| import torch
|
| from tqdm import tqdm
|
| from torch.amp import autocast
|
|
|
| def train(model, device, train_loader, optimizer, criterion, epoch, accumulation_steps=4):
|
| model.train()
|
| running_loss = 0.0
|
| correct1 = 0
|
| correct5 = 0
|
| total = 0
|
| pbar = tqdm(train_loader)
|
|
|
| for batch_idx, (inputs, targets) in enumerate(pbar):
|
| inputs, targets = inputs.to(device), targets.to(device)
|
|
|
| with autocast(device_type='cuda'):
|
| outputs = model(inputs)
|
| loss = criterion(outputs, targets) / accumulation_steps
|
|
|
| loss.backward()
|
|
|
| if (batch_idx + 1) % accumulation_steps == 0 or (batch_idx + 1) == len(train_loader):
|
| optimizer.step()
|
| optimizer.zero_grad()
|
|
|
| running_loss += loss.item() * accumulation_steps
|
| _, predicted = outputs.topk(5, 1, True, True)
|
| total += targets.size(0)
|
| correct1 += predicted[:, :1].eq(targets.view(-1, 1).expand_as(predicted[:, :1])).sum().item()
|
| correct5 += predicted.eq(targets.view(-1, 1).expand_as(predicted)).sum().item()
|
|
|
| pbar.set_description(desc=f'Epoch {epoch} | Loss: {running_loss / (batch_idx + 1):.4f} | Top-1 Acc: {100. * correct1 / total:.2f} | Top-5 Acc: {100. * correct5 / total:.2f}')
|
|
|
| if (batch_idx + 1) % 50 == 0:
|
| torch.cuda.empty_cache()
|
|
|
| return 100. * correct1 / total, 100. * correct5 / total, running_loss / len(train_loader)
|
|
|
| def test(model, device, test_loader, criterion):
|
| model.eval()
|
| test_loss = 0
|
| correct1 = 0
|
| correct5 = 0
|
| total = 0
|
| misclassified_images = []
|
| misclassified_labels = []
|
| misclassified_preds = []
|
|
|
| with torch.no_grad():
|
| for inputs, targets in test_loader:
|
| inputs, targets = inputs.to(device), targets.to(device)
|
| outputs = model(inputs)
|
| loss = criterion(outputs, targets)
|
|
|
| test_loss += loss.item()
|
| _, predicted = outputs.topk(5, 1, True, True)
|
| total += targets.size(0)
|
| correct1 += predicted[:, :1].eq(targets.view(-1, 1).expand_as(predicted[:, :1])).sum().item()
|
| correct5 += predicted.eq(targets.view(-1, 1).expand_as(predicted)).sum().item()
|
|
|
|
|
| '''
|
| for i in range(inputs.size(0)):
|
| if targets[i] not in predicted[i, :1]:
|
| misclassified_images.append(inputs[i].cpu())
|
| misclassified_labels.append(targets[i].cpu())
|
| misclassified_preds.append(predicted[i, :1].cpu())
|
| '''
|
|
|
| test_accuracy1 = 100. * correct1 / total
|
| test_accuracy5 = 100. * correct5 / total
|
| print(f'Test Loss: {test_loss/len(test_loader):.4f}, Top-1 Accuracy: {test_accuracy1:.2f}, Top-5 Accuracy: {test_accuracy5:.2f}')
|
| return test_accuracy1, test_accuracy5, test_loss / len(test_loader), misclassified_images, misclassified_labels, misclassified_preds
|
|
|
