mindspore/model_zoo/official/cv/openpose/export.py

# 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.
# ============================================================================
"""export"""

import argparse
import numpy as np
from mindspore import Tensor
from mindspore import context
from mindspore.train.serialization import load_checkpoint, load_param_into_net, export

from src.openposenet import OpenPoseNet
from src.config import params

parser = argparse.ArgumentParser(description="openpose export")
parser.add_argument("--device_id", type=int, default=0, help="Device id")
parser.add_argument("--batch_size", type=int, default=1, help="batch size")
parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
parser.add_argument("--file_name", type=str, default="openpose", help="output file name.")
parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
parser.add_argument("--device_target", type=str, default="Ascend",
                    choices=["Ascend", "GPU", "CPU"], help="device target (default: Ascend)")
args = parser.parse_args()

context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=args.device_id)

if __name__ == "__main__":
    context.set_context(mode=context.GRAPH_MODE, save_graphs=False)
    # define net
    net = OpenPoseNet()

    # load checkpoint
    param_dict = load_checkpoint(args.ckpt_file)
    load_param_into_net(net, param_dict)
    inputs = np.ones([args.batch_size, 3, params["insize"], params["insize"]]).astype(np.float32)
    export(net, Tensor(inputs), file_name=args.file_name, file_format=args.file_format)