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import os |
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import csv |
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import torch |
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from torch import nn |
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from torch.utils.data import DataLoader |
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from torchvision import datasets |
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from torchvision.transforms import ToTensor, Normalize, RandomCrop, RandomHorizontalFlip, Compose |
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from contextualizer_uniform_mlp import Contextualizer |
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transform = Compose([ |
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RandomCrop(32, padding=4), |
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RandomHorizontalFlip(), |
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ToTensor(), |
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Normalize((0.5, 0.5,0.5),(0.5, 0.5,0.5)) |
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]) |
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training_data = datasets.CIFAR10( |
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root='data', |
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train=True, |
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download=True, |
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transform=transform |
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) |
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test_data = datasets.CIFAR10( |
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root='data', |
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train=False, |
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download=True, |
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transform=transform |
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) |
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batch_size = 128 |
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train_dataloader = DataLoader(training_data, batch_size=batch_size,shuffle=True) |
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test_dataloader = DataLoader(test_data, batch_size=batch_size) |
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for X, y in test_dataloader: |
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print(f"Shape of X [N,C,H,W]:{X.shape}") |
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print(f"Shape of y:{y.shape}{y.dtype}") |
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break |
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def check_sizes(image_size, patch_size): |
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sqrt_num_patches, remainder = divmod(image_size, patch_size) |
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assert remainder == 0, "`image_size` must be divisibe by `patch_size`" |
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num_patches = sqrt_num_patches ** 2 |
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return num_patches |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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print(f"using {device} device") |
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class ContextualizerumlpImageClassification(Contextualizer): |
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def __init__( |
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self, |
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image_size=32, |
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patch_size=4, |
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in_channels=3, |
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num_classes=10, |
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d_model = 256, |
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num_tokens = 64, |
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num_layers=4, |
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dropout=0.5 |
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): |
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num_patches = check_sizes(image_size, patch_size) |
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super().__init__(d_model,num_layers,dropout, num_tokens) |
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self.patcher = nn.Conv2d( |
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in_channels, d_model, kernel_size=patch_size, stride=patch_size |
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) |
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self.classifier = nn.Linear(d_model, num_classes) |
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def forward(self, x): |
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patches = self.patcher(x) |
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batch_size, num_channels, _, _ = patches.shape |
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patches = patches.permute(0, 2, 3, 1) |
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patches = patches.view(batch_size, -1, num_channels) |
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embedding = self.model(patches) |
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embedding = embedding.mean(dim=1) |
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out = self.classifier(embedding) |
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return out |
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model = ContextualizerumlpImageClassification().to(device) |
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print(model) |
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loss_fn = nn.CrossEntropyLoss() |
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optimizer = torch.optim.Adam(model.parameters(),lr=1e-3) |
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def train(dataloader, model, loss_fn, optimizer): |
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size = len(dataloader.dataset) |
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num_batches = len(dataloader) |
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model.train() |
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train_loss = 0 |
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correct = 0 |
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for batch, (X,y) in enumerate(dataloader): |
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X, y = X.to(device), y.to(device) |
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pred = model(X) |
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loss = loss_fn(pred,y) |
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optimizer.zero_grad() |
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loss.backward() |
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optimizer.step() |
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train_loss += loss.item() |
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_, labels = torch.max(pred.data, 1) |
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correct += labels.eq(y.data).type(torch.float).sum() |
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if batch % 100 == 0: |
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loss, current = loss.item(), batch * len(X) |
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print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]") |
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train_loss /= num_batches |
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train_accuracy = 100. * correct.item() / size |
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print(train_accuracy) |
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return train_loss,train_accuracy |
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def test(dataloader, model, loss_fn): |
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size = len(dataloader.dataset) |
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num_batches = len(dataloader) |
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model.eval() |
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test_loss = 0 |
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correct = 0 |
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with torch.no_grad(): |
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for X,y in dataloader: |
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X,y = X.to(device), y.to(device) |
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pred = model(X) |
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test_loss += loss_fn(pred, y).item() |
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correct += (pred.argmax(1) == y).type(torch.float).sum().item() |
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test_loss /= num_batches |
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correct /= size |
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print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n") |
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test_accuracy = 100*correct |
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return test_loss, test_accuracy |
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logname = "/PATH/Contextualizer_uniform_mlp/Experiments_cifar10/logs_contextualizer/logs_cifar10.csv" |
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if not os.path.exists(logname): |
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with open(logname, 'w') as logfile: |
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logwriter = csv.writer(logfile, delimiter=',') |
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logwriter.writerow(['epoch', 'train loss', 'train acc', |
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'test loss', 'test acc']) |
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epochs = 100 |
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for epoch in range(epochs): |
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print(f"Epoch {epoch+1}\n-----------------------------------") |
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train_loss, train_acc = train(train_dataloader, model, loss_fn, optimizer) |
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test_loss, test_acc = test(test_dataloader, model, loss_fn) |
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with open(logname, 'a') as logfile: |
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logwriter = csv.writer(logfile, delimiter=',') |
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logwriter.writerow([epoch+1, train_loss, train_acc, |
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test_loss, test_acc]) |
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print("Done!") |
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path = "/PATH/Contextualizer_uniform_mlp/Experiments_cifar10/weights_contextualizer" |
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model_name = "ContextualizerumlpImageClassification_cifar10" |
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torch.save(model.state_dict(), f"{path}/{model_name}.pth") |
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print(f"Saved Model State to {path}/{model_name}.pth ") |
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