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| import pytorch_lightning as pl | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| from torch.utils.data import random_split, DataLoader | |
| import torch.optim as optim | |
| from torchmetrics import Accuracy | |
| from torchvision import transforms | |
| class BasicBlock(pl.LightningModule): | |
| 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(pl.LightningModule): | |
| def __init__(self, block, num_blocks, num_classes=10): | |
| super(ResNet, self).__init__() | |
| self.in_planes = 64 | |
| self.learning_rate = 0.001 | |
| self.accuracy = Accuracy(task='multiclass', num_classes=10) | |
| 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 | |
| ################################################################################ | |
| #train loop | |
| def training_step(self, batch, batch_idx): | |
| x, y = batch | |
| preds = self(x) | |
| loss = F.cross_entropy(preds, y) | |
| acc = self.accuracy(preds, y) | |
| # perform logging | |
| self.log("train_loss", loss, on_step=True, on_epoch=True, prog_bar=True, logger=True) | |
| self.log("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 | |
| preds = self(x) | |
| loss = F.cross_entropy(preds, y) | |
| acc = self.accuracy(preds, y) | |
| # perform logging | |
| self.log("val_loss", loss, on_epoch=True, prog_bar=False, logger=True) | |
| self.log("val_acc", acc, on_epoch=True, prog_bar=True, logger=True) | |
| return loss | |
| def test_step(self, batch, batch_idx): | |
| x, y = batch | |
| preds = self(x) | |
| loss = F.cross_entropy(preds, y) | |
| acc = self.accuracy(preds, y) | |
| # perform logging | |
| self.log("test_loss", loss, on_step=True, prog_bar=True, logger=True) | |
| self.log("test_acc", acc, on_step=True, prog_bar=True, logger=True) | |
| return loss | |
| def configure_optimizers(self): | |
| optimizer = optim.SGD(self.parameters(), lr=self.learning_rate, momentum=0.9) | |
| return { | |
| "optimizer": optimizer, | |
| "lr_scheduler": { | |
| "scheduler": torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=2), | |
| "monitor": "val_loss", | |
| "frequency": 1, | |
| }, | |
| } | |
| ################################### | |
| def ResNet18(): | |
| return ResNet(BasicBlock, [2, 2, 2, 2]) |