<gh_stars>10-100
# -*- coding: utf-8 -*-
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# 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
from lib.config import config, update_config, hrnet


def parse_args():
    parser = argparse.ArgumentParser(description='Train classification network')
    parser.add_argument('--cfg',
                        help='experiment configure file name',
                        required=False,
                        type=str,
                        default='./experiments/cls_hrnet_w18_sgd_lr5e-2_wd1e-4_bs32_x100.yaml')

    parser.add_argument('--modelDir',
                        help='model directory',
                        type=str,
                        default='')
    parser.add_argument('--logDir',
                        help='log directory',
                        type=str,
                        default='')
    parser.add_argument('--dataDir',
                        help='data directory',
                        type=str,
                        default='')
    parser.add_argument('--testModel',
                        help='testModel',
                        type=str,
                        default='')

    args = parser.parse_args()
    update_config(config, args)

    return args


def build_model():
    args = parse_args()
    checkpoint = torch.load("./output/imagenet/cls_hrnet_w18_sgd_lr5e-2_wd1e-4_bs32_x100/model_best.pth.tar", map_location='cpu')
    model = hrnet.get_cls_net(config)
    model.load_state_dict(checkpoint)
    model.eval()
    return model


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 pre_process(raw_data):
    from torchvision import transforms
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])
    transforms_list = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        normalize
    ])
    input_data = transforms_list(raw_data)
    return input_data.unsqueeze(0)


def post_process(output_tensor):
    return  torch.argmax(output_tensor, 1)


if __name__ == '__main__':
    # 1. get raw data
    raw_data = get_raw_data()

    # 2. buid model
    model = build_model()

    # 3. pre process data
    input_tensor = pre_process(raw_data)

    # 4. run forward
    output_tensor = model(input_tensor)

    # 5. post process
    result = post_process(output_tensor)

    # 6. print result
    print(result)
