# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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 os
import argparse

import paddle

import utils as ut
from cvlibs import Config
from script import evaluate
from paddleseg.cvlibs import manager
from paddleseg.core import evaluate
from paddleseg.utils import get_sys_env, logger, utils
from datasets import CityDataset
from script import val


def get_test_config(cfg, args):
    test_config = cfg.test_config
    if args.aug_eval:
        test_config['aug_eval'] = args.aug_eval
        test_config['scales'] = args.scales

    if args.flip_horizontal:
        test_config['flip_horizontal'] = args.flip_horizontal

    if args.flip_vertical:
        test_config['flip_vertical'] = args.flip_vertical

    if args.is_slide:
        test_config['is_slide'] = args.is_slide
        test_config['crop_size'] = args.crop_size
        test_config['stride'] = args.stride

    return test_config


def parse_args():
    parser = argparse.ArgumentParser(description='Model evaluation')

    # params of evaluate
    parser.add_argument(
        "--config", dest="cfg", help="The config file.", default=None, type=str)
    parser.add_argument(
        '--model_path',
        dest='model_path',
        help='The path of model for evaluation',
        type=str,
        default=None)
    parser.add_argument(
        '--num_workers',
        dest='num_workers',
        help='Num workers for data loader',
        type=int,
        default=0)

    # augment for evaluation
    parser.add_argument(
        '--aug_eval',
        dest='aug_eval',
        help='Whether to use mulit-scales and flip augment for evaluation',
        action='store_true')
    parser.add_argument(
        '--scales',
        dest='scales',
        nargs='+',
        help='Scales for augment',
        type=float,
        default=1.0)
    parser.add_argument(
        '--flip_horizontal',
        dest='flip_horizontal',
        help='Whether to use flip horizontally augment',
        action='store_true')
    parser.add_argument(
        '--flip_vertical',
        dest='flip_vertical',
        help='Whether to use flip vertically augment',
        action='store_true')

    # sliding window evaluation
    parser.add_argument(
        '--is_slide',
        dest='is_slide',
        help='Whether to evaluate by sliding window',
        action='store_true')
    parser.add_argument(
        '--crop_size',
        dest='crop_size',
        nargs=2,
        help='The crop size of sliding window, the first is width and the second is height.',
        type=int,
        default=None)
    parser.add_argument(
        '--stride',
        dest='stride',
        nargs=2,
        help='The stride of sliding window, the first is width and the second is height.',
        type=int,
        default=None)
    parser.add_argument(
        '--data_format',
        dest='data_format',
        help='Data format that specifies the layout of input. It can be "NCHW" or "NHWC". Default: "NCHW".',
        type=str,
        default='NCHW')

    return parser.parse_args()


def main(args):
    env_info = get_sys_env()
    place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
        'GPUs used'] else 'cpu'

    paddle.set_device(place)
    if not args.cfg:
        raise RuntimeError('No configuration file specified.')

    cfg = Config(args.cfg)

    if cfg.dic["data"]["target"]["dataset"] == 'cityscapes':
        val_dataset = CityDataset(
            split='val', **cfg.dic["data"]["target"]["kwargs"])
    else:
        raise NotImplementedError()

    if len(val_dataset) < 500:
        print(len(val_dataset))
        for i in range(len(val_dataset)):
            print(val_dataset[i])

    if val_dataset is None:
        raise RuntimeError(
            'The verification dataset is not specified in the configuration file.'
        )
    elif len(val_dataset) == 0:
        raise ValueError(
            'The length of val_dataset is 0. Please check if your dataset is valid'
        )

    msg = '\n---------------Config Information---------------\n'
    msg += str(cfg)
    msg += '------------------------------------------------'
    logger.info(msg)

    model = cfg.model
    if args.model_path:
        utils.load_entire_model(model, args.model_path)
        logger.info('Loaded trained params of model successfully')

    test_config = get_test_config(cfg, args)

    val.evaluate(
        model, val_dataset, num_workers=args.num_workers, **test_config)


if __name__ == '__main__':
    args = parse_args()
    main(args)