utilities/resnet.py

"""
ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.

Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
    Deep Residual Learning for Image Recognition. arXiv:1512.03385
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import pytorch_lightning as pl
from torchmetrics.functional import accuracy
from torchvision import transforms
from torch.utils.data import DataLoader
from torchvision.datasets import CIFAR10
import albumentations as A
from albumentations.pytorch import ToTensorV2


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class LitResNet(pl.LightningModule):
    def __init__(self, block, num_blocks, num_classes=10,batch_size=128):
        super(LitResNet, self).__init__()
        self.batch_size = batch_size
        self.in_planes = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.linear = nn.Linear(512*block.expansion, num_classes)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

        
    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out
    
        
    
    def training_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        # Calculate loss
        loss = F.cross_entropy(y_hat, y)
        #Calculate accuracy
        acc = accuracy(y_hat, y)
        self.log_dict(
            {"train_loss": loss, "train_acc": acc},
            on_step=True,
            on_epoch=True,
            prog_bar=True,
            logger=True,
        )
        return loss
    
    def validation_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = F.cross_entropy(y_hat, y)
        acc = accuracy(y_hat, y)
        self.log_dict(
            {"val_loss": loss, "val_acc": acc},
            on_step=True,
            on_epoch=True,
            prog_bar=True,
            logger=True,
        )
        return loss

    def test_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)

        argmax_pred = y_hat.argmax(dim=1).cpu()
        loss = F.cross_entropy(y_hat, y)
        acc = accuracy(y_hat, y)
        self.log_dict(
            {"test_loss": loss, "test_acc": acc},
            on_step=True,
            on_epoch=True,
            prog_bar=True,
            logger=True,
        )

        # Update the confusion matrix
        self.confusion_matrix.update(y_hat, y)

        # Store the predictions, labels and incorrect predictions
        x, y, y_hat, argmax_pred = (
            x.cpu(),
            y.cpu(),
            y_hat.cpu(),
            argmax_pred.cpu(),
        )
        self.pred_store["test_preds"] = torch.cat(
            (self.pred_store["test_preds"], argmax_pred), dim=0
        )
        self.pred_store["test_labels"] = torch.cat(
            (self.pred_store["test_labels"], y), dim=0
        )
        for d, t, p, o in zip(x, y, argmax_pred, y_hat):
            if p.eq(t.view_as(p)).item() == False:
                self.pred_store["test_incorrect"].append(
                    (d.cpu(), t, p, o[p.item()].cpu())
                )

        return loss


def configure_optimizers(self):
        return torch.optim.Adam(self.parameters(), lr=0.02)

def LitResNet18():
    return LitResNet(BasicBlock, [2, 2, 2, 2])

def LitResNet34(): 
    return LitResNet(BasicBlock, [3, 4, 6, 3])