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| # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. | |
| # | |
| # NVIDIA CORPORATION and its licensors retain all intellectual property | |
| # and proprietary rights in and to this software, related documentation | |
| # and any modifications thereto. Any use, reproduction, disclosure or | |
| # distribution of this software and related documentation without an express | |
| # license agreement from NVIDIA CORPORATION is strictly prohibited. | |
| import numpy as np | |
| import torch | |
| from torch_utils import training_stats | |
| from torch_utils import misc | |
| from torch_utils.ops import conv2d_gradfix | |
| from losses.pcp import PerceptualLoss | |
| #---------------------------------------------------------------------------- | |
| class Loss: | |
| def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, sync, gain): # to be overridden by subclass | |
| raise NotImplementedError() | |
| #---------------------------------------------------------------------------- | |
| class TwoStageLoss(Loss): | |
| def __init__(self, device, G_mapping, G_synthesis, D, augment_pipe=None, style_mixing_prob=0.9, r1_gamma=10, pl_batch_shrink=2, pl_decay=0.01, pl_weight=2, truncation_psi=1, pcp_ratio=1.0): | |
| super().__init__() | |
| self.device = device | |
| self.G_mapping = G_mapping | |
| self.G_synthesis = G_synthesis | |
| self.D = D | |
| self.augment_pipe = augment_pipe | |
| self.style_mixing_prob = style_mixing_prob | |
| self.r1_gamma = r1_gamma | |
| self.pl_batch_shrink = pl_batch_shrink | |
| self.pl_decay = pl_decay | |
| self.pl_weight = pl_weight | |
| self.pl_mean = torch.zeros([], device=device) | |
| self.truncation_psi = truncation_psi | |
| self.pcp = PerceptualLoss(layer_weights=dict(conv4_4=1/4, conv5_4=1/2)).to(device) | |
| self.pcp_ratio = pcp_ratio | |
| def run_G(self, img_in, mask_in, z, c, sync): | |
| with misc.ddp_sync(self.G_mapping, sync): | |
| ws = self.G_mapping(z, c, truncation_psi=self.truncation_psi) | |
| if self.style_mixing_prob > 0: | |
| with torch.autograd.profiler.record_function('style_mixing'): | |
| cutoff = torch.empty([], dtype=torch.int64, device=ws.device).random_(1, ws.shape[1]) | |
| cutoff = torch.where(torch.rand([], device=ws.device) < self.style_mixing_prob, cutoff, torch.full_like(cutoff, ws.shape[1])) | |
| ws[:, cutoff:] = self.G_mapping(torch.randn_like(z), c, truncation_psi=self.truncation_psi, skip_w_avg_update=True)[:, cutoff:] | |
| with misc.ddp_sync(self.G_synthesis, sync): | |
| img, img_stg1 = self.G_synthesis(img_in, mask_in, ws, return_stg1=True) | |
| return img, ws, img_stg1 | |
| def run_D(self, img, mask, img_stg1, c, sync): | |
| # if self.augment_pipe is not None: | |
| # # img = self.augment_pipe(img) | |
| # # !!!!! have to remove the color transform | |
| # tmp_img = torch.cat([img, mask], dim=1) | |
| # tmp_img = self.augment_pipe(tmp_img) | |
| # img, mask = torch.split(tmp_img, [3, 1]) | |
| with misc.ddp_sync(self.D, sync): | |
| logits, logits_stg1 = self.D(img, mask, img_stg1, c) | |
| return logits, logits_stg1 | |
| def accumulate_gradients(self, phase, real_img, mask, real_c, gen_z, gen_c, sync, gain): | |
| assert phase in ['Gmain', 'Greg', 'Gboth', 'Dmain', 'Dreg', 'Dboth'] | |
| do_Gmain = (phase in ['Gmain', 'Gboth']) | |
| do_Dmain = (phase in ['Dmain', 'Dboth']) | |
| do_Gpl = (phase in ['Greg', 'Gboth']) and (self.pl_weight != 0) | |
| do_Dr1 = (phase in ['Dreg', 'Dboth']) and (self.r1_gamma != 0) | |
| # Gmain: Maximize logits for generated images. | |
| if do_Gmain: | |
| with torch.autograd.profiler.record_function('Gmain_forward'): | |
| gen_img, _gen_ws, gen_img_stg1 = self.run_G(real_img, mask, gen_z, gen_c, sync=(sync and not do_Gpl)) # May get synced by Gpl. | |
| gen_logits, gen_logits_stg1 = self.run_D(gen_img, mask, gen_img_stg1, gen_c, sync=False) | |
| training_stats.report('Loss/scores/fake', gen_logits) | |
| training_stats.report('Loss/signs/fake', gen_logits.sign()) | |
| training_stats.report('Loss/scores/fake_s1', gen_logits_stg1) | |
| training_stats.report('Loss/signs/fake_s1', gen_logits_stg1.sign()) | |
| loss_Gmain = torch.nn.functional.softplus(-gen_logits) # -log(sigmoid(gen_logits)) | |
| training_stats.report('Loss/G/loss', loss_Gmain) | |
| loss_Gmain_stg1 = torch.nn.functional.softplus(-gen_logits_stg1) | |
| training_stats.report('Loss/G/loss_s1', loss_Gmain_stg1) | |
| # just for showing | |
| l1_loss = torch.mean(torch.abs(gen_img - real_img)) | |
| training_stats.report('Loss/G/l1_loss', l1_loss) | |
| pcp_loss, _ = self.pcp(gen_img, real_img) | |
| training_stats.report('Loss/G/pcp_loss', pcp_loss) | |
| with torch.autograd.profiler.record_function('Gmain_backward'): | |
| loss_Gmain_all = loss_Gmain + loss_Gmain_stg1 + pcp_loss * self.pcp_ratio | |
| loss_Gmain_all.mean().mul(gain).backward() | |
| # # Gpl: Apply path length regularization. | |
| # if do_Gpl: | |
| # with torch.autograd.profiler.record_function('Gpl_forward'): | |
| # batch_size = gen_z.shape[0] // self.pl_batch_shrink | |
| # gen_img, gen_ws = self.run_G(real_img[:batch_size], mask[:batch_size], gen_z[:batch_size], gen_c[:batch_size], sync=sync) | |
| # pl_noise = torch.randn_like(gen_img) / np.sqrt(gen_img.shape[2] * gen_img.shape[3]) | |
| # with torch.autograd.profiler.record_function('pl_grads'), conv2d_gradfix.no_weight_gradients(): | |
| # pl_grads = torch.autograd.grad(outputs=[(gen_img * pl_noise).sum()], inputs=[gen_ws], create_graph=True, only_inputs=True)[0] | |
| # pl_lengths = pl_grads.square().sum(2).mean(1).sqrt() | |
| # pl_mean = self.pl_mean.lerp(pl_lengths.mean(), self.pl_decay) | |
| # self.pl_mean.copy_(pl_mean.detach()) | |
| # pl_penalty = (pl_lengths - pl_mean).square() | |
| # training_stats.report('Loss/pl_penalty', pl_penalty) | |
| # loss_Gpl = pl_penalty * self.pl_weight | |
| # training_stats.report('Loss/G/reg', loss_Gpl) | |
| # with torch.autograd.profiler.record_function('Gpl_backward'): | |
| # (gen_img[:, 0, 0, 0] * 0 + loss_Gpl).mean().mul(gain).backward() | |
| # Dmain: Minimize logits for generated images. | |
| loss_Dgen = 0 | |
| loss_Dgen_stg1 = 0 | |
| if do_Dmain: | |
| with torch.autograd.profiler.record_function('Dgen_forward'): | |
| gen_img, _gen_ws, gen_img_stg1 = self.run_G(real_img, mask, gen_z, gen_c, sync=False) | |
| gen_logits, gen_logits_stg1 = self.run_D(gen_img, mask, gen_img_stg1, gen_c, sync=False) # Gets synced by loss_Dreal. | |
| training_stats.report('Loss/scores/fake', gen_logits) | |
| training_stats.report('Loss/signs/fake', gen_logits.sign()) | |
| loss_Dgen = torch.nn.functional.softplus(gen_logits) # -log(1 - sigmoid(gen_logits)) | |
| training_stats.report('Loss/scores/fake_s1', gen_logits_stg1) | |
| training_stats.report('Loss/signs/fake_s1', gen_logits_stg1.sign()) | |
| loss_Dgen_stg1 = torch.nn.functional.softplus(gen_logits_stg1) # -log(1 - sigmoid(gen_logits)) | |
| with torch.autograd.profiler.record_function('Dgen_backward'): | |
| loss_Dgen_all = loss_Dgen + loss_Dgen_stg1 | |
| loss_Dgen_all.mean().mul(gain).backward() | |
| # Dmain: Maximize logits for real images. | |
| # Dr1: Apply R1 regularization. | |
| if do_Dmain or do_Dr1: | |
| name = 'Dreal_Dr1' if do_Dmain and do_Dr1 else 'Dreal' if do_Dmain else 'Dr1' | |
| with torch.autograd.profiler.record_function(name + '_forward'): | |
| real_img_tmp = real_img.detach().requires_grad_(do_Dr1) | |
| mask_tmp = mask.detach().requires_grad_(do_Dr1) | |
| real_img_tmp_stg1 = real_img.detach().requires_grad_(do_Dr1) | |
| real_logits, real_logits_stg1 = self.run_D(real_img_tmp, mask_tmp, real_img_tmp_stg1, real_c, sync=sync) | |
| training_stats.report('Loss/scores/real', real_logits) | |
| training_stats.report('Loss/signs/real', real_logits.sign()) | |
| training_stats.report('Loss/scores/real_s1', real_logits_stg1) | |
| training_stats.report('Loss/signs/real_s1', real_logits_stg1.sign()) | |
| loss_Dreal = 0 | |
| loss_Dreal_stg1 = 0 | |
| if do_Dmain: | |
| loss_Dreal = torch.nn.functional.softplus(-real_logits) # -log(sigmoid(real_logits)) | |
| loss_Dreal_stg1 = torch.nn.functional.softplus(-real_logits_stg1) # -log(sigmoid(real_logits)) | |
| training_stats.report('Loss/D/loss', loss_Dgen + loss_Dreal) | |
| training_stats.report('Loss/D/loss_s1', loss_Dgen_stg1 + loss_Dreal_stg1) | |
| loss_Dr1 = 0 | |
| loss_Dr1_stg1 = 0 | |
| if do_Dr1: | |
| with torch.autograd.profiler.record_function('r1_grads'), conv2d_gradfix.no_weight_gradients(): | |
| r1_grads = torch.autograd.grad(outputs=[real_logits.sum()], inputs=[real_img_tmp], create_graph=True, only_inputs=True)[0] | |
| r1_grads_stg1 = torch.autograd.grad(outputs=[real_logits_stg1.sum()], inputs=[real_img_tmp_stg1], create_graph=True, only_inputs=True)[0] | |
| r1_penalty = r1_grads.square().sum([1,2,3]) | |
| loss_Dr1 = r1_penalty * (self.r1_gamma / 2) | |
| training_stats.report('Loss/r1_penalty', r1_penalty) | |
| training_stats.report('Loss/D/reg', loss_Dr1) | |
| r1_penalty_stg1 = r1_grads_stg1.square().sum([1, 2, 3]) | |
| loss_Dr1_stg1 = r1_penalty_stg1 * (self.r1_gamma / 2) | |
| training_stats.report('Loss/r1_penalty_s1', r1_penalty_stg1) | |
| training_stats.report('Loss/D/reg_s1', loss_Dr1_stg1) | |
| with torch.autograd.profiler.record_function(name + '_backward'): | |
| ((real_logits + real_logits_stg1) * 0 + loss_Dreal + loss_Dreal_stg1 + loss_Dr1 + loss_Dr1_stg1).mean().mul(gain).backward() | |
| #---------------------------------------------------------------------------- | |