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- # 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 checkpoint file into air, mindir models"""
- import re
- import numpy as np
- from mindspore import Tensor, context, load_checkpoint, export, load_param_into_net
- from src.model_utils.config import config
- from src.model_utils.device_adapter import get_device_id
- from src.model_utils.moxing_adapter import moxing_wrapper
- from src.network_define import MaskRcnn_Mobilenetv1_Infer
-
- def config_(cfg):
- train_cls = [i for i in re.findall(r'[a-zA-Z\s]+', cfg.coco_classes) if i != ' ']
- cfg.coco_classes = np.array(train_cls)
- lss = [int(re.findall(r'[0-9]+', i)[0]) for i in cfg.feature_shapes]
- cfg.feature_shapes = [(lss[2*i], lss[2*i+1]) for i in range(int(len(lss)/2))]
- cfg.roi_layer = dict(type='RoIAlign', out_size=7, mask_out_size=14, sample_num=2)
- cfg.warmup_ratio = 1/3.0
- cfg.mask_shape = (28, 28)
- return cfg
-
- config = config_(config)
-
- 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())
-
- def modelarts_process():
- pass
-
- @moxing_wrapper(pre_process=modelarts_process)
- def export_maskrcnn_mobilenetv1():
- """ export_maskrcnn_mobilenetv1 """
- config.test_batch_size = config.batch_size_export
- net = MaskRcnn_Mobilenetv1_Infer(config)
-
- config.batch_size = config.batch_size_export
-
- 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)
- net.set_train(False)
-
- img_data = Tensor(np.zeros([config.batch_size, 3, config.img_height, config.img_width], np.float16))
- img_metas = Tensor(np.zeros([config.batch_size, 4], np.float16))
-
- input_data = [img_data, img_metas]
- export(net, *input_data, file_name=config.file_name, file_format=config.file_format)
-
- if __name__ == '__main__':
- export_maskrcnn_mobilenetv1()
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