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August 27, 2021 18:47
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training loop for segmenter, brain mri dataset
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| def run_experiment(model_name, model, optimizer, criterion, train_loader, val_loader, device='cuda', num_epochs=50, clear_mem=True): | |
| ####################### | |
| #Train model # | |
| ####################### | |
| print('Model sent to ' + str(device)) | |
| model.to(device) | |
| losses = [] | |
| train_scores = [] # hold IoU scores | |
| iters = 0 | |
| for epoch in range(num_epochs): | |
| if epoch % 10 == 0: | |
| print(f'Epoch {epoch+1}/{num_epochs}') | |
| for i,batch in enumerate(train_loader): | |
| img = batch[0].to(device) | |
| msk = batch[1].to(device) | |
| optimizer.zero_grad() | |
| output = model(img) | |
| loss = criterion(output, msk) | |
| loss.backward() | |
| optimizer.step() | |
| losses.append(loss.item()) | |
| train_scores.append(iou_pytorch(output.detach(), msk)) | |
| iters += 1 | |
| #if iters % 500 == 0: | |
| #print(f'Loss: [{loss}]') | |
| #for i in range(len(train_scores)): | |
| # train_scores[i] = train_scores[i].mean() | |
| ####################### | |
| #Validate model # | |
| ####################### | |
| model.eval() | |
| val_losses = [] | |
| val_scores = [] | |
| for i,batch in enumerate(val_loader): | |
| img = batch[0].to(device) | |
| msk = batch[1].to(device) | |
| output = model(img) | |
| loss = criterion(output, msk) | |
| val_scores.append(iou_pytorch(output.detach(), msk)) | |
| val_losses.append(loss.item()) | |
| results = { | |
| 'model_name': model_name, | |
| 'train_losses': losses, | |
| 'train_scores': train_scores, | |
| 'val_losses': val_losses, | |
| 'val_scores': val_scores | |
| } | |
| if clear_mem: | |
| del model | |
| del optimizer | |
| del criterion | |
| torch.cuda.empty_cache() | |
| return results |
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