import torch import torch.nn as nn import torch.nn.functional as F import sys sys.path.insert(0, '.') # nopep8 from foleycrafter.models.specvqgan.modules.discriminator.model import (NLayerDiscriminator, NLayerDiscriminator1dFeats, NLayerDiscriminator1dSpecs, weights_init) from foleycrafter.models.specvqgan.modules.losses.lpaps import LPAPS class DummyLoss(nn.Module): def __init__(self): super().__init__() def adopt_weight(weight, global_step, threshold=0, value=0.): if global_step < threshold: weight = value return weight def hinge_d_loss(logits_real, logits_fake): loss_real = torch.mean(F.relu(1. - logits_real)) loss_fake = torch.mean(F.relu(1. + 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(torch.nn.functional.softplus(-logits_real)) + torch.mean(torch.nn.functional.softplus(logits_fake))) return d_loss class VQLPAPSWithDiscriminator(nn.Module): def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0, disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0, perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, disc_ndf=64, disc_loss="hinge", min_adapt_weight=0.0, max_adapt_weight=1e4): super().__init__() assert disc_loss in ["hinge", "vanilla"] self.codebook_weight = codebook_weight self.pixel_weight = pixelloss_weight self.perceptual_loss = LPAPS().eval() self.perceptual_weight = perceptual_weight self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm, ndf=disc_ndf ).apply(weights_init) self.discriminator_iter_start = disc_start if disc_loss == "hinge": self.disc_loss = hinge_d_loss elif disc_loss == "vanilla": self.disc_loss = vanilla_d_loss else: raise ValueError(f"Unknown GAN loss '{disc_loss}'.") print(f"VQLPAPSWithDiscriminator running with {disc_loss} loss.") self.disc_factor = disc_factor self.discriminator_weight = disc_weight self.disc_conditional = disc_conditional self.min_adapt_weight = min_adapt_weight self.max_adapt_weight = max_adapt_weight 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, self.min_adapt_weight, self.max_adapt_weight).detach() d_weight = d_weight * self.discriminator_weight return d_weight def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx, global_step, last_layer=None, cond=None, split="train"): 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 else: p_loss = torch.tensor([0.0]) nll_loss = rec_loss # nll_loss = torch.sum(nll_loss) / nll_loss.shape[0] nll_loss = torch.mean(nll_loss) # now the GAN part if optimizer_idx == 0: # generator update if cond is None: assert not self.disc_conditional logits_fake = self.discriminator(reconstructions.contiguous()) else: assert self.disc_conditional logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1)) g_loss = -torch.mean(logits_fake) 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) disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean() log = {"{}/total_loss".format(split): loss.clone().detach().mean(), "{}/quant_loss".format(split): codebook_loss.detach().mean(), "{}/nll_loss".format(split): nll_loss.detach().mean(), "{}/rec_loss".format(split): rec_loss.detach().mean(), "{}/p_loss".format(split): p_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 if cond is None: logits_real = self.discriminator(inputs.contiguous().detach()) logits_fake = self.discriminator(reconstructions.contiguous().detach()) else: logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1)) logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1)) 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 class VQLPAPSWithDiscriminator1dFeats(VQLPAPSWithDiscriminator): def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0, disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0, perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, disc_ndf=64, disc_loss="hinge", min_adapt_weight=0.0, max_adapt_weight=1e4): super().__init__(disc_start=disc_start, codebook_weight=codebook_weight, pixelloss_weight=pixelloss_weight, disc_num_layers=disc_num_layers, disc_in_channels=disc_in_channels, disc_factor=disc_factor, disc_weight=disc_weight, perceptual_weight=perceptual_weight, use_actnorm=use_actnorm, disc_conditional=disc_conditional, disc_ndf=disc_ndf, disc_loss=disc_loss, min_adapt_weight=min_adapt_weight, max_adapt_weight=max_adapt_weight) self.discriminator = NLayerDiscriminator1dFeats(input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init) class VQLPAPSWithDiscriminator1dSpecs(VQLPAPSWithDiscriminator): def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0, disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0, perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, disc_ndf=64, disc_loss="hinge", min_adapt_weight=0.0, max_adapt_weight=1e4): super().__init__(disc_start=disc_start, codebook_weight=codebook_weight, pixelloss_weight=pixelloss_weight, disc_num_layers=disc_num_layers, disc_in_channels=disc_in_channels, disc_factor=disc_factor, disc_weight=disc_weight, perceptual_weight=perceptual_weight, use_actnorm=use_actnorm, disc_conditional=disc_conditional, disc_ndf=disc_ndf, disc_loss=disc_loss, min_adapt_weight=min_adapt_weight, max_adapt_weight=max_adapt_weight) self.discriminator = NLayerDiscriminator1dSpecs(input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init) if __name__ == '__main__': from foleycrafter.models.specvqgan.modules.diffusionmodules.model import Decoder, Decoder1d optimizer_idx = 0 loss_config = { 'disc_conditional': False, 'disc_start': 30001, 'disc_weight': 0.8, 'codebook_weight': 1.0, } ddconfig = { 'ch': 128, 'num_res_blocks': 2, 'dropout': 0.0, 'z_channels': 256, 'double_z': False, } qloss = torch.rand(1, requires_grad=True) ## AUDIO loss_config['disc_in_channels'] = 1 ddconfig['in_channels'] = 1 ddconfig['resolution'] = 848 ddconfig['attn_resolutions'] = [53] ddconfig['out_ch'] = 1 ddconfig['ch_mult'] = [1, 1, 2, 2, 4] decoder = Decoder(**ddconfig) loss = VQLPAPSWithDiscriminator(**loss_config) x = torch.rand(16, 1, 80, 848) # subtracting something which uses dec_conv_out so that it will be in a graph xrec = torch.rand(16, 1, 80, 848) - decoder.conv_out(torch.rand(16, 128, 80, 848)).mean() aeloss, log_dict_ae = loss(qloss, x, xrec, optimizer_idx, global_step=0,last_layer=decoder.conv_out.weight) print(aeloss) print(log_dict_ae)