Add the readme file of quantization tools (#1198)

tools/quantize/README.md

@@ -0,0 +1,30 @@
+# Post Training Quantization Tools
+
+To support int8 model deployment on mobile devices,we provide the universal post training quantization tools which can convert the float32 model to int8 model.
+
+The old convert tool and technical detail is in [Caffe-Int8-Convert-Tools](https://github.com/BUG1989/caffe-int8-convert-tools)
+
+## User Guide
+
+Example with mobilenet,just need three steps.
+
+### 1. Optimization graphic 
+
+```
+./ncnnoptimize mobilenet-fp32.param mobilenet-fp32.bin mobilenet-nobn-fp32.param mobilenet-nobn-fp32.bin 
+```
+
+### 2. Create the calibration table file
+
+We suggest that using the verification dataset for calibration, which is more than 5000 images.
+
+```
+./ncnn2table --param mobilenet-nobn-fp32.param --bin mobilenet-nobn-fp32.bin --images images/ --output mobilenet-nobn.table --mean 104,117,123 --norm 0.017,0.017,0.017 --size 224,224 --thread 2
+```
+
+### 3. Quantization
+
+```
+./ncnn2int8 mobilenet-nobn-fp32.param mobilenet-nobn-fp32.bin mobilenet-int8.param mobilenet-int8.bin 
+```
+

tools/quantize/ncnn2int8.cpp

@@ -195,7 +195,7 @@ int NetQuantize::quantize_convolution()
 
         // find convolution layer
         std::map<std::string, std::vector<float> >::iterator iter_data = blob_int8scale_table.find(layers[i]->name);
-        if (iter_data == weight_int8scale_table.end())
+        if (iter_data == blob_int8scale_table.end())
             continue;
 
         char key[256];
@@ -261,7 +261,7 @@ int NetQuantize::quantize_convolutiondepthwise()
 
         // find convolutiondepthwise layer
         std::map<std::string, std::vector<float> >::iterator iter_data = blob_int8scale_table.find(layers[i]->name);
-        if (iter_data == weight_int8scale_table.end())
+        if (iter_data == blob_int8scale_table.end())
             continue;
 
         char key[256];
@@ -327,7 +327,7 @@ int NetQuantize::quantize_innerproduct()
 
         // find InnerProduct layer
         std::map<std::string, std::vector<float> >::iterator iter_data = blob_int8scale_table.find(layers[i]->name);
-        if (iter_data == weight_int8scale_table.end())
+        if (iter_data == blob_int8scale_table.end())
             continue;
 
         char key[256];

tools/quantize/ncnn2table.cpp

@@ -685,6 +685,7 @@ void showUsage()
     std::cout << " -h, --help       show this help message and exit" << std::endl;
     std::cout << " -p, --param      path to ncnn.param file" << std::endl;
     std::cout << " -b, --bin        path to ncnn.bin file" << std::endl;
+    std::cout << " -i, --images     path to calibration images" << std::endl;
     std::cout << " -o, --output     path to output calibration tbale file" << std::endl;
     std::cout << " -m, --mean       value of mean" << std::endl;
     std::cout << " -n, --norm       value of normalize(scale value,defualt is 1)" << std::endl;