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import torch
import torch.nn as nn
import torchvision.models as models
from modules.basic_layers import GroupNorm
class Extractor(nn.Module):
def __init__(self, channels: list[int], num_groups: int = 32, use_residual: bool = True):
super().__init__()
self.use_residual = use_residual
self.layers = nn.ModuleList([
nn.Sequential(
nn.Conv2d(in_channels=channels[i], out_channels=channels[i + 1], kernel_size=3, stride=2, padding=1),
GroupNorm(channels[i + 1], num_groups = num_groups),
nn.SiLU(),
nn.Conv2d(in_channels=channels[i + 1], out_channels=channels[i + 1], kernel_size=3, stride=1, padding=1),
GroupNorm(channels[i + 1], num_groups = num_groups),
nn.SiLU()
) for i in range(len(channels) - 1)
])
if self.use_residual:
self.residual = nn.ModuleList([
nn.Sequential(
nn.Conv2d(in_channels=channels[i], out_channels=channels[i + 1], kernel_size=3, stride=2, padding=1),
) for i in range(len(channels) - 1)
])
def forward(self, x: torch.Tensor) -> list[torch.Tensor]:
features = []
for residual, layer in zip(self.residual, self.layers):
if self.use_residual:
x = layer(x) + residual(x)
else:
x = layer(x)
features.append(x)
return features
class ResNetExtractor(nn.Module):
def __init__(self, pretrained: bool = True, layers_to_extract: list[str] = ["layer1", "layer2", "layer3"]):
super(ResNetExtractor, self).__init__()
resnet = models.resnet18(pretrained=pretrained)
self.initial_layers = nn.Sequential(
resnet.conv1,
resnet.bn1,
resnet.relu
)
self.layers = nn.ModuleDict({
"layer1": resnet.layer1,
"layer2": resnet.layer2,
"layer3": resnet.layer3,
})
self.layers_to_extract = layers_to_extract
def forward(self, x: torch.Tensor) -> list[torch.Tensor]:
features = []
x = self.initial_layers(x)
for name, layer in self.layers.items():
x = layer(x)
if name in self.layers_to_extract:
features.append(x)
return features
class VGGExtractor(nn.Module):
def __init__(self, layers_to_extract: list[int] = [8, 15, 22, 29]):
super(VGGExtractor, self).__init__()
self.vgg = models.vgg16(pretrained=True).features
self.layers_to_extract = layers_to_extract
self.selected_layers = [self.vgg[i] for i in layers_to_extract]
def forward(self, x: torch.Tensor) -> list[torch.Tensor]:
features = []
for i, layer in enumerate(self.vgg):
x = layer(x)
if i in self.layers_to_extract:
features.append(x)
return features
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