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| """ | |
| 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 os | |
| import torch | |
| import utils | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torchmetrics import Accuracy | |
| from torchvision.datasets import CIFAR10 | |
| from pytorch_lightning import LightningModule | |
| from torch.utils.data import DataLoader, random_split | |
| 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 ResNet(LightningModule): | |
| def __init__(self, block, num_blocks, num_classes=10, loss='cross_entropy', learning_rate=2e-4, momentum=0.9, optimizer="SGD", | |
| epochs=20): | |
| super(ResNet, self).__init__() | |
| 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) | |
| self.accuracy = Accuracy(task="multiclass", num_classes=num_classes) | |
| self.learning_rate = learning_rate | |
| self.optimizer = optimizer | |
| self.momentum = momentum | |
| self.loss = utils.get_criterion(loss) | |
| self.epochs = epochs | |
| 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 | |
| loss = self.loss(self(x), y) | |
| return loss | |
| def validation_step(self, batch, batch_idx): | |
| x, y = batch | |
| logits = self(x) | |
| loss = self.loss(logits, y) | |
| preds = torch.argmax(logits, dim=1) | |
| self.accuracy(preds, y) | |
| # Calling self.log will surface up scalars for you in TensorBoard | |
| 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): | |
| # Here we just reuse the validation_step for testing | |
| return self.validation_step(batch, batch_idx) | |
| def configure_optimizers(self): | |
| optimizer = utils.get_optimizer(self, lr=self.learning_rate, momentum=self.momentum, optimizer_type="SGD") | |
| max_lr = utils.get_learning_rate(self, optimizer, self.loss, self.trainer.datamodule.train_dataloader()) | |
| scheduler = utils.get_OneCycleLR_scheduler(optimizer, max_lr=max_lr, epochs=self.epochs, | |
| steps_per_epoch=len(self.trainer.datamodule.train_dataloader()), max_at_epoch=5, | |
| anneal_strategy = 'linear', div_factor=10, | |
| final_div_factor=1) | |
| return [optimizer],[{"scheduler": scheduler, "interval": "step", "frequency": 1}] | |
| def ResNet18(loss='cross_entropy', learning_rate=2e-4, momentum=0.9, optimizer="SGD", epochs=20): | |
| return ResNet(BasicBlock, [2, 2, 2, 2], loss=loss, learning_rate=learning_rate, momentum=momentum, | |
| optimizer=optimizer, epochs=epochs) | |
| def ResNet34(loss='cross_entropy', learning_rate=2e-4, momentum=0.9, optimizer="SGD", epochs=20): | |
| return ResNet(BasicBlock, [3, 4, 6, 3], loss=loss, learning_rate=learning_rate, momentum=momentum, | |
| optimizer=optimizer, epochs=epochs) | |