classifier.py

import argparse
import os
import random
import shutil
import time
import warnings
import sys

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.models as models

from utils.torchsummary import summary
from utils.commons import data_loaders, load_cls_model, AverageMeter

parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('--dataset', type=str, default='cifar100',
                    help='name of the dataset (default: cifar100)')
parser.add_argument('--data-folder', type=str, default='./data',
                    help='name of the data folder (default: ./data)')
parser.add_argument('--size', type=int, default=64,
                    help='size of image (default: 64)')
parser.add_argument('--arch', default='alexnet',
                    help='model architecture (default: resnet18)')
parser.add_argument('--pretrained', default=None,
                    help='path of pre-trained model')
parser.add_argument('-j', '--workers', default=4, type=int,
                    help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=90, type=int,
                    help='number of total epochs to run')
parser.add_argument('--step', default=30, type=int,
                    help='step for scheduler')
parser.add_argument('--start-epoch', default=0, type=int,
                    help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=256, type=int,
                    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=0.1, type=float
                    , help='initial learning rate', dest='lr')
parser.add_argument('--momentum', default=0.9, type=float,
                    help='momentum')
parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,
                    help='weight decay (default: 1e-4)',
                    dest='weight_decay')
parser.add_argument('-p', '--print-freq', default=10, type=int,
                    help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str,
                    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('--freeze', type=int, default=0,
                    help='freeze feature layers, default none')
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',
                    help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='nccl',
                    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('--inet', default=False, action='store_true', help='Bool type')

# Patchy
parser.add_argument('--encoder-arch', type=str, default='resnet',
                    help='encoder architecture (default: resnet)')
parser.add_argument('--decoder-arch', type=str, default='pyramid',
                    help='decoder architecture (default: pyramid)')
parser.add_argument('--ngf', type=int, default=64,
                    help='depth of first layer of encoder (default: 16)')
parser.add_argument('--ndf', type=int, default=64,
                    help='depth of first layer of decoder (default: 16)')
parser.add_argument('--num-parts', type=int, default=16,
                    help='number of parts (default: 16)')
parser.add_argument('--hidden-size', type=int, default=6,
                    help='size of the latent vectors (default: 6)')
parser.add_argument('--scale', type=int, default=8,
                    help='scale down feature maps (default: 8)')
parser.add_argument('--categorical', action='store_true', default=False,
                    help='take only 1 part per location (default: False)')
parser.add_argument('--independent', action='store_true', default=False,
                    help='independent decoders (default: False)')
parser.add_argument('--hard', action='store_true', default=False,
                    help='hard samples from bernoulli (default: False)')

parser.add_argument('--output-folder', type=str, default='./scratch',
                    help='name of the output folder (default: ./scratch)')

best_acc1 = 0


def main():
    args = parser.parse_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

    print("All arguments")
    print(args)
    print("PID: ", os.getpid())

    # Slurm
    if 'SLURM_JOB_NAME' in os.environ and 'SLURM_JOB_ID' in os.environ:
        # running with sbatch and not srun
        if os.environ['SLURM_JOB_NAME'] != 'bash':
            args.output_folder = os.path.join(args.output_folder,
                                              os.environ['SLURM_JOB_ID'])
            print("SLURM_JOB_ID: ", os.environ['SLURM_JOB_ID'])
        else:
            args.output_folder = os.path.join(args.output_folder, str(os.getpid()))
    else:
        args.output_folder = os.path.join(args.output_folder, str(os.getpid()))

    # Create logs and models folder if they don't exist
    if not os.path.exists(args.output_folder):
        print("Creating output directory: %s" % args.output_folder)
        os.makedirs(args.output_folder)

    log_dir = os.path.join(args.output_folder, 'logs')
    if not os.path.exists(log_dir):
        print("Creating log directory: %s" % log_dir)
        os.makedirs(log_dir)

    model_dir = os.path.join(args.output_folder, 'models')
    if not os.path.exists(model_dir):
        print("Creating model directory: %s" % model_dir)
        os.makedirs(model_dir)

    args.log_dir = log_dir
    args.model_dir = model_dir

    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)

    # Data loading code
    if args.distributed and args.gpu is not None:
        args.workers = int(args.workers / ngpus_per_node)
    train_loader, val_loader, (channels, height, width), classes, train_sampler = \
        data_loaders(args.dataset, data_folder=args.data_folder, inet=args.inet,
                     batch_size=args.batch_size, num_workers=args.workers,
                     distributed=args.distributed, classify=True, size=args.size)

    # create model
    model = load_cls_model(args.arch, num_classes=classes,
                           pretrained=args.pretrained, freeze=args.freeze,
                           input_size=(channels, height, width),
                           encoder_arch=args.encoder_arch,
                           decoder_arch=args.decoder_arch,
                           hidden_size=args.hidden_size,
                           num_parts=args.num_parts,
                           base_depth=args.ngf,
                           scale=args.scale,
                           independent=args.independent,
                           hard=args.hard,
                           categorical=args.categorical,
                           )

    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)
            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)

    optimizer = torch.optim.SGD(model.parameters(), args.lr,
                                momentum=args.momentum,
                                weight_decay=args.weight_decay)
    # optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)

    # optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            print("=> loading checkpoint '{}'".format(args.resume))
            checkpoint = torch.load(args.resume)
            args.start_epoch = checkpoint['epoch']
            best_acc1 = checkpoint['best_acc1']
            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

    # Training code
    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)

        # evaluate on validation set
        acc1 = validate(val_loader, model, criterion, args, epoch)

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

        print('End of epoch:{} Best Acc@1 {:.3f}'.format(epoch, best_acc1.item()))

        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, model_dir=args.model_dir)

    summary(model, (channels, height, width))


def train(train_loader, model, criterion, optimizer, epoch, args):
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    # switch to train mode
    model.train()

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

        if args.gpu is not None:
            input = input.cuda(args.gpu, non_blocking=True)
        target = target.cuda(args.gpu, non_blocking=True)

        # compute output
        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:
            print('Epoch: [{0}][{1}/{2}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                  'Acc@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                   epoch, i, len(train_loader), batch_time=batch_time,
                   data_time=data_time, loss=losses, top1=top1, top5=top5))


def validate(val_loader, model, criterion, args, epoch):
    batch_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    # switch to evaluate mode
    model.eval()

    with torch.no_grad():
        end = time.time()
        for i, (input, target) in enumerate(val_loader):
            if args.gpu is not None:
                input = input.cuda(args.gpu, non_blocking=True)
            target = target.cuda(args.gpu, non_blocking=True)

            # compute output
            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))

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

            if i % args.print_freq == 0:
                print('Test: [{0}/{1}]\t'
                      'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                      'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                      'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                      'Acc@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                       i, len(val_loader), batch_time=batch_time, loss=losses,
                       top1=top1, top5=top5))

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

    return top1.avg


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


def adjust_learning_rate(optimizer, epoch, args):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    """Sets the learning rate to the initial LR decayed by 10 every 10 epochs"""
    lr = args.lr * (0.1 ** (epoch // args.step))
    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


if __name__ == '__main__':
    main()