93 lines
3.2 KiB
Python
93 lines
3.2 KiB
Python
import torch
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import torch.nn as nn
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import torch.optim as optim
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import time
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import copy
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from save_model import save_model
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from early_stopping import EarlyStopping
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def train_model(model, dataloaders, criterion, optimizer, lr_scheduler, device, snapshot_path, model_name = 'model_ft', num_epochs=25, early_stop = False, patience = 7):
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since = time.time()
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val_acc_history = []
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model = model.to(device)
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best_model_wts = copy.deepcopy(model.state_dict())
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best_acc = 0.0
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# initialize the early_stopping object
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early_stopping = EarlyStopping(model_name, patience=patience, verbose=True, path = snapshot_path)
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for epoch in range(num_epochs):
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print('Epoch {}/{}'.format(epoch, num_epochs - 1))
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print('-' * 10)
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# Each epoch has a training and validation phase
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for phase in ['train', 'val']:
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if phase == 'train':
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model.train() # Set model to training mode
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else:
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model.eval() # Set model to evaluate mode
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running_loss = 0.0
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running_corrects = 0
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# Iterate over data.
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for inputs, labels in dataloaders[phase]:
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inputs = inputs.to(device)
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labels = labels.to(device)
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# zero the parameter gradients
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optimizer.zero_grad()
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# forward
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# track history if only in train
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with torch.set_grad_enabled(phase == 'train'):
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# Get model outputs and calculate loss
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outputs = model(inputs)
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loss = criterion(outputs, labels)
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_, preds = torch.max(outputs, 1)
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# backward + optimize only if in training phase
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if phase == 'train':
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loss.backward()
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optimizer.step()
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# statistics
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running_loss += loss.item() * inputs.size(0)
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running_corrects += torch.sum(preds == labels.data)
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epoch_loss = running_loss / len(dataloaders[phase].dataset)
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epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)
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print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))
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# deep copy the model
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if phase == 'val' and epoch_acc >= best_acc:
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best_acc = epoch_acc
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best_model_wts = copy.deepcopy(model.state_dict())
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if phase == 'val':
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val_acc_history.append(epoch_acc)
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lr_scheduler.step(epoch_acc)
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print()
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if early_stop:
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early_stopping(epoch_loss, model, epoch)
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if early_stopping.early_stop:
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print("Early stopping")
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break
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elif epoch%10 == 9:
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save_model(model, model_name, snapshot_path, epoch, device)
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time_elapsed = time.time() - since
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print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
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print('Best val Acc: {:4f}'.format(best_acc))
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# load best model weights
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model.load_state_dict(best_model_wts)
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return model, val_acc_history
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