# Copyright (c) 2020 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 utils
import argparse
import numpy as np

import paddle.fluid as fluid


def parse_args():
    def str2bool(v):
        return v.lower() in ("true", "t", "1")

    parser = argparse.ArgumentParser()
    parser.add_argument("-i", "--image_file", type=str)
    parser.add_argument("-d", "--model_dir", type=str)
    parser.add_argument("-m", "--model_file", type=str)
    parser.add_argument("-p", "--params_file", type=str)
    parser.add_argument("--use_gpu", type=str2bool, default=True)

    return parser.parse_args()


def create_predictor(args):
    if args.use_gpu:
        place = fluid.CUDAPlace(0)
    else:
        place = fluid.CPUPlace()

    exe = fluid.Executor(place)
    [program, feed_names, fetch_lists] = fluid.io.load_inference_model(
        args.model_dir,
        exe,
        model_filename=args.model_file,
        params_filename=args.params_file)
    compiled_program = fluid.compiler.CompiledProgram(program)

    return exe, compiled_program, feed_names, fetch_lists


def create_operators():
    size = 224
    img_mean = [0.485, 0.456, 0.406]
    img_std = [0.229, 0.224, 0.225]
    img_scale = 1.0 / 255.0

    decode_op = utils.DecodeImage()
    resize_op = utils.ResizeImage(resize_short=256)
    crop_op = utils.CropImage(size=(size, size))
    normalize_op = utils.NormalizeImage(
        scale=img_scale, mean=img_mean, std=img_std)
    totensor_op = utils.ToTensor()

    return [decode_op, resize_op, crop_op, normalize_op, totensor_op]


def preprocess(fname, ops):
    data = open(fname).read()
    for op in ops:
        data = op(data)

    return data


def postprocess(outputs, topk=5):
    output = outputs[0]
    prob = np.array(output).flatten()
    index = prob.argsort(axis=0)[-topk:][::-1].astype('int32')
    return zip(index, prob[index])


def main():
    args = parse_args()
    operators = create_operators()
    exe, program, feed_names, fetch_lists = create_predictor(args)

    data = preprocess(args.image_file, operators)
    outputs = exe.run(program,
                      feed={feed_names[0]: data},
                      fetch_list=fetch_lists,
                      return_numpy=False)
    probs = postprocess(outputs)

    for idx, prob in probs:
        print("class id: {:d}, probability: {:.4f}".format(idx, prob))


if __name__ == "__main__":
    main()