models.py

import torch
import torch.nn as nn
from torchvision import models


class Stem(nn.Module):
    def __init__(self):
        super(Stem, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=2),
            nn.MaxPool2d(kernel_size=3, stride=2),
        )

    def forward(self, x):
        x = self.conv(x)
        return x


class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super().__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(
                in_channels,
                out_channels,
                kernel_size=3,
                stride=stride,
                padding=1,
                bias=False,
            ),
            nn.BatchNorm2d(out_channels),
            nn.LeakyReLU(inplace=True),
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(
                out_channels,
                out_channels,
                kernel_size=3,
                stride=1,
                padding=1,
                bias=False,
            ),
            nn.BatchNorm2d(out_channels),
        )

        self.shortcut = (
            nn.Identity()
            if in_channels == out_channels and stride == 1
            else nn.Sequential(
                nn.Conv2d(
                    in_channels, out_channels, kernel_size=1, stride=stride, bias=False
                ),
                nn.BatchNorm2d(out_channels),
            )
        )

        self.act = nn.LeakyReLU(inplace=True)

    def forward(self, x):
        identity = self.shortcut(x)
        x = self.conv1(x)
        x = self.conv2(x)
        x += identity
        return self.act(x)


class FromZero(nn.Module):
    def __init__(self, num_classes=10):
        super(FromZero, self).__init__()
        self.stem = nn.Sequential(Stem())
        self.layer1 = nn.Sequential(ResidualBlock(64, 64), ResidualBlock(64, 64))
        self.layer2 = nn.Sequential(
            ResidualBlock(64, 128, stride=2), ResidualBlock(128, 128)
        )
        self.layer3 = nn.Sequential(
            ResidualBlock(128, 256, stride=2), ResidualBlock(256, 256)
        )
        self.layer4 = nn.Sequential(
            ResidualBlock(256, 512, stride=2), ResidualBlock(512, 512), nn.Dropout(0.2)
        )

        self.flatten = nn.Flatten()
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Sequential(
            nn.Linear(512, num_classes),
        )

    def forward(self, x):
        x = self.stem(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = self.flatten(x)
        x = self.fc(x)
        return x