# BSD 3-Clause License
#
# Copyright (c) 2017 xxxx
# All rights reserved.
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
#   list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
#   this list of conditions and the following disclaimer in the documentation
#   and/or other materials provided with the distribution.
#
# * Neither the name of the copyright holder nor the names of its
#   contributors may be used to endorse or promote products derived from
#   this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# ============================================================================
import argparse
import os
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from utils import accuracy, ProgressMeter, AverageMeter
from repvgg import get_RepVGG_func_by_name
from utils import load_checkpoint, get_default_ImageNet_val_loader

parser = argparse.ArgumentParser(description='PyTorch ImageNet Test')
parser.add_argument('data', metavar='DIR', help='path to dataset')
parser.add_argument('mode', metavar='MODE', default='train', choices=['train', 'deploy'], help='train or deploy')
parser.add_argument('weights', metavar='WEIGHTS', help='path to the weights file')
parser.add_argument('-a', '--arch', metavar='ARCH', default='RepVGG-A0')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
parser.add_argument('-b', '--batch-size', default=100, type=int,
                    metavar='N',
                    help='mini-batch size (default: 100) for test')
parser.add_argument('-r', '--resolution', default=224, type=int,
                    metavar='R',
                    help='resolution (default: 224) for test')

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

    repvgg_build_func = get_RepVGG_func_by_name(args.arch)

    model = repvgg_build_func(deploy=args.mode=='deploy')

    if not torch.cuda.is_available():
        print('using CPU, this will be slow')
        use_gpu = False
    else:
        model = model.cuda()
        use_gpu = True

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

    if os.path.isfile(args.weights):
        print("=> loading checkpoint '{}'".format(args.weights))
        load_checkpoint(model, args.weights)
    else:
        print("=> no checkpoint found at '{}'".format(args.weights))


    cudnn.benchmark = True

    val_loader = get_default_ImageNet_val_loader(args)

    validate(val_loader, model, criterion, use_gpu)


def validate(val_loader, model, criterion, use_gpu):
    batch_time = AverageMeter('Time', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(
        len(val_loader),
        [batch_time, losses, top1, top5],
        prefix='Test: ')

    # switch to evaluate mode
    model.eval()

    with torch.no_grad():
        end = time.time()
        for i, (images, target) in enumerate(val_loader):
            if use_gpu:
                images = images.cuda(non_blocking=True)
                target = target.cuda(non_blocking=True)

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

            # measure accuracy and record loss
            acc1, acc5 = accuracy(output, target, topk=(1, 5))
            losses.update(loss.item(), images.size(0))
            top1.update(acc1[0], images.size(0))
            top5.update(acc5[0], images.size(0))

            # measure elapsed time
            batch_time.update(time.time() - end)
            end = time.time()

            if i % 10 == 0:
                progress.display(i)

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

    return top1.avg




if __name__ == '__main__':
    test()