"""

BSD 3-Clause License



Copyright (c) Soumith Chintala 2016,

All rights reserved.



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.



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  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.





Copyright 2020 Huawei Technologies Co., Ltd



Licensed under the BSD 3-Clause License (the "License");

you may not use this file except in compliance with the License.

You may obtain a copy of the License at



https://spdx.org/licenses/BSD-3-Clause.html



Unless required by applicable law or agreed to in writing, software

distributed under the License is distributed on an "AS IS" BASIS,

WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

See the License for the specific language governing permissions and

limitations under the License.

"""

import argparse

import torch

import torchvision

from torchvision import datasets, transforms

from collections import OrderedDict



from timm.models import create_model

from timm.data.transforms import _pil_interp, RandomResizedCropAndInterpolation, ToNumpy, ToTensor



DEFAULT_CROP_PCT = 0.875



parser = argparse.ArgumentParser(description='ImageNet Training')

parser.add_argument('--checkpoint', type=str, default='',

                    help='checkpoint path')

args = parser.parse_args()



def proc_node_module(checkpoint, attr_name):

    new_state_dict = OrderedDict()

    for k, v in checkpoint[attr_name].items():

        if(k[0: 7] == "module."):

            name = k[7:]

        else:

            name = k[0:]

        new_state_dict[name] = v

    return new_state_dict



def get_raw_data():

    from PIL import Image

    from urllib.request import urlretrieve

    IMAGE_URL = 'https://bbs-img.huaweicloud.com/blogs/img/thumb/1591951315139_8989_1363.png'

    urlretrieve(IMAGE_URL, 'tmp.jpg')

    img = Image.open("tmp.jpg")

    img = img.convert('RGB')

    return img



def test():

    loc = 'npu:0'

    loc_cpu = 'cpu'

    torch.npu.set_device(loc)

    if args.checkpoint == '':

        print("please give the checkpoint path using --checkpoint param")

        exit(0)

    checkpoint = torch.load(args.checkpoint, map_location=loc)

    checkpoint['state_dict'] = proc_node_module(checkpoint, 'state_dict')

    

    model = create_model(

        'volo_d1',

        pretrained=False,

        num_classes=None,

        drop_rate=0.0,

        drop_connect_rate=None,  # DEPRECATED, use drop_path

        drop_path_rate=0.0,

        drop_block_rate=None,

        global_pool=None,

        bn_tf=False,

        bn_momentum=None,

        bn_eps=None,

        scriptable=False,

        checkpoint_path='',

        img_size=224)

    

    model = model.to(loc)

    model.load_state_dict(checkpoint['state_dict'])

    model.eval()



    crop_pct = DEFAULT_CROP_PCT

    img_size = 224

    scale_size = int(math.floor(img_size / crop_pct))

    interpolation = 'bilinear'

    tfl = [

        transforms.Resize(scale_size, _pil_interp(interpolation)),

        transforms.CenterCrop(img_size),

    ]

    tfl += [ToNumpy()]

    data_transfrom = transforms.Compose(tfl)



    rd = get_raw_data()



    inputs = data_transfrom(rd)

    inputs = inputs.unsqueeze(0)

    inputs = inputs.to(loc)

    output = model(inputs)

    output = output.to(loc_cpu)



    _, pred = output.topk(1, 1, True, True)

    result = torch.argmax(output, 1)

    print("class: ", pred[0][0].item())

    print(result)



if __name__ == "__main__":

    test()