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- # 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#################
- python export.py
- """
- import argparse
- import ast
- import os
-
- import numpy as np
- import mindspore.common.dtype as mstype
- from mindspore import Tensor, load_checkpoint, load_param_into_net, export, context
-
- from src.pointnet2 import PointNet2
-
- parser = argparse.ArgumentParser(description='PointNet2 export')
- parser.add_argument("--enable_modelarts", type=ast.literal_eval, default=False,
- help="Run on modelArt, default is false.")
- parser.add_argument('--data_url', default=None, help='Directory contains dataset.')
- parser.add_argument('--train_url', default=None, help='Directory contains checkpoint file')
- parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file name.")
- parser.add_argument("--batch_size", type=int, default=1, help="batch size")
- parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='MINDIR', help='file format')
- parser.add_argument('--num_category', default=40, type=int, choices=[10, 40], help='training on ModelNet10/40')
- parser.add_argument('--use_normals', action='store_true', default=False, help='use normals') # channels = 6 if true
- args = parser.parse_args()
-
- context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
- context.set_context(device_id=int(os.getenv('DEVICE_ID', '0')))
- context.set_context(max_call_depth=2048)
-
- if args.enable_modelarts:
- import moxing as mox
-
- local_data_url = "/cache/data"
- mox.file.copy_parallel(args.data_url, local_data_url)
- device_id = int(os.getenv('DEVICE_ID'))
- local_output_url = '/cache/ckpt' + str(device_id)
- mox.file.copy_parallel(src_url=os.path.join(args.train_url, args.ckpt_file),
- dst_url=os.path.join(local_output_url, args.ckpt_file))
- else:
- local_output_url = '.'
-
- if __name__ == '__main__':
-
- net = PointNet2(args.num_category, args.use_normals)
-
- param_dict = load_checkpoint(os.path.join(local_output_url, args.ckpt_file))
- print('load ckpt')
- load_param_into_net(net, param_dict)
- print('load ckpt to net')
- net.set_train(False)
- input_arr = Tensor(np.ones([args.batch_size, 1024, 3]), mstype.float32)
- print('input')
- export(net, input_arr, file_name="PointNet2", file_format=args.file_format)
- if args.enable_modelarts:
- file_name = "PointNet2." + args.file_format.lower()
- mox.file.copy_parallel(src_url=file_name,
- dst_url=os.path.join(args.train_url, file_name))
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