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.nn as nn
import torch.nn.functional as F
# imports
import os

import torch
from pytorch_lightning import LightningModule, Trainer
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader, random_split
from torchmetrics import Accuracy
from torchvision import transforms
from torchvision.datasets import CIFAR10
# from pytorch_lightning.callbacks import ModelSummary
# from lightning.pytorch.callbacks import ModelCheckpoint
from pytorch_lightning.callbacks import ModelCheckpoint, ModelSummary
import torchvision.transforms as transforms


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 CIFAR10Model(LightningModule):
    def __init__(self, block, num_blocks, num_classes=10, data_dir=PATH_DATASETS, learning_rate=0.01):
        super(CIFAR10Model, self).__init__()
        self.in_planes = 64

        # Define transformations using Albumentations
        normalize = transforms.Normalize(mean=(0.4914, 0.4822, 0.4465), std=(0.2470, 0.2434, 0.2615))
        random_crop = transforms.RandomCrop((32, 32))
        horizontal_flip = transforms.RandomHorizontalFlip()
        to_tensor = transforms.ToTensor()
        self.transform = transforms.Compose([
                        random_crop,
                        horizontal_flip,
                        to_tensor,
                        normalize
        ])

        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)

        self.accuracy = Accuracy(task="MULTICLASS", num_classes=10)

        self.data_dir = data_dir
        self.learning_rate = learning_rate

    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
        logits = self(x)
        loss = F.cross_entropy(logits, y)
        return loss

    def validation_step(self, batch, batch_idx):
        x, y = batch
        logits = self(x)
        loss = F.cross_entropy(logits, y)
        preds = torch.argmax(logits, dim=1)
        self.accuracy(preds, y)
        self.log("val_loss", loss, prog_bar=True)
        self.log("val_acc", self.accuracy, prog_bar=True)
        return loss

    def test_step(self, batch, batch_idx):
        return self.validation_step(batch, batch_idx)

    def configure_optimizers(self):
        optimizer = torch.optim.SGD(self.parameters(), lr=self.learning_rate)
        return optimizer

    def prepare_data(self):
        CIFAR10(self.data_dir, train=True, download=True)
        CIFAR10(self.data_dir, train=False, download=True)

    def setup(self, stage=None):
        if stage == "fit" or stage is None:
            cifar10_full = CIFAR10(self.data_dir, train=True, transform=self.transform)
            train_size = int(len(cifar10_full) * 0.9)
            val_size = len(cifar10_full) - train_size
            self.cifar10_train, self.cifar10_val = random_split(cifar10_full, [train_size, val_size])

        if stage == "test" or stage is None:
            self.cifar10_test = CIFAR10(self.data_dir, train=False, transform=self.transform)

    def train_dataloader(self):
        return DataLoader(self.cifar10_train, batch_size=BATCH_SIZE, num_workers=os.cpu_count())

    def val_dataloader(self):
        return DataLoader(self.cifar10_val, batch_size=BATCH_SIZE, num_workers=os.cpu_count(), persistent_workers=True)

    def test_dataloader(self):
        return DataLoader(self.cifar10_test, batch_size=BATCH_SIZE, num_workers=os.cpu_count())



def ResNet18():
    return CIFAR10Model(BasicBlock, [2, 2, 2, 2])


def ResNet34():
    return ResNet(BasicBlock, [3, 4, 6, 3])