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113 lines
4.0 KiB
113 lines
4.0 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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import paddle
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import numbers
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import numpy as np
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from ..framework import in_dygraph_mode
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from .. import core, layers
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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 and paddle.Tensor, 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, paddle.Tensor):
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return layers.stack(batch, axis=0)
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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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return outputs
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def default_convert_fn(batch):
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"""
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Default batch converting 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 and paddle.Tensor.
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.. note::
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This function is default :attr:`collate_fn` in **Distable
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automatic batching** mode, for **Distable automatic batching**
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mode, please ses :attr:`paddle.io.DataLoader`
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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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if isinstance(batch, (paddle.Tensor, np.ndarray)):
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return batch
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elif isinstance(batch, (str, bytes)):
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return batch
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elif isinstance(batch, Mapping):
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return {key: default_convert_fn(batch[key]) for key in batch}
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elif isinstance(batch, Sequence):
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return [default_convert_fn(d) for d in batch]
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else:
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return batch
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