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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()
# Collect misclassified samples
'''
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
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