models/tta/autoencoder/autoencoder_loss.py

# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn
import functools
import torch.nn.functional as F


def hinge_d_loss(logits_real, logits_fake):
    loss_real = torch.mean(F.relu(1.0 - logits_real))
    loss_fake = torch.mean(F.relu(1.0 + logits_fake))
    d_loss = 0.5 * (loss_real + loss_fake)
    return d_loss


def vanilla_d_loss(logits_real, logits_fake):
    d_loss = 0.5 * (
        torch.mean(F.softplus(-logits_real)) + torch.mean(F.softplus(logits_fake))
    )
    return d_loss


def adopt_weight(weight, global_step, threshold=0, value=0.0):
    if global_step < threshold:
        weight = value
    return weight


class ActNorm(nn.Module):
    def __init__(
        self, num_features, logdet=False, affine=True, allow_reverse_init=False
    ):
        assert affine
        super().__init__()
        self.logdet = logdet
        self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1))
        self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1))
        self.allow_reverse_init = allow_reverse_init

        self.register_buffer("initialized", torch.tensor(0, dtype=torch.uint8))

    def initialize(self, input):
        with torch.no_grad():
            flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1)
            mean = (
                flatten.mean(1)
                .unsqueeze(1)
                .unsqueeze(2)
                .unsqueeze(3)
                .permute(1, 0, 2, 3)
            )
            std = (
                flatten.std(1)
                .unsqueeze(1)
                .unsqueeze(2)
                .unsqueeze(3)
                .permute(1, 0, 2, 3)
            )

            self.loc.data.copy_(-mean)
            self.scale.data.copy_(1 / (std + 1e-6))

    def forward(self, input, reverse=False):
        if reverse:
            return self.reverse(input)
        if len(input.shape) == 2:
            input = input[:, :, None, None]
            squeeze = True
        else:
            squeeze = False

        _, _, height, width = input.shape

        if self.training and self.initialized.item() == 0:
            self.initialize(input)
            self.initialized.fill_(1)

        h = self.scale * (input + self.loc)

        if squeeze:
            h = h.squeeze(-1).squeeze(-1)

        if self.logdet:
            log_abs = torch.log(torch.abs(self.scale))
            logdet = height * width * torch.sum(log_abs)
            logdet = logdet * torch.ones(input.shape[0]).to(input)
            return h, logdet

        return h

    def reverse(self, output):
        if self.training and self.initialized.item() == 0:
            if not self.allow_reverse_init:
                raise RuntimeError(
                    "Initializing ActNorm in reverse direction is "
                    "disabled by default. Use allow_reverse_init=True to enable."
                )
            else:
                self.initialize(output)
                self.initialized.fill_(1)

        if len(output.shape) == 2:
            output = output[:, :, None, None]
            squeeze = True
        else:
            squeeze = False

        h = output / self.scale - self.loc

        if squeeze:
            h = h.squeeze(-1).squeeze(-1)
        return h


def weights_init(m):
    classname = m.__class__.__name__
    if classname.find("Conv") != -1:
        nn.init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find("BatchNorm") != -1:
        nn.init.normal_(m.weight.data, 1.0, 0.02)
        nn.init.constant_(m.bias.data, 0)


class NLayerDiscriminator(nn.Module):
    """Defines a PatchGAN discriminator as in Pix2Pix
    --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
    """

    def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False):
        """Construct a PatchGAN discriminator
        Parameters:
            input_nc (int)  -- the number of channels in input images
            ndf (int)       -- the number of filters in the last conv layer
            n_layers (int)  -- the number of conv layers in the discriminator
            norm_layer      -- normalization layer
        """
        super(NLayerDiscriminator, self).__init__()
        if not use_actnorm:
            norm_layer = nn.BatchNorm2d
        else:
            norm_layer = ActNorm
        if (
            type(norm_layer) == functools.partial
        ):  # no need to use bias as BatchNorm2d has affine parameters
            use_bias = norm_layer.func != nn.BatchNorm2d
        else:
            use_bias = norm_layer != nn.BatchNorm2d

        kw = 4
        padw = 1
        sequence = [
            nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
            nn.LeakyReLU(0.2, True),
        ]
        nf_mult = 1
        nf_mult_prev = 1
        for n in range(1, n_layers):  # gradually increase the number of filters
            nf_mult_prev = nf_mult
            nf_mult = min(2**n, 8)
            sequence += [
                nn.Conv2d(
                    ndf * nf_mult_prev,
                    ndf * nf_mult,
                    kernel_size=kw,
                    stride=2,
                    padding=padw,
                    bias=use_bias,
                ),
                norm_layer(ndf * nf_mult),
                nn.LeakyReLU(0.2, True),
            ]

        nf_mult_prev = nf_mult
        nf_mult = min(2**n_layers, 8)
        sequence += [
            nn.Conv2d(
                ndf * nf_mult_prev,
                ndf * nf_mult,
                kernel_size=kw,
                stride=1,
                padding=padw,
                bias=use_bias,
            ),
            norm_layer(ndf * nf_mult),
            nn.LeakyReLU(0.2, True),
        ]

        sequence += [
            nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)
        ]  # output 1 channel prediction map
        self.main = nn.Sequential(*sequence)

    def forward(self, input):
        """Standard forward."""
        return self.main(input)


class AutoencoderLossWithDiscriminator(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.cfg = cfg
        self.kl_weight = cfg.kl_weight
        self.logvar = nn.Parameter(torch.ones(size=()) * cfg.logvar_init)

        self.discriminator = NLayerDiscriminator(
            input_nc=cfg.disc_in_channels,
            n_layers=cfg.disc_num_layers,
            use_actnorm=cfg.use_actnorm,
        ).apply(weights_init)

        self.discriminator_iter_start = cfg.disc_start
        self.discriminator_weight = cfg.disc_weight
        self.disc_factor = cfg.disc_factor
        self.disc_loss = hinge_d_loss

    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer):
        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]

        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
        d_weight = torch.clamp(
            d_weight, self.cfg.min_adapt_d_weight, self.cfg.max_adapt_d_weight
        ).detach()
        d_weight = d_weight * self.discriminator_weight
        return d_weight

    def forward(
        self,
        inputs,
        reconstructions,
        posteriors,
        optimizer_idx,
        global_step,
        last_layer,
        split="train",
        weights=None,
    ):
        rec_loss = torch.abs(
            inputs.contiguous() - reconstructions.contiguous()
        )  # l1 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]
        weighted_nll_loss = torch.mean(weighted_nll_loss)
        # nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
        nll_loss = torch.mean(nll_loss)
        kl_loss = posteriors.kl()
        kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
        # ? kl_loss = torch.mean(kl_loss)

        # now the GAN part
        if optimizer_idx == 0:
            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
            )

            total_loss = (
                weighted_nll_loss
                + self.kl_weight * kl_loss
                + d_weight * disc_factor * g_loss
            )

            return {
                "loss": total_loss,
                "kl_loss": kl_loss,
                "rec_loss": rec_loss.mean(),
                "nll_loss": nll_loss,
                "g_loss": g_loss,
                "d_weight": d_weight,
                "disc_factor": torch.tensor(disc_factor),
            }

        if optimizer_idx == 1:
            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)

            return {
                "d_loss": d_loss,
                "logits_real": logits_real.mean(),
                "logits_fake": logits_fake.mean(),
            }