Introducing fastxtend

A Collection of Tools, Extensions, & Addons for fastai

Benjamin Warner

June 6, 2022

4 min. read

My new python package, fastxtend, is a collection of tools, extensions, and addons for fastaiSome fastxtend features were originally intended to be added directly to fastai, but was put on hold pending the start of active development of fastai version 3. version 2. You can read the documentation, view the source code and/or contribute on GitHub, or install from pypi.

Keep reading as I highlight some of fastxtend’s current best features.

# Install fastxtend

Fastxtend is on pypi and can be installed from pip:

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]

# Import fastxtend

Like fastai, fastxtend provides safe wildcard importsIf you don’t like using wildcard imports, you can import methods, classes, or modules directly too. 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.

# Metrics

Fastxtend metricsFastxtend metrics were originally created for my cloud segmentation project. are a backward compatible, reimplementation of fastai metrics which allows almost anyThe exception being AvgSmoothMetricX which can only log on the training set. metric to independently log on train, valid, or both. Like fastai, by default all fastxtend metrics log only on the validation set.

This adds support for easily logging individual losses when training with multiple lossesEasy multiple loss support was my original reason for the metrics refactor..

All fastxtend metrics inherit from fastai’s Metric and run on Learner via a modified Recorder callback. Fastxtend metrics can:

• Mix and match with any fastai metrics or other fastai compatible metrics
• Log metrics for training set, validation set, or both
• Change the metric type on creation for fastxtend nativescikit-Learn derived metrics are currently always AccumMetricX metrics. metrics
• Set the metric name on creation

# Metrics Examples

Fastxtend metricsAll fastxtend metrics require a class initialization. can be mixed with fastai metrics:

Learner(..., metrics=[accuracy, Accuracy()])

To log accuracy on the training set as a smooth metric and log accuracy on the validation set like normal, create the following two metrics:

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

For more information, check out the metrics documentation.

# Log Multiple Losses

MultiLoss is a simple loss function wrapper which in conjunction with MultiLossCallback to log individual losses as metricsUsing fastxtend metrics under the hood. while training.

An example of using MultiLoss to log MSE and L1 loss on a regression task:

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

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

which results in the following output.

Two epochs of Multiloss Training Output

epoch	train_loss	train_mse_loss	train_l1_loss	valid_loss	valid_mse_loss	valid_l1_loss	valid_rmse	time
0	23.598301	12.719514	10.878788	17.910727	9.067028	8.843699	3.011151	00:00
1	22.448792	11.937573	10.511218	15.481797	7.464430	8.017367	2.732111	00:00

MultiTargetLoss inherits from Multiloss and supports multiple predictions and targets each with one loss functionMore complicated multi-loss scenarios can inherit from MultiLoss to add support for multiple losses on multiple targets..

For more information, check out the MultiLoss documentation.

# Simple Profiler

Inspired by PyTorch Lightning’s SimpleProfiler, Simple Profiler allows easy profiling by piping .profile() to an initialized Learner and then fitting like normal.

from fastxtend.callback import simpleprofiler

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

Raw results are stored in the callback for later use, results are optionally displayedFormatted or plain, and with or without markdown for copying to a report or blog. in a table, optionally exported to a csv file, and formatted and raw results are automatically logged to Weights and Biases if using fastai’s WandBCallback.

Example of Simple Profiler Output

Phase	Action	Mean Duration	Duration Std Dev	Number of Calls	Total Time	Percent of Total
fit	fit	-	-	1	404.7 s	100%
	epoch	202.4 s	2.721 s	2	404.7 s	100%
	train	178.4 s	2.020 s	2	356.7 s	88%
	validate	23.99 s	699.9ms	2	47.98 s	12%
train	batch	1.203 s	293.3ms	294	353.7 s	87%
	step	726.8ms	35.05ms	294	213.7 s	53%
	backward	411.3ms	159.6ms	294	120.9 s	30%
	pred	32.90ms	107.5ms	294	9.673 s	2%
	draw	28.49ms	78.12ms	294	8.375 s	2%
	zero_grad	2.437ms	324.4µs	294	716.4ms	0%
	loss	958.6µs	107.4µs	294	281.8ms	0%
valid	batch	72.83ms	176.0ms	124	9.031 s	2%
	pred	40.13ms	126.7ms	124	4.976 s	1%
	draw	31.58ms	121.3ms	124	3.916 s	1%
	loss	967.8µs	1.034ms	124	120.0ms	0%

Simple Profiler is one of the few callbacks not imported by fastxtend’s wildcard imports as it modifies the fastai training loopThis is just to be extra safe. I have not observed any differences when training with simple profiler or without. by adding the draw step.

Check out the simple profiler documentation for more information.

# Audio

Fastxtend audio contains an audio module inspired by the fastaudioFastaudio is no longer under active development. project.

It consists of:

• TensorAudio, TensorSpec, TensorMel 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 TensorMel mel spectrograms using the GPU.
• Out of the box support for converting one TensorAudio to one or multiple TensorSpec or TensorMel objects from the Datablock api.
• Audio MixUp and CutMix Callbacks.
• audio_learner which mergesAnd preserves audio channel order. multiple TensorSpec or TensorMel objects before passing to the model.

# Vision

Fastxtend vision has:

• A more flexible implementation of fastai’s xresnetEasily use a custom_head, set stem_pool, block_pool, and head_pool pooling layers on creation, and per ResBlock stochastic depth stoch_depth.
• More attention modules
• MaxBlurPool and BlurPool from kornia compatible with fastai & fastxtend’s xresnet
• Additional image augmentations
• Support for running fastai batch transforms on CPU

# LRFinder

Fastai’s LRFinder only restores the model and optimizer state. Fastxtend’s LRFinder additionally restores the DataLoader and random state, so as far as your runtime is concerned you might as well have never ran it.

# But Wait, There’s More

Check out the documentation for additionalFastxtend is under active development with new features added regularly. splitters, callbacks, schedulers, utilities, and more.