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54 lines
2.5 KiB
54 lines
2.5 KiB
# 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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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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from src.maskrcnn_mobilenetv1.mask_rcnn_mobilenetv1 import Mask_Rcnn_Mobilenetv1
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from src.config import config
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parser = argparse.ArgumentParser(description="maskrcnn mobilnetv1 export")
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument("--batch_size", type=int, default=1, help="batch size")
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parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
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parser.add_argument("--file_name", type=str, default="maskrcnn_mobilenetv1", help="output file name.")
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parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "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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args = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
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if args.device_target == "Ascend":
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context.set_context(device_id=args.device_id)
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if __name__ == '__main__':
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config.test_batch_size = args.batch_size
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net = Mask_Rcnn_Mobilenetv1(config)
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load_checkpoint(args.ckpt_file, net=net)
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net.set_train(False)
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img_data = Tensor(np.zeros([args.batch_size, 3, config.img_height, config.img_width], np.float16))
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img_metas = Tensor(np.zeros([args.batch_size, 4], np.float16))
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gt_bboxes = Tensor(np.zeros([args.batch_size, config.num_gts, 4], np.float16))
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gt_labels = Tensor(np.zeros([args.batch_size, config.num_gts], np.int32))
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gt_num = Tensor(np.zeros([args.batch_size, config.num_gts], np.bool))
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gt_mask = Tensor(np.zeros([args.batch_size, 1, 1, 1], np.bool))
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input_data = [img_data, img_metas, gt_bboxes, gt_labels, gt_num, gt_mask]
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export(net, *input_data, file_name=args.file_name, file_format=args.file_format)
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