[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

@@ -2,6 +2,9 @@
 
 This model is ported from [PaddleHub](https://github.com/PaddlePaddle/PaddleHub) using [this script from OpenCV](https://github.com/opencv/opencv/blob/master/samples/dnn/dnn_model_runner/dnn_conversion/paddlepaddle/paddle_humanseg.py).
 
+**Note**:
+- `human_segmentation_pphumanseg_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`.
+
 ## Demo
 
 ### Python
@@ -46,11 +49,13 @@ Results of accuracy evaluation with [tools/eval](../../tools/eval).
 
 | Models             | Accuracy       | mIoU          |
 | ------------------ | -------------- | ------------- |
-| PPHumanSeg         | 0.9581         | 0.8996        |
-| PPHumanSeg quant   | 0.4365         | 0.2788        |
+| PPHumanSeg         | 0.9656         | 0.9164        |
+| PPHumanSeg block   | 0.9655         | 0.9162        |
+| PPHumanSeg quant   | 0.7285         | 0.3642        |
 
 
 \*: 'quant' stands for 'quantized'.
+\*\*: 'block' stands for 'blockwise quantized'.
 
 ---
 ## License