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- # 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, onnx, mindir models"""
- import numpy as np
-
- import mindspore.common.dtype as mstype
- 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
- from src.model_utils.device_adapter import get_device_id
-
-
- 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())
-
- if config.backbone in ("resnet_v1.5_50", "resnet_v1_101", "resnet_v1_152"):
- from src.FasterRcnn.faster_rcnn_resnet import FasterRcnn_Infer
- elif config.backbone == "resnet_v1_50":
- from src.FasterRcnn.faster_rcnn_resnet50v1 import FasterRcnn_Infer
-
- def modelarts_pre_process():
- pass
-
- @moxing_wrapper(pre_process=modelarts_pre_process)
- def export_fasterrcnn():
- """ export_fasterrcnn """
- net = FasterRcnn_Infer(config=config)
-
- param_dict = load_checkpoint(config.ckpt_file)
-
- param_dict_new = {}
- for key, value in param_dict.items():
- param_dict_new["network." + key] = value
-
- load_param_into_net(net, param_dict_new)
-
- device_type = "Ascend" if context.get_context("device_target") == "Ascend" else "Others"
- if device_type == "Ascend":
- net.to_float(mstype.float16)
-
- img = Tensor(np.zeros([config.test_batch_size, 3, config.img_height, config.img_width]), mstype.float32)
- img_metas = Tensor(np.random.uniform(0.0, 1.0, size=[config.test_batch_size, 4]), mstype.float32)
-
- export(net, img, img_metas, file_name=config.file_name, file_format=config.file_format)
-
- if __name__ == '__main__':
- export_fasterrcnn()
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