[GSoC] Add block quantized models (#270)

* 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

@@ -8,15 +8,20 @@ Notes:
 - This model can detect **faces of pixels between around 10x10 to 300x300** due to the training scheme.
 - For details on training this model, please visit https://github.com/ShiqiYu/libfacedetection.train.
 - This ONNX model has fixed input shape, but OpenCV DNN infers on the exact shape of input image. See https://github.com/opencv/opencv_zoo/issues/44 for more information.
+- `face_detection_yunet_2023mar_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`.
 
 Results of accuracy evaluation with [tools/eval](../../tools/eval).
 
 | Models      | Easy AP | Medium AP | Hard AP |
 | ----------- | ------- | --------- | ------- |
-| YuNet       | 0.8871  | 0.8710    | 0.7681  |
-| YuNet quant | 0.8838  | 0.8683    | 0.7676  |
+| YuNet       | 0.8844  | 0.8656    | 0.7503  |
+| YuNet block | 0.8845  | 0.8652    | 0.7504  |
+| YuNet quant | 0.8810  | 0.8629    | 0.7503  |
+
 
 \*: 'quant' stands for 'quantized'.
+\*\*: 'block' stands for 'blockwise quantized'.
+
 
 ## Demo