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141 lines
7.8 KiB
Python
141 lines
7.8 KiB
Python
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# NVIDIA CORPORATION and its licensors retain all intellectual property
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# and proprietary rights in and to this software, related documentation
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# and any modifications thereto. Any use, reproduction, disclosure or
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# distribution of this software and related documentation without an express
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# license agreement from NVIDIA CORPORATION is strictly prohibited.
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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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#----------------------------------------------------------------------------
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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): # to be overridden by subclass
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raise NotImplementedError()
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#----------------------------------------------------------------------------
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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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# Gmain: Maximize logits for generated images.
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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) # -log(sigmoid(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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# Gpl: Apply path length regularization.
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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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# Dmain: Minimize logits for generated images.
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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) # -log(1 - sigmoid(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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# Dmain: Maximize logits for real images.
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# Dr1: Apply R1 regularization.
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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) # -log(sigmoid(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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#----------------------------------------------------------------------------
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