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46 lines
1.8 KiB
46 lines
1.8 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. |
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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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from .operators import * |
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from .batch_operators import BatchRandomResize, BatchRandomResizeByShort, _BatchPadding |
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from paddlers import transforms as T |
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def arrange_transforms(model_type, transforms, mode='train'): |
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# 给transforms添加arrange操作 |
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if model_type == 'segmenter': |
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if mode == 'eval': |
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transforms.apply_im_only = True |
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else: |
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transforms.apply_im_only = False |
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arrange_transform = ArrangeSegmenter(mode) |
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elif model_type == 'classifier': |
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arrange_transform = ArrangeClassifier(mode) |
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elif model_type == 'detector': |
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arrange_transform = ArrangeDetector(mode) |
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else: |
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raise Exception("Unrecognized model type: {}".format(model_type)) |
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transforms.arrange_outputs = arrange_transform |
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def build_transforms(transforms_info): |
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transforms = list() |
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for op_info in transforms_info: |
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op_name = list(op_info.keys())[0] |
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op_attr = op_info[op_name] |
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if not hasattr(T, op_name): |
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raise Exception("There's no transform named '{}'".format(op_name)) |
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transforms.append(getattr(T, op_name)(**op_attr)) |
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eval_transforms = T.Compose(transforms) |
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return eval_transforms
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