export.py

# Copyright 2020-2021 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 checkpoint file into air and onnx models#################
python export.py
"""
import os
import numpy as np

from mindspore import Tensor, load_checkpoint, load_param_into_net, export, context
from src.model_utils.config import config
from src.model_utils.moxing_adapter import moxing_wrapper

# -----> Ascend 
context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
if config.device_target == "Ascend":
    context.set_context(device_id=config.device_id)

def modelarts_pre_process():
    '''modelarts pre process function.'''
    config.file_name = os.path.join(config.output_path, config.file_name)

@moxing_wrapper(pre_process=modelarts_pre_process)
def run_export():
    """run export."""
    if config.network_dataset == 'resnet18_cifar10':
        from src.resnet import resnet18 as resnet
    elif config.network_dataset == 'resnet18_imagenet2012':
        from src.resnet import resnet18 as resnet
    elif config.network_dataset == 'resnet34_imagenet2012':
        from src.resnet import resnet34 as resnet
    elif config.network_dataset == 'resnet50_cifar10':
        from src.resnet import resnet50 as resnet
    elif config.network_dataset == 'resnet50_imagenet2012':
        from src.resnet import resnet50 as resnet
    elif config.network_dataset == 'resnet101_imagenet2012':
        from src.resnet import resnet101 as resnet
    elif config.network_dataset == 'se-resnet50_imagenet2012':
        from src.resnet import se_resnet50 as resnet
    else:
        raise ValueError("network and dataset is not support.")

    net = resnet(config.class_num)

    assert config.checkpoint_file_path is not None, "checkpoint_path is None."

    param_dict = load_checkpoint(config.checkpoint_file_path)
    load_param_into_net(net, param_dict)

    input_arr = Tensor(np.zeros([config.batch_size, 3, config.height, config.width], np.float32))
    export(net, input_arr, file_name=config.file_name, file_format=config.file_format)

if __name__ == '__main__':
    run_export()