train_search_imagenet.py

import os
import sys
import time
import glob
import numpy as np
import torch
import utils
import logging
import argparse
import torch.nn as nn
import torch.utils
import torch.nn.functional as F
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torch.backends.cudnn as cudnn
import copy

from torch.autograd import Variable
from model_search_imagenet import Network
from architect import Architect


parser = argparse.ArgumentParser("imagenet")
parser.add_argument('--workers', type=int, default=4, help='number of workers to load dataset')
parser.add_argument('--data', type=str, default='/tmp/cache/', help='location of the data corpus')
parser.add_argument('--batch_size', type=int, default=1024, help='batch size')
parser.add_argument('--learning_rate', type=float, default=0.5, help='init learning rate')
parser.add_argument('--learning_rate_min', type=float, default=0.0, help='min learning rate')
parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
parser.add_argument('--weight_decay', type=float, default=3e-4, help='weight decay')
parser.add_argument('--report_freq', type=float, default=50, help='report frequency')
parser.add_argument('--epochs', type=int, default=50, help='num of training epochs')
parser.add_argument('--init_channels', type=int, default=16, help='num of init channels')
parser.add_argument('--layers', type=int, default=8, help='total number of layers')
parser.add_argument('--model_path', type=str, default='saved_models', help='path to save the model')
parser.add_argument('--cutout', action='store_true', default=False, help='use cutout')
parser.add_argument('--cutout_length', type=int, default=16, help='cutout length')
parser.add_argument('--drop_path_prob', type=float, default=0.3, help='drop path probability')
parser.add_argument('--save', type=str, default='/tmp/checkpoints/', help='experiment name')
parser.add_argument('--seed', type=int, default=2, help='random seed')
parser.add_argument('--grad_clip', type=float, default=5, help='gradient clipping')
parser.add_argument('--unrolled', action='store_true', default=False, help='use one-step unrolled validation loss')
parser.add_argument('--arch_learning_rate', type=float, default=6e-3, help='learning rate for arch encoding')
parser.add_argument('--arch_weight_decay', type=float, default=1e-3, help='weight decay for arch encoding')
parser.add_argument('--begin', type=int, default=35, help='batch size')

parser.add_argument('--tmp_data_dir', type=str, default='/cache/', help='temp data dir')
parser.add_argument('--note', type=str, default='try', help='note for this run')

args = parser.parse_args()

args.save = '{}search-{}-{}'.format(args.save, args.note, time.strftime("%Y%m%d-%H%M%S"))
utils.create_exp_dir(args.save, scripts_to_save=glob.glob('*.py'))

log_format = '%(asctime)s %(message)s'
logging.basicConfig(stream=sys.stdout, level=logging.INFO,
    format=log_format, datefmt='%m/%d %I:%M:%S %p')
fh = logging.FileHandler(os.path.join(args.save, 'log.txt'))
fh.setFormatter(logging.Formatter(log_format))
logging.getLogger().addHandler(fh)

data_dir = os.path.join(args.tmp_data_dir, 'imagenet_search')
 #data preparation, we random sample 10% and 2.5% from training set(each class) as train and val, respectively.
#Note that the data sampling can not use torch.utils.data.sampler.SubsetRandomSampler as imagenet is too large   
CLASSES = 1000



def main():
    if not torch.cuda.is_available():
        logging.info('no gpu device available')
        sys.exit(1)

    np.random.seed(args.seed)
    #torch.cuda.set_device(args.gpu)
    cudnn.benchmark = True
    torch.manual_seed(args.seed)
    cudnn.enabled=True
    torch.cuda.manual_seed(args.seed)
    #logging.info('gpu device = %d' % args.gpu)
    logging.info("args = %s", args)
    #dataset_dir = '/cache/'
    #pre.split_dataset(dataset_dir)
    #sys.exit(1)
   # dataset prepare
    traindir = data_dir = os.path.join(data_dir, 'train')
    valdir = data_dir = os.path.join(data_dir,  'val')
        
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                           std=[0.229, 0.224, 0.225])
    criterion = nn.CrossEntropyLoss()
    criterion = criterion.cuda()
    #dataset split     
    train_data1 = dset.ImageFolder(traindir, transforms.Compose([
            transforms.RandomResizedCrop(224),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            normalize,
        ]))
    train_data2 = dset.ImageFolder(valdir, transforms.Compose([
            transforms.RandomResizedCrop(224),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            normalize,
        ]))
    valid_data = dset.ImageFolder(valdir, transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            normalize,
        ]))
    num_train = len(train_data1)
    num_val = len(train_data2)
    print('# images to train network: %d' % num_train)
    print('# images to validate network: %d' % num_val)
    
    model = Network(args.init_channels, CLASSES, args.layers, criterion)
    model = torch.nn.DataParallel(model)
    model = model.cuda()
    logging.info("param size = %fMB", utils.count_parameters_in_MB(model))

    optimizer = torch.optim.SGD(
        model.parameters(),
        args.learning_rate,
        momentum=args.momentum,
        weight_decay=args.weight_decay)
    optimizer_a = torch.optim.Adam(model.module.arch_parameters(),
               lr=args.arch_learning_rate, betas=(0.5, 0.999), 
               weight_decay=args.arch_weight_decay)
    
    test_queue = torch.utils.data.DataLoader(
                        valid_data, 
                        batch_size=args.batch_size, 
                        shuffle=False, 
                        pin_memory=True, 
                        num_workers=args.workers)

    train_queue = torch.utils.data.DataLoader(
        train_data1, batch_size=args.batch_size, shuffle=True,
        pin_memory=True, num_workers=args.workers)

    valid_queue = torch.utils.data.DataLoader(
        train_data2, batch_size=args.batch_size, shuffle=True,
        pin_memory=True, num_workers=args.workers)

    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
            optimizer, float(args.epochs), eta_min=args.learning_rate_min)

    #architect = Architect(model, args)
    lr=args.learning_rate
    for epoch in range(args.epochs):
        scheduler.step()
        current_lr = scheduler.get_lr()[0]
        logging.info('Epoch: %d lr: %e', epoch, current_lr)
        if epoch < 5 and args.batch_size > 256:
            for param_group in optimizer.param_groups:
                param_group['lr'] = lr * (epoch + 1) / 5.0
            logging.info('Warming-up Epoch: %d, LR: %e', epoch, lr * (epoch + 1) / 5.0)
            print(optimizer) 
        genotype = model.module.genotype()
        logging.info('genotype = %s', genotype)
        arch_param = model.module.arch_parameters()
        logging.info(F.softmax(arch_param[0], dim=-1))
        logging.info(F.softmax(arch_param[1], dim=-1))
        # training
        train_acc, train_obj = train(train_queue, valid_queue, model, optimizer, optimizer_a, criterion, lr,epoch)
        logging.info('Train_acc %f', train_acc)
        
        # validation
        if epoch>= 47:
            valid_acc, valid_obj = infer(valid_queue, model, criterion)
            #test_acc, test_obj = infer(test_queue, model, criterion)
            logging.info('Valid_acc %f', valid_acc)
            #logging.info('Test_acc %f', test_acc)

        #utils.save(model, os.path.join(args.save, 'weights.pt'))

def train(train_queue, valid_queue, model, optimizer, optimizer_a, criterion, lr,epoch):
    objs = utils.AvgrageMeter()
    top1 = utils.AvgrageMeter()
    top5 = utils.AvgrageMeter()

    for step, (input, target) in enumerate(train_queue):
        model.train()
        n = input.size(0)

        input = input.cuda(non_blocking=True)
        target = target.cuda(non_blocking=True)

        # get a random minibatch from the search queue with replacement
        try:
            input_search, target_search = next(valid_queue_iter)
        except:
            valid_queue_iter = iter(valid_queue)
            input_search, target_search = next(valid_queue_iter)
        input_search = input_search.cuda(non_blocking=True)
        target_search = target_search.cuda(non_blocking=True)
        
        if epoch >=args.begin:
            optimizer_a.zero_grad()
            logits = model(input_search)
            loss_a = criterion(logits, target_search)
            loss_a.sum().backward()
            nn.utils.clip_grad_norm_(model.module.arch_parameters(), args.grad_clip)
            optimizer_a.step()
        #architect.step(input, target, input_search, target_search, lr, optimizer, unrolled=args.unrolled)

        optimizer.zero_grad()
        logits = model(input)
        loss = criterion(logits, target)

        loss.backward()
        nn.utils.clip_grad_norm_(model.module.parameters(), args.grad_clip)
        optimizer.step()


        prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
        objs.update(loss.data.item(), n)
        top1.update(prec1.data.item(), n)
        top5.update(prec5.data.item(), n)

        if step % args.report_freq == 0:
            logging.info('TRAIN Step: %03d Objs: %e R1: %f R5: %f', step, objs.avg, top1.avg, top5.avg)

    return top1.avg, objs.avg



def infer(valid_queue, model, criterion):
    objs = utils.AvgrageMeter()
    top1 = utils.AvgrageMeter()
    top5 = utils.AvgrageMeter()
    model.eval()

    for step, (input, target) in enumerate(valid_queue):
        input = input.cuda()
        target = target.cuda(non_blocking=True)
        with torch.no_grad():
            logits = model(input)
            loss = criterion(logits, target)

        prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
        n = input.size(0)
        objs.update(loss.data.item(), n)
        top1.update(prec1.data.item(), n)
        top5.update(prec5.data.item(), n)

        if step % args.report_freq == 0:
            logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)

    return top1.avg, objs.avg


if __name__ == '__main__':
    main()