main_lincls.py

#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import argparse
import builtins
import math
import os
import random
import shutil
import time
import warnings

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import architectures as models

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 ImageNet Training')
parser.add_argument('data', metavar='DIR',
                    help='path to dataset')
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet50',
                    choices=model_names,
                    help='model architecture: ' +
                         ' | '.join(model_names) +
                         ' (default: resnet50)')
parser.add_argument('-j', '--workers', default=32, type=int, metavar='N',
                    help='number of data loading workers (default: 32)')
parser.add_argument('--epochs', default=90, 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('-b', '--batch-size', default=4096, type=int,
                    metavar='N',
                    help='mini-batch size (default: 4096), this is the total '
                         'batch size of all GPUs on the current node when '
                         'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
                    metavar='LR', help='initial (base) learning rate', dest='lr')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                    help='momentum')
parser.add_argument('--wd', '--weight-decay', default=0., type=float,
                    metavar='W', help='weight decay (default: 0.)',
                    dest='weight_decay')
parser.add_argument('-p', '--print-freq', default=10, type=int,
                    metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
                    help='evaluate model on validation set')
parser.add_argument('--world-size', default=-1, type=int,
                    help='number of nodes for distributed training')
parser.add_argument('--rank', default=-1, type=int,
                    help='node rank for distributed training')
parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
                    help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='nccl', type=str,
                    help='distributed backend')
parser.add_argument('--seed', default=None, type=int,
                    help='seed for initializing training. ')
parser.add_argument('--gpu', default=None, type=int,
                    help='GPU id to use.')
parser.add_argument('--multiprocessing-distributed', action='store_true',
                    help='Use multi-processing distributed training to launch '
                         'N processes per node, which has N GPUs. This is the '
                         'fastest way to use PyTorch for either single node or '
                         'multi node data parallel training')

# additional configs:
parser.add_argument('--pretrained', default='', type=str,
                    help='path to simsiam pretrained checkpoint')
parser.add_argument('--lars', action='store_true',
                    help='Use LARS')

best_acc1 = 0


def main():
	args = parser.parse_args()
	
	args.output_dir = os.path.join(os.path.dirname(args.pretrained),
	                               os.path.basename(args.pretrained).split('.')[0] + '_lincls_lr:' + str(args.lr))
	print(args.output_dir)
	if not os.path.isdir(args.output_dir):
		os.makedirs(args.output_dir)
	
	if args.seed is not None:
		random.seed(args.seed)
		torch.manual_seed(args.seed)
		cudnn.deterministic = True
		warnings.warn('You have chosen to seed training. '
		              'This will turn on the CUDNN deterministic setting, '
		              'which can slow down your training considerably! '
		              'You may see unexpected behavior when restarting '
		              'from checkpoints.')
	
	if args.gpu is not None:
		warnings.warn('You have chosen a specific GPU. This will completely '
		              'disable data parallelism.')
	
	if args.dist_url == "env://" and args.world_size == -1:
		args.world_size = int(os.environ["WORLD_SIZE"])
	
	args.distributed = args.world_size > 1 or args.multiprocessing_distributed
	
	ngpus_per_node = torch.cuda.device_count()
	if args.multiprocessing_distributed:
		# Since we have ngpus_per_node processes per node, the total world_size
		# needs to be adjusted accordingly
		args.world_size = ngpus_per_node * args.world_size
		# Use torch.multiprocessing.spawn to launch distributed processes: the
		# main_worker process function
		mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
	else:
		# Simply call main_worker function
		main_worker(args.gpu, ngpus_per_node, args)


def main_worker(gpu, ngpus_per_node, args):
	global best_acc1
	args.gpu = gpu
	
	# suppress printing if not master
	if args.multiprocessing_distributed and args.gpu != 0:
		def print_pass(*args):
			pass
		builtins.print = print_pass
	
	if args.gpu is not None:
		print("Use GPU: {} for training".format(args.gpu))
	
	if args.distributed:
		if args.dist_url == "env://" and args.rank == -1:
			args.rank = int(os.environ["RANK"])
		if args.multiprocessing_distributed:
			# For multiprocessing distributed training, rank needs to be the
			# global rank among all the processes
			args.rank = args.rank * ngpus_per_node + gpu
		dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
		                        world_size=args.world_size, rank=args.rank)
		torch.distributed.barrier()
	# create model
	print("=> creating model '{}'".format(args.arch))
	model = models.__dict__[args.arch]()
	
	# freeze all layers but the last fc
	for name, param in model.named_parameters():
		if name not in ['fc.weight', 'fc.bias']:
			param.requires_grad = False
	# init the fc layer
	model.fc.weight.data.normal_(mean=0.0, std=0.01)
	model.fc.bias.data.zero_()
	
	# load from pre-trained, before DistributedDataParallel constructor
	if args.pretrained:
		if os.path.isfile(args.pretrained):
			print("=> loading checkpoint '{}'".format(args.pretrained))
			checkpoint = torch.load(args.pretrained, map_location="cpu")
			
			# rename moco pre-trained keys
			state_dict = checkpoint['state_dict']
			for k in list(state_dict.keys()):
				# retain only encoder up to before the embedding layer
				if k.startswith('module.encoder') and not k.startswith('module.encoder.fc'):
					# remove prefix
					state_dict[k[len("module.encoder."):]] = state_dict[k]
				# delete renamed or unused k
				del state_dict[k]
			
			args.start_epoch = 0
			msg = model.load_state_dict(state_dict, strict=False)
			assert set(msg.missing_keys) == {"fc.weight", "fc.bias"}
			
			print("=> loaded pre-trained model '{}'".format(args.pretrained))
		else:
			print("=> no checkpoint found at '{}'".format(args.pretrained))
	
	# infer learning rate before changing batch size
	init_lr = args.lr * args.batch_size / 256
	
	if args.distributed:
		# For multiprocessing distributed, DistributedDataParallel constructor
		# should always set the single device scope, otherwise,
		# DistributedDataParallel will use all available devices.
		if args.gpu is not None:
			torch.cuda.set_device(args.gpu)
			model.cuda(args.gpu)
			# When using a single GPU per process and per
			# DistributedDataParallel, we need to divide the batch size
			# ourselves based on the total number of GPUs we have
			args.batch_size = int(args.batch_size / ngpus_per_node)
			args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)
			model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
		else:
			model.cuda()
			# DistributedDataParallel will divide and allocate batch_size to all
			# available GPUs if device_ids are not set
			model = torch.nn.parallel.DistributedDataParallel(model)
	elif args.gpu is not None:
		torch.cuda.set_device(args.gpu)
		model = model.cuda(args.gpu)
	else:
		# DataParallel will divide and allocate batch_size to all available GPUs
		if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
			model.features = torch.nn.DataParallel(model.features)
			model.cuda()
		else:
			model = torch.nn.DataParallel(model).cuda()
	
	# define loss function (criterion) and optimizer
	criterion = nn.CrossEntropyLoss().cuda(args.gpu)
	
	# optimize only the linear classifier
	parameters = list(filter(lambda p: p.requires_grad, model.parameters()))
	assert len(parameters) == 2  # fc.weight, fc.bias
	
	optimizer = torch.optim.SGD(parameters, init_lr,
	                            momentum=args.momentum,
	                            weight_decay=args.weight_decay)
	if args.lars:
		print("=> use LARS optimizer.")
		from LARC import LARC
		optimizer = LARC(optimizer=optimizer, trust_coefficient=.001, clip=False)
	
	# optionally resume from a checkpoint
	if args.resume:
		if os.path.isfile(args.resume):
			print("=> loading checkpoint '{}'".format(args.resume))
			if args.gpu is None:
				checkpoint = torch.load(args.resume)
			else:
				# Map model to be loaded to specified single gpu.
				loc = 'cuda:{}'.format(args.gpu)
				checkpoint = torch.load(args.resume, map_location=loc)
			args.start_epoch = checkpoint['epoch']
			best_acc1 = checkpoint['best_acc1']
			if args.gpu is not None:
				# best_acc1 may be from a checkpoint from a different GPU
				best_acc1 = best_acc1.to(args.gpu)
			model.load_state_dict(checkpoint['state_dict'])
			optimizer.load_state_dict(checkpoint['optimizer'])
			print("=> loaded checkpoint '{}' (epoch {})"
			      .format(args.resume, checkpoint['epoch']))
		else:
			print("=> no checkpoint found at '{}'".format(args.resume))
	
	cudnn.benchmark = True
	
	# Data loading code
	traindir = os.path.join(args.data, 'train')
	valdir = os.path.join(args.data, 'val')
	normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
	                                 std=[0.229, 0.224, 0.225])
	
	train_dataset = datasets.ImageFolder(
		traindir,
		transforms.Compose([
			transforms.RandomResizedCrop(224),
			transforms.RandomHorizontalFlip(),
			transforms.ToTensor(),
			normalize,
		]))
	
	if args.distributed:
		train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
	else:
		train_sampler = None
	
	train_loader = torch.utils.data.DataLoader(
		train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
		num_workers=args.workers, pin_memory=True, sampler=train_sampler)
	
	val_loader = torch.utils.data.DataLoader(
		datasets.ImageFolder(valdir, transforms.Compose([
			transforms.Resize(256),
			transforms.CenterCrop(224),
			transforms.ToTensor(),
			normalize,
		])),
		batch_size=256, shuffle=False,
		num_workers=args.workers, pin_memory=True)
	
	if args.evaluate:
		validate(val_loader, model, criterion, args)
		return
	
	for epoch in range(args.start_epoch, args.epochs):
		if args.distributed:
			train_sampler.set_epoch(epoch)
		adjust_learning_rate(optimizer, init_lr, epoch, args)
		
		# train for one epoch
		train(train_loader, model, criterion, optimizer, epoch, args)
		
		# evaluate on validation set
		acc1 = validate(val_loader, model, criterion, args)
		
		# remember best acc@1 and save checkpoint
		is_best = acc1 > best_acc1
		best_acc1 = max(acc1, best_acc1)
		
		if not args.multiprocessing_distributed or (args.multiprocessing_distributed
		                                            and args.rank % ngpus_per_node == 0):
			save_checkpoint({
				'epoch': epoch + 1,
				'arch': args.arch,
				'state_dict': model.state_dict(),
				'best_acc1': best_acc1,
				'optimizer': optimizer.state_dict(),
			}, is_best, args)
			if epoch == args.start_epoch:
				sanity_check(model.state_dict(), args.pretrained)


def train(train_loader, model, criterion, optimizer, epoch, args):
	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 eval mode:
	Under the protocol of linear classification on frozen features/models,
	it is not legitimate to change any part of the pre-trained model.
	BatchNorm in train mode may revise running mean/std (even if it receives
	no gradient), which are part of the model parameters too.
	"""
	model.eval()
	
	end = time.time()
	for i, (images, target) in enumerate(train_loader):
		# measure data loading time
		data_time.update(time.time() - end)
		
		if args.gpu is not None:
			images = images.cuda(args.gpu, non_blocking=True)
		target = target.cuda(args.gpu, non_blocking=True)
		
		# 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 i % args.print_freq == 0:
			progress.display(i)


def validate(val_loader, model, criterion, args):
	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 i, (images, target) in enumerate(val_loader):
			if args.gpu is not None:
				images = images.cuda(args.gpu, non_blocking=True)
			target = target.cuda(args.gpu, non_blocking=True)
			
			# 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 i % args.print_freq == 0:
				progress.display(i)
		
		# TODO: this should also be done with the ProgressMeter
		print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
		      .format(top1=top1, top5=top5))
	
	return top1.avg


def save_checkpoint(state, is_best, args, filename='checkpoint.pth.tar'):
	filename = os.path.join(args.output_dir, filename)
	torch.save(state, filename)
	if is_best:
		shutil.copyfile(filename, os.path.join(args.output_dir, 'model_best.pth.tar'))


def sanity_check(state_dict, pretrained_weights):
	"""
	Linear classifier should not change any weights other than the linear layer.
	This sanity check asserts nothing wrong happens (e.g., BN stats updated).
	"""
	print("=> loading '{}' for sanity check".format(pretrained_weights))
	checkpoint = torch.load(pretrained_weights, map_location="cpu")
	state_dict_pre = checkpoint['state_dict']
	
	for k in list(state_dict.keys()):
		# only ignore fc layer
		if 'fc.weight' in k or 'fc.bias' in k:
			continue
		
		# name in pretrained model
		k_pre = 'module.encoder.' + k[len('module.'):] \
			if k.startswith('module.') else 'module.encoder.' + k
		
		assert ((state_dict[k].cpu() == state_dict_pre[k_pre]).all()), \
			'{} is changed in linear classifier training.'.format(k)
	
	print("=> sanity check passed.")


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__)


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) + ']'


def adjust_learning_rate(optimizer, init_lr, epoch, args):
	"""Decay the learning rate based on schedule"""
	cur_lr = init_lr * 0.5 * (1. + math.cos(math.pi * epoch / args.epochs))
	for param_group in optimizer.param_groups:
		param_group['lr'] = cur_lr


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].reshape(-1).float().sum(0, keepdim=True)
			res.append(correct_k.mul_(100.0 / batch_size))
		return res


if __name__ == '__main__':
	main()