train_imagenet_teacher.py

"""
image training framework.
Train only the single network without any KD which is usually used as teacher.
This use multi-GPU training

"""
from __future__ import print_function

import os
import argparse
import time

import tensorboard_logger as tb_logger
import torch
import torch.optim as optim
import torch.nn as nn
import torch.backends.cudnn as cudnn

from helper.util import adjust_learning_rate
from helper.loops import train_vanilla as train, validate


import shutil

from models.imagenet import model_dict_imagenet_teacher
from dataset.imagenet import get_imagenet_dataloaders
from models.imagenet import model_channels_imagenet

cudnn.benchmark = True

def parse_option():

    parser = argparse.ArgumentParser('argument for training')

    parser.add_argument('--print_freq', type=int, default=500, help='print frequency')
    parser.add_argument('--tb_freq', type=int, default=500, help='tb 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=8, help='num of workers to use')
    parser.add_argument('--epochs', type=int, default=100, help='number of training epochs')

    # optimization
    parser.add_argument('--learning_rate', type=float, default=0.1, help='learning rate')
    parser.add_argument('--lr_decay_epochs', type=str, default='30, 60, 90', 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('--lr_decay', type=str, default='step', choices=['step', 'cos'], help='learning decay')
    parser.add_argument('--weight_decay', type=float, default=1e-4, help='weight decay')
    parser.add_argument('--momentum', type=float, default=0.9, help='momentum')

    parser.add_argument('-c', '--ceta', type=float, default=2.5, help='weight balance for other losses')
    # parser.add_argument('-s', '--s_norm', type=int, default=1, help='NORM ratio')
    # dataset
    parser.add_argument('--model', type=str, default='resnet50',
                        choices=['resnet50', 'resnet34', 'resnet18', 'resnet50_2', 'MobileNet'])
    parser.add_argument('--dataset', type=str, default='imagenet', choices=['imagenet'], help='dataset')

    parser.add_argument('-t', '--trial', type=int, default=0, help='the experiment id')

    opt = parser.parse_args()

    # set the path according to the environment

    opt.model_path = './save/models'
    opt.tb_path = './save/tensorboard'

    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 = '{}_{}_lr_{}_decay_{}_trial_{}'.format(opt.model, opt.dataset, opt.learning_rate,
                                                            opt.weight_decay, opt.trial)

    opt.tb_folder = os.path.join(opt.tb_path, opt.model_name)
    if not os.path.isdir(opt.tb_folder):
        os.makedirs(opt.tb_folder)

    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)

    return opt


def main():

    opt = parse_option()
    # dataloader
    if opt.dataset == 'imagenet':
        train_loader, val_loader = get_imagenet_dataloaders(batch_size=opt.batch_size, num_workers=opt.num_workers)
        n_cls = 1000
    else:
        raise NotImplementedError(opt.dataset)

    # model
    model = model_dict_imagenet_teacher[opt.model](num_classes=n_cls)
    model = nn.DataParallel(model)
    # optimizer
    optimizer = optim.SGD(model.parameters(),
                          lr=opt.learning_rate,
                          momentum=opt.momentum,
                          weight_decay=opt.weight_decay)

    criterion = nn.CrossEntropyLoss()

    if torch.cuda.is_available():
        model = model.cuda()
        criterion = criterion.cuda()
        cudnn.benchmark = True

    # tensorboard
    logger = tb_logger.Logger(logdir=opt.tb_folder, flush_secs=2)

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

        adjust_learning_rate(epoch, opt, optimizer)
        print("==> training...")

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

        logger.log_value('train_acc', train_acc, epoch)
        logger.log_value('train_loss', train_loss, epoch)

        test_acc, test_acc_top5, test_loss = validate(val_loader, model, criterion, opt)

        logger.log_value('test_acc', test_acc, epoch)
        logger.log_value('test_acc_top5', test_acc_top5, epoch)
        logger.log_value('test_loss', test_loss, epoch)

        # save the best model
        if test_acc > best_acc:
            best_acc = test_acc
            state = {
                'epoch': epoch,
                'model': model.state_dict(),
                'best_acc': best_acc,
                'optimizer': optimizer.state_dict(),
            }
            save_file = os.path.join(opt.save_folder, '{}_best.pth'.format(opt.model))
            print('saving the best model!')
            torch.save(state, save_file)

        # regular saving
        if epoch % opt.save_freq == 0:
            print('==> Saving...')
            state = {
                'epoch': epoch,
                'model': model.state_dict(),
                'accuracy': test_acc,
                'optimizer': optimizer.state_dict(),
            }
            save_file = os.path.join(opt.save_folder, 'ckpt_epoch_{epoch}.pth'.format(epoch=epoch))
            torch.save(state, save_file)

    print('best accuracy:', best_acc)
    with open('save/results.txt', 'a') as f:
        f.write('{}'.format(time.time())+'  ')
        f.write(opt.model_name+'  ')
        f.write('best accuracy:{}\n'.format(best_acc))

    # save model
    state = {
        'opt': opt,
        'model': model.state_dict(),
        'optimizer': optimizer.state_dict(),
    }
    save_file = os.path.join(opt.save_folder, '{}_last.pth'.format(opt.model))
    torch.save(state, save_file)

if __name__ == '__main__':
    main()