add the tools/quantize (#509)

tools/quantize/README.md

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+# Tengine Post Training Quantization Tools
+
+To support int8 model deployment on AIoT devices, we provide some universal post training quantization tools which can convert the **Float32** tmfile model to **Int8**/**UInt8** tmfile model.
+
+## 1 Symmetric per-channel quantization tool
+
+| Type                  | Note                                                         |
+| --------------------- | ------------------------------------------------------------ |
+| Adaptive              | TENGINE_MODE_INT8                                            |
+| Activation data       | Int8                                                         |
+| Weight date           | Int8                                                         |
+| Bias date             | Int32                                                        |
+| Example               | [**tm_classification_int8.c**](https://github.com/OAID/Tengine/blob/tengine-lite/examples/tm_classification_int8.c) |
+| Execution environment | Ubuntu 18.04                                                 |
+
+## 1.1 Download
+
+Currently, only provide the per-compiled file, you can get it from [quant_tool_int8](https://github.com/OAID/Tengine/releases/download/lite-v1.2-pre/quant_tool_int8).
+
+## 1.2 Install dependent libraries
+
+```
+sudo apt install libopencv-dev
+```
+
+## 1.3 Description params
+
+```
+$ ./quant_tool_int8 -h
+[Quant Tools Info]: optional arguments:
+-h    help            show this help message and exit
+-m    input model     path to input float32 tmfile
+-i    image dir       path to calibration images folder
+-o    output model    path to output int8 tmfile
+-a    algorithm       the type of quant algorithm(0:min-max, 1:kl, default is 1)
+-g    size            the size of input image(using the resize the original image,default is 3,224,224
+-w    mean            value of mean (mean value, default is 104.0,117.0,123.0
+-s    scale           value of normalize (scale value, default is 1.0,1.0,1.0)
+-b    swapRB          flag which indicates that swap first and last channels in 3-channel image is necessary(0:OFF, 1:ON, default is 1)
+-c    center crop     flag which indicates that center crop process image is necessary(0:OFF, 1:ON, default is 0)
+-y    letter box      flag which indicates that letter box process image is necessary(maybe using for YOLO, 0:OFF, 1:ON, default is 0)
+-t    num thread      count of processing threads(default is 4)
+```
+
+## 1.4 Demo
+
+Before use the quant tool, **you need Float32 tmfile and Calibration Dataset**, the image num of calibration dataset we suggest to use 500-1000.
+
+```
+$ .quant_tool_int8  -m ./mobilenet_fp32.tmfile -i ./dataset -o ./mobilenet_int8.tmfile -g 3,224,224 -w 104.007,116.669,122.679 -s 0.017,0.017,0.017
+
+---- Tengine Post Training Quantization Tool ----
+
+Version     : v1.0, 17:32:30 Dec 24 2020
+Status      : int8, per-channel, symmetric
+Input model : ./mobilenet_fp32.tmfile
+Output model: ./mobilenet_int8.tmfile
+Calib images: ./dataset
+Algorithm   : KL
+Dims        : 3 224 224
+Mean        : 104.007 116.669 122.679
+Scale       : 0.017 0.017 0.017
+BGR2RGB     : ON
+Center crop : OFF
+Letter box  : OFF
+Thread num  : 1
+
+[Quant Tools Info]: Step 0, load FP32 tmfile.
+[Quant Tools Info]: Step 0, load FP32 tmfile done.
+[Quant Tools Info]: Step 0, load calibration image files.
+[Quant Tools Info]: Step 0, load calibration image files done, image num is 55.
+[Quant Tools Info]: Step 1, find original calibration table.
+[Quant Tools Info]: Step 1, find original calibration table done, output ./table_minmax.scale
+[Quant Tools Info]: Step 2, find calibration table.
+[Quant Tools Info]: Step 2, find calibration table done, output ./table_kl.scale
+[Quant Tools Info]: Thread 1, image nums 55, total time 1964.24 ms, avg time 35.71 ms
+[Quant Tools Info]: Calibration file is using table_kl.scale
+[Quant Tools Info]: Step 3, load FP32 tmfile once again
+[Quant Tools Info]: Step 3, load FP32 tmfile once again done.
+[Quant Tools Info]: Step 3, load calibration table file table_kl.scale.
+[Quant Tools Info]: Step 4, optimize the calibration table.
+[Quant Tools Info]: Step 4, quantize activation tensor done.
+[Quant Tools Info]: Step 5, quantize weight tensor done.
+[Quant Tools Info]: Step 6, save Int8 tmfile done, ./mobilenet_int8.tmfile
+
+---- Tengine Int8 tmfile create success, best wish for your INT8 inference has a low accuracy loss...\(^0^)/ ----
+```
+
+## 2 Asymmetric per-layer quantization tool
+
+## 2.1 Symmetric per-channel quantization tool
+
+| Type                  | Note                                                         |
+| --------------------- | ------------------------------------------------------------ |
+| Adaptive              | TENGINE_MODE_UINT8                                           |
+| Activation data       | UInt8                                                        |
+| Weight date           | UInt8                                                        |
+| Bias date             | Int32                                                        |
+| Example               | [**tm_classification_uint8.c**](https://github.com/OAID/Tengine/blob/tengine-lite/examples/tm_classification_uint8.c) |
+| Execution environment | Ubuntu 18.04                                                 |
+
+## 2.2 Download
+
+Currently, only provide the per-compiled file, you can get it from [quant_tool](https://github.com/OAID/Tengine/releases/download/lite-v1.2-pre/quant_tool).
+
+***To be continue ......***