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from torch import tanh, Tensor
import torch.nn as nn
from dataclasses import dataclass
from abc import ABC, abstractmethod
@dataclass
class GeneratorConfig:
channels: int = 3
num_features: int = 64
num_residuals: int = 12
depth: int = 4
class BaseGenerator(ABC, nn.Module):
def __init__(self, channels: int = 3):
super().__init__()
self.channels = channels
@abstractmethod
def forward(self, x: Tensor) -> Tensor:
pass
class Generator(BaseGenerator):
def __init__(self, cfg: GeneratorConfig):
super().__init__(cfg.channels)
self.cfg = cfg
self.model = self._construct_model()
def _construct_model(self):
initial_layer = nn.Sequential(
nn.Conv2d(
self.cfg.channels,
self.cfg.num_features,
kernel_size=7,
stride=1,
padding=3,
padding_mode="reflect",
),
nn.ReLU(inplace=True),
)
down_blocks = nn.Sequential(
ConvBlock(
self.cfg.num_features,
self.cfg.num_features * 2,
kernel_size=3,
stride=2,
padding=1,
),
ConvBlock(
self.cfg.num_features * 2,
self.cfg.num_features * 4,
kernel_size=3,
stride=2,
padding=1,
),
)
residual_blocks = nn.Sequential(
*[
ResidualBlock(self.cfg.num_features * 4)
for _ in range(self.cfg.num_residuals)
]
)
up_blocks = nn.Sequential(
ConvBlock(
self.cfg.num_features * 4,
self.cfg.num_features * 2,
down=False,
kernel_size=3,
stride=2,
padding=1,
output_padding=1,
),
ConvBlock(
self.cfg.num_features * 2,
self.cfg.num_features,
down=False,
kernel_size=3,
stride=2,
padding=1,
output_padding=1,
),
)
last_layer = nn.Conv2d(
self.cfg.num_features,
self.cfg.channels,
kernel_size=7,
stride=1,
padding=3,
padding_mode="reflect",
)
return nn.Sequential(
initial_layer, down_blocks, residual_blocks, up_blocks, last_layer
)
def forward(self, x: Tensor) -> Tensor:
return tanh(self.model(x))
class ConvBlock(nn.Module):
def __init__(
self, in_channels, out_channels, down=True, use_activation=True, **kwargs
):
super().__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, padding_mode="reflect", **kwargs)
if down
else nn.ConvTranspose2d(in_channels, out_channels, **kwargs),
nn.InstanceNorm2d(out_channels),
nn.ReLU(inplace=True) if use_activation else nn.Identity(),
)
def forward(self, x: Tensor) -> Tensor:
return self.conv(x)
class ResidualBlock(nn.Module):
def __init__(self, channels: int):
super().__init__()
self.block = nn.Sequential(
ConvBlock(channels, channels, kernel_size=3, padding=1),
ConvBlock(
channels, channels, use_activation=False, kernel_size=3, padding=1
),
)
def forward(self, x: Tensor) -> Tensor:
return x + self.block(x)
if __name__ == '__main__':
import torch
cfg = GeneratorConfig()
generator = Generator(cfg)
generator.load_state_dict(torch.load('generator.pth'))
generator.eval()
out = generator(torch.randn([1, cfg.channels, 256, 256]))
print(out.shape) |