forked from huawei/mindspore2022
69 lines
2.7 KiB
Python
69 lines
2.7 KiB
Python
# 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 os
|
|
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, SsdInferWithDecoder, ssd_mobilenet_v2, ssd_mobilenet_v1_fpn, ssd_resnet50_fpn, ssd_vgg16
|
|
from src.model_utils.config import config
|
|
from src.model_utils.moxing_adapter import moxing_wrapper
|
|
from src.box_utils import default_boxes
|
|
|
|
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 hasattr(config, 'num_ssd_boxes') and config.num_ssd_boxes == -1:
|
|
num = 0
|
|
h, w = config.img_shape
|
|
for i in range(len(config.steps)):
|
|
num += (h // config.steps[i]) * (w // config.steps[i]) * config.num_default[i]
|
|
config.num_ssd_boxes = num
|
|
|
|
if config.model_name == "ssd300":
|
|
net = SSD300(ssd_mobilenet_v2(), config, is_training=False)
|
|
elif config.model_name == "ssd_vgg16":
|
|
net = ssd_vgg16(config=config)
|
|
elif config.model_name == "ssd_mobilenet_v1_fpn":
|
|
net = ssd_mobilenet_v1_fpn(config=config)
|
|
elif config.model_name == "ssd_resnet50_fpn":
|
|
net = ssd_resnet50_fpn(config=config)
|
|
else:
|
|
raise ValueError(f'config.model: {config.model_name} is not supported')
|
|
|
|
net = SsdInferWithDecoder(net, Tensor(default_boxes), config)
|
|
|
|
param_dict = load_checkpoint(config.checkpoint_file_path)
|
|
net.init_parameters_data()
|
|
load_param_into_net(net, param_dict)
|
|
net.set_train(False)
|
|
|
|
input_shp = [config.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=config.file_name, file_format=config.file_format)
|
|
|
|
if __name__ == '__main__':
|
|
run_export()
|