torchoutil

Collection of functions and modules to help development in PyTorch.

Installation

bash pip install torchoutil

The main requirement is PyTorch.

To check if the package is installed and show the package version, you can use the following command in your terminal: bash torchoutil-info

This library works on all Python versions >=3.8, all PyTorch versions >= 3.10, and on Linux, Mac and Windows systems.

Examples

torchoutil functions and modules can be used like torch ones. The default acronym for torchoutil is to.

Label conversions

Supports multiclass labels conversions between probabilities, classes indices, classes names and onehot encoding.

```python import torchoutil as to

probs = to.astensor([[0.9, 0.1], [0.4, 0.6]]) names = to.probstoname(probs, idxto_name={0: "Cat", 1: "Dog"})

["Cat", "Dog"]

```

This package also supports multilabel labels conversions between probabilities, classes multi-indices, classes multi-names and multihot encoding.

```python import torchoutil as to

multihot = to.astensor([[1, 0, 0], [0, 1, 1], [0, 0, 0]]) indices = to.multihotto_indices(multihot)

[[0], [1, 2], []]

```

Typing

```python import torchoutil as to

x1 = to.astensor([1, 2]) print(isinstance(x1, to.Tensor2D)) # False x2 = to.astensor([[1, 2], [3, 4]]) print(isinstance(x2, to.Tensor2D)) # True ```

```python import torchoutil as to

x1 = to.as_tensor([1, 2], dtype=to.int) print(isinstance(x1, to.SignedIntegerTensor)) # True

x2 = to.as_tensor([1, 2], dtype=to.long) print(isinstance(x2, to.SignedIntegerTensor)) # True

x3 = to.as_tensor([1, 2], dtype=to.float) print(isinstance(x3, to.SignedIntegerTensor)) # False ```

Padding

```python import torchoutil as to

x1 = to.rand(10, 3, 1) x2 = to.paddim(x, targetlength=5, dim=1, pad_value=-1)

x2 has shape (10, 5, 1)

```

```python import torchoutil as to

tensors = [to.rand(10, 2), to.rand(5, 3), to.rand(0, 5)] padded = to.padandstackrec(tensors, padvalue=0)

padded has shape (10, 5)

```

Masking

```python import torchoutil as to

x = to.astensor([3, 1, 2]) mask = to.lengthstononpadmask(x, maxlen=4)

Each row i contains x[i] True values for non-padding mask

tensor([[True, True, True, False],

[True, False, False, False],

[True, True, False, False]])

```

```python import torchoutil as to

x = to.astensor([1, 2, 3, 4]) mask = to.astensor([True, True, False, False]) result = to.masked_mean(x, mask)

result contains the mean of the values marked as True: 1.5

```

Others tensors manipulations!

```python import torchoutil as to

x = to.astensor([1, 2, 3, 4]) result = to.insertat_indices(x, indices=[0, 2], values=5)

result contains tensor with inserted values: tensor([5, 1, 2, 5, 3, 4])

```

```python import torchoutil as to

perm = to.randperm(10) invperm = to.getinverse_perm(perm)

x1 = to.rand(10) x2 = x1[perm] x3 = x2[inv_perm]

inv_perm are indices that allow us to get x3 from x2, i.e. x1 == x3 here

```

Pre-compute datasets to HDF files

Here is an example of pre-computing spectrograms of torchaudio SPEECHCOMMANDS dataset, using pack_dataset function:

```python from torchaudio.datasets import SPEECHCOMMANDS from torchaudio.transforms import Spectrogram from torchoutil import nn from torchoutil.extras.hdf import packtohdf

speechcommandsroot = "path/to/speechcommands" packedroot = "path/to/packed_dataset.hdf"

dataset = SPEECHCOMMANDS(speechcommandsroot, download=True, subset="validation")

dataset[0] is a tuple, contains waveform and other metadata

class MyTransform(nn.Module): def init(self) -> None: super().init() self.spectrogram_extractor = Spectrogram()

def forward(self, item):
    waveform = item[0]
    spectrogram = self.spectrogram_extractor(waveform)
    return (spectrogram,) + item[1:]

packtohdf(dataset, packed_root, MyTransform()) ```

Then you can load the pre-computed dataset using HDFDataset: ```python from torchoutil.extras.hdf import HDFDataset

packedroot = "path/to/packeddataset.hdf" packeddataset = HDFDataset(packedroot) packed_dataset[0] # == first transformed item, i.e. transform(dataset[0]) ```

Extras requirements

torchoutil also provides additional modules when some specific package are already installed in your environment. All extras can be installed with pip install torchoutil[extras]

• If tensorboard is installed, the function load_event_file can be used. It is useful to load manually all data contained in an tensorboard event file.
• If numpy is installed, the classes NumpyToTensor and ToNumpy can be used and their related function. It is meant to be used to compose dynamic transforms into Sequential module.
• If h5py is installed, the function pack_to_hdf and class HDFDataset can be used. Can be used to pack/read dataset to HDF files, and supports variable-length sequences of data.
• If pyyaml is installed, the functions to_yaml and load_yaml can be used.

Contact

Maintainer: - Étienne Labbé "Labbeti": labbeti.pub@gmail.com