imagenet_official_retrain.py

import argparse
import os
import random
import shutil
import time
import warnings
import numpy as np
import glob
from tqdm import tqdm
from logger import Logger

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 imagenet_dataset as datasets
import torchvision.models 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 imagenet dataset')
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18',
                    choices=model_names,
                    help='model architecture: ' +
                        ' | '.join(model_names) +
                        ' (default: resnet18)')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=10, 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=32, type=int,
                    metavar='N',
                    help='mini-batch size (default: 256), 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=0.001, type=float,
                    metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                    help='momentum')
parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,
                    metavar='W', help='weight decay (default: 1e-4)',
                    dest='weight_decay')
parser.add_argument('--gamma', type=float, default=0.1, help='LR is multiplied by gamma on schedule.')
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('--pretrained', dest='pretrained', action='store_true',
                    help='use pre-trained model')
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='0', type=str,
                    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')
parser.add_argument('--config', default=None, type=str, metavar='PATH',
                    help='Path of the grouping configuration file')
parser.add_argument('-c', '--checkpoint', default='checkpoint', type=str, metavar='PATH',
                    help='path to save checkpoint (default: checkpoint)')
parser.add_argument('--schedule', type=int, nargs='+', default=[150, 225],
                        help='Decrease learning rate at these epochs.')
args = parser.parse_args() 

best_acc1 = 0
state = {k: v for k, v in args._get_kwargs()}
if args.gpu is not None:
    print("Use GPU: {} for training".format(args.gpu))
    os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu  

def main():
    global args
    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

    if args.gpu is not None:
        print("Use GPU: {} for training".format(args.gpu))
        os.environ['CUDA_VISIBLE_DEVICES'] = 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)
    # optionally resume from a checkpoint
    title = 'imagenet-1k-' + args.arch
    if args.resume:
        if os.path.isfile(args.resume):
            print("=> loading checkpoint '{}'".format(args.resume))
            model = torch.load(args.resume, map_location=torch.device("cuda"))
            # model = torch.nn.DataParallel(model) # TODO DataParallel
            print("checkpoint loading model in porgess..........")
            model = model.cuda()   ### Deleted: DataParelle(model)
            print('    Total params: %.2fM' % (sum(p.numel() for p in model.parameters())/1000000.0))
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))

    cudnn.benchmark = True


    # define loss function (criterion) and optimizer
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.SGD(model.parameters(), args.lr,
                                momentum=args.momentum,
                                weight_decay=args.weight_decay)


    # Load the grouping config
    group_id = filename_to_index(args.resume)
    group_config = np.load(open(args.config, "rb"))[group_id[0]]
    group_id = group_id[0]
    model_prefix = args.checkpoint + '/' + args.arch + '_' + str(group_id) + '_' + 'pruned_group_model'
    log_prefix = args.checkpoint + '/' + args.arch + '_' + str(group_id) + '_' + 'pruned_group_model'
    logger = Logger(log_prefix + '_log.txt', title=title)
    logger.set_names(['Learning Rate', 'Train Loss', 'Valid Loss', 'Train Acc.', 'Valid Acc.'])


    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])
    if args.evaluate:
        val_loader = torch.utils.data.DataLoader(
        datasets.ImageFolder(
            valdir,
            transforms.Compose([
                transforms.RandomResizedCrop(224),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                normalize,
            ]), group=group_config)) # Retrain true, performs dataset balancing
        validate(val_loader, model, criterion, args)
        return

    if not os.path.isdir(args.checkpoint):
        mkdir_p(args.checkpoint)
   
    if args.epochs == 0:
        torch.save(model, model_prefix + '.pth')
 
    for epoch in range(args.start_epoch, args.epochs):
        if args.distributed:
            train_sampler.set_epoch(epoch)
        adjust_learning_rate(optimizer, epoch, args)


        ## Fast and dirty way to resample negative images after each epoch.
        val_loader = torch.utils.data.DataLoader(
            datasets.ImageFolder(
            valdir,
            transforms.Compose([
                transforms.RandomResizedCrop(224),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                normalize,
            ]), group=group_config), \
            batch_size=args.batch_size, shuffle=False, \
            num_workers=args.workers, pin_memory=True) 


        train_dataset = datasets.ImageFolder(
            traindir,
            transforms.Compose([
                transforms.RandomResizedCrop(224),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                normalize,
            ]), group=group_config, retrain=True) # Retrain true, performs dataset balancing

 
        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)

        # train for one epoch
        train_loss, train_acc = train(train_loader, model, criterion, optimizer, epoch, args, val_loader)

        # Validate
        test_loss, 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 is_best:
            torch.save(model, model_prefix + '.pth')


        logger.append([state['lr'], train_loss, test_loss, train_acc, acc1])
        

    logger.close()
    logger.plot()

def train(train_loader, model, criterion, optimizer, epoch, args, val_loader=None):
    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 i, (input, target) in tqdm(enumerate(train_loader)):
        # measure data loading time
        data_time.update(time.time() - end)
        input = input.cuda(non_blocking=True)
        target = target.cuda(non_blocking=True)

        # compute output
        #print("model.src_device_obj: ", model.src_device_obj)
        output = model(input)
        loss = criterion(output, target)

        # measure accuracy and record loss
        acc1, acc5 = accuracy(output, target, topk=(1, 5))
        losses.update(loss.item(), input.size(0))
        top1.update(acc1[0], input.size(0))
        top5.update(acc5[0], input.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.print(i)
            
        del output
        del loss
    return (losses.avg, top1.avg)

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()
    # define loss function (criterion) and optimizer
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.SGD(model.parameters(), args.lr, \
                                momentum=args.momentum, \
                                weight_decay=args.weight_decay)

    for i, (input, target) in tqdm(enumerate(val_loader)):
        input = input.cuda(non_blocking=True)
        target = target.cuda(non_blocking=True)

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

        # measure accuracy and record loss
        acc1, acc5 = accuracy(output, target, topk=(1, 2))
        losses.update(loss.item(), input.size(0))
        top1.update(acc1[0], input.size(0))
        top5.update(acc5[0], input.size(0))

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

        if i % args.print_freq == 0:
            progress.print(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))
        del output
        del loss
    return (losses.avg, top1.avg)

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

def filename_to_index(filename):
    filename = [int(s) for s in filename.split('_') if s.isdigit()]
    return filename

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 print(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) + ']'

"""
# Original adjust learning rate, every 30 epochs, decay by 10
def adjust_learning_rate(optimizer, epoch, args):
    # Sets the learning rate to the initial LR decayed by 10 every 30 epochs
    lr = args.lr * (0.1 ** (epoch // 30)
    for param_group in optimizer.param_groups:
        param_group['lr'] = 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].view(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res

def adjust_learning_rate(optimizer, epoch, args):
    global state
    if epoch in args.schedule:
        state['lr'] *= args.gamma
        print("Learning rate decayed to {}".format(state['lr']))
        for param_group in optimizer.param_groups:
            param_group['lr'] = state['lr']

if __name__ == '__main__':
    main()