pytorch_train_10.py

# 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, 10)  # 40 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 fixed_feature_model(num_epochs, data_dir, learning_rate, momentum):
  	
    run.log('lr', np.float(learning_rate))
    run.log('momentum', np.float(momentum))
    model_conv = models.resnet18(pretrained=True)
    for param in model_conv.parameters():
        param.requires_grad = False

    # Parameters of newly constructed modules have requires_grad=True by default
    num_ftrs = model_conv.fc.in_features
    model_conv.fc = nn.Linear(num_ftrs, 10)

    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
    model_conv = model_conv.to(device)

    criterion = nn.CrossEntropyLoss()

    # Observe that only parameters of final layer are being optimized as
    # opoosed to before.
    optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr =learning_rate,momentum=momentum)

    # Decay LR by a factor of 0.1 every 7 epochs
    exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)
    model = train_model(model_conv, criterion, optimizer_conv,
                         exp_lr_scheduler, num_epochs,data_dir)

    return model

def main():
    # get command-line arguments
    parser = argparse.ArgumentParser()
    parser.add_argument('--data_dir', type=str, help='directory of training data')
    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()

    print("data directory is: " + args.data_dir)
    model = fixed_feature_model(args.num_epochs, args.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()