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"""Loss functions.""" |
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import numpy as np |
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import torch |
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from torch_utils import training_stats |
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from torch_utils.ops import conv2d_gradfix |
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from torch_utils.ops import upfirdn2d |
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class Loss: |
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def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, gain, cur_nimg): |
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raise NotImplementedError() |
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class StyleGAN2Loss(Loss): |
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def __init__(self, device, G, D, augment_pipe=None, r1_gamma=10, style_mixing_prob=0, pl_weight=0, pl_batch_shrink=2, pl_decay=0.01, pl_no_weight_grad=False, blur_init_sigma=0, blur_fade_kimg=0): |
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super().__init__() |
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self.device = device |
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self.G = G |
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self.D = D |
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self.augment_pipe = augment_pipe |
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self.r1_gamma = r1_gamma |
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self.style_mixing_prob = style_mixing_prob |
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self.pl_weight = pl_weight |
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self.pl_batch_shrink = pl_batch_shrink |
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self.pl_decay = pl_decay |
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self.pl_no_weight_grad = pl_no_weight_grad |
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self.pl_mean = torch.zeros([], device=device) |
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self.blur_init_sigma = blur_init_sigma |
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self.blur_fade_kimg = blur_fade_kimg |
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def run_G(self, z, c, update_emas=False): |
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ws = self.G.mapping(z, c, update_emas=update_emas) |
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if self.style_mixing_prob > 0: |
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with torch.autograd.profiler.record_function('style_mixing'): |
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cutoff = torch.empty([], dtype=torch.int64, device=ws.device).random_(1, ws.shape[1]) |
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cutoff = torch.where(torch.rand([], device=ws.device) < self.style_mixing_prob, cutoff, torch.full_like(cutoff, ws.shape[1])) |
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ws[:, cutoff:] = self.G.mapping(torch.randn_like(z), c, update_emas=False)[:, cutoff:] |
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img = self.G.synthesis(ws, update_emas=update_emas) |
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return img, ws |
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def run_D(self, img, c, blur_sigma=0, update_emas=False): |
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blur_size = np.floor(blur_sigma * 3) |
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if blur_size > 0: |
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with torch.autograd.profiler.record_function('blur'): |
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f = torch.arange(-blur_size, blur_size + 1, device=img.device).div(blur_sigma).square().neg().exp2() |
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img = upfirdn2d.filter2d(img, f / f.sum()) |
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if self.augment_pipe is not None: |
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img = self.augment_pipe(img) |
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logits = self.D(img, c, update_emas=update_emas) |
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return logits |
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def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, gain, cur_nimg): |
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assert phase in ['Gmain', 'Greg', 'Gboth', 'Dmain', 'Dreg', 'Dboth'] |
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if self.pl_weight == 0: |
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phase = {'Greg': 'none', 'Gboth': 'Gmain'}.get(phase, phase) |
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if self.r1_gamma == 0: |
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phase = {'Dreg': 'none', 'Dboth': 'Dmain'}.get(phase, phase) |
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blur_sigma = max(1 - cur_nimg / (self.blur_fade_kimg * 1e3), 0) * self.blur_init_sigma if self.blur_fade_kimg > 0 else 0 |
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if phase in ['Gmain', 'Gboth']: |
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with torch.autograd.profiler.record_function('Gmain_forward'): |
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gen_img, _gen_ws = self.run_G(gen_z, gen_c) |
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gen_logits = self.run_D(gen_img, gen_c, blur_sigma=blur_sigma) |
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training_stats.report('Loss/scores/fake', gen_logits) |
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training_stats.report('Loss/signs/fake', gen_logits.sign()) |
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loss_Gmain = torch.nn.functional.softplus(-gen_logits) |
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training_stats.report('Loss/G/loss', loss_Gmain) |
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with torch.autograd.profiler.record_function('Gmain_backward'): |
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loss_Gmain.mean().mul(gain).backward() |
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if phase in ['Greg', 'Gboth']: |
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with torch.autograd.profiler.record_function('Gpl_forward'): |
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batch_size = gen_z.shape[0] // self.pl_batch_shrink |
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gen_img, gen_ws = self.run_G(gen_z[:batch_size], gen_c[:batch_size]) |
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pl_noise = torch.randn_like(gen_img) / np.sqrt(gen_img.shape[2] * gen_img.shape[3]) |
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with torch.autograd.profiler.record_function('pl_grads'), conv2d_gradfix.no_weight_gradients(self.pl_no_weight_grad): |
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pl_grads = torch.autograd.grad(outputs=[(gen_img * pl_noise).sum()], inputs=[gen_ws], create_graph=True, only_inputs=True)[0] |
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pl_lengths = pl_grads.square().sum(2).mean(1).sqrt() |
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pl_mean = self.pl_mean.lerp(pl_lengths.mean(), self.pl_decay) |
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self.pl_mean.copy_(pl_mean.detach()) |
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pl_penalty = (pl_lengths - pl_mean).square() |
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training_stats.report('Loss/pl_penalty', pl_penalty) |
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loss_Gpl = pl_penalty * self.pl_weight |
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training_stats.report('Loss/G/reg', loss_Gpl) |
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with torch.autograd.profiler.record_function('Gpl_backward'): |
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loss_Gpl.mean().mul(gain).backward() |
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loss_Dgen = 0 |
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if phase in ['Dmain', 'Dboth']: |
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with torch.autograd.profiler.record_function('Dgen_forward'): |
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gen_img, _gen_ws = self.run_G(gen_z, gen_c, update_emas=True) |
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gen_logits = self.run_D(gen_img, gen_c, blur_sigma=blur_sigma, update_emas=True) |
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training_stats.report('Loss/scores/fake', gen_logits) |
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training_stats.report('Loss/signs/fake', gen_logits.sign()) |
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loss_Dgen = torch.nn.functional.softplus(gen_logits) |
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with torch.autograd.profiler.record_function('Dgen_backward'): |
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loss_Dgen.mean().mul(gain).backward() |
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if phase in ['Dmain', 'Dreg', 'Dboth']: |
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name = 'Dreal' if phase == 'Dmain' else 'Dr1' if phase == 'Dreg' else 'Dreal_Dr1' |
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with torch.autograd.profiler.record_function(name + '_forward'): |
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real_img_tmp = real_img.detach().requires_grad_(phase in ['Dreg', 'Dboth']) |
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real_logits = self.run_D(real_img_tmp, real_c, blur_sigma=blur_sigma) |
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training_stats.report('Loss/scores/real', real_logits) |
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training_stats.report('Loss/signs/real', real_logits.sign()) |
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loss_Dreal = 0 |
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if phase in ['Dmain', 'Dboth']: |
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loss_Dreal = torch.nn.functional.softplus(-real_logits) |
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training_stats.report('Loss/D/loss', loss_Dgen + loss_Dreal) |
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loss_Dr1 = 0 |
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if phase in ['Dreg', 'Dboth']: |
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with torch.autograd.profiler.record_function('r1_grads'), conv2d_gradfix.no_weight_gradients(): |
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r1_grads = torch.autograd.grad(outputs=[real_logits.sum()], inputs=[real_img_tmp], create_graph=True, only_inputs=True)[0] |
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r1_penalty = r1_grads.square().sum([1,2,3]) |
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loss_Dr1 = r1_penalty * (self.r1_gamma / 2) |
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training_stats.report('Loss/r1_penalty', r1_penalty) |
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training_stats.report('Loss/D/reg', loss_Dr1) |
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with torch.autograd.profiler.record_function(name + '_backward'): |
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(loss_Dreal + loss_Dr1).mean().mul(gain).backward() |
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