resnet18.py

"""
Builds ResNet18 from scratch using PyTorch.
This does not build generalized blocks for all ResNets, just for ResNet18.
Paper => Deep Residual Learning for Image Recognition.
Link => https://arxiv.org/pdf/1512.03385v1.pdf
"""

import torch.nn as nn
import torch
from torch.nn import functional as F

from torch import Tensor
from typing import Type

class BasicBlock(nn.Module):
    def __init__(
        self, 
        in_channels: int,
        out_channels: int,
        stride: int = 1,
        expansion: int = 1,
        downsample: nn.Module = None
    ) -> None:
        super(BasicBlock, self).__init__()
        # Multiplicative factor for the subsequent conv2d layer's output channels.
        # It is 1 for ResNet18 and ResNet34.
        self.expansion = expansion
        self.downsample = downsample
        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.dropout = nn.Dropout(p=0.1)
        self.conv2 = nn.Conv2d(
            out_channels, 
            out_channels*self.expansion, 
            kernel_size=3, 
            padding=1,
            bias=False
        )
        self.bn2 = nn.BatchNorm2d(out_channels*self.expansion)



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

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        # out = self.dropout(out)
        # out = F.dropout(out, p=0.1, training=self.training)

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

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

        out += identity
        out = self.relu(out)
        # out = F.dropout(out, p=0.1, training=self.training)
        return  out


class ResNet(nn.Module):
    def __init__(
        self, 
        img_channels: int,
        num_layers: int,
        block: Type[BasicBlock],
        num_classes: int  = 1000
    ) -> None:
        super(ResNet, self).__init__()
        if num_layers == 18:
            # The following `layers` list defines the number of `BasicBlock` 
            # to use to build the network and how many basic blocks to stack
            # together.
            layers = [2, 2, 2, 2]
            self.expansion = 1
        
        self.in_channels = 64
        # All ResNets (18 to 152) contain a Conv2d => BN => ReLU for the first
        # three layers. Here, kernel size is 7.
        self.conv1 = nn.Conv2d(
            in_channels=img_channels,
            out_channels=self.in_channels,
            kernel_size=7, 
            stride=2,
            padding=3,
            bias=False
        )
        self.bn1 = nn.BatchNorm2d(self.in_channels)
        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.dropout = nn.Dropout(p=0.1)

        # self.conv_1 = nn.Conv2d(512,512, kernel_size=(5,3), stride=(1,1), padding=(0,1), bias=False)
        # self.bn1 = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        # self.relu = nn.ReLU(inplace=True)
        # self.conv_2 = nn.Conv2d(512, 512, kernel_size=(5, 3), stride=(1, 1), padding=(0, 1), bias=False)
        # self.bn2 = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        # self.fc = nn.Linear(512*self.expansion, num_classes)


    def _make_layer(
        self, 
        block: Type[BasicBlock],
        out_channels: int,
        blocks: int,
        stride: int = 1
    ) -> nn.Sequential:
        downsample = None
        if stride != 1:
            """
            This should pass from `layer2` to `layer4` or 
            when building ResNets50 and above. Section 3.3 of the paper
            Deep Residual Learning for Image Recognition
            (https://arxiv.org/pdf/1512.03385v1.pdf).
            """
            downsample = nn.Sequential(
                nn.Conv2d(
                    self.in_channels, 
                    out_channels*self.expansion,
                    kernel_size=1,
                    stride=stride,
                    bias=False 
                ),
                nn.BatchNorm2d(out_channels * self.expansion),
            )
        layers = []
        layers.append(
            block(
                self.in_channels, out_channels, stride, self.expansion, downsample
            )
        )
        self.in_channels = out_channels * self.expansion

        for i in range(1, blocks):
            layers.append(block(
                self.in_channels,
                out_channels,
                expansion=self.expansion
            ))
        return nn.Sequential(*layers)

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

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        # print("Layer4 input dim: ", x.shape)
        x = self.layer4(x)
        # The spatial dimension of the final layer's feature 
        # map should be (7, 7) for all ResNets.
        # x = self.Conv()
        # print('Dimensions of the last convolutional feature map: ', x.shape)
        #
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        # x = self.fc(x)

        return x





# if __name__ == '__main__':
#     tensor = torch.rand([1, 1, 500, 224])
#     model = ResNet(img_channels=1, num_layers=18, block=BasicBlock, num_classes=1000)
#     print(model)
#
#
#
#     # Total parameters and trainable parameters.
#     total_params = sum(p.numel() for p in model.parameters())
#     print(f"{total_params:,} total parameters.")
#     total_trainable_params = sum(
#         p.numel() for p in model.parameters() if p.requires_grad)
#     print(f"{total_trainable_params:,} training parameters.")