fastxtend

fastxtend (fastai extended) is a collection of tools, extensions, and addons for fastai

Feature overview

General Features

• Fused optimizers which are 21 to 293 percent faster relative to fastai native optimizers.
• Flexible metrics which can log on train, valid, or both. Backwards compatible with fastai metrics.
• Easily use multiple losses and log each individual loss on train and valid.
• A simple profiler for profiling fastai training.

Vision

• Increase training speed using ProgressiveResize to automaticly apply progressive resizing.
• Apply MixUp, CutMix, or Augmentations with CutMixUp or CutMixUpAugment.
• Additional image augmentations.
• Support for running fastai batch transforms on CPU.
• More attention and pooling modules
• A flexible implementation of fastai’s XResNet.

Audio

• TensorAudio, TensorSpec, TensorMelSpec objects which maintain metadata and support plotting themselves using librosa.
• A selection of performant audio augmentations inspired by fastaudio and torch-audiomentations.
• Uses TorchAudio to quickly convert TensorAudio waveforms into TensorSpec spectrograms or TensorMelSpec mel spectrograms using the GPU.
• Out of the box support for converting one TensorAudio to one or multiple TensorSpec or TensorMelSpec objects from the Datablock api.
• Audio MixUp and CutMix Callbacks.
• audio_learner which merges multiple TensorSpec or TensorMelSpec objects before passing to the model.

Check out the documentation for additional splitters, callbacks, schedulers, utilities, and more.

Install

fastxtend is avalible on pypi:

pip install fastxtend

To install with dependencies for vision, audio, or all tasks run one of:

pip install fastxtend[vision]

pip install fastxtend[audio]

pip install fastxtend[all]

Or to create an editable install:

git clone https://github.com/warner-benjamin/fastxtend.git
cd fastxtend
pip install -e ".[dev]"

Usage

Like fastai, fastxtend provides safe wildcard imports using python’s __all__.

from fastai.vision.all import *
from fastxtend.vision.all import *

In general, import fastxtend after all fastai imports, as fastxtend modifies fastai. Any method modified by fastxtend is backwards compatible with the original fastai code.

Examples

Use a fused ForEach optimizer:

Learner(..., opt_func=adam(fused=True))

Log an accuracy metric on the training set as a smoothed metric and validation set like normal:

Learner(..., metrics=[Accuracy(log_metric=LogMetric.Train, metric_type=MetricType.Smooth),
                      Accuracy()])

Log multiple losses as individual metrics on train and valid:

mloss = MultiLoss(loss_funcs=[nn.MSELoss, nn.L1Loss], 
                  weights=[1, 3.5], loss_names=['mse_loss', 'l1_loss'])

Learner(..., loss_func=mloss, metrics=RMSE(), cbs=MultiLossCallback)

Apply MixUp, CutMix, or Augmentation while training:

Learner(..., cbs=CutMixUpAugment)

Profile a fastai training loop:

from fastxtend.callback import simpleprofiler

learn = Learner(...).profile()
learn.fit_one_cycle(2, 3e-3)

Train in channels last format:

Learner(...).to_channelslast()

Requirements

fastxtend requires fastai to be installed. See http://docs.fast.ai for installation instructions.