# 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.
# ============================================================================
"""Export Resnet50 on ImageNet"""

import argparse
import numpy as np

import mindspore
from mindspore import Tensor, context, load_checkpoint, load_param_into_net, export
from mindspore.compression.quant import QuantizationAwareTraining

from models.resnet_quant_manual import resnet50_quant
from src.config import config_quant

parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
parser.add_argument('--file_format', type=str, choices=["AIR", "MINDIR"], default="MINDIR", help="file format")
parser.add_argument('--device_target', type=str, default=None, help='Run device target')
args_opt = parser.parse_args()

if __name__ == '__main__':
    context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, save_graphs=False)
    # define fusion network
    network = resnet50_quant(class_num=config_quant.class_num)
    # convert fusion network to quantization aware network
    quantizer = QuantizationAwareTraining(bn_fold=True,
                                          per_channel=[True, False],
                                          symmetric=[True, False])
    network = quantizer.quantize(network)
    # load checkpoint
    if args_opt.checkpoint_path:
        param_dict = load_checkpoint(args_opt.checkpoint_path)
        not_load_param = load_param_into_net(network, param_dict)
        if not_load_param:
            raise ValueError("Load param into network fail!")
    # export network
    print("============== Starting export ==============")
    inputs = Tensor(np.ones([1, 3, 224, 224]), mindspore.float32)
    export(network, inputs, file_name="resnet50_quant", file_format=args_opt.file_format,
           quant_mode='MANUAL', mean=0., std_dev=48.106)
    print("============== End export ==============")