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🎨 updated tensor.py

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This commit is contained in:
Faylixe
2020-12-07 19:32:38 +01:00
parent ae9269525d
commit 3d3212719f
2 changed files with 125 additions and 75 deletions
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2
setup.py
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@@ -55,7 +55,7 @@ setup(
'norbert==0.2.1',
'numpy<1.19.0,>=1.16.0',
'pandas==1.1.2',
'requests',
'httpx[h2]',
'scipy==1.4.1',
'setuptools>=41.0.0',
'librosa==0.8.0',

198
spleeter/utils/tensor.py
View File

@@ -3,6 +3,9 @@
""" Utility function for tensorflow. """
from typing import Any, Callable, Dict
# pyright: reportMissingImports=false
# pylint: disable=import-error
import tensorflow as tf
import pandas as pd
@@ -13,29 +16,35 @@ __author__ = 'Deezer Research'
__license__ = 'MIT License'
def sync_apply(tensor_dict, func, concat_axis=1):
""" Return a function that applies synchronously the provided func on the
provided dictionnary of tensor. This means that func is applied to the
concatenation of the tensors in tensor_dict. This is useful for performing
random operation that needs the same drawn value on multiple tensor, such
as a random time-crop on both input data and label (the same crop should be
applied to both input data and label, so random crop cannot be applied
separately on each of them).
def sync_apply(
tensor_dict: tf.Tensor,
func: Callable,
concat_axis: int = 1) -> Dict[str, tf.Tensor]:
"""
Return a function that applies synchronously the provided func on the
provided dictionnary of tensor. This means that func is applied to the
concatenation of the tensors in tensor_dict. This is useful for
performing random operation that needs the same drawn value on multiple
tensor, such as a random time-crop on both input data and label (the
same crop should be applied to both input data and label, so random
crop cannot be applied separately on each of them).
IMPORTANT NOTE: all tensor are assumed to be the same shape.
Notes:
All tensor are assumed to be the same shape.
Params:
- tensor_dict: dictionary (key: strings, values: tf.tensor)
a dictionary of tensor.
- func: function
function to be applied to the concatenation of the tensors in
tensor_dict
- concat_axis: int
The axis on which to perform the concatenation.
Parameters:
tensor_dict (Dict[str, tensorflow.Tensor]):
A dictionary of tensor.
func (Callable):
Function to be applied to the concatenation of the tensors in
`tensor_dict`.
concat_axis (int):
The axis on which to perform the concatenation.
Returns:
processed tensors dictionary with the same name (keys) as input
tensor_dict.
Dict[str, tensorflow.Tensor]:
Processed tensors dictionary with the same name (keys) as input
tensor_dict.
"""
if concat_axis not in {0, 1}:
raise NotImplementedError(
@@ -48,26 +57,27 @@ def sync_apply(tensor_dict, func, concat_axis=1):
if concat_axis == 0:
return {
name: processed_concat_tensor[index * D:(index + 1) * D, :, :]
for index, name in enumerate(tensor_dict)
}
for index, name in enumerate(tensor_dict)}
return {
name: processed_concat_tensor[:, index * D:(index + 1) * D, :]
for index, name in enumerate(tensor_dict)
}
for index, name in enumerate(tensor_dict)}
def from_float32_to_uint8(
tensor,
tensor_key='tensor',
min_key='min',
max_key='max'):
tensor: tf.Tensor,
tensor_key: str = 'tensor',
min_key: str = 'min',
max_key: str = 'max') -> tf.Tensor:
"""
:param tensor:
:param tensor_key:
:param min_key:
:param max_key:
:returns:
Parameters:
tensor (tensorflow.Tensor):
tensor_key (str):
min_key (str):
max_key (str):
Returns:
tensorflow.Tensor:
"""
tensor_min = tf.reduce_min(tensor)
tensor_max = tf.reduce_max(tensor)
@@ -76,17 +86,22 @@ def from_float32_to_uint8(
(tensor - tensor_min) / (tensor_max - tensor_min + 1e-16)
* 255.9999, dtype=tf.uint8),
min_key: tensor_min,
max_key: tensor_max
}
max_key: tensor_max}
def from_uint8_to_float32(tensor, tensor_min, tensor_max):
def from_uint8_to_float32(
tensor: tf.Tensor,
tensor_min: tf.Tensor,
tensor_max: tf.Tensor) -> tf.Tensor:
"""
:param tensor:
:param tensor_min:
:param tensor_max:
:returns:
Parameters:
tensor (tensorflow.Tensor):
tensor_min (tensorflow.Tensor):
tensor_max (tensorflow.Tensor):
Returns:
tensorflow.Tensor:
"""
return (
tf.cast(tensor, tf.float32)
@@ -94,23 +109,31 @@ def from_uint8_to_float32(tensor, tensor_min, tensor_max):
/ 255.9999 + tensor_min)
def pad_and_partition(tensor, segment_len):
""" Pad and partition a tensor into segment of len segment_len
along the first dimension. The tensor is padded with 0 in order
to ensure that the first dimension is a multiple of segment_len.
def pad_and_partition(
tensor: tf.Tensor,
segment_len: int) -> tf.Tensor:
"""
Pad and partition a tensor into segment of len `segment_len`
along the first dimension. The tensor is padded with 0 in order
to ensure that the first dimension is a multiple of `segment_len`.
Tensor must be of known fixed rank
Tensor must be of known fixed rank
:Example:
Examples:
>>> tensor = [[1, 2, 3], [4, 5, 6]]
>>> segment_len = 2
>>> pad_and_partition(tensor, segment_len)
[[[1, 2], [4, 5]], [[3, 0], [6, 0]]]
```python
>>> tensor = [[1, 2, 3], [4, 5, 6]]
>>> segment_len = 2
>>> pad_and_partition(tensor, segment_len)
[[[1, 2], [4, 5]], [[3, 0], [6, 0]]]
````
:param tensor:
:param segment_len:
:returns:
Parameters:
tensor (tensorflow.Tensor):
segment_len (int):
Returns:
tensorflow.Tensor:
"""
tensor_size = tf.math.floormod(tf.shape(tensor)[0], segment_len)
pad_size = tf.math.floormod(segment_len - tensor_size, segment_len)
@@ -125,12 +148,15 @@ def pad_and_partition(tensor, segment_len):
axis=0))
def pad_and_reshape(instr_spec, frame_length, F):
def pad_and_reshape(instr_spec, frame_length, F) -> Any:
"""
:param instr_spec:
:param frame_length:
:param F:
:returns:
Parameters:
instr_spec:
frame_length:
F:
Returns:
Any:
"""
spec_shape = tf.shape(instr_spec)
extension_row = tf.zeros((spec_shape[0], spec_shape[1], 1, spec_shape[-1]))
@@ -146,12 +172,18 @@ def pad_and_reshape(instr_spec, frame_length, F):
return processed_instr_spec
def dataset_from_csv(csv_path, **kwargs):
""" Load dataset from a CSV file using Pandas. kwargs if any are
forwarded to the `pandas.read_csv` function.
def dataset_from_csv(csv_path: str, **kwargs) -> Any:
"""
Load dataset from a CSV file using Pandas. kwargs if any are
forwarded to the `pandas.read_csv` function.
:param csv_path: Path of the CSV file to load dataset from.
:returns: Loaded dataset.
Parameters:
csv_path (str):
Path of the CSV file to load dataset from.
Returns:
Any:
Loaded dataset.
"""
df = pd.read_csv(csv_path, **kwargs)
dataset = (
@@ -161,14 +193,23 @@ def dataset_from_csv(csv_path, **kwargs):
return dataset
def check_tensor_shape(tensor_tf, target_shape):
""" Return a Tensorflow boolean graph that indicates whether
sample[features_key] has the specified target shape. Only check
not None entries of target_shape.
def check_tensor_shape(
tensor_tf: tf.Tensor,
target_shape: Any) -> bool:
"""
Return a Tensorflow boolean graph that indicates whether
sample[features_key] has the specified target shape. Only check
not None entries of target_shape.
:param tensor_tf: Tensor to check shape for.
:param target_shape: Target shape to compare tensor to.
:returns: True if shape is valid, False otherwise (as TF boolean).
Parameters:
tensor_tf (tensorflow.Tensor):
Tensor to check shape for.
target_shape (Any):
Target shape to compare tensor to.
Returns:
bool:
`True` if shape is valid, `False` otherwise (as TF boolean).
"""
result = tf.constant(True)
for i, target_length in enumerate(target_shape):
@@ -179,12 +220,21 @@ def check_tensor_shape(tensor_tf, target_shape):
return result
def set_tensor_shape(tensor, tensor_shape):
""" Set shape for a tensor (not in place, as opposed to tf.set_shape)
def set_tensor_shape(
tensor: tf.Tensor,
tensor_shape: Any) -> tf.Tensor:
"""
Set shape for a tensor (not in place, as opposed to tf.set_shape)
:param tensor: Tensor to reshape.
:param tensor_shape: Shape to apply to the tensor.
:returns: A reshaped tensor.
Parameters:
tensor (tensorflow.Tensor):
Tensor to reshape.
tensor_shape (Any):
Shape to apply to the tensor.
Returns:
tensorflow.Tensor:
A reshaped tensor.
"""
# NOTE: That SOUND LIKE IN PLACE HERE ?
tensor.set_shape(tensor_shape)