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@wonjuleee wonjuleee released this 20 May 05:23
· 5 commits to release_v220 since this release
v2.2.0
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Learn about vigilant mode.

New features

  • Pre-production quality
  • (TensorFlow) Added TensorFlow 2.5.x support.
  • (TensorFlow) The SubclassedConverter class was added to create NNCFGraph for the tf.Graph Keras model.
  • (TensorFlow) Added TFOpLambda layer support with TFModelConverter, TFModelTransformer, and TFOpLambdaMetatype.
  • (TensorFlow) Patterns from MatMul and Conv2D to BiasAdd and Metatypes of TensorFlow operations with weights TFOpWithWeightsMetatype are added.
  • (PyTorch, TensorFlow) Added prunings for Reshape and Linear as ReshapePruningOp and LinearPruningOp.
  • (PyTorch) Added mixed precision quantization config with HAWQ for Resnet50 and Mobilenet_v2 for the latest VPU.
  • (PyTorch) Splitted NNCFBatchNorm into NNCFBatchNorm1d, NNCFBatchNorm2d, NNCFBatchNorm3d.
  • (PyTorch - Experimental) Added the BNASTrainingController and BNASTrainingAlgorithm for BootstrapNAS to search the model's architecture.
  • (Experimental) ONNX ModelProto is now converted to NNCFGraph through GraphConverter.
  • (Experimental) ONNXOpMetatype and extended patterns for fusing HW config is now available.
  • (Experimental) Added ONNXPostTrainingQuantization and MinMaxQuantization supports for ONNX.

Bugfixes

  • (PyTorch, TensorFlow) Added exception handling of BN adaptation for zero sample values.
  • (PyTorch, TensorFlow) Fixed learning rate after validation step for EarlyExitCompressionTrainingLoop.
  • (PyTorch) Fixed FakeQuantizer to make exact zeros.
  • (PyTorch) Fixed Quantizer misplacements during ONNX export.
  • (PyTorch) Restored device information during ONNX export.
  • (PyTorch) Fixed the statistics collection from the pruned model.
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