train_classifier.py

# this file is based on code publicly available at
#   https://github.com/bearpaw/pytorch-classification
# written by Wei Yang.


from architectures import CLASSIFIERS_ARCHITECTURES, get_architecture
from datasets import get_dataset, DATASETS
from torch.nn import CrossEntropyLoss
from torch.optim import SGD, Optimizer
from torch.optim.lr_scheduler import StepLR
from torch.utils.data import DataLoader
from train_utils import AverageMeter, accuracy, init_logfile, log, copy_code

import argparse
import datetime
import numpy as np
import os
import time
import torch

parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('--dataset', type=str, choices=DATASETS)
parser.add_argument('--arch', type=str, choices=CLASSIFIERS_ARCHITECTURES)
parser.add_argument('--outdir', type=str, help='folder to save model and training log)')
parser.add_argument('--workers', default=4, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=90, type=int, metavar='N',
                    help='number of total epochs to run')
parser.add_argument('--batch', default=256, type=int, metavar='N',
                    help='batchsize (default: 256)')
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
                    help='initial learning rate', dest='lr')
parser.add_argument('--lr_step_size', type=int, default=30,
                    help='How often to decrease learning by gamma.')
parser.add_argument('--gamma', type=float, default=0.1,
                    help='LR is multiplied by gamma on schedule.')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                    help='momentum')
parser.add_argument('--weight-decay', '--wd', default=5e-4, type=float,
                    metavar='W', help='weight decay (default: 5e-4)')
parser.add_argument('--gpu', default=None, type=str,
                    help='id(s) for CUDA_VISIBLE_DEVICES')
parser.add_argument('--print-freq', default=10, type=int,
                    metavar='N', help='print frequency (default: 10)')
parser.add_argument('--noise_sd', default=0.0, type=float,
                    help="standard deviation of noise distribution for data augmentation")
parser.add_argument('--resume', action='store_true',
                    help='if true, tries to resume training from an existing checkpoint')
parser.add_argument('--azure_datastore_path', type=str, default='',
                    help='Path to imagenet on azure')
parser.add_argument('--philly_imagenet_path', type=str, default='',
                    help='Path to imagenet on philly')
args = parser.parse_args()

if args.azure_datastore_path:
    os.environ['IMAGENET_DIR_AZURE'] = os.path.join(args.azure_datastore_path, 'datasets/imagenet_zipped')
if args.philly_imagenet_path:
    os.environ['IMAGENET_DIR_PHILLY'] = os.path.join(args.philly_imagenet_path, './')

torch.manual_seed(0)
torch.cuda.manual_seed_all(0)

def main():
    if args.gpu:
        os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
    
    if not os.path.exists(args.outdir):
        os.makedirs(args.outdir)

    # Copy code to output directory
    copy_code(args.outdir)

    train_dataset = get_dataset(args.dataset, 'train')
    test_dataset = get_dataset(args.dataset, 'test')
    pin_memory = (args.dataset == "imagenet")
    train_loader = DataLoader(train_dataset, shuffle=True, batch_size=args.batch,
                              num_workers=args.workers, pin_memory=pin_memory)
    test_loader = DataLoader(test_dataset, shuffle=False, batch_size=args.batch,
                             num_workers=args.workers, pin_memory=pin_memory)

    model = get_architecture(args.arch, args.dataset)

    criterion = CrossEntropyLoss().cuda()
    optimizer = SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
    scheduler = StepLR(optimizer, step_size=args.lr_step_size, gamma=args.gamma)

    starting_epoch = 0
    logfilename = os.path.join(args.outdir, 'log.txt')

    ## Resume from checkpoint if exists and if resume flag is True
    model_path = os.path.join(args.outdir, 'checkpoint.pth.tar')
    if args.resume and os.path.isfile(model_path):
        print("=> loading checkpoint '{}'".format(model_path))
        checkpoint = torch.load(model_path,
                                map_location=lambda storage, loc: storage)
        starting_epoch = checkpoint['epoch']
        model.load_state_dict(checkpoint['state_dict'])
        optimizer.load_state_dict(checkpoint['optimizer'])
        print("=> loaded checkpoint '{}' (epoch {})"
                        .format(model_path, checkpoint['epoch']))
    else:
        if args.resume: print("=> no checkpoint found at '{}'".format(args.outdir))
        init_logfile(logfilename, "epoch\ttime\tlr\ttrainloss\ttestloss\ttrainAcc\ttestAcc")


    for epoch in range(starting_epoch, args.epochs):
        before = time.time()
        train_loss, train_acc = train(train_loader, model, criterion, optimizer, epoch, args.noise_sd)
        test_loss, test_acc = test(test_loader, model, criterion, args.noise_sd)
        after = time.time()

        log(logfilename, "{}\t{:.3}\t{:.3}\t{:.3}\t{:.3}\t{:.3}\t{:.3}".format(
            epoch, after - before,
            scheduler.get_lr()[0], train_loss, test_loss, train_acc, test_acc))

        scheduler.step(epoch)

        torch.save({
            'epoch': epoch + 1,
            'arch': args.arch,
            'state_dict': model.state_dict(),
            'optimizer': optimizer.state_dict(),
        }, os.path.join(args.outdir, 'checkpoint.pth.tar'))


def train(loader: DataLoader, model: torch.nn.Module, criterion, optimizer: Optimizer, epoch: int, noise_sd: float):
    """
    Function to do one training epoch
        :param loader:DataLoader: dataloader (train) 
        :param model:torch.nn.Module: the classifer being trained
        :param criterion: the loss function
        :param optimizer:Optimizer: the optimizer used during trainined
        :param epoch:int: the current epoch number (for logging)
        :param noise_sd:float: the std-dev of the Guassian noise perturbation of the input
    """
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()
    end = time.time()

    # switch to train mode
    model.train()

    for i, (inputs, targets) in enumerate(loader):
        # measure data loading time
        data_time.update(time.time() - end)

        inputs = inputs.cuda()
        targets = targets.cuda()

        # augment inputs with noise
        inputs = inputs + torch.randn_like(inputs, device='cuda') * noise_sd

        # compute output
        outputs = model(inputs)
        loss = criterion(outputs, targets)

        # measure accuracy and record loss
        acc1, acc5 = accuracy(outputs, targets, topk=(1, 5))
        losses.update(loss.item(), inputs.size(0))
        top1.update(acc1.item(), inputs.size(0))
        top5.update(acc5.item(), inputs.size(0))

        # compute gradient and do SGD step
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % args.print_freq == 0:
            print('Epoch: [{0}][{1}/{2}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                  'Acc@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                epoch, i, len(loader), batch_time=batch_time,
                data_time=data_time, loss=losses, top1=top1, top5=top5))

    return (losses.avg, top1.avg)


def test(loader: DataLoader, model: torch.nn.Module, criterion, noise_sd: float):
    """
    Function to evaluate the trained model
        :param loader:DataLoader: dataloader (train)
        :param model:torch.nn.Module: the classifer being evaluated
        :param criterion: the loss function
        :param noise_sd:float: the std-dev of the Guassian noise perturbation of the input
    """
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()
    end = time.time()

    # switch to eval mode
    model.eval()

    with torch.no_grad():
        for i, (inputs, targets) in enumerate(loader):
            # measure data loading time
            data_time.update(time.time() - end)

            inputs = inputs.cuda()
            targets = targets.cuda()

            # augment inputs with noise
            inputs = inputs + torch.randn_like(inputs, device='cuda') * noise_sd

            # compute output
            outputs = model(inputs)
            loss = criterion(outputs, targets)

            # measure accuracy and record loss
            acc1, acc5 = accuracy(outputs, targets, topk=(1, 5))
            losses.update(loss.item(), inputs.size(0))
            top1.update(acc1.item(), inputs.size(0))
            top5.update(acc5.item(), inputs.size(0))

            # measure elapsed time
            batch_time.update(time.time() - end)
            end = time.time()

            if i % args.print_freq == 0:
                print('Test: [{0}/{1}]\t'
                      'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                      'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
                      'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                      'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                      'Acc@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                    i, len(loader), batch_time=batch_time,
                    data_time=data_time, loss=losses, top1=top1, top5=top5))

        return (losses.avg, top1.avg)


if __name__ == "__main__":
    main()