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"""Pix2pix discriminator loss."""
from torch import nn, Tensor
from text_recognizer.networks.vqvae.norm import Normalize
class NLayerDiscriminator(nn.Module):
"""Defines a PatchGAN discriminator loss in Pix2Pix."""
def __init__(
self, in_channels: int = 1, num_channels: int = 32, num_layers: int = 3
) -> None:
super().__init__()
self.in_channels = in_channels
self.num_channels = num_channels
self.num_layers = num_layers
self.discriminator = self._build_discriminator()
def _build_discriminator(self) -> nn.Sequential:
"""Builds discriminator."""
discriminator = [
nn.Sigmoid(),
nn.Conv2d(
in_channels=self.in_channels,
out_channels=self.num_channels,
kernel_size=4,
stride=2,
padding=1,
),
nn.Mish(inplace=True),
]
in_channels = self.num_channels
for n in range(1, self.num_layers):
discriminator += [
nn.Conv2d(
in_channels=in_channels,
out_channels=in_channels * n,
kernel_size=4,
stride=2,
padding=1,
),
# Normalize(num_channels=in_channels * n),
nn.Mish(inplace=True),
]
in_channels *= n
discriminator += [
nn.Conv2d(
in_channels=self.num_channels * (self.num_layers - 1),
out_channels=1,
kernel_size=4,
padding=1,
)
]
return nn.Sequential(*discriminator)
def forward(self, x: Tensor) -> Tensor:
"""Forward pass through discriminator."""
return self.discriminator(x)
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