# Copyright (c) 2023 speechbrain Authors. All Rights Reserved.
# Copyright (c) 2023 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.
#
# Modified from speechbrain 2023 (https://github.com/speechbrain/speechbrain/blob/develop/speechbrain/utils/data_utils.py)
import collections.abc
import csv
import os
import pathlib
import re
import shutil
import urllib.request
import numpy as np
import paddle
import tqdm
def batch_pad_right(array: list, mode="constant", value=0):
"""Given a list of paddle tensors it batches them together by padding to the right
on each dimension in order to get same length for all.
Parameters
----------
array : list
List of tensor we wish to pad together.
mode : str
Padding mode see numpy.pad documentation.
value : float
Padding value see numpy.pad documentation.
Returns
-------
batched : numpy array
Padded numpy array.
valid_vals : list
List containing proportion for each dimension of original, non-padded values.
"""
if not len(array):
raise IndexError("Tensors list must not be empty")
if len(array) == 1:
# if there is only one tensor in the batch we simply unsqueeze it.
return np.expand_dims(array[0], 0), np.array([1.0], dtype="float32")
if not (any(
[array[i].ndim == array[0].ndim for i in range(1, len(array))])):
raise IndexError("All array must have same number of dimensions")
# FIXME we limit the support here: we allow padding of only the first dimension
# need to remove this when feat extraction is updated to handle multichannel.
max_shape = []
for dim in range(array[0].ndim):
if dim != 0:
if not all(
[x.shape[dim] == array[0].shape[dim] for x in array[1:]]):
raise EnvironmentError(
"Tensors should have same dimensions except for the first one"
)
max_shape.append(max([x.shape[dim] for x in array]))
batched = []
valid = []
for t in array:
# for each tensor we apply pad_right_to
padded, valid_percent = pad_right_to(
t, max_shape, mode=mode, value=value)
batched.append(padded)
valid.append(valid_percent[0])
batched = np.stack(batched)
return batched, np.array(valid, dtype="float32")
np_str_obj_array_pattern = re.compile(r"[SaUO]")
def pad_right_to(
array: np.ndarray,
target_shape: (list, tuple),
mode="constant",
value=0, ):
"""
This function takes a numpy of arbitrary shape and pads it to target
shape by appending values on the right.
Parameters
----------
array : input numpy array
Input tensor whose dimension we need to pad.
target_shape : (list, tuple)
Target shape we want for the target tensor its len must be equal to tensor.ndim
mode : str
Pad mode, please refer to numpy.pad documentation.
value : float
Pad value, please refer to numpy.pad documentation.
Returns
-------
array : numpy array
Padded numpy array.
valid_vals : list
List containing proportion for each dimension of original, non-padded values.
"""
assert len(target_shape) == array.ndim
pads = [] # this contains the abs length of the padding for each dimension.
valid_vals = [] # this contains the relative lengths for each dimension.
i = len(target_shape) - 1 # iterating over target_shape ndims
j = 0
while i >= 0:
assert (target_shape[i] >= array.shape[i]
), "Target shape must be >= original shape for every dim"
pads.extend([0, target_shape[i] - array.shape[i]])
valid_vals.append(array.shape[j] / target_shape[j])
i -= 1
j += 1
array = np.pad(array, pads, mode, constant_values=(value, value))
return array, valid_vals
def mod_default_collate(batch):
"""Makes a tensor from list of batch values.
Note that this doesn't need to zip(*) values together
as PaddedBatch connects them already (by key).
Here the idea is not to error out.
"""
elem = batch[0]
elem_type = type(elem)
if isinstance(elem, paddle.Tensor):
out = None
try:
if paddle.io.get_worker_info() is not None:
# If we're in a background process, concatenate directly into a
# shared memory tensor to avoid an extra copy
numel = sum([x.numel() for x in batch])
storage = elem.storage()._new_shared(numel)
out = elem.new(storage)
return paddle.stack(batch, 0, name=out)
except RuntimeError: # Unequal size:
return batch
elif (elem_type.__module__ == "numpy" and elem_type.__name__ != "str_" and
elem_type.__name__ != "string_"):
try:
if (elem_type.__name__ == "ndarray" or
elem_type.__name__ == "memmap"):
# array of string classes and object
if np_str_obj_array_pattern.search(elem.dtype.str) is not None:
return batch
return mod_default_collate(
[paddle.to_tensor(b, dtype=b.dtype) for b in batch])
elif elem.shape == (): # scalars
return paddle.to_tensor(batch, dtype=batch.dtype)
except RuntimeError: # Unequal size
return batch
elif isinstance(elem, float):
return paddle.to_tensor(batch, dtype=paddle.float64)
elif isinstance(elem, int):
return paddle.to_tensor(batch, dtype=paddle.int64)
else:
return batch