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| import torch | |
| from torch import nn | |
| from . import weights_init, l1, l2, hinge_d_loss, vanilla_d_loss, measure_perplexity, square_dist_loss | |
| from .geometric import GeoConverter | |
| from .discriminator import NLayerDiscriminator, LiDARNLayerDiscriminator, LiDARNLayerDiscriminatorV2 | |
| from .perceptual import PerceptualLoss | |
| VERSION2DISC = {'v0': NLayerDiscriminator, 'v1': LiDARNLayerDiscriminator, 'v2': LiDARNLayerDiscriminatorV2} | |
| class VQGeoLPIPSWithDiscriminator(nn.Module): | |
| def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0, | |
| disc_num_layers=3, disc_in_channels=3, disc_out_channels=1, disc_factor=1.0, disc_weight=1.0, | |
| mask_factor=0.0, use_actnorm=False, disc_conditional=False, | |
| disc_ndf=64, disc_loss="hinge", n_classes=None, pixel_loss="l1", disc_version='v1', | |
| geo_factor=1.0, curve_length=4, perceptual_factor=1.0, perceptual_type='rangenet_final', | |
| dataset_config=dict()): | |
| super().__init__() | |
| assert disc_loss in ["hinge", "vanilla"] | |
| assert pixel_loss in ["l1", "l2"] | |
| self.codebook_weight = codebook_weight | |
| self.pixel_weight = pixelloss_weight | |
| self.mask_factor = mask_factor | |
| self.geo_factor = geo_factor | |
| # scale of reconstruction loss | |
| self.rec_scale = 1 | |
| if mask_factor > 0: | |
| self.rec_scale += 1. | |
| if geo_factor > 0: | |
| self.rec_scale += 1. | |
| if perceptual_factor > 0: | |
| self.rec_scale += 1. | |
| if pixel_loss == "l1": | |
| self.pixel_loss = l1 | |
| else: | |
| self.pixel_loss = l2 | |
| self.perceptual_factor = perceptual_factor | |
| if perceptual_factor > 0.: | |
| print(f"{self.__class__.__name__}: Running with LPIPS.") | |
| self.perceptual_loss = PerceptualLoss(perceptual_type, dataset_config.depth_scale, | |
| dataset_config.log_scale).eval() | |
| disc_cls = VERSION2DISC[disc_version] | |
| self.discriminator = disc_cls(input_nc=disc_in_channels, | |
| output_nc=disc_out_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"VQGeoLPIPSWithDiscriminator running with {disc_loss} loss.") | |
| self.disc_factor = disc_factor | |
| self.discriminator_weight = disc_weight | |
| self.disc_conditional = disc_conditional | |
| self.n_classes = n_classes | |
| self.geometry_converter = GeoConverter(curve_length, False, dataset_config) # force converting xyz output | |
| self.geo_loss = square_dist_loss | |
| 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, codebook_loss, inputs, reconstructions, optimizer_idx, | |
| global_step, last_layer=None, cond=None, split="train", predicted_indices=None, masks=None): | |
| input_coord = self.geometry_converter(inputs) | |
| rec_coord = self.geometry_converter(reconstructions[:, 0:1].contiguous()) | |
| ############# Reconstruction ############# | |
| # pixel reconstruction loss | |
| if self.mask_factor > 0 and masks is not None: | |
| pixel_rec_loss = self.pixel_loss(inputs.contiguous(), reconstructions[:, 0:1].contiguous()) | |
| mask_rec_loss = self.pixel_loss(masks.contiguous(), reconstructions[:, 1:2].contiguous()) * self.mask_factor | |
| else: | |
| pixel_rec_loss = self.pixel_loss(inputs.contiguous(), reconstructions.contiguous()) | |
| mask_rec_loss = torch.tensor(0.0) | |
| # geometry reconstruction loss (bev) | |
| if self.geo_factor > 0: | |
| geo_rec_loss = self.geo_loss(input_coord[:, :2], rec_coord[:, :2]) * self.geo_factor | |
| else: | |
| geo_rec_loss = torch.tensor(0.0) | |
| # perceptual loss | |
| if self.perceptual_factor > 0: | |
| perceptual_loss = self.perceptual_loss((inputs.contiguous(), input_coord), | |
| (reconstructions[:, 0:1].contiguous(), rec_coord)) * self.perceptual_factor | |
| else: | |
| perceptual_loss = torch.tensor(0.0) | |
| # overall reconstruction loss | |
| rec_loss = (pixel_rec_loss + mask_rec_loss + geo_rec_loss + perceptual_loss) / self.rec_scale | |
| nll_loss = rec_loss | |
| nll_loss = torch.mean(nll_loss) | |
| ############# GAN ############# | |
| disc_factor = 0. if global_step > self.discriminator_iter_start else self.disc_factor | |
| # update generator (input: img, mask, coord, [cond]) | |
| if optimizer_idx == 0: | |
| disc_recons = reconstructions.contiguous() | |
| if self.geo_factor > 0: | |
| disc_recons = torch.cat((disc_recons, rec_coord[:, :2].contiguous()), dim=1) | |
| if cond is not None and self.disc_conditional: | |
| disc_recons = torch.cat((disc_recons, cond), dim=1) | |
| logits_fake = self.discriminator(disc_recons) | |
| # adversarial loss | |
| 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) | |
| 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(), | |
| "{}/rec_loss".format(split): rec_loss.detach().mean(), | |
| "{}/pix_rec_loss".format(split): pixel_rec_loss.detach().mean(), | |
| "{}/geo_rec_loss".format(split): geo_rec_loss.detach().mean(), | |
| "{}/mask_rec_loss".format(split): mask_rec_loss.detach().mean(), | |
| "{}/perceptual_loss".format(split): perceptual_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()} | |
| if predicted_indices is not None: | |
| assert self.n_classes is not None | |
| with torch.no_grad(): | |
| perplexity, cluster_usage = measure_perplexity(predicted_indices, self.n_classes) | |
| log[f"{split}/perplexity"] = perplexity | |
| log[f"{split}/cluster_usage"] = cluster_usage | |
| return loss, log | |
| # update discriminator (input: img, mask, coord, [cond]) | |
| if optimizer_idx == 1: | |
| disc_inputs, disc_recons = inputs.contiguous().detach(), reconstructions.contiguous().detach() | |
| if self.mask_factor > 0 and masks is not None: | |
| disc_inputs = torch.cat((disc_inputs, masks.contiguous().detach()), dim=1) | |
| if self.geo_factor > 0: | |
| disc_inputs = torch.cat((disc_inputs, input_coord[:, :2].contiguous()), dim=1) | |
| disc_recons = torch.cat((disc_recons, rec_coord[:, :2].contiguous()), dim=1) | |
| if cond is not None: | |
| disc_inputs = torch.cat((disc_inputs, cond), dim=1) | |
| disc_recons = torch.cat((disc_recons, cond), dim=1) | |
| logits_real = self.discriminator(disc_inputs) | |
| logits_fake = self.discriminator(disc_recons) | |
| # gan loss | |
| d_loss = self.disc_loss(logits_real, logits_fake) * disc_factor | |
| 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 | |