train.py

import argparse
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
from torch.optim.lr_scheduler import CosineAnnealingLR
from utils import WarmupCosineAnnealingLR
from utils import AverageMeter, accuracy, ProgressMeter, get_default_ImageNet_val_loader, get_default_ImageNet_train_sampler_loader, log_msg

IMAGENET_TRAINSET_SIZE = 1281167

parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('--data', metavar='DIR', type=str, help='path to dataset')

parser.add_argument('-a', '--arch', metavar='ARCH', default='ResNet-18')
parser.add_argument('-t', '--blocktype', metavar='BLK', default='base')

parser.add_argument('-j', '--workers', default=64, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=120, 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=256, 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('--val-batch-size', default=100, type=int, metavar='V',
                    help='validation batch size')
parser.add_argument('--lr', '--learning-rate', default=0.1, 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('-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=0, type=int,
                    help='node rank for distributed training')
parser.add_argument('--dist-url', default='20333', 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')
parser.add_argument('--custwd', dest='custwd', action='store_true',
                    help='Use custom weight decay. It improves the accuracy and makes quantization easier.')
parser.add_argument('--tag', default='', type=str,
                    help='the tag for identifying the log and model files. Just a string.')

best_acc1 = 0

def sgd_optimizer(model, lr, momentum, weight_decay, use_custwd):
    params = []
    for key, value in model.named_parameters():
        if not value.requires_grad:
            continue
        apply_weight_decay = weight_decay
        apply_lr = lr

        if (use_custwd and ('blk' in key)) or (use_custwd and ('rbr_dense' in key or 'rbr_1x1' in key)) or 'bias' in key or 'bn' in key or 'prior' in key:
            apply_weight_decay = 0
            print('set weight decay=0 for {}'.format(key))
        if 'bias' in key:
            apply_lr = 2 * lr       #   Just a Caffe-style common practice. Made no difference.
        params += [{'params': [value], 'lr': apply_lr, 'weight_decay': apply_weight_decay}]
    optimizer = torch.optim.SGD(params, lr, momentum=momentum)
    return optimizer

def adam_optimizer(model):
    params = []
    for key, value in model.named_parameters():
        if not value.requires_grad or not 'prior' in key:
            continue
        params += [{'params': [value], 'lr': 1e-4, 'betas': (0.5, 0.999)}]
        print('adam opt for {}'.format(key))
    optimizer = torch.optim.Adam(params, 1e-4)
    return optimizer

def main():
    args = parser.parse_args()
    
    args.multiprocessing_distributed = True
    args.dist_backend = 'nccl'
    #args.data = '/gruntdata/humu.hm/ILSVRC2015/Data/CLS-LOC/'
    args.data='/disk1/humu.hm/ImageNet/ILSVRC2015/Data/CLS-LOC/' 
    args.dist_url = 'tcp://127.0.0.1:' + args.dist_url

    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
    log_file = 'results/train_{}_{}_{}_exp.txt'.format(args.arch, args.blocktype, args.tag)

    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)

    from convnet_utils import switch_deploy_flag, switch_conv_bn_impl, build_model
    switch_deploy_flag(False)
    switch_conv_bn_impl(args.blocktype)
    model = build_model(args.arch)

    is_main = not args.multiprocessing_distributed or (
                args.multiprocessing_distributed and args.rank % ngpus_per_node == 0)

    if is_main:
        for n, p in model.named_parameters():
            print(n, p.size())
        for n, p in model.named_buffers():
            print(n, p.size())
        log_msg('epochs {}, lr {}, weight_decay {}'.format(args.epochs, args.lr, args.weight_decay), log_file)

    if not torch.cuda.is_available():
        print('using CPU, this will be slow')
    elif 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
        model = torch.nn.DataParallel(model).cuda()


    # define loss function (criterion) and optimizer
    criterion = nn.CrossEntropyLoss().cuda(args.gpu)

    optimizer = sgd_optimizer(model, args.lr, args.momentum, args.weight_decay, args.custwd)
    #arch_optimizer = adam_optimizer(model)

    #lr_scheduler = CosineAnnealingLR(optimizer=optimizer, T_max=args.epochs * IMAGENET_TRAINSET_SIZE // args.batch_size // ngpus_per_node)
    lr_scheduler = WarmupCosineAnnealingLR(optimizer=optimizer, T_cosine_max=args.epochs * IMAGENET_TRAINSET_SIZE // args.batch_size // ngpus_per_node, warmup=args.epochs/24)

    # 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'])
            lr_scheduler.load_state_dict(checkpoint['scheduler'])
            print("=> loaded checkpoint '{}' (epoch {})"
                  .format(args.resume, checkpoint['epoch']))
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))

    cudnn.benchmark = True

    train_sampler, train_loader = get_default_ImageNet_train_sampler_loader(args)
    val_loader = get_default_ImageNet_val_loader(args)

    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, epoch, args)

        # train for one epoch
        train(train_loader, model, criterion, optimizer, epoch, args, lr_scheduler, is_main=is_main)

        if is_main:
            # evaluate on validation set
            acc1 = validate(val_loader, model, criterion, args)
            msg = '{}, epoch {}, acc {}'.format(args.arch, epoch, acc1)
            log_msg(msg, log_file)

            # remember best acc@1 and save checkpoint
            is_best = acc1 > best_acc1
            best_acc1 = max(acc1, best_acc1)

            save_checkpoint({
                'epoch': epoch + 1,
                'arch': args.arch,
                'state_dict': model.state_dict(),
                'best_acc1': best_acc1,
                'optimizer' : optimizer.state_dict(),
                'scheduler': lr_scheduler.state_dict(),
            }, is_best, filename = '/disk1/humu.hm/ckpt/{}_{}_{}.pth.tar'.format(args.arch, args.blocktype, args.tag),
                best_filename='/disk1/humu.hm/ckpt/{}_{}_{}_best.pth.tar'.format(args.arch, args.blocktype, args.tag))


def train(train_loader, model, criterion, optimizer, epoch, args, lr_scheduler, is_main):
    batch_time = AverageMeter('Time', ':6.3f', warm=True)
    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()

    torch.cuda.synchronize()
    end = time.time()
    for i, (images, target) in enumerate(train_loader):
        # measure data loading time
        torch.cuda.synchronize()
        data_time.update(time.time() - end)

        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)

        if args.custwd:
            for module in model.modules():
                if hasattr(module, 'get_custom_L2'):
                    loss += args.weight_decay * 0.5 * module.get_custom_L2()
                #if hasattr(module, 'weight_gen'):
                    #loss += args.weight_decay * 0.5 * ((module.weight_gen()**2).sum())
                #if hasattr(module, 'weight'):
                    #loss += args.weight_decay * 0.5 * ((module.weight**2).sum())

        # 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
        torch.cuda.synchronize()
        batch_time.update(time.time() - end)
        end = time.time()

        lr_scheduler.step()

        if is_main and i % args.print_freq == 0:
            progress.display(i)
        if is_main and i % 1000 == 0:
            print('cur lr: ', lr_scheduler.get_lr()[0])



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)
            if torch.cuda.is_available():
                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)

        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, filename, best_filename):
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, best_filename)


if __name__ == '__main__':
    main()