Highlights

• Continue RNN optimization. We support both LSTM and GRU integration with MKL-DNN which acheives ~3x performance

• ONNX support. We support loading third party framework models via ONNX

• Richer data preprocssing support and segmentation inference pipeline support

Details

• [New Feature] Full MaskRCNN model support with data processing
• [New Feature] Support variable-size Resize
• [New Feature] Support batch input for region proposal
• [New Feature] Support samples of different size in one minibatch
• [New Feature] MAP validation method implementation
• [New Feature] ROILabel enhancement to support both object detection and segmentation
• [New Feature] Grey image support for segmentation
• [New Feature] Add TopBlocks support for Feature Pyramid Networks (FPN)
• [New Feature] GRU integration with MKL-DNN support
• [New Feature] MaskHead support for MaskRCNN
• [New Feature] BoxHead support for MaskRCNN
• [New Feature] RegionalProposal support for MaskRCNN
• [New Feature] Shape operation support for ONNX
• [New Feature] Gemm operation support for ONNX
• [New Feature] Gather operation support for ONNX
• [New Feature] AveragePool operation support for ONNX
• [New Feature] BatchNormalization operation support for ONNX
• [New Feature] Concat operation support for ONNX
• [New Feature] Conv operation support for ONNX
• [New Feature] MaxPool operation support for ONNX
• [New Feature] Reshape operation support for ONNX
• [New Feature] Relu operation support for ONNX
• [New Feature] SoftMax operation support for ONNX
• [New Feature] Sum operation support for ONNX
• [New Feature] Squeeze operation support for ONNX
• [New Feature] Const operation support for ONNX
• [New Feature] ONNX model loader implementation
• [New Feature] RioAlign layer support
• [Enhancement] Align batch normalization layer between mklblas and mkl-dnn
• [Enhancement] Python API enhancement to support nested list input
• [Enhancement] Multi-model training/inference support with MKL-DNN
• [Enhancement] BatchNormalization fusion with Scale
• [Enhancement] SoftMax companion object support no argument initialization
• [Enhancement] Python support for training with MKL-DNN
• [Enhancement] Docs enhancement
• [Bug Fix] Fix model version comparison
• [Bug Fix] Fix graph backward bug for ParallelTable
• [Bug Fix] Fix memory leak for training with MKL-DNN
• [Bug Fix] Fix performance caused by denormal values during training
• [Bug Fix] Fix SoftMax segment fault issue under MKL-DNN
• [Bug Fix] Fix TimeDistributedCriterion python API inconsistent with Scala