Pytorch-toolbelt

New features

- Added normalized focal loss

Bugfixes

- Fixed wrong shape of intermediate layers of DenseNet

Catalyst contrib

- Refactor Dice/IoU loss into single metric `IoUMetricsCallback` with a few cool features: `metric="dice|jaccard"` to choose what metric should be used; `mode=binary|multiclass|multilabel` to specify problem type (binary, multiclass or multi-label segmentation)'; `classes_of_interest=[1,2,4]` to select for which set of classes metric should be computed and `nan_score_on_empty=False` to compute `Dice Accuracy` (Counts as a 1.0 if both `y_true` and `y_pred` are empty; 0.0 if `y_pred` is not empty).
- Added L-p regularization callback to apply L1 and L2 regularization to model with support of regularization strength scheduling.


Losses

- Refactor `DiceLoss`/`JaccardLoss` losses in a same fashion as metrics.

Models

- Add Densenet encoders
- Bugfix: Fix missing BN+Relu in `UNetDecoder`
- Global pooling modules can squeeze spatial channel dimensions if `flatten=True`.

Misc

- Add more unit tests
- Code-style is now managed with Black
- `to_numpy` now supports `int`, `float` scalar types

* Minor release to update Catalyst contrib modules to latest Catalyst (requires catalyst>=19.8)

1. Added `ignore_index` for focal loss
2. Added `ignore_index` to some metrics for Catalyst
3. Added `tif` extension for `find_images_in_dir`

New functionality / breaking changes
* Added visualization functions to render best/worst batches for binary and semantic segmentation.
* JaccardScoreCallback now is a single callback for computing IoU for binary/multiclass/multilabel segmentation.
* Added HFF module (Hierarchical feature fusion).
* Added `set_trainable` function to enable/disabled training and batch-norm on module and it's childs.
* RLE encoding/decoding (Hi, Kaggle)

API changes
* `rgb_image_from_tensor` now accepts `dtype` parameters for returned image

Bugfixes
* Fixed wrong implementation of UpsampleAddConv (There was extra residual connection)

New stuff:
1. EfficientNet
2. Multiscale TTA module
3. New activations: Swish, HardSwish, HardSigmoid
4. AGN module (Activated Group Norm), mimicks ABN

Changes:
1. `SpatialGate2d` now accepts `squeeze_channels` for explicit number of squeeze channels.

Misc
1. Code formatting