main_IN100.py

"""Implementation of ResNet train and eval on ImageNet-100"""
"""The implementation code is partially based on SupContrast GitHub repo"""

from __future__ import print_function

import os
import sys
import argparse
import time
import math

import torch
import torch.backends.cudnn as cudnn
from torchvision import transforms, datasets

from util import AverageMeter
from util import adjust_learning_rate, warmup_learning_rate, accuracy
from util import set_optimizer, save_model
from networks.resnet import ResNet_Model

try:
    import apex
    from apex import amp, optimizers
except ImportError:
    pass


def parse_option():
    parser = argparse.ArgumentParser('argument for training ResNets on ImageNet-100')

    parser.add_argument('--data_folder', type=str,
     default='', help='dataset')
    parser.add_argument('--print_freq', type=int, default=10,
                        help='print frequency')
    parser.add_argument('--save_freq', type=int, default=50,
                        help='save frequency')
    parser.add_argument('--batch_size', type=int, default=256,
                        help='batch_size')
    parser.add_argument('--num_workers', type=int, default=4,
                        help='num of workers to use')
    parser.add_argument('--epochs', type=int, default=200,
                        help='number of training epochs')

    # optimization
    parser.add_argument('--learning_rate', type=float, default=0.2,
                        help='learning rate')
    parser.add_argument('--lr_decay_epochs', type=str, default='120,160',
                        help='where to decay lr, can be a list')
    parser.add_argument('--lr_decay_rate', type=float, default=0.1,
                        help='decay rate for learning rate')
    parser.add_argument('--weight_decay', type=float, default=1e-4,
                        help='weight decay')
    parser.add_argument('--momentum', type=float, default=0.9,
                        help='momentum')

    # model dataset
    parser.add_argument('--model', type=str, default='resnet50')
    parser.add_argument('--dataset', type=str, default='imagenet100',
                        choices=['imagenet100', 'imagenet'], help='dataset')

    # other setting
    parser.add_argument('--cosine', action='store_true',
                        help='using cosine annealing')
    parser.add_argument('--warm', action='store_true',
                        help='warm-up for large batch training')

    opt = parser.parse_args()

    # set the path according to the environment
    opt.traindir = os.path.join(opt.data_folder, 'train')
    opt.valdir =  os.path.join(opt.data_folder, 'val')


    opt.model_path = './save/{}_models'.format(opt.dataset)

    iterations = opt.lr_decay_epochs.split(',')
    opt.lr_decay_epochs = list([])
    for it in iterations:
        opt.lr_decay_epochs.append(int(it))

    opt.model_name = 'CE_{}_{}_lr_{}_decay_{}_bsz_{}'.\
        format(opt.dataset, opt.model, opt.learning_rate, opt.weight_decay,
               opt.batch_size)

    if opt.cosine:
        opt.model_name = '{}_cosine'.format(opt.model_name)

    # warm-up for large-batch training,
    if opt.batch_size > 256:
        opt.warm = True
    if opt.warm:
        opt.model_name = '{}_warm'.format(opt.model_name)
        opt.warmup_from = 0.01
        opt.warm_epochs = 10
        if opt.cosine:
            eta_min = opt.learning_rate * (opt.lr_decay_rate ** 3)
            opt.warmup_to = eta_min + (opt.learning_rate - eta_min) * (
                    1 + math.cos(math.pi * opt.warm_epochs / opt.epochs)) / 2
        else:
            opt.warmup_to = opt.learning_rate

    opt.save_folder = os.path.join(opt.model_path, opt.model_name)
    if not os.path.isdir(opt.save_folder):
        os.makedirs(opt.save_folder)

    opt.n_cls = 100


    return opt


def set_loader(opt):
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])
    train_dataset = datasets.ImageFolder(
        opt.traindir,
        transform=transforms.Compose([
            transforms.RandomResizedCrop(224),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            normalize,
        ]))
    val_dataset = datasets.ImageFolder(
        opt.valdir,
        transform=transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            normalize,
        ]))
    
    train_loader = torch.utils.data.DataLoader(
    train_dataset, batch_size=opt.batch_size, shuffle=True, num_workers=opt.num_workers, pin_memory=True)
    val_loader = torch.utils.data.DataLoader(
    val_dataset, batch_size=opt.batch_size, shuffle=False, num_workers=opt.num_workers, pin_memory=True)

    return train_loader, val_loader


def set_model(opt):
    model = ResNet_Model(name=opt.model, num_classes=opt.n_cls)
    criterion = torch.nn.CrossEntropyLoss()

    if torch.cuda.is_available():
        if torch.cuda.device_count() > 1:
            model = torch.nn.DataParallel(model)
        model = model.cuda()
        criterion = criterion.cuda()
        cudnn.benchmark = True

    return model, criterion


def train(train_loader, model, criterion, optimizer, epoch, opt):
    """one epoch training"""
    model.train()

    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()

    end = time.time()
    for idx, (images, labels) in enumerate(train_loader):
        data_time.update(time.time() - end)

        images = images.cuda(non_blocking=True)
        labels = labels.cuda(non_blocking=True)
        bsz = labels.shape[0]

        # warm-up learning rate
        warmup_learning_rate(opt, epoch, idx, len(train_loader), optimizer)

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

        # update metric
        losses.update(loss.item(), bsz)
        acc1, acc5 = accuracy(output, labels, topk=(1, 5))
        top1.update(acc1[0], bsz)

        # SGD
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

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

        # print info
        if (idx + 1) % opt.print_freq == 0:
            print('Train: [{0}][{1}/{2}]\t'
                  'BT {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'DT {data_time.val:.3f} ({data_time.avg:.3f})\t'
                  'loss {loss.val:.3f} ({loss.avg:.3f})\t'
                  'Acc@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
                   epoch, idx + 1, len(train_loader), batch_time=batch_time,
                   data_time=data_time, loss=losses, top1=top1))
            sys.stdout.flush()

    return losses.avg, top1.avg


def validate(val_loader, model, criterion, opt):
    """validation"""
    model.eval()

    batch_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()

    with torch.no_grad():
        end = time.time()
        for idx, (images, labels) in enumerate(val_loader):
            images = images.float().cuda()
            labels = labels.cuda()
            bsz = labels.shape[0]

            # forward
            output = model(images)
            loss = criterion(output, labels)

            # update metric
            losses.update(loss.item(), bsz)
            acc1, acc5 = accuracy(output, labels, topk=(1, 5))
            top1.update(acc1[0], bsz)

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

            if idx % opt.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})'.format(
                       idx, len(val_loader), batch_time=batch_time,
                       loss=losses, top1=top1))

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


def main():
    best_acc = 0
    opt = parse_option()

    # build data loader
    train_loader, val_loader = set_loader(opt)
    # build model and criterion
    model, criterion = set_model(opt)
    # build optimizer
    optimizer = set_optimizer(opt, model)

    # training routine
    for epoch in range(1, opt.epochs + 1):
        adjust_learning_rate(opt, optimizer, epoch)

        # train for one epoch
        time1 = time.time()
        loss, train_acc = train(train_loader, model, criterion, optimizer, epoch, opt)
        time2 = time.time()
        print('epoch {}, total time {:.2f}'.format(epoch, time2 - time1))

        # evaluation
        loss, val_acc = validate(val_loader, model, criterion, opt)

        if val_acc > best_acc:
            best_acc = val_acc

        if epoch % opt.save_freq == 0:
            save_file = os.path.join(
                opt.save_folder, 'ckpt_epoch_{epoch}.pth'.format(epoch=epoch))
            save_model(model, optimizer, opt, epoch, save_file)

    # save the last model
    save_file = os.path.join(
        opt.save_folder, 'last.pth')
    save_model(model, optimizer, opt, opt.epochs, save_file)

    print('best accuracy: {:.2f}'.format(best_acc))


if __name__ == '__main__':
    main()