''' Training script for Fashion-MNIST ''' from __future__ import print_function import argparse import os import shutil import time import random import torch import torch.nn as nn import torch.nn.parallel import torch.backends.cudnn as cudnn import torch.optim as optim import torch.utils.data as data import torchvision.datasets as datasets import models.fashion as models import transforms import numpy as np from utils import Bar, Logger, AverageMeter, accuracy, mkdir_p, savefig model_names = sorted(name for name in models.__dict__ if name.islower() and not name.startswith("__") and callable(models.__dict__[name])) parser = argparse.ArgumentParser(description='PyTorch Fashion-MNIST Training') # Datasets parser.add_argument('-d', '--dataset', default='fashionmnist', type=str) parser.add_argument('-j', '--workers', default=4, type=int, metavar='N', help='number of data loading workers (default: 4)') # Optimization options parser.add_argument('--epochs', default=300, type=int, metavar='N', help='number of total epochs to run') parser.add_argument('--start-epoch', default=0, type=int, metavar='N', help='manual epoch number (useful on restarts)') parser.add_argument('--train-batch', default=128, type=int, metavar='N', help='train batchsize') parser.add_argument('--test-batch', default=100, type=int, metavar='N', help='test batchsize') parser.add_argument('--lr', '--learning-rate', default=0.1, type=float, metavar='LR', help='initial learning rate') parser.add_argument('--drop', '--dropout', default=0, type=float, metavar='Dropout', help='Dropout ratio') parser.add_argument('--schedule', type=int, nargs='+', default=[150, 225], help='Decrease learning rate at these epochs.') 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: 1e-4)') # Checkpoints parser.add_argument('-c', '--checkpoint', default='checkpoint', type=str, metavar='PATH', help='path to save checkpoint (default: checkpoint)') parser.add_argument('--resume', default='', type=str, metavar='PATH', help='path to latest checkpoint (default: none)') # Architecture parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet', choices=model_names, help='model architecture: ' + ' | '.join(model_names) + ' (default: resnet20)') parser.add_argument('--depth', type=int, default=20, help='Model depth.') parser.add_argument('--widen-factor', type=int, default=10, help='Widen factor. 10') parser.add_argument('--growthRate', type=int, default=12, help='Growth rate for DenseNet.') parser.add_argument('--compressionRate', type=int, default=2, help='Compression Rate (theta) for DenseNet.') # Miscs parser.add_argument('--manualSeed', type=int, help='manual seed') parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true', help='evaluate model on validation set') # Random Erasing parser.add_argument('--p', default=0, type=float, help='Random Erasing probability') parser.add_argument('--sh', default=0.4, type=float, help='max erasing area') parser.add_argument('--r1', default=0.3, type=float, help='aspect of erasing area') args = parser.parse_args() state = {k: v for k, v in args._get_kwargs()} # Validate dataset assert args.dataset == 'fashionmnist' # Use CUDA use_cuda = torch.cuda.is_available() # Random seed if args.manualSeed is None: args.manualSeed = random.randint(1, 10000) random.seed(args.manualSeed) torch.manual_seed(args.manualSeed) if use_cuda: torch.cuda.manual_seed_all(args.manualSeed) best_acc = 0 # best test accuracy def main(): global best_acc start_epoch = args.start_epoch # start from epoch 0 or last checkpoint epoch if not os.path.isdir(args.checkpoint): mkdir_p(args.checkpoint) # Data print('==> Preparing dataset %s' % args.dataset) transform_train = transforms.Compose([ transforms.RandomCrop(28, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)), transforms.RandomErasing(probability = args.p, sh = args.sh, r1 = args.r1, mean = [0.4914]), ]) transform_test = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)), ]) if args.dataset == 'fashionmnist': dataloader = datasets.FashionMNIST num_classes = 10 trainset = dataloader(root='./data', train=True, download=True, transform=transform_train) trainloader = data.DataLoader(trainset, batch_size=args.train_batch, shuffle=True, num_workers=args.workers) testset = dataloader(root='./data', train=False, download=False, transform=transform_test) testloader = data.DataLoader(testset, batch_size=args.test_batch, shuffle=False, num_workers=args.workers) # Model print("==> creating model '{}'".format(args.arch)) if args.arch.startswith('wrn'): model = models.__dict__[args.arch]( num_classes=num_classes, depth=args.depth, widen_factor=args.widen_factor, dropRate=args.drop, ) elif args.arch.endswith('resnet'): model = models.__dict__[args.arch]( num_classes=num_classes, depth=args.depth, ) model = torch.nn.DataParallel(model).cuda() cudnn.benchmark = True print(' Total params: %.2fM' % (sum(p.numel() for p in model.parameters())/1000000.0)) criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay) # Resume title = 'fashionmnist-' + args.arch if args.resume: # Load checkpoint. print('==> Resuming from checkpoint..') assert os.path.isfile(args.resume), 'Error: no checkpoint directory found!' args.checkpoint = os.path.dirname(args.resume) checkpoint = torch.load(args.resume) best_acc = checkpoint['best_acc'] start_epoch = checkpoint['epoch'] model.load_state_dict(checkpoint['state_dict']) optimizer.load_state_dict(checkpoint['optimizer']) logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title, resume=True) else: logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title) logger.set_names(['Learning Rate', 'Train Loss', 'Valid Loss', 'Train Acc.', 'Valid Acc.']) if args.evaluate: print('\nEvaluation only') test_loss, test_acc = test(testloader, model, criterion, start_epoch, use_cuda) print(' Test Loss: %.8f, Test Acc: %.2f' % (test_loss, test_acc)) return # Train and val for epoch in range(start_epoch, args.epochs): adjust_learning_rate(optimizer, epoch) print('\nEpoch: [%d | %d] LR: %f' % (epoch + 1, args.epochs, state['lr'])) train_loss, train_acc = train(trainloader, model, criterion, optimizer, epoch, use_cuda) test_loss, test_acc = test(testloader, model, criterion, epoch, use_cuda) # append logger file logger.append([state['lr'], train_loss, test_loss, train_acc, test_acc]) # save model is_best = test_acc > best_acc best_acc = max(test_acc, best_acc) save_checkpoint({ 'epoch': epoch + 1, 'state_dict': model.state_dict(), 'acc': test_acc, 'best_acc': best_acc, 'optimizer' : optimizer.state_dict(), }, is_best, checkpoint=args.checkpoint) logger.close() logger.plot() savefig(os.path.join(args.checkpoint, 'log.eps')) print('Best acc:') print(best_acc) def train(trainloader, model, criterion, optimizer, epoch, use_cuda): # switch to train mode model.train() batch_time = AverageMeter() data_time = AverageMeter() losses = AverageMeter() top1 = AverageMeter() top5 = AverageMeter() end = time.time() bar = Bar('Processing', max=len(trainloader)) for batch_idx, (inputs, targets) in enumerate(trainloader): # measure data loading time data_time.update(time.time() - end) if use_cuda: inputs, targets = inputs.cuda(), targets.cuda(async=True) inputs, targets = torch.autograd.Variable(inputs), torch.autograd.Variable(targets) # compute output outputs = model(inputs) loss = criterion(outputs, targets) # measure accuracy and record loss prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5)) losses.update(loss.item(), inputs.size(0)) top1.update(prec1.item(), inputs.size(0)) top5.update(prec5.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() # plot progress bar.suffix = '({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | Total: {total:} | ETA: {eta:} | Loss: {loss:.4f} | top1: {top1: .4f} | top5: {top5: .4f}'.format( batch=batch_idx + 1, size=len(trainloader), data=data_time.avg, bt=batch_time.avg, total=bar.elapsed_td, eta=bar.eta_td, loss=losses.avg, top1=top1.avg, top5=top5.avg, ) bar.next() bar.finish() return (losses.avg, top1.avg) def test(testloader, model, criterion, epoch, use_cuda): global best_acc batch_time = AverageMeter() data_time = AverageMeter() losses = AverageMeter() top1 = AverageMeter() top5 = AverageMeter() # switch to evaluate mode model.eval() end = time.time() bar = Bar('Processing', max=len(testloader)) for batch_idx, (inputs, targets) in enumerate(testloader): # measure data loading time data_time.update(time.time() - end) if use_cuda: inputs, targets = inputs.cuda(), targets.cuda() inputs, targets = torch.autograd.Variable(inputs, volatile=True), torch.autograd.Variable(targets) # compute output outputs = model(inputs) loss = criterion(outputs, targets) # measure accuracy and record loss prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5)) losses.update(loss.item(), inputs.size(0)) top1.update(prec1.item(), inputs.size(0)) top5.update(prec5.item(), inputs.size(0)) # measure elapsed time batch_time.update(time.time() - end) end = time.time() # plot progress bar.suffix = '({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | Total: {total:} | ETA: {eta:} | Loss: {loss:.4f} | top1: {top1: .4f} | top5: {top5: .4f}'.format( batch=batch_idx + 1, size=len(testloader), data=data_time.avg, bt=batch_time.avg, total=bar.elapsed_td, eta=bar.eta_td, loss=losses.avg, top1=top1.avg, top5=top5.avg, ) bar.next() bar.finish() return (losses.avg, top1.avg) def save_checkpoint(state, is_best, checkpoint='checkpoint', filename='checkpoint.pth.tar'): filepath = os.path.join(checkpoint, filename) torch.save(state, filepath) if is_best: shutil.copyfile(filepath, os.path.join(checkpoint, 'model_best.pth.tar')) def adjust_learning_rate(optimizer, epoch): global state if epoch in args.schedule: state['lr'] *= args.gamma for param_group in optimizer.param_groups: param_group['lr'] = state['lr'] if __name__ == '__main__': main()