File size: 8,301 Bytes
a0bcaae
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
bb0f5a9
 
a0bcaae
 
bb0f5a9
 
a0bcaae
 
bb0f5a9
 
a0bcaae
 
 
 
bb0f5a9
 
 
 
 
 
 
 
 
 
 
 
 
a0bcaae
 
 
 
 
bb0f5a9
 
 
 
 
 
a0bcaae
 
 
 
 
 
 
bb0f5a9
 
a0bcaae
 
 
 
 
 
 
 
 
 
 
 
bb0f5a9
 
a0bcaae
 
 
 
 
 
 
 
bb0f5a9
 
a0bcaae
 
 
 
 
 
 
 
bb0f5a9
 
 
 
a0bcaae
bb0f5a9
 
a0bcaae
 
 
 
 
 
 
 
 
 
 
 
 
 
 
bb0f5a9
 
a0bcaae
 
bb0f5a9
 
a0bcaae
 
 
 
 
 
 
 
bb0f5a9
 
 
 
a0bcaae
 
 
 
 
bb0f5a9
 
 
 
a0bcaae
 
 
 
bb0f5a9
 
 
a0bcaae
 
 
 
 
 
 
bb0f5a9
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  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.

"""Loss functions."""

import numpy as np
import torch
from torch_utils import training_stats
from torch_utils.ops import conv2d_gradfix
from torch_utils.ops import upfirdn2d

# ----------------------------------------------------------------------------


class Loss:
    # to be overridden by subclass
    def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, gain, cur_nimg):
        raise NotImplementedError()

# ----------------------------------------------------------------------------


class StyleGAN2Loss(Loss):
    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):
        super().__init__()
        self.device = device
        self.G = G
        self.D = D
        self.augment_pipe = augment_pipe
        self.r1_gamma = r1_gamma
        self.style_mixing_prob = style_mixing_prob
        self.pl_weight = pl_weight
        self.pl_batch_shrink = pl_batch_shrink
        self.pl_decay = pl_decay
        self.pl_no_weight_grad = pl_no_weight_grad
        self.pl_mean = torch.zeros([], device=device)
        self.blur_init_sigma = blur_init_sigma
        self.blur_fade_kimg = blur_fade_kimg

    def run_G(self, z, c, update_emas=False):
        ws = self.G.mapping(z, c, update_emas=update_emas)
        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, update_emas=False)[:, cutoff:]
        img = self.G.synthesis(ws, update_emas=update_emas)
        return img, ws

    def run_D(self, img, c, blur_sigma=0, update_emas=False):
        blur_size = np.floor(blur_sigma * 3)
        if blur_size > 0:
            with torch.autograd.profiler.record_function('blur'):
                f = torch.arange(-blur_size, blur_size + 1,
                                 device=img.device).div(blur_sigma).square().neg().exp2()
                img = upfirdn2d.filter2d(img, f / f.sum())
        if self.augment_pipe is not None:
            img = self.augment_pipe(img)
        logits = self.D(img, c, update_emas=update_emas)
        return logits

    def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, gain, cur_nimg):
        assert phase in ['Gmain', 'Greg', 'Gboth', 'Dmain', 'Dreg', 'Dboth']
        if self.pl_weight == 0:
            phase = {'Greg': 'none', 'Gboth': 'Gmain'}.get(phase, phase)
        if self.r1_gamma == 0:
            phase = {'Dreg': 'none', 'Dboth': 'Dmain'}.get(phase, phase)
        blur_sigma = max(1 - cur_nimg / (self.blur_fade_kimg * 1e3), 0) * \
            self.blur_init_sigma if self.blur_fade_kimg > 0 else 0

        # Gmain: Maximize logits for generated images.
        if phase in ['Gmain', 'Gboth']:
            with torch.autograd.profiler.record_function('Gmain_forward'):
                gen_img, _gen_ws = self.run_G(gen_z, gen_c)
                gen_logits = self.run_D(gen_img, gen_c, blur_sigma=blur_sigma)
                training_stats.report('Loss/scores/fake', gen_logits)
                training_stats.report('Loss/signs/fake', gen_logits.sign())
                # -log(sigmoid(gen_logits))
                loss_Gmain = torch.nn.functional.softplus(-gen_logits)
                training_stats.report('Loss/G/loss', loss_Gmain)
            with torch.autograd.profiler.record_function('Gmain_backward'):
                loss_Gmain.mean().mul(gain).backward()

        # Gpl: Apply path length regularization.
        if phase in ['Greg', 'Gboth']:
            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(
                    gen_z[:batch_size], gen_c[:batch_size])
                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(self.pl_no_weight_grad):
                    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'):
                loss_Gpl.mean().mul(gain).backward()

        # Dmain: Minimize logits for generated images.
        loss_Dgen = 0
        if phase in ['Dmain', 'Dboth']:
            with torch.autograd.profiler.record_function('Dgen_forward'):
                gen_img, _gen_ws = self.run_G(gen_z, gen_c, update_emas=True)
                gen_logits = self.run_D(
                    gen_img, gen_c, blur_sigma=blur_sigma, update_emas=True)
                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))
            with torch.autograd.profiler.record_function('Dgen_backward'):
                loss_Dgen.mean().mul(gain).backward()

        # Dmain: Maximize logits for real images.
        # Dr1: Apply R1 regularization.
        if phase in ['Dmain', 'Dreg', 'Dboth']:
            name = 'Dreal' if phase == 'Dmain' else 'Dr1' if phase == 'Dreg' else 'Dreal_Dr1'
            with torch.autograd.profiler.record_function(name + '_forward'):
                real_img_tmp = real_img.detach().requires_grad_(
                    phase in ['Dreg', 'Dboth'])
                real_logits = self.run_D(
                    real_img_tmp, real_c, blur_sigma=blur_sigma)
                training_stats.report('Loss/scores/real', real_logits)
                training_stats.report('Loss/signs/real', real_logits.sign())

                loss_Dreal = 0
                if phase in ['Dmain', 'Dboth']:
                    # -log(sigmoid(real_logits))
                    loss_Dreal = torch.nn.functional.softplus(-real_logits)
                    training_stats.report(
                        'Loss/D/loss', loss_Dgen + loss_Dreal)

                loss_Dr1 = 0
                if phase in ['Dreg', 'Dboth']:
                    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_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)

            with torch.autograd.profiler.record_function(name + '_backward'):
                (loss_Dreal + loss_Dr1).mean().mul(gain).backward()

# ----------------------------------------------------------------------------