[GSoC] Add block quantized models (#270)
Browse files* Gemm and MatMul block quantization support
* refactoring
* fix indentation
* node name independent
* Block quantization tool:
- constant weight category supported
- add data type saturation
- handled the case in which all the elements within a block are the same
benchmark script modified to support block quantized models
block quantized some models
* add missing block quantized models
* formatting
* add blocked models to eval script. Evaluation yunet
* Add sface and pphumanseg evaluation, block quantization tool fix, handpose blocked model fix, removed blocked CRNN EN,
* changed evaluation metric in block_quantize script and add verbose mode
* Add evaluation for PP-ResNet and Mobilenet
* changed file suffix and update readmes
* renamed int8bq
README.md
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Nanodet: NanoDet is a FCOS-style one-stage anchor-free object detection model which using Generalized Focal Loss as classification and regression loss.In NanoDet-Plus, we propose a novel label assignment strategy with a simple assign guidance module (AGM) and a dynamic soft label assigner (DSLA) to solve the optimal label assignment problem in lightweight model training.
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Note
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- This version of nanodet: Nanodet-m-plus-1.5x_416
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## Demo
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Nanodet: NanoDet is a FCOS-style one-stage anchor-free object detection model which using Generalized Focal Loss as classification and regression loss.In NanoDet-Plus, we propose a novel label assignment strategy with a simple assign guidance module (AGM) and a dynamic soft label assigner (DSLA) to solve the optimal label assignment problem in lightweight model training.
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**Note**:
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- This version of nanodet: Nanodet-m-plus-1.5x_416
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- `object_detection_nanodet_2022nov_int8bq.onnx` represents the block-quantized version in int8 precision and is generated using [block_quantize.py](../../tools/quantize/block_quantize.py) with `block_size=64`.
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## Demo
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