You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
88 lines
3.0 KiB
88 lines
3.0 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import paddle
|
|
import numbers
|
|
import numpy as np
|
|
from ..framework import in_dygraph_mode
|
|
from .. import core, layers
|
|
|
|
try:
|
|
from collections.abc import Sequence, Mapping
|
|
except:
|
|
from collections import Sequence, Mapping
|
|
|
|
|
|
def default_collate_fn(batch):
|
|
"""
|
|
Default batch collating function for :code:`paddle.io.DataLoader`,
|
|
batch should be a list of samples, and each sample should be a list
|
|
of fields as follows:
|
|
|
|
[[filed1, filed2, ...], [filed1, filed2, ...], ...]
|
|
|
|
This default collate function zipped each filed together and stack
|
|
each filed as the batch field as follows:
|
|
|
|
[batch_filed1, batch_filed2, ...]
|
|
|
|
Args:
|
|
batch(list of list of numpy array|paddle.Tensor): the batch data, each fields
|
|
should be a numpy array, each sample should be a list of
|
|
fileds, and batch should be a list of sample.
|
|
|
|
Returns:
|
|
a list of numpy array|Paddle.Tensor: collated batch of input batch data,
|
|
fields data type as same as fields in each sample.
|
|
"""
|
|
sample = batch[0]
|
|
if isinstance(sample, np.ndarray):
|
|
batch = np.stack(batch, axis=0)
|
|
return batch
|
|
elif isinstance(sample, paddle.Tensor):
|
|
return layers.stack(batch, axis=0)
|
|
elif isinstance(sample, numbers.Number):
|
|
batch = np.array(batch)
|
|
return batch
|
|
elif isinstance(sample, (str, bytes)):
|
|
return batch
|
|
elif isinstance(sample, Mapping):
|
|
return {
|
|
key: default_collate_fn([d[key] for d in batch])
|
|
for key in sample
|
|
}
|
|
elif isinstance(sample, Sequence):
|
|
sample_fields_num = len(sample)
|
|
if not all(len(sample) == sample_fields_num for sample in iter(batch)):
|
|
raise RuntimeError(
|
|
"fileds number not same among samples in a batch")
|
|
return [default_collate_fn(fields) for fields in zip(*batch)]
|
|
|
|
raise TypeError("batch data con only contains: tensor, numpy.ndarray, "
|
|
"dict, list, number, but got {}".format(type(sample)))
|
|
return outputs
|
|
|
|
|
|
def default_convert_fn(batch):
|
|
if isinstance(batch, (paddle.Tensor, np.ndarray)):
|
|
return batch
|
|
elif isinstance(batch, (str, bytes)):
|
|
return batch
|
|
elif isinstance(batch, Mapping):
|
|
return {key: default_convert_fn(batch[key]) for key in batch}
|
|
elif isinstance(batch, Sequence):
|
|
return [default_convert_fn(d) for d in batch]
|
|
else:
|
|
return batch
|