v3.1.0

3.1.0

• Move to VS 2019
• Fix a codegen error that was resulting in incorrect functional behavior
• Fix regressions in audio training tutorial (#232)
• Add importing of Sum nodes to ONNX importer
• Fix crash in LLVMContext::SetName
• Improved performance of CNN models on Pi3 with new implementations of spatial, pointwise and regular convolutions
• Improved performance of reorder node
• New nodes: ReorderDataCodeNode, SpatialConvolutionNode, MatrixMatrixMultiplyCodeNode
• Implement parallelization strategies for matrix multiplication nodes.
• Only enable new MatrixMatrixMultipleCodeNode path for select ARM targets like Pi, and not Intel/AMD CPUs
• Add the flag --skip_ellcode to compile and wrap.py tools to use OpenBLAS for linear algebra computations.

Check out the latest model benchmarks here!

v3.0.2

3.0.2

• Add FastGRNN node to ELL, train_classifier.py and the onnx_importer.

v3.0.1

3.0.1

• Fix make_vad.py so the VAD model works properly.
• Fix smoothing of classifier output.
• Improvements to noise mixing in audio training scripts
• Use featurizer metadata to get correct defaults for some audio script parameters
• Improvements and code clean up in view_audio.py
• make_dataset will fill the featurizer internal buffer before returning features so it doesn't featurize zeros.
• onnx_importer: fixes to ConstantFill, Reshape, Slice, Transpose, and support for constant folding involving Cast, and Mul and fix handling of nodes with multiple outputs.
• Fix bug causing OptimizeReorderDataNodesTransformation to fail caused by input port and memory layout mismatch
• move to SWIG 4.0.0.

v3.0.0

3.0.0

• Improve Python API so Map and CompiledMap have a way of calling compute on models with multiple inputs and outputs (see ComputeMultiple)
• Improve Python Callback handling so Python apps can handle models with any number of and type of callbacks.
• Simplify datasetFromImages.py so it can create training, testing and validation set from one set of images on disk.
• Update repurposing tutorial setup to take advantage of dataFromImages.py changes
• Update index.md to fix bug in Linux instructions
• Fix FFTNode so that behavior is identical to NumPy behavior
• Update make_featurizer.py to calculate features correctly

v2.5.4

2.5.4

• Add support for ONNX GlobalAveragePooling
• Publish 13 new audio models trained using Azure ML
• Add ONNX importer support for ConstantOfShape
• Add a -mean option to audio test runs to output mean prediction score
• Fix InputNode unarchival to correctly set the memory layout

v2.5.3

2.5.3

• Fixed build break in C++ tutorial on Windows

v2.5.2

2.5.2

• Moved to LLVM 8.0
• Added Asparagus and Pear models to the gallery, removed Elderflower
• Added support for image preprocessing metadata and image normalization defaults for pytorch + imagenet
• Updated find_ell.py (and thus other python tools) to work if your working directory contains ELL's build output

v2.5.1

• Fixed API wrapper for PortMemoryLayout to produce correct extent
• Fixed processing of ConcatenationNode in importer engine
• Added four build target aliases that build groups of targets: bindings, libraries, tests, and tools
• Add support for ONNX Cast operator (thanks to Ehsan Azar for PR #218)
• Fixed build error on macOS (GitHub issue #216)

v2.5.0

• Improve audio python library and update audio training tutorial
• Add support for non-unrolled loops in the Value library
• Add new methods to Python API to support FastGRNN models
• Add easy node-creation functions
• Fix bugs in ReinterpretLayoutNode
• Fix importing of DenseNet models
• Improve FFT and MelFilterBank nodes so that non-power of 2 sized input buffers can be processed
• Fix darknet importer
• Add shuffling of test and validation sets to audio train_classifier
• Update OpenBLASSetup.cmake so it can find a locally built version of OpenBLAS
• Fix compiling for cortex-m4 targets
• Add a link back to the original model file name in our emitted header file
• Clean up Python API, removing "TensorShape" specific API in favor of the more general PortMemoryLayout
• Fix bug in Microphone and WavReader classes so they properly handle auto-resampling of audio with multiple channels
• Add an -auto_scale option to audio scripts to control whether audio is scaled or not before featurization
• Add ability to mix noise with audio during make_dataset process
• Add support for shuffling and cleaning the audio training list
• Add many new options to audio train_classifier, including ability to control learning rate schedulers
• Fix link error when building on a Mac
• Fix exception in DetectLowPrecisionConvolutionTransformation when running Compile in Debug mode

v2.4.0

• Initial release of the finetune utility
• Remove stale references to VS 2015
• Add logging of build tool options
• Improve error reporting of compile failures
• Suppress alias analysis on emitted IR code
• Improve logging in ELL pythonlibs
• Remove volatile attribute from memcpy, memmove, and memset LLVM intrinsic calls
• Allow compilation of models with more than one input or output
• Fix parsing of boolean command-line arguments
• Add a RAM requirement for audio tutorial
• Add support for optimized models
• Fix ONNX import of no-op concat blocks
• Gallery updates for Pi3 specific ELL models
• Added documentation detailing ELL's versioning strategy