exp/baseline2/model.py

import torch
import torch.nn as nn
from huggingface_hub import PyTorchModelHubMixin


class SEBlock(nn.Module):
    def __init__(self, channels, reduction=16):
        super().__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(channels, channels // reduction, bias=False),
            nn.ReLU(inplace=True),
            nn.Linear(channels // reduction, channels, bias=False),
            nn.Sigmoid(),
        )

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        return x * y.expand_as(x)


class ResBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super().__init__()
        self.conv1 = nn.Conv2d(
            in_channels,
            out_channels,
            kernel_size=3,
            stride=stride,
            padding=1,
            bias=False,
        )
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(
            out_channels, out_channels, kernel_size=3, padding=1, bias=False
        )
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.se = SEBlock(out_channels)
        self.downsample = downsample

    def forward(self, x):
        identity = x
        if self.downsample is not None:
            identity = self.downsample(x)

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.se(out)

        out += identity
        out = self.relu(out)
        return out


class ResNet(nn.Module, PyTorchModelHubMixin):
    def __init__(
        self, layers=[2, 2, 2, 2], channels=[16, 24, 48, 96], dropout_rate=0.5
    ):
        super().__init__()
        self.in_channels = 16

        # Stem
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(16)
        self.relu = nn.ReLU(inplace=True)

        # Stages
        self.layer1 = self._make_layer(channels[0], layers[0], stride=1)
        self.layer2 = self._make_layer(channels[1], layers[1], stride=2)
        self.layer3 = self._make_layer(channels[2], layers[2], stride=2)
        self.layer4 = self._make_layer(channels[3], layers[3], stride=2)

        self.dropout = nn.Dropout(p=dropout_rate)

        # Final classification head
        # H, W will reduce. Assuming input is (3, 80, 101)
        # L1: (16, 80, 101) (stride 1)
        # L2: (32, 40, 51)  (stride 2)
        # L3: (64, 20, 26)  (stride 2)
        # L4: (128, 10, 13) (stride 2)

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(channels[3], 1)
        self.sigmoid = nn.Sigmoid()

    def _make_layer(self, out_channels, blocks, stride=1):
        downsample = None
        if stride != 1 or self.in_channels != out_channels:
            downsample = nn.Sequential(
                nn.Conv2d(
                    self.in_channels,
                    out_channels,
                    kernel_size=1,
                    stride=stride,
                    bias=False,
                ),
                nn.BatchNorm2d(out_channels),
            )

        layers = []
        layers.append(ResBlock(self.in_channels, out_channels, stride, downsample))
        self.in_channels = out_channels
        for _ in range(1, blocks):
            layers.append(ResBlock(self.in_channels, out_channels))

        return nn.Sequential(*layers)

    def forward(self, x):
        # x: (B, 3, 80, 101)
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)  # (B, 128, 1, 1)
        x = torch.flatten(x, 1)  # (B, 128)
        x = self.dropout(x)
        x = self.fc(x)
        x = self.sigmoid(x)

        return x


if __name__ == "__main__":
    from torchinfo import summary

    model = ResNet()
    summary(model, (1, 3, 80, 101))