DeepSparse v0.9.0
·
3 commits
to release/0.9
since this release
New Features:
- Support optimized for resize operators with coordinate transformations of pytorch_half_pixel and align_corners.
- Up-to-date version check implemented for DeepSparse.
- YOLACT and DeepSparse integration added in examples/dbolya-yolact.
Changes:
- The parameter for the number of sockets to use has been removed -- the Python interface now only takes only the number of cores as a parameter.
- Tensor columns have been optimized. Users will see performance improvements specifically for pruned quantized BERT models:
- The softmax operator can now take advantage of tensor columns.
- Inference batch sizes that are not divisible by 16 are now supported.
- Various performance improvements made to:
- certain networks, such as YOLOv5, on AVX2 systems.
- dense convolutions on some AVX-512 systems.
- API docs recompiled.
Resolved Issues:
- In rare circumstances, users could have experienced an assertion error when executing networks with depthwise convolutions.
Known Issues:
- YOLACT models fail with a mismatched shape error on multi-socket systems with batch size greater than 1. This issue applies to any model with a constant output.
- In some circumstances, GEMM operations where the number of columns of the output matrix is not divisible by 16 may fail with an assertion error.