xunge/pytorch_imagenet.py

## pytorch_imagenet.py
import time
import shutil
import os

import torch
import torch.nn as nn
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torchvision.models as models
import torch.backends.cudnn as cudnn


class DefaultConfigs(object):
    # 1.string parameters
    train_dir = "/home/ubuntu/share/dataset/imagenet/train"
    val_dir = '/home/ubuntu/share/dataset/imagenet/val'
    model_name = "resnet18"
    weights = "./checkpoints/"
    best_models = weights + "best_model/"

    # 2.numeric parameters
    epochs = 40
    start_epoch = 0
    batch_size = 256
    momentum = 0.9
    lr = 0.1
    weight_decay = 1e-4
    interval = 10
    workers = 5

    # 3.boolean parameters
    evaluate = False
    pretrained = False
    resume = False


device = "cuda" if torch.cuda.is_available() else "cpu"
best_acc = 0
config = DefaultConfigs()


class ProgressMeter(object):
    def __init__(self, num_batches, meters, prefix=""):
        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
        self.meters = meters
        self.prefix = prefix

    def display(self, batch):
        entries = [self.prefix + self.batch_fmtstr.format(batch)]
        entries += [str(meter) for meter in self.meters]
        print('\t'.join(entries))

    def _get_batch_fmtstr(self, num_batches):
        num_digits = len(str(num_batches // 1))
        fmt = '{:' + str(num_digits) + 'd}'
        return '[' + fmt + '/' + fmt.format(num_batches) + ']'


class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self, name, fmt=':f'):
        self.name = name
        self.fmt = fmt
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count

    def __str__(self):
        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
        return fmtstr.format(**self.__dict__)


def accuracy(output, target, topk=(1,)):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)

        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.eq(target.view(1, -1).expand_as(pred))

        res = []
        for k in topk:
            correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res


def adjust_learning_rate(optimizer, epoch):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    lr = config.lr * (0.1 ** (epoch // 30))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


def save_checkpoint(state, is_best):
    filename = config.weights + config.model_name + os.sep + "_checkpoint.pth.tar"
    torch.save(state, filename)
    if is_best:
        message = config.best_models + config.model_name + os.sep + 'model_best.pth.tar'
        shutil.copyfile(filename, message)


def validate(val_loader, model, criterion):
    batch_time = AverageMeter('Time', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(
        len(val_loader),
        [batch_time, losses, top1, top5],
        prefix='Test: ')
    # switch to evaluate mode
    model.eval()

    with torch.no_grad():
        end = time.time()
        for batch_id, (images, target) in enumerate(val_loader):
            images, target = images.to(device), target.to(device)
            # compute output
            output = model(images)
            loss = criterion(output, target)

            # measure accuracy and record loss
            acc1, acc5 = accuracy(output, target, topk=(1, 5))
            losses.update(loss.item(), images.size(0))
            top1.update(acc1[0], images.size(0))
            top5.update(acc5[0], images.size(0))

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

            if (batch_id + 1) % config.interval == 0:
                progress.display(batch_id + 1)

        print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
              .format(top1=top1, top5=top5))
    return top1.avg


def train(train_loader, model, criterion, optimizer, epoch):
    batch_time = AverageMeter('Time', ':6.3f')
    data_time = AverageMeter('Data', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(
        len(train_loader),
        [batch_time, data_time, losses, top1, top5],
        prefix="Epoch: [{}]".format(epoch))
    # switch to train mode
    model.train()

    end = time.time()
    for batch_id, (images, target) in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)
        images, target = images.to(device), target.to(device)

        # compute output
        output = model(images)
        loss = criterion(output, target)

        # measure accuracy and record loss
        acc1, acc5 = accuracy(output, target, topk=(1, 5))
        losses.update(loss.item(), images.size(0))
        top1.update(acc1[0], images.size(0))
        top5.update(acc5[0], images.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 (batch_id + 1) % config.interval == 0:
            progress.display(batch_id + 1)


def main():
    global best_acc

    if config.pretrained:
        print("=> using pre-trained model '{}'".format(config.model_name))
        model = models.__dict__[config.model_name](pretrained=True)
    else:
        print("=> creating model '{}'".format(config.model_name))
        model = models.__dict__[config.model_name]()
    model.to(device)

    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])

    criterion = nn.CrossEntropyLoss().to(device)
    optimizer = torch.optim.SGD(model.parameters(), config.lr,
                                momentum=config.momentum,
                                weight_decay=config.weight_decay)

    cudnn.benchmark = True

    if config.resume:
        checkpoint = torch.load(config.best_models + "model_best.pth.tar")
        config.start_epoch = checkpoint['epoch']
        best_acc = checkpoint['best_acc']
        model.load_state_dict(checkpoint['state_dict'])
        optimizer.load_state_dict(checkpoint['optimizer'])

    train_loader = torch.utils.data.DataLoader(
        datasets.ImageFolder(config.train_dir, transforms.Compose([
            transforms.RandomResizedCrop(224),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            normalize,
        ])),
        batch_size=config.batch_size, shuffle=True,
        num_workers=config.workers, pin_memory=True)

    val_loader = torch.utils.data.DataLoader(
        datasets.ImageFolder(config.val_dir, transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            normalize,
        ])),
        batch_size=config.batch_size, shuffle=False,
        num_workers=config.workers, pin_memory=True)

    if config.evaluate:
        validate(val_loader, model, criterion)
        return

    for epoch in range(config.start_epoch, config.epochs):
        adjust_learning_rate(optimizer, epoch)

        print('\nEpoch: [%d | %d]' % (epoch + 1, config.epochs))

        train(train_loader, model, criterion, optimizer, epoch)
        test_acc = validate(val_loader, model, criterion)

        # save model
        is_best = test_acc > best_acc
        best_acc = max(test_acc, best_acc)
        save_checkpoint({
            'epoch': epoch + 1,
            "model_name": config.model_name,
            'state_dict': model.state_dict(),
            'acc': test_acc,
            'best_acc': best_acc,
            'optimizer': optimizer.state_dict(),
        }, is_best)


if __name__ == '__main__':
    main()
	import time
	import shutil
	import os

	import torch
	import torch.nn as nn
	import torchvision.datasets as datasets
	import torchvision.transforms as transforms
	import torchvision.models as models
	import torch.backends.cudnn as cudnn


	class DefaultConfigs(object):
	# 1.string parameters
	train_dir = "/home/ubuntu/share/dataset/imagenet/train"
	val_dir = '/home/ubuntu/share/dataset/imagenet/val'
	model_name = "resnet18"
	weights = "./checkpoints/"
	best_models = weights + "best_model/"

	# 2.numeric parameters
	epochs = 40
	start_epoch = 0
	batch_size = 256
	momentum = 0.9
	lr = 0.1
	weight_decay = 1e-4
	interval = 10
	workers = 5

	# 3.boolean parameters
	evaluate = False
	pretrained = False
	resume = False


	device = "cuda" if torch.cuda.is_available() else "cpu"
	best_acc = 0
	config = DefaultConfigs()


	class ProgressMeter(object):
	def __init__(self, num_batches, meters, prefix=""):
	self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
	self.meters = meters
	self.prefix = prefix

	def display(self, batch):
	entries = [self.prefix + self.batch_fmtstr.format(batch)]
	entries += [str(meter) for meter in self.meters]
	print('\t'.join(entries))

	def _get_batch_fmtstr(self, num_batches):
	num_digits = len(str(num_batches // 1))
	fmt = '{:' + str(num_digits) + 'd}'
	return '[' + fmt + '/' + fmt.format(num_batches) + ']'


	class AverageMeter(object):
	"""Computes and stores the average and current value"""

	def __init__(self, name, fmt=':f'):
	self.name = name
	self.fmt = fmt
	self.reset()

	def reset(self):
	self.val = 0
	self.avg = 0
	self.sum = 0
	self.count = 0

	def update(self, val, n=1):
	self.val = val
	self.sum += val * n
	self.count += n
	self.avg = self.sum / self.count

	def __str__(self):
	fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
	return fmtstr.format(**self.__dict__)


	def accuracy(output, target, topk=(1,)):
	"""Computes the accuracy over the k top predictions for the specified values of k"""
	with torch.no_grad():
	maxk = max(topk)
	batch_size = target.size(0)

	_, pred = output.topk(maxk, 1, True, True)
	pred = pred.t()
	correct = pred.eq(target.view(1, -1).expand_as(pred))

	res = []
	for k in topk:
	correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
	res.append(correct_k.mul_(100.0 / batch_size))
	return res


	def adjust_learning_rate(optimizer, epoch):
	"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
	lr = config.lr * (0.1 ** (epoch // 30))
	for param_group in optimizer.param_groups:
	param_group['lr'] = lr


	def save_checkpoint(state, is_best):
	filename = config.weights + config.model_name + os.sep + "_checkpoint.pth.tar"
	torch.save(state, filename)
	if is_best:
	message = config.best_models + config.model_name + os.sep + 'model_best.pth.tar'
	shutil.copyfile(filename, message)


	def validate(val_loader, model, criterion):
	batch_time = AverageMeter('Time', ':6.3f')
	losses = AverageMeter('Loss', ':.4e')
	top1 = AverageMeter('Acc@1', ':6.2f')
	top5 = AverageMeter('Acc@5', ':6.2f')
	progress = ProgressMeter(
	len(val_loader),
	[batch_time, losses, top1, top5],
	prefix='Test: ')
	# switch to evaluate mode
	model.eval()

	with torch.no_grad():
	end = time.time()
	for batch_id, (images, target) in enumerate(val_loader):
	images, target = images.to(device), target.to(device)
	# compute output
	output = model(images)
	loss = criterion(output, target)

	# measure accuracy and record loss
	acc1, acc5 = accuracy(output, target, topk=(1, 5))
	losses.update(loss.item(), images.size(0))
	top1.update(acc1[0], images.size(0))
	top5.update(acc5[0], images.size(0))

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

	if (batch_id + 1) % config.interval == 0:
	progress.display(batch_id + 1)

	print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
	.format(top1=top1, top5=top5))
	return top1.avg


	def train(train_loader, model, criterion, optimizer, epoch):
	batch_time = AverageMeter('Time', ':6.3f')
	data_time = AverageMeter('Data', ':6.3f')
	losses = AverageMeter('Loss', ':.4e')
	top1 = AverageMeter('Acc@1', ':6.2f')
	top5 = AverageMeter('Acc@5', ':6.2f')
	progress = ProgressMeter(
	len(train_loader),
	[batch_time, data_time, losses, top1, top5],
	prefix="Epoch: [{}]".format(epoch))
	# switch to train mode
	model.train()

	end = time.time()
	for batch_id, (images, target) in enumerate(train_loader):
	# measure data loading time
	data_time.update(time.time() - end)
	images, target = images.to(device), target.to(device)

	# compute output
	output = model(images)
	loss = criterion(output, target)

	# measure accuracy and record loss
	acc1, acc5 = accuracy(output, target, topk=(1, 5))
	losses.update(loss.item(), images.size(0))
	top1.update(acc1[0], images.size(0))
	top5.update(acc5[0], images.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 (batch_id + 1) % config.interval == 0:
	progress.display(batch_id + 1)


	def main():
	global best_acc

	if config.pretrained:
	print("=> using pre-trained model '{}'".format(config.model_name))
	model = models.__dict__[config.model_name](pretrained=True)
	else:
	print("=> creating model '{}'".format(config.model_name))
	model = models.__dict__[config.model_name]()
	model.to(device)

	normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
	std=[0.229, 0.224, 0.225])

	criterion = nn.CrossEntropyLoss().to(device)
	optimizer = torch.optim.SGD(model.parameters(), config.lr,
	momentum=config.momentum,
	weight_decay=config.weight_decay)

	cudnn.benchmark = True

	if config.resume:
	checkpoint = torch.load(config.best_models + "model_best.pth.tar")
	config.start_epoch = checkpoint['epoch']
	best_acc = checkpoint['best_acc']
	model.load_state_dict(checkpoint['state_dict'])
	optimizer.load_state_dict(checkpoint['optimizer'])

	train_loader = torch.utils.data.DataLoader(
	datasets.ImageFolder(config.train_dir, transforms.Compose([
	transforms.RandomResizedCrop(224),
	transforms.RandomHorizontalFlip(),
	transforms.ToTensor(),
	normalize,
	])),
	batch_size=config.batch_size, shuffle=True,
	num_workers=config.workers, pin_memory=True)

	val_loader = torch.utils.data.DataLoader(
	datasets.ImageFolder(config.val_dir, transforms.Compose([
	transforms.Resize(256),
	transforms.CenterCrop(224),
	transforms.ToTensor(),
	normalize,
	])),
	batch_size=config.batch_size, shuffle=False,
	num_workers=config.workers, pin_memory=True)

	if config.evaluate:
	validate(val_loader, model, criterion)
	return

	for epoch in range(config.start_epoch, config.epochs):
	adjust_learning_rate(optimizer, epoch)

	print('\nEpoch: [%d \| %d]' % (epoch + 1, config.epochs))

	train(train_loader, model, criterion, optimizer, epoch)
	test_acc = validate(val_loader, model, criterion)

	# save model
	is_best = test_acc > best_acc
	best_acc = max(test_acc, best_acc)
	save_checkpoint({
	'epoch': epoch + 1,
	"model_name": config.model_name,
	'state_dict': model.state_dict(),
	'acc': test_acc,
	'best_acc': best_acc,
	'optimizer': optimizer.state_dict(),
	}, is_best)


	if __name__ == '__main__':
	main()