#!/usr/bin/env python3

# 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 torch

import numpy as np

import pycls.core.config as config

from pycls.core.config import cfg

import pycls.datasets.transforms as transforms

from pycls.models.effnet import EffNet





def build_model():

    config.merge_from_file('configs/dds_baselines/effnet/EN-B1_dds_8npu.yaml')

    cfg.freeze()

    model = EffNet()

    checkpoint = torch.load('result/model.pyth')

    model.load_state_dict(checkpoint["model_state"], False)

    model.eval()

    return model





def get_raw_data():

    from PIL import Image

    from urllib.request import urlretrieve

    with open('url.ini', 'r') as f:

        content = f.read()

        img_url = content.split('img_url=')[1].split('\n')[0]

    IMAGE_URL = img_url

    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(274),

        transforms.CenterCrop(240),

        transforms.ToTensor(),

        normalize

    ])

    input_data = transforms_list(raw_data)

    return input_data.unsqueeze(0)





if __name__ == '__main__':

    raw_data = get_raw_data()

    model = build_model()

    input_tensor = pre_process(raw_data)

    output_tensor = model(input_tensor)

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

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