PyTorch Toolbelt 0.4.3

PyTorch Toolbelt 0.4.3

Modules

• Added missing sigmoid activation support to get_activation_block
• Make Encoders support JIT & Tracing
• Better support for encoders from timm (They named with prefix Timm)

Utils

• rgb_image_from_tensor now clip values

TTA & Ensembling

• Ensembler now supports arithmetic, geometric & harmonic averaging via reduction parameter.
• Bring geometric & harmonic averaging to all TTA functions as well

Datasets

• read_binary_mask
• Refactor SegmentationDataset to support strided masks for deep supervision
• Added RandomSubsetDataset and RandomSubsetWithMaskDataset to sample dataset based on some condition (E.g. sample only samples of particular class)

Other

As usual, more tests, better type annotations & comments

PyTorch Toolbelt 0.4.2

Breaking Changes

• Bump up minimal PyTorch version to 1.7.1

New features

• New dataset classes ClassificationDataset, SegmentationDataset for easy every-day use in Kaggle
• New losses: FocalCosineLoss, BiTemperedLogisticLoss, SoftF1Loss
• Support of new activations for get_activation_block (Silu, Softplus, Gelu)
• More encoders from timm package: NFNets, NFRegNet, HRNet, DPN
• RocAucMetricCallback for Catalyst
• MultilabelAccuracyCallback and AccuracyCallback with DDP support

Bugfixes

• Fix invalid prefix in catalyst registry to from tbt to tbt.

PyTorch Toolbelt 0.4.1

New features

• Added Soft-F1 loss for direct optimization of F1 score (Binary case only)
• Fully rework TTA (Kept backward compatibility where it's possible) module for inference.
• Added support of ignore_index to Dice & Jaccard losses.
• Improved Lovasz loss to work in fp16 mode.
• Added option to override selected params in make_n_channel_input.
• More Encoders, from timm package.
• FPNFuse module not works on 2D, 3D and N-D inputs.
• Added Global K-Max 2D pooling block.
• Added Generalized mean pooling 2D block.
• Added softmax_over_dim_X, argmax_over_dim_X shorthand functions for use in metrics to get soft/hard labels without using lambda functions.
• Added helper visualization functions to add fancy header to image, stack images of different sizes.
• Improved rendering of confusion matrix.

Catalyst goodies

• Encoders & Losses are available in Catalyst registry
• StopIfNanCallback
• Added OutputDistributionCallback to log distribtion of predictions to TensorBoard.
• Added UMAPCallback to visualize embedding space using UMAP in TensorBoard.

Breaking Changes

• Renamed CudaTileMerger to TileMerger. TileMerger allows to specify target device explicitly.
• tensor_from_rgb_image removed in favor of image_to_tensor.

Bug fixes & Improvements

• Improve numeric stability of focal_loss_with_logits when reduction="sum"
• Prevent NaN in FocalLoss when all elements are equal to ignore_index value.
• A LOT of type hints.

PyTorch Toolbelt 0.4.0

New features

• Memory-efficient Swish and Mish activation functions (Credits goes to http://github.com/rwightman/pytorch-image-models)
• Refactor EfficientNet encoders (no pretrained weights yet)

Fixes

• Fixed incorrect default value for ignore_index in SoftCrossEntropyLoss

Breaking changes

• All catalyst-related utils updated to be compatible with Catalyst 20.8.2
• Remove PIL package dependency

Improvements

• More comments, more type hints

Pytorch Toolbelt 0.3.2

New features

• Many helpful callbacks for Catalyst library: HyperParameterCallback, LossAdapter to name a few.
• New losses for deep model supervision (Helpful, when size of target and output mask are different)
• Stacked Hourglass encoder
• Context Aggregation Network decoder

Breaking Changes

• ABN module will now resolve as nn.Sequential(BatchNorm2d, Activation) instead of a hand-crafted module. This enables easier conversion of batch normalization modules to the nn.SyncBatchNorm.

• Almost every Encoder/Decoder implementation has been refactored for better clarity and flexibility. Please double-check your pipelines.

Important bugfixes

• Improved numerical stability of Dice / Jaccard losses (Using log_sigmoid() + exp() instead of plain sigmoid() )

Other

• A lots of comments for functions and modules
• Code cleanup, thanks for DeepSource
• Type annotations for modules and functions
• Update of README

Pytorch toolbelt 0.3.1

Fixes

• Fixed bug in computation IoU metric in binary_dice_iou_score function
• Fixed incorrect default value in SoftCrossEntropyLoss #38

Improvements

• Function draw_binary_segmentation_predictions now has parameter image_format (rgb|bgr|gray) to specify format of the image to visualize correctly images in TB
• More type annotations across the codebase

New features

• New visualization function draw_multilabel_segmentation_predictions

Pytorch Toolbel 0.3.0

Pytorch Toolbel 0.3.0

This release has a huge set of new features, bugfixes and breaking changes. So be careful, when upgrading.
pip install pytorch-toolbelt==0.3.0

New features

Encoders

• HRNetV2
• DenseNets
• EfficientNet
• Encoder class has change_input_channels method to change number of channels in input image

New losses

• BCELoss with support of ignore_index
• SoftBCELoss (Label smoothing loss for binary case with support of ignore_index)
• SoftCrossEntropyLoss (Label smoothing loss for multiclass case with support of ignore_index)

Catalyst goodies

• Online pseudolabeling callback
• Training signal annealing callback

Other

• New activation functions support in ABN block: Swish, Mish, HardSigmoid
• New decoders (Unet, FPN, DeeplabV3, PPM) to simplify creation of segmentation models
• CREDITS.md to include all the references to code/articles. Existing list is definitely not complete, so feel free to make PR's
• Object context block from OCNet

API changes

• Focal loss now supports normalized focal loss and reduced focal loss extensions.
• Optimize computation of pyramid weight matrix #34
• Default value align_corners=False in F.interpolate when doing bilinear upsampling.

Bugfixes

• Fix missing call to batch normalization block in FPNBottleneckBN
• Fix numerical stability for DiceLoss and JaccardLoss when log_loss=True
• Fix numerical stability when computing normalized focal loss

PyTorch Toolbelt 0.2.1

New features

• Added normalized focal loss

Bugfixes

• Fixed wrong shape of intermediate layers of DenseNet

PyTorch Toolbelt 0.2.0

PyTorch Toolbelt 0.2.0

This release dedicated to housekeeping work. Dice/IoU metrics and losses have been redesigned to reduce amount of duplicated code and bring more clarity. Code is now auto-formatted using Black.

pip install pytorch_toolbelt==0.2.0

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

0.1.4

PyTorch 0.1.4

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