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Updated notebook folders
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Roope Astala
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# Copyright (c) 2017, PyTorch contributors
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# Modifications copyright (C) Microsoft Corporation
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# Licensed under the BSD license
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# Adapted from https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
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from __future__ import print_function, division
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torch.optim import lr_scheduler
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from torchvision import datasets, models, transforms
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import numpy as np
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import time
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import os
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import copy
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import argparse
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from azureml.core.run import Run
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# get the Azure ML run object
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run = Run.get_context()
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def load_data(data_dir):
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"""Load the train/val data."""
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# Data augmentation and normalization for training
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# Just normalization for validation
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data_transforms = {
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'train': transforms.Compose([
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transforms.RandomResizedCrop(224),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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]),
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'val': transforms.Compose([
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transforms.Resize(256),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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]),
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}
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image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
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data_transforms[x])
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for x in ['train', 'val']}
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dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
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shuffle=True, num_workers=0)
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for x in ['train', 'val']}
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dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
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class_names = image_datasets['train'].classes
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return dataloaders, dataset_sizes, class_names
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def train_model(model, criterion, optimizer, scheduler, num_epochs, data_dir):
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"""Train the model."""
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# load training/validation data
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dataloaders, dataset_sizes, class_names = load_data(data_dir)
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device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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since = time.time()
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best_model_wts = copy.deepcopy(model.state_dict())
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best_acc = 0.0
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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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scheduler.step()
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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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outputs = model(inputs)
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_, preds = torch.max(outputs, 1)
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loss = criterion(outputs, labels)
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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 / dataset_sizes[phase]
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epoch_acc = running_corrects.double() / dataset_sizes[phase]
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print('{} Loss: {:.4f} Acc: {:.4f}'.format(
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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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# log the best val accuracy to AML run
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run.log('best_val_acc', np.float(best_acc))
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print()
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time_elapsed = time.time() - since
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print('Training complete in {:.0f}m {:.0f}s'.format(
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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
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def fine_tune_model(num_epochs, data_dir, learning_rate, momentum):
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"""Load a pretrained model and reset the final fully connected layer."""
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# log the hyperparameter metrics to the AML run
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run.log('lr', np.float(learning_rate))
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run.log('momentum', np.float(momentum))
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model_ft = models.resnet18(pretrained=True)
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num_ftrs = model_ft.fc.in_features
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model_ft.fc = nn.Linear(num_ftrs, 2) # only 2 classes to predict
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device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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model_ft = model_ft.to(device)
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criterion = nn.CrossEntropyLoss()
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# Observe that all parameters are being optimized
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optimizer_ft = optim.SGD(model_ft.parameters(), lr=learning_rate, momentum=momentum)
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# Decay LR by a factor of 0.1 every 7 epochs
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exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
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model = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs, data_dir)
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return model
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def main():
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# get command-line arguments
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parser = argparse.ArgumentParser()
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parser.add_argument('--data_dir', type=str, help='directory of training data')
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parser.add_argument('--num_epochs', type=int, default=25, help='number of epochs to train')
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parser.add_argument('--output_dir', type=str, help='output directory')
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parser.add_argument('--learning_rate', type=float, default=0.001, help='learning rate')
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parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
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args = parser.parse_args()
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print("data directory is: " + args.data_dir)
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model = fine_tune_model(args.num_epochs, args.data_dir, args.learning_rate, args.momentum)
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os.makedirs(args.output_dir, exist_ok=True)
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torch.save(model, os.path.join(args.output_dir, 'model.pt'))
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if __name__ == "__main__":
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main()
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