You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
49 lines
2.1 KiB
49 lines
2.1 KiB
# 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.
|
|
# ============================================================================
|
|
|
|
import argparse
|
|
import numpy as np
|
|
|
|
import mindspore
|
|
from mindspore import context, Tensor
|
|
from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
|
|
from src.ssd import SSD300, ssd_mobilenet_v2, ssd_mobilenet_v1_fpn
|
|
from src.config import config
|
|
|
|
parser = argparse.ArgumentParser(description='SSD 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="ssd", help="output file name.")
|
|
parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR', help='file format')
|
|
args = parser.parse_args()
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args.device_id)
|
|
|
|
if __name__ == '__main__':
|
|
if config.model == "ssd300":
|
|
net = SSD300(ssd_mobilenet_v2(), config, is_training=False)
|
|
else:
|
|
net = ssd_mobilenet_v1_fpn(config=config)
|
|
|
|
param_dict = load_checkpoint(args.ckpt_file)
|
|
net.init_parameters_data()
|
|
load_param_into_net(net, param_dict)
|
|
net.set_train(False)
|
|
|
|
input_shp = [args.batch_size, 3] + config.img_shape
|
|
input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp), mindspore.float32)
|
|
export(net, input_array, file_name=args.file_name, file_format=args.file_format)
|