search_one_level.py

from __future__ import division
import os
import sys
import time
import glob
import logging
from tqdm import tqdm
import re
import torch
import torch.nn as nn
import torch.utils
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
import torchvision.datasets as dset
from torch.autograd import Variable

import torch.distributed as dist
import torch.multiprocessing as mp
import torch.utils.data.distributed

from datasets import prepare_train_data, prepare_test_data, prepare_train_data_for_search, prepare_test_data_for_search

import time

from tensorboardX import SummaryWriter

from config_search import config

import numpy as np
import matplotlib
# Force matplotlib to not use any Xwindows backend.
matplotlib.use('Agg')
from matplotlib import pyplot as plt
from PIL import Image

from architect import Architect
from model_search import FBNet as Network
from model_infer import FBNet_Infer

from lr import LambdaLR
from perturb import Random_alpha

import argparse

from thop import profile
# from thop.count_hooks import count_convNd


parser = argparse.ArgumentParser(description='DNA')
parser.add_argument('--dataset', type=str, default=None,
                    help='which dataset to use')
parser.add_argument('--search_space', type=str, default=None,
                    help='which dataset to use')
parser.add_argument('--pretrain_epoch', type=int, default=None,
                    help='pretrain epochs')
parser.add_argument('--load_epoch', type=int, default=None,
                    help='which epoch to load')
parser.add_argument('--pretrain', type=str, default=None,
                    help='path to save')
parser.add_argument('--act_num', type=int, default=None,
                    help='path to activate')
parser.add_argument('--dataset_path', type=str, default=None,
                    help='path to dataset')
parser.add_argument('--running_mode', type=str, default=None,
                    help='HW-NAS algorithm to run, select from FBNet, ProxylessNAS')
parser.add_argument('--hw_platform_path', type=str, default=None,
                    help='path to hardware platform data')
parser.add_argument('--efficiency_metric', type=str, default=None,
                    help='efficiency metric, select from latency, flops, energy')
parser.add_argument('-b', '--batch_size', type=int, default=None,
                    help='batch size')
parser.add_argument('--num_workers', type=int, default=None,
                    help='number of workers')
# parser.add_argument('--num_classes', type=int, default=None,
#                     help='numbe')
parser.add_argument('--flops_weight', type=float, default=None,
                    help='weight of FLOPs loss')
parser.add_argument('--lr', type=float, default=0.05,
                    help='weight of learning rate')
parser.add_argument('--gpu', type=str, default='0', 
                    help='gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU')
# parser.add_argument('--gpu', nargs='+', type=int, default=None,
#                     help='specify gpus')
# distributed parallel
parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument("--port", type=str, default="10001")
parser.add_argument('--distributed', type=bool, default=False, 
                    help='whether to use distributed training')
parser.add_argument("--ngpus_per_node", type=int, default=0)
parser.add_argument('--world_size', type=int, default=1,
                    help='number of nodes')
parser.add_argument('--rank', type=int, default=None,
                    help='node rank')
parser.add_argument('--dist_url', type=str, default=None,
                    help='url used to set up distributed training')
# parser.add_argument('--seed', type=int, default=123456,
#                     help='random seed')
parser.add_argument('--seed', type=int, default=2,
                    help='random seed')
args = parser.parse_args()


np.random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.benchmark = True

def main():
    if args.dataset is not None:
        config.dataset = args.dataset
    if args.search_space is not None:
        config.search_space = args.search_space
    if args.pretrain_epoch is not None:
        config.pretrain_epoch = args.pretrain_epoch
    if args.load_epoch is not None:
        config.load_epoch = args.load_epoch
    if args.dataset_path is not None:
        config.dataset_path = args.dataset_path
    if args.pretrain is not None:
        config.pretrain = args.pretrain
    if args.act_num is not None:
        config.act_num = args.act_num
    if args.lr is not None:
        config.lr = args.lr
    if args.running_mode is not None:
        print('Mannually change HW-NAS running mode')
        if args.running_mode == 'FBNet':
            config.mode = 'soft'
        elif args.running_mode == 'ProxylessNAS':
            config.mode = 'proxy_hard'
        else:
            print('HW-NAS algorithm {} is not supported'.format(args.running_mode))
            sys.exit(0)
    if args.hw_platform_path is not None:
        config.hw_platform_path = args.hw_platform_path
    if args.efficiency_metric is not None:
        config.efficiency_metric = args.efficiency_metric
    if args.batch_size is not None:
        config.batch_size = args.batch_size
    if args.num_workers is not None:
        config.num_workers = args.num_workers
    if args.flops_weight is not None:
        config.flops_weight = args.flops_weight
    if args.world_size is not None:
        config.world_size = args.world_size
    if args.rank is not None:
        config.rank = args.rank
    if args.dist_url is not None:
        config.dist_url = args.dist_url
    if args.gpu is not None:
        config.gpu = args.gpu
    if args.port is not None:
        config.port = args.port
    # if args.distributed:
    # print("args.distributed",args.distributed)
    # print("config.distributed",config.distributed)
    config.distributed = args.distributed
    # print("args.distributed",args.distributed)
    # print("config.distributed",config.distributed)
    if args.local_rank is not None:
        config.local_rank = args.local_rank
    if args.ngpus_per_node is not None:
        ngpus_per_node = args.ngpus_per_node
    # config.distributed = config.world_size > 1 or config.multiprocessing_distributed
    
    gpu_ids = config.gpu.split(',')
    # print("gpu_ids",gpu_ids)
    config.gpu = []
    for gpu_id in gpu_ids:
        id = int(gpu_id)
        # print("id",id)
        config.gpu.append(id)
    gpu = config.gpu
    print("gpu",gpu)

    if config.dataset == 'cifar10':
        config.num_classes = 10
    elif config.dataset == 'cifar100':
        config.num_classes = 100
    else:
        config.num_classes = 100
        print('Dataset: imagenet !')
        # sys.exit()

    # TODO:
    config.nepochs = 90 + config.pretrain_epoch

    # ngpus_per_node = torch.cuda.device_count()
    # config.ngpus_per_node = ngpus_per_node
    config.num_workers = config.num_workers * ngpus_per_node
    # print(config)
    if config.multiprocessing_distributed:
        # Since we have ngpus_per_node processes per node, the total world_size
        # needs to be adjusted accordingly
        config.world_size = ngpus_per_node * config.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, config))
    else:
        # Simply call main_worker function
        config.world_size = ngpus_per_node * config.world_size
        main_worker(config.gpu, ngpus_per_node, config)
    


def main_worker(gpu, ngpus_per_node, config):
    config.gpu = gpu
    pretrain = config.pretrain
    if not os.path.exists(pretrain):
        os.makedirs(pretrain)

    if config.gpu is not None:
        print("Use GPU: {} for training".format(config.gpu))

    if config.distributed:
        if config.multiprocessing_distributed:
            # For multiprocessing distributed training, rank needs to be the
            # global rank among all the processes
            config.rank = config.rank * ngpus_per_node + gpu
            # 1. 获取环境信息
            # rank = int(os.environ['SLURM_PROCID'])
            # world_size = int(os.environ['SLURM_NTASKS'])
            # local_rank = int(os.environ['SLURM_LOCALID'])
            node_list = str(os.environ['SLURM_NODELIST'])       

            # 对ip进行操作
            node_parts = re.findall('[0-9]+', node_list)
            host_ip = '{}.{}.{}.{}'.format(node_parts[1], node_parts[2], node_parts[3], node_parts[4])
            # 注意端口一定要没有被使用
            port = "23456"                                         
            # 使用TCP初始化方法
            init_method = 'tcp://{}:{}'.format(host_ip, port)      

            # 多进程初始化,初始化通信环境
            # dist.init_process_group("nccl", init_method=init_method,
            #                         world_size=world_size, rank=rank) 
        
        os.environ['MASTER_PORT'] = config.port
        dist.init_process_group(backend="nccl")
        # dist.init_process_group(backend=config.dist_backend, init_method=init_method,
        #                         world_size=config.world_size, rank=config.rank)
        # print("Rank: {}".format(config.rank))


    # if not config.multiprocessing_distributed or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0):
    if not (config.multiprocessing_distributed or config.distributed) or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0) or (config.distributed and dist.get_rank() == 0):
        if type(pretrain) == str:
            config.save = pretrain
        else:
            config.save = 'ckpt/{}-{}'.format(config.save, time.strftime("%Y%m%d-%H%M%S"))

        logger = SummaryWriter(config.save)

        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(config.save, 'log.txt'))
        fh.setFormatter(logging.Formatter(log_format))
        logging.getLogger().addHandler(fh)

        logging.info("args = %s", str(config))
    else:
        logger = None


    model = Network(config=config)
    # print(model)

    print('config.gpu:', config.gpu)
    if config.distributed:
        # For multiprocessing distributed, DistributedDataParallel constructor
        # should always set the single device scope, otherwise,
        # DistributedDataParallel will use all available devices.
        if len(config.gpu) > 1:
            # print("config.gpu",config.gpu)
            # torch.cuda.set_device(config.gpu)
            # model.cuda(config.gpu)
            # device = torch.device("cuda", config.local_rank)
	        # model = model.to(device)
            # model = Network(config=config).to(device)
            model.cuda()
            # 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
            # config.batch_size = int(config.batch_size / ngpus_per_node)
            config.num_workers = int((config.num_workers + ngpus_per_node - 1) / ngpus_per_node)
            model = torch.nn.parallel.DistributedDataParallel(model, device_ids=config.gpu, find_unused_parameters=True)
            # model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[config.local_rank], find_unused_parameters=True)
        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, find_unused_parameters=True)
    else:
        model = torch.nn.DataParallel(model).cuda()
        # model = torch.nn.DataParallel(model, config.gpu)


    architect = Architect(model, config)

    # Optimizer ###################################
    base_lr = config.lr
    parameters = []
    parameters += list(model.module.stem.parameters())
    parameters += list(model.module.cells.parameters())
    parameters += list(model.module.header.parameters())
    parameters += list(model.module.fc.parameters())
    
    if config.opt == 'Adam':
        optimizer = torch.optim.Adam(
            parameters,
            lr=config.lr,
            betas=config.betas)
    elif config.opt == 'Sgd':
        optimizer = torch.optim.SGD(
            parameters,
            lr=config.lr,
            momentum=config.momentum,
            weight_decay=config.weight_decay)
    else:
        print("Wrong Optimizer Type.")
        sys.exit()

    # lr policy ##############################
    # total_iteration = config.nepochs * config.niters_per_epoch
    
    if config.lr_schedule == 'linear':
        lr_policy = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=LambdaLR(config.nepochs, 0, config.decay_epoch).step)
    elif config.lr_schedule == 'exponential':
        lr_policy = torch.optim.lr_scheduler.ExponentialLR(optimizer, config.lr_decay)
    elif config.lr_schedule == 'multistep':
        lr_policy = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=config.milestones, gamma=config.gamma)
    elif config.lr_schedule == 'cosine':
        lr_policy = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(config.nepochs), eta_min=config.learning_rate_min)
    else:
        print("Wrong Learning Rate Schedule Type.")
        sys.exit()


    # TODO:
    # if use multi machines, the pretrained weight and arch need to be duplicated on all the machines
    if type(pretrain) == str and os.path.exists(pretrain + "/weights_pretrain_%d.pth" %(config.pretrain_epoch)):
    # TODO:
    # if type(pretrain) == str and os.path.exists(pretrain + "/weights_pretrain_110.pth"):
        partial = torch.load(pretrain + "/weights_pretrain_%d.pth" %(config.pretrain_epoch))
        # partial = torch.load(pretrain + "/weights_pretrain_110.pth")
        state = model.state_dict()
        pretrained_dict = {k: v for k, v in partial.items() if k in state and state[k].size() == partial[k].size()}
        state.update(pretrained_dict)
        model.load_state_dict(state)

        pretrain_arch = torch.load(pretrain + "/arch_pretrain_%d.pth" %(config.pretrain_epoch))
        # pretrain_arch = torch.load(pretrain + "/arch_pretrain_110.pth")
        
        model.module.alpha.data = pretrain_arch['alpha'].data
        # print(pretrain_arch['alpha'])
        start_epoch = pretrain_arch['epoch'] + 1

        optimizer.load_state_dict(pretrain_arch['optimizer'])
        lr_policy.load_state_dict(pretrain_arch['lr_scheduler'])
        architect.optimizer.load_state_dict(pretrain_arch['arch_optimizer'])

        print('Resume from Epoch %d. Load pretrained weight and arch.' % start_epoch)
    else:
        start_epoch = 0
        print('No checkpoint. Search from scratch.')


    # # data loader ###########################
    if 'cifar' in config.dataset:
        transform_train = transforms.Compose([
            transforms.RandomCrop(32, padding=4),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            transforms.Normalize((0.4914, 0.4822, 0.4465),
                                 (0.2023, 0.1994, 0.2010)),
        ])

        transform_test = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize((0.4914, 0.4822, 0.4465),
                                 (0.2023, 0.1994, 0.2010)),
        ])

        if config.dataset == 'cifar10':
            train_data = dset.CIFAR10(root=config.dataset_path, train=True, download=False, transform=transform_train)
            test_data = dset.CIFAR10(root=config.dataset_path, train=False, download=False, transform=transform_test)
        elif config.dataset == 'cifar100':
            train_data = dset.CIFAR100(root=config.dataset_path, train=True, download=False, transform=transform_train)
            # train_data.data = train_data.data[:32]
            test_data = dset.CIFAR100(root=config.dataset_path, train=False, download=False, transform=transform_test)
            # test_data.data = test_data.data[:32]
        else:
            print('Wrong dataset.')
            sys.exit()


    elif config.dataset == 'imagenet':
        train_data = prepare_train_data_for_search(dataset=config.dataset,
                                          datadir=config.dataset_path+'/train', num_class=config.num_classes)
        test_data = prepare_test_data_for_search(dataset=config.dataset,
                                        datadir=config.dataset_path+'/val', num_class=config.num_classes)

    elif config.dataset == 'tinyimagenet':
        print('Wrong dataset.')
        traindir = os.path.join(config.dataset_path, 'train')
        valdir = os.path.join(config.dataset_path, 'val')
        normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                        std=[0.229, 0.224, 0.225])

        train_data = dset.ImageFolder(
            traindir,
            transforms.Compose([
                transforms.RandomResizedCrop(224),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                normalize,
            ]))
        
        test_data = dset.ImageFolder(valdir, transforms.Compose([
                transforms.Resize(256),
                transforms.CenterCrop(224),
                transforms.ToTensor(),
                normalize,
            ]))
    else:
        print("Wrong dataset!")
        


    num_train = len(train_data)
    indices = list(range(num_train))
    split = int(np.floor(config.train_portion * num_train))

    # TODO:
    if config.distributed:
        # train_sampler_model = torch.utils.data.sampler. SubsetRandomSampler(indices[:split])
        # train_sampler_arch = torch.utils.data.sampler.SubsetRandomSampler(indices[split:num_train])
        train_sampler = torch.utils.data.sampler. SubsetRandomSampler(indices[:])

    else:
        # train_sampler_model = torch.utils.data.sampler.SubsetRandomSampler(indices[:split])
        # train_sampler_arch = torch.utils.data.sampler.SubsetRandomSampler(indices[split:num_train])
        train_sampler = torch.utils.data.sampler.SubsetRandomSampler(indices[:])
    

    train_loader = torch.utils.data.DataLoader(
        train_data, batch_size=config.batch_size, 
        sampler=train_sampler, shuffle=(train_sampler is None),
        pin_memory=False, num_workers=config.num_workers, drop_last=True)

    # train_loader_arch = torch.utils.data.DataLoader(
    #     train_data, batch_size=config.batch_size,
    #     sampler=train_sampler_arch, shuffle=(train_sampler_arch is None),
    #     pin_memory=False, num_workers=config.num_workers, drop_last=True)

    test_loader = torch.utils.data.DataLoader(test_data,
                                          batch_size=config.batch_size,
                                          shuffle=False,
                                          num_workers=config.num_workers)


    # tbar = tqdm(range(config.nepochs), ncols=80)
    # TODO:
    for epoch in range(start_epoch, config.nepochs):
        # if config.distributed:
        #     train_loader_model.sampler.set_epoch(epoch)
        #     train_loader_arch.sampler.set_epoch(epoch)
            # train_sampler_model.set_epoch(epoch)
            # train_sampler_arch.set_epoch(epoch)

        if config.perturb_alpha:
            epsilon_alpha = 0.03 + (config.epsilon_alpha - 0.03) * epoch / config.nepochs

            # if not config.multiprocessing_distributed or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0):
            if not (config.multiprocessing_distributed or config.distributed) or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0) or (config.distributed and dist.get_rank() == 0):
                logging.info('Epoch %d epsilon_alpha %e', epoch, epsilon_alpha)
        else:
            epsilon_alpha = 0

        if epoch < config.pretrain_epoch:
            update_arch = False
            model.module.set_search_mode(mode=config.pretrain_mode, act_num=config.pretrain_act_num)

        else:
            model.module.set_search_mode(mode=config.mode, act_num=config.act_num)

            update_arch = True

        temp = config.temp_init * config.temp_decay ** epoch
        # if not config.multiprocessing_distributed or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0):
        if not (config.multiprocessing_distributed or config.distributed) or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0) or (config.distributed and dist.get_rank() == 0):
            logging.info("Temperature: " + str(temp))
            logging.info("[Epoch %d/%d] lr=%f" % (epoch + 1, config.nepochs, optimizer.param_groups[0]['lr']))
            logging.info("update arch: " + str(update_arch))

        train_iterwise(train_loader, model, architect, optimizer, lr_policy, logger, epoch, 
            update_arch=update_arch, epsilon_alpha=epsilon_alpha, temp=temp, arch_update_frec=config.arch_update_frec)

        lr_policy.step()
        torch.cuda.empty_cache()

        # validation
        # if epoch and not (epoch+1) % config.eval_epoch:
        if epoch:
            # if ((epoch+1) == (config.pretrain_epoch+90)):
            #         # if not config.multiprocessing_distributed or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0):
            #             save(model, os.path.join(config.save, 'weights_%d.pth'%(epoch+1)))
                        # logging.info("Save Sucessfully in ",config.save)
                        # save(model, os.path.join(config.save, 'weights_latest.pth'))
            if ((epoch+1) == config.pretrain_epoch):
                    # if not config.multiprocessing_distributed or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0):
                    if not (config.multiprocessing_distributed or config.distributed) or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0) or (config.distributed and dist.get_rank() == 0):
                        save(model, os.path.join(config.save, 'weights_pretrain_%d.pth' %(config.pretrain_epoch)))
            # TODO:
            if (config.pretrain_epoch == 150):
                if ((epoch+1) == 30 or (epoch+1) == 50 or (epoch+1) == 70 or (epoch+1) == 90 or (epoch+1) == 100 or (epoch+1) == 110 or (epoch+1) == 130):
                        # if not config.multiprocessing_distributed or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0):
                        if not (config.multiprocessing_distributed or config.distributed) or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0) or (config.distributed and dist.get_rank() == 0):
                            save(model, os.path.join(config.save, 'weights_pretrain_%d.pth' %(epoch+1)))
            # if ((epoch+1)%30 == 0):
            #     save(model, os.path.join(config.save, 'weights_%d.pth'%(epoch+1)))

            with torch.no_grad():
                if pretrain == True:
                    acc = infer(epoch, model, test_loader, logger, temp=temp)

                    if config.distributed:
                        acc = reduce_tensor(acc, config.world_size)

                    # if not config.multiprocessing_distributed or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0):
                    if not (config.multiprocessing_distributed or config.distributed) or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0) or (config.distributed and dist.get_rank() == 0):
                        logger.add_scalar('acc/val', acc, epoch)
                        logging.info("Epoch %d: acc %.3f"%(epoch, acc))

                else:
                    # TODO:
                    # acc, metric = infer(epoch, model, test_loader, logger, temp=temp, finalize=True)
                    acc = infer(epoch, model, test_loader, logger, temp=temp, finalize=False)

                    # if config.distributed:
                    #     acc = reduce_tensor(acc, config.world_size)

                    # if not config.multiprocessing_distributed or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0):
                    if not (config.multiprocessing_distributed or config.distributed) or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0) or (config.distributed and dist.get_rank() == 0):
                        logger.add_scalar('acc/val', acc, epoch)
                        logging.info("Epoch %d: acc %.3f"%(epoch, acc))
                        
                        state = {}
                        # TODO：
                        # if config.efficiency_metric == 'flops':
                        #     logger.add_scalar('flops/val', metric, epoch)
                        #     logging.info("Epoch %d: FLOPs %.3f"%(epoch, metric))
                        #     state['flops'] = metric

                        # # For latency aware search, the returned metris FPS
                        # if config.efficiency_metric == 'latency':
                        #     logger.add_scalar('fps/val', metric, epoch)
                        #     logging.info("Epoch %d: FPS %.3f"%(epoch, metric))
                        #     state['fps'] = metric
                        state['alpha'] = getattr(model.module, 'alpha')
                        model.module.show_arch(alpha=state['alpha'])
                        # print("alpha,", state['alpha'])
                        state['acc'] = acc
                        state['epoch'] = epoch

                        state['optimizer'] = optimizer.state_dict()
                        state['lr_scheduler'] = lr_policy.state_dict()

                        state['arch_optimizer'] = architect.optimizer.state_dict()
                        
                        if ((epoch+1) == config.pretrain_epoch):
                            # if not config.multiprocessing_distributed or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0):
                            if not (config.multiprocessing_distributed or config.distributed) or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0) or (config.distributed and dist.get_rank() == 0):
                                torch.save(state, os.path.join(config.save, "arch_pretrain_%d.pth" %config.pretrain_epoch))
                        
                        if (config.pretrain_epoch == 150):
                            if ((epoch+1) == 30 or (epoch+1) == 50 or (epoch+1) == 70 or (epoch+1) == 90 or (epoch+1) == 100 or (epoch+1) == 110 or (epoch+1) == 130):
                                # if not config.multiprocessing_distributed or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0):
                                if not (config.multiprocessing_distributed or config.distributed) or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0) or (config.distributed and dist.get_rank() == 0):
                                    torch.save(state, os.path.join(config.save, "arch_pretrain_%d.pth" %(epoch+1)))
                        # if ((epoch+1) % 30 == 0):
                        # # if ((epoch+1) == (config.pretrain_epoch+90)):
                        #     torch.save(state, os.path.join(config.save, "arch_%d.pth"%(epoch+1)))
                            # torch.save(state, os.path.join(config.save, "arch_latest.pth"))
                    
                    # TODO:
                    # if config.efficiency_metric == 'flops':
                    #     if config.flops_weight > 0 and update_arch:
                    #         if metric < config.flops_min:
                    #             architect.flops_weight /= 2
                    #         elif metric > config.flops_max:
                    #             architect.flops_weight *= 2

                            # # if not config.multiprocessing_distributed or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0):
                            #     logger.add_scalar("arch/flops_weight", architect.flops_weight, epoch+1)
                            #     logging.info("arch_flops_weight = " + str(architect.flops_weight))

                    # For latency aware search, the returned metris FPS
                    # elif config.efficiency_metric == 'latency':
                    #     if config.latency_weight > 0 and update_arch:
                    #         if metric < config.fps_min:
                    #             architect.latency_weight *= 2
                    #         elif metric > config.fps_max:
                    #             architect.latency_weight /= 2

                    #         # if not config.multiprocessing_distributed or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0):
                    #             logger.add_scalar("arch/latency_weight", architect.latency_weight, epoch+1)
                    #             logging.info("arch_latency_weight = " + str(architect.latency_weight))



    # if not config.multiprocessing_distributed or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0):
    if not (config.multiprocessing_distributed or config.distributed) or (config.multiprocessing_distributed and config.rank % ngpus_per_node == 0) or (config.distributed and dist.get_rank() == 0):
        if update_arch:
            torch.save(state, os.path.join(config.save, "arch_%d.pth"%(epoch+1)))
            save(model, os.path.join(config.save, "weight_%d.pth"%(epoch+1)))


        # if config.efficiency_metric == 'latency':
        #     model_infer = FBNet_Infer(getattr(model.module, 'alpha'), config=config)
        #     latency = model_infer.forward_latency(size=(3, config.image_height, config.image_width))
        #     fps = 1000 / latency

        #     flops, params = profile(model_infer, inputs=(torch.randn(1, 3, config.image_height, config.image_width),))
        #     bitops = model_infer.forward_bitops(size=(3, config.image_height, config.image_width))
            
        #     logging.info("params = %fM, FLOPs = %fM, BitOPs = %fG", params / 1e6, flops / 1e6, bitops / 1e9)
        #     logging.info("FPS of Final Arch: %f", fps)


def crossentropyloss(x,y):

    softmax_func=nn.Softmax(dim=1)
    soft_output=softmax_func(x)
    soft_output=torch.clamp(soft_output, 1e-4, 1, out=None)

    log_output=torch.log(soft_output)
    # print("log_output",log_output)

    #pytorch中关于NLLLoss的默认参数配置为：reducetion=True、size_average=True
    nllloss_func=nn.NLLLoss()
    nlloss_output=nllloss_func(log_output,y)
    return nlloss_output


def train_iterwise(train_loader, model, architect, optimizer, lr_policy, logger, epoch, update_arch=True, epsilon_alpha=0, temp=1, arch_update_frec=1):
    model.train()

    # bar_format = '{desc}[{elapsed}<{remaining},{rate_fmt}]'
    # pbar = tqdm(range(len(train_loader_model)), file=sys.stdout, bar_format=bar_format, ncols=80)
    dataloader = iter(train_loader)
    # dataloader_arch = iter(train_loader_arch)

    for step in range(len(train_loader)):
        start_time = time.time()

        input, target = dataloader.next()

        # data_time = time.time() - start_time

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

        if update_arch:
            architect.set_weight_optimizer(optimizer)
            loss = architect.step(input, target, config=config, temp=temp)

            if not (config.multiprocessing_distributed or config.distributed) or (config.multiprocessing_distributed and config.rank % config.ngpus_per_node == 0) or (config.distributed and dist.get_rank() == 0):
                logger.add_scalar('loss/train', loss, epoch*len(train_loader)+step)
        else:
            logit = model(input, temp, update_arch)
            loss = crossentropyloss(logit, target)
            # print("loss",loss)
            optimizer.zero_grad()
            loss.backward()
            # print('loss.backward()',loss.backward())
            nn.utils.clip_grad_norm_(model.parameters(), config.grad_clip)
            optimizer.step() 

        if epsilon_alpha and update_arch:
            Random_alpha(model, epsilon_alpha)

    torch.cuda.empty_cache()
    
    del loss
    # if update_arch: del loss_arch


def infer(epoch, model, test_loader, logger, temp=1, finalize=False):
    with torch.no_grad():
        model.eval()
        prec1_list = []

        for i, (input, target) in enumerate(test_loader):
            input_var = Variable(input).cuda()
            target_var = Variable(target).cuda()

            output = model(input_var)
            prec1, = accuracy(output.data, target_var, topk=(1,))
            prec1_list.append(prec1)

        acc = sum(prec1_list)/len(prec1_list)

    if finalize:
        model_infer = FBNet_Infer(getattr(model.module, 'alpha'), config=config)
        if config.efficiency_metric == 'flops':
            flops = model_infer.forward_flops((3, config.image_height, config.image_width))
            return acc, flops
        elif config.efficiency_metric == 'latency':
            latency = model_infer.forward_latency([3, config.image_height, config.image_width])
            fps = 1000 / latency


            return acc, fps
    else:
        return acc


def reduce_tensor(rt, n):
    dist.all_reduce(rt, op=dist.ReduceOp.SUM)
    rt /= n
    return rt


def accuracy(output, target, topk=(1,)):
    """Computes the precision@k for the specified values of k"""
    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)
        res.append(correct_k.mul_(100.0 / batch_size))
    return res


def save(model, model_path):
    torch.save(model.state_dict(), model_path)


if __name__ == '__main__':
    main()