Releases: albumentations-team/autoalbument
Releases · albumentations-team/autoalbument
0.4.0
Major changes
- AutoAlbument core is rewritten. Now AutoAlbument uses PyTorch Lightning to search for the best augmentation policies.
- Some configuration parameters in
search.yaml
are changed to work with PyTorch Lightning. The new release contains a new command namedautoalbument-migrate
which automatically converts an old config file to the new format. If you try to callautoalbument-search
with the old config file, you will get instructions on how to useautoalbument-migrate
.
0.3.1
0.3.0
Bug fixes
CutoutFixedNumberOfHoles
andCutoutFixedSize
are now wrapped in a straight-through estimator.- Fixed a bug that prevented passing a list as an argument to augmentations defined in
preprocessing
. - TensorBoarad logs are now saved to a file without a leading
.
.
Minor changes
- Added example configs for SVHN, ImageNet, and Cityscapes.
- The CIFAR-10 example is now using WideResNet 28-10 as a custom model.
- The default config for classification now uses
num_sub_policies: 50
instead ofnum_sub_policies: 20
. - The default config for semantic segmentation now uses
num_sub_policies: 25
instead ofnum_sub_policies: 20
. - The same parameters for Cutout coordinates are applied to each image in a chunk
- Datasets in example configs are renamed from
SearchDataset
to a custom class name for that dataset.
0.2.0
Minor changes
- AutoAlbument now uses the
instantiate
function from Hydra to create instances of Classification and Semantic Segmentation models. This change allows using custom models inside AutoAlbument. See the CIFAR10 example that uses a custom ResNet-18 model. - AutoAlbument now requires
timm>=0.3.2
andsegmentation-models-pytorch>=0.1.3
. - All arguments to SearchDataset classes in examples are now defined in Hydra configs.
- Fixed typos in code.
0.1.1
0.1.0
- Major internal refactoring to follow best practices for Hydra configs.
autoalbument-create
will now ask for confirmation to overwrite a file if it already exists.- The default way of configuring PyTorch Dataset in
search.yaml
is changed from thedataset_file
parameter (which required a path to a Python file with the dataset) to thedataset
parameter (which requires a Python class located within PYTHONPATH). The previous option of providing a dataset filepath is also supported.