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| 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, data_dir=PATH_DATASETS, num_classes=10, hidden_size=16, learning_rate=2e-4): | |
| super().__init__() | |
| # Set our init args as class attributes | |
| self.data_dir = data_dir | |
| self.hidden_size = hidden_size | |
| self.learning_rate = learning_rate | |
| # Hardcode some dataset specific attributes | |
| self.num_classes = 10 | |
| self.transform = transforms.Compose( | |
| [ | |
| transforms.ToTensor(), | |
| transforms.Normalize((0.1307,), (0.3081,)), | |
| ] | |
| ) | |
| 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=10) | |
| 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.nll_loss(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) | |
| # 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 = torch.optim.SGD(self.parameters(), lr=self.learning_rate) | |
| return optimizer | |
| #################### | |
| # DATA RELATED HOOKS | |
| #################### | |
| def prepare_data(self): | |
| # download | |
| CIFAR10(self.data_dir, train=True, download=True) | |
| CIFAR10(self.data_dir, train=False, download=True) | |
| def setup(self, stage=None): | |
| # Assign train/val datasets for use in dataloaders | |
| if stage == "fit" or stage is None: | |
| cifar10_full = CIFAR10(self.data_dir, train=True, transform=self.transform) | |
| # Calculate the sizes of train and validation splits based on percentages | |
| train_size = int(len(cifar10_full) * 0.9) # 90% for training | |
| val_size = len(cifar10_full) - train_size # Remaining for validation | |
| # Use random_split with split sizes | |
| self.cifar10_train, self.cifar10_val = random_split(cifar10_full, [train_size, val_size]) | |
| # Assign test dataset for use in dataloader(s) | |
| 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(),persistent_workers=True) | |
| def val_dataloader(self): | |
| return DataLoader(self.cifar10_val, batch_size=BATCH_SIZE, num_workers=os.cpu_count()) | |
| def test_dataloader(self): | |
| return DataLoader(self.cifar10_test, batch_size=BATCH_SIZE, num_workers=os.cpu_count()) |