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72 lines
2.8 KiB
72 lines
2.8 KiB
# Copyright (c) 2022 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 paddle |
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import numbers |
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import numpy as np |
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try: |
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from collections.abc import Sequence, Mapping |
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except: |
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from collections import Sequence, Mapping |
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def default_collate_fn(batch): |
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""" |
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Default batch collating function for :code:`paddle.io.DataLoader`, |
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get input data as a list of sample datas, each element in list |
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if the data of a sample, and sample data should composed of list, |
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dictionary, string, number, numpy array, this |
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function will parse input data recursively and stack number, |
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numpy array and paddle.Tensor datas as batch datas. e.g. for |
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following input data: |
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[{'image': np.array(shape=[3, 224, 224]), 'label': 1}, |
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{'image': np.array(shape=[3, 224, 224]), 'label': 3}, |
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{'image': np.array(shape=[3, 224, 224]), 'label': 4}, |
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{'image': np.array(shape=[3, 224, 224]), 'label': 5},] |
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This default collate function zipped each number and numpy array |
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field together and stack each field as the batch field as follows: |
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{'image': np.array(shape=[4, 3, 224, 224]), 'label': np.array([1, 3, 4, 5])} |
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Args: |
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batch(list of sample data): batch should be a list of sample data. |
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Returns: |
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Batched data: batched each number, numpy array and paddle.Tensor |
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in input data. |
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""" |
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sample = batch[0] |
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if isinstance(sample, np.ndarray): |
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batch = np.stack(batch, axis=0) |
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return batch |
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elif isinstance(sample, numbers.Number): |
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batch = np.array(batch) |
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return batch |
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elif isinstance(sample, (str, bytes)): |
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return batch |
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elif isinstance(sample, Mapping): |
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return { |
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key: default_collate_fn([d[key] for d in batch]) |
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for key in sample |
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} |
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elif isinstance(sample, Sequence): |
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sample_fields_num = len(sample) |
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if not all(len(sample) == sample_fields_num for sample in iter(batch)): |
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raise RuntimeError( |
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"fileds number not same among samples in a batch") |
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return [default_collate_fn(fields) for fields in zip(*batch)] |
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raise TypeError("batch data con only contains: tensor, numpy.ndarray, " |
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"dict, list, number, but got {}".format(type(sample)))
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