models.py

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

def ConvBlock(in_channels, out_channels, pool=False):
    layers = [nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
             nn.BatchNorm2d(out_channels),
             nn.ReLU(inplace=True)]
    if pool:
        layers.append(nn.MaxPool2d(4))
    return nn.Sequential(*layers)

class ImageClassificationBase(nn.Module):
    def training_step(self, batch):
        images, labels = batch
        out = self(images)
        loss = F.cross_entropy(out, labels)
        return loss
        
    def validation_step(self, batch):
        images, labels = batch
        out = self(images)
        loss = F.cross_entropy(out, labels)
        acc = (out.argmax(dim=1) == labels).float().mean()
        return {"val_loss": loss.detach(), "val_accuracy": acc}
        
    def validation_epoch_end(self, outputs):
        batch_losses = [x["val_loss"] for x in outputs]
        batch_accuracy = [x["val_accuracy"] for x in outputs]
        epoch_loss = torch.stack(batch_losses).mean()
        epoch_accuracy = torch.stack(batch_accuracy).mean()
        return {"val_loss": epoch_loss, "val_accuracy": epoch_accuracy}
        
    def epoch_end(self, epoch, result):
        print(f"Epoch [{epoch}], train_loss: {result['train_loss']:.4f}, val_loss: {result['val_loss']:.4f}, val_acc: {result['val_accuracy']:.4f}")


class ResNet9(ImageClassificationBase):
    def __init__(self, in_channels, num_classes):
        super().__init__()
        self.conv1 = ConvBlock(in_channels, 64)
        self.conv2 = ConvBlock(64, 128, pool=True)
        self.res1 = nn.Sequential(ConvBlock(128, 128), ConvBlock(128, 128))
        self.conv3 = ConvBlock(128, 256, pool=True)
        self.conv4 = ConvBlock(256, 512, pool=True)
        self.res2 = nn.Sequential(ConvBlock(512, 512), ConvBlock(512, 512))
        self.classifier = nn.Sequential(
            nn.MaxPool2d(4),
            nn.Flatten(),
            nn.Linear(512, num_classes)
        )
    def forward(self, xb):
        out = self.conv1(xb)
        out = self.conv2(out)
        out = self.res1(out) + out
        out = self.conv3(out)
        out = self.conv4(out)
        out = self.res2(out) + out
        out = self.classifier(out)
        return out