-
Notifications
You must be signed in to change notification settings - Fork 0
/
export.py
46 lines (40 loc) · 1.83 KB
/
export.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
# Copyright 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, onnx, mindir models"""
import numpy as np
import mindspore as ms
from mindspore import Tensor, load_checkpoint, load_param_into_net, export, context
from src.image_classification import CSPDarknet53
from model_utils.config import config
from model_utils.device_adapter import get_device_id
if __name__ == '__main__':
context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
if config.device_target == "Ascend":
context.set_context(device_id=get_device_id())
net = CSPDarknet53(num_classes=config.num_classes)
param_dict = load_checkpoint(config.ckpt_file)
param_dict_new = {}
for k, v in param_dict.items():
if k.startswith('moments.'):
continue
elif k.startswith('network.'):
param_dict_new[k[8:]] = v
else:
param_dict_new[k] = v
load_param_into_net(net, param_dict_new)
net.set_train(False)
input_shape = [config.export_batch_size, 3, config.width, config.height]
input_arr = Tensor(np.random.uniform(0.0, 1.0, size=input_shape), ms.float32)
export(net, input_arr, file_name=config.file_name, file_format=config.file_format)