import argparse
import os
import shutil
import time
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.utils.data
import torch.utils.data.distributed
from torchvision import datasets, transforms
from src_code.lmdbdataset import lmdbDataset
from compress_model.new_model import vgg16_GAP

parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
'''parser.add_argument('data', metavar='DIR',
                    help='path to dataset')
'''
parser.add_argument('-j', '--workers', default=8, type=int, metavar='N',
                    help='number of data loading workers (default: 8)')
parser.add_argument('--epochs', default=24, type=int, metavar='N',
                    help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
                    help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=256, type=int,
                    metavar='N', help='mini-batch size (default: 128)')
parser.add_argument('--lr', '--learning-rate', default=0.001, type=float,
                    metavar='LR', help='initial learning rate')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                    help='momentum')
parser.add_argument('--weight-decay', '--wd', default=5e-4, type=float,
                    metavar='W', help='weight decay (default: 1e-4)')
parser.add_argument('--print-freq', '-p', default=10, type=int,
                    metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
parser.add_argument('--evaluate', default=False, type=bool,
                    help='evaluate model on validation set')
parser.add_argument('--pretrained', dest='pretrained', action='store_true',
                    help='use pre-trained model')
parser.add_argument('--world-size', default=1, type=int,
                    help='number of distributed processes')
parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
                    help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='gloo', type=str,
                    help='distributed backend')
parser.add_argument('--gpu_id', default='4,5,6,7', type=str,
                    help='id(s) for CUDA_VISIBLE_DEVICES')
parser.add_argument('--data_base', default='/data/zhangcl/ImageNet', type=str, help='the path of dataset')
parser.add_argument('--load_from_lmdb', default=True, type=bool, help='load image data from lmdb or not')
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
best_prec1 = 0
print(args)


def main():
    global args, best_prec1
    args = parser.parse_args()
    args.distributed = args.world_size > 1

    if args.distributed:
        dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                world_size=args.world_size)

    # create model
    model = vgg16_GAP('checkpoint/model.pth')
    print(model)
    model = torch.nn.DataParallel(model.cuda(), device_ids=range(torch.cuda.device_count()))
    cudnn.benchmark = True

    # define loss function (criterion) and optimizer
    criterion = nn.CrossEntropyLoss().cuda()

    optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), args.lr,
                                momentum=args.momentum,
                                weight_decay=args.weight_decay)

    cudnn.benchmark = True

    # Data loading code from lmdb
    if args.load_from_lmdb:
        train_loader = torch.utils.data.DataLoader(
            lmdbDataset(os.path.join(args.data_base, 'ILSVRC-train.lmdb'), True),
            batch_size=args.batch_size,
            num_workers=16,
            pin_memory=True
        )
        print('train_loader_success!')
        val_loader = torch.utils.data.DataLoader(
            lmdbDataset(os.path.join(args.data_base, 'ILSVRC-val.lmdb'), False),
            batch_size=args.batch_size,
            num_workers=16,
            pin_memory=True
        )
    else:
        traindir = os.path.join('/opt/luojh/Dataset/ImageNet/images', 'train')
        valdir = os.path.join('/opt/luojh/Dataset/ImageNet/images', 'val')
        normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                         std=[0.229, 0.224, 0.225])

        train_loader = torch.utils.data.DataLoader(
            datasets.ImageFolder(traindir, transforms.Compose([
                transforms.RandomResizedCrop(224),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                normalize,
            ])),
            batch_size=args.batch_size, shuffle=True,
            num_workers=args.workers, pin_memory=True)

        val_loader = torch.utils.data.DataLoader(
            datasets.ImageFolder(valdir, transforms.Compose([
                transforms.Resize(256),
                transforms.CenterCrop(224),
                transforms.ToTensor(),
                normalize,
            ])),
            batch_size=args.batch_size, shuffle=False,
            num_workers=args.workers, pin_memory=True)

    # evaluate and train
    if args.evaluate:
        validate(val_loader, model, criterion)

        return
    for epoch in range(args.start_epoch, args.epochs):
        adjust_learning_rate(optimizer, epoch, int(args.epochs/3.0))

        # train for one epoch
        train(train_loader, model, criterion, optimizer, epoch)

        # evaluate on validation set
        prec1 = validate(val_loader, model, criterion)

        # remember best prec@1 and save checkpoint
        is_best = prec1 > best_prec1
        best_prec1 = max(prec1, best_prec1)
        if is_best:
            folder_path = 'checkpoint/fine_tune_GAP'
            if not os.path.exists(folder_path):
                os.makedirs(folder_path)
            torch.save(model.state_dict(), folder_path + '/model.pth')
        print('best acc is %.3f' % best_prec1)


def train(train_loader, model, criterion, optimizer, epoch):
    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)

        target = target.cuda(async=True)
        input_var = torch.autograd.Variable(input).cuda()
        target_var = torch.autograd.Variable(target).cuda()

        # compute output
        output = model(input_var)
        loss = criterion(output, target_var)

        # measure accuracy and record loss
        prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
        losses.update(loss.data[0], input.size(0))
        top1.update(prec1[0], input.size(0))
        top5.update(prec5[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'
                  'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                  'Prec@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))
            sys.stdout.flush()


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

    # switch to evaluate mode
    model.eval()

    end = time.time()
    for i, (input, target) in enumerate(val_loader):
        target = target.cuda(async=True)
        input_var = torch.autograd.Variable(input, volatile=True)
        target_var = torch.autograd.Variable(target, volatile=True)

        # compute output
        output = model(input_var)
        loss = criterion(output, target_var)

        # measure accuracy and record loss
        prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
        losses.update(loss.data[0], input.size(0))
        top1.update(prec1[0], input.size(0))
        top5.update(prec5[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'
                  'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                  'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                   i, len(val_loader), batch_time=batch_time, loss=losses,
                   top1=top1, top5=top5))
            sys.stdout.flush()

    print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'
          .format(top1=top1, top5=top5))

    return top1.avg


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


class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count


def adjust_learning_rate(optimizer, epoch, epoch_num):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    lr = args.lr * (0.1 ** (epoch // epoch_num))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


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, keepdim=True)
        res.append(correct_k.mul_(100.0 / batch_size))
    return res


if __name__ == '__main__':
    main()
