resnet.py

import torch
from torch import nn, Tensor
from typing import List, Optional, Type, Union


def conv3x3(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
    """3x3 convolution with padding"""
    return nn.Conv2d(
        in_planes,
        out_planes,
        kernel_size=3,
        stride=stride,
        padding=1,
        bias=False,
    )


def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    expansion: int = 1

    def __init__(
        self,
        inplanes: int,
        planes: int,
        stride: int = 1,
        downsample: Optional[nn.Module] = None,
    ) -> None:
        super().__init__()
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x: Tensor) -> Tensor:
        identity = x

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

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

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

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

        return out


class Bottleneck(nn.Module):
    expansion: int = 4

    def __init__(
        self,
        inplanes: int,
        planes: int,
        stride: int = 1,
        downsample: Optional[nn.Module] = None,
    ) -> None:
        super().__init__()
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, planes)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = conv3x3(planes, planes, stride)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = conv1x1(planes, planes * self.expansion)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x: Tensor) -> Tensor:
        identity = x

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

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

        out = self.conv3(out)
        out = self.bn3(out)

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

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

        return out


class ResNet(nn.Module):
    def __init__(
        self,
        block: Type[Union[BasicBlock, Bottleneck]],
        layers: List[int],
        num_classes: int = 1000,
    ) -> None:
        super().__init__()
        self.inplanes = 64
        self.conv1 = nn.Conv2d(
            3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False
        )
        self.bn1 = nn.BatchNorm2d(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

    def _make_layer(
        self,
        block: Type[Union[BasicBlock, Bottleneck]],
        planes: int,
        blocks: int,
        stride: int = 1,
    ) -> nn.Sequential:
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(
            block(
                self.inplanes,
                planes,
                stride,
                downsample,
            )
        )
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x: Tensor) -> Tensor:
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

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

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)

        return x


def resnet18(num_classes: int) -> ResNet:
    return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes)


def resnet34(num_classes: int) -> ResNet:
    return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes)


def resnet50(num_classes: int) -> ResNet:
    return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes)


def resnet101(num_classes: int) -> ResNet:
    return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes)


def resnet152(num_classes: int) -> ResNet:
    return ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes)


if __name__ == "__main__":
    # Load pre-trained weights from torchvision into our simplified implementation
    from torchvision.models import resnet

    tch_model = resnet.resnet18(weights=resnet.ResNet18_Weights.IMAGENET1K_V1).eval()
    model = resnet18(num_classes=1000).eval()

    model.load_state_dict(tch_model.state_dict())

    # Inference comparison
    x = torch.rand(1, 3, 224, 224)

    with torch.no_grad():
        out1 = tch_model(x)
        out2 = model(x)

    assert torch.equal(out1, out2)

    # Inference sample
    import numpy as np
    from PIL import Image
    from torchvision.transforms import Normalize

    CLASSES = resnet.ResNet18_Weights.IMAGENET1K_V1.meta["categories"]

    img = Image.open("assets/dog.jpg").resize((224, 224), Image.Resampling.BILINEAR)

    norm = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    x = (
        torch.from_numpy(np.array(img, dtype=np.float32) / 255.0)
        .permute(2, 0, 1)
        .unsqueeze(0)
    )

    with torch.no_grad():
        x = norm(x)
        out: Tensor = model(x)
        predicted = out.max(dim=1)

        idx = predicted.indices.item()
        print(f"Predicted: {CLASSES[idx]}")
        print(f"Category Id: {idx}")
        print(f"Score: {predicted.values.item():.4f}")