192 lines
7.4 KiB
Python
192 lines
7.4 KiB
Python
import os
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import torch
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from torch import nn
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import torch.nn.functional as F
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from einops import rearrange
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from .modeling_lpips import LPIPS
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from .modeling_discriminator import NLayerDiscriminator, NLayerDiscriminator3D, weights_init
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#from IPython import embed
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class AdaptiveLossWeight:
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def __init__(self, timestep_range=[0, 1], buckets=300, weight_range=[1e-7, 1e7]):
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self.bucket_ranges = torch.linspace(timestep_range[0], timestep_range[1], buckets-1)
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self.bucket_losses = torch.ones(buckets)
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self.weight_range = weight_range
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def weight(self, timestep):
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indices = torch.searchsorted(self.bucket_ranges.to(timestep.device), timestep)
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return (1/self.bucket_losses.to(timestep.device)[indices]).clamp(*self.weight_range)
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def update_buckets(self, timestep, loss, beta=0.99):
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indices = torch.searchsorted(self.bucket_ranges.to(timestep.device), timestep).cpu()
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self.bucket_losses[indices] = self.bucket_losses[indices]*beta + loss.detach().cpu() * (1-beta)
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def hinge_d_loss(logits_real, logits_fake):
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loss_real = torch.mean(F.relu(1.0 - logits_real))
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loss_fake = torch.mean(F.relu(1.0 + logits_fake))
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d_loss = 0.5 * (loss_real + loss_fake)
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return d_loss
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def vanilla_d_loss(logits_real, logits_fake):
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d_loss = 0.5 * (
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torch.mean(torch.nn.functional.softplus(-logits_real))
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+ torch.mean(torch.nn.functional.softplus(logits_fake))
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)
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return d_loss
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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 LPIPSWithDiscriminator(nn.Module):
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def __init__(
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self,
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disc_start,
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logvar_init=0.0,
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kl_weight=1.0,
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pixelloss_weight=1.0,
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perceptual_weight=1.0,
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# --- Discriminator Loss ---
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disc_num_layers=4,
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disc_in_channels=3,
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disc_factor=1.0,
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disc_weight=0.5,
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disc_loss="hinge",
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add_discriminator=True,
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using_3d_discriminator=False,
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):
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super().__init__()
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assert disc_loss in ["hinge", "vanilla"]
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self.kl_weight = kl_weight
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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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if add_discriminator:
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disc_cls = NLayerDiscriminator3D if using_3d_discriminator else NLayerDiscriminator
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self.discriminator = disc_cls(
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input_nc=disc_in_channels, n_layers=disc_num_layers,
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).apply(weights_init)
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else:
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self.discriminator = None
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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.using_3d_discriminator = using_3d_discriminator
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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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inputs,
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reconstructions,
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posteriors,
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optimizer_idx,
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global_step,
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split="train",
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last_layer=None,
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):
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t = reconstructions.shape[2]
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inputs = rearrange(inputs, "b c t h w -> (b t) c h w").contiguous()
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reconstructions = rearrange(reconstructions, "b c t h w -> (b t) c h w").contiguous()
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if optimizer_idx == 0:
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# rec_loss = torch.mean(torch.abs(inputs - reconstructions), dim=(1,2,3), keepdim=True)
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rec_loss = torch.mean(F.mse_loss(inputs, reconstructions, reduction='none'), dim=(1,2,3), keepdim=True)
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if self.perceptual_weight > 0:
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p_loss = self.perceptual_loss(inputs, reconstructions)
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nll_loss = self.pixel_weight * rec_loss + self.perceptual_weight * p_loss
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nll_loss = nll_loss / torch.exp(self.logvar) + self.logvar
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weighted_nll_loss = 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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kl_loss = posteriors.kl()
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kl_loss = torch.mean(kl_loss)
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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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if disc_factor > 0.0:
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if self.using_3d_discriminator:
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reconstructions = rearrange(reconstructions, '(b t) c h w -> b c t h w', t=t)
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logits_fake = self.discriminator(reconstructions.contiguous())
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g_loss = -torch.mean(logits_fake)
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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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g_loss = torch.tensor(0.0)
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loss = (
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weighted_nll_loss
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+ self.kl_weight * kl_loss
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+ d_weight * disc_factor * g_loss
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)
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log = {
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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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"{}/kl_loss".format(split): kl_loss.detach().mean(),
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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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"{}/perception_loss".format(split): p_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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return loss, log
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if optimizer_idx == 1:
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if self.using_3d_discriminator:
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inputs = rearrange(inputs, '(b t) c h w -> b c t h w', t=t)
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reconstructions = rearrange(reconstructions, '(b t) c h w -> b c t h w', t=t)
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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 |