#!/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
from pycls.models.effnet import EffNet
import pycls.core.optimizer as optim


def build_model():
    config.merge_from_file('configs/dds_baselines/effnet/EN-B3_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(256),
        transforms.CenterCrop(224),
        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())