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from typing import Any, Union |
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import torch |
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import torch.nn as nn |
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from einops import rearrange |
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from taming.modules.discriminator.model import NLayerDiscriminator, weights_init |
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from taming.modules.losses.lpips import LPIPS |
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from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss |
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from ....util import default, instantiate_from_config |
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def adopt_weight(weight, global_step, threshold=0, value=0.0): |
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if global_step < threshold: |
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weight = value |
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return weight |
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class LatentLPIPS(nn.Module): |
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def __init__( |
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self, |
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decoder_config, |
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perceptual_weight=1.0, |
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latent_weight=1.0, |
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scale_input_to_tgt_size=False, |
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scale_tgt_to_input_size=False, |
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perceptual_weight_on_inputs=0.0, |
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): |
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super().__init__() |
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self.scale_input_to_tgt_size = scale_input_to_tgt_size |
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self.scale_tgt_to_input_size = scale_tgt_to_input_size |
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self.init_decoder(decoder_config) |
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self.perceptual_loss = LPIPS().eval() |
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self.perceptual_weight = perceptual_weight |
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self.latent_weight = latent_weight |
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self.perceptual_weight_on_inputs = perceptual_weight_on_inputs |
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def init_decoder(self, config): |
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self.decoder = instantiate_from_config(config) |
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if hasattr(self.decoder, "encoder"): |
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del self.decoder.encoder |
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def forward(self, latent_inputs, latent_predictions, image_inputs, split="train"): |
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log = dict() |
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loss = (latent_inputs - latent_predictions) ** 2 |
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log[f"{split}/latent_l2_loss"] = loss.mean().detach() |
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image_reconstructions = None |
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if self.perceptual_weight > 0.0: |
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image_reconstructions = self.decoder.decode(latent_predictions) |
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image_targets = self.decoder.decode(latent_inputs) |
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perceptual_loss = self.perceptual_loss( |
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image_targets.contiguous(), image_reconstructions.contiguous() |
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) |
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loss = ( |
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self.latent_weight * loss.mean() |
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+ self.perceptual_weight * perceptual_loss.mean() |
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) |
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log[f"{split}/perceptual_loss"] = perceptual_loss.mean().detach() |
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if self.perceptual_weight_on_inputs > 0.0: |
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image_reconstructions = default( |
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image_reconstructions, self.decoder.decode(latent_predictions) |
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) |
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if self.scale_input_to_tgt_size: |
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image_inputs = torch.nn.functional.interpolate( |
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image_inputs, |
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image_reconstructions.shape[2:], |
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mode="bicubic", |
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antialias=True, |
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) |
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elif self.scale_tgt_to_input_size: |
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image_reconstructions = torch.nn.functional.interpolate( |
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image_reconstructions, |
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image_inputs.shape[2:], |
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mode="bicubic", |
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antialias=True, |
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) |
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perceptual_loss2 = self.perceptual_loss( |
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image_inputs.contiguous(), image_reconstructions.contiguous() |
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) |
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loss = loss + self.perceptual_weight_on_inputs * perceptual_loss2.mean() |
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log[f"{split}/perceptual_loss_on_inputs"] = perceptual_loss2.mean().detach() |
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return loss, log |
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class GeneralLPIPSWithDiscriminator(nn.Module): |
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def __init__( |
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self, |
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disc_start: int, |
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logvar_init: float = 0.0, |
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pixelloss_weight=1.0, |
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disc_num_layers: int = 3, |
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disc_in_channels: int = 3, |
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disc_factor: float = 1.0, |
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disc_weight: float = 1.0, |
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perceptual_weight: float = 1.0, |
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disc_loss: str = "hinge", |
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scale_input_to_tgt_size: bool = False, |
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dims: int = 2, |
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learn_logvar: bool = False, |
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regularization_weights: Union[None, dict] = None, |
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): |
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super().__init__() |
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self.dims = dims |
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if self.dims > 2: |
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print( |
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f"running with dims={dims}. This means that for perceptual loss calculation, " |
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f"the LPIPS loss will be applied to each frame independently. " |
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) |
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self.scale_input_to_tgt_size = scale_input_to_tgt_size |
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assert disc_loss in ["hinge", "vanilla"] |
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self.pixel_weight = pixelloss_weight |
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self.perceptual_loss = LPIPS().eval() |
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self.perceptual_weight = perceptual_weight |
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self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init) |
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self.learn_logvar = learn_logvar |
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self.discriminator = NLayerDiscriminator( |
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input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=False |
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).apply(weights_init) |
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self.discriminator_iter_start = disc_start |
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self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss |
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self.disc_factor = disc_factor |
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self.discriminator_weight = disc_weight |
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self.regularization_weights = default(regularization_weights, {}) |
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def get_trainable_parameters(self) -> Any: |
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return self.discriminator.parameters() |
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def get_trainable_autoencoder_parameters(self) -> Any: |
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if self.learn_logvar: |
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yield self.logvar |
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yield from () |
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def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): |
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if last_layer is not None: |
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nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0] |
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g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0] |
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else: |
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nll_grads = torch.autograd.grad( |
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nll_loss, self.last_layer[0], retain_graph=True |
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)[0] |
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g_grads = torch.autograd.grad( |
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g_loss, self.last_layer[0], retain_graph=True |
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)[0] |
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d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4) |
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d_weight = torch.clamp(d_weight, 0.0, 1e4).detach() |
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d_weight = d_weight * self.discriminator_weight |
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return d_weight |
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def forward( |
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self, |
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regularization_log, |
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inputs, |
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reconstructions, |
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optimizer_idx, |
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global_step, |
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last_layer=None, |
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split="train", |
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weights=None, |
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): |
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if self.scale_input_to_tgt_size: |
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inputs = torch.nn.functional.interpolate( |
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inputs, reconstructions.shape[2:], mode="bicubic", antialias=True |
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) |
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if self.dims > 2: |
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inputs, reconstructions = map( |
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lambda x: rearrange(x, "b c t h w -> (b t) c h w"), |
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(inputs, reconstructions), |
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) |
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rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()) |
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if self.perceptual_weight > 0: |
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p_loss = self.perceptual_loss( |
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inputs.contiguous(), reconstructions.contiguous() |
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) |
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rec_loss = rec_loss + self.perceptual_weight * p_loss |
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nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar |
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weighted_nll_loss = nll_loss |
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if weights is not None: |
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weighted_nll_loss = weights * nll_loss |
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weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0] |
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nll_loss = torch.sum(nll_loss) / nll_loss.shape[0] |
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if optimizer_idx == 0: |
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logits_fake = self.discriminator(reconstructions.contiguous()) |
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g_loss = -torch.mean(logits_fake) |
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if self.disc_factor > 0.0: |
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try: |
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d_weight = self.calculate_adaptive_weight( |
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nll_loss, g_loss, last_layer=last_layer |
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) |
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except RuntimeError: |
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assert not self.training |
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d_weight = torch.tensor(0.0) |
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else: |
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d_weight = torch.tensor(0.0) |
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disc_factor = adopt_weight( |
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self.disc_factor, global_step, threshold=self.discriminator_iter_start |
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) |
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loss = weighted_nll_loss + d_weight * disc_factor * g_loss |
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log = dict() |
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for k in regularization_log: |
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if k in self.regularization_weights: |
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loss = loss + self.regularization_weights[k] * regularization_log[k] |
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log[f"{split}/{k}"] = regularization_log[k].detach().mean() |
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log.update( |
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{ |
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"{}/total_loss".format(split): loss.clone().detach().mean(), |
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"{}/logvar".format(split): self.logvar.detach(), |
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"{}/nll_loss".format(split): nll_loss.detach().mean(), |
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"{}/rec_loss".format(split): rec_loss.detach().mean(), |
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"{}/d_weight".format(split): d_weight.detach(), |
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"{}/disc_factor".format(split): torch.tensor(disc_factor), |
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"{}/g_loss".format(split): g_loss.detach().mean(), |
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} |
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) |
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return loss, log |
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if optimizer_idx == 1: |
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logits_real = self.discriminator(inputs.contiguous().detach()) |
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logits_fake = self.discriminator(reconstructions.contiguous().detach()) |
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disc_factor = adopt_weight( |
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self.disc_factor, global_step, threshold=self.discriminator_iter_start |
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) |
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d_loss = disc_factor * self.disc_loss(logits_real, logits_fake) |
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log = { |
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"{}/disc_loss".format(split): d_loss.clone().detach().mean(), |
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"{}/logits_real".format(split): logits_real.detach().mean(), |
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"{}/logits_fake".format(split): logits_fake.detach().mean(), |
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} |
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return d_loss, log |
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