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Functions639 in github.com/KeepTryingTo/Pytorch-GAN

⨍ Functions 639◇ Types & classes 107
↓ 23 callersMethodloss_function
(self, *args, **kwargs)
PyTorch-VAE/models/hvae.py:192
↓ 13 callersMethodsample
(self, batch_size:int, current_device: int, **kwargs)
PyTorch-VAE/models/hvae.py:233
↓ 9 callersMethod__init__
(self,channel,w_dim)
StyleGAN/net/model.py:109
↓ 6 callersFunctionshowImage
:param root: 主窗口 :return:
pix2pix/mainWindows.py:61
↓ 5 callersMethodgenerate
Given an input image x, returns the reconstructed image :param x: (Tensor) [B x C x H x W] :return: (Tensor) [B x C x H x W]
PyTorch-VAE/models/hvae.py:252
↓ 5 callersFunctionshowImage
:param root: 主窗口 :return:
SRGAN/mainWindows.py:62
↓ 4 callersMethod__init__
(self,in_channels,out_channels, discriminator = False,use_act = True, use_bn
SRGAN/models.py:12
↓ 4 callersMethod__init__
( self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, gain=2 )
ProGAN/model.py:48
↓ 4 callersMethod_block
(self,in_channels,out_channels,kernel_size,stride,padding)
github/net/Generator.py:37
↓ 4 callersMethod_block
(self,in_channels,out_channels,kernel_size,stride,padding)
WGANGPCode/net/Generator.py:37
↓ 4 callersFunctionshowImage
:param root: 主窗口 :return:
Code/mainWindows.py:62
↓ 3 callersFunctionCanvas_
(root)
SRGAN/mainWindows.py:45
↓ 3 callersFunctionCanvas_
(root)
Code/mainWindows.py:45
↓ 3 callersFunctionCanvas_
(root)
pix2pix/mainWindows.py:35
↓ 3 callersMethod_block
(self,in_channels,out_channels,kernel_size,stride,padding)
github/net/Discriminator.py:29
↓ 3 callersMethod_block
(self,in_channels,out_channels,kernel_size,stride,padding)
WGANGPCode/net/Discriminator.py:29
↓ 3 callersMethodcompute_kernel
(self, x1: Tensor, x2: Tensor)
PyTorch-VAE/models/info_vae.py:150
↓ 3 callersMethodcompute_kernel
(self, x1: Tensor, x2: Tensor)
PyTorch-VAE/models/wae_mmd.py:125
↓ 3 callersMethoddecode
(self, input: Tensor)
PyTorch-VAE/models/hvae.py:153
↓ 3 callersMethodlog_density_gaussian
Computes the log pdf of the Gaussian with parameters mu and logvar at x :param x: (Tensor) Point at whichGaussian PDF is to be evalua
PyTorch-VAE/models/betatc_vae.py:132
↓ 3 callersFunctionloss_fn
(inputs,outputs,loss_fn,z_mean,z_log_var,num_features = 784)
VAE_AutoEncoder/utils.py:101
↓ 3 callersMethodreparameterize
Will a single z be enough ti compute the expectation for the loss?? :param mu: (Tensor) Mean of the latent Gaussian :
PyTorch-VAE/models/hvae.py:158
↓ 3 callersMethodreparameterize
Reparameterization trick to sample from N(mu, var) from N(0,1). :param mu: (Tensor) Mean of the latent Gaussian [B x D]
PyTorch-VAE/models/lvae.py:197
↓ 2 callersFunctionCanvas_
(root)
StyleGAN/mainWindow.py:29
↓ 2 callersFunctionCanvas_
(root)
fc-CGANCode/mainWindow.py:34
↓ 2 callersFunctionCanvas_
(root)
GANCode/mainWindow.py:27
↓ 2 callersFunctionCanvas_
(root)
github/mainWindow.py:29
↓ 2 callersFunctionCanvas_
(root)
ProGAN/mainWindow.py:29
↓ 2 callersFunctionCanvas_
(root)
DCGAN/mainWindow.py:28
↓ 2 callersFunctionCanvas_
(root)
WGANGPCode/mainWindow.py:29
↓ 2 callersFunctionCanvas_
(root)
cnn-CGANCode/mainWindow.py:34
↓ 2 callersMethodI_function
(self, a, b, c, d)
PyTorch-VAE/models/gamma_vae.py:168
↓ 2 callersMethod__init__
(self,in_channels,out_channels,down=True,use_act=True,**kwargs)
Code/net/Generator.py:14
↓ 2 callersMethod__init__
(self, in_channels: int, latent_dims: List, hidden_dims: Li
PyTorch-VAE/models/lvae.py:64
↓ 2 callersMethod__init__
(self, in_channels: int, embedding_dim: int, num_embeddings
PyTorch-VAE/models/vq_vae.py:75
↓ 2 callersFunctionchoose_bytes_unit
(num_bytes)
StyleGAN/download_ffhq.py:134
↓ 2 callersFunctionconv_out_shape
(img_size)
PyTorch-VAE/models/lvae.py:9
↓ 2 callersMethoddecode
Maps the given latent codes of S samples onto the image space. :param z: (Tensor) [B x S x D] :return: (Tensor) [B x
PyTorch-VAE/models/miwae.py:98
↓ 2 callersMethoddecode
Maps the given latent codes onto the image space. :param z: (Tensor) [B x D] :return: (Tensor) [B x C x H x W]
PyTorch-VAE/models/dfcvae.py:107
↓ 2 callersMethoddecode
(self, z: Tensor)
PyTorch-VAE/models/swae.py:99
↓ 2 callersMethoddecode
Maps the given latent codes onto the image space. :param z: (Tensor) [B x D] :return: (Tensor) [B x C x H x W]
PyTorch-VAE/models/logcosh_vae.py:95
↓ 2 callersMethoddecode
Maps the given latent codes onto the image space. :param z: (Tensor) [B x D x Q] :return: (Tensor) [B x C x H x W]
PyTorch-VAE/models/cat_vae.py:105
↓ 2 callersMethoddecode
(self, z: Tensor)
PyTorch-VAE/models/info_vae.py:104
↓ 2 callersMethoddecode
Maps the given latent codes onto the image space. :param z: (Tensor) [B x D] :return: (Tensor) [B x C x H x W]
PyTorch-VAE/models/vanilla_vae.py:94
↓ 2 callersMethoddecode
Maps the given latent codes onto the image space. :param z: (Tensor) [B x D] :return: (Tensor) [B x C x H x W]
PyTorch-VAE/models/lvae.py:151
↓ 2 callersMethoddecode
Maps the given latent codes onto the image space. :param z: (Tensor) [B x D] :return: (Tensor) [B x C x H x W]
PyTorch-VAE/models/dip_vae.py:95
↓ 2 callersMethoddecode
(self, z: Tensor)
PyTorch-VAE/models/wae_mmd.py:96
↓ 2 callersMethoddecode
(self, z: Tensor)
PyTorch-VAE/models/beta_vae.py:105
↓ 2 callersMethoddecode
(self, z: Tensor)
PyTorch-VAE/models/cvae.py:100
↓ 2 callersMethoddecode
Maps the given latent codes onto the image space. :param z: (Tensor) [B x D x Q] :return: (Tensor) [B x C x H x W]
PyTorch-VAE/models/joint_vae.py:129
↓ 2 callersMethoddecode
Maps the given latent codes onto the image space. :param z: (Tensor) [B x D] :return: (Tensor) [B x C x H x W]
PyTorch-VAE/models/betatc_vae.py:102
↓ 2 callersMethoddecode
Maps the given latent codes onto the image space. :param z: (Tensor) [B x D] :return: (Tensor) [B x C x H x W]
PyTorch-VAE/models/twostage_vae.py:117
↓ 2 callersMethoddecode
Maps the given latent codes onto the image space. :param z: (Tensor) [B x D] :return: (Tensor) [B x C x H x W]
PyTorch-VAE/models/mssim_vae.py:101
↓ 2 callersMethoddecode
(self, z: Tensor)
PyTorch-VAE/models/vampvae.py:99
↓ 2 callersMethoddecode
Maps the given latent codes onto the image space. :param z: (Tensor) [B x D] :return: (Tensor) [B x C x H x W]
PyTorch-VAE/models/fvae.py:109
↓ 2 callersMethoddecode
Maps the given latent codes of S samples onto the image space. :param z: (Tensor) [B x S x D] :return: (Tensor) [B x
PyTorch-VAE/models/iwae.py:95
↓ 2 callersMethoddecode
(self, z: Tensor)
PyTorch-VAE/models/gamma_vae.py:109
↓ 2 callersFunctiondownload_files
(file_specs, num_threads=32, status_delay=0.2, timing_window=50, **download_kwargs)
StyleGAN/download_ffhq.py:154
↓ 2 callersMethodencode
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
PyTorch-VAE/models/vampvae.py:82
↓ 2 callersMethodextract_features
Extracts the features from the pretrained model at the layers indicated by feature_layers. :param input: (Tensor) [B x C x H
PyTorch-VAE/models/dfcvae.py:142
↓ 2 callersMethodforward
(self, input: Tensor, **kwargs)
PyTorch-VAE/experiment.py:31
↓ 2 callersFunctioninitialize_weights
(model)
github/initialize.py:9
↓ 2 callersFunctioninitialize_weights
(model)
WGANGPCode/initialize.py:9
↓ 2 callersFunctionloadModel
:param model: :return:
Code/mainWindows.py:133
↓ 2 callersFunctionload_checkpoint
(checkpoint_file, model, optimizer, lr)
StyleGAN/utils.py:68
↓ 2 callersFunctionload_checkpoint
(checkpoint_file, model, optimizer, lr)
SRGAN/utils.py:47
↓ 2 callersFunctionload_checkpoint
(checkpoint_file, model, optimizer, lr)
ProGAN/utils.py:65
↓ 2 callersFunctionload_checkpoint
(checkpoint_file, model, optimizer, lr)
ProGAN/demo.py:261
↓ 2 callersFunctionload_checkpoint
(checkpoint_file, model, optimizer, lr)
pix2pix/utils.py:41
↓ 2 callersMethodminibatch_std
(self,x)
StyleGAN/net/model.py:259
↓ 2 callersMethodminibatch_std
(self, x)
ProGAN/model.py:198
↓ 2 callersFunctionsave_checkpoint
(model, optimizer, filename="models/my_checkpoint.pth.tar")
StyleGAN/utils.py:58
↓ 2 callersFunctionsave_checkpoint
(model, optimizer, filename="my_checkpoint.pth.tar")
SRGAN/utils.py:38
↓ 2 callersFunctionsave_checkpoint
(model, optimizer, filename="my_checkpoint.pth.tar")
ProGAN/utils.py:56
↓ 2 callersFunctionsave_checkpoint
(model, optimizer, filename="my_checkpoint.pth.tar")
ProGAN/demo.py:252
↓ 2 callersFunctionsave_checkpoint
(model, optimizer, filename="my_checkpoint.pth.tar")
pix2pix/utils.py:32
↓ 2 callersFunctionshowImage
:param root: 主窗口 :return:
StyleGAN/mainWindow.py:39
↓ 2 callersFunctionshowImage
:param root: 主窗口 :return:
fc-CGANCode/mainWindow.py:44
↓ 2 callersFunctionshowImage
:param root: 主窗口 :return:
GANCode/mainWindow.py:37
↓ 2 callersFunctionshowImage
:param root: 主窗口 :return:
github/mainWindow.py:39
↓ 2 callersFunctionshowImage
:param root: 主窗口 :return:
ProGAN/mainWindow.py:39
↓ 2 callersFunctionshowImage
:param root: 主窗口 :return:
DCGAN/mainWindow.py:38
↓ 2 callersFunctionshowImage
:param root: 主窗口 :return:
WGANGPCode/mainWindow.py:39
↓ 2 callersFunctionshowImage
:param root: 主窗口 :return:
cnn-CGANCode/mainWindow.py:44
↓ 1 callersFunctionButtonImage
(model,root)
StyleGAN/mainWindow.py:107
↓ 1 callersFunctionButtonImage
(model1,root)
SRGAN/mainWindows.py:155
↓ 1 callersFunctionButtonImage
(model1,model2,root)
Code/mainWindows.py:151
↓ 1 callersFunctionButtonImage
(model,root)
fc-CGANCode/mainWindow.py:109
↓ 1 callersFunctionButtonImage
(model,root)
GANCode/mainWindow.py:104
↓ 1 callersFunctionButtonImage
(model,root)
github/mainWindow.py:112
↓ 1 callersFunctionButtonImage
(model,root)
ProGAN/mainWindow.py:108
↓ 1 callersFunctionButtonImage
(model1,root)
pix2pix/mainWindows.py:175
↓ 1 callersFunctionButtonImage
(model,root)
DCGAN/mainWindow.py:111
↓ 1 callersFunctionButtonImage
(model,root)
WGANGPCode/mainWindow.py:112
↓ 1 callersFunctionButtonImage
(model,root)
cnn-CGANCode/mainWindow.py:109
↓ 1 callersFunctionDrawGen
:param model: 生成器训练的模型 :param epoch: 迭代次数 :param test_input: 对产生的噪声生成图像 :return:
GANCode/GAN.py:52
↓ 1 callersFunctionDrawGen
:param model: 生成器训练的模型 :param epoch: 迭代次数 :param test_input: 对产生的噪声生成图像 :return:
github/draw.py:12
↓ 1 callersFunctionDrawGen
:param model: 生成器训练的模型 :param epoch: 迭代次数 :param test_input: 对产生的噪声生成图像 :return:
DCGAN/DCGAN.py:63
↓ 1 callersFunctionDrawGen
:param model: 生成器训练的模型 :param epoch: 迭代次数 :param test_input: 对产生的噪声生成图像 :return:
WGANGPCode/draw.py:12
↓ 1 callersFunctionParser
()
fc-CGANCode/train.py:20
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