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| 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) | |