# Copyright 2020 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.
# ============================================================================
"""Evaluate MobilenetV2 on ImageNet"""

import os
import argparse

from mindspore import context
from mindspore import nn
from mindspore.train.model import Model
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.train.quant import quant

from src.mobilenetV2 import mobilenetV2
from src.dataset import create_dataset
from src.config import config_ascend

parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
parser.add_argument('--device_target', type=str, default=None, help='Run device target')
parser.add_argument('--quantization_aware', type=bool, default=False, help='Use quantization aware training')
args_opt = parser.parse_args()

if __name__ == '__main__':
    config_device_target = None
    if args_opt.device_target == "Ascend":
        config_device_target = config_ascend
        device_id = int(os.getenv('DEVICE_ID'))
        context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
                            device_id=device_id, save_graphs=False)

    else:
        raise ValueError("Unsupported device target: {}.".format(args_opt.device_target))

    # define fusion network
    network = mobilenetV2(num_classes=config_device_target.num_classes)
    if args_opt.quantization_aware:
        # convert fusion network to quantization aware network
        network = quant.convert_quant_network(network, bn_fold=True, per_channel=[True, False], symmetric=[True, False])
    # define network loss
    loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')

    # define dataset
    dataset = create_dataset(dataset_path=args_opt.dataset_path,
                             do_train=False,
                             config=config_device_target,
                             device_target=args_opt.device_target,
                             batch_size=config_device_target.batch_size)
    step_size = dataset.get_dataset_size()

    # load checkpoint
    if args_opt.checkpoint_path:
        param_dict = load_checkpoint(args_opt.checkpoint_path)
        load_param_into_net(network, param_dict)
    network.set_train(False)

    # define model
    model = Model(network, loss_fn=loss, metrics={'acc'})

    print("============== Starting Validation ==============")
    res = model.eval(dataset)
    print("result:", res, "ckpt=", args_opt.checkpoint_path)
    print("============== End Validation ==============")