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"""This library gathers utilities for data io operation.
Authors
* Mirco Ravanelli 2020
* Aku Rouhe 2020
* Samuele Cornell 2020
* Adel Moumen 2024
* Pierre Champion 2023
"""
import
collections
.
abc
import
csv
import
gzip
import
math
import
os
import
pathlib
import
re
import
shutil
import
urllib
.
request
from
numbers
import
Number
import
torch
import
tqdm
import
speechbrain
as
sb
def
undo_padding
(
batch
,
lengths
):
"""Produces Python lists given a batch of sentences with
their corresponding relative lengths.
Arguments
---------
batch : torch.Tensor
Batch of sentences gathered in a batch.
lengths : torch.Tensor
Relative length of each sentence in the batch.
Returns
-------
as_list : list
A python list of the corresponding input tensor.
Example
-------
>>> batch = torch.rand([4, 100])
>>> lengths = torch.tensor([0.5, 0.6, 0.7, 1.0])
>>> snt_list = undo_padding(batch, lengths)
>>> len(snt_list)
4
"""
batch_max_len
=
batch
.
shape
[
1
]
as_list
=
[]
for
seq
,
seq_length
in
zip
(
batch
,
lengths
):
actual_size
=
int
(
torch
.
round
(
seq_length
*
batch_max_len
))
seq_true
=
seq
.
narrow
(
0
,
0
,
actual_size
)
as_list
.
append
(
seq_true
.
tolist
())
return
as_list
def
get_all_files
(
dirName
,
match_and
=
None
,
match_or
=
None
,
exclude_and
=
None
,
exclude_or
=
None
):
"""Returns a list of files found within a folder.
Different options can be used to restrict the search to some specific
patterns.
Arguments
---------
dirName : str
The directory to search.
match_and : list
A list that contains patterns to match. The file is
returned if it matches all the entries in `match_and`.
match_or : list
A list that contains patterns to match. The file is
returned if it matches one or more of the entries in `match_or`.
exclude_and : list
A list that contains patterns to match. The file is
returned if it matches none of the entries in `exclude_and`.
exclude_or : list
A list that contains pattern to match. The file is
returned if it fails to match one of the entries in `exclude_or`.
Returns
-------
allFiles : list
The list of files matching the patterns.
Example
-------
>>> get_all_files("tests/samples/RIRs", match_and=["3.wav"])
['tests/samples/RIRs/rir3.wav']
"""
# Match/exclude variable initialization
match_and_entry
=
True
match_or_entry
=
True
exclude_or_entry
=
False
exclude_and_entry
=
False
# Create a list of file and sub directories
listOfFile
=
os
.
listdir
(
dirName
)
allFiles
=
list
()
# Iterate over all the entries
for
entry
in
listOfFile
:
# Create full path
fullPath
=
os
.
path
.
join
(
dirName
,
entry
)
# If entry is a directory then get the list of files in this directory
if
os
.
path
.
isdir
(
fullPath
):
allFiles
=
allFiles
+
get_all_files
(
fullPath
,
match_and
=
match_and
,
match_or
=
match_or
,
exclude_and
=
exclude_and
,
exclude_or
=
exclude_or
,
)
else
:
# Check match_and case
if
match_and
is
not
None
:
match_and_entry
=
False
match_found
=
0
for
ele
in
match_and
:
if
ele
in
fullPath
:
match_found
=
match_found
+
1
if
match_found
==
len
(
match_and
):
match_and_entry
=
True
# Check match_or case
if
match_or
is
not
None
:
match_or_entry
=
False
for
ele
in
match_or
:
if
ele
in
fullPath
:
match_or_entry
=
True
break
# Check exclude_and case
if
exclude_and
is
not
None
:
match_found
=
0
for
ele
in
exclude_and
:
if
ele
in
fullPath
:
match_found
=
match_found
+
1
if
match_found
==
len
(
exclude_and
):
exclude_and_entry
=
True
# Check exclude_or case
if
exclude_or
is
not
None
:
exclude_or_entry
=
False
for
ele
in
exclude_or
:
if
ele
in
fullPath
:
exclude_or_entry
=
True
break
# If needed, append the current file to the output list
if
(
match_and_entry
and
match_or_entry
and
not
(
exclude_and_entry
)
and
not
(
exclude_or_entry
)
):
allFiles
.
append
(
fullPath
)
return
allFiles
def
get_list_from_csv
(
csvfile
,
field
,
delimiter
=
","
,
skipinitialspace
=
True
):
"""Gets a list from the selected field of the input csv file.
Arguments
---------
csvfile: path
Path to the csv file.
field: str
Field of the csv file used to create the list.
delimiter: str
Delimiter of the csv file.
skipinitialspace: bool
Set it to true to skip initial spaces in the entries.
Returns
-------
The list of files in the given field of a csv
"""
lst
=
[]
with
open
(
csvfile
,
newline
=
""
,
encoding
=
"utf-8"
)
as
csvf
:
reader
=
csv
.
DictReader
(
csvf
,
delimiter
=
delimiter
,
skipinitialspace
=
skipinitialspace
)
for
row
in
reader
:
lst
.
append
(
row
[
field
])
return
lst
def
split_list
(
seq
,
num
):
"""Returns a list of splits in the sequence.
Arguments
---------
seq : iterable
The input list, to be split.
num : int
The number of chunks to produce.
Returns
-------
A list of lists, length num and containing all elements of seq.
Example
-------
>>> split_list([1, 2, 3, 4, 5, 6, 7, 8, 9], 4)
[[1, 2], [3, 4], [5, 6], [7, 8, 9]]
"""
# Average length of the chunk
avg
=
len
(
seq
)
/
float
(
num
)
out
=
[]
last
=
0.0
# Creating the chunks
while
last
<
len
(
seq
):
out
.
append
(
seq
[
int
(
last
) :
int
(
last
+
avg
)])
last
+=
avg
return
out
def
recursive_items
(
dictionary
):
"""Yield each (key, value) of a nested dictionary.
Arguments
---------
dictionary : dict
The nested dictionary to list.
Yields
------
`(key, value)` tuples from the dictionary.
Example
-------
>>> rec_dict = {"lev1": {"lev2": {"lev3": "current_val"}}}
>>> [item for item in recursive_items(rec_dict)]
[('lev3', 'current_val')]
"""
for
key
,
value
in
dictionary
.
items
():
if
type
(
value
)
is
dict
:
yield
from
recursive_items
(
value
)
else
:
yield
(
key
,
value
)
def
recursive_update
(
d
,
u
,
must_match
=
False
):
"""Similar function to `dict.update`, but for a nested `dict`.
From: https://stackoverflow.com/a/3233356
If you have to a nested mapping structure, for example:
{"a": 1, "b": {"c": 2}}
Say you want to update the above structure with:
{"b": {"d": 3}}
This function will produce:
{"a": 1, "b": {"c": 2, "d": 3}}
Instead of:
{"a": 1, "b": {"d": 3}}
Arguments
---------
d : dict
Mapping to be updated.
u : dict
Mapping to update with.
must_match : bool
Whether to throw an error if the key in `u` does not exist in `d`.
Example
-------
>>> d = {"a": 1, "b": {"c": 2}}
>>> recursive_update(d, {"b": {"d": 3}})
>>> d
{'a': 1, 'b': {'c': 2, 'd': 3}}
"""
# TODO: Consider cases where u has branch off k, but d does not.
# e.g. d = {"a":1}, u = {"a": {"b": 2 }}
for
k
,
v
in
u
.
items
():
if
isinstance
(
v
,
collections
.
abc
.
Mapping
)
and
k
in
d
:
recursive_update
(
d
.
get
(
k
, {}),
v
)
elif
must_match
and
k
not
in
d
:
raise
KeyError
(
f"Override '
{
k
}
' not found in:
{
[
key
for
key
in
d
.
keys
()]
}
"
)
else
:
d
[
k
]
=
v
def
download_file
(
source
,
dest
,
unpack
=
False
,
dest_unpack
=
None
,
replace_existing
=
False
,
write_permissions
=
False
,
):
"""Downloads the file from the given source and saves it in the given
destination path.
Arguments
---------
source : path or url
Path of the source file. If the source is an URL, it downloads it from
the web.
dest : path
Destination path.
unpack : bool
If True, it unpacks the data in the dest folder.
The archive is preserved.
File formats supported for unpacking/decompression are:
- any format enumerated by `shutil.get_archive_formats()`, usually
including `.tar`, `.tar.gz`, `.zip`.
- plain `.gz` file (when not a `.tar` archive)
Note that you should ALWAYS trust an archive you are extracting, for
security reasons.
dest_unpack: path
Path where to store the unpacked dataset
replace_existing : bool
If True, replaces the existing files.
write_permissions: bool
When set to True, all the files in the dest_unpack directory will be granted write permissions.
This option is active only when unpack=True.
"""
try
:
# make sure all processing reached here before main process create dest_dir
sb
.
utils
.
distributed
.
ddp_barrier
()
if
sb
.
utils
.
distributed
.
if_main_process
():
class
DownloadProgressBar
(
tqdm
.
tqdm
):
"""DownloadProgressBar class."""
def
update_to
(
self
,
b
=
1
,
bsize
=
1
,
tsize
=
None
):
"""Needed to support multigpu training."""
if
tsize
is
not
None
:
self
.
total
=
tsize
self
.
update
(
b
*
bsize
-
self
.
n
)
# Create the destination directory if it doesn't exist
dest_dir
=
pathlib
.
Path
(
dest
).
resolve
().
parent
dest_dir
.
mkdir
(
parents
=
True
,
exist_ok
=
True
)
if
"http"
not
in
source
:
shutil
.
copyfile
(
source
,
dest
)
elif
not
os
.
path
.
isfile
(
dest
)
or
(
os
.
path
.
isfile
(
dest
)
and
replace_existing
):
print
(
f"Downloading
{
source
}
to
{
dest
}
"
)
with
DownloadProgressBar
(
unit
=
"B"
,
unit_scale
=
True
,
miniters
=
1
,
desc
=
source
.
split
(
"/"
)[
-
1
],
)
as
t
:
urllib
.
request
.
urlretrieve
(
source
,
filename
=
dest
,
reporthook
=
t
.
update_to
)
else
:
print
(
f"
{
dest
}
exists. Skipping download"
)
# Unpack if necessary
if
unpack
:
if
dest_unpack
is
None
:
dest_unpack
=
os
.
path
.
dirname
(
dest
)
print
(
f"Extracting
{
dest
}
to
{
dest_unpack
}
"
)
if
dest
.
endswith
(
".gz"
)
and
not
dest
.
endswith
(
".tar.gz"
):
# just a gzip'd file, but not an actual archive.
# merely uncompress it and remove the `.gz`.
with
gzip
.
open
(
dest
,
"rb"
)
as
f_in
:
with
open
(
dest
[:
-
3
],
"wb"
)
as
f_out
:
shutil
.
copyfileobj
(
f_in
,
f_out
)
else
:
shutil
.
unpack_archive
(
dest
,
dest_unpack
)
if
write_permissions
:
set_writing_permissions
(
dest_unpack
)
finally
:
sb
.
utils
.
distributed
.
ddp_barrier
()
def
set_writing_permissions
(
folder_path
):
"""
This function sets user writing permissions to all the files in the given folder.
Arguments
---------
folder_path : folder
Folder whose files will be granted write permissions.
"""
for
root
,
dirs
,
files
in
os
.
walk
(
folder_path
):
for
file_name
in
files
:
file_path
=
os
.
path
.
join
(
root
,
file_name
)
# Set writing permissions (mode 0o666) to the file
os
.
chmod
(
file_path
,
0o666
)
def
pad_right_to
(
tensor
,
target_shape
,
mode
=
"constant"
,
value
=
0
):
"""
This function takes a torch tensor of arbitrary shape and pads it to target
shape by appending values on the right.
Arguments
---------
tensor : torch.Tensor
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 torch.nn.functional.pad documentation.
value : float
Pad value, please refer to torch.nn.functional.pad documentation.
Returns
-------
tensor : torch.Tensor
Padded tensor.
valid_vals : list
List containing proportion for each dimension of original, non-padded values.
"""
assert
len
(
target_shape
)
==
tensor
.
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
]
>=
tensor
.
shape
[
i
], (
"Target shape must be >= original shape for every dim"
)
pads
.
extend
([
0
,
target_shape
[
i
]
-
tensor
.
shape
[
i
]])
valid_vals
.
append
(
tensor
.
shape
[
j
]
/
target_shape
[
j
])
i
-=
1
j
+=
1
tensor
=
torch
.
nn
.
functional
.
pad
(
tensor
,
pads
,
mode
=
mode
,
value
=
value
)
return
tensor
,
valid_vals
def
batch_pad_right
(
tensors
:
list
,
mode
=
"constant"
,
value
=
0
):
"""Given a list of torch tensors it batches them together by padding to the right
on each dimension in order to get same length for all.
Arguments
---------
tensors : list
List of tensor we wish to pad together.
mode : str
Padding mode see torch.nn.functional.pad documentation.
value : float
Padding value see torch.nn.functional.pad documentation.
Returns
-------
tensor : torch.Tensor
Padded tensor.
valid_vals : list
List containing proportion for each dimension of original, non-padded values.
"""
if
not
len
(
tensors
):
raise
IndexError
(
"Tensors list must not be empty"
)
if
len
(
tensors
)
==
1
:
# if there is only one tensor in the batch we simply unsqueeze it.
return
tensors
[
0
].
unsqueeze
(
0
),
torch
.
tensor
([
1.0
])
if
not
(
all
(
[
tensors
[
i
].
ndim
==
tensors
[
0
].
ndim
for
i
in
range
(
1
,
len
(
tensors
))]
)
):
raise
IndexError
(
"All tensors 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
(
tensors
[
0
].
ndim
):
if
dim
!=
0
:
if
not
all
(
[
x
.
shape
[
dim
]
==
tensors
[
0
].
shape
[
dim
]
for
x
in
tensors
[
1
:]]
):
raise
OSError
(
"Tensors should have same dimensions except for the first one"
)
max_shape
.
append
(
max
([
x
.
shape
[
dim
]
for
x
in
tensors
]))
batched
=
[]
valid
=
[]
for
t
in
tensors
:
# 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
=
torch
.
stack
(
batched
)
return
batched
,
torch
.
tensor
(
valid
)
def
split_by_whitespace
(
text
):
"""A very basic functional version of str.split"""
return
text
.
split
()
def
recursive_to
(
data
,
*
args
,
**
kwargs
):
"""Moves data to device, or other type, and handles containers.
Very similar to torch.utils.data._utils.pin_memory.pin_memory,
but applies .to() instead.
"""
if
isinstance
(
data
,
torch
.
Tensor
):
return
data
.
to
(
*
args
,
**
kwargs
)
elif
isinstance
(
data
,
collections
.
abc
.
Mapping
):
return
{
k
:
recursive_to
(
sample
,
*
args
,
**
kwargs
)
for
k
,
sample
in
data
.
items
()
}
elif
isinstance
(
data
,
tuple
)
and
hasattr
(
data
,
"_fields"
):
# namedtuple
return
type
(
data
)(
*
(
recursive_to
(
sample
,
*
args
,
**
kwargs
)
for
sample
in
data
)
)
elif
isinstance
(
data
,
collections
.
abc
.
Sequence
):
return
[
recursive_to
(
sample
,
*
args
,
**
kwargs
)
for
sample
in
data
]
elif
hasattr
(
data
,
"to"
):
return
data
.
to
(
*
args
,
**
kwargs
)
# What should be done with unknown data?
# For now, just return as they are
else
:
return
data
np_str_obj_array_pattern
=
re
.
compile
(
r"[SaUO]"
)
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.
This is modified from:
https://github.com/pytorch/pytorch/blob/c0deb231db76dbea8a9d326401417f7d1ce96ed5/torch/utils/data/_utils/collate.py#L42
"""
elem
=
batch
[
0
]
elem_type
=
type
(
elem
)
if
isinstance
(
elem
,
torch
.
Tensor
):
out
=
None
try
:
if
torch
.
utils
.
data
.
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
torch
.
stack
(
batch
,
0
,
out
=
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
([
torch
.
as_tensor
(
b
)
for
b
in
batch
])
elif
elem
.
shape
==
():
# scalars
return
torch
.
as_tensor
(
batch
)
except
RuntimeError
:
# Unequal size
return
batch
elif
isinstance
(
elem
,
float
):
return
torch
.
tensor
(
batch
,
dtype
=
torch
.
float64
)
elif
isinstance
(
elem
,
int
):
return
torch
.
tensor
(
batch
)
else
:
return
batch
def
split_path
(
path
):
"""Splits a path to source and filename
This also handles URLs and Huggingface hub paths, in addition to
regular paths.
Arguments
---------
path : str or FetchSource
Returns
-------
str
Source
str
Filename
"""
def
split
(
src
):
"""Core function to split path."""
if
"/"
in
src
:
return
src
.
rsplit
(
"/"
,
maxsplit
=
1
)
else
:
# Interpret as path to file in current directory.
return
"./"
,
src
if
isinstance
(
path
,
sb
.
utils
.
fetching
.
FetchSource
):
fetch_from
,
fetch_path
=
path
source
,
filename
=
split
(
fetch_path
)
return
sb
.
utils
.
fetching
.
FetchSource
(
fetch_from
,
source
),
filename
else
:
return
split
(
path
)
def
scalarize
(
value
):
"""Converts a namedtuple or dictionary containing tensors
to their scalar value
Arguments
---------
value: dict or namedtuple
a dictionary or named tuple of tensors
Returns
-------
result: dict
a result dictionary
"""
if
hasattr
(
value
,
"_asdict"
):
value_dict
=
value
.
_asdict
()
else
:
value_dict
=
value
return
{
key
:
item_value
.
item
()
for
key
,
item_value
in
value_dict
.
items
()}
def
unsqueeze_as
(
x
,
target
):
"""Reshape the tensor to be of a shape compatible with the target
tensor, only valid if x.dim() <= y.dim()
Arguments
---------
x: torch.Tensor
the original tensor
target: torch.Tensor
the tensor whose shape
Returns
-------
result: torch.Tensor
a view of tensor x reshaped to a shape compatible with y
"""
return
x
.
view
(
x
.
shape
+
(
1
,)
*
(
target
.
dim
()
-
x
.
dim
()))
def
pad_divisible
(
tensor
,
length
=
None
,
factor
=
2
,
len_dim
=
1
,
pad_value
=
0
):
"""Adds extra padding to the specified dimension of a tensor to make
it divisible by the specified factor. This is useful when passing
variable-length sequences to downsampling UNets or other similar
architectures in which inputs are expected to be divisible by the
downsampling factor
Arguments
---------
tensor: torch.Tensor
the tensor to be padded, of arbitrary dimension
length: torch.Tensor
a 1-D tensor of relative lengths
factor: int
the divisibility factor
len_dim: int
the index of the dimension used as the length
pad_value: int
the value with which outputs will be padded
Returns
-------
tensor_padded: torch.Tensor
the tensor, with additional padding if required
length: torch.Tensor
the adjusted length tensor, if provided
Example
-------
>>> x = torch.tensor([[1, 2, 3, 4], [5, 6, 0, 0]])
>>> lens = torch.tensor([1.0, 0.5])
>>> x_pad, lens_pad = pad_divisible(x, length=lens, factor=5)
>>> x_pad
tensor([[1, 2, 3, 4, 0],
[5, 6, 0, 0, 0]])
>>> lens_pad
tensor([0.8000, 0.4000])
"""
time_dim
=
tensor
.
size
(
len_dim
)
desired_time_dim
=
time_dim
gap
=
time_dim
%
factor
if
gap
>
0
:
desired_time_dim
+=
factor
-
gap
new_shape
=
list
(
tensor
.
shape
)
new_shape
[
len_dim
]
=
desired_time_dim
tensor_padded
,
_
=
pad_right_to
(
tensor
,
new_shape
,
value
=
pad_value
)
# Adjust lengths to the new dimension, post-padding
if
length
is
not
None
:
length
=
length
*
(
time_dim
/
desired_time_dim
)
return
tensor_padded
,
length
def
trim_to_shape
(
tensor
,
shape
):
"""Trims the specified tensor to match the specified shape
Arguments
---------
tensor: torch.Tensor
a tensor
shape: enumerable
the desired shape
Returns
-------
tensor: torch.Tensor
the trimmed tensor
"""
for
dim
,
size
in
enumerate
(
shape
):
tensor
=
tensor
.
narrow
(
dim
,
0
,
size
)
return
tensor
def
trim_as
(
tensor
,
other
):
"""Trims the specified tensor to match the shape of another
tensor (at most)
Arguments
---------
tensor: torch.Tensor:
a tensor
other: torch.Tensor
the tensor whose shape to match
Returns
-------
tensor: torch.Tensor
the trimmed tensor
"""
return
trim_to_shape
(
tensor
,
other
.
shape
)
def
match_shape
(
tensor
,
other
):
"""A swiss-army-knife helper function to match the shape of a tensor to
match that of another tensor - useful for masks, etc.
Arguments
---------
tensor: torch.Tensor:
a tensor
other: torch.Tensor
the tensor whose shape to match
Returns
-------
tensor: torch.Tensor
the tensor with matching shape
"""
result
=
unsqueeze_as
(
tensor
,
other
)
result
=
result
.
expand_as
(
other
)
result
=
trim_as
(
result
,
other
)
return
result
def
batch_shuffle
(
items
,
batch_size
):
"""Shuffles batches of fixed size within a sequence
Arguments
---------
items: sequence
a tensor or an indexable sequence, such as a list
batch_size: int
the batch size
Returns
-------
items: sequence
the original items. If a tensor was passed, a tensor
will be returned. Otherwise, it will return a list
"""
batch_count
=
math
.
floor
(
len
(
items
)
/
batch_size
)
batches
=
torch
.
randperm
(
batch_count
)
batch_idx
=
(
batches
.
unsqueeze
(
-
1
).
expand
(
batch_count
,
batch_size
)
*
batch_size
)
batch_offset
=
torch
.
arange
(
batch_size
).
unsqueeze
(
0
)
batch_idx
+=
batch_offset
tail
=
torch
.
arange
(
batch_count
*
batch_size
,
len
(
items
))
batch_idx
=
torch
.
concat
((
batch_idx
.
flatten
(),
tail
))
if
torch
.
is_tensor
(
items
):
result
=
items
[
batch_idx
]
else
:
result
=
[
items
[
idx
]
for
idx
in
batch_idx
]
return
result
def
concat_padded_features
(
feats
,
lens
,
dim
=
1
,
feats_slice_start
=
None
,
feats_slice_end
=
None
):
"""Concatenates multiple padded feature tensors into a single
padded tensor in a vectorized manner without including the
padding in the final tensor, adding padding only at the end.
The function supports optional relative sicing of the tensors.
One possible use case is to concatenate batches of spectrograms
or audio.
Arguments
---------
feats: list
a list of padded tensors
lens: list
a list of length tensors
dim: int
The dimension on which to perform concatenation
feats_slice_start: list
offsets, relative to the beginning of the sequence, for each
of the tensors being concatenated. This is useful if only
a subsequence of some slices is included
feats_slice_end: list
offsets, relative to the end of the sequence, for each
of the tensors being concatenated. This is useful if only
a subsequence of some slices is included
Returns
-------
out: torch.Tensor
a concatenated tensor
"""
first_item
=
feats
[
0
]
item_lengths
=
torch
.
tensor
([
item
.
size
(
dim
)
for
item
in
feats
]).
to
(
first_item
.
device
)
lens
=
torch
.
concat
([
len_rel
.
unsqueeze
(
0
)
for
len_rel
in
lens
])
lens_abs
=
(
lens
*
item_lengths
.
unsqueeze
(
-
1
)).
int
()
feats_slice_start
=
_offset_to_tensor
(
feats_slice_start
,
lens_abs
)
feats_slice_end
=
_offset_to_tensor
(
feats_slice_end
,
lens_abs
)
out_start
,
out_end
=
_lens_to_boundaries
(
lens_abs
,
feats_slice_start
,
feats_slice_end
,
cumulative
=
True
)
in_start
,
in_end
=
_lens_to_boundaries
(
lens_abs
,
feats_slice_start
,
feats_slice_end
,
cumulative
=
False
)
total_length
=
out_end
.
max
().
int
().
item
()
out_shape
=
list
(
first_item
.
shape
)
out_shape
[
dim
]
=
total_length
out
=
torch
.
zeros
(
out_shape
).
to
(
first_item
.
device
)
for
item
,
item_in_start
,
item_in_end
,
item_out_start
,
item_out_end
in
zip
(
feats
,
in_start
,
in_end
,
out_start
,
out_end
):
in_mask
=
_boundaries_to_mask
(
item
,
item_in_start
,
item_in_end
,
dim
)
out_mask
=
_boundaries_to_mask
(
out
,
item_out_start
,
item_out_end
,
dim
)
out
[
out_mask
]
=
item
[
in_mask
]
out_lens
=
out_end
[
-
1
, :].
float
()
/
total_length
return
out
,
out_lens
def
_offset_to_tensor
(
offset
,
lengths
):
"""Converts a variety of offset representations to a component x batch tensor,
used by concat_padded_features. offset can be a tensor, a list of tensors (where
each element is a tensor of relative offsets similar to lengths), a list of floats
(in which case all batch elements are presumed to have the same offset)
Arguments
---------
offset: list|Tensor
a list or tensor of offsets
lengths: torch.Tensor
a length tensor
Returns
-------
result: torch.Tensor
a tensor of offsets
"""
if
offset
is
None
:
result
=
None
elif
torch
.
is_tensor
(
offset
):
result
=
offset
elif
isinstance
(
offset
,
Number
):
result
=
torch
.
ones_like
(
lengths
)
*
offset
elif
isinstance
(
offset
,
list
):
if
isinstance
(
offset
[
0
],
Number
):
result
=
torch
.
tensor
(
offset
).
unsqueeze
(
-
1
).
to
(
lengths
.
device
)
else
:
result
=
torch
.
concat
([
item
.
unsqueeze
(
0
)
for
item
in
offset
])
else
:
raise
ValueError
(
"The offset must be a number, a tensor or a list of tensors"
)
return
result
def
_lens_to_boundaries
(
lengths
,
slice_start
=
None
,
slice_end
=
None
,
cumulative
=
True
):
"""Converts a tensor of lengths to a tensor of start and end
boundaries, used for concat_padded_features
Arguments
---------
lengths: torch.Tensor
a (component x batch) tensor of absolute lengths
slice_start: torch.Tensor
a (component x batch) tensor of relative start offsets
slice_end: torch.Tensor
a (component x batch) tensor of relative end offsets
cumulative: True
if true, the start of a given component is assumed to
be at the end of the previous component.
if false, all components start at the beginning of the
length dimension
Returns
-------
start: torch.Tensor
the starting boundary
end: torch.Tensor
the ending boundary
"""
batch_size
=
lengths
.
size
(
-
1
)
batch_padding
=
torch
.
zeros
((
1
,
batch_size
)).
int
().
to
(
lengths
.
device
)
if
slice_start
is
None
:
start_offset
=
torch
.
tensor
(
0
).
to
(
lengths
.
device
)
else
:
start_offset
=
(
lengths
*
slice_start
).
floor
().
int
()
if
slice_end
is
None
:
end_offset
=
torch
.
tensor
(
0
).
to
(
lengths
.
device
)
else
:
end_offset
=
(
lengths
*
slice_end
).
floor
().
int
()
if
cumulative
:
effective_lengths
=
lengths
-
start_offset
-
end_offset
effective_lengths_zpad
=
torch
.
concat
(
[
batch_padding
,
effective_lengths
],
dim
=
0
)
start
=
effective_lengths_zpad
.
cumsum
(
dim
=
0
)[:
-
1
, :]
else
:
start
=
torch
.
zeros
(
*
lengths
.
shape
).
to
(
lengths
.
device
)
start
+=
start_offset
end
=
start
+
lengths
-
end_offset
return
start
,
end
def
_boundaries_to_mask
(
target
,
start
,
end
,
len_dim
=
1
):
"""For a given features tensor and tensors of start and end indexes,
computes the corresponding Boolean mask
Arguments
---------
target: torch.Tensor
the target tensor
start: torch.Tensor
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