update results

README.md

@@ -4,7 +4,8 @@ PyTorch implementation of [Data Free Quantization Through Weight Equalization an
 ## Results
 Int8**: Fake quantization; 8 bits weight, 8 bits activation, 16 bits bias  
 Int8*: Fake quantization; 8 bits weight, 8 bits activation, 8 bits bias  
-Int8: Inference using [ncnn](https://github.com/Tencent/ncnn); 8 bits weight, 8 bits activation, 32 bits bias  
+Int8': Fake quantization; 8 bits weight(symmetric), 8 bits activation(symmetric), 32 bits bias  
+Int8: Int8 Inference using [ncnn](https://github.com/Tencent/ncnn); 8 bits weight(symmetric), 8 bits activation(symmetric), 32 bits bias  
 
 ### On classification task
 - Tested with [MobileNetV2](https://github.com/tonylins/pytorch-mobilenet-v2) and [ResNet-18](https://pytorch.org/docs/stable/torchvision/models.html)
@@ -13,29 +14,29 @@ Int8: Inference using [ncnn](https://github.com/Tencent/ncnn); 8 bits weight, 8
 <tr><th>MobileNetV2   </th><th>ResNet-18</th></tr>
 <tr><td>
 
-model/precision | FP32 | Int8** | Int8* | Int8
------------|------|------| ------ | ------
-Original   | 71.81 | 0.102 | 0.1 | --
-+ReLU | 71.78 | 0.102 | 0.096 | --
-+ReLU+LE | 71.78 | 70.32 | 68.78 | --
-+ReLU+LE +DR | -- | 70.47 | 68.87 | --
-+BC  |  --  | 57.07 | 0.12 | --
-+BC +clip_15  |  --  | 65.37 | 0.13 | --
-+ReLU+LE+BC  |  --  | 70.79 | 68.17 | --
-+ReLU+LE+BC +DR  |  --  | 70.9 | 68.41 | --
+model/precision | FP32 | Int8** | Int8* | Int8' | Int8<br>(FP32-69.19)
+-----------|------|------| ------ | ------|------
+Original   | 71.81 | 0.102 | 0.1 | 0.062 | 0.082
++ReLU | 71.78 | 0.102 | 0.096 | 0.094 | 0.082
++ReLU+LE | 71.78 | 70.32 | 68.78 | 67.5 | 65.21
++ReLU+LE +DR | -- | 70.47 | 68.87 | -- | --
++BC  |  --  | 57.07 | 0.12 | 26.25 | 5.57
++BC +clip_15  |  --  | 65.37 | 0.13 | 65.96 | 45.13
++ReLU+LE+BC  |  --  | 70.79 | 68.17 | 68.65 | 62.19
++ReLU+LE+BC +DR  |  --  | 70.9 | 68.41 | -- | --
 
 </td><td>
 
-model/precision | FP32 | Int8** | Int8* | Int8
------------|------|------|------|------
-Original   | 69.76 | 69.13 | 69.09 | --
-+ReLU | 69.76 | 69.13 | 69.09 | --
-+ReLU+LE | 69.76 | 69.2 | 69.2 | --
-+ReLU+LE +DR | -- | 67.74 | 67.75 | --
-+BC  |  --  | 69.04 | 68.56 | --
-+BC +clip_15  |  --  | 69.04 | 68.56 | --
-+ReLU+LE+BC  |  --  | 69.04 | 68.56 | --
-+ReLU+LE+BC +DR  |  --  | 67.65 | 67.62 | --
+model/precision | FP32 | Int8** | Int8* 
+-----------|------|------|------
+Original   | 69.76 | 69.13 | 69.09 
++ReLU | 69.76 | 69.13 | 69.09 
++ReLU+LE | 69.76 | 69.2 | 69.2 
++ReLU+LE +DR | -- | 67.74 | 67.75 
++BC  |  --  | 69.04 | 68.56 
++BC +clip_15  |  --  | 69.04 | 68.56 
++ReLU+LE+BC  |  --  | 69.04 | 68.56 
++ReLU+LE+BC +DR  |  --  | 67.65 | 67.62
 
 </td></tr> </table>
 
@@ -164,10 +165,24 @@ python convert_ncnn.py --equalize --correction --quantize --relu --ncnn_build pa
   [ncnn](https://github.com/Tencent/ncnn)  
   [onnx-simplifier](https://github.com/daquexian/onnx-simplifier)  
 
-  Basic steps are:  
+  Inference_cls.cpp only implements mobilenetv2. Basic steps are:  
 
-  1. Run convert_ncnn.py to convert pytorch model (with layer equalization or bias correction) to ncnn int8 model and generate calibration table file.
-  2. Inference! [link](https://github.com/Tencent/ncnn/wiki/quantized-int8-inference)
+  1. Run convert_ncnn.py to convert pytorch model (with layer equalization or bias correction) to ncnn int8 model and generate calibration table file. The name of out_layer will be printed to console.  
+  ```
+    python convert_ncnn.py --quantize --relu --equalize --correction
+  ```
+
+  2. compile inference_cls.cpp
+  ```
+    mkdir build
+    cd build
+    cmake ..
+    make
+  ```
+  3. Inference! [link](https://github.com/Tencent/ncnn/wiki/quantized-int8-inference)
+  ```
+    ./inference_cls --images=path_to_imagenet_validation_set --param=../modeling/ncnn/model_int8.param --bin=../modeling/ncnn/model_int8.bin --out_layer=name_from_step1
+  ```
 
 ## TODO
 - [x] cross layer equalization
@@ -178,7 +193,7 @@ python convert_ncnn.py --equalize --correction --quantize --relu --ncnn_build pa
 - [x] use distilled data to set min/max activation range
 - [ ] ~~use distilled data to find optimal scale matrix~~
 - [ ] ~~use distilled data to do bias correction~~
-- [ ] True Int8 inference
+- [x] True Int8 inference
 
 ## Acknowledgment
 - https://github.com/jfzhang95/pytorch-deeplab-xception