All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog.
-
Added support
learn2learn
training_strategy forImageClassifier
(#737) -
Added
vissl
training_strategies forImageEmbedder
(#682) -
Added support for
from_data_frame
toTextClassificationData
(#785)
- Changed the default
num_workers
on linux to0
(matching the default for other OS) (#759)
- Fixed a bug where additional kwargs (e.g. sampler) passed to tabular data would be ignored (#792)
- Added support for (input, target) style datasets (e.g. torchvision) to the from_datasets method (#552)
- Added support for
from_csv
andfrom_data_frame
toImageClassificationData
(#556) - Added SimCLR, SwAV, Barlow-twins pretrained weights for resnet50 backbone in ImageClassifier task (#560)
- Added support for Semantic Segmentation backbones and heads from
segmentation-models.pytorch
(#562) - Added support for nesting of
Task
objects (#575) - Added
PointCloudSegmentation
Task (#566) - Added
PointCloudObjectDetection
Task (#600) - Added a
GraphClassifier
task (#73) - Added the option to pass
pretrained
as a string toSemanticSegmentation
to change pretrained weights to load fromsegmentation-models.pytorch
(#587) - Added support for
field
parameter for loadng JSON based datasets in text tasks. (#585) - Added
AudioClassificationData
and an example for classifying audio spectrograms (#594) - Added a
SpeechRecognition
task for speech to text using Wav2Vec (#586) - Added Flash Zero, a zero code command line ML platform built with flash (#611)
- Added support for
.npy
and.npz
files toImageClassificationData
andAudioClassificationData
(#651) - Added support for
from_csv
to theAudioClassificationData
(#651) - Added option to pass a
resolver
to thefrom_csv
andfrom_pandas
methods ofImageClassificationData
, which is used to resolve filenames given IDs (#651) - Added integration with IceVision for the
ObjectDetector
(#608) - Added keypoint detection task (#608)
- Added instance segmentation task (#608)
- Added Torch ORT support to Transformer based tasks (#667)
- Added support for flash zero with the
InstanceSegmentation
andKeypointDetector
tasks (#672) - Added support for
in_chans
argument to the flash ResNet to control the expected number of input channels (#673) - Added a
QuestionAnswering
task for extractive question answering (#607) - Added automatic unwrapping of IceVision prediction objects (#727)
- Added support for the
ObjectDetector
with FiftyOne (#727) - Added support for MP3 files to the
SpeechRecognition
task with librosa (#726) - Added support for
from_numpy
andfrom_tensors
toAudioClassificationData
(#745)
- Changed how pretrained flag works for loading weights for ImageClassifier task (#560)
- Removed bolts pretrained weights for SSL from ImageClassifier task (#560)
- Changed the behaviour of the
sampler
argument of theDataModule
to take aSampler
type rather than instantiated object (#651) - Changed arguments to
ObjectDetector
, usehead
instead ofmodel
and append_fpn
to the backbone name instead of thefpn
argument (#608)
- Fixed a bug where serve sanity checking would not be triggered using the latest PyTorchLightning version (#493)
- Fixed a bug where train and validation metrics weren't being correctly computed (#559)
- Fixed a bug where an uncaught ValueError could be raised when checking if a module is available (#615)
- Fixed a bug where some tasks were not compatible with PyTorch 1.7 due to use of
torch.jit.isinstance
(#611) - Fixed a bug where custom samplers would not be properly forwarded to the data loader (#651)
- Fixed a bug where it was not possible to pass no metrics to the
ImageClassifier
orTestClassifier
(#660) - Fixed a bug where
drop_last
would be set to True during prediction and testing (#671) - Fixed a bug where flash was not compatible with pytorch-lightning >= 1.4.3 (#690)
- Added integration with FiftyOne (#360)
- Added
flash.serve
(#399) - Added support for
torch.jit
to tasks where possible and documented task JIT compatibility (#389) - Added option to provide a
Sampler
to theDataModule
to use when creating aDataLoader
(#390) - Added support for multi-label text classification and toxic comments example (#401)
- Added a sanity checking feature to flash.serve (#423)
- Split
backbone
argument toSemanticSegmentation
intobackbone
andhead
arguments (#412)
- Fixed a bug where the
DefaultDataKeys.METADATA
couldn't be a dict (#393) - Fixed a bug where the
SemanticSegmentation
task would not work as expected with finetuning callbacks (#412) - Fixed a bug where predict batches could not be visualized with
ImageClassificationData
(#438)
- Fixed a bug where
flash.Trainer.from_argparse_args
+finetune
would not work (#382)
- Added
deeplabv3
,lraspp
, andunet
backbones for theSemanticSegmentation
task (#370)
- Changed the installation command for extra features (#346)
- Change resize interpolation default mode to nearest (#352)
- Deprecated
SemanticSegmentation
backbone namestorchvision/fcn_resnet50
andtorchvision/fcn_resnet101
, usefc_resnet50
andfcn_resnet101
instead (#370)
- Fixed
flash.Trainer.add_argparse_args
not adding any arguments (#343) - Fixed a bug where the translation task wasn't decoding tokens properly (#332)
- Fixed a bug where huggingface tokenizers were sometimes being pickled (#332)
- Fixed issue with
KorniaParallelTransforms
to assure to share the random state between transforms (#351) - Fixed a bug where using
val_split
withoverfit_batches
would give an infinite recursion (#375) - Fixed a bug where some timm models were mistakenly given a
global_pool
argument (#377) - Fixed
flash.Trainer.from_argparse_args
not passing arguments correctly (#380)
- Added DataPipeline API (#188 #141 #207)
- Added timm integration (#196)
- Added BaseViz Callback (#201)
- Added backbone API (#204)
- Added support for Iterable auto dataset (#227)
- Added multi label support (#230)
- Added support for schedulers (#232)
- Added visualisation callback for image classification (#228)
- Added Video Classification task (#216)
- Added Dino backbone for image classification (#259)
- Added Data Sources API (#256 #264 #272)
- Refactor preprocess_cls to preprocess, add Serializer, add DataPipelineState (#229)
- Added Semantic Segmentation task (#239 #287 #290)
- Added Object detection prediction example (#283)
- Added Style Transfer task and accompanying finetuning and prediction examples (#262)
- Added a Template task and tutorials showing how to contribute a task to flash (#306)
- Rename valid_ to val_ (#197)
- Refactor preprocess_cls to preprocess, add Serializer, add DataPipelineState (#229)
- Fix DataPipeline resolution in Task (#212)
- Fixed a bug where the backbone used in summarization was not correctly passed to the postprocess (#296)
- Added TIMM integration as backbones (#196)
- Fixed nltk.download (#210)
-
Switch to use
torchmetrics
(#169) -
Better support for
optimizer
andschedulers
(#232) -
Update lightning version to v1.2 (#133)
-
Fixed classification softmax (#169)
-
Fixed a bug where loading from a local checkpoint that had
pretrained=True
without an internet connection would sometimes raise an error (#237) -
Don't download data if exists (#157)
- Added
RetinaNet
&backbones
toObjectDetector
Task (#121) - Added .csv image loading utils (#116, #117, #118)
- Set inputs as optional (#109)
- Added
ObjectDetector
Task (#56) - Added TabNet for tabular classification (#101)
- Added support for more backbones(mobilnet, vgg, densenet, resnext) (#45)
- Added backbones for image embedding model (#63)
- Added SWAV and SimCLR models to
imageclassifier
+ backbone reorg (#68)
- Applied transform in
FilePathDataset
(#97) - Moved classification integration from vision root to folder (#86)