0.7.6

This release changes the way Batch.apply_parallel works: now it accepts both init and post functions, and should be the preferrable way to decorate batch methods (by marking them with decorators.apply_parallel).

Other than that, there are a few new building blocks for TorchModel, parameter to pad the last microbatches to full microbatch_size, and small bug fixes.

0.7.5

Models

• added gradient clipping and new layers

Plot

• refactored existing plots across the library to rely on plot, introduced in the previous release

Research improvements

• modified stored configs to use aliases instead of actual values: that fixes some pickling problems

0.7.0

Models

• refactored model building procedure: split modules into separate entities like EncoderModule and DecoderModule
• introduced new modules that import ready-to-use networks from other libraries: currently, we support TIMM and HuggingFace libraries
• better module repr
• check #645 for other changes

Plot

• introduced plot module with utilities for displaying images and curves
• plot has a few tutorials with lots of examples: refer to them to get a more in-depth understanding of plot usages

Research improvements

• added separate Storage class, that manages output streams of research results.
• various fixes and QoL changes

0.6.0

Named expressions

Added BA

Models

Removed tensorflow

Research

Added research module for massive parallel model training / evaluation.

Tutorials and examples

New tutorials.

Also added some tests.

0.5.0

Lots of improvements and bug fixes

v0.5.0beta3

Update __init__.py

v0.5.0beta1

Update __init__.py

v0.5.0beta2

Update release-check.yml

More layers and models

Bug fixes and a lot of refactoring.

Batch

Components can be added dynamically during execution.
Parameters order is changed in apply_transform and apply_transform_all.

Named expressions:

• B() returns the batch itself.
• F takes args and kwargs.
• added R (random) and L (lambda).

Pipeline

Refactored models directory and variables directory.
Added print.
Removed print_variable.

Tensorflow

Layers

Added:

• 1d and 3d bilinear resize
• 3d depth to space
• separable transposed convolutions
• subpixel convolutions
• bilinear additive resize
• upsample
• alpha dropout
• universal pooling and global_pooling

Changed:

• conv_block support residuals (with sum and concat) and upsample layers.

TFModel:

• new methods: upsample, Pyramid Pooling module, Atrous Spatial Pyramid Pooling module
• model predictions can be an output of predefined operations (sigmoid, softmax, argmax, etc)

Model zoo

Added DenseNetFC, ResNetAttention, VNet, RefineNet, Faster-RCNN, Global Convolution Network, Encoder-decoder, Inception-ResNet v2, MobileNet v2.

New models

• Changed model structure and configuration (with default_config() and build_config())

• Added ready to use TensorFlow models: VGG, Inception v1, v3, v4, ResNet, MobileNet, SqueezeNet, DenseNet, FCN32, FCN16, FCN8, UNet, LinkNet.

• Added new layers: fractional_max_pooling.

• Dimensionality for all layers is now inferred from the input tensor shape.

• Added fake njit decorator for environments without numba installed.