Upload __init__.py

sgm/__init__.py

@@ -0,0 +1,246 @@
+from typing import Any, Union
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+
+from ....util import default, instantiate_from_config
+from ..lpips.loss.lpips import LPIPS
+from ..lpips.model.model import NLayerDiscriminator, weights_init
+from ..lpips.vqperceptual import hinge_d_loss, vanilla_d_loss
+
+
+def adopt_weight(weight, global_step, threshold=0, value=0.0):
+    if global_step < threshold:
+        weight = value
+    return weight
+
+
+class LatentLPIPS(nn.Module):
+    def __init__(
+        self,
+        decoder_config,
+        perceptual_weight=1.0,
+        latent_weight=1.0,
+        scale_input_to_tgt_size=False,
+        scale_tgt_to_input_size=False,
+        perceptual_weight_on_inputs=0.0,
+    ):
+        super().__init__()
+        self.scale_input_to_tgt_size = scale_input_to_tgt_size
+        self.scale_tgt_to_input_size = scale_tgt_to_input_size
+        self.init_decoder(decoder_config)
+        self.perceptual_loss = LPIPS().eval()
+        self.perceptual_weight = perceptual_weight
+        self.latent_weight = latent_weight
+        self.perceptual_weight_on_inputs = perceptual_weight_on_inputs
+
+    def init_decoder(self, config):
+        self.decoder = instantiate_from_config(config)
+        if hasattr(self.decoder, "encoder"):
+            del self.decoder.encoder
+
+    def forward(self, latent_inputs, latent_predictions, image_inputs, split="train"):
+        log = dict()
+        loss = (latent_inputs - latent_predictions) ** 2
+        log[f"{split}/latent_l2_loss"] = loss.mean().detach()
+        image_reconstructions = None
+        if self.perceptual_weight > 0.0:
+            image_reconstructions = self.decoder.decode(latent_predictions)
+            image_targets = self.decoder.decode(latent_inputs)
+            perceptual_loss = self.perceptual_loss(
+                image_targets.contiguous(), image_reconstructions.contiguous()
+            )
+            loss = (
+                self.latent_weight * loss.mean()
+                + self.perceptual_weight * perceptual_loss.mean()
+            )
+            log[f"{split}/perceptual_loss"] = perceptual_loss.mean().detach()
+
+        if self.perceptual_weight_on_inputs > 0.0:
+            image_reconstructions = default(
+                image_reconstructions, self.decoder.decode(latent_predictions)
+            )
+            if self.scale_input_to_tgt_size:
+                image_inputs = torch.nn.functional.interpolate(
+                    image_inputs,
+                    image_reconstructions.shape[2:],
+                    mode="bicubic",
+                    antialias=True,
+                )
+            elif self.scale_tgt_to_input_size:
+                image_reconstructions = torch.nn.functional.interpolate(
+                    image_reconstructions,
+                    image_inputs.shape[2:],
+                    mode="bicubic",
+                    antialias=True,
+                )
+
+            perceptual_loss2 = self.perceptual_loss(
+                image_inputs.contiguous(), image_reconstructions.contiguous()
+            )
+            loss = loss + self.perceptual_weight_on_inputs * perceptual_loss2.mean()
+            log[f"{split}/perceptual_loss_on_inputs"] = perceptual_loss2.mean().detach()
+        return loss, log
+
+
+class GeneralLPIPSWithDiscriminator(nn.Module):
+    def __init__(
+        self,
+        disc_start: int,
+        logvar_init: float = 0.0,
+        pixelloss_weight=1.0,
+        disc_num_layers: int = 3,
+        disc_in_channels: int = 3,
+        disc_factor: float = 1.0,
+        disc_weight: float = 1.0,
+        perceptual_weight: float = 1.0,
+        disc_loss: str = "hinge",
+        scale_input_to_tgt_size: bool = False,
+        dims: int = 2,
+        learn_logvar: bool = False,
+        regularization_weights: Union[None, dict] = None,
+    ):
+        super().__init__()
+        self.dims = dims
+        if self.dims > 2:
+            print(
+                f"running with dims={dims}. This means that for perceptual loss calculation, "
+                f"the LPIPS loss will be applied to each frame independently. "
+            )
+        self.scale_input_to_tgt_size = scale_input_to_tgt_size
+        assert disc_loss in ["hinge", "vanilla"]
+        self.pixel_weight = pixelloss_weight
+        self.perceptual_loss = LPIPS().eval()
+        self.perceptual_weight = perceptual_weight
+        # output log variance
+        self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)
+        self.learn_logvar = learn_logvar
+
+        self.discriminator = NLayerDiscriminator(
+            input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=False
+        ).apply(weights_init)
+        self.discriminator_iter_start = disc_start
+        self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
+        self.disc_factor = disc_factor
+        self.discriminator_weight = disc_weight
+        self.regularization_weights = default(regularization_weights, {})
+
+    def get_trainable_parameters(self) -> Any:
+        return self.discriminator.parameters()
+
+    def get_trainable_autoencoder_parameters(self) -> Any:
+        if self.learn_logvar:
+            yield self.logvar
+        yield from ()
+
+    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
+        if last_layer is not None:
+            nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
+            g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
+        else:
+            nll_grads = torch.autograd.grad(
+                nll_loss, self.last_layer[0], retain_graph=True
+            )[0]
+            g_grads = torch.autograd.grad(
+                g_loss, self.last_layer[0], retain_graph=True
+            )[0]
+
+        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
+        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
+        d_weight = d_weight * self.discriminator_weight
+        return d_weight
+
+    def forward(
+        self,
+        regularization_log,
+        inputs,
+        reconstructions,
+        optimizer_idx,
+        global_step,
+        last_layer=None,
+        split="train",
+        weights=None,
+    ):
+        if self.scale_input_to_tgt_size:
+            inputs = torch.nn.functional.interpolate(
+                inputs, reconstructions.shape[2:], mode="bicubic", antialias=True
+            )
+
+        if self.dims > 2:
+            inputs, reconstructions = map(
+                lambda x: rearrange(x, "b c t h w -> (b t) c h w"),
+                (inputs, reconstructions),
+            )
+
+        rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
+        if self.perceptual_weight > 0:
+            p_loss = self.perceptual_loss(
+                inputs.contiguous(), reconstructions.contiguous()
+            )
+            rec_loss = rec_loss + self.perceptual_weight * p_loss
+
+        nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
+        weighted_nll_loss = nll_loss
+        if weights is not None:
+            weighted_nll_loss = weights * nll_loss
+        weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
+        nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
+
+        # now the GAN part
+        if optimizer_idx == 0:
+            # generator update
+            logits_fake = self.discriminator(reconstructions.contiguous())
+            g_loss = -torch.mean(logits_fake)
+
+            if self.disc_factor > 0.0:
+                try:
+                    d_weight = self.calculate_adaptive_weight(
+                        nll_loss, g_loss, last_layer=last_layer
+                    )
+                except RuntimeError:
+                    assert not self.training
+                    d_weight = torch.tensor(0.0)
+            else:
+                d_weight = torch.tensor(0.0)
+
+            disc_factor = adopt_weight(
+                self.disc_factor, global_step, threshold=self.discriminator_iter_start
+            )
+            loss = weighted_nll_loss + d_weight * disc_factor * g_loss
+            log = dict()
+            for k in regularization_log:
+                if k in self.regularization_weights:
+                    loss = loss + self.regularization_weights[k] * regularization_log[k]
+                log[f"{split}/{k}"] = regularization_log[k].detach().mean()
+
+            log.update(
+                {
+                    "{}/total_loss".format(split): loss.clone().detach().mean(),
+                    "{}/logvar".format(split): self.logvar.detach(),
+                    "{}/nll_loss".format(split): nll_loss.detach().mean(),
+                    "{}/rec_loss".format(split): rec_loss.detach().mean(),
+                    "{}/d_weight".format(split): d_weight.detach(),
+                    "{}/disc_factor".format(split): torch.tensor(disc_factor),
+                    "{}/g_loss".format(split): g_loss.detach().mean(),
+                }
+            )
+
+            return loss, log
+
+        if optimizer_idx == 1:
+            # second pass for discriminator update
+            logits_real = self.discriminator(inputs.contiguous().detach())
+            logits_fake = self.discriminator(reconstructions.contiguous().detach())
+
+            disc_factor = adopt_weight(
+                self.disc_factor, global_step, threshold=self.discriminator_iter_start
+            )
+            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
+
+            log = {
+                "{}/disc_loss".format(split): d_loss.clone().detach().mean(),
+                "{}/logits_real".format(split): logits_real.detach().mean(),
+                "{}/logits_fake".format(split): logits_fake.detach().mean(),
+            }
+            return d_loss, log