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github.com/KeepTryingTo/Pytorch-GAN
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Functions
639 in github.com/KeepTryingTo/Pytorch-GAN
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Functions
639
◇
Types & classes
107
Method
forward
(self,x)
Code/net/Discriminator.py:53
Method
forward
(self,x)
Code/net/Generator.py:23
Method
forward
(self,x)
Code/net/Generator.py:33
Method
forward
(self,x)
Code/net/Generator.py:63
Method
forward
(self,x)
AutoEncoder/net/AE.py:32
Method
forward
(self, *inputs: Tensor)
PyTorch-VAE/models/base.py:23
Method
forward
(self, input: Tensor)
PyTorch-VAE/models/lvae.py:32
Method
forward
(self, z: Tensor)
PyTorch-VAE/models/lvae.py:55
Method
forward
(self, img1: Tensor, img2: Tensor)
PyTorch-VAE/models/mssim_vae.py:250
Method
forward
(self, latents: Tensor)
PyTorch-VAE/models/vq_vae.py:24
Method
forward
(self, input: Tensor)
PyTorch-VAE/models/vq_vae.py:69
Method
forward
(self,x)
VAE_AutoEncoder/net/DenseVAE.py:24
Method
forward
(self,x)
VAE_AutoEncoder/net/DenseVAE.py:46
Method
forward
(self,input,label)
fc-CGANCode/models.py:36
Method
forward
(self,input,label)
fc-CGANCode/models.py:71
Method
forward
(self,x)
Adversarial_AutoEncoder/net/Encoder.py:23
Method
forward
(self,x)
Adversarial_AutoEncoder/net/Decoder.py:23
Method
forward
(self,x)
Adversarial_AutoEncoder/net/Discriminator.py:30
Method
forward
(self,input)
GANCode/dModel.py:25
Method
forward
(self,input)
GANCode/gModel.py:23
Method
forward
(self,input)
github/net/Discriminator.py:37
Method
forward
(self,input)
github/net/Generator.py:47
Method
forward
(self, x)
ProGAN/model.py:61
Method
forward
(self, x)
ProGAN/model.py:70
Method
forward
(self, x)
ProGAN/model.py:83
Method
forward
(self, x, alpha, steps)
ProGAN/model.py:127
Method
forward
(self, x, alpha, steps)
ProGAN/model.py:207
Method
forward
(self,x)
Dropout_AutoEncoder/net/AE.py:36
Method
forward
(self,input)
pix2pix/net/Discriminator.py:20
Method
forward
(self,x,y)
pix2pix/net/Discriminator.py:46
Method
forward
(self,x)
pix2pix/net/Generator.py:25
Method
forward
(self,x)
pix2pix/net/Generator.py:64
Method
forward
(self,input)
DCGAN/DCDis.py:42
Method
forward
(self,randomNoise)
DCGAN/DCGen.py:44
Method
forward
(self,input)
WGANGPCode/net/Discriminator.py:37
Method
forward
(self,input)
WGANGPCode/net/Generator.py:47
Method
forward
(self,x)
Denoising_AutoEncoder/net/AE.py:32
Method
forward
(self,input,label)
cnn-CGANCode/models.py:72
Method
forward
(self,input,label)
cnn-CGANCode/models.py:136
Function
func_wrapper
(self)
PyTorch-VAE/utils.py:15
Method
generate
Given an input image x, returns the reconstructed image. Returns only the first reconstructed sample :param x: (Tensor) [B x
PyTorch-VAE/models/miwae.py:184
Method
generate
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/dfcvae.py:210
Method
generate
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/swae.py:199
Method
generate
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/logcosh_vae.py:175
Method
generate
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/cat_vae.py:202
Method
generate
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/info_vae.py:249
Method
generate
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/vanilla_vae.py:166
Method
generate
(self, x: Tensor, **kwargs)
PyTorch-VAE/models/base.py:19
Method
generate
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/lvae.py:264
Method
generate
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/dip_vae.py:184
Method
generate
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/wae_mmd.py:224
Method
generate
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/beta_vae.py:172
Method
generate
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/cvae.py:170
Method
generate
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/joint_vae.py:261
Method
generate
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/betatc_vae.py:231
Method
generate
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/twostage_vae.py:190
Method
generate
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/mssim_vae.py:173
Method
generate
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/vampvae.py:188
Method
generate
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/fvae.py:221
Method
generate
Given an input image x, returns the reconstructed image. Returns only the first reconstructed sample :param x: (Tensor) [B x
PyTorch-VAE/models/iwae.py:180
Method
generate
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/gamma_vae.py:230
Method
generate
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/vq_vae.py:218
Function
generate_and_save_images
:param save_dir: :param gen: :param epoch: :return:
VAE_AutoEncoder/utils.py:44
Function
generate_examples
Tried using truncation trick here but not sure it actually helped anything, you can remove it if you like and just sample from torch.randn
ProGAN/utils.py:86
Function
generate_examples
Tried using truncation trick here but not sure it actually helped anything, you can remove it if you like and just sample from torch.randn
ProGAN/demo.py:282
Function
gradient_penalty
(critic, real, fake, device)
SRGAN/utils.py:15
Function
gradient_penalty
(critic, real, fake, device)
fc-CGANCode/utils.py:29
Function
gradient_penalty
(critic, real, fake, device)
cnn-CGANCode/utils.py:30
Function
load_checkPoint
(model,filename,optimizer,lr)
AutoEncoder/utils.py:57
Function
load_checkPoint
(model,filename,optimizer,lr)
Adversarial_AutoEncoder/utils.py:58
Function
load_checkPoint
(model,filename,optimizer,lr)
Dropout_AutoEncoder/utils.py:57
Function
load_checkPoint
(model,filename,optimizer,lr)
Denoising_AutoEncoder/utils.py:57
Function
load_checkpoin
(checkpoint_file,model,optimizer,lr)
Code/utils.py:23
Function
load_checkpoin
(checkpoint_file, model, optimizer, lr)
VAE_AutoEncoder/utils.py:35
Function
load_checkpoint
(checkpoint_file, model, optimizer, lr)
fc-CGANCode/utils.py:61
Function
load_checkpoint
(checkpoint_file, model, optimizer, lr)
cnn-CGANCode/utils.py:62
Method
loss_function
KL(N(\mu, \sigma), N(0, 1)) = \log \frac{1}{\sigma} + \frac{\sigma^2 + \mu^2}{2} - \frac{1}{2} :param args: :param kwargs:
PyTorch-VAE/models/miwae.py:132
Method
loss_function
Computes the VAE loss function. KL(N(\mu, \sigma), N(0, 1)) = \log \frac{1}{\sigma} + \frac{\sigma^2 + \mu^2}{2} - \frac{1}{2}
PyTorch-VAE/models/dfcvae.py:163
Method
loss_function
(self, *args, **kwargs)
PyTorch-VAE/models/swae.py:110
Method
loss_function
Computes the VAE loss function. KL(N(\mu, \sigma), N(0, 1)) = \log \frac{1}{\sigma} + \frac{\sigma^2 + \mu^2}{2} - \frac{1}{2}
PyTorch-VAE/models/logcosh_vae.py:125
Method
loss_function
Computes the VAE loss function. KL(N(\mu, \sigma), N(0, 1)) = \log \frac{1}{\sigma} + \frac{\sigma^2 + \mu^2}{2} - \frac{1}{2}
PyTorch-VAE/models/cat_vae.py:139
Method
loss_function
(self, *args, **kwargs)
PyTorch-VAE/models/info_vae.py:128
Method
loss_function
Computes the VAE loss function. KL(N(\mu, \sigma), N(0, 1)) = \log \frac{1}{\sigma} + \frac{\sigma^2 + \mu^2}{2} - \frac{1}{2}
PyTorch-VAE/models/vanilla_vae.py:124
Method
loss_function
(self, *inputs: Any, **kwargs)
PyTorch-VAE/models/base.py:27
Method
loss_function
Computes the VAE loss function. KL(N(\mu, \sigma), N(0, 1)) = \log \frac{1}{\sigma} + \frac{\sigma^2 + \mu^2}{2} - \frac{1}{2}
PyTorch-VAE/models/lvae.py:218
Method
loss_function
Computes the VAE loss function. KL(N(\mu, \sigma), N(0, 1)) = \log \frac{1}{\sigma} + \frac{\sigma^2 + \mu^2}{2} - \frac{1}{2}
PyTorch-VAE/models/dip_vae.py:125
Method
loss_function
(self, *args, **kwargs)
PyTorch-VAE/models/wae_mmd.py:107
Method
loss_function
(self, *args, **kwargs)
PyTorch-VAE/models/beta_vae.py:129
Method
loss_function
(self, *args, **kwargs)
PyTorch-VAE/models/cvae.py:133
Method
loss_function
Computes the VAE loss function. KL(N(\mu, \sigma), N(0, 1)) = \log \frac{1}{\sigma} + \frac{\sigma^2 + \mu^2}{2} - \frac{1}{2}
PyTorch-VAE/models/joint_vae.py:175
Method
loss_function
Computes the VAE loss function. KL(N(\mu, \sigma), N(0, 1)) = \log \frac{1}{\sigma} + \frac{\sigma^2 + \mu^2}{2} - \frac{1}{2}
PyTorch-VAE/models/betatc_vae.py:144
Method
loss_function
Computes the VAE loss function. KL(N(\mu, \sigma), N(0, 1)) = \log \frac{1}{\sigma} + \frac{\sigma^2 + \mu^2}{2} - \frac{1}{2}
PyTorch-VAE/models/twostage_vae.py:148
Method
loss_function
Computes the VAE loss function. KL(N(\mu, \sigma), N(0, 1)) = \log \frac{1}{\sigma} + \frac{\sigma^2 + \mu^2}{2} - \frac{1}{2}
PyTorch-VAE/models/mssim_vae.py:131
Method
loss_function
(self, *args, **kwargs)
PyTorch-VAE/models/vampvae.py:123
Method
loss_function
Computes the VAE loss function. KL(N(\mu, \sigma), N(0, 1)) = \log \frac{1}{\sigma} + \frac{\sigma^2 + \mu^2}{2} - \frac{1}{2}
PyTorch-VAE/models/fvae.py:151
Method
loss_function
KL(N(\mu, \sigma), N(0, 1)) = \log \frac{1}{\sigma} + \frac{\sigma^2 + \mu^2}{2} - \frac{1}{2} :param args: :param kwargs:
PyTorch-VAE/models/iwae.py:129
Method
loss_function
(self, *args, **kwargs)
PyTorch-VAE/models/gamma_vae.py:184
Method
loss_function
:param args: :param kwargs: :return:
PyTorch-VAE/models/vq_vae.py:194
Method
on_validation_end
(self)
PyTorch-VAE/experiment.py:61
Function
plot_predictions
:param y_true: 真实的图像 :param y_pred: 网络输出的图像 :return:
VAE_AutoEncoder/utils.py:76
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