """ - def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False): - """Construct a PatchGAN discriminator - Parameters: - input_nc (int) -- the number of channels in input feats - ndf (int) -- the number of filters in the last conv layer - n_layers (int) -- the number of conv layers in the discriminator - norm_layer -- normalization layer - """ - super().__init__(input_nc=input_nc, ndf=64, n_layers=n_layers, use_actnorm=use_actnorm) - - if not use_actnorm: - norm_layer = nn.BatchNorm1d - else: - norm_layer = ActNorm - if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm has affine parameters - use_bias = norm_layer.func != nn.BatchNorm1d - else: - use_bias = norm_layer != nn.BatchNorm1d - - kw = 4 - padw = 1 - sequence = [nn.Conv1d(input_nc, input_nc//2, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)] - nf_mult = input_nc//2 - nf_mult_prev = 1 - for n in range(1, n_layers): # gradually decrease the number of filters - nf_mult_prev = nf_mult - nf_mult = max(nf_mult_prev // (2 ** n), 8) - sequence += [ - nn.Conv1d(nf_mult_prev, nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), - norm_layer(nf_mult), - nn.LeakyReLU(0.2, True) - ] - - nf_mult_prev = nf_mult - nf_mult = max(nf_mult_prev // (2 ** n), 8) - sequence += [ - nn.Conv1d(nf_mult_prev, nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), - norm_layer(nf_mult), - nn.LeakyReLU(0.2, True) - ] - nf_mult_prev = nf_mult - nf_mult = max(nf_mult_prev // (2 ** n), 8) - sequence += [ - nn.Conv1d(nf_mult_prev, nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), - norm_layer(nf_mult), - nn.LeakyReLU(0.2, True) - ] - # output 1 channel prediction map - sequence += [nn.Conv1d(nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] - self.main = nn.Sequential(*sequence) - - -class NLayerDiscriminator1dSpecs(NLayerDiscriminator): - """Defines a PatchGAN discriminator as in Pix2Pix - --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py - """ - def __init__(self, input_nc=80, ndf=64, n_layers=3, use_actnorm=False): - """Construct a PatchGAN discriminator - Parameters: - input_nc (int) -- the number of channels in input specs - ndf (int) -- the number of filters in the last conv layer - n_layers (int) -- the number of conv layers in the discriminator - norm_layer -- normalization layer - """ - super().__init__(input_nc=input_nc, ndf=64, n_layers=n_layers, use_actnorm=use_actnorm) - - if not use_actnorm: - norm_layer = nn.BatchNorm1d - else: - norm_layer = ActNorm - if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm has affine parameters - use_bias = norm_layer.func != nn.BatchNorm1d - else: - use_bias = norm_layer != nn.BatchNorm1d - - kw = 4 - padw = 1 - sequence = [nn.Conv1d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)] - nf_mult = 1 - nf_mult_prev = 1 - for n in range(1, n_layers): # gradually decrease the number of filters - nf_mult_prev = nf_mult - nf_mult = min(2 ** n, 8) - sequence += [ - nn.Conv1d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), - norm_layer(ndf * nf_mult), - nn.LeakyReLU(0.2, True) - ] - - nf_mult_prev = nf_mult - nf_mult = min(2 ** n_layers, 8) - sequence += [ - nn.Conv1d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), - norm_layer(ndf * nf_mult), - nn.LeakyReLU(0.2, True) - ] - # output 1 channel prediction map - sequence += [nn.Conv1d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] - self.main = nn.Sequential(*sequence) - - def forward(self, input): - """Standard forward.""" - # (B, C, L) - input = input.squeeze(1) - input = self.main(input) - return input - - -if __name__ == '__main__': - import torch - - ## FEATURES - disc_in_channels = 2048 - disc_num_layers = 2 - use_actnorm = False - disc_ndf = 64 - discriminator = NLayerDiscriminator1dFeats(input_nc=disc_in_channels, n_layers=disc_num_layers, - use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init) - inputs = torch.rand((6, 2048, 212)) - outputs = discriminator(inputs) - print(outputs.shape) - - ## AUDIO - disc_in_channels = 1 - disc_num_layers = 3 - use_actnorm = False - disc_ndf = 64 - discriminator = NLayerDiscriminator(input_nc=disc_in_channels, n_layers=disc_num_layers, - use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init) - inputs = torch.rand((6, 1, 80, 848)) - outputs = discriminator(inputs) - print(outputs.shape) - - ## IMAGE - disc_in_channels = 3 - disc_num_layers = 3 - use_actnorm = False - disc_ndf = 64 - discriminator = NLayerDiscriminator(input_nc=disc_in_channels, n_layers=disc_num_layers, - use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init) - inputs = torch.rand((6, 3, 256, 256)) - outputs = discriminator(inputs) - print(outputs.shape) diff --git a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/encoders/open_clap/pretrained.py b/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/encoders/open_clap/pretrained.py deleted file mode 100644 index 723619a9fd511cf8619def49c4631ec701891b93..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/encoders/open_clap/pretrained.py +++ /dev/null @@ -1,147 +0,0 @@ -import hashlib -import os -import urllib -import warnings - -from tqdm import tqdm - -_RN50 = dict( - openai="https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt", - yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt", - cc12m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt" -) - -_RN50_quickgelu = dict( - openai="https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt", - yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt", - cc12m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt" -) - -_RN101 = dict( - openai="https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt", - yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt" -) - -_RN101_quickgelu = dict( - openai="https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt", - yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt" -) - -_RN50x4 = dict( - openai="https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt", -) - -_RN50x16 = dict( - openai="https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt", -) - -_RN50x64 = dict( - openai="https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt", -) - -_VITB32 = dict( - openai="https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt", - laion400m_e31="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt", - laion400m_e32="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt", - laion400m_avg="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_avg-8a00ab3c.pt", -) - -_VITB32_quickgelu = dict( - openai="https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt", - laion400m_e31="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt", - laion400m_e32="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt", - laion400m_avg="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_avg-8a00ab3c.pt", -) - -_VITB16 = dict( - openai="https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt", -) - -_VITL14 = dict( - openai="https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt", -) - -_PRETRAINED = { - "RN50": _RN50, - "RN50-quickgelu": _RN50_quickgelu, - "RN101": _RN101, - "RN101-quickgelu": _RN101_quickgelu, - "RN50x4": _RN50x4, - "RN50x16": _RN50x16, - "ViT-B-32": _VITB32, - "ViT-B-32-quickgelu": _VITB32_quickgelu, - "ViT-B-16": _VITB16, - "ViT-L-14": _VITL14, -} - - -def list_pretrained(as_str: bool = False): - """ returns list of pretrained models - Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True - """ - return [':'.join([k, t]) if as_str else (k, t) for k in _PRETRAINED.keys() for t in _PRETRAINED[k].keys()] - - -def list_pretrained_tag_models(tag: str): - """ return all models having the specified pretrain tag """ - models = [] - for k in _PRETRAINED.keys(): - if tag in _PRETRAINED[k]: - models.append(k) - return models - - -def list_pretrained_model_tags(model: str): - """ return all pretrain tags for the specified model architecture """ - tags = [] - if model in _PRETRAINED: - tags.extend(_PRETRAINED[model].keys()) - return tags - - -def get_pretrained_url(model: str, tag: str): - if model not in _PRETRAINED: - return '' - model_pretrained = _PRETRAINED[model] - if tag not in model_pretrained: - return '' - return model_pretrained[tag] - - -def download_pretrained(url: str, root: str = os.path.expanduser("~/.cache/clip")): - os.makedirs(root, exist_ok=True) - filename = os.path.basename(url) - - if 'openaipublic' in url: - expected_sha256 = url.split("/")[-2] - else: - expected_sha256 = '' - - download_target = os.path.join(root, filename) - - if os.path.exists(download_target) and not os.path.isfile(download_target): - raise RuntimeError(f"{download_target} exists and is not a regular file") - - if os.path.isfile(download_target): - if expected_sha256: - if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256: - return download_target - else: - warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") - else: - return download_target - - with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: - with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop: - while True: - buffer = source.read(8192) - if not buffer: - break - - output.write(buffer) - loop.update(len(buffer)) - - if expected_sha256 and hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256: - raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match") - - return download_target diff --git a/spaces/AgentVerse/agentVerse/agentverse/agentverse.py b/spaces/AgentVerse/agentVerse/agentverse/agentverse.py deleted file mode 100644 index 8b1505075ccd942f0859b779eff6b6e3a2915aa1..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/agentverse/agentverse.py +++ /dev/null @@ -1,65 +0,0 @@ -import asyncio -import logging -from typing import List - -# from agentverse.agents import Agent -from agentverse.agents.conversation_agent import BaseAgent -from agentverse.environments import BaseEnvironment -from agentverse.initialization import load_agent, load_environment, prepare_task_config - -logging.basicConfig( - format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", - datefmt="%m/%d/%Y %H:%M:%S", - level=logging.INFO, -) - -openai_logger = logging.getLogger("openai") -openai_logger.setLevel(logging.WARNING) - - -class AgentVerse: - def __init__(self, agents: List[BaseAgent], environment: BaseEnvironment): - self.agents = agents - self.environment = environment - - @classmethod - def from_task(cls, task: str, tasks_dir: str): - """Build an AgentVerse from a task name. - The task name should correspond to a directory in `tasks` directory. - Then this method will load the configuration from the yaml file in that directory. - """ - # Prepare the config of the task - task_config = prepare_task_config(task, tasks_dir) - - # Build the agents - agents = [] - for agent_configs in task_config["agents"]: - agent = load_agent(agent_configs) - agents.append(agent) - - # Build the environment - env_config = task_config["environment"] - env_config["agents"] = agents - environment = load_environment(env_config) - - return cls(agents, environment) - - def run(self): - """Run the environment from scratch until it is done.""" - self.environment.reset() - while not self.environment.is_done(): - asyncio.run(self.environment.step()) - - def reset(self): - self.environment.reset() - for agent in self.agents: - agent.reset() - - def next(self, *args, **kwargs): - """Run the environment for one step and return the return message.""" - return_message = asyncio.run(self.environment.step(*args, **kwargs)) - return return_message - - def update_state(self, *args, **kwargs): - """Run the environment for one step and return the return message.""" - self.environment.update_state(*args, **kwargs) diff --git a/spaces/AgentVerse/agentVerse/agentverse/tasksolving.py b/spaces/AgentVerse/agentVerse/agentverse/tasksolving.py deleted file mode 100644 index 2bd2bd9b7793d21de6cc4f0089c33eaed955e721..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/agentverse/tasksolving.py +++ /dev/null @@ -1,91 +0,0 @@ -import asyncio -import os -import copy - -import logging - -from agentverse.environments.tasksolving_env.basic import BasicEnvironment -from agentverse.initialization import load_agent, load_environment, prepare_task_config -from agentverse.utils import AGENT_TYPES - - -openai_logger = logging.getLogger("openai") -openai_logger.setLevel(logging.WARNING) - - -class TaskSolving: - environment: BasicEnvironment - task: str = "" - logs: list = [] - - def __init__(self, environment: BasicEnvironment, task: str = ""): - self.environment = environment - self.task = task - - @classmethod - def from_task(cls, task: str, tasks_dir: str): - """Build an AgentVerse from a task name. - The task name should correspond to a directory in `tasks` directory. - Then this method will load the configuration from the yaml file in that directory. - """ - # Prepare the config of the task - task_config = prepare_task_config(task, tasks_dir) - - # Build the environment - env_config = task_config["environment"] - - # Build agents for all pipeline (task) - agents = {} - for i, agent_config in enumerate(task_config["agents"]): - agent_type = AGENT_TYPES(i) - if i == 2 and agent_config.get("agent_type", "") == "critic": - agent = load_agent(agent_config) - agents[agent_type] = [ - copy.deepcopy(agent) - for _ in range(task_config.get("cnt_agents", 1) - 1) - ] - else: - agents[agent_type] = load_agent(agent_config) - - env_config["agents"] = agents - - env_config["task_description"] = task_config.get("task_description", "") - env_config["max_rounds"] = task_config.get("max_rounds", 3) - - environment: BasicEnvironment = load_environment(env_config) - - return cls(environment=environment, task=task) - - def run(self): - """Run the environment from scratch until it is done.""" - self.environment.reset() - self.logs = [] - advice = "No advice yet." - previous_plan = "No solution yet." - while not self.environment.is_done(): - result, advice, previous_plan, logs, success = asyncio.run( - self.environment.step(advice, previous_plan) - ) - self.logs += logs - self.environment.report_metrics() - self.save_result(previous_plan, result, self.environment.get_spend()) - return previous_plan, result, self.logs - - def singleagent_thinking(self, preliminary_solution, advice) -> str: - preliminary_solution = self.environment.solve( - former_solution=preliminary_solution, - critic_opinions=[(self.environment.evaluator, advice)], - ) - return preliminary_solution - - def reset(self): - self.environment.reset() - - def save_result(self, plan: str, result: str, spend: float): - """Save the result to the result file""" - result_file_path = "./results/" + self.task + ".txt" - os.makedirs(os.path.dirname(result_file_path), exist_ok=True) - with open(result_file_path, "w") as f: - f.write("[Final Plan]\n" + plan + "\n\n") - f.write("[Result]\n" + result) - f.write(f"[Spent]\n${spend}") diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/namevaluelabel/Factory.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/namevaluelabel/Factory.js deleted file mode 100644 index db094ab8b8ad86b74fcc5419879e69ef4e599031..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/namevaluelabel/Factory.js +++ /dev/null @@ -1,13 +0,0 @@ -import NameValueLabel from './NameValueLabel.js'; -import ObjectFactory from '../ObjectFactory.js'; -import SetValue from '../../../plugins/utils/object/SetValue.js'; - -ObjectFactory.register('nameValueLabel', function (config) { - var gameObject = new NameValueLabel(this.scene, config); - this.scene.add.existing(gameObject); - return gameObject; -}); - -SetValue(window, 'RexPlugins.UI.NameValueLabel', NameValueLabel); - -export default NameValueLabel; \ No newline at end of file diff --git a/spaces/AkitoP/umamusume_bert_vits2/text/cleaner.py b/spaces/AkitoP/umamusume_bert_vits2/text/cleaner.py deleted file mode 100644 index 3ba3739816aabbe16663b68c74fcda0588c14bab..0000000000000000000000000000000000000000 --- a/spaces/AkitoP/umamusume_bert_vits2/text/cleaner.py +++ /dev/null @@ -1,28 +0,0 @@ -from text import chinese, japanese, cleaned_text_to_sequence - - -language_module_map = {"ZH": chinese, "JP": japanese} - - -def clean_text(text, language): - language_module = language_module_map[language] - norm_text = language_module.text_normalize(text) - phones, tones, word2ph = language_module.g2p(norm_text) - return norm_text, phones, tones, word2ph - - -def clean_text_bert(text, language): - language_module = language_module_map[language] - norm_text = language_module.text_normalize(text) - phones, tones, word2ph = language_module.g2p(norm_text) - bert = language_module.get_bert_feature(norm_text, word2ph) - return phones, tones, bert - - -def text_to_sequence(text, language): - norm_text, phones, tones, word2ph = clean_text(text, language) - return cleaned_text_to_sequence(phones, tones, language) - - -if __name__ == "__main__": - pass diff --git a/spaces/AlekseyKorshuk/model-evaluation/tabs/arena_battle.py b/spaces/AlekseyKorshuk/model-evaluation/tabs/arena_battle.py deleted file mode 100644 index b5996af803e55759018000c19a35eb940dde2a79..0000000000000000000000000000000000000000 --- a/spaces/AlekseyKorshuk/model-evaluation/tabs/arena_battle.py +++ /dev/null @@ -1,260 +0,0 @@ -import time - -import gradio as gr -import random -from conversation import Conversation -from utils import get_matchmaking - - -def get_tab_arena_battle(download_bot_config, get_bot_profile, model_mapping, client): - gr.Markdown(""" - # ⚔️ Chatbot Arena (battle) ⚔️ - ## Rules - * Chat with two anonymous models side-by-side and vote for which one is better! - * You can do multiple rounds of conversations before voting or vote for each message. - * The names of the models will be revealed of the top after your voted and pressed "Show models". - * Click “Restart” to start a new round with new models. - """) - default_bot_id = "_bot_e21de304-6151-4a04-b025-4c553ae8cbca" - bot_config = download_bot_config(default_bot_id) - user_state = gr.State( - bot_config - ) - with gr.Row(): - bot_id = gr.Textbox(label="Chai bot ID", value=default_bot_id, interactive=True) - reload_bot_button = gr.Button("Reload bot") - bot_profile = gr.HTML(get_bot_profile(bot_config)) - with gr.Accordion("Bot config:", open=False): - bot_config_text = gr.Markdown(f"# Memory\n{bot_config['memory']}\n# Prompt\n{bot_config['prompt']}\n") - - with gr.Row(): - values = list(model_mapping.keys()) - first_message = (None, bot_config["firstMessage"]) - height = 450 - model_a_value, model_b_value = get_matchmaking(client, values, is_anonymous=True) - with gr.Column(): - model_a = gr.Textbox(value=model_a_value, label="Model A", interactive=False, visible=False) - chatbot_a = gr.Chatbot([first_message]) - chatbot_a.style(height=height) - with gr.Column(): - model_b = gr.Textbox(value=model_b_value, label="Model B", interactive=False, visible=False) - chatbot_b = gr.Chatbot([first_message]) - chatbot_b.style(height=height) - - with gr.Row(): - with gr.Column(scale=3): - msg = gr.Textbox(show_label=False, value="Hi there!", interactive=True) - with gr.Column(scale=3): - send = gr.Button("Send") - with gr.Row(): - vote_a = gr.Button("👈 A is better", interactive=False) - vote_b = gr.Button("👉 B is better", interactive=False) - vote_tie = gr.Button("🤝 Tie", interactive=False) - vote_bad = gr.Button("💩 Both are bad", interactive=False) - show_models_button = gr.Button("Show models", interactive=False) - with gr.Row(): - regenerate = gr.Button("Regenerate", interactive=False) - clear = gr.Button("Restart") - - with gr.Accordion("Generation parameters for model A", open=False): - model = model_mapping[model_a.value] - temperature_model_a = gr.Slider(minimum=0.0, maximum=1.0, value=model.generation_params["temperature"], - interactive=True, label="Temperature") - repetition_penalty_model_a = gr.Slider(minimum=0.0, maximum=2.0, - value=model.generation_params["repetition_penalty"], - interactive=True, label="Repetition penalty") - max_new_tokens_model_a = gr.Slider(minimum=1, maximum=512, value=model.generation_params["max_new_tokens"], - interactive=True, label="Max new tokens") - top_k_model_a = gr.Slider(minimum=1, maximum=100, value=model.generation_params["top_k"], - interactive=True, label="Top-K") - top_p_model_a = gr.Slider(minimum=0.0, maximum=1.0, value=model.generation_params["top_p"], - interactive=True, label="Top-P") - - with gr.Accordion("Generation parameters for model B", open=False): - model = model_mapping[model_b.value] - temperature_model_b = gr.Slider(minimum=0.0, maximum=1.0, value=model.generation_params["temperature"], - interactive=True, label="Temperature") - repetition_penalty_model_b = gr.Slider(minimum=0.0, maximum=2.0, - value=model.generation_params["repetition_penalty"], - interactive=True, label="Repetition penalty") - max_new_tokens_model_b = gr.Slider(minimum=1, maximum=512, value=model.generation_params["max_new_tokens"], - interactive=True, label="Max new tokens") - top_k_model_b = gr.Slider(minimum=1, maximum=100, value=model.generation_params["top_k"], - interactive=True, label="Top-K") - top_p_model_b = gr.Slider(minimum=0.0, maximum=1.0, value=model.generation_params["top_p"], - interactive=True, label="Top-P") - - def clear_chat(user_state): - return "", [(None, user_state["firstMessage"])], [(None, user_state["firstMessage"])] - - def reload_bot(bot_id): - bot_config = download_bot_config(bot_id) - bot_profile = get_bot_profile(bot_config) - return bot_profile, [(None, bot_config["firstMessage"])], [(None, bot_config[ - "firstMessage"])], bot_config, f"# Memory\n{bot_config['memory']}\n# Prompt\n{bot_config['prompt']}" - - def get_generation_args(model_tag): - model = model_mapping[model_tag] - return ( - model.generation_params["temperature"], - model.generation_params["repetition_penalty"], - model.generation_params["max_new_tokens"], - model.generation_params["top_k"], - model.generation_params["top_p"], - ) - - def respond(message, chat_history, user_state, model_tag, - temperature, repetition_penalty, max_new_tokens, top_k, top_p): - custom_generation_params = { - 'temperature': temperature, - 'repetition_penalty': repetition_penalty, - 'max_new_tokens': max_new_tokens, - 'top_k': top_k, - 'top_p': top_p, - } - conv = Conversation(user_state) - conv.set_chat_history(chat_history) - conv.add_user_message(message) - model = model_mapping[model_tag] - bot_message = model.generate_response(conv, custom_generation_params) - chat_history.append( - (message, bot_message) - ) - return "", chat_history - - def record_vote(user_state, vote, - chat_history_a, model_tag_a, - chat_history_b, model_tag_b): - conv_a = Conversation(user_state) - conv_a.set_chat_history(chat_history_a) - conv_b = Conversation(user_state) - conv_b.set_chat_history(chat_history_b) - if "A is better" in vote: - vote_str = "model_a" - elif "B is better" in vote: - vote_str = "model_b" - elif "Tie" in vote: - vote_str = "tie" - else: - vote_str = "tie (bothbad)" - row = { - "timestamp": time.time(), - "bot_id": user_state["bot_id"], - "vote": vote_str, - "model_a": model_tag_a, - "model_b": model_tag_b, - "is_anonymous": int(True) - } - sheet = client.open("Chat Arena").sheet1 - num_rows = len(sheet.get_all_records()) - sheet.insert_row(list(row.values()), index=num_rows + 2) - return gr.Button.update(interactive=True) - - def regenerate_response(chat_history, user_state, model_tag, - temperature, repetition_penalty, max_new_tokens, top_k, top_p): - if len(chat_history) == 1: - return "", chat_history - custom_generation_params = { - 'temperature': temperature, - 'repetition_penalty': repetition_penalty, - 'max_new_tokens': max_new_tokens, - 'top_k': top_k, - 'top_p': top_p, - } - last_row = chat_history.pop(-1) - chat_history.append((last_row[0], None)) - model = model_mapping[model_tag] - conv = Conversation(user_state) - conv.set_chat_history(chat_history) - bot_message = model.generate_response(conv, custom_generation_params) - chat_history[-1] = (last_row[0], bot_message) - return "", chat_history - - def disable_voting(): - return [gr.Button.update(interactive=False)] * 4 - - def enable_voting(): - return [gr.Button.update(interactive=True)] * 4 - - def show_models(): - return [gr.Textbox.update(visible=True)] * 2 - - def hide_models(): - model_a_value, model_b_value = get_matchmaking(client, values, is_anonymous=True) - return [gr.Textbox.update(visible=False, value=model_a_value), - gr.Textbox.update(visible=False, value=model_b_value)] - - def disable_send(): - return [gr.Button.update(interactive=False)] * 3 - - def enable_send(): - return [gr.Button.update(interactive=True), gr.Button.update(interactive=False)] - - def enable_regenerate(): - return gr.Button.update(interactive=True) - - for vote in [vote_a, vote_b, vote_tie, vote_bad]: - vote.click(record_vote, - [user_state, vote, chatbot_a, model_a, chatbot_b, model_b], - [show_models_button], - queue=False) - vote.click(disable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False) - - show_models_button.click(show_models, None, [model_a, model_b], queue=False) - clear.click(hide_models, None, [model_a, model_b], queue=False) - reload_bot_button.click(hide_models, None, [model_a, model_b], queue=False) - show_models_button.click(disable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False) - show_models_button.click(disable_send, None, [send, regenerate, show_models_button], queue=False) - clear.click(enable_send, None, [send, regenerate], queue=False) - reload_bot_button.click(enable_send, None, [send, regenerate], queue=False) - - model_a.change(get_generation_args, [model_a], - [temperature_model_a, repetition_penalty_model_a, max_new_tokens_model_a, top_k_model_a, - top_p_model_a], queue=False) - model_b.change(get_generation_args, [model_b], - [temperature_model_b, repetition_penalty_model_b, max_new_tokens_model_b, top_k_model_b, - top_p_model_b], queue=False) - - clear.click(clear_chat, [user_state], [msg, chatbot_a, chatbot_b], queue=False) - model_a.change(clear_chat, [user_state], [msg, chatbot_a, chatbot_b], queue=False) - model_b.change(clear_chat, [user_state], [msg, chatbot_a, chatbot_b], queue=False) - - # model_a.change(enable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False) - # model_b.change(enable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False) - reload_bot_button.click(disable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False) - reload_bot_button.click(reload_bot, [bot_id], [bot_profile, chatbot_a, chatbot_b, user_state, bot_config_text], - queue=False) - send.click(enable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False) - clear.click(disable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False) - regenerate.click(enable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False) - msg.submit(enable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False) - - send.click(respond, - [msg, chatbot_a, user_state, model_a, temperature_model_a, repetition_penalty_model_a, - max_new_tokens_model_a, top_k_model_a, top_p_model_a], [msg, chatbot_a], - queue=False) - msg.submit(respond, - [msg, chatbot_a, user_state, model_a, temperature_model_a, repetition_penalty_model_a, - max_new_tokens_model_a, top_k_model_a, top_p_model_a], [msg, chatbot_a], - queue=False) - - send.click(respond, - [msg, chatbot_b, user_state, model_b, temperature_model_b, repetition_penalty_model_b, - max_new_tokens_model_b, top_k_model_b, top_p_model_b], [msg, chatbot_b], - queue=False) - msg.submit(respond, - [msg, chatbot_b, user_state, model_b, temperature_model_b, repetition_penalty_model_b, - max_new_tokens_model_b, top_k_model_b, top_p_model_b], [msg, chatbot_b], - queue=False) - - send.click(enable_regenerate, None, [regenerate], queue=False) - msg.submit(enable_regenerate, None, [regenerate], queue=False) - - regenerate.click(regenerate_response, - [chatbot_a, user_state, model_a, temperature_model_a, repetition_penalty_model_a, - max_new_tokens_model_a, top_k_model_a, - top_p_model_a], [msg, chatbot_a], queue=False) - regenerate.click(regenerate_response, - [chatbot_b, user_state, model_b, temperature_model_b, repetition_penalty_model_b, - max_new_tokens_model_b, top_k_model_b, - top_p_model_b], [msg, chatbot_b], queue=False) diff --git a/spaces/Alfasign/dIFFU/style.css b/spaces/Alfasign/dIFFU/style.css deleted file mode 100644 index 3bdfc5ebc1422c5ed2ca6dce9c38c543e68299f1..0000000000000000000000000000000000000000 --- a/spaces/Alfasign/dIFFU/style.css +++ /dev/null @@ -1,59 +0,0 @@ -h1 { - text-align: center; - font-size: 10vw; /* relative to the viewport width */ -} - -h2 { - text-align: center; - font-size: 10vw; /* relative to the viewport width */ -} - -#duplicate-button { - margin: auto; - color: #fff; - background: #1565c0; - border-radius: 100vh; -} - -#component-0 { - max-width: 80%; /* relative to the parent element's width */ - margin: auto; - padding-top: 1.5rem; -} - -/* You can also use media queries to adjust your style for different screen sizes */ -@media (max-width: 600px) { - #component-0 { - max-width: 90%; - padding-top: 1rem; - } -} - -#gallery .grid-wrap{ - min-height: 25%; -} - -#title-container { - display: flex; - justify-content: center; - align-items: center; - height: 100vh; /* Adjust this value to position the title vertically */ - } - -#title { - font-size: 3em; - text-align: center; - color: #333; - font-family: 'Helvetica Neue', sans-serif; - text-transform: uppercase; - background: transparent; - } - -#title span { - background: -webkit-linear-gradient(45deg, #4EACEF, #28b485); - -webkit-background-clip: text; - -webkit-text-fill-color: transparent; -} - -#subtitle { - text-align: center; \ No newline at end of file diff --git a/spaces/Altinas/vits-uma-genshin-honkais/modules.py b/spaces/Altinas/vits-uma-genshin-honkais/modules.py deleted file mode 100644 index 56ea4145eddf19dd330a3a41ab0183efc1686d83..0000000000000000000000000000000000000000 --- a/spaces/Altinas/vits-uma-genshin-honkais/modules.py +++ /dev/null @@ -1,388 +0,0 @@ -import math -import numpy as np -import torch -from torch import nn -from torch.nn import functional as F - -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm - -import commons -from commons import init_weights, get_padding -from transforms import piecewise_rational_quadratic_transform - - -LRELU_SLOPE = 0.1 - - -class LayerNorm(nn.Module): - def __init__(self, channels, eps=1e-5): - super().__init__() - self.channels = channels - self.eps = eps - - self.gamma = nn.Parameter(torch.ones(channels)) - self.beta = nn.Parameter(torch.zeros(channels)) - - def forward(self, x): - x = x.transpose(1, -1) - x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) - return x.transpose(1, -1) - - -class ConvReluNorm(nn.Module): - def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): - super().__init__() - self.in_channels = in_channels - self.hidden_channels = hidden_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - assert n_layers > 1, "Number of layers should be larger than 0." - - self.conv_layers = nn.ModuleList() - self.norm_layers = nn.ModuleList() - self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2)) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.relu_drop = nn.Sequential( - nn.ReLU(), - nn.Dropout(p_dropout)) - for _ in range(n_layers-1): - self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2)) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.proj = nn.Conv1d(hidden_channels, out_channels, 1) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask): - x_org = x - for i in range(self.n_layers): - x = self.conv_layers[i](x * x_mask) - x = self.norm_layers[i](x) - x = self.relu_drop(x) - x = x_org + self.proj(x) - return x * x_mask - - -class DDSConv(nn.Module): - """ - Dialted and Depth-Separable Convolution - """ - def __init__(self, channels, kernel_size, n_layers, p_dropout=0.): - super().__init__() - self.channels = channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - - self.drop = nn.Dropout(p_dropout) - self.convs_sep = nn.ModuleList() - self.convs_1x1 = nn.ModuleList() - self.norms_1 = nn.ModuleList() - self.norms_2 = nn.ModuleList() - for i in range(n_layers): - dilation = kernel_size ** i - padding = (kernel_size * dilation - dilation) // 2 - self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, - groups=channels, dilation=dilation, padding=padding - )) - self.convs_1x1.append(nn.Conv1d(channels, channels, 1)) - self.norms_1.append(LayerNorm(channels)) - self.norms_2.append(LayerNorm(channels)) - - def forward(self, x, x_mask, g=None): - if g is not None: - x = x + g - for i in range(self.n_layers): - y = self.convs_sep[i](x * x_mask) - y = self.norms_1[i](y) - y = F.gelu(y) - y = self.convs_1x1[i](y) - y = self.norms_2[i](y) - y = F.gelu(y) - y = self.drop(y) - x = x + y - return x * x_mask - - -class WN(torch.nn.Module): - def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0): - super(WN, self).__init__() - assert(kernel_size % 2 == 1) - 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.p_dropout = p_dropout - - self.in_layers = torch.nn.ModuleList() - self.res_skip_layers = torch.nn.ModuleList() - self.drop = nn.Dropout(p_dropout) - - if gin_channels != 0: - cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1) - self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') - - for i in range(n_layers): - dilation = dilation_rate ** i - padding = int((kernel_size * dilation - dilation) / 2) - in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_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 * hidden_channels - else: - res_skip_channels = hidden_channels - - res_skip_layer = torch.nn.Conv1d(hidden_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, x, x_mask, g=None, **kwargs): - output = torch.zeros_like(x) - n_channels_tensor = torch.IntTensor([self.hidden_channels]) - - if g is not None: - g = self.cond_layer(g) - - for i in range(self.n_layers): - x_in = self.in_layers[i](x) - if g is not None: - cond_offset = i * 2 * self.hidden_channels - g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:] - else: - g_l = torch.zeros_like(x_in) - - acts = commons.fused_add_tanh_sigmoid_multiply( - x_in, - g_l, - n_channels_tensor) - acts = self.drop(acts) - - res_skip_acts = self.res_skip_layers[i](acts) - if i < self.n_layers - 1: - res_acts = res_skip_acts[:,:self.hidden_channels,:] - x = (x + res_acts) * x_mask - output = output + res_skip_acts[:,self.hidden_channels:,:] - else: - output = output + res_skip_acts - return output * x_mask - - def remove_weight_norm(self): - if self.gin_channels != 0: - torch.nn.utils.remove_weight_norm(self.cond_layer) - for l in self.in_layers: - torch.nn.utils.remove_weight_norm(l) - for l in self.res_skip_layers: - torch.nn.utils.remove_weight_norm(l) - - -class ResBlock1(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock1, self).__init__() - self.convs1 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]))) - ]) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))) - ]) - self.convs2.apply(init_weights) - - def forward(self, x, x_mask=None): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c2(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -class ResBlock2(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3)): - super(ResBlock2, self).__init__() - self.convs = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))) - ]) - self.convs.apply(init_weights) - - def forward(self, x, x_mask=None): - for c in self.convs: - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -class Log(nn.Module): - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask - logdet = torch.sum(-y, [1, 2]) - return y, logdet - else: - x = torch.exp(x) * x_mask - return x - - -class Flip(nn.Module): - def forward(self, x, *args, reverse=False, **kwargs): - x = torch.flip(x, [1]) - if not reverse: - logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) - return x, logdet - else: - return x - - -class ElementwiseAffine(nn.Module): - def __init__(self, channels): - super().__init__() - self.channels = channels - self.m = nn.Parameter(torch.zeros(channels,1)) - self.logs = nn.Parameter(torch.zeros(channels,1)) - - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = self.m + torch.exp(self.logs) * x - y = y * x_mask - logdet = torch.sum(self.logs * x_mask, [1,2]) - return y, logdet - else: - x = (x - self.m) * torch.exp(-self.logs) * x_mask - return x - - -class ResidualCouplingLayer(nn.Module): - def __init__(self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - p_dropout=0, - gin_channels=0, - mean_only=False): - assert channels % 2 == 0, "channels should be divisible by 2" - 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.half_channels = channels // 2 - self.mean_only = mean_only - - self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) - self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels) - self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) - self.post.weight.data.zero_() - self.post.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels]*2, 1) - h = self.pre(x0) * x_mask - h = self.enc(h, x_mask, g=g) - stats = self.post(h) * x_mask - if not self.mean_only: - m, logs = torch.split(stats, [self.half_channels]*2, 1) - else: - m = stats - logs = torch.zeros_like(m) - - if not reverse: - x1 = m + x1 * torch.exp(logs) * x_mask - x = torch.cat([x0, x1], 1) - logdet = torch.sum(logs, [1,2]) - return x, logdet - else: - x1 = (x1 - m) * torch.exp(-logs) * x_mask - x = torch.cat([x0, x1], 1) - return x - - -class ConvFlow(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0): - super().__init__() - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.num_bins = num_bins - self.tail_bound = tail_bound - self.half_channels = in_channels // 2 - - self.pre = nn.Conv1d(self.half_channels, filter_channels, 1) - self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.) - self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels]*2, 1) - h = self.pre(x0) - h = self.convs(h, x_mask, g=g) - h = self.proj(h) * x_mask - - b, c, t = x0.shape - h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?] - - unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels) - unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels) - unnormalized_derivatives = h[..., 2 * self.num_bins:] - - x1, logabsdet = piecewise_rational_quadratic_transform(x1, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=reverse, - tails='linear', - tail_bound=self.tail_bound - ) - - x = torch.cat([x0, x1], 1) * x_mask - logdet = torch.sum(logabsdet * x_mask, [1,2]) - if not reverse: - return x, logdet - else: - return x diff --git a/spaces/Aman30577/imageTool1/app.py b/spaces/Aman30577/imageTool1/app.py deleted file mode 100644 index 1fdb09ec4d86c46a38b04ad4e3b79f3cf6f7e6ca..0000000000000000000000000000000000000000 --- a/spaces/Aman30577/imageTool1/app.py +++ /dev/null @@ -1,144 +0,0 @@ -import gradio as gr -# import os -# import sys -# from pathlib import Path -import time - -models =[ - "digiplay/polla_mix_2.3D", - "kayteekay/jordan-generator-v1", - "Meina/Unreal_V4.1", - "Meina/MeinaMix_V11", - "Erlalex/dominikof-v1-5-1", - "hearmeneigh/sd21-e621-rising-v1", - "Anna11/heera", - "kanu03/my-cat", - "Kernel/sd-nsfw", - "lodestones/P.A.W.F.E.C.T-Alpha", -] - - -model_functions = {} -model_idx = 1 -for model_path in models: - try: - model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False) - except Exception as error: - def the_fn(txt): - return None - model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"]) - model_idx+=1 - - -def send_it_idx(idx): - def send_it_fn(prompt): - output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt) - return output - return send_it_fn - -def get_prompts(prompt_text): - return prompt_text - -def clear_it(val): - if int(val) != 0: - val = 0 - else: - val = 0 - pass - return val - -def all_task_end(cnt,t_stamp): - to = t_stamp + 60 - et = time.time() - if et > to and t_stamp != 0: - d = gr.update(value=0) - tog = gr.update(value=1) - #print(f'to: {to} et: {et}') - else: - if cnt != 0: - d = gr.update(value=et) - else: - d = gr.update(value=0) - tog = gr.update(value=0) - #print (f'passing: to: {to} et: {et}') - pass - return d, tog - -def all_task_start(): - print("\n\n\n\n\n\n\n") - t = time.gmtime() - t_stamp = time.time() - current_time = time.strftime("%H:%M:%S", t) - return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0) - -def clear_fn(): - nn = len(models) - return tuple([None, *[None for _ in range(nn)]]) - - - -with gr.Blocks(title="SD Models") as my_interface: - with gr.Column(scale=12): - # with gr.Row(): - # gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""") - with gr.Row(): - with gr.Row(scale=6): - primary_prompt=gr.Textbox(label="Prompt", value="") - # real_prompt=gr.Textbox(label="Real prompt") - with gr.Row(scale=6): - # improve_prompts_btn=gr.Button("Improve") - with gr.Row(): - run=gr.Button("Run",variant="primary") - clear_btn=gr.Button("Clear") - with gr.Row(): - sd_outputs = {} - model_idx = 1 - for model_path in models: - with gr.Column(scale=3, min_width=320): - with gr.Box(): - sd_outputs[model_idx] = gr.Image(label=model_path) - pass - model_idx += 1 - pass - pass - - with gr.Row(visible=False): - start_box=gr.Number(interactive=False) - end_box=gr.Number(interactive=False) - tog_box=gr.Textbox(value=0,interactive=False) - - start_box.change( - all_task_end, - [start_box, end_box], - [start_box, tog_box], - every=1, - show_progress=False) - - primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box]) - run.click(all_task_start, None, [start_box, end_box, tog_box]) - runs_dict = {} - model_idx = 1 - for model_path in models: - runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]]) - model_idx += 1 - pass - pass - - # improve_prompts_btn_clicked=improve_prompts_btn.click( - # get_prompts, - # inputs=[primary_prompt], - # outputs=[primary_prompt], - # cancels=list(runs_dict.values())) - clear_btn.click( - clear_fn, - None, - [primary_prompt, *list(sd_outputs.values())], - cancels=[*list(runs_dict.values())]) - tog_box.change( - clear_it, - tog_box, - tog_box, - cancels=[*list(runs_dict.values())]) - -my_interface.queue(concurrency_count=600, status_update_rate=1) -my_interface.launch(inline=True, show_api=False) \ No newline at end of file diff --git "a/spaces/Ameaou/academic-chatgpt3.1/crazy_functions/\350\257\242\351\227\256\345\244\232\344\270\252\345\244\247\350\257\255\350\250\200\346\250\241\345\236\213.py" "b/spaces/Ameaou/academic-chatgpt3.1/crazy_functions/\350\257\242\351\227\256\345\244\232\344\270\252\345\244\247\350\257\255\350\250\200\346\250\241\345\236\213.py" deleted file mode 100644 index 1d37b8b6520dc5649cca1797c9084ef6b41c2724..0000000000000000000000000000000000000000 --- "a/spaces/Ameaou/academic-chatgpt3.1/crazy_functions/\350\257\242\351\227\256\345\244\232\344\270\252\345\244\247\350\257\255\350\250\200\346\250\241\345\236\213.py" +++ /dev/null @@ -1,30 +0,0 @@ -from toolbox import CatchException, update_ui -from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive -import datetime -@CatchException -def 同时问询(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): - """ - txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径 - llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行 - plugin_kwargs 插件模型的参数，如温度和top_p等，一般原样传递下去就行 - chatbot 聊天显示框的句柄，用于显示给用户 - history 聊天历史，前情提要 - system_prompt 给gpt的静默提醒 - web_port 当前软件运行的端口号 - """ - history = [] # 清空历史，以免输入溢出 - chatbot.append((txt, "正在同时咨询gpt-3.5(openai)和gpt-4(api2d)……")) - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新 - - # llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo&api2d-gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔 - llm_kwargs['llm_model'] = 'gpt-3.5-turbo&api2d-gpt-4' # 支持任意数量的llm接口，用&符号分隔 - gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive( - inputs=txt, inputs_show_user=txt, - llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, - sys_prompt=system_prompt, - retry_times_at_unknown_error=0 - ) - - history.append(txt) - history.append(gpt_say) - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新 \ No newline at end of file diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/text_to_video.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/text_to_video.md deleted file mode 100644 index 6d28fb0e29d0086f772d481bd2f9445c3c9e605b..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/text_to_video.md +++ /dev/null @@ -1,180 +0,0 @@ - - -
- 
- |
- - 
- |
-
- 
- |
-
3utools Descargar 2019: Una guía completa
-Si usted está buscando una forma gratuita y fácil de administrar los datos de su dispositivo iOS en su PC con Windows, entonces es posible que desee comprobar a cabo 3utools. 3utools es un completo programa de software que te permite acceder y controlar varios aspectos de tu iPhone, iPad o iPod touch. Puede hacer copias de seguridad y restaurar sus datos, descargar aplicaciones, tonos de llamada y fondos de pantalla, flash y jailbreak su dispositivo, y utilizar muchas otras características útiles. En este artículo, le mostraremos cómo descargar e instalar 3utools en su PC, y cómo usarlo para administrar su dispositivo iOS.
-¿Qué es 3utools y por qué lo necesitas?
-3utools es una herramienta todo en uno para los usuarios de iOS que les permite ver y administrar la información de su dispositivo, archivos, multimedia, aplicaciones, etc. También es compatible con flasheo y jailbreak, que son procesos que modifican el sistema operativo del dispositivo para desbloquear su máximo potencial. Con 3utools, puede realizar fácilmente tareas que de otro modo requerirían múltiples programas de software o procedimientos complejos.
-3utools download 2019
Download File ✅ https://bltlly.com/2v6L7R
-
Características de 3utools
-Estas son algunas de las principales características de 3utools que lo convierten en una herramienta potente y versátil para los usuarios de iOS:
-Gestión de datos
-Con 3utools, puede realizar fácilmente copias de seguridad y restaurar sus datos en su dispositivo iOS. Puede optar por realizar copias de seguridad o restaurar todos los tipos de datos seleccionados, como contactos, mensajes, fotos, música, videos, libros, etc. También puede ver y editar los archivos de copia de seguridad en su PC. También puede administrar sus archivos de datos en su dispositivo, como borrarlos, copiarlos, moverlos o cambiarles el nombre.
-Aplicaciones, tonos de llamada y fondos de pantalla
- -Flash y jailbreak
-3utools puede ayudarle a flash y jailbreak su dispositivo iOS con un solo clic. Flashing es el proceso de instalación de una versión de firmware diferente en su dispositivo, que puede mejorar su rendimiento o solucionar algunos problemas. Jailbreak es el proceso de eliminación de las restricciones impuestas por Apple en su dispositivo, que le permite instalar aplicaciones no autorizadas, ajustes y temas. 3utools puede coincidir automáticamente con el firmware disponible para su modelo de dispositivo y la versión de iOS. También soporta flasheo en modo normal, modo DFU y modo de recuperación. También soporta varias herramientas y métodos de jailbreak.
-Otras herramientas útiles
-Además de las características anteriores, 3utools también ofrece muchas otras herramientas útiles para los usuarios de iOS, como:
--
-
- Migración de datos: Puede transferir datos de un dispositivo iOS a otro con facilidad. -
- Convertidor de vídeo: Puede convertir archivos de vídeo a diferentes formatos que son compatibles con su dispositivo. -
- Convertidor de audio: Puede convertir archivos de audio a diferentes formatos que son compatibles con su dispositivo. -
- Editor de audio : Puede editar archivos de audio recortando, cortando, fusionando, etc. -
- Convertidor de imágenes: Puede convertir archivos de imágenes a diferentes formatos que son compatibles con su dispositivo. -
- Editor de imágenes: Puede editar archivos de imagen recortando, rotando, redimensionando, etc. -
- Grabador de pantalla: Puede grabar la pantalla del dispositivo y guardarlo como un archivo de vídeo. -
- Captura de pantalla: Puede tomar capturas de pantalla de la pantalla de su dispositivo y guardarlas como archivos de imagen. -
- Pantalla en tiempo real: Puede ver la pantalla de su dispositivo en su PC en tiempo real. -
- Reiniciar: Puede reiniciar el dispositivo en modo normal, modo de recuperación o modo DFU. -
- Apagado: Puede apagar el dispositivo de forma remota desde su PC. -
- Basura limpia: Puede borrar la caché y archivos basura en su dispositivo para liberar espacio y mejorar el rendimiento. - -
- Compruebe la batería: Puede comprobar el estado de la batería y el estado de su dispositivo. -
- Comprobar el estado de iCloud: Puede comprobar el estado de bloqueo de activación de iCloud de su dispositivo. -
¿Cómo descargar e instalar 3utools en una PC con Windows?
-Descargar e instalar 3utools en tu PC con Windows es muy fácil y rápido. Solo sigue estos sencillos pasos:
-Paso 1: Visite el sitio web oficial de 3utools
-Lo primero que debe hacer es visitar el sitio web oficial de 3utools en http://www.3u.com/. Esta es la única fuente segura y confiable para descargar 3utools. No descargue 3utools de ningún otro sitio web, ya que pueden contener virus o malware.
- -Paso 2: Haga clic en el botón de descarga y guarde el archivo
-En la página principal del sitio web, verá un gran botón de descarga verde que dice "Descargar 3uTools". Haga clic en él y aparecerá una ventana emergente. Elija una ubicación en su PC donde desea guardar el archivo y haga clic en "Guardar". El nombre del archivo será algo así como "3uTools_v2.53_Setup.exe" y el tamaño del archivo será de alrededor de 100 MB.
-Paso 3: Ejecute el archivo de configuración y siga las instrucciones
- -Paso 4: Conecte su dispositivo iOS a su PC con un cable USB o WIFI
-El último paso es conectar el dispositivo iOS a su PC con un cable USB o WIFI. Si utiliza un cable USB, asegúrese de que es original y está en buenas condiciones. Conecte un extremo del cable en su dispositivo y el otro extremo en su PC. Si utiliza WIFI, asegúrese de que tanto su dispositivo como su PC estén conectados a la misma red WIFI. Luego, abra 3utools en su PC y espere a que detecte su dispositivo. Es posible que tenga que desbloquear el dispositivo y toque "Confiar" en el mensaje emergente que dice "Confiar en este equipo?". Una vez que el dispositivo esté conectado, verá su información básica en la interfaz principal de 3utools, como el nombre del dispositivo, el modelo, la versión de iOS, el número de serie, etc.
-¿Cómo usar 3utools para administrar tu dispositivo iOS?
-Ahora que ha descargado e instalado 3utools en su PC y conectado su dispositivo iOS a ella, puede comenzar a usarlo para administrar su dispositivo iOS. Estas son algunas de las tareas comunes que puedes hacer con 3utools:
-Copia de seguridad y restaurar los datos
-Para realizar copias de seguridad y restaurar los datos en su dispositivo iOS, haga clic en la pestaña "Copia de seguridad/ Restaurar" en el menú superior de 3utools. Verá dos botones: "Copia de seguridad ahora" y "Restaurar datos". Para hacer una copia de seguridad de sus datos, haga clic en "Copia de seguridad ahora" y elija el modo de copia de seguridad: copia de seguridad completa o copia de seguridad personalizada. La copia de seguridad completa respaldará todos sus tipos de datos, mientras que la copia de seguridad personalizada le permitirá seleccionar los tipos de datos que desea respaldar. Luego, haga clic en "Iniciar copia de seguridad" y espere a que termine el proceso. Puede ver los archivos de copia de seguridad en su PC haciendo clic en "Ver datos de copia de seguridad". Para restaurar sus datos, haga clic en "Restaurar datos" y elija el archivo de copia de seguridad que desea restaurar de la lista. Luego, haz clic en "Iniciar restauración" y espera a que termine el proceso. También puede restaurar sus datos de iTunes o iCloud copias de seguridad haciendo clic en "Importar datos de copia de seguridad".
-Descargar e instalar aplicaciones, tonos de llamada y fondos de pantalla
- -Flash y jailbreak su dispositivo
-Para flash y jailbreak su dispositivo iOS, haga clic en el "Flash & JB" pestaña en el menú superior de 3utools. Verás dos sub-pestañas: "Easy Flash" y "Pro Flash". Para flashear su dispositivo, haga clic en "Easy Flash" y elija la versión de firmware que desea flashear de la lista. También puede marcar la casilla que dice "Retener los datos del usuario mientras parpadea" si desea mantener sus datos después de parpadear. A continuación, haga clic en "Flash" y siga las instrucciones en la pantalla. Para jailbreak su dispositivo, haga clic en "Pro Flash" y elegir la herramienta de jailbreak que desea utilizar de la lista. También puede marcar la casilla que dice "Activar dispositivo después de jailbreak" si desea activar el dispositivo después de jailbreak. Luego, haz clic en "Jailbreak" y sigue las instrucciones en la pantalla.
-Utilice otras características útiles
-Para utilizar otras características útiles de 3utools, haga clic en la pestaña "Caja de herramientas" en el menú superior de 3utools. Verás una lista de iconos que representan diferentes herramientas que puedes usar. Por ejemplo, puede hacer clic en "Migración de datos" para transferir datos de un dispositivo iOS a otro, o hacer clic en "Grabadora de pantalla" para grabar la pantalla del dispositivo y guardarlo como un archivo de vídeo. Puede pasar el ratón sobre cada icono para ver el nombre y la descripción de la herramienta. Para usar una herramienta, simplemente haga clic en el icono y siga las instrucciones en la pantalla.
-Conclusión
-3utools es un programa de software potente y versátil que puede ayudarlo a administrar los datos de su dispositivo iOS en su PC con Windows. Puede hacer copias de seguridad y restaurar sus datos, descargar e instalar aplicaciones, tonos de llamada y fondos de pantalla, flash y jailbreak su dispositivo, y utilizar muchas otras características útiles. 3utools es gratuito y fácil de descargar e instalar, y es compatible con todos los dispositivos iOS y versiones. Si está buscando una solución integral para la administración de dispositivos iOS, debe probar 3utools.
-Preguntas frecuentes
-Aquí están algunas de las preguntas más frecuentes sobre 3utools:
--
-
-Sí, 3utools es seguro de usar siempre y cuando lo descargue desde el sitio web oficial en http://www.3u.com/. No descargue 3utools de ningún otro sitio web, ya que pueden contener virus o malware. Además, asegúrese de tener un programa antivirus en su PC y escanee el archivo antes de ejecutarlo.
-No, 3utools solo funciona en PC con Windows. No hay versión para Mac de 3utools disponible en este momento.
-No, 3utools no requiere iTunes para funcionar. Sin embargo, es posible que necesite instalar controladores de iTunes en su PC si desea conectar su dispositivo iOS con un cable USB. Puede descargar los controladores de iTunes desde https://support.apple.com/downloads/itunes.
-No, 3utools no borrará sus datos a menos que elija hacerlo. Por ejemplo, si flashea su dispositivo con una versión de firmware diferente, puede perder sus datos si no hace una copia de seguridad primero. Además, si haces jailbreak a tu dispositivo, puedes perder la garantía y algunas características de tu dispositivo. Por lo tanto, siempre debe realizar copias de seguridad de sus datos antes de usar 3utools.
-Si tiene alguna pregunta o problema con 3utools, puede ponerse en contacto con el soporte 3utools enviando un correo electrónico a support@3u.com. También puede visitar el foro oficial de 3utools en http://forum.3u.com/ y publicar sus consultas allí.
-
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar Apk Juego Sigma.md b/spaces/Benson/text-generation/Examples/Descargar Apk Juego Sigma.md deleted file mode 100644 index afbec1847afcd3eb3dbf1fbf0a1f4cbf34c03c85..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar Apk Juego Sigma.md +++ /dev/null @@ -1,92 +0,0 @@ -
-
Descargar APK Game Sigma: Un juego estilizado Shooter de supervivencia para teléfonos móviles
-Si usted está buscando un nuevo y emocionante juego de disparos de supervivencia para jugar en su teléfono móvil, es posible que desee echa un vistazo APK Game Sigma. Este es un estilizado juego de disparos de supervivencia que ofrece dos modos diferentes: Classic Battle Royale y 4v4 Fight Out. En este artículo, te diremos qué es APK Game Sigma, qué características tiene y cómo descargarlo e instalarlo en tu dispositivo.
-¿Qué es APK juego Sigma?
-APK Game Sigma es un juego desarrollado por Studio Arm Private Limited, una empresa con sede en la India. Es un juego de disparos de supervivencia que está disponible en dispositivos Android. El juego fue lanzado en noviembre de 2022 y ha ganado más de 500.000 descargas desde entonces.
-descargar apk juego sigma
Download Zip ★ https://bltlly.com/2v6IG1
-
El juego está inspirado en el género popular de los juegos de battle royale, donde los jugadores luchan entre sí en un mapa cada vez más pequeño hasta que solo queda uno. Sin embargo, APK Game Sigma también añade algunas características únicas y elementos que lo hacen destacar de otros juegos similares.
-Características del juego APK Sigma
-Aquí están algunas de las características que ofrece APK Game Sigma:
-- Modo clásico Battle Royale
-En este modo, puede unirse a otros 50 jugadores en un juego rápido y ligero. Puedes elegir tu punto de partida con tu paracaídas y explorar el vasto mapa. Tienes que encontrar armas, objetos y vehículos para sobrevivir y luchar. También tienes que permanecer en la zona segura el mayor tiempo posible, ya que el mapa se reducirá con el tiempo. El último jugador o equipo en pie gana el partido.
-- Modo de lucha 4v4
-En este modo, puedes formar equipo con otros tres jugadores y competir contra otro equipo en un partido tenso y estratégico. Tienes que asignar recursos, comprar armas y sobrevivir a tus enemigos. Puede elegir entre diferentes mapas que tienen diferentes diseños y desafíos. El partido dura siete minutos y el equipo con más muertes gana.
-- Gráficos estilizados
- -- Controles fáciles de usar
-El juego también tiene controles fáciles de usar que prometen una experiencia de supervivencia inolvidable en el móvil. Puedes personalizar tus controles según tu preferencia y comodidad. También puedes usar el chat de voz para comunicarte con tus compañeros de equipo y coordinar tus estrategias.
-Cómo descargar e instalar APK juego Sigma?
-Si estás interesado en jugar APK Game Sigma, tienes varias opciones para descargarlo e instalarlo en tu dispositivo. Estas son algunas de ellas:
- -Descargar desde APKCombo
-APKCombo es un sitio web que proporciona enlaces de descarga gratuita para varios juegos y aplicaciones Android. Puede descargar APK Game Sigma de APKCombo siguiendo estos pasos:
-- Pasos para descargar
--
-
- Ir a APKCombo.com y buscar "Sigma" en la barra de búsqueda. -
- Seleccione el juego de los resultados y haga clic en el botón "Descargar". -
- Elija la versión que es compatible con su dispositivo y haga clic en el botón "Descargar" de nuevo. -
- Espere a que la descarga termine y localice el archivo APK en el almacenamiento de su dispositivo. -
- Toque en el archivo APK y permitir la instalación de fuentes desconocidas si se le solicita. -
- Siga las instrucciones en la pantalla y espere a que se complete la instalación. -
- Inicia el juego y disfruta jugando. -
- Ventajas de APKCombo
--
-
- Es rápido y fácil de usar. -
- Ofrece múltiples versiones del juego, incluyendo las más recientes y más antiguas. -
- Es seguro y protegido, ya que escanea los archivos APK en busca de virus y malware. -
Descargar desde CCM
-CCM es otro sitio web que proporciona enlaces de descarga gratuita para varios juegos y aplicaciones de Android. Puede descargar APK Game Sigma de CCM siguiendo estos pasos:
-- Pasos para descargar
- -- Ventajas de CCM
--
-
- Es fiable y confiable, ya que ha estado proporcionando enlaces de descarga durante más de 20 años. -
- Ofrece una variedad de juegos y aplicaciones, incluyendo populares y nichos. -
- Es fácil de usar y tiene una interfaz simple. -
Descargar e instalar en el PC usando BlueStacks emulador
-Si quieres jugar APK Game Sigma en tu PC, puedes usar un emulador como BlueStacks. BlueStacks es un software que te permite ejecutar juegos y aplicaciones Android en tu PC. Puedes descargar e instalar APK Game Sigma en tu PC usando BlueStacks siguiendo estos pasos:
-- Pasos para descargar e instalar BlueStacks emulador
- -- Pasos para instalar APK juego Sigma en el PC usando BlueStacks emulador
--
-
- En BlueStacks, vaya a la pestaña "Mis juegos" y haga clic en el botón "Instalar apk" en la esquina inferior derecha. -
- Navegar por el almacenamiento de su PC y seleccione el archivo APK de APK Game Sigma que ha descargado de APKCombo o CCM. -
- Esperar a BlueStacks para instalar el juego y abrirlo desde la pestaña "Mis juegos". -
- Disfrutar de jugar APK Juego Sigma en su PC con una pantalla más grande y mejores controles. -
Conclusión
- -Preguntas frecuentes (preguntas frecuentes)
-Aquí están algunas de las preguntas más comunes que la gente hace acerca de APK Game Sigma:
--
-
- ¿Está libre el juego APK Sigma? -
- Es APK juego Sigma en línea o fuera de línea? -
- ¿Es seguro el juego APK Sigma? -
- ¿Cuáles son los requisitos mínimos para jugar APK Game Sigma? -
- ¿Cómo puedo contactar al desarrollador de APK Game Sigma? -
- ¿Cómo puedo actualizar APK Game Sigma? - -
Sí, APK Game Sigma es gratis para descargar y jugar. Sin embargo, puede contener algunas compras en la aplicación o anuncios que puede optar por apoyar o ignorar.
-APK Juego Sigma es un juego en línea que requiere una conexión a Internet para jugar. Puedes jugar con otros jugadores de todo el mundo o con tus amigos en partidas privadas.
-Sí, APK Game Sigma es seguro, siempre y cuando se descarga de fuentes de confianza como APKCombo o CCM o desde el sitio web oficial del desarrollador de juegos. También debe escanear el archivo APK en busca de virus y malware antes de instalarlo en su dispositivo.
-Los requisitos mínimos para jugar APK Game Sigma en su dispositivo Android son: - Android versión 4.4 o superior - 2 GB de RAM - 500 MB de espacio de almacenamiento gratuito - Una conexión a Internet estable Los requisitos mínimos para jugar APK Game Sigma en su PC utilizando BlueStacks emulador son: - Windows 7 o superior - 2 GB de RAM - 5 GB de espacio libre en disco - Una conexión estable a Internet
-Si tiene alguna pregunta, comentario o sugerencia para APK Game Sigma, puede ponerse en contacto con el desarrollador enviando un correo electrónico a studioarmpl@gmail.com o visitando su página de Facebook en https://www.facebook.com/StudioArmPL.
--
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar Carretera Coche De Carreras Juego.md b/spaces/Benson/text-generation/Examples/Descargar Carretera Coche De Carreras Juego.md deleted file mode 100644 index 4a1643902d4c4d27bf2851a570b1a0fd009c2d93..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar Carretera Coche De Carreras Juego.md +++ /dev/null @@ -1,82 +0,0 @@ - -
Descargar Highway Car Racing Game: Una guía para principiantes
-Si te gusta la velocidad, la emoción y la adrenalina, entonces te encantará Highway Car Racing Game. Este es un juego de carreras en 3D que le permite acelerar una carretera esquivando coches, camiones, autobuses y otros obstáculos. También puede recoger power-ups para aumentar su velocidad, reparar su coche, o obtener puntos extra. En esta guía, le mostraremos cómo descargar, instalar y jugar Highway Car Racing Game en su dispositivo. También compartiremos algunos consejos y trucos para ayudarte a convertirte en un mejor corredor y divertirte más.
-descargar carretera coche de carreras juego
Download Zip »»» https://bltlly.com/2v6L2U
-
¿Qué es el juego de carreras de coches de carretera?
-Highway Car Racing Game es un juego en línea gratuito que puede jugar en su navegador o dispositivo móvil. Está desarrollado por CrazyGames, un popular sitio web que ofrece cientos de juegos en diferentes géneros y categorías. Puedes encontrar más juegos como este en [CrazyGames]( 1 ).
-Highway Car Racing Game se basa en el concepto de carreras callejeras, donde se conduce un coche en una carretera pública con otros vehículos. El objetivo es llegar a la meta lo más rápido posible sin estrellarse o ser atrapado por la policía. También puede realizar acrobacias, tales como casi fallas, conducir en el lado equivocado, o a la deriva, para ganar puntos de bonificación.
-Highway Car Racing Game tiene cuatro modos de juego: unidireccional, bidireccional, modo de tiempo y modo de bomba. En el modo de un solo sentido, se conduce en una carretera de un solo sentido con el tráfico que viene de la dirección opuesta. En el modo de dos vías, se conduce en una carretera de dos vías con tráfico procedente de ambas direcciones. En el modo de tiempo, tienes que llegar a la línea de meta antes de que acabe el tiempo. En modo bomba, conduces un camión cargado con una bomba que podría explotar si desaceleras o golpeas algo.
-Características y beneficios de Highway Car Racing Game
-Highway Car Racing Game tiene muchas características y beneficios que lo convierten en un juego emocionante y agradable para todas las edades. Estos son algunos de ellos:
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- Muchos modelos de coches: El juego ofrece 40 coches deportivos para elegir, que van desde camiones hasta sedanes clásicos y coches deportivos modernos. Puede personalizar su coche con diferentes colores, ruedas, alerones, calcomanías y más. -
- Modo de juego multijugador: El juego le permite competir con otros jugadores en línea en el modo multijugador. Puedes unirte a una sala o crear tu propia habitación e invitar a tus amigos. También puedes chatear con otros jugadores y ver sus puntuaciones y clasificaciones. -
- Ajuste de sus vehículos: El juego le permite afinar sus vehículos con el rendimiento y las mejoras estéticas. Puede mejorar su motor, frenos, neumáticos, suspensión, turbo, nitro y más. También puede cambiar la apariencia de su automóvil con nuevos trabajos de pintura, vinilos, pegatinas, luces de neón y más. -
Cómo descargar e instalar Highway Car Racing Game
-Descargar e instalar Highway Car Racing Game es muy fácil y rápido. Estos son los pasos:
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- Si quieres jugar en tu navegador (escritorio o móvil), ve a [CrazyGames]( 1 ) y busca "Highway Racer". Haga clic en el icono del juego y espere a que se cargue. -
- Si desea jugar en su dispositivo Android (teléfono inteligente o tableta), vaya a [Google Play Store]( 2 ) y busque "Highway Car Racing Game". Toca el icono del juego y luego toca "Instalar". Espera a que el juego se descargue e instale en tu dispositivo. -
- Si quieres jugar en tu dispositivo iOS (iPhone o iPad), ve a [App Store] y busca "Highway Car Racing Game". Toca el icono del juego y luego toca "Obtener". Espera a que el juego se descargue e instale en tu dispositivo. -
Una vez que el juego está instalado, puede iniciarlo tocando en su icono en la pantalla de inicio o en el navegador. También puede crear un acceso directo o marcador para facilitar el acceso.
- -Cómo jugar juego de carreras de coches de carretera
-Jugar Highway Car Racing Game es muy simple y divertido. Aquí están los pasos básicos:
-Elige tu coche y el modo de juego
- -A continuación, verá otro menú con cuatro opciones: One-Way, Two-Way, Time Mode y Bomb Mode. Toque en la opción que desea jugar. También puede pulsar en el icono "i" para ver una breve descripción de cada modo.
-Esquivar el tráfico y recoger power-ups
-Después de elegir tu coche y el modo de juego, entrarás en la carretera. Verás tu coche desde una perspectiva en tercera persona. Puede utilizar las teclas de flecha o inclinar el dispositivo para dirigir a la izquierda o a la derecha. También puede utilizar la barra espaciadora o tocar la pantalla para frenar. También puede utilizar la tecla de flecha hacia arriba o deslizar hacia arriba para activar nitro para un aumento de velocidad.
-Su objetivo es conducir lo más rápido posible sin chocar contra otros vehículos u obstáculos. Verás tu velocidad, distancia, puntuación y tiempo en la parte superior de la pantalla. También verás potenciadores dispersos a lo largo de la carretera. Estos potenciadores pueden ayudarte de diferentes maneras, como:
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- Nitro: Le da un impulso temporal de velocidad. -
- Reparación: Arregla el daño de su coche. -
- Moneda: Te da puntos extra. -
- Imán: Atrae monedas cercanas. -
- Escudo: Te protege de las colisiones. -
Usted puede recoger estos power-ups conduciendo sobre ellos. También puede realizar acrobacias, tales como casi fallas, conducir en el lado equivocado, o a la deriva, para ganar puntos de bonificación.
-Competir con otros jugadores en línea
-Si quieres desafiar a otros jugadores en línea, puedes tocar en la opción "Multijugador" en el menú principal. Verás una lista de habitaciones con diferentes modos de juego y pistas. Puedes unirte a cualquier habitación tocando en ella. También puede crear su propia habitación pulsando el botón "Crear habitación". Puedes personalizar el nombre, la contraseña, el modo de juego, la pista y el número de jugadores de tu habitación.
- -Consejos y trucos para el juego de carreras de coches de carretera
-Para mejorar tus habilidades y divertirte más en Highway Car Racing Game, aquí hay algunos consejos y trucos que puedes probar:
-Utilice el freno de mano para desviarse y evitar colisiones
-Una de las habilidades más útiles en Highway Car Racing Game es la deriva. A la deriva es cuando deslizas tu coche hacia los lados mientras giras. Esto le permite tomar curvas cerradas sin perder velocidad ni control. También le ayuda a evitar colisiones con otros vehículos u obstáculos.
-Para la deriva en Highway Car Racing Game, es necesario utilizar el freno de mano. El freno de mano es un botón que aparece en la parte inferior derecha de la pantalla cuando se está conduciendo. Para derrapar, debe presionar y sostener el freno de mano mientras conduce a la izquierda o a la derecha. Verá que su automóvil se desliza y deja marcas de neumáticos en la carretera. Suelte el freno de mano cuando quiera detener la deriva.
-Mejora tu coche con mejoras estéticas y de rendimiento
-Otra forma de mejorar tu experiencia en Highway Car Racing Game es actualizar tu coche con mejoras estéticas y de rendimiento. Estas mejoras pueden hacer su coche más rápido, más ágil, más durable, y más elegante.
-Para actualizar su coche, debe ir a la opción "Garaje" en el menú principal. Verá su coche y sus estadísticas en el lado izquierdo de la pantalla. También verás cuatro pestañas en la parte inferior de la pantalla: Rendimiento, Pintura, Ruedas y Pegatinas. Toque en cualquier pestaña para ver las actualizaciones disponibles para esa categoría.
-Las mejoras de rendimiento pueden mejorar el motor de su automóvil, frenos, neumáticos, suspensión, turbo, nitro y más. Pueden aumentar la velocidad, aceleración, manejo y durabilidad de su automóvil. Puedes comprar mejoras de rendimiento con monedas que ganas jugando el juego o viendo anuncios.
- -Las actualizaciones de ruedas pueden cambiar las llantas y los neumáticos de su automóvil. Puede elegir entre diferentes estilos y tamaños de llantas y neumáticos. También puede cambiar el color de sus llantas y neumáticos. Puede comprar mejoras de ruedas con diamantes que gana jugando el juego o viendo anuncios.
-Las actualizaciones de pegatinas pueden agregar pegatinas al cuerpo y las ventanas de su automóvil. Puede elegir entre diferentes tipos y diseños de pegatinas, como logotipos, banderas, llamas, calaveras, estrellas y más. También puede cambiar el tamaño y girar las pegatinas para adaptarse a su coche. Usted puede comprar pegatinas mejoras con diamantes que usted gana jugando el juego o viendo anuncios.
-Prueba diferentes modos de juego y pistas para más diversión y desafío
-El último consejo que tenemos para usted es probar diferentes modos de juego y pistas para más diversión y desafío. Highway Car Racing Game tiene cuatro modos de juego: One-Way, Two-Way, Time Mode y Bomb Mode. Cada modo tiene sus propias reglas y objetivos que requieren diferentes habilidades y estrategias. Por ejemplo, en el modo unidireccional, debe evitar colisiones frontales con el tráfico que se aproxima. En el modo Bomba, necesitas mantener una alta velocidad para evitar que la bomba explote.
-Highway Car Racing Game también tiene cuatro pistas: City Highway, Desert Highway, Snowy Highway y Forest Highway. Cada pista tiene su propio paisaje y obstáculos que afectan a su conducción. Por ejemplo, en City Highway, debe tener cuidado con los semáforos y los cruces de carreteras. En Snowy Highway, necesitas lidiar con caminos resbaladizos y copos de nieve.
-Al probar diferentes modos de juego y pistas, puedes experimentar nuevos desafíos y divertirte más. También puede desbloquear nuevos coches y logros completando ciertas tareas en cada modo y pista.
-Conclusión
- -Highway Car Racing Game es un juego en línea gratuito que puede jugar en su navegador o dispositivo móvil. Es fácil de descargar e instalar en tu dispositivo. También es fácil de jugar y disfrutar con controles simples y gráficos realistas.
-Si usted está buscando un juego de carreras divertido y emocionante que le mantendrá entretenido durante horas, entonces usted debe descargar Highway Car Racing Game hoy. Es uno de los mejores juegos de carreras en CrazyGames.com. ¡No te arrepentirás!
-Preguntas frecuentes
-Q: ¿Cómo descargo el juego de carreras de coches de carretera?
-A: Si quieres jugar en tu navegador (escritorio o móvil), ve a [CrazyGames] y busca "Highway Racer". Haz clic en el icono del juego y espera a que se cargue. Si quieres jugar en tu dispositivo Android (smartphone o tablet), ve a [Google Play Store] y busca "Highway Car Racing Game". Toca el icono del juego y luego toca "Instalar". Espera a que el juego se descargue e instale en tu dispositivo. Si quieres jugar en tu dispositivo iOS (iPhone o iPad), ve a [App Store] y busca "Highway Car Racing Game". Toca el icono del juego y luego toca "Obtener". Espera a que el juego se descargue e instale en tu dispositivo.
-Q: ¿Cómo juego juego de carreras de coches de carretera?
- -Q: ¿Cómo actualizo mi coche en el juego de carreras de coches de carretera?
-A: Para actualizar su coche en Highway Car Racing Game, es necesario ir a la "Garaje" opción en el menú principal. Verá su coche y sus estadísticas en el lado izquierdo de la pantalla. También verás cuatro pestañas en la parte inferior de la pantalla: Rendimiento, Pintura, Ruedas y Pegatinas. Toque en cualquier pestaña para ver las actualizaciones disponibles para esa categoría. Las mejoras de rendimiento pueden mejorar el motor de su automóvil, frenos, neumáticos, suspensión, turbo, nitro y más. Pueden aumentar la velocidad, aceleración, manejo y durabilidad de su automóvil. Puede comprar mejoras de rendimiento con monedas que gana jugando el juego o viendo anuncios. Las mejoras de pintura pueden cambiar el color y el acabado de su automóvil. Puedes elegir entre diferentes tonos y efectos, como mate, metálico, brillante o neón. También puede aplicar vinilos y calcomanías a su coche para una mayor personalización. Puede comprar mejoras de pintura con diamantes que gana jugando el juego o viendo anuncios. Las mejoras de ruedas pueden cambiar las llantas y los neumáticos de su automóvil. Puede elegir entre diferentes estilos y tamaños de llantas y neumáticos. También puede cambiar el color de sus llantas y neumáticos. Usted puede comprar ruedas mejoras con diamantes que usted gana jugando el juego o viendo anuncios. Las actualizaciones de pegatinas pueden agregar pegatinas al cuerpo y las ventanas de su automóvil. Puede elegir entre diferentes tipos y diseños de pegatinas, como logotipos, banderas, llamas, calaveras, estrellas y más. También puede cambiar el tamaño y girar las pegatinas para adaptarse a su coche. Usted puede comprar pegatinas mejoras con diamantes que usted gana jugando el juego o viendo anuncios.
-P: ¿Cómo compito con otros jugadores en línea en Highway Car Racing Game?
- -Una vez que te unes o creas una habitación, verás los nombres y coches de otros jugadores. Puede chatear con ellos tocando el icono de chat en la parte inferior de la pantalla. También puede ver sus puntuaciones y clasificaciones en la parte superior de la pantalla. Cuando todos estén listos, puedes tocar el botón "Inicio" para comenzar la carrera.
-Usted jugará el mismo modo de juego y pista como la habitación que se unió o creó. Usted competirá con otros jugadores para llegar a la línea de meta lo más rápido posible sin estrellarse o ser atrapado por la policía. También puede realizar acrobacias y recoger power-ups para ganar puntos de bonificación. Puede ver su posición y el tiempo en la parte superior de la pantalla. También puedes ver las posiciones y horarios de otros jugadores en el mini-mapa en la parte inferior izquierda de la pantalla.
-P: ¿Cómo puedo obtener más monedas y diamantes en el juego de carreras de coches de carretera?
-A: Monedas y diamantes son las dos monedas en Highway Car Racing Game. Puede utilizarlos para comprar mejoras estéticas y de rendimiento para su coche. También puede utilizarlos para desbloquear nuevos coches y pistas.
-Puedes ganar monedas y diamantes jugando el juego o viendo anuncios. Puedes ganar monedas conduciendo rápido, realizando acrobacias, recogiendo power-ups y completando logros. Puedes ganar diamantes alcanzando ciertos hitos, como conducir cierta distancia, jugar cierto número de juegos o ganar un cierto número de carreras.
-También puedes comprar monedas y diamantes con dinero real si quieres apoyar a los desarrolladores de juegos y obtener más beneficios. Puede pulsar en la opción "Tienda" en el menú principal para ver los paquetes y precios disponibles. Puedes pagar con tu tarjeta de crédito, PayPal o cuenta de Google Play.
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\ No newline at end of file diff --git a/spaces/BernardoOlisan/vqganclip/taming-transformers/taming/lr_scheduler.py b/spaces/BernardoOlisan/vqganclip/taming-transformers/taming/lr_scheduler.py deleted file mode 100644 index e598ed120159c53da6820a55ad86b89f5c70c82d..0000000000000000000000000000000000000000 --- a/spaces/BernardoOlisan/vqganclip/taming-transformers/taming/lr_scheduler.py +++ /dev/null @@ -1,34 +0,0 @@ -import numpy as np - - -class LambdaWarmUpCosineScheduler: - """ - note: use with a base_lr of 1.0 - """ - def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0): - self.lr_warm_up_steps = warm_up_steps - self.lr_start = lr_start - self.lr_min = lr_min - self.lr_max = lr_max - self.lr_max_decay_steps = max_decay_steps - self.last_lr = 0. - self.verbosity_interval = verbosity_interval - - def schedule(self, n): - if self.verbosity_interval > 0: - if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}") - if n < self.lr_warm_up_steps: - lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start - self.last_lr = lr - return lr - else: - t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps) - t = min(t, 1.0) - lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * ( - 1 + np.cos(t * np.pi)) - self.last_lr = lr - return lr - - def __call__(self, n): - return self.schedule(n) - diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/_collections.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/_collections.py deleted file mode 100644 index da9857e986d89acac3ba05a6735dc08c249bde1a..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/_collections.py +++ /dev/null @@ -1,337 +0,0 @@ -from __future__ import absolute_import - -try: - from collections.abc import Mapping, MutableMapping -except ImportError: - from collections import Mapping, MutableMapping -try: - from threading import RLock -except ImportError: # Platform-specific: No threads available - - class RLock: - def __enter__(self): - pass - - def __exit__(self, exc_type, exc_value, traceback): - pass - - -from collections import OrderedDict - -from .exceptions import InvalidHeader -from .packages import six -from .packages.six import iterkeys, itervalues - -__all__ = ["RecentlyUsedContainer", "HTTPHeaderDict"] - - -_Null = object() - - -class RecentlyUsedContainer(MutableMapping): - """ - Provides a thread-safe dict-like container which maintains up to - ``maxsize`` keys while throwing away the least-recently-used keys beyond - ``maxsize``. - - :param maxsize: - Maximum number of recent elements to retain. - - :param dispose_func: - Every time an item is evicted from the container, - ``dispose_func(value)`` is called. Callback which will get called - """ - - ContainerCls = OrderedDict - - def __init__(self, maxsize=10, dispose_func=None): - self._maxsize = maxsize - self.dispose_func = dispose_func - - self._container = self.ContainerCls() - self.lock = RLock() - - def __getitem__(self, key): - # Re-insert the item, moving it to the end of the eviction line. - with self.lock: - item = self._container.pop(key) - self._container[key] = item - return item - - def __setitem__(self, key, value): - evicted_value = _Null - with self.lock: - # Possibly evict the existing value of 'key' - evicted_value = self._container.get(key, _Null) - self._container[key] = value - - # If we didn't evict an existing value, we might have to evict the - # least recently used item from the beginning of the container. - if len(self._container) > self._maxsize: - _key, evicted_value = self._container.popitem(last=False) - - if self.dispose_func and evicted_value is not _Null: - self.dispose_func(evicted_value) - - def __delitem__(self, key): - with self.lock: - value = self._container.pop(key) - - if self.dispose_func: - self.dispose_func(value) - - def __len__(self): - with self.lock: - return len(self._container) - - def __iter__(self): - raise NotImplementedError( - "Iteration over this class is unlikely to be threadsafe." - ) - - def clear(self): - with self.lock: - # Copy pointers to all values, then wipe the mapping - values = list(itervalues(self._container)) - self._container.clear() - - if self.dispose_func: - for value in values: - self.dispose_func(value) - - def keys(self): - with self.lock: - return list(iterkeys(self._container)) - - -class HTTPHeaderDict(MutableMapping): - """ - :param headers: - An iterable of field-value pairs. Must not contain multiple field names - when compared case-insensitively. - - :param kwargs: - Additional field-value pairs to pass in to ``dict.update``. - - A ``dict`` like container for storing HTTP Headers. - - Field names are stored and compared case-insensitively in compliance with - RFC 7230. Iteration provides the first case-sensitive key seen for each - case-insensitive pair. - - Using ``__setitem__`` syntax overwrites fields that compare equal - case-insensitively in order to maintain ``dict``'s api. For fields that - compare equal, instead create a new ``HTTPHeaderDict`` and use ``.add`` - in a loop. - - If multiple fields that are equal case-insensitively are passed to the - constructor or ``.update``, the behavior is undefined and some will be - lost. - - >>> headers = HTTPHeaderDict() - >>> headers.add('Set-Cookie', 'foo=bar') - >>> headers.add('set-cookie', 'baz=quxx') - >>> headers['content-length'] = '7' - >>> headers['SET-cookie'] - 'foo=bar, baz=quxx' - >>> headers['Content-Length'] - '7' - """ - - def __init__(self, headers=None, **kwargs): - super(HTTPHeaderDict, self).__init__() - self._container = OrderedDict() - if headers is not None: - if isinstance(headers, HTTPHeaderDict): - self._copy_from(headers) - else: - self.extend(headers) - if kwargs: - self.extend(kwargs) - - def __setitem__(self, key, val): - self._container[key.lower()] = [key, val] - return self._container[key.lower()] - - def __getitem__(self, key): - val = self._container[key.lower()] - return ", ".join(val[1:]) - - def __delitem__(self, key): - del self._container[key.lower()] - - def __contains__(self, key): - return key.lower() in self._container - - def __eq__(self, other): - if not isinstance(other, Mapping) and not hasattr(other, "keys"): - return False - if not isinstance(other, type(self)): - other = type(self)(other) - return dict((k.lower(), v) for k, v in self.itermerged()) == dict( - (k.lower(), v) for k, v in other.itermerged() - ) - - def __ne__(self, other): - return not self.__eq__(other) - - if six.PY2: # Python 2 - iterkeys = MutableMapping.iterkeys - itervalues = MutableMapping.itervalues - - __marker = object() - - def __len__(self): - return len(self._container) - - def __iter__(self): - # Only provide the originally cased names - for vals in self._container.values(): - yield vals[0] - - def pop(self, key, default=__marker): - """D.pop(k[,d]) -> v, remove specified key and return the corresponding value. - If key is not found, d is returned if given, otherwise KeyError is raised. - """ - # Using the MutableMapping function directly fails due to the private marker. - # Using ordinary dict.pop would expose the internal structures. - # So let's reinvent the wheel. - try: - value = self[key] - except KeyError: - if default is self.__marker: - raise - return default - else: - del self[key] - return value - - def discard(self, key): - try: - del self[key] - except KeyError: - pass - - def add(self, key, val): - """Adds a (name, value) pair, doesn't overwrite the value if it already - exists. - - >>> headers = HTTPHeaderDict(foo='bar') - >>> headers.add('Foo', 'baz') - >>> headers['foo'] - 'bar, baz' - """ - key_lower = key.lower() - new_vals = [key, val] - # Keep the common case aka no item present as fast as possible - vals = self._container.setdefault(key_lower, new_vals) - if new_vals is not vals: - vals.append(val) - - def extend(self, *args, **kwargs): - """Generic import function for any type of header-like object. - Adapted version of MutableMapping.update in order to insert items - with self.add instead of self.__setitem__ - """ - if len(args) > 1: - raise TypeError( - "extend() takes at most 1 positional " - "arguments ({0} given)".format(len(args)) - ) - other = args[0] if len(args) >= 1 else () - - if isinstance(other, HTTPHeaderDict): - for key, val in other.iteritems(): - self.add(key, val) - elif isinstance(other, Mapping): - for key in other: - self.add(key, other[key]) - elif hasattr(other, "keys"): - for key in other.keys(): - self.add(key, other[key]) - else: - for key, value in other: - self.add(key, value) - - for key, value in kwargs.items(): - self.add(key, value) - - def getlist(self, key, default=__marker): - """Returns a list of all the values for the named field. Returns an - empty list if the key doesn't exist.""" - try: - vals = self._container[key.lower()] - except KeyError: - if default is self.__marker: - return [] - return default - else: - return vals[1:] - - # Backwards compatibility for httplib - getheaders = getlist - getallmatchingheaders = getlist - iget = getlist - - # Backwards compatibility for http.cookiejar - get_all = getlist - - def __repr__(self): - return "%s(%s)" % (type(self).__name__, dict(self.itermerged())) - - def _copy_from(self, other): - for key in other: - val = other.getlist(key) - if isinstance(val, list): - # Don't need to convert tuples - val = list(val) - self._container[key.lower()] = [key] + val - - def copy(self): - clone = type(self)() - clone._copy_from(self) - return clone - - def iteritems(self): - """Iterate over all header lines, including duplicate ones.""" - for key in self: - vals = self._container[key.lower()] - for val in vals[1:]: - yield vals[0], val - - def itermerged(self): - """Iterate over all headers, merging duplicate ones together.""" - for key in self: - val = self._container[key.lower()] - yield val[0], ", ".join(val[1:]) - - def items(self): - return list(self.iteritems()) - - @classmethod - def from_httplib(cls, message): # Python 2 - """Read headers from a Python 2 httplib message object.""" - # python2.7 does not expose a proper API for exporting multiheaders - # efficiently. This function re-reads raw lines from the message - # object and extracts the multiheaders properly. - obs_fold_continued_leaders = (" ", "\t") - headers = [] - - for line in message.headers: - if line.startswith(obs_fold_continued_leaders): - if not headers: - # We received a header line that starts with OWS as described - # in RFC-7230 S3.2.4. This indicates a multiline header, but - # there exists no previous header to which we can attach it. - raise InvalidHeader( - "Header continuation with no previous header: %s" % line - ) - else: - key, value = headers[-1] - headers[-1] = (key, value + " " + line.strip()) - continue - - key, value = line.split(":", 1) - headers.append((key, value.strip())) - - return cls(headers) diff --git a/spaces/Billyosoro/ESRGAN/realesrgan/train.py b/spaces/Billyosoro/ESRGAN/realesrgan/train.py deleted file mode 100644 index 8a9cec9ed80d9f362984779548dcec921a636a04..0000000000000000000000000000000000000000 --- a/spaces/Billyosoro/ESRGAN/realesrgan/train.py +++ /dev/null @@ -1,11 +0,0 @@ -# flake8: noqa -import os.path as osp -from basicsr.train import train_pipeline - -import realesrgan.archs -import realesrgan.data -import realesrgan.models - -if __name__ == '__main__': - root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir)) - train_pipeline(root_path) diff --git a/spaces/Blackroot/Fancy-Audiogen/README.md b/spaces/Blackroot/Fancy-Audiogen/README.md deleted file mode 100644 index ef76bf946f1db0f0f473308ccb2b39555e6afda2..0000000000000000000000000000000000000000 --- a/spaces/Blackroot/Fancy-Audiogen/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Fancy Audiogen -emoji: 📉 -colorFrom: green -colorTo: yellow -sdk: gradio -sdk_version: 3.34.0 -app_file: app.py -pinned: false -license: unlicense ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/setup.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/setup.py deleted file mode 100644 index 40db8844e4d682d1ec347357c34aaa02da1d7d80..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/setup.py +++ /dev/null @@ -1,149 +0,0 @@ -#!/usr/bin/env python -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved - -import glob -import os -import shutil -from os import path -from setuptools import find_packages, setup -from typing import List -import torch -from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension - -torch_ver = [int(x) for x in torch.__version__.split(".")[:2]] -assert torch_ver >= [1, 3], "Requires PyTorch >= 1.3" - - -def get_version(): - init_py_path = path.join(path.abspath(path.dirname(__file__)), "detectron2", "__init__.py") - init_py = open(init_py_path, "r").readlines() - version_line = [l.strip() for l in init_py if l.startswith("__version__")][0] - version = version_line.split("=")[-1].strip().strip("'\"") - - # The following is used to build release packages. - # Users should never use it. - suffix = os.getenv("D2_VERSION_SUFFIX", "") - version = version + suffix - if os.getenv("BUILD_NIGHTLY", "0") == "1": - from datetime import datetime - - date_str = datetime.today().strftime("%y%m%d") - version = version + ".dev" + date_str - - new_init_py = [l for l in init_py if not l.startswith("__version__")] - new_init_py.append('__version__ = "{}"\n'.format(version)) - with open(init_py_path, "w") as f: - f.write("".join(new_init_py)) - return version - - -def get_extensions(): - this_dir = path.dirname(path.abspath(__file__)) - extensions_dir = path.join(this_dir, "detectron2", "layers", "csrc") - - main_source = path.join(extensions_dir, "vision.cpp") - sources = glob.glob(path.join(extensions_dir, "**", "*.cpp")) - source_cuda = glob.glob(path.join(extensions_dir, "**", "*.cu")) + glob.glob( - path.join(extensions_dir, "*.cu") - ) - - sources = [main_source] + sources - extension = CppExtension - - extra_compile_args = {"cxx": []} - define_macros = [] - - if ( - torch.cuda.is_available() and CUDA_HOME is not None and os.path.isdir(CUDA_HOME) - ) or os.getenv("FORCE_CUDA", "0") == "1": - extension = CUDAExtension - sources += source_cuda - define_macros += [("WITH_CUDA", None)] - extra_compile_args["nvcc"] = [ - "-DCUDA_HAS_FP16=1", - "-D__CUDA_NO_HALF_OPERATORS__", - "-D__CUDA_NO_HALF_CONVERSIONS__", - "-D__CUDA_NO_HALF2_OPERATORS__", - ] - - # It's better if pytorch can do this by default .. - CC = os.environ.get("CC", None) - if CC is not None: - extra_compile_args["nvcc"].append("-ccbin={}".format(CC)) - - include_dirs = [extensions_dir] - - ext_modules = [ - extension( - "detectron2._C", - sources, - include_dirs=include_dirs, - define_macros=define_macros, - extra_compile_args=extra_compile_args, - ) - ] - - return ext_modules - - -def get_model_zoo_configs() -> List[str]: - """ - Return a list of configs to include in package for model zoo. Copy over these configs inside - detectron2/model_zoo. - """ - - # Use absolute paths while symlinking. - source_configs_dir = path.join(path.dirname(path.realpath(__file__)), "configs") - destination = path.join( - path.dirname(path.realpath(__file__)), "detectron2", "model_zoo", "configs" - ) - # Symlink the config directory inside package to have a cleaner pip install. - - # Remove stale symlink/directory from a previous build. - if path.exists(source_configs_dir): - if path.islink(destination): - os.unlink(destination) - elif path.isdir(destination): - shutil.rmtree(destination) - - if not path.exists(destination): - try: - os.symlink(source_configs_dir, destination) - except OSError: - # Fall back to copying if symlink fails: ex. on Windows. - shutil.copytree(source_configs_dir, destination) - - config_paths = glob.glob("configs/**/*.yaml", recursive=True) - return config_paths - - -setup( - name="detectron2", - version=get_version(), - author="FAIR", - url="https://github.com/facebookresearch/detectron2", - description="Detectron2 is FAIR's next-generation research " - "platform for object detection and segmentation.", - packages=find_packages(exclude=("configs", "tests")), - package_data={"detectron2.model_zoo": get_model_zoo_configs()}, - python_requires=">=3.6", - install_requires=[ - "termcolor>=1.1", - "Pillow", # you can also use pillow-simd for better performance - "yacs>=0.1.6", - "tabulate", - "cloudpickle", - "matplotlib", - "tqdm>4.29.0", - "tensorboard", - "fvcore", - "future", # used by caffe2 - "pydot", # used to save caffe2 SVGs - ], - extras_require={ - "all": ["shapely", "psutil"], - "dev": ["flake8", "isort", "black==19.3b0", "flake8-bugbear", "flake8-comprehensions"], - }, - ext_modules=get_extensions(), - cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension}, -) diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/docs/_source/basic/model_zoo.md b/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/docs/_source/basic/model_zoo.md deleted file mode 100644 index 23b06f06c0e2a2fed5b6276bce4b188683937b60..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/docs/_source/basic/model_zoo.md +++ /dev/null @@ -1,96 +0,0 @@ -# Benchmark and Model Zoo - -## Environment - -We use the following environment to run all the experiments in this page. - -- Python 3.6 -- PyTorch 0.4.1 -- CUDA 9.0.176 -- CUDNN 7.0.4 - -## VQA-v2 - -We provide three groups of results (including the accuracies of *Overall*, *Yes/No*, *Number* and *Other*) for each model on VQA-v2 using different training schemes as follows. We provide pre-trained models for the latter two schemes. - -- **Train -> Val**: trained on the `train` split and evaluated on the `val` split. -- **Train+val -> Test-dev**: trained on the `train+val` splits and evaluated on the `test-dev` split. - -- **Train+val+vg -> Test-dev**: trained on the `train+val+vg` splits and evaluated on the `test-dev` split. - -**Note that for one model, the used base learning rate in the two schemes may be different, you should modify this setting in the config file to reproduce the results.** - - - -#### Train -> Val - -| Model | Base lr | Overall (%) | Yes/No (%) | Number (%) | Other (%) | -|:--------------------------------------------------------------------------------------:|:-------:|:-----------:|:----------:|:----------:|:---------:| -| [BUTD](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/butd.yml) | 2e-3 | 63.84 | 81.40 | 43.81 | 55.78 | -| [MFB](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/mfb.yml) | 7e-4 | 65.35 | 83.23 | 45.31 | 57.05 | -| [MFH](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/mfh.yml) | 7e-4 | 66.18 | 84.07 | 46.55 | 57.78 | -| [BAN-4](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/ban_4.yml) | 2e-3 | 65.86 | 83.53 | 46.36 | 57.56 | -| [BAN-8](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/ban_8.yml) | 2e-3 | 66.00 | 83.61 | 47.04 | 57.62 | -| [MCAN-small](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/mcan_small.yml) | 1e-4 | 67.17 | 84.82 | 49.31 | 58.48 | -| [MCAN-large](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/mcan_large.yml) | 7e-5 | 67.50 | 85.14 | 49.66 | 58.80 | -| [MMNasNet-small](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/mmnasnet_small.yml) | 1.2e-4 | 67.79 | 85.02 | 52.25 | 58.80 | -| [MMNasNet-large](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/mmnasnet_large.yml) | 7e-5 | 67.98 | 85.22 | 52.04 | 59.09 | - -#### Train+val -> Test-dev - -| Model | Base lr | Overall (%) | Yes/No (%) | Number (%) | Other (%) | Download | -|:--------------------------------------------------------------------------------------:|:-------:|:-----------:|:----------:|:----------:|:---------:|:-------------------------------------------------------------------------------------------------------------------------:| -| [BUTD](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/butd.yml) | 2e-3 | 66.98 | 83.28 | 46.19 | 57.85 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/EWSOkcCVGMpAot9ol0IJP3ABv3cWFRvGFB67980PHiCk3Q?download=1) | -| [MFB](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/mfb.yml) | 7e-4 | 68.29 | 84.64 | 48.29 | 58.89 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/ET-B23hG7UNPrQ0hha77V5kBMxAokIr486lB3YwMt-zhow?download=1) | -| [MFH](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/mfh.yml) | 7e-4 | 69.11 | 85.56 | 48.81 | 59.69 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/EUpvJD3c7NZJvBAbFOXTS0IBk1jCSz46bi7Pfq1kzJ35PA?download=1) | -| [BAN-4](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/ban_4.yml) | 1.4e-3 | 68.9 | 85.0 | 49.5 | 59.56 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/EVUabhYppDBImgV6b0DdGr0BrxTdSLm7ux9rN65T_8DZ0Q?download=1) | -| [BAN-8](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/ban_8.yml) | 1.4e-3 | 69.07 | 85.2 | 49.63 | 59.71 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/EbJgyL7FPTFAqzMm3HB1xDIBjXpWygOoXrdnDZKEIu34rg?download=1) | -| [MCAN-small](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/mcan_small.yml) | 1e-4 | 70.33 | 86.77 | 52.14 | 60.40 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/EcFeQCi_9MVBn6MeESly8OYBZCeBEuaPQqZjT-oXidgKKg?download=1) | -| [MCAN-large](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/mcan_large.yml) | 5e-5 | 70.48 | 86.90 | 52.11 | 60.63 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/Ee6HdFN_FcZAsQEm85WesHgBZBkY8dZ-278dDYG_ty_IwA?download=1) | - -#### Train+val+vg -> Test-dev - -| Model | Base lr | Overall (%) | Yes/No (%) | Number (%) | Other (%) | Download | -|:--------------------------------------------------------------------------------------:|:-------:|:-----------:|:----------:|:----------:|:---------:|:-------------------------------------------------------------------------------------------------------------------------:| -| [BUTD](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/butd.yml) | 2e-3 | 67.54 | 83.48 | 46.97 | 58.62 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/EbLMhJsx9AVJi-ipqtkzHckBS5TWo_au3T8wHPEdDKMgPQ?download=1) | -| [MFB](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/mfb.yml) | 7e-4 | 68.25 | 84.79 | 48.24 | 58.68 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/EfLYkv1XBgNJgOMU5PAo04YBHxAVmpeJtnZecqJztJdNig?download=1) | -| [MFH](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/mfh.yml) | 7e-4 | 68.86 | 85.38 | 49.27 | 59.21 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/EXGNuWmba8JOnQkkpfqokqcBzJ6Yw1ID6hl7hj2nyJaNJA?download=1) | -| [BAN-4](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/ban_4.yml) | 1.4e-3 | 69.31 | 85.42 | 50.15 | 59.91 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/ERAUbsBJzcNHjXcINxDoWOQByR0jSbdNp8nonuFdbyc8yA?download=1) | -| [BAN-8](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/ban_8.yml) | 1.4e-3 | 69.48 | 85.40 | 50.82 | 60.14 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/EW6v-dZOdJhFoKwT3bIx8M8B_U998hE8YD9zUJsUpo0rjQ?download=1) | -| [MCAN-small](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/mcan_small.yml) | 1e-4 | 70.69 | 87.08 | 53.16 | 60.66 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/EWSniKgB8Y9PropErzcAedkBKwJCeBP6b5x5oT_I4LiWtg?download=1) | -| [MCAN-large](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/mcan_large.yml) | 5e-5 | 70.82 | 87.19 | 52.56 | 60.98 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/EQvT2mjBm4ZGnE-jBgAJCbIBC9RBiHwl-XEDr8T63DS10w?download=1) | -| [MMNasNet-small](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/mmnasnet_small.yml) | 1e-4 | 71.24 | 87.11 | 56.15 | 61.08 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/EaUf4tRcw0FPghbwRoVcMo8BQT9SWzgiZBpD2CrFRfS54w?download=1) | -| [MMNasNet-large](https://github.com/MILVLG/openvqa/tree/master/configs/vqa/mmnasnet_large.yml) | 5e-5 | 71.45 | 87.29 | 55.71 | 61.45 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/EQwNsq0AVehGqhWS4iwuWsYBPtP78xEqRgFKuRGKodkQWA?download=1) | - -## GQA -We provide a group of results (including *Accuracy*, *Binary*, *Open*, *Validity*, *Plausibility*, *Consistency*, *Distribution*) for each model on GQA as follows. - -- **Train+val -> Test-dev**: trained on the `train(balance) + val(balance)` splits and evaluated on the `test-dev(balance)` split. - -**The results shown in the following are obtained from the [online server](https://evalai.cloudcv.org/web/challenges/challenge-page/225/overview). Note that the offline Test-dev result is evaluated by the provided offical script, which results in slight difference compared to the online result due to some unknown reasons.** - -#### Train+val -> Test-dev - -| Model | Base lr | Accuracy (%) | Binary (%) | Open (%) | Validity (%) | Plausibility (%) | Consistency (%) | Distribution | Download | -|:------:|:-------:|:------------:|:----------:|:--------:|:------------:|:----------------:|:----------------:|:------------:|:--------:| -| [BUTD (frcn+bbox)](https://github.com/MILVLG/openvqa/tree/master/configs/gqa/butd.yml) | 2e-3 | 53.38 | 67.78 | 40.72 | 96.62 | 84.81 | 77.62 | 1.26 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/EaalaQ6VmBJCgeoZiPp45_gBn20g7tpkp-Uq8IVFcun64w?download=1) | -| [BAN-4 (frcn+bbox)](https://github.com/MILVLG/openvqa/tree/master/configs/gqa/ban_4.yml) | 2e-3 | 55.01 | 72.02 | 40.06 | 96.94 | 85.67 | 81.85 | 1.04 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/EdRIuVXaJqBJoXg3T7N0xfYBsPl-GlgW2hq2toqm2gOxXg?download=1) | -| [BAN-8 (frcn+bbox)](https://github.com/MILVLG/openvqa/tree/master/configs/gqa/ban_8.yml) | 1e-3 | 56.19 | 73.31 | 41.13 | 96.77 | 85.58 | 84.64 | 1.09 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/ES8FCQxFsqJBnvdoOcF_724BJgJml6iStYYK9UeUbI8Uyw?download=1) | -| [MCAN-small (frcn)](https://github.com/MILVLG/openvqa/tree/master/configs/gqa/mcan_small.yml) | 1e-4 | 53.41 | 70.29 | 38.56 | 96.77 | 85.32 | 82.29 | 1.40 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/ER_i5xbPuXNCiC15iVtxBvgBTe7IBRpqpWTmeAY5svv3Ew?download=1) | -| [MCAN-small (frcn+grid)](https://github.com/MILVLG/openvqa/tree/master/configs/gqa/mcan_small.yml) | 1e-4 | 54.28 | 71.68 | 38.97 | 96.79 | 85.11 | 84.49 | 1.20 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/EbsPhIGkvpNKtqBbFmIFIucBQO_dM6lDgQL-gdd3RnzziQ?download=1) | -| [MCAN-small (frcn+bbox)](https://github.com/MILVLG/openvqa/tree/master/configs/gqa/mcan_small.yml) | 1e-4 | 58.20 | 75.87 | 42.66 | 97.01 | 85.41 | 87.99 | 1.25 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/EQCUNFPnpC1HliLDFCSDUc4BUdbdq40iPZVi5tLOCrVaQA?download=1) | -| [MCAN-small (frcn+bbox+grid)](https://github.com/MILVLG/openvqa/tree/master/configs/gqa/mcan_small.yml) | 1e-4 | 58.38 | 76.49 | 42.45 | 96.98 | 84.47 | 87.36 | 1.29 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/EcrY2vDlzERLksouT5_cbcIBM1BCPkPdg4MyPmci8xrQig?download=1) | -| [MCAN-large (frcn+bbox+grid)](https://github.com/MILVLG/openvqa/tree/master/configs/gqa/mcan_large.yml) | 5e-5 | 58.10 | 76.98 | 41.50 | 97.01 | 85.43 | 87.34 | 1.20 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/Ed6PBjIDEHpDot3vY__T-OIBJGdW51RFo2u_pm-7S5TMPA?download=1) | - - -## CLEVR - -We provide a group of results (including *Overall*, *Count*, *Exist*, *Compare Numbers*, *Query Attribute*, *Compare Attribute*) for each model on CLEVR as follows. - -- **Train -> Val**: trained on the `train` split and evaluated on the `val` split. - -#### Train -> Val - -| Model | Base lr | Overall (%) | Count (%) | Exist (%) | Compare Numbers (%) | Query Attribute (%) | Compare Attribute (%) | Download | -|:-----:|:-------:|:-------------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| -| [MCAN-small](https://github.com/MILVLG/openvqa/tree/master/configs/clevr/mcan_small.yml) | 4e-5 | 98.74 | 96.81 | 99.27 | 98.89 | 99.53 | 99.19 | [model](https://awma1-my.sharepoint.com/:u:/g/personal/yuz_l0_tn/ERtwnuAoeHNKjs0qTkWC3cYBWVuUk7BLk88cnCKNFxYYlQ?download=1) | diff --git a/spaces/CVPR/transfiner/configs/common/README.md b/spaces/CVPR/transfiner/configs/common/README.md deleted file mode 100644 index 912cc29927542bfe4258d3208cf52d73cb0ea477..0000000000000000000000000000000000000000 --- a/spaces/CVPR/transfiner/configs/common/README.md +++ /dev/null @@ -1,6 +0,0 @@ -This directory provides definitions for a few common models, dataloaders, scheduler, -and optimizers that are often used in training. -The definition of these objects are provided in the form of lazy instantiation: -their arguments can be edited by users before constructing the objects. - -They can be imported, or loaded by `model_zoo.get_config` API in users' own configs. diff --git a/spaces/ChandraMohanNayal/AutoGPT/autogpt/config/config.py b/spaces/ChandraMohanNayal/AutoGPT/autogpt/config/config.py deleted file mode 100644 index 4b53df10e8d2832be7ffb321d9036aec5a47a79d..0000000000000000000000000000000000000000 --- a/spaces/ChandraMohanNayal/AutoGPT/autogpt/config/config.py +++ /dev/null @@ -1,251 +0,0 @@ -"""Configuration class to store the state of bools for different scripts access.""" -import os - -import openai -import yaml -from colorama import Fore -from dotenv import load_dotenv - -from autogpt.config.singleton import Singleton - -load_dotenv(verbose=True) - - -class Config(metaclass=Singleton): - """ - Configuration class to store the state of bools for different scripts access. - """ - - def __init__(self) -> None: - """Initialize the Config class""" - self.debug_mode = False - self.continuous_mode = False - self.continuous_limit = 0 - self.speak_mode = False - self.skip_reprompt = False - self.allow_downloads = False - self.skip_news = False - - self.ai_settings_file = os.getenv("AI_SETTINGS_FILE", "ai_settings.yaml") - self.fast_llm_model = os.getenv("FAST_LLM_MODEL", "gpt-3.5-turbo") - self.smart_llm_model = os.getenv("SMART_LLM_MODEL", "gpt-4") - self.fast_token_limit = int(os.getenv("FAST_TOKEN_LIMIT", 4000)) - self.smart_token_limit = int(os.getenv("SMART_TOKEN_LIMIT", 8000)) - self.browse_chunk_max_length = int(os.getenv("BROWSE_CHUNK_MAX_LENGTH", 8192)) - - self.openai_api_key = os.getenv("OPENAI_API_KEY") - self.temperature = float(os.getenv("TEMPERATURE", "1")) - self.use_azure = os.getenv("USE_AZURE") == "True" - self.execute_local_commands = ( - os.getenv("EXECUTE_LOCAL_COMMANDS", "False") == "True" - ) - self.restrict_to_workspace = ( - os.getenv("RESTRICT_TO_WORKSPACE", "True") == "True" - ) - - if self.use_azure: - self.load_azure_config() - openai.api_type = self.openai_api_type - openai.api_base = self.openai_api_base - openai.api_version = self.openai_api_version - - self.elevenlabs_api_key = os.getenv("ELEVENLABS_API_KEY") - self.elevenlabs_voice_1_id = os.getenv("ELEVENLABS_VOICE_1_ID") - self.elevenlabs_voice_2_id = os.getenv("ELEVENLABS_VOICE_2_ID") - - self.use_mac_os_tts = False - self.use_mac_os_tts = os.getenv("USE_MAC_OS_TTS") - - self.use_brian_tts = False - self.use_brian_tts = os.getenv("USE_BRIAN_TTS") - - self.github_api_key = os.getenv("GITHUB_API_KEY") - self.github_username = os.getenv("GITHUB_USERNAME") - - self.google_api_key = os.getenv("GOOGLE_API_KEY") - self.custom_search_engine_id = os.getenv("CUSTOM_SEARCH_ENGINE_ID") - - self.pinecone_api_key = os.getenv("PINECONE_API_KEY") - self.pinecone_region = os.getenv("PINECONE_ENV") - - self.weaviate_host = os.getenv("WEAVIATE_HOST") - self.weaviate_port = os.getenv("WEAVIATE_PORT") - self.weaviate_protocol = os.getenv("WEAVIATE_PROTOCOL", "http") - self.weaviate_username = os.getenv("WEAVIATE_USERNAME", None) - self.weaviate_password = os.getenv("WEAVIATE_PASSWORD", None) - self.weaviate_scopes = os.getenv("WEAVIATE_SCOPES", None) - self.weaviate_embedded_path = os.getenv("WEAVIATE_EMBEDDED_PATH") - self.weaviate_api_key = os.getenv("WEAVIATE_API_KEY", None) - self.use_weaviate_embedded = ( - os.getenv("USE_WEAVIATE_EMBEDDED", "False") == "True" - ) - - # milvus configuration, e.g., localhost:19530. - self.milvus_addr = os.getenv("MILVUS_ADDR", "localhost:19530") - self.milvus_collection = os.getenv("MILVUS_COLLECTION", "autogpt") - - self.image_provider = os.getenv("IMAGE_PROVIDER") - self.image_size = int(os.getenv("IMAGE_SIZE", 256)) - self.huggingface_api_token = os.getenv("HUGGINGFACE_API_TOKEN") - self.huggingface_image_model = os.getenv( - "HUGGINGFACE_IMAGE_MODEL", "CompVis/stable-diffusion-v1-4" - ) - self.huggingface_audio_to_text_model = os.getenv( - "HUGGINGFACE_AUDIO_TO_TEXT_MODEL" - ) - self.sd_webui_url = os.getenv("SD_WEBUI_URL", "http://localhost:7860") - self.sd_webui_auth = os.getenv("SD_WEBUI_AUTH") - - # Selenium browser settings - self.selenium_web_browser = os.getenv("USE_WEB_BROWSER", "chrome") - self.selenium_headless = os.getenv("HEADLESS_BROWSER", "True") == "True" - - # User agent header to use when making HTTP requests - # Some websites might just completely deny request with an error code if - # no user agent was found. - self.user_agent = os.getenv( - "USER_AGENT", - "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36" - " (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36", - ) - - self.redis_host = os.getenv("REDIS_HOST", "localhost") - self.redis_port = os.getenv("REDIS_PORT", "6379") - self.redis_password = os.getenv("REDIS_PASSWORD", "") - self.wipe_redis_on_start = os.getenv("WIPE_REDIS_ON_START", "True") == "True" - self.memory_index = os.getenv("MEMORY_INDEX", "auto-gpt") - # Note that indexes must be created on db 0 in redis, this is not configurable. - - self.memory_backend = os.getenv("MEMORY_BACKEND", "local") - # Initialize the OpenAI API client - openai.api_key = self.openai_api_key - - def get_azure_deployment_id_for_model(self, model: str) -> str: - """ - Returns the relevant deployment id for the model specified. - - Parameters: - model(str): The model to map to the deployment id. - - Returns: - The matching deployment id if found, otherwise an empty string. - """ - if model == self.fast_llm_model: - return self.azure_model_to_deployment_id_map[ - "fast_llm_model_deployment_id" - ] # type: ignore - elif model == self.smart_llm_model: - return self.azure_model_to_deployment_id_map[ - "smart_llm_model_deployment_id" - ] # type: ignore - elif model == "text-embedding-ada-002": - return self.azure_model_to_deployment_id_map[ - "embedding_model_deployment_id" - ] # type: ignore - else: - return "" - - AZURE_CONFIG_FILE = os.path.join(os.path.dirname(__file__), "..", "azure.yaml") - - def load_azure_config(self, config_file: str = AZURE_CONFIG_FILE) -> None: - """ - Loads the configuration parameters for Azure hosting from the specified file - path as a yaml file. - - Parameters: - config_file(str): The path to the config yaml file. DEFAULT: "../azure.yaml" - - Returns: - None - """ - try: - with open(config_file) as file: - config_params = yaml.load(file, Loader=yaml.FullLoader) - except FileNotFoundError: - config_params = {} - self.openai_api_type = config_params.get("azure_api_type") or "azure" - self.openai_api_base = config_params.get("azure_api_base") or "" - self.openai_api_version = ( - config_params.get("azure_api_version") or "2023-03-15-preview" - ) - self.azure_model_to_deployment_id_map = config_params.get("azure_model_map", []) - - def set_continuous_mode(self, value: bool) -> None: - """Set the continuous mode value.""" - self.continuous_mode = value - - def set_continuous_limit(self, value: int) -> None: - """Set the continuous limit value.""" - self.continuous_limit = value - - def set_speak_mode(self, value: bool) -> None: - """Set the speak mode value.""" - self.speak_mode = value - - def set_fast_llm_model(self, value: str) -> None: - """Set the fast LLM model value.""" - self.fast_llm_model = value - - def set_smart_llm_model(self, value: str) -> None: - """Set the smart LLM model value.""" - self.smart_llm_model = value - - def set_fast_token_limit(self, value: int) -> None: - """Set the fast token limit value.""" - self.fast_token_limit = value - - def set_smart_token_limit(self, value: int) -> None: - """Set the smart token limit value.""" - self.smart_token_limit = value - - def set_browse_chunk_max_length(self, value: int) -> None: - """Set the browse_website command chunk max length value.""" - self.browse_chunk_max_length = value - - def set_openai_api_key(self, value: str) -> None: - """Set the OpenAI API key value.""" - self.openai_api_key = value - - def set_elevenlabs_api_key(self, value: str) -> None: - """Set the ElevenLabs API key value.""" - self.elevenlabs_api_key = value - - def set_elevenlabs_voice_1_id(self, value: str) -> None: - """Set the ElevenLabs Voice 1 ID value.""" - self.elevenlabs_voice_1_id = value - - def set_elevenlabs_voice_2_id(self, value: str) -> None: - """Set the ElevenLabs Voice 2 ID value.""" - self.elevenlabs_voice_2_id = value - - def set_google_api_key(self, value: str) -> None: - """Set the Google API key value.""" - self.google_api_key = value - - def set_custom_search_engine_id(self, value: str) -> None: - """Set the custom search engine id value.""" - self.custom_search_engine_id = value - - def set_pinecone_api_key(self, value: str) -> None: - """Set the Pinecone API key value.""" - self.pinecone_api_key = value - - def set_pinecone_region(self, value: str) -> None: - """Set the Pinecone region value.""" - self.pinecone_region = value - - def set_debug_mode(self, value: bool) -> None: - """Set the debug mode value.""" - self.debug_mode = value - - -def check_openai_api_key() -> None: - """Check if the OpenAI API key is set in config.py or as an environment variable.""" - cfg = Config() - if not cfg.openai_api_key: - print( - Fore.RED - + "Please set your OpenAI API key in .env or as an environment variable." - ) - print("You can get your key from https://platform.openai.com/account/api-keys") - exit(1) diff --git a/spaces/ChandraMohanNayal/AutoGPT/autogpt/spinner.py b/spaces/ChandraMohanNayal/AutoGPT/autogpt/spinner.py deleted file mode 100644 index 4e33d74213881352546f334ccb1eb4772b8b7b70..0000000000000000000000000000000000000000 --- a/spaces/ChandraMohanNayal/AutoGPT/autogpt/spinner.py +++ /dev/null @@ -1,65 +0,0 @@ -"""A simple spinner module""" -import itertools -import sys -import threading -import time - - -class Spinner: - """A simple spinner class""" - - def __init__(self, message: str = "Loading...", delay: float = 0.1) -> None: - """Initialize the spinner class - - Args: - message (str): The message to display. - delay (float): The delay between each spinner update. - """ - self.spinner = itertools.cycle(["-", "/", "|", "\\"]) - self.delay = delay - self.message = message - self.running = False - self.spinner_thread = None - - def spin(self) -> None: - """Spin the spinner""" - while self.running: - sys.stdout.write(f"{next(self.spinner)} {self.message}\r") - sys.stdout.flush() - time.sleep(self.delay) - sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r") - - def __enter__(self): - """Start the spinner""" - self.running = True - self.spinner_thread = threading.Thread(target=self.spin) - self.spinner_thread.start() - - return self - - def __exit__(self, exc_type, exc_value, exc_traceback) -> None: - """Stop the spinner - - Args: - exc_type (Exception): The exception type. - exc_value (Exception): The exception value. - exc_traceback (Exception): The exception traceback. - """ - self.running = False - if self.spinner_thread is not None: - self.spinner_thread.join() - sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r") - sys.stdout.flush() - - def update_message(self, new_message, delay=0.1): - """Update the spinner message - Args: - new_message (str): New message to display - delay: Delay in seconds before updating the message - """ - time.sleep(delay) - sys.stdout.write( - f"\r{' ' * (len(self.message) + 2)}\r" - ) # Clear the current message - sys.stdout.flush() - self.message = new_message diff --git a/spaces/CofAI/chat.b4/client/css/style.css b/spaces/CofAI/chat.b4/client/css/style.css deleted file mode 100644 index 6281753f30cc140e82bcf8343377e3828b8261f5..0000000000000000000000000000000000000000 --- a/spaces/CofAI/chat.b4/client/css/style.css +++ /dev/null @@ -1,18 +0,0 @@ -@import "./global.css"; -@import "./hljs.css"; -@import "./main.css"; -@import "./sidebar.css"; -@import "./conversation.css"; -@import "./message.css"; -@import "./stop-generating.css"; -@import "./typing.css"; -@import "./checkbox.css"; -@import "./label.css"; -@import "./button.css"; -@import "./buttons.css"; -@import "./dropdown.css"; -@import "./field.css"; -@import "./select.css"; -@import "./options.css"; -@import "./theme-toggler.css"; -@import "./message-input.css"; diff --git a/spaces/CyberHarem/find_my_waifu/app.py b/spaces/CyberHarem/find_my_waifu/app.py deleted file mode 100644 index 50937cb5e8d3e30bab78309f492bb13967de2281..0000000000000000000000000000000000000000 --- a/spaces/CyberHarem/find_my_waifu/app.py +++ /dev/null @@ -1,144 +0,0 @@ -import io -import json -import os - -import gradio as gr -import markdown -import pandas as pd -from gchar.games.dispatch.access import get_character -from gchar.generic import import_generic -from gchar.resources.pixiv import get_pixiv_keywords, get_pixiv_posts -from gchar.resources.sites import list_available_sites, get_site_tag -from gchar.utils import get_requests_session -from huggingface_hub import hf_hub_url, configure_http_backend -from pycivitai import civitai_find_online -from pycivitai.client import ModelNotFound - -from character import get_ch_name -from civitai import try_find_title -from huggingface import get_hf_fs - -import_generic() - -hf_fs = get_hf_fs() -configure_http_backend(get_requests_session) - - -def query(chr_name): - ch = get_character(chr_name, allow_fuzzy=True) - - # get character info - info_columns = ['Property', 'Value'] - info_data = [] - info_data.append(('Index', ch.index)) - ennames = [str(enname) for enname in ch.ennames] - if ennames: - info_data.append(('EN Name', ', '.join(ennames))) - cnnames = [str(cnname) for cnname in ch.cnnames] - if cnnames: - info_data.append(('CN Name', ', '.join(cnnames))) - jpnames = [str(jpname) for jpname in ch.jpnames] - if jpnames: - info_data.append(('JP Name', ', '.join(jpnames))) - if hasattr(ch, 'krnames'): - krnames = [str(krname) for krname in ch.krnames] - if krnames: - info_data.append(('KR Name', ', '.join(krnames))) - info_data.append(('Sex', ch.gender.name)) - info_data.append(('Source', ch.__official_name__)) - info_df = pd.DataFrame(columns=info_columns, data=info_data) - - # get skins - skin_dir = f'datasets/{ch.__skin_repository__}/{ch.__game_name__}/{ch.index}' - meta_json = f'{skin_dir}/.meta.json' - skin_urls = [] - if hf_fs.exists(meta_json): - meta = json.loads(hf_fs.read_text(meta_json)) - for item in meta['files']: - skin_url = hf_hub_url( - ch.__skin_repository__, - filename=f'{ch.__game_name__}/{ch.index}/{item["name"]}', - repo_type='dataset', - ) - skin_name = item['metadata']['name'] - skin_urls.append((skin_url, skin_name)) - - # get repo info - repo = f'CyberHarem/{get_ch_name(ch)}' - with io.StringIO() as sf: - if hf_fs.exists(f'{repo}/meta.json'): - model_url = f'https://huggingface.co/{repo}' - print(f'Model: [{model_url}]({model_url})', file=sf) - else: - print(f'Model not found.', file=sf) - print(file=sf) - - if hf_fs.exists(f'datasets/{repo}/dataset-raw.zip'): - ds_url = f'https://huggingface.co/datasets/{repo}' - print(f'Dataset: [{ds_url}]({ds_url})', file=sf) - else: - print('Dataset not found.', file=sf) - print(file=sf) - - try: - model_name = try_find_title(str(ch.enname), ch.__game_name__) - resource = civitai_find_online(model_name) - civit_url = f'https://civitai.com/models/{resource.model_id}' - print(f'CivitAI Model: [{civit_url}]({civit_url})', file=sf) - except ModelNotFound: - print('No CivitAI published model found.', file=sf) - print(file=sf) - - html = markdown.markdown(sf.getvalue()) - - # get tags on all sites - tags_columns = ['Site', 'Posts', 'Tag'] - tags_data = [] - tags_data.append(('Pixiv (ALL)', get_pixiv_posts(ch)[0], get_pixiv_keywords(ch))) - tags_data.append(('Pixiv (R18)', get_pixiv_posts(ch)[1], get_pixiv_keywords(ch, includes=['R-18']))) - for site in list_available_sites(): - tag_retval = get_site_tag(ch, site, with_posts=True, sure_only=True) - if tag_retval is not None: - tag_name, tag_cnt = tag_retval - tags_data.append((site, tag_cnt, tag_name)) - tags_data = sorted(tags_data, key=lambda x: (-x[1], x[0])) - tags_df = pd.DataFrame(columns=tags_columns, data=tags_data) - - return info_df, skin_urls, html, tags_df - - -if __name__ == '__main__': - with gr.Blocks() as demo: - gr_input = gr.Textbox( - label='Character Name', - placeholder='Enter name or alias of the character.' - ) - gr_submit = gr.Button(value='Find My Waifu', variant='primary') - - with gr.Row(): - with gr.Column(): - with gr.Row(): - gr_info = gr.DataFrame(label='Character Info') - with gr.Row(): - gr_skins = gr.Gallery(label='Skins') - - with gr.Column(): - with gr.Row(): - gr_html = gr.HTML(label='Entry of Model and Dataset', value='(N/A)') - with gr.Row(): - gr_tags = gr.DataFrame(label='Character Tags') - - gr_submit.click( - query, - inputs=[ - gr_input, - ], - outputs=[ - gr_info, - gr_skins, - gr_html, - gr_tags, - ] - ) - - demo.queue(os.cpu_count()).launch() diff --git a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/postprocessing/dula/layout.py b/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/postprocessing/dula/layout.py deleted file mode 100644 index 9101a65800d866a660a99b2e4ee809517ffeedf1..0000000000000000000000000000000000000000 --- a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/postprocessing/dula/layout.py +++ /dev/null @@ -1,226 +0,0 @@ -""" -@Date: 2021/10/06 -@description: Use the approach proposed by DuLa-Net -""" -import cv2 -import numpy as np -import math -import matplotlib.pyplot as plt - -from visualization.floorplan import draw_floorplan - - -def merge_near(lst, diag): - group = [[0, ]] - for i in range(1, len(lst)): - if lst[i][1] == 0 and lst[i][0] - np.mean(group[-1]) < diag * 0.02: - group[-1].append(lst[i][0]) - else: - group.append([lst[i][0], ]) - if len(group) == 1: - group = [lst[0][0], lst[-1][0]] - else: - group = [int(np.mean(x)) for x in group] - return group - - -def fit_layout(floor_xz, need_cube=False, show=False, block_eps=0.2): - show_radius = np.linalg.norm(floor_xz, axis=-1).max() - side_l = 512 - floorplan = draw_floorplan(xz=floor_xz, show_radius=show_radius, show=show, scale=1, side_l=side_l).astype(np.uint8) - center = np.array([side_l / 2, side_l / 2]) - polys = cv2.findContours(floorplan, 1, 2) - if isinstance(polys, tuple): - if len(polys) == 3: - # opencv 3 - polys = list(polys[1]) - else: - polys = list(polys[0]) - polys.sort(key=lambda x: cv2.contourArea(x), reverse=True) - poly = polys[0] - sub_x, sub_y, w, h = cv2.boundingRect(poly) - floorplan_sub = floorplan[sub_y:sub_y + h, sub_x:sub_x + w] - sub_center = center - np.array([sub_x, sub_y]) - polys = cv2.findContours(floorplan_sub, 1, 2) - if isinstance(polys, tuple): - if len(polys) == 3: - polys = polys[1] - else: - polys = polys[0] - poly = polys[0] - epsilon = 0.005 * cv2.arcLength(poly, True) - poly = cv2.approxPolyDP(poly, epsilon, True) - - x_lst = [[0, 0], ] - y_lst = [[0, 0], ] - - ans = np.zeros((floorplan_sub.shape[0], floorplan_sub.shape[1])) - - for i in range(len(poly)): - p1 = poly[i][0] - p2 = poly[(i + 1) % len(poly)][0] - # We added occlusion detection - cp1 = p1 - sub_center - cp2 = p2 - sub_center - p12 = p2 - p1 - l1 = np.linalg.norm(cp1) - l2 = np.linalg.norm(cp2) - l3 = np.linalg.norm(p12) - # We added occlusion detection - is_block1 = abs(np.cross(cp1/l1, cp2/l2)) < block_eps - is_block2 = abs(np.cross(cp2/l2, p12/l3)) < block_eps*2 - is_block = is_block1 and is_block2 - - if (p2[0] - p1[0]) == 0: - slope = 10 - else: - slope = abs((p2[1] - p1[1]) / (p2[0] - p1[0])) - - if is_block: - s = p1[1] if l1 < l2 else p2[1] - y_lst.append([s, 1]) - s = p1[0] if l1 < l2 else p2[0] - x_lst.append([s, 1]) - - left = p1[0] if p1[0] < p2[0] else p2[0] - right = p1[0] if p1[0] > p2[0] else p2[0] - top = p1[1] if p1[1] < p2[1] else p2[1] - bottom = p1[1] if p1[1] > p2[1] else p2[1] - sample = floorplan_sub[top:bottom, left:right] - score = 0 if sample.size == 0 else sample.mean() - if score >= 0.3: - ans[top:bottom, left:right] = 1 - - else: - if slope <= 1: - s = int((p1[1] + p2[1]) / 2) - y_lst.append([s, 0]) - elif slope > 1: - s = int((p1[0] + p2[0]) / 2) - x_lst.append([s, 0]) - - debug_show = False - if debug_show: - plt.figure(dpi=300) - plt.axis('off') - a = cv2.drawMarker(floorplan_sub.copy()*0.5, tuple([floorplan_sub.shape[1] // 2, floorplan_sub.shape[0] // 2]), [1], markerType=0, markerSize=10, thickness=2) - plt.imshow(cv2.drawContours(a, [poly], 0, 1, 1)) - plt.savefig('src/1.png', bbox_inches='tight', transparent=True, pad_inches=0) - plt.show() - - plt.figure(dpi=300) - plt.axis('off') - a = cv2.drawMarker(ans.copy()*0.5, tuple([floorplan_sub.shape[1] // 2, floorplan_sub.shape[0] // 2]), [1], markerType=0, markerSize=10, thickness=2) - plt.imshow(cv2.drawContours(a, [poly], 0, 1, 1)) - # plt.show() - plt.savefig('src/2.png', bbox_inches='tight', transparent=True, pad_inches=0) - plt.show() - - x_lst.append([floorplan_sub.shape[1], 0]) - y_lst.append([floorplan_sub.shape[0], 0]) - x_lst.sort(key=lambda x: x[0]) - y_lst.sort(key=lambda x: x[0]) - - diag = math.sqrt(math.pow(floorplan_sub.shape[1], 2) + math.pow(floorplan_sub.shape[0], 2)) - x_lst = merge_near(x_lst, diag) - y_lst = merge_near(y_lst, diag) - if need_cube and len(x_lst) > 2: - x_lst = [x_lst[0], x_lst[-1]] - if need_cube and len(y_lst) > 2: - y_lst = [y_lst[0], y_lst[-1]] - - for i in range(len(x_lst) - 1): - for j in range(len(y_lst) - 1): - sample = floorplan_sub[y_lst[j]:y_lst[j + 1], x_lst[i]:x_lst[i + 1]] - score = 0 if sample.size == 0 else sample.mean() - if score >= 0.3: - ans[y_lst[j]:y_lst[j + 1], x_lst[i]:x_lst[i + 1]] = 1 - - if debug_show: - plt.figure(dpi=300) - plt.axis('off') - a = cv2.drawMarker(ans.copy() * 0.5, tuple([floorplan_sub.shape[1] // 2, floorplan_sub.shape[0] // 2]), [1], - markerType=0, markerSize=10, thickness=2) - plt.imshow(cv2.drawContours(a, [poly], 0, 1, 1)) - # plt.show() - plt.savefig('src/3.png', bbox_inches='tight', transparent=True, pad_inches=0) - plt.show() - - pred = np.uint8(ans) - pred_polys = cv2.findContours(pred, 1, 3) - if isinstance(pred_polys, tuple): - if len(pred_polys) == 3: - pred_polys = pred_polys[1] - else: - pred_polys = pred_polys[0] - - pred_polys.sort(key=lambda x: cv2.contourArea(x), reverse=True) - pred_polys = pred_polys[0] - - if debug_show: - plt.figure(dpi=300) - plt.axis('off') - a = cv2.drawMarker(ans.copy() * 0.5, tuple([floorplan_sub.shape[1] // 2, floorplan_sub.shape[0] // 2]), [1], - markerType=0, markerSize=10, thickness=2) - a = cv2.drawContours(a, [poly], 0, 0.8, 1) - a = cv2.drawContours(a, [pred_polys], 0, 1, 1) - plt.imshow(a) - # plt.show() - plt.savefig('src/4.png', bbox_inches='tight', transparent=True, pad_inches=0) - plt.show() - - polygon = [(p[0][1], p[0][0]) for p in pred_polys[::-1]] - - v = np.array([p[0] + sub_y for p in polygon]) - u = np.array([p[1] + sub_x for p in polygon]) - # side_l - # v<-----------|o - # | | | - # | ----|----z | side_l - # | | | - # | x \|/ - # |------------u - side_l = floorplan.shape[0] - pred_xz = np.concatenate((u[:, np.newaxis] - side_l // 2, side_l // 2 - v[:, np.newaxis]), axis=1) - - pred_xz = pred_xz * show_radius / (side_l // 2) - if show: - draw_floorplan(pred_xz, show_radius=show_radius, show=show) - - show_process = False - if show_process: - img = np.zeros((floorplan_sub.shape[0], floorplan_sub.shape[1], 3)) - for x in x_lst: - cv2.line(img, (x, 0), (x, floorplan_sub.shape[0]), (0, 255, 0), 1) - for y in y_lst: - cv2.line(img, (0, y), (floorplan_sub.shape[1], y), (255, 0, 0), 1) - - fig = plt.figure() - plt.axis('off') - ax1 = fig.add_subplot(2, 2, 1) - ax1.imshow(floorplan) - ax3 = fig.add_subplot(2, 2, 2) - ax3.imshow(floorplan_sub) - ax4 = fig.add_subplot(2, 2, 3) - ax4.imshow(img) - ax5 = fig.add_subplot(2, 2, 4) - ax5.imshow(ans) - plt.show() - - return pred_xz - - -if __name__ == '__main__': - from utils.conversion import uv2xyz - - pano_img = np.zeros([512, 1024, 3]) - corners = np.array([[0.1, 0.7], - [0.4, 0.7], - [0.3, 0.6], - [0.6, 0.6], - [0.8, 0.7]]) - xz = uv2xyz(corners)[..., ::2] - draw_floorplan(xz, show=True, marker_color=None, center_color=0.8) - - xz = fit_layout(xz) - draw_floorplan(xz, show=True, marker_color=None, center_color=0.8) diff --git a/spaces/DmitriiKhizbullin/camel-data-explorer/apps/data_explorer/loader.py b/spaces/DmitriiKhizbullin/camel-data-explorer/apps/data_explorer/loader.py deleted file mode 100644 index 4df77fd429a6635577de5e0cc010209ff2b08736..0000000000000000000000000000000000000000 --- a/spaces/DmitriiKhizbullin/camel-data-explorer/apps/data_explorer/loader.py +++ /dev/null @@ -1,172 +0,0 @@ -""" -Everything related to parsing the data JSONs into UI-compatible format. -""" - -import glob -import json -import os -import re -import zipfile -from typing import Any, Dict, List, Optional, Tuple, Union - -from tqdm import tqdm - -ChatHistory = Dict[str, Any] -ParsedChatHistory = Dict[str, Any] -AllChats = Dict[str, Any] -Datasets = Dict[str, AllChats] - -REPO_ROOT = os.path.realpath( - os.path.join(os.path.dirname(os.path.abspath(__file__)), "../..")) - - -class AutoZip: - def __init__(self, zip_path: str, ext: str = ".json"): - self.zip_path = zip_path - self.zip = zipfile.ZipFile(zip_path, "r") - self.fl = [f for f in self.zip.filelist if f.filename.endswith(ext)] - - def __next__(self): - if self.index >= len(self.fl): - raise StopIteration - else: - finfo = self.fl[self.index] - with self.zip.open(finfo) as f: - raw_json = json.loads(f.read().decode("utf-8")) - self.index += 1 - return raw_json - - def __len__(self): - return len(self.fl) - - def __iter__(self): - self.index = 0 - return self - - -def parse(raw_chat: ChatHistory) -> Union[ParsedChatHistory, None]: - """ Gets the JSON raw chat data, validates it and transforms - into an easy to work with form. - - Args: - raw_chat (ChatHistory): In-memory loaded JSON data file. - - Returns: - Union[ParsedChatHistory, None]: Parsed chat data or None - if there were parsing errors. - """ - - if "role_1" not in raw_chat: - return None - - role_1 = raw_chat["role_1"] - if "_RoleType.ASSISTANT" not in role_1: - return None - assistant_role = role_1.split("_RoleType.ASSISTANT") - if len(assistant_role) < 1: - return None - if len(assistant_role[0]) <= 0: - return None - assistant_role = assistant_role[0] - - role_2 = raw_chat["role_2"] - if "_RoleType.USER" not in role_2: - return None - user_role = role_2.split("_RoleType.USER") - if len(user_role) < 1: - return None - if len(user_role[0]) <= 0: - return None - user_role = user_role[0] - - original_task = raw_chat["original_task"] - if len(original_task) <= 0: - return None - - specified_task = raw_chat["specified_task"] - if len(specified_task) <= 0: - return None - - messages = dict() - for key in raw_chat: - match = re.search("message_(?P
ChatGPT Academic Optimization
-
-**Если вам понравился этот проект, пожалуйста, поставьте ему звезду. Если вы придумали более полезные академические ярлыки или функциональные плагины, не стесняйтесь создавать запросы на изменение или пул-запросы. Мы также имеем [README на английском языке](docs/README_EN.md), переведенный этим же проектом.
-
-> **Примечание**
->
-> 1. Пожалуйста, обратите внимание, что только функциonal plugins (buttons) с **красным цветом** могут читать файлы, некоторые из которых находятся в **выпадающем меню** плагинов. Кроме того, мы приветствуем и обрабатываем любые новые плагины с **наивысшим приоритетом**!
->
-> 2. Функции каждого файла в этом проекте подробно описаны в собственном анализе [`self_analysis.md`](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) . При повторных итерациях вы также можете вызывать обновленный отчет функций проекта, щелкнув соответствующий функциональный плагин GPT. Часто задаваемые вопросы собраны в [`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98) .
-
-
-
-Функция | Описание
---- | ---
-Редактирование одним кликом | Поддержка редактирования одним кликом, поиск грамматических ошибок в академических статьях
-Переключение языков "Английский-Китайский" одним кликом | Одним кликом переключайте языки "Английский-Китайский"
-Разъяснение программного кода одним кликом | Вы можете правильно отобразить и объяснить программный код.
-[Настраиваемые сочетания клавиш](https://www.bilibili.com/video/BV14s4y1E7jN) | Поддержка настраиваемых сочетаний клавиш
-[Настройка сервера-прокси](https://www.bilibili.com/video/BV1rc411W7Dr) | Поддержка настройки сервера-прокси
-Модульный дизайн | Поддержка настраиваемых функциональных плагинов высших порядков и функциональных плагинов, поддерживающих [горячее обновление](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
-[Автоанализ программы](https://www.bilibili.com/video/BV1cj411A7VW) | [Функциональный плагин] [Прочтение в один клик](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) кода программы проекта
-[Анализ программы](https://www.bilibili.com/video/BV1cj411A7VW) | [Функциональный плагин] Один клик для проанализирования дерева других проектов Python/C/C++/Java/Lua/...
-Чтение статей| [Функциональный плагин] Одним кликом прочитайте весь латех (LaTex) текст статьи и сгенерируйте краткое описание
-Перевод и редактирование всех статей из LaTex | [Функциональный плагин] Перевод или редактирование LaTex-статьи всего одним нажатием кнопки
-Генерация комментариев в пакетном режиме | [Функциональный плагин] Одним кликом сгенерируйте комментарии к функциям в пакетном режиме
-Генерация отчетов пакета CHAT | [Функциональный плагин] Автоматически создавайте сводные отчеты после выполнения
-[Помощник по arxiv](https://www.bilibili.com/video/BV1LM4y1279X) | [Функциональный плагин] Введите URL статьи arxiv, чтобы легко перевести резюме и загрузить PDF-файл
-[Перевод полного текста статьи в формате PDF](https://www.bilibili.com/video/BV1KT411x7Wn) | [Функциональный плагин] Извлеките заголовок статьи, резюме и переведите весь текст статьи (многопоточно)
-[Помощник интеграции Google Scholar](https://www.bilibili.com/video/BV19L411U7ia) | [Функциональный плагин] Дайте GPT выбрать для вас интересные статьи на любой странице поиска Google Scholar.
-Отображение формул/изображений/таблиц | Одновременно отображается tex-форма и рендер-форма формул, поддержка формул, высокоскоростных кодов
-Поддержка функциональных плагинов многопоточности | Поддержка многопоточной работы с плагинами, обрабатывайте огромные объемы текста или программы одним кликом
-Запуск темной темы gradio[подробнее](https://github.com/binary-husky/chatgpt_academic/issues/173) | Добавьте / ?__dark-theme=true в конец URL браузера, чтобы переключиться на темную тему.
-[Поддержка нескольких моделей LLM](https://www.bilibili.com/video/BV1wT411p7yf), поддержка API2D | Находиться между GPT3.5, GPT4 и [清华ChatGLM](https://github.com/THUDM/ChatGLM-6B) должно быть очень приятно, не так ли?
-Альтернатива huggingface без использования научной сети [Онлайн-эксперимент](https://huggingface.co/spaces/qingxu98/gpt-academic) | Войдите в систему, скопируйте пространство [этот пространственный URL](https://huggingface.co/spaces/qingxu98/gpt-academic)
-…… | ……
-
-
-
-
-- Новый интерфейс (вы можете изменить настройку LAYOUT в config.py, чтобы переключаться между "горизонтальным расположением" и "вертикальным расположением")
-
-
-
-
-
-Вы профессиональный переводчик научных статей.
-
-- Все кнопки генерируются динамически путем чтения functional.py и могут быть легко настроены под пользовательские потребности, освобождая буфер обмена.
-
-
-
-
-
-- Редактирование/корректирование
-
-
-
-
-
-- Если вывод содержит формулы, они отображаются одновременно как в формате tex, так и в рендеринговом формате для удобства копирования и чтения.
-
-
-
-
-
-- Лень смотреть код проекта? Просто покажите chatgpt.
-
-
-
-
-
-- Несколько моделей больших языковых моделей смешиваются (ChatGLM + OpenAI-GPT3.5 + [API2D] (https://api2d.com/) -GPT4)
-
-
-
-
-
-Несколько моделей больших языковых моделей смешиваются в [бета-версии huggingface] (https://huggingface.co/spaces/qingxu98/academic-chatgpt-beta) (huggingface-версия не поддерживает chatglm).
-
-
----
-
-## Установка - Метод 1: Запуск (Windows, Linux или MacOS)
-
-1. Скачайте проект
-```sh
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
-```
-
-2. Настройка API_KEY и настройки прокси
-
-В файле `config.py` настройте зарубежный прокси и OpenAI API KEY, пояснения ниже
-```
-1. Если вы находитесь в Китае, вам нужно настроить зарубежный прокси, чтобы использовать OpenAI API. Пожалуйста, внимательно прочитайте config.py для получения инструкций (1. Измените USE_PROXY на True; 2. Измените прокси в соответствии с инструкциями).
-2. Настройка API KEY OpenAI. Вам необходимо зарегистрироваться на сайте OpenAI и получить API KEY. После получения API KEY настройте его в файле config.py.
-3. Вопросы, связанные с сетевыми проблемами (тайм-аут сети, прокси не работает), можно найти здесь: https://github.com/binary-husky/chatgpt_academic/issues/1
-```
-(Примечание: при запуске программы будет проверяться наличие конфиденциального файла конфигурации с именем `config_private.py` и использоваться в нем конфигурация параметров, которая перезаписывает параметры с такими же именами в `config.py`. Поэтому, если вы понимаете логику чтения нашей конфигурации, мы настоятельно рекомендуем вам создать новый файл конфигурации с именем `config_private.py` рядом с `config.py` и переместить (скопировать) настройки из `config.py` в `config_private.py`. `config_private.py` не подвергается контролю git, что делает конфиденциальную информацию более безопасной.)
-
-
-3. Установить зависимости
-```sh
-# (Выбор 1) Рекомендуется
-python -m pip install -r requirements.txt
-
-# (Выбор 2) Если вы используете anaconda, то шаги будут аналогичны:
-# (Шаг 2.1) conda create -n gptac_venv python=3.11
-# (Шаг 2.2) conda activate gptac_venv
-# (Шаг 2.3) python -m pip install -r requirements.txt
-
-# Примечание: используйте официальный источник pip или источник pip.aliyun.com. Другие источники pip могут вызывать проблемы. временный метод замены источника:
-# python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
-```
-
-Если требуется поддержка TUNA ChatGLM, необходимо установить дополнительные зависимости (если вы неудобны с python, необходимо иметь хорошую конфигурацию компьютера):
-```sh
-python -m pip install -r request_llm/requirements_chatglm.txt
-```
-
-4. Запустите
-```sh
-python main.py
-```
-
-5. Тестовые функции плагина
-```
-- Тестирвоание анализа проекта Python
- В основной области введите `./crazy_functions/test_project/python/dqn` , а затем нажмите "Анализировать весь проект Python"
-- Тестирование самостоятельного чтения кода
- Щелкните " [Демонстрационный режим многопоточности] Проанализируйте сам проект (расшифровка источника кода)"
-- Тестирование функций шаблонного плагина (вы можете использовать эту функцию как шаблон для более сложных функций, требующих ответа от gpt в связи с тем, что произошло сегодня в истории)
- Щелкните " [Функции шаблонного плагина] День в истории"
-- На нижней панели дополнительные функции для выбора
-```
-
-## Установка - Метод 2: Использование docker (Linux)
-
-
-1. Только ChatGPT (рекомендуется для большинства пользователей):
-``` sh
-# Скачать проект
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
-# Настроить прокси за границей и OpenAI API KEY
-Отредактируйте файл config.py в любом текстовом редакторе.
-# Установка
-docker build -t gpt-academic .
-# Запустить
-docker run --rm -it --net=host gpt-academic
-
-# Проверка функциональности плагина
-## Проверка шаблонной функции плагина (требуется, чтобы gpt ответил, что произошло "в истории на этот день"), вы можете использовать эту функцию в качестве шаблона для реализации более сложных функций.
-Нажмите "[Шаблонный демонстрационный плагин] История на этот день".
-## Тест абстрактного резюме для проекта на Latex
-В области ввода введите ./crazy_functions/test_project/latex/attention, а затем нажмите "Чтение реферата о тезисах статьи на LaTeX".
-## Тестовый анализ проекта на Python
-Введите в область ввода ./crazy_functions/test_project/python/dqn, затем нажмите "Проанализировать весь проект на Python".
-
-Выбирайте больше функциональных плагинов в нижнем выпадающем меню.
-```
-
-2. ChatGPT + ChatGLM (требуется глубокое знание Docker и достаточно мощное компьютерное оборудование):
-
-``` sh
-# Изменение Dockerfile
-cd docs && nano Dockerfile+ChatGLM
-# Как построить | Как запустить (Dockerfile+ChatGLM в пути docs, сначала перейдите в папку с помощью cd docs)
-docker build -t gpt-academic --network=host -f Dockerfile+ChatGLM .
-# Как запустить | Как запустить (2) я хочу войти в контейнер и сделать какие-то настройки до запуска:
-docker run --rm -it --net=host --gpus=all gpt-academic bash
-```
-
-
-## Установка-Метод 3: Другие способы развертывания
-
-1. Развертывание на удаленном облачном сервере
-Пожалуйста, посетите [Deploy Wiki-1] (https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
-
-2. Использование WSL2 (Windows Subsystem for Linux)
-Пожалуйста, посетите [Deploy Wiki-2] (https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
-
-
-## Установка-Настройки прокси
-### Метод 1: Обычный способ
-[Конфигурация прокси] (https://github.com/binary-husky/chatgpt_academic/issues/1)
-
-### Метод 2: Руководство новичка
-[Руководство новичка] (https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BB%A3%E7%90%86%E8%BD%AF%E4%BB%B6%E9%97%AE%E9%A2%98%E7%9A%84%E6%96%B0%E6%89%8B%E8%A7%A3%E5%86%B3%E6%96%B9%E6%B3%95%EF%BC%88%E6%96%B9%E6%B3%95%E5%8F%AA%E9%80%82%E7%94%A8%E4%BA%8E%E6%96%B0%E6%89%8B%EF%BC%89)
-
-
----
-
-## Настройка новой удобной кнопки (настройка быстрой клавиши для научной работы)
-Откройте `core_functional.py` любым текстовым редактором, добавьте элементы, как показано ниже, затем перезапустите программу. (Если кнопка уже успешно добавлена и видна, то префикс и суффикс поддерживают горячее изменение, чтобы они оказались в действии, не нужно перезапускать программу.)
-например
-```
-"Супер анг-рус": {
- # Префикс, будет добавлен перед вашим вводом. Например, используется для описания ваших потребностей, таких как перевод, кодинг, редактирование и т. д.
- "Prefix": "Пожалуйста, переведите этот фрагмент на русский язык, а затем создайте пошаговую таблицу в markdown, чтобы объяснить все специализированные термины, которые встречаются в тексте:\n\n",
-
- # Суффикс, будет добавлен после вашего ввода. Например, совместно с префиксом можно обрамить ваш ввод в кавычки.
- "Suffix": "",
-},
-```
-
-
-
-
-
----
-
-
-## Демонстрация некоторых возможностей
-
-### Отображение изображений:
-
-
-
-
-
-
-
-### Если программа может понимать и разбирать сама себя:
-
-
-
-
-
-
-
-
-
-
-
-
-### Анализ других проектов на Python/Cpp:
-
-
-
-
-
-
-
-
-
-
-### Генерация понимания и абстрактов с помощью Латех статей в один клик
-
-
-
-
-
-### Автоматическое создание отчетов
-
-
-
-
-
-
-
-### Модульный дизайн функций
-
-
-
-
-
-
-
-
-
-### Трансляция исходного кода на английский язык
-
-
-
-
-
-
-
-## Todo и планирование версий:
-- version 3.2+ (todo): функция плагины поддерживают более многочисленные интерфейсы параметров
-- version 3.1: поддержка одновременного опроса нескольких моделей gpt! Поддержка api2d, поддержка балансировки нагрузки множества apikey.
-- version 3.0: поддержка chatglm и других маленьких llm
-- version 2.6: реструктурировал структуру плагинов, повысил интерактивность, добавил больше плагинов
-- version 2.5: само обновление, решение проблемы слишком длинного текста и переполнения токена при переводе всего проекта исходного кода
-- version 2.4: (1) добавлена функция перевода всего PDF-документа; (2) добавлена функция изменения положения входной области; (3) добавлена опция вертикального макета; (4) оптимизация функций многопоточности плагина.
-- version 2.3: улучшение многопоточной интерактивности
-- version 2.2: функция плагинов поддерживает горячую перезагрузку
-- version 2.1: блочная раскладка
-- version 2.0: модульный дизайн функций плагина
-- version 1.0: основные функции
-
-## Ссылки на изучение и обучение
-
-```
-В коде использовано много хороших дизайнерских решений из других отличных проектов, в том числе:
-
-# Project1: использование многих приемов из ChuanhuChatGPT
-https://github.com/GaiZhenbiao/ChuanhuChatGPT
-
-# Project2: ChatGLM-6B в Тхуде:
-https://github.com/THUDM/ChatGLM-6B
-```
-
diff --git a/spaces/Fox1997/vits-uma-genshin-honkai/Docker/Dockerfile b/spaces/Fox1997/vits-uma-genshin-honkai/Docker/Dockerfile
deleted file mode 100644
index 4d39cdf02a2ec151686cc1d61234bf723068fed8..0000000000000000000000000000000000000000
--- a/spaces/Fox1997/vits-uma-genshin-honkai/Docker/Dockerfile
+++ /dev/null
@@ -1,12 +0,0 @@
-FROM python:3.9-bullseye
-VOLUME ["/app"]
-WORKDIR /app
-# Set apt to Chinese mirror
-RUN sed -i 's/deb.debian.org/mirrors.ustc.edu.cn/g' /etc/apt/sources.list
-RUN apt-get update && apt-get -y install cmake git
-RUN git clone https://huggingface.co/spaces/ikechan8370/vits-uma-genshin-honkai
-WORKDIR /app/vits-uma-genshin-honkai
-RUN sed -i "s/\.launch()/\.launch(server_name=\"0.0.0.0\")/" /app/vits-uma-genshin-honkai/app.py
-ADD vits.sh /app/vits.sh
-EXPOSE 7860
-ENTRYPOINT [ "/app/vits.sh" ]
\ No newline at end of file
diff --git a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/modules.py b/spaces/FrankZxShen/vits-fast-fineturning-models-ba/modules.py
deleted file mode 100644
index 9c7fd9cd6eb8b7e0ec0e08957e970744a374a924..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/modules.py
+++ /dev/null
@@ -1,390 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
- def __init__(self, channels, eps=1e-5):
- super().__init__()
- self.channels = channels
- self.eps = eps
-
- self.gamma = nn.Parameter(torch.ones(channels))
- self.beta = nn.Parameter(torch.zeros(channels))
-
- def forward(self, x):
- x = x.transpose(1, -1)
- x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
- return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
- def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
- super().__init__()
- self.in_channels = in_channels
- self.hidden_channels = hidden_channels
- self.out_channels = out_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
- assert n_layers > 1, "Number of layers should be larger than 0."
-
- self.conv_layers = nn.ModuleList()
- self.norm_layers = nn.ModuleList()
- self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.relu_drop = nn.Sequential(
- nn.ReLU(),
- nn.Dropout(p_dropout))
- for _ in range(n_layers-1):
- self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask):
- x_org = x
- for i in range(self.n_layers):
- x = self.conv_layers[i](x * x_mask)
- x = self.norm_layers[i](x)
- x = self.relu_drop(x)
- x = x_org + self.proj(x)
- return x * x_mask
-
-
-class DDSConv(nn.Module):
- """
- Dialted and Depth-Separable Convolution
- """
- def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
- super().__init__()
- self.channels = channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
-
- self.drop = nn.Dropout(p_dropout)
- self.convs_sep = nn.ModuleList()
- self.convs_1x1 = nn.ModuleList()
- self.norms_1 = nn.ModuleList()
- self.norms_2 = nn.ModuleList()
- for i in range(n_layers):
- dilation = kernel_size ** i
- padding = (kernel_size * dilation - dilation) // 2
- self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
- groups=channels, dilation=dilation, padding=padding
- ))
- self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
- self.norms_1.append(LayerNorm(channels))
- self.norms_2.append(LayerNorm(channels))
-
- def forward(self, x, x_mask, g=None):
- if g is not None:
- x = x + g
- for i in range(self.n_layers):
- y = self.convs_sep[i](x * x_mask)
- y = self.norms_1[i](y)
- y = F.gelu(y)
- y = self.convs_1x1[i](y)
- y = self.norms_2[i](y)
- y = F.gelu(y)
- y = self.drop(y)
- x = x + y
- return x * x_mask
-
-
-class WN(torch.nn.Module):
- def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
- super(WN, self).__init__()
- assert(kernel_size % 2 == 1)
- 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.p_dropout = p_dropout
-
- self.in_layers = torch.nn.ModuleList()
- self.res_skip_layers = torch.nn.ModuleList()
- self.drop = nn.Dropout(p_dropout)
-
- if gin_channels != 0:
- cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
- self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
- for i in range(n_layers):
- dilation = dilation_rate ** i
- padding = int((kernel_size * dilation - dilation) / 2)
- in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_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 * hidden_channels
- else:
- res_skip_channels = hidden_channels
-
- res_skip_layer = torch.nn.Conv1d(hidden_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, x, x_mask, g=None, **kwargs):
- output = torch.zeros_like(x)
- n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
- if g is not None:
- g = self.cond_layer(g)
-
- for i in range(self.n_layers):
- x_in = self.in_layers[i](x)
- if g is not None:
- cond_offset = i * 2 * self.hidden_channels
- g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
- else:
- g_l = torch.zeros_like(x_in)
-
- acts = commons.fused_add_tanh_sigmoid_multiply(
- x_in,
- g_l,
- n_channels_tensor)
- acts = self.drop(acts)
-
- res_skip_acts = self.res_skip_layers[i](acts)
- if i < self.n_layers - 1:
- res_acts = res_skip_acts[:,:self.hidden_channels,:]
- x = (x + res_acts) * x_mask
- output = output + res_skip_acts[:,self.hidden_channels:,:]
- else:
- output = output + res_skip_acts
- return output * x_mask
-
- def remove_weight_norm(self):
- if self.gin_channels != 0:
- torch.nn.utils.remove_weight_norm(self.cond_layer)
- for l in self.in_layers:
- torch.nn.utils.remove_weight_norm(l)
- for l in self.res_skip_layers:
- torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.convs1 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2])))
- ])
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1)))
- ])
- self.convs2.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c2(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.convs = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1])))
- ])
- self.convs.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class Log(nn.Module):
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
- logdet = torch.sum(-y, [1, 2])
- return y, logdet
- else:
- x = torch.exp(x) * x_mask
- return x
-
-
-class Flip(nn.Module):
- def forward(self, x, *args, reverse=False, **kwargs):
- x = torch.flip(x, [1])
- if not reverse:
- logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
- return x, logdet
- else:
- return x
-
-
-class ElementwiseAffine(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.channels = channels
- self.m = nn.Parameter(torch.zeros(channels,1))
- self.logs = nn.Parameter(torch.zeros(channels,1))
-
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = self.m + torch.exp(self.logs) * x
- y = y * x_mask
- logdet = torch.sum(self.logs * x_mask, [1,2])
- return y, logdet
- else:
- x = (x - self.m) * torch.exp(-self.logs) * x_mask
- return x
-
-
-class ResidualCouplingLayer(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=0,
- gin_channels=0,
- mean_only=False):
- assert channels % 2 == 0, "channels should be divisible by 2"
- 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.half_channels = channels // 2
- self.mean_only = mean_only
-
- self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
- self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
- self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
- self.post.weight.data.zero_()
- self.post.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0) * x_mask
- h = self.enc(h, x_mask, g=g)
- stats = self.post(h) * x_mask
- if not self.mean_only:
- m, logs = torch.split(stats, [self.half_channels]*2, 1)
- else:
- m = stats
- logs = torch.zeros_like(m)
-
- if not reverse:
- x1 = m + x1 * torch.exp(logs) * x_mask
- x = torch.cat([x0, x1], 1)
- logdet = torch.sum(logs, [1,2])
- return x, logdet
- else:
- x1 = (x1 - m) * torch.exp(-logs) * x_mask
- x = torch.cat([x0, x1], 1)
- return x
-
-
-class ConvFlow(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
- super().__init__()
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.num_bins = num_bins
- self.tail_bound = tail_bound
- self.half_channels = in_channels // 2
-
- self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
- self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
- self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0)
- h = self.convs(h, x_mask, g=g)
- h = self.proj(h) * x_mask
-
- b, c, t = x0.shape
- h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
- unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
- x1, logabsdet = piecewise_rational_quadratic_transform(x1,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=reverse,
- tails='linear',
- tail_bound=self.tail_bound
- )
-
- x = torch.cat([x0, x1], 1) * x_mask
- logdet = torch.sum(logabsdet * x_mask, [1,2])
- if not reverse:
- return x, logdet
- else:
- return x
diff --git "a/spaces/Gmq-x/gpt-academic/crazy_functions/\350\247\243\346\236\220\351\241\271\347\233\256\346\272\220\344\273\243\347\240\201.py" "b/spaces/Gmq-x/gpt-academic/crazy_functions/\350\247\243\346\236\220\351\241\271\347\233\256\346\272\220\344\273\243\347\240\201.py"
deleted file mode 100644
index 375b87aee6d20c16255fb3b0af58fa58a048b81f..0000000000000000000000000000000000000000
--- "a/spaces/Gmq-x/gpt-academic/crazy_functions/\350\247\243\346\236\220\351\241\271\347\233\256\346\272\220\344\273\243\347\240\201.py"
+++ /dev/null
@@ -1,266 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-
-def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
- import os, copy
- from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
- from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
- msg = '正常'
- inputs_array = []
- inputs_show_user_array = []
- history_array = []
- sys_prompt_array = []
- report_part_1 = []
-
- assert len(file_manifest) <= 512, "源文件太多（超过512个）, 请缩减输入文件的数量。或者，您也可以选择删除此行警告，并修改代码拆分file_manifest列表，从而实现分批次处理。"
- ############################## <第一步，逐个文件分析，多线程> ##################################
- for index, fp in enumerate(file_manifest):
- # 读取文件
- with open(fp, 'r', encoding='utf-8', errors='replace') as f:
- file_content = f.read()
- prefix = "接下来请你逐文件分析下面的工程" if index==0 else ""
- i_say = prefix + f'请对下面的程序文件做一个概述文件名是{os.path.relpath(fp, project_folder)}，文件代码是 ```{file_content}```'
- i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述: {os.path.abspath(fp)}'
- # 装载请求内容
- inputs_array.append(i_say)
- inputs_show_user_array.append(i_say_show_user)
- history_array.append([])
- sys_prompt_array.append("你是一个程序架构分析师，正在分析一个源代码项目。你的回答必须简单明了。")
-
- # 文件读取完成，对每一个源代码文件，生成一个请求线程，发送到chatgpt进行分析
- gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
- inputs_array = inputs_array,
- inputs_show_user_array = inputs_show_user_array,
- history_array = history_array,
- sys_prompt_array = sys_prompt_array,
- llm_kwargs = llm_kwargs,
- chatbot = chatbot,
- show_user_at_complete = True
- )
-
- # 全部文件解析完成，结果写入文件，准备对工程源代码进行汇总分析
- report_part_1 = copy.deepcopy(gpt_response_collection)
- history_to_return = report_part_1
- res = write_results_to_file(report_part_1)
- chatbot.append(("完成？", "逐个文件分析已完成。" + res + "\n\n正在开始汇总。"))
- yield from update_ui(chatbot=chatbot, history=history_to_return) # 刷新界面
-
- ############################## <第二步，综合，单线程，分组+迭代处理> ##################################
- batchsize = 16 # 10个文件为一组
- report_part_2 = []
- previous_iteration_files = []
- last_iteration_result = ""
- while True:
- if len(file_manifest) == 0: break
- this_iteration_file_manifest = file_manifest[:batchsize]
- this_iteration_gpt_response_collection = gpt_response_collection[:batchsize*2]
- file_rel_path = [os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)]
- # 把“请对下面的程序文件做一个概述” 替换成 精简的 "文件名：{all_file[index]}"
- for index, content in enumerate(this_iteration_gpt_response_collection):
- if index%2==0: this_iteration_gpt_response_collection[index] = f"{file_rel_path[index//2]}" # 只保留文件名节省token
- previous_iteration_files.extend([os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)])
- previous_iteration_files_string = ', '.join(previous_iteration_files)
- current_iteration_focus = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)])
- i_say = f'根据以上分析，对程序的整体功能和构架重新做出概括。然后用一张markdown表格整理每个文件的功能（包括{previous_iteration_files_string}）。'
- inputs_show_user = f'根据以上分析，对程序的整体功能和构架重新做出概括，由于输入长度限制，可能需要分组处理，本组文件为 {current_iteration_focus} + 已经汇总的文件组。'
- this_iteration_history = copy.deepcopy(this_iteration_gpt_response_collection)
- this_iteration_history.append(last_iteration_result)
- result = yield from request_gpt_model_in_new_thread_with_ui_alive(
- inputs=i_say, inputs_show_user=inputs_show_user, llm_kwargs=llm_kwargs, chatbot=chatbot,
- history=this_iteration_history, # 迭代之前的分析
- sys_prompt="你是一个程序架构分析师，正在分析一个项目的源代码。")
- report_part_2.extend([i_say, result])
- last_iteration_result = result
-
- file_manifest = file_manifest[batchsize:]
- gpt_response_collection = gpt_response_collection[batchsize*2:]
-
- ##############################
-Visual ChatGPT combines visual foundation models and ChatGPT to send,receive and process images during chatting
This demo currently only supports text, image captioning, image generation, visual question answering, edge detection on image and generating image conditioned on canny and pose images using the diffusers implementation of ControlNet
") - gr.Markdown("For example usages please refer to the official GIF
") - gr.Markdown('
For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
-
-
-
diff --git a/spaces/SeViLA/SeViLA/lavis/models/sevila_models/__init__.py b/spaces/SeViLA/SeViLA/lavis/models/sevila_models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/SerdarHelli/Knee-View-Merchant-Landmark-Detection/app.py b/spaces/SerdarHelli/Knee-View-Merchant-Landmark-Detection/app.py
deleted file mode 100644
index 8f3ace32d42a3a02e1a056ef4573f442a57a046d..0000000000000000000000000000000000000000
--- a/spaces/SerdarHelli/Knee-View-Merchant-Landmark-Detection/app.py
+++ /dev/null
@@ -1,143 +0,0 @@
-
-
-
-
-import streamlit as st
-import os
-from PIL import Image
-import numpy as np
-import cv2
-from Utils import *
-
-from huggingface_hub import hf_hub_download,from_pretrained_keras
-
-
-model = from_pretrained_keras("SerdarHelli/Knee-View-Merchant-Landmark-Detection")
-st.header("Knee Merchant Landmark Detection")
-
-
-
-st.markdown("***Measurement of Merchant Angles is a fully automated method to measure Patellar Congruence Angle and Tilt Angle on Merchant Knee radiographs, employing CNN landmark localizers*** ")
-
-link='[S.Serdar Helli and Andaç Hamamcı - Yeditepe Medical Imaging Lab. ! ](https://imagingyeditepe.github.io/software.html)'
-
-st.markdown(link,unsafe_allow_html=True)
-
-
-
-
-image_file = st.file_uploader("Upload Images", type=["dcm"])
-
-
-st.text("Merchant Knee View Dicom Examples ")
-
-examples=["1.3.46.670589.30.1.6.1.149885691756583.1510655758812.1.dcm"
- ,"1.2.392.200036.9125.9.0.235868094.418384128.208354950.dcm",
- "1.2.392.200036.9107.500.304.423.20170526.173028.10423.dcm"]
-
-colx1, colx2, colx3 = st.columns(3)
-
-
-
-with colx1:
- st.text("Example -1 ")
-
- if st.button('Example 1'):
- image_file=examples[0]
-
-with colx2:
- st.text("Example -2 ")
-
- if st.button('Example 2'):
- image_file=examples[1]
-
-
-with colx3:
- st.text("Example -3 ")
-
- if st.button('Example 3'):
- image_file=examples[2]
-
-
-if image_file is not None:
- st.text("Making A Prediction ....")
-
-
- try:
- data,PatientName,PatientID,SOPInstanceUID,StudyDate,InstitutionAddress,PatientAge,PatientSex=read_dicom(image_file,False,True)
- except:
- data,PatientName,PatientID,SOPInstanceUID,StudyDate,InstitutionAddress,PatientAge,PatientSex=read_dicom(image_file,True,True)
- pass
-
-
-
- img = np.copy(data)
-
- #Denoise Image
- kernel =( np.ones((5,5), dtype=np.float32))
- img2=cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel,iterations=2 )
- img2=cv2.erode(img2,kernel,iterations =2)
- if len(img2.shape)==3:
- img2=img2[:,:,0]
-
- #Threshhold 100- 4096
- ret,thresh = cv2.threshold(img2,100, 4096, cv2.THRESH_BINARY)
-
- #To Thresh uint8 becasue "findContours" doesnt accept uint16
- thresh =((thresh/np.max(thresh))*255).astype('uint8')
- a1,b1=thresh.shape
- #Find Countours
- contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-
- #If There is no countour
- if len(contours)==0:
- roi= thresh
-
- else:
- #Get Areas
- c_area=np.zeros([len(contours)])
- for i in range(len(contours)):
- c_area[i]= cv2.contourArea(contours[i])
-
- #Find Max Countour
- cnts=contours[np.argmax(c_area)]
- x, y, w, h = cv2.boundingRect(cnts)
-
- #Posibble Square
- roi = croping(data, x, y, w, h)
-
- # Absolute Square
- roi=modification_cropping(roi)
-
- # Resize to 256x256 with Inter_Nearest
- roi=cv2.resize(roi,(256,256),interpolation=cv2.INTER_NEAREST)
-
- pre=predict(roi,model)
- heatpoint=points_max_value(pre)
- output=put_text_point(roi,heatpoint)
- output,PatellerCongruenceAngle,ParalelTiltAngle=draw_angle(output,heatpoint)
- data_text = {'PatientID': PatientID, 'PatientName': PatientName,
- 'Pateller_Congruence_Angle': PatellerCongruenceAngle,
- 'Paralel_Tilt_Angle':ParalelTiltAngle,
- 'SOP_Instance_UID':SOPInstanceUID,
- "StudyDate" :StudyDate,
- "InstitutionName" :InstitutionAddress,
- "PatientAge" :PatientAge ,
- "PatientSex" :PatientSex,
- }
-
-
-
- st.text("Original Dicom Image")
-
- st.image(np.uint8((data/np.max(data)*255)),width=450)
-
-
- st.text("Predicted and Cropped-Resized Image ")
-
- st.image(np.uint8(output),width=450)
-
-
-
- st.write(data_text)
-
diff --git a/spaces/Shivam29rathore/shorter-finbert/README.md b/spaces/Shivam29rathore/shorter-finbert/README.md
deleted file mode 100644
index 11e50738e49d849c98d1fca16cc5f325f7f4ec61..0000000000000000000000000000000000000000
--- a/spaces/Shivam29rathore/shorter-finbert/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Shorter Finbert
-emoji: 📊
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.1.5
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/aiofiles/tempfile/temptypes.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/aiofiles/tempfile/temptypes.py
deleted file mode 100644
index b17e0257b7999e2512f125f8b74d266158f23820..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/aiofiles/tempfile/temptypes.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""Async wrappers for spooled temp files and temp directory objects"""
-
-# Imports
-import asyncio
-from types import coroutine
-
-from ..base import AsyncBase
-from ..threadpool.utils import (
- delegate_to_executor,
- proxy_property_directly,
- cond_delegate_to_executor,
-)
-from functools import partial
-
-
-@delegate_to_executor("fileno", "rollover")
-@cond_delegate_to_executor(
- "close",
- "flush",
- "isatty",
- "read",
- "readline",
- "readlines",
- "seek",
- "tell",
- "truncate",
-)
-@proxy_property_directly("closed", "encoding", "mode", "name", "newlines")
-class AsyncSpooledTemporaryFile(AsyncBase):
- """Async wrapper for SpooledTemporaryFile class"""
-
- async def _check(self):
- if self._file._rolled:
- return
- max_size = self._file._max_size
- if max_size and self._file.tell() > max_size:
- await self.rollover()
-
- async def write(self, s):
- """Implementation to anticipate rollover"""
- if self._file._rolled:
- cb = partial(self._file.write, s)
- return await self._loop.run_in_executor(self._executor, cb)
- else:
- file = self._file._file # reference underlying base IO object
- rv = file.write(s)
- await self._check()
- return rv
-
- async def writelines(self, iterable):
- """Implementation to anticipate rollover"""
- if self._file._rolled:
- cb = partial(self._file.writelines, iterable)
- return await self._loop.run_in_executor(self._executor, cb)
- else:
- file = self._file._file # reference underlying base IO object
- rv = file.writelines(iterable)
- await self._check()
- return rv
-
-
-@delegate_to_executor("cleanup")
-@proxy_property_directly("name")
-class AsyncTemporaryDirectory:
- """Async wrapper for TemporaryDirectory class"""
-
- def __init__(self, file, loop, executor):
- self._file = file
- self._loop = loop
- self._executor = executor
-
- async def close(self):
- await self.cleanup()
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/aiofiles/threadpool/text.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/aiofiles/threadpool/text.py
deleted file mode 100644
index 1323009ac25f10cfa6908318e0fcb387f85fb99c..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/aiofiles/threadpool/text.py
+++ /dev/null
@@ -1,68 +0,0 @@
-from ..base import AsyncBase, AsyncIndirectBase
-from .utils import (
- delegate_to_executor,
- proxy_method_directly,
- proxy_property_directly,
-)
-
-
-@delegate_to_executor(
- "close",
- "flush",
- "isatty",
- "read",
- "readable",
- "readline",
- "readlines",
- "seek",
- "seekable",
- "tell",
- "truncate",
- "write",
- "writable",
- "writelines",
-)
-@proxy_method_directly("detach", "fileno", "readable")
-@proxy_property_directly(
- "buffer",
- "closed",
- "encoding",
- "errors",
- "line_buffering",
- "newlines",
- "name",
- "mode",
-)
-class AsyncTextIOWrapper(AsyncBase):
- """The asyncio executor version of io.TextIOWrapper."""
-
-
-@delegate_to_executor(
- "close",
- "flush",
- "isatty",
- "read",
- "readable",
- "readline",
- "readlines",
- "seek",
- "seekable",
- "tell",
- "truncate",
- "write",
- "writable",
- "writelines",
-)
-@proxy_method_directly("detach", "fileno", "readable")
-@proxy_property_directly(
- "buffer",
- "closed",
- "encoding",
- "errors",
- "line_buffering",
- "newlines",
- "name",
- "mode",
-)
-class AsyncTextIndirectIOWrapper(AsyncIndirectBase):
- """The indirect asyncio executor version of io.TextIOWrapper."""
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/abc/_subprocesses.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/abc/_subprocesses.py
deleted file mode 100644
index 704b44a2dda9e21997acf52c268e414d01bd2eb5..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/abc/_subprocesses.py
+++ /dev/null
@@ -1,79 +0,0 @@
-from __future__ import annotations
-
-from abc import abstractmethod
-from signal import Signals
-
-from ._resources import AsyncResource
-from ._streams import ByteReceiveStream, ByteSendStream
-
-
-class Process(AsyncResource):
- """An asynchronous version of :class:`subprocess.Popen`."""
-
- @abstractmethod
- async def wait(self) -> int:
- """
- Wait until the process exits.
-
- :return: the exit code of the process
- """
-
- @abstractmethod
- def terminate(self) -> None:
- """
- Terminates the process, gracefully if possible.
-
- On Windows, this calls ``TerminateProcess()``.
- On POSIX systems, this sends ``SIGTERM`` to the process.
-
- .. seealso:: :meth:`subprocess.Popen.terminate`
- """
-
- @abstractmethod
- def kill(self) -> None:
- """
- Kills the process.
-
- On Windows, this calls ``TerminateProcess()``.
- On POSIX systems, this sends ``SIGKILL`` to the process.
-
- .. seealso:: :meth:`subprocess.Popen.kill`
- """
-
- @abstractmethod
- def send_signal(self, signal: Signals) -> None:
- """
- Send a signal to the subprocess.
-
- .. seealso:: :meth:`subprocess.Popen.send_signal`
-
- :param signal: the signal number (e.g. :data:`signal.SIGHUP`)
- """
-
- @property
- @abstractmethod
- def pid(self) -> int:
- """The process ID of the process."""
-
- @property
- @abstractmethod
- def returncode(self) -> int | None:
- """
- The return code of the process. If the process has not yet terminated, this will be
- ``None``.
- """
-
- @property
- @abstractmethod
- def stdin(self) -> ByteSendStream | None:
- """The stream for the standard input of the process."""
-
- @property
- @abstractmethod
- def stdout(self) -> ByteReceiveStream | None:
- """The stream for the standard output of the process."""
-
- @property
- @abstractmethod
- def stderr(self) -> ByteReceiveStream | None:
- """The stream for the standard error output of the process."""
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/base_doc/mixins/__init__.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/base_doc/mixins/__init__.py
deleted file mode 100644
index bfa675df9a1b200e8d4c6a41f5da9cfe95d9fdfd..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/base_doc/mixins/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from docarray.base_doc.mixins.io import IOMixin
-from docarray.base_doc.mixins.update import UpdateMixin
-
-__all__ = ['IOMixin', 'UpdateMixin']
diff --git a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/solver/build.py b/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/solver/build.py
deleted file mode 100644
index 5e526df1e05b1ad8943c18cc7a1e5e43c58d57c8..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/solver/build.py
+++ /dev/null
@@ -1,310 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import copy
-import itertools
-import logging
-from collections import defaultdict
-from enum import Enum
-from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Type, Union
-import torch
-from fvcore.common.param_scheduler import (
- CosineParamScheduler,
- MultiStepParamScheduler,
- StepWithFixedGammaParamScheduler,
-)
-
-from annotator.oneformer.detectron2.config import CfgNode
-from annotator.oneformer.detectron2.utils.env import TORCH_VERSION
-
-from .lr_scheduler import LRMultiplier, LRScheduler, WarmupParamScheduler
-
-_GradientClipperInput = Union[torch.Tensor, Iterable[torch.Tensor]]
-_GradientClipper = Callable[[_GradientClipperInput], None]
-
-
-class GradientClipType(Enum):
- VALUE = "value"
- NORM = "norm"
-
-
-def _create_gradient_clipper(cfg: CfgNode) -> _GradientClipper:
- """
- Creates gradient clipping closure to clip by value or by norm,
- according to the provided config.
- """
- cfg = copy.deepcopy(cfg)
-
- def clip_grad_norm(p: _GradientClipperInput):
- torch.nn.utils.clip_grad_norm_(p, cfg.CLIP_VALUE, cfg.NORM_TYPE)
-
- def clip_grad_value(p: _GradientClipperInput):
- torch.nn.utils.clip_grad_value_(p, cfg.CLIP_VALUE)
-
- _GRADIENT_CLIP_TYPE_TO_CLIPPER = {
- GradientClipType.VALUE: clip_grad_value,
- GradientClipType.NORM: clip_grad_norm,
- }
- return _GRADIENT_CLIP_TYPE_TO_CLIPPER[GradientClipType(cfg.CLIP_TYPE)]
-
-
-def _generate_optimizer_class_with_gradient_clipping(
- optimizer: Type[torch.optim.Optimizer],
- *,
- per_param_clipper: Optional[_GradientClipper] = None,
- global_clipper: Optional[_GradientClipper] = None,
-) -> Type[torch.optim.Optimizer]:
- """
- Dynamically creates a new type that inherits the type of a given instance
- and overrides the `step` method to add gradient clipping
- """
- assert (
- per_param_clipper is None or global_clipper is None
- ), "Not allowed to use both per-parameter clipping and global clipping"
-
- def optimizer_wgc_step(self, closure=None):
- if per_param_clipper is not None:
- for group in self.param_groups:
- for p in group["params"]:
- per_param_clipper(p)
- else:
- # global clipper for future use with detr
- # (https://github.com/facebookresearch/detr/pull/287)
- all_params = itertools.chain(*[g["params"] for g in self.param_groups])
- global_clipper(all_params)
- super(type(self), self).step(closure)
-
- OptimizerWithGradientClip = type(
- optimizer.__name__ + "WithGradientClip",
- (optimizer,),
- {"step": optimizer_wgc_step},
- )
- return OptimizerWithGradientClip
-
-
-def maybe_add_gradient_clipping(
- cfg: CfgNode, optimizer: Type[torch.optim.Optimizer]
-) -> Type[torch.optim.Optimizer]:
- """
- If gradient clipping is enabled through config options, wraps the existing
- optimizer type to become a new dynamically created class OptimizerWithGradientClip
- that inherits the given optimizer and overrides the `step` method to
- include gradient clipping.
-
- Args:
- cfg: CfgNode, configuration options
- optimizer: type. A subclass of torch.optim.Optimizer
-
- Return:
- type: either the input `optimizer` (if gradient clipping is disabled), or
- a subclass of it with gradient clipping included in the `step` method.
- """
- if not cfg.SOLVER.CLIP_GRADIENTS.ENABLED:
- return optimizer
- if isinstance(optimizer, torch.optim.Optimizer):
- optimizer_type = type(optimizer)
- else:
- assert issubclass(optimizer, torch.optim.Optimizer), optimizer
- optimizer_type = optimizer
-
- grad_clipper = _create_gradient_clipper(cfg.SOLVER.CLIP_GRADIENTS)
- OptimizerWithGradientClip = _generate_optimizer_class_with_gradient_clipping(
- optimizer_type, per_param_clipper=grad_clipper
- )
- if isinstance(optimizer, torch.optim.Optimizer):
- optimizer.__class__ = OptimizerWithGradientClip # a bit hacky, not recommended
- return optimizer
- else:
- return OptimizerWithGradientClip
-
-
-def build_optimizer(cfg: CfgNode, model: torch.nn.Module) -> torch.optim.Optimizer:
- """
- Build an optimizer from config.
- """
- params = get_default_optimizer_params(
- model,
- base_lr=cfg.SOLVER.BASE_LR,
- weight_decay_norm=cfg.SOLVER.WEIGHT_DECAY_NORM,
- bias_lr_factor=cfg.SOLVER.BIAS_LR_FACTOR,
- weight_decay_bias=cfg.SOLVER.WEIGHT_DECAY_BIAS,
- )
- sgd_args = {
- "params": params,
- "lr": cfg.SOLVER.BASE_LR,
- "momentum": cfg.SOLVER.MOMENTUM,
- "nesterov": cfg.SOLVER.NESTEROV,
- "weight_decay": cfg.SOLVER.WEIGHT_DECAY,
- }
- if TORCH_VERSION >= (1, 12):
- sgd_args["foreach"] = True
- return maybe_add_gradient_clipping(cfg, torch.optim.SGD(**sgd_args))
-
-
-def get_default_optimizer_params(
- model: torch.nn.Module,
- base_lr: Optional[float] = None,
- weight_decay: Optional[float] = None,
- weight_decay_norm: Optional[float] = None,
- bias_lr_factor: Optional[float] = 1.0,
- weight_decay_bias: Optional[float] = None,
- lr_factor_func: Optional[Callable] = None,
- overrides: Optional[Dict[str, Dict[str, float]]] = None,
-) -> List[Dict[str, Any]]:
- """
- Get default param list for optimizer, with support for a few types of
- overrides. If no overrides needed, this is equivalent to `model.parameters()`.
-
- Args:
- base_lr: lr for every group by default. Can be omitted to use the one in optimizer.
- weight_decay: weight decay for every group by default. Can be omitted to use the one
- in optimizer.
- weight_decay_norm: override weight decay for params in normalization layers
- bias_lr_factor: multiplier of lr for bias parameters.
- weight_decay_bias: override weight decay for bias parameters.
- lr_factor_func: function to calculate lr decay rate by mapping the parameter names to
- corresponding lr decay rate. Note that setting this option requires
- also setting ``base_lr``.
- overrides: if not `None`, provides values for optimizer hyperparameters
- (LR, weight decay) for module parameters with a given name; e.g.
- ``{"embedding": {"lr": 0.01, "weight_decay": 0.1}}`` will set the LR and
- weight decay values for all module parameters named `embedding`.
-
- For common detection models, ``weight_decay_norm`` is the only option
- needed to be set. ``bias_lr_factor,weight_decay_bias`` are legacy settings
- from Detectron1 that are not found useful.
-
- Example:
- ::
- torch.optim.SGD(get_default_optimizer_params(model, weight_decay_norm=0),
- lr=0.01, weight_decay=1e-4, momentum=0.9)
- """
- if overrides is None:
- overrides = {}
- defaults = {}
- if base_lr is not None:
- defaults["lr"] = base_lr
- if weight_decay is not None:
- defaults["weight_decay"] = weight_decay
- bias_overrides = {}
- if bias_lr_factor is not None and bias_lr_factor != 1.0:
- # NOTE: unlike Detectron v1, we now by default make bias hyperparameters
- # exactly the same as regular weights.
- if base_lr is None:
- raise ValueError("bias_lr_factor requires base_lr")
- bias_overrides["lr"] = base_lr * bias_lr_factor
- if weight_decay_bias is not None:
- bias_overrides["weight_decay"] = weight_decay_bias
- if len(bias_overrides):
- if "bias" in overrides:
- raise ValueError("Conflicting overrides for 'bias'")
- overrides["bias"] = bias_overrides
- if lr_factor_func is not None:
- if base_lr is None:
- raise ValueError("lr_factor_func requires base_lr")
- norm_module_types = (
- torch.nn.BatchNorm1d,
- torch.nn.BatchNorm2d,
- torch.nn.BatchNorm3d,
- torch.nn.SyncBatchNorm,
- # NaiveSyncBatchNorm inherits from BatchNorm2d
- torch.nn.GroupNorm,
- torch.nn.InstanceNorm1d,
- torch.nn.InstanceNorm2d,
- torch.nn.InstanceNorm3d,
- torch.nn.LayerNorm,
- torch.nn.LocalResponseNorm,
- )
- params: List[Dict[str, Any]] = []
- memo: Set[torch.nn.parameter.Parameter] = set()
- for module_name, module in model.named_modules():
- for module_param_name, value in module.named_parameters(recurse=False):
- if not value.requires_grad:
- continue
- # Avoid duplicating parameters
- if value in memo:
- continue
- memo.add(value)
-
- hyperparams = copy.copy(defaults)
- if isinstance(module, norm_module_types) and weight_decay_norm is not None:
- hyperparams["weight_decay"] = weight_decay_norm
- if lr_factor_func is not None:
- hyperparams["lr"] *= lr_factor_func(f"{module_name}.{module_param_name}")
-
- hyperparams.update(overrides.get(module_param_name, {}))
- params.append({"params": [value], **hyperparams})
- return reduce_param_groups(params)
-
-
-def _expand_param_groups(params: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
- # Transform parameter groups into per-parameter structure.
- # Later items in `params` can overwrite parameters set in previous items.
- ret = defaultdict(dict)
- for item in params:
- assert "params" in item
- cur_params = {x: y for x, y in item.items() if x != "params"}
- for param in item["params"]:
- ret[param].update({"params": [param], **cur_params})
- return list(ret.values())
-
-
-def reduce_param_groups(params: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
- # Reorganize the parameter groups and merge duplicated groups.
- # The number of parameter groups needs to be as small as possible in order
- # to efficiently use the PyTorch multi-tensor optimizer. Therefore instead
- # of using a parameter_group per single parameter, we reorganize the
- # parameter groups and merge duplicated groups. This approach speeds
- # up multi-tensor optimizer significantly.
- params = _expand_param_groups(params)
- groups = defaultdict(list) # re-group all parameter groups by their hyperparams
- for item in params:
- cur_params = tuple((x, y) for x, y in item.items() if x != "params")
- groups[cur_params].extend(item["params"])
- ret = []
- for param_keys, param_values in groups.items():
- cur = {kv[0]: kv[1] for kv in param_keys}
- cur["params"] = param_values
- ret.append(cur)
- return ret
-
-
-def build_lr_scheduler(cfg: CfgNode, optimizer: torch.optim.Optimizer) -> LRScheduler:
- """
- Build a LR scheduler from config.
- """
- name = cfg.SOLVER.LR_SCHEDULER_NAME
-
- if name == "WarmupMultiStepLR":
- steps = [x for x in cfg.SOLVER.STEPS if x <= cfg.SOLVER.MAX_ITER]
- if len(steps) != len(cfg.SOLVER.STEPS):
- logger = logging.getLogger(__name__)
- logger.warning(
- "SOLVER.STEPS contains values larger than SOLVER.MAX_ITER. "
- "These values will be ignored."
- )
- sched = MultiStepParamScheduler(
- values=[cfg.SOLVER.GAMMA**k for k in range(len(steps) + 1)],
- milestones=steps,
- num_updates=cfg.SOLVER.MAX_ITER,
- )
- elif name == "WarmupCosineLR":
- end_value = cfg.SOLVER.BASE_LR_END / cfg.SOLVER.BASE_LR
- assert end_value >= 0.0 and end_value <= 1.0, end_value
- sched = CosineParamScheduler(1, end_value)
- elif name == "WarmupStepWithFixedGammaLR":
- sched = StepWithFixedGammaParamScheduler(
- base_value=1.0,
- gamma=cfg.SOLVER.GAMMA,
- num_decays=cfg.SOLVER.NUM_DECAYS,
- num_updates=cfg.SOLVER.MAX_ITER,
- )
- else:
- raise ValueError("Unknown LR scheduler: {}".format(name))
-
- sched = WarmupParamScheduler(
- sched,
- cfg.SOLVER.WARMUP_FACTOR,
- min(cfg.SOLVER.WARMUP_ITERS / cfg.SOLVER.MAX_ITER, 1.0),
- cfg.SOLVER.WARMUP_METHOD,
- cfg.SOLVER.RESCALE_INTERVAL,
- )
- return LRMultiplier(optimizer, multiplier=sched, max_iter=cfg.SOLVER.MAX_ITER)
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/models/decode_heads/psa_head.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/models/decode_heads/psa_head.py
deleted file mode 100644
index 480dbd1a081262e45bf87e32c4a339ac8f8b4ffb..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/models/decode_heads/psa_head.py
+++ /dev/null
@@ -1,196 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from annotator.uniformer.mmcv.cnn import ConvModule
-
-from annotator.uniformer.mmseg.ops import resize
-from ..builder import HEADS
-from .decode_head import BaseDecodeHead
-
-try:
- from annotator.uniformer.mmcv.ops import PSAMask
-except ModuleNotFoundError:
- PSAMask = None
-
-
-@HEADS.register_module()
-class PSAHead(BaseDecodeHead):
- """Point-wise Spatial Attention Network for Scene Parsing.
-
- This head is the implementation of `PSANet
- Welcome to your static Space!
-You can modify this app directly by editing index.html in the Files and versions tab.
-- Also don't forget to check the - Spaces documentation. -
-
- 
-
- 
-
- 
-
- 
-
- 
-
- 
-
- 
-
- 
-
- 
-
- 
-
- 
-
- 
-
- 
-
- 
-
- 
-
- 
-
Andrew Lim Bloom Test
")
- gr.Markdown("""Generate your story with a sentence or ask a question:
- 
""")
- gr.Markdown("""-
{json.dumps(y)}
" - return super().postprocess(y) - - def get_block_name(self) -> str: - return "html" - - -def create_msg(name, data): - return {"name": name, "data": data} - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--share", action="store_true", default=False, help="share gradio app") - parser.add_argument("--port", type=int, default=7860, help="gradio server port") - opt = parser.parse_args() - - print('Loading model...') - - SEQ_LEN = 2048 - - # instantiate the model - - model = TransformerWrapper( - num_tokens = 3344, - max_seq_len = SEQ_LEN, - attn_layers = Decoder(dim = 1024, depth = 60, heads = 8) - ) - - model = AutoregressiveWrapper(model) - - model = torch.nn.DataParallel(model) - - # model.cuda() - print('=' * 70) - - print('Loading model checkpoint...') - - model.load_state_dict( - torch.load('Euterpe_X_Large_Trained_Model_100000_steps_0.477_loss_0.8533_acc.pth' - )) - - print('=' * 70) - - model.eval() - - print('Done!') - - load_javascript() - app = gr.Blocks() - with app: - gr.Markdown("Euterpe X
") - - gr.Markdown("\n\n" - "[SOTA] [PyTorch 2.0] [638M] [85.33% acc] Full-attention multi-instrumental music transformer for supervised music generation, optimized for speed, efficiency, and performance\n\n" - "Check out [Euterpe X](https://github.com/asigalov61/Euterpe-X) on GitHub!\n\n" - "Special thanks go out to [SkyTNT](https://github.com/SkyTNT/midi-model) for fantastic FluidSynth Synthesizer and MIDI Visualizer code\n\n" - "[Open In Colab]" - "(https://colab.research.google.com/github/asigalov61/Euterpe-X/blob/main/Euterpe_X.ipynb)" - " for faster execution and endless generation" - ) - - js_msg = JSMsgReceiver() - input_drums = gr.Checkbox(label="Add Drums", value=False, info="Add drums to the composition") - input_instrument = gr.Radio( - ["Piano", "Guitar", "Bass", "Violin", "Cello", "Harp", "Trumpet", "Sax", "Flute", "Choir", "Organ"], - value="Piano", label="Lead Instrument Controls", info="Desired lead instrument") - input_num_tokens = gr.Slider(16, 256, value=128, label="Number of Tokens", info="Number of tokens to generate") - run_btn = gr.Button("generate", variant="primary") - interrupt_btn = gr.Button("interrupt") - - output_midi_seq = gr.Variable() - output_midi_visualizer = gr.HTML(elem_id="midi_visualizer_container") - output_audio = gr.Audio(label="output audio", format="mp3", elem_id="midi_audio") - output_midi = gr.File(label="output midi", file_types=[".mid"]) - run_event = run_btn.click(GenerateMIDI, [input_num_tokens, input_drums, input_instrument], - [output_midi_seq, output_midi, output_audio, js_msg]) - interrupt_btn.click(cancel_run, output_midi_seq, [output_midi, output_audio, js_msg], - cancels=run_event, queue=False) - app.queue(concurrency_count=1).launch(server_port=opt.port, share=opt.share, inbrowser=True) \ No newline at end of file diff --git a/spaces/auto-academic/auto-draft/latex_templates/ICLR2022/experiments.tex b/spaces/auto-academic/auto-draft/latex_templates/ICLR2022/experiments.tex deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/awacke1/AI-ChatGPT-Provider/README.md b/spaces/awacke1/AI-ChatGPT-Provider/README.md deleted file mode 100644 index 8ba25d2a1f6cfcf41cbb622670410417c7ce9a9e..0000000000000000000000000000000000000000 --- a/spaces/awacke1/AI-ChatGPT-Provider/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: AI ChatGPT Provider -emoji: 🏃 -colorFrom: yellow -colorTo: yellow -sdk: streamlit -sdk_version: 1.19.0 -app_file: app.py -pinned: false -license: mit ---- - -# This space tests everything known in datasets for ChatGPT regarding specific web sites - diff --git a/spaces/awacke1/ASRGenerateStoryandVideo/app.py b/spaces/awacke1/ASRGenerateStoryandVideo/app.py deleted file mode 100644 index 802d78aff8e7fa6fc5ed4494c961c6cf4b75cebb..0000000000000000000000000000000000000000 --- a/spaces/awacke1/ASRGenerateStoryandVideo/app.py +++ /dev/null @@ -1,174 +0,0 @@ -import gradio as gr -from transformers import pipeline -import io, base64 -from PIL import Image -import numpy as np -import tensorflow as tf -import mediapy -import os -import sys -from huggingface_hub import snapshot_download - -import streamlit as st -import firebase_admin -from firebase_admin import credentials -from firebase_admin import firestore -import datetime -import tempfile -from typing import Optional -import numpy as np -from TTS.utils.manage import ModelManager -from TTS.utils.synthesizer import Synthesizer - - -# firestore singleton is a cached multiuser instance to persist shared crowdsource memory -@st.experimental_singleton -def get_db_firestore(): - cred = credentials.Certificate('test.json') - firebase_admin.initialize_app(cred, {'projectId': u'clinical-nlp-b9117',}) - db = firestore.client() - return db - -#start firestore singleton -db = get_db_firestore() - -# create ASR ML pipeline -asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h") - -# create Text Classification pipeline -classifier = pipeline("text-classification") - -# create text generator pipeline -story_gen = pipeline("text-generation", "pranavpsv/gpt2-genre-story-generator") - -# transcribe function -def transcribe(audio): - text = asr(audio)["text"] - return text - -def speech_to_text(speech): - text = asr(speech)["text"] - return text - -def text_to_sentiment(text): - sentiment = classifier(text)[0]["label"] - return sentiment - -def upsert(text): - date_time =str(datetime.datetime.today()) - doc_ref = db.collection('Text2SpeechSentimentSave').document(date_time) - doc_ref.set({u'firefield': 'Recognize Speech', u'first': 'https://huggingface.co/spaces/awacke1/Text2SpeechSentimentSave', u'last': text, u'born': date_time,}) - saved = select('Text2SpeechSentimentSave', date_time) - # check it here: https://console.firebase.google.com/u/0/project/clinical-nlp-b9117/firestore/data/~2FStreamlitSpaces - return saved - -def select(collection, document): - doc_ref = db.collection(collection).document(document) - doc = doc_ref.get() - docid = ("The id is: ", doc.id) - contents = ("The contents are: ", doc.to_dict()) - return contents - -def selectall(text): - docs = db.collection('Text2SpeechSentimentSave').stream() - doclist='' - for doc in docs: - r=(f'{doc.id} => {doc.to_dict()}') - doclist += r - return doclist - -# story gen -def generate_story(choice, input_text): - query = "-
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-
-
- diff --git a/spaces/birsardar/stable-diffusion-mat-outpainting-primer/datasets/dataset_512_val.py b/spaces/birsardar/stable-diffusion-mat-outpainting-primer/datasets/dataset_512_val.py deleted file mode 100644 index ef802863464db76ae79f7e06bdb9722b3525f0cf..0000000000000000000000000000000000000000 --- a/spaces/birsardar/stable-diffusion-mat-outpainting-primer/datasets/dataset_512_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_512 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_512_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 512x512 - res = 512 - 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/bobsingh149/chestxray-classification-streamlit-demo/app.py b/spaces/bobsingh149/chestxray-classification-streamlit-demo/app.py deleted file mode 100644 index 8a78dc16e08ca0b02832f6f472decdff1e747023..0000000000000000000000000000000000000000 --- a/spaces/bobsingh149/chestxray-classification-streamlit-demo/app.py +++ /dev/null @@ -1,162 +0,0 @@ -"""Streamlit web app for radiological condition prediction from chest X-ray images""" - -import os -os.environ['CUDA_VISIBLE_DEVICES'] = '-1' -import matplotlib.pyplot as plt -import tensorflow as tf -import keras.backend.tensorflow_backend as tb -tb._SYMBOLIC_SCOPE.value = True -import numpy as np -import time -import cv2 -import streamlit as st -import pandas as pd -from tensorflow.keras.models import load_model -from keras.utils.data_utils import get_file -from tensorflow import keras -from PIL import Image -from explain import get_gradcam - -st.set_option("deprecation.showfileUploaderEncoding", False) - -IMAGE_SIZE = 224 - -classes = [ - 'Normal', # No Finding - 'Enlarged- \nCardiomediastinum', - 'Cardiomegaly', - 'Lung Opacity', - 'Lung Lesion', - 'Edema', - 'Consolidation', - 'Pneumonia', - 'Atelectasis', - 'Pneumothorax', - 'Pleural Effusion', - 'Pleural Other', - 'Fracture', - 'Support Devices' -] - - -@st.cache(allow_output_mutation=True) -def cached_model(): - URL = "https://github.com/hasibzunair/cxr-predictor/releases/latest/download/CheXpert_DenseNet121_res224.h5" - weights_path = get_file( - "CheXpert_DenseNet121_res224.h5", - URL) - model = load_model(weights_path, compile = False) - return model - - -def preprocess_image(uploaded_file): - # Load image - img_array = np.array(Image.open(uploaded_file)) - # Normalize to [0,1] - img_array = img_array.astype('float32') - img_array /= 255 - # Check that images are 2D arrays - if len(img_array.shape) > 2: - img_array = img_array[:, :, 0] - # Convert to 3-channel - img_array = np.stack((img_array, img_array, img_array), axis=-1) - # Convert to array - img_array = cv2.resize(img_array, (IMAGE_SIZE, IMAGE_SIZE)) - return img_array - - -def make_prediction(file): - # Preprocess input image - image = preprocess_image(file) - # Add batch axis - image = np.expand_dims(image, 0) - # Predict - predictions = model.predict(image) - return predictions - - -# Get model -model = cached_model() - -if __name__ == '__main__': - - logo = np.array(Image.open("media/logo_rs.png")) - st.image(logo, use_column_width=True) - - st.write(""" - # AI Assisted Radiology Tool - :red_circle: NOT FOR MEDICAL USE! - - This is a prototype application which demonstrates how artifical intelligence based systems can identify - medical conditions from images. Using this tool, medical professionals can process an image to - confirm or aid in their diagnosis which may serve as a second opinion. - - The tool predicts the presence of 14 different radiological conditions from a given chest X-ray image. This is built using data from a large public - [database](https://stanfordmlgroup.github.io/competitions/chexpert/). - - Questions? Email me at `hasibzunair@gmail.com`. - - If you continue, you assume all liability when using the system. - Please upload a posterior to anterior (PA) view chest X-ray image file (PNG, JPG, JPEG) - to predict the presence of the radiological conditions. Here's an example. - """) - - example_image = np.array(Image.open("media/example.jpg")) - st.image(example_image, caption="An example input.", width=100) - - uploaded_file = st.file_uploader("Upload file either by clicking 'Browze Files' or drag and drop the image.", type=None) - - if uploaded_file is not None: - # Uploaded image - original_image = np.array(Image.open(uploaded_file)) - - st.image(original_image, caption="Input chest X-ray image", use_column_width=True) - st.write("") - st.write("Analyzing the input image. Please wait...") - - start_time = time.time() - - # Preprocess input image - image = preprocess_image(uploaded_file) - image = np.expand_dims(image, 0) - - # Predict - predictions = make_prediction(uploaded_file) - - st.write("Took {} seconds to run.".format( - round(time.time() - start_time, 3))) - - # Convert probabilty scores to percent for easy interpretation - predictions_percent = [x*100 for x in predictions[0]] - - df = pd.DataFrame({'classes' : classes, 'predictions' : predictions_percent}) - df = df.sort_values('predictions') - - # Top predicted class - top_predicted_class = list(df['classes'])[-1] - - fig, ax = plt.subplots() - ax.grid(False) - ax.set_xlim(0, 1) - ax.set_xticks([x for x in range(0,110, 10)]) - #ax.set_xticklabels(['Low','Average','High']) - ax.tick_params(axis='y', labelcolor='r', which='major', labelsize=8) - ax.barh(df['classes'], df['predictions'], color='green') - ax.set_xlabel('Confidence (in percent)', fontsize=15) - ax.set_ylabel('Radiological conditions', fontsize=15) - st.pyplot(fig) - - st.write(""" - For more information about the top most predicted radiological finding, - you can click on 'Get Heatmap' button which will highlight the most influential features - in the image affecting the prediction. - """) - if st.button('Get Heatmap'): - st.write('Generating heatmap for regions predictive of {}. Indicated by red.'.format(top_predicted_class)) - heatmap = get_gradcam(uploaded_file, model, "conv5_block16_2_conv", predictions) - orig_heatmap = cv2.resize(heatmap, (original_image.shape[1], original_image.shape[0])) - # Convert to 3 channel if not - if len(original_image.shape) > 2: - original_image = original_image[:, :, 0] - original_image = np.stack((original_image, original_image, original_image), axis=-1) - st.image(np.concatenate((original_image,orig_heatmap),axis=1), caption="Input image + Heatmap ", use_column_width=True) \ No newline at end of file diff --git a/spaces/bradarrML/stablediffusion-infinity/js/toolbar.js b/spaces/bradarrML/stablediffusion-infinity/js/toolbar.js deleted file mode 100644 index 6c721bc84d3a41a0761ead58e6034ba4dfd4a6ef..0000000000000000000000000000000000000000 --- a/spaces/bradarrML/stablediffusion-infinity/js/toolbar.js +++ /dev/null @@ -1,581 +0,0 @@ -// import { w2ui,w2toolbar,w2field,query,w2alert, w2utils,w2confirm} from "https://rawgit.com/vitmalina/w2ui/master/dist/w2ui.es6.min.js" -// import { w2ui,w2toolbar,w2field,query,w2alert, w2utils,w2confirm} from "https://cdn.jsdelivr.net/gh/vitmalina/w2ui@master/dist/w2ui.es6.min.js" - -// https://stackoverflow.com/questions/36280818/how-to-convert-file-to-base64-in-javascript -function getBase64(file) { - var reader = new FileReader(); - reader.readAsDataURL(file); - reader.onload = function () { - add_image(reader.result); - // console.log(reader.result); - }; - reader.onerror = function (error) { - console.log("Error: ", error); - }; -} - -function getText(file) { - var reader = new FileReader(); - reader.readAsText(file); - reader.onload = function () { - window.postMessage(["load",reader.result],"*") - // console.log(reader.result); - }; - reader.onerror = function (error) { - console.log("Error: ", error); - }; -} - -document.querySelector("#upload_file").addEventListener("change", (event)=>{ - console.log(event); - let file = document.querySelector("#upload_file").files[0]; - getBase64(file); -}) - -document.querySelector("#upload_state").addEventListener("change", (event)=>{ - console.log(event); - let file = document.querySelector("#upload_state").files[0]; - getText(file); -}) - -open_setting = function() { - if (!w2ui.foo) { - new w2form({ - name: "foo", - style: "border: 0px; background-color: transparent;", - fields: [{ - field: "canvas_width", - type: "int", - required: true, - html: { - label: "Canvas Width" - } - }, - { - field: "canvas_height", - type: "int", - required: true, - html: { - label: "Canvas Height" - } - }, - ], - record: { - canvas_width: 1200, - canvas_height: 600, - }, - actions: { - Save() { - this.validate(); - let record = this.getCleanRecord(); - window.postMessage(["resize",record.canvas_width,record.canvas_height],"*"); - w2popup.close(); - }, - custom: { - text: "Cancel", - style: "text-transform: uppercase", - onClick(event) { - w2popup.close(); - } - } - } - }); - } - w2popup.open({ - title: "Form in a Popup", - body: "", - style: "padding: 15px 0px 0px 0px", - width: 500, - height: 280, - showMax: true, - async onToggle(event) { - await event.complete - w2ui.foo.resize(); - } - }) - .then((event) => { - w2ui.foo.render("#form") - }); -} - -var button_lst=["clear", "load", "save", "export", "upload", "selection", "canvas", "eraser", "outpaint", "accept", "cancel", "retry", "prev", "current", "next", "eraser_size_btn", "eraser_size", "resize_selection", "scale", "zoom_in", "zoom_out", "help"]; -var upload_button_lst=['clear', 'load', 'save', "upload", 'export', 'outpaint', 'resize_selection', 'help', "setting"]; -var resize_button_lst=['clear', 'load', 'save', "upload", 'export', "selection", "canvas", "eraser", 'outpaint', 'resize_selection',"zoom_in", "zoom_out", 'help', "setting"]; -var outpaint_button_lst=['clear', 'load', 'save', "canvas", "eraser", "upload", 'export', 'resize_selection', "zoom_in", "zoom_out",'help', "setting"]; -var outpaint_result_lst=["accept", "cancel", "retry", "prev", "current", "next"]; -var outpaint_result_func_lst=["accept", "retry", "prev", "current", "next"]; - -function check_button(id,text="",checked=true,tooltip="") -{ - return { type: "check", id: id, text: text, icon: checked?"fa-solid fa-square-check":"fa-regular fa-square", checked: checked, tooltip: tooltip }; -} - -var toolbar=new w2toolbar({ - box: "#toolbar", - name: "toolbar", - tooltip: "top", - items: [ - { type: "button", id: "clear", text: "Reset", tooltip: "Reset Canvas", icon: "fa-solid fa-rectangle-xmark" }, - { type: "break" }, - { type: "button", id: "load", tooltip: "Load Canvas", icon: "fa-solid fa-file-import" }, - { type: "button", id: "save", tooltip: "Save Canvas", icon: "fa-solid fa-file-export" }, - { type: "button", id: "export", tooltip: "Export Image", icon: "fa-solid fa-floppy-disk" }, - { type: "break" }, - { type: "button", id: "upload", text: "Upload Image", icon: "fa-solid fa-upload" }, - { type: "break" }, - { type: "radio", id: "selection", group: "1", tooltip: "Selection", icon: "fa-solid fa-arrows-up-down-left-right", checked: true }, - { type: "radio", id: "canvas", group: "1", tooltip: "Canvas", icon: "fa-solid fa-image" }, - { type: "radio", id: "eraser", group: "1", tooltip: "Eraser", icon: "fa-solid fa-eraser" }, - { type: "break" }, - { type: "button", id: "outpaint", text: "Outpaint", tooltip: "Run Outpainting", icon: "fa-solid fa-brush" }, - { type: "break" }, - { type: "button", id: "accept", text: "Accept", tooltip: "Accept current result", icon: "fa-solid fa-check", hidden: true, disable:true,}, - { type: "button", id: "cancel", text: "Cancel", tooltip: "Cancel current outpainting/error", icon: "fa-solid fa-ban", hidden: true}, - { type: "button", id: "retry", text: "Retry", tooltip: "Retry", icon: "fa-solid fa-rotate", hidden: true, disable:true,}, - { type: "button", id: "prev", tooltip: "Prev Result", icon: "fa-solid fa-caret-left", hidden: true, disable:true,}, - { type: "html", id: "current", hidden: true, disable:true, - async onRefresh(event) { - await event.complete - let fragment = query.html(` -
-
`)
- query(this.box).find("#tb_toolbar_item_current").append(fragment)
- }
- },
- { type: "button", id: "next", tooltip: "Next Result", icon: "fa-solid fa-caret-right", hidden: true,disable:true,},
- { type: "button", id: "add_image", text: "Add Image", icon: "fa-solid fa-file-circle-plus", hidden: true,disable:true,},
- { type: "button", id: "delete_image", text: "Delete Image", icon: "fa-solid fa-trash-can", hidden: true,disable:true,},
- { type: "button", id: "confirm", text: "Confirm", icon: "fa-solid fa-check", hidden: true,disable:true,},
- { type: "button", id: "cancel_overlay", text: "Cancel", icon: "fa-solid fa-ban", hidden: true,disable:true,},
- { type: "break" },
- { type: "spacer" },
- { type: "break" },
- { type: "button", id: "eraser_size_btn", tooltip: "Eraser Size", text:"Size", icon: "fa-solid fa-eraser", hidden: true, count: 32},
- { type: "html", id: "eraser_size", hidden: true,
- async onRefresh(event) {
- await event.complete
- // let fragment = query.html(`
- //
- // `)
- let fragment = query.html(`
-
- `)
- fragment.filter("input").on("change", event => {
- this.eraser_size = event.target.value;
- window.overlay.freeDrawingBrush.width=this.eraser_size;
- this.setCount("eraser_size_btn", event.target.value);
- window.postMessage(["eraser_size", event.target.value],"*")
- this.refresh();
- })
- query(this.box).find("#tb_toolbar_item_eraser_size").append(fragment)
- }
- },
- // { type: "button", id: "resize_eraser", tooltip: "Resize Eraser", icon: "fa-solid fa-sliders" },
- { type: "button", id: "resize_selection", text: "Resize Selection", tooltip: "Resize Selection", icon: "fa-solid fa-expand" },
- { type: "break" },
- { type: "html", id: "scale",
- async onRefresh(event) {
- await event.complete
- let fragment = query.html(`
-
- ${this.sel_value ?? "1/1"}
-
-
`)
- query(this.box).find("#tb_toolbar_item_scale").append(fragment)
- }
- },
- { type: "button", id: "zoom_in", tooltip: "Zoom In", icon: "fa-solid fa-magnifying-glass-plus" },
- { type: "button", id: "zoom_out", tooltip: "Zoom Out", icon: "fa-solid fa-magnifying-glass-minus" },
- { type: "break" },
- { type: "button", id: "help", tooltip: "Help", icon: "fa-solid fa-circle-info" },
- { type: "new-line"},
- { type: "button", id: "setting", text: "Canvas Setting", tooltip: "Resize Canvas Here", icon: "fa-solid fa-sliders" },
- { type: "break" },
- check_button("enable_img2img","Enable Img2Img",false),
- // check_button("use_correction","Photometric Correction",false),
- check_button("resize_check","Resize Small Input",true),
- check_button("enable_safety","Enable Safety Checker",true),
- check_button("square_selection","Square Selection Only",false),
- {type: "break"},
- check_button("use_seed","Use Seed:",false),
- { type: "html", id: "seed_val",
- async onRefresh(event) {
- await event.complete
- let fragment = query.html(`
- `)
- fragment.filter("input").on("change", event => {
- this.config_obj.seed_val = event.target.value;
- parent.config_obj=this.config_obj;
- this.refresh();
- })
- query(this.box).find("#tb_toolbar_item_seed_val").append(fragment)
- }
- },
- { type: "button", id: "random_seed", tooltip: "Set a random seed", icon: "fa-solid fa-dice" },
- ],
- onClick(event) {
- switch(event.target){
- case "setting":
- open_setting();
- break;
- case "upload":
- this.upload_mode=true
- document.querySelector("#overlay_container").style.pointerEvents="auto";
- this.click("canvas");
- this.click("selection");
- this.show("confirm","cancel_overlay","add_image","delete_image");
- this.enable("confirm","cancel_overlay","add_image","delete_image");
- this.disable(...upload_button_lst);
- query("#upload_file").click();
- if(this.upload_tip)
- {
- this.upload_tip=false;
- w2utils.notify("Note that only visible images will be added to canvas",{timeout:10000,where:query("#container")})
- }
- break;
- case "resize_selection":
- this.resize_mode=true;
- this.disable(...resize_button_lst);
- this.enable("confirm","cancel_overlay");
- this.show("confirm","cancel_overlay");
- window.postMessage(["resize_selection",""],"*");
- document.querySelector("#overlay_container").style.pointerEvents="auto";
- break;
- case "confirm":
- if(this.upload_mode)
- {
- export_image();
- }
- else
- {
- let sel_box=this.selection_box;
- window.postMessage(["resize_selection",sel_box.x,sel_box.y,sel_box.width,sel_box.height],"*");
- }
- case "cancel_overlay":
- end_overlay();
- this.hide("confirm","cancel_overlay","add_image","delete_image");
- if(this.upload_mode){
- this.enable(...upload_button_lst);
- }
- else
- {
- this.enable(...resize_button_lst);
- window.postMessage(["resize_selection","",""],"*");
- if(event.target=="cancel_overlay")
- {
- this.selection_box=this.selection_box_bak;
- }
- }
- if(this.selection_box)
- {
- this.setCount("resize_selection",`${Math.floor(this.selection_box.width/8)*8}x${Math.floor(this.selection_box.height/8)*8}`);
- }
- this.disable("confirm","cancel_overlay","add_image","delete_image");
- this.upload_mode=false;
- this.resize_mode=false;
- this.click("selection");
- break;
- case "add_image":
- query("#upload_file").click();
- break;
- case "delete_image":
- let active_obj = window.overlay.getActiveObject();
- if(active_obj)
- {
- window.overlay.remove(active_obj);
- window.overlay.renderAll();
- }
- else
- {
- w2utils.notify("You need to select an image first",{error:true,timeout:2000,where:query("#container")})
- }
- break;
- case "load":
- query("#upload_state").click();
- this.selection_box=null;
- this.setCount("resize_selection","");
- break;
- case "next":
- case "prev":
- window.postMessage(["outpaint", "", event.target], "*");
- break;
- case "outpaint":
- this.click("selection");
- this.disable(...outpaint_button_lst);
- this.show(...outpaint_result_lst);
- if(this.outpaint_tip)
- {
- this.outpaint_tip=false;
- w2utils.notify("The canvas stays locked until you accept/cancel current outpainting",{timeout:10000,where:query("#container")})
- }
- document.querySelector("#container").style.pointerEvents="none";
- case "retry":
- this.disable(...outpaint_result_func_lst);
- window.postMessage(["transfer",""],"*")
- break;
- case "accept":
- case "cancel":
- this.hide(...outpaint_result_lst);
- this.disable(...outpaint_result_func_lst);
- this.enable(...outpaint_button_lst);
- document.querySelector("#container").style.pointerEvents="auto";
- window.postMessage(["click", event.target],"*");
- let app=parent.document.querySelector("gradio-app");
- app=app.shadowRoot??app;
- app.querySelector("#cancel").click();
- break;
- case "eraser":
- case "selection":
- case "canvas":
- if(event.target=="eraser")
- {
- this.show("eraser_size","eraser_size_btn");
- window.overlay.freeDrawingBrush.width=this.eraser_size;
- window.overlay.isDrawingMode = true;
- }
- else
- {
- this.hide("eraser_size","eraser_size_btn");
- window.overlay.isDrawingMode = false;
- }
- if(this.upload_mode)
- {
- if(event.target=="canvas")
- {
- window.postMessage(["mode", event.target],"*")
- document.querySelector("#overlay_container").style.pointerEvents="none";
- document.querySelector("#overlay_container").style.opacity = 0.5;
- }
- else
- {
- document.querySelector("#overlay_container").style.pointerEvents="auto";
- document.querySelector("#overlay_container").style.opacity = 1.0;
- }
- }
- else
- {
- window.postMessage(["mode", event.target],"*")
- }
- break;
- case "help":
- w2popup.open({
- title: "Document",
- body: "Usage: https://github.com/lkwq007/stablediffusion-infinity/blob/master/docs/usage.md"
- })
- break;
- case "clear":
- w2confirm("Reset canvas?").yes(() => {
- window.postMessage(["click", event.target],"*");
- }).no(() => {})
- break;
- case "random_seed":
- this.config_obj.seed_val=Math.floor(Math.random() * 3000000000);
- parent.config_obj=this.config_obj;
- this.refresh();
- break;
- case "enable_img2img":
- case "use_correction":
- case "resize_check":
- case "enable_safety":
- case "use_seed":
- case "square_selection":
- let target=this.get(event.target);
- target.icon=target.checked?"fa-regular fa-square":"fa-solid fa-square-check";
- this.config_obj[event.target]=!target.checked;
- parent.config_obj=this.config_obj;
- this.refresh();
- break;
- case "save":
- case "export":
- ask_filename(event.target);
- break;
- default:
- // clear, save, export, outpaint, retry
- // break, save, export, accept, retry, outpaint
- window.postMessage(["click", event.target],"*")
- }
- console.log("Target: "+ event.target, event)
- }
-})
-window.w2ui=w2ui;
-w2ui.toolbar.config_obj={
- resize_check: true,
- enable_safety: true,
- use_correction: false,
- enable_img2img: false,
- use_seed: false,
- seed_val: 0,
- square_selection: false,
-};
-w2ui.toolbar.outpaint_tip=true;
-w2ui.toolbar.upload_tip=true;
-window.update_count=function(cur,total){
- w2ui.toolbar.sel_value=`${cur}/${total}`;
- w2ui.toolbar.refresh();
-}
-window.update_eraser=function(val,max_val){
- w2ui.toolbar.eraser_size=`${val}`;
- w2ui.toolbar.eraser_max=`${max_val}`;
- w2ui.toolbar.setCount("eraser_size_btn", `${val}`);
- w2ui.toolbar.refresh();
-}
-window.update_scale=function(val){
- w2ui.toolbar.scale_value=`${val}`;
- w2ui.toolbar.refresh();
-}
-window.enable_result_lst=function(){
- w2ui.toolbar.enable(...outpaint_result_lst);
-}
-function onObjectScaled(e)
-{
- let object = e.target;
- if(object.isType("rect"))
- {
- let width=object.getScaledWidth();
- let height=object.getScaledHeight();
- object.scale(1);
- width=Math.max(Math.min(width,window.overlay.width-object.left),256);
- height=Math.max(Math.min(height,window.overlay.height-object.top),256);
- let l=Math.max(Math.min(object.left,window.overlay.width-width-object.strokeWidth),0);
- let t=Math.max(Math.min(object.top,window.overlay.height-height-object.strokeWidth),0);
- if(window.w2ui.toolbar.config_obj.square_selection)
- {
- let max_val = Math.min(Math.max(width,height),window.overlay.width,window.overlay.height);
- width=max_val;
- height=max_val;
- }
- object.set({ width: width, height: height, left:l,top:t})
- window.w2ui.toolbar.selection_box={width: width, height: height, x:object.left, y:object.top};
- window.w2ui.toolbar.setCount("resize_selection",`${Math.floor(width/8)*8}x${Math.floor(height/8)*8}`);
- window.w2ui.toolbar.refresh();
- }
-}
-function onObjectMoved(e)
-{
- let object = e.target;
- if(object.isType("rect"))
- {
- let l=Math.max(Math.min(object.left,window.overlay.width-object.width-object.strokeWidth),0);
- let t=Math.max(Math.min(object.top,window.overlay.height-object.height-object.strokeWidth),0);
- object.set({left:l,top:t});
- window.w2ui.toolbar.selection_box={width: object.width, height: object.height, x:object.left, y:object.top};
- }
-}
-window.setup_overlay=function(width,height)
-{
- if(window.overlay)
- {
- window.overlay.setDimensions({width:width,height:height});
- let app=parent.document.querySelector("gradio-app");
- app=app.shadowRoot??app;
- app.querySelector("#sdinfframe").style.height=80+Number(height)+"px";
- document.querySelector("#container").style.height= height+"px";
- document.querySelector("#container").style.width = width+"px";
- }
- else
- {
- canvas=new fabric.Canvas("overlay_canvas");
- canvas.setDimensions({width:width,height:height});
- let app=parent.document.querySelector("gradio-app");
- app=app.shadowRoot??app;
- app.querySelector("#sdinfframe").style.height=80+Number(height)+"px";
- canvas.freeDrawingBrush = new fabric.EraserBrush(canvas);
- canvas.on("object:scaling", onObjectScaled);
- canvas.on("object:moving", onObjectMoved);
- window.overlay=canvas;
- }
- document.querySelector("#overlay_container").style.pointerEvents="none";
-}
-window.update_overlay=function(width,height)
-{
- window.overlay.setDimensions({width:width,height:height},{backstoreOnly:true});
- // document.querySelector("#overlay_container").style.pointerEvents="none";
-}
-window.adjust_selection=function(x,y,width,height)
-{
- var rect = new fabric.Rect({
- left: x,
- top: y,
- fill: "rgba(0,0,0,0)",
- strokeWidth: 3,
- stroke: "rgba(0,0,0,0.7)",
- cornerColor: "red",
- cornerStrokeColor: "red",
- borderColor: "rgba(255, 0, 0, 1.0)",
- width: width,
- height: height,
- lockRotation: true,
- });
- rect.setControlsVisibility({ mtr: false });
- window.overlay.add(rect);
- window.overlay.setActiveObject(window.overlay.item(0));
- window.w2ui.toolbar.selection_box={width: width, height: height, x:x, y:y};
- window.w2ui.toolbar.selection_box_bak={width: width, height: height, x:x, y:y};
-}
-function add_image(url)
-{
- fabric.Image.fromURL(url,function(img){
- window.overlay.add(img);
- window.overlay.setActiveObject(img);
- },{left:100,top:100});
-}
-function export_image()
-{
- data=window.overlay.toDataURL();
- document.querySelector("#upload_content").value=data;
- window.postMessage(["upload",""],"*");
- end_overlay();
-}
-function end_overlay()
-{
- window.overlay.clear();
- document.querySelector("#overlay_container").style.opacity = 1.0;
- document.querySelector("#overlay_container").style.pointerEvents="none";
-}
-function ask_filename(target)
-{
- w2prompt({
- label: "Enter filename",
- value: `outpaint_${((new Date(Date.now() -(new Date()).getTimezoneOffset() * 60000))).toISOString().replace("T","_").replace(/[^0-9_]/g, "").substring(0,15)}`,
- })
- .change((event) => {
- console.log("change", event.detail.originalEvent.target.value);
- })
- .ok((event) => {
- console.log("value=", event.detail.value);
- window.postMessage(["click",target,event.detail.value],"*");
- })
- .cancel((event) => {
- console.log("cancel");
- });
-}
-
-document.querySelector("#container").addEventListener("wheel",(e)=>{e.preventDefault()})
-window.setup_shortcut=function(json)
-{
- var config=JSON.parse(json);
- var key_map={};
- Object.keys(config.shortcut).forEach(k=>{
- key_map[config.shortcut[k]]=k;
- })
- document.addEventListener("keydown",(e)=>{
- if(e.target.tagName!="INPUT")
- {
- let key=e.key;
- if(e.ctrlKey)
- {
- key="Ctrl+"+e.key;
- if(key in key_map)
- {
- e.preventDefault();
- }
- }
- if(key in key_map)
- {
- w2ui.toolbar.click(key_map[key]);
- }
- }
- })
-}
\ No newline at end of file
diff --git a/spaces/brainblow/MusiCreator/tests/modules/test_lstm.py b/spaces/brainblow/MusiCreator/tests/modules/test_lstm.py
deleted file mode 100644
index 1248964c8191e19f27661f0974bef9cc967eb015..0000000000000000000000000000000000000000
--- a/spaces/brainblow/MusiCreator/tests/modules/test_lstm.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import random
-import torch
-
-from audiocraft.modules.lstm import StreamableLSTM
-
-
-class TestStreamableLSTM:
-
- def test_lstm(self):
- B, C, T = 4, 2, random.randint(1, 100)
-
- lstm = StreamableLSTM(C, 3, skip=False)
- x = torch.randn(B, C, T)
- y = lstm(x)
-
- print(y.shape)
- assert y.shape == torch.Size([B, C, T])
-
- def test_lstm_skip(self):
- B, C, T = 4, 2, random.randint(1, 100)
-
- lstm = StreamableLSTM(C, 3, skip=True)
- x = torch.randn(B, C, T)
- y = lstm(x)
-
- assert y.shape == torch.Size([B, C, T])
diff --git a/spaces/caffeinum/VToonify/vtoonify/model/stylegan/dataset.py b/spaces/caffeinum/VToonify/vtoonify/model/stylegan/dataset.py
deleted file mode 100644
index 7713ea2f8bc94d202d2dfbe830af3cb96b1e803d..0000000000000000000000000000000000000000
--- a/spaces/caffeinum/VToonify/vtoonify/model/stylegan/dataset.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from io import BytesIO
-
-import lmdb
-from PIL import Image
-from torch.utils.data import Dataset
-
-
-class MultiResolutionDataset(Dataset):
- def __init__(self, path, transform, resolution=256):
- self.env = lmdb.open(
- path,
- max_readers=32,
- readonly=True,
- lock=False,
- readahead=False,
- meminit=False,
- )
-
- if not self.env:
- raise IOError('Cannot open lmdb dataset', path)
-
- with self.env.begin(write=False) as txn:
- self.length = int(txn.get('length'.encode('utf-8')).decode('utf-8'))
-
- self.resolution = resolution
- self.transform = transform
-
- def __len__(self):
- return self.length
-
- def __getitem__(self, index):
- with self.env.begin(write=False) as txn:
- key = f'{self.resolution}-{str(index).zfill(5)}'.encode('utf-8')
- img_bytes = txn.get(key)
-
- buffer = BytesIO(img_bytes)
- img = Image.open(buffer)
- img = self.transform(img)
-
- return img
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/MpegImagePlugin.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/MpegImagePlugin.py
deleted file mode 100644
index d96d3a11c4966e94a53c67f13c3bf8f7987c0c83..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/MpegImagePlugin.py
+++ /dev/null
@@ -1,82 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# MPEG file handling
-#
-# History:
-# 95-09-09 fl Created
-#
-# Copyright (c) Secret Labs AB 1997.
-# Copyright (c) Fredrik Lundh 1995.
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-from . import Image, ImageFile
-from ._binary import i8
-
-#
-# Bitstream parser
-
-
-class BitStream:
- def __init__(self, fp):
- self.fp = fp
- self.bits = 0
- self.bitbuffer = 0
-
- def next(self):
- return i8(self.fp.read(1))
-
- def peek(self, bits):
- while self.bits < bits:
- c = self.next()
- if c < 0:
- self.bits = 0
- continue
- self.bitbuffer = (self.bitbuffer << 8) + c
- self.bits += 8
- return self.bitbuffer >> (self.bits - bits) & (1 << bits) - 1
-
- def skip(self, bits):
- while self.bits < bits:
- self.bitbuffer = (self.bitbuffer << 8) + i8(self.fp.read(1))
- self.bits += 8
- self.bits = self.bits - bits
-
- def read(self, bits):
- v = self.peek(bits)
- self.bits = self.bits - bits
- return v
-
-
-##
-# Image plugin for MPEG streams. This plugin can identify a stream,
-# but it cannot read it.
-
-
-class MpegImageFile(ImageFile.ImageFile):
- format = "MPEG"
- format_description = "MPEG"
-
- def _open(self):
- s = BitStream(self.fp)
-
- if s.read(32) != 0x1B3:
- msg = "not an MPEG file"
- raise SyntaxError(msg)
-
- self.mode = "RGB"
- self._size = s.read(12), s.read(12)
-
-
-# --------------------------------------------------------------------
-# Registry stuff
-
-Image.register_open(MpegImageFile.format, MpegImageFile)
-
-Image.register_extensions(MpegImageFile.format, [".mpg", ".mpeg"])
-
-Image.register_mime(MpegImageFile.format, "video/mpeg")
diff --git a/spaces/candlend/vits-hoshimi/sovits/README.md b/spaces/candlend/vits-hoshimi/sovits/README.md
deleted file mode 100644
index df1199c319929c2efea98ca39b62bd12541e2dcb..0000000000000000000000000000000000000000
--- a/spaces/candlend/vits-hoshimi/sovits/README.md
+++ /dev/null
@@ -1,96 +0,0 @@
-# SoftVC VITS Singing Voice Conversion
-
-## 模型简介
-歌声音色转换模型，通过SoftVC内容编码器提取源音频语音特征，与F0同时输入VITS替换原本的文本输入达到歌声转换的效果。同时，更换声码器为 [NSF HiFiGAN](https://github.com/openvpi/DiffSinger/tree/refactor/modules/nsf_hifigan) 解决断音问题
-## 注意
-+ 当前分支是32khz版本的分支，32khz模型推理更快，显存占用大幅减小，数据集所占硬盘空间也大幅降低，推荐训练该版本模型
-+ 如果是使用git clone 下载的仓库需要先 git checkout 32k 切换至32k分支
-+ 如果之前训练了48khz的模型想切换到32khz, 则需要重新执行完整的预处理过程（重采样、生成配置文件、生成f0）
-但是可以直接加载旧的48khz模型进行加训
-## 预先下载的模型文件
-+ soft vc hubert：[hubert-soft-0d54a1f4.pt](https://github.com/bshall/hubert/releases/download/v0.1/hubert-soft-0d54a1f4.pt)
- + 放在hubert目录下
-+ 预训练底模文件 [G_0.pth](https://huggingface.co/innnky/sovits_pretrained/resolve/main/G_0.pth) 与 [D_0.pth](https://huggingface.co/innnky/sovits_pretrained/resolve/main/D_0.pth)
- + 放在logs/32k 目录下
- + 预训练底模为必选项，因为据测试从零开始训练有概率不收敛，同时底模也能加快训练速度
- + 预训练底模训练数据集包含云灏 即霜 辉宇·星AI 派蒙 绫地宁宁，覆盖男女生常见音域，可以认为是相对通用的底模
- + 底模删除了optimizer speaker_embedding 等无关权重, 只可以用于初始化训练，无法用于推理
- + 该底模和48khz底模通用
-```shell
-# 一键下载
-# hubert
-wget -P hubert/ https://github.com/bshall/hubert/releases/download/v0.1/hubert-soft-0d54a1f4.pt
-# G与D预训练模型
-wget -P logs/32k/ https://huggingface.co/innnky/sovits_pretrained/resolve/main/G_0.pth
-wget -P logs/32k/ https://huggingface.co/innnky/sovits_pretrained/resolve/main/D_0.pth
-
-```
-## colab一键数据集制作、训练脚本
-[32khz一键colab](https://colab.research.google.com/drive/1FL-zRnYOkKMDmJ8EJIgGO8ehMhElroLZ?usp=sharing)
-
-[48khz一键colab](https://colab.research.google.com/drive/1rCUOOVG7-XQlVZuWRAj5IpGrMM8t07pE?usp=sharing)
-## 数据集准备
-仅需要以以下文件结构将数据集放入dataset_raw目录即可
-```shell
-dataset_raw
-├───speaker0
-│ ├───xxx1-xxx1.wav
-│ ├───...
-│ └───Lxx-0xx8.wav
-└───speaker1
- ├───xx2-0xxx2.wav
- ├───...
- └───xxx7-xxx007.wav
-```
-
-## 数据预处理
-1. 重采样至 32khz
-
-```shell
-python resample.py
- ```
-2. 自动划分训练集 验证集 测试集 以及自动生成配置文件
-```shell
-python preprocess_flist_config.py
-# 注意
-# 自动生成的配置文件中，说话人数量n_speakers会自动按照数据集中的人数而定
-# 为了给之后添加说话人留下一定空间，n_speakers自动设置为 当前数据集人数乘2
-# 如果想多留一些空位可以在此步骤后 自行修改生成的config.json中n_speakers数量
-# 一旦模型开始训练后此项不可再更改
-```
-3. 生成hubert与f0
-```shell
-python preprocess_hubert_f0.py
-```
-执行完以上步骤后 dataset 目录便是预处理完成的数据，可以删除dataset_raw文件夹了
-
-## 训练
-```shell
-python train.py -c configs/config.json -m 32k
-```
-
-## 训练中追加说话人数据
-基本类似预处理过程
-1. 将新追加的说话人数据按之前的结构放入dataset_raw目录下，并重采样至32khz
-```shell
-python resample.py
- ```
-2. 使用`add_speaker.py`重新生成训练集、验证集，重新生成配置文件
-```shell
-python add_speaker.py
-```
-3. 重新生成hubert与f0
-```shell
-python preprocess_hubert_f0.py
-```
-之后便可以删除dataset_raw文件夹了
-
-## 推理
-
-使用[inference_main.py](inference_main.py)
-+ 更改model_path为你自己训练的最新模型记录点
-+ 将待转换的音频放在raw文件夹下
-+ clean_names 写待转换的音频名称
-+ trans 填写变调半音数量
-+ spk_list 填写合成的说话人名称
-
diff --git a/spaces/ccolas/TastyPiano/src/music/representation_learning/mlm_pretrain/pretrain_span.py b/spaces/ccolas/TastyPiano/src/music/representation_learning/mlm_pretrain/pretrain_span.py
deleted file mode 100644
index 513e8e2c6e6373eae9f80d5a21b90f8c37d9b0e0..0000000000000000000000000000000000000000
--- a/spaces/ccolas/TastyPiano/src/music/representation_learning/mlm_pretrain/pretrain_span.py
+++ /dev/null
@@ -1,527 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...)
-on a text file or a dataset without using HuggingFace Trainer.
-
-Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
-https://huggingface.co/models?filter=fill-mask
-"""
-# You can also adapt this script on your own mlm task. Pointers for this are left as comments.
-
-import argparse
-import logging
-import math
-import os
-from pathlib import Path
-from accelerate import DistributedDataParallelKwargs
-import numpy as np
-import torch
-import datasets
-from datasets import load_dataset
-from torch.utils.data import DataLoader
-from tqdm.auto import tqdm
-from data_collators import DataCollatorForMusicModeling, DataCollatorForSpanMusicModeling
-
-import transformers
-from accelerate import Accelerator, DistributedType
-from huggingface_hub import Repository
-from transformers import (
- CONFIG_MAPPING,
- MODEL_MAPPING,
- AutoConfig,
- AutoModelForMaskedLM,
- AutoTokenizer,
- SchedulerType,
- get_scheduler,
- set_seed,
-)
-from transformers.file_utils import get_full_repo_name
-from transformers.utils.versions import require_version
-from src.music.config import DATASET_PATH, EXPERIMENT_PATH
-
-
-logger = logging.getLogger(__name__)
-require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
-MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
-MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
-
-
-def parse_args():
- parser = argparse.ArgumentParser(description="Finetune a transformers model on a Masked Language Modeling task")
- parser.add_argument(
- "--dataset_name",
- type=str,
- default=None,
- help="The name of the dataset to use (via the datasets library).",
- )
- parser.add_argument(
- "--dataset_config_name",
- type=str,
- default=None,
- help="The configuration name of the dataset to use (via the datasets library).",
- )
- parser.add_argument("--train_file",
- type=str,
- default=DATASET_PATH + "/small/train.txt",
- help="A csv or a json file containing the training data."
- )
- parser.add_argument("--validation_file",
- type=str,
- default=DATASET_PATH + "/small/test.txt",
- help="A csv or a json file containing the validation data."
- )
- parser.add_argument(
- "--validation_split_percentage",
- default=5,
- help="The percentage of the train set used as validation set in case there's no validation split",
- )
- parser.add_argument(
- "--pad_to_max_length",
- action="store_true",
- help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.",
- )
- parser.add_argument(
- "--model_name_or_path",
- type=str,
- default="./models/music-spanbert",
- help="Path to pretrained model or model identifier from huggingface.co/models.",
- required=False,
- )
- parser.add_argument(
- "--config_name",
- type=str,
- default="./models/music-spanbert",
- help="Pretrained config name or path if not the same as model_name",
- )
- parser.add_argument(
- "--tokenizer_name",
- type=str,
- default="./models/music-spanbert",
- help="Pretrained tokenizer name or path if not the same as model_name",
- )
- parser.add_argument(
- "--use_slow_tokenizer",
- action="store_true",
- help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
- )
- parser.add_argument(
- "--per_device_train_batch_size",
- type=int,
- default=8,
- help="Batch size (per device) for the training dataloader.",
- )
- parser.add_argument(
- "--logging_steps",
- type=int,
- default=500,
- help="Batch size (per device) for the training dataloader.",
- )
- parser.add_argument(
- "--save_steps",
- type=int,
- default=500,
- help="Batch size (per device) for the training dataloader.",
- )
- parser.add_argument(
- "--eval_steps",
- type=int,
- default=1000,
- help="Batch size (per device) for the training dataloader.",
- )
- parser.add_argument(
- "--nb_eval_steps",
- type=int,
- default=100,
- help="Batch size (per device) for the training dataloader.",
- )
- parser.add_argument(
- "--per_device_eval_batch_size",
- type=int,
- default=8,
- help="Batch size (per device) for the evaluation dataloader.",
- )
- parser.add_argument(
- "--learning_rate",
- type=float,
- default=5e-5,
- help="Initial learning rate (after the potential warmup period) to use.",
- )
- parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
- parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
- parser.add_argument(
- "--max_train_steps",
- type=int,
- default=None,
- help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
- )
- parser.add_argument(
- "--gradient_accumulation_steps",
- type=int,
- default=1,
- help="Number of updates steps to accumulate before performing a backward/update pass.",
- )
- parser.add_argument(
- "--lr_scheduler_type",
- type=SchedulerType,
- default="linear",
- help="The scheduler type to use.",
- choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
- )
- parser.add_argument(
- "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
- )
- parser.add_argument("--output_dir", type=str, default=EXPERIMENT_PATH + './music/representation_learning/saved_models/pretraining_mlm/music-spanbert/', help="Where to store the final model.")
- parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
- parser.add_argument(
- "--model_type",
- type=str,
- default='bert',
- help="Model type to use if training from scratch.",
- choices=MODEL_TYPES,
- )
- parser.add_argument(
- "--max_seq_length",
- type=int,
- default=512,
- help="The maximum total input sequence length after tokenization. Sequences longer than this will be truncated.",
- )
- parser.add_argument(
- "--nb_tokens_per_note",
- type=int,
- default=5,
- help="The maximum total input sequence length after tokenization. Sequences longer than this will be truncated.",
- )
- parser.add_argument("--cache_dir", type=str, default=None, help="The token to use to push to the Model Hub.")
-
- parser.add_argument(
- "--line_by_line",
- type=bool,
- default=False,
- help="Whether distinct lines of text in the dataset are to be handled as distinct sequences.",
- )
- parser.add_argument(
- "--preprocessing_num_workers",
- type=int,
- default=1,
- help="The number of processes to use for the preprocessing.",
- )
- parser.add_argument(
- "--overwrite_cache", type=bool, default=False, help="Overwrite the cached training and evaluation sets"
- )
- parser.add_argument(
- "--mlm_probability", type=float, default=0.15, help="Ratio of tokens to mask for masked language modeling loss"
- )
- parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
- parser.add_argument(
- "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
- )
- parser.add_argument("--hub_token", type=str, default="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", help="The token to use to push to the Model Hub.")
- args = parser.parse_args()
- # Sanity checks
- if args.dataset_name is None and args.train_file is None and args.validation_file is None:
- raise ValueError("Need either a dataset name or a training/validation file.")
- else:
- if args.train_file is not None:
- extension = args.train_file.split(".")[-1]
- if extension not in ["csv", "json", "txt"]:
- raise ValueError("`train_file` should be a csv, json or txt file.")
- if args.validation_file is not None:
- extension = args.validation_file.split(".")[-1]
- if extension not in ["csv", "json", "txt"]:
- raise ValueError("`validation_file` should be a csv, json or txt file.")
-
- if args.push_to_hub:
- assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
-
- return args
-
-
-def main():
- args = parse_args()
-
- args.max_seq_length = (args.max_seq_length // args.nb_tokens_per_note) * args.nb_tokens_per_note
-
-
- ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
- # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
- accelerator = Accelerator(kwargs_handlers=[ddp_kwargs])
- # Make one log on every process with the configuration for debugging.
- logging.basicConfig(
- format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
- datefmt="%m/%d/%Y %H:%M:%S",
- level=logging.INFO,
- )
- logger.info(accelerator.state)
- logger.info(accelerator.device)
-
- # Setup logging, we only want one process per machine to log things on the screen.
- # accelerator.is_local_main_process is only True for one process per machine.
- logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
- if accelerator.is_local_main_process:
- datasets.utils.logging.set_verbosity_warning()
- transformers.utils.logging.set_verbosity_info()
- else:
- datasets.utils.logging.set_verbosity_error()
- transformers.utils.logging.set_verbosity_error()
-
- if accelerator.is_main_process:
- save_dir = args.output_dir + 'run'
- candidate_save_dir = save_dir
- trial_id = 0
- while os.path.exists(candidate_save_dir):
- trial_id += 1
- candidate_save_dir = save_dir + f'_{trial_id}'
- save_dir = candidate_save_dir
- os.makedirs(save_dir)
- args.output_dir = save_dir
-
- # If passed along, set the training seed now.
- if args.seed is not None:
- set_seed(args.seed)
-
- # Handle the repository creation
- if accelerator.is_main_process:
- if args.push_to_hub:
- if args.hub_model_id is None:
- repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
- else:
- repo_name = args.hub_model_id
- repo = Repository(args.output_dir, clone_from=repo_name)
- elif args.output_dir is not None:
- os.makedirs(args.output_dir, exist_ok=True)
- accelerator.wait_for_everyone()
-
- # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
- # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
- # (the dataset will be downloaded automatically from the datasets Hub).
- #
- # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
- # 'text' is found. You can easily tweak this behavior (see below).
- #
- # In distributed training, the load_dataset function guarantee that only one local process can concurrently
- # download the dataset.
- if accelerator.is_main_process:
- print("\nLoading Dataset")
- data_files = {}
- data_files["train"] = args.train_file
- data_files["validation"] = args.validation_file
- dataset = load_dataset("text", data_files=data_files, cache_dir=args.cache_dir)
-
- tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, cache_dir=args.cache_dir, use_fast=not args.use_slow_tokenizer)
-
- # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
- # Since we make sure that all sequences are of the same length, no attention_mask is needed.
- def tokenize_function(examples):
- return tokenizer(examples['text'], return_attention_mask=False)
- with accelerator.main_process_first():
- tokenized_datasets = dataset.map(tokenize_function,
- batched=True,
- num_proc=args.preprocessing_num_workers,
- remove_columns=['text'],
- load_from_cache_file=not args.overwrite_cache,
- )
-
- def group_texts(examples):
- results = dict()
- for k in examples.keys():
- results[k] = []
- ex = examples[k]
- for e in ex:
- results[k] += [e[i: i + args.max_seq_length] for i in range(0, len(e) - args.max_seq_length, args.max_seq_length)]
- return results
-
- # chunk into groups of size args.max_sequence_len
- with accelerator.main_process_first():
- tokenized_datasets = tokenized_datasets.map(group_texts,
- batched=True,
- num_proc=args.preprocessing_num_workers,
- load_from_cache_file=not args.overwrite_cache,
- )
- if accelerator.is_main_process:
- print(" len of train_loader", len(tokenized_datasets['train']))
- print(" len of valid_loader", len(tokenized_datasets['validation']))
-
- if accelerator.is_main_process:
- if torch.cuda.is_available():
- print("Use %d GPUS" % torch.cuda.device_count())
- else:
- print('Use cpu.')
-
-
-
- # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
- # https://huggingface.co/docs/datasets/loading_datasets.html.
-
- # Load pretrained model and tokenizer
- #
- # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
- # download model & vocab.
- if args.config_name:
- config = AutoConfig.from_pretrained(args.config_name)
- elif args.model_name_or_path:
- config = AutoConfig.from_pretrained(args.model_name_or_path)
- else:
- config = CONFIG_MAPPING[args.model_type]()
- logger.warning("You are instantiating a new config instance from scratch.")
-
-
-
- logger.info("Training new model from scratch")
- model = AutoModelForMaskedLM.from_config(config)
-
- model.resize_token_embeddings(len(tokenizer))
-
- train_dataset = tokenized_datasets["train"]
- eval_dataset = tokenized_datasets["validation"]
-
- # Data collator
- # This one will take care of randomly masking the tokens.
- data_collator = DataCollatorForSpanMusicModeling(tokenizer=tokenizer, mlm_probability=args.mlm_probability)
-
- # DataLoaders creation:
- train_dataloader = DataLoader(train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size)
- eval_dataloader = DataLoader(eval_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size)
- print('eval dataloader len', len(eval_dataloader))
- # Optimizer
- # Split weights in two groups, one with weight decay and the other not.
- no_decay = ["bias", "LayerNorm.weight"]
- optimizer_grouped_parameters = [
- {
- "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
- "weight_decay": args.weight_decay,
- },
- {
- "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
- "weight_decay": 0.0,
- },
- ]
- optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
-
- # Prepare everything with our `accelerator`.
- model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(model, optimizer, train_dataloader, eval_dataloader)
-
- # On TPU, the tie weights in our model have been disconnected, so we need to restore the ties.
- if accelerator.distributed_type == DistributedType.TPU:
- model.tie_weights()
-
- # Note -> the training dataloader needs to be prepared before we grab his length below (cause its length will be
- # shorter in multiprocess)
-
- # Scheduler and math around the number of training steps.
- num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
- if args.max_train_steps is None:
- args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
- else:
- args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
- lr_scheduler = get_scheduler(name=args.lr_scheduler_type,
- optimizer=optimizer,
- num_warmup_steps=args.num_warmup_steps,
- num_training_steps=args.max_train_steps,
- )
-
- # Train!
- total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-
- logger.info("***** Running training *****")
- logger.info(f" Num examples = {len(train_dataset)}")
- logger.info(f" Num Epochs = {args.num_train_epochs}")
- logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}")
- logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
- logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
- logger.info(f" Total optimization steps = {args.max_train_steps}")
- # Only show the progress bar once on each machine.
- progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
- completed_steps = 0
-
- n_steps = 0
- best_eval_loss = float("inf")
- best_eval_acc = -float("inf")
- for epoch in range(args.num_train_epochs):
- model.train()
- train_losses = []
- for step, batch in enumerate(train_dataloader):
- n_steps += 1
- outputs = model(**batch)
- loss = outputs.loss
- loss = loss / args.gradient_accumulation_steps
- accelerator.backward(loss)
- train_losses.append(accelerator.gather(loss.repeat(args.per_device_train_batch_size)))
- if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
- optimizer.step()
- lr_scheduler.step()
- optimizer.zero_grad()
- progress_bar.update(1)
- completed_steps += 1
-
- if completed_steps >= args.max_train_steps:
- break
-
- if n_steps % args.eval_steps == 0 or step == len(train_dataloader) - 1:
- model.eval()
- eval_losses = []
- eval_accuracies = []
- for step, batch in enumerate(eval_dataloader):
- if step > args.nb_eval_steps:
- break
- with torch.no_grad():
- outputs = model(**batch)
- eval_accuracies.append(accelerator.gather(compute_accuracy(outputs, batch)))
- loss = outputs.loss
- eval_losses.append(accelerator.gather(loss.repeat(args.per_device_eval_batch_size)))
-
- eval_losses = torch.cat(eval_losses)
- eval_accuracies = torch.cat(eval_accuracies)
- try:
- perplexity = math.exp(torch.mean(eval_losses))
- except OverflowError:
- perplexity = float("inf")
-
- if torch.mean(eval_accuracies) > best_eval_acc:
- best_eval_acc = torch.mean(eval_accuracies)
- accelerator.wait_for_everyone()
- unwrapped_model = accelerator.unwrap_model(model)
- unwrapped_model.save_pretrained(args.output_dir, save_function=accelerator.save)
- if accelerator.is_main_process:
- logger.info("New best score, saving checkpoint")
- tokenizer.save_pretrained(args.output_dir)
- if args.push_to_hub:
- repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
- if accelerator.is_main_process:
- logger.info(f"\nEval: epoch {epoch}, step: {step} (total step: {n_steps}), --- perplexity: {perplexity:.3f}, loss: {torch.mean(eval_losses):.3f}, "
- f"acc: {torch.mean(eval_accuracies):.3f}")
-
- if n_steps % args.logging_steps == 0 or step == len(train_dataloader) - 1:
- this_train_losses = torch.cat(train_losses)
- this_train_losses = this_train_losses[-args.logging_steps:]
- try:
- perplexity = math.exp(torch.mean(this_train_losses))
- except OverflowError:
- perplexity = float("inf")
- if accelerator.is_main_process:
- logger.info(f"\nTrain: epoch {epoch}, step: {step} (total step: {n_steps}) --- perplexity: {perplexity:.3f}, loss: {torch.mean(this_train_losses):.3f}")
-
-def compute_accuracy(outputs, batch):
- predictions = torch.argmax(outputs['logits'], dim=2)
- labels = batch['labels']
- inds = torch.where(labels != -100)
- accuracy = torch.mean((predictions[inds] == labels[inds]).float())
- return torch.atleast_1d(accuracy)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/character-aware-diffusion/charred/app.py b/spaces/character-aware-diffusion/charred/app.py
deleted file mode 100644
index 85d27ae80c58ba06ac1ad74febae951fa0f2430d..0000000000000000000000000000000000000000
--- a/spaces/character-aware-diffusion/charred/app.py
+++ /dev/null
@@ -1,231 +0,0 @@
-import gradio as gr
-import jax
-
-import numpy as np
-
-import jax
-import jax.numpy as jnp
-
-from PIL import Image
-
-from diffusers import (
- FlaxAutoencoderKL,
- FlaxDPMSolverMultistepScheduler,
- FlaxUNet2DConditionModel,
-)
-
-from transformers import ByT5Tokenizer, FlaxT5ForConditionalGeneration
-
-
-def get_inference_lambda(seed):
- tokenizer = ByT5Tokenizer()
-
- language_model = FlaxT5ForConditionalGeneration.from_pretrained(
- "google/byt5-base",
- dtype=jnp.float32,
- )
- text_encoder = language_model.encode
- text_encoder_params = language_model.params
- max_length = 1024
- tokenized_negative_prompt = tokenizer(
- "", padding="max_length", max_length=max_length, return_tensors="np"
- ).input_ids
- negative_prompt_text_encoder_hidden_states = text_encoder(
- tokenized_negative_prompt,
- params=text_encoder_params,
- train=False,
- )[0]
-
- scheduler = FlaxDPMSolverMultistepScheduler.from_config(
- config={
- "_diffusers_version": "0.16.0",
- "beta_end": 0.012,
- "beta_schedule": "scaled_linear",
- "beta_start": 0.00085,
- "clip_sample": False,
- "num_train_timesteps": 1000,
- "prediction_type": "v_prediction",
- "set_alpha_to_one": False,
- "skip_prk_steps": True,
- "steps_offset": 1,
- "trained_betas": None,
- }
- )
- timesteps = 20
- guidance_scale = jnp.array([7.5], dtype=jnp.bfloat16)
-
- unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(
- "character-aware-diffusion/charred",
- dtype=jnp.bfloat16,
- )
-
- vae, vae_params = FlaxAutoencoderKL.from_pretrained(
- "flax/stable-diffusion-2-1",
- subfolder="vae",
- dtype=jnp.bfloat16,
- )
- vae_scale_factor = 2 ** (len(vae.config.block_out_channels) - 1)
-
- image_width = image_height = 256
-
- # Generating latent shape
- latent_shape = (
- negative_prompt_text_encoder_hidden_states.shape[0], # is th
- unet.in_channels,
- image_width // vae_scale_factor,
- image_height // vae_scale_factor,
- )
-
- def __tokenize_prompt(prompt: str):
- return tokenizer(
- text=prompt,
- max_length=1024,
- padding="max_length",
- truncation=True,
- return_tensors="jax",
- ).input_ids
-
- def __convert_image(image):
- # create PIL image from JAX tensor converted to numpy
- return Image.fromarray(np.asarray(image), mode="RGB")
-
- def __get_context(tokenized_prompt: jnp.array):
- # Get the text embedding
- text_encoder_hidden_states = text_encoder(
- tokenized_prompt,
- params=text_encoder_params,
- train=False,
- )[0]
-
- # context = empty negative prompt embedding + prompt embedding
- return jnp.concatenate(
- [negative_prompt_text_encoder_hidden_states, text_encoder_hidden_states]
- )
-
- def __predict_image(context: jnp.array):
- def ___timestep(step, step_args):
- latents, scheduler_state = step_args
-
- t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step]
-
- # For classifier-free guidance, we need to do two forward passes.
- # Here we concatenate the unconditional and text embeddings into a single batch
- # to avoid doing two forward passes
- latent_input = jnp.concatenate([latents] * 2)
-
- timestep = jnp.broadcast_to(t, latent_input.shape[0])
-
- scaled_latent_input = scheduler.scale_model_input(
- scheduler_state, latent_input, t
- )
-
- # predict the noise residual
- unet_prediction_sample = unet.apply(
- {"params": unet_params},
- jnp.array(scaled_latent_input),
- jnp.array(timestep, dtype=jnp.int32),
- context,
- ).sample
-
- # perform guidance
- unet_prediction_sample_uncond, unet_prediction_text = jnp.split(
- unet_prediction_sample, 2, axis=0
- )
- guided_unet_prediction_sample = (
- unet_prediction_sample_uncond
- + guidance_scale
- * (unet_prediction_text - unet_prediction_sample_uncond)
- )
-
- # compute the previous noisy sample x_t -> x_t-1
- latents, scheduler_state = scheduler.step(
- scheduler_state, guided_unet_prediction_sample, t, latents
- ).to_tuple()
-
- return latents, scheduler_state
-
- # initialize scheduler state
- initial_scheduler_state = scheduler.set_timesteps(
- scheduler.create_state(), num_inference_steps=timesteps, shape=latent_shape
- )
-
- # initialize latents
- initial_latents = (
- jax.random.normal(
- jax.random.PRNGKey(seed), shape=latent_shape, dtype=jnp.bfloat16
- )
- * initial_scheduler_state.init_noise_sigma
- )
-
- final_latents, _ = jax.lax.fori_loop(
- 0, timesteps, ___timestep, (initial_latents, initial_scheduler_state)
- )
-
- vae_output = vae.apply(
- {"params": vae_params},
- 1 / vae.config.scaling_factor * final_latents,
- method=vae.decode,
- ).sample
-
- # return 8 bit RGB image (width, height, rgb)
- return (
- ((vae_output / 2 + 0.5).transpose(0, 2, 3, 1).clip(0, 1) * 255)
- .round()
- .astype(jnp.uint8)[0]
- )
-
- jax_jit_compiled_accel_predict_image = jax.jit(__predict_image)
-
- jax_jit_compiled_cpu_get_context = jax.jit(
- __get_context, device=jax.devices(backend="cpu")[0]
- )
-
- return lambda prompt: __convert_image(
- jax_jit_compiled_accel_predict_image(
- jax_jit_compiled_cpu_get_context(__tokenize_prompt(prompt))
- )
- )
-
-
-generate_image_for_prompt = get_inference_lambda(87)
-
-
-with gr.Blocks(theme="gradio/soft") as demo:
-
- gr.Markdown("# Character-Aware Stable Diffusion (CHARRED)")
-
- with gr.Tab("Journal"):
- gr.Markdown(
- """
- ## On How Four Crazy Fellows Embarked on Training a JAX U-Net from Scratch in Five Days and Almost Died in the End
-
- Lorem ipsum dolor sit amet, consectetur adipiscing elit. Mauris vitae varius libero. Nullam laoreet eget sapien quis tristique. Cras odio odio, consequat sed cursus quis, dignissim hendrerit ligula. Curabitur non lorem tellus. Nam bibendum malesuada mi sed faucibus. Sed euismod enim metus, sit amet venenatis elit elementum vel. Duis nec rhoncus tellus, rhoncus auctor justo. Proin id gravida dolor. Sed nulla lectus, finibus non fringilla ac, fermentum in sapien. Cras lobortis est augue, vel posuere justo pretium vitae. Aliquam lorem dolor, condimentum et finibus rutrum, rhoncus eget nunc.
-
- In varius eu nulla non tempor. Maecenas laoreet scelerisque ipsum, eu placerat enim luctus sed. In malesuada, nibh finibus finibus sollicitudin, lacus massa pulvinar sem, vel venenatis nibh sem eget lorem. Cras at augue magna. Nullam elementum porta turpis, et tristique sapien placerat vel. Etiam eu lorem malesuada, ornare leo a, commodo erat. Mauris a velit vulputate, placerat lectus vel, varius lorem. Sed volutpat porttitor venenatisLorem ipsum dolor sit amet, consectetur adipiscing elit. Mauris vitae varius libero. Nullam laoreet eget sapien quis tristique. Cras odio odio, consequat sed cursus quis, dignissim hendrerit ligula. Curabitur non lorem tellus. Nam bibendum malesuada mi sed faucibus. Sed euismod enim metus, sit amet venenatis elit elementum vel. Duis nec rhoncus tellus, rhoncus auctor justo. Proin id gravida dolor. Sed nulla lectus, finibus non fringilla ac, fermentum in sapien. Cras lobortis est augue, vel posuere justo pretium vitae. Aliquam lorem dolor, condimentum et finibus rutrum, rhoncus eget nunc.
-
- Sed pellentesque gravida consectetur. Mauris molestie nunc quis lacinia egestas. Curabitur aliquam varius quam, nec venenatis leo efficitur a. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Ut fermentum gravida mauris, at blandit diam suscipit dapibus. Maecenas ac condimentum justo. Pellentesque aliquet risus vitae massa molestie iaculis. Quisque at libero tincidunt dui ornare vulputate. Sed tristique dolor lacinia pellentesque maximus. Donec bibendum tempus orci, eu gravida metus vehicula sit amet. Donec quis sodales neque, id consequat elit.
-
- Sed molestie diam a massa sodales porta. Sed et ex vitae felis blandit consectetur porttitor in lectus. Interdum et malesuada fames ac ante ipsum primis in faucibus. Praesent est mi, lacinia ut egestas sed, dapibus sed augue. Sed scelerisque est a ex porta suscipit. Curabitur eleifend massa vitae suscipit finibus. Cras lobortis pellentesque est. Pellentesque semper justo nibh, vitae convallis lectus ultrices sed. Nunc auctor dignissim pretium. Praesent orci justo, posuere a diam at, tincidunt viverra leo. Quisque sit amet dignissim erat, id varius massa. Phasellus fringilla vestibulum elit, id eleifend erat hendrerit ut.
-
- Duis scelerisque sit amet est at iaculis. Suspendisse sed ipsum vitae massa placerat semper. Pellentesque vitae sapien tristique, congue ligula sed, dapibus nunc. Suspendisse sed maximus neque, a lobortis risus. Nam lorem nisi, commodo a neque ut, volutpat porttitor ipsum. Quisque in tortor blandit, ultrices leo eget, venenatis nisl. Vestibulum ultricies sapien at sapien tincidunt vehicula vel in lacus. Sed ultricies mattis quam ac aliquet. Nulla a ullamcorper urna. Duis lacus ligula, auctor in orci sed, hendrerit maximus lectus. Nam a enim at nibh aliquam rhoncus. Pellentesque nulla justo, varius eget molestie sit amet, ultricies id tortor.
- """
- )
-
- with gr.Tab("☢️ DEMO ☢️"):
- gr.Markdown(
- "## This is a demo of the CHARRED character-aware stable diffusion model for you to enjoy at your own leisure, risk and peril"
- )
- prompt_input_charr = gr.Textbox(label="Prompt")
- charred_output = gr.Image(label="Output Image")
- submit_btn = gr.Button(value="Submit")
- charred_inputs = [prompt_input_charr]
- submit_btn.click(
- fn=generate_image_for_prompt,
- inputs=charred_inputs,
- outputs=[charred_output],
- )
- # examples = [["postage stamp from california", "low quality", "charr_output.png", "charr_output.png" ]]
- # gr.Examples(fn = infer_sd, inputs = ["text", "text", "image", "image"], examples=examples, cache_examples=True)
-
-demo.queue(concurrency_count=1)
-demo.launch(debug=True, show_error=True)
diff --git a/spaces/chendl/compositional_test/transformers/docker/transformers-pytorch-gpu/Dockerfile b/spaces/chendl/compositional_test/transformers/docker/transformers-pytorch-gpu/Dockerfile
deleted file mode 100644
index 3cb8625339baf27964c3b8fbf01665ac376bc4e4..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/docker/transformers-pytorch-gpu/Dockerfile
+++ /dev/null
@@ -1,32 +0,0 @@
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu20.04
-LABEL maintainer="Hugging Face"
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-RUN apt update
-RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg
-RUN python3 -m pip install --no-cache-dir --upgrade pip
-
-ARG REF=main
-RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF
-RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch,testing,video]
-
-# If set to nothing, will install the latest version
-ARG PYTORCH='2.0.0'
-ARG TORCH_VISION=''
-ARG TORCH_AUDIO=''
-# Example: `cu102`, `cu113`, etc.
-ARG CUDA='cu117'
-
-RUN [ ${#PYTORCH} -gt 0 ] && VERSION='torch=='$PYTORCH'.*' || VERSION='torch'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA
-RUN [ ${#TORCH_VISION} -gt 0 ] && VERSION='torchvision=='TORCH_VISION'.*' || VERSION='torchvision'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA
-RUN [ ${#TORCH_AUDIO} -gt 0 ] && VERSION='torchaudio=='TORCH_AUDIO'.*' || VERSION='torchaudio'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA
-
-RUN python3 -m pip uninstall -y tensorflow flax
-
-RUN python3 -m pip install --no-cache-dir git+https://github.com/facebookresearch/detectron2.git pytesseract
-RUN python3 -m pip install -U "itsdangerous<2.1.0"
-
-# When installing in editable mode, `transformers` is not recognized as a package.
-# this line must be added in order for python to be aware of transformers.
-RUN cd transformers && python3 setup.py develop
diff --git a/spaces/chendl/compositional_test/transformers/examples/research_projects/seq2seq-distillation/_test_make_student.py b/spaces/chendl/compositional_test/transformers/examples/research_projects/seq2seq-distillation/_test_make_student.py
deleted file mode 100644
index 73df66315cbd7952e7779108d0dd7adcf738cd3d..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/research_projects/seq2seq-distillation/_test_make_student.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import tempfile
-import unittest
-
-from make_student import create_student_by_copying_alternating_layers
-
-from transformers import AutoConfig
-from transformers.file_utils import cached_property
-from transformers.testing_utils import require_torch
-
-
-TINY_BART = "sshleifer/bart-tiny-random"
-TINY_T5 = "patrickvonplaten/t5-tiny-random"
-
-
-@require_torch
-class MakeStudentTester(unittest.TestCase):
- @cached_property
- def teacher_config(self):
- return AutoConfig.from_pretrained(TINY_BART)
-
- def test_valid_t5(self):
- student, *_ = create_student_by_copying_alternating_layers(TINY_T5, tempfile.mkdtemp(), e=1, d=1)
- self.assertEqual(student.config.num_hidden_layers, 1)
-
- def test_asymmetric_t5(self):
- student, *_ = create_student_by_copying_alternating_layers(TINY_T5, tempfile.mkdtemp(), e=1, d=None)
-
- def test_same_decoder_small_encoder(self):
- student, *_ = create_student_by_copying_alternating_layers(TINY_BART, tempfile.mkdtemp(), e=1, d=None)
- self.assertEqual(student.config.encoder_layers, 1)
- self.assertEqual(student.config.decoder_layers, self.teacher_config.encoder_layers)
-
- def test_small_enc_small_dec(self):
- student, *_ = create_student_by_copying_alternating_layers(TINY_BART, tempfile.mkdtemp(), e=1, d=1)
- self.assertEqual(student.config.encoder_layers, 1)
- self.assertEqual(student.config.decoder_layers, 1)
-
- def test_raises_assert(self):
- with self.assertRaises(AssertionError):
- create_student_by_copying_alternating_layers(TINY_BART, tempfile.mkdtemp(), e=None, d=None)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/blis/tests/test_dotv.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/blis/tests/test_dotv.py
deleted file mode 100644
index a066ad39afc9d90c26913bf0d7b483956afa4027..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/blis/tests/test_dotv.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# Copyright ExplosionAI GmbH, released under BSD.
-from __future__ import division
-from hypothesis import given, assume
-
-from blis.tests.common import *
-from blis.py import dotv
-
-
-@given(
- ndarrays(min_len=10, max_len=100, min_val=-100.0, max_val=100.0, dtype="float64"),
- ndarrays(min_len=10, max_len=100, min_val=-100.0, max_val=100.0, dtype="float64"),
-)
-def test_memoryview_double_noconj(A, B):
- if len(A) < len(B):
- B = B[: len(A)]
- else:
- A = A[: len(B)]
- assume(A is not None)
- assume(B is not None)
- numpy_result = A.dot(B)
- result = dotv(A, B)
- assert_allclose([numpy_result], result, atol=1e-4, rtol=1e-4)
-
-
-@given(
- ndarrays(min_len=10, max_len=100, min_val=-100.0, max_val=100.0, dtype="float32"),
- ndarrays(min_len=10, max_len=100, min_val=-100.0, max_val=100.0, dtype="float32"),
-)
-def test_memoryview_float_noconj(A, B):
- if len(A) < len(B):
- B = B[: len(A)]
- else:
- A = A[: len(B)]
- assume(A is not None)
- assume(B is not None)
- numpy_result = A.dot(B)
- result = dotv(A, B)
- assert_allclose([numpy_result], result, atol=1e-4, rtol=1e-3)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/pens/reportLabPen.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/pens/reportLabPen.py
deleted file mode 100644
index 2cb89c8bf4c772b7a987edb0593c40c83cc2201b..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/pens/reportLabPen.py
+++ /dev/null
@@ -1,80 +0,0 @@
-from fontTools.pens.basePen import BasePen
-from reportlab.graphics.shapes import Path
-
-
-__all__ = ["ReportLabPen"]
-
-
-class ReportLabPen(BasePen):
-
- """A pen for drawing onto a ``reportlab.graphics.shapes.Path`` object."""
-
- def __init__(self, glyphSet, path=None):
- BasePen.__init__(self, glyphSet)
- if path is None:
- path = Path()
- self.path = path
-
- def _moveTo(self, p):
- (x, y) = p
- self.path.moveTo(x, y)
-
- def _lineTo(self, p):
- (x, y) = p
- self.path.lineTo(x, y)
-
- def _curveToOne(self, p1, p2, p3):
- (x1, y1) = p1
- (x2, y2) = p2
- (x3, y3) = p3
- self.path.curveTo(x1, y1, x2, y2, x3, y3)
-
- def _closePath(self):
- self.path.closePath()
-
-
-if __name__ == "__main__":
- import sys
-
- if len(sys.argv) < 3:
- print(
- "Usage: reportLabPen.py
- ${this.scale_value ?? "100%"}
-
Exterminate It Serial Crack 212rar
Download File === https://tinurli.com/2uwi6w
-
- aaccfb2cb3
-
-
- diff --git a/spaces/cihyFjudo/fairness-paper-search/FSX AEROSOFT STUTTGART-X V 1.02 Patch lennorlen The Ultimate Guide to Stuttgart Airport Add-on.md b/spaces/cihyFjudo/fairness-paper-search/FSX AEROSOFT STUTTGART-X V 1.02 Patch lennorlen The Ultimate Guide to Stuttgart Airport Add-on.md deleted file mode 100644 index a31b0a3a9d5f8b28f2846929e4b19e34e3699121..0000000000000000000000000000000000000000 --- a/spaces/cihyFjudo/fairness-paper-search/FSX AEROSOFT STUTTGART-X V 1.02 Patch lennorlen The Ultimate Guide to Stuttgart Airport Add-on.md +++ /dev/null @@ -1,6 +0,0 @@ -
FSX AEROSOFT STUTTGART-X V 1.02 Patch lennorlen
Download Zip https://tinurli.com/2uwkUe
- - aaccfb2cb3
-
-
- diff --git a/spaces/cihyFjudo/fairness-paper-search/Mega Pack Stadium Pes 2013 Aquario Babbo Cataly ((HOT)).md b/spaces/cihyFjudo/fairness-paper-search/Mega Pack Stadium Pes 2013 Aquario Babbo Cataly ((HOT)).md deleted file mode 100644 index f7b8d7c066999888bd7f7173e013d7d0690a2f5b..0000000000000000000000000000000000000000 --- a/spaces/cihyFjudo/fairness-paper-search/Mega Pack Stadium Pes 2013 Aquario Babbo Cataly ((HOT)).md +++ /dev/null @@ -1,6 +0,0 @@ -
Mega Pack Stadium Pes 2013 aquario babbo cataly
Download Zip >>> https://tinurli.com/2uwj1x
-
- aaccfb2cb3
-
-
- diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/cycler.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/cycler.py deleted file mode 100644 index f86b68de64b8066b98d8fa2d92bf5983ea582237..0000000000000000000000000000000000000000 --- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/cycler.py +++ /dev/null @@ -1,501 +0,0 @@ -""" -Cycler -====== - -Cycling through combinations of values, producing dictionaries. - -You can add cyclers:: - - from cycler import cycler - cc = (cycler(color=list('rgb')) + - cycler(linestyle=['-', '--', '-.'])) - for d in cc: - print(d) - -Results in:: - - {'color': 'r', 'linestyle': '-'} - {'color': 'g', 'linestyle': '--'} - {'color': 'b', 'linestyle': '-.'} - - -You can multiply cyclers:: - - from cycler import cycler - cc = (cycler(color=list('rgb')) * - cycler(linestyle=['-', '--', '-.'])) - for d in cc: - print(d) - -Results in:: - - {'color': 'r', 'linestyle': '-'} - {'color': 'r', 'linestyle': '--'} - {'color': 'r', 'linestyle': '-.'} - {'color': 'g', 'linestyle': '-'} - {'color': 'g', 'linestyle': '--'} - {'color': 'g', 'linestyle': '-.'} - {'color': 'b', 'linestyle': '-'} - {'color': 'b', 'linestyle': '--'} - {'color': 'b', 'linestyle': '-.'} -""" - - -import copy -from functools import reduce -from itertools import product, cycle -from operator import mul, add - -__version__ = '0.10.0' - - -def _process_keys(left, right): - """ - Helper function to compose cycler keys. - - Parameters - ---------- - left, right : iterable of dictionaries or None - The cyclers to be composed. - - Returns - ------- - keys : set - The keys in the composition of the two cyclers. - """ - l_peek = next(iter(left)) if left is not None else {} - r_peek = next(iter(right)) if right is not None else {} - l_key = set(l_peek.keys()) - r_key = set(r_peek.keys()) - if l_key & r_key: - raise ValueError("Can not compose overlapping cycles") - return l_key | r_key - - -def concat(left, right): - r""" - Concatenate `Cycler`\s, as if chained using `itertools.chain`. - - The keys must match exactly. - - Examples - -------- - >>> num = cycler('a', range(3)) - >>> let = cycler('a', 'abc') - >>> num.concat(let) - cycler('a', [0, 1, 2, 'a', 'b', 'c']) - - Returns - ------- - `Cycler` - The concatenated cycler. - """ - if left.keys != right.keys: - raise ValueError("Keys do not match:\n" - "\tIntersection: {both!r}\n" - "\tDisjoint: {just_one!r}".format( - both=left.keys & right.keys, - just_one=left.keys ^ right.keys)) - _l = left.by_key() - _r = right.by_key() - return reduce(add, (_cycler(k, _l[k] + _r[k]) for k in left.keys)) - - -class Cycler: - """ - Composable cycles. - - This class has compositions methods: - - ``+`` - for 'inner' products (zip) - - ``+=`` - in-place ``+`` - - ``*`` - for outer products (`itertools.product`) and integer multiplication - - ``*=`` - in-place ``*`` - - and supports basic slicing via ``[]``. - - Parameters - ---------- - left, right : Cycler or None - The 'left' and 'right' cyclers. - op : func or None - Function which composes the 'left' and 'right' cyclers. - """ - - def __call__(self): - return cycle(self) - - def __init__(self, left, right=None, op=None): - """ - Semi-private init. - - Do not use this directly, use `cycler` function instead. - """ - if isinstance(left, Cycler): - self._left = Cycler(left._left, left._right, left._op) - elif left is not None: - # Need to copy the dictionary or else that will be a residual - # mutable that could lead to strange errors - self._left = [copy.copy(v) for v in left] - else: - self._left = None - - if isinstance(right, Cycler): - self._right = Cycler(right._left, right._right, right._op) - elif right is not None: - # Need to copy the dictionary or else that will be a residual - # mutable that could lead to strange errors - self._right = [copy.copy(v) for v in right] - else: - self._right = None - - self._keys = _process_keys(self._left, self._right) - self._op = op - - def __contains__(self, k): - return k in self._keys - - @property - def keys(self): - """The keys this Cycler knows about.""" - return set(self._keys) - - def change_key(self, old, new): - """ - Change a key in this cycler to a new name. - Modification is performed in-place. - - Does nothing if the old key is the same as the new key. - Raises a ValueError if the new key is already a key. - Raises a KeyError if the old key isn't a key. - """ - if old == new: - return - if new in self._keys: - raise ValueError( - "Can't replace {old} with {new}, {new} is already a key" - .format(old=old, new=new) - ) - if old not in self._keys: - raise KeyError("Can't replace {old} with {new}, {old} is not a key" - .format(old=old, new=new)) - - self._keys.remove(old) - self._keys.add(new) - - if self._right is not None and old in self._right.keys: - self._right.change_key(old, new) - - # self._left should always be non-None - # if self._keys is non-empty. - elif isinstance(self._left, Cycler): - self._left.change_key(old, new) - else: - # It should be completely safe at this point to - # assume that the old key can be found in each - # iteration. - self._left = [{new: entry[old]} for entry in self._left] - - @classmethod - def _from_iter(cls, label, itr): - """ - Class method to create 'base' Cycler objects - that do not have a 'right' or 'op' and for which - the 'left' object is not another Cycler. - - Parameters - ---------- - label : str - The property key. - - itr : iterable - Finite length iterable of the property values. - - Returns - ------- - `Cycler` - New 'base' cycler. - """ - ret = cls(None) - ret._left = list({label: v} for v in itr) - ret._keys = {label} - return ret - - def __getitem__(self, key): - # TODO : maybe add numpy style fancy slicing - if isinstance(key, slice): - trans = self.by_key() - return reduce(add, (_cycler(k, v[key]) for k, v in trans.items())) - else: - raise ValueError("Can only use slices with Cycler.__getitem__") - - def __iter__(self): - if self._right is None: - for left in self._left: - yield dict(left) - else: - for a, b in self._op(self._left, self._right): - out = {} - out.update(a) - out.update(b) - yield out - - def __add__(self, other): - """ - Pair-wise combine two equal length cyclers (zip). - - Parameters - ---------- - other : Cycler - """ - if len(self) != len(other): - raise ValueError("Can only add equal length cycles, " - f"not {len(self)} and {len(other)}") - return Cycler(self, other, zip) - - def __mul__(self, other): - """ - Outer product of two cyclers (`itertools.product`) or integer - multiplication. - - Parameters - ---------- - other : Cycler or int - """ - if isinstance(other, Cycler): - return Cycler(self, other, product) - elif isinstance(other, int): - trans = self.by_key() - return reduce(add, (_cycler(k, v*other) for k, v in trans.items())) - else: - return NotImplemented - - def __rmul__(self, other): - return self * other - - def __len__(self): - op_dict = {zip: min, product: mul} - if self._right is None: - return len(self._left) - l_len = len(self._left) - r_len = len(self._right) - return op_dict[self._op](l_len, r_len) - - def __iadd__(self, other): - """ - In-place pair-wise combine two equal length cyclers (zip). - - Parameters - ---------- - other : Cycler - """ - if not isinstance(other, Cycler): - raise TypeError("Cannot += with a non-Cycler object") - # True shallow copy of self is fine since this is in-place - old_self = copy.copy(self) - self._keys = _process_keys(old_self, other) - self._left = old_self - self._op = zip - self._right = Cycler(other._left, other._right, other._op) - return self - - def __imul__(self, other): - """ - In-place outer product of two cyclers (`itertools.product`). - - Parameters - ---------- - other : Cycler - """ - if not isinstance(other, Cycler): - raise TypeError("Cannot *= with a non-Cycler object") - # True shallow copy of self is fine since this is in-place - old_self = copy.copy(self) - self._keys = _process_keys(old_self, other) - self._left = old_self - self._op = product - self._right = Cycler(other._left, other._right, other._op) - return self - - def __eq__(self, other): - if len(self) != len(other): - return False - if self.keys ^ other.keys: - return False - return all(a == b for a, b in zip(self, other)) - - def __ne__(self, other): - return not (self == other) - - __hash__ = None - - def __repr__(self): - op_map = {zip: '+', product: '*'} - if self._right is None: - lab = self.keys.pop() - itr = list(v[lab] for v in self) - return f"cycler({lab!r}, {itr!r})" - else: - op = op_map.get(self._op, '?') - msg = "({left!r} {op} {right!r})" - return msg.format(left=self._left, op=op, right=self._right) - - def _repr_html_(self): - # an table showing the value of each key through a full cycle - output = "
| {key!r} | " - for d in iter(self): - output += "
|---|
| {d[k]!r} | " - output += "
Agar.io Hack Bot Download Free: How to Get Unlimited Coins, Mass, and Zoom
-If you are a fan of online multiplayer games, you might have heard of Agar.io, a popular browser-based game where you control a cell and try to eat other cells to grow bigger. The game is simple but addictive, and it has millions of players around the world.
-However, playing Agar.io can also be frustrating, especially when you encounter bigger and faster cells that can easily devour you. You might also want to customize your cell with skins, colors, or nicknames, but you need coins to do that. And you might wish you could zoom out more to see the whole map and plan your strategy better.
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Fortunately, there is a way to overcome these challenges and enjoy Agar.io more. You can use a hack bot for Agar.io, which is a user script that modifies the game code and gives you access to various features that can enhance your gameplay. With a hack bot for Agar.io, you can get unlimited coins, unlimited mass, and unlimited zoom, as well as other functions such as macro feed, double split, minimap, chat, helpers, themes, and more.
-In this article, we will show you how to download and install a hack bot for Agar.io, how to use it, and some tips and tricks for using it. We will also answer some frequently asked questions about using a hack bot for Agar.io. By the end of this article, you will be able to play Agar.io like never before.
-How to Download and Install a Hack Bot for Agar.io
-The first step to using a hack bot for Agar.io is to download and install it on your browser. Here are the steps you need to follow:
-Step 1: Find a reliable source for the hack bot script
-A hack bot script is a piece of code that runs on your browser and modifies the game code of Agar.io. There are many sources where you can find hack bot scripts for Agar.io, but not all of them are safe, updated, or working. You need to find a reliable source that offers a hack bot script that is compatible with your browser, has good reviews, and has the features you want.
-One of the most popular and trusted sources for hack bot scripts for Agar.io is Agar.io Bots, which is a website that provides free and premium hack bot scripts for Agar.io. The free version of the script offers unlimited coins, mass, and zoom, as well as macro feed, double split, dark theme, and minimap. The premium version of the script offers more features such as chat, helpers, skins, colors, nicknames, themes, and more. You can also request custom features or report bugs on the website.
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To download the hack bot script from Agar.io Bots, you need to visit their website and click on the "Download" button. You will be redirected to a page where you can choose between the free or premium version of the script. You can also see the list of features and instructions for each version. Choose the version you want and click on the "Download" button again. You will get a file named "agario-hack-bot.user.js" on your computer.
-Step 2: Install a user script manager extension for your browser
-A user script manager extension is a tool that allows you to run user scripts on your browser. User scripts are pieces of code that modify the behavior or appearance of websites. A hack bot script for Agar.io is a user script that modifies the game code of Agar.io.
-There are many user script manager extensions available for different browsers, but one of the most popular and compatible ones is Tampermonkey, which is a free and open-source extension that works on Chrome, Firefox, Safari, Edge, Opera, and other browsers. Tampermonkey makes it easy to install, manage, and update user scripts on your browser.
-To install Tampermonkey on your browser, you need to visit their website and click on the "Download" button. You will be redirected to a page where you can choose your browser and download the extension. Follow the instructions on the page to install Tampermonkey on your browser. You will see a Tampermonkey icon on your browser toolbar after installation.
-Step 3: Install the hack bot script from the source
-Now that you have downloaded the hack bot script from Agar.io Bots and installed Tampermonkey on your browser, you need to install the hack bot script on Tampermonkey. This will allow Tampermonkey to run the hack bot script on Agar.io whenever you launch the game.
-To install the hack bot script on Tampermonkey, you need to open the file "agario-hack-bot.user.js" that you downloaded from Agar.io Bots. You can do this by double-clicking on the file or dragging it to your browser window. You will see a Tampermonkey installation page with the details of the hack bot script. Click on the "Install" button to install the hack bot script on Tampermonkey. You will see a confirmation message after installation.
-Step 4: Enable the hack bot script in your browser settings
-The final step to using a hack bot for Agar.io is to enable the hack bot script in your browser settings. This will allow Tampermonkey to run the hack bot script on Agar.io whenever you launch the game.
-To enable the hack bot script in your browser settings, you need to click on the Tampermonkey icon on your browser toolbar and select "Dashboard". You will see a list of user scripts that you have installed on Tampermonkey. Find the one named "Agar.io Hack Bot" and make sure it has a green check mark next to it. If it does not, click on it to enable it. You can also edit or delete the hack bot script from this page.
-How to Use a Hack Bot for Agar.io
-Now that you have installed and enabled a hack bot for Agar.io, you are ready to use it and enjoy the game more. Here are the steps you need to follow:
-Step 1: Launch Agar.io in your browser
-To use a hack bot for Agar.io, you need to launch Agar.io in your browser as usual. You can do this by visiting agar.io, which is the official website of the game. You will see the Agar.io logo and a menu with options such as "Play", "Skins", "Shop", "Settings", and "Social". You will also see a message saying "Agar.io Hack Bot is active" at the bottom of the screen. This means that the hack bot script is running and ready to use.
-Step 2: Configure the hack bot settings according to your preferences
-Before you start playing Agar.io with the hack bot, you might want to configure the hack bot settings according to your preferences. You can do this by clicking on the "Settings" option in the menu and then clicking on the "Hack Bot" tab. You will see a list of features and options that you can enable or disable, such as:
--
-
- Unlimited Coins: This feature allows you to get unlimited coins in Agar.io, which you can use to buy skins, colors, or nicknames for your cell. You can also set the amount of coins you want to get per second. -
- Unlimited Mass: This feature allows you to get unlimited mass in Agar.io, which means that your cell will never shrink or lose size. You can also set the amount of mass you want to start with and the maximum mass you want to reach. -
- Unlimited Zoom: This feature allows you to zoom out more in Agar.io, which means that you can see more of the map and other cells. You can also set the zoom level you want to use. -
- Macro Feed: This feature allows you to feed your cell faster and more efficiently by pressing a key. You can also set the key you want to use for macro feed. -
- Double Split: This feature allows you to split your cell twice by pressing a key. This can help you escape or attack other cells more easily. You can also set the key you want to use for double split. -
- Dark Theme: This feature allows you to change the background color of Agar.io to black, which can make it easier to see other cells and avoid distractions. You can also set the opacity of the dark theme. -
- Minimap: This feature allows you to see a small map of Agar.io on the top right corner of the screen, which can help you navigate and locate other cells. You can also set the size and position of the minimap. -
- Chat: This feature allows you to chat with other players who use the hack bot for Agar.io. You can also set the key you want to use for chat and the color of your chat messages. -
- Helpers: This feature allows you to see indicators and guides on Agar.io, such as arrows, circles, lines, or text, that can help you play better. You can also set the color and opacity of the helpers. -
- Skins: This feature allows you to choose from hundreds of skins for your cell, such as flags, animals, memes, logos, or celebrities. You can also set the skin mode and skin rotation speed. -
- Colors: This feature allows you to choose from different colors for your cell, such as rainbow, gradient, random, or custom. You can also set the color mode and color rotation speed. -
- Nicknames: This feature allows you to choose from different nicknames for your cell, such as funny, cool, or offensive. You can also set the nickname mode and nickname rotation speed. -
- Themes: This feature allows you to choose from different themes for Agar.io, such as winter, halloween, or space. You can also set the theme mode and theme rotation speed. -
You can enable or disable any of these features by clicking on the switch next to them. You can also adjust their settings by using the sliders or input boxes next to them. You can save your settings by clicking on the "Save" button at the bottom of the page. You can also reset your settings by clicking on the "Reset" button at the bottom of the page.
-Step 3: Enjoy the game with unlimited coins, mass, and zoom
-After you have configured the hack bot settings according to your preferences, you are ready to enjoy Agar.io with unlimited coins, mass, and zoom. You can do this by clicking on the "Play" option in the menu and entering a nickname for your cell. You will be taken to the game screen where you can see your cell, other cells, pellets, viruses, and borders. You can control your cell with your mouse and use the keys you have set for the hack bot features. You can also see your score, rank, and coins at the top of the screen.
-Your goal is to eat as many pellets and smaller cells as possible to grow bigger and avoid being eaten by bigger cells. You can also split your cell into smaller pieces to move faster or eat other cells, or eject some mass to feed other cells or viruses. You can use the hack bot features to gain an advantage over other players and have more fun. For example, you can use unlimited coins to buy skins, colors, or nicknames for your cell, unlimited mass to never shrink or lose size, unlimited zoom to see more of the map and other cells, macro feed to feed your cell faster and more efficiently, double split to split your cell twice, dark theme to improve your visibility, minimap to navigate and locate other cells, chat to communicate with other players, helpers to see indicators and guides, skins to choose from hundreds of skins, colors to choose from different colors, nicknames to choose from different nicknames, and themes to choose from different themes.
-You can play Agar.io with the hack bot for as long as you want and enjoy the game more. You can also switch between different game modes such as FFA, Teams, Experimental, or Party by clicking on the "Mode" option in the menu. You can also invite your friends to play with you by clicking on the "Social" option in the menu and sharing your party code with them.
-Tips and Tricks for Using a Hack Bot for Agar.io
-Using a hack bot for Agar.io can make the game more fun and easy, but it can also make it more challenging and risky. Here are some tips and tricks for using a hack bot for Agar.io:
-Tip 1: Use the macro feed and split functions to gain an edge over other players
-The macro feed and split functions are two of the most useful features of the hack bot for Agar.io. They allow you to feed your cell faster and more efficiently by pressing a key, and split your cell twice by pressing another key. These functions can help you escape or attack other cells more easily.
-For example, you can use the macro feed function to feed a virus near a bigger cell and make it explode into smaller pieces that you can eat. You can also use the macro feed function to feed a smaller cell that is allied with you and make it bigger and stronger. You can use the double split function to split your cell twice and catch a smaller cell that is running away from you. You can also use the double split function to split your cell twice and escape from a bigger cell that is chasing you.
-However, you need to be careful when using these functions, as they can also backfire on you. For example, you might accidentally feed a bigger cell that is hiding behind a virus or a smaller cell that is not allied with you. You might also accidentally split your cell into too many pieces that are vulnerable to being eaten by other cells.
-Tip 2: Use the dark theme and minimap to improve your visibility and awareness
-The dark theme and minimap are two of the most helpful features of the hack bot for Agar.io. They allow you to change the background color of Agar.io to black and see a small map of Agar.io on the top right corner of the screen. These features can help you improve your visibility and awareness in the game.
-For example, you can use the dark theme to make it easier to see other cells and avoid distractions from the bright colors of Agar.io. You can also use the dark theme to make it harder for other players to see you if you have a dark-colored skin or nickname. You can use the minimap to see more of the map and other cells and plan your strategy better. You can also use the minimap to locate pellets, viruses, or borders and avoid or target them accordingly.
-However, you need to be aware that the dark theme and minimap might not work on some game modes or servers, as they might be disabled or blocked by the game developers or administrators. You might also need to adjust the opacity of the dark theme and the size and position of the minimap according to your screen resolution and preference.
-Tip 3: Use the chat and helpers features to communicate and cooperate with other players
-The chat and helpers features are two of the most fun and social features of the hack bot for Agar.io. They allow you to chat with other players who use the hack bot for Agar.io and see indicators and guides on Agar.io that can help you play better. These features can help you communicate and cooperate with other players in the game.
-For example, you can use the chat feature to send messages to other players who use the hack bot for Agar.io and make friends, enemies, or allies with them. You can also use the chat feature to share tips, tricks, or jokes with them. You can use the helpers feature to see arrows, circles, lines, or text on Agar.io that can indicate where to go, what to do, or what to avoid. You can also use the helpers feature to see the names, skins, colors, or nicknames of other players who use the hack bot for Agar.io and identify them easily.
-However, you need to be respectful and polite when using these features, as they can also cause problems or conflicts with other players. For example, you might annoy or offend other players who use the hack bot for Agar.io or who do not use it with your messages or indicators. You might also reveal your position or strategy to other players who use the hack bot for Agar.io or who do not use it with your messages or indicators.
-Conclusion
-In conclusion, using a hack bot for Agar.io can make the game more fun and easy, but it can also make it more challenging and risky. You need to download and install a hack bot script from a reliable source, install a user script manager extension for your browser, enable the hack bot script in your browser settings, configure the hack bot settings according to your preferences, and enjoy the game with unlimited coins, mass, and zoom. You also need to use the macro feed and split functions to gain an edge over other players, use the dark theme and minimap to improve your visibility and awareness, and use the chat and helpers features to communicate and cooperate with other players. You also need to be careful when using these features, as they can also backfire on you or cause problems or conflicts with other players.
-If you are looking for a way to enhance your gameplay in Agar.io, you should try using a hack bot for Agar.io. It is free, easy, and fun to use. However, you should also be aware of the risks and responsibilities that come with using a hack bot for Agar.io. It is not a cheat or a guarantee of winning. It is a tool that can help you play better and have more fun.
-So what are you waiting for? Download a hack bot for Agar.io today and experience Agar.io like never before.
-FAQs
-Q1: Is using a hack bot for Agar.io safe and legal?
-A1: Using a hack bot for Agar.io is generally safe and legal, as long as you use it from a reliable source, install it on a trusted extension, enable it on a compatible browser, configure it according to your preferences, and enjoy it responsibly. However, there are some risks and consequences that you should be aware of when using a hack bot for Agar.io. For example:
--
-
- You might get banned or blocked from playing Agar.io if you use a hack bot for Agar.io that is detected or reported by the game developers or administrators. -
- You might get infected or hacked by malware or viruses if you download or install a hack bot for Agar.io that is from an untrusted source or extension. -
- You might get scammed or cheated by fake or fraudulent websites or services that claim to offer a hack bot for Agar.io but actually steal your personal information or money. -
- You might get disliked or hated by other players who do not use a hack bot for Agar.io or who think that using a hack bot for Agar.io is unfair or unethical. -
Therefore, you should always be careful and cautious when using a hack bot for Agar.io. You should only use it from a reliable source, install it on a trusted extension, enable it on a compatible browser, configure it according to your preferences, and enjoy it responsibly. You should also respect the rules and rights of the game developers, administrators, and other players when using a hack bot for Agar.io.
-Q2: How can I update or uninstall the hack bot for Agar.io?
-A2: Updating or uninstalling the hack bot for Agar.io is easy and simple. You can do it by following these steps:
-Updating the hack bot for Agar.io
-To update the hack bot for Agar.io, you need to visit the source where you downloaded the hack bot script from and check if there is a newer version available. If there is, you need to download the new version of the hack bot script and install it on Tampermonkey. You can also enable the auto-update feature on Tampermonkey to automatically update the hack bot script whenever there is a new version available.
-Uninstalling the hack bot for Agar.io
-To uninstall the hack bot for Agar.io, you need to click on the Tampermonkey icon on your browser toolbar and select "Dashboard". You will see a list of user scripts that you have installed on Tampermonkey. Find the one named "Agar.io Hack Bot" and click on the trash icon next to it. You will see a confirmation message asking if you want to delete the hack bot script. Click on "OK" to delete the hack bot script from Tampermonkey. You can also disable the hack bot script by clicking on the switch next to it.
-Q3: What are some alternatives to using a hack bot for Agar.io?
-A3: Using a hack bot for Agar.io is not the only way to enhance your gameplay in Agar.io. There are some alternatives that you can try, such as:
--
-
- Using mods or extensions for Agar.io: Mods or extensions are tools that modify or add features to Agar.io, such as skins, colors, nicknames, themes, sounds, or modes. There are many mods or extensions available for different browsers, such as Agario Mods, Agario Powerups, or Agario Extended. You can download and install them from their websites or from your browser's web store. -
- Using cheats or codes for Agar.io: Cheats or codes are commands that activate or deactivate certain functions or effects in Agar.io, such as mass, speed, invisibility, or invincibility. There are many cheats or codes available for Agar.io, such as Agario Cheats, Agario Codes, or Agario Hacks. You can find them online or in some forums or videos. -
- Using tips or tricks for Agar.io: Tips or tricks are strategies or techniques that can help you play better and have more fun in Agar.io, such as how to move, split, feed, merge, hide, chase, escape, or cooperate with other players. There are many tips or tricks available for Agar.io, such as Agario Tips, Agario Tricks, or Agario Guide. You can learn them online or from other players. -
However, you need to be careful and cautious when using these alternatives, as they might also have some risks and consequences similar to using a hack bot for Agar.io. For example:
--
-
- You might get banned or blocked from playing Agar.io if you use mods, extensions, cheats, or codes that are detected or reported by the game developers or administrators. -
- You might get infected or hacked by malware or viruses if you download or install mods, extensions, cheats, or codes that are from untrusted sources or websites. -
- You might get scammed or cheated by fake or fraudulent websites or services that claim to offer mods, extensions, cheats, or codes for Agar.io but actually steal your personal information or money. -
- You might get disliked or hated by other players who do not use mods, extensions, cheats, or codes for Agar.io or who think that using them is unfair or unethical. -
Therefore, you should always be careful and cautious when using these alternatives. You should only use them from reliable sources, install them on trusted extensions, enable them on compatible browsers, configure them according to your preferences, and enjoy them responsibly. You should also respect the rules and rights of the game developers, administrators, and other players when using them.
-Q4: How can I report or avoid players who use a hack bot for Agar.io?
-A4: If you encounter players who use a hack bot for Agar.io and you think that they are ruining the game for you or others, you can report or avoid them in different ways. For example:
--
-
- Report them to the game developers or administrators: You can report players who use a hack bot for Agar.io to the game developers or administrators by sending them an email, a message, or a feedback form with the details of the players, such as their names, skins, colors, nicknames, or screenshots. You can find the contact information of the game developers or administrators on their website or social media accounts. -
- Report them to the hack bot source or extension: You can report players who use a hack bot for Agar.io to the source or extension where they got the hack bot script from by sending them an email, a message, or a feedback form with the details of the players, such as their names, skins, colors, nicknames, or screenshots. You can find the contact information of the source or extension on their website or web store. -
- Avoid them by switching game modes or servers: You can avoid players who use a hack bot for Agar.io by switching to a different game mode or server where they are not present or less common. You can do this by clicking on the "Mode" option in the menu and choosing a different game mode such as FFA, Teams, Experimental, or Party. You can also click on the "Region" option in the menu and choosing a different server such as North America, South America, Europe, Asia, Oceania, or Russia. -
- Avoid them by playing with your friends: You can avoid players who use a hack bot for Agar.io by playing with your friends who do not use a hack bot for Agar.io or who use it responsibly. You can do this by clicking on the "Social" option in the menu and creating or joining a party with your friends. You can also invite your friends to play with you by sharing your party code with them. -
However, you need to be fair and respectful when reporting or avoiding players who use a hack bot for Agar.io. You should not report or avoid players who use a hack bot for Agar.io just because they are better than you or because you do not like them. You should also not harass or insult players who use a hack bot for Agar.io just because they use it. You should also remember that some players who use a hack bot for Agar.io might be newbies or kids who do not know better or who just want to have fun. You should try to be understanding and tolerant of them and maybe even teach them how to play better and have more fun without using a hack bot for Agar.io.
-Q5: Where can I find more information or support for using a hack bot for Agar.io?
-A5: If you need more information or support for using a hack bot for Agar.io, you can find it in different places. For example:
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- Find it on the source or extension website: You can find more information or support for using a hack bot for Agar.io on the website of the source or extension where you got the hack bot script from. You can find FAQs, tutorials, videos, forums, blogs, or contact forms that can help you with your questions or issues. -
- Find it on the game website or social media accounts: You can find more information or support for using a hack bot for Agar.io on the website or social media accounts of the game developers or administrators. You can find news, updates, announcements, events, contests, polls, surveys, or feedback forms that can help you with your questions or issues. -
- Find it on the online communities or platforms: You can find more information or support for using a hack bot for Agar.io on the online communities or platforms where other players who use a hack bot for Agar.io gather and share their experiences and opinions. You can find Reddit, Discord, YouTube, Facebook, Twitter, Instagram, or TikTok pages, groups, channels, or accounts that can help you with your questions or issues. -
However, you need to be careful and cautious when finding information or support for using a hack bot for Agar.io. You should not trust or follow any information or support that is from an unknown, unverified, or suspicious source, website, extension, account, or person. You should also not share any personal information or money with anyone who claims to offer information or support for using a hack bot for Agar.io. You should also verify and cross-check any information or support that you find with other sources before using it.
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Experience the Thrill of Flying with Indian Air Force A Cut Above Game APK.md b/spaces/congsaPfin/Manga-OCR/logs/Experience the Thrill of Flying with Indian Air Force A Cut Above Game APK.md deleted file mode 100644 index 22352541ca7f6f49d212be4226732f59ab6568e2..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Experience the Thrill of Flying with Indian Air Force A Cut Above Game APK.md +++ /dev/null @@ -1,101 +0,0 @@ -
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Indian Air Force: A Cut Above - A Mobile Game for Aspiring Pilots
-Have you ever dreamed of becoming a fighter pilot in the Indian Air Force (IAF)? Do you want to experience the thrill of flying some of the most advanced aircraft in the world? If yes, then you should try out Indian Air Force: A Cut Above, a mobile game that lets you simulate various roles of an IAF air warrior.
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Introduction
-Indian Air Force: A Cut Above is a free air combat mobile gaming application developed by Threye Interactive, a Delhi-based game development studio. It was officially launched on 31 July 2019 by the Air Chief Marshal B. S. Dhanoa for Android and iOS devices. The game is part of IAF's promotional exercise to attract youth to join the profession of military aviation.
-The game has several features that make it realistic and engaging. It allows the player to control a variety of IAF's aircraft assets, including the MI-17 V5 helicopter, the Mirage 2000, and the Rafale. It also features a character similar to Wing Commander Abhinandan Varthaman, who shot down a Pakistani F-16 in February 2019. The game has three difficulty levels and three game modes: training, single player, and free flight. The developers have also announced that they will introduce a multiplayer mode in October 2019.
-How to Download and Install the Game
-The game is available for both Android and iOS devices. However, there are some minimum system requirements that need to be met before downloading and installing the game. For Android devices, the game needs at least Android 5.0 or higher, 2 GB of RAM, and 5 GB of free disk space. For iOS devices, the game needs at least iOS 9 or higher, 4 GB of RAM, and 1.1 GB of free disk space.
-The download size of the game is about 330 MB on Android and 1.1 GB on iOS. The game can be downloaded from Google Play Store or Apple App Store by searching for "Indian Air Force: A Cut Above" or by clicking on these links:
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The game requires some permissions from the user to access certain features. These include access to photos, files, phone calls, internet connection, location, camera, microphone, etc. The user also needs to create a separate login ID and password to play all the modes of the game.
-How to Play the Game
-The game offers three modes of gameplay: training, single player, and free flight. Each mode has its own objectives and challenges.
-Training Mode
-The training mode teaches the player the basics of flying and combat. It consists of four lessons: takeoff, landing, navigation, and weapons. The player has to follow the instructions of the instructor and complete the tasks given. The player can also practice flying in different weather conditions and time of day. The training mode is essential for learning the controls and mechanics of the game.
-Single Player Mode
-The single player mode offers 10 missions based on real-life scenarios that IAF has faced or may face in the future. The missions include air strikes, air-to-air combat, air-to-ground combat, rescue operations, reconnaissance, etc. The player has to select an aircraft and a weapon loadout for each mission and complete the objectives within a given time limit. The missions are graded on the basis of accuracy, speed, and damage. The player can also earn medals and badges for completing the missions with excellence.
-Free Flight Mode
-The free flight mode lets the player fly any aircraft in any location without any restrictions or objectives. The player can choose from a variety of locations, such as Leh, Srinagar, Port Blair, Car Nicobar, etc. The player can also customize the weather conditions and time of day. The free flight mode is ideal for exploring the game's graphics and physics and enjoying the freedom of flying.
-Multiplayer Mode
-The multiplayer mode is an upcoming feature that will allow players to compete with each other online. The multiplayer mode will have two sub-modes: team deathmatch and capture the flag. In team deathmatch, two teams of players will try to shoot down each other's aircraft and score points. In capture the flag, two teams of players will try to capture each other's base and defend their own. The multiplayer mode will also have leaderboards and rankings to show the performance of the players.
-Tips and Tricks for Playing the Game
-The game is not very difficult to play, but it does require some skills and strategies to master it. Here are some tips and tricks for playing the game:
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- Use the motion controls and buttons effectively. The game allows the player to control the aircraft using either motion controls or buttons. The motion controls use the device's accelerometer and gyroscope to tilt and turn the aircraft. The buttons use virtual joysticks and buttons to control the aircraft's speed, altitude, direction, etc. The player can choose whichever option suits them better, but they should practice both to get familiar with them. -
- Choose the right aircraft and weapons for each mission. The game offers a variety of aircraft and weapons to choose from, but not all of them are suitable for every mission. The player should read the mission briefings carefully and select an aircraft and a weapon loadout that matches the mission objectives and requirements. For example, for air-to-ground combat missions, the player should choose an aircraft that has bombs or rockets, such as the Mirage 2000 or the Rafale. For air-to-air combat missions, the player should choose an aircraft that has missiles or guns, such as the MiG-21 or the Sukhoi-30. -
- Avoid crashing and losing hitpoints. The game has realistic physics and damage models that make flying challenging and risky. The player should avoid crashing into obstacles or enemies, as this will cause damage to their aircraft and reduce their hitpoints. The player should also avoid flying too low or too high, as this will affect their performance and visibility. The player should also monitor their fuel level and radar alerts to avoid running out of fuel or being detected by enemies. -
- Earn rewards and unlock new features. The game rewards the player with coins and stars for completing missions and achieving goals. The coins can be used to buy new aircraft and weapons from the hangar. The stars can be used to unlock new features, such as skins, decals, badges, etc. The player can also earn achievements for performing certain actions or reaching certain milestones in the game. -
Conclusion
-Indian Air Force: A Cut Above is a mobile game that simulates various roles of an IAF air warrior. It is a fun and educational game that teaches the player about IAF's history, culture, values, and operations. It is also a game that inspires and motivates the player to join IAF as a pilot or a technician. It is a game that showcases IAF's capabilities and achievements in defending India's sovereignty and security.
-If you are interested in playing Indian Air Force: A Cut Above, you can download it from Google Play Store or Apple App Store for free. You can also visit IAF's official website or social media pages for more information about IAF's career opportunities and events.
-So what are you waiting for? Download Indian Air Force: A Cut Above today and experience the thrill of flying with IAF!
FAQs -Here are some frequently asked questions and answers related to the game:
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- Is the game based on real events? -
- How realistic is the game? -
- Can I play the game offline? -
- Can I customize my aircraft? -
- Can I interact with other players? -
Yes, the game is based on real events that IAF has faced or may face in the future. The game depicts some of the historical and contemporary missions that IAF has undertaken, such as the Balakot airstrike, the Kargil war, the surgical strike, etc. The game also features some of the real-life aircraft and weapons that IAF uses or plans to use, such as the Rafale, the Tejas, the BrahMos, etc.
-The game is realistic in terms of graphics, physics, and damage models. The game uses high-quality 3D models and textures to create realistic environments and aircraft. The game also uses realistic physics and aerodynamics to simulate the flight dynamics and performance of the aircraft. The game also uses realistic damage models to show the effects of bullets, missiles, bombs, etc. on the aircraft and the environment.
-Yes, you can play the game offline. The game does not require an internet connection to play the training mode, the single player mode, or the free flight mode. However, you will need an internet connection to play the multiplayer mode, to access some of the features of the game, such as skins, decals, badges, etc., and to update the game with new content and patches.
-Yes, you can customize your aircraft. The game allows you to choose from a variety of aircraft and weapons for each mission. You can also change the appearance of your aircraft by applying different skins, decals, badges, etc. You can earn these customization options by completing missions and earning coins and stars.
-Yes, you can interact with other players. The game has a chat feature that lets you communicate with other players online. You can also join or create a squadron with other players and fly together in the multiplayer mode. You can also challenge other players to a duel or a race in the free flight mode.
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/FIFA 19 Download Key The Ultimate Guide to Enjoy the Game.md b/spaces/congsaPfin/Manga-OCR/logs/FIFA 19 Download Key The Ultimate Guide to Enjoy the Game.md deleted file mode 100644 index c967b8682854b43ef66e5dcae29109678d05f016..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/FIFA 19 Download Key The Ultimate Guide to Enjoy the Game.md +++ /dev/null @@ -1,108 +0,0 @@ - -
Download Key for FIFA 19: How to Get It and Play the Game
-If you are a fan of football simulation games, you might have heard of FIFA 19, the latest installment in the popular FIFA series developed by EA Sports. FIFA 19 is a game that offers a realistic and immersive experience of playing football, with stunning graphics, authentic gameplay, and a variety of modes and features. But how can you get a download key for FIFA 19 and enjoy the game on your PC or console? In this article, we will explain what FIFA 19 is, what are its features and system requirements, and how you can download key for FIFA 19 from different sources.
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What is FIFA 19?
-FIFA 19 is a football simulation video game that was released in September 2018 by EA Sports. It is the 26th edition of the FIFA series, and the third and final chapter of the story mode called The Journey, which follows the career of Alex Hunter, a young footballer who tries to make his mark in the world of football. FIFA 19 also features the UEFA Champions League, Europa League, and Super Cup competitions, as well as new gameplay innovations such as Active Touch System, Dynamic Tactics, Timed Finishing, and more.
-Features of FIFA 19
-FIFA 19 has many features that make it one of the best football games ever made. Some of these features are:
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- UEFA Champions League: For the first time in the FIFA series, you can play in the prestigious UEFA Champions League, Europa League, and Super Cup competitions, with official match presentation, commentary, and graphics. You can also create your own custom tournaments with any European club. -
- The Journey: Champions: The story mode of FIFA 19 concludes the saga of Alex Hunter, who joins Real Madrid and competes for the Champions League trophy. You can also play as Alex's sister Kim Hunter, who plays for the US women's national team in the Women's World Cup, and his friend Danny Williams, who plays for a Premier League club. -
- Kick Off Mode: The classic mode of playing a single match with any team has been revamped with new options and features. You can play in different match types such as Classic Match, UEFA Champions League, Home & Away, Best Of Series, Cup Finals, and House Rules. You can also track your stats and records against your friends with Kick Off Name. -
- Ultimate Team: The most popular mode of FIFA 19 allows you to create your own dream team with players from different leagues and eras. You can compete in online and offline modes such as Division Rivals, Squad Battles, FUT Champions, FUT Draft, and more. You can also enjoy new content such as Icons, Ones to Watch, Team of the Year, Team of the Season, and more. -
- Career Mode: The mode that lets you manage your own club or play as a single player has been improved with new features such as interactive press conferences, player morale system, dynamic player potential, and more. You can also experience the UEFA Champions League and Europa League in your career. -
- Other Modes: FIFA 19 also has other modes such as Pro Clubs, Online Seasons, Co-Op Seasons, Skill Games, Practice Arena, and more. -
System Requirements for FIFA 19
-If you want to play FIFA 19 on your PC, you need to make sure that your system meets the minimum or recommended requirements for the game. According to EA Sports, these are the system requirements for FIFA 19:
-| How to Download Key for FIFA 19?
- Now that you know what FIFA 19 is and what are its features and system requirements, you might be wondering how to download key for FIFA 19 and play the game on your PC or console. There are two main sources of getting a download key for FIFA 19: official and unofficial. Let's take a look at each of them and see their pros and cons. -Official Sources-The official sources of getting a download key for FIFA 19 are the ones that are authorized by EA Sports, the developer and publisher of the game. These sources include EA Origin, Steam, PlayStation Store, and Xbox Store. These are the most reliable and secure ways of getting a download key for FIFA 19, as they guarantee that you will get a valid and working key that will allow you to play the game without any issues. However, these sources also have some disadvantages, such as higher prices, regional restrictions, and limited availability. -download key for fifa 19 pc EA Origin-EA Origin is the official digital distribution platform of EA Sports, where you can buy and download games developed by EA Sports, including FIFA 19. You can also access other features such as cloud saves, achievements, friends list, chat, and more. To get a download key for FIFA 19 from EA Origin, you need to create an account on the website, add the game to your cart, and complete the payment process. You will then receive an email with your download key, which you can redeem on the Origin client or website. You can also buy a physical copy of the game from a retailer or online store, which will come with a download key that you can redeem on EA Origin. -Steam-Steam is another popular digital distribution platform that offers a wide range of games from different developers and publishers, including EA Sports. Steam also has features such as cloud saves, achievements, friends list, chat, and more. To get a download key for FIFA 19 from Steam, you need to create an account on the website, add the game to your cart, and complete the payment process. You will then receive an email with your download key, which you can redeem on the Steam client or website. You can also buy a physical copy of the game from a retailer or online store, which will come with a download key that you can redeem on Steam. -PlayStation Store-PlayStation Store is the official online store of Sony PlayStation, where you can buy and download games for your PlayStation 4 console, including FIFA 19. You can also access other features such as cloud saves, trophies, friends list, chat, and more. To get a download key for FIFA 19 from PlayStation Store, you need to create an account on the website or on your console, add the game to your cart, and complete the payment process. You will then receive an email with your download key, which you can redeem on your console or on the website. You can also buy a physical copy of the game from a retailer or online store, which will come with a download key that you can redeem on PlayStation Store. -Xbox Store-Xbox Store is the official online store of Microsoft Xbox, where you can buy and download games for your Xbox One console, including FIFA 19. You can also access other features such as cloud saves, achievements, friends list, chat, and more. To get a download key for FIFA 19 from Xbox Store, you need to create an account on the website or on your console, add the game to your cart, and complete the payment process. You will then receive an email with your download key, which you can redeem on your console or on the website. You can also buy a physical copy of the game from a retailer or online store, which will come with a download key that you can redeem on Xbox Store. -Unofficial Sources-The unofficial sources of getting a download key for FIFA 19 are the ones that are not authorized by EA Sports, but claim to offer download keys for FIFA 19 at lower prices or for free. These sources include key generator sites, torrent sites , and other shady websites. These are the most risky and unreliable ways of getting a download key for FIFA 19, as they may not work, may contain viruses or malware, may get your account banned, or may even be illegal. We do not recommend using these sources, as they may cause you more harm than good. -Key Generator Sites-Key generator sites are websites that claim to generate random download keys for FIFA 19 or other games, which you can use to activate the game on your PC or console. These sites usually ask you to complete surveys, download apps, or perform other tasks in order to get the key. However, most of these sites are scams, as they either do not give you a valid key, or give you a key that has already been used by someone else. Some of these sites may also infect your device with viruses or malware, or steal your personal information. -Torrent Sites-Torrent sites are websites that allow you to download files from other users who share them, using a peer-to-peer network. Some of these files may be download keys for FIFA 19 or other games, which you can use to activate the game on your PC or console. However, most of these files are fake, as they either do not work, or contain viruses or malware that can harm your device. Some of these files may also be illegal, as they violate the copyright laws and the terms of service of EA Sports. Downloading and using these files may get your account banned, or even get you in trouble with the law. -Risks and Disadvantages of Unofficial Sources-As you can see, using unofficial sources to get a download key for FIFA 19 is not a good idea, as it comes with many risks and disadvantages. Some of these are: -
Conclusion-In conclusion, FIFA 19 is a great football simulation game that offers a realistic and immersive experience of playing football, with stunning graphics, authentic gameplay, and a variety of modes and features. However, if you want to play the game on your PC or console, you need to get a download key for FIFA 19 from a reliable and secure source. The best way to do that is to use official sources such as EA Origin, Steam, PlayStation Store, and Xbox Store, which guarantee that you will get a valid and working key that will allow you to play the game without any issues. However, these sources may have some disadvantages such as higher prices, regional restrictions, and limited availability. You should avoid using unofficial sources such as key generator sites, torrent sites , and other shady websites, which may not work, may contain viruses or malware, may get your account banned, or may even be illegal. We hope that this article has helped you understand how to download key for FIFA 19 and play the game on your PC or console. If you have any questions or comments, feel free to leave them below. -FAQs-Here are some frequently asked questions about downloading key for FIFA 19: -
- - \ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Money Quiz Can You Answer These Questions About Money?.md b/spaces/congsaPfin/Manga-OCR/logs/Money Quiz Can You Answer These Questions About Money?.md deleted file mode 100644 index 6960ed04db6018a8be1b4682d2587fe25b3dfe50..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Money Quiz Can You Answer These Questions About Money?.md +++ /dev/null @@ -1,123 +0,0 @@ - - Quiz Money: What Is It and How to Earn From It?-Do you love trivia games and quizzes? Do you want to test your knowledge and learn new things while having fun? Do you also want to earn some extra cash or rewards for your efforts? If you answered yes to any of these questions, then you might be interested in quiz money. -Quiz money is a term that refers to the phenomenon of earning money or other incentives by playing online quizzes. It is a popular way of making money online, especially among students, freelancers, and anyone who wants to earn some passive income. In this article, we will explain what quiz money is, how it works, how to choose the best quiz money app or website, and how to maximize your earnings from it. Let's get started! -quiz moneyDownload Zip ->>> https://urlca.com/2uObyF - How Does Quiz Money Work?-The basic concept of quiz money is simple: you sign up for an app or a website that offers quizzes on various topics, such as general knowledge, sports, entertainment, history, geography, science, etc. You then choose a quiz that suits your interest and skill level, and answer the questions within a given time limit. Depending on your performance, you will earn points, coins, cash, gift cards, vouchers, or other rewards. Some platforms also offer bonuses, referrals, daily challenges, leaderboards, tournaments, and other features to make the experience more exciting and rewarding. -The types of quizzes and rewards vary from platform to platform. Some platforms offer multiple-choice questions, while others offer true/false questions, fill-in-the-blanks, image recognition, etc. Some platforms offer cash rewards that can be withdrawn via PayPal or other methods, while others offer gift cards from Amazon, iTunes, Google Play, etc. Some platforms offer fixed rewards for each quiz, while others offer variable rewards based on the difficulty level and the number of participants. Some platforms require a minimum amount of points or coins to redeem rewards, while others allow instant redemption. -The pros and cons of quiz money also depend on the platform you choose. The main advantages of quiz money are that it is fun, easy, flexible, educational, and potentially lucrative. You can play quizzes anytime and anywhere, as long as you have an internet connection and a device. You can also learn new facts and information while playing quizzes. You can also earn some extra income or rewards that can help you with your expenses or savings. However, there are also some drawbacks of quiz money that you should be aware of. The main disadvantages are that it is competitive, addictive, unpredictable, and risky. You may face tough competition from other players who are more knowledgeable or faster than you. You may also get addicted to playing quizzes and neglect your other responsibilities or hobbies. You may also face fluctuations in your earnings or rewards due to changes in the platform's policies or algorithms. You may also encounter scams or frauds that may steal your personal information or money. -How to Choose the Best Quiz Money App or Website?-With so many quiz money platforms available online, how do you choose the best one for you? Here are some criteria that you should look for when selecting a quiz money platform: -
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How to Maximize Your Earnings from Quiz Money?-Now that you know how quiz money works and how to choose the best quiz money app or website, you might be wondering how to maximize your earnings from it. Here are some tips and tricks that can help you increase your chances of winning quizzes and getting more rewards: -
Conclusion-Quiz money is a great way to have fun, learn new things, and earn some extra income or rewards online. However, you need to be careful and smart when choosing and playing quiz money platforms. You need to check the reputation, variety, rewards, and user experience of each platform before signing up. You also need to play regularly, strategically, and safely to maximize your earnings from quiz money. If you follow these tips and tricks, you will be able to enjoy quiz money without any hassle or risk. So what are you waiting for? Start playing quiz money today and see how much you can earn! -FAQs-Here are some frequently asked questions and answers about quiz money: -quiz money app
Yes, quiz money is legal as long as the platform you use is registered and licensed by the relevant authorities. However, you may need to check the laws and regulations of your country or state regarding online gambling or taxation before playing quiz money. -Quiz money is fair as long as the platform you use is transparent and honest about its rules, policies, algorithms, and outcomes. However, you may encounter some platforms that are rigged, biased, or fraudulent. You should avoid these platforms and report them to the authorities or the review sites. -Quiz money is worth it if you enjoy playing quizzes and earning rewards. However, you should not expect to make a lot of money or quit your job from quiz money. Quiz money is best seen as a hobby or a side hustle that can supplement your income or savings. 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-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/crashedice/signify/signify/gan/util/__init__.py b/spaces/crashedice/signify/signify/gan/util/__init__.py deleted file mode 100644 index ae36f63d8859ec0c60dcbfe67c4ac324e751ddf7..0000000000000000000000000000000000000000 --- a/spaces/crashedice/signify/signify/gan/util/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""This package includes a miscellaneous collection of useful helper functions.""" diff --git a/spaces/cxm1207/ChatBOT/ChuanhuChatbot.py b/spaces/cxm1207/ChatBOT/ChuanhuChatbot.py deleted file mode 100644 index 086dc6a1e3da91f4078e163ffac03ab54ed0a7d0..0000000000000000000000000000000000000000 --- a/spaces/cxm1207/ChatBOT/ChuanhuChatbot.py +++ /dev/null @@ -1,159 +0,0 @@ -import gradio as gr -# import openai -import os -import sys -import argparse -from utils import * -from presets import * - - -my_api_key = "" # 在这里输入你的 API 密钥 - -#if we are running in Docker -if os.environ.get('dockerrun') == 'yes': - dockerflag = True -else: - dockerflag = False - -authflag = False - -if dockerflag: - my_api_key = os.environ.get('my_api_key') - if my_api_key == "empty": - print("Please give a api key!") - sys.exit(1) - #auth - username = os.environ.get('USERNAME') - password = os.environ.get('PASSWORD') - if not (isinstance(username, type(None)) or isinstance(password, type(None))): - authflag = True -else: - if not my_api_key and os.path.exists("api_key.txt") and os.path.getsize("api_key.txt"): - with open("api_key.txt", "r") as f: - my_api_key = f.read().strip() - if os.path.exists("auth.json"): - with open("auth.json", "r") as f: - auth = json.load(f) - username = auth["username"] - password = auth["password"] - if username != "" and password != "": - authflag = True - -gr.Chatbot.postprocess = postprocess - -with gr.Blocks(css=customCSS) as demo: - gr.HTML(title) - with gr.Row(): - keyTxt = gr.Textbox(show_label=False, placeholder=f"在这里输入你的OpenAI API-key...", - value=my_api_key, type="password", visible=not HIDE_MY_KEY).style(container=True) - use_streaming_checkbox = gr.Checkbox(label="实时传输回答", value=True, visible=enable_streaming_option) - chatbot = gr.Chatbot() # .style(color_map=("#1D51EE", "#585A5B")) - history = gr.State([]) - token_count = gr.State([]) - promptTemplates = gr.State(load_template(get_template_names(plain=True)[0], mode=2)) - TRUECOMSTANT = gr.State(True) - FALSECONSTANT = gr.State(False) - topic = gr.State("未命名对话历史记录") - - with gr.Row(): - with gr.Column(scale=12): - user_input = gr.Textbox(show_label=False, placeholder="在这里输入").style( - container=False) - with gr.Column(min_width=50, scale=1): - submitBtn = gr.Button("🚀", variant="primary") - with gr.Row(): - emptyBtn = gr.Button("🧹 新的对话") - retryBtn = gr.Button("🔄 重新生成") - delLastBtn = gr.Button("🗑️ 删除最近一条对话") - reduceTokenBtn = gr.Button("♻️ 总结对话") - status_display = gr.Markdown("status: ready") - systemPromptTxt = gr.Textbox(show_label=True, placeholder=f"在这里输入System Prompt...", - label="System prompt", value=initial_prompt).style(container=True) - with gr.Accordion(label="加载Prompt模板", open=False): - with gr.Column(): - with gr.Row(): - with gr.Column(scale=6): - templateFileSelectDropdown = gr.Dropdown(label="选择Prompt模板集合文件", choices=get_template_names(plain=True), multiselect=False, value=get_template_names(plain=True)[0]) - with gr.Column(scale=1): - templateRefreshBtn = gr.Button("🔄 刷新") - templaeFileReadBtn = gr.Button("📂 读入模板") - with gr.Row(): - with gr.Column(scale=6): - templateSelectDropdown = gr.Dropdown(label="从Prompt模板中加载", choices=load_template(get_template_names(plain=True)[0], mode=1), multiselect=False, value=load_template(get_template_names(plain=True)[0], mode=1)[0]) - with gr.Column(scale=1): - templateApplyBtn = gr.Button("⬇️ 应用") - with gr.Accordion(label="保存/加载对话历史记录", open=False): - with gr.Column(): - with gr.Row(): - with gr.Column(scale=6): - saveFileName = gr.Textbox( - show_label=True, placeholder=f"在这里输入保存的文件名...", label="设置保存文件名", value="对话历史记录").style(container=True) - with gr.Column(scale=1): - saveHistoryBtn = gr.Button("💾 保存对话") - with gr.Row(): - with gr.Column(scale=6): - historyFileSelectDropdown = gr.Dropdown(label="从列表中加载对话", choices=get_history_names(plain=True), multiselect=False, value=get_history_names(plain=True)[0]) - with gr.Column(scale=1): - historyRefreshBtn = gr.Button("🔄 刷新") - historyReadBtn = gr.Button("📂 读入对话") - #inputs, top_p, temperature, top_k, repetition_penalty - with gr.Accordion("参数", open=False): - top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.05, - interactive=True, label="Top-p (nucleus sampling)",) - temperature = gr.Slider(minimum=-0, maximum=5.0, value=1.0, - step=0.1, interactive=True, label="Temperature",) - #top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",) - #repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", ) - gr.Markdown(description) - - - user_input.submit(predict, [keyTxt, systemPromptTxt, history, user_input, chatbot, token_count, top_p, temperature, use_streaming_checkbox], [chatbot, history, status_display, token_count], show_progress=True) - user_input.submit(reset_textbox, [], [user_input]) - - submitBtn.click(predict, [keyTxt, systemPromptTxt, history, user_input, chatbot, token_count, top_p, temperature, use_streaming_checkbox], [chatbot, history, status_display, token_count], show_progress=True) - submitBtn.click(reset_textbox, [], [user_input]) - - emptyBtn.click(reset_state, outputs=[chatbot, history, token_count, status_display], show_progress=True) - - retryBtn.click(retry, [keyTxt, systemPromptTxt, history, chatbot, token_count, top_p, temperature, use_streaming_checkbox], [chatbot, history, status_display, token_count], show_progress=True) - - delLastBtn.click(delete_last_conversation, [chatbot, history, token_count, use_streaming_checkbox], [ - chatbot, history, token_count, status_display], show_progress=True) - - reduceTokenBtn.click(reduce_token_size, [keyTxt, systemPromptTxt, history, chatbot, token_count, top_p, temperature, use_streaming_checkbox], [chatbot, history, status_display, token_count], show_progress=True) - - saveHistoryBtn.click(save_chat_history, [ - saveFileName, systemPromptTxt, history, chatbot], None, show_progress=True) - - saveHistoryBtn.click(get_history_names, None, [historyFileSelectDropdown]) - - historyRefreshBtn.click(get_history_names, None, [historyFileSelectDropdown]) - - historyReadBtn.click(load_chat_history, [historyFileSelectDropdown, systemPromptTxt, history, chatbot], [saveFileName, systemPromptTxt, history, chatbot], show_progress=True) - - templateRefreshBtn.click(get_template_names, None, [templateFileSelectDropdown]) - - templaeFileReadBtn.click(load_template, [templateFileSelectDropdown], [promptTemplates, templateSelectDropdown], show_progress=True) - - templateApplyBtn.click(get_template_content, [promptTemplates, templateSelectDropdown, systemPromptTxt], [systemPromptTxt], show_progress=True) - -print("川虎的温馨提示：访问 http://localhost:7860 查看界面") -# 默认开启本地服务器，默认可以直接从IP访问，默认不创建公开分享链接 -demo.title = "川虎ChatGPT 🚀" - -if __name__ == "__main__": - #if running in Docker - if dockerflag: - if authflag: - demo.queue().launch(server_name="0.0.0.0", server_port=7860,auth=(username, password)) - else: - demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False) - #if not running in Docker - else: - if authflag: - demo.queue().launch(share=False, auth=(username, password)) - else: - demo.queue().launch(share=False) # 改为 share=True 可以创建公开分享链接 - #demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False) # 可自定义端口 - #demo.queue().launch(server_name="0.0.0.0", server_port=7860,auth=("在这里填写用户名", "在这里填写密码")) # 可设置用户名与密码 - #demo.queue().launch(auth=("在这里填写用户名", "在这里填写密码")) # 适合Nginx反向代理 diff --git a/spaces/cybercorejapan/human-detection-docker/models/detectors/__init__.py b/spaces/cybercorejapan/human-detection-docker/models/detectors/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/damo-vilab/modelscope-text-to-video-synthesis/style.css b/spaces/damo-vilab/modelscope-text-to-video-synthesis/style.css deleted file mode 100644 index 2f399e973fe275a7299ae85c6a85bd5d35eb64cb..0000000000000000000000000000000000000000 --- a/spaces/damo-vilab/modelscope-text-to-video-synthesis/style.css +++ /dev/null @@ -1,191 +0,0 @@ -/* -This CSS file is copied from here: -https://huggingface.co/spaces/stabilityai/stable-diffusion/blob/2794a3c3ba66115c307075098e713f572b08bf80/app.py -*/ - -h1 { - text-align: center; -} - -.gradio-container { - font-family: 'IBM Plex Sans', sans-serif; -} - -.gr-button { - color: white; - border-color: black; - background: black; -} - -input[type='range'] { - accent-color: black; -} - -.dark input[type='range'] { - accent-color: #dfdfdf; -} - -.container { - max-width: 730px; - margin: auto; - padding-top: 1.5rem; -} - -#gallery { - min-height: 22rem; - margin-bottom: 15px; - margin-left: auto; - margin-right: auto; - border-bottom-right-radius: .5rem !important; - border-bottom-left-radius: .5rem !important; -} - -#gallery>div>.h-full { - min-height: 20rem; -} - -.details:hover { - text-decoration: underline; -} - -.gr-button { - white-space: nowrap; -} - -.gr-button:focus { - border-color: rgb(147 197 253 / var(--tw-border-opacity)); - outline: none; - box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000); - --tw-border-opacity: 1; - --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); - --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color); - --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity)); - --tw-ring-opacity: .5; -} - -#advanced-btn { - font-size: .7rem !important; - line-height: 19px; - margin-top: 12px; - margin-bottom: 12px; - padding: 2px 8px; - border-radius: 14px !important; -} - -#advanced-options { - display: none; - margin-bottom: 20px; -} - -.footer { - margin-bottom: 45px; - margin-top: 35px; - text-align: center; - border-bottom: 1px solid #e5e5e5; -} - -.footer>p { - font-size: .8rem; - display: inline-block; - padding: 0 10px; - transform: translateY(10px); - background: white; -} - -.dark .footer { - border-color: #303030; -} - -.dark .footer>p { - background: #0b0f19; -} - -.acknowledgments h4 { - margin: 1.25em 0 .25em 0; - font-weight: bold; - font-size: 115%; -} - -.animate-spin { - animation: spin 1s linear infinite; -} - -@keyframes spin { - from { - transform: rotate(0deg); - } - - to { - transform: rotate(360deg); - } -} - -#share-btn-container { - display: flex; - padding-left: 0.5rem !important; - padding-right: 0.5rem !important; - background-color: #000000; - justify-content: center; - align-items: center; - border-radius: 9999px !important; - width: 13rem; - margin-top: 10px; - margin-left: auto; -} - -#share-btn { - all: initial; - color: #ffffff; - font-weight: 600; - cursor: pointer; - font-family: 'IBM Plex Sans', sans-serif; - margin-left: 0.5rem !important; - padding-top: 0.25rem !important; - padding-bottom: 0.25rem !important; - right: 0; -} - -#share-btn * { - all: unset; -} - -#share-btn-container div:nth-child(-n+2) { - width: auto !important; - min-height: 0px !important; -} - -#share-btn-container .wrap { - display: none !important; -} - -.gr-form { - flex: 1 1 50%; - border-top-right-radius: 0; - border-bottom-right-radius: 0; -} - -#prompt-container { - gap: 0; -} - -#prompt-text-input, -#negative-prompt-text-input { - padding: .45rem 0.625rem -} - -#component-16 { - border-top-width: 1px !important; - margin-top: 1em -} - -.image_duplication { - position: absolute; - width: 100px; - left: 50px -} - -#component-0 { - max-width: 730px; - margin: auto; - padding-top: 1.5rem; -} diff --git a/spaces/dandan4272/hand_gesture_rec/test.py b/spaces/dandan4272/hand_gesture_rec/test.py deleted file mode 100644 index 21b026fcdf538f00e360dcedbdbdaaeea94bf9fe..0000000000000000000000000000000000000000 --- a/spaces/dandan4272/hand_gesture_rec/test.py +++ /dev/null @@ -1,224 +0,0 @@ -# python imports -# external imports -import torch -import torchvision -from ignite.metrics import Accuracy, Recall, Precision -from matplotlib import pyplot as plt -from torch.utils.data import DataLoader -import torch.nn as nn - -# model imports -from Mydataset import MyDataset2 -from torch.nn import functional as F - -from model.stgcn.Models import TwoStreamSpatialTemporalGraph - - -def eval1(net,testloader,use_cuda): - # 假设您已经定义了模型和测试数据加载器 - net.eval() - confusion_matrix = torch.zeros(3, 3) - with torch.no_grad(): - for data, target in testloader: - if use_cuda: - data, target = data.cuda(), target.cuda() - output = model(data) - predict = output.argmax(dim=1) - for t, p in zip(predict.view(-1), target.view(-1)): - confusion_matrix[t.long(), p.long()] += 1 - - TP = confusion_matrix[0, 0] - FP = confusion_matrix[0, 1] - FN = confusion_matrix[1, 0] - TN = confusion_matrix[1, 1] - - accuracy = (TP + TN) / (TP + FP + FN + TN) - precision = TP / (TP + FP) - recall = TP / (TP + FN) - f1_score = 2 * precision * recall / (precision + recall) - - print(f'Accuracy: {accuracy}') - print(f'Precision: {precision}') - print(f'Recall: {recall}') - print(f'F1 Score: {f1_score}') - -def eval(net,testloader,use_cuda): - net.eval() - test_loss = 0 - correct = 0 - total = 0 - classnum = 3 - target_num = torch.zeros((1,classnum)) - predict_num = torch.zeros((1,classnum)) - acc_num = torch.zeros((1,classnum)) - for batch_idx, (inputs, targets) in enumerate(testloader): - if use_cuda: - inputs, targets = inputs.cuda(), targets.cuda() - # inputs, targets = Variable(inputs, volatile=True), Variable(targets) - outputs = net(inputs) - loss = F.cross_entropy(outputs, targets) - # loss = criterion(outputs, targets) - # loss is variable , if add it(+=loss) directly, there will be a bigger ang bigger graph. - test_loss += loss.data - _, predicted = torch.max(outputs.data, 1) - total += targets.size(0) - correct += predicted.eq(targets.data).cpu().sum() - pre_mask = torch.zeros(outputs.size()).scatter_(1, predicted.cpu().view(-1, 1), 1.) - predict_num += pre_mask.sum(0) - tar_mask = torch.zeros(outputs.size()).scatter_(1, targets.data.cpu().view(-1, 1), 1.) - target_num += tar_mask.sum(0) - acc_mask = pre_mask*tar_mask - acc_num += acc_mask.sum(0) - recall = acc_num/target_num - precision = acc_num/predict_num - F1 = 2*recall*precision/(recall+precision) - accuracy = acc_num.sum(1)/target_num.sum(1) -#精度调整 - recall = (recall.numpy()[0]*100).round(3) - precision = (precision.numpy()[0]*100).round(3) - F1 = (F1.numpy()[0]*100).round(3) - accuracy = (accuracy.numpy()[0]*100).round(3) -# 打印格式方便复制 - print('recall'," ".join('%s' % id for id in recall)) - print('precision'," ".join('%s' % id for id in precision)) - print('F1'," ".join('%s' % id for id in F1)) - print('accuracy',accuracy) - - -def test_loop(dataloader, model, loss_fn, num_class): - # 实例化相关metrics的计算对象 - test_acc = Accuracy() - test_recall = Recall() - test_precision = Precision() - - size = len(dataloader.dataset) - num_batches = len(dataloader) - test_loss, correct = 0, 0 - - with torch.no_grad(): - for X, z in dataloader: - X = X.cuda() - z = z.cuda() - pred = model(X) - test_loss += loss_fn(pred, z).item() - correct += (pred.argmax(1) == z).type(torch.float).sum().item() - # 一个batch进行计算迭代 - test_acc.update((pred, z)) - test_recall.update((pred, z)) - test_precision.update((pred, z)) - - test_loss /= num_batches - correct /= size - - # 计算一个epoch的accuray、recall、precision - total_acc = test_acc.compute() - total_recall = test_recall.compute() - total_precision = test_precision.compute() - AV_precision = torch.sum(total_precision)/num_class - print(f"Test Error: \n Accuracy: {(100 * correct):>0.1f}%, " - f"Avg loss: {test_loss:>8f}, " - f"ignite acc: {(100 * total_acc):>0.1f}%\n") - print("recall of every test dataset class: ", total_recall) - print("precision of every test dataset class: ", total_precision) - - # 清空计算对象 - test_precision.reset() - test_acc.reset() - test_recall.reset() - return test_loss,correct,AV_precision - - -#%% Visualizing the STN results -def convert_image_np(inp): - """Convert a Tensor to numpy image.""" - # inp = torch.squeeze(inp) - inp = inp.numpy().transpose((1, 2, 3, 0)) - - # for i in inp: - # inp = inp.numpy().transpose((1, 2,3, 0)) - # inp = inp[5,:,:,:] - # mean = np.array([0.485, 0.456, 0.406]) - # std = np.array([0.229, 0.224, 0.225]) - # inp = std * inp + mean - # inp = np.clip(inp, 0, 1) - return inp - -def visualize_stn(test_loader): - with torch.no_grad(): - # Get a batch of training data - data = next(iter(test_loader))[0].cuda() - - input_tensor = data.cpu() - transformed_input_tensor = model.stn(data).cpu() - in_grid = convert_image_np( - torchvision.utils.make_grid(input_tensor)) - - out_grid = convert_image_np( - torchvision.utils.make_grid(transformed_input_tensor)) - - # x = (in_grid,out_grid) - # Plot the results side-by-side - for g in range(len(in_grid)): - f, axarr = plt.subplots(1, 2) - axarr[0].imshow(in_grid[g]) - axarr[0].set_title('Dataset Images') - - axarr[1].imshow(out_grid[g]) - axarr[1].set_title('Transformed Images') - - plt.show() -if __name__ == "__main__": - actions = ['shake_hand', 'palm', 'fist', 'clock_wise', 'anti_clockwise', 'ok', 'thumb', 'v', 'heart','no_gesture'] - # test() - device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - epoch = 100 # 加载之前训练的模型(指定迭代次数) - class_names = ['shake_hand', 'palm', 'fist', 'clock_wise', 'anti_clockwise', 'ok', 'thumb', 'v', 'heart','no_gesture'] - num_class = len(class_names) - graph_args = {'strategy': 'spatial'} - model = TwoStreamSpatialTemporalGraph(graph_args, num_class).to(device) - model.eval() - los = nn.CrossEntropyLoss() - # 加载权重 - if(epoch!=0): - # pre = torch.load(os.path.join('weights/5', '_hand_stgcn_fps15_%d.pth' % epoch)) - pre = torch.load('weights/5/_hand_stgcn_fps15_21node_sgd_200.pth') - - model.load_state_dict(pre) - # model = STGCN(weight_file='weights/_hand_stgcn_100.pth', device=device) - - # model.cuda() - # 加载训练数据 - Data_test = 'datasets/hand/dataset/test' - dataset = MyDataset2(Data_test) - dataloader = DataLoader(dataset, batch_size=8, shuffle=True, num_workers=0, drop_last=True) - - # eval1(model,dataloader,use_cuda=True) - # eval(model,dataloader,use_cuda=True) - test_loop(dataloader,model,los,num_class) - # visualize_stn(test_loader=dataloader) -# Test -# Error: -# Accuracy: 97.0 %, Avg -# loss: 0.014478, ignite -# acc: 99.4 % -# -# recall -# of -# every -# test -# dataset -# -# -# class: tensor([1.0000, 1.0000, 0.8889, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000], -# dtype=torch.float64) -# -# -# precision -# of -# every -# test -# dataset -# -# -# class: tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 0.9091, 1.0000, 1.0000], -# dtype=torch.float64) \ No newline at end of file diff --git a/spaces/darragh/bloom_demo_long/spaces_info.py b/spaces/darragh/bloom_demo_long/spaces_info.py deleted file mode 100644 index 649c3185305b49bc3fe3fad8bc6be26719f599fb..0000000000000000000000000000000000000000 --- a/spaces/darragh/bloom_demo_long/spaces_info.py +++ /dev/null @@ -1,76 +0,0 @@ -description = """Gradio Demo for BLOOM. To use it, simply add your text, or click one of the examples to load them. -Tips: -- Do NOT talk to BLOOM as an entity, it's not a chatbot but a webpage/blog/article completion model. -- For the best results: MIMIC a few sentences of a webpage similar to the content you want to generate. -Start a paragraph as if YOU were writing a blog, webpage, math post, coding article and BLOOM will generate a coherent follow-up. Longer prompts usually give more interesting results. -- Content: Please see our [content disclaimer](https://hf.co/spaces/bigscience/bloom-book) before using the model, as it may sometimes behave in unexpected ways. - -Options: -- sampling: imaginative completions (may be not super accurate e.g. math/history) -- greedy: accurate completions (may be more boring or have repetitions) -""" - -wip_description = """JAX / Flax Gradio Demo for BLOOM. The 176B BLOOM model running on a TPU v3-256 pod, with 2D model parallelism and custom mesh axes. -Note: -1. For this WIP demo, only **sampling** is supported. -2. Rendering of the screenshot is currently not optimised. To experience the true speed of JAX / Flax, tick 'just output raw text'. -""" - -examples = [ - [ - 'A "whatpu" is a small, furry animal native to Tanzania. An example of a sentence that uses the word whatpu is: We were traveling in Africa and we saw these very cute whatpus. To do a "farduddle" means to jump up and down really fast. An example of a sentence that uses the word farduddle is:', - 32, - "Sample", - "Sample 1", - ], - [ - "A poem about the beauty of science by Alfred Edgar Brittle\nTitle: The Magic Craft\nIn the old times", - 50, - "Sample", - "Sample 1", - ], - ["استخراج العدد العاملي في لغة بايثون:", 30, "Greedy", "Sample 1"], - ["Pour déguster un ortolan, il faut tout d'abord", 32, "Sample", "Sample 1"], - [ - "Traduce español de España a español de Argentina\nEl coche es rojo - el auto es rojo\nEl ordenador es nuevo - la computadora es nueva\nel boligrafo es negro -", - 16, - "Sample", - "Sample 1", - ], - [ - "Estos ejemplos quitan vocales de las palabras\nEjemplos:\nhola - hl\nmanzana - mnzn\npapas - pps\nalacran - lcrn\npapa -", - 16, - "Sample", - "Sample 1", - ], - [ - "Question: If I put cheese into the fridge, will it melt?\nAnswer:", - 32, - "Sample", - "Sample 1", - ], - ["Math exercise - answers:\n34+10=44\n54+20=", 16, "Greedy", "Sample 1"], - [ - "Question: Where does the Greek Goddess Persephone spend half of the year when she is not with her mother?\nAnswer:", - 24, - "Greedy", - "Sample 1", - ], - [ - "spelling test answers.\nWhat are the letters in « language »?\nAnswer: l-a-n-g-u-a-g-e\nWhat are the letters in « Romanian »?\nAnswer:", - 24, - "Greedy", - "Sample 1", - ], -] - -initial_prompt_value = """استخراج العدد العاملي في لغة بايثون : -def factorial(n): - if n == 0: - return 1 - else: - result = 1 - for i in range(1, n + 1) : - result *= i - return result -print(factorial(5))""" \ No newline at end of file diff --git a/spaces/dawdqd/ChuanhuChatGPT/web_assets/stylesheet/override-gradio.css b/spaces/dawdqd/ChuanhuChatGPT/web_assets/stylesheet/override-gradio.css deleted file mode 100644 index 4705139f87d3625c43bb688c9e62d200a956f57a..0000000000000000000000000000000000000000 --- a/spaces/dawdqd/ChuanhuChatGPT/web_assets/stylesheet/override-gradio.css +++ /dev/null @@ -1,67 +0,0 @@ - -/* 解决container=False时的错误填充 */ -div.form { - background: none !important; -} -div.no-container { - padding: 10px 0 0 0 !important; -} - -/* gradio的页脚信息 */ -footer { - /* display: none !important; */ - margin-top: .2em !important; - font-size: 85%; -} - -/* 覆盖 gradio 丑陋的复制按钮样式 */ -.message pre button[title="copy"] { - border-radius: 5px; - transition: background-color .2s ease; -} -.message pre button[title="copy"]:hover { - background-color: #333232; -} -.message pre button .check { - color: #fff !important; - background: var(--neutral-950) !important; -} - - - - -/* 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; */ - 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; -} diff --git a/spaces/dawood17/SayBot_Enchancer/CodeFormer/facelib/detection/yolov5face/__init__.py b/spaces/dawood17/SayBot_Enchancer/CodeFormer/facelib/detection/yolov5face/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/test_utils.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/test_utils.py deleted file mode 100644 index fcda2f3ddc045a381470012ba331c75299af4981..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/test_utils.py +++ /dev/null @@ -1,706 +0,0 @@ -"""Utilities shared by tests.""" - -import asyncio -import contextlib -import gc -import inspect -import ipaddress -import os -import socket -import sys -import warnings -from abc import ABC, abstractmethod -from types import TracebackType -from typing import ( - TYPE_CHECKING, - Any, - Callable, - Iterator, - List, - Optional, - Type, - Union, - cast, -) -from unittest import mock - -from aiosignal import Signal -from multidict import CIMultiDict, CIMultiDictProxy -from yarl import URL - -import aiohttp -from aiohttp.client import _RequestContextManager, _WSRequestContextManager - -from . import ClientSession, hdrs -from .abc import AbstractCookieJar -from .client_reqrep import ClientResponse -from .client_ws import ClientWebSocketResponse -from .helpers import PY_38, sentinel -from .http import HttpVersion, RawRequestMessage -from .web import ( - Application, - AppRunner, - BaseRunner, - Request, - Server, - ServerRunner, - SockSite, - UrlMappingMatchInfo, -) -from .web_protocol import _RequestHandler - -if TYPE_CHECKING: # pragma: no cover - from ssl import SSLContext -else: - SSLContext = None - -if PY_38: - from unittest import IsolatedAsyncioTestCase as TestCase -else: - from asynctest import TestCase # type: ignore[no-redef] - -REUSE_ADDRESS = os.name == "posix" and sys.platform != "cygwin" - - -def get_unused_port_socket( - host: str, family: socket.AddressFamily = socket.AF_INET -) -> socket.socket: - return get_port_socket(host, 0, family) - - -def get_port_socket( - host: str, port: int, family: socket.AddressFamily -) -> socket.socket: - s = socket.socket(family, socket.SOCK_STREAM) - if REUSE_ADDRESS: - # Windows has different semantics for SO_REUSEADDR, - # so don't set it. Ref: - # https://docs.microsoft.com/en-us/windows/win32/winsock/using-so-reuseaddr-and-so-exclusiveaddruse - s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) - s.bind((host, port)) - return s - - -def unused_port() -> int: - """Return a port that is unused on the current host.""" - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: - s.bind(("127.0.0.1", 0)) - return cast(int, s.getsockname()[1]) - - -class BaseTestServer(ABC): - __test__ = False - - def __init__( - self, - *, - scheme: Union[str, object] = sentinel, - loop: Optional[asyncio.AbstractEventLoop] = None, - host: str = "127.0.0.1", - port: Optional[int] = None, - skip_url_asserts: bool = False, - socket_factory: Callable[ - [str, int, socket.AddressFamily], socket.socket - ] = get_port_socket, - **kwargs: Any, - ) -> None: - self._loop = loop - self.runner: Optional[BaseRunner] = None - self._root: Optional[URL] = None - self.host = host - self.port = port - self._closed = False - self.scheme = scheme - self.skip_url_asserts = skip_url_asserts - self.socket_factory = socket_factory - - async def start_server( - self, loop: Optional[asyncio.AbstractEventLoop] = None, **kwargs: Any - ) -> None: - if self.runner: - return - self._loop = loop - self._ssl = kwargs.pop("ssl", None) - self.runner = await self._make_runner(**kwargs) - await self.runner.setup() - if not self.port: - self.port = 0 - try: - version = ipaddress.ip_address(self.host).version - except ValueError: - version = 4 - family = socket.AF_INET6 if version == 6 else socket.AF_INET - _sock = self.socket_factory(self.host, self.port, family) - self.host, self.port = _sock.getsockname()[:2] - site = SockSite(self.runner, sock=_sock, ssl_context=self._ssl) - await site.start() - server = site._server - assert server is not None - sockets = server.sockets - assert sockets is not None - self.port = sockets[0].getsockname()[1] - if self.scheme is sentinel: - if self._ssl: - scheme = "https" - else: - scheme = "http" - self.scheme = scheme - self._root = URL(f"{self.scheme}://{self.host}:{self.port}") - - @abstractmethod # pragma: no cover - async def _make_runner(self, **kwargs: Any) -> BaseRunner: - pass - - def make_url(self, path: str) -> URL: - assert self._root is not None - url = URL(path) - if not self.skip_url_asserts: - assert not url.is_absolute() - return self._root.join(url) - else: - return URL(str(self._root) + path) - - @property - def started(self) -> bool: - return self.runner is not None - - @property - def closed(self) -> bool: - return self._closed - - @property - def handler(self) -> Server: - # for backward compatibility - # web.Server instance - runner = self.runner - assert runner is not None - assert runner.server is not None - return runner.server - - async def close(self) -> None: - """Close all fixtures created by the test client. - - After that point, the TestClient is no longer usable. - - This is an idempotent function: running close multiple times - will not have any additional effects. - - close is also run when the object is garbage collected, and on - exit when used as a context manager. - - """ - if self.started and not self.closed: - assert self.runner is not None - await self.runner.cleanup() - self._root = None - self.port = None - self._closed = True - - def __enter__(self) -> None: - raise TypeError("Use async with instead") - - def __exit__( - self, - exc_type: Optional[Type[BaseException]], - exc_value: Optional[BaseException], - traceback: Optional[TracebackType], - ) -> None: - # __exit__ should exist in pair with __enter__ but never executed - pass # pragma: no cover - - async def __aenter__(self) -> "BaseTestServer": - await self.start_server(loop=self._loop) - return self - - async def __aexit__( - self, - exc_type: Optional[Type[BaseException]], - exc_value: Optional[BaseException], - traceback: Optional[TracebackType], - ) -> None: - await self.close() - - -class TestServer(BaseTestServer): - def __init__( - self, - app: Application, - *, - scheme: Union[str, object] = sentinel, - host: str = "127.0.0.1", - port: Optional[int] = None, - **kwargs: Any, - ): - self.app = app - super().__init__(scheme=scheme, host=host, port=port, **kwargs) - - async def _make_runner(self, **kwargs: Any) -> BaseRunner: - return AppRunner(self.app, **kwargs) - - -class RawTestServer(BaseTestServer): - def __init__( - self, - handler: _RequestHandler, - *, - scheme: Union[str, object] = sentinel, - host: str = "127.0.0.1", - port: Optional[int] = None, - **kwargs: Any, - ) -> None: - self._handler = handler - super().__init__(scheme=scheme, host=host, port=port, **kwargs) - - async def _make_runner(self, debug: bool = True, **kwargs: Any) -> ServerRunner: - srv = Server(self._handler, loop=self._loop, debug=debug, **kwargs) - return ServerRunner(srv, debug=debug, **kwargs) - - -class TestClient: - """ - A test client implementation. - - To write functional tests for aiohttp based servers. - - """ - - __test__ = False - - def __init__( - self, - server: BaseTestServer, - *, - cookie_jar: Optional[AbstractCookieJar] = None, - loop: Optional[asyncio.AbstractEventLoop] = None, - **kwargs: Any, - ) -> None: - if not isinstance(server, BaseTestServer): - raise TypeError( - "server must be TestServer " "instance, found type: %r" % type(server) - ) - self._server = server - self._loop = loop - if cookie_jar is None: - cookie_jar = aiohttp.CookieJar(unsafe=True, loop=loop) - self._session = ClientSession(loop=loop, cookie_jar=cookie_jar, **kwargs) - self._closed = False - self._responses: List[ClientResponse] = [] - self._websockets: List[ClientWebSocketResponse] = [] - - async def start_server(self) -> None: - await self._server.start_server(loop=self._loop) - - @property - def host(self) -> str: - return self._server.host - - @property - def port(self) -> Optional[int]: - return self._server.port - - @property - def server(self) -> BaseTestServer: - return self._server - - @property - def app(self) -> Optional[Application]: - return cast(Optional[Application], getattr(self._server, "app", None)) - - @property - def session(self) -> ClientSession: - """An internal aiohttp.ClientSession. - - Unlike the methods on the TestClient, client session requests - do not automatically include the host in the url queried, and - will require an absolute path to the resource. - - """ - return self._session - - def make_url(self, path: str) -> URL: - return self._server.make_url(path) - - async def _request(self, method: str, path: str, **kwargs: Any) -> ClientResponse: - resp = await self._session.request(method, self.make_url(path), **kwargs) - # save it to close later - self._responses.append(resp) - return resp - - def request(self, method: str, path: str, **kwargs: Any) -> _RequestContextManager: - """Routes a request to tested http server. - - The interface is identical to aiohttp.ClientSession.request, - except the loop kwarg is overridden by the instance used by the - test server. - - """ - return _RequestContextManager(self._request(method, path, **kwargs)) - - def get(self, path: str, **kwargs: Any) -> _RequestContextManager: - """Perform an HTTP GET request.""" - return _RequestContextManager(self._request(hdrs.METH_GET, path, **kwargs)) - - def post(self, path: str, **kwargs: Any) -> _RequestContextManager: - """Perform an HTTP POST request.""" - return _RequestContextManager(self._request(hdrs.METH_POST, path, **kwargs)) - - def options(self, path: str, **kwargs: Any) -> _RequestContextManager: - """Perform an HTTP OPTIONS request.""" - return _RequestContextManager(self._request(hdrs.METH_OPTIONS, path, **kwargs)) - - def head(self, path: str, **kwargs: Any) -> _RequestContextManager: - """Perform an HTTP HEAD request.""" - return _RequestContextManager(self._request(hdrs.METH_HEAD, path, **kwargs)) - - def put(self, path: str, **kwargs: Any) -> _RequestContextManager: - """Perform an HTTP PUT request.""" - return _RequestContextManager(self._request(hdrs.METH_PUT, path, **kwargs)) - - def patch(self, path: str, **kwargs: Any) -> _RequestContextManager: - """Perform an HTTP PATCH request.""" - return _RequestContextManager(self._request(hdrs.METH_PATCH, path, **kwargs)) - - def delete(self, path: str, **kwargs: Any) -> _RequestContextManager: - """Perform an HTTP PATCH request.""" - return _RequestContextManager(self._request(hdrs.METH_DELETE, path, **kwargs)) - - def ws_connect(self, path: str, **kwargs: Any) -> _WSRequestContextManager: - """Initiate websocket connection. - - The api corresponds to aiohttp.ClientSession.ws_connect. - - """ - return _WSRequestContextManager(self._ws_connect(path, **kwargs)) - - async def _ws_connect(self, path: str, **kwargs: Any) -> ClientWebSocketResponse: - ws = await self._session.ws_connect(self.make_url(path), **kwargs) - self._websockets.append(ws) - return ws - - async def close(self) -> None: - """Close all fixtures created by the test client. - - After that point, the TestClient is no longer usable. - - This is an idempotent function: running close multiple times - will not have any additional effects. - - close is also run on exit when used as a(n) (asynchronous) - context manager. - - """ - if not self._closed: - for resp in self._responses: - resp.close() - for ws in self._websockets: - await ws.close() - await self._session.close() - await self._server.close() - self._closed = True - - def __enter__(self) -> None: - raise TypeError("Use async with instead") - - def __exit__( - self, - exc_type: Optional[Type[BaseException]], - exc: Optional[BaseException], - tb: Optional[TracebackType], - ) -> None: - # __exit__ should exist in pair with __enter__ but never executed - pass # pragma: no cover - - async def __aenter__(self) -> "TestClient": - await self.start_server() - return self - - async def __aexit__( - self, - exc_type: Optional[Type[BaseException]], - exc: Optional[BaseException], - tb: Optional[TracebackType], - ) -> None: - await self.close() - - -class AioHTTPTestCase(TestCase): - """A base class to allow for unittest web applications using aiohttp. - - Provides the following: - - * self.client (aiohttp.test_utils.TestClient): an aiohttp test client. - * self.loop (asyncio.BaseEventLoop): the event loop in which the - application and server are running. - * self.app (aiohttp.web.Application): the application returned by - self.get_application() - - Note that the TestClient's methods are asynchronous: you have to - execute function on the test client using asynchronous methods. - """ - - async def get_application(self) -> Application: - """Get application. - - This method should be overridden - to return the aiohttp.web.Application - object to test. - """ - return self.get_app() - - def get_app(self) -> Application: - """Obsolete method used to constructing web application. - - Use .get_application() coroutine instead. - """ - raise RuntimeError("Did you forget to define get_application()?") - - def setUp(self) -> None: - if not PY_38: - asyncio.get_event_loop().run_until_complete(self.asyncSetUp()) - - async def asyncSetUp(self) -> None: - try: - self.loop = asyncio.get_running_loop() - except (AttributeError, RuntimeError): # AttributeError->py36 - self.loop = asyncio.get_event_loop_policy().get_event_loop() - - return await self.setUpAsync() - - async def setUpAsync(self) -> None: - self.app = await self.get_application() - self.server = await self.get_server(self.app) - self.client = await self.get_client(self.server) - - await self.client.start_server() - - def tearDown(self) -> None: - if not PY_38: - self.loop.run_until_complete(self.asyncTearDown()) - - async def asyncTearDown(self) -> None: - return await self.tearDownAsync() - - async def tearDownAsync(self) -> None: - await self.client.close() - - async def get_server(self, app: Application) -> TestServer: - """Return a TestServer instance.""" - return TestServer(app, loop=self.loop) - - async def get_client(self, server: TestServer) -> TestClient: - """Return a TestClient instance.""" - return TestClient(server, loop=self.loop) - - -def unittest_run_loop(func: Any, *args: Any, **kwargs: Any) -> Any: - """ - A decorator dedicated to use with asynchronous AioHTTPTestCase test methods. - - In 3.8+, this does nothing. - """ - warnings.warn( - "Decorator `@unittest_run_loop` is no longer needed in aiohttp 3.8+", - DeprecationWarning, - stacklevel=2, - ) - return func - - -_LOOP_FACTORY = Callable[[], asyncio.AbstractEventLoop] - - -@contextlib.contextmanager -def loop_context( - loop_factory: _LOOP_FACTORY = asyncio.new_event_loop, fast: bool = False -) -> Iterator[asyncio.AbstractEventLoop]: - """A contextmanager that creates an event_loop, for test purposes. - - Handles the creation and cleanup of a test loop. - """ - loop = setup_test_loop(loop_factory) - yield loop - teardown_test_loop(loop, fast=fast) - - -def setup_test_loop( - loop_factory: _LOOP_FACTORY = asyncio.new_event_loop, -) -> asyncio.AbstractEventLoop: - """Create and return an asyncio.BaseEventLoop instance. - - The caller should also call teardown_test_loop, - once they are done with the loop. - """ - loop = loop_factory() - try: - module = loop.__class__.__module__ - skip_watcher = "uvloop" in module - except AttributeError: # pragma: no cover - # Just in case - skip_watcher = True - asyncio.set_event_loop(loop) - if sys.platform != "win32" and not skip_watcher: - policy = asyncio.get_event_loop_policy() - watcher: asyncio.AbstractChildWatcher - try: # Python >= 3.8 - # Refs: - # * https://github.com/pytest-dev/pytest-xdist/issues/620 - # * https://stackoverflow.com/a/58614689/595220 - # * https://bugs.python.org/issue35621 - # * https://github.com/python/cpython/pull/14344 - watcher = asyncio.ThreadedChildWatcher() - except AttributeError: # Python < 3.8 - watcher = asyncio.SafeChildWatcher() - watcher.attach_loop(loop) - with contextlib.suppress(NotImplementedError): - policy.set_child_watcher(watcher) - return loop - - -def teardown_test_loop(loop: asyncio.AbstractEventLoop, fast: bool = False) -> None: - """Teardown and cleanup an event_loop created by setup_test_loop.""" - closed = loop.is_closed() - if not closed: - loop.call_soon(loop.stop) - loop.run_forever() - loop.close() - - if not fast: - gc.collect() - - asyncio.set_event_loop(None) - - -def _create_app_mock() -> mock.MagicMock: - def get_dict(app: Any, key: str) -> Any: - return app.__app_dict[key] - - def set_dict(app: Any, key: str, value: Any) -> None: - app.__app_dict[key] = value - - app = mock.MagicMock(spec=Application) - app.__app_dict = {} - app.__getitem__ = get_dict - app.__setitem__ = set_dict - - app._debug = False - app.on_response_prepare = Signal(app) - app.on_response_prepare.freeze() - return app - - -def _create_transport(sslcontext: Optional[SSLContext] = None) -> mock.Mock: - transport = mock.Mock() - - def get_extra_info(key: str) -> Optional[SSLContext]: - if key == "sslcontext": - return sslcontext - else: - return None - - transport.get_extra_info.side_effect = get_extra_info - return transport - - -def make_mocked_request( - method: str, - path: str, - headers: Any = None, - *, - match_info: Any = sentinel, - version: HttpVersion = HttpVersion(1, 1), - closing: bool = False, - app: Any = None, - writer: Any = sentinel, - protocol: Any = sentinel, - transport: Any = sentinel, - payload: Any = sentinel, - sslcontext: Optional[SSLContext] = None, - client_max_size: int = 1024**2, - loop: Any = ..., -) -> Request: - """Creates mocked web.Request testing purposes. - - Useful in unit tests, when spinning full web server is overkill or - specific conditions and errors are hard to trigger. - """ - task = mock.Mock() - if loop is ...: - loop = mock.Mock() - loop.create_future.return_value = () - - if version < HttpVersion(1, 1): - closing = True - - if headers: - headers = CIMultiDictProxy(CIMultiDict(headers)) - raw_hdrs = tuple( - (k.encode("utf-8"), v.encode("utf-8")) for k, v in headers.items() - ) - else: - headers = CIMultiDictProxy(CIMultiDict()) - raw_hdrs = () - - chunked = "chunked" in headers.get(hdrs.TRANSFER_ENCODING, "").lower() - - message = RawRequestMessage( - method, - path, - version, - headers, - raw_hdrs, - closing, - None, - False, - chunked, - URL(path), - ) - if app is None: - app = _create_app_mock() - - if transport is sentinel: - transport = _create_transport(sslcontext) - - if protocol is sentinel: - protocol = mock.Mock() - protocol.transport = transport - - if writer is sentinel: - writer = mock.Mock() - writer.write_headers = make_mocked_coro(None) - writer.write = make_mocked_coro(None) - writer.write_eof = make_mocked_coro(None) - writer.drain = make_mocked_coro(None) - writer.transport = transport - - protocol.transport = transport - protocol.writer = writer - - if payload is sentinel: - payload = mock.Mock() - - req = Request( - message, payload, protocol, writer, task, loop, client_max_size=client_max_size - ) - - match_info = UrlMappingMatchInfo( - {} if match_info is sentinel else match_info, mock.Mock() - ) - match_info.add_app(app) - req._match_info = match_info - - return req - - -def make_mocked_coro( - return_value: Any = sentinel, raise_exception: Any = sentinel -) -> Any: - """Creates a coroutine mock.""" - - async def mock_coro(*args: Any, **kwargs: Any) -> Any: - if raise_exception is not sentinel: - raise raise_exception - if not inspect.isawaitable(return_value): - return return_value - await return_value - - return mock.Mock(wraps=mock_coro) diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/varLib/interpolatable.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/varLib/interpolatable.py deleted file mode 100644 index d5428c2002286b7de284fff89a79f62cd6ebd656..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/varLib/interpolatable.py +++ /dev/null @@ -1,583 +0,0 @@ -""" -Tool to find wrong contour order between different masters, and -other interpolatability (or lack thereof) issues. - -Call as: -$ fonttools varLib.interpolatable font1 font2 ... -""" - -from fontTools.pens.basePen import AbstractPen, BasePen -from fontTools.pens.pointPen import SegmentToPointPen -from fontTools.pens.recordingPen import RecordingPen -from fontTools.pens.statisticsPen import StatisticsPen -from fontTools.pens.momentsPen import OpenContourError -from collections import OrderedDict -import math -import itertools -import sys - - -def _rot_list(l, k): - """Rotate list by k items forward. Ie. item at position 0 will be - at position k in returned list. Negative k is allowed.""" - n = len(l) - k %= n - if not k: - return l - return l[n - k :] + l[: n - k] - - -class PerContourPen(BasePen): - def __init__(self, Pen, glyphset=None): - BasePen.__init__(self, glyphset) - self._glyphset = glyphset - self._Pen = Pen - self._pen = None - self.value = [] - - def _moveTo(self, p0): - self._newItem() - self._pen.moveTo(p0) - - def _lineTo(self, p1): - self._pen.lineTo(p1) - - def _qCurveToOne(self, p1, p2): - self._pen.qCurveTo(p1, p2) - - def _curveToOne(self, p1, p2, p3): - self._pen.curveTo(p1, p2, p3) - - def _closePath(self): - self._pen.closePath() - self._pen = None - - def _endPath(self): - self._pen.endPath() - self._pen = None - - def _newItem(self): - self._pen = pen = self._Pen() - self.value.append(pen) - - -class PerContourOrComponentPen(PerContourPen): - def addComponent(self, glyphName, transformation): - self._newItem() - self.value[-1].addComponent(glyphName, transformation) - - -class RecordingPointPen(BasePen): - def __init__(self): - self.value = [] - - def beginPath(self, identifier=None, **kwargs): - pass - - def endPath(self) -> None: - pass - - def addPoint(self, pt, segmentType=None): - self.value.append((pt, False if segmentType is None else True)) - - -def _vdiff(v0, v1): - return tuple(b - a for a, b in zip(v0, v1)) - - -def _vlen(vec): - v = 0 - for x in vec: - v += x * x - return v - - -def _complex_vlen(vec): - v = 0 - for x in vec: - v += abs(x) * abs(x) - return v - - -def _matching_cost(G, matching): - return sum(G[i][j] for i, j in enumerate(matching)) - - -def min_cost_perfect_bipartite_matching(G): - n = len(G) - try: - from scipy.optimize import linear_sum_assignment - - rows, cols = linear_sum_assignment(G) - assert (rows == list(range(n))).all() - return list(cols), _matching_cost(G, cols) - except ImportError: - pass - - try: - from munkres import Munkres - - cols = [None] * n - for row, col in Munkres().compute(G): - cols[row] = col - return cols, _matching_cost(G, cols) - except ImportError: - pass - - if n > 6: - raise Exception("Install Python module 'munkres' or 'scipy >= 0.17.0'") - - # Otherwise just brute-force - permutations = itertools.permutations(range(n)) - best = list(next(permutations)) - best_cost = _matching_cost(G, best) - for p in permutations: - cost = _matching_cost(G, p) - if cost < best_cost: - best, best_cost = list(p), cost - return best, best_cost - - -def test(glyphsets, glyphs=None, names=None, ignore_missing=False): - if names is None: - names = glyphsets - if glyphs is None: - # `glyphs = glyphsets[0].keys()` is faster, certainly, but doesn't allow for sparse TTFs/OTFs given out of order - # ... risks the sparse master being the first one, and only processing a subset of the glyphs - glyphs = {g for glyphset in glyphsets for g in glyphset.keys()} - - hist = [] - problems = OrderedDict() - - def add_problem(glyphname, problem): - problems.setdefault(glyphname, []).append(problem) - - for glyph_name in glyphs: - try: - m0idx = 0 - allVectors = [] - allNodeTypes = [] - allContourIsomorphisms = [] - for glyphset, name in zip(glyphsets, names): - glyph = glyphset[glyph_name] - - if glyph is None: - if not ignore_missing: - add_problem(glyph_name, {"type": "missing", "master": name}) - allNodeTypes.append(None) - allVectors.append(None) - allContourIsomorphisms.append(None) - continue - - perContourPen = PerContourOrComponentPen( - RecordingPen, glyphset=glyphset - ) - try: - glyph.draw(perContourPen, outputImpliedClosingLine=True) - except TypeError: - glyph.draw(perContourPen) - contourPens = perContourPen.value - del perContourPen - - contourVectors = [] - contourIsomorphisms = [] - nodeTypes = [] - allNodeTypes.append(nodeTypes) - allVectors.append(contourVectors) - allContourIsomorphisms.append(contourIsomorphisms) - for ix, contour in enumerate(contourPens): - nodeVecs = tuple(instruction[0] for instruction in contour.value) - nodeTypes.append(nodeVecs) - - stats = StatisticsPen(glyphset=glyphset) - try: - contour.replay(stats) - except OpenContourError as e: - add_problem( - glyph_name, - {"master": name, "contour": ix, "type": "open_path"}, - ) - continue - size = math.sqrt(abs(stats.area)) * 0.5 - vector = ( - int(size), - int(stats.meanX), - int(stats.meanY), - int(stats.stddevX * 2), - int(stats.stddevY * 2), - int(stats.correlation * size), - ) - contourVectors.append(vector) - # print(vector) - - # Check starting point - if nodeVecs[0] == "addComponent": - continue - assert nodeVecs[0] == "moveTo" - assert nodeVecs[-1] in ("closePath", "endPath") - points = RecordingPointPen() - converter = SegmentToPointPen(points, False) - contour.replay(converter) - # points.value is a list of pt,bool where bool is true if on-curve and false if off-curve; - # now check all rotations and mirror-rotations of the contour and build list of isomorphic - # possible starting points. - bits = 0 - for pt, b in points.value: - bits = (bits << 1) | b - n = len(points.value) - mask = (1 << n) - 1 - isomorphisms = [] - contourIsomorphisms.append(isomorphisms) - for i in range(n): - b = ((bits << i) & mask) | ((bits >> (n - i))) - if b == bits: - isomorphisms.append( - _rot_list([complex(*pt) for pt, bl in points.value], i) - ) - # Add mirrored rotations - mirrored = list(reversed(points.value)) - reversed_bits = 0 - for pt, b in mirrored: - reversed_bits = (reversed_bits << 1) | b - for i in range(n): - b = ((reversed_bits << i) & mask) | ((reversed_bits >> (n - i))) - if b == bits: - isomorphisms.append( - _rot_list([complex(*pt) for pt, bl in mirrored], i) - ) - - # m0idx should be the index of the first non-None item in allNodeTypes, - # else give it the first index of None, which is likely 0 - m0idx = allNodeTypes.index( - next((x for x in allNodeTypes if x is not None), None) - ) - # m0 is the first non-None item in allNodeTypes, or the first item if all are None - m0 = allNodeTypes[m0idx] - for i, m1 in enumerate(allNodeTypes[m0idx + 1 :]): - if m1 is None: - continue - if len(m0) != len(m1): - add_problem( - glyph_name, - { - "type": "path_count", - "master_1": names[m0idx], - "master_2": names[m0idx + i + 1], - "value_1": len(m0), - "value_2": len(m1), - }, - ) - if m0 == m1: - continue - for pathIx, (nodes1, nodes2) in enumerate(zip(m0, m1)): - if nodes1 == nodes2: - continue - if len(nodes1) != len(nodes2): - add_problem( - glyph_name, - { - "type": "node_count", - "path": pathIx, - "master_1": names[m0idx], - "master_2": names[m0idx + i + 1], - "value_1": len(nodes1), - "value_2": len(nodes2), - }, - ) - continue - for nodeIx, (n1, n2) in enumerate(zip(nodes1, nodes2)): - if n1 != n2: - add_problem( - glyph_name, - { - "type": "node_incompatibility", - "path": pathIx, - "node": nodeIx, - "master_1": names[m0idx], - "master_2": names[m0idx + i + 1], - "value_1": n1, - "value_2": n2, - }, - ) - continue - - # m0idx should be the index of the first non-None item in allVectors, - # else give it the first index of None, which is likely 0 - m0idx = allVectors.index( - next((x for x in allVectors if x is not None), None) - ) - # m0 is the first non-None item in allVectors, or the first item if all are None - m0 = allVectors[m0idx] - for i, m1 in enumerate(allVectors[m0idx + 1 :]): - if m1 is None: - continue - if len(m0) != len(m1): - # We already reported this - continue - if not m0: - continue - costs = [[_vlen(_vdiff(v0, v1)) for v1 in m1] for v0 in m0] - matching, matching_cost = min_cost_perfect_bipartite_matching(costs) - identity_matching = list(range(len(m0))) - identity_cost = sum(costs[i][i] for i in range(len(m0))) - if ( - matching != identity_matching - and matching_cost < identity_cost * 0.95 - ): - add_problem( - glyph_name, - { - "type": "contour_order", - "master_1": names[m0idx], - "master_2": names[m0idx + i + 1], - "value_1": list(range(len(m0))), - "value_2": matching, - }, - ) - break - - # m0idx should be the index of the first non-None item in allContourIsomorphisms, - # else give it the first index of None, which is likely 0 - m0idx = allContourIsomorphisms.index( - next((x for x in allContourIsomorphisms if x is not None), None) - ) - # m0 is the first non-None item in allContourIsomorphisms, or the first item if all are None - m0 = allContourIsomorphisms[m0idx] - for i, m1 in enumerate(allContourIsomorphisms[m0idx + 1 :]): - if m1 is None: - continue - if len(m0) != len(m1): - # We already reported this - continue - if not m0: - continue - for ix, (contour0, contour1) in enumerate(zip(m0, m1)): - c0 = contour0[0] - costs = [ - v for v in (_complex_vlen(_vdiff(c0, c1)) for c1 in contour1) - ] - min_cost = min(costs) - first_cost = costs[0] - if min_cost < first_cost * 0.95: - add_problem( - glyph_name, - { - "type": "wrong_start_point", - "contour": ix, - "master_1": names[m0idx], - "master_2": names[m0idx + i + 1], - }, - ) - - except ValueError as e: - add_problem( - glyph_name, - {"type": "math_error", "master": name, "error": e}, - ) - return problems - - -def main(args=None): - """Test for interpolatability issues between fonts""" - import argparse - - parser = argparse.ArgumentParser( - "fonttools varLib.interpolatable", - description=main.__doc__, - ) - parser.add_argument( - "--glyphs", - action="store", - help="Space-separate name of glyphs to check", - ) - parser.add_argument( - "--json", - action="store_true", - help="Output report in JSON format", - ) - parser.add_argument( - "--quiet", - action="store_true", - help="Only exit with code 1 or 0, no output", - ) - parser.add_argument( - "--ignore-missing", - action="store_true", - help="Will not report glyphs missing from sparse masters as errors", - ) - parser.add_argument( - "inputs", - metavar="FILE", - type=str, - nargs="+", - help="Input a single DesignSpace/Glyphs file, or multiple TTF/UFO files", - ) - - args = parser.parse_args(args) - - glyphs = set(args.glyphs.split()) if args.glyphs else None - - from os.path import basename - - fonts = [] - names = [] - - if len(args.inputs) == 1: - if args.inputs[0].endswith(".designspace"): - from fontTools.designspaceLib import DesignSpaceDocument - - designspace = DesignSpaceDocument.fromfile(args.inputs[0]) - args.inputs = [master.path for master in designspace.sources] - - elif args.inputs[0].endswith(".glyphs"): - from glyphsLib import GSFont, to_ufos - - gsfont = GSFont(args.inputs[0]) - fonts.extend(to_ufos(gsfont)) - names = ["%s-%s" % (f.info.familyName, f.info.styleName) for f in fonts] - args.inputs = [] - - elif args.inputs[0].endswith(".ttf"): - from fontTools.ttLib import TTFont - - font = TTFont(args.inputs[0]) - if "gvar" in font: - # Is variable font - gvar = font["gvar"] - # Gather all "master" locations - locs = set() - for variations in gvar.variations.values(): - for var in variations: - loc = [] - for tag, val in sorted(var.axes.items()): - loc.append((tag, val[1])) - locs.add(tuple(loc)) - # Rebuild locs as dictionaries - new_locs = [{}] - names.append("()") - for loc in sorted(locs, key=lambda v: (len(v), v)): - names.append(str(loc)) - l = {} - for tag, val in loc: - l[tag] = val - new_locs.append(l) - locs = new_locs - del new_locs - # locs is all master locations now - - for loc in locs: - fonts.append(font.getGlyphSet(location=loc, normalized=True)) - - args.inputs = [] - - for filename in args.inputs: - if filename.endswith(".ufo"): - from fontTools.ufoLib import UFOReader - - fonts.append(UFOReader(filename)) - else: - from fontTools.ttLib import TTFont - - fonts.append(TTFont(filename)) - - names.append(basename(filename).rsplit(".", 1)[0]) - - glyphsets = [] - for font in fonts: - if hasattr(font, "getGlyphSet"): - glyphset = font.getGlyphSet() - else: - glyphset = font - glyphsets.append({k: glyphset[k] for k in glyphset.keys()}) - - if not glyphs: - glyphs = set([gn for glyphset in glyphsets for gn in glyphset.keys()]) - - for glyphset in glyphsets: - glyphSetGlyphNames = set(glyphset.keys()) - diff = glyphs - glyphSetGlyphNames - if diff: - for gn in diff: - glyphset[gn] = None - - problems = test( - glyphsets, glyphs=glyphs, names=names, ignore_missing=args.ignore_missing - ) - - if not args.quiet: - if args.json: - import json - - print(json.dumps(problems)) - else: - for glyph, glyph_problems in problems.items(): - print(f"Glyph {glyph} was not compatible: ") - for p in glyph_problems: - if p["type"] == "missing": - print(" Glyph was missing in master %s" % p["master"]) - if p["type"] == "open_path": - print(" Glyph has an open path in master %s" % p["master"]) - if p["type"] == "path_count": - print( - " Path count differs: %i in %s, %i in %s" - % (p["value_1"], p["master_1"], p["value_2"], p["master_2"]) - ) - if p["type"] == "node_count": - print( - " Node count differs in path %i: %i in %s, %i in %s" - % ( - p["path"], - p["value_1"], - p["master_1"], - p["value_2"], - p["master_2"], - ) - ) - if p["type"] == "node_incompatibility": - print( - " Node %o incompatible in path %i: %s in %s, %s in %s" - % ( - p["node"], - p["path"], - p["value_1"], - p["master_1"], - p["value_2"], - p["master_2"], - ) - ) - if p["type"] == "contour_order": - print( - " Contour order differs: %s in %s, %s in %s" - % ( - p["value_1"], - p["master_1"], - p["value_2"], - p["master_2"], - ) - ) - if p["type"] == "wrong_start_point": - print( - " Contour %d start point differs: %s, %s" - % ( - p["contour"], - p["master_1"], - p["master_2"], - ) - ) - if p["type"] == "math_error": - print( - " Miscellaneous error in %s: %s" - % ( - p["master"], - p["error"], - ) - ) - if problems: - return problems - - -if __name__ == "__main__": - import sys - - problems = main() - sys.exit(int(bool(problems))) diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/chat_interface.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/chat_interface.py deleted file mode 100644 index 5e2f22d3a9c05fddc94d07e33ee6b6a4b48a3ebb..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/chat_interface.py +++ /dev/null @@ -1,509 +0,0 @@ -""" -This file defines a useful high-level abstraction to build Gradio chatbots: ChatInterface. -""" - - -from __future__ import annotations - -import inspect -from typing import AsyncGenerator, Callable - -import anyio -from gradio_client import utils as client_utils -from gradio_client.documentation import document, set_documentation_group - -from gradio.blocks import Blocks -from gradio.components import ( - Button, - Chatbot, - IOComponent, - Markdown, - State, - Textbox, - get_component_instance, -) -from gradio.events import Dependency, EventListenerMethod -from gradio.helpers import create_examples as Examples # noqa: N812 -from gradio.layouts import Accordion, Column, Group, Row -from gradio.themes import ThemeClass as Theme -from gradio.utils import SyncToAsyncIterator, async_iteration - -set_documentation_group("chatinterface") - - -@document() -class ChatInterface(Blocks): - """ - ChatInterface is Gradio's high-level abstraction for creating chatbot UIs, and allows you to create - a web-based demo around a chatbot model in a few lines of code. Only one parameter is required: fn, which - takes a function that governs the response of the chatbot based on the user input and chat history. Additional - parameters can be used to control the appearance and behavior of the demo. - - Example: - import gradio as gr - - def echo(message, history): - return message - - demo = gr.ChatInterface(fn=echo, examples=["hello", "hola", "merhaba"], title="Echo Bot") - demo.launch() - Demos: chatinterface_random_response, chatinterface_streaming_echo - Guides: creating-a-chatbot-fast, sharing-your-app - """ - - def __init__( - self, - fn: Callable, - *, - chatbot: Chatbot | None = None, - textbox: Textbox | None = None, - additional_inputs: str | IOComponent | list[str | IOComponent] | None = None, - additional_inputs_accordion_name: str = "Additional Inputs", - examples: list[str] | None = None, - cache_examples: bool | None = None, - title: str | None = None, - description: str | None = None, - theme: Theme | str | None = None, - css: str | None = None, - analytics_enabled: bool | None = None, - submit_btn: str | None | Button = "Submit", - stop_btn: str | None | Button = "Stop", - retry_btn: str | None | Button = "🔄 Retry", - undo_btn: str | None | Button = "↩️ Undo", - clear_btn: str | None | Button = "🗑️ Clear", - autofocus: bool = True, - ): - """ - Parameters: - fn: the function to wrap the chat interface around. Should accept two parameters: a string input message and list of two-element lists of the form [[user_message, bot_message], ...] representing the chat history, and return a string response. See the Chatbot documentation for more information on the chat history format. - chatbot: an instance of the gr.Chatbot component to use for the chat interface, if you would like to customize the chatbot properties. If not provided, a default gr.Chatbot component will be created. - textbox: an instance of the gr.Textbox component to use for the chat interface, if you would like to customize the textbox properties. If not provided, a default gr.Textbox component will be created. - additional_inputs: an instance or list of instances of gradio components (or their string shortcuts) to use as additional inputs to the chatbot. If components are not already rendered in a surrounding Blocks, then the components will be displayed under the chatbot, in an accordion. - additional_inputs_accordion_name: the label of the accordion to use for additional inputs, only used if additional_inputs is provided. - examples: sample inputs for the function; if provided, appear below the chatbot and can be clicked to populate the chatbot input. - cache_examples: If True, caches examples in the server for fast runtime in examples. The default option in HuggingFace Spaces is True. The default option elsewhere is False. - title: a title for the interface; if provided, appears above chatbot in large font. Also used as the tab title when opened in a browser window. - description: a description for the interface; if provided, appears above the chatbot and beneath the title in regular font. Accepts Markdown and HTML content. - theme: Theme to use, loaded from gradio.themes. - css: custom css or path to custom css file to use with interface. - analytics_enabled: Whether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED environment variable if defined, or default to True. - submit_btn: Text to display on the submit button. If None, no button will be displayed. If a Button object, that button will be used. - stop_btn: Text to display on the stop button, which replaces the submit_btn when the submit_btn or retry_btn is clicked and response is streaming. Clicking on the stop_btn will halt the chatbot response. If set to None, stop button functionality does not appear in the chatbot. If a Button object, that button will be used as the stop button. - retry_btn: Text to display on the retry button. If None, no button will be displayed. If a Button object, that button will be used. - undo_btn: Text to display on the delete last button. If None, no button will be displayed. If a Button object, that button will be used. - clear_btn: Text to display on the clear button. If None, no button will be displayed. If a Button object, that button will be used. - autofocus: If True, autofocuses to the textbox when the page loads. - """ - super().__init__( - analytics_enabled=analytics_enabled, - mode="chat_interface", - css=css, - title=title or "Gradio", - theme=theme, - ) - self.fn = fn - self.is_async = inspect.iscoroutinefunction( - self.fn - ) or inspect.isasyncgenfunction(self.fn) - self.is_generator = inspect.isgeneratorfunction( - self.fn - ) or inspect.isasyncgenfunction(self.fn) - self.examples = examples - if self.space_id and cache_examples is None: - self.cache_examples = True - else: - self.cache_examples = cache_examples or False - self.buttons: list[Button] = [] - - if additional_inputs: - if not isinstance(additional_inputs, list): - additional_inputs = [additional_inputs] - self.additional_inputs = [ - get_component_instance(i, render=False) for i in additional_inputs # type: ignore - ] - else: - self.additional_inputs = [] - self.additional_inputs_accordion_name = additional_inputs_accordion_name - - with self: - if title: - Markdown( - f" {self.title}" - ) - if description: - Markdown(description) - - with Column(variant="panel"): - if chatbot: - self.chatbot = chatbot.render() - else: - self.chatbot = Chatbot(label="Chatbot") - - with Group(): - with Row(): - if textbox: - textbox.container = False - textbox.show_label = False - self.textbox = textbox.render() - else: - self.textbox = Textbox( - container=False, - show_label=False, - label="Message", - placeholder="Type a message...", - scale=7, - autofocus=autofocus, - ) - if submit_btn: - if isinstance(submit_btn, Button): - submit_btn.render() - elif isinstance(submit_btn, str): - submit_btn = Button( - submit_btn, - variant="primary", - scale=1, - min_width=150, - ) - else: - raise ValueError( - f"The submit_btn parameter must be a gr.Button, string, or None, not {type(submit_btn)}" - ) - if stop_btn: - if isinstance(stop_btn, Button): - stop_btn.visible = False - stop_btn.render() - elif isinstance(stop_btn, str): - stop_btn = Button( - stop_btn, - variant="stop", - visible=False, - scale=1, - min_width=150, - ) - else: - raise ValueError( - f"The stop_btn parameter must be a gr.Button, string, or None, not {type(stop_btn)}" - ) - self.buttons.extend([submit_btn, stop_btn]) - - with Row(): - for btn in [retry_btn, undo_btn, clear_btn]: - if btn: - if isinstance(btn, Button): - btn.render() - elif isinstance(btn, str): - btn = Button(btn, variant="secondary") - else: - raise ValueError( - f"All the _btn parameters must be a gr.Button, string, or None, not {type(btn)}" - ) - self.buttons.append(btn) - - self.fake_api_btn = Button("Fake API", visible=False) - self.fake_response_textbox = Textbox( - label="Response", visible=False - ) - ( - self.submit_btn, - self.stop_btn, - self.retry_btn, - self.undo_btn, - self.clear_btn, - ) = self.buttons - - if examples: - if self.is_generator: - examples_fn = self._examples_stream_fn - else: - examples_fn = self._examples_fn - - self.examples_handler = Examples( - examples=examples, - inputs=[self.textbox] + self.additional_inputs, - outputs=self.chatbot, - fn=examples_fn, - ) - - any_unrendered_inputs = any( - not inp.is_rendered for inp in self.additional_inputs - ) - if self.additional_inputs and any_unrendered_inputs: - with Accordion(self.additional_inputs_accordion_name, open=False): - for input_component in self.additional_inputs: - if not input_component.is_rendered: - input_component.render() - - # The example caching must happen after the input components have rendered - if cache_examples: - client_utils.synchronize_async(self.examples_handler.cache) - - self.saved_input = State() - self.chatbot_state = State([]) - - self._setup_events() - self._setup_api() - - def _setup_events(self) -> None: - submit_fn = self._stream_fn if self.is_generator else self._submit_fn - submit_event = ( - self.textbox.submit( - self._clear_and_save_textbox, - [self.textbox], - [self.textbox, self.saved_input], - api_name=False, - queue=False, - ) - .then( - self._display_input, - [self.saved_input, self.chatbot_state], - [self.chatbot, self.chatbot_state], - api_name=False, - queue=False, - ) - .then( - submit_fn, - [self.saved_input, self.chatbot_state] + self.additional_inputs, - [self.chatbot, self.chatbot_state], - api_name=False, - ) - ) - self._setup_stop_events(self.textbox.submit, submit_event) - - if self.submit_btn: - click_event = ( - self.submit_btn.click( - self._clear_and_save_textbox, - [self.textbox], - [self.textbox, self.saved_input], - api_name=False, - queue=False, - ) - .then( - self._display_input, - [self.saved_input, self.chatbot_state], - [self.chatbot, self.chatbot_state], - api_name=False, - queue=False, - ) - .then( - submit_fn, - [self.saved_input, self.chatbot_state] + self.additional_inputs, - [self.chatbot, self.chatbot_state], - api_name=False, - ) - ) - self._setup_stop_events(self.submit_btn.click, click_event) - - if self.retry_btn: - retry_event = ( - self.retry_btn.click( - self._delete_prev_fn, - [self.chatbot_state], - [self.chatbot, self.saved_input, self.chatbot_state], - api_name=False, - queue=False, - ) - .then( - self._display_input, - [self.saved_input, self.chatbot_state], - [self.chatbot, self.chatbot_state], - api_name=False, - queue=False, - ) - .then( - submit_fn, - [self.saved_input, self.chatbot_state] + self.additional_inputs, - [self.chatbot, self.chatbot_state], - api_name=False, - ) - ) - self._setup_stop_events(self.retry_btn.click, retry_event) - - if self.undo_btn: - self.undo_btn.click( - self._delete_prev_fn, - [self.chatbot_state], - [self.chatbot, self.saved_input, self.chatbot_state], - api_name=False, - queue=False, - ).then( - lambda x: x, - [self.saved_input], - [self.textbox], - api_name=False, - queue=False, - ) - - if self.clear_btn: - self.clear_btn.click( - lambda: ([], [], None), - None, - [self.chatbot, self.chatbot_state, self.saved_input], - queue=False, - api_name=False, - ) - - def _setup_stop_events( - self, event_trigger: EventListenerMethod, event_to_cancel: Dependency - ) -> None: - if self.stop_btn and self.is_generator: - if self.submit_btn: - event_trigger( - lambda: (Button.update(visible=False), Button.update(visible=True)), - None, - [self.submit_btn, self.stop_btn], - api_name=False, - queue=False, - ) - event_to_cancel.then( - lambda: (Button.update(visible=True), Button.update(visible=False)), - None, - [self.submit_btn, self.stop_btn], - api_name=False, - queue=False, - ) - else: - event_trigger( - lambda: Button.update(visible=True), - None, - [self.stop_btn], - api_name=False, - queue=False, - ) - event_to_cancel.then( - lambda: Button.update(visible=False), - None, - [self.stop_btn], - api_name=False, - queue=False, - ) - self.stop_btn.click( - None, - None, - None, - cancels=event_to_cancel, - api_name=False, - ) - - def _setup_api(self) -> None: - api_fn = self._api_stream_fn if self.is_generator else self._api_submit_fn - - self.fake_api_btn.click( - api_fn, - [self.textbox, self.chatbot_state] + self.additional_inputs, - [self.textbox, self.chatbot_state], - api_name="chat", - ) - - def _clear_and_save_textbox(self, message: str) -> tuple[str, str]: - return "", message - - def _display_input( - self, message: str, history: list[list[str | None]] - ) -> tuple[list[list[str | None]], list[list[str | None]]]: - history.append([message, None]) - return history, history - - async def _submit_fn( - self, - message: str, - history_with_input: list[list[str | None]], - *args, - ) -> tuple[list[list[str | None]], list[list[str | None]]]: - history = history_with_input[:-1] - if self.is_async: - response = await self.fn(message, history, *args) - else: - response = await anyio.to_thread.run_sync( - self.fn, message, history, *args, limiter=self.limiter - ) - history.append([message, response]) - return history, history - - async def _stream_fn( - self, - message: str, - history_with_input: list[list[str | None]], - *args, - ) -> AsyncGenerator: - history = history_with_input[:-1] - if self.is_async: - generator = self.fn(message, history, *args) - else: - generator = await anyio.to_thread.run_sync( - self.fn, message, history, *args, limiter=self.limiter - ) - generator = SyncToAsyncIterator(generator, self.limiter) - try: - first_response = await async_iteration(generator) - update = history + [[message, first_response]] - yield update, update - except StopIteration: - update = history + [[message, None]] - yield update, update - async for response in generator: - update = history + [[message, response]] - yield update, update - - async def _api_submit_fn( - self, message: str, history: list[list[str | None]], *args - ) -> tuple[str, list[list[str | None]]]: - if self.is_async: - response = await self.fn(message, history, *args) - else: - response = await anyio.to_thread.run_sync( - self.fn, message, history, *args, limiter=self.limiter - ) - history.append([message, response]) - return response, history - - async def _api_stream_fn( - self, message: str, history: list[list[str | None]], *args - ) -> AsyncGenerator: - if self.is_async: - generator = self.fn(message, history, *args) - else: - generator = await anyio.to_thread.run_sync( - self.fn, message, history, *args, limiter=self.limiter - ) - generator = SyncToAsyncIterator(generator, self.limiter) - try: - first_response = await async_iteration(generator) - yield first_response, history + [[message, first_response]] - except StopIteration: - yield None, history + [[message, None]] - async for response in generator: - yield response, history + [[message, response]] - - async def _examples_fn(self, message: str, *args) -> list[list[str | None]]: - if self.is_async: - response = await self.fn(message, [], *args) - else: - response = await anyio.to_thread.run_sync( - self.fn, message, [], *args, limiter=self.limiter - ) - return [[message, response]] - - async def _examples_stream_fn( - self, - message: str, - *args, - ) -> AsyncGenerator: - if self.is_async: - generator = self.fn(message, [], *args) - else: - generator = await anyio.to_thread.run_sync( - self.fn, message, [], *args, limiter=self.limiter - ) - generator = SyncToAsyncIterator(generator, self.limiter) - async for response in generator: - yield [[message, response]] - - def _delete_prev_fn( - self, history: list[list[str | None]] - ) -> tuple[list[list[str | None]], str, list[list[str | None]]]: - try: - message, _ = history.pop() - except IndexError: - message = "" - return history, message or "", history diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/h11/tests/test_against_stdlib_http.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/h11/tests/test_against_stdlib_http.py deleted file mode 100644 index d2ee13149d34c9882432cdebfec87dff9814076d..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/h11/tests/test_against_stdlib_http.py +++ /dev/null @@ -1,115 +0,0 @@ -import json -import os.path -import socket -import socketserver -import threading -from contextlib import closing, contextmanager -from http.server import SimpleHTTPRequestHandler -from typing import Callable, Generator -from urllib.request import urlopen - -import h11 - - -@contextmanager -def socket_server( - handler: Callable[..., socketserver.BaseRequestHandler] -) -> Generator[socketserver.TCPServer, None, None]: - httpd = socketserver.TCPServer(("127.0.0.1", 0), handler) - thread = threading.Thread( - target=httpd.serve_forever, kwargs={"poll_interval": 0.01} - ) - thread.daemon = True - try: - thread.start() - yield httpd - finally: - httpd.shutdown() - - -test_file_path = os.path.join(os.path.dirname(__file__), "data/test-file") -with open(test_file_path, "rb") as f: - test_file_data = f.read() - - -class SingleMindedRequestHandler(SimpleHTTPRequestHandler): - def translate_path(self, path: str) -> str: - return test_file_path - - -def test_h11_as_client() -> None: - with socket_server(SingleMindedRequestHandler) as httpd: - with closing(socket.create_connection(httpd.server_address)) as s: - c = h11.Connection(h11.CLIENT) - - s.sendall( - c.send( # type: ignore[arg-type] - h11.Request( - method="GET", target="/foo", headers=[("Host", "localhost")] - ) - ) - ) - s.sendall(c.send(h11.EndOfMessage())) # type: ignore[arg-type] - - data = bytearray() - while True: - event = c.next_event() - print(event) - if event is h11.NEED_DATA: - # Use a small read buffer to make things more challenging - # and exercise more paths :-) - c.receive_data(s.recv(10)) - continue - if type(event) is h11.Response: - assert event.status_code == 200 - if type(event) is h11.Data: - data += event.data - if type(event) is h11.EndOfMessage: - break - assert bytes(data) == test_file_data - - -class H11RequestHandler(socketserver.BaseRequestHandler): - def handle(self) -> None: - with closing(self.request) as s: - c = h11.Connection(h11.SERVER) - request = None - while True: - event = c.next_event() - if event is h11.NEED_DATA: - # Use a small read buffer to make things more challenging - # and exercise more paths :-) - c.receive_data(s.recv(10)) - continue - if type(event) is h11.Request: - request = event - if type(event) is h11.EndOfMessage: - break - assert request is not None - info = json.dumps( - { - "method": request.method.decode("ascii"), - "target": request.target.decode("ascii"), - "headers": { - name.decode("ascii"): value.decode("ascii") - for (name, value) in request.headers - }, - } - ) - s.sendall(c.send(h11.Response(status_code=200, headers=[]))) # type: ignore[arg-type] - s.sendall(c.send(h11.Data(data=info.encode("ascii")))) - s.sendall(c.send(h11.EndOfMessage())) - - -def test_h11_as_server() -> None: - with socket_server(H11RequestHandler) as httpd: - host, port = httpd.server_address - url = "http://{}:{}/some-path".format(host, port) - with closing(urlopen(url)) as f: - assert f.getcode() == 200 - data = f.read() - info = json.loads(data.decode("ascii")) - print(info) - assert info["method"] == "GET" - assert info["target"] == "/some-path" - assert "urllib" in info["headers"]["user-agent"] diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/inference/_generated/_async_client.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/inference/_generated/_async_client.py deleted file mode 100644 index 7a40403abefc0c6d5aa896b85e350c721ede5300..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/inference/_generated/_async_client.py +++ /dev/null @@ -1,1269 +0,0 @@ -# coding=utf-8 -# Copyright 2023-present, the HuggingFace Inc. team. -# -# 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. -# -# WARNING -# This entire file has been adapted from the sync-client code in `src/huggingface_hub/inference/_client.py`. -# Any change in InferenceClient will be automatically reflected in AsyncInferenceClient. -# To re-generate the code, run `make style` or `python ./utils/generate_async_inference_client.py --update`. -# WARNING -import logging -import time -import warnings -from dataclasses import asdict -from typing import ( - TYPE_CHECKING, - Any, - AsyncIterable, - Dict, - List, - Optional, - Union, - overload, -) - -from requests.structures import CaseInsensitiveDict - -from huggingface_hub.constants import INFERENCE_ENDPOINT -from huggingface_hub.inference._common import ( - ContentT, - InferenceTimeoutError, - _async_stream_text_generation_response, - _b64_encode, - _b64_to_image, - _bytes_to_dict, - _bytes_to_image, - _get_recommended_model, - _import_numpy, - _is_tgi_server, - _open_as_binary, - _set_as_non_tgi, -) -from huggingface_hub.inference._text_generation import ( - TextGenerationParameters, - TextGenerationRequest, - TextGenerationResponse, - TextGenerationStreamResponse, - raise_text_generation_error, -) -from huggingface_hub.inference._types import ClassificationOutput, ConversationalOutput, ImageSegmentationOutput -from huggingface_hub.utils import ( - build_hf_headers, -) -from huggingface_hub.utils._typing import Literal - -from .._common import _async_yield_from, _import_aiohttp - - -if TYPE_CHECKING: - import numpy as np - from PIL import Image - -logger = logging.getLogger(__name__) - - -class AsyncInferenceClient: - """ - Initialize a new Inference Client. - - [`InferenceClient`] aims to provide a unified experience to perform inference. The client can be used - seamlessly with either the (free) Inference API or self-hosted Inference Endpoints. - - Args: - model (`str`, `optional`): - The model to run inference with. Can be a model id hosted on the Hugging Face Hub, e.g. `bigcode/starcoder` - or a URL to a deployed Inference Endpoint. Defaults to None, in which case a recommended model is - automatically selected for the task. - token (`str`, *optional*): - Hugging Face token. Will default to the locally saved token. Pass `token=False` if you don't want to send - your token to the server. - timeout (`float`, `optional`): - The maximum number of seconds to wait for a response from the server. Loading a new model in Inference - API can take up to several minutes. Defaults to None, meaning it will loop until the server is available. - headers (`Dict[str, str]`, `optional`): - Additional headers to send to the server. By default only the authorization and user-agent headers are sent. - Values in this dictionary will override the default values. - cookies (`Dict[str, str]`, `optional`): - Additional cookies to send to the server. - """ - - def __init__( - self, - model: Optional[str] = None, - token: Union[str, bool, None] = None, - timeout: Optional[float] = None, - headers: Optional[Dict[str, str]] = None, - cookies: Optional[Dict[str, str]] = None, - ) -> None: - self.model: Optional[str] = model - self.headers = CaseInsensitiveDict(build_hf_headers(token=token)) # contains 'authorization' + 'user-agent' - if headers is not None: - self.headers.update(headers) - self.cookies = cookies - self.timeout = timeout - - def __repr__(self): - return f"Download Denah Rumah Format DwgDownload Zip ⚙⚙⚙ https://gohhs.com/2uFTvS - - 1fdad05405 - - - diff --git a/spaces/diacanFperku/AutoGPT/Fifa 12 Pc Universal Gamepads Configuration By Obievasquez16.rar.md b/spaces/diacanFperku/AutoGPT/Fifa 12 Pc Universal Gamepads Configuration By Obievasquez16.rar.md deleted file mode 100644 index e0912d9f212bac2497d930a0d0578b258bbe5016..0000000000000000000000000000000000000000 --- a/spaces/diacanFperku/AutoGPT/Fifa 12 Pc Universal Gamepads Configuration By Obievasquez16.rar.md +++ /dev/null @@ -1,6 +0,0 @@ - Fifa 12 Pc Universal Gamepads Configuration By Obievasquez16.rarDownload File »»» https://gohhs.com/2uFVwl - -Fifa 12 Pc Universal Gamepads Configuration By Obievasquez16.rar - DOWNLOAD. 53075fed5d Starlino,,,Gamepad,,,Configuration,,,allows,, ... 4d29de3e1b - - - diff --git a/spaces/digitalxingtong/Miiu-Bert-Vits2/train_ms.py b/spaces/digitalxingtong/Miiu-Bert-Vits2/train_ms.py deleted file mode 100644 index 5d109003d40497ea4493e7c73f47c1eb7370a81e..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Miiu-Bert-Vits2/train_ms.py +++ /dev/null @@ -1,402 +0,0 @@ -import os -import json -import argparse -import itertools -import math -import torch -import shutil -from torch import nn, optim -from torch.nn import functional as F -from torch.utils.data import DataLoader -from torch.utils.tensorboard import SummaryWriter -import torch.multiprocessing as mp -import torch.distributed as dist -from torch.nn.parallel import DistributedDataParallel as DDP -from torch.cuda.amp import autocast, GradScaler -from tqdm import tqdm -import logging -logging.getLogger('numba').setLevel(logging.WARNING) -import commons -import utils -from data_utils import ( - TextAudioSpeakerLoader, - TextAudioSpeakerCollate, - DistributedBucketSampler -) -from models import ( - SynthesizerTrn, - MultiPeriodDiscriminator, - DurationDiscriminator, -) -from losses import ( - generator_loss, - discriminator_loss, - feature_loss, - kl_loss -) -from mel_processing import mel_spectrogram_torch, spec_to_mel_torch -from text.symbols import symbols - -torch.backends.cudnn.benchmark = True -torch.backends.cuda.matmul.allow_tf32 = True -torch.backends.cudnn.allow_tf32 = True -torch.set_float32_matmul_precision('medium') -global_step = 0 - - -def main(): - """Assume Single Node Multi GPUs Training Only""" - assert torch.cuda.is_available(), "CPU training is not allowed." - - n_gpus = torch.cuda.device_count() - os.environ['MASTER_ADDR'] = 'localhost' - os.environ['MASTER_PORT'] = '65280' - - hps = utils.get_hparams() - if not hps.cont: - shutil.copy('./pretrained_models/D_0.pth','./logs/OUTPUT_MODEL/D_0.pth') - shutil.copy('./pretrained_models/G_0.pth','./logs/OUTPUT_MODEL/G_0.pth') - shutil.copy('./pretrained_models/DUR_0.pth','./logs/OUTPUT_MODEL/DUR_0.pth') - mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,)) - - -def run(rank, n_gpus, hps): - global global_step - if rank == 0: - logger = utils.get_logger(hps.model_dir) - logger.info(hps) - utils.check_git_hash(hps.model_dir) - writer = SummaryWriter(log_dir=hps.model_dir) - writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval")) - - dist.init_process_group(backend= 'gloo' if os.name == 'nt' else 'nccl', init_method='env://', world_size=n_gpus, rank=rank) - torch.manual_seed(hps.train.seed) - torch.cuda.set_device(rank) - - train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data) - train_sampler = DistributedBucketSampler( - train_dataset, - hps.train.batch_size, - [32, 300, 400, 500, 600, 700, 800, 900, 1000], - num_replicas=n_gpus, - rank=rank, - shuffle=True) - collate_fn = TextAudioSpeakerCollate() - train_loader = DataLoader(train_dataset, num_workers=2, shuffle=False, pin_memory=True, - collate_fn=collate_fn, batch_sampler=train_sampler) - if rank == 0: - eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data) - eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False, - batch_size=1, pin_memory=True, - drop_last=False, collate_fn=collate_fn) - if "use_noise_scaled_mas" in hps.model.keys() and hps.model.use_noise_scaled_mas == True: - print("Using noise scaled MAS for VITS2") - use_noise_scaled_mas = True - mas_noise_scale_initial = 0.01 - noise_scale_delta = 2e-6 - else: - print("Using normal MAS for VITS1") - use_noise_scaled_mas = False - mas_noise_scale_initial = 0.0 - noise_scale_delta = 0.0 - if "use_duration_discriminator" in hps.model.keys() and hps.model.use_duration_discriminator == True: - print("Using duration discriminator for VITS2") - use_duration_discriminator = True - net_dur_disc = DurationDiscriminator( - hps.model.hidden_channels, - hps.model.hidden_channels, - 3, - 0.1, - gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0, - ).cuda(rank) - if "use_spk_conditioned_encoder" in hps.model.keys() and hps.model.use_spk_conditioned_encoder == True: - if hps.data.n_speakers == 0: - raise ValueError("n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model") - use_spk_conditioned_encoder = True - else: - print("Using normal encoder for VITS1") - use_spk_conditioned_encoder = False - - net_g = SynthesizerTrn( - len(symbols), - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - n_speakers=hps.data.n_speakers, - mas_noise_scale_initial = mas_noise_scale_initial, - noise_scale_delta = noise_scale_delta, - **hps.model).cuda(rank) - - freeze_enc = getattr(hps.model, "freeze_enc", False) - if freeze_enc: - print("freeze encoder !!!") - for param in net_g.enc_p.parameters(): - param.requires_grad = False - - net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank) - optim_g = torch.optim.AdamW( - filter(lambda p: p.requires_grad, net_g.parameters()), - hps.train.learning_rate, - betas=hps.train.betas, - eps=hps.train.eps) - optim_d = torch.optim.AdamW( - net_d.parameters(), - hps.train.learning_rate, - betas=hps.train.betas, - eps=hps.train.eps) - if net_dur_disc is not None: - optim_dur_disc = torch.optim.AdamW( - net_dur_disc.parameters(), - hps.train.learning_rate, - betas=hps.train.betas, - eps=hps.train.eps) - else: - optim_dur_disc = None - net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True) - net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True) - if net_dur_disc is not None: - net_dur_disc = DDP(net_dur_disc, device_ids=[rank], find_unused_parameters=True) - - pretrain_dir = None - if pretrain_dir is None: - try: - if net_dur_disc is not None: - _, optim_dur_disc, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"), net_dur_disc, optim_dur_disc, skip_optimizer=not hps.cont) - _, optim_g, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, - optim_g, skip_optimizer=not hps.cont) - _, optim_d, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, - optim_d, skip_optimizer=not hps.cont) - - epoch_str = max(epoch_str, 1) - global_step = (epoch_str - 1) * len(train_loader) - except Exception as e: - print(e) - epoch_str = 1 - global_step = 0 - else: - _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "G_*.pth"), net_g, - optim_g, True) - _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "D_*.pth"), net_d, - optim_d, True) - - - - scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2) - scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2) - if net_dur_disc is not None: - scheduler_dur_disc = torch.optim.lr_scheduler.ExponentialLR(optim_dur_disc, gamma=hps.train.lr_decay, last_epoch=epoch_str-2) - else: - scheduler_dur_disc = None - scaler = GradScaler(enabled=hps.train.fp16_run) - - for epoch in range(epoch_str, hps.train.epochs + 1): - if rank == 0: - train_and_evaluate(rank, epoch, hps, [net_g, net_d, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, eval_loader], logger, [writer, writer_eval]) - else: - train_and_evaluate(rank, epoch, hps, [net_g, net_d, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, None], None, None) - scheduler_g.step() - scheduler_d.step() - if net_dur_disc is not None: - scheduler_dur_disc.step() - - -def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers): - net_g, net_d, net_dur_disc = nets - optim_g, optim_d, optim_dur_disc = optims - scheduler_g, scheduler_d, scheduler_dur_disc = schedulers - train_loader, eval_loader = loaders - if writers is not None: - writer, writer_eval = writers - - train_loader.batch_sampler.set_epoch(epoch) - global global_step - - net_g.train() - net_d.train() - if net_dur_disc is not None: - net_dur_disc.train() - for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in tqdm(enumerate(train_loader)): - if net_g.module.use_noise_scaled_mas: - current_mas_noise_scale = net_g.module.mas_noise_scale_initial - net_g.module.noise_scale_delta * global_step - net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0) - x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True) - spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True) - y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True) - speakers = speakers.cuda(rank, non_blocking=True) - tone = tone.cuda(rank, non_blocking=True) - language = language.cuda(rank, non_blocking=True) - bert = bert.cuda(rank, non_blocking=True) - - with autocast(enabled=hps.train.fp16_run): - y_hat, l_length, attn, ids_slice, x_mask, z_mask, \ - (z, z_p, m_p, logs_p, m_q, logs_q), (hidden_x, logw, logw_) = net_g(x, x_lengths, spec, spec_lengths, speakers, tone, language, bert) - mel = spec_to_mel_torch( - spec, - hps.data.filter_length, - hps.data.n_mel_channels, - hps.data.sampling_rate, - hps.data.mel_fmin, - hps.data.mel_fmax) - y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length) - y_hat_mel = mel_spectrogram_torch( - y_hat.squeeze(1), - hps.data.filter_length, - hps.data.n_mel_channels, - hps.data.sampling_rate, - hps.data.hop_length, - hps.data.win_length, - hps.data.mel_fmin, - hps.data.mel_fmax - ) - - y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice - - # Discriminator - y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach()) - with autocast(enabled=False): - loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g) - loss_disc_all = loss_disc - if net_dur_disc is not None: - y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach()) - with autocast(enabled=False): - # TODO: I think need to mean using the mask, but for now, just mean all - loss_dur_disc, losses_dur_disc_r, losses_dur_disc_g = discriminator_loss(y_dur_hat_r, y_dur_hat_g) - loss_dur_disc_all = loss_dur_disc - optim_dur_disc.zero_grad() - scaler.scale(loss_dur_disc_all).backward() - scaler.unscale_(optim_dur_disc) - grad_norm_dur_disc = commons.clip_grad_value_(net_dur_disc.parameters(), None) - scaler.step(optim_dur_disc) - - optim_d.zero_grad() - scaler.scale(loss_disc_all).backward() - scaler.unscale_(optim_d) - grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None) - scaler.step(optim_d) - - with autocast(enabled=hps.train.fp16_run): - # Generator - y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat) - if net_dur_disc is not None: - y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_) - with autocast(enabled=False): - loss_dur = torch.sum(l_length.float()) - loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel - loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl - - loss_fm = feature_loss(fmap_r, fmap_g) - loss_gen, losses_gen = generator_loss(y_d_hat_g) - loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl - if net_dur_disc is not None: - loss_dur_gen, losses_dur_gen = generator_loss(y_dur_hat_g) - loss_gen_all += loss_dur_gen - optim_g.zero_grad() - scaler.scale(loss_gen_all).backward() - scaler.unscale_(optim_g) - grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None) - scaler.step(optim_g) - scaler.update() - - if rank == 0: - if global_step % hps.train.log_interval == 0: - lr = optim_g.param_groups[0]['lr'] - losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl] - logger.info('Train Epoch: {} [{:.0f}%]'.format( - epoch, - 100. * batch_idx / len(train_loader))) - logger.info([x.item() for x in losses] + [global_step, lr]) - - scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr, - "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g} - scalar_dict.update( - {"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl}) - scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)}) - scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)}) - scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)}) - - image_dict = { - "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()), - "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()), - "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()), - "all/attn": utils.plot_alignment_to_numpy(attn[0, 0].data.cpu().numpy()) - } - utils.summarize( - writer=writer, - global_step=global_step, - images=image_dict, - scalars=scalar_dict) - - if global_step % hps.train.eval_interval == 0: - evaluate(hps, net_g, eval_loader, writer_eval) - utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch, - os.path.join(hps.model_dir, "G_{}.pth".format(global_step))) - utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch, - os.path.join(hps.model_dir, "D_{}.pth".format(global_step))) - if net_dur_disc is not None: - utils.save_checkpoint(net_dur_disc, optim_dur_disc, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "DUR_{}.pth".format(global_step))) - keep_ckpts = getattr(hps.train, 'keep_ckpts', 5) - if keep_ckpts > 0: - utils.clean_checkpoints(path_to_models=hps.model_dir, n_ckpts_to_keep=keep_ckpts, sort_by_time=True) - - - global_step += 1 - - if rank == 0: - logger.info('====> Epoch: {}'.format(epoch)) - - - -def evaluate(hps, generator, eval_loader, writer_eval): - generator.eval() - image_dict = {} - audio_dict = {} - print("Evaluating ...") - with torch.no_grad(): - for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in enumerate(eval_loader): - x, x_lengths = x.cuda(), x_lengths.cuda() - spec, spec_lengths = spec.cuda(), spec_lengths.cuda() - y, y_lengths = y.cuda(), y_lengths.cuda() - speakers = speakers.cuda() - bert = bert.cuda() - tone = tone.cuda() - language = language.cuda() - for use_sdp in [True, False]: - y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers, tone, language, bert, y=spec, max_len=1000, sdp_ratio=0.0 if not use_sdp else 1.0) - y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length - - mel = spec_to_mel_torch( - spec, - hps.data.filter_length, - hps.data.n_mel_channels, - hps.data.sampling_rate, - hps.data.mel_fmin, - hps.data.mel_fmax) - y_hat_mel = mel_spectrogram_torch( - y_hat.squeeze(1).float(), - hps.data.filter_length, - hps.data.n_mel_channels, - hps.data.sampling_rate, - hps.data.hop_length, - hps.data.win_length, - hps.data.mel_fmin, - hps.data.mel_fmax - ) - image_dict.update({ - f"gen/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy()) - }) - audio_dict.update({ - f"gen/audio_{batch_idx}_{use_sdp}": y_hat[0, :, :y_hat_lengths[0]] - }) - image_dict.update({f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())}) - audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, :y_lengths[0]]}) - - utils.summarize( - writer=writer_eval, - global_step=global_step, - images=image_dict, - audios=audio_dict, - audio_sampling_rate=hps.data.sampling_rate - ) - generator.train() - -if __name__ == "__main__": - main() diff --git a/spaces/dineshreddy/WALT/cwalt_generate.py b/spaces/dineshreddy/WALT/cwalt_generate.py deleted file mode 100644 index 18e16c8912d8fe879bfe82ad47d4c04abe44766e..0000000000000000000000000000000000000000 --- a/spaces/dineshreddy/WALT/cwalt_generate.py +++ /dev/null @@ -1,14 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Sat Jun 4 16:55:58 2022 - -@author: dinesh -""" -from cwalt.CWALT import CWALT_Generation -from cwalt.Clip_WALT_Generate import Get_unoccluded_objects - -if __name__ == '__main__': - camera_name = 'cam2' - Get_unoccluded_objects(camera_name) - CWALT_Generation(camera_name) diff --git a/spaces/dirge/voicevox/test/test_connect_base64_waves.py b/spaces/dirge/voicevox/test/test_connect_base64_waves.py deleted file mode 100644 index e50c8f517e64e178f180abab0ed2372878848f86..0000000000000000000000000000000000000000 --- a/spaces/dirge/voicevox/test/test_connect_base64_waves.py +++ /dev/null @@ -1,130 +0,0 @@ -import base64 -import io -from unittest import TestCase - -import numpy as np -import numpy.testing -import soundfile -from scipy.signal import resample - -from voicevox_engine.utility import ConnectBase64WavesException, connect_base64_waves - - -def generate_sine_wave_ndarray( - seconds: float, samplerate: int, frequency: float -) -> np.ndarray: - x = np.linspace(0, seconds, int(seconds * samplerate), endpoint=False) - wave = np.sin(2 * np.pi * frequency * x).astype(np.float32) - - return wave - - -def encode_bytes(wave_ndarray: np.ndarray, samplerate: int) -> bytes: - wave_bio = io.BytesIO() - soundfile.write( - file=wave_bio, - data=wave_ndarray, - samplerate=samplerate, - format="WAV", - subtype="FLOAT", - ) - wave_bio.seek(0) - - return wave_bio.getvalue() - - -def generate_sine_wave_bytes( - seconds: float, samplerate: int, frequency: float -) -> bytes: - wave_ndarray = generate_sine_wave_ndarray(seconds, samplerate, frequency) - return encode_bytes(wave_ndarray, samplerate) - - -def encode_base64(wave_bytes: bytes) -> str: - return base64.standard_b64encode(wave_bytes).decode("utf-8") - - -def generate_sine_wave_base64(seconds: float, samplerate: int, frequency: float) -> str: - wave_bytes = generate_sine_wave_bytes(seconds, samplerate, frequency) - wave_base64 = encode_base64(wave_bytes) - return wave_base64 - - -class TestConnectBase64Waves(TestCase): - def test_connect(self): - samplerate = 1000 - wave = generate_sine_wave_ndarray( - seconds=2, samplerate=samplerate, frequency=10 - ) - wave_base64 = encode_base64(encode_bytes(wave, samplerate=samplerate)) - - wave_x2_ref = np.concatenate([wave, wave]) - - wave_x2, _ = connect_base64_waves(waves=[wave_base64, wave_base64]) - - self.assertEqual(wave_x2_ref.shape, wave_x2.shape) - - self.assertTrue((wave_x2_ref == wave_x2).all()) - - def test_no_wave_error(self): - self.assertRaises(ConnectBase64WavesException, connect_base64_waves, waves=[]) - - def test_invalid_base64_error(self): - wave_1000hz = generate_sine_wave_base64( - seconds=2, samplerate=1000, frequency=10 - ) - wave_1000hz_broken = wave_1000hz[1:] # remove head 1 char - - self.assertRaises( - ConnectBase64WavesException, - connect_base64_waves, - waves=[ - wave_1000hz_broken, - ], - ) - - def test_invalid_wave_file_error(self): - wave_1000hz = generate_sine_wave_bytes(seconds=2, samplerate=1000, frequency=10) - wave_1000hz_broken_bytes = wave_1000hz[1:] # remove head 1 byte - wave_1000hz_broken = encode_base64(wave_1000hz_broken_bytes) - - self.assertRaises( - ConnectBase64WavesException, - connect_base64_waves, - waves=[ - wave_1000hz_broken, - ], - ) - - def test_different_frequency(self): - wave_24000hz = generate_sine_wave_ndarray( - seconds=1, samplerate=24000, frequency=10 - ) - wave_1000hz = generate_sine_wave_ndarray( - seconds=2, samplerate=1000, frequency=10 - ) - wave_24000_base64 = encode_base64(encode_bytes(wave_24000hz, samplerate=24000)) - wave_1000_base64 = encode_base64(encode_bytes(wave_1000hz, samplerate=1000)) - - wave_1000hz_to2400hz = resample(wave_1000hz, 24000 * len(wave_1000hz) // 1000) - wave_x2_ref = np.concatenate([wave_24000hz, wave_1000hz_to2400hz]) - - wave_x2, _ = connect_base64_waves(waves=[wave_24000_base64, wave_1000_base64]) - - self.assertEqual(wave_x2_ref.shape, wave_x2.shape) - numpy.testing.assert_array_almost_equal(wave_x2_ref, wave_x2) - - def test_different_channels(self): - wave_1000hz = generate_sine_wave_ndarray( - seconds=2, samplerate=1000, frequency=10 - ) - wave_2ch_1000hz = np.array([wave_1000hz, wave_1000hz]).T - wave_1ch_base64 = encode_base64(encode_bytes(wave_1000hz, samplerate=1000)) - wave_2ch_base64 = encode_base64(encode_bytes(wave_2ch_1000hz, samplerate=1000)) - - wave_x2_ref = np.concatenate([wave_2ch_1000hz, wave_2ch_1000hz]) - - wave_x2, _ = connect_base64_waves(waves=[wave_1ch_base64, wave_2ch_base64]) - - self.assertEqual(wave_x2_ref.shape, wave_x2.shape) - self.assertTrue((wave_x2_ref == wave_x2).all()) diff --git a/spaces/dmeck/RVC-Speakers/speakers/load/serializable.py b/spaces/dmeck/RVC-Speakers/speakers/load/serializable.py deleted file mode 100644 index 8f0e5ccf8dd0f6d95e569f8b3b0965b4557f519b..0000000000000000000000000000000000000000 --- a/spaces/dmeck/RVC-Speakers/speakers/load/serializable.py +++ /dev/null @@ -1,163 +0,0 @@ -from abc import ABC -from typing import Any, Dict, List, Literal, TypedDict, Union, cast - -from pydantic import BaseModel, PrivateAttr - - -class BaseSerialized(TypedDict): - """Base class for serialized objects.""" - - lc: int - id: List[str] - - -class SerializedConstructor(BaseSerialized): - """Serialized constructor.""" - - type: Literal["constructor"] - kwargs: Dict[str, Any] - - -class SerializedSecret(BaseSerialized): - """Serialized secret.""" - - type: Literal["secret"] - - -class SerializedNotImplemented(BaseSerialized): - """Serialized not implemented.""" - - type: Literal["not_implemented"] - - -class Serializable(BaseModel, ABC): - """Serializable base class.""" - - @property - def lc_serializable(self) -> bool: - """ - Return whether or not the class is serializable. - """ - return False - - @property - def lc_namespace(self) -> List[str]: - """ - Return the namespace of the langchain object. - eg. ["langchain", "llms", "openai"] - """ - return self.__class__.__module__.split(".") - - @property - def lc_secrets(self) -> Dict[str, str]: - """ - Return a map of constructor argument names to secret ids. - eg. {"openai_api_key": "OPENAI_API_KEY"} - """ - return dict() - - @property - def lc_attributes(self) -> Dict: - """ - Return a list of attribute names that should be included in the - serialized kwargs. These attributes must be accepted by the - constructor. - """ - return {} - - class Config: - extra = "ignore" - - _lc_kwargs = PrivateAttr(default_factory=dict) - - def __init__(self, **kwargs: Any) -> None: - super().__init__(**kwargs) - self._lc_kwargs = kwargs - - def to_json(self) -> Union[SerializedConstructor, SerializedNotImplemented]: - if not self.lc_serializable: - return self.to_json_not_implemented() - - secrets = dict() - # Get latest values for kwargs if there is an attribute with same name - lc_kwargs = { - k: getattr(self, k, v) - for k, v in self._lc_kwargs.items() - if not (self.__exclude_fields__ or {}).get(k, False) # type: ignore - } - - # Merge the lc_secrets and lc_attributes from every class in the MRO - for cls in [None, *self.__class__.mro()]: - # Once we get to Serializable, we're done - if cls is Serializable: - break - - # Get a reference to self bound to each class in the MRO - this = cast(Serializable, self if cls is None else super(cls, self)) - - secrets.update(this.lc_secrets) - lc_kwargs.update(this.lc_attributes) - - # include all secrets, even if not specified in kwargs - # as these secrets may be passed as an environment variable instead - for key in secrets.keys(): - secret_value = getattr(self, key, None) or lc_kwargs.get(key) - if secret_value is not None: - lc_kwargs.update({key: secret_value}) - - return { - "lc": 1, - "type": "constructor", - "id": [*self.lc_namespace, self.__class__.__name__], - "kwargs": lc_kwargs - if not secrets - else _replace_secrets(lc_kwargs, secrets), - } - - def to_json_not_implemented(self) -> SerializedNotImplemented: - return to_json_not_implemented(self) - - -def _replace_secrets( - root: Dict[Any, Any], secrets_map: Dict[str, str] -) -> Dict[Any, Any]: - result = root.copy() - for path, secret_id in secrets_map.items(): - [*parts, last] = path.split(".") - current = result - for part in parts: - if part not in current: - break - current[part] = current[part].copy() - current = current[part] - if last in current: - current[last] = { - "lc": 1, - "type": "secret", - "id": [secret_id], - } - return result - - -def to_json_not_implemented(obj: object) -> SerializedNotImplemented: - """Serialize a "not implemented" object. - - Args: - obj: object to serialize - - Returns: - SerializedNotImplemented - """ - _id: List[str] = [] - try: - if hasattr(obj, "__name__"): - _id = [*obj.__module__.split("."), obj.__name__] - elif hasattr(obj, "__class__"): - _id = [*obj.__class__.__module__.split("."), obj.__class__.__name__] - except Exception: - pass - return { - "lc": 1, - "type": "not_implemented", - "id": _id, - } diff --git a/spaces/dnth/rice-disease-classifier/README.md b/spaces/dnth/rice-disease-classifier/README.md deleted file mode 100644 index 16fab93489752662dfbbab0cceb3278173515002..0000000000000000000000000000000000000000 --- a/spaces/dnth/rice-disease-classifier/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Rice Disease Classifier -emoji: 📉 -colorFrom: yellow -colorTo: pink -sdk: gradio -app_file: app.py -pinned: false -license: afl-3.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/doevent/df/README.md b/spaces/doevent/df/README.md deleted file mode 100644 index 86dac2e2d91118d8bc4dc4ba44e5c63bb2307ed4..0000000000000000000000000000000000000000 --- a/spaces/doevent/df/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: DF -emoji: 📉 -colorFrom: red -colorTo: red -sdk: gradio -sdk_version: 3.40.1 -app_file: app.py -pinned: false -license: unknown -duplicated_from: doevent/dl ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/spaces/dolceschokolade/chatbot-mini/components/Chat/PromptList.tsx b/spaces/dolceschokolade/chatbot-mini/components/Chat/PromptList.tsx deleted file mode 100644 index 3e5ad100b11223736446b4187e600fd97f193ac7..0000000000000000000000000000000000000000 --- a/spaces/dolceschokolade/chatbot-mini/components/Chat/PromptList.tsx +++ /dev/null @@ -1,45 +0,0 @@ -import { FC, MutableRefObject } from 'react'; - -import { Prompt } from '@/types/prompt'; - -interface Props { - prompts: Prompt[]; - activePromptIndex: number; - onSelect: () => void; - onMouseOver: (index: number) => void; - promptListRef: MutableRefObject
How to Activate Adobe Media Encoder Cs6 131-Adobe Media Encoder Cs6 is a powerful software that allows you to encode, transcode, and compress video and audio files for various formats and platforms. However, to use this software, you need to activate it with a valid serial number. In this article, we will show you how to activate Adobe Media Encoder Cs6 131 in a few simple steps. -Step 1: Download and Install Adobe Media Encoder Cs6 131-The first step is to download and install Adobe Media Encoder Cs6 131 on your computer. You can download it from the official Adobe website or from a trusted third-party source. Make sure you have enough disk space and system requirements to run the software smoothly. Follow the installation instructions and agree to the terms and conditions. -Activar Adobe Media Encoder Cs6 131DOWNLOAD https://jinyurl.com/2uEpiP - Step 2: Launch Adobe Media Encoder Cs6 131-The next step is to launch Adobe Media Encoder Cs6 131 on your computer. You will see a welcome screen that asks you to sign in with your Adobe ID or create a new one. If you already have an Adobe ID, enter your email and password and click Sign In. If you don't have an Adobe ID, click Create an Account and follow the steps to create one. -Step 3: Enter Your Serial Number-The final step is to enter your serial number to activate Adobe Media Encoder Cs6 131. You can find your serial number on the back of the DVD case or in the confirmation email if you purchased the software online. Enter the 24-digit serial number in the boxes provided and click Next. If your serial number is valid, you will see a confirmation message that says "Your product has been activated successfully". Click Done to finish the activation process. -Congratulations! You Have Activated Adobe Media Encoder Cs6 131-You have successfully activated Adobe Media Encoder Cs6 131 on your computer. You can now enjoy all the features and functions of this software. You can use it to encode, transcode, and compress video and audio files for various formats and platforms. You can also customize your settings and preferences according to your needs. -If you have any questions or issues regarding the activation process, you can contact Adobe customer support or visit their online forums for help. You can also check out their tutorials and guides for more tips and tricks on how to use Adobe Media Encoder Cs6 131 effectively. -
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-Learn more about Adobe Media Encoder Cs6 131 here d5da3c52bf- - \ No newline at end of file diff --git a/spaces/erwann/Face-editor/img_processing.py b/spaces/erwann/Face-editor/img_processing.py deleted file mode 100644 index 9cd4af11d1c43786a761dace62d500c88643c8c4..0000000000000000000000000000000000000000 --- a/spaces/erwann/Face-editor/img_processing.py +++ /dev/null @@ -1,55 +0,0 @@ -import io - -import numpy as np -import PIL -import requests -import torch -import torchvision.transforms as T -import torchvision.transforms.functional as TF -from PIL import Image, ImageDraw, ImageFont - - -def preprocess(img, target_image_size=256): - s = min(img.size) - - if s < target_image_size: - raise ValueError(f"min dim for image {s} < {target_image_size}") - - r = target_image_size / s - s = (round(r * img.size[1]), round(r * img.size[0])) - img = TF.resize(img, s, interpolation=PIL.Image.LANCZOS) - img = TF.center_crop(img, output_size=2 * [target_image_size]) - img = torch.unsqueeze(T.ToTensor()(img), 0) - return img - - -def preprocess_vqgan(x): - x = 2.0 * x - 1.0 - return x - - -def custom_to_pil(x, process=True, mode="RGB"): - x = x.detach().cpu() - if process: - x = torch.clamp(x, -1.0, 1.0) - x = (x + 1.0) / 2.0 - x = x.permute(1, 2, 0).numpy() - if process: - x = (255 * x).astype(np.uint8) - x = Image.fromarray(x) - if not x.mode == mode: - x = x.convert(mode) - return x - - -def get_pil(x): - x = torch.clamp(x, -1.0, 1.0) - x = (x + 1.0) / 2.0 - x = x.permute(1, 2, 0) - return x - - -def loop_post_process(x): - x = get_pil(x.squeeze()) - return x.permute(2, 0, 1).unsqueeze(0) - diff --git a/spaces/eson/tokenizer-arena/util.py b/spaces/eson/tokenizer-arena/util.py deleted file mode 100644 index 29be9421ae99d61238ccb38c7e24cd5876a6a633..0000000000000000000000000000000000000000 --- a/spaces/eson/tokenizer-arena/util.py +++ /dev/null @@ -1,149 +0,0 @@ -import gradio as gr -import json -import socket -import pandas as pd -from vocab import load_tokener -from utils.zh_util import iter_vocab -from utils.log_util import logger -from functools import lru_cache -from urllib.parse import urlparse, parse_qs - - -@lru_cache -def tokenize(text, tokenizer_type, color_num=5): - """ - """ - logger.info("param=" + json.dumps({"text": text, "tokenizer_type": tokenizer_type}, ensure_ascii=False)) - pos_tokens = [] - tokenizer = load_tokener(tokenizer_type) - encoding = tokenizer.encode(text) - - table = [] - - for idx, token_id in enumerate(encoding): - decode_text = tokenizer.decode([token_id]) # 特殊字符解码后会统一变成 �，对应 "\ufffd" - pos_tokens.extend([(decode_text, str(idx % color_num))]) - - # token "Byte": # 这是 utf-8编码吧？ - token = tokenizer.convert_ids_to_tokens([token_id])[0] - if isinstance(token, bytes): - try: - token_str = token.decode("utf-8") - except: - token_str = token.decode("utf-8", errors="ignore") - logger.error("decode_error: " + json.dumps( - {"tokenizer_type": tokenizer_type, "token": str(token), "token_str": token_str}, - ensure_ascii=False)) - - token_bytes = token - # json_dumps = json.dumps(token_str) - elif isinstance(token, str): - token_str = token - token_bytes = bytes(token_str, "utf-8") - # json_dumps = json.dumps(token_str) - else: - return - - # ⭐ - table.append( - {"TokenID": token_id, - "Token": token_str, # utf-8解码后的字符串，为什么有些是 <0xE7>，表示什么？比如llama - "Text": decode_text, # - # "Bytes": token_bytes, # bytes类型在gradio前端页面被解码成字符串，比如 b'\xe4\xb8\xad' 仍然显示成 "中"。因此 str(token_bytes) - "UTF8 Bytes": str(token_bytes), - # "Unicode": json_dumps # unicode, 如果是ascii码，就直接显示。如果不是ascii码，就显示unicode - } - ) - - table_df = pd.DataFrame(table) - logger.info(f"Tokens={table[:2]}") - # print(table_df) - - return gr.update(value=pos_tokens, label=f"Tokens: {len(encoding)}"), table_df - - -@lru_cache -def tokenize_pair(text, tokenizer_type_1, tokenizer_type_2): - """ - input_text.change - """ - pos_tokens_1, table_df_1 = tokenize(text, tokenizer_type_1) - pos_tokens_2, table_df_2 = tokenize(text, tokenizer_type_2) - return pos_tokens_1, table_df_1, pos_tokens_2, table_df_2 - - -@lru_cache -def basic_count(tokenizer_type): - tokenizer = load_tokener(tokenizer_type) - stats = iter_vocab(tokenizer, tokenizer_type) - return tokenizer.vocab_size, f'{stats["中文汉字数"]["中文单字"]}/{stats["中文汉字数"]["中文多字"]}' - - -@lru_cache -def get_overlap_token_size(tokenizer_type_1, tokenizer_type_2): - tokenizer1 = load_tokener(tokenizer_type_1) - tokenizer2 = load_tokener(tokenizer_type_2) - - vocab_set_1 = tokenizer1.get_vocab().keys() - vocab_set_2 = tokenizer2.get_vocab().keys() - - token1 = next(iter(vocab_set_1)) - token2 = next(iter(vocab_set_2)) - if type(token1) != type(token2): # bytes str - if isinstance(token1, str): - vocab_set_1 = set([token.encode("utf-8") for token in vocab_set_1]) - if isinstance(token2, str): - vocab_set_2 = set([token.encode("utf-8") for token in vocab_set_2]) - - overlap_tokens = vocab_set_1 & vocab_set_2 - overlap_token_size = len(overlap_tokens) - logger.info( - f"{overlap_token_size} OverlapTokens of {tokenizer_type_1} {tokenizer_type_2}: {list(overlap_tokens)[:10]}") - return overlap_token_size, overlap_token_size - - -default_user_input = """Replace this text in the input field to see how tokenization works -华为发布Mate60手机 -ラグビーワールドカップ2023フランス""" -default_tokenizer_type_1 = "llama" -# default_tokenizer_type_2 = "internlm_chat_7b" -default_tokenizer_type_2 = "gpt_35_turbo" - - -def on_load(url_params, request: gr.Request): - """ - onLoad - """ - text = None - tokenizer_type_1 = None - tokenizer_type_2 = None - try: - url_params = json.loads(url_params) - except: - url_params = {} - if request: - logger.info(str(request.headers)) - client_ip = request.client.host - # local_ip = socket.gethostbyname(socket.gethostbyname("")) - # headers = request.kwargs['headers'] - # if headers and 'x-forwarded-for' in headers: - # x_forwarded_for = headers['x-forwarded-for'] - # client_ip = x_forwarded_for.split(' ')[0] if x_forwarded_for else "" - # if "referer" in request.headers: # not work for huggingface-space - # url_params = parse_qs(urlparse(request.headers["referer"]).query) - # url_params = {k: v[0] for k, v in url_params.items() if len(v) > 0} - tokenizer_type_1 = url_params.get("tokenizer1", default_tokenizer_type_1) - tokenizer_type_2 = url_params.get("tokenizer2", default_tokenizer_type_2) - text = url_params.get("text", default_user_input) - logger.info(f"client_ip: {client_ip}; params: {url_params}") - return text, tokenizer_type_1, tokenizer_type_2 - - -def test_coding(): - bytes1 = b'\xe4\xb8\xad' - print(bytes1) # b'\xe4\xb8\xad' - - -if __name__ == "__main__": - print(get_overlap_token_size("gpt_35_turbo", "gpt_4")) - # print(basic_count("internlm_chat_7b")) diff --git a/spaces/evaluate-metric/code_eval/execute.py b/spaces/evaluate-metric/code_eval/execute.py deleted file mode 100644 index 53517a805cf84758b858612b62794b874590159b..0000000000000000000000000000000000000000 --- a/spaces/evaluate-metric/code_eval/execute.py +++ /dev/null @@ -1,236 +0,0 @@ -# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. -# -# 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. - -# This code is adapted from OpenAI's release -# https://github.com/openai/human-eval/blob/master/human_eval/execution.py - -import contextlib -import faulthandler -import io -import multiprocessing -import os -import platform -import signal -import tempfile - - -def check_correctness(check_program, timeout, task_id, completion_id): - """ - Evaluates the functional correctness of a completion by running the test - suite provided in the problem. - - :param completion_id: an optional completion ID so we can match - the results later even if execution finishes asynchronously. - """ - manager = multiprocessing.Manager() - result = manager.list() - - p = multiprocessing.Process(target=unsafe_execute, args=(check_program, result, timeout)) - p.start() - p.join(timeout=timeout + 1) - if p.is_alive(): - p.kill() - - if not result: - result.append("timed out") - - return dict( - task_id=task_id, - passed=result[0] == "passed", - result=result[0], - completion_id=completion_id, - ) - - -def unsafe_execute(check_program, result, timeout): - - with create_tempdir(): - - # These system calls are needed when cleaning up tempdir. - import os - import shutil - - rmtree = shutil.rmtree - rmdir = os.rmdir - chdir = os.chdir - - # Disable functionalities that can make destructive changes to the test. - reliability_guard() - - # Run program. - try: - exec_globals = {} - with swallow_io(): - with time_limit(timeout): - exec(check_program, exec_globals) - result.append("passed") - except TimeoutException: - result.append("timed out") - except BaseException as e: - result.append(f"failed: {e}") - - # Needed for cleaning up. - shutil.rmtree = rmtree - os.rmdir = rmdir - os.chdir = chdir - - -@contextlib.contextmanager -def time_limit(seconds): - def signal_handler(signum, frame): - raise TimeoutException("Timed out!") - - signal.setitimer(signal.ITIMER_REAL, seconds) - signal.signal(signal.SIGALRM, signal_handler) - try: - yield - finally: - signal.setitimer(signal.ITIMER_REAL, 0) - - -@contextlib.contextmanager -def swallow_io(): - stream = WriteOnlyStringIO() - with contextlib.redirect_stdout(stream): - with contextlib.redirect_stderr(stream): - with redirect_stdin(stream): - yield - - -@contextlib.contextmanager -def create_tempdir(): - with tempfile.TemporaryDirectory() as dirname: - with chdir(dirname): - yield dirname - - -class TimeoutException(Exception): - pass - - -class WriteOnlyStringIO(io.StringIO): - """StringIO that throws an exception when it's read from""" - - def read(self, *args, **kwargs): - raise OSError - - def readline(self, *args, **kwargs): - raise OSError - - def readlines(self, *args, **kwargs): - raise OSError - - def readable(self, *args, **kwargs): - """Returns True if the IO object can be read.""" - return False - - -class redirect_stdin(contextlib._RedirectStream): # type: ignore - _stream = "stdin" - - -@contextlib.contextmanager -def chdir(root): - if root == ".": - yield - return - cwd = os.getcwd() - os.chdir(root) - try: - yield - except BaseException as exc: - raise exc - finally: - os.chdir(cwd) - - -def reliability_guard(maximum_memory_bytes=None): - """ - This disables various destructive functions and prevents the generated code - from interfering with the test (e.g. fork bomb, killing other processes, - removing filesystem files, etc.) - - WARNING - This function is NOT a security sandbox. Untrusted code, including, model- - generated code, should not be blindly executed outside of one. See the - Codex paper for more information about OpenAI's code sandbox, and proceed - with caution. - """ - - if maximum_memory_bytes is not None: - import resource - - resource.setrlimit(resource.RLIMIT_AS, (maximum_memory_bytes, maximum_memory_bytes)) - resource.setrlimit(resource.RLIMIT_DATA, (maximum_memory_bytes, maximum_memory_bytes)) - if not platform.uname().system == "Darwin": - resource.setrlimit(resource.RLIMIT_STACK, (maximum_memory_bytes, maximum_memory_bytes)) - - faulthandler.disable() - - import builtins - - builtins.exit = None - builtins.quit = None - - import os - - os.environ["OMP_NUM_THREADS"] = "1" - - os.kill = None - os.system = None - os.putenv = None - os.remove = None - os.removedirs = None - os.rmdir = None - os.fchdir = None - os.setuid = None - os.fork = None - os.forkpty = None - os.killpg = None - os.rename = None - os.renames = None - os.truncate = None - os.replace = None - os.unlink = None - os.fchmod = None - os.fchown = None - os.chmod = None - os.chown = None - os.chroot = None - os.fchdir = None - os.lchflags = None - os.lchmod = None - os.lchown = None - os.getcwd = None - os.chdir = None - - import shutil - - shutil.rmtree = None - shutil.move = None - shutil.chown = None - - import subprocess - - subprocess.Popen = None # type: ignore - - __builtins__["help"] = None - - import sys - - sys.modules["ipdb"] = None - sys.modules["joblib"] = None - sys.modules["resource"] = None - sys.modules["psutil"] = None - sys.modules["tkinter"] = None diff --git a/spaces/evanpierce/3D_Photo_Inpainting2/mesh_tools.py b/spaces/evanpierce/3D_Photo_Inpainting2/mesh_tools.py deleted file mode 100644 index ca2a065e08f41b35358b82e89b5045ad8172c54b..0000000000000000000000000000000000000000 --- a/spaces/evanpierce/3D_Photo_Inpainting2/mesh_tools.py +++ /dev/null @@ -1,1083 +0,0 @@ -import os -import numpy as np -try: - import cynetworkx as netx -except ImportError: - import networkx as netx - -import json -import scipy.misc as misc -#import OpenEXR -import scipy.signal as signal -import matplotlib.pyplot as plt -import cv2 -import scipy.misc as misc -from skimage import io -from functools import partial -from vispy import scene, io -from vispy.scene import visuals -from functools import reduce -# from moviepy.editor import ImageSequenceClip -import scipy.misc as misc -from vispy.visuals.filters import Alpha -import cv2 -from skimage.transform import resize -import copy -import torch -import os -from utils import refine_depth_around_edge, smooth_cntsyn_gap -from utils import require_depth_edge, filter_irrelevant_edge_new, open_small_mask -from skimage.feature import canny -from scipy import ndimage -import time -import transforms3d - -def relabel_node(mesh, nodes, cur_node, new_node): - if cur_node == new_node: - return mesh - mesh.add_node(new_node) - for key, value in nodes[cur_node].items(): - nodes[new_node][key] = value - for ne in mesh.neighbors(cur_node): - mesh.add_edge(new_node, ne) - mesh.remove_node(cur_node) - - return mesh - -def filter_edge(mesh, edge_ccs, config, invalid=False): - context_ccs = [set() for _ in edge_ccs] - mesh_nodes = mesh.nodes - for edge_id, edge_cc in enumerate(edge_ccs): - if config['context_thickness'] == 0: - continue - edge_group = {} - for edge_node in edge_cc: - far_nodes = mesh_nodes[edge_node].get('far') - if far_nodes is None: - continue - for far_node in far_nodes: - context_ccs[edge_id].add(far_node) - if mesh_nodes[far_node].get('edge_id') is not None: - if edge_group.get(mesh_nodes[far_node]['edge_id']) is None: - edge_group[mesh_nodes[far_node]['edge_id']] = set() - edge_group[mesh_nodes[far_node]['edge_id']].add(far_node) - if len(edge_cc) > 2: - for edge_key in [*edge_group.keys()]: - if len(edge_group[edge_key]) == 1: - context_ccs[edge_id].remove([*edge_group[edge_key]][0]) - valid_edge_ccs = [] - for xidx, yy in enumerate(edge_ccs): - if invalid is not True and len(context_ccs[xidx]) > 0: - # if len(context_ccs[xidx]) > 0: - valid_edge_ccs.append(yy) - elif invalid is True and len(context_ccs[xidx]) == 0: - valid_edge_ccs.append(yy) - else: - valid_edge_ccs.append(set()) - # valid_edge_ccs = [yy for xidx, yy in enumerate(edge_ccs) if len(context_ccs[xidx]) > 0] - - return valid_edge_ccs - -def extrapolate(global_mesh, - info_on_pix, - image, - depth, - other_edge_with_id, - edge_map, - edge_ccs, - depth_edge_model, - depth_feat_model, - rgb_feat_model, - config, - direc='right-up'): - h_off, w_off = global_mesh.graph['hoffset'], global_mesh.graph['woffset'] - noext_H, noext_W = global_mesh.graph['noext_H'], global_mesh.graph['noext_W'] - - if "up" in direc.lower() and "-" not in direc.lower(): - all_anchor = [0, h_off + config['context_thickness'], w_off, w_off + noext_W] - global_shift = [all_anchor[0], all_anchor[2]] - mask_anchor = [0, h_off, w_off, w_off + noext_W] - context_anchor = [h_off, h_off + config['context_thickness'], w_off, w_off + noext_W] - valid_line_anchor = [h_off, h_off + 1, w_off, w_off + noext_W] - valid_anchor = [min(mask_anchor[0], context_anchor[0]), max(mask_anchor[1], context_anchor[1]), - min(mask_anchor[2], context_anchor[2]), max(mask_anchor[3], context_anchor[3])] - elif "down" in direc.lower() and "-" not in direc.lower(): - all_anchor = [h_off + noext_H - config['context_thickness'], 2 * h_off + noext_H, w_off, w_off + noext_W] - global_shift = [all_anchor[0], all_anchor[2]] - mask_anchor = [h_off + noext_H, 2 * h_off + noext_H, w_off, w_off + noext_W] - context_anchor = [h_off + noext_H - config['context_thickness'], h_off + noext_H, w_off, w_off + noext_W] - valid_line_anchor = [h_off + noext_H - 1, h_off + noext_H, w_off, w_off + noext_W] - valid_anchor = [min(mask_anchor[0], context_anchor[0]), max(mask_anchor[1], context_anchor[1]), - min(mask_anchor[2], context_anchor[2]), max(mask_anchor[3], context_anchor[3])] - elif "left" in direc.lower() and "-" not in direc.lower(): - all_anchor = [h_off, h_off + noext_H, 0, w_off + config['context_thickness']] - global_shift = [all_anchor[0], all_anchor[2]] - mask_anchor = [h_off, h_off + noext_H, 0, w_off] - context_anchor = [h_off, h_off + noext_H, w_off, w_off + config['context_thickness']] - valid_line_anchor = [h_off, h_off + noext_H, w_off, w_off + 1] - valid_anchor = [min(mask_anchor[0], context_anchor[0]), max(mask_anchor[1], context_anchor[1]), - min(mask_anchor[2], context_anchor[2]), max(mask_anchor[3], context_anchor[3])] - elif "right" in direc.lower() and "-" not in direc.lower(): - all_anchor = [h_off, h_off + noext_H, w_off + noext_W - config['context_thickness'], 2 * w_off + noext_W] - global_shift = [all_anchor[0], all_anchor[2]] - mask_anchor = [h_off, h_off + noext_H, w_off + noext_W, 2 * w_off + noext_W] - context_anchor = [h_off, h_off + noext_H, w_off + noext_W - config['context_thickness'], w_off + noext_W] - valid_line_anchor = [h_off, h_off + noext_H, w_off + noext_W - 1, w_off + noext_W] - valid_anchor = [min(mask_anchor[0], context_anchor[0]), max(mask_anchor[1], context_anchor[1]), - min(mask_anchor[2], context_anchor[2]), max(mask_anchor[3], context_anchor[3])] - elif "left" in direc.lower() and "up" in direc.lower() and "-" in direc.lower(): - all_anchor = [0, h_off + config['context_thickness'], 0, w_off + config['context_thickness']] - global_shift = [all_anchor[0], all_anchor[2]] - mask_anchor = [0, h_off, 0, w_off] - context_anchor = "inv-mask" - valid_line_anchor = None - valid_anchor = all_anchor - elif "left" in direc.lower() and "down" in direc.lower() and "-" in direc.lower(): - all_anchor = [h_off + noext_H - config['context_thickness'], 2 * h_off + noext_H, 0, w_off + config['context_thickness']] - global_shift = [all_anchor[0], all_anchor[2]] - mask_anchor = [h_off + noext_H, 2 * h_off + noext_H, 0, w_off] - context_anchor = "inv-mask" - valid_line_anchor = None - valid_anchor = all_anchor - elif "right" in direc.lower() and "up" in direc.lower() and "-" in direc.lower(): - all_anchor = [0, h_off + config['context_thickness'], w_off + noext_W - config['context_thickness'], 2 * w_off + noext_W] - global_shift = [all_anchor[0], all_anchor[2]] - mask_anchor = [0, h_off, w_off + noext_W, 2 * w_off + noext_W] - context_anchor = "inv-mask" - valid_line_anchor = None - valid_anchor = all_anchor - elif "right" in direc.lower() and "down" in direc.lower() and "-" in direc.lower(): - all_anchor = [h_off + noext_H - config['context_thickness'], 2 * h_off + noext_H, w_off + noext_W - config['context_thickness'], 2 * w_off + noext_W] - global_shift = [all_anchor[0], all_anchor[2]] - mask_anchor = [h_off + noext_H, 2 * h_off + noext_H, w_off + noext_W, 2 * w_off + noext_W] - context_anchor = "inv-mask" - valid_line_anchor = None - valid_anchor = all_anchor - - global_mask = np.zeros_like(depth) - global_mask[mask_anchor[0]:mask_anchor[1],mask_anchor[2]:mask_anchor[3]] = 1 - mask = global_mask[valid_anchor[0]:valid_anchor[1], valid_anchor[2]:valid_anchor[3]] * 1 - context = 1 - mask - global_context = np.zeros_like(depth) - global_context[all_anchor[0]:all_anchor[1],all_anchor[2]:all_anchor[3]] = context - # context = global_context[valid_anchor[0]:valid_anchor[1], valid_anchor[2]:valid_anchor[3]] * 1 - - - - valid_area = mask + context - input_rgb = image[valid_anchor[0]:valid_anchor[1], valid_anchor[2]:valid_anchor[3]] / 255. * context[..., None] - input_depth = depth[valid_anchor[0]:valid_anchor[1], valid_anchor[2]:valid_anchor[3]] * context - log_depth = np.log(input_depth + 1e-8) - log_depth[mask > 0] = 0 - input_mean_depth = np.mean(log_depth[context > 0]) - input_zero_mean_depth = (log_depth - input_mean_depth) * context - input_disp = 1./np.abs(input_depth) - input_disp[mask > 0] = 0 - input_disp = input_disp / input_disp.max() - valid_line = np.zeros_like(depth) - if valid_line_anchor is not None: - valid_line[valid_line_anchor[0]:valid_line_anchor[1], valid_line_anchor[2]:valid_line_anchor[3]] = 1 - valid_line = valid_line[all_anchor[0]:all_anchor[1], all_anchor[2]:all_anchor[3]] - # f, ((ax1, ax2)) = plt.subplots(1, 2, sharex=True, sharey=True); ax1.imshow(global_context * 1 + global_mask * 2); ax2.imshow(image); plt.show() - # f, ((ax1, ax2, ax3)) = plt.subplots(1, 3, sharex=True, sharey=True); ax1.imshow(context * 1 + mask * 2); ax2.imshow(input_rgb); ax3.imshow(valid_line); plt.show() - # import pdb; pdb.set_trace() - # return - input_edge_map = edge_map[all_anchor[0]:all_anchor[1], all_anchor[2]:all_anchor[3]] * context - input_other_edge_with_id = other_edge_with_id[all_anchor[0]:all_anchor[1], all_anchor[2]:all_anchor[3]] - end_depth_maps = ((valid_line * input_edge_map) > 0) * input_depth - - - if isinstance(config["gpu_ids"], int) and (config["gpu_ids"] >= 0): - device = config["gpu_ids"] - else: - device = "cpu" - - valid_edge_ids = sorted(list(input_other_edge_with_id[(valid_line * input_edge_map) > 0])) - valid_edge_ids = valid_edge_ids[1:] if (len(valid_edge_ids) > 0 and valid_edge_ids[0] == -1) else valid_edge_ids - edge = reduce(lambda x, y: (x + (input_other_edge_with_id == y).astype(np.uint8)).clip(0, 1), [np.zeros_like(mask)] + list(valid_edge_ids)) - t_edge = torch.FloatTensor(edge).to(device)[None, None, ...] - t_rgb = torch.FloatTensor(input_rgb).to(device).permute(2,0,1).unsqueeze(0) - t_mask = torch.FloatTensor(mask).to(device)[None, None, ...] - t_context = torch.FloatTensor(context).to(device)[None, None, ...] - t_disp = torch.FloatTensor(input_disp).to(device)[None, None, ...] - t_depth_zero_mean_depth = torch.FloatTensor(input_zero_mean_depth).to(device)[None, None, ...] - - depth_edge_output = depth_edge_model.forward_3P(t_mask, t_context, t_rgb, t_disp, t_edge, unit_length=128, - cuda=device) - t_output_edge = (depth_edge_output> config['ext_edge_threshold']).float() * t_mask + t_edge - output_raw_edge = t_output_edge.data.cpu().numpy().squeeze() - # import pdb; pdb.set_trace() - mesh = netx.Graph() - hxs, hys = np.where(output_raw_edge * mask > 0) - valid_map = mask + context - for hx, hy in zip(hxs, hys): - node = (hx, hy) - mesh.add_node((hx, hy)) - eight_nes = [ne for ne in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1), \ - (hx + 1, hy + 1), (hx - 1, hy - 1), (hx - 1, hy + 1), (hx + 1, hy - 1)]\ - if 0 <= ne[0] < output_raw_edge.shape[0] and 0 <= ne[1] < output_raw_edge.shape[1] and 0 < output_raw_edge[ne[0], ne[1]]] - for ne in eight_nes: - mesh.add_edge(node, ne, length=np.hypot(ne[0] - hx, ne[1] - hy)) - if end_depth_maps[ne[0], ne[1]] != 0: - mesh.nodes[ne[0], ne[1]]['cnt'] = True - mesh.nodes[ne[0], ne[1]]['depth'] = end_depth_maps[ne[0], ne[1]] - ccs = [*netx.connected_components(mesh)] - end_pts = [] - for cc in ccs: - end_pts.append(set()) - for node in cc: - if mesh.nodes[node].get('cnt') is not None: - end_pts[-1].add((node[0], node[1], mesh.nodes[node]['depth'])) - fpath_map = np.zeros_like(output_raw_edge) - 1 - npath_map = np.zeros_like(output_raw_edge) - 1 - for end_pt, cc in zip(end_pts, ccs): - sorted_end_pt = [] - if len(end_pt) >= 2: - continue - if len(end_pt) == 0: - continue - if len(end_pt) == 1: - sub_mesh = mesh.subgraph(list(cc)).copy() - pnodes = netx.periphery(sub_mesh) - ends = [*end_pt] - edge_id = global_mesh.nodes[(ends[0][0] + all_anchor[0], ends[0][1] + all_anchor[2], -ends[0][2])]['edge_id'] - pnodes = sorted(pnodes, - key=lambda x: np.hypot((x[0] - ends[0][0]), (x[1] - ends[0][1])), - reverse=True)[0] - npath = [*netx.shortest_path(sub_mesh, (ends[0][0], ends[0][1]), pnodes, weight='length')] - for np_node in npath: - npath_map[np_node[0], np_node[1]] = edge_id - fpath = [] - if global_mesh.nodes[(ends[0][0] + all_anchor[0], ends[0][1] + all_anchor[2], -ends[0][2])].get('far') is None: - print("None far") - import pdb; pdb.set_trace() - else: - fnodes = global_mesh.nodes[(ends[0][0] + all_anchor[0], ends[0][1] + all_anchor[2], -ends[0][2])].get('far') - fnodes = [(xx[0] - all_anchor[0], xx[1] - all_anchor[2], xx[2]) for xx in fnodes] - dmask = mask + 0 - did = 0 - while True: - did += 1 - dmask = cv2.dilate(dmask, np.ones((3, 3)), iterations=1) - if did > 3: - break - # ffnode = [fnode for fnode in fnodes if (dmask[fnode[0], fnode[1]] > 0)] - ffnode = [fnode for fnode in fnodes if (dmask[fnode[0], fnode[1]] > 0 and mask[fnode[0], fnode[1]] == 0)] - if len(ffnode) > 0: - fnode = ffnode[0] - break - if len(ffnode) == 0: - continue - fpath.append((fnode[0], fnode[1])) - for step in range(0, len(npath) - 1): - parr = (npath[step + 1][0] - npath[step][0], npath[step + 1][1] - npath[step][1]) - new_loc = (fpath[-1][0] + parr[0], fpath[-1][1] + parr[1]) - new_loc_nes = [xx for xx in [(new_loc[0] + 1, new_loc[1]), (new_loc[0] - 1, new_loc[1]), - (new_loc[0], new_loc[1] + 1), (new_loc[0], new_loc[1] - 1)]\ - if xx[0] >= 0 and xx[0] < fpath_map.shape[0] and xx[1] >= 0 and xx[1] < fpath_map.shape[1]] - if np.sum([fpath_map[nlne[0], nlne[1]] for nlne in new_loc_nes]) != -4: - break - if npath_map[new_loc[0], new_loc[1]] != -1: - if npath_map[new_loc[0], new_loc[1]] != edge_id: - break - else: - continue - if valid_area[new_loc[0], new_loc[1]] == 0: - break - new_loc_nes_eight = [xx for xx in [(new_loc[0] + 1, new_loc[1]), (new_loc[0] - 1, new_loc[1]), - (new_loc[0], new_loc[1] + 1), (new_loc[0], new_loc[1] - 1), - (new_loc[0] + 1, new_loc[1] + 1), (new_loc[0] + 1, new_loc[1] - 1), - (new_loc[0] - 1, new_loc[1] - 1), (new_loc[0] - 1, new_loc[1] + 1)]\ - if xx[0] >= 0 and xx[0] < fpath_map.shape[0] and xx[1] >= 0 and xx[1] < fpath_map.shape[1]] - if np.sum([int(npath_map[nlne[0], nlne[1]] == edge_id) for nlne in new_loc_nes_eight]) == 0: - break - fpath.append((fpath[-1][0] + parr[0], fpath[-1][1] + parr[1])) - if step != len(npath) - 2: - for xx in npath[step+1:]: - if npath_map[xx[0], xx[1]] == edge_id: - npath_map[xx[0], xx[1]] = -1 - if len(fpath) > 0: - for fp_node in fpath: - fpath_map[fp_node[0], fp_node[1]] = edge_id - # import pdb; pdb.set_trace() - far_edge = (fpath_map > -1).astype(np.uint8) - update_edge = (npath_map > -1) * mask + edge - t_update_edge = torch.FloatTensor(update_edge).to(device)[None, None, ...] - depth_output = depth_feat_model.forward_3P(t_mask, t_context, t_depth_zero_mean_depth, t_update_edge, unit_length=128, - cuda=device) - depth_output = depth_output.cpu().data.numpy().squeeze() - depth_output = np.exp(depth_output + input_mean_depth) * mask # + input_depth * context - # if "right" in direc.lower() and "-" not in direc.lower(): - # plt.imshow(depth_output); plt.show() - # import pdb; pdb.set_trace() - # f, ((ax1, ax2)) = plt.subplots(1, 2, sharex=True, sharey=True); ax1.imshow(depth_output); ax2.imshow(npath_map + fpath_map); plt.show() - for near_id in np.unique(npath_map[npath_map > -1]): - depth_output = refine_depth_around_edge(depth_output.copy(), - (fpath_map == near_id).astype(np.uint8) * mask, # far_edge_map_in_mask, - (fpath_map == near_id).astype(np.uint8), # far_edge_map, - (npath_map == near_id).astype(np.uint8) * mask, - mask.copy(), - np.zeros_like(mask), - config) - # if "right" in direc.lower() and "-" not in direc.lower(): - # plt.imshow(depth_output); plt.show() - # import pdb; pdb.set_trace() - # f, ((ax1, ax2)) = plt.subplots(1, 2, sharex=True, sharey=True); ax1.imshow(depth_output); ax2.imshow(npath_map + fpath_map); plt.show() - rgb_output = rgb_feat_model.forward_3P(t_mask, t_context, t_rgb, t_update_edge, unit_length=128, - cuda=device) - - # rgb_output = rgb_feat_model.forward_3P(t_mask, t_context, t_rgb, t_update_edge, unit_length=128, cuda=config['gpu_ids']) - if config.get('gray_image') is True: - rgb_output = rgb_output.mean(1, keepdim=True).repeat((1,3,1,1)) - rgb_output = ((rgb_output.squeeze().data.cpu().permute(1,2,0).numpy() * mask[..., None] + input_rgb) * 255).astype(np.uint8) - image[all_anchor[0]:all_anchor[1], all_anchor[2]:all_anchor[3]][mask > 0] = rgb_output[mask > 0] # np.array([255,0,0]) # rgb_output[mask > 0] - depth[all_anchor[0]:all_anchor[1], all_anchor[2]:all_anchor[3]][mask > 0] = depth_output[mask > 0] - # nxs, nys = np.where(mask > -1) - # for nx, ny in zip(nxs, nys): - # info_on_pix[(nx, ny)][0]['color'] = rgb_output[] - - - nxs, nys = np.where((npath_map > -1)) - for nx, ny in zip(nxs, nys): - n_id = npath_map[nx, ny] - four_nes = [xx for xx in [(nx + 1, ny), (nx - 1, ny), (nx, ny + 1), (nx, ny - 1)]\ - if 0 <= xx[0] < fpath_map.shape[0] and 0 <= xx[1] < fpath_map.shape[1]] - for nex, ney in four_nes: - if fpath_map[nex, ney] == n_id: - na, nb = (nx + all_anchor[0], ny + all_anchor[2], info_on_pix[(nx + all_anchor[0], ny + all_anchor[2])][0]['depth']), \ - (nex + all_anchor[0], ney + all_anchor[2], info_on_pix[(nex + all_anchor[0], ney + all_anchor[2])][0]['depth']) - if global_mesh.has_edge(na, nb): - global_mesh.remove_edge(na, nb) - nxs, nys = np.where((fpath_map > -1)) - for nx, ny in zip(nxs, nys): - n_id = fpath_map[nx, ny] - four_nes = [xx for xx in [(nx + 1, ny), (nx - 1, ny), (nx, ny + 1), (nx, ny - 1)]\ - if 0 <= xx[0] < npath_map.shape[0] and 0 <= xx[1] < npath_map.shape[1]] - for nex, ney in four_nes: - if npath_map[nex, ney] == n_id: - na, nb = (nx + all_anchor[0], ny + all_anchor[2], info_on_pix[(nx + all_anchor[0], ny + all_anchor[2])][0]['depth']), \ - (nex + all_anchor[0], ney + all_anchor[2], info_on_pix[(nex + all_anchor[0], ney + all_anchor[2])][0]['depth']) - if global_mesh.has_edge(na, nb): - global_mesh.remove_edge(na, nb) - nxs, nys = np.where(mask > 0) - for x, y in zip(nxs, nys): - x = x + all_anchor[0] - y = y + all_anchor[2] - cur_node = (x, y, 0) - new_node = (x, y, -abs(depth[x, y])) - disp = 1. / -abs(depth[x, y]) - mapping_dict = {cur_node: new_node} - info_on_pix, global_mesh = update_info(mapping_dict, info_on_pix, global_mesh) - global_mesh.nodes[new_node]['color'] = image[x, y] - global_mesh.nodes[new_node]['old_color'] = image[x, y] - global_mesh.nodes[new_node]['disp'] = disp - info_on_pix[(x, y)][0]['depth'] = -abs(depth[x, y]) - info_on_pix[(x, y)][0]['disp'] = disp - info_on_pix[(x, y)][0]['color'] = image[x, y] - - - nxs, nys = np.where((npath_map > -1)) - for nx, ny in zip(nxs, nys): - self_node = (nx + all_anchor[0], ny + all_anchor[2], info_on_pix[(nx + all_anchor[0], ny + all_anchor[2])][0]['depth']) - if global_mesh.has_node(self_node) is False: - break - n_id = int(round(npath_map[nx, ny])) - four_nes = [xx for xx in [(nx + 1, ny), (nx - 1, ny), (nx, ny + 1), (nx, ny - 1)]\ - if 0 <= xx[0] < fpath_map.shape[0] and 0 <= xx[1] < fpath_map.shape[1]] - for nex, ney in four_nes: - ne_node = (nex + all_anchor[0], ney + all_anchor[2], info_on_pix[(nex + all_anchor[0], ney + all_anchor[2])][0]['depth']) - if global_mesh.has_node(ne_node) is False: - continue - if fpath_map[nex, ney] == n_id: - if global_mesh.nodes[self_node].get('edge_id') is None: - global_mesh.nodes[self_node]['edge_id'] = n_id - edge_ccs[n_id].add(self_node) - info_on_pix[(self_node[0], self_node[1])][0]['edge_id'] = n_id - if global_mesh.has_edge(self_node, ne_node) is True: - global_mesh.remove_edge(self_node, ne_node) - if global_mesh.nodes[self_node].get('far') is None: - global_mesh.nodes[self_node]['far'] = [] - global_mesh.nodes[self_node]['far'].append(ne_node) - - global_fpath_map = np.zeros_like(other_edge_with_id) - 1 - global_fpath_map[all_anchor[0]:all_anchor[1], all_anchor[2]:all_anchor[3]] = fpath_map - fpath_ids = np.unique(global_fpath_map) - fpath_ids = fpath_ids[1:] if fpath_ids.shape[0] > 0 and fpath_ids[0] == -1 else [] - fpath_real_id_map = np.zeros_like(global_fpath_map) - 1 - for fpath_id in fpath_ids: - fpath_real_id = np.unique(((global_fpath_map == fpath_id).astype(np.int) * (other_edge_with_id + 1)) - 1) - fpath_real_id = fpath_real_id[1:] if fpath_real_id.shape[0] > 0 and fpath_real_id[0] == -1 else [] - fpath_real_id = fpath_real_id.astype(np.int) - fpath_real_id = np.bincount(fpath_real_id).argmax() - fpath_real_id_map[global_fpath_map == fpath_id] = fpath_real_id - nxs, nys = np.where((fpath_map > -1)) - for nx, ny in zip(nxs, nys): - self_node = (nx + all_anchor[0], ny + all_anchor[2], info_on_pix[(nx + all_anchor[0], ny + all_anchor[2])][0]['depth']) - n_id = fpath_map[nx, ny] - four_nes = [xx for xx in [(nx + 1, ny), (nx - 1, ny), (nx, ny + 1), (nx, ny - 1)]\ - if 0 <= xx[0] < npath_map.shape[0] and 0 <= xx[1] < npath_map.shape[1]] - for nex, ney in four_nes: - ne_node = (nex + all_anchor[0], ney + all_anchor[2], info_on_pix[(nex + all_anchor[0], ney + all_anchor[2])][0]['depth']) - if global_mesh.has_node(ne_node) is False: - continue - if npath_map[nex, ney] == n_id or global_mesh.nodes[ne_node].get('edge_id') == n_id: - if global_mesh.has_edge(self_node, ne_node) is True: - global_mesh.remove_edge(self_node, ne_node) - if global_mesh.nodes[self_node].get('near') is None: - global_mesh.nodes[self_node]['near'] = [] - if global_mesh.nodes[self_node].get('edge_id') is None: - f_id = int(round(fpath_real_id_map[self_node[0], self_node[1]])) - global_mesh.nodes[self_node]['edge_id'] = f_id - info_on_pix[(self_node[0], self_node[1])][0]['edge_id'] = f_id - edge_ccs[f_id].add(self_node) - global_mesh.nodes[self_node]['near'].append(ne_node) - - return info_on_pix, global_mesh, image, depth, edge_ccs - # for edge_cc in edge_ccs: - # for edge_node in edge_cc: - # edge_ccs - # context_ccs, mask_ccs, broken_mask_ccs, edge_ccs, erode_context_ccs, init_mask_connect, edge_maps, extend_context_ccs, extend_edge_ccs - -def get_valid_size(imap): - x_max = np.where(imap.sum(1).squeeze() > 0)[0].max() + 1 - x_min = np.where(imap.sum(1).squeeze() > 0)[0].min() - y_max = np.where(imap.sum(0).squeeze() > 0)[0].max() + 1 - y_min = np.where(imap.sum(0).squeeze() > 0)[0].min() - size_dict = {'x_max':x_max, 'y_max':y_max, 'x_min':x_min, 'y_min':y_min} - - return size_dict - -def dilate_valid_size(isize_dict, imap, dilate=[0, 0]): - osize_dict = copy.deepcopy(isize_dict) - osize_dict['x_min'] = max(0, osize_dict['x_min'] - dilate[0]) - osize_dict['x_max'] = min(imap.shape[0], osize_dict['x_max'] + dilate[0]) - osize_dict['y_min'] = max(0, osize_dict['y_min'] - dilate[0]) - osize_dict['y_max'] = min(imap.shape[1], osize_dict['y_max'] + dilate[1]) - - return osize_dict - -def size_operation(size_a, size_b, operation): - assert operation == '+' or operation == '-', "Operation must be '+' (union) or '-' (exclude)" - osize = {} - if operation == '+': - osize['x_min'] = min(size_a['x_min'], size_b['x_min']) - osize['y_min'] = min(size_a['y_min'], size_b['y_min']) - osize['x_max'] = max(size_a['x_max'], size_b['x_max']) - osize['y_max'] = max(size_a['y_max'], size_b['y_max']) - assert operation != '-', "Operation '-' is undefined !" - - return osize - -def fill_dummy_bord(mesh, info_on_pix, image, depth, config): - context = np.zeros_like(depth).astype(np.uint8) - context[mesh.graph['hoffset']:mesh.graph['hoffset'] + mesh.graph['noext_H'], - mesh.graph['woffset']:mesh.graph['woffset'] + mesh.graph['noext_W']] = 1 - mask = 1 - context - xs, ys = np.where(mask > 0) - depth = depth * context - image = image * context[..., None] - cur_depth = 0 - cur_disp = 0 - color = [0, 0, 0] - for x, y in zip(xs, ys): - cur_node = (x, y, cur_depth) - mesh.add_node(cur_node, color=color, - synthesis=False, - disp=cur_disp, - cc_id=set(), - ext_pixel=True) - info_on_pix[(x, y)] = [{'depth':cur_depth, - 'color':mesh.nodes[(x, y, cur_depth)]['color'], - 'synthesis':False, - 'disp':mesh.nodes[cur_node]['disp'], - 'ext_pixel':True}] - # for x, y in zip(xs, ys): - four_nes = [(xx, yy) for xx, yy in [(x + 1, y), (x - 1, y), (x, y + 1), (x, y - 1)] if\ - 0 <= x < mesh.graph['H'] and 0 <= y < mesh.graph['W'] and info_on_pix.get((xx, yy)) is not None] - for ne in four_nes: - # if (ne[0] - x) + (ne[1] - y) == 1 and info_on_pix.get((ne[0], ne[1])) is not None: - mesh.add_edge(cur_node, (ne[0], ne[1], info_on_pix[(ne[0], ne[1])][0]['depth'])) - - return mesh, info_on_pix - - -def enlarge_border(mesh, info_on_pix, depth, image, config): - mesh.graph['hoffset'], mesh.graph['woffset'] = config['extrapolation_thickness'], config['extrapolation_thickness'] - mesh.graph['bord_up'], mesh.graph['bord_left'], mesh.graph['bord_down'], mesh.graph['bord_right'] = \ - 0, 0, mesh.graph['H'], mesh.graph['W'] - # new_image = np.pad(image, - # pad_width=((config['extrapolation_thickness'], config['extrapolation_thickness']), - # (config['extrapolation_thickness'], config['extrapolation_thickness']), (0, 0)), - # mode='constant') - # new_depth = np.pad(depth, - # pad_width=((config['extrapolation_thickness'], config['extrapolation_thickness']), - # (config['extrapolation_thickness'], config['extrapolation_thickness'])), - # mode='constant') - - return mesh, info_on_pix, depth, image - -def fill_missing_node(mesh, info_on_pix, image, depth): - for x in range(mesh.graph['bord_up'], mesh.graph['bord_down']): - for y in range(mesh.graph['bord_left'], mesh.graph['bord_right']): - if info_on_pix.get((x, y)) is None: - print("fill missing node = ", x, y) - import pdb; pdb.set_trace() - re_depth, re_count = 0, 0 - for ne in [(x + 1, y), (x - 1, y), (x, y + 1), (x, y - 1)]: - if info_on_pix.get(ne) is not None: - re_depth += info_on_pix[ne][0]['depth'] - re_count += 1 - if re_count == 0: - re_depth = -abs(depth[x, y]) - else: - re_depth = re_depth / re_count - depth[x, y] = abs(re_depth) - info_on_pix[(x, y)] = [{'depth':re_depth, - 'color':image[x, y], - 'synthesis':False, - 'disp':1./re_depth}] - mesh.add_node((x, y, re_depth), color=image[x, y], - synthesis=False, - disp=1./re_depth, - cc_id=set()) - return mesh, info_on_pix, depth - - - -def refresh_bord_depth(mesh, info_on_pix, image, depth): - H, W = mesh.graph['H'], mesh.graph['W'] - corner_nodes = [(mesh.graph['bord_up'], mesh.graph['bord_left']), - (mesh.graph['bord_up'], mesh.graph['bord_right'] - 1), - (mesh.graph['bord_down'] - 1, mesh.graph['bord_left']), - (mesh.graph['bord_down'] - 1, mesh.graph['bord_right'] - 1)] - # (0, W - 1), (H - 1, 0), (H - 1, W - 1)] - bord_nodes = [] - bord_nodes += [(mesh.graph['bord_up'], xx) for xx in range(mesh.graph['bord_left'] + 1, mesh.graph['bord_right'] - 1)] - bord_nodes += [(mesh.graph['bord_down'] - 1, xx) for xx in range(mesh.graph['bord_left'] + 1, mesh.graph['bord_right'] - 1)] - bord_nodes += [(xx, mesh.graph['bord_left']) for xx in range(mesh.graph['bord_up'] + 1, mesh.graph['bord_down'] - 1)] - bord_nodes += [(xx, mesh.graph['bord_right'] - 1) for xx in range(mesh.graph['bord_up'] + 1, mesh.graph['bord_down'] - 1)] - for xy in bord_nodes: - tgt_loc = None - if xy[0] == mesh.graph['bord_up']: - tgt_loc = (xy[0] + 1, xy[1])# (1, xy[1]) - elif xy[0] == mesh.graph['bord_down'] - 1: - tgt_loc = (xy[0] - 1, xy[1]) # (H - 2, xy[1]) - elif xy[1] == mesh.graph['bord_left']: - tgt_loc = (xy[0], xy[1] + 1) - elif xy[1] == mesh.graph['bord_right'] - 1: - tgt_loc = (xy[0], xy[1] - 1) - if tgt_loc is not None: - ne_infos = info_on_pix.get(tgt_loc) - if ne_infos is None: - import pdb; pdb.set_trace() - # if ne_infos is not None and len(ne_infos) == 1: - tgt_depth = ne_infos[0]['depth'] - tgt_disp = ne_infos[0]['disp'] - new_node = (xy[0], xy[1], tgt_depth) - src_node = (tgt_loc[0], tgt_loc[1], tgt_depth) - tgt_nes_loc = [(xx[0], xx[1]) \ - for xx in mesh.neighbors(src_node)] - tgt_nes_loc = [(xx[0] - tgt_loc[0] + xy[0], xx[1] - tgt_loc[1] + xy[1]) for xx in tgt_nes_loc \ - if abs(xx[0] - xy[0]) == 1 and abs(xx[1] - xy[1]) == 1] - tgt_nes_loc = [xx for xx in tgt_nes_loc if info_on_pix.get(xx) is not None] - tgt_nes_loc.append(tgt_loc) - # if (xy[0], xy[1]) == (559, 60): - # import pdb; pdb.set_trace() - if info_on_pix.get(xy) is not None and len(info_on_pix.get(xy)) > 0: - old_depth = info_on_pix[xy][0].get('depth') - old_node = (xy[0], xy[1], old_depth) - mesh.remove_edges_from([(old_ne, old_node) for old_ne in mesh.neighbors(old_node)]) - mesh.add_edges_from([((zz[0], zz[1], info_on_pix[zz][0]['depth']), old_node) for zz in tgt_nes_loc]) - mapping_dict = {old_node: new_node} - # if old_node[2] == new_node[2]: - # print("mapping_dict = ", mapping_dict) - info_on_pix, mesh = update_info(mapping_dict, info_on_pix, mesh) - else: - info_on_pix[xy] = [] - info_on_pix[xy][0] = info_on_pix[tgt_loc][0] - info_on_pix['color'] = image[xy[0], xy[1]] - info_on_pix['old_color'] = image[xy[0], xy[1]] - mesh.add_node(new_node) - mesh.add_edges_from([((zz[0], zz[1], info_on_pix[zz][0]['depth']), new_node) for zz in tgt_nes_loc]) - mesh.nodes[new_node]['far'] = None - mesh.nodes[new_node]['near'] = None - if mesh.nodes[src_node].get('far') is not None: - redundant_nodes = [ne for ne in mesh.nodes[src_node]['far'] if (ne[0], ne[1]) == xy] - [mesh.nodes[src_node]['far'].remove(aa) for aa in redundant_nodes] - if mesh.nodes[src_node].get('near') is not None: - redundant_nodes = [ne for ne in mesh.nodes[src_node]['near'] if (ne[0], ne[1]) == xy] - [mesh.nodes[src_node]['near'].remove(aa) for aa in redundant_nodes] - for xy in corner_nodes: - hx, hy = xy - four_nes = [xx for xx in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1)] if \ - mesh.graph['bord_up'] <= xx[0] < mesh.graph['bord_down'] and \ - mesh.graph['bord_left'] <= xx[1] < mesh.graph['bord_right']] - ne_nodes = [] - ne_depths = [] - for ne_loc in four_nes: - if info_on_pix.get(ne_loc) is not None: - ne_depths.append(info_on_pix[ne_loc][0]['depth']) - ne_nodes.append((ne_loc[0], ne_loc[1], info_on_pix[ne_loc][0]['depth'])) - new_node = (xy[0], xy[1], float(np.mean(ne_depths))) - if info_on_pix.get(xy) is not None and len(info_on_pix.get(xy)) > 0: - old_depth = info_on_pix[xy][0].get('depth') - old_node = (xy[0], xy[1], old_depth) - mesh.remove_edges_from([(old_ne, old_node) for old_ne in mesh.neighbors(old_node)]) - mesh.add_edges_from([(zz, old_node) for zz in ne_nodes]) - mapping_dict = {old_node: new_node} - info_on_pix, mesh = update_info(mapping_dict, info_on_pix, mesh) - else: - info_on_pix[xy] = [] - info_on_pix[xy][0] = info_on_pix[ne_loc[-1]][0] - info_on_pix['color'] = image[xy[0], xy[1]] - info_on_pix['old_color'] = image[xy[0], xy[1]] - mesh.add_node(new_node) - mesh.add_edges_from([(zz, new_node) for zz in ne_nodes]) - mesh.nodes[new_node]['far'] = None - mesh.nodes[new_node]['near'] = None - for xy in bord_nodes + corner_nodes: - # if (xy[0], xy[1]) == (559, 60): - # import pdb; pdb.set_trace() - depth[xy[0], xy[1]] = abs(info_on_pix[xy][0]['depth']) - for xy in bord_nodes: - cur_node = (xy[0], xy[1], info_on_pix[xy][0]['depth']) - nes = mesh.neighbors(cur_node) - four_nes = set([(xy[0] + 1, xy[1]), (xy[0] - 1, xy[1]), (xy[0], xy[1] + 1), (xy[0], xy[1] - 1)]) - \ - set([(ne[0], ne[1]) for ne in nes]) - four_nes = [ne for ne in four_nes if mesh.graph['bord_up'] <= ne[0] < mesh.graph['bord_down'] and \ - mesh.graph['bord_left'] <= ne[1] < mesh.graph['bord_right']] - four_nes = [(ne[0], ne[1], info_on_pix[(ne[0], ne[1])][0]['depth']) for ne in four_nes] - mesh.nodes[cur_node]['far'] = [] - mesh.nodes[cur_node]['near'] = [] - for ne in four_nes: - if abs(ne[2]) >= abs(cur_node[2]): - mesh.nodes[cur_node]['far'].append(ne) - else: - mesh.nodes[cur_node]['near'].append(ne) - - return mesh, info_on_pix, depth - -def get_union_size(mesh, dilate, *alls_cc): - all_cc = reduce(lambda x, y: x | y, [set()] + [*alls_cc]) - min_x, min_y, max_x, max_y = mesh.graph['H'], mesh.graph['W'], 0, 0 - H, W = mesh.graph['H'], mesh.graph['W'] - for node in all_cc: - if node[0] < min_x: - min_x = node[0] - if node[0] > max_x: - max_x = node[0] - if node[1] < min_y: - min_y = node[1] - if node[1] > max_y: - max_y = node[1] - max_x = max_x + 1 - max_y = max_y + 1 - # mask_size = dilate_valid_size(mask_size, edge_dict['mask'], dilate=[20, 20]) - osize_dict = dict() - osize_dict['x_min'] = max(0, min_x - dilate[0]) - osize_dict['x_max'] = min(H, max_x + dilate[0]) - osize_dict['y_min'] = max(0, min_y - dilate[1]) - osize_dict['y_max'] = min(W, max_y + dilate[1]) - - return osize_dict - -def incomplete_node(mesh, edge_maps, info_on_pix): - vis_map = np.zeros((mesh.graph['H'], mesh.graph['W'])) - - for node in mesh.nodes: - if mesh.nodes[node].get('synthesis') is not True: - connect_all_flag = False - nes = [xx for xx in mesh.neighbors(node) if mesh.nodes[xx].get('synthesis') is not True] - if len(nes) < 3 and 0 < node[0] < mesh.graph['H'] - 1 and 0 < node[1] < mesh.graph['W'] - 1: - if len(nes) <= 1: - connect_all_flag = True - else: - dan_ne_node_a = nes[0] - dan_ne_node_b = nes[1] - if abs(dan_ne_node_a[0] - dan_ne_node_b[0]) > 1 or \ - abs(dan_ne_node_a[1] - dan_ne_node_b[1]) > 1: - connect_all_flag = True - if connect_all_flag == True: - vis_map[node[0], node[1]] = len(nes) - four_nes = [(node[0] - 1, node[1]), (node[0] + 1, node[1]), (node[0], node[1] - 1), (node[0], node[1] + 1)] - for ne in four_nes: - for info in info_on_pix[(ne[0], ne[1])]: - ne_node = (ne[0], ne[1], info['depth']) - if info.get('synthesis') is not True and mesh.has_node(ne_node): - mesh.add_edge(node, ne_node) - break - - return mesh - -def edge_inpainting(edge_id, context_cc, erode_context_cc, mask_cc, edge_cc, extend_edge_cc, - mesh, edge_map, edge_maps_with_id, config, union_size, depth_edge_model, inpaint_iter): - edge_dict = get_edge_from_nodes(context_cc, erode_context_cc, mask_cc, edge_cc, extend_edge_cc, - mesh.graph['H'], mesh.graph['W'], mesh) - edge_dict['edge'], end_depth_maps, _ = \ - filter_irrelevant_edge_new(edge_dict['self_edge'] + edge_dict['comp_edge'], - edge_map, - edge_maps_with_id, - edge_id, - edge_dict['context'], - edge_dict['depth'], mesh, context_cc | erode_context_cc, spdb=True) - patch_edge_dict = dict() - patch_edge_dict['mask'], patch_edge_dict['context'], patch_edge_dict['rgb'], \ - patch_edge_dict['disp'], patch_edge_dict['edge'] = \ - crop_maps_by_size(union_size, edge_dict['mask'], edge_dict['context'], - edge_dict['rgb'], edge_dict['disp'], edge_dict['edge']) - tensor_edge_dict = convert2tensor(patch_edge_dict) - if require_depth_edge(patch_edge_dict['edge'], patch_edge_dict['mask']) and inpaint_iter == 0: - with torch.no_grad(): - device = config["gpu_ids"] if isinstance(config["gpu_ids"], int) and config["gpu_ids"] >= 0 else "cpu" - depth_edge_output = depth_edge_model.forward_3P(tensor_edge_dict['mask'], - tensor_edge_dict['context'], - tensor_edge_dict['rgb'], - tensor_edge_dict['disp'], - tensor_edge_dict['edge'], - unit_length=128, - cuda=device) - depth_edge_output = depth_edge_output.cpu() - tensor_edge_dict['output'] = (depth_edge_output > config['ext_edge_threshold']).float() * tensor_edge_dict['mask'] + tensor_edge_dict['edge'] - else: - tensor_edge_dict['output'] = tensor_edge_dict['edge'] - depth_edge_output = tensor_edge_dict['edge'] + 0 - patch_edge_dict['output'] = tensor_edge_dict['output'].squeeze().data.cpu().numpy() - edge_dict['output'] = np.zeros((mesh.graph['H'], mesh.graph['W'])) - edge_dict['output'][union_size['x_min']:union_size['x_max'], union_size['y_min']:union_size['y_max']] = \ - patch_edge_dict['output'] - - return edge_dict, end_depth_maps - -def depth_inpainting(context_cc, extend_context_cc, erode_context_cc, mask_cc, mesh, config, union_size, depth_feat_model, edge_output, given_depth_dict=False, spdb=False): - if given_depth_dict is False: - depth_dict = get_depth_from_nodes(context_cc | extend_context_cc, erode_context_cc, mask_cc, mesh.graph['H'], mesh.graph['W'], mesh, config['log_depth']) - if edge_output is not None: - depth_dict['edge'] = edge_output - else: - depth_dict = given_depth_dict - patch_depth_dict = dict() - patch_depth_dict['mask'], patch_depth_dict['context'], patch_depth_dict['depth'], \ - patch_depth_dict['zero_mean_depth'], patch_depth_dict['edge'] = \ - crop_maps_by_size(union_size, depth_dict['mask'], depth_dict['context'], - depth_dict['real_depth'], depth_dict['zero_mean_depth'], depth_dict['edge']) - tensor_depth_dict = convert2tensor(patch_depth_dict) - resize_mask = open_small_mask(tensor_depth_dict['mask'], tensor_depth_dict['context'], 3, 41) - with torch.no_grad(): - device = config["gpu_ids"] if isinstance(config["gpu_ids"], int) and config["gpu_ids"] >= 0 else "cpu" - depth_output = depth_feat_model.forward_3P(resize_mask, - tensor_depth_dict['context'], - tensor_depth_dict['zero_mean_depth'], - tensor_depth_dict['edge'], - unit_length=128, - cuda=device) - depth_output = depth_output.cpu() - tensor_depth_dict['output'] = torch.exp(depth_output + depth_dict['mean_depth']) * \ - tensor_depth_dict['mask'] + tensor_depth_dict['depth'] - patch_depth_dict['output'] = tensor_depth_dict['output'].data.cpu().numpy().squeeze() - depth_dict['output'] = np.zeros((mesh.graph['H'], mesh.graph['W'])) - depth_dict['output'][union_size['x_min']:union_size['x_max'], union_size['y_min']:union_size['y_max']] = \ - patch_depth_dict['output'] - depth_output = depth_dict['output'] * depth_dict['mask'] + depth_dict['depth'] * depth_dict['context'] - depth_output = smooth_cntsyn_gap(depth_dict['output'].copy() * depth_dict['mask'] + depth_dict['depth'] * depth_dict['context'], - depth_dict['mask'], depth_dict['context'], - init_mask_region=depth_dict['mask']) - if spdb is True: - f, ((ax1, ax2)) = plt.subplots(1, 2, sharex=True, sharey=True); - ax1.imshow(depth_output * depth_dict['mask'] + depth_dict['depth']); ax2.imshow(depth_dict['output'] * depth_dict['mask'] + depth_dict['depth']); plt.show() - import pdb; pdb.set_trace() - depth_dict['output'] = depth_output * depth_dict['mask'] + depth_dict['depth'] * depth_dict['context'] - - return depth_dict - -def update_info(mapping_dict, info_on_pix, *meshes): - rt_meshes = [] - for mesh in meshes: - rt_meshes.append(relabel_node(mesh, mesh.nodes, [*mapping_dict.keys()][0], [*mapping_dict.values()][0])) - x, y, _ = [*mapping_dict.keys()][0] - info_on_pix[(x, y)][0]['depth'] = [*mapping_dict.values()][0][2] - - return [info_on_pix] + rt_meshes - -def build_connection(mesh, cur_node, dst_node): - if (abs(cur_node[0] - dst_node[0]) + abs(cur_node[1] - dst_node[1])) < 2: - mesh.add_edge(cur_node, dst_node) - if abs(cur_node[0] - dst_node[0]) > 1 or abs(cur_node[1] - dst_node[1]) > 1: - return mesh - ne_nodes = [*mesh.neighbors(cur_node)].copy() - for ne_node in ne_nodes: - if mesh.has_edge(ne_node, dst_node) or ne_node == dst_node: - continue - else: - mesh = build_connection(mesh, ne_node, dst_node) - - return mesh - -def recursive_add_edge(edge_mesh, mesh, info_on_pix, cur_node, mark): - ne_nodes = [(x[0], x[1]) for x in edge_mesh.neighbors(cur_node)] - for node_xy in ne_nodes: - node = (node_xy[0], node_xy[1], info_on_pix[node_xy][0]['depth']) - if mark[node[0], node[1]] != 3: - continue - else: - mark[node[0], node[1]] = 0 - mesh.remove_edges_from([(xx, node) for xx in mesh.neighbors(node)]) - mesh = build_connection(mesh, cur_node, node) - re_info = dict(depth=0, count=0) - for re_ne in mesh.neighbors(node): - re_info['depth'] += re_ne[2] - re_info['count'] += 1. - try: - re_depth = re_info['depth'] / re_info['count'] - except: - re_depth = node[2] - re_node = (node_xy[0], node_xy[1], re_depth) - mapping_dict = {node: re_node} - info_on_pix, edge_mesh, mesh = update_info(mapping_dict, info_on_pix, edge_mesh, mesh) - - edge_mesh, mesh, mark, info_on_pix = recursive_add_edge(edge_mesh, mesh, info_on_pix, re_node, mark) - - return edge_mesh, mesh, mark, info_on_pix - -def resize_for_edge(tensor_dict, largest_size): - resize_dict = {k: v.clone() for k, v in tensor_dict.items()} - frac = largest_size / np.array([*resize_dict['edge'].shape[-2:]]).max() - if frac < 1: - resize_mark = torch.nn.functional.interpolate(torch.cat((resize_dict['mask'], - resize_dict['context']), - dim=1), - scale_factor=frac, - mode='bilinear') - resize_dict['mask'] = (resize_mark[:, 0:1] > 0).float() - resize_dict['context'] = (resize_mark[:, 1:2] == 1).float() - resize_dict['context'][resize_dict['mask'] > 0] = 0 - resize_dict['edge'] = torch.nn.functional.interpolate(resize_dict['edge'], - scale_factor=frac, - mode='bilinear') - resize_dict['edge'] = (resize_dict['edge'] > 0).float() - resize_dict['edge'] = resize_dict['edge'] * resize_dict['context'] - resize_dict['disp'] = torch.nn.functional.interpolate(resize_dict['disp'], - scale_factor=frac, - mode='nearest') - resize_dict['disp'] = resize_dict['disp'] * resize_dict['context'] - resize_dict['rgb'] = torch.nn.functional.interpolate(resize_dict['rgb'], - scale_factor=frac, - mode='bilinear') - resize_dict['rgb'] = resize_dict['rgb'] * resize_dict['context'] - return resize_dict - -def get_map_from_nodes(nodes, height, width): - omap = np.zeros((height, width)) - for n in nodes: - omap[n[0], n[1]] = 1 - - return omap - -def get_map_from_ccs(ccs, height, width, condition_input=None, condition=None, real_id=False, id_shift=0): - if condition is None: - condition = lambda x, condition_input: True - - if real_id is True: - omap = np.zeros((height, width)) + (-1) + id_shift - else: - omap = np.zeros((height, width)) - for cc_id, cc in enumerate(ccs): - for n in cc: - if condition(n, condition_input): - if real_id is True: - omap[n[0], n[1]] = cc_id + id_shift - else: - omap[n[0], n[1]] = 1 - return omap - -def revise_map_by_nodes(nodes, imap, operation, limit_constr=None): - assert operation == '+' or operation == '-', "Operation must be '+' (union) or '-' (exclude)" - omap = copy.deepcopy(imap) - revise_flag = True - if operation == '+': - for n in nodes: - omap[n[0], n[1]] = 1 - if limit_constr is not None and omap.sum() > limit_constr: - omap = imap - revise_flag = False - elif operation == '-': - for n in nodes: - omap[n[0], n[1]] = 0 - if limit_constr is not None and omap.sum() < limit_constr: - omap = imap - revise_flag = False - - return omap, revise_flag - -def repaint_info(mesh, cc, x_anchor, y_anchor, source_type): - if source_type == 'rgb': - feat = np.zeros((3, x_anchor[1] - x_anchor[0], y_anchor[1] - y_anchor[0])) - else: - feat = np.zeros((1, x_anchor[1] - x_anchor[0], y_anchor[1] - y_anchor[0])) - for node in cc: - if source_type == 'rgb': - feat[:, node[0] - x_anchor[0], node[1] - y_anchor[0]] = np.array(mesh.nodes[node]['color']) / 255. - elif source_type == 'd': - feat[:, node[0] - x_anchor[0], node[1] - y_anchor[0]] = abs(node[2]) - - return feat - -def get_context_from_nodes(mesh, cc, H, W, source_type=''): - if 'rgb' in source_type or 'color' in source_type: - feat = np.zeros((H, W, 3)) - else: - feat = np.zeros((H, W)) - context = np.zeros((H, W)) - for node in cc: - if 'rgb' in source_type or 'color' in source_type: - feat[node[0], node[1]] = np.array(mesh.nodes[node]['color']) / 255. - context[node[0], node[1]] = 1 - else: - feat[node[0], node[1]] = abs(node[2]) - - return feat, context - -def get_mask_from_nodes(mesh, cc, H, W): - mask = np.zeros((H, W)) - for node in cc: - mask[node[0], node[1]] = abs(node[2]) - - return mask - - -def get_edge_from_nodes(context_cc, erode_context_cc, mask_cc, edge_cc, extend_edge_cc, H, W, mesh): - context = np.zeros((H, W)) - mask = np.zeros((H, W)) - rgb = np.zeros((H, W, 3)) - disp = np.zeros((H, W)) - depth = np.zeros((H, W)) - real_depth = np.zeros((H, W)) - edge = np.zeros((H, W)) - comp_edge = np.zeros((H, W)) - fpath_map = np.zeros((H, W)) - 1 - npath_map = np.zeros((H, W)) - 1 - near_depth = np.zeros((H, W)) - for node in context_cc: - rgb[node[0], node[1]] = np.array(mesh.nodes[node]['color']) - disp[node[0], node[1]] = mesh.nodes[node]['disp'] - depth[node[0], node[1]] = node[2] - context[node[0], node[1]] = 1 - for node in erode_context_cc: - rgb[node[0], node[1]] = np.array(mesh.nodes[node]['color']) - disp[node[0], node[1]] = mesh.nodes[node]['disp'] - depth[node[0], node[1]] = node[2] - context[node[0], node[1]] = 1 - rgb = rgb / 255. - disp = np.abs(disp) - disp = disp / disp.max() - real_depth = depth.copy() - for node in context_cc: - if mesh.nodes[node].get('real_depth') is not None: - real_depth[node[0], node[1]] = mesh.nodes[node]['real_depth'] - for node in erode_context_cc: - if mesh.nodes[node].get('real_depth') is not None: - real_depth[node[0], node[1]] = mesh.nodes[node]['real_depth'] - for node in mask_cc: - mask[node[0], node[1]] = 1 - near_depth[node[0], node[1]] = node[2] - for node in edge_cc: - edge[node[0], node[1]] = 1 - for node in extend_edge_cc: - comp_edge[node[0], node[1]] = 1 - rt_dict = {'rgb': rgb, 'disp': disp, 'depth': depth, 'real_depth': real_depth, 'self_edge': edge, 'context': context, - 'mask': mask, 'fpath_map': fpath_map, 'npath_map': npath_map, 'comp_edge': comp_edge, 'valid_area': context + mask, - 'near_depth': near_depth} - - return rt_dict - -def get_depth_from_maps(context_map, mask_map, depth_map, H, W, log_depth=False): - context = context_map.astype(np.uint8) - mask = mask_map.astype(np.uint8).copy() - depth = np.abs(depth_map) - real_depth = depth.copy() - zero_mean_depth = np.zeros((H, W)) - - if log_depth is True: - log_depth = np.log(real_depth + 1e-8) * context - mean_depth = np.mean(log_depth[context > 0]) - zero_mean_depth = (log_depth - mean_depth) * context - else: - zero_mean_depth = real_depth - mean_depth = 0 - edge = np.zeros_like(depth) - - rt_dict = {'depth': depth, 'real_depth': real_depth, 'context': context, 'mask': mask, - 'mean_depth': mean_depth, 'zero_mean_depth': zero_mean_depth, 'edge': edge} - - return rt_dict - -def get_depth_from_nodes(context_cc, erode_context_cc, mask_cc, H, W, mesh, log_depth=False): - context = np.zeros((H, W)) - mask = np.zeros((H, W)) - depth = np.zeros((H, W)) - real_depth = np.zeros((H, W)) - zero_mean_depth = np.zeros((H, W)) - for node in context_cc: - depth[node[0], node[1]] = node[2] - context[node[0], node[1]] = 1 - for node in erode_context_cc: - depth[node[0], node[1]] = node[2] - context[node[0], node[1]] = 1 - depth = np.abs(depth) - real_depth = depth.copy() - for node in context_cc: - if mesh.nodes[node].get('real_depth') is not None: - real_depth[node[0], node[1]] = mesh.nodes[node]['real_depth'] - for node in erode_context_cc: - if mesh.nodes[node].get('real_depth') is not None: - real_depth[node[0], node[1]] = mesh.nodes[node]['real_depth'] - real_depth = np.abs(real_depth) - for node in mask_cc: - mask[node[0], node[1]] = 1 - if log_depth is True: - log_depth = np.log(real_depth + 1e-8) * context - mean_depth = np.mean(log_depth[context > 0]) - zero_mean_depth = (log_depth - mean_depth) * context - else: - zero_mean_depth = real_depth - mean_depth = 0 - - rt_dict = {'depth': depth, 'real_depth': real_depth, 'context': context, 'mask': mask, - 'mean_depth': mean_depth, 'zero_mean_depth': zero_mean_depth} - - return rt_dict - -def get_rgb_from_nodes(context_cc, erode_context_cc, mask_cc, H, W, mesh): - context = np.zeros((H, W)) - mask = np.zeros((H, W)) - rgb = np.zeros((H, W, 3)) - erode_context = np.zeros((H, W)) - for node in context_cc: - rgb[node[0], node[1]] = np.array(mesh.nodes[node]['color']) - context[node[0], node[1]] = 1 - rgb = rgb / 255. - for node in mask_cc: - mask[node[0], node[1]] = 1 - for node in erode_context_cc: - erode_context[node[0], node[1]] = 1 - mask[node[0], node[1]] = 1 - rt_dict = {'rgb': rgb, 'context': context, 'mask': mask, - 'erode': erode_context} - - return rt_dict - -def crop_maps_by_size(size, *imaps): - omaps = [] - for imap in imaps: - omaps.append(imap[size['x_min']:size['x_max'], size['y_min']:size['y_max']].copy()) - - return omaps - -def convert2tensor(input_dict): - rt_dict = {} - for key, value in input_dict.items(): - if 'rgb' in key or 'color' in key: - rt_dict[key] = torch.FloatTensor(value).permute(2, 0, 1)[None, ...] - else: - rt_dict[key] = torch.FloatTensor(value)[None, None, ...] - - return rt_dict diff --git a/spaces/failfast/2D-GameCreator/src/components/Introduction.tsx b/spaces/failfast/2D-GameCreator/src/components/Introduction.tsx deleted file mode 100644 index 49055d017688a63b617bfd2fd549bdb5190349c2..0000000000000000000000000000000000000000 --- a/spaces/failfast/2D-GameCreator/src/components/Introduction.tsx +++ /dev/null @@ -1,109 +0,0 @@ -import { - Stack, - Grid, - Typography, - Paper, - List, - ListSubheader, - ListItem, - ListItemIcon, - ListItemText, - Link, - Button, -} from "@mui/material"; -import KeyIcon from "@mui/icons-material/Key"; -import SmartButtonIcon from "@mui/icons-material/SmartButton"; -import PlayArrowIcon from "@mui/icons-material/PlayArrow"; -import { PlayArrow } from "@mui/icons-material"; - -export default function Introduction() { - return ( -
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You can also join their community forum [here] or follow them on social media [here]. 401be4b1e0- - \ No newline at end of file diff --git a/spaces/fbeckk/cell-seg/transforms.py b/spaces/fbeckk/cell-seg/transforms.py deleted file mode 100644 index 75fbffb31917f7f3f9ebe634e2cf81420250b431..0000000000000000000000000000000000000000 --- a/spaces/fbeckk/cell-seg/transforms.py +++ /dev/null @@ -1,265 +0,0 @@ -import logging -import warnings -from functools import lru_cache -from typing import Tuple, Optional, List - -import cv2 -import numpy as np - -transforms_logger = logging.getLogger(__name__) - - -@lru_cache(maxsize=None) -def _patch_intensity_mask(patch_height: int = 224, patch_width: int = 224, sig: float = 7.5): - """ - Provides an intensity mask, given a patch size, based on an exponential function. - Values close to the center of the patch are close to 1. - When we are 20 pixels from the edges, we are ~0.88. Then, we have a drastic drop - to 0 at the edges. - Args: - patch_height: Input patch height - patch_width: Input patch width - sig: Sigma that divides the exponential. - - Returns: - An intensity map as a numpy array, with shape == (patch_height, patch_width) - """ - max_size = max(224, patch_height, patch_width) - xm = np.arange(max_size) - xm = np.abs(xm - xm.mean()) - mask = 1 / (1 + np.exp((xm - (max_size / 2 - 20)) / sig)) - mask = mask * mask[:, np.newaxis] - mask = mask[ - max_size // 2 - patch_height // 2: max_size // 2 + patch_height // 2 + patch_height % 2, - max_size // 2 - patch_width // 2: max_size // 2 + patch_width // 2 + patch_width % 2] - return mask - - -def average_patches(patches: np.ndarray, y_sub: List[Tuple[int, int]], x_sub: List[Tuple[int, int]], - height: int, width: int): - """ - Average the patch values over an image of (height, width). - Args: - patches: numpy array of (# patches, # classes == 3, patch_height, patch_width) - y_sub: list of integer tuples. Each tuple contains the start and ending position of - the patch in the y-axis. - x_sub: list of integer tuples. Each tuple contains the start and ending position of - the patch in the x-axis. - height: output image height - width: output image width - - Returns: - A numpy array of (height, width) with the average of the patches with appropriate overlap interpolation. - """ - intensity_mask = np.zeros((height, width), dtype=np.float32) - mean_output = np.zeros((patches.shape[1], height, width), dtype=np.float32) - patch_intensity_mask = _patch_intensity_mask(patch_height=patches.shape[-2], patch_width=patches.shape[-1]) - - for i in range(len(y_sub)): - mean_output[:, y_sub[i][0]:y_sub[i][1], x_sub[i][0]:x_sub[i][1]] += patches[i] * patch_intensity_mask - intensity_mask[y_sub[i][0]:y_sub[i][1], x_sub[i][0]:x_sub[i][1]] += patch_intensity_mask - - return mean_output / intensity_mask - - -def split_in_patches(x: np.ndarray, patch_size: int = 224, tile_overlap: float = 0.1): - """ make tiles of image to run at test-time - - Parameters - ---------- - x : float32 - array that's n_channels x height x width - - patch_size : int (optional, default 224) - size of tiles - - - tile_overlap: float (optional, default 0.1) - fraction of overlap of tiles - - Returns - ------- - patches : float32 - array that's ntiles x n_channels x bsize x bsize - - y_sub : list - list of arrays with start and end of tiles in Y of length ntiles - - x_sub : list - list of arrays with start and end of tiles in X of length ntiles - - - """ - - n_channels, height, width = x.shape - - tile_overlap = min(0.5, max(0.05, tile_overlap)) - patch_height = np.int32(min(patch_size, height)) - patch_width = np.int32(min(patch_size, width)) - - # tiles overlap by 10% tile size - ny = 1 if height <= patch_size else int(np.ceil((1. + 2 * tile_overlap) * height / patch_size)) - nx = 1 if width <= patch_size else int(np.ceil((1. + 2 * tile_overlap) * width / patch_size)) - - y_start = np.linspace(0, height - patch_height, ny).astype(np.int32) - x_start = np.linspace(0, width - patch_width, nx).astype(np.int32) - - y_sub, x_sub = [], [] - patches = np.zeros((len(y_start), len(x_start), n_channels, patch_height, patch_width), np.float32) - for j in range(len(y_start)): - for i in range(len(x_start)): - y_sub.append([y_start[j], y_start[j] + patch_height]) - x_sub.append([x_start[i], x_start[i] + patch_width]) - patches[j, i] = x[:, y_sub[-1][0]:y_sub[-1][1], x_sub[-1][0]:x_sub[-1][1]] - - return patches, y_sub, x_sub - - -def convert_image_grayscale(x: np.ndarray): - assert x.ndim == 2 - x = x.astype(np.float32) - x = x[:, :, np.newaxis] - x = np.concatenate((x, np.zeros_like(x)), axis=-1) - return x - - -def convert_image(x, channels: Tuple[int, int]): - assert len(channels) == 2 - - return reshape(x, channels=channels) - - -def reshape(x: np.ndarray, channels=(0, 0)): - """ reshape data using channels - - Parameters - ---------- - x : Numpy array, channel last. - - channels : list of int of length 2 (optional, default [0,0]) - First element of list is the channel to segment (0=grayscale, 1=red, 2=green, 3=blue). - Second element of list is the optional nuclear channel (0=none, 1=red, 2=green, 3=blue). - For instance, to train on grayscale images, input [0,0]. To train on images with cells - in green and nuclei in blue, input [2,3]. - - - Returns - ------- - data : numpy array that's (Z x ) Ly x Lx x nchan (if chan_first==False) - - """ - x = x.astype(np.float32) - if x.ndim < 3: - x = x[:, :, np.newaxis] - - if x.shape[-1] == 1: - x = np.concatenate((x, np.zeros_like(x)), axis=-1) - else: - if channels[0] == 0: - x = x.mean(axis=-1, keepdims=True) - x = np.concatenate((x, np.zeros_like(x)), axis=-1) - else: - channels_index = [channels[0] - 1] - if channels[1] > 0: - channels_index.append(channels[1] - 1) - x = x[..., channels_index] - for i in range(x.shape[-1]): - if np.ptp(x[..., i]) == 0.0: - if i == 0: - warnings.warn("chan to seg' has value range of ZERO") - else: - warnings.warn("'chan2 (opt)' has value range of ZERO, can instead set chan2 to 0") - if x.shape[-1] == 1: - x = np.concatenate((x, np.zeros_like(x)), axis=-1) - - return np.transpose(x, (2, 0, 1)) - - -def resize_image(image, height: Optional[int] = None, width: Optional[int] = None, resize: Optional[float] = None, - interpolation=cv2.INTER_LINEAR, no_channels=False): - """ resize image for computing flows / unresize for computing dynamics - - Parameters - ------------- - - image: ND-array - image of size [Y x X x nchan] or [Lz x Y x X x nchan] or [Lz x Y x X] - - height: int, optional - - width: int, optional - - resize: float, optional - resize coefficient(s) for image; if Ly is None then rsz is used - - interpolation: cv2 interp method (optional, default cv2.INTER_LINEAR) - - Returns - -------------- - - imgs: ND-array - image of size [Ly x Lx x nchan] or [Lz x Ly x Lx x nchan] - - """ - if height is None and resize is None: - error_message = 'must give size to resize to or factor to use for resizing' - transforms_logger.critical(error_message) - raise ValueError(error_message) - - if height is None: - # determine Ly and Lx using rsz - if not isinstance(resize, list) and not isinstance(resize, np.ndarray): - resize = [resize, resize] - if no_channels: - height = int(image.shape[-2] * resize[-2]) - width = int(image.shape[-1] * resize[-1]) - else: - height = int(image.shape[-3] * resize[-2]) - width = int(image.shape[-2] * resize[-1]) - - return cv2.resize(image, (width, height), interpolation=interpolation) - - -def pad_image(x: np.ndarray, div: int = 16): - """ pad image for test-time so that its dimensions are a multiple of 16 (2D or 3D) - - Parameters - ------------- - - x: ND-array - image of size [nchan (x Lz) x height x width] - - div: int (optional, default 16) - - Returns - -------------- - - output: ND-array - padded image - - y_sub: array, int - yrange of pixels in output corresponding to img0 - - x_sub: array, int - xrange of pixels in output corresponding to img0 - - """ - x_pad = int(div * np.ceil(x.shape[-2] / div) - x.shape[-2]) - x_pad_left = div // 2 + x_pad // 2 - x_pad_right = div // 2 + x_pad - x_pad // 2 - - y_pad = int(div * np.ceil(x.shape[-1] / div) - x.shape[-1]) - y_pad_left = div // 2 + y_pad // 2 - y_pad_right = div // 2 + y_pad - y_pad // 2 - - if x.ndim > 3: - pads = np.array([[0, 0], [0, 0], [x_pad_left, x_pad_right], [y_pad_left, y_pad_right]]) - else: - pads = np.array([[0, 0], [x_pad_left, x_pad_right], [y_pad_left, y_pad_right]]) - - output = np.pad(x, pads, mode='constant') - - height, width = x.shape[-2:] - y_sub = np.arange(x_pad_left, x_pad_left + height) - x_sub = np.arange(y_pad_left, y_pad_left + width) - return output, y_sub, x_sub \ No newline at end of file diff --git a/spaces/fclong/summary/fengshen/examples/clue1.1/data_preprocessing/c3_preprocessing.py b/spaces/fclong/summary/fengshen/examples/clue1.1/data_preprocessing/c3_preprocessing.py deleted file mode 100644 index 46f8c268cf829384cc05d2b4c3c01e826d1ad892..0000000000000000000000000000000000000000 --- a/spaces/fclong/summary/fengshen/examples/clue1.1/data_preprocessing/c3_preprocessing.py +++ /dev/null @@ -1,72 +0,0 @@ -import json -from tqdm import tqdm -import os -import argparse - - -def load_data(file_path,is_training=False): - with open(file_path, 'r', encoding='utf8') as f: - lines = json.loads(''.join(f.readlines())) - result=[] - for line in tqdm(lines): - data = line - texta = '\n'.join(data[0]) - textb ='' - for qa in data[1]: - question=qa['question'] - choice=qa['choice'] - answer=qa['answer'] if 'answer' in qa.keys() else '' - label = qa['choice'].index(answer) if 'answer' in qa.keys() else 0 - text_id = qa['id'] if 'id' in qa.keys() else 0 - result.append({'texta':texta, - 'textb':textb, - 'question':question, - 'choice':choice, - 'answer':answer, - 'label':label, - 'id':text_id}) - return result - - -def save_data(data,file_path): - with open(file_path, 'w', encoding='utf8') as f: - for line in data: - json_data=json.dumps(line,ensure_ascii=False) - f.write(json_data+'\n') - - - - -if __name__=="__main__": - parser = argparse.ArgumentParser(description="train") - parser.add_argument("--data_path", type=str,default="") - parser.add_argument("--save_path", type=str,default="") - - args = parser.parse_args() - - - data_path = args.data_path - save_path = args.save_path - - if not os.path.exists(save_path): - os.makedirs(save_path) - - file_list=['d-train','d-dev','c3-m-train','m-train','m-dev','test1.0','test1.1'] - train_data = [] - dev_data = [] - for file in file_list: - file_path = os.path.join(data_path,file+'.json') - data=load_data(file_path=file_path) - if 'train' in file or 'd-dev' in file: - train_data.extend(data) - elif 'm-dev' in file: - dev_data.extend(data) - elif 'test' in file: - output_path = os.path.join(save_path,file+'.json') - save_data(data,output_path) - - output_path = os.path.join(save_path,'train.json') - save_data(train_data,output_path) - - output_path = os.path.join(save_path,'dev.json') - save_data(dev_data,output_path) \ No newline at end of file diff --git a/spaces/fclong/summary/fengshen/examples/wenzhong_qa/finetune_medicalQA.py b/spaces/fclong/summary/fengshen/examples/wenzhong_qa/finetune_medicalQA.py deleted file mode 100644 index 1a79948d5f7fe736856e44392a834edfa6ac51d9..0000000000000000000000000000000000000000 --- a/spaces/fclong/summary/fengshen/examples/wenzhong_qa/finetune_medicalQA.py +++ /dev/null @@ -1,176 +0,0 @@ -from transformers import GPT2LMHeadModel -from data.task_dataloader.medicalQADataset import GPT2QADataModel -from transformers.optimization import get_linear_schedule_with_warmup -from pytorch_lightning import Trainer, loggers -from pytorch_lightning.callbacks import ModelCheckpoint -import pytorch_lightning as pl -import argparse -import torch -import os -import sys -sys.path.insert(0, '/cognitive_comp/wuziwei/codes/fengshen/fengshen') -# sys.path.append('../../') -# sys.path.append('../') -# os.environ["CUDA_VISIBLE_DEVICES"] = '4,5,6,7' - - -class GPT2FinetuneMedicalQAModelCheckpoint: - @staticmethod - def add_argparse_args(parent_args): - parser = parent_args.add_argument_group('BaseModel') - - parser.add_argument('--monitor', default='train_loss', type=str) - parser.add_argument('--mode', default='min', type=str) - parser.add_argument('--dirpath', default='./ckpt/', type=str) - parser.add_argument( - '--filename', default='model-{epoch:02d}-{train_loss:.4f}', type=str) - parser.add_argument('--save_last', action='store_true', default=True) - parser.add_argument('--save_top_k', default=3, type=float) - parser.add_argument('--every_n_train_steps', default=1000, type=float) - parser.add_argument('--save_weights_only', default=True, type=bool) - - return parent_args - - def __init__(self, args): - self.callbacks = ModelCheckpoint(monitor=args.monitor, - save_top_k=args.save_top_k, - mode=args.mode, - # every_n_train_steps=args.every_n_train_steps, - save_weights_only=args.save_weights_only, - dirpath=args.dirpath, - filename=args.filename, - save_last=args.save_last) - - -class GPT2FinetuneMedicalQA(pl.LightningModule): - - @staticmethod - def add_model_specific_args(parent_args): - parser = parent_args.add_argument_group('BaseModel') - parser.add_argument('--learning_rate', default=1e-4, type=float) - parser.add_argument('--weight_decay', default=0.1, type=float) - parser.add_argument('--warmup', default=0.01, type=float) - return parent_args - - def __init__(self, args, num_data): - super().__init__() - self.args = args - self.num_data = num_data - print('num_data:', num_data) - self.model = GPT2LMHeadModel.from_pretrained( - args.pretrained_model_path) - - def setup(self, stage) -> None: - if stage == 'fit': - num_gpus = self.trainer.gpus if self.trainer.gpus is not None else 0 - self.total_step = int(self.trainer.max_epochs * self.num_data / - (max(1, num_gpus) * self.trainer.accumulate_grad_batches)) - print('Total training step:', self.total_step) - - def training_step(self, batch, batch_idx): - output = self.model(input_ids=batch['input_ids'], - attention_mask=batch['attention_mask'], labels=batch['labels']) - # output = self.model(input_ids=batch['input_ids'], labels=batch['labels']) - # acc = self.comput_metrix(output.logits, batch['labels']) - self.log('train_loss', output.loss) - return output.loss - - def comput_metrix(self, logits, labels): - y_pred = torch.argmax(logits, dim=-1) - y_pred = y_pred.view(size=(-1,)) - y_true = labels.view(size=(-1,)).float() - corr = torch.eq(y_pred, y_true) - acc = torch.sum(corr.float())/labels.size()[0] - return acc - - def validation_step(self, batch, batch_idx): - output = self.model(input_ids=batch['input_ids'], - attention_mask=batch['attention_mask'], labels=batch['labels']) - # output = self.model(input_ids=batch['input_ids'], labels=batch['labels']) - # acc = self.comput_metrix(output.logits, batch['labels']) - self.log('val_loss', output.loss) - # self.log('val_acc', acc) - - def configure_optimizers(self): - no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] - paras = list( - filter(lambda p: p[1].requires_grad, self.named_parameters())) - paras = [{ - 'params': - [p for n, p in paras if not any(nd in n for nd in no_decay)], - 'weight_decay': self.args.weight_decay - }, { - 'params': [p for n, p in paras if any(nd in n for nd in no_decay)], - 'weight_decay': 0.0 - }] - optimizer = torch.optim.AdamW(paras, lr=self.args.learning_rate) - scheduler = get_linear_schedule_with_warmup( - optimizer, int(self.total_step * self.args.warmup), - self.total_step) - - return [{ - 'optimizer': optimizer, - 'lr_scheduler': { - 'scheduler': scheduler, - 'interval': 'step', - 'frequency': 1 - } - }] - - -def main(): - total_parser = argparse.ArgumentParser("Summary Task") - total_parser.add_argument( - '--do_eval_only', action='store_true', default=False) - total_parser.add_argument( - '--pretrained_model_path', default=None, type=str) - total_parser.add_argument('--output_save_path', - default='./predict.json', type=str) - # * Args for data preprocessing - total_parser = GPT2QADataModel.add_data_specific_args(total_parser) - # * Args for training - total_parser = Trainer.add_argparse_args(total_parser) - total_parser = GPT2FinetuneMedicalQAModelCheckpoint.add_argparse_args( - total_parser) - total_parser = GPT2FinetuneMedicalQA.add_model_specific_args(total_parser) - # * Args for base model - args = total_parser.parse_args() - - data_model = GPT2QADataModel(args) - if not args.do_eval_only: - model = GPT2FinetuneMedicalQA(args, len(data_model.train_dataloader())) - checkpoint_callback = GPT2FinetuneMedicalQAModelCheckpoint( - args).callbacks - logger = loggers.TensorBoardLogger(save_dir=os.path.join( - args.default_root_dir, 'log/'), name='MedicalQA-GPT2') - trainer = Trainer.from_argparse_args(args, - logger=logger, - callbacks=[checkpoint_callback] - ) - trainer.fit(model, data_model) - - # result = trainer.predict(model, data_model) - # with open('test_results.txt', 'wt', encoding='utf-8') as w: - # for line in result: - # w.writelines(line) - - model.model.save_pretrained( - '/cognitive_comp/wuziwei/pretrained_model_hf') - else: - print('save to hf.....') - trainer = Trainer.from_argparse_args(args) - model = GPT2FinetuneMedicalQA( - args, len(data_model.predict_dataloader())) - - result = trainer.predict( - model, data_model, ckpt_path='/cognitive_comp/wuziwei/task/fs_medical_qa_finetune/ckpt/last.ckpt') - # with open('test_results.txt','wt',encoding='utf-8') as w: - # for line in result: - # w.writelines(line) - - model.model.save_pretrained( - '/cognitive_comp/wuziwei/pretrained_model_hf') - - -if __name__ == '__main__': - main() diff --git a/spaces/ferdmartin/DogBreedsApp/App/Dockerfile b/spaces/ferdmartin/DogBreedsApp/App/Dockerfile deleted file mode 100644 index 538b61e4c0c3f6454759d782109a27232e1ba302..0000000000000000000000000000000000000000 --- a/spaces/ferdmartin/DogBreedsApp/App/Dockerfile +++ /dev/null @@ -1,10 +0,0 @@ -FROM python:3 -RUN pip install flask -RUN pip install requests -RUN pip install gcloud -RUN pip install --upgrade google-cloud-translate -EXPOSE 5003/tcp -COPY app.py . -COPY fmartinezlopezGTranslateKey.json . -COPY templates/index.html templates/ -ENTRYPOINT [ "python3", "app.py" ] \ No newline at end of file diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/iconv-lite/README.md b/spaces/fffiloni/controlnet-animation-doodle/node_modules/iconv-lite/README.md deleted file mode 100644 index c981c3708582a563f0f463c1e01715257d5148bd..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/iconv-lite/README.md +++ /dev/null @@ -1,156 +0,0 @@ -## Pure JS character encoding conversion [](https://travis-ci.org/ashtuchkin/iconv-lite) - - * Doesn't need native code compilation. Works on Windows and in sandboxed environments like [Cloud9](http://c9.io). - * Used in popular projects like [Express.js (body_parser)](https://github.com/expressjs/body-parser), - [Grunt](http://gruntjs.com/), [Nodemailer](http://www.nodemailer.com/), [Yeoman](http://yeoman.io/) and others. - * Faster than [node-iconv](https://github.com/bnoordhuis/node-iconv) (see below for performance comparison). - * Intuitive encode/decode API - * Streaming support for Node v0.10+ - * [Deprecated] Can extend Node.js primitives (buffers, streams) to support all iconv-lite encodings. - * In-browser usage via [Browserify](https://github.com/substack/node-browserify) (~180k gzip compressed with Buffer shim included). - * Typescript [type definition file](https://github.com/ashtuchkin/iconv-lite/blob/master/lib/index.d.ts) included. - * React Native is supported (need to explicitly `npm install` two more modules: `buffer` and `stream`). - * License: MIT. - -[](https://npmjs.org/packages/iconv-lite/) - -## Usage -### Basic API -```javascript -var iconv = require('iconv-lite'); - -// Convert from an encoded buffer to js string. -str = iconv.decode(Buffer.from([0x68, 0x65, 0x6c, 0x6c, 0x6f]), 'win1251'); - -// Convert from js string to an encoded buffer. -buf = iconv.encode("Sample input string", 'win1251'); - -// Check if encoding is supported -iconv.encodingExists("us-ascii") -``` - -### Streaming API (Node v0.10+) -```javascript - -// Decode stream (from binary stream to js strings) -http.createServer(function(req, res) { - var converterStream = iconv.decodeStream('win1251'); - req.pipe(converterStream); - - converterStream.on('data', function(str) { - console.log(str); // Do something with decoded strings, chunk-by-chunk. - }); -}); - -// Convert encoding streaming example -fs.createReadStream('file-in-win1251.txt') - .pipe(iconv.decodeStream('win1251')) - .pipe(iconv.encodeStream('ucs2')) - .pipe(fs.createWriteStream('file-in-ucs2.txt')); - -// Sugar: all encode/decode streams have .collect(cb) method to accumulate data. -http.createServer(function(req, res) { - req.pipe(iconv.decodeStream('win1251')).collect(function(err, body) { - assert(typeof body == 'string'); - console.log(body); // full request body string - }); -}); -``` - -### [Deprecated] Extend Node.js own encodings -> NOTE: This doesn't work on latest Node versions. See [details](https://github.com/ashtuchkin/iconv-lite/wiki/Node-v4-compatibility). - -```javascript -// After this call all Node basic primitives will understand iconv-lite encodings. -iconv.extendNodeEncodings(); - -// Examples: -buf = new Buffer(str, 'win1251'); -buf.write(str, 'gbk'); -str = buf.toString('latin1'); -assert(Buffer.isEncoding('iso-8859-15')); -Buffer.byteLength(str, 'us-ascii'); - -http.createServer(function(req, res) { - req.setEncoding('big5'); - req.collect(function(err, body) { - console.log(body); - }); -}); - -fs.createReadStream("file.txt", "shift_jis"); - -// External modules are also supported (if they use Node primitives, which they probably do). -request = require('request'); -request({ - url: "http://github.com/", - encoding: "cp932" -}); - -// To remove extensions -iconv.undoExtendNodeEncodings(); -``` - -## Supported encodings - - * All node.js native encodings: utf8, ucs2 / utf16-le, ascii, binary, base64, hex. - * Additional unicode encodings: utf16, utf16-be, utf-7, utf-7-imap. - * All widespread singlebyte encodings: Windows 125x family, ISO-8859 family, - IBM/DOS codepages, Macintosh family, KOI8 family, all others supported by iconv library. - Aliases like 'latin1', 'us-ascii' also supported. - * All widespread multibyte encodings: CP932, CP936, CP949, CP950, GB2312, GBK, GB18030, Big5, Shift_JIS, EUC-JP. - -See [all supported encodings on wiki](https://github.com/ashtuchkin/iconv-lite/wiki/Supported-Encodings). - -Most singlebyte encodings are generated automatically from [node-iconv](https://github.com/bnoordhuis/node-iconv). Thank you Ben Noordhuis and libiconv authors! - -Multibyte encodings are generated from [Unicode.org mappings](http://www.unicode.org/Public/MAPPINGS/) and [WHATWG Encoding Standard mappings](http://encoding.spec.whatwg.org/). Thank you, respective authors! - - -## Encoding/decoding speed - -Comparison with node-iconv module (1000x256kb, on MacBook Pro, Core i5/2.6 GHz, Node v0.12.0). -Note: your results may vary, so please always check on your hardware. - - operation iconv@2.1.4 iconv-lite@0.4.7 - ---------------------------------------------------------- - encode('win1251') ~96 Mb/s ~320 Mb/s - decode('win1251') ~95 Mb/s ~246 Mb/s - -## BOM handling - - * Decoding: BOM is stripped by default, unless overridden by passing `stripBOM: false` in options - (f.ex. `iconv.decode(buf, enc, {stripBOM: false})`). - A callback might also be given as a `stripBOM` parameter - it'll be called if BOM character was actually found. - * If you want to detect UTF-8 BOM when decoding other encodings, use [node-autodetect-decoder-stream](https://github.com/danielgindi/node-autodetect-decoder-stream) module. - * Encoding: No BOM added, unless overridden by `addBOM: true` option. - -## UTF-16 Encodings - -This library supports UTF-16LE, UTF-16BE and UTF-16 encodings. First two are straightforward, but UTF-16 is trying to be -smart about endianness in the following ways: - * Decoding: uses BOM and 'spaces heuristic' to determine input endianness. Default is UTF-16LE, but can be - overridden with `defaultEncoding: 'utf-16be'` option. Strips BOM unless `stripBOM: false`. - * Encoding: uses UTF-16LE and writes BOM by default. Use `addBOM: false` to override. - -## Other notes - -When decoding, be sure to supply a Buffer to decode() method, otherwise [bad things usually happen](https://github.com/ashtuchkin/iconv-lite/wiki/Use-Buffers-when-decoding). -Untranslatable characters are set to � or ?. No transliteration is currently supported. -Node versions 0.10.31 and 0.11.13 are buggy, don't use them (see #65, #77). - -## Testing - -```bash -$ git clone git@github.com:ashtuchkin/iconv-lite.git -$ cd iconv-lite -$ npm install -$ npm test - -$ # To view performance: -$ node test/performance.js - -$ # To view test coverage: -$ npm run coverage -$ open coverage/lcov-report/index.html -``` diff --git a/spaces/flax-community/multilingual-image-captioning/apps/mic.py b/spaces/flax-community/multilingual-image-captioning/apps/mic.py deleted file mode 100644 index 7fca991c1f9cd7f3a615889d863b9e315b4cd7e9..0000000000000000000000000000000000000000 --- a/spaces/flax-community/multilingual-image-captioning/apps/mic.py +++ /dev/null @@ -1,147 +0,0 @@ -from .utils import get_transformed_image - -import streamlit as st -import numpy as np -import pandas as pd -import os -import matplotlib.pyplot as plt -import re -from mtranslate import translate -from .utils import ( - read_markdown, - tokenizer, - language_mapping, - code_to_name, - voicerss_tts -) -import requests -from PIL import Image -from .model.flax_clip_vision_mbart.modeling_clip_vision_mbart import ( - FlaxCLIPVisionMBartForConditionalGeneration, -) -from streamlit import caching - - -def app(state): - mic_state = state - with st.beta_expander("Usage"): - st.write(read_markdown("usage.md")) - st.write("\n") - st.write(read_markdown("intro.md")) - - # st.sidebar.title("Generation Parameters") - max_length = 64 - - with st.sidebar.beta_expander('Generation Parameters'): - do_sample = st.checkbox("Sample", value=False, help="Sample from the model instead of using beam search.") - top_k = st.number_input("Top K", min_value=10, max_value=200, value=50, step=1, help="The number of highest probability vocabulary tokens to keep for top-k-filtering.") - num_beams = st.number_input(label="Number of Beams", min_value=2, max_value=10, value=4, step=1, help="Number of beams to be used in beam search.") - temperature = st.select_slider(label="Temperature", options = list(np.arange(0.0,1.1, step=0.1)), value=1.0, help ="The value used to module the next token probabilities.", format_func=lambda x: f"{x:.2f}") - top_p = st.select_slider(label = "Top-P", options = list(np.arange(0.0,1.1, step=0.1)),value=1.0, help="Nucleus Sampling : If set to float < 1, only the most probable tokens with probabilities that add up to :obj:`top_p` or higher are kept for generation.", format_func=lambda x: f"{x:.2f}") - if st.button("Clear All Cache"): - caching.clear_cache() - - - @st.cache - def load_model(ckpt): - return FlaxCLIPVisionMBartForConditionalGeneration.from_pretrained(ckpt) - - @st.cache - def generate_sequence(pixel_values, lang_code, num_beams, temperature, top_p, do_sample, top_k, max_length): - lang_code = language_mapping[lang_code] - output_ids = mic_state.model.generate(input_ids=pixel_values, forced_bos_token_id=tokenizer.lang_code_to_id[lang_code], max_length=max_length, num_beams=num_beams, temperature=temperature, top_p = top_p, top_k=top_k, do_sample=do_sample) - print(output_ids) - output_sequence = tokenizer.batch_decode(output_ids[0], skip_special_tokens=True, max_length=max_length) - return output_sequence - - mic_checkpoints = ["flax-community/clip-vit-base-patch32_mbart-large-50"] # TODO: Maybe add more checkpoints? - dummy_data = pd.read_csv("reference.tsv", sep="\t") - - first_index = 25 - # Init Session State - if mic_state.image_file is None: - mic_state.image_file = dummy_data.loc[first_index, "image_file"] - mic_state.caption = dummy_data.loc[first_index, "caption"].strip("- ") - mic_state.lang_id = dummy_data.loc[first_index, "lang_id"] - - image_path = os.path.join("images", mic_state.image_file) - image = plt.imread(image_path) - mic_state.image = image - - if mic_state.model is None: - # Display Top-5 Predictions - with st.spinner("Loading model..."): - mic_state.model = load_model(mic_checkpoints[0]) - - query1 = st.text_input( - "Enter a URL to an image", - value="http://images.cocodataset.org/val2017/000000397133.jpg", - ) - - col1, col2, col3 = st.beta_columns([2,1, 2]) - if col1.button( - "Get a random example", - help="Get a random example from the 100 `seeded` image-text pairs.", - ): - sample = dummy_data.sample(1).reset_index() - mic_state.image_file = sample.loc[0, "image_file"] - mic_state.caption = sample.loc[0, "caption"].strip("- ") - mic_state.lang_id = sample.loc[0, "lang_id"] - - image_path = os.path.join("images", mic_state.image_file) - image = plt.imread(image_path) - mic_state.image = image - - col2.write("OR") - - if col3.button("Use above URL"): - image_data = requests.get(query1, stream=True).raw - image = np.asarray(Image.open(image_data)) - mic_state.image = image - - transformed_image = get_transformed_image(mic_state.image) - - new_col1, new_col2 = st.beta_columns([5,5]) - - # Display Image - new_col1.image(mic_state.image, use_column_width="always") - # Display Reference Caption - with new_col1.beta_expander("Reference Caption"): - st.write("**Reference Caption**: " + mic_state.caption) - st.markdown( - f"""**English Translation**: {mic_state.caption if mic_state.lang_id == "en" else translate(mic_state.caption, 'en')}""" - ) - - # Select Language - options = list(code_to_name.keys()) - lang_id = new_col2.selectbox( - "Language", - index=options.index(mic_state.lang_id), - options=options, - format_func=lambda x: code_to_name[x], - help="The language in which caption is to be generated." - ) - - sequence = [''] - if new_col2.button("Generate Caption", help="Generate a caption in the specified language."): - with st.spinner("Generating Sequence... This might take some time, you can read our Article meanwhile!"): - sequence = generate_sequence(transformed_image, lang_id, num_beams, temperature, top_p, do_sample, top_k, max_length) - # print(sequence) - - if sequence!=['']: - new_col2.write( - "**Generated Caption**: "+sequence[0] - ) - - new_col2.write( - "**English Translation**: "+ (sequence[0] if lang_id=="en" else translate(sequence[0])) - ) - with new_col2: - try: - clean_text = re.sub(r'[^A-Za-z0-9 ]+', '', sequence[0]) - # st.write("**Cleaned Text**: ",clean_text) - audio_bytes = voicerss_tts(clean_text, lang_id) - st.markdown("**Audio for the generated caption**") - st.audio(audio_bytes) - except: - st.info("Unabled to generate audio. Please try again in some time.") \ No newline at end of file diff --git a/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/minigrid.py b/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/minigrid.py deleted file mode 100644 index e3e84d386ede9d362c1cf4ecd202844cf8e104cb..0000000000000000000000000000000000000000 --- a/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/minigrid.py +++ /dev/null @@ -1,3492 +0,0 @@ -import math -import random -import hashlib -import gym -from enum import IntEnum -import numpy as np -from gym import error, spaces, utils -from gym.utils import seeding -from .rendering import * -from abc import ABC, abstractmethod -import warnings -import astar - -import traceback -import warnings -from functools import wraps - -SocialAINPCActionsDict = { - "go_forward": 0, - "rotate_left": 1, - "rotate_right": 2, - "toggle_action": 3, - "point_stop_point": 4, - "point_E": 5, - "point_S": 6, - "point_W": 7, - "point_N": 8, - "stop_point": 9, - "no_op": 10 -} - -point_dir_encoding = { - "point_E": 0, - "point_S": 1, - "point_W": 2, - "point_N": 3, -} - -def get_traceback(): - tb = traceback.extract_stack() - return "".join(traceback.format_list(tb)[:-1]) - - -# Size in pixels of a tile in the full-scale human view -TILE_PIXELS = 32 - -# Map of color names to RGB values -COLORS = { - 'red' : np.array([255, 0, 0]), - 'green' : np.array([0, 255, 0]), - 'blue' : np.array([0, 0, 255]), - 'purple': np.array([112, 39, 195]), - 'yellow': np.array([255, 255, 0]), - 'grey' : np.array([100, 100, 100]), - 'brown': np.array([82, 36, 19]) -} - -COLOR_NAMES = sorted(list(COLORS.keys())) - -# Used to map colors to integers -COLOR_TO_IDX = { - 'red' : 0, - 'green' : 1, - 'blue' : 2, - 'purple': 3, - 'yellow': 4, - 'grey' : 5, - 'brown' : 6, -} - -IDX_TO_COLOR = dict(zip(COLOR_TO_IDX.values(), COLOR_TO_IDX.keys())) - -# Map of object type to integers -OBJECT_TO_IDX = { - 'unseen' : 0, - 'empty' : 1, - 'wall' : 2, - 'floor' : 3, - 'door' : 4, - 'key' : 5, - 'ball' : 6, - 'box' : 7, - 'goal' : 8, - 'lava' : 9, - 'agent' : 10, - 'npc' : 11, - 'switch' : 12, - 'lockablebox' : 13, - 'apple' : 14, - 'applegenerator' : 15, - 'generatorplatform': 16, - 'marble' : 17, - 'marbletee' : 18, - 'fence' : 19, - 'remotedoor' : 20, - 'lever' : 21, -} - -IDX_TO_OBJECT = dict(zip(OBJECT_TO_IDX.values(), OBJECT_TO_IDX.keys())) - -# Map of state names to integers -STATE_TO_IDX = { - 'open' : 0, - 'closed': 1, - 'locked': 2, -} - -# Map of agent direction indices to vectors -DIR_TO_VEC = [ - # Pointing right (positive X) - np.array((1, 0)), - # Down (positive Y) - np.array((0, 1)), - # Pointing left (negative X) - np.array((-1, 0)), - # Up (negative Y) - np.array((0, -1)), -] - -class WorldObj: - """ - Base class for grid world objects - """ - - def __init__(self, type, color): - assert type in OBJECT_TO_IDX, type - assert color in COLOR_TO_IDX, color - self.type = type - self.color = color - self.contains = None - - # Initial position of the object - self.init_pos = None - - # Current position of the object - self.cur_pos = np.array((0, 0)) - - def can_overlap(self): - """Can the agent overlap with this?""" - return False - - def can_push(self): - """Can the agent push the object?""" - return False - - def can_pickup(self): - """Can the agent pick this up?""" - return False - - def can_contain(self): - """Can this contain another object?""" - return False - - def see_behind(self): - """Can the agent see behind this object?""" - return True - - def toggle(self, env, pos): - """Method to trigger/toggle an action this object performs""" - return False - - def encode(self, nb_dims=3, absolute_coordinates=False): - """Encode the a description of this object as a nb_dims-tuple of integers""" - if absolute_coordinates: - core = (OBJECT_TO_IDX[self.type], *self.cur_pos, COLOR_TO_IDX[self.color]) - else: - core = (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color]) - - return core + (0,) * (nb_dims - len(core)) - - def cache(self, *args, **kwargs): - """Used for cached rendering.""" - return self.encode(*args, **kwargs) - - @staticmethod - def decode(type_idx, color_idx, state): - """Create an object from a 3-tuple state description""" - - obj_type = IDX_TO_OBJECT[type_idx] - color = IDX_TO_COLOR[color_idx] - - if obj_type == 'empty' or obj_type == 'unseen': - return None - - if obj_type == 'wall': - v = Wall(color) - elif obj_type == 'floor': - v = Floor(color) - elif obj_type == 'ball': - v = Ball(color) - elif obj_type == 'marble': - v = Marble(color) - elif obj_type == 'apple': - eaten = state == 1 - v = Apple(color, eaten=eaten) - elif obj_type == 'apple_generator': - is_pressed = state == 2 - v = AppleGenerator(color, is_pressed=is_pressed) - elif obj_type == 'key': - v = Key(color) - elif obj_type == 'box': - v = Box(color) - elif obj_type == 'lockablebox': - is_locked = state == 2 - v = LockableBox(color, is_locked=is_locked) - elif obj_type == 'door': - # State, 0: open, 1: closed, 2: locked - is_open = state == 0 - is_locked = state == 2 - v = Door(color, is_open, is_locked) - elif obj_type == 'remotedoor': - # State, 0: open, 1: closed - is_open = state == 0 - v = RemoteDoor(color, is_open) - elif obj_type == 'goal': - v = Goal() - elif obj_type == 'lava': - v = Lava() - elif obj_type == 'fence': - v = Fence() - elif obj_type == 'switch': - v = Switch(color, is_on=state) - elif obj_type == 'lever': - v = Lever(color, is_on=state) - elif obj_type == 'npc': - warnings.warn("NPC's internal state cannot be decoded. Only the icon is shown.") - v = NPC(color) - v.npc_type=0 - else: - assert False, "unknown object type in decode '%s'" % obj_type - - return v - - def render(self, r): - """Draw this object with the given renderer""" - raise NotImplementedError - - -class BlockableWorldObj(WorldObj): - - def __init__(self, type, color, block_set): - super(BlockableWorldObj, self).__init__(type, color) - self.block_set = block_set - self.blocked = False - - - def can_push(self): - return True - - def push(self, *args, **kwargs): - return self.block_block_set() - - def toggle(self, *args, **kwargs): - return self.block_block_set() - - def block_block_set(self): - """A function that blocks the block set""" - if not self.blocked: - if self.block_set is not None: - # cprint("BLOCKED!", "red") - for e in self.block_set: - e.block() - - return True - - else: - return False - - def block(self): - self.blocked = True - - -class Goal(WorldObj): - def __init__(self): - super().__init__('goal', 'green') - - def can_overlap(self): - return True - - def render(self, img): - fill_coords(img, point_in_rect(0, 1, 0, 1), COLORS[self.color]) - - -class Floor(WorldObj): - """ - Colored floor tile the agent can walk over - """ - - def __init__(self, color='blue'): - super().__init__('floor', color) - - def can_overlap(self): - return True - - def render(self, img): - # Give the floor a pale color - color = COLORS[self.color] / 2 - fill_coords(img, point_in_rect(0.031, 1, 0.031, 1), color) - - -class Lava(WorldObj): - def __init__(self): - super().__init__('lava', 'red') - - def can_overlap(self): - return True - - def render(self, img): - c = (255, 128, 0) - - # Background color - fill_coords(img, point_in_rect(0, 1, 0, 1), c) - - # Little waves - for i in range(3): - ylo = 0.3 + 0.2 * i - yhi = 0.4 + 0.2 * i - fill_coords(img, point_in_line(0.1, ylo, 0.3, yhi, r=0.03), (0,0,0)) - fill_coords(img, point_in_line(0.3, yhi, 0.5, ylo, r=0.03), (0,0,0)) - fill_coords(img, point_in_line(0.5, ylo, 0.7, yhi, r=0.03), (0,0,0)) - fill_coords(img, point_in_line(0.7, yhi, 0.9, ylo, r=0.03), (0,0,0)) - - -class Fence(WorldObj): - """ - Same as Lava but can't overlap. - """ - def __init__(self): - super().__init__('fence', 'grey') - - def can_overlap(self): - return False - - def render(self, img): - c = COLORS[self.color] - - # ugly fence - fill_coords(img, point_in_rect( - 0.1, 0.9, 0.5, 0.9 - # (0.15, 0.9), - # (0.10, 0.5), - # (0.95, 0.9), - # (0.90, 0.5), - # (0.10, 0.9), - # (0.10, 0.5), - # (0.95, 0.9), - # (0.95, 0.5), - ), c) - fill_coords(img, point_in_quadrangle( - # (0.15, 0.9), - # (0.10, 0.5), - # (0.95, 0.9), - # (0.90, 0.5), - (0.10, 0.9), - (0.10, 0.5), - (0.95, 0.9), - (0.95, 0.5), - ), c) - return - - # preety fence - fill_coords(img, point_in_quadrangle( - (0.15, 0.3125), - (0.15, 0.4125), - (0.85, 0.4875), - (0.85, 0.5875), - ), c) - - # h2 - fill_coords(img, point_in_quadrangle( - (0.15, 0.6125), - (0.15, 0.7125), - (0.85, 0.7875), - (0.85, 0.8875), - ), c) - - # vm - fill_coords(img, point_in_quadrangle( - (0.45, 0.2875), - (0.45, 0.8875), - (0.55, 0.3125), - (0.55, 0.9125), - ), c) - fill_coords(img, point_in_triangle( - (0.45, 0.2875), - (0.55, 0.3125), - (0.5, 0.25), - ), c) - - # vl - fill_coords(img, point_in_quadrangle( - (0.25, 0.2375), - (0.25, 0.8375), - (0.35, 0.2625), - (0.35, 0.8625), - ), c) - # vl - fill_coords(img, point_in_triangle( - (0.25, 0.2375), - (0.35, 0.2625), - (0.3, 0.2), - ), c) - - - # vr - fill_coords(img, point_in_quadrangle( - (0.65, 0.3375), - (0.65, 0.9375), - (0.75, 0.3625), - (0.75, 0.9625), - ), c) - fill_coords(img, point_in_triangle( - (0.65, 0.3375), - (0.75, 0.3625), - (0.7, 0.3), - ), c) - - -class Wall(WorldObj): - def __init__(self, color='grey'): - super().__init__('wall', color) - - def see_behind(self): - return False - - def render(self, img): - fill_coords(img, point_in_rect(0, 1, 0, 1), COLORS[self.color]) - - -class Lever(BlockableWorldObj): - def __init__(self, color, object=None, is_on=False, block_set=None, active_steps=None): - super().__init__('lever', color, block_set) - self.is_on = is_on - self.object = object - - self.active_steps = active_steps - self.countdown = None # countdown timer - - self.was_activated = False - - if self.block_set is not None: - if self.is_on: - raise ValueError("If using a block set, a Switch must be initialized as OFF") - - def can_overlap(self): - """The agent can only walk over this cell when the door is open""" - return False - - def see_behind(self): - return True - - def step(self): - if self.countdown is not None: - self.countdown = self.countdown - 1 - - if self.countdown <= 0: - # if nothing is on the door, close the door and deactivate timer - self.toggle() - self.countdown = None - - def toggle(self, env=None, pos=None): - - if self.blocked: - return False - - if self.was_activated and not self.is_on: - # cannot be activated twice - return False - - self.is_on = not self.is_on - - if self.is_on: - if self.active_steps is not None: - # activate countdown to shutdown - self.countdown = self.active_steps - self.was_activated = True - - if self.object is not None: - # open object - self.object.open_close() - - if self.is_on: - self.block_block_set() - - return True - - def block(self): - self.blocked = True - - def encode(self, nb_dims=3, absolute_coordinates=False): - """Encode the a description of this object as a 3-tuple of integers""" - - # State, 0: off, 1: on - state = 1 if self.is_on else 0 - - count = self.countdown if self.countdown is not None else 255 - - if absolute_coordinates: - v = (OBJECT_TO_IDX[self.type], *self.cur_pos, COLOR_TO_IDX[self.color], state, count) - else: - v = (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color], state, count) - - v += (0,) * (nb_dims-len(v)) - - return v - - def render(self, img): - c = COLORS[self.color] - black = (0, 0, 0) - - # off_angle = -math.pi/3 - off_angle = -math.pi/2 - on_angle = -math.pi/8 - - - rotating_lever_shapes = [] - rotating_lever_shapes.append((point_in_rect(0.5, 0.9, 0.77, 0.83), c)) - rotating_lever_shapes.append((point_in_circle(0.9, 0.8, 0.1), c)) - - rotating_lever_shapes.append((point_in_circle(0.5, 0.8, 0.08), c)) - - if self.is_on: - if self.countdown is None: - angle = on_angle - else: - angle = (self.countdown/self.active_steps) * (on_angle-off_angle) + off_angle - - else: - angle = off_angle - - fill_coords(img, point_in_circle_clip(0.5, 0.8, 0.12, theta_end=-math.pi), c) - # fill_coords(img, point_in_circle_clip(0.5, 0.8, 0.08, theta_end=-math.pi), black) - - rotating_lever_shapes = [(rotate_fn(v, cx=0.5, cy=0.8, theta=angle), col) for v, col in rotating_lever_shapes] - - for v, col in rotating_lever_shapes: - fill_coords(img, v, col) - - fill_coords(img, point_in_rect(0.2, 0.8, 0.78, 0.82), c) - fill_coords(img, point_in_circle(0.5, 0.8, 0.03), (0, 0, 0)) - - -class RemoteDoor(BlockableWorldObj): - """Door that are unlocked by a lever""" - def __init__(self, color, is_open=False, block_set=None): - super().__init__('remotedoor', color, block_set) - self.is_open = is_open - - def can_overlap(self): - """The agent can only walk over this cell when the door is open""" - return self.is_open - - def see_behind(self): - return self.is_open - - # def toggle(self, env, pos=None): - # return False - - def open_close(self): - # If the player has the right key to open the door - - self.is_open = not self.is_open - return True - - def encode(self, nb_dims=3, absolute_coordinates=False): - """Encode the a description of this object as a 3-tuple of integers""" - - # State, 0: open, 1: closed - state = 0 if self.is_open else 1 - - if absolute_coordinates: - v = (OBJECT_TO_IDX[self.type], *self.cur_pos, COLOR_TO_IDX[self.color], state) - else: - v = (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color], state) - - v += (0,) * (nb_dims-len(v)) - return v - - def block(self): - self.blocked = True - - def render(self, img): - c = COLORS[self.color] - - if self.is_open: - fill_coords(img, point_in_rect(0.88, 1.00, 0.00, 1.00), c) - fill_coords(img, point_in_rect(0.92, 0.96, 0.04, 0.96), (0,0,0)) - else: - - fill_coords(img, point_in_rect(0.00, 1.00, 0.00, 1.00), c) - fill_coords(img, point_in_rect(0.04, 0.96, 0.04, 0.96), (0,0,0)) - fill_coords(img, point_in_rect(0.08, 0.92, 0.08, 0.92), c) - fill_coords(img, point_in_rect(0.12, 0.88, 0.12, 0.88), (0,0,0)) - - # wifi symbol - fill_coords(img, point_in_circle_clip(cx=0.5, cy=0.8, r=0.5, theta_start=-np.pi/3, theta_end=-2*np.pi/3), c) - fill_coords(img, point_in_circle_clip(cx=0.5, cy=0.8, r=0.45, theta_start=-np.pi/3, theta_end=-2*np.pi/3), (0,0,0)) - fill_coords(img, point_in_circle_clip(cx=0.5, cy=0.8, r=0.4, theta_start=-np.pi/3, theta_end=-2*np.pi/3), c) - fill_coords(img, point_in_circle_clip(cx=0.5, cy=0.8, r=0.35, theta_start=-np.pi/3, theta_end=-2*np.pi/3), (0,0,0)) - fill_coords(img, point_in_circle_clip(cx=0.5, cy=0.8, r=0.3, theta_start=-np.pi/3, theta_end=-2*np.pi/3), c) - fill_coords(img, point_in_circle_clip(cx=0.5, cy=0.8, r=0.25, theta_start=-np.pi/3, theta_end=-2*np.pi/3), (0,0,0)) - fill_coords(img, point_in_circle_clip(cx=0.5, cy=0.8, r=0.2, theta_start=-np.pi/3, theta_end=-2*np.pi/3), c) - fill_coords(img, point_in_circle_clip(cx=0.5, cy=0.8, r=0.15, theta_start=-np.pi/3, theta_end=-2*np.pi/3), (0,0,0)) - fill_coords(img, point_in_circle_clip(cx=0.5, cy=0.8, r=0.1, theta_start=-np.pi/3, theta_end=-2*np.pi/3), c) - - return - - -class Door(BlockableWorldObj): - def __init__(self, color, is_open=False, is_locked=False, block_set=None): - super().__init__('door', color, block_set) - self.is_open = is_open - self.is_locked = is_locked - - def can_overlap(self): - """The agent can only walk over this cell when the door is open""" - return self.is_open - - def see_behind(self): - return self.is_open - - def toggle(self, env, pos=None): - - if self.blocked: - return False - - # If the player has the right key to open the door - if self.is_locked: - if isinstance(env.carrying, Key) and env.carrying.color == self.color: - self.is_locked = False - self.is_open = True - ret = True - ret = False - - else: - self.is_open = not self.is_open - ret = True - - self.block_block_set() - - return ret - - - def encode(self, nb_dims=3, absolute_coordinates=False): - """Encode the a description of this object as a 3-tuple of integers""" - - # State, 0: open, 1: closed, 2: locked - if self.is_open: - state = 0 - elif self.is_locked: - state = 2 - elif not self.is_open: - state = 1 - - if absolute_coordinates: - v = (OBJECT_TO_IDX[self.type], *self.cur_pos, COLOR_TO_IDX[self.color], state) - else: - v = (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color], state) - - v += (0,) * (nb_dims-len(v)) - return v - - def render(self, img): - c = COLORS[self.color] - - if self.is_open: - fill_coords(img, point_in_rect(0.88, 1.00, 0.00, 1.00), c) - fill_coords(img, point_in_rect(0.92, 0.96, 0.04, 0.96), (0,0,0)) - return - - # Door frame and door - if self.is_locked: - fill_coords(img, point_in_rect(0.00, 1.00, 0.00, 1.00), c) - fill_coords(img, point_in_rect(0.06, 0.94, 0.06, 0.94), 0.45 * np.array(c)) - - # Draw key slot - fill_coords(img, point_in_rect(0.52, 0.75, 0.50, 0.56), c) - else: - fill_coords(img, point_in_rect(0.00, 1.00, 0.00, 1.00), c) - fill_coords(img, point_in_rect(0.04, 0.96, 0.04, 0.96), (0,0,0)) - fill_coords(img, point_in_rect(0.08, 0.92, 0.08, 0.92), c) - fill_coords(img, point_in_rect(0.12, 0.88, 0.12, 0.88), (0,0,0)) - - # Draw door handle - fill_coords(img, point_in_circle(cx=0.75, cy=0.50, r=0.08), c) - - -class Switch(BlockableWorldObj): - def __init__(self, color, lockable_object=None, is_on=False, no_turn_off=True, no_light=True, locker_switch=False, block_set=None): - super().__init__('switch', color, block_set) - self.is_on = is_on - self.lockable_object = lockable_object - self.no_turn_off = no_turn_off - self.no_light = no_light - self.locker_switch = locker_switch - - if self.block_set is not None: - - if self.is_on: - raise ValueError("If using a block set, a Switch must be initialized as OFF") - - if not self.no_turn_off: - raise ValueError("If using a block set, a Switch must be initialized can't be turned off") - - - def can_overlap(self): - """The agent can only walk over this cell when the door is open""" - return False - - def see_behind(self): - return True - - def toggle(self, env, pos=None): - - if self.blocked: - return False - - if self.is_on: - if self.no_turn_off: - return False - - self.is_on = not self.is_on - if self.lockable_object is not None: - if self.locker_switch: - # locker/unlocker switch - self.lockable_object.is_locked = not self.lockable_object.is_locked - else: - # opener switch - self.lockable_object.toggle(env, pos) - - - if self.is_on: - self.block_block_set() - - if self.no_turn_off: - # assert that obj is toggled only once - assert not hasattr(self, "was_toggled") - self.was_toggled = True - - return True - - def block(self): - self.blocked = True - - def encode(self, nb_dims=3, absolute_coordinates=False): - """Encode the a description of this object as a 3-tuple of integers""" - - # State, 0: off, 1: on - state = 1 if self.is_on else 0 - - if self.no_light: - state = 0 - - if absolute_coordinates: - v = (OBJECT_TO_IDX[self.type], *self.cur_pos, COLOR_TO_IDX[self.color], state) - else: - v = (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color], state) - - v += (0,) * (nb_dims-len(v)) - - return v - - - def render(self, img): - c = COLORS[self.color] - - # Door frame and door - if self.is_on and not self.no_light: - fill_coords(img, point_in_rect(0.00, 1.00, 0.00, 1.00), c) - fill_coords(img, point_in_rect(0.04, 0.96, 0.04, 0.96), (0,0,0)) - fill_coords(img, point_in_rect(0.08, 0.92, 0.08, 0.92), c) - fill_coords(img, point_in_rect(0.12, 0.88, 0.12, 0.88), 0.45 * np.array(c)) - - else: - - fill_coords(img, point_in_rect(0.00, 1.00, 0.00, 1.00), c) - fill_coords(img, point_in_rect(0.04, 0.96, 0.04, 0.96), (0,0,0)) - fill_coords(img, point_in_rect(0.08, 0.92, 0.08, 0.92), c) - fill_coords(img, point_in_rect(0.12, 0.88, 0.12, 0.88), (0,0,0)) - - -class Key(WorldObj): - def __init__(self, color='blue'): - super(Key, self).__init__('key', color) - - def can_pickup(self): - return True - - def render(self, img): - c = COLORS[self.color] - - # Vertical quad - fill_coords(img, point_in_rect(0.50, 0.63, 0.31, 0.88), c) - - # Teeth - fill_coords(img, point_in_rect(0.38, 0.50, 0.59, 0.66), c) - fill_coords(img, point_in_rect(0.38, 0.50, 0.81, 0.88), c) - - # Ring - fill_coords(img, point_in_circle(cx=0.56, cy=0.28, r=0.190), c) - fill_coords(img, point_in_circle(cx=0.56, cy=0.28, r=0.064), (0,0,0)) - - -class MarbleTee(WorldObj): - def __init__(self, color="red"): - super(MarbleTee, self).__init__('marbletee', color) - - def can_pickup(self): - return False - - def can_push(self): - return False - - def render(self, img): - c = COLORS[self.color] - - fill_coords(img, point_in_quadrangle( - (0.2, 0.5), - (0.8, 0.5), - (0.4, 0.6), - (0.6, 0.6), - ), c) - - fill_coords(img, point_in_triangle( - (0.4, 0.6), - (0.6, 0.6), - (0.5, 0.9), - ), c) - - -class Marble(WorldObj): - def __init__(self, color='blue', env=None): - super(Marble, self).__init__('marble', color) - self.is_tagged = False - self.moving_dir = None - self.env = env - self.was_pushed = False - self.tee = MarbleTee(color) - self.tee_uncovered = False - - def can_pickup(self): - return True - - def step(self): - if self.moving_dir is not None: - prev_pos = self.cur_pos - self.go_forward() - - if any(prev_pos != self.cur_pos) and not self.tee_uncovered: - assert self.was_pushed - - # if Marble was moved for the first time, uncover the Tee - # self.env.grid.set(*prev_pos, self.tee) - self.env.put_obj_np(self.tee, prev_pos) - self.tee_uncovered = True - - @property - def is_moving(self): - return self.moving_dir is not None - - @property - def dir_vec(self): - """ - Get the direction vector for the agent, pointing in the direction - of forward movement. - """ - if self.moving_dir is not None: - return DIR_TO_VEC[self.moving_dir] - else: - return np.array((0, 0)) - - @property - def front_pos(self): - """ - Get the position of the cell that is right in front of the agent - """ - return self.cur_pos + self.dir_vec - - def go_forward(self): - # Get the position in front of the agent - fwd_pos = self.front_pos - - # Get the contents of the cell in front of the agent - fwd_cell = self.env.grid.get(*fwd_pos) - # Don't move if you are going to collide - if fwd_pos.tolist() != self.env.agent_pos.tolist() and (fwd_cell is None or fwd_cell.can_overlap()): - self.env.grid.set(*self.cur_pos, None) - self.env.grid.set(*fwd_pos, self) - self.cur_pos = fwd_pos - return True - - # push object if pushable - if fwd_pos.tolist() != self.env.agent_pos.tolist() and (fwd_cell is not None and fwd_cell.can_push()): - fwd_cell.push(push_dir=self.moving_dir, pusher=self) - self.moving_dir = None - return True - - else: - self.moving_dir = None - return False - - def can_push(self): - return True - - def push(self, push_dir, pusher=None): - if type(push_dir) is not int: - raise ValueError("Direction must be of type int and is of type {}".format(type(push_dir))) - - self.moving_dir = push_dir - if self.moving_dir is not None: - self.was_pushed = True - - def render(self, img): - color = COLORS[self.color] - if self.is_tagged: - color = color / 2 - - fill_coords(img, point_in_circle(0.5, 0.5, 0.20), color) - fill_coords(img, point_in_circle(0.55, 0.45, 0.07), (0, 0, 0)) - - def tag(self,): - self.is_tagged = True - - def encode(self, nb_dims=3, absolute_coordinates=False): - """Encode the a description of this object as a nb_dims-tuple of integers""" - if absolute_coordinates: - core = (OBJECT_TO_IDX[self.type], *self.cur_pos, COLOR_TO_IDX[self.color]) - else: - core = (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color]) - - return core + (1 if self.is_tagged else 0,) * (nb_dims - len(core)) - - -class Ball(WorldObj): - def __init__(self, color='blue'): - super(Ball, self).__init__('ball', color) - self.is_tagged = False - - def can_pickup(self): - return True - - def render(self, img): - color = COLORS[self.color] - if self.is_tagged: - color = color / 2 - fill_coords(img, point_in_circle(0.5, 0.5, 0.31), color) - - def tag(self,): - self.is_tagged = True - - def encode(self, nb_dims=3, absolute_coordinates=False): - """Encode the a description of this object as a nb_dims-tuple of integers""" - if absolute_coordinates: - core = (OBJECT_TO_IDX[self.type], *self.cur_pos, COLOR_TO_IDX[self.color]) - else: - core = (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color]) - - return core + (1 if self.is_tagged else 0,) * (nb_dims - len(core)) - - -class Apple(WorldObj): - def __init__(self, color='red', eaten=False): - super(Apple, self).__init__('apple', color) - self.is_tagged = False - self.eaten = eaten - assert self.color != "yellow" - - def revert(self, color='red', eaten=False): - self.color = color - self.is_tagged = False - self.eaten = eaten - assert self.color != "yellow" - - def can_pickup(self): - return False - - def render(self, img): - color = COLORS[self.color] - - if self.is_tagged: - color = color / 2 - - fill_coords(img, point_in_circle(0.5, 0.5, 0.31), color) - fill_coords(img, point_in_rect(0.1, 0.9, 0.1, 0.55), (0, 0, 0)) - fill_coords(img, point_in_circle(0.35, 0.5, 0.15), color) - fill_coords(img, point_in_circle(0.65, 0.5, 0.15), color) - - fill_coords(img, point_in_rect(0.48, 0.52, 0.2, 0.45), COLORS["brown"]) - - # quadrangle - fill_coords(img, point_in_quadrangle( - (0.52, 0.25), - (0.65, 0.1), - (0.75, 0.3), - (0.90, 0.15), - ), COLORS["green"]) - - - if self.eaten: - assert self.color == "yellow" - fill_coords(img, point_in_circle(0.74, 0.6, 0.23), (0,0,0)) - fill_coords(img, point_in_circle(0.26, 0.6, 0.23), (0,0,0)) - - def toggle(self, env, pos): - if not self.eaten: - self.eaten = True - - assert self.color != "yellow" - self.color = "yellow" - - return True - - else: - assert self.color == "yellow" - return False - - def tag(self,): - self.is_tagged = True - - def encode(self, nb_dims=3, absolute_coordinates=False): - """Encode the a description of this object as a nb_dims-tuple of integers""" - - # eaten <=> yellow - assert self.eaten == (self.color == "yellow") - if absolute_coordinates: - core = (OBJECT_TO_IDX[self.type], *self.cur_pos, COLOR_TO_IDX[self.color]) - else: - core = (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color]) - - return core + (1 if self.is_tagged else 0,) * (nb_dims - len(core)) - - -class GeneratorPlatform(WorldObj): - def __init__(self, color="red"): - super(GeneratorPlatform, self).__init__('generatorplatform', color) - - def can_pickup(self): - return False - - def can_push(self): - return False - - def render(self, img): - c = COLORS[self.color] - - fill_coords(img, point_in_circle(0.5, 0.5, 0.2), c) - fill_coords(img, point_in_circle(0.5, 0.5, 0.18), (0,0,0)) - - fill_coords(img, point_in_circle(0.5, 0.5, 0.16), c) - fill_coords(img, point_in_circle(0.5, 0.5, 0.14), (0,0,0)) - - -class AppleGenerator(BlockableWorldObj): - def __init__(self, color="red", is_pressed=False, block_set=None, on_push=None, marble_activation=False): - super(AppleGenerator, self).__init__('applegenerator', color, block_set) - self.is_pressed = is_pressed - self.on_push = on_push - self.marble_activation = marble_activation - - def can_pickup(self): - return False - - def block(self): - self.blocked = True - - def can_push(self): - return True - - def push(self, push_dir=None, pusher=None): - - if self.marble_activation: - # check that it is marble that pushed the generator - if type(pusher) != Marble: - return self.block_block_set() - - if not self.blocked: - self.is_pressed = True - - if self.on_push: - self.on_push() - - return self.block_block_set() - - else: - return False - - def render(self, img): - c = COLORS[self.color] - - if not self.marble_activation: - # Outline - fill_coords(img, point_in_rect(0.15, 0.85, 0.15, 0.85), c) - # fill_coords(img, point_in_rect(0.17, 0.83, 0.17, 0.83), (0, 0, 0)) - fill_coords(img, point_in_rect(0.16, 0.84, 0.16, 0.84), (0, 0, 0)) - - # Outline - fill_coords(img, point_in_rect(0.22, 0.78, 0.22, 0.78), c) - fill_coords(img, point_in_rect(0.24, 0.76, 0.24, 0.76), (0, 0, 0)) - else: - # Outline - fill_coords(img, point_in_circle(0.5, 0.5, 0.37), c) - fill_coords(img, point_in_circle(0.5, 0.5, 0.35), (0, 0, 0)) - - # Outline - fill_coords(img, point_in_circle(0.5, 0.5, 0.32), c) - fill_coords(img, point_in_circle(0.5, 0.5, 0.30), (0, 0, 0)) - - # Apple inside - fill_coords(img, point_in_circle(0.5, 0.5, 0.2), COLORS["red"]) - # fill_coords(img, point_in_rect(0.18, 0.82, 0.18, 0.55), (0, 0, 0)) - fill_coords(img, point_in_rect(0.30, 0.65, 0.30, 0.55), (0, 0, 0)) - fill_coords(img, point_in_circle(0.42, 0.5, 0.12), COLORS["red"]) - fill_coords(img, point_in_circle(0.58, 0.5, 0.12), COLORS["red"]) - - # peteljka - fill_coords(img, point_in_rect(0.49, 0.50, 0.25, 0.45), COLORS["brown"]) - - # leaf - fill_coords(img, point_in_quadrangle( - (0.52, 0.32), - (0.60, 0.21), - (0.70, 0.34), - (0.80, 0.23), - ), COLORS["green"]) - - def encode(self, nb_dims=3, absolute_coordinates=False): - """Encode the a description of this object as a 3-tuple of integers""" - - type = 2 if self.marble_activation else 1 - - if absolute_coordinates: - v = (OBJECT_TO_IDX[self.type], *self.cur_pos, COLOR_TO_IDX[self.color], type) - else: - v = (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color], type) - - v += (0,) * (nb_dims - len(v)) - - return v - - -class Box(WorldObj): - def __init__(self, color="red", contains=None): - super(Box, self).__init__('box', color) - self.contains = contains - - def can_pickup(self): - return True - - def render(self, img): - c = COLORS[self.color] - - # Outline - fill_coords(img, point_in_rect(0.12, 0.88, 0.12, 0.88), c) - fill_coords(img, point_in_rect(0.18, 0.82, 0.18, 0.82), (0,0,0)) - - # Horizontal slit - fill_coords(img, point_in_rect(0.16, 0.84, 0.47, 0.53), c) - - def toggle(self, env, pos): - # Replace the box by its contents - env.grid.set(*pos, self.contains) - return True - - -class LockableBox(BlockableWorldObj): - def __init__(self, color="red", is_locked=False, contains=None, block_set=None): - super(LockableBox, self).__init__('lockablebox', color, block_set) - self.contains = contains - self.is_locked = is_locked - - self.is_open = False - - def can_pickup(self): - return True - - def encode(self, nb_dims=3, absolute_coordinates=False): - """Encode the a description of this object as a 3-tuple of integers""" - - # State, 0: open, 1: closed, 2: locked - # 2 and 1 to be consistent with doors - if self.is_locked: - state = 2 - else: - state = 1 - - if absolute_coordinates: - v = (OBJECT_TO_IDX[self.type], *self.cur_pos, COLOR_TO_IDX[self.color], state) - else: - v = (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color], state) - - v += (0,) * (nb_dims - len(v)) - - return v - - def render(self, img): - c = COLORS[self.color] - - # Outline - fill_coords(img, point_in_rect(0.12, 0.88, 0.12, 0.88), c) - - if self.is_locked: - fill_coords(img, point_in_rect(0.18, 0.82, 0.18, 0.82), 0.45 * np.array(c)) - else: - fill_coords(img, point_in_rect(0.18, 0.82, 0.18, 0.82), (0, 0, 0)) - - # Horizontal slit - fill_coords(img, point_in_rect(0.16, 0.84, 0.47, 0.53), c) - - def toggle(self, env, pos): - if self.blocked: - return False - - if self.is_locked: - if isinstance(env.carrying, Key) and env.carrying.color == self.color: - self.is_locked = False - self.is_open = True - return True - return False - - self.is_open = True - # Replace the box by its contents - env.grid.set(*pos, self.contains) - - self.block_block_set() - - # assert that obj is toggled only once - assert not hasattr(self, "was_toggled") - self.was_toggled = True - - return True - - def block(self): - self.blocked = True - - -class NPC(ABC, WorldObj): - def __init__(self, color, view_size=7): - super().__init__('npc', color) - self.point_dir = 255 # initially no point - self.introduction_statement = "Help please " - self.list_of_possible_utterances = NPC.get_list_of_possible_utterances() - self.view_size = view_size - self.carrying = False - self.prim_actions_dict = SocialAINPCActionsDict - - self.reset_last_action() - - @staticmethod - def get_list_of_possible_utterances(): - return ["no_op"] - - def _npc_action(func): - """ - Decorator that logs the last action - """ - @wraps(func) - def func_wrapper(self, *args, **kwargs): - - if self.env.add_npc_last_prim_action: - self.last_action = func.__name__ - - return func(self, *args, **kwargs) - - return func_wrapper - - def reset_last_action(self): - self.last_action = "no_op" - - def step(self): - self.reset_last_action() - - if self.env.hidden_npc: - info = { - "prim_action": "no_op", - "utterance": "no_op", - "was_introduced_to": self.was_introduced_to - } - return None, info - - else: - return None, None - - def handle_introduction(self, utterance): - reply, action = None, None - # introduction and language - if self.env.parameters.get("Pragmatic_frame_complexity", "No") == "No": - - # only once - if not self.was_introduced_to: - self.was_introduced_to = True - - elif self.env.parameters["Pragmatic_frame_complexity"] == "Eye_contact": - - # only first time at eye contact - if self.is_eye_contact() and not self.was_introduced_to: - self.was_introduced_to = True - - # if not self.was_introduced_to: - # rotate to see the agent - # action = self.look_at_action(self.env.agent_pos) - - elif self.env.parameters["Pragmatic_frame_complexity"] == "Ask": - - # every time asked - if utterance == self.introduction_statement: - self.was_introduced_to = True - - elif self.env.parameters["Pragmatic_frame_complexity"] == "Ask_Eye_contact": - - # only first time at eye contact with the introduction statement - if (self.is_eye_contact() and utterance == self.introduction_statement) and not self.was_introduced_to: - self.was_introduced_to = True - - # if not self.was_introduced_to: - # # rotate to see the agent - # action = self.look_at_action(self.env.agent_pos) - - else: - raise NotImplementedError() - - return reply, action - - def look_at_action(self, target_pos): - # rotate to see the target_pos - wanted_dir = self.compute_wanted_dir(target_pos) - action = self.compute_turn_action(wanted_dir) - return action - - @_npc_action - def rotate_left(self): - self.npc_dir -= 1 - if self.npc_dir < 0: - self.npc_dir += 4 - return True - - @_npc_action - def rotate_right(self): - self.npc_dir = (self.npc_dir + 1) % 4 - return True - - def path_to_toggle_pos(self, goal_pos): - """ - Return the next action from the path to toggling an object at goal_pos - """ - if type(goal_pos) != np.ndarray or goal_pos.shape != (2,): - raise ValueError(f"goal_pos must be a np.ndarray of shape (2,) and is {goal_pos}") - - assert type(self.front_pos) == np.ndarray and self.front_pos.shape == (2,) - - if all(self.front_pos == goal_pos): - # in front of door - return self.toggle_action - - else: - return self.path_to_pos(goal_pos) - - def turn_to_see_agent(self): - wanted_dir = self.compute_wanted_dir(self.env.agent_pos) - action = self.compute_turn_action(wanted_dir) - return action - - def relative_coords(self, x, y): - """ - Check if a grid position belongs to the npc's field of view, and returns the corresponding coordinates - """ - - vx, vy = self.get_view_coords(x, y) - - if vx < 0 or vy < 0 or vx >= self.view_size or vy >= self.view_size: - return None - - return vx, vy - - - def get_view_coords(self, i, j): - """ - Translate and rotate absolute grid coordinates (i, j) into the - npc's partially observable view (sub-grid). Note that the resulting - coordinates may be negative or outside of the npc's view size. - """ - - ax, ay = self.cur_pos - dx, dy = self.dir_vec - rx, ry = self.right_vec - - # Compute the absolute coordinates of the top-left view corner - sz = self.view_size - hs = self.view_size // 2 - tx = ax + (dx * (sz-1)) - (rx * hs) - ty = ay + (dy * (sz-1)) - (ry * hs) - - lx = i - tx - ly = j - ty - - # Project the coordinates of the object relative to the top-left - # corner onto the agent's own coordinate system - vx = (rx*lx + ry*ly) - vy = -(dx*lx + dy*ly) - - return vx, vy - - def is_pointing(self): - return self.point_dir != 255 - - def path_to_pos(self, goal_pos): - """ - Return the next action from the path to goal_pos - """ - - if type(goal_pos) != np.ndarray or goal_pos.shape != (2,): - raise ValueError(f"goal_pos must be a np.ndarray of shape (2,) and is {goal_pos}") - - def neighbors(n): - - n_nd = np.array(n) - - adjacent_positions = [ - n_nd + np.array([ 0, 1]), - n_nd + np.array([ 0,-1]), - n_nd + np.array([ 1, 0]), - n_nd + np.array([-1, 0]), - ] - adjacent_cells = map(lambda pos: self.env.grid.get(*pos), adjacent_positions) - - # keep the positions that don't have anything on or can_overlap - neighbors = [ - tuple(pos) for pos, cell in - zip(adjacent_positions, adjacent_cells) if ( - all(pos == goal_pos) - or cell is None - or cell.can_overlap() - ) and not all(pos == self.env.agent_pos) - ] - - for n1 in neighbors: - yield n1 - - def distance(n1, n2): - return 1 - - def cost(n, goal): - # manhattan - return int(np.abs(np.array(n) - np.array(goal)).sum()) - - # def is_goal_reached(n, goal): - # return all(n == goal) - - path = astar.find_path( - # tuples because np.ndarray is not hashable - tuple(self.cur_pos), - tuple(goal_pos), - neighbors_fnct=neighbors, - heuristic_cost_estimate_fnct=cost, - distance_between_fnct=distance, - # is_goal_reached_fnct=is_goal_reached - ) - - if path is None: - # no possible path - return None - - path = list(path) - assert all(path[0] == self.cur_pos) - next_step = path[1] - wanted_dir = self.compute_wanted_dir(next_step) - - if self.npc_dir == wanted_dir: - return self.go_forward - - else: - return self.compute_turn_action(wanted_dir) - - def gen_obs_grid(self): - """ - Generate the sub-grid observed by the npc. - This method also outputs a visibility mask telling us which grid - cells the npc can actually see. - """ - view_size = self.view_size - - topX, topY, botX, botY = self.env.get_view_exts(dir=self.npc_dir, view_size=view_size, pos=self.cur_pos) - - grid = self.env.grid.slice(topX, topY, view_size, view_size) - - for i in range(self.npc_dir + 1): - grid = grid.rotate_left() - - # Process ocluders and visibility - # Note that this incurs some performance cost - if not self.env.see_through_walls: - vis_mask = grid.process_vis(agent_pos=(view_size // 2, view_size - 1)) - else: - vis_mask = np.ones(shape=(grid.width, grid.height), dtype=np.bool) - - # Make it so the npc sees what it's carrying - # We do this by placing the carried object at the agent's position - # in the agent's partially observable view - npc_pos = grid.width // 2, grid.height - 1 - if self.carrying: - grid.set(*npc_pos, self.carrying) - else: - grid.set(*npc_pos, None) - - return grid, vis_mask - - def is_near_agent(self): - ax, ay = self.env.agent_pos - wx, wy = self.cur_pos - if (ax == wx and abs(ay - wy) == 1) or (ay == wy and abs(ax - wx) == 1): - return True - return False - - def is_eye_contact(self): - """ - Returns true if the agent and the NPC are looking at each other - """ - if self.cur_pos[1] == self.env.agent_pos[1]: - # same y - if self.cur_pos[0] > self.env.agent_pos[0]: - return self.npc_dir == 2 and self.env.agent_dir == 0 - else: - return self.npc_dir == 0 and self.env.agent_dir == 2 - - if self.cur_pos[0] == self.env.agent_pos[0]: - # same x - if self.cur_pos[1] > self.env.agent_pos[1]: - return self.npc_dir == 3 and self.env.agent_dir == 1 - else: - return self.npc_dir == 1 and self.env.agent_dir == 3 - - return False - - def compute_wanted_dir(self, target_pos): - """ - Computes the direction in which the NPC should look to see target pos - """ - - distance_vec = target_pos - self.cur_pos - angle = np.degrees(np.arctan2(*distance_vec)) - if angle < 0: - angle += 360 - - if angle < 45: - wanted_dir = 1 # S - elif angle < 135: - wanted_dir = 0 # E - elif angle < 225: - wanted_dir = 3 # N - elif angle < 315: - wanted_dir = 2 # W - elif angle < 360: - wanted_dir = 1 # S - - return wanted_dir - - def compute_wanted_point_dir(self, target_pos): - point_dir = self.compute_wanted_dir(target_pos) - - return point_dir - - # dir = 0 # E - # dir = 1 # S - # dir = 2 # W - # dir = 3 # N - # dir = 255 # no point - - @_npc_action - def stop_point(self): - self.point_dir = 255 - return True - - @_npc_action - def point_E(self): - self.point_dir = point_dir_encoding["point_E"] - return True - - @_npc_action - def point_S(self): - self.point_dir = point_dir_encoding["point_S"] - return True - - @_npc_action - def point_W(self): - self.point_dir = point_dir_encoding["point_W"] - return True - - @_npc_action - def point_N(self): - self.point_dir = point_dir_encoding["point_N"] - return True - - def compute_wanted_point_action(self, target_pos): - point_dir = self.compute_wanted_dir(target_pos) - - if point_dir == point_dir_encoding["point_E"]: - return self.point_E - elif point_dir == point_dir_encoding["point_S"]: - return self.point_S - elif point_dir == point_dir_encoding["point_W"]: - return self.point_W - elif point_dir == point_dir_encoding["point_N"]: - return self.point_N - else: - raise ValueError("Unknown direction {}".format(point_dir)) - - - def compute_turn_action(self, wanted_dir): - """ - Return the action turning for in the direction of wanted_dir - """ - if self.npc_dir == wanted_dir: - # return lambda *args: None - return None - if (wanted_dir - self.npc_dir) == 1 or (wanted_dir == 0 and self.npc_dir == 3): - return self.rotate_right - if (wanted_dir - self.npc_dir) == - 1 or (wanted_dir == 3 and self.npc_dir == 0): - return self.rotate_left - else: - return self.env._rand_elem([self.rotate_left, self.rotate_right]) - - @_npc_action - def go_forward(self): - # Get the position in front of the agent - fwd_pos = self.front_pos - - # Get the contents of the cell in front of the agent - fwd_cell = self.env.grid.get(*fwd_pos) - # Don't move if you are going to collide - if fwd_pos.tolist() != self.env.agent_pos.tolist() and (fwd_cell is None or fwd_cell.can_overlap()): - self.env.grid.set(*self.cur_pos, None) - self.env.grid.set(*fwd_pos, self) - self.cur_pos = fwd_pos - return True - - # push object if pushable - if fwd_pos.tolist() != self.env.agent_pos.tolist() and (fwd_cell is not None and fwd_cell.can_push()): - fwd_cell.push(push_dir=self.npc_dir, pusher=self) - - else: - return False - - @_npc_action - def toggle_action(self): - fwd_pos = self.front_pos - fwd_cell = self.env.grid.get(*fwd_pos) - if fwd_cell: - return fwd_cell.toggle(self.env, fwd_pos) - - return False - - @property - def dir_vec(self): - """ - Get the direction vector for the agent, pointing in the direction - of forward movement. - """ - - assert self.npc_dir >= 0 and self.npc_dir < 4 - return DIR_TO_VEC[self.npc_dir] - - @property - def right_vec(self): - """ - Get the vector pointing to the right of the agent. - """ - - dx, dy = self.dir_vec - return np.array((-dy, dx)) - - - @property - def front_pos(self): - """ - Get the position of the cell that is right in front of the agent - """ - - return self.cur_pos + self.dir_vec - - @property - def back_pos(self): - """ - Get the position of the cell that is right in front of the agent - """ - - return self.cur_pos - self.dir_vec - - @property - def right_pos(self): - """ - Get the position of the cell that is right in front of the agent - """ - - return self.cur_pos + self.right_vec - - @property - def left_pos(self): - """ - Get the position of the cell that is right in front of the agent - """ - - return self.cur_pos - self.right_vec - - def draw_npc_face(self, c): - assert self.npc_type == 0 - - assert all(COLORS[self.color] == c) - - shapes = [] - shapes_colors = [] - - # Draw eyes - shapes.append(point_in_circle(cx=0.70, cy=0.50, r=0.10)) - shapes_colors.append(c) - shapes.append(point_in_circle(cx=0.30, cy=0.50, r=0.10)) - shapes_colors.append(c) - - # Draw mouth - shapes.append(point_in_rect(0.20, 0.80, 0.72, 0.81)) - shapes_colors.append(c) - - # Draw bottom hat - shapes.append(point_in_triangle((0.15, 0.28), - (0.85, 0.28), - (0.50, 0.05))) - shapes_colors.append(c) - # Draw top hat - shapes.append(point_in_rect(0.30, 0.70, 0.05, 0.28)) - shapes_colors.append(c) - return shapes, shapes_colors - - def render(self, img): - - - c = COLORS[self.color] - - npc_shapes = [] - npc_shapes_colors = [] - - - npc_face_shapes, npc_face_shapes_colors = self.draw_npc_face(c=c) - - npc_shapes.extend(npc_face_shapes) - npc_shapes_colors.extend(npc_face_shapes_colors) - - if hasattr(self, "npc_dir"): - # Pre-rotation to ensure npc_dir = 1 means NPC looks downwards - npc_shapes = [rotate_fn(v, cx=0.5, cy=0.5, theta=-1*(math.pi / 2)) for v in npc_shapes] - # Rotate npc based on its direction - npc_shapes = [rotate_fn(v, cx=0.5, cy=0.5, theta=(math.pi/2) * self.npc_dir) for v in npc_shapes] - - if hasattr(self, "point_dir"): - if self.is_pointing(): - # default points east - finger = point_in_triangle((0.85, 0.1), - (0.85, 0.3), - (0.99, 0.2)) - finger = rotate_fn(finger, cx=0.5, cy=0.5, theta=(math.pi/2) * self.point_dir) - - npc_shapes.append(finger) - npc_shapes_colors.append(c) - - if self.last_action == self.toggle_action.__name__: - # T symbol - t_symbol = [point_in_rect(0.8, 0.84, 0.02, 0.18), point_in_rect(0.8, 0.95, 0.08, 0.12)] - t_symbol = [rotate_fn(v, cx=0.5, cy=0.5, theta=(math.pi/2) * self.npc_dir) for v in t_symbol] - npc_shapes.extend(t_symbol) - npc_shapes_colors.extend([c, c]) - - elif self.last_action == self.go_forward.__name__: - # symbol for Forward (ommited for speed) - pass - - if self.env.hidden_npc: - # crossed eye symbol - dx, dy = 0.15, -0.2 - - # draw eye - npc_shapes.append(point_in_circle(cx=0.70+dx, cy=0.48+dy, r=0.11)) - npc_shapes_colors.append((128,128,128)) - - npc_shapes.append(point_in_circle(cx=0.30+dx, cy=0.52+dy, r=0.11)) - npc_shapes_colors.append((128,128,128)) - - npc_shapes.append(point_in_circle(0.5+dx, 0.5+dy, 0.25)) - npc_shapes_colors.append((128, 128, 128)) - - npc_shapes.append(point_in_circle(0.5+dx, 0.5+dy, 0.20)) - npc_shapes_colors.append((0, 0, 0)) - - npc_shapes.append(point_in_circle(0.5+dx, 0.5+dy, 0.1)) - npc_shapes_colors.append((128, 128, 128)) - - # cross it - npc_shapes.append(point_in_line(0.2+dx, 0.7+dy, 0.8+dx, 0.3+dy, 0.04)) - npc_shapes_colors.append((128, 128, 128)) - - - # Draw shapes - for v, c in zip(npc_shapes, npc_shapes_colors): - fill_coords(img, v, c) - - def cache(self, *args, **kwargs): - """Used for cached rendering.""" - # adding npc_dir and point_dir because, when egocentric coordinates are used, - # they can result in the same encoding but require new rendering - return self.encode(*args, **kwargs) + (self.npc_dir, self.point_dir,) - - def can_overlap(self): - # If the NPC is hidden, agent can overlap on it - return self.env.hidden_npc - - def encode(self, nb_dims=3, absolute_coordinates=False): - if not hasattr(self, "npc_type"): - raise ValueError("An NPC class must implement the npc_type (int)") - - if not hasattr(self, "env"): - raise ValueError("An NPC class must have the env") - - assert nb_dims == 6+2*bool(absolute_coordinates) - - if self.env.hidden_npc: - return (1,) + (0,) * (nb_dims-1) - - assert self.env.egocentric_observation == (not absolute_coordinates) - - if absolute_coordinates: - v = (OBJECT_TO_IDX[self.type], *self.cur_pos, COLOR_TO_IDX[self.color], self.npc_type) - else: - v = (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color], self.npc_type) - - if self.env.add_npc_direction: - assert hasattr(self, "npc_dir"), "4D but there is no npc dir in NPC state" - assert self.npc_dir >= 0 - - if self.env.egocentric_observation: - assert self.env.agent_dir >= 0 - - # 0 - eye contact; 2 - gaze in same direction; 1 - to left; 3 - to right - npc_dir_enc = (self.npc_dir - self.env.agent_dir + 2) % 4 - - v += (npc_dir_enc,) - else: - v += (self.npc_dir,) - - if self.env.add_npc_point_direction: - assert hasattr(self, "point_dir"), "5D but there is no npc point dir in NPC state" - - if self.point_dir == 255: - # no pointing - v += (self.point_dir,) - - elif 0 <= self.point_dir <= 3: - # pointing - - if self.env.egocentric_observation: - assert self.env.agent_dir >= 0 - - # 0 - pointing at agent; 2 - point in direction of agent gaze; 1 - to left; 3 - to right - point_enc = (self.point_dir - self.env.agent_dir + 2) % 4 - v += (point_enc,) - - else: - v += (self.point_dir,) - - else: - raise ValueError(f"Undefined point direction {self.point_dir}") - - if self.env.add_npc_last_prim_action: - assert hasattr(self, "last_action"), "6D but there is no last action in NPC state" - - if self.last_action in ["point_E", "point_S", "point_W", "point_N"] and self.env.egocentric_observation: - - # get the direction of the last point - last_action_point_dir = point_dir_encoding[self.last_action] - - # convert to relative dir - # 0 - pointing at agent; 2 - point in direction of agent gaze; 1 - to left; 3 - to right - last_action_relative_point_dir = (last_action_point_dir - self.env.agent_dir + 2) % 4 - - # the point_X action ids are in range [point_E, ... , point_N] - # id of point_E is the starting one, we use the same range [E, S, W ,N ] -> [at, left, same, right] - last_action_id = self.prim_actions_dict["point_E"] + last_action_relative_point_dir - - else: - last_action_id = self.prim_actions_dict[self.last_action] - - v += (last_action_id,) - - assert self.point_dir >= 0 - assert len(v) == nb_dims - - return v - - -class Grid: - """ - Represent a grid and operations on it - """ - - # Static cache of pre-renderer tiles - tile_cache = {} - - def __init__(self, width, height, nb_obj_dims): - assert width >= 3 - assert height >= 3 - - self.width = width - self.height = height - self.nb_obj_dims = nb_obj_dims - - self.grid = [None] * width * height - - def __contains__(self, key): - if isinstance(key, WorldObj): - for e in self.grid: - if e is key: - return True - elif isinstance(key, tuple): - for e in self.grid: - if e is None: - continue - if (e.color, e.type) == key: - return True - if key[0] is None and key[1] == e.type: - return True - return False - - def __eq__(self, other): - grid1 = self.encode() - grid2 = other.encode() - return np.array_equal(grid2, grid1) - - def __ne__(self, other): - return not self == other - - def copy(self): - from copy import deepcopy - return deepcopy(self) - - def set(self, i, j, v): - assert i >= 0 and i < self.width - assert j >= 0 and j < self.height - self.grid[j * self.width + i] = v - - def get(self, i, j): - assert i >= 0 and i < self.width - assert j >= 0 and j < self.height - return self.grid[j * self.width + i] - - def horz_wall(self, x, y, length=None, obj_type=Wall): - if length is None: - length = self.width - x - for i in range(0, length): - o = obj_type() - o.cur_pos = np.array((x+i, y)) - self.set(x + i, y, o) - - def vert_wall(self, x, y, length=None, obj_type=Wall): - if length is None: - length = self.height - y - for j in range(0, length): - o = obj_type() - o.cur_pos = np.array((x, y+j)) - self.set(x, y + j, o) - - def wall_rect(self, x, y, w, h): - self.horz_wall(x, y, w) - self.horz_wall(x, y+h-1, w) - self.vert_wall(x, y, h) - self.vert_wall(x+w-1, y, h) - - def rotate_left(self): - """ - Rotate the grid to the left (counter-clockwise) - """ - - grid = Grid(self.height, self.width, self.nb_obj_dims) - - for i in range(self.width): - for j in range(self.height): - v = self.get(i, j) - grid.set(j, grid.height - 1 - i, v) - - return grid - - def slice(self, topX, topY, width, height): - """ - Get a subset of the grid - """ - - grid = Grid(width, height, self.nb_obj_dims) - - for j in range(0, height): - for i in range(0, width): - x = topX + i - y = topY + j - - if x >= 0 and x < self.width and \ - y >= 0 and y < self.height: - v = self.get(x, y) - else: - v = Wall() - - grid.set(i, j, v) - - return grid - - @classmethod - def render_tile( - cls, - obj, - agent_dir=None, - highlight=False, - tile_size=TILE_PIXELS, - subdivs=3, - nb_obj_dims=3, - mask_unobserved=False - ): - """ - Render a tile and cache the result - """ - # Hash map lookup key for the cache - key = (agent_dir, highlight, tile_size, mask_unobserved) - # key = obj.encode(nb_dims=nb_obj_dims) + key if obj else key - key = obj.cache(nb_dims=nb_obj_dims) + key if obj else key - - if key in cls.tile_cache: - return cls.tile_cache[key] - - img = np.zeros(shape=(tile_size * subdivs, tile_size * subdivs, 3), dtype=np.uint8) # 3D for rendering - - # Draw the grid lines (top and left edges) - fill_coords(img, point_in_rect(0, 0.031, 0, 1), (100, 100, 100)) - fill_coords(img, point_in_rect(0, 1, 0, 0.031), (100, 100, 100)) - - if obj != None: - obj.render(img) - - # Overlay the agent on top - if agent_dir is not None: - tri_fn = point_in_triangle( - (0.12, 0.19), - (0.87, 0.50), - (0.12, 0.81), - ) - - # Rotate the agent based on its direction - tri_fn = rotate_fn(tri_fn, cx=0.5, cy=0.5, theta=0.5*math.pi*agent_dir) - fill_coords(img, tri_fn, (255, 0, 0)) - - # Highlight the cell if needed - if highlight: - highlight_img(img) - elif mask_unobserved: - # mask unobserved and not highlighted -> unobserved by the agent - img *= 0 - - # Downsample the image to perform supersampling/anti-aliasing - img = downsample(img, subdivs) - - # Cache the rendered tile - cls.tile_cache[key] = img - - return img - - def render( - self, - tile_size, - agent_pos=None, - agent_dir=None, - highlight_mask=None, - mask_unobserved=False, - ): - """ - Render this grid at a given scale - :param r: target renderer object - :param tile_size: tile size in pixels - """ - - if highlight_mask is None: - highlight_mask = np.zeros(shape=(self.width, self.height), dtype=np.bool) - - # Compute the total grid size - width_px = self.width * tile_size - height_px = self.height * tile_size - img = np.zeros(shape=(height_px, width_px, 3), dtype=np.uint8) - - # Render the grid - for j in range(0, self.height): - for i in range(0, self.width): - cell = self.get(i, j) - - agent_here = np.array_equal(agent_pos, (i, j)) - tile_img = Grid.render_tile( - cell, - agent_dir=agent_dir if agent_here else None, - highlight=highlight_mask[i, j], - tile_size=tile_size, - nb_obj_dims=self.nb_obj_dims, - mask_unobserved=mask_unobserved - ) - - ymin = j * tile_size - ymax = (j+1) * tile_size - xmin = i * tile_size - xmax = (i+1) * tile_size - img[ymin:ymax, xmin:xmax, :] = tile_img - - return img - - def encode(self, vis_mask=None, absolute_coordinates=False): - """ - Produce a compact numpy encoding of the grid - """ - - if vis_mask is None: - vis_mask = np.ones((self.width, self.height), dtype=bool) - - array = np.zeros((self.width, self.height, self.nb_obj_dims), dtype='uint8') - - for i in range(self.width): - for j in range(self.height): - if vis_mask[i, j]: - v = self.get(i, j) - - if v is None: - array[i, j, 0] = OBJECT_TO_IDX['empty'] - array[i, j, 1:] = 0 - - else: - array[i, j, :] = v.encode(nb_dims=self.nb_obj_dims, absolute_coordinates=absolute_coordinates) - - return array - - @staticmethod - def decode(array): - """ - Decode an array grid encoding back into a grid - """ - - width, height, channels = array.shape - assert channels in [5, 4, 3] - - vis_mask = np.ones(shape=(width, height), dtype=np.bool) - - grid = Grid(width, height, nb_obj_dims=channels) - for i in range(width): - for j in range(height): - if len(array[i, j]) == 3: - type_idx, color_idx, state = array[i, j] - else: - type_idx, color_idx, state, orient = array[i, j] - - v = WorldObj.decode(type_idx, color_idx, state) - grid.set(i, j, v) - vis_mask[i, j] = (type_idx != OBJECT_TO_IDX['unseen']) - - return grid, vis_mask - - def process_vis(grid, agent_pos): - # mask = np.zeros(shape=(grid.width, grid.height), dtype=np.bool) - # - # mask[agent_pos[0], agent_pos[1]] = True - # - # for j in reversed(range(0, grid.height)): - # for i in range(0, grid.width-1): - # if not mask[i, j]: - # continue - # - # cell = grid.get(i, j) - # if cell and not cell.see_behind(): - # continue - # - # mask[i+1, j] = True - # if j > 0: - # mask[i+1, j-1] = True - # mask[i, j-1] = True - # - # for i in reversed(range(1, grid.width)): - # if not mask[i, j]: - # continue - # - # cell = grid.get(i, j) - # if cell and not cell.see_behind(): - # continue - # - # mask[i-1, j] = True - # if j > 0: - # mask[i-1, j-1] = True - # mask[i, j-1] = True - - mask = np.ones(shape=(grid.width, grid.height), dtype=np.bool) - # handle frontal occlusions - - # 45 deg - for j in reversed(range(0, agent_pos[1]+1)): - dy = abs(agent_pos[1] - j) - - # in front of the agent - i = agent_pos[0] - cell = grid.get(i, j) - if (cell and not cell.see_behind()) or mask[i, j] == False: - - if j < agent_pos[1] and j > 0: - # 45 deg - mask[i-1,j-1] = False - mask[i,j-1] = False - mask[i+1,j-1] = False - - # agent -> to the left - for i in reversed(range(1, agent_pos[0])): - dx = abs(agent_pos[0] - i) - cell = grid.get(i, j) - - if (cell and not cell.see_behind()) or mask[i,j] == False: - # angle - if dx >= dy: - mask[i - 1, j] = False - - if j > 0: - mask[i - 1, j - 1] = False - if dy >= dx: - mask[i, j - 1] = False - - # agent -> to the right - for i in range(agent_pos[0]+1, grid.width-1): - dx = abs(agent_pos[0] - i) - cell = grid.get(i, j) - - if (cell and not cell.see_behind()) or mask[i,j] == False: - # angle - if dx >= dy: - mask[i + 1, j] = False - - if j > 0: - mask[i + 1, j - 1] = False - if dy >= dx: - mask[i, j - 1] = False - - # for i in range(0, grid.width): - # cell = grid.get(i, j) - # if (cell and not cell.see_behind()) or mask[i,j] == False: - # mask[i, j-1] = False - - # grid - # for j in reversed(range(0, agent_pos[1]+1)): - # - # i = agent_pos[0] - # cell = grid.get(i, j) - # if (cell and not cell.see_behind()) or mask[i, j] == False: - # if j < agent_pos[1]: - # # grid - # mask[i,j-1] = False - # - # for i in reversed(range(1, agent_pos[0])): - # # agent -> to the left - # cell = grid.get(i, j) - # if (cell and not cell.see_behind()) or mask[i,j] == False: - # # grid - # mask[i-1, j] = False - # if j < agent_pos[1] and j > 0: - # mask[i, j-1] = False - # - # for i in range(agent_pos[0]+1, grid.width-1): - # # agent -> to the right - # cell = grid.get(i, j) - # if (cell and not cell.see_behind()) or mask[i,j] == False: - # # grid - # mask[i+1, j] = False - # if j < agent_pos[1] and j > 0: - # mask[i, j-1] = False - - for j in range(0, grid.height): - for i in range(0, grid.width): - if not mask[i, j]: - grid.set(i, j, None) - - return mask - - -class MiniGridEnv(gym.Env): - """ - 2D grid world game environment - """ - - metadata = { - 'render.modes': ['human', 'rgb_array'], - 'video.frames_per_second' : 10 - } - - # Enumeration of possible actions - class Actions(IntEnum): - # Turn left, turn right, move forward - left = 0 - right = 1 - forward = 2 - - # Pick up an object - pickup = 3 - # Drop an object - drop = 4 - # Toggle/activate an object - toggle = 5 - - # Done completing task - done = 6 - - def __init__( - self, - grid_size=None, - width=None, - height=None, - max_steps=100, - see_through_walls=False, - full_obs=False, - seed=None, - agent_view_size=7, - actions=None, - action_space=None, - add_npc_direction=False, - add_npc_point_direction=False, - add_npc_last_prim_action=False, - reward_diminish_factor=0.9, - egocentric_observation=True, - ): - - # sanity check params for SocialAI experiments - if "SocialAI" in type(self).__name__: - assert egocentric_observation - assert grid_size == 10 - assert not see_through_walls - assert max_steps == 80 - assert agent_view_size == 7 - assert not full_obs - assert add_npc_direction and add_npc_point_direction and add_npc_last_prim_action - - self.egocentric_observation = egocentric_observation - - if hasattr(self, "lever_active_steps"): - assert self.lever_active_steps == 10 - - # Can't set both grid_size and width/height - if grid_size: - assert width == None and height == None - width = grid_size - height = grid_size - - # Action enumeration for this environment - if actions: - self.actions = actions - else: - self.actions = MiniGridEnv.Actions - - # Actions are discrete integer values - if action_space: - self.action_space = action_space - else: - self.action_space = spaces.MultiDiscrete([len(self.actions)]) - - # Number of cells (width and height) in the agent view - assert agent_view_size % 2 == 1 - assert agent_view_size >= 3 - self.agent_view_size = agent_view_size - - # Number of object dimensions (i.e. number of channels in symbolic image) - self.add_npc_direction = add_npc_direction - self.add_npc_point_direction = add_npc_point_direction - self.add_npc_last_prim_action = add_npc_last_prim_action - self.nb_obj_dims = 3 + 2*bool(not self.egocentric_observation) + int(self.add_npc_direction) + int(self.add_npc_point_direction) + int(self.add_npc_last_prim_action) - - # Observations are dictionaries containing an - # encoding of the grid and a textual 'mission' string - self.observation_space = spaces.Box( - low=0, - high=255, - shape=(self.agent_view_size, self.agent_view_size, self.nb_obj_dims), - dtype='uint8' - ) - self.observation_space = spaces.Dict({ - 'image': self.observation_space - }) - - # Range of possible rewards - self.reward_range = (0, 1) - - # Window to use for human rendering mode - self.window = None - - # Environment configuration - self.width = width - self.height = height - self.max_steps = max_steps - self.see_through_walls = see_through_walls - self.full_obs = full_obs - - self.reward_diminish_factor = reward_diminish_factor - - # Current position and direction of the agent - self.agent_pos = None - self.agent_dir = None - - # Initialize the RNG - self.seed(seed=seed) - - # Initialize the state - self.reset() - - def reset(self): - # Current position and direction of the agent - self.agent_pos = None - self.agent_dir = None - - # Generate a new random grid at the start of each episode - # To keep the same grid for each episode, call env.seed() with - # the same seed before calling env.reset() - self._gen_grid(self.width, self.height) - - # These fields should be defined by _gen_grid - assert self.agent_pos is not None - assert self.agent_dir is not None - - # Check that the agent doesn't overlap with an object - start_cell = self.grid.get(*self.agent_pos) - assert start_cell is None or start_cell.can_overlap() - - # Item picked up, being carried, initially nothing - self.carrying = None - - # Step count since episode start - self.step_count = 0 - - # Return first observation - obs = self.gen_obs(full_obs=self.full_obs) - return obs - - def reset_with_info(self, *args, **kwargs): - obs = self.reset(*args, **kwargs) - info = self.generate_info(done=False, reward=0) - return obs, info - - def seed(self, seed=1337): - # Seed the random number generator - self.np_random, _ = seeding.np_random(seed) - return [seed] - - def hash(self, size=16): - """Compute a hash that uniquely identifies the current state of the environment. - :param size: Size of the hashing - """ - sample_hash = hashlib.sha256() - - to_encode = [self.grid.encode(), self.agent_pos, self.agent_dir] - for item in to_encode: - sample_hash.update(str(item).encode('utf8')) - - return sample_hash.hexdigest()[:size] - - @property - def steps_remaining(self): - return self.max_steps - self.step_count - - def is_near(self, pos1, pos2): - ax, ay = pos1 - wx, wy = pos2 - if (ax == wx and abs(ay - wy) == 1) or (ay == wy and abs(ax - wx) == 1): - return True - return False - - def get_cell(self, x, y): - return self.grid.get(x, y) - - def __str__(self): - """ - Produce a pretty string of the environment's grid along with the agent. - A grid cell is represented by 2-character string, the first one for - the object and the second one for the color. - """ - - # Map of object types to short string - OBJECT_TO_STR = { - 'wall' : 'W', - 'floor' : 'F', - 'door' : 'D', - 'key' : 'K', - 'ball' : 'A', - 'box' : 'B', - 'goal' : 'G', - 'lava' : 'V', - } - - # Short string for opened door - OPENDED_DOOR_IDS = '_' - - # Map agent's direction to short string - AGENT_DIR_TO_STR = { - 0: '>', - 1: 'V', - 2: '<', - 3: '^' - } - - str = '' - - for j in range(self.grid.height): - - for i in range(self.grid.width): - if i == self.agent_pos[0] and j == self.agent_pos[1]: - str += 2 * AGENT_DIR_TO_STR[self.agent_dir] - continue - - c = self.grid.get(i, j) - - if c == None: - str += ' ' - continue - - if c.type == 'door': - if c.is_open: - str += '__' - elif c.is_locked: - str += 'L' + c.color[0].upper() - else: - str += 'D' + c.color[0].upper() - continue - - str += OBJECT_TO_STR[c.type] + c.color[0].upper() - - if j < self.grid.height - 1: - str += '\n' - - return str - - def _gen_grid(self, width, height): - assert False, "_gen_grid needs to be implemented by each environment" - - def _reward(self): - """ - Compute the reward to be given upon success - """ - - return 1 - self.reward_diminish_factor * (self.step_count / self.max_steps) - - def _rand_int(self, low, high): - """ - Generate random integer in [low,high[ - """ - return self.np_random.randint(low, high) - - def _rand_float(self, low, high): - """ - Generate random float in [low,high[ - """ - - return self.np_random.uniform(low, high) - - def _rand_bool(self): - """ - Generate random boolean value - """ - - return (self.np_random.randint(0, 2) == 0) - - def _rand_elem(self, iterable): - """ - Pick a random element in a list - """ - - lst = list(iterable) - idx = self._rand_int(0, len(lst)) - return lst[idx] - - def _rand_subset(self, iterable, num_elems): - """ - Sample a random subset of distinct elements of a list - """ - - lst = list(iterable) - assert num_elems <= len(lst) - - out = [] - - while len(out) < num_elems: - elem = self._rand_elem(lst) - lst.remove(elem) - out.append(elem) - - return out - - def _rand_color(self): - """ - Generate a random color name (string) - """ - - return self._rand_elem(COLOR_NAMES) - - def _rand_pos(self, xLow, xHigh, yLow, yHigh): - """ - Generate a random (x,y) position tuple - """ - - return ( - self.np_random.randint(xLow, xHigh), - self.np_random.randint(yLow, yHigh) - ) - - def find_loc(self, - top=None, - size=None, - reject_fn=None, - max_tries=math.inf, - reject_agent_pos=True, - reject_taken_pos=True - ): - """ - Place an object at an empty position in the grid - - :param top: top-left position of the rectangle where to place - :param size: size of the rectangle where to place - :param reject_fn: function to filter out potential positions - """ - - if top is None: - top = (0, 0) - else: - top = (max(top[0], 0), max(top[1], 0)) - - if size is None: - size = (self.grid.width, self.grid.height) - - num_tries = 0 - - while True: - # This is to handle with rare cases where rejection sampling - # gets stuck in an infinite loop - if num_tries > max_tries: - raise RecursionError('rejection sampling failed in place_obj') - if num_tries % 10000 == 0 and num_tries > 0: - warnings.warn("num_tries = {}. This is probably an infinite loop. {}".format(num_tries, get_traceback())) - # warnings.warn("num_tries = {}. This is probably an infinite loop.".format(num_tries)) - exit() - break - - num_tries += 1 - - pos = np.array(( - self._rand_int(top[0], min(top[0] + size[0], self.grid.width)), - self._rand_int(top[1], min(top[1] + size[1], self.grid.height)) - )) - - # Don't place the object on top of another object - if reject_taken_pos: - if self.grid.get(*pos) != None: - continue - - # Don't place the object where the agent is - if reject_agent_pos and np.array_equal(pos, self.agent_pos): - continue - - # Check if there is a filtering criterion - if reject_fn and reject_fn(self, pos): - continue - - break - - return pos - - def place_obj(self, - obj, - top=None, - size=None, - reject_fn=None, - max_tries=math.inf - ): - """ - Place an object at an empty position in the grid - - :param top: top-left position of the rectangle where to place - :param size: size of the rectangle where to place - :param reject_fn: function to filter out potential positions - """ - - # if top is None: - # top = (0, 0) - # else: - # top = (max(top[0], 0), max(top[1], 0)) - # - # if size is None: - # size = (self.grid.width, self.grid.height) - # - # num_tries = 0 - # - # while True: - # # This is to handle with rare cases where rejection sampling - # # gets stuck in an infinite loop - # if num_tries > max_tries: - # raise RecursionError('rejection sampling failed in place_obj') - # - # num_tries += 1 - # - # pos = np.array(( - # self._rand_int(top[0], min(top[0] + size[0], self.grid.width)), - # self._rand_int(top[1], min(top[1] + size[1], self.grid.height)) - # )) - # - # # Don't place the object on top of another object - # if self.grid.get(*pos) != None: - # continue - # - # # Don't place the object where the agent is - # if np.array_equal(pos, self.agent_pos): - # continue - # - # # Check if there is a filtering criterion - # if reject_fn and reject_fn(self, pos): - # continue - # - # break - # - # self.grid.set(*pos, obj) - # - # if obj is not None: - # obj.init_pos = pos - # obj.cur_pos = pos - # - # return pos - - pos = self.find_loc( - top=top, - size=size, - reject_fn=reject_fn, - max_tries=max_tries - ) - - if obj is None: - self.grid.set(*pos, obj) - else: - self.put_obj_np(obj, pos) - - return pos - - def put_obj_np(self, obj, pos): - """ - Put an object at a specific position in the grid - """ - - assert isinstance(pos, np.ndarray) - - i, j = pos - - cell = self.grid.get(i, j) - if cell is not None: - raise ValueError("trying to put {} on {}".format(obj, cell)) - - self.grid.set(i, j, obj) - obj.init_pos = np.array((i, j)) - obj.cur_pos = np.array((i, j)) - - def put_obj(self, obj, i, j): - """ - Put an object at a specific position in the grid - """ - warnings.warn( - "This function is kept for backwards compatiblity with minigrid. It is recommended to use put_object_np()." - ) - raise DeprecationWarning("Deprecated use put_obj_np. (or remove this Warning)") - - self.grid.set(i, j, obj) - obj.init_pos = (i, j) - obj.cur_pos = (i, j) - - def place_agent( - self, - top=None, - size=None, - rand_dir=True, - max_tries=math.inf - ): - """ - Set the agent's starting point at an empty position in the grid - """ - - self.agent_pos = None - pos = self.place_obj(None, top, size, max_tries=max_tries) - self.agent_pos = pos - - if rand_dir: - self.agent_dir = self._rand_int(0, 4) - - return pos - - @property - def dir_vec(self): - """ - Get the direction vector for the agent, pointing in the direction - of forward movement. - """ - assert self.agent_dir >= 0 and self.agent_dir < 4 - return DIR_TO_VEC[self.agent_dir] - - @property - def right_vec(self): - """ - Get the vector pointing to the right of the agent. - """ - - dx, dy = self.dir_vec - return np.array((-dy, dx)) - - @property - def front_pos(self): - """ - Get the position of the cell that is right in front of the agent - """ - - return self.agent_pos + self.dir_vec - - @property - def back_pos(self): - """ - Get the position of the cell that is right in front of the agent - """ - - return self.agent_pos - self.dir_vec - - @property - def right_pos(self): - """ - Get the position of the cell that is right in front of the agent - """ - - return self.agent_pos + self.right_vec - - @property - def left_pos(self): - """ - Get the position of the cell that is right in front of the agent - """ - - return self.agent_pos - self.right_vec - - def get_view_coords(self, i, j): - """ - Translate and rotate absolute grid coordinates (i, j) into the - agent's partially observable view (sub-grid). Note that the resulting - coordinates may be negative or outside of the agent's view size. - """ - - ax, ay = self.agent_pos - dx, dy = self.dir_vec - rx, ry = self.right_vec - - # Compute the absolute coordinates of the top-left view corner - sz = self.agent_view_size - hs = self.agent_view_size // 2 - tx = ax + (dx * (sz-1)) - (rx * hs) - ty = ay + (dy * (sz-1)) - (ry * hs) - - lx = i - tx - ly = j - ty - - # Project the coordinates of the object relative to the top-left - # corner onto the agent's own coordinate system - vx = (rx*lx + ry*ly) - vy = -(dx*lx + dy*ly) - - return vx, vy - - def get_view_exts(self, dir=None, view_size=None, pos=None): - """ - Get the extents of the square set of tiles visible to the agent (or to an npc if specified - Note: the bottom extent indices are not included in the set - """ - - # by default compute view exts for agent - if (dir is None) and (view_size is None) and (pos is None): - dir = self.agent_dir - view_size = self.agent_view_size - pos = self.agent_pos - - # Facing right - if dir == 0: - topX = pos[0] - topY = pos[1] - view_size // 2 - # Facing down - elif dir == 1: - topX = pos[0] - view_size // 2 - topY = pos[1] - # Facing left - elif dir == 2: - topX = pos[0] - view_size + 1 - topY = pos[1] - view_size // 2 - # Facing up - elif dir == 3: - topX = pos[0] - view_size // 2 - topY = pos[1] - view_size + 1 - else: - assert False, "invalid agent direction: {}".format(dir) - - botX = topX + view_size - botY = topY + view_size - - return (topX, topY, botX, botY) - - def relative_coords(self, x, y): - """ - Check if a grid position belongs to the agent's field of view, and returns the corresponding coordinates - """ - - vx, vy = self.get_view_coords(x, y) - - if vx < 0 or vy < 0 or vx >= self.agent_view_size or vy >= self.agent_view_size: - return None - - return vx, vy - - def in_view(self, x, y): - """ - check if a grid position is visible to the agent - """ - - return self.relative_coords(x, y) is not None - - def agent_sees(self, x, y): - """ - Check if a non-empty grid position is visible to the agent - """ - - coordinates = self.relative_coords(x, y) - if coordinates is None: - return False - vx, vy = coordinates - assert not self.full_obs, "agent sees function not implemented with full_obs" - obs = self.gen_obs() - obs_grid, _ = Grid.decode(obs['image']) - obs_cell = obs_grid.get(vx, vy) - world_cell = self.grid.get(x, y) - - return obs_cell is not None and obs_cell.type == world_cell.type - - def step(self, action): - self.step_count += 1 - - reward = 0 - done = False - - # Get the position in front of the agent - fwd_pos = self.front_pos - - # Get the contents of the cell in front of the agent - fwd_cell = self.grid.get(*fwd_pos) - - # Rotate left - if action == self.actions.left: - self.agent_dir -= 1 - if self.agent_dir < 0: - self.agent_dir += 4 - - # Rotate right - elif action == self.actions.right: - self.agent_dir = (self.agent_dir + 1) % 4 - - # Move forward - elif action == self.actions.forward: - if fwd_cell != None and fwd_cell.can_push(): - fwd_cell.push(push_dir=self.agent_dir, pusher="agent") - - if fwd_cell == None or fwd_cell.can_overlap(): - self.agent_pos = fwd_pos - if fwd_cell != None and fwd_cell.type == 'goal': - done = True - reward = self._reward() - if fwd_cell != None and fwd_cell.type == 'lava': - done = True - - # Pick up an object - elif hasattr(self.actions, "pickup") and action == self.actions.pickup: - if fwd_cell and fwd_cell.can_pickup(): - if self.carrying is None: - self.carrying = fwd_cell - self.carrying.cur_pos = np.array([-1, -1]) - self.grid.set(*fwd_pos, None) - - # Drop an object - elif hasattr(self.actions, "drop") and action == self.actions.drop: - if not fwd_cell and self.carrying: - self.grid.set(*fwd_pos, self.carrying) - self.carrying.cur_pos = fwd_pos - self.carrying = None - - # Toggle/activate an object - elif action == self.actions.toggle: - if fwd_cell: - fwd_cell.toggle(self, fwd_pos) - - # Done action (not used by default) - elif action == self.actions.done: - pass - - elif action in map(int, self.actions): - # action that was added in an inheriting class (ex. talk action) - pass - - elif np.isnan(action): - # action skip - pass - - else: - assert False, f"unknown action {action}" - - if self.step_count >= self.max_steps: - done = True - - obs = self.gen_obs(full_obs=self.full_obs) - - info = self.generate_info(done, reward) - - return obs, reward, done, info - - def generate_info(self, done, reward): - - success = done and reward > 0 - - info = {"success": success} - - gen_extra_info_dict = self.gen_extra_info() # add stuff needed for textual observations here - - assert not any(item in info for item in gen_extra_info_dict), "Duplicate keys found with gen_extra_info" - - info = { - **info, - **gen_extra_info_dict, - } - return info - - def gen_extra_info(self): - grid, vis_mask = self.gen_obs_grid() - carrying = self.carrying - agent_pos_vx, agent_pos_vy = self.get_view_coords(self.agent_pos[0], self.agent_pos[1]) - npc_actions_dict = SocialAINPCActionsDict - - extra_info = { - "image": grid.encode(vis_mask), - "vis_mask": vis_mask, - "carrying": carrying, - "agent_pos_vx": agent_pos_vx, - "agent_pos_vy": agent_pos_vy, - "npc_actions_dict": npc_actions_dict - } - return extra_info - - def gen_obs_grid(self): - """ - Generate the sub-grid observed by the agent. - This method also outputs a visibility mask telling us which grid - cells the agent can actually see. - """ - - topX, topY, botX, botY = self.get_view_exts() - - grid = self.grid.slice(topX, topY, self.agent_view_size, self.agent_view_size) - - for i in range(self.agent_dir + 1): - grid = grid.rotate_left() - - # Process occluders and visibility - # Note that this incurs some performance cost - if not self.see_through_walls: - vis_mask = grid.process_vis(agent_pos=(self.agent_view_size // 2, self.agent_view_size - 1)) - else: - vis_mask = np.ones(shape=(grid.width, grid.height), dtype=np.bool) - - # Make it so the agent sees what it's carrying - # We do this by placing the carried object at the agent's position - # in the agent's partially observable view - agent_pos = grid.width // 2, grid.height - 1 - if self.carrying: - grid.set(*agent_pos, self.carrying) - else: - grid.set(*agent_pos, None) - - return grid, vis_mask - - def add_agent_to_grid(self, image): - """ - Add agent to symbolic pixel image, used only for full observation - """ - ax, ay = self.agent_pos - image[ax,ay] = [9,9,9,self.agent_dir] # could be made cleaner by creating an Agent_id (here we use Lava_id) - return image - - def gen_obs(self, full_obs=False): - """ - Generate the agent's view (partially observable, low-resolution encoding) - Fully observable view can be returned when full_obs is set to True - """ - if full_obs: - image = self.add_agent_to_grid(self.grid.encode()) - - else: - grid, vis_mask = self.gen_obs_grid() - - # Encode the partially observable view into a numpy array - image = grid.encode(vis_mask, absolute_coordinates=not self.egocentric_observation) - - assert hasattr(self, 'mission'), "environments must define a textual mission string" - - # Observations are dictionaries containing: - # - an image (partially observable view of the environment) - # - the agent's direction/orientation (acting as a compass) - # - a textual mission string (instructions for the agent) - obs = { - 'image': image, - 'direction': self.agent_dir, - 'mission': self.mission - } - - return obs - - def get_obs_render(self, obs, tile_size=TILE_PIXELS//2): - """ - Render an agent observation for visualization - """ - - grid, vis_mask = Grid.decode(obs) - - # Render the whole grid - img = grid.render( - tile_size, - agent_pos=(self.agent_view_size // 2, self.agent_view_size - 1), - agent_dir=3, - highlight_mask=vis_mask - ) - - return img - - def render(self, mode='human', close=False, highlight=True, tile_size=TILE_PIXELS, mask_unobserved=False): - """ - Render the whole-grid human view - """ - if mode == 'human' and close: - if self.window: - self.window.close() - return - - if mode == 'human' and not self.window: - import gym_minigrid.window - self.window = gym_minigrid.window.Window('gym_minigrid') - self.window.show(block=False) - - # Compute which cells are visible to the agent - _, vis_mask = self.gen_obs_grid() - - # Compute the world coordinates of the bottom-left corner - # of the agent's view area - f_vec = self.dir_vec - r_vec = self.right_vec - top_left = self.agent_pos + f_vec * (self.agent_view_size-1) - r_vec * (self.agent_view_size // 2) - - # Mask of which cells to highlight - highlight_mask = np.zeros(shape=(self.width, self.height), dtype=np.bool) - - # For each cell in the visibility mask - for vis_j in range(0, self.agent_view_size): - for vis_i in range(0, self.agent_view_size): - # If this cell is not visible, don't highlight it - if not vis_mask[vis_i, vis_j]: - continue - - # Compute the world coordinates of this cell - abs_i, abs_j = top_left - (f_vec * vis_j) + (r_vec * vis_i) - - if abs_i < 0 or abs_i >= self.width: - continue - if abs_j < 0 or abs_j >= self.height: - continue - - # Mark this cell to be highlighted - highlight_mask[abs_i, abs_j] = True - - # Render the whole grid - img = self.grid.render( - tile_size, - self.agent_pos, - self.agent_dir, - highlight_mask=highlight_mask if highlight else None, - mask_unobserved=mask_unobserved - ) - if mode == 'human': - # self.window.set_caption(self.mission) - self.window.show_img(img) - - return img - - def get_mission(self): - return self.mission - - def close(self): - if self.window: - self.window.close() - return - - def gen_text_obs(self): - grid, vis_mask = self.gen_obs_grid() - - # Encode the partially observable view into a numpy array - image = grid.encode(vis_mask) - - # (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color], state) - # State, 0: open, 1: closed, 2: locked - IDX_TO_COLOR = dict(zip(COLOR_TO_IDX.values(), COLOR_TO_IDX.keys())) - IDX_TO_OBJECT = dict(zip(OBJECT_TO_IDX.values(), OBJECT_TO_IDX.keys())) - - list_textual_descriptions = [] - - if self.carrying is not None: - list_textual_descriptions.append("You carry a {} {}".format(self.carrying.color, self.carrying.type)) - - agent_pos_vx, agent_pos_vy = self.get_view_coords(self.agent_pos[0], self.agent_pos[1]) - - view_field_dictionary = dict() - - for i in range(image.shape[0]): - for j in range(image.shape[1]): - if image[i][j][0] != 0 and image[i][j][0] != 1 and image[i][j][0] != 2: - if i not in view_field_dictionary.keys(): - view_field_dictionary[i] = dict() - view_field_dictionary[i][j] = image[i][j] - else: - view_field_dictionary[i][j] = image[i][j] - - # Find the wall if any - # We describe a wall only if there is no objects between the agent and the wall in straight line - - # Find wall in front - add_wall_descr = False - if add_wall_descr: - j = agent_pos_vy - 1 - object_seen = False - while j >= 0 and not object_seen: - if image[agent_pos_vx][j][0] != 0 and image[agent_pos_vx][j][0] != 1: - if image[agent_pos_vx][j][0] == 2: - list_textual_descriptions.append( - f"A wall is {agent_pos_vy - j} steps in front of you. \n") # forward - object_seen = True - else: - object_seen = True - j -= 1 - # Find wall left - i = agent_pos_vx - 1 - object_seen = False - while i >= 0 and not object_seen: - if image[i][agent_pos_vy][0] != 0 and image[i][agent_pos_vy][0] != 1: - if image[i][agent_pos_vy][0] == 2: - list_textual_descriptions.append( - f"A wall is {agent_pos_vx - i} steps to the left. \n") # left - object_seen = True - else: - object_seen = True - i -= 1 - # Find wall right - i = agent_pos_vx + 1 - object_seen = False - while i < image.shape[0] and not object_seen: - if image[i][agent_pos_vy][0] != 0 and image[i][agent_pos_vy][0] != 1: - if image[i][agent_pos_vy][0] == 2: - list_textual_descriptions.append( - f"A wall is {i - agent_pos_vx} steps to the right. \n") # right - object_seen = True - else: - object_seen = True - i += 1 - - # list_textual_descriptions.append("You see the following objects: ") - # returns the position of seen objects relative to you - for i in view_field_dictionary.keys(): - for j in view_field_dictionary[i].keys(): - if i != agent_pos_vx or j != agent_pos_vy: - object = view_field_dictionary[i][j] - - front_dist = agent_pos_vy - j - left_right_dist = i-agent_pos_vx - - loc_descr = "" - if front_dist == 1 and left_right_dist == 0: - loc_descr += "Right in front of you " - - elif left_right_dist == 1 and front_dist == 0: - loc_descr += "Just to the right of you" - - elif left_right_dist == -1 and front_dist == 0: - loc_descr += "Just to the left of you" - - else: - front_str = str(front_dist)+" steps in front of you " if front_dist > 0 else "" - - loc_descr += front_str - - suff = "s" if abs(left_right_dist) > 0 else "" - and_ = "and" if loc_descr != "" else "" - - if left_right_dist < 0: - left_right_str = f"{and_} {-left_right_dist} step{suff} to the left" - loc_descr += left_right_str - - elif left_right_dist > 0: - left_right_str = f"{and_} {left_right_dist} step{suff} to the right" - loc_descr += left_right_str - - else: - left_right_str = "" - loc_descr += left_right_str - - loc_descr += f" there is a " - - obj_type = IDX_TO_OBJECT[object[0]] - if obj_type == "npc": - IDX_TO_STATE = {0: 'friendly', 1: 'antagonistic'} - - description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} peer. " - - # gaze - gaze_dir = { - 0: "towards you", - 1: "to the left of you", - 2: "in the same direction as you", - 3: "to the right of you", - } - description += f"It is looking {gaze_dir[object[3]]}. " - - # point - point_dir = { - 0: "towards you", - 1: "to the left of you", - 2: "in the same direction as you", - 3: "to the right of you", - } - - if object[4] != 255: - description += f"It is pointing {point_dir[object[4]]}. " - - # last action - last_action = {v: k for k, v in SocialAINPCActionsDict.items()}[object[5]] - - - last_action = { - "go_forward": "foward", - "rotate_left": "turn left", - "rotate_right": "turn right", - "toggle_action": "toggle", - "point_stop_point": "stop pointing", - "point_E": "", - "point_S": "", - "point_W": "", - "point_N": "", - "stop_point": "stop pointing", - "no_op": "" - }[last_action] - - if last_action not in ["no_op", ""]: - description += f"It's last action is {last_action}. " - - elif obj_type in ["switch", "apple", "generatorplatform", "marble", "marbletee", "fence"]: - - if obj_type == "switch": - # assumes that Switch.no_light == True - assert object[-1] == 0 - - description = f"{IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} " - assert object[2:].mean() == 0 - - elif obj_type == "lockablebox": - IDX_TO_STATE = {0: 'open', 1: 'closed', 2: 'locked'} - description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} " - assert object[3:].mean() == 0 - - elif obj_type == "applegenerator": - IDX_TO_STATE = {1: 'square', 2: 'round'} - description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} " - assert object[3:].mean() == 0 - - elif obj_type == "remotedoor": - IDX_TO_STATE = {0: 'open', 1: 'closed'} - description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} " - assert object[3:].mean() == 0 - - elif obj_type == "door": - IDX_TO_STATE = {0: 'open', 1: 'closed', 2: 'locked'} - description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} " - assert object[3:].mean() == 0 - - elif obj_type == "lever": - IDX_TO_STATE = {1: 'activated', 0: 'unactivated'} - if object[3] == 255: - countdown_txt = "" - else: - countdown_txt = f"with {object[3]} timesteps left. " - - description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} {countdown_txt}" - - assert object[4:].mean() == 0 - else: - raise ValueError(f"Undefined object type {obj_type}") - - full_destr = loc_descr + description + "\n" - - list_textual_descriptions.append(full_destr) - - if len(list_textual_descriptions) == 0: - list_textual_descriptions.append("\n") - - return {'descriptions': list_textual_descriptions} - -class MultiModalMiniGridEnv(MiniGridEnv): - - grammar = None - - def reset(self, *args, **kwargs): - obs = super().reset() - self.append_existing_utterance_to_history() - obs = self.add_utterance_to_observation(obs) - self.reset_utterance() - return obs - - def append_existing_utterance_to_history(self): - if self.utterance != self.empty_symbol: - if self.utterance.startswith(self.empty_symbol): - self.utterance_history += self.utterance[len(self.empty_symbol):] - else: - assert self.utterance == self.beginning_string - self.utterance_history += self.utterance - - def add_utterance_to_observation(self, obs): - obs["utterance"] = self.utterance - obs["utterance_history"] = self.utterance_history - obs["mission"] = "Hidden" - return obs - - def reset_utterance(self): - # set utterance to empty indicator - self.utterance = self.empty_symbol - - def render(self, *args, show_dialogue=True, **kwargs): - - obs = super().render(*args, **kwargs) - - if args and args[0] == 'human': - # draw text to the side of the image - self.window.clear_text() # erase previous text - if show_dialogue: - self.window.set_caption(self.full_conversation) - - # self.window.ax.set_title("correct color: {}".format(self.box.target_color), loc="left", fontsize=10) - - if self.outcome_info: - color = None - if "SUCCESS" in self.outcome_info: - color = "lime" - elif "FAILURE" in self.outcome_info: - color = "red" - self.window.add_text(*(0.01, 0.85, self.outcome_info), - **{'fontsize': 15, 'color': color, 'weight': "bold"}) - - self.window.show_img(obs) # re-draw image to add changes to window - return obs - - def add_obstacles(self): - self.obstacles = self.parameters.get("Obstacles", "No") if self.parameters else "No" - - if self.obstacles != "No": - n_stumps_range = { - "A_bit": (1, 2), - "Medium": (3, 4), - "A_lot": (5, 6), - }[self.obstacles] - - n_stumps = random.randint(*n_stumps_range) - - for _ in range(n_stumps): - self.wall_start_x = self._rand_int(1, self.current_width - 2) - self.wall_start_y = self._rand_int(1, self.current_height - 2) - if random.choice([True, False]): - self.grid.horz_wall( - x=self.wall_start_x, - y=self.wall_start_y, - length=1 - ) - else: - self.grid.horz_wall( - x=self.wall_start_x, - y=self.wall_start_y, - length=1 - ) \ No newline at end of file diff --git a/spaces/flowers-team/SocialAISchool/torch-ac/torch_ac/algos/a2c.py b/spaces/flowers-team/SocialAISchool/torch-ac/torch_ac/algos/a2c.py deleted file mode 100644 index fc720cd54ce95cd90cac40827c697810427e85bd..0000000000000000000000000000000000000000 --- a/spaces/flowers-team/SocialAISchool/torch-ac/torch_ac/algos/a2c.py +++ /dev/null @@ -1,110 +0,0 @@ -import numpy -import torch -import torch.nn.functional as F - -from torch_ac.algos.base import BaseAlgo - -class A2CAlgo(BaseAlgo): - """The Advantage Actor-Critic algorithm.""" - - def __init__(self, envs, acmodel, device=None, num_frames_per_proc=None, discount=0.99, lr=0.01, gae_lambda=0.95, - entropy_coef=0.01, value_loss_coef=0.5, max_grad_norm=0.5, recurrence=4, - rmsprop_alpha=0.99, rmsprop_eps=1e-8, preprocess_obss=None, reshape_reward=None): - num_frames_per_proc = num_frames_per_proc or 8 - - super().__init__(envs, acmodel, device, num_frames_per_proc, discount, lr, gae_lambda, entropy_coef, - value_loss_coef, max_grad_norm, recurrence, preprocess_obss, reshape_reward) - self.optimizer = torch.optim.RMSprop(self.acmodel.parameters(), lr, - alpha=rmsprop_alpha, eps=rmsprop_eps) - raise NotImplementedError("This needs to be refactored to work with mm actions") - - def update_parameters(self, exps): - # Compute starting indexes - - inds = self._get_starting_indexes() - - # Initialize update values - - update_entropy = 0 - update_value = 0 - update_policy_loss = 0 - update_value_loss = 0 - update_loss = 0 - - # Initialize memory - - if self.acmodel.recurrent: - memory = exps.memory[inds] - - for i in range(self.recurrence): - # Create a sub-batch of experience - - sb = exps[inds + i] - - # Compute loss - - if self.acmodel.recurrent: - dist, value, memory = self.acmodel(sb.obs, memory * sb.mask) - else: - dist, value = self.acmodel(sb.obs) - - entropy = dist.entropy().mean() - - policy_loss = -(dist.log_prob(sb.action) * sb.advantage).mean() - - value_loss = (value - sb.returnn).pow(2).mean() - - loss = policy_loss - self.entropy_coef * entropy + self.value_loss_coef * value_loss - - # Update batch values - - update_entropy += entropy.item() - update_value += value.mean().item() - update_policy_loss += policy_loss.item() - update_value_loss += value_loss.item() - update_loss += loss - - # Update update values - - update_entropy /= self.recurrence - update_value /= self.recurrence - update_policy_loss /= self.recurrence - update_value_loss /= self.recurrence - update_loss /= self.recurrence - - # Update actor-critic - - self.optimizer.zero_grad() - update_loss.backward() - update_grad_norm = sum(p.grad.data.norm(2) ** 2 for p in self.acmodel.parameters()) ** 0.5 - torch.nn.utils.clip_grad_norm_(self.acmodel.parameters(), self.max_grad_norm) - self.optimizer.step() - - # Log some values - - logs = { - "entropy": update_entropy, - "value": update_value, - "policy_loss": update_policy_loss, - "value_loss": update_value_loss, - "grad_norm": update_grad_norm - } - - return logs - - def _get_starting_indexes(self): - """Gives the indexes of the observations given to the model and the - experiences used to compute the loss at first. - - The indexes are the integers from 0 to `self.num_frames` with a step of - `self.recurrence`. If the model is not recurrent, they are all the - integers from 0 to `self.num_frames`. - - Returns - ------- - starting_indexes : list of int - the indexes of the experiences to be used at first - """ - - starting_indexes = numpy.arange(0, self.num_frames, self.recurrence) - return starting_indexes diff --git a/spaces/freddyaboulton/3.1.4.9-all-demos/demos/zip_to_json/run.py b/spaces/freddyaboulton/3.1.4.9-all-demos/demos/zip_to_json/run.py deleted file mode 100644 index 2500b409d12175e25cc4062696624e85f5f78124..0000000000000000000000000000000000000000 --- a/spaces/freddyaboulton/3.1.4.9-all-demos/demos/zip_to_json/run.py +++ /dev/null @@ -1,23 +0,0 @@ -from zipfile import ZipFile - -import gradio as gr - - -def zip_to_json(file_obj): - files = [] - with ZipFile(file_obj.name) as zfile: - for zinfo in zfile.infolist(): - files.append( - { - "name": zinfo.filename, - "file_size": zinfo.file_size, - "compressed_size": zinfo.compress_size, - } - ) - return files - - -demo = gr.Interface(zip_to_json, "file", "json") - -if __name__ == "__main__": - demo.launch() diff --git a/spaces/freddyaboulton/ts-lags/app.py b/spaces/freddyaboulton/ts-lags/app.py deleted file mode 100644 index e9099b1fae53d693e69a05ce6ab63e45b52dfcf8..0000000000000000000000000000000000000000 --- a/spaces/freddyaboulton/ts-lags/app.py +++ /dev/null @@ -1,79 +0,0 @@ -import gradio as gr -import pandas as pd -import numpy as np -from scipy.signal import find_peaks -from statsmodels.tsa import stattools -import plotly.graph_objects as go -import matplotlib.pyplot as plt - - -MIN_ALPHA = 0.01 -MAX_ALPHA = 0.2 -STEP = 0.01 - -target_names = {"SeoulBikeData": "Rented Bike Count", - "daily-total-female-births": "Births", - "southern_oscillation_evenly_spaced": "oscillation", - "monthly_co2": "CO2", - "monthly-sunspots": "Sunspots", - "daily_bike_shares": "cnt", - "AirPassengers": "#Passengers", - "Alcohol_Sales": "S4248SM144NCEN", - "yahoo_stock": "Adj Close", - "Miles_Traveled": "TRFVOLUSM227NFWA", - "DailyDelhiClimateTrain": "meantemp", - "peyton_manning": "y"} - - - -def plot_significant_lags(conf_level: float, dataset: str): - df = pd.read_csv(dataset + ".csv") - y = df.pop(target_names[dataset]) - acf_values, ci_intervals = stattools.acf(y, nlags=min(len(y) - 1, 400), fft=True, alpha=conf_level) - peaks, _ = find_peaks(acf_values) - index = np.arange(len(acf_values)) - significant = np.logical_or(ci_intervals[:, 0] > 0, ci_intervals[:, 1] < 0) - first_significant_10 = index[:10][significant[:10]] - significant_lags = sorted(set(index[significant]).intersection(peaks).union(first_significant_10)) - not_significant_lags = sorted(set(index).difference(significant_lags)) - assert not set(significant_lags).intersection(not_significant_lags) - - fig1 = go.Figure() - fig1.add_trace(go.Scatter(x=significant_lags, - y=acf_values[significant_lags], - mode = 'markers', - marker_color ='red', - marker_size = 10)) - fig1.add_trace(go.Scatter(x=not_significant_lags, - y=acf_values[not_significant_lags], - mode = 'markers', - marker_color ='black', - opacity=0.2, - marker_size = 10)) - fig1.add_trace(go.Scatter(x=np.arange(len(acf_values)), y=ci_intervals[:, 0] - acf_values, - line_color='blue', opacity=0.1)) - fig1.add_trace(go.Scatter(x=np.arange(len(acf_values)), y=ci_intervals[:, 1] - acf_values, - line_color='blue', opacity=0.1, fill='tonexty')) - fig1.update_yaxes(range=[min(-0.2, min(acf_values), min(ci_intervals[:, 0])) - 0.1, 1.1]) - fig1.update_layout(showlegend=False, - title_text=f"{dataset} Statistically significant lags for a {int((1 - conf_level) * 100)}% confidence interval: {significant_lags}") - return fig1 - - -demo = gr.Blocks() - -with demo: - with gr.Column(): - conf_level_slider = gr.Slider(minimum=MIN_ALPHA, maximum=MAX_ALPHA, step=STEP) - dataset = gr.Dropdown(choices=["SeoulBikeData", "montly_co2", "daily_bike_shares", "peyton_manning"], - value="peyton_manning") - inputs = [conf_level_slider, dataset] - with gr.Column(): - plot = gr.Plot() - button = gr.Button("Visualize") - button.click(plot_significant_lags, inputs=inputs, outputs=plot) - -demo.launch() - - - diff --git a/spaces/geraskalnas/TheBloke-stable-vicuna-13B-HF/app.py b/spaces/geraskalnas/TheBloke-stable-vicuna-13B-HF/app.py deleted file mode 100644 index 7239a6282fcebda631c725b867b40980d06b0a1d..0000000000000000000000000000000000000000 --- a/spaces/geraskalnas/TheBloke-stable-vicuna-13B-HF/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/TheBloke/stable-vicuna-13B-HF").launch() \ No newline at end of file diff --git a/spaces/giswqs/solara/app.py b/spaces/giswqs/solara/app.py deleted file mode 100644 index 4e0d7d61d4943eedf3e2167f59bbb66a8c92413d..0000000000000000000000000000000000000000 --- a/spaces/giswqs/solara/app.py +++ /dev/null @@ -1,28 +0,0 @@ -""" -# ipyleaflet -Map visualization using [ipyleaflet](https://ipyleaflet.readthedocs.io/), a ipywidgets wrapper for [leaflet.js](https://leafletjs.com/) -""" -import leafmap - -import solara - -zoom = solara.reactive(5) -center = solara.reactive((53.2305799, 6.5323552)) - - -@solara.component -def Page(): - with solara.Column(style={"min-width": "500px", "height": "500px"}): - # solara components support reactive variables - solara.SliderInt(label="Zoom level", value=zoom, min=1, max=20) - # using 3rd party widget library require wiring up the events manually - # using zoom.value and zoom.set - leafmap.Map.element( # type: ignore - zoom=zoom.value, - on_zoom=zoom.set, - center=center.value, - on_center=center.set, - scroll_wheel_zoom=True, - ) - solara.Text(f"Zoom: {zoom.value}") - solara.Text(f"Center: {center.value}") diff --git a/spaces/glyszt/vt/vtoonify/model/raft/core/utils/utils.py b/spaces/glyszt/vt/vtoonify/model/raft/core/utils/utils.py deleted file mode 100644 index 741ccfe4d0d778c3199c586d368edc2882d4fff8..0000000000000000000000000000000000000000 --- a/spaces/glyszt/vt/vtoonify/model/raft/core/utils/utils.py +++ /dev/null @@ -1,82 +0,0 @@ -import torch -import torch.nn.functional as F -import numpy as np -from scipy import interpolate - - -class InputPadder: - """ Pads images such that dimensions are divisible by 8 """ - def __init__(self, dims, mode='sintel'): - self.ht, self.wd = dims[-2:] - pad_ht = (((self.ht // 8) + 1) * 8 - self.ht) % 8 - pad_wd = (((self.wd // 8) + 1) * 8 - self.wd) % 8 - if mode == 'sintel': - self._pad = [pad_wd//2, pad_wd - pad_wd//2, pad_ht//2, pad_ht - pad_ht//2] - else: - self._pad = [pad_wd//2, pad_wd - pad_wd//2, 0, pad_ht] - - def pad(self, *inputs): - return [F.pad(x, self._pad, mode='replicate') for x in inputs] - - def unpad(self,x): - ht, wd = x.shape[-2:] - c = [self._pad[2], ht-self._pad[3], self._pad[0], wd-self._pad[1]] - return x[..., c[0]:c[1], c[2]:c[3]] - -def forward_interpolate(flow): - flow = flow.detach().cpu().numpy() - dx, dy = flow[0], flow[1] - - ht, wd = dx.shape - x0, y0 = np.meshgrid(np.arange(wd), np.arange(ht)) - - x1 = x0 + dx - y1 = y0 + dy - - x1 = x1.reshape(-1) - y1 = y1.reshape(-1) - dx = dx.reshape(-1) - dy = dy.reshape(-1) - - valid = (x1 > 0) & (x1 < wd) & (y1 > 0) & (y1 < ht) - x1 = x1[valid] - y1 = y1[valid] - dx = dx[valid] - dy = dy[valid] - - flow_x = interpolate.griddata( - (x1, y1), dx, (x0, y0), method='nearest', fill_value=0) - - flow_y = interpolate.griddata( - (x1, y1), dy, (x0, y0), method='nearest', fill_value=0) - - flow = np.stack([flow_x, flow_y], axis=0) - return torch.from_numpy(flow).float() - - -def bilinear_sampler(img, coords, mode='bilinear', mask=False): - """ Wrapper for grid_sample, uses pixel coordinates """ - H, W = img.shape[-2:] - xgrid, ygrid = coords.split([1,1], dim=-1) - xgrid = 2*xgrid/(W-1) - 1 - ygrid = 2*ygrid/(H-1) - 1 - - grid = torch.cat([xgrid, ygrid], dim=-1) - img = F.grid_sample(img, grid, align_corners=True) - - if mask: - mask = (xgrid > -1) & (ygrid > -1) & (xgrid < 1) & (ygrid < 1) - return img, mask.float() - - return img - - -def coords_grid(batch, ht, wd, device): - coords = torch.meshgrid(torch.arange(ht, device=device), torch.arange(wd, device=device)) - coords = torch.stack(coords[::-1], dim=0).float() - return coords[None].repeat(batch, 1, 1, 1) - - -def upflow8(flow, mode='bilinear'): - new_size = (8 * flow.shape[2], 8 * flow.shape[3]) - return 8 * F.interpolate(flow, size=new_size, mode=mode, align_corners=True) diff --git a/spaces/gotiQspiryo/whisper-ui/examples/DDT2000 Diag Renault Hit.md b/spaces/gotiQspiryo/whisper-ui/examples/DDT2000 Diag Renault Hit.md deleted file mode 100644 index de012c8bc58db1dd0dcfd776260e19a1f8865563..0000000000000000000000000000000000000000 --- a/spaces/gotiQspiryo/whisper-ui/examples/DDT2000 Diag Renault Hit.md +++ /dev/null @@ -1,133 +0,0 @@ - - DDT2000 diag renault hit: What is it and how to use it?- -If you are looking for a diagnostic tool for Renault vehicles, you might have come across the term "DDT2000 diag renault hit". But what does it mean and how can you use it? In this article, we will explain what DDT2000 diag renault hit is and how to use it effectively. - -What is DDT2000 diag renault hit?- -DDT2000 diag renault hit is a combination of software and hardware that allows you to perform deep diagnostics of Renault vehicles. It consists of: -DDT2000 diag renault hitDownload ► https://urlgoal.com/2uyLXd - -
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Conclusion- -DDT2000 diag renault hit is a powerful diagnostic tool for Renault vehicles that can help you solve various problems and perform various functions. It consists of a software, a hardware, and a database that work together to access all the ECUs of Renault vehicles in a more advanced mode than via the conventional Renault CAN CLIP software. To use DDT2000 diag renault hit, you need to have some requirements and follow some steps. - -We hope this article has helped you understand what DDT2000 diag renault hit is and how to use it. If you have any questions or comments, please feel free to leave them below. We would love to hear from you. Have a great day! -What are the advantages of DDT2000 diag renault hit over other diagnostic tools?- -DDT2000 diag renault hit has several advantages over other diagnostic tools for Renault vehicles, such as: - - -
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Conclusion- -DDT2000 diag renault hit is a powerful diagnostic tool for Renault vehicles that can help you solve various problems and perform various functions. It consists of a software, a hardware, and a database that work together to access all the ECUs of Renault vehicles in a more advanced mode than via the conventional Renault CAN CLIP software. To use DDT2000 diag renault hit, you need to have some requirements and follow some steps. You also need to be aware of some advantages and disadvantages of DDT2000 diag renault hit and how to overcome them. You also have some alternatives to DDT2000 diag renault hit if you are looking for other diagnostic tools for Renault vehicles. - -We hope this article has helped you understand what DDT2000 diag renault hit is and how to use it. If you have any questions or comments, please feel free to leave them below. We would love to hear from you. Have a great day! 3cee63e6c2- - \ No newline at end of file diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Mere Photo Ko Seene Se Yaar MP3 Download Listen to the Fevicol Se Item Song Online.md b/spaces/gotiQspiryo/whisper-ui/examples/Mere Photo Ko Seene Se Yaar MP3 Download Listen to the Fevicol Se Item Song Online.md deleted file mode 100644 index c9dced377035083fb3258631d8ef5751a0e884ef..0000000000000000000000000000000000000000 --- a/spaces/gotiQspiryo/whisper-ui/examples/Mere Photo Ko Seene Se Yaar MP3 Download Listen to the Fevicol Se Item Song Online.md +++ /dev/null @@ -1,6 +0,0 @@ - mere photo ko seene se yaar song download mp3DOWNLOAD ✑ ✑ ✑ https://urlgoal.com/2uyMh3 - - aaccfb2cb3 - - - diff --git a/spaces/gradio/keras-image-classifier/app.py b/spaces/gradio/keras-image-classifier/app.py deleted file mode 100644 index 704062af215e339b654b0eca4b874155768f863e..0000000000000000000000000000000000000000 --- a/spaces/gradio/keras-image-classifier/app.py +++ /dev/null @@ -1,28 +0,0 @@ -import requests -import tensorflow as tf - -inception_net = tf.keras.applications.MobileNetV2() - -import requests - -# Download human-readable labels for ImageNet. -response = requests.get("https://git.io/JJkYN") -labels = response.text.split("\n") - - -def classify_image(inp): - inp = inp.reshape((-1, 224, 224, 3)) - inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp) - prediction = inception_net.predict(inp).flatten() - confidences = {labels[i]: float(prediction[i]) for i in range(1000)} - return confidences - - -import gradio as gr - -gr.Interface(fn=classify_image, - inputs=gr.inputs.Image(shape=(224, 224)), - outputs=gr.outputs.Label(num_top_classes=3), - examples=["banana.jpg", "car.jpg"], - theme="default", - css=".footer{display:none !important}").launch() diff --git a/spaces/gregojoh/layoutlmv3_document/README.md b/spaces/gregojoh/layoutlmv3_document/README.md deleted file mode 100644 index 25ac0e690741797937eebc13109361e8531f125b..0000000000000000000000000000000000000000 --- a/spaces/gregojoh/layoutlmv3_document/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Layoutlmv3 Document -emoji: 🏢 -colorFrom: pink -colorTo: yellow -sdk: streamlit -sdk_version: 1.17.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/guohuiyuan/Text-to-Music/utils.py b/spaces/guohuiyuan/Text-to-Music/utils.py deleted file mode 100644 index d302528fd6fc9be8d782f78b6c44f4d894147d07..0000000000000000000000000000000000000000 --- a/spaces/guohuiyuan/Text-to-Music/utils.py +++ /dev/null @@ -1,50 +0,0 @@ -import json -import numpy as np -import httpx - -from constants import MUBERT_TAGS, MUBERT_LICENSE, MUBERT_MODE, MUBERT_TOKEN - - -def get_mubert_tags_embeddings(w2v_model): - return w2v_model.encode(MUBERT_TAGS) - - -def get_pat(email: str): - r = httpx.post('https://api-b2b.mubert.com/v2/GetServiceAccess', - json={ - "method": "GetServiceAccess", - "params": { - "email": email, - "license": MUBERT_LICENSE, - "token": MUBERT_TOKEN, - "mode": MUBERT_MODE, - } - }) - - rdata = json.loads(r.text) - assert rdata['status'] == 1, "probably incorrect e-mail" - pat = rdata['data']['pat'] - return pat - - -def find_similar(em, embeddings, method='cosine'): - scores = [] - for ref in embeddings: - if method == 'cosine': - scores.append(1 - np.dot(ref, em) / (np.linalg.norm(ref) * np.linalg.norm(em))) - if method == 'norm': - scores.append(np.linalg.norm(ref - em)) - return np.array(scores), np.argsort(scores) - - -def get_tags_for_prompts(w2v_model, mubert_tags_embeddings, prompts, top_n=3, debug=False): - prompts_embeddings = w2v_model.encode(prompts) - ret = [] - for i, pe in enumerate(prompts_embeddings): - scores, idxs = find_similar(pe, mubert_tags_embeddings) - top_tags = MUBERT_TAGS[idxs[:top_n]] - top_prob = 1 - scores[idxs[:top_n]] - if debug: - print(f"Prompt: {prompts[i]}\nTags: {', '.join(top_tags)}\nScores: {top_prob}\n\n\n") - ret.append((prompts[i], list(top_tags))) - return ret diff --git a/spaces/gwang-kim/DATID-3D/eg3d/training/crosssection_utils.py b/spaces/gwang-kim/DATID-3D/eg3d/training/crosssection_utils.py deleted file mode 100644 index 72d49f29534ae98b6c4cbd807677dcc55fd5e5ce..0000000000000000000000000000000000000000 --- a/spaces/gwang-kim/DATID-3D/eg3d/training/crosssection_utils.py +++ /dev/null @@ -1,26 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: LicenseRef-NvidiaProprietary -# -# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual -# property and proprietary rights in and to this material, related -# documentation and any modifications thereto. Any use, reproduction, -# disclosure or distribution of this material and related documentation -# without an express license agreement from NVIDIA CORPORATION or -# its affiliates is strictly prohibited. - -import torch - -def sample_cross_section(G, ws, resolution=256, w=1.2): - axis=0 - A, B = torch.meshgrid(torch.linspace(w/2, -w/2, resolution, device=ws.device), torch.linspace(-w/2, w/2, resolution, device=ws.device), indexing='ij') - A, B = A.reshape(-1, 1), B.reshape(-1, 1) - C = torch.zeros_like(A) - coordinates = [A, B] - coordinates.insert(axis, C) - coordinates = torch.cat(coordinates, dim=-1).expand(ws.shape[0], -1, -1) - - sigma = G.sample_mixed(coordinates, torch.randn_like(coordinates), ws)['sigma'] - return sigma.reshape(-1, 1, resolution, resolution) - -# if __name__ == '__main__': -# sample_crossection(None) \ No newline at end of file diff --git a/spaces/gwang-kim/DATID-3D/eg3d/visualizer.py b/spaces/gwang-kim/DATID-3D/eg3d/visualizer.py deleted file mode 100644 index 65779220e0c5105193e5832ff2b472ee4ad5ebc8..0000000000000000000000000000000000000000 --- a/spaces/gwang-kim/DATID-3D/eg3d/visualizer.py +++ /dev/null @@ -1,324 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: LicenseRef-NvidiaProprietary -# -# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual -# property and proprietary rights in and to this material, related -# documentation and any modifications thereto. Any use, reproduction, -# disclosure or distribution of this material and related documentation -# without an express license agreement from NVIDIA CORPORATION or -# its affiliates is strictly prohibited. - -import click -import os - -import multiprocessing -import numpy as np -import imgui -import dnnlib -from gui_utils import imgui_window -from gui_utils import imgui_utils -from gui_utils import gl_utils -from gui_utils import text_utils -from viz import renderer -from viz import pickle_widget -from viz import latent_widget -from viz import stylemix_widget -from viz import trunc_noise_widget -from viz import performance_widget -from viz import capture_widget -from viz import backbone_cache_widget -from viz import layer_widget -from viz import pose_widget -from viz import zoom_widget -from viz import conditioning_pose_widget -from viz import render_type_widget -from viz import render_depth_sample_widget - -#---------------------------------------------------------------------------- - -class Visualizer(imgui_window.ImguiWindow): - def __init__(self, capture_dir=None): - super().__init__(title='Cat Machine', window_width=3840, window_height=2160) - - # Internals. - self._last_error_print = None - self._async_renderer = AsyncRenderer() - self._defer_rendering = 0 - self._tex_img = None - self._tex_obj = None - - # Widget interface. - self.args = dnnlib.EasyDict() - self.result = dnnlib.EasyDict() - self.pane_w = 0 - self.label_w = 0 - self.button_w = 0 - - # Widgets. - self.pickle_widget = pickle_widget.PickleWidget(self) - self.latent_widget = latent_widget.LatentWidget(self) - self.stylemix_widget = stylemix_widget.StyleMixingWidget(self) - self.trunc_noise_widget = trunc_noise_widget.TruncationNoiseWidget(self) - self.perf_widget = performance_widget.PerformanceWidget(self) - self.capture_widget = capture_widget.CaptureWidget(self) - self.backbone_cache_widget = backbone_cache_widget.BackboneCacheWidget(self) - self.layer_widget = layer_widget.LayerWidget(self) - self.pose_widget = pose_widget.PoseWidget(self) - self.zoom_widget = zoom_widget.ZoomWidget(self) - self.conditioning_pose_widget = conditioning_pose_widget.ConditioningPoseWidget(self) - self.render_type_widget = render_type_widget.RenderTypeWidget(self) - self.render_depth_sample_widget = render_depth_sample_widget.RenderDepthSampleWidget(self) - - if capture_dir is not None: - self.capture_widget.path = capture_dir - - # Initialize window. - self.set_position(0, 0) - self._adjust_font_size() - self.skip_frame() # Layout may change after first frame. - - def close(self): - super().close() - if self._async_renderer is not None: - self._async_renderer.close() - self._async_renderer = None - - def add_recent_pickle(self, pkl, ignore_errors=False): - self.pickle_widget.add_recent(pkl, ignore_errors=ignore_errors) - - def load_pickle(self, pkl, ignore_errors=False): - self.pickle_widget.load(pkl, ignore_errors=ignore_errors) - - def print_error(self, error): - error = str(error) - if error != self._last_error_print: - print('\n' + error + '\n') - self._last_error_print = error - - def defer_rendering(self, num_frames=1): - self._defer_rendering = max(self._defer_rendering, num_frames) - - def clear_result(self): - self._async_renderer.clear_result() - - def set_async(self, is_async): - if is_async != self._async_renderer.is_async: - self._async_renderer.set_async(is_async) - self.clear_result() - if 'image' in self.result: - self.result.message = 'Switching rendering process...' - self.defer_rendering() - - def _adjust_font_size(self): - old = self.font_size - self.set_font_size(min(self.content_width / 120, self.content_height / 60)) - if self.font_size != old: - self.skip_frame() # Layout changed. - - def draw_frame(self): - self.begin_frame() - self.args = dnnlib.EasyDict() - self.pane_w = self.font_size * 50 - self.button_w = self.font_size * 5 - self.label_w = round(self.font_size * 5.5) - - # Detect mouse dragging in the result area. - dragging, dx, dy = imgui_utils.drag_hidden_window('##result_area', x=self.pane_w, y=0, width=self.content_width-self.pane_w, height=self.content_height) - if dragging: - self.pose_widget.drag(dx, dy) - - # Begin control pane. - imgui.set_next_window_position(0, 0) - imgui.set_next_window_size(self.pane_w, self.content_height) - imgui.begin('##control_pane', closable=False, flags=(imgui.WINDOW_NO_TITLE_BAR | imgui.WINDOW_NO_RESIZE | imgui.WINDOW_NO_MOVE)) - - # Widgets. - expanded, _visible = imgui_utils.collapsing_header('Network & latent', default=True) - self.pickle_widget(expanded) - self.pose_widget(expanded) - self.zoom_widget(expanded) - self.conditioning_pose_widget(expanded) - self.render_type_widget(expanded) - self.render_depth_sample_widget(expanded) - self.latent_widget(expanded) - self.stylemix_widget(expanded) - self.trunc_noise_widget(expanded) - expanded, _visible = imgui_utils.collapsing_header('Performance & capture', default=True) - self.perf_widget(expanded) - self.capture_widget(expanded) - expanded, _visible = imgui_utils.collapsing_header('Layers & channels', default=True) - self.backbone_cache_widget(expanded) - self.layer_widget(expanded) - - # Render. - if self.is_skipping_frames(): - pass - elif self._defer_rendering > 0: - self._defer_rendering -= 1 - elif self.args.pkl is not None: - self._async_renderer.set_args(**self.args) - result = self._async_renderer.get_result() - if result is not None: - self.result = result - - # Display. - max_w = self.content_width - self.pane_w - max_h = self.content_height - pos = np.array([self.pane_w + max_w / 2, max_h / 2]) - if 'image' in self.result: - if self._tex_img is not self.result.image: - self._tex_img = self.result.image - if self._tex_obj is None or not self._tex_obj.is_compatible(image=self._tex_img): - self._tex_obj = gl_utils.Texture(image=self._tex_img, bilinear=False, mipmap=False) - else: - self._tex_obj.update(self._tex_img) - zoom = min(max_w / self._tex_obj.width, max_h / self._tex_obj.height) - # print(zoom) - zoom = np.floor(zoom) if zoom >= 1 else zoom - # zoom = 1 - self._tex_obj.draw(pos=pos, zoom=zoom, align=0.5, rint=True) - if 'error' in self.result: - self.print_error(self.result.error) - if 'message' not in self.result: - self.result.message = str(self.result.error) - if 'message' in self.result: - tex = text_utils.get_texture(self.result.message, size=self.font_size, max_width=max_w, max_height=max_h, outline=2) - tex.draw(pos=pos, align=0.5, rint=True, color=1) - - # End frame. - self._adjust_font_size() - imgui.end() - self.end_frame() - -#---------------------------------------------------------------------------- - -class AsyncRenderer: - def __init__(self): - self._closed = False - self._is_async = False - self._cur_args = None - self._cur_result = None - self._cur_stamp = 0 - self._renderer_obj = None - self._args_queue = None - self._result_queue = None - self._process = None - - def close(self): - self._closed = True - self._renderer_obj = None - if self._process is not None: - self._process.terminate() - self._process = None - self._args_queue = None - self._result_queue = None - - @property - def is_async(self): - return self._is_async - - def set_async(self, is_async): - self._is_async = is_async - - def set_args(self, **args): - assert not self._closed - if args != self._cur_args: - if self._is_async: - self._set_args_async(**args) - else: - self._set_args_sync(**args) - self._cur_args = args - - def _set_args_async(self, **args): - if self._process is None: - self._args_queue = multiprocessing.Queue() - self._result_queue = multiprocessing.Queue() - try: - multiprocessing.set_start_method('spawn') - except RuntimeError: - pass - self._process = multiprocessing.Process(target=self._process_fn, args=(self._args_queue, self._result_queue), daemon=True) - self._process.start() - self._args_queue.put([args, self._cur_stamp]) - - def _set_args_sync(self, **args): - if self._renderer_obj is None: - self._renderer_obj = renderer.Renderer() - self._cur_result = self._renderer_obj.render(**args) - - def get_result(self): - assert not self._closed - if self._result_queue is not None: - while self._result_queue.qsize() > 0: - result, stamp = self._result_queue.get() - if stamp == self._cur_stamp: - self._cur_result = result - return self._cur_result - - def clear_result(self): - assert not self._closed - self._cur_args = None - self._cur_result = None - self._cur_stamp += 1 - - @staticmethod - def _process_fn(args_queue, result_queue): - renderer_obj = renderer.Renderer() - cur_args = None - cur_stamp = None - while True: - args, stamp = args_queue.get() - while args_queue.qsize() > 0: - args, stamp = args_queue.get() - if args != cur_args or stamp != cur_stamp: - result = renderer_obj.render(**args) - if 'error' in result: - result.error = renderer.CapturedException(result.error) - result_queue.put([result, stamp]) - cur_args = args - cur_stamp = stamp - -#---------------------------------------------------------------------------- - -@click.command() -@click.argument('pkls', metavar='PATH', nargs=-1) -@click.option('--capture-dir', help='Where to save screenshot captures', metavar='PATH', default=None) -@click.option('--browse-dir', help='Specify model path for the \'Browse...\' button', metavar='PATH') -def main( - pkls, - capture_dir, - browse_dir -): - """Interactive model visualizer. - - Optional PATH argument can be used specify which .pkl file to load. - """ - viz = Visualizer(capture_dir=capture_dir) - - if browse_dir is not None: - viz.pickle_widget.search_dirs = [browse_dir] - - # List pickles. - pretrained = [ - 'https://api.ngc.nvidia.com/v2/models/nvidia/research/eg3d/versions/1/files/ffhq512-128.pkl', - 'https://api.ngc.nvidia.com/v2/models/nvidia/research/eg3d/versions/1/files/afhqcats512-128.pkl', - 'https://api.ngc.nvidia.com/v2/models/nvidia/research/eg3d/versions/1/files/ffhqrebalanced512-64.pkl', - 'https://api.ngc.nvidia.com/v2/models/nvidia/research/eg3d/versions/1/files/ffhqrebalanced512-128.pkl', - 'https://api.ngc.nvidia.com/v2/models/nvidia/research/eg3d/versions/1/files/shapenetcars128-64.pkl', - ] - - # Populate recent pickles list with pretrained model URLs. - for url in pretrained: - viz.add_recent_pickle(url) - - # Run. - while not viz.should_close(): - viz.draw_frame() - viz.close() - -#---------------------------------------------------------------------------- - -if __name__ == "__main__": - main() - -#---------------------------------------------------------------------------- diff --git a/spaces/h2oai/wave-tour/examples/plot_interaction_brush.py b/spaces/h2oai/wave-tour/examples/plot_interaction_brush.py deleted file mode 100644 index c75a577103feb157dac4c82a4862f41c583315bd..0000000000000000000000000000000000000000 --- a/spaces/h2oai/wave-tour/examples/plot_interaction_brush.py +++ /dev/null @@ -1,27 +0,0 @@ -# Plot / Interaction / Brush -# Make a scatterplot with brush enabled. #plot -# --- - -from h2o_wave import main, app, Q, ui, data - - -@app('/demo') -async def serve(q: Q): - q.page['example'] = ui.plot_card( - box='1 1 4 5', - title='Line plot brush', - data=data('year value', 8, rows=[ - ('1991', 3), - ('1992', 4), - ('1993', 3.5), - ('1994', 5), - ('1995', 4.9), - ('1996', 6), - ('1997', 7), - ('1998', 9), - ('1999', 13), - ]), - plot=ui.plot([ui.mark(type='line', x_scale='time', x='=year', y='=value', y_min=0)]), - interactions=['brush'] - ) - await q.page.save() diff --git a/spaces/hackathon-pln-es/Sentence-Embedding-Bertin/app.py b/spaces/hackathon-pln-es/Sentence-Embedding-Bertin/app.py deleted file mode 100644 index defe8b01f465d755d9c7edc067e065010cb9ebff..0000000000000000000000000000000000000000 --- a/spaces/hackathon-pln-es/Sentence-Embedding-Bertin/app.py +++ /dev/null @@ -1,79 +0,0 @@ -import streamlit as st -import pandas as pd -import numpy as np - -from sentence_transformers.util import cos_sim -from sentence_transformers import SentenceTransformer -from bokeh.plotting import figure, output_notebook, show, save -from bokeh.io import output_file, show -from bokeh.models import ColumnDataSource, HoverTool -from sklearn.manifold import TSNE - - -@st.cache -def load_model(): - model = SentenceTransformer('hackathon-pln-es/bertin-roberta-base-finetuning-esnli') - model.eval() - return model - -@st.cache -def load_plot_data(): - embs = np.load('semeval2015-embs.npy') - data = pd.read_csv('semeval2015-data.csv') - return embs, data - -st.title("Sentence Embedding for Spanish with Bertin") -st.write("Sentence embedding for spanish trained on NLI. Used for Sentence Textual Similarity. Based on the model hackathon-pln-es/bertin-roberta-base-finetuning-esnli.") -st.write("Introduce two sentence to see their cosine similarity and a graph showing them in the embedding space.") -st.write("Authors: Anibal Pérez, Emilio Tomás Ariza, Lautaro Gesuelli y Mauricio Mazuecos.") - -sent1 = st.text_area('Enter sentence 1') -sent2 = st.text_area('Enter sentence 2') - -if st.button('Compute similarity'): - if sent1 and sent2: - model = load_model() - encodings = model.encode([sent1, sent2]) - sim = cos_sim(encodings[0], encodings[1]).numpy().tolist()[0][0] - st.text('Cosine Similarity: {0:.4f}'.format(sim)) - - print('Generating visualization...') - sentembs, data = load_plot_data() - X_embedded = TSNE(n_components=2, learning_rate='auto', - init='random').fit_transform(np.concatenate([sentembs, encodings], axis=0)) - - data = data.append({'sent': sent1, 'color': '#F0E442'}, ignore_index=True) # sentence 1 - data = data.append({'sent': sent2, 'color': '#D55E00'}, ignore_index=True) # sentence 2 - data['x'] = X_embedded[:,0] - data['y'] = X_embedded[:,1] - - source = ColumnDataSource(data) - - p = figure(title="Embeddings in space") - p.circle( - x='x', - y='y', - legend_label="Objects", - #fill_color=["red"], - color='color', - fill_alpha=0.5, - line_color="blue", - size=14, - source=source - ) - p.add_tools(HoverTool( - tooltips=[ - ('sent', '@sent') - ], - formatters={ - '@sent': 'printf' - }, - mode='mouse' - )) - st.bokeh_chart(p, use_container_width=True) - else: - st.write('Missing a sentences') -else: - pass - - diff --git a/spaces/hahahafofo/vits-uma-genshin-honkai/transforms.py b/spaces/hahahafofo/vits-uma-genshin-honkai/transforms.py deleted file mode 100644 index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000 --- a/spaces/hahahafofo/vits-uma-genshin-honkai/transforms.py +++ /dev/null @@ -1,193 +0,0 @@ -import torch -from torch.nn import functional as F - -import numpy as np - - -DEFAULT_MIN_BIN_WIDTH = 1e-3 -DEFAULT_MIN_BIN_HEIGHT = 1e-3 -DEFAULT_MIN_DERIVATIVE = 1e-3 - - -def piecewise_rational_quadratic_transform(inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - tails=None, - tail_bound=1., - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE): - - if tails is None: - spline_fn = rational_quadratic_spline - spline_kwargs = {} - else: - spline_fn = unconstrained_rational_quadratic_spline - spline_kwargs = { - 'tails': tails, - 'tail_bound': tail_bound - } - - outputs, logabsdet = spline_fn( - inputs=inputs, - unnormalized_widths=unnormalized_widths, - unnormalized_heights=unnormalized_heights, - unnormalized_derivatives=unnormalized_derivatives, - inverse=inverse, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height, - min_derivative=min_derivative, - **spline_kwargs - ) - return outputs, logabsdet - - -def searchsorted(bin_locations, inputs, eps=1e-6): - bin_locations[..., -1] += eps - return torch.sum( - inputs[..., None] >= bin_locations, - dim=-1 - ) - 1 - - -def unconstrained_rational_quadratic_spline(inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - tails='linear', - tail_bound=1., - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE): - inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound) - outside_interval_mask = ~inside_interval_mask - - outputs = torch.zeros_like(inputs) - logabsdet = torch.zeros_like(inputs) - - if tails == 'linear': - unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1)) - constant = np.log(np.exp(1 - min_derivative) - 1) - unnormalized_derivatives[..., 0] = constant - unnormalized_derivatives[..., -1] = constant - - outputs[outside_interval_mask] = inputs[outside_interval_mask] - logabsdet[outside_interval_mask] = 0 - else: - raise RuntimeError('{} tails are not implemented.'.format(tails)) - - outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline( - inputs=inputs[inside_interval_mask], - unnormalized_widths=unnormalized_widths[inside_interval_mask, :], - unnormalized_heights=unnormalized_heights[inside_interval_mask, :], - unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :], - inverse=inverse, - left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height, - min_derivative=min_derivative - ) - - return outputs, logabsdet - -def rational_quadratic_spline(inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - left=0., right=1., bottom=0., top=1., - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE): - if torch.min(inputs) < left or torch.max(inputs) > right: - raise ValueError('Input to a transform is not within its domain') - - num_bins = unnormalized_widths.shape[-1] - - if min_bin_width * num_bins > 1.0: - raise ValueError('Minimal bin width too large for the number of bins') - if min_bin_height * num_bins > 1.0: - raise ValueError('Minimal bin height too large for the number of bins') - - widths = F.softmax(unnormalized_widths, dim=-1) - widths = min_bin_width + (1 - min_bin_width * num_bins) * widths - cumwidths = torch.cumsum(widths, dim=-1) - cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0) - cumwidths = (right - left) * cumwidths + left - cumwidths[..., 0] = left - cumwidths[..., -1] = right - widths = cumwidths[..., 1:] - cumwidths[..., :-1] - - derivatives = min_derivative + F.softplus(unnormalized_derivatives) - - heights = F.softmax(unnormalized_heights, dim=-1) - heights = min_bin_height + (1 - min_bin_height * num_bins) * heights - cumheights = torch.cumsum(heights, dim=-1) - cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0) - cumheights = (top - bottom) * cumheights + bottom - cumheights[..., 0] = bottom - cumheights[..., -1] = top - heights = cumheights[..., 1:] - cumheights[..., :-1] - - if inverse: - bin_idx = searchsorted(cumheights, inputs)[..., None] - else: - bin_idx = searchsorted(cumwidths, inputs)[..., None] - - input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0] - input_bin_widths = widths.gather(-1, bin_idx)[..., 0] - - input_cumheights = cumheights.gather(-1, bin_idx)[..., 0] - delta = heights / widths - input_delta = delta.gather(-1, bin_idx)[..., 0] - - input_derivatives = derivatives.gather(-1, bin_idx)[..., 0] - input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0] - - input_heights = heights.gather(-1, bin_idx)[..., 0] - - if inverse: - a = (((inputs - input_cumheights) * (input_derivatives - + input_derivatives_plus_one - - 2 * input_delta) - + input_heights * (input_delta - input_derivatives))) - b = (input_heights * input_derivatives - - (inputs - input_cumheights) * (input_derivatives - + input_derivatives_plus_one - - 2 * input_delta)) - c = - input_delta * (inputs - input_cumheights) - - discriminant = b.pow(2) - 4 * a * c - assert (discriminant >= 0).all() - - root = (2 * c) / (-b - torch.sqrt(discriminant)) - outputs = root * input_bin_widths + input_cumwidths - - theta_one_minus_theta = root * (1 - root) - denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta) - * theta_one_minus_theta) - derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2) - + 2 * input_delta * theta_one_minus_theta - + input_derivatives * (1 - root).pow(2)) - logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) - - return outputs, -logabsdet - else: - theta = (inputs - input_cumwidths) / input_bin_widths - theta_one_minus_theta = theta * (1 - theta) - - numerator = input_heights * (input_delta * theta.pow(2) - + input_derivatives * theta_one_minus_theta) - denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta) - * theta_one_minus_theta) - outputs = input_cumheights + numerator / denominator - - derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2) - + 2 * input_delta * theta_one_minus_theta - + input_derivatives * (1 - theta).pow(2)) - logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) - - return outputs, logabsdet diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/rpn/rpn.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/rpn/rpn.py deleted file mode 100644 index 0dacdec59b3619f1452cc83f86d841d8ea50c440..0000000000000000000000000000000000000000 --- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/rpn/rpn.py +++ /dev/null @@ -1,171 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. -import torch -import torch.nn.functional as F -from torch import nn - -from maskrcnn_benchmark.modeling import registry -from maskrcnn_benchmark.modeling.box_coder import BoxCoder -from .loss import make_rpn_loss_evaluator -from .anchor_generator import make_anchor_generator -from .inference import make_rpn_postprocessor - - -@registry.RPN_HEADS.register("SimpleRPNHead") -class mRPNHead(nn.Module): - """ - Adds a simple RPN Head with classification and regression heads - """ - - def __init__(self, cfg, in_channels, num_anchors): - """ - Arguments: - cfg : config - in_channels (int): number of channels of the input feature - num_anchors (int): number of anchors to be predicted - """ - super(mRPNHead, self).__init__() - self.cls_logits = nn.Conv2d(in_channels, num_anchors, kernel_size=1, stride=1) - self.bbox_pred = nn.Conv2d( - in_channels, num_anchors * 4, kernel_size=1, stride=1 - ) - - for l in [self.cls_logits, self.bbox_pred]: - torch.nn.init.normal_(l.weight, std=0.01) - torch.nn.init.constant_(l.bias, 0) - - def forward(self, x): - logits = [] - bbox_reg = [] - for feature in x: - t = F.relu(feature) - logits.append(self.cls_logits(t)) - bbox_reg.append(self.bbox_pred(t)) - return logits, bbox_reg - - -@registry.RPN_HEADS.register("SingleConvRPNHead") -class RPNHead(nn.Module): - """ - Adds a simple RPN Head with classification and regression heads - """ - - def __init__(self, cfg, in_channels, num_anchors): - """ - Arguments: - cfg : config - in_channels (int): number of channels of the input feature - num_anchors (int): number of anchors to be predicted - """ - super(RPNHead, self).__init__() - self.conv = nn.Conv2d( - in_channels, in_channels, kernel_size=3, stride=1, padding=1 - ) - self.cls_logits = nn.Conv2d(in_channels, num_anchors, kernel_size=1, stride=1) - self.bbox_pred = nn.Conv2d( - in_channels, num_anchors * 4, kernel_size=1, stride=1 - ) - - for l in [self.conv, self.cls_logits, self.bbox_pred]: - torch.nn.init.normal_(l.weight, std=0.01) - torch.nn.init.constant_(l.bias, 0) - - def forward(self, x): - logits = [] - bbox_reg = [] - for feature in x: - t = F.relu(self.conv(feature)) - logits.append(self.cls_logits(t)) - bbox_reg.append(self.bbox_pred(t)) - return logits, bbox_reg - - -class RPNModule(torch.nn.Module): - """ - Module for RPN computation. Takes feature maps from the backbone and RPN - proposals and losses. Works for both FPN and non-FPN. - """ - - def __init__(self, cfg): - super(RPNModule, self).__init__() - - self.cfg = cfg.clone() - - anchor_generator = make_anchor_generator(cfg) - - in_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS - rpn_head = registry.RPN_HEADS[cfg.MODEL.RPN.RPN_HEAD] - head = rpn_head( - cfg, in_channels, anchor_generator.num_anchors_per_location()[0] - ) - - rpn_box_coder = BoxCoder(weights=(1.0, 1.0, 1.0, 1.0)) - - box_selector_train = make_rpn_postprocessor(cfg, rpn_box_coder, is_train=True) - box_selector_test = make_rpn_postprocessor(cfg, rpn_box_coder, is_train=False) - - loss_evaluator = make_rpn_loss_evaluator(cfg, rpn_box_coder) - - self.anchor_generator = anchor_generator - self.head = head - self.box_selector_train = box_selector_train - self.box_selector_test = box_selector_test - self.loss_evaluator = loss_evaluator - - def forward(self, images, features, targets=None): - """ - Arguments: - images (ImageList): images for which we want to compute the predictions - features (list[Tensor]): features computed from the images that are - used for computing the predictions. Each tensor in the list - correspond to different feature levels - targets (list[BoxList): ground-truth boxes present in the image (optional) - - Returns: - boxes (list[BoxList]): the predicted boxes from the RPN, one BoxList per - image. - losses (dict[Tensor]): the losses for the model during training. During - testing, it is an empty dict. - """ - objectness, rpn_box_regression = self.head(features) - anchors = self.anchor_generator(images, features) - - if self.training: - return self._forward_train(anchors, objectness, rpn_box_regression, targets) - else: - return self._forward_test(anchors, objectness, rpn_box_regression) - - def _forward_train(self, anchors, objectness, rpn_box_regression, targets): - if self.cfg.MODEL.RPN_ONLY: - # When training an RPN-only model, the loss is determined by the - # predicted objectness and rpn_box_regression values and there is - # no need to transform the anchors into predicted boxes; this is an - # optimization that avoids the unnecessary transformation. - boxes = anchors - else: - # For end-to-end models, anchors must be transformed into boxes and - # sampled into a training batch. - with torch.no_grad(): - boxes = self.box_selector_train( - anchors, objectness, rpn_box_regression, targets - ) - loss_objectness, loss_rpn_box_reg = self.loss_evaluator( - anchors, objectness, rpn_box_regression, targets - ) - losses = { - "loss_objectness": loss_objectness, - "loss_rpn_box_reg": loss_rpn_box_reg, - } - return boxes, losses - - def _forward_test(self, anchors, objectness, rpn_box_regression): - boxes = self.box_selector_test(anchors, objectness, rpn_box_regression) - if self.cfg.MODEL.RPN_ONLY: - # For end-to-end models, the RPN proposals are an intermediate state - # and don't bother to sort them in decreasing score order. For RPN-only - # models, the proposals are the final output and we return them in - # high-to-low confidence order. - inds = [ - box.get_field("objectness").sort(descending=True)[1] for box in boxes - ] - boxes = [box[ind] for box, ind in zip(boxes, inds)] - return boxes, {} \ No newline at end of file diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/export/patcher.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/export/patcher.py deleted file mode 100644 index 3f0b0fd8122d12c10d06cfc1b0720e3c3374c737..0000000000000000000000000000000000000000 --- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/export/patcher.py +++ /dev/null @@ -1,153 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved - -import contextlib -import mock -import torch - -from detectron2.modeling import poolers -from detectron2.modeling.proposal_generator import rpn -from detectron2.modeling.roi_heads import keypoint_head, mask_head -from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers - -from .c10 import ( - Caffe2Compatible, - Caffe2FastRCNNOutputsInference, - Caffe2KeypointRCNNInference, - Caffe2MaskRCNNInference, - Caffe2ROIPooler, - Caffe2RPN, -) - - -class GenericMixin(object): - pass - - -class Caffe2CompatibleConverter(object): - """ - A GenericUpdater which implements the `create_from` interface, by modifying - module object and assign it with another class replaceCls. - """ - - def __init__(self, replaceCls): - self.replaceCls = replaceCls - - def create_from(self, module): - # update module's class to the new class - assert isinstance(module, torch.nn.Module) - if issubclass(self.replaceCls, GenericMixin): - # replaceCls should act as mixin, create a new class on-the-fly - new_class = type( - "{}MixedWith{}".format(self.replaceCls.__name__, module.__class__.__name__), - (self.replaceCls, module.__class__), - {}, # {"new_method": lambda self: ...}, - ) - module.__class__ = new_class - else: - # replaceCls is complete class, this allow arbitrary class swap - module.__class__ = self.replaceCls - - # initialize Caffe2Compatible - if isinstance(module, Caffe2Compatible): - module.tensor_mode = False - - return module - - -def patch(model, target, updater, *args, **kwargs): - """ - recursively (post-order) update all modules with the target type and its - subclasses, make a initialization/composition/inheritance/... via the - updater.create_from. - """ - for name, module in model.named_children(): - model._modules[name] = patch(module, target, updater, *args, **kwargs) - if isinstance(model, target): - return updater.create_from(model, *args, **kwargs) - return model - - -def patch_generalized_rcnn(model): - ccc = Caffe2CompatibleConverter - model = patch(model, rpn.RPN, ccc(Caffe2RPN)) - model = patch(model, poolers.ROIPooler, ccc(Caffe2ROIPooler)) - - return model - - -@contextlib.contextmanager -def mock_fastrcnn_outputs_inference( - tensor_mode, check=True, box_predictor_type=FastRCNNOutputLayers -): - with mock.patch.object( - box_predictor_type, - "inference", - autospec=True, - side_effect=Caffe2FastRCNNOutputsInference(tensor_mode), - ) as mocked_func: - yield - if check: - assert mocked_func.call_count > 0 - - -@contextlib.contextmanager -def mock_mask_rcnn_inference(tensor_mode, patched_module, check=True): - with mock.patch( - "{}.mask_rcnn_inference".format(patched_module), side_effect=Caffe2MaskRCNNInference() - ) as mocked_func: - yield - if check: - assert mocked_func.call_count > 0 - - -@contextlib.contextmanager -def mock_keypoint_rcnn_inference(tensor_mode, patched_module, use_heatmap_max_keypoint, check=True): - with mock.patch( - "{}.keypoint_rcnn_inference".format(patched_module), - side_effect=Caffe2KeypointRCNNInference(use_heatmap_max_keypoint), - ) as mocked_func: - yield - if check: - assert mocked_func.call_count > 0 - - -class ROIHeadsPatcher: - def __init__(self, cfg, heads): - self.heads = heads - - self.use_heatmap_max_keypoint = cfg.EXPORT_CAFFE2.USE_HEATMAP_MAX_KEYPOINT - - @contextlib.contextmanager - def mock_roi_heads(self, tensor_mode=True): - """ - Patching several inference functions inside ROIHeads and its subclasses - - Args: - tensor_mode (bool): whether the inputs/outputs are caffe2's tensor - format or not. Default to True. - """ - # NOTE: this requries the `keypoint_rcnn_inference` and `mask_rcnn_inference` - # are called inside the same file as BaseXxxHead due to using mock.patch. - kpt_heads_mod = keypoint_head.BaseKeypointRCNNHead.__module__ - mask_head_mod = mask_head.BaseMaskRCNNHead.__module__ - - mock_ctx_managers = [ - mock_fastrcnn_outputs_inference( - tensor_mode=tensor_mode, - check=True, - box_predictor_type=type(self.heads.box_predictor), - ) - ] - if getattr(self.heads, "keypoint_on", False): - mock_ctx_managers += [ - mock_keypoint_rcnn_inference( - tensor_mode, kpt_heads_mod, self.use_heatmap_max_keypoint - ) - ] - if getattr(self.heads, "mask_on", False): - mock_ctx_managers += [mock_mask_rcnn_inference(tensor_mode, mask_head_mod)] - - with contextlib.ExitStack() as stack: # python 3.3+ - for mgr in mock_ctx_managers: - stack.enter_context(mgr) - yield diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/layers/mask_ops.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/layers/mask_ops.py deleted file mode 100644 index 0fe115dbbe15c354575c67d7d10f055eab0bdf91..0000000000000000000000000000000000000000 --- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/layers/mask_ops.py +++ /dev/null @@ -1,248 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -import numpy as np -import torch -from PIL import Image -from torch.nn import functional as F - -__all__ = ["paste_masks_in_image"] - - -BYTES_PER_FLOAT = 4 -# TODO: This memory limit may be too much or too little. It would be better to -# determine it based on available resources. -GPU_MEM_LIMIT = 1024 ** 3 # 1 GB memory limit - - -def _do_paste_mask(masks, boxes, img_h, img_w, skip_empty=True): - """ - Args: - masks: N, 1, H, W - boxes: N, 4 - img_h, img_w (int): - skip_empty (bool): only paste masks within the region that - tightly bound all boxes, and returns the results this region only. - An important optimization for CPU. - - Returns: - if skip_empty == False, a mask of shape (N, img_h, img_w) - if skip_empty == True, a mask of shape (N, h', w'), and the slice - object for the corresponding region. - """ - # On GPU, paste all masks together (up to chunk size) - # by using the entire image to sample the masks - # Compared to pasting them one by one, - # this has more operations but is faster on COCO-scale dataset. - device = masks.device - if skip_empty: - x0_int, y0_int = torch.clamp(boxes.min(dim=0).values.floor()[:2] - 1, min=0).to( - dtype=torch.int32 - ) - x1_int = torch.clamp(boxes[:, 2].max().ceil() + 1, max=img_w).to(dtype=torch.int32) - y1_int = torch.clamp(boxes[:, 3].max().ceil() + 1, max=img_h).to(dtype=torch.int32) - else: - x0_int, y0_int = 0, 0 - x1_int, y1_int = img_w, img_h - x0, y0, x1, y1 = torch.split(boxes, 1, dim=1) # each is Nx1 - - N = masks.shape[0] - - img_y = torch.arange(y0_int, y1_int, device=device, dtype=torch.float32) + 0.5 - img_x = torch.arange(x0_int, x1_int, device=device, dtype=torch.float32) + 0.5 - img_y = (img_y - y0) / (y1 - y0) * 2 - 1 - img_x = (img_x - x0) / (x1 - x0) * 2 - 1 - # img_x, img_y have shapes (N, w), (N, h) - - gx = img_x[:, None, :].expand(N, img_y.size(1), img_x.size(1)) - gy = img_y[:, :, None].expand(N, img_y.size(1), img_x.size(1)) - grid = torch.stack([gx, gy], dim=3) - - img_masks = F.grid_sample(masks.to(dtype=torch.float32), grid, align_corners=False) - - if skip_empty: - return img_masks[:, 0], (slice(y0_int, y1_int), slice(x0_int, x1_int)) - else: - return img_masks[:, 0], () - - -def paste_masks_in_image(masks, boxes, image_shape, threshold=0.5): - """ - Paste a set of masks that are of a fixed resolution (e.g., 28 x 28) into an image. - The location, height, and width for pasting each mask is determined by their - corresponding bounding boxes in boxes. - - Note: - This is a complicated but more accurate implementation. In actual deployment, it is - often enough to use a faster but less accurate implementation. - See :func:`paste_mask_in_image_old` in this file for an alternative implementation. - - Args: - masks (tensor): Tensor of shape (Bimg, Hmask, Wmask), where Bimg is the number of - detected object instances in the image and Hmask, Wmask are the mask width and mask - height of the predicted mask (e.g., Hmask = Wmask = 28). Values are in [0, 1]. - boxes (Boxes or Tensor): A Boxes of length Bimg or Tensor of shape (Bimg, 4). - boxes[i] and masks[i] correspond to the same object instance. - image_shape (tuple): height, width - threshold (float): A threshold in [0, 1] for converting the (soft) masks to - binary masks. - - Returns: - img_masks (Tensor): A tensor of shape (Bimg, Himage, Wimage), where Bimg is the - number of detected object instances and Himage, Wimage are the image width - and height. img_masks[i] is a binary mask for object instance i. - """ - - assert masks.shape[-1] == masks.shape[-2], "Only square mask predictions are supported" - N = len(masks) - if N == 0: - return masks.new_empty((0,) + image_shape, dtype=torch.uint8) - if not isinstance(boxes, torch.Tensor): - boxes = boxes.tensor - device = boxes.device - assert len(boxes) == N, boxes.shape - - img_h, img_w = image_shape - - # The actual implementation split the input into chunks, - # and paste them chunk by chunk. - if device.type == "cpu": - # CPU is most efficient when they are pasted one by one with skip_empty=True - # so that it performs minimal number of operations. - num_chunks = N - else: - # GPU benefits from parallelism for larger chunks, but may have memory issue - # int(img_h) because shape may be tensors in tracing - num_chunks = int(np.ceil(N * int(img_h) * int(img_w) * BYTES_PER_FLOAT / GPU_MEM_LIMIT)) - assert ( - num_chunks <= N - ), "Default GPU_MEM_LIMIT in mask_ops.py is too small; try increasing it" - chunks = torch.chunk(torch.arange(N, device=device), num_chunks) - - img_masks = torch.zeros( - N, img_h, img_w, device=device, dtype=torch.bool if threshold >= 0 else torch.uint8 - ) - for inds in chunks: - masks_chunk, spatial_inds = _do_paste_mask( - masks[inds, None, :, :], boxes[inds], img_h, img_w, skip_empty=device.type == "cpu" - ) - - if threshold >= 0: - masks_chunk = (masks_chunk >= threshold).to(dtype=torch.bool) - else: - # for visualization and debugging - masks_chunk = (masks_chunk * 255).to(dtype=torch.uint8) - - img_masks[(inds,) + spatial_inds] = masks_chunk - return img_masks - - -# The below are the original paste function (from Detectron1) which has -# larger quantization error. -# It is faster on CPU, while the aligned one is faster on GPU thanks to grid_sample. - - -def paste_mask_in_image_old(mask, box, img_h, img_w, threshold): - """ - Paste a single mask in an image. - This is a per-box implementation of :func:`paste_masks_in_image`. - This function has larger quantization error due to incorrect pixel - modeling and is not used any more. - - Args: - mask (Tensor): A tensor of shape (Hmask, Wmask) storing the mask of a single - object instance. Values are in [0, 1]. - box (Tensor): A tensor of shape (4, ) storing the x0, y0, x1, y1 box corners - of the object instance. - img_h, img_w (int): Image height and width. - threshold (float): Mask binarization threshold in [0, 1]. - - Returns: - im_mask (Tensor): - The resized and binarized object mask pasted into the original - image plane (a tensor of shape (img_h, img_w)). - """ - # Conversion from continuous box coordinates to discrete pixel coordinates - # via truncation (cast to int32). This determines which pixels to paste the - # mask onto. - box = box.to(dtype=torch.int32) # Continuous to discrete coordinate conversion - # An example (1D) box with continuous coordinates (x0=0.7, x1=4.3) will map to - # a discrete coordinates (x0=0, x1=4). Note that box is mapped to 5 = x1 - x0 + 1 - # pixels (not x1 - x0 pixels). - samples_w = box[2] - box[0] + 1 # Number of pixel samples, *not* geometric width - samples_h = box[3] - box[1] + 1 # Number of pixel samples, *not* geometric height - - # Resample the mask from it's original grid to the new samples_w x samples_h grid - mask = Image.fromarray(mask.cpu().numpy()) - mask = mask.resize((samples_w, samples_h), resample=Image.BILINEAR) - mask = np.array(mask, copy=False) - - if threshold >= 0: - mask = np.array(mask > threshold, dtype=np.uint8) - mask = torch.from_numpy(mask) - else: - # for visualization and debugging, we also - # allow it to return an unmodified mask - mask = torch.from_numpy(mask * 255).to(torch.uint8) - - im_mask = torch.zeros((img_h, img_w), dtype=torch.uint8) - x_0 = max(box[0], 0) - x_1 = min(box[2] + 1, img_w) - y_0 = max(box[1], 0) - y_1 = min(box[3] + 1, img_h) - - im_mask[y_0:y_1, x_0:x_1] = mask[ - (y_0 - box[1]) : (y_1 - box[1]), (x_0 - box[0]) : (x_1 - box[0]) - ] - return im_mask - - -# Our pixel modeling requires extrapolation for any continuous -# coordinate < 0.5 or > length - 0.5. When sampling pixels on the masks, -# we would like this extrapolation to be an interpolation between boundary values and zero, -# instead of using absolute zero or boundary values. -# Therefore `paste_mask_in_image_old` is often used with zero padding around the masks like this: -# masks, scale = pad_masks(masks[:, 0, :, :], 1) -# boxes = scale_boxes(boxes.tensor, scale) - - -def pad_masks(masks, padding): - """ - Args: - masks (tensor): A tensor of shape (B, M, M) representing B masks. - padding (int): Number of cells to pad on all sides. - - Returns: - The padded masks and the scale factor of the padding size / original size. - """ - B = masks.shape[0] - M = masks.shape[-1] - pad2 = 2 * padding - scale = float(M + pad2) / M - padded_masks = masks.new_zeros((B, M + pad2, M + pad2)) - padded_masks[:, padding:-padding, padding:-padding] = masks - return padded_masks, scale - - -def scale_boxes(boxes, scale): - """ - Args: - boxes (tensor): A tensor of shape (B, 4) representing B boxes with 4 - coords representing the corners x0, y0, x1, y1, - scale (float): The box scaling factor. - - Returns: - Scaled boxes. - """ - w_half = (boxes[:, 2] - boxes[:, 0]) * 0.5 - h_half = (boxes[:, 3] - boxes[:, 1]) * 0.5 - x_c = (boxes[:, 2] + boxes[:, 0]) * 0.5 - y_c = (boxes[:, 3] + boxes[:, 1]) * 0.5 - - w_half *= scale - h_half *= scale - - scaled_boxes = torch.zeros_like(boxes) - scaled_boxes[:, 0] = x_c - w_half - scaled_boxes[:, 2] = x_c + w_half - scaled_boxes[:, 1] = y_c - h_half - scaled_boxes[:, 3] = y_c + h_half - return scaled_boxes diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/modeling/backbone/resnet.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/modeling/backbone/resnet.py deleted file mode 100644 index f1faae012f346166a311902826fb9e4b61e24e54..0000000000000000000000000000000000000000 --- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/modeling/backbone/resnet.py +++ /dev/null @@ -1,591 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -import numpy as np -import fvcore.nn.weight_init as weight_init -import torch -import torch.nn.functional as F -from torch import nn - -from detectron2.layers import ( - CNNBlockBase, - Conv2d, - DeformConv, - ModulatedDeformConv, - ShapeSpec, - get_norm, -) - -from .backbone import Backbone -from .build import BACKBONE_REGISTRY - -__all__ = [ - "ResNetBlockBase", - "BasicBlock", - "BottleneckBlock", - "DeformBottleneckBlock", - "BasicStem", - "ResNet", - "make_stage", - "build_resnet_backbone", -] - - -ResNetBlockBase = CNNBlockBase -""" -Alias for backward compatibiltiy. -""" - - -class BasicBlock(CNNBlockBase): - """ - The basic residual block for ResNet-18 and ResNet-34 defined in :paper:`ResNet`, - with two 3x3 conv layers and a projection shortcut if needed. - """ - - def __init__(self, in_channels, out_channels, *, stride=1, norm="BN"): - """ - Args: - in_channels (int): Number of input channels. - out_channels (int): Number of output channels. - stride (int): Stride for the first conv. - norm (str or callable): normalization for all conv layers. - See :func:`layers.get_norm` for supported format. - """ - super().__init__(in_channels, out_channels, stride) - - if in_channels != out_channels: - self.shortcut = Conv2d( - in_channels, - out_channels, - kernel_size=1, - stride=stride, - bias=False, - norm=get_norm(norm, out_channels), - ) - else: - self.shortcut = None - - self.conv1 = Conv2d( - in_channels, - out_channels, - kernel_size=3, - stride=stride, - padding=1, - bias=False, - norm=get_norm(norm, out_channels), - ) - - self.conv2 = Conv2d( - out_channels, - out_channels, - kernel_size=3, - stride=1, - padding=1, - bias=False, - norm=get_norm(norm, out_channels), - ) - - for layer in [self.conv1, self.conv2, self.shortcut]: - if layer is not None: # shortcut can be None - weight_init.c2_msra_fill(layer) - - def forward(self, x): - out = self.conv1(x) - out = F.relu_(out) - out = self.conv2(out) - - if self.shortcut is not None: - shortcut = self.shortcut(x) - else: - shortcut = x - - out += shortcut - out = F.relu_(out) - return out - - -class BottleneckBlock(CNNBlockBase): - """ - The standard bottleneck residual block used by ResNet-50, 101 and 152 - defined in :paper:`ResNet`. It contains 3 conv layers with kernels - 1x1, 3x3, 1x1, and a projection shortcut if needed. - """ - - def __init__( - self, - in_channels, - out_channels, - *, - bottleneck_channels, - stride=1, - num_groups=1, - norm="BN", - stride_in_1x1=False, - dilation=1, - ): - """ - Args: - bottleneck_channels (int): number of output channels for the 3x3 - "bottleneck" conv layers. - num_groups (int): number of groups for the 3x3 conv layer. - norm (str or callable): normalization for all conv layers. - See :func:`layers.get_norm` for supported format. - stride_in_1x1 (bool): when stride>1, whether to put stride in the - first 1x1 convolution or the bottleneck 3x3 convolution. - dilation (int): the dilation rate of the 3x3 conv layer. - """ - super().__init__(in_channels, out_channels, stride) - - if in_channels != out_channels: - self.shortcut = Conv2d( - in_channels, - out_channels, - kernel_size=1, - stride=stride, - bias=False, - norm=get_norm(norm, out_channels), - ) - else: - self.shortcut = None - - # The original MSRA ResNet models have stride in the first 1x1 conv - # The subsequent fb.torch.resnet and Caffe2 ResNe[X]t implementations have - # stride in the 3x3 conv - stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride) - - self.conv1 = Conv2d( - in_channels, - bottleneck_channels, - kernel_size=1, - stride=stride_1x1, - bias=False, - norm=get_norm(norm, bottleneck_channels), - ) - - self.conv2 = Conv2d( - bottleneck_channels, - bottleneck_channels, - kernel_size=3, - stride=stride_3x3, - padding=1 * dilation, - bias=False, - groups=num_groups, - dilation=dilation, - norm=get_norm(norm, bottleneck_channels), - ) - - self.conv3 = Conv2d( - bottleneck_channels, - out_channels, - kernel_size=1, - bias=False, - norm=get_norm(norm, out_channels), - ) - - for layer in [self.conv1, self.conv2, self.conv3, self.shortcut]: - if layer is not None: # shortcut can be None - weight_init.c2_msra_fill(layer) - - # Zero-initialize the last normalization in each residual branch, - # so that at the beginning, the residual branch starts with zeros, - # and each residual block behaves like an identity. - # See Sec 5.1 in "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour": - # "For BN layers, the learnable scaling coefficient γ is initialized - # to be 1, except for each residual block's last BN - # where γ is initialized to be 0." - - # nn.init.constant_(self.conv3.norm.weight, 0) - # TODO this somehow hurts performance when training GN models from scratch. - # Add it as an option when we need to use this code to train a backbone. - - def forward(self, x): - out = self.conv1(x) - out = F.relu_(out) - - out = self.conv2(out) - out = F.relu_(out) - - out = self.conv3(out) - - if self.shortcut is not None: - shortcut = self.shortcut(x) - else: - shortcut = x - - out += shortcut - out = F.relu_(out) - return out - - -class DeformBottleneckBlock(ResNetBlockBase): - """ - Similar to :class:`BottleneckBlock`, but with :paper:`deformable conv
")
- gr.HTML(" |
|---|
")
-
-
- with gr.Row():
- mkd = gr.Markdown('Upload a PDF
',scale=1)
- mkd = gr.Markdown('OR
',scale=1)
- mkd = gr.Markdown('Provide a PDF link
',scale=1)
-
-
- with gr.Row(equal_height=True):
- pdf_file = gr.File(label='PDF 📑', file_count='single', scale=1)
- pdf_link = gr.Textbox(placeholder='Enter an arxiv link here', label='Link to Paper🔗', scale=1)
- with gr.Row():
- btn = gr.Button('Run Nougat 🍫')
- with gr.Row():
- clr = gr.Button('Clear Inputs & Outputs 🧼')
-
- output_headline = gr.Markdown("## PDF converted to markup language through Nougat-OCR👇")
- with gr.Row():
- parsed_output = gr.Markdown(elem_id='mkd', value='Output Text 📝')
- output_file = gr.File(file_types = ["txt"], label="Output File 📑")
-
- btn.click(inference, [pdf_file, pdf_link], [parsed_output, output_file])
- clr.click(lambda : (gr.update(value=None),
- gr.update(value=None),
- gr.update(value=None),
- gr.update(value=None)),
- [],
- [pdf_file, pdf_link, parsed_output, output_file]
- )
- gr.Examples(
- [["nougat.pdf", ""], [None, "https://arxiv.org/pdf/2308.08316.pdf"]],
- inputs = [pdf_file, pdf_link],
- outputs = [parsed_output, output_file],
- fn=inference,
- cache_examples=True,
- label='Click on any Examples below to get Nougat OCR results quickly:'
- )
-
-
-
-demo.queue()
-demo.launch(debug=True)
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/data_augmentation/pyramid_augmentations.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/data_augmentation/pyramid_augmentations.py
deleted file mode 100644
index 60bb280558b7c3df0b795b003a6a83022668ab2b..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/data_augmentation/pyramid_augmentations.py
+++ /dev/null
@@ -1,189 +0,0 @@
-# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
-#
-# 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.
-
-from copy import deepcopy
-
-from batchgenerators.transforms.abstract_transforms import AbstractTransform
-from skimage.morphology import label, ball
-from skimage.morphology.binary import binary_erosion, binary_dilation, binary_closing, binary_opening
-import numpy as np
-
-
-class RemoveRandomConnectedComponentFromOneHotEncodingTransform(AbstractTransform):
- def __init__(self, channel_idx, key="data", p_per_sample=0.2, fill_with_other_class_p=0.25,
- dont_do_if_covers_more_than_X_percent=0.25, p_per_label=1):
- """
- :param dont_do_if_covers_more_than_X_percent: dont_do_if_covers_more_than_X_percent=0.25 is 25\%!
- :param channel_idx: can be list or int
- :param key:
- """
- self.p_per_label = p_per_label
- self.dont_do_if_covers_more_than_X_percent = dont_do_if_covers_more_than_X_percent
- self.fill_with_other_class_p = fill_with_other_class_p
- self.p_per_sample = p_per_sample
- self.key = key
- if not isinstance(channel_idx, (list, tuple)):
- channel_idx = [channel_idx]
- self.channel_idx = channel_idx
-
- def __call__(self, **data_dict):
- data = data_dict.get(self.key)
- for b in range(data.shape[0]):
- if np.random.uniform() < self.p_per_sample:
- for c in self.channel_idx:
- if np.random.uniform() < self.p_per_label:
- workon = np.copy(data[b, c])
- num_voxels = np.prod(workon.shape, dtype=np.uint64)
- lab, num_comp = label(workon, return_num=True)
- if num_comp > 0:
- component_ids = []
- component_sizes = []
- for i in range(1, num_comp + 1):
- component_ids.append(i)
- component_sizes.append(np.sum(lab == i))
- component_ids = [i for i, j in zip(component_ids, component_sizes) if j < num_voxels*self.dont_do_if_covers_more_than_X_percent]
- #_ = component_ids.pop(np.argmax(component_sizes))
- #else:
- # component_ids = list(range(1, num_comp + 1))
- if len(component_ids) > 0:
- random_component = np.random.choice(component_ids)
- data[b, c][lab == random_component] = 0
- if np.random.uniform() < self.fill_with_other_class_p:
- other_ch = [i for i in self.channel_idx if i != c]
- if len(other_ch) > 0:
- other_class = np.random.choice(other_ch)
- data[b, other_class][lab == random_component] = 1
- data_dict[self.key] = data
- return data_dict
-
-
-class MoveSegAsOneHotToData(AbstractTransform):
- def __init__(self, channel_id, all_seg_labels, key_origin="seg", key_target="data", remove_from_origin=True):
- self.remove_from_origin = remove_from_origin
- self.all_seg_labels = all_seg_labels
- self.key_target = key_target
- self.key_origin = key_origin
- self.channel_id = channel_id
-
- def __call__(self, **data_dict):
- origin = data_dict.get(self.key_origin)
- target = data_dict.get(self.key_target)
- seg = origin[:, self.channel_id:self.channel_id+1]
- seg_onehot = np.zeros((seg.shape[0], len(self.all_seg_labels), *seg.shape[2:]), dtype=seg.dtype)
- for i, l in enumerate(self.all_seg_labels):
- seg_onehot[:, i][seg[:, 0] == l] = 1
- target = np.concatenate((target, seg_onehot), 1)
- data_dict[self.key_target] = target
-
- if self.remove_from_origin:
- remaining_channels = [i for i in range(origin.shape[1]) if i != self.channel_id]
- origin = origin[:, remaining_channels]
- data_dict[self.key_origin] = origin
- return data_dict
-
-
-class ApplyRandomBinaryOperatorTransform(AbstractTransform):
- def __init__(self, channel_idx, p_per_sample=0.3, any_of_these=(binary_dilation, binary_erosion, binary_closing,
- binary_opening),
- key="data", strel_size=(1, 10), p_per_label=1):
- self.p_per_label = p_per_label
- self.strel_size = strel_size
- self.key = key
- self.any_of_these = any_of_these
- self.p_per_sample = p_per_sample
-
- assert not isinstance(channel_idx, tuple), "bäh"
-
- if not isinstance(channel_idx, list):
- channel_idx = [channel_idx]
- self.channel_idx = channel_idx
-
- def __call__(self, **data_dict):
- data = data_dict.get(self.key)
- for b in range(data.shape[0]):
- if np.random.uniform() < self.p_per_sample:
- ch = deepcopy(self.channel_idx)
- np.random.shuffle(ch)
- for c in ch:
- if np.random.uniform() < self.p_per_label:
- operation = np.random.choice(self.any_of_these)
- selem = ball(np.random.uniform(*self.strel_size))
- workon = np.copy(data[b, c]).astype(int)
- res = operation(workon, selem).astype(workon.dtype)
- data[b, c] = res
-
- # if class was added, we need to remove it in ALL other channels to keep one hot encoding
- # properties
- # we modify data
- other_ch = [i for i in ch if i != c]
- if len(other_ch) > 0:
- was_added_mask = (res - workon) > 0
- for oc in other_ch:
- data[b, oc][was_added_mask] = 0
- # if class was removed, leave it at background
- data_dict[self.key] = data
- return data_dict
-
-
-class ApplyRandomBinaryOperatorTransform2(AbstractTransform):
- def __init__(self, channel_idx, p_per_sample=0.3, p_per_label=0.3, any_of_these=(binary_dilation, binary_closing),
- key="data", strel_size=(1, 10)):
- """
- 2019_11_22: I have no idea what the purpose of this was...
-
- the same as above but here we should use only expanding operations. Expansions will replace other labels
- :param channel_idx: can be list or int
- :param p_per_sample:
- :param any_of_these:
- :param fill_diff_with_other_class:
- :param key:
- :param strel_size:
- """
- self.strel_size = strel_size
- self.key = key
- self.any_of_these = any_of_these
- self.p_per_sample = p_per_sample
- self.p_per_label = p_per_label
-
- assert not isinstance(channel_idx, tuple), "bäh"
-
- if not isinstance(channel_idx, list):
- channel_idx = [channel_idx]
- self.channel_idx = channel_idx
-
- def __call__(self, **data_dict):
- data = data_dict.get(self.key)
- for b in range(data.shape[0]):
- if np.random.uniform() < self.p_per_sample:
- ch = deepcopy(self.channel_idx)
- np.random.shuffle(ch)
- for c in ch:
- if np.random.uniform() < self.p_per_label:
- operation = np.random.choice(self.any_of_these)
- selem = ball(np.random.uniform(*self.strel_size))
- workon = np.copy(data[b, c]).astype(int)
- res = operation(workon, selem).astype(workon.dtype)
- data[b, c] = res
-
- # if class was added, we need to remove it in ALL other channels to keep one hot encoding
- # properties
- # we modify data
- other_ch = [i for i in ch if i != c]
- if len(other_ch) > 0:
- was_added_mask = (res - workon) > 0
- for oc in other_ch:
- data[b, oc][was_added_mask] = 0
- # if class was removed, leave it at backgound
- data_dict[self.key] = data
- return data_dict
diff --git a/spaces/huggingface-tools/image-transformation/app.py b/spaces/huggingface-tools/image-transformation/app.py
deleted file mode 100644
index fdba856f256fc708f577b8dbf38d34b25745359d..0000000000000000000000000000000000000000
--- a/spaces/huggingface-tools/image-transformation/app.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from transformers.tools.base import launch_gradio_demo
-from image_transformation import ImageTransformationTool
-
-launch_gradio_demo(ImageTransformationTool)
diff --git a/spaces/imseldrith/Article-Rewriter/app.py b/spaces/imseldrith/Article-Rewriter/app.py
deleted file mode 100644
index b6fb0be918e0968622cef2183aefdd4b6a475ff1..0000000000000000000000000000000000000000
--- a/spaces/imseldrith/Article-Rewriter/app.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import torch
-from transformers import PegasusForConditionalGeneration, PegasusTokenizer
-
-model_name = 'tuner007/pegasus_paraphrase'
-torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
-tokenizer = PegasusTokenizer.from_pretrained(model_name)
-model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
-
-def get_response(input_text,num_return_sequences):
- batch = tokenizer.prepare_seq2seq_batch([input_text],truncation=True,padding='longest',max_length=60, return_tensors="pt").to(torch_device)
- translated = model.generate(**batch,max_length=60,num_beams=10, num_return_sequences=num_return_sequences, temperature=1.5)
- tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
- return tgt_text
-
-from sentence_splitter import SentenceSplitter, split_text_into_sentences
-
-splitter = SentenceSplitter(language='en')
-
-def paraphraze(text):
- sentence_list = splitter.split(text)
- paraphrase = []
-
- for i in sentence_list:
- a = get_response(i,1)
- paraphrase.append(a)
- paraphrase2 = [' '.join(x) for x in paraphrase]
- paraphrase3 = [' '.join(x for x in paraphrase2) ]
- paraphrased_text = str(paraphrase3).strip('[]').strip("'")
- return paraphrased_text
-
-import gradio as gr
-def summarize(text):
-
- paraphrased_text = paraphraze(text)
- return paraphrased_text
-gr.Interface(fn=summarize, inputs=gr.inputs.Textbox(lines=7, placeholder="Enter text here"), outputs=[gr.outputs.Textbox(label="Paraphrased Text")],examples=[["This Api is the best quillbot api alternative with no words limit."
-]]).launch(inline=False)
\ No newline at end of file
diff --git a/spaces/innovatorved/whisper.api/app/tests/test_api/test_users.py b/spaces/innovatorved/whisper.api/app/tests/test_api/test_users.py
deleted file mode 100644
index b7e95a6d757058b8cd9285546f8a7992dfe0f881..0000000000000000000000000000000000000000
--- a/spaces/innovatorved/whisper.api/app/tests/test_api/test_users.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import uuid
-
-from fastapi.testclient import TestClient
-from app.main import app
-
-from app.tests.utils.utils import fake_user_details, get_new_fake_pwd
-
-client = TestClient(app)
-
-test_user_details = fake_user_details()
-
-
-def test_create_user():
- data = {
- "email": test_user_details["email"],
- "username": test_user_details["username"],
- "password": test_user_details["password"],
- }
- response = client.post("/api/v1/users/", json=data)
- print(response.content)
- test_user_details["user_id"] = response.json()["id"]
- assert response.status_code == 201
- assert response.json()["email"] == test_user_details["email"]
- assert response.json()["username"] == test_user_details["username"]
-
-
-def test_create_user_invalid_email():
- test_user_details_2 = fake_user_details()
-
- data = {
- "email": "invalid_email",
- "username": test_user_details_2["username"],
- "password": test_user_details_2["password"],
- }
- response = client.post(f"/api/v1/users/", json=data)
- assert response.status_code == 422
- assert "value_error" in response.json()["detail"][0]["type"]
- assert "Invalid email" in response.json()["detail"][0]["msg"]
-
-
-def test_create_user_invalid_password():
- data = {
- "email": test_user_details["email"],
- "username": test_user_details["username"],
- "password": "short",
- }
- response = client.post("/api/v1/users/", json=data)
- assert response.status_code == 422
- assert (
- "Password must be at least 6 characters long"
- in response.json()["detail"][0]["msg"]
- )
-
-
-def test_read_user():
- response = client.get(f"/api/v1/users/{test_user_details['user_id']}")
- assert response.status_code == 200
- assert response.json()["email"] == test_user_details["email"]
- assert response.json()["username"] == test_user_details["username"]
-
-
-def test_read_user_not_found():
- response = client.get(f"/api/v1/users/{uuid.uuid4()}")
-
- assert response.status_code == 404
- assert response.json()["detail"] == "User not found"
-
-
-def test_check_update_password():
- new_password = get_new_fake_pwd()
- data = {
- "current_password": test_user_details["password"],
- "new_password": new_password,
- }
- response = client.put(
- f"/api/v1/users/{test_user_details['user_id']}/update_password/", json=data
- )
- assert response.status_code == 200
- assert response.json()["email"] == test_user_details["email"]
- assert response.json()["username"] == test_user_details["username"]
-
-
-def test_check_update_password_invalid_current_password():
- new_password = get_new_fake_pwd()
- data = {"current_password": "wrong_password", "new_password": new_password}
- response = client.put(
- f"/api/v1/users/{test_user_details['user_id']}/update_password/", json=data
- )
- assert response.status_code == 400
- assert response.json()["detail"] == "Incorrect password"
-
-
-def test_check_update_password_not_found():
- data = {"current_password": "wrong_password", "new_password": "new_password"}
- response = client.put(f"/api/v1/users/{uuid.uuid4()}/update_password/", json=data)
- assert response.status_code == 404
- assert response.json()["detail"] == "User not found"
-
-
-def test_delete_user_not_found():
- response = client.delete(f"/api/v1/users/{uuid.uuid4()}/delete")
- assert response.status_code == 404
- assert response.json()["detail"] == "User not found"
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Jv16 PowerTools X 5.0.0.484 Crack Product Key 2020 EXCLUSIVE.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Jv16 PowerTools X 5.0.0.484 Crack Product Key 2020 EXCLUSIVE.md
deleted file mode 100644
index 829249c87fb10baf35a8d3164fa53296b3ca8b44..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Jv16 PowerTools X 5.0.0.484 Crack Product Key 2020 EXCLUSIVE.md
+++ /dev/null
@@ -1,6 +0,0 @@
-jv16 PowerTools X 5.0.0.484 Crack Product Key 2020
Download ✅ https://urlin.us/2uEwwU
-
-SimLab Composer 9.1.10 Crack is a professional software for 3D ... SimLab Composer ... jv16 PowerTools X 5.0.0.484 Crack Product Key 2020 4d29de3e1b
-
-
- diff --git a/spaces/inreVtussa/clothingai/Examples/Casio Fx-cg20 Emulator.md b/spaces/inreVtussa/clothingai/Examples/Casio Fx-cg20 Emulator.md deleted file mode 100644 index 5aa5d4b60fc943d789cf9bc8cdc3154b40343f28..0000000000000000000000000000000000000000 --- a/spaces/inreVtussa/clothingai/Examples/Casio Fx-cg20 Emulator.md +++ /dev/null @@ -1,155 +0,0 @@ -
-
Casio Fx-cg20 Emulator: Everything You Need to Know
- -The Casio Fx-cg20 is a color graphing calculator that can do more than just calculations. It can also emulate other devices and systems, such as computers, game consoles, and scientific instruments. In this article, we will explain what an emulator is, why you might want to use one on your Casio Fx-cg20, and how to download and install emulator software for your calculator.
-Casio Fx-cg20 Emulator
Download File === https://tiurll.com/2uCl7v
- -
What is an emulator?
- -An emulator is a software program that mimics the behavior of another device or system. For example, you can use an emulator to run games or applications that were designed for a different platform, such as a Nintendo Entertainment System (NES) or a Windows PC. Emulators can also be used to create virtual environments for testing or experimenting with different settings and scenarios.
- -Why use an emulator on your Casio Fx-cg20?
- -There are many benefits of using an emulator on your Casio Fx-cg20 calculator. Here are some of them:
- --
-
- You can enhance your learning experience by exploring different topics and concepts with interactive simulations and games. -
- You can prepare teaching materials and present them in the classroom with the help of an emulator. -
- You can have fun and enjoy your favorite games or applications on your calculator. -
- You can expand your calculator's capabilities and functionality by adding new features and options. -
How to download and install emulator software for your Casio Fx-cg20?
- -There are different types of emulator software available for your Casio Fx-cg20 calculator. Some of them are official products from Casio, while others are created by independent developers. Here are some examples of emulator software that you can download and install on your Casio Fx-cg20:
- - -fx-CG Manager PLUS Subscription
- -This is an official product from Casio that emulates the operation of the fx-CG50 series of graphing calculators on your computer. You can use it to create and edit files, transfer data, and perform calculations on your PC or Mac. You can also use it to project your calculator's screen on a larger display for classroom presentations. You can download the software from Casio's website, and purchase a subscription license for one year or three years.
- -NESizm
- -This is an unofficial product from an independent developer that emulates the Nintendo Entertainment System (NES) on your Casio Fx-cg20 calculator. You can use it to play NES games on your calculator, such as Super Mario Bros., The Legend of Zelda, Tetris, and more. You can download the software from GitHub, and follow the instructions to install it on your calculator.
- -How to use emulator software on your Casio Fx-cg20?
- -Once you have downloaded and installed the emulator software on your Casio Fx-cg20 calculator, you can start using it by following these steps:
- --
-
- Turn on your calculator and press the MENU key. -
- Select the PRGM icon to enter the Program menu. -
- Select the emulator software that you want to run from the list of programs. -
- Follow the instructions on the screen to load or run the files that you want to use with the emulator. -
- Enjoy using the emulator software on your Casio Fx-cg20 calculator. -
Conclusion
- -In this article, we have shown you how to use your Casio Fx-cg20 calculator as a powerful learning tool by using emulator software. You can download and install different types of emulator software for your calculator, such as fx-CG Manager PLUS Subscription or NESizm, and use them to explore different topics and concepts, prepare teaching materials, have fun, and expand your calculator's capabilities. We hope you find this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below.
-What are the advantages and disadvantages of using an emulator on your Casio Fx-cg20?
- -Using an emulator on your Casio Fx-cg20 calculator can have both advantages and disadvantages. Here are some of them:
- -Advantages
- --
-
- You can access a wider range of games and applications that are not available for your calculator. -
- You can learn new skills and concepts by using different types of emulators, such as computer programming, game design, or scientific modeling. -
- You can save money and space by using your calculator as an emulator instead of buying separate devices or systems. -
- You can customize your emulator settings and preferences to suit your needs and preferences. -
Disadvantages
- --
-
- You may encounter compatibility issues or errors when using some emulators or files on your calculator. -
- You may experience lower performance or quality when using some emulators or files on your calculator. -
- You may violate some intellectual property rights or terms of use when using some emulators or files on your calculator. -
- You may damage your calculator or lose your data if you install or use some emulators or files incorrectly on your calculator. -
How to choose the best emulator software for your Casio Fx-cg20?
- -There are many factors to consider when choosing the best emulator software for your Casio Fx-cg20 calculator. Here are some of them:
- --
-
- The compatibility and availability of the emulator software for your calculator model and operating system. -
- The features and functions of the emulator software, such as graphics, sound, controls, options, etc. -
- The quality and performance of the emulator software, such as speed, accuracy, stability, etc. -
- The legality and safety of the emulator software, such as licenses, permissions, viruses, etc. -
- The reviews and ratings of the emulator software from other users and experts. -
To find the best emulator software for your Casio Fx-cg20 calculator, you can do some research online, read some articles or blogs, watch some videos or tutorials, or ask some friends or experts for recommendations. You can also try out different emulator software yourself and compare them based on your own criteria and preferences.
-What are some tips and tricks for using an emulator on your Casio Fx-cg20?
- -Using an emulator on your Casio Fx-cg20 calculator can be fun and easy if you follow some tips and tricks. Here are some of them:
- --
-
- Make sure you have enough free memory on your calculator before installing or running an emulator software. You can check your memory status by pressing SHIFT + 9 + 1. -
- Make sure you have the latest operating system (OS) version on your calculator before installing or running an emulator software. You can check your OS version by pressing MENU + 5 + 4 + 1. You can download the latest OS from Casio's website. -
- Make sure you have the correct files for the emulator software that you want to use. For example, if you want to use NESizm, you need to have the NESizm.g3a file and the NES ROM files that you want to play. You can transfer files from your computer to your calculator using a USB cable and a software such as FA-124 or fx-CG Manager PLUS. -
- Make sure you follow the instructions and guidelines of the emulator software that you want to use. For example, if you want to use fx-CG Manager PLUS, you need to activate your subscription license and register your calculator serial number. You can find the instructions and guidelines on the emulator software's website or manual. -
- Make sure you backup your data and settings before installing or running an emulator software. You can backup your data and settings by pressing MENU + 5 + 4 + 2. You can restore your data and settings by pressing MENU + 5 + 4 + 3. -
What are some alternatives to using an emulator on your Casio Fx-cg20?
- -If you don't want to use an emulator on your Casio Fx-cg20 calculator, or if you can't find an emulator software that suits your needs, you can try some alternatives. Here are some of them:
- --
-
- You can use the built-in applications and features of your calculator, such as eActivity, Picture Plot, Geometry, Spreadsheet, etc. You can find more information about these applications and features on Casio's website. -
- You can download and install add-in applications for your calculator, such as games, utilities, educational tools, etc. You can find some add-in applications on Cemetech, Planet-Casio, or Omnimaga. -
- You can create your own programs or applications for your calculator using a programming language such as Basic or C/C++. You can find some tutorials and resources on Casio's website, Cemetech, or Omnimaga. -
What are some examples of using an emulator on your Casio Fx-cg20?
- -Using an emulator on your Casio Fx-cg20 calculator can open up many possibilities and opportunities for learning and fun. Here are some examples of using an emulator on your calculator:
- --
-
- You can use fx-CG Manager PLUS to create eActivities that combine text, images, graphs, and calculations for different subjects and topics. You can also use it to share your eActivities with other users or download eActivities from Casio's website. -
- You can use NESizm to play classic NES games on your calculator, such as Super Mario Bros., The Legend of Zelda, Tetris, and more. You can also use it to create your own games or modify existing games using a programming language such as C or Assembly. -
- You can use other emulator software to emulate other devices or systems on your calculator, such as computers, game consoles, scientific instruments, etc. You can find some other emulator software on Cemetech, Planet-Casio, or Omnimaga. -
How to troubleshoot problems with using an emulator on your Casio Fx-cg20?
- -Sometimes, you may encounter problems or issues with using an emulator on your Casio Fx-cg20 calculator. Here are some common problems and solutions:
- -The emulator software does not run or crashes on your calculator.
- -This may be caused by several reasons, such as low memory, incompatible OS version, corrupted files, etc. To fix this problem, you can try the following solutions:
- --
-
- Check your memory status by pressing SHIFT + 9 + 1. If you have low memory, delete some files or programs that you don't need. -
- Check your OS version by pressing MENU + 5 + 4 + 1. If you have an outdated OS version, update it from Casio's website. -
- Check your files by connecting your calculator to your computer and scanning them for errors or viruses. If you have corrupted files, delete them or replace them with new ones. -
- Check your emulator software by downloading it again from its original source and installing it correctly on your calculator. -
The emulator software does not work properly or has low performance or quality on your calculator.
- -This may be caused by several reasons, such as incompatible files, incorrect settings, hardware limitations, etc. To fix this problem, you can try the following solutions:
- --
-
- Check your files by making sure they are compatible with the emulator software that you are using. For example, if you are using NESizm, make sure you have NES ROM files that work with NESizm. -
- Check your settings by making sure they are appropriate for the emulator software that you are using. For example, if you are using fx-CG Manager PLUS, make sure you have activated your subscription license and registered your calculator serial number. -
- Check your hardware by making sure it meets the requirements for the emulator software that you are using. For example, if you are using NESizm, make sure you have enough battery power and a fast processor speed. -
The emulator software violates some intellectual property rights or terms of use.
- -This may be caused by several reasons, such as illegal downloads, unauthorized modifications, unfair use, etc. To avoid this problem, you should follow these guidelines:
- --
-
- Download emulator software only from official or trusted sources. -
- Use emulator software only for personal or educational purposes. -
- Respect the rights and wishes of the original creators and owners of the emulator software and the files that you use with it. -
- Do not distribute or sell emulator software or files without permission or license. -
Conclusion
- -In this article, we have shown you how to use your Casio Fx-cg20 calculator as an emulator of other devices and systems. You can download and install different types of emulator software for your calculator, such as fx-CG Manager PLUS Subscription or NESizm, and use them to enhance your learning experience, prepare teaching materials, have fun, and expand your calculator's capabilities. We have also given you some tips and tricks, some examples, and some solutions for common problems with using an emulator on your calculator. We hope you find this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below.
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\ No newline at end of file diff --git a/spaces/ixxan/multilingual-vqa/app.py b/spaces/ixxan/multilingual-vqa/app.py deleted file mode 100644 index ec95709d1d3debb068638cdea445fea0e5a01447..0000000000000000000000000000000000000000 --- a/spaces/ixxan/multilingual-vqa/app.py +++ /dev/null @@ -1,85 +0,0 @@ -import gradio as gr -import torch -from transformers.utils import logging - -from googletrans import Translator -from transformers import ViltProcessor, ViltForQuestionAnswering, T5Tokenizer, T5ForConditionalGeneration - -logging.set_verbosity_info() -logger = logging.get_logger("transformers") - -# Translation -def google_translate(question, dest): - translator = Translator() - translation = translator.translate(question, dest=dest) - logger.info("Translation text: " + translation.text) - logger.info("Translation src: " + translation.src) - return (translation.text, translation.src) - -# Load Vilt -vilt_processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa") -vilt_model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa") - -def vilt_vqa(image, question): - inputs = vilt_processor(image, question, return_tensors="pt") - with torch.no_grad(): - outputs = vilt_model(**inputs) - logits = outputs.logits - idx = logits.argmax(-1).item() - answer = vilt_model.config.id2label[idx] - logger.info("ViLT: " + answer) - return answer - -# Load FLAN-T5 -t5_tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large") -t5_model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large", device_map="auto") - -def flan_t5_complete_sentence(question, answer): - input_text = f"A question: {question} An answer: {answer}. Based on these, answer the question with a complete sentence without extra information." - logger.info("T5 input: " + input_text) - inputs = t5_tokenizer(input_text, return_tensors="pt") - outputs = t5_model.generate(**inputs, max_length=50) - result_sentence = t5_tokenizer.batch_decode(outputs, skip_special_tokens=True) - logger.info("T5 output: " + str(result_sentence)) - return result_sentence - -# Main function -def vqa_main(image, question): - en_question, question_src_lang = google_translate(question, dest='en') - vqa_answer = vilt_vqa(image, en_question) - llm_answer = flan_t5_complete_sentence(en_question, vqa_answer)[0] - final_answer, answer_src_lang = google_translate(llm_answer, dest=question_src_lang) - logger.info("Final Answer: " + final_answer) - return final_answer - -# Home page text -title = "Interactive demo: Multilingual VQA" -description = """ -Upload an image, type a question, click 'submit', or click one of the examples to load them. - -Note: You may see "Error" displayed for output due to Gradio's incompatibility, if that is the case, please click "Logs" above to obtain the output for your request. - -""" -article = """ -Supported 107 Languages: 'afrikaans', 'albanian', 'amharic', 'arabic', 'armenian', 'azerbaijani', 'basque', 'belarusian', 'bengali', 'bosnian', 'bulgarian', 'catalan', 'cebuano', 'chichewa', 'chinese (simplified)', 'chinese (traditional)', 'corsican', 'croatian', 'czech', 'danish', 'dutch', 'english', 'esperanto', 'estonian', 'filipino', 'finnish', 'french', 'frisian', 'galician', 'georgian', 'german', 'greek', 'gujarati', 'haitian creole', 'hausa', 'hawaiian', 'hebrew', 'hebrew', 'hindi', 'hmong', 'hungarian', 'icelandic', 'igbo', 'indonesian', 'irish', 'italian', 'japanese', 'javanese', 'kannada', 'kazakh', 'khmer', 'korean', 'kurdish (kurmanji)', 'kyrgyz', 'lao', 'latin', 'latvian', 'lithuanian', 'luxembourgish', 'macedonian', 'malagasy', 'malay', 'malayalam', 'maltese', 'maori', 'marathi', 'mongolian', 'myanmar (burmese)', 'nepali', 'norwegian', 'odia', 'pashto', 'persian', 'polish', 'portuguese', 'punjabi', 'romanian', 'russian', 'samoan', 'scots gaelic', 'serbian', 'sesotho', 'shona', 'sindhi', 'sinhala', 'slovak', 'slovenian', 'somali', 'spanish', 'sundanese', 'swahili', 'swedish', 'tajik', 'tamil', 'telugu', 'thai', 'turkish', 'ukrainian', 'urdu', 'uyghur', 'uzbek', 'vietnamese', 'welsh', 'xhosa', 'yiddish', 'yoruba', 'zulu' -""" - -# Load example images -torch.hub.download_url_to_file('http://farm3.staticflickr.com/2710/4520550856_7a9f9ea59d_z.jpg', 'apple.jpg') -torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg') - -# Define home page variables -image = gr.inputs.Image(type="pil") -question = gr.inputs.Textbox(label="Question") -answer = gr.outputs.Textbox(label="Predicted answer") -examples = [["apple.jpg", "Qu'est-ce que c'est dans ma main?"], ["cats.jpg", "What are the cats doing?"]] - -demo = gr.Interface(fn=vqa_main, - inputs=[image, question], - outputs="text", - examples=examples, - title=title, - description=description, - article=article, - enable_queue = True) -demo.launch(debug=True, show_error = True) \ No newline at end of file diff --git a/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/__init__.py b/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/__init__.py deleted file mode 100644 index fe08549e9c92f0e8a7bc96cead3277dedd12583c..0000000000000000000000000000000000000000 --- a/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -""" -from .save_images import save_samples, get_output_folder -from .depth import DepthModel -from .prompt import sanitize -from .animation import construct_RotationMatrixHomogenous, getRotationMatrixManual, getPoints_for_PerspectiveTranformEstimation, warpMatrix, anim_frame_warp_2d, anim_frame_warp_3d -from .generate import add_noise, load_img, load_mask_latent, prepare_mask -""" \ No newline at end of file diff --git a/spaces/jackyliang42/code-as-policies/lmp.py b/spaces/jackyliang42/code-as-policies/lmp.py deleted file mode 100644 index 7603fcb775ad957771724ac6bf41c250b1cc33ae..0000000000000000000000000000000000000000 --- a/spaces/jackyliang42/code-as-policies/lmp.py +++ /dev/null @@ -1,258 +0,0 @@ -from time import sleep -import ast -import astunparse -import openai -from openai.error import RateLimitError, APIConnectionError -from pygments import highlight -from pygments.lexers import PythonLexer -from pygments.formatters import TerminalFormatter - - -class LMP: - - def __init__(self, name, cfg, lmp_fgen, fixed_vars, variable_vars, md_logger): - self._name = name - self._cfg = cfg - self._md_logger = md_logger - - with open(self._cfg['prompt_path'], 'r') as f: - self._base_prompt = f.read() - - self._stop_tokens = list(self._cfg['stop']) - - self._lmp_fgen = lmp_fgen - - self._fixed_vars = fixed_vars - self._variable_vars = variable_vars - self.exec_hist = '' - - def clear_exec_hist(self): - self.exec_hist = '' - - def build_prompt(self, query, context=''): - if len(self._variable_vars) > 0: - variable_vars_imports_str = f"from utils import {', '.join(self._variable_vars.keys())}" - else: - variable_vars_imports_str = '' - prompt = self._base_prompt.replace('{variable_vars_imports}', variable_vars_imports_str) - - if self._cfg['maintain_session']: - prompt += f'\n{self.exec_hist}' - - if context != '': - prompt += f'\n{context}' - - use_query = f'{self._cfg["query_prefix"]}{query}{self._cfg["query_suffix"]}' - prompt += f'\n{use_query}' - - return prompt, use_query - - def __call__(self, query, context='', **kwargs): - prompt, use_query = self.build_prompt(query, context=context) - - while True: - try: - code_str = openai.Completion.create( - prompt=prompt, - stop=self._stop_tokens, - temperature=self._cfg['temperature'], - engine=self._cfg['engine'], - max_tokens=self._cfg['max_tokens'] - )['choices'][0]['text'].strip() - break - except (RateLimitError, APIConnectionError) as e: - print(f'OpenAI API got err {e}') - print('Retrying after 10s.') - sleep(10) - - if self._cfg['include_context'] and context != '': - to_exec = f'{context}\n{code_str}' - to_log = f'{context}\n{use_query}\n{code_str}' - else: - to_exec = code_str - to_log = f'{use_query}\n{to_exec}' - - to_log_pretty = highlight(to_log, PythonLexer(), TerminalFormatter()) - print(f'LMP {self._name} generated code:\n{to_log_pretty}') - self._md_logger.log_text(f'LMP {self._name} Generated Code:') - self._md_logger.log_code(to_log) - - new_fs = self._lmp_fgen.create_new_fs_from_code(code_str) - self._variable_vars.update(new_fs) - - gvars = merge_dicts([self._fixed_vars, self._variable_vars]) - lvars = kwargs - - if not self._cfg['debug_mode']: - exec_safe(to_exec, gvars, lvars) - - self.exec_hist += f'\n{to_exec}' - - if self._cfg['maintain_session']: - self._variable_vars.update(lvars) - - if self._cfg['has_return']: - return lvars[self._cfg['return_val_name']] - - -class LMPFGen: - - def __init__(self, cfg, fixed_vars, variable_vars, md_logger): - self._cfg = cfg - - self._stop_tokens = list(self._cfg['stop']) - self._fixed_vars = fixed_vars - self._variable_vars = variable_vars - self._md_logger = md_logger - - with open(self._cfg['prompt_path'], 'r') as f: - self._base_prompt = f.read() - - def create_f_from_sig(self, f_name, f_sig, other_vars=None, fix_bugs=False, return_src=False): - print(f'Creating function: {f_sig}') - - use_query = f'{self._cfg["query_prefix"]}{f_sig}{self._cfg["query_suffix"]}' - prompt = f'{self._base_prompt}\n{use_query}' - - while True: - try: - f_src = openai.Completion.create( - prompt=prompt, - stop=self._stop_tokens, - temperature=self._cfg['temperature'], - engine=self._cfg['engine'], - max_tokens=self._cfg['max_tokens'] - )['choices'][0]['text'].strip() - break - except (RateLimitError, APIConnectionError) as e: - print(f'OpenAI API got err {e}') - print('Retrying after 10s.') - sleep(10) - - if fix_bugs: - f_src = openai.Edit.create( - model='code-davinci-edit-001', - input='# ' + f_src, - temperature=0, - instruction='Fix the bug if there is one. Improve readability. Keep same inputs and outputs. Only small changes. No comments.', - )['choices'][0]['text'].strip() - - if other_vars is None: - other_vars = {} - gvars = merge_dicts([self._fixed_vars, self._variable_vars, other_vars]) - lvars = {} - - exec_safe(f_src, gvars, lvars) - - f = lvars[f_name] - - to_print = f'{use_query}\n{f_src}' - to_print_pretty = highlight(to_print, PythonLexer(), TerminalFormatter()) - print(f'LMPFGen generated code:\n{to_print_pretty}') - self._md_logger.log_text('Generated Function:') - self._md_logger.log_code(to_print) - - if return_src: - return f, f_src - return f - - def create_new_fs_from_code(self, code_str, other_vars=None, fix_bugs=False, return_src=False): - fs, f_assigns = {}, {} - f_parser = FunctionParser(fs, f_assigns) - f_parser.visit(ast.parse(code_str)) - for f_name, f_assign in f_assigns.items(): - if f_name in fs: - fs[f_name] = f_assign - - if other_vars is None: - other_vars = {} - - new_fs = {} - srcs = {} - for f_name, f_sig in fs.items(): - all_vars = merge_dicts([self._fixed_vars, self._variable_vars, new_fs, other_vars]) - if not var_exists(f_name, all_vars): - f, f_src = self.create_f_from_sig(f_name, f_sig, new_fs, fix_bugs=fix_bugs, return_src=True) - - # recursively define child_fs in the function body if needed - f_def_body = astunparse.unparse(ast.parse(f_src).body[0].body) - child_fs, child_f_srcs = self.create_new_fs_from_code( - f_def_body, other_vars=all_vars, fix_bugs=fix_bugs, return_src=True - ) - - if len(child_fs) > 0: - new_fs.update(child_fs) - srcs.update(child_f_srcs) - - # redefine parent f so newly created child_fs are in scope - gvars = merge_dicts([self._fixed_vars, self._variable_vars, new_fs, other_vars]) - lvars = {} - - exec_safe(f_src, gvars, lvars) - - f = lvars[f_name] - - new_fs[f_name], srcs[f_name] = f, f_src - - if return_src: - return new_fs, srcs - return new_fs - - -class FunctionParser(ast.NodeTransformer): - - def __init__(self, fs, f_assigns): - super().__init__() - self._fs = fs - self._f_assigns = f_assigns - - def visit_Call(self, node): - self.generic_visit(node) - if isinstance(node.func, ast.Name): - f_sig = astunparse.unparse(node).strip() - f_name = astunparse.unparse(node.func).strip() - self._fs[f_name] = f_sig - return node - - def visit_Assign(self, node): - self.generic_visit(node) - if isinstance(node.value, ast.Call): - assign_str = astunparse.unparse(node).strip() - f_name = astunparse.unparse(node.value.func).strip() - self._f_assigns[f_name] = assign_str - return node - - -def var_exists(name, all_vars): - try: - eval(name, all_vars) - except: - exists = False - else: - exists = True - return exists - - -def merge_dicts(dicts): - return { - k : v - for d in dicts - for k, v in d.items() - } - - -def exec_safe(code_str, gvars=None, lvars=None): - banned_phrases = ['import', '__'] - for phrase in banned_phrases: - assert phrase not in code_str - - if gvars is None: - gvars = {} - if lvars is None: - lvars = {} - empty_fn = lambda *args, **kwargs: None - custom_gvars = merge_dicts([ - gvars, - {'exec': empty_fn, 'eval': empty_fn} - ]) - exec(code_str, custom_gvars, lvars) \ No newline at end of file diff --git a/spaces/jbilcke-hf/AnimateDiff/download_bashscripts/7-FilmVelvia.sh b/spaces/jbilcke-hf/AnimateDiff/download_bashscripts/7-FilmVelvia.sh deleted file mode 100644 index 53aa688a0d5b2ac1bfa5a080cc4151e1a92ed9f0..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/AnimateDiff/download_bashscripts/7-FilmVelvia.sh +++ /dev/null @@ -1,3 +0,0 @@ -#!/bin/bash -wget https://civitai.com/api/download/models/90115 -P models/DreamBooth_LoRA/ --content-disposition --no-check-certificate -wget https://civitai.com/api/download/models/92475 -P models/DreamBooth_LoRA/ --content-disposition --no-check-certificate diff --git a/spaces/jbilcke-hf/LifeSim/src/app/agents/types.ts b/spaces/jbilcke-hf/LifeSim/src/app/agents/types.ts deleted file mode 100644 index 4e0201fb4dfbe24dafff2ae0828e77b4ec1a82f0..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/LifeSim/src/app/agents/types.ts +++ /dev/null @@ -1,15 +0,0 @@ -export type AgentType = 'ant' | 'fish' | 'fox' | 'smith' | 'city' | 'dungeon' - -export interface Scene { - action: string - position: string - light: string - actionnables: string[] - prompt: string -} - -export interface Agent { - title: string - type: AgentType - simulate: () => Scene -} diff --git a/spaces/jcenaa/Segment-Any-RGBD/datasets/scannet_preprocess/preprocess_scannet.py b/spaces/jcenaa/Segment-Any-RGBD/datasets/scannet_preprocess/preprocess_scannet.py deleted file mode 100644 index 714a18d9ceeb7825824ce1c8e0f81d7e33969894..0000000000000000000000000000000000000000 --- a/spaces/jcenaa/Segment-Any-RGBD/datasets/scannet_preprocess/preprocess_scannet.py +++ /dev/null @@ -1,215 +0,0 @@ -""" -Preprocessing Script for ScanNet 20/200 - -Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com) -Please cite our work if the code is helpful to you. -""" - -import warnings - -import torch - -warnings.filterwarnings("ignore", category=DeprecationWarning) - -import sys -import os -import argparse -import glob -import json -import plyfile -import numpy as np -import pandas as pd -import multiprocessing as mp -from concurrent.futures import ProcessPoolExecutor -from itertools import repeat - -# Load external constants -from meta_data.scannet200_constants import VALID_CLASS_IDS_200, VALID_CLASS_IDS_20 - -CLOUD_FILE_PFIX = '_vh_clean_2' -SEGMENTS_FILE_PFIX = '.0.010000.segs.json' -AGGREGATIONS_FILE_PFIX = '.aggregation.json' -CLASS_IDS200 = VALID_CLASS_IDS_200 -CLASS_IDS20 = VALID_CLASS_IDS_20 -IGNORE_INDEX = -1 - - -def read_plymesh(filepath): - """Read ply file and return it as numpy array. Returns None if emtpy.""" - with open(filepath, 'rb') as f: - plydata = plyfile.PlyData.read(f) - if plydata.elements: - vertices = pd.DataFrame(plydata['vertex'].data).values - faces = np.stack(plydata['face'].data['vertex_indices'], axis=0) - return vertices, faces - - -# Map the raw category id to the point cloud -def point_indices_from_group(seg_indices, group, labels_pd): - group_segments = np.array(group['segments']) - label = group['label'] - - # Map the category name to id - label_id20 = labels_pd[labels_pd['raw_category'] == label]['nyu40id'] - label_id20 = int(label_id20.iloc[0]) if len(label_id20) > 0 else 0 - label_id200 = labels_pd[labels_pd['raw_category'] == label]['id'] - label_id200 = int(label_id200.iloc[0]) if len(label_id200) > 0 else 0 - - # Only store for the valid categories - if label_id20 in CLASS_IDS20: - label_id20 = CLASS_IDS20.index(label_id20) - else: - label_id20 = IGNORE_INDEX - - if label_id200 in CLASS_IDS200: - label_id200 = CLASS_IDS200.index(label_id200) - else: - label_id200 = IGNORE_INDEX - - # get points, where segment indices (points labelled with segment ids) are in the group segment list - point_idx = np.where(np.isin(seg_indices, group_segments))[0] - return point_idx, label_id20, label_id200 - - -def face_normal(vertex, face): - v01 = vertex[face[:, 1]] - vertex[face[:, 0]] - v02 = vertex[face[:, 2]] - vertex[face[:, 0]] - vec = np.cross(v01, v02) - length = np.sqrt(np.sum(vec ** 2, axis=1, keepdims=True)) + 1.0e-8 - nf = vec / length - area = length * 0.5 - return nf, area - - -def vertex_normal(vertex, face): - nf, area = face_normal(vertex, face) - nf = nf * area - - nv = np.zeros_like(vertex) - for i in range(face.shape[0]): - nv[face[i]] += nf[i] - - length = np.sqrt(np.sum(nv ** 2, axis=1, keepdims=True)) + 1.0e-8 - nv = nv / length - return nv - - -def handle_process(scene_path, output_path, labels_pd, train_scenes, val_scenes, parse_normals=True): - scene_id = os.path.basename(scene_path) - mesh_path = os.path.join(scene_path, f'{scene_id}{CLOUD_FILE_PFIX}.ply') - segments_file = os.path.join(scene_path, f'{scene_id}{CLOUD_FILE_PFIX}{SEGMENTS_FILE_PFIX}') - aggregations_file = os.path.join(scene_path, f'{scene_id}{AGGREGATIONS_FILE_PFIX}') - info_file = os.path.join(scene_path, f'{scene_id}.txt') - - if scene_id in train_scenes: - output_file = os.path.join(output_path, 'train', f'{scene_id}.pth') - split_name = 'train' - elif scene_id in val_scenes: - output_file = os.path.join(output_path, 'val', f'{scene_id}.pth') - split_name = 'val' - else: - output_file = os.path.join(output_path, 'test', f'{scene_id}.pth') - split_name = 'test' - - print(f'Processing: {scene_id} in {split_name}') - - vertices, faces = read_plymesh(mesh_path) - coords = vertices[:, :3] - colors = vertices[:, 3:6] - save_dict = dict(coord=coords, color=colors, scene_id=scene_id) - - # # Rotating the mesh to axis aligned - # info_dict = {} - # with open(info_file) as f: - # for line in f: - # (key, val) = line.split(" = ") - # info_dict[key] = np.fromstring(val, sep=' ') - # - # if 'axisAlignment' not in info_dict: - # rot_matrix = np.identity(4) - # else: - # rot_matrix = info_dict['axisAlignment'].reshape(4, 4) - # r_coords = coords.transpose() - # r_coords = np.append(r_coords, np.ones((1, r_coords.shape[1])), axis=0) - # r_coords = np.dot(rot_matrix, r_coords) - # coords = r_coords - - # Parse Normals - if parse_normals: - save_dict["normal"] = vertex_normal(coords, faces) - - # Load segments file - if split_name != "test": - with open(segments_file) as f: - segments = json.load(f) - seg_indices = np.array(segments['segIndices']) - - # Load Aggregations file - with open(aggregations_file) as f: - aggregation = json.load(f) - seg_groups = np.array(aggregation['segGroups']) - - # Generate new labels - semantic_gt20 = np.ones((vertices.shape[0])) * IGNORE_INDEX - semantic_gt200 = np.ones((vertices.shape[0])) * IGNORE_INDEX - instance_ids = np.ones((vertices.shape[0])) * IGNORE_INDEX - for group in seg_groups: - point_idx, label_id20, label_id200 = \ - point_indices_from_group(seg_indices, group, labels_pd) - - semantic_gt20[point_idx] = label_id20 - semantic_gt200[point_idx] = label_id200 - instance_ids[point_idx] = group['id'] - - semantic_gt20 = semantic_gt20.astype(int) - semantic_gt200 = semantic_gt200.astype(int) - instance_ids = instance_ids.astype(int) - - save_dict["semantic_gt20"] = semantic_gt20 - save_dict["semantic_gt200"] = semantic_gt200 - save_dict["instance_gt"] = instance_ids - - # Concatenate with original cloud - processed_vertices = np.hstack((semantic_gt200, instance_ids)) - - if np.any(np.isnan(processed_vertices)) or not np.all(np.isfinite(processed_vertices)): - raise ValueError(f'Find NaN in Scene: {scene_id}') - - # Save processed data - torch.save(save_dict, output_file) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--dataset_root', required=True, help='Path to the ScanNet dataset containing scene folders') - parser.add_argument('--output_root', required=True, help='Output path where train/val folders will be located') - parser.add_argument('--parse_normals', default=True, type=bool, help='Whether parse point normals') - config = parser.parse_args() - - # Load label map - labels_pd = pd.read_csv('scannet-preprocess/meta_data/scannetv2-labels.combined.tsv', - sep='\t', header=0) - - # Load train/val splits - with open('scannet-preprocess/meta_data/scannetv2_train.txt') as train_file: - train_scenes = train_file.read().splitlines() - with open('scannet-preprocess/meta_data/scannetv2_val.txt') as val_file: - val_scenes = val_file.read().splitlines() - - # Create output directories - train_output_dir = os.path.join(config.output_root, 'train') - os.makedirs(train_output_dir, exist_ok=True) - val_output_dir = os.path.join(config.output_root, 'val') - os.makedirs(val_output_dir, exist_ok=True) - test_output_dir = os.path.join(config.output_root, 'test') - os.makedirs(test_output_dir, exist_ok=True) - - # Load scene paths - scene_paths = sorted(glob.glob(config.dataset_root + '/scans*/scene*')) - - # Preprocess data. - print('Processing scenes...') - pool = ProcessPoolExecutor(max_workers=mp.cpu_count()) - # pool = ProcessPoolExecutor(max_workers=1) - _ = list(pool.map(handle_process, scene_paths, repeat(config.output_root), repeat(labels_pd), repeat(train_scenes), - repeat(val_scenes), repeat(config.parse_normals))) diff --git a/spaces/jergra43/llama2-7b-ggml-chat-app/llama_chat_format.py b/spaces/jergra43/llama2-7b-ggml-chat-app/llama_chat_format.py deleted file mode 100644 index dad9e9eb48d2f9939006cbf19c5c1fdb09e6a397..0000000000000000000000000000000000000000 --- a/spaces/jergra43/llama2-7b-ggml-chat-app/llama_chat_format.py +++ /dev/null @@ -1,38 +0,0 @@ -BOS, EOS = "
Augmenting Convolutional networks with attention-based aggregation
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How to Download and Install AMD A8-4500M APU with Radeon HD 7640G Drivers
-If you have a laptop with an AMD A8-4500M APU with Radeon HD 7640G graphics, you may need to update your drivers to get the best performance and compatibility. Drivers are software that allow your hardware to communicate with your operating system and applications. Updating your drivers can improve your system stability, security, and gaming experience.
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If you encounter any problems or have any questions, you can visit the AMD Radeon Software Help Center [^1^] or contact the AMD Customer Care Team [^1^] for assistance.
-The AMD A8-4500M APU with Radeon HD 7640G is a mobile processor that was launched in May 2012. It has four cores, four threads, and a base clock of 1.9 GHz that can boost up to 2.8 GHz. It also has a built-in graphics card that supports DirectX 11 and AMD HD3D Technology. It is part of the AMD A-Series Processors family and uses the Trinity architecture with Socket FS1r2. It has a default TDP of 35W and a L2 cache of 4MB [^2^] [^3^].
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- -If you want to see how your AMD A8-4500M APU with Radeon HD 7640G graphics performs in various tasks and games, you can check out some benchmarks online. Benchmarks are tests that measure the speed and performance of a hardware component or a system. They can help you compare your processor and graphics card with other models and see if they meet your expectations and needs.
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-Another website that offers detailed CPU and GPU benchmarks is NotebookCheck [^3^]. It provides comprehensive reviews and tests of various laptops, processors, graphics cards, and games. According to NotebookCheck, the AMD A8-4500M APU with Radeon HD 7640G graphics can handle some older and less demanding games at low to medium settings, such as FIFA 13, Diablo III, or Counter-Strike: Global Offensive. However, it struggles with newer and more demanding games at high settings, such as Battlefield 3, Crysis 3, or Far Cry 3. It also has a low performance in synthetic benchmarks, such as Cinebench R11.5 or x264 HD Benchmark.
-As you can see, the AMD A8-4500M APU with Radeon HD 7640G graphics is not a very powerful processor and graphics card combination. It may be enough for some users who only need their laptop for basic tasks and occasional gaming, but it may not satisfy those who need more speed and performance for their work or entertainment. If you are looking for a better processor and graphics card for your laptop, you may want to consider upgrading to a newer model or switching to a different brand.
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\ No newline at end of file diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Albania Una E Mille Pdf 15.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Albania Una E Mille Pdf 15.md deleted file mode 100644 index 7d325019f18dbeb3a548717783541e75c20b0471..0000000000000000000000000000000000000000 --- a/spaces/lincquiQcaudo/Top-20-Diffusion/Albania Una E Mille Pdf 15.md +++ /dev/null @@ -1,11 +0,0 @@ -
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-e time to complete degree.. the united states and one of only 15 operating within a medical school. ashley miller, clinical support coordinator. data on substance use among 15- to 16-year-old students in. levels (10 % or less) of illicit drug use were noted in albania,.
-ambassador of the republic of albania to the united kingdom. ms. julcileia da silva de s e vasconcelos. mr. darren miller. albania. labour force survey. labour force survey (click on icon publications. page 18-22 of its report and questionnaire: pages 131 to 166 of its pdf.
-the last national study on alcohol consumption in albania, conducted in 1979, showed that there was no serious problem. when the study was. alcohol consumption in albania, which was not. data on substance use among 15- to 16-year-old students in. levels (10 % or less) of illicit drug use were noted in albania,.
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\ No newline at end of file diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Free Download Microsoft Sql Server 2000 Setup For Windows 7 LINK.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Free Download Microsoft Sql Server 2000 Setup For Windows 7 LINK.md deleted file mode 100644 index 73d90ee7e3788ff13e710bf2fbf851cda96961f6..0000000000000000000000000000000000000000 --- a/spaces/lincquiQcaudo/Top-20-Diffusion/Free Download Microsoft Sql Server 2000 Setup For Windows 7 LINK.md +++ /dev/null @@ -1,6 +0,0 @@ -
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- diff --git a/spaces/logasja/LowKey/backbone/__init__.py b/spaces/logasja/LowKey/backbone/__init__.py deleted file mode 100644 index 8b137891791fe96927ad78e64b0aad7bded08bdc..0000000000000000000000000000000000000000 --- a/spaces/logasja/LowKey/backbone/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/spaces/ls291/ChatSQL/utility/__init__.py b/spaces/ls291/ChatSQL/utility/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/luxuedong/lxd/src/pages/api/kblob.ts b/spaces/luxuedong/lxd/src/pages/api/kblob.ts deleted file mode 100644 index 0ce7e6063cdc06838e76f1cff1d5982d34ef52de..0000000000000000000000000000000000000000 --- a/spaces/luxuedong/lxd/src/pages/api/kblob.ts +++ /dev/null @@ -1,56 +0,0 @@ -'use server' - -import { NextApiRequest, NextApiResponse } from 'next' -import FormData from 'form-data' -import { fetch } from '@/lib/isomorphic' -import { KBlobRequest } from '@/lib/bots/bing/types' - -const API_DOMAIN = 'https://bing.vcanbb.top' - -export const config = { - api: { - bodyParser: { - sizeLimit: '10mb' // Set desired value here - } - } -} - -export default async function handler(req: NextApiRequest, res: NextApiResponse) { - try { - const { knowledgeRequest, imageBase64 } = req.body as KBlobRequest - - const formData = new FormData() - formData.append('knowledgeRequest', JSON.stringify(knowledgeRequest)) - if (imageBase64) { - formData.append('imageBase64', imageBase64) - } - - const response = await fetch(`${API_DOMAIN}/images/kblob`, - { - method: 'POST', - body: formData.getBuffer(), - headers: { - "sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"", - "sec-ch-ua-mobile": "?0", - "sec-ch-ua-platform": "\"Windows\"", - "Referer": `${API_DOMAIN}/web/index.html`, - "Referrer-Policy": "origin-when-cross-origin", - 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32', - ...formData.getHeaders() - } - } - ).then(res => res.text()) - - res.writeHead(200, { - 'Content-Type': 'application/json', - }) - res.end(response || JSON.stringify({ result: { value: 'UploadFailed', message: '请更换 IP 或代理后重试' } })) - } catch (e) { - return res.json({ - result: { - value: 'UploadFailed', - message: `${e}` - } - }) - } -} diff --git a/spaces/ma-xu/LIVE/thrust/thrust/cmake/FindTBB.cmake b/spaces/ma-xu/LIVE/thrust/thrust/cmake/FindTBB.cmake deleted file mode 100644 index f0d5c8119b56036c23f930b6c7ea3f470f513d72..0000000000000000000000000000000000000000 --- a/spaces/ma-xu/LIVE/thrust/thrust/cmake/FindTBB.cmake +++ /dev/null @@ -1,440 +0,0 @@ -# - Find ThreadingBuildingBlocks include dirs and libraries -# Use this module by invoking find_package with the form: -# find_package(TBB -# [REQUIRED] # Fail with error if TBB is not found -# ) # -# Once done, this will define -# -# TBB_FOUND - system has TBB -# TBB_INCLUDE_DIRS - the TBB include directories -# TBB_LIBRARIES - TBB libraries to be lined, doesn't include malloc or -# malloc proxy -# TBB::tbb - imported target for the TBB library -# -# TBB_VERSION - Product Version Number ("MAJOR.MINOR") -# TBB_VERSION_MAJOR - Major Product Version Number -# TBB_VERSION_MINOR - Minor Product Version Number -# TBB_INTERFACE_VERSION - Engineering Focused Version Number -# TBB_COMPATIBLE_INTERFACE_VERSION - The oldest major interface version -# still supported. This uses the engineering -# focused interface version numbers. -# -# TBB_MALLOC_FOUND - system has TBB malloc library -# TBB_MALLOC_INCLUDE_DIRS - the TBB malloc include directories -# TBB_MALLOC_LIBRARIES - The TBB malloc libraries to be lined -# TBB::malloc - imported target for the TBB malloc library -# -# TBB_MALLOC_PROXY_FOUND - system has TBB malloc proxy library -# TBB_MALLOC_PROXY_INCLUDE_DIRS = the TBB malloc proxy include directories -# TBB_MALLOC_PROXY_LIBRARIES - The TBB malloc proxy libraries to be lined -# TBB::malloc_proxy - imported target for the TBB malloc proxy library -# -# -# This module reads hints about search locations from variables: -# ENV TBB_ARCH_PLATFORM - for eg. set it to "mic" for Xeon Phi builds -# ENV TBB_ROOT or just TBB_ROOT - root directory of tbb installation -# ENV TBB_BUILD_PREFIX - specifies the build prefix for user built tbb -# libraries. Should be specified with ENV TBB_ROOT -# and optionally... -# ENV TBB_BUILD_DIR - if build directory is different than ${TBB_ROOT}/build -# -# -# Modified by Robert Maynard from the original OGRE source -# -#------------------------------------------------------------------- -# This file is part of the CMake build system for OGRE -# (Object-oriented Graphics Rendering Engine) -# For the latest info, see http://www.ogre3d.org/ -# -# The contents of this file are placed in the public domain. Feel -# free to make use of it in any way you like. -#------------------------------------------------------------------- -# -#============================================================================= -# Copyright 2010-2012 Kitware, Inc. -# Copyright 2012 Rolf Eike Beer
5th BIGDATA CONFERENCE
' -st.markdown(head, unsafe_allow_html=True) - -st.title('시각장애인을 위한 안내見 서비스') - -personal_selected = st.sidebar.selectbox( - "Input Image Formation", - ('Real-Time', 'Saved', 'Video') -) - -st.sidebar.write('Selected: ', personal_selected) - -# torch : 1.13.0, cuda : 11.6, nvidia : 512.78 -device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') -model = torch.load('./model_pt/Faster-RCNN_0.3116.pt') - -if personal_selected == "Real-Time" : - # 실시간 사진 찍기 - picture = st.camera_input("Take a picture") - - if picture: - st.image(picture) - - # Object Detection 결과 이미지 출력 - image = Image.open(picture) - img = asarray(image) - - # 후처리 문장 만들어주기 - object_text, object_list, cut_list = object_detection(img) - ocr_text = ocr(object_list, cut_list) - text = object_text + ocr_text - - # 문장 결과 오디오로 출력하기 - tts = gTTS(text, lang="ko") - tts.save("sample_audio.mp3") - - audio_file = open('sample_audio.mp3', 'rb') - audio_bytes = audio_file.read() - - st.audio(audio_bytes, format='audio/mp3') - - -elif personal_selected == "Saved" : - # 이미지 업로드 - upload_file = st.file_uploader('Select an Image', type='jpg') - - if upload_file is not None: - image = Image.open(f'./image/{upload_file.name}') - st.image(image, caption='The image file has been uploaded.') - img = asarray(image) - - # 후처리 문장 만들어주기 - object_text, object_list, cut_list = object_detection(img) - ocr_text = ocr(object_list, cut_list) - text = object_text + ocr_text - - # 문장 결과 오디오로 출력하기 - tts = gTTS(text, lang="ko") - tts.save("sample_audio.mp3") - - audio_file = open('sample_audio.mp3', 'rb') - audio_bytes = audio_file.read() - - st.audio(audio_bytes, format='audio/mp3') - - -elif personal_selected == "Video" : - # 비디오 업로드 - uploaded_video = st.file_uploader("Choose video", type=["mp4", "mov"]) - - if uploaded_video is not None: - video_file = open(f'./video/{uploaded_video.name}', 'rb') - video_bytes = video_file.read() - - st.video(video_bytes) - - # 이미지 캡쳐 - capture = cv2.VideoCapture(uploaded_video.name) - ret, frame = capture.read() - - now = datetime.datetime.now().strftime("%d_%H-%M-%S") - key = cv2.waitKey(33) - - if st.button('캡처할 버튼'): - st.write("캡쳐") - st.image(frame) - # frame.save(f'./{str(now)}.png') - cv2.imwrite("./" + str(now) + ".png", frame) - - capture.release() - cv2.destroyAllWindows() - - # 문장 결과 오디오로 출력하기 - text = "" - tts = gTTS(text, lang="ko") - tts.save("sample_audio.mp3") - - audio_file = open('sample_audio.mp3', 'rb') - audio_bytes = audio_file.read() - - st.audio(audio_bytes, format='audio/mp3') - - \ No newline at end of file diff --git a/spaces/manhkhanhUIT/BOPBTL/Face_Enhancement/options/base_options.py b/spaces/manhkhanhUIT/BOPBTL/Face_Enhancement/options/base_options.py deleted file mode 100644 index af67450092b2428c71f2a40941efe612378ea0bd..0000000000000000000000000000000000000000 --- a/spaces/manhkhanhUIT/BOPBTL/Face_Enhancement/options/base_options.py +++ /dev/null @@ -1,294 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. - -import sys -import argparse -import os -from util import util -import torch -import models -import data -import pickle - - -class BaseOptions: - def __init__(self): - self.initialized = False - - def initialize(self, parser): - # experiment specifics - parser.add_argument( - "--name", - type=str, - default="label2coco", - help="name of the experiment. It decides where to store samples and models", - ) - - parser.add_argument( - "--gpu_ids", type=str, default="0", help="gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU" - ) - parser.add_argument( - "--checkpoints_dir", type=str, default="./checkpoints", help="models are saved here" - ) - parser.add_argument("--model", type=str, default="pix2pix", help="which model to use") - parser.add_argument( - "--norm_G", - type=str, - default="spectralinstance", - help="instance normalization or batch normalization", - ) - parser.add_argument( - "--norm_D", - type=str, - default="spectralinstance", - help="instance normalization or batch normalization", - ) - parser.add_argument( - "--norm_E", - type=str, - default="spectralinstance", - help="instance normalization or batch normalization", - ) - parser.add_argument("--phase", type=str, default="train", help="train, val, test, etc") - - # input/output sizes - parser.add_argument("--batchSize", type=int, default=1, help="input batch size") - parser.add_argument( - "--preprocess_mode", - type=str, - default="scale_width_and_crop", - help="scaling and cropping of images at load time.", - choices=( - "resize_and_crop", - "crop", - "scale_width", - "scale_width_and_crop", - "scale_shortside", - "scale_shortside_and_crop", - "fixed", - "none", - "resize", - ), - ) - parser.add_argument( - "--load_size", - type=int, - default=1024, - help="Scale images to this size. The final image will be cropped to --crop_size.", - ) - parser.add_argument( - "--crop_size", - type=int, - default=512, - help="Crop to the width of crop_size (after initially scaling the images to load_size.)", - ) - parser.add_argument( - "--aspect_ratio", - type=float, - default=1.0, - help="The ratio width/height. The final height of the load image will be crop_size/aspect_ratio", - ) - parser.add_argument( - "--label_nc", - type=int, - default=182, - help="# of input label classes without unknown class. If you have unknown class as class label, specify --contain_dopntcare_label.", - ) - parser.add_argument( - "--contain_dontcare_label", - action="store_true", - help="if the label map contains dontcare label (dontcare=255)", - ) - parser.add_argument("--output_nc", type=int, default=3, help="# of output image channels") - - # for setting inputs - parser.add_argument("--dataroot", type=str, default="./datasets/cityscapes/") - parser.add_argument("--dataset_mode", type=str, default="coco") - parser.add_argument( - "--serial_batches", - action="store_true", - help="if true, takes images in order to make batches, otherwise takes them randomly", - ) - parser.add_argument( - "--no_flip", - action="store_true", - help="if specified, do not flip the images for data argumentation", - ) - parser.add_argument("--nThreads", default=0, type=int, help="# threads for loading data") - parser.add_argument( - "--max_dataset_size", - type=int, - default=sys.maxsize, - help="Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.", - ) - parser.add_argument( - "--load_from_opt_file", - action="store_true", - help="load the options from checkpoints and use that as default", - ) - parser.add_argument( - "--cache_filelist_write", - action="store_true", - help="saves the current filelist into a text file, so that it loads faster", - ) - parser.add_argument( - "--cache_filelist_read", action="store_true", help="reads from the file list cache" - ) - - # for displays - parser.add_argument("--display_winsize", type=int, default=400, help="display window size") - - # for generator - parser.add_argument( - "--netG", type=str, default="spade", help="selects model to use for netG (pix2pixhd | spade)" - ) - parser.add_argument("--ngf", type=int, default=64, help="# of gen filters in first conv layer") - parser.add_argument( - "--init_type", - type=str, - default="xavier", - help="network initialization [normal|xavier|kaiming|orthogonal]", - ) - parser.add_argument( - "--init_variance", type=float, default=0.02, help="variance of the initialization distribution" - ) - parser.add_argument("--z_dim", type=int, default=256, help="dimension of the latent z vector") - parser.add_argument( - "--no_parsing_map", action="store_true", help="During training, we do not use the parsing map" - ) - - # for instance-wise features - parser.add_argument( - "--no_instance", action="store_true", help="if specified, do *not* add instance map as input" - ) - parser.add_argument( - "--nef", type=int, default=16, help="# of encoder filters in the first conv layer" - ) - parser.add_argument("--use_vae", action="store_true", help="enable training with an image encoder.") - parser.add_argument( - "--tensorboard_log", action="store_true", help="use tensorboard to record the resutls" - ) - - # parser.add_argument('--img_dir',) - parser.add_argument( - "--old_face_folder", type=str, default="", help="The folder name of input old face" - ) - parser.add_argument( - "--old_face_label_folder", type=str, default="", help="The folder name of input old face label" - ) - - parser.add_argument("--injection_layer", type=str, default="all", help="") - - self.initialized = True - return parser - - def gather_options(self): - # initialize parser with basic options - if not self.initialized: - parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) - parser = self.initialize(parser) - - # get the basic options - opt, unknown = parser.parse_known_args() - - # modify model-related parser options - model_name = opt.model - model_option_setter = models.get_option_setter(model_name) - parser = model_option_setter(parser, self.isTrain) - - # modify dataset-related parser options - # dataset_mode = opt.dataset_mode - # dataset_option_setter = data.get_option_setter(dataset_mode) - # parser = dataset_option_setter(parser, self.isTrain) - - opt, unknown = parser.parse_known_args() - - # if there is opt_file, load it. - # The previous default options will be overwritten - if opt.load_from_opt_file: - parser = self.update_options_from_file(parser, opt) - - opt = parser.parse_args() - self.parser = parser - return opt - - def print_options(self, opt): - message = "" - message += "----------------- Options ---------------\n" - for k, v in sorted(vars(opt).items()): - comment = "" - default = self.parser.get_default(k) - if v != default: - comment = "\t[default: %s]" % str(default) - message += "{:>25}: {:<30}{}\n".format(str(k), str(v), comment) - message += "----------------- End -------------------" - # print(message) - - def option_file_path(self, opt, makedir=False): - expr_dir = os.path.join(opt.checkpoints_dir, opt.name) - if makedir: - util.mkdirs(expr_dir) - file_name = os.path.join(expr_dir, "opt") - return file_name - - def save_options(self, opt): - file_name = self.option_file_path(opt, makedir=True) - with open(file_name + ".txt", "wt") as opt_file: - for k, v in sorted(vars(opt).items()): - comment = "" - default = self.parser.get_default(k) - if v != default: - comment = "\t[default: %s]" % str(default) - opt_file.write("{:>25}: {:<30}{}\n".format(str(k), str(v), comment)) - - with open(file_name + ".pkl", "wb") as opt_file: - pickle.dump(opt, opt_file) - - def update_options_from_file(self, parser, opt): - new_opt = self.load_options(opt) - for k, v in sorted(vars(opt).items()): - if hasattr(new_opt, k) and v != getattr(new_opt, k): - new_val = getattr(new_opt, k) - parser.set_defaults(**{k: new_val}) - return parser - - def load_options(self, opt): - file_name = self.option_file_path(opt, makedir=False) - new_opt = pickle.load(open(file_name + ".pkl", "rb")) - return new_opt - - def parse(self, save=False): - - opt = self.gather_options() - opt.isTrain = self.isTrain # train or test - opt.contain_dontcare_label = False - - self.print_options(opt) - if opt.isTrain: - self.save_options(opt) - - # Set semantic_nc based on the option. - # This will be convenient in many places - opt.semantic_nc = ( - opt.label_nc + (1 if opt.contain_dontcare_label else 0) + (0 if opt.no_instance else 1) - ) - - # set gpu ids - str_ids = opt.gpu_ids.split(",") - opt.gpu_ids = [] - for str_id in str_ids: - int_id = int(str_id) - if int_id >= 0: - opt.gpu_ids.append(int_id) - - if len(opt.gpu_ids) > 0: - print("The main GPU is ") - print(opt.gpu_ids[0]) - torch.cuda.set_device(opt.gpu_ids[0]) - - assert ( - len(opt.gpu_ids) == 0 or opt.batchSize % len(opt.gpu_ids) == 0 - ), "Batch size %d is wrong. It must be a multiple of # GPUs %d." % (opt.batchSize, len(opt.gpu_ids)) - - self.opt = opt - return self.opt diff --git a/spaces/manhkhanhUIT/Image_Restoration_Colorization/Global/util/visualizer.py b/spaces/manhkhanhUIT/Image_Restoration_Colorization/Global/util/visualizer.py deleted file mode 100644 index 1a88df203aa95750ba911c77b32f6234863b8e79..0000000000000000000000000000000000000000 --- a/spaces/manhkhanhUIT/Image_Restoration_Colorization/Global/util/visualizer.py +++ /dev/null @@ -1,143 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. - -import numpy as np -import os -import ntpath -import time -from . import util -#from . import html -import scipy.misc -try: - from StringIO import StringIO # Python 2.7 -except ImportError: - from io import BytesIO # Python 3.x - -class Visualizer(): - def __init__(self, opt): - # self.opt = opt - self.tf_log = opt.tf_log - self.use_html = opt.isTrain and not opt.no_html - self.win_size = opt.display_winsize - self.name = opt.name - if self.tf_log: - import tensorflow as tf - self.tf = tf - self.log_dir = os.path.join(opt.checkpoints_dir, opt.name, 'logs') - self.writer = tf.summary.FileWriter(self.log_dir) - - if self.use_html: - self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web') - self.img_dir = os.path.join(self.web_dir, 'images') - print('create web directory %s...' % self.web_dir) - util.mkdirs([self.web_dir, self.img_dir]) - self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt') - with open(self.log_name, "a") as log_file: - now = time.strftime("%c") - log_file.write('================ Training Loss (%s) ================\n' % now) - - # |visuals|: dictionary of images to display or save - def display_current_results(self, visuals, epoch, step): - if self.tf_log: # show images in tensorboard output - img_summaries = [] - for label, image_numpy in visuals.items(): - # Write the image to a string - try: - s = StringIO() - except: - s = BytesIO() - scipy.misc.toimage(image_numpy).save(s, format="jpeg") - # Create an Image object - img_sum = self.tf.Summary.Image(encoded_image_string=s.getvalue(), height=image_numpy.shape[0], width=image_numpy.shape[1]) - # Create a Summary value - img_summaries.append(self.tf.Summary.Value(tag=label, image=img_sum)) - - # Create and write Summary - summary = self.tf.Summary(value=img_summaries) - self.writer.add_summary(summary, step) - - if self.use_html: # save images to a html file - for label, image_numpy in visuals.items(): - if isinstance(image_numpy, list): - for i in range(len(image_numpy)): - img_path = os.path.join(self.img_dir, 'epoch%.3d_%s_%d.jpg' % (epoch, label, i)) - util.save_image(image_numpy[i], img_path) - else: - img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.jpg' % (epoch, label)) - util.save_image(image_numpy, img_path) - - # update website - webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, refresh=30) - for n in range(epoch, 0, -1): - webpage.add_header('epoch [%d]' % n) - ims = [] - txts = [] - links = [] - - for label, image_numpy in visuals.items(): - if isinstance(image_numpy, list): - for i in range(len(image_numpy)): - img_path = 'epoch%.3d_%s_%d.jpg' % (n, label, i) - ims.append(img_path) - txts.append(label+str(i)) - links.append(img_path) - else: - img_path = 'epoch%.3d_%s.jpg' % (n, label) - ims.append(img_path) - txts.append(label) - links.append(img_path) - if len(ims) < 10: - webpage.add_images(ims, txts, links, width=self.win_size) - else: - num = int(round(len(ims)/2.0)) - webpage.add_images(ims[:num], txts[:num], links[:num], width=self.win_size) - webpage.add_images(ims[num:], txts[num:], links[num:], width=self.win_size) - webpage.save() - - # errors: dictionary of error labels and values - def plot_current_errors(self, errors, step): - if self.tf_log: - for tag, value in errors.items(): - summary = self.tf.Summary(value=[self.tf.Summary.Value(tag=tag, simple_value=value)]) - self.writer.add_summary(summary, step) - - # errors: same format as |errors| of plotCurrentErrors - def print_current_errors(self, epoch, i, errors, t, lr): - message = '(epoch: %d, iters: %d, time: %.3f lr: %.5f) ' % (epoch, i, t, lr) - for k, v in errors.items(): - if v != 0: - message += '%s: %.3f ' % (k, v) - - print(message) - with open(self.log_name, "a") as log_file: - log_file.write('%s\n' % message) - - - def print_save(self,message): - - print(message) - - with open(self.log_name,"a") as log_file: - log_file.write('%s\n'%message) - - - # save image to the disk - def save_images(self, webpage, visuals, image_path): - image_dir = webpage.get_image_dir() - short_path = ntpath.basename(image_path[0]) - name = os.path.splitext(short_path)[0] - - webpage.add_header(name) - ims = [] - txts = [] - links = [] - - for label, image_numpy in visuals.items(): - image_name = '%s_%s.jpg' % (name, label) - save_path = os.path.join(image_dir, image_name) - util.save_image(image_numpy, save_path) - - ims.append(image_name) - txts.append(label) - links.append(image_name) - webpage.add_images(ims, txts, links, width=self.win_size) diff --git a/spaces/manhkhanhUIT/Image_Restoration_Colorization/README.md b/spaces/manhkhanhUIT/Image_Restoration_Colorization/README.md deleted file mode 100644 index 2d7edf460ec6adbe74b72c6ac6768f8e82ff36f0..0000000000000000000000000000000000000000 --- a/spaces/manhkhanhUIT/Image_Restoration_Colorization/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: Image Restoration and Colorization -emoji: 🌟 -colorFrom: blue -colorTo: green -sdk: gradio -app_file: app.py -pinned: false ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio` or `streamlit` - -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. \ No newline at end of file diff --git a/spaces/marioboy/neil-breen/samples/README.md b/spaces/marioboy/neil-breen/samples/README.md deleted file mode 100644 index 1a392d86e42f72e83954619f563f4881da327236..0000000000000000000000000000000000000000 --- a/spaces/marioboy/neil-breen/samples/README.md +++ /dev/null @@ -1,22 +0,0 @@ -The audio files in this folder are provided for toolbox testing and -benchmarking purposes. These are the same reference utterances -used by the SV2TTS authors to generate the audio samples located at: -https://google.github.io/tacotron/publications/speaker_adaptation/index.html - -The `p240_00000.mp3` and `p260_00000.mp3` files are compressed -versions of audios from the VCTK corpus available at: -https://datashare.is.ed.ac.uk/handle/10283/3443 -VCTK.txt contains the copyright notices and licensing information. - -The `1320_00000.mp3`, `3575_00000.mp3`, `6829_00000.mp3` -and `8230_00000.mp3` files are compressed versions of audios -from the LibriSpeech dataset available at: https://openslr.org/12 -For these files, the following notice applies: -``` -LibriSpeech (c) 2014 by Vassil Panayotov - -LibriSpeech ASR corpus is licensed under a -Creative Commons Attribution 4.0 International License. - -See