!7610 add export for mobilenetv2, mobilenetv3, resnext50
Merge pull request !7610 from zhaoting/mobilenetpull/7610/MERGE
mindspore-ci-bot
4 years ago
committed by
Gitee
commit
d96193f62e
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# 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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"""
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mobilenetv2 export mindir.
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"""
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import numpy as np
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from mindspore import Tensor
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from mindspore.train.serialization import export
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from src.config import set_config
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from src.args import export_parse_args
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from src.models import define_net, load_ckpt
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from src.utils import set_context
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if __name__ == '__main__':
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args_opt = export_parse_args()
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cfg = set_config(args_opt)
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set_context(cfg)
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_, _, net = define_net(cfg, args_opt.is_training)
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load_ckpt(net, args_opt.pretrain_ckpt)
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input_shp = [1, 3, cfg.image_height, cfg.image_width]
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input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp).astype(np.float32))
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export(net, input_array, file_name=cfg.export_file, file_format=cfg.export_format)
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# 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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"""
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mobilenetv3 export mindir.
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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 context, Tensor
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from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
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from src.config import config_gpu
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from src.mobilenetV3 import mobilenet_v3_large
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parser = argparse.ArgumentParser(description='Image classification')
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parser.add_argument('--checkpoint_path', type=str, required=True, help='Checkpoint file path')
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parser.add_argument('--device_target', type=str, default="GPU", help='run device_target')
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args_opt = parser.parse_args()
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if __name__ == '__main__':
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cfg = None
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if args_opt.device_target == "GPU":
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cfg = config_gpu
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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else:
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raise ValueError("Unsupported device_target.")
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net = mobilenet_v3_large(num_classes=cfg.num_classes, activation="Softmax")
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param_dict = load_checkpoint(args_opt.checkpoint_path)
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load_param_into_net(net, param_dict)
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input_shp = [1, 3, cfg.image_height, cfg.image_width]
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input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp).astype(np.float32))
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export(net, input_array, file_name=cfg.export_file, file_format=cfg.export_format)
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# 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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"""
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resnext export mindir.
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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 context, Tensor
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from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
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from src.config import config
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from src.image_classification import get_network
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def parse_args():
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"""parse_args"""
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parser = argparse.ArgumentParser('mindspore classification test')
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parser.add_argument('--platform', type=str, default='Ascend', choices=('Ascend', 'GPU'), help='run platform')
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parser.add_argument('--pretrained', type=str, required=True, help='fully path of pretrained model to load. '
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'If it is a direction, it will test all ckpt')
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args, _ = parser.parse_known_args()
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args.image_size = config.image_size
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args.num_classes = config.num_classes
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args.backbone = config.backbone
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args.image_size = list(map(int, config.image_size.split(',')))
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args.image_height = args.image_size[0]
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args.image_width = args.image_size[1]
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args.export_format = config.export_format
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args.export_file = config.export_file
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return args
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if __name__ == '__main__':
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args_export = parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args_export.platform)
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net = get_network(args_export.backbone, num_classes=args_export.num_classes, platform=args_export.platform)
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param_dict = load_checkpoint(args_export.pretrained)
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load_param_into_net(net, param_dict)
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input_shp = [1, 3, args_export.image_height, args_export.image_width]
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input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp).astype(np.float32))
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export(net, input_array, file_name=args_export.export_file, file_format=args_export.export_format)
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