forked from mindspore-Ecosystem/mindspore
54 lines
2.2 KiB
Python
54 lines
2.2 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""export checkpoint file into air, onnx, mindir models"""
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import argparse
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import numpy as np
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from mindspore import Tensor, context, load_checkpoint, export
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import mindspore.common.dtype as mstype
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from src.config import Config_CNNCTC
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from src.cnn_ctc import CNNCTC_Model
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parser = argparse.ArgumentParser(description="CNNCTC_export")
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument("--file_name", type=str, default="cnn_ctc", help="CNN&CTC output air name.")
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parser.add_argument("--file_format", type=str, choices=["AIR", "MINDIR"], default="AIR", help="file format")
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parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
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help="device target")
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parser.add_argument("--ckpt_file", type=str, default="./ckpts/cnn_ctc.ckpt", help="CNN&CTC ckpt file.")
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args_opt = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
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if args_opt.device_target == "Ascend":
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context.set_context(device_id=args_opt.device_id)
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if __name__ == "__main__":
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cfg = Config_CNNCTC()
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ckpt_path = cfg.CKPT_PATH
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if args_opt.ckpt_file != "":
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ckpt_path = args_opt.ckpt_file
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net = CNNCTC_Model(cfg.NUM_CLASS, cfg.HIDDEN_SIZE, cfg.FINAL_FEATURE_WIDTH)
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load_checkpoint(ckpt_path, net=net)
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bs = cfg.TEST_BATCH_SIZE
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input_data = Tensor(np.zeros([bs, 3, cfg.IMG_H, cfg.IMG_W]), mstype.float32)
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export(net, input_data, file_name=args_opt.file_name, file_format=args_opt.file_format)
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