models/custom_resnet.py

import torch
import torch.nn as nn


class ResBlock(nn.Module):
    def __init__(self, channels):
        super(ResBlock, self).__init__()

        self.resblock = nn.Sequential(
            nn.Conv2d(
                in_channels=channels,
                out_channels=channels,
                kernel_size=3,
                stride=1,
                padding=1,
                bias=False,
            ),
            nn.BatchNorm2d(channels),
            nn.ReLU(),
            nn.Conv2d(
                in_channels=channels,
                out_channels=channels,
                kernel_size=3,
                stride=1,
                padding=1,
                bias=False,
            ),
            nn.BatchNorm2d(channels),
            nn.ReLU(),
        )

    def forward(self, x):
        return x + self.resblock(x)


class CustomResnet(nn.Module):
    def __init__(self):
        super(CustomResnet, self).__init__()

        self.prep = nn.Sequential(
            nn.Conv2d(
                in_channels=3,
                out_channels=64,
                kernel_size=3,
                stride=1,
                padding=1,
                bias=False,
            ),
            nn.BatchNorm2d(64),
            nn.ReLU(),
        )

        self.layer1 = nn.Sequential(
            nn.Conv2d(
                in_channels=64,
                out_channels=128,
                kernel_size=3,
                padding=1,
                stride=1,
                bias=False,
            ),
            nn.MaxPool2d(kernel_size=2),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            ResBlock(channels=128),
        )

        self.layer2 = nn.Sequential(
            nn.Conv2d(
                in_channels=128,
                out_channels=256,
                kernel_size=3,
                padding=1,
                stride=1,
                bias=False,
            ),
            nn.MaxPool2d(kernel_size=2),
            nn.BatchNorm2d(256),
            nn.ReLU(),
        )

        self.layer3 = nn.Sequential(
            nn.Conv2d(
                in_channels=256,
                out_channels=512,
                kernel_size=3,
                padding=1,
                stride=1,
                bias=False,
            ),
            nn.MaxPool2d(kernel_size=2),
            nn.BatchNorm2d(512),
            nn.ReLU(),
            ResBlock(channels=512),
        )

        self.pool = nn.MaxPool2d(kernel_size=4)

        self.fc = nn.Linear(in_features=512, out_features=10, bias=False)

        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x):
        x = self.prep(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.pool(x)
        x = x.view(-1, 512)
        x = self.fc(x)
        # x = self.softmax(x)
        return x