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
|
"""VQGAN loss for PyTorch Lightning."""
from typing import Dict, Optional
from click.types import Tuple
import torch
from torch import nn, Tensor
import torch.nn.functional as F
from text_recognizer.criterions.n_layer_discriminator import NLayerDiscriminator
def adopt_weight(
weight: Tensor, global_step: int, threshold: int = 0, value: float = 0.0
) -> float:
if global_step < threshold:
weight = value
return weight
class VQGANLoss(nn.Module):
"""VQGAN loss."""
def __init__(
self,
reconstruction_loss: nn.L1Loss,
discriminator: NLayerDiscriminator,
vq_loss_weight: float = 1.0,
discriminator_weight: float = 1.0,
discriminator_factor: float = 1.0,
discriminator_iter_start: int = 1000,
) -> None:
super().__init__()
self.reconstruction_loss = reconstruction_loss
self.discriminator = discriminator
self.vq_loss_weight = vq_loss_weight
self.discriminator_weight = discriminator_weight
self.discriminator_factor = discriminator_factor
self.discriminator_iter_start = discriminator_iter_start
@staticmethod
def adversarial_loss(logits_real: Tensor, logits_fake: Tensor) -> Tensor:
"""Calculates the adversarial loss."""
loss_real = torch.mean(F.relu(1.0 - logits_real))
loss_fake = torch.mean(F.relu(1.0 + logits_fake))
d_loss = (loss_real + loss_fake) / 2.0
return d_loss
def _adaptive_weight(
self, rec_loss: Tensor, g_loss: Tensor, decoder_last_layer: Tensor
) -> Tensor:
rec_grads = torch.autograd.grad(
rec_loss, decoder_last_layer, retain_graph=True
)[0]
g_grads = torch.autograd.grad(g_loss, decoder_last_layer, retain_graph=True)[0]
d_weight = torch.norm(rec_grads) / (torch.norm(g_grads) + 1.0e-4)
d_weight = torch.clamp(d_weight, 0.0, 1.0e4).detach()
d_weight *= self.discriminator_weight
return d_weight
def forward(
self,
data: Tensor,
reconstructions: Tensor,
vq_loss: Tensor,
decoder_last_layer: Tensor,
optimizer_idx: int,
global_step: int,
stage: str,
) -> Optional[Tuple]:
"""Calculates the VQGAN loss."""
rec_loss: Tensor = self.reconstruction_loss(
data.contiguous(), reconstructions.contiguous()
)
# GAN part.
if optimizer_idx == 0:
logits_fake = self.discriminator(reconstructions.contiguous())
g_loss = -torch.mean(logits_fake)
if self.training:
d_weight = self._adaptive_weight(
rec_loss=rec_loss,
g_loss=g_loss,
decoder_last_layer=decoder_last_layer,
)
else:
d_weight = torch.tensor(0.0)
d_factor = adopt_weight(
self.discriminator_factor,
global_step=global_step,
threshold=self.discriminator_iter_start,
)
loss: Tensor = (
rec_loss + d_factor * d_weight * g_loss + self.vq_loss_weight * vq_loss
)
log = {
f"{stage}/total_loss": loss,
f"{stage}/vq_loss": vq_loss,
f"{stage}/rec_loss": rec_loss,
f"{stage}/g_loss": g_loss,
}
return loss, log
if optimizer_idx == 1:
logits_fake = self.discriminator(reconstructions.contiguous().detach())
logits_real = self.discriminator(data.contiguous().detach())
d_factor = adopt_weight(
self.discriminator_factor,
global_step=global_step,
threshold=self.discriminator_iter_start,
)
d_loss = d_factor * self.adversarial_loss(
logits_real=logits_real, logits_fake=logits_fake
)
log = {
f"{stage}/d_loss": d_loss,
}
return d_loss, log
|
