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Functions334 in github.com/AntixK/PyTorch-VAE

⨍ Functions 334◇ Types & classes 53
↓ 23 callersMethodloss_function
(self, *args, **kwargs)
models/hvae.py:192
↓ 13 callersMethodsample
(self, batch_size:int, current_device: int, **kwargs)
models/hvae.py:233
↓ 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]
models/hvae.py:252
↓ 3 callersMethodcompute_kernel
(self, x1: Tensor, x2: Tensor)
models/info_vae.py:150
↓ 3 callersMethodcompute_kernel
(self, x1: Tensor, x2: Tensor)
models/wae_mmd.py:125
↓ 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
models/betatc_vae.py:132
↓ 3 callersMethodreparameterize
Will a single z be enough ti compute the expectation for the loss?? :param mu: (Tensor) Mean of the latent Gaussian :
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]
models/lvae.py:197
↓ 2 callersMethodI_function
(self, a, b, c, d)
models/gamma_vae.py:168
↓ 2 callersMethod__init__
(self, in_channels: int, latent_dims: List, hidden_dims: Li
models/lvae.py:64
↓ 2 callersMethod__init__
(self, in_channels: int, embedding_dim: int, num_embeddings
models/vq_vae.py:75
↓ 2 callersFunctionconv_out_shape
(img_size)
models/lvae.py:9
↓ 2 callersMethoddecode
(self, input: Tensor)
models/hvae.py:153
↓ 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
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]
models/dfcvae.py:107
↓ 2 callersMethoddecode
(self, z: Tensor)
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]
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]
models/cat_vae.py:105
↓ 2 callersMethoddecode
(self, z: Tensor)
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]
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]
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]
models/dip_vae.py:95
↓ 2 callersMethoddecode
(self, z: Tensor)
models/wae_mmd.py:96
↓ 2 callersMethoddecode
(self, z: Tensor)
models/beta_vae.py:105
↓ 2 callersMethoddecode
(self, z: Tensor)
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]
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]
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]
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]
models/mssim_vae.py:101
↓ 2 callersMethoddecode
(self, z: Tensor)
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]
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
models/iwae.py:95
↓ 2 callersMethoddecode
(self, z: Tensor)
models/gamma_vae.py:109
↓ 2 callersMethodencode
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
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
models/dfcvae.py:142
↓ 2 callersMethodforward
(self, input: Tensor, **kwargs)
experiment.py:31
↓ 1 callersMethod__init__
(self)
dataset.py:16
↓ 1 callersMethod__init__
Computes the differentiable MS-SSIM loss Reference: [1] https://github.com/jorge-pessoa/pytorch-msssim/blob/dev/pytorch_msssi
models/mssim_vae.py:184
↓ 1 callersMethodcompute_inv_mult_quad
Computes the Inverse Multi-Quadratics Kernel between x1 and x2, given by k(x_1, x_2) = \sum \frac{C}{C + \|x_1 - x_2
models/info_vae.py:195
↓ 1 callersMethodcompute_inv_mult_quad
Computes the Inverse Multi-Quadratics Kernel between x1 and x2, given by k(x_1, x_2) = \sum \frac{C}{C + \|x_1 - x_2
models/wae_mmd.py:170
↓ 1 callersMethodcompute_kl_divergence
(self, z: Tensor, q_params: Tuple, p_params: Tuple)
models/lvae.py:186
↓ 1 callersMethodcompute_mmd
(self, z: Tensor)
models/info_vae.py:218
↓ 1 callersMethodcompute_mmd
(self, z: Tensor, reg_weight: float)
models/wae_mmd.py:193
↓ 1 callersMethodcompute_rbf
Computes the RBF Kernel between x1 and x2. :param x1: (Tensor) :param x2: (Tensor) :param eps: (Float) :retur
models/info_vae.py:178
↓ 1 callersMethodcompute_rbf
Computes the RBF Kernel between x1 and x2. :param x1: (Tensor) :param x2: (Tensor) :param eps: (Float) :retur
models/wae_mmd.py:153
↓ 1 callersMethodcompute_swd
Computes the Sliced Wasserstein Distance (SWD) - which consists of randomly projecting the encoded and prior vectors and computing
models/swae.py:151
↓ 1 callersMethodcreate_window
(self, window_size, in_channels)
models/mssim_vae.py:208
↓ 1 callersMethoddecode
Maps the given latent codes onto the image space. :param z: (Tensor) [B x D x H x W] :return: (Tensor) [B x C x H x W
models/vq_vae.py:178
↓ 1 callersMethodencode
(self, input: Tensor)
models/hvae.py:145
↓ 1 callersMethodencode
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
models/miwae.py:81
↓ 1 callersMethodencode
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
models/dfcvae.py:90
↓ 1 callersMethodencode
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
models/swae.py:84
↓ 1 callersMethodencode
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
models/logcosh_vae.py:78
↓ 1 callersMethodencode
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
models/cat_vae.py:89
↓ 1 callersMethodencode
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
models/info_vae.py:88
↓ 1 callersMethodencode
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
models/vanilla_vae.py:77
↓ 1 callersMethodencode
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
models/lvae.py:134
↓ 1 callersMethodencode
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
models/dip_vae.py:78
↓ 1 callersMethodencode
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
models/wae_mmd.py:81
↓ 1 callersMethodencode
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
models/beta_vae.py:88
↓ 1 callersMethodencode
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
models/cvae.py:83
↓ 1 callersMethodencode
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
models/joint_vae.py:111
↓ 1 callersMethodencode
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
models/betatc_vae.py:84
↓ 1 callersMethodencode
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
models/twostage_vae.py:100
↓ 1 callersMethodencode
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
models/mssim_vae.py:84
↓ 1 callersMethodencode
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
models/fvae.py:92
↓ 1 callersMethodencode
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
models/iwae.py:78
↓ 1 callersMethodencode
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
models/gamma_vae.py:92
↓ 1 callersMethodencode
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
models/vq_vae.py:168
↓ 1 callersMethodencode_z1
(self, input: Tensor, z2: Tensor)
models/hvae.py:132
↓ 1 callersMethodencode_z2
Encodes the input by passing through the encoder network and returns the latent codes. :param input: (Tensor) Input tensor to
models/hvae.py:115
↓ 1 callersMethodforward
(self, input: Tensor, **kwargs)
models/hvae.py:170
↓ 1 callersMethodforward
(self, input: Tensor, **kwargs)
models/miwae.py:124
↓ 1 callersMethodforward
(self, input: Tensor, **kwargs)
models/dfcvae.py:132
↓ 1 callersMethodforward
(self, input: Tensor, **kwargs)
models/swae.py:106
↓ 1 callersMethodforward
(self, input: Tensor, **kwargs)
models/logcosh_vae.py:120
↓ 1 callersMethodforward
(self, input: Tensor, **kwargs)
models/cat_vae.py:134
↓ 1 callersMethodforward
(self, input: Tensor, **kwargs)
models/info_vae.py:123
↓ 1 callersMethodforward
(self, input: Tensor, **kwargs)
models/vanilla_vae.py:119
↓ 1 callersMethodforward
(self, input: Tensor, **kwargs)
models/lvae.py:209
↓ 1 callersMethodforward
(self, input: Tensor, **kwargs)
models/dip_vae.py:120
↓ 1 callersMethodforward
(self, input: Tensor, **kwargs)
models/wae_mmd.py:103
↓ 1 callersMethodforward
(self, input: Tensor, **kwargs)
models/beta_vae.py:124
↓ 1 callersMethodforward
(self, input: Tensor, **kwargs)
models/cvae.py:119
↓ 1 callersMethodforward
(self, input: Tensor, **kwargs)
models/joint_vae.py:170
↓ 1 callersMethodforward
(self, input: Tensor, **kwargs)
models/betatc_vae.py:127
↓ 1 callersMethodforward
(self, input: Tensor, **kwargs)
models/twostage_vae.py:142
↓ 1 callersMethodforward
(self, input: Tensor, **kwargs)
models/mssim_vae.py:126
↓ 1 callersMethodforward
(self, input: Tensor, **kwargs)
models/vampvae.py:118
↓ 1 callersMethodforward
(self, input: Tensor, **kwargs)
models/fvae.py:134
↓ 1 callersMethodforward
(self, input: Tensor, **kwargs)
models/iwae.py:121
↓ 1 callersMethodforward
(self, input: Tensor, **kwargs)
models/gamma_vae.py:158
↓ 1 callersMethodforward
(self, input: Tensor, **kwargs)
models/vq_vae.py:189
↓ 1 callersMethodgaussian_window
(self, window_size:int, sigma: float)
models/mssim_vae.py:203
↓ 1 callersMethodget_random_projections
Returns random samples from latent distribution's (Gaussian) unit sphere for projecting the encoded samples and the distribut
models/swae.py:129
↓ 1 callersMethodh_func
Reparameterize a sample eps ~ N(0, 1) so that h(z) ~ Gamma(alpha, 1) :param alpha: (Tensor) Shape parameter :param eps: (Tens
models/gamma_vae.py:137
↓ 1 callersFunctioninit_
(m)
models/gamma_vae.py:239
↓ 1 callersMethodinv_h_func
Inverse reparameterize the given z into eps. :param alpha: (Tensor) :param z: (Tensor) :return: (Tensor)
models/gamma_vae.py:148
↓ 1 callersMethodmerge_gauss
(self, mu_1: Tensor, mu_2: Tensor, log_var_1: Tens
models/lvae.py:173
↓ 1 callersMethodpermute_latent
Permutes each of the latent codes in the batch :param z: [B x D] :return: [B x D]
models/fvae.py:139
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