# Copyright (c) 2017, PyTorch contributors # Modifications copyright (C) Microsoft Corporation # Licensed under the BSD license # Adapted from https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html from __future__ import print_function, division import torch import torch.nn as nn import torch.optim as optim from torch.optim import lr_scheduler from torchvision import datasets, models, transforms import numpy as np import time import os import copy import argparse from azureml.core.run import Run # get the Azure ML run object run = Run.get_context() def load_data(data_dir): """Load the train/val data.""" # Data augmentation and normalization for training # Just normalization for validation data_transforms = { 'train': transforms.Compose([ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]), 'val': transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]), } image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'val']} dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, shuffle=True, num_workers=4) for x in ['train', 'val']} dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} class_names = image_datasets['train'].classes return dataloaders, dataset_sizes, class_names def train_model(model, criterion, optimizer, scheduler, num_epochs, data_dir): """Train the model.""" # load training/validation data dataloaders, dataset_sizes, class_names = load_data(data_dir) device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') since = time.time() best_model_wts = copy.deepcopy(model.state_dict()) best_acc = 0.0 for epoch in range(num_epochs): print('Epoch {}/{}'.format(epoch, num_epochs - 1)) print('-' * 10) # Each epoch has a training and validation phase for phase in ['train', 'val']: if phase == 'train': scheduler.step() model.train() # Set model to training mode else: model.eval() # Set model to evaluate mode running_loss = 0.0 running_corrects = 0 # Iterate over data. for inputs, labels in dataloaders[phase]: inputs = inputs.to(device) labels = labels.to(device) # zero the parameter gradients optimizer.zero_grad() # forward # track history if only in train with torch.set_grad_enabled(phase == 'train'): outputs = model(inputs) _, preds = torch.max(outputs, 1) loss = criterion(outputs, labels) # backward + optimize only if in training phase if phase == 'train': loss.backward() optimizer.step() # statistics running_loss += loss.item() * inputs.size(0) running_corrects += torch.sum(preds == labels.data) epoch_loss = running_loss / dataset_sizes[phase] epoch_acc = running_corrects.double() / dataset_sizes[phase] print('{} Loss: {:.4f} Acc: {:.4f}'.format( phase, epoch_loss, epoch_acc)) # deep copy the model if phase == 'val' and epoch_acc > best_acc: best_acc = epoch_acc best_model_wts = copy.deepcopy(model.state_dict()) # log the best val accuracy to AML run run.log('best_val_acc', np.float(best_acc)) print() time_elapsed = time.time() - since print('Training complete in {:.0f}m {:.0f}s'.format( time_elapsed // 60, time_elapsed % 60)) print('Best val Acc: {:4f}'.format(best_acc)) # load best model weights model.load_state_dict(best_model_wts) return model def fine_tune_model(num_epochs, data_dir, learning_rate, momentum): """Load a pretrained model and reset the final fully connected layer.""" # log the hyperparameter metrics to the AML run run.log('lr', np.float(learning_rate)) run.log('momentum', np.float(momentum)) model_ft = models.resnet18(pretrained=True) num_ftrs = model_ft.fc.in_features model_ft.fc = nn.Linear(num_ftrs, 2) # only 2 classes to predict device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') model_ft = model_ft.to(device) criterion = nn.CrossEntropyLoss() # Observe that all parameters are being optimized optimizer_ft = optim.SGD(model_ft.parameters(), lr=learning_rate, momentum=momentum) # Decay LR by a factor of 0.1 every 7 epochs exp_lr_scheduler = lr_scheduler.StepLR( optimizer_ft, step_size=7, gamma=0.1) model = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs, data_dir) return model def download_data(): """Download and extract the training data.""" import urllib from zipfile import ZipFile # download data data_file = './hymenoptera_data.zip' download_url = 'https://download.pytorch.org/tutorial/hymenoptera_data.zip' urllib.request.urlretrieve(download_url, filename=data_file) # extract files with ZipFile(data_file, 'r') as zip: print('extracting files...') zip.extractall() print('finished extracting') data_dir = zip.namelist()[0] # delete zip file os.remove(data_file) return data_dir def main(): print("Torch version:", torch.__version__) # get command-line arguments parser = argparse.ArgumentParser() parser.add_argument('--num_epochs', type=int, default=25, help='number of epochs to train') parser.add_argument('--output_dir', type=str, help='output directory') parser.add_argument('--learning_rate', type=float, default=0.001, help='learning rate') parser.add_argument('--momentum', type=float, default=0.9, help='momentum') args = parser.parse_args() data_dir = download_data() print("data directory is: " + data_dir) model = fine_tune_model(args.num_epochs, data_dir, args.learning_rate, args.momentum) os.makedirs(args.output_dir, exist_ok=True) torch.save(model, os.path.join(args.output_dir, 'model.pt')) if __name__ == "__main__": main()