ResNet.py

import torch 
import torch.nn as nn
import torchvision 
import time

from torch.nn import functional as F 
from torch.utils import data 
from torchvision import transforms

class Residual(nn.Module):  
    def __init__(self, input_channels, num_channels,use_1x1conv=True, strides=1):
        super().__init__()
        self.conv1 = nn.Conv2d(input_channels, num_channels,kernel_size=3, padding=1, stride=strides)
        self.conv2 = nn.Conv2d(num_channels, num_channels,kernel_size=3, padding=1)
        if use_1x1conv:
            self.conv3 = nn.Conv2d(input_channels, num_channels,kernel_size=1, stride=strides)
        else:
            self.conv3 = None
        self.bn1 = nn.BatchNorm2d(num_channels)
        self.bn2 = nn.BatchNorm2d(num_channels)

    def forward(self, X):
        Y = F.relu(self.bn1(self.conv1(X)))
        Y = self.bn2(self.conv2(Y))
        if self.conv3:
            X = self.conv3(X)
        return F.relu(Y + X)

def resnet_block(input_channels, num_channels, num_residuals,first_block=False):
    blk = []
    for i in range(num_residuals):
        if i == 0 and not first_block:
            blk.append(Residual(input_channels, num_channels,use_1x1conv=True, strides=2))
        else:
            blk.append(Residual(num_channels, num_channels))
    return blk

class resnet(nn.Module):
    def __init__(self):
        super().__init__()
        b1 = nn.Sequential(
        nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),
                nn.BatchNorm2d(64), 
                nn.ReLU(),
                nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        )
        b2 = nn.Sequential(*resnet_block(64, 64, 2, first_block=True))
        b3 = nn.Sequential(*resnet_block(64, 128, 2))
        b4 = nn.Sequential(*resnet_block(128, 256, 2))
        b5 = nn.Sequential(*resnet_block(256, 512, 2))
        self.model = nn.Sequential(b1, b2, b3, b4, b5,
                                    nn.AdaptiveAvgPool2d((1,1)),
                                    nn.Flatten(), nn.Linear(512, 10))
    
    def forward(self, t):
        return self.model(t)