from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import os
import time
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data as data


from models import create_model, transforms_imagenet_eval
from dataset import Dataset


parser = argparse.ArgumentParser(description='PyTorch ImageNet Validation')
parser.add_argument('data', metavar='DIR',
                    help='path to dataset')
parser.add_argument('--model', '-m', metavar='MODEL', default='dpn92',
                    help='model architecture (default: dpn92)')
parser.add_argument('-j', '--workers', default=2, type=int, metavar='N',
                    help='number of data loading workers (default: 2)')
parser.add_argument('-b', '--batch-size', default=256, type=int,
                    metavar='N', help='mini-batch size (default: 256)')
parser.add_argument('--img-size', default=224, type=int,
                    metavar='N', help='Input image dimension')
parser.add_argument('--print-freq', '-p', default=10, type=int,
                    metavar='N', help='print frequency (default: 10)')
parser.add_argument('--checkpoint', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
parser.add_argument('--pretrained', dest='pretrained', action='store_true',
                    help='use pre-trained model')
parser.add_argument('--num-gpu', type=int, default=1,
                    help='Number of GPUS to use')
parser.add_argument('--no-test-pool', dest='no_test_pool', action='store_true',
                    help='disable test time pool for DPN models')


def main():
    args = parser.parse_args()

    test_time_pool = False
    if 'dpn' in args.model and args.img_size > 224 and not args.no_test_pool:
        test_time_pool = True

    # create model
    num_classes = 1000
    model = create_model(
        args.model,
        num_classes=num_classes,
        pretrained=args.pretrained,
        test_time_pool=test_time_pool)

    print('Model %s created, param count: %d' %
          (args.model, sum([m.numel() for m in model.parameters()])))

    # optionally resume from a checkpoint
    if args.checkpoint and os.path.isfile(args.checkpoint):
        print("=> loading checkpoint '{}'".format(args.checkpoint))
        checkpoint = torch.load(args.checkpoint)
        if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
            model.load_state_dict(checkpoint['state_dict'])
        else:
            model.load_state_dict(checkpoint)
        print("=> loaded checkpoint '{}'".format(args.checkpoint))
    elif not args.pretrained:
        print("=> no checkpoint found at '{}'".format(args.checkpoint))
        exit(1)

    if args.num_gpu > 1:
        model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda()
    else:
        model = model.cuda()

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

    cudnn.benchmark = True

    dataset = Dataset(
        args.data,
        transforms_imagenet_eval(args.model, args.img_size))

    loader = data.DataLoader(
        dataset,
        batch_size=args.batch_size, shuffle=False,
        num_workers=args.workers, pin_memory=True)

    batch_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    # switch to evaluate mode
    model.eval()
    end = time.time()
    with torch.no_grad():
        for i, (input, target) in enumerate(loader):
            target = target.cuda()
            input = input.cuda()

            # compute output
            output = model(input)
            loss = criterion(output, target)

            # measure accuracy and record loss
            prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
            losses.update(loss.item(), input.size(0))
            top1.update(prec1.item(), input.size(0))
            top5.update(prec5.item(), 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(loader), batch_time=batch_time, loss=losses,
                    top1=top1, top5=top5))

    print(' * Prec@1 {top1.avg:.3f} ({top1a:.3f}) Prec@5 {top5.avg:.3f} ({top5a:.3f})'.format(
        top1=top1, top1a=100-top1.avg, top5=top5, top5a=100.-top5.avg))


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 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


if __name__ == '__main__':
    main()