From bca76f02efeebd0e66d87a62acfe9c47f93aaaba Mon Sep 17 00:00:00 2001 From: ralphc1212 <55773420+ralphc1212@users.noreply.github.com> Date: Thu, 23 Jun 2022 20:29:19 -0400 Subject: [PATCH 1/7] Add files via upload --- configs/vndae.yaml | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) create mode 100644 configs/vndae.yaml diff --git a/configs/vndae.yaml b/configs/vndae.yaml new file mode 100644 index 00000000..20028ea5 --- /dev/null +++ b/configs/vndae.yaml @@ -0,0 +1,28 @@ +model_params: + name: 'VNDAE' + in_channels: 3 + latent_dim: 128 + + +data_params: + data_path: "data/" + train_batch_size: 64 + val_batch_size: 64 + patch_size: 64 + num_workers: 4 + + +exp_params: + LR: 0.005 + weight_decay: 0 + scheduler_gamma: 0.95 + kld_weight: 0.00025 + manual_seed: 1265 + +trainer_params: + gpus: [0] + max_epochs: 50 + +logging_params: + save_dir: "logs/" + name: "VNDAE" \ No newline at end of file From 6370b10544ed97e51d07274dc87fdcf2687c055a Mon Sep 17 00:00:00 2001 From: ralphc1212 <55773420+ralphc1212@users.noreply.github.com> Date: Thu, 23 Jun 2022 20:30:45 -0400 Subject: [PATCH 2/7] Add files via upload --- models/vnd_ae.py | 218 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 218 insertions(+) create mode 100644 models/vnd_ae.py diff --git a/models/vnd_ae.py b/models/vnd_ae.py new file mode 100644 index 00000000..1a637f15 --- /dev/null +++ b/models/vnd_ae.py @@ -0,0 +1,218 @@ +import torch +from models import BaseVAE +from torch import nn +from torch.nn import functional as F +from .types_ import * + +TAU = 1. +PI = 0.95 +RSV_DIM = 1 +EPS = 1e-8 +SAMPLE_LEN = 1. + +class VNDAE(BaseVAE): + + + def __init__(self, + in_channels: int, + latent_dim: int, + hidden_dims: List = None, + **kwargs) -> None: + super(VNDAE, self).__init__() + + self.latent_dim = latent_dim + + modules = [] + if hidden_dims is None: + hidden_dims = [32, 64, 128, 256, 512] + + # Build Encoder + for h_dim in hidden_dims: + modules.append( + nn.Sequential( + nn.Conv2d(in_channels, out_channels=h_dim, + kernel_size= 3, stride= 2, padding = 1), + nn.BatchNorm2d(h_dim), + nn.LeakyReLU()) + ) + in_channels = h_dim + + self.encoder = nn.Sequential(*modules) + self.fc_mu = nn.Linear(hidden_dims[-1] * 4, latent_dim) + self.fc_var = nn.Linear(hidden_dims[-1] * 4, latent_dim) + self.fc_p_vnd = nn.Linear(hidden_dims[-1] * 4, latent_dim) + + Pi = nn.Parameter(PI * torch.ones(latent_dim - RSV_DIM), requires_grad=False) + + self.ZERO = nn.Parameter(torch.tensor([0.]), requires_grad=False) + self.ONE = nn.Parameter(torch.tensor([1.]), requires_grad=False) + self.pv = nn.Parameter(torch.cat([self.ONE, torch.cumprod(Pi, dim=0)]) + * torch.cat([1 - Pi, self.ONE]), requires_grad=False) + + # Build Decoder + modules = [] + + self.decoder_input = nn.Linear(latent_dim, hidden_dims[-1] * 4) + + hidden_dims.reverse() + + for i in range(len(hidden_dims) - 1): + modules.append( + nn.Sequential( + nn.ConvTranspose2d(hidden_dims[i], + hidden_dims[i + 1], + kernel_size=3, + stride = 2, + padding=1, + output_padding=1), + nn.BatchNorm2d(hidden_dims[i + 1]), + nn.LeakyReLU()) + ) + + self.decoder = nn.Sequential(*modules) + + self.final_layer = nn.Sequential( + nn.ConvTranspose2d(hidden_dims[-1], + hidden_dims[-1], + kernel_size=3, + stride=2, + padding=1, + output_padding=1), + nn.BatchNorm2d(hidden_dims[-1]), + nn.LeakyReLU(), + nn.Conv2d(hidden_dims[-1], out_channels= 3, + kernel_size= 3, padding= 1), + nn.Tanh()) + + @staticmethod + def clip_beta(tensor, to=5.): + """ + Shrink all tensor's values to range [-to,to] + """ + return torch.clamp(tensor, -to, to) + + def encode(self, input: Tensor) -> List[Tensor]: + """ + Encodes the input by passing through the encoder network + and returns the latent codes. + :param input: (Tensor) Input tensor to encoder [N x C x H x W] + :return: (Tensor) List of latent codes + """ + result = self.encoder(input) + result = torch.flatten(result, start_dim=1) + + # Split the result into mu, var and p_vnd components + # of the latent mixture + mu = self.fc_mu(result) + log_var = self.fc_var(result) + p_vnd = self.fc_p_vnd(result) + + return [mu, log_var, p_vnd] + + def decode(self, z: Tensor) -> Tensor: + """ + Maps the given latent codes + onto the image space. + :param z: (Tensor) [B x D] + :return: (Tensor) [B x C x H x W] + """ + result = self.decoder_input(z) + result = result.view(-1, 512, 2, 2) + result = self.decoder(result) + result = self.final_layer(result) + return result + + def reparameterize(self, mu: Tensor, logvar: Tensor, p_vnd: Tensor) -> Tensor: + """ + Reparameterization trick to sample from the mixture posterior shown in Eq. 28 in [https://arxiv.org/pdf/2101.11353.pdf]. + :param mu: (Tensor) Mean of the latent Gaussian [B x D] + :param logvar: (Tensor) Standard deviation of the latent Gaussian [B x D] + :param p_vnd: (Tensor) Parameter for the Downhill distribution [B x D] + :return: (Tensor) [B x D] + """ + std = torch.exp(0.5 * logvar) + + # Generate samples for the Downhill distribution + + eps = torch.randn_like(std) + beta = torch.sigmoid(self.clip_beta(p_vnd[:,RSV_DIM:])) + ONES = torch.ones_like(beta[:,0:1]) + qv = torch.cat([ONES, torch.cumprod(beta, dim=1)], dim = -1) * torch.cat([1 - beta, ONES], dim = -1) + s_vnd = F.gumbel_softmax(qv, tau=TAU, hard=True) + + cumsum = torch.cumsum(s_vnd, dim=1) + dif = cumsum - s_vnd + mask0 = dif[:, 1:] + mask1 = 1. - mask0 + s_vnd = torch.cat([torch.ones_like(p_vnd[:,:RSV_DIM]), mask1], dim = -1) + + return (eps * std + mu) * s_vnd + + def forward(self, input: Tensor, **kwargs) -> List[Tensor]: + mu, log_var, p_vnd = self.encode(input) + z = self.reparameterize(mu, log_var, p_vnd) + return [self.decode(z), input, mu, log_var, p_vnd] + + def loss_function(self, + *args, + **kwargs) -> dict: + """ + Computes the VNDAE loss function shown in Eq.29 in [https://arxiv.org/pdf/2101.11353.pdf]. + :param args: + :param kwargs: + :return: + """ + recons = args[0] + input = args[1] + mu = args[2] + log_var = args[3] + p_vnd = args[4] + beta = torch.sigmoid(self.clip_beta(p_vnd[:,RSV_DIM:])) + ONES = torch.ones_like(beta[:,0:1]) + qv = torch.cat([ONES, torch.cumprod(beta, dim=1)], dim = -1) * torch.cat([1 - beta, ONES], dim = -1) + + ZEROS = torch.zeros_like(beta[:, 0:1]) + cum_sum = torch.cat([ZEROS, torch.cumsum(qv[:, 1:], dim = 1)], dim = -1)[:, :-1] + coef1 = torch.sum(qv, dim=1, keepdim=True) - cum_sum + coef1 = torch.cat([torch.ones_like(p_vnd[:,:RSV_DIM]), coef1], dim = -1) + + kld_weight = kwargs['M_N'] # Account for the minibatch samples from the dataset + recons_loss =F.mse_loss(recons, input) + + kld_gaussian = -0.5 * (1 + log_var - mu ** 2 - log_var.exp()) + + kld_weighted_gaussian = torch.diagonal(kld_gaussian.mm(coef1.t()), 0).mean() + + log_frac = torch.log(qv / self.pv + EPS) + kld_vnd = torch.diagonal(qv.mm(log_frac.t()), 0).mean() + + kld_loss = kld_vnd + kld_weighted_gaussian + loss = recons_loss + kld_weight * kld_loss + return {'loss': loss, 'Reconstruction_Loss':recons_loss.detach(), 'KLD': - kld_loss.detach()} + + def sample(self, + num_samples:int, + current_device: int, **kwargs) -> Tensor: + """ + Samples from the latent space given fixed width SAMPLE_LEN. + :param num_samples: (Int) Number of samples + :param current_device: (Int) Device to run the model + :return: (Tensor) + """ + z = torch.randn(num_samples, + self.latent_dim) + + z = torch.cat([z[:, :int(SAMPLE_LEN * self.latent_dim)], torch.zeros_like(z[:, :int((1 - SAMPLE_LEN) * self.latent_dim)])], dim = -1) + z = z.to(current_device) + + samples = self.decode(z) + return samples + + def generate(self, x: Tensor, **kwargs) -> Tensor: + """ + 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] + """ + + return self.forward(x)[0] From 46631df387abe5b0605f6fd70baf9d3aefcb8a66 Mon Sep 17 00:00:00 2001 From: ralphc1212 <55773420+ralphc1212@users.noreply.github.com> Date: Thu, 23 Jun 2022 20:32:13 -0400 Subject: [PATCH 3/7] Update __init__.py --- models/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/models/__init__.py b/models/__init__.py index 3f310f29..e0e59fee 100644 --- a/models/__init__.py +++ b/models/__init__.py @@ -21,7 +21,7 @@ from .vq_vae import * from .betatc_vae import * from .dip_vae import * - +from .vnd_ae import * # Aliases VAE = VanillaVAE @@ -35,6 +35,7 @@ 'SWAE':SWAE, 'MIWAE':MIWAE, 'VQVAE':VQVAE, + 'VNDAE':VNDAE, 'DFCVAE':DFCVAE, 'DIPVAE':DIPVAE, 'BetaVAE':BetaVAE, From deaac5a3165ea1048cfb129aa72b0a1c33f55041 Mon Sep 17 00:00:00 2001 From: ralphc1212 <55773420+ralphc1212@users.noreply.github.com> Date: Thu, 23 Jun 2022 20:49:49 -0400 Subject: [PATCH 4/7] Add files via upload --- assets/VNDAE_1.png | Bin 0 -> 900623 bytes assets/recons_VNDAE_1.png | Bin 0 -> 831967 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 assets/VNDAE_1.png create mode 100644 assets/recons_VNDAE_1.png diff --git a/assets/VNDAE_1.png b/assets/VNDAE_1.png new file mode 100644 index 0000000000000000000000000000000000000000..2f8dfdf90dd7e8788821347b75a6dc36aea410a1 GIT binary patch literal 900623 zcmaf)`#;nF|HfaNVGh}bL{2jj<**!b&M=1%<&d1pxzLdF`8bT^P;$y42_Y(MPC0u= zIo32~PRprU4w3WeyU$oVsd+wEzI7 zv;TWQ!Hjp(QI*U92mr2Pb*w@Q{~VcQxY?8BppPaBz4P;*wHfwByB7zv1pU6P!yyLm z1l`Mio@^y60XGU{R}aiey(uP!_hC~- zt|SNMoxf0?9d87BRqf$18R1K9#o1FyhwU{N<68wW@GL(#3V}w5Bf)qkC+sUVr@ZZx z-8)D9zVhf( zER;LNbd&b?@2|r{+WPvfiZBQHQZ)!Sm=$HB!)Kt2=|Sp(IkCE6ECB0=?hq;_$8+7I zBHE+(4)^yL7i0Fdk9R=NpNY9(LV-{~H`**VHWq*aei#V;um(z4_uHu@Qkv-0t<6FD zo6f_-LHQUTYDIgM9flW$xKeDEVLrNCxsy!xELc(>wzz- zyYeS1<}sV=>s>tJ51-T|Cwi{gw?!Rp{F;y#jXBy_9Hi3SZ7Dt?Tm(eR{e=7!qrAM# zEX_3?xle@kSSaSx`C%y5)Ci9O@Nfjb2tT1~Xj4h6*xY=%zrVL7dtdv&=7b$MUhVvM zCjgLyye61Rm;y%pnKoQTP$>-ZzD|k55&7@X6jXnU zrVuP6qq2YV*WNUlJiocO_lvf-7aFZuXhsf{A)6|QJ-}ji*G?8=gn{Gj5bZFCm;nYe zr=8-Q72+X70@4VvU2kGe0sIFc3w(nIf(Ioo2B+S4UIfRt83Gp3dJ8ZNCZp@%U{yOG z9#FOvv1H$(fjrr!z5&)_4zy41RH)nMCASnTYa}JK~k9Psgh$Ib1Xp=AvY z4oyw>4r`&h<~vVB56ywO7{*W30`arhKOd?sYcQ=Ez01yo)=YhwVpOA zmc&&WsLKtQE@Pb{9s9RLMMkyBP^&591qF6_ZENP?F;<|gHkPT)y|m3 zD?C*&UsU~i^`1w54Z0_0H)g*R+;uXqz1KM!)PsQp24e8B>ln^~2%2*kY^Yav+WPBe zqE2608dl)xGz!Jr1D<-v#2@=#)FbuK=KN=dx0;_Ng#kSE*%l^ML}Qxq05&YztuQWa z-8HOL{}w)ZGG=G?U#I-hOMSgp`c&{Xoq-q&Ii=O z4nhH0Y$>k@ri>Z0X^@(&K1x;^F6Vu0VImvEEsfGO?MqBakx)r&X-kN>^;{zyiTuzk zl7b0IzyzL%9>GpD7&B%+<9L}!$-1$ixAp!v5WBukz}dga9bC>RnVG3;qT(Fw4X*CI z;}8I`CFbRO(ZZ^et3;NG)Rv{s!^8Rr#^kJA}|!5LJVO>ZaO{i zN-~0Cu|jhI$Ovl$5R9;RtPuncbOCcqygv~-uEUg`xul;hoQNBF`2L;xw6O+JBibsW zw60<@oL>9Pb7IDxLor0Lq3-t*KMiD2Ik>nIMxF64F@tUX)$wxAo>IgL5D=M2*elaS zgdayhHxS={tG`a-Wku*^K?{Su+VN@6L?}U^sV=5-;u?KGt99Cvh;0wWyZ<+ zvi8w7{mtOkHLAy?y-fxjq8r&}nmf1{*`}qXt*u!I>klZB%!yCy(d8DufM7BJAW8ZF z5(G-p31xgT76ZpZKrCpN=VFWJ|4jFeJ-~89{ruM9(BxM#chxn&{2j_TukP~P{*^{j zSb%+C8*XrUaeuGXVS*I6209DuSSn@0g%KcObd%$P0c6Jy7SfOD!a!Uw5-e`IEPX?N zU4)(^;(+HO({CgE;JJsFmJl?4qlNr?N#by3ICHZ&f*B6r6# z(kwTT2<3;>3C1fT?uw*Buoyf#I?XU{g+ld@I{LS``KIK`@q33il(Wqm5QwpKVO6

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