mirror of
https://github.com/hpcaitech/Open-Sora.git
synced 2026-04-15 03:15:20 +02:00
233 lines
9.9 KiB
Python
233 lines
9.9 KiB
Python
import numpy as np
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import torch
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import torch.nn as nn
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# from taming.modules.losses.lpips import LPIPS # need to pip install https://github.com/CompVis/taming-transformers
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# from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
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from einops import rearrange
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"""Stripped version of https://github.com/richzhang/PerceptualSimilarity/tree/master/models"""
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## NOTE: not used since we only have 'GN'
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# def get_norm_layer(norm_type, dtype):
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# if norm_type == 'LN':
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# # supply a few args with partial function and pass the rest of the args when this norm_fn is called
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# norm_fn = functools.partial(nn.LayerNorm, dtype=dtype)
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# elif norm_type == 'GN': #
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# norm_fn = functools.partial(nn.GroupNorm, dtype=dtype)
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# elif norm_type is None:
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# norm_fn = lambda: (lambda x: x)
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# else:
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# raise NotImplementedError(f'norm_type: {norm_type}')
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# return norm_fn
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class DiagonalGaussianDistribution(object):
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def __init__(
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self,
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parameters,
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deterministic=False,
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):
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self.parameters = parameters
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self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) # SCH: channels dim
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self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
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self.deterministic = deterministic
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self.std = torch.exp(0.5 * self.logvar)
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self.var = torch.exp(self.logvar)
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if self.deterministic:
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self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device, dtype=self.mean.dtype)
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def sample(self):
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# torch.randn: standard normal distribution
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x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device, dtype=self.mean.dtype)
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return x
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def kl(self, other=None):
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if self.deterministic:
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return torch.Tensor([0.0])
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else:
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if other is None: # SCH: assumes other is a standard normal distribution
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return 0.5 * torch.sum(torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, dim=[1, 2, 3, 4])
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else:
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return 0.5 * torch.sum(
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torch.pow(self.mean - other.mean, 2) / other.var
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+ self.var / other.var
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- 1.0
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- self.logvar
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+ other.logvar,
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dim=[1, 2, 3, 4],
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)
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def nll(self, sample, dims=[1, 2, 3, 4]): # TODO: what does this do?
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if self.deterministic:
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return torch.Tensor([0.0])
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logtwopi = np.log(2.0 * np.pi)
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return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, dim=dims)
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def mode(self): # SCH: used for vae inference?
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return self.mean
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class VEA3DLoss(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=0.1,
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disc_loss="hinge",
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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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# output log variance
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self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)
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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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weights=None,
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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: # NOTE: need in_channels == 3 in order to use!
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assert inputs.size(1) == 3, f"using vgg16 that requires 3 input channels but got {inputs.size(1)}"
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# SCH: transform to [(B,T), C, H, W] shape for percetual loss over each frame
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B = inputs.shape[0]
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inputs = rearrange(inputs, "B C T H W -> (B T) C H W")
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reconstructions = rearrange(reconstructions, "B C T H W -> (B T) C H W")
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# permutated_input = torch.permute(inputs, (0, 2, 1, 3, 4)) # [B, C, T, H, W] --> [B, T, C, H, W]
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# permutated_rec = torch.permute(reconstructions, (0, 2, 1, 3, 4))
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# data_shape = permutated_input.size()
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# p_loss = self.perceptual_loss(
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# permutated_input.reshape(-1, data_shape[-3], data_shape[-2],data_shape[-1]).contiguous(),
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# permutated_rec.reshape(-1, data_shape[-3], data_shape[-2],data_shape[-1]).contiguous()
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# )
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p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
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# SCH: shape back p_loss
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# permuted_p_loss = torch.permute(p_loss.reshape(data_shape[0], data_shape[1], 1, 1, 1), (0,2,1,3,4))
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# rec_loss = rec_loss + self.perceptual_weight * permuted_p_loss
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p_loss = rearrange(p_loss, "(B T) C H W -> B C T H W", B=B)
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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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kl_loss = posteriors.kl()
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kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
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loss = weighted_nll_loss + self.kl_weight * kl_loss # TODO: add discriminator loss later
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return loss
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class VEA3DLossWithDiscriminator(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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disc_num_layers=3,
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disc_in_channels=3,
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disc_factor=1.0,
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disc_weight=1.0,
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perceptual_weight=1.0,
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use_actnorm=False,
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disc_conditional=False,
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disc_loss="hinge",
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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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# output log variance
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self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)
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# self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
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# n_layers=disc_num_layers,
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# use_actnorm=use_actnorm
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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.disc_conditional = disc_conditional
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# TODO: for 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(nll_loss, self.last_layer[0], retain_graph=True)[0]
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# g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[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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last_layer=None,
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cond=None,
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split="train",
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weights=None,
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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: # NOTE: need in_channels == 3 in order to use!
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assert inputs.size(1) == 3, f"using vgg16 that requires 3 input channels but got {inputs.size(1)} "
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# SCH: transform to [(B,T), C, H, W] shape for percetual loss over each frame
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permutated_input = torch.permute(inputs, (0, 2, 1, 3, 4)) # [B, C, T, H, W] --> [B, T, C, H, W]
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permutated_rec = torch.permute(reconstructions, (0, 2, 1, 3, 4))
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data_shape = permutated_input.size()
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p_loss = self.perceptual_loss(
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permutated_input.reshape(-1, data_shape[-3], data_shape[-2], data_shape[-1]).contiguous(),
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permutated_rec.reshape(-1, data_shape[-3], data_shape[-2], data_shape[-1]).contiguous(),
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)
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# SCH: shape back p_loss
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permuted_p_loss = torch.permute(p_loss.reshape(data_shape[0], data_shape[1], 1, 1, 1), (0, 2, 1, 3, 4))
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rec_loss = rec_loss + self.perceptual_weight * permuted_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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kl_loss = posteriors.kl()
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kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
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loss = weighted_nll_loss + self.kl_weight * kl_loss # TODO: add discriminator loss later
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# log = {"{}/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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# # "{}/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
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