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59 lines
2.3 KiB
59 lines
2.3 KiB
# Copyright (c) 2020 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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import numpy as np |
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def config_check(cfg, train_dataset=None, val_dataset=None): |
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""" |
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To check config。 |
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Args: |
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cfg (paddleseg.cvlibs.Config): An object of paddleseg.cvlibs.Config. |
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train_dataset (paddle.io.Dataset): Used to read and process training datasets. |
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val_dataset (paddle.io.Dataset, optional): Used to read and process validation datasets. |
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""" |
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num_classes_check(cfg, train_dataset, val_dataset) |
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def num_classes_check(cfg, train_dataset, val_dataset): |
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"""" |
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Check that the num_classes in model, train_dataset and val_dataset is consistent. |
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""" |
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num_classes_set = set() |
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if train_dataset and hasattr(train_dataset, 'num_classes'): |
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num_classes_set.add(train_dataset.num_classes) |
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if val_dataset and hasattr(val_dataset, 'num_classes'): |
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num_classes_set.add(val_dataset.num_classes) |
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if cfg.dic.get('model', None) and cfg.dic['model'].get('num_classes', None): |
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num_classes_set.add(cfg.dic['model'].get('num_classes')) |
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if (not cfg.train_dataset) and (not cfg.val_dataset): |
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raise ValueError( |
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'One of `train_dataset` or `val_dataset should be given, but there are none.' |
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) |
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if len(num_classes_set) == 0: |
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raise ValueError( |
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'`num_classes` is not found. Please set it in model, train_dataset or val_dataset' |
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) |
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elif len(num_classes_set) > 1: |
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raise ValueError( |
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'`num_classes` is not consistent: {}. Please set it consistently in model or train_dataset or val_dataset' |
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.format(num_classes_set)) |
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else: |
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num_classes = num_classes_set.pop() |
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if train_dataset: |
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train_dataset.num_classes = num_classes |
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if val_dataset: |
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val_dataset.num_classes = num_classes
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