diff --git a/examples/caffe/README.md b/examples/caffe/README.md
new file mode 100644
index 0000000..d3ca5de
--- /dev/null
+++ b/examples/caffe/README.md
@@ -0,0 +1,168 @@
+# Caffe模型转换示例
+
+本文档以 lenet_caffe 为例，介绍如何使用 Netrans 对 Caffe 模型进行转换。
+
+Netrans 支持所有的 Caffe 模型。
+
+## 安装Netrans
+1. 先确定您的 Netrans 下载目录，使用以下命令将 Netrans 加入系统配置文件。记得使用您真实的 Netrans下载目录 替换下行命令中的文字。
+```bash
+export NETRANS_PATH=Netrans下载目录/bin
+```
+2. 安装 netrans_py 
+```bash
+cd netrans_py
+pip3 install -e .
+```
+
+## 数据准备
+转换 Caffe 模型时，模型工程目录应包含以下文件：
+- 以 .prototxt 结尾的模型结构定义文件
+- 以 .caffemode 结尾的模型权重文件
+- dataset.txt 包含数据路径的文本文件（支持图像和NPY格式）
+
+我们的示例 已经完成数据准备，可以使用下面命令进入目录执行。
+
+```bash
+cd netrans/
+cd examples/caffe
+```
+
+此时目录如下：
+```bash
+lenet_caffe/
+├── 0.jpg                   # 校准数据
+├── dataset.txt             # 指定数据地址的文件
+├── lenet_caffe.caffemodel  # caffe 模型权重
+└── lenet_caffe.prototxt    # caffe 模型结构
+```
+
+## 使用 nertans_cli 命令行工具
+使用 netrans_cli 之前，请先使用以下命令将 命令行脚本 拷贝至当前目录。
+```bash
+cp ../../netrans_cli/*sh ./
+```
+此时目录如下：
+```bash
+caffe/
+├── example.py
+├── export.sh
+├── gen_inputmeta.sh
+├── import_model.sh
+├── infer.sh
+├── lenet_caffe
+│   ├── 0.jpg
+│   ├── dataset.txt
+│   ├── lenet_caffe.caffemodel
+│   └── lenet_caffe.prototxt
+└── quantize.sh
+```
+### 模型导入
+```bash
+./import_model.sh lenet_caffe
+```
+
+该步骤会生成 .json 结尾的网络结构文件和 .data 结尾的权重数据文件。
+
+此时 lenet_caffe 的目录结构如下：
+```bash
+lenet_caffe/
+├── 0.jpg
+├── dataset.txt
+├── lenet_caffe.caffemodel
+├── lenet_caffe.data
+├── lenet_caffe.json
+└── lenet_caffe.prototxt
+```
+
+### 配置文件生成
+数据在推理前一般会经过预处理，为了确保模型可以正确的输入数据，需要生产对应的配置文件。
+
+```bash
+./gen_inputmeta.sh lenet_caffe
+```
+
+此时 lenet_caffe 的目录结构如下：
+```bash
+lenet_caffe/
+├── 0.jpg
+├── dataset.txt
+├── lenet_caffe.caffemodel
+├── lenet_caffe.data
+├── lenet_caffe_inputmeta.yml
+├── lenet_caffe.json
+└── lenet_caffe.prototxt
+```
+
+### 模型量化 
+为了优化模型的推理效率，加快模型的推理速度，我们使用下行命令对模型进行量化处理。
+量化模型需要两个参数，目录（模型）名字和量化类型。量化类型包括：float,int16, int8 和 uint8。
+
+```bash
+./quantize.sh lenet_caffe uint8
+
+```
+
+此时 lenet_caffe 的目录结构如下：
+```bash
+lenet_caffe/
+├── 0.jpg
+├── dataset.txt
+├── lenet_caffe_asymmetric_affine.quantize
+├── lenet_caffe.caffemodel
+├── lenet_caffe.data
+├── lenet_caffe_inputmeta.yml
+├── lenet_caffe.json
+└── lenet_caffe.prototxt
+```
+### 模型导出
+最后我们使用 export.sh 将模型导出到nbg格式并生成应用程序工程。量化模型需要两个参数，目录（模型）名字和量化类型。量化类型包括：float,int16, int8 和 uint8。量化类型应于 quantize.sh 使用的一致。
+
+```bash
+./export.sh lenet_caffe uint8
+```
+此时 lenet_caffe 的目录结构如下：
+
+```bash
+lenet_caffe/
+├── 0.jpg
+├── dataset.txt
+├── lenet_caffe_asymmetric_affine.quantize
+├── lenet_caffe.caffemodel
+├── lenet_caffe.data
+├── lenet_caffe_inputmeta.yml
+├── lenet_caffe.json
+├── lenet_caffe.prototxt
+└── wksp
+    └── asymmetric_affine
+        ├── BUILD
+        ├── dump_core_graph.json
+        ├── graph.json
+        ├── lenetcaffeasymmetricaffine.2012.vcxproj
+        ├── lenet_caffe_asymmetric_affine.export.data
+        ├── lenetcaffeasymmetricaffine.vcxproj
+        ├── main.c
+        ├── makefile.linux
+        ├── network_binary.nb
+        ├── vnn_global.h
+        ├── vnn_lenetcaffeasymmetricaffine.c
+        ├── vnn_lenetcaffeasymmetricaffine.h
+        ├── vnn_post_process.c
+        ├── vnn_post_process.h
+        ├── vnn_pre_process.c
+        └── vnn_pre_process.h
+```
+## 使用 netrans_py python api
+本文档提供基于 python api 实现的python脚本，请先使用以下命令将 api脚本 拷贝至当前目录。
+
+### 准备示例脚本
+```bash
+cp ../../netrans_py/example.py ./
+
+```
+
+### 运行示例脚本
+
+```bash
+python3 example.py lenet_caffe -q uint8
+```
diff --git a/examples/caffe/lenet_caffe/0.jpg b/examples/caffe/lenet_caffe/0.jpg
new file mode 100644
index 0000000..5e822c8
Binary files /dev/null and b/examples/caffe/lenet_caffe/0.jpg differ
diff --git a/examples/caffe/lenet_caffe/dataset.txt b/examples/caffe/lenet_caffe/dataset.txt
new file mode 100644
index 0000000..26bbc2e
--- /dev/null
+++ b/examples/caffe/lenet_caffe/dataset.txt
@@ -0,0 +1 @@
+0.jpg
diff --git a/examples/caffe/lenet_caffe/lenet_caffe.caffemodel b/examples/caffe/lenet_caffe/lenet_caffe.caffemodel
new file mode 100644
index 0000000..227e182
Binary files /dev/null and b/examples/caffe/lenet_caffe/lenet_caffe.caffemodel differ
diff --git a/examples/caffe/lenet_caffe/lenet_caffe.prototxt b/examples/caffe/lenet_caffe/lenet_caffe.prototxt
new file mode 100644
index 0000000..a768465
--- /dev/null
+++ b/examples/caffe/lenet_caffe/lenet_caffe.prototxt
@@ -0,0 +1,136 @@
+name: "LeNet"
+layer {
+  name: "input"
+  type: "Input"
+  top: "data"
+  input_param {
+    shape {
+      dim: 64
+      dim: 1
+      dim: 28
+      dim: 28
+    }
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 20
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 50
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "conv2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "ip1"
+  type: "InnerProduct"
+  bottom: "pool2"
+  top: "ip1"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  inner_product_param {
+    num_output: 500
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "ip1"
+  top: "ip1"
+}
+layer {
+  name: "ip2"
+  type: "InnerProduct"
+  bottom: "ip1"
+  top: "ip2"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  inner_product_param {
+    num_output: 10
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "prob"
+  type: "Softmax"
+  bottom: "ip2"
+  top: "prob"
+}
diff --git a/examples/darknet/README.md b/examples/darknet/README.md
new file mode 100644
index 0000000..f994ea9
--- /dev/null
+++ b/examples/darknet/README.md
@@ -0,0 +1,167 @@
+# Darknet模型转换示例
+
+本文档以 yolov4_tiny 为例，介绍如何使用 Netrans 对 Darknet 模型进行转换。
+
+Netrans 支持 Darknet[官网](https://pjreddie.com/darknet/)列出 darknet 模型
+
+## 安装Netrans
+1. 先确定您的 Netrans 下载目录，使用以下命令将 Netrans 加入系统配置文件。记得使用您真实的 Netrans下载目录 替换下行命令中的文字。
+```bash
+export NETRANS_PATH=Netrans下载目录/bin
+```
+2. 安装 netrans_py 
+```bash
+cd netrans_py
+pip3 install -e .
+```
+
+## 数据准备
+转换 Darknet 模型时，模型工程目录应包含以下文件：
+- .cfg 文件：网络结构配置文件
+- .weights 文件：训练权重文件
+- .dataset.txt：数据路径配置文件
+
+我们的示例 已经完成数据准备，可以使用下面命令进入目录执行。
+
+```bash
+cd netrans/
+cd examples/darknet
+```
+
+此时目录如下：
+```bash
+yolov4_tiny/
+├── 0.jpg                 # 校准数据
+├── dataset.txt           # 指定数据地址的文件 
+├── yolov4_tiny.cfg       # 网络结构配置文件
+└── yolov4_tiny.weights   # 预训练权重文件
+```
+
+## 使用 nertans_cli 命令行工具
+使用 netrans_cli 之前，请先使用以下命令将 命令行脚本 拷贝至当前目录。
+```bash
+cp ../../netrans_cli/*sh ./
+```
+此时目录如下：
+```bash
+darknet/
+├── export.sh
+├── gen_inputmeta.sh
+├── import_model.sh
+├── infer.sh
+├── quantize.sh
+└── yolov4_tiny
+    ├── 0.jpg
+    ├── dataset.txt
+    ├── yolov4_tiny.cfg
+    └── yolov4_tiny.weights
+```
+### 模型导入
+```bash
+./import_model.sh yolov4_tiny
+```
+
+该步骤会生成 .json 结尾的网络结构文件和 .data 结尾的权重数据文件。
+
+此时 yolov4_tiny 的目录结构如下：
+```bash
+yolov4_tiny/
+├── 0.jpg
+├── dataset.txt
+├── yolov4_tiny.cfg
+├── yolov4_tiny.data
+├── yolov4_tiny.json
+└── yolov4_tiny.weights
+```
+
+### 配置文件生成
+数据在推理前一般会经过预处理，为了确保模型可以正确的输入数据，需要生产对应的配置文件。
+
+```bash
+./gen_inputmeta.sh yolov4_tiny
+```
+
+此时 yolov4_tiny 的目录结构如下：
+```bash
+yolov4_tiny/
+├── 0.jpg
+├── dataset.txt
+├── yolov4_tiny.cfg
+├── yolov4_tiny.data
+├── yolov4_tiny_inputmeta.yml
+├── yolov4_tiny.json
+└── yolov4_tiny.weights
+```
+
+### 模型量化 
+为了优化模型的推理效率，加快模型的推理速度，我们使用下行命令对模型进行量化处理。
+量化模型需要两个参数，目录（模型）名字和量化类型。量化类型包括：float,int16, int8 和 uint8。
+
+```bash
+./quantize.sh yolov4_tiny uint8
+
+```
+
+此时 yolov4_tiny 的目录结构如下：
+```bash
+yolov4_tiny/
+├── 0.jpg
+├── dataset.txt
+├── yolov4_tiny_asymmetric_affine.quantize
+├── yolov4_tiny.cfg
+├── yolov4_tiny.data
+├── yolov4_tiny_inputmeta.yml
+├── yolov4_tiny.json
+└── yolov4_tiny.weights
+```
+### 模型导出
+最后我们使用 export.sh 将模型导出到nbg格式并生成应用程序工程。量化模型需要两个参数，目录（模型）名字和量化类型。量化类型包括：float,int16, int8 和 uint8。量化类型应于 quantize.sh 使用的一致。
+
+```bash
+./export.sh yolov4_tiny uint8
+```
+此时 yolov4_tiny 的目录结构如下：
+
+```bash
+yolov4_tiny/
+├── 0.jpg
+├── dataset.txt
+├── inputs_outputs.txt
+├── yolov4_tiny_asymmetric_affine.quantize
+├── yolov4_tiny.data
+├── yolov4_tiny_inputmeta.yml
+├── yolov4_tiny.json
+├── yolov4_tiny.weights
+└── wksp
+    └── asymmetric_affine
+        ├── BUILD
+        ├── dump_core_graph.json
+        ├── graph.json
+        ├── yolov4_tinyasymmetricaffine.2012.vcxproj
+        ├── yolov4_tiny_asymmetric_affine.export.data
+        ├── yolov4_tinyasymmetricaffine.vcxproj
+        ├── main.c
+        ├── makefile.linux
+        ├── network_binary.nb
+        ├── vnn_global.h
+        ├── vnn_yolov4_tinyasymmetricaffine.c
+        ├── vnn_yolov4_tinyasymmetricaffine.h
+        ├── vnn_post_process.c
+        ├── vnn_post_process.h
+        ├── vnn_pre_process.c
+        └── vnn_pre_process.h
+```
+## 使用 netrans_py python api
+本文档提供基于 python api 实现的python脚本，请先使用以下命令将 api脚本 拷贝至当前目录。
+
+### 准备示例脚本
+```bash
+cp ../../netrans_py/example.py ./
+
+```
+
+### 运行示例脚本
+
+```bash
+python3 example.py yolov4_tiny -q uint8
+```
diff --git a/examples/darknet/yolov4_tiny/0.jpg b/examples/darknet/yolov4_tiny/0.jpg
new file mode 100644
index 0000000..9420443
Binary files /dev/null and b/examples/darknet/yolov4_tiny/0.jpg differ
diff --git a/examples/darknet/yolov4_tiny/dataset.txt b/examples/darknet/yolov4_tiny/dataset.txt
new file mode 100644
index 0000000..26bbc2e
--- /dev/null
+++ b/examples/darknet/yolov4_tiny/dataset.txt
@@ -0,0 +1 @@
+0.jpg
diff --git a/examples/darknet/yolov4_tiny/yolov4_tiny.cfg b/examples/darknet/yolov4_tiny/yolov4_tiny.cfg
new file mode 100644
index 0000000..d990b51
--- /dev/null
+++ b/examples/darknet/yolov4_tiny/yolov4_tiny.cfg
@@ -0,0 +1,294 @@
+[net]
+# Testing
+#batch=1
+#subdivisions=1
+# Training
+batch=64
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.00261
+burn_in=1000
+
+max_batches = 2000200
+policy=steps
+steps=1600000,1800000
+scales=.1,.1
+
+
+#weights_reject_freq=1001
+#ema_alpha=0.9998
+#equidistant_point=1000
+#num_sigmas_reject_badlabels=3
+#badlabels_rejection_percentage=0.2
+
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers=-1
+groups=2
+group_id=1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1,-2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -6,-1
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers=-1
+groups=2
+group_id=1
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1,-2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -6,-1
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers=-1
+groups=2
+group_id=1
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1,-2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -6,-1
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+##################################
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+
+[yolo]
+mask = 3,4,5
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=80
+num=6
+jitter=.3
+scale_x_y = 1.05
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+ignore_thresh = .7
+truth_thresh = 1
+random=0
+resize=1.5
+nms_kind=greedynms
+beta_nms=0.6
+#new_coords=1
+#scale_x_y = 2.0
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 23
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+[yolo]
+mask = 1,2,3
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=80
+num=6
+jitter=.3
+scale_x_y = 1.05
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+ignore_thresh = .7
+truth_thresh = 1
+random=0
+resize=1.5
+nms_kind=greedynms
+beta_nms=0.6
+#new_coords=1
+#scale_x_y = 2.0
diff --git a/examples/darknet/yolov4_tiny/yolov4_tiny.weights b/examples/darknet/yolov4_tiny/yolov4_tiny.weights
new file mode 100644
index 0000000..27edc5d
Binary files /dev/null and b/examples/darknet/yolov4_tiny/yolov4_tiny.weights differ
diff --git a/examples/onnx/README.md b/examples/onnx/README.md
new file mode 100644
index 0000000..dcb96cd
--- /dev/null
+++ b/examples/onnx/README.md
@@ -0,0 +1,186 @@
+# Onnx模型转换示例
+本文档以 yolov5s 为例介绍如何使用 Netrans 对 Onnx 模型进行转换。
+
+Netrans 支持 ONNX 至 1.14.0， opset支持至19。
+
+## 安装Netrans
+1. 先确定您的 Netrans 下载目录，使用以下命令将 Netrans 加入系统配置文件。记得使用您真实的 Netrans下载目录 替换下行命令中的文字。
+```bash
+export NETRANS_PATH=Netrans下载目录/bin
+```
+2. 安装 netrans_py 
+```bash
+cd netrans_py
+pip3 install -e .
+```
+## 数据准备
+
+转换ONNX模型需准备：
+
+- .onnx 文件：网络模型
+- dataset.txt：数据路径配置文件
+
+我们的示例 已经完成数据准备，可以使用下面命令进入目录执行。
+
+```bash
+cd netrans/
+cd examples/onnx
+```
+
+此时目录如下：
+
+```
+yolov5s/
+├── 0.jpg          # 校准数据
+├── dataset.txt    # 指定数据地址的文件 
+└── yolov5s.onnx   # 网络模型
+```
+
+### 3.1 使用 netrans_cli 转换 onnx 示例模型 yolov5s 
+使用 netrans_cli 之前，请先使用以下命令将 命令行脚本 拷贝至当前目录。
+```bash
+cp ../../netrans_cli/*sh ./
+```
+此时目录如下：
+
+```
+onnx/
+├── export.sh
+├── gen_inputmeta.sh
+├── import_model.sh
+├── infer.sh
+├── quantize.sh
+└── yolov5s
+    ├── 0.jpg
+    ├── dataset.txt
+    └── yolov5s.onnx
+```
+
+#### 3.1.1 导入模型
+
+```bash
+./import_model.sh yolov5s
+```
+
+该步骤会生成 .json 结尾的网络结构文件和 .data 结尾的权重数据文件。
+
+此时 yolov5s 的目录结构如下
+```
+yolov5s/
+├── 0.jpg
+├── dataset.txt
+├── yolov5s.data
+├── yolov5s.json
+└── yolov5s.onnx
+```
+
+#### 3.1.2 生成配置文件
+数据在推理前一般会经过预处理，为了确保模型可以正确的输入数据，需要生产对应的配置文件。
+
+```bash
+./gen_inputmeta.sh yolov5s
+```
+
+此时 yolov5s 的目录结构如下：
+```
+yolov5s/
+├── 0.jpg
+├── dataset.txt
+├── yolov5s.data
+├── yolov5s_inputmeta.yml
+├── yolov5s.json
+└── yolov5s.onnx
+
+```
+根据 yolov5s 的实际情况 ，我们需要修改yml中的 mean 为 0，scale为 0.003921568627。
+打开 ` yolov5s_inputmeta.yml ` 文件，
+修改第30-33行为：
+```
+        scale:
+        - 0.003921568627
+        - 0.003921568627
+        - 0.003921568627
+
+```
+关闭并保存。
+
+#### 3.1.3 量化模型
+
+```bash
+./quantize.sh yolov5s uint8
+
+```
+量化模型需要两个参数，目录（模型）名字和量化类型。量化类型包括：float， int8和uint8。
+
+此时 yolov5s 的目录结构如下：
+
+```
+yolov5s/
+├── 0.jpg
+├── dataset.txt
+├── yolov5s_asymmetric_affine.quantize
+├── yolov5s.data
+├── yolov5s_inputmeta.yml
+├── yolov5s.json
+└── yolov5s.onnx
+```
+
+#### 3.1.4 导出模型
+
+```bash
+./export.sh yolov5s uint8
+```
+此时 yolov5s 的目录结构如下：
+
+```
+yolov5s/
+├── 0.jpg
+├── dataset.txt
+├── wksp
+│   └── asymmetric_affine
+│       ├── BUILD
+│       ├── dump_core_graph.json
+│       ├── graph.json
+│       ├── main.c
+│       ├── makefile.linux
+│       ├── network_binary.nb
+│       ├── vnn_global.h
+│       ├── vnn_post_process.c
+│       ├── vnn_post_process.h
+│       ├── vnn_pre_process.c
+│       ├── vnn_pre_process.h
+│       ├── vnn_yolov5sasymmetricaffine.c
+│       ├── vnn_yolov5sasymmetricaffine.h
+│       ├── yolov5sasymmetricaffine.2012.vcxproj
+│       ├── yolov5s_asymmetric_affine.export.data
+│       └── yolov5sasymmetricaffine.vcxproj
+├── yolov5s_asymmetric_affine.quantize
+├── yolov5s.data
+├── yolov5s_inputmeta.yml
+├── yolov5s.json
+└── yolov5s.onnx
+```
+
+
+### 3.2 使用 netrans_py 转换 onnx 示例模型 yolov5s
+
+#### 3.2.1 安装netrans_py 
+
+```bash
+cd netrans_py
+pip3 install -e .
+```
+
+#### 3.2.2 准备示例脚本
+
+```bash
+cd ../example/onnx
+cp ../../netrans_py/example.py ./
+
+```
+
+#### 3.2.3 运行示例脚本
+
+```bash
+python3 example.py yolov5s -q uint8 -m 0 -s 0.003921568627
+```
diff --git a/examples/onnx/yolov5s/0.jpg b/examples/onnx/yolov5s/0.jpg
new file mode 100644
index 0000000..9420443
Binary files /dev/null and b/examples/onnx/yolov5s/0.jpg differ
diff --git a/examples/onnx/yolov5s/dataset.txt b/examples/onnx/yolov5s/dataset.txt
new file mode 100644
index 0000000..26bbc2e
--- /dev/null
+++ b/examples/onnx/yolov5s/dataset.txt
@@ -0,0 +1 @@
+0.jpg
diff --git a/examples/onnx/yolov5s/yolov5s.onnx b/examples/onnx/yolov5s/yolov5s.onnx
new file mode 100644
index 0000000..e520f5a
Binary files /dev/null and b/examples/onnx/yolov5s/yolov5s.onnx differ
diff --git a/examples/tensorflow/README.md b/examples/tensorflow/README.md
new file mode 100644
index 0000000..3eac253
--- /dev/null
+++ b/examples/tensorflow/README.md
@@ -0,0 +1,175 @@
+# TensorFlow模型转换示例
+
+本文档以 lenet 为例，介绍如何使用 Netrans 对 Tensorflow 模型进行转换。
+
+Netrans 支持 TensorFlow 版本1.4.x, 2.0.x, 2.3.x, 2.6.x, 2.8.x, 2.10.x, 2.12.x 以tf.io.write_graph()保存的模型。
+
+## 安装Netrans
+1. 先确定您的 Netrans 下载目录，使用以下命令将 Netrans 加入系统配置文件。记得使用您真实的 Netrans下载目录 替换下行命令中的文字。
+```bash
+export NETRANS_PATH=Netrans下载目录/bin
+```
+2. 安装 netrans_py 
+```bash
+cd netrans_py
+pip3 install -e .
+```
+
+## 数据准备
+转换 TensorFlow 模型时，模型工程目录应包含以下文件：
+- .pb 文件：冻结图模型文件
+- inputs_outputs.txt：输入输出节点定义文件
+- dataset.txt：数据路径配置文件
+
+我们的示例 已经完成数据准备，可以使用下面命令进入目录执行。
+
+```bash
+cd netrans/
+cd examples/tensorflow
+```
+
+此时目录如下：
+```bash
+lenet/
+├── 0.jpg                # 校准数据
+├── dataset.txt          # 指定数据地址的文件 
+├── inputs_outputs.txt   # 输入输出节点定义文件
+└── lenet.pb             # 冻结图模型文件
+```
+
+
+## 使用 nertans_cli 命令行工具
+使用 netrans_cli 之前，请先使用以下命令将 命令行脚本 拷贝至当前目录。
+```bash
+cp ../../netrans_cli/*sh ./
+```
+此时目录如下：
+```bash
+tensorflow/
+├── export.sh
+├── gen_inputmeta.sh
+├── import_model.sh
+├── infer.sh
+├── lenet
+│   ├── 0.jpg
+│   ├── dataset.txt
+│   ├── inputs_outputs.txt
+│   └── lenet.pb
+└── quantize.sh
+```
+### 模型导入
+```bash
+./import_model.sh lenet
+```
+
+该步骤会生成 .json 结尾的网络结构文件和 .data 结尾的权重数据文件。
+
+此时 lenet 的目录结构如下：
+```bash
+lenet/
+├── 0.jpg
+├── dataset.txt
+├── inputs_outputs.txt
+├── lenet.data
+├── lenet.json
+└── lenet.pb
+```
+
+### 配置文件生成
+数据在推理前一般会经过预处理，为了确保模型可以正确的输入数据，需要生产对应的配置文件。
+
+```bash
+./gen_inputmeta.sh lenet
+```
+
+此时 lenet 的目录结构如下：
+```bash
+lenet/
+├── 0.jpg
+├── dataset.txt
+├── inputs_outputs.txt
+├── lenet.data
+├── lenet_inputmeta.yml
+├── lenet.json
+└── lenet.pb
+```
+
+### 模型量化 
+为了优化模型的推理效率，加快模型的推理速度，我们使用下行命令对模型进行量化处理。
+量化模型需要两个参数，目录（模型）名字和量化类型。量化类型包括：float,int16, int8 和 uint8。
+
+```bash
+./quantize.sh lenet uint8
+
+```
+
+此时 lenet 的目录结构如下：
+```bash
+lenet/
+├── 0.jpg
+├── dataset.txt
+├── inputs_outputs.txt
+├── lenet_asymmetric_affine.quantize
+├── lenet.data
+├── lenet_inputmeta.yml
+├── lenet.json
+└── lenet.pb
+```
+### 模型导出
+最后我们使用 export.sh 将模型导出到nbg格式并生成应用程序工程。量化模型需要两个参数，目录（模型）名字和量化类型。量化类型包括：float,int16, int8 和 uint8。量化类型应于 quantize.sh 使用的一致。
+
+```bash
+./export.sh lenet uint8
+```
+此时 lenet 的目录结构如下：
+
+```bash
+lenet/
+├── 0.jpg
+├── dataset.txt
+├── inputs_outputs.txt
+├── lenet_asymmetric_affine.quantize
+├── lenet.data
+├── lenet_inputmeta.yml
+├── lenet.json
+├── lenet.pb
+└── wksp
+    └── asymmetric_affine
+        ├── BUILD
+        ├── dump_core_graph.json
+        ├── graph.json
+        ├── lenetasymmetricaffine.2012.vcxproj
+        ├── lenet_asymmetric_affine.export.data
+        ├── lenetasymmetricaffine.vcxproj
+        ├── main.c
+        ├── makefile.linux
+        ├── network_binary.nb
+        ├── vnn_global.h
+        ├── vnn_lenetasymmetricaffine.c
+        ├── vnn_lenetasymmetricaffine.h
+        ├── vnn_post_process.c
+        ├── vnn_post_process.h
+        ├── vnn_pre_process.c
+        └── vnn_pre_process.h
+```
+## 使用 netrans_py python api
+
+### 3.2.1 安装netrans_py 
+```bash
+cd netrans_py
+pip3 install -e .
+```
+
+### 准备示例脚本
+
+```bash
+cd ../example/tensorflow
+cp ../../netrans_py/example.py ./
+
+```
+
+### 运行示例脚本
+
+```bash
+python3 example.py lenet -q uint8
+```
diff --git a/examples/tensorflow/export.sh b/examples/tensorflow/export.sh
new file mode 100755
index 0000000..6504941
--- /dev/null
+++ b/examples/tensorflow/export.sh
@@ -0,0 +1,137 @@
+#!/bin/bash
+
+if [ -z "$NETRANS_PATH" ]; then
+    echo "Need to set enviroment variable NETRANS_PATH"
+    exit 1
+fi
+
+OVXGENERATOR=$NETRANS_PATH/pnnacc
+
+OVXGENERATOR="$OVXGENERATOR export ovxlib"
+
+DATASET=dataset.txt
+
+VERIFT='FLASE'
+function export_network()
+{
+    NAME=$1
+    pushd $NAME
+    
+    QUANTIZED=$2
+    
+    if [ ${QUANTIZED} = 'float' ]; then 
+        TYPE=float;
+        quantization_type="none_quantized"
+        generate_path='./wksp/none_quantized'
+    elif [ ${QUANTIZED} = 'uint8' ]; then 
+        quantization_type="asymmetric_affine"
+        generate_path='./wksp/asymmetric_affine'
+        TYPE=quantized;
+    elif [ ${QUANTIZED} = 'int8' ]; then 
+        quantization_type="dynamic_fixed_point-8"
+        generate_path='./wksp/dynamic_fixed_point-8'
+        TYPE=quantized;
+    elif [ ${QUANTIZED} = 'int16' ]; then       
+        quantization_type="dynamic_fixed_point-16"
+        generate_path='./wksp/dynamic_fixed_point-16'
+        TYPE=quantized;
+    else
+        echo "=========== wrong quantization_type ! ( float / uint8 / int8 / int16 )==========="
+        exit -1 
+    fi
+
+    echo " ======================================================================="
+    echo " =========== Start Generate $NAME ovx C code with type of ${quantization_type} ==========="
+    echo " ======================================================================="
+    
+    mkdir -p "${generate_path}"
+    
+	# if want to import c code into win IDE , change --target-ide-project command-line param from 'linux64' -> 'win32'
+    if [ ${QUANTIZED} = 'float' ]; then 
+        cmd="$OVXGENERATOR \
+            --model ${NAME}.json \
+            --model-data ${NAME}.data \
+  	    --model-quantize ${NAME}.quantize \
+            --dtype   ${TYPE} \
+	    --pack-nbg-viplite \
+            --model-quantize ${NAME}_${quantization_type}.quantize \
+            --with-input-meta    ${NAME}_inputmeta.yml\
+            --optimize 'VIP8000NANOQI_PLUS_PID0XB1'\
+            #--optimize None\
+            --target-ide-project 'linux64' \
+            --viv-sdk ${NETRANS_PATH}/pnna_sdk \
+            --output-path ${generate_path}/${NAME}_${quantization_type}"
+    else
+    
+        if [ -f ${NAME}_${quantization_type}.quantize ]; then
+            echo -e "\033[31m using  ${NAME}_${quantization_type}.quantize \033[0m" 
+        else
+            echo -e "\033[31m Can not find  ${NAME}_${quantization_type}.quantize \033[0m" 
+            exit -1;
+        fi  
+        
+        cmd="$OVXGENERATOR \
+            --model ${NAME}.json \
+            --model-data ${NAME}.data \
+  	    --model-quantize ${NAME}.quantize \
+            --dtype ${TYPE} \
+	    --pack-nbg-viplite \
+            --model-quantize ${NAME}_${quantization_type}.quantize \
+            --with-input-meta    ${NAME}_inputmeta.yml\
+            --optimize 'VIP8000NANOQI_PLUS_PID0XB1'\
+            --target-ide-project 'linux64' \
+            --viv-sdk ${NETRANS_PATH}/pnna_sdk \
+            --output-path ${generate_path}/${NAME}_${quantization_type}"   
+    fi  
+    if [${VERIFY}='TRUE']; then
+        echo $cmd
+    fi
+    eval $cmd
+
+    # copy input file into source code folder
+    # sourcefile="`cat ${DATASET}`"
+    # cpcmd="cp -fr $sourcefile ${generate_path}/"
+    
+    # echo $cpcmd
+    # eval $cpcmd
+ 
+    # temp='wksp/temp'
+
+    # mkcmd="mkdir -p ${temp}"
+    # eval $mkcmd
+
+    # sourcefile="`cat ${DATASET}`"
+    # cpcmd="cp -fr $sourcefile ${temp}/"
+    
+    # echo $cpcmd
+    # eval $cpcmd
+    
+    cpcmd="cp ${generate_path}_nbg_viplite/network_binary.nb ${generate_path}/"
+    eval $cpcmd
+     
+    delcmd="rm -rf ${generate_path}_nbg_viplite"
+    eval $delcmd
+
+    # rm -rf ${generate_path}
+    # mvcmd="mv ${temp} ${generate_path}"
+
+    # eval $mvcmd
+    
+    echo " ======================================================================="
+    echo " =========== End  Generate $NAME ovx C code with type of ${quantization_type} ==========="
+    echo " ======================================================================="
+    
+    popd
+}
+
+if [ "$#" -lt 2 ]; then
+    echo "Input a network name and quantized type ( float / uint8 / int8 / int16 )"
+    exit -1
+fi
+
+if [ ! -e "${1%/}" ]; then
+    echo "Directory ${1%/} does not exist !"
+    exit -2
+fi
+
+export_network ${1%/} ${2%/}
diff --git a/examples/tensorflow/gen_inputmeta.sh b/examples/tensorflow/gen_inputmeta.sh
new file mode 100755
index 0000000..1299473
--- /dev/null
+++ b/examples/tensorflow/gen_inputmeta.sh
@@ -0,0 +1,28 @@
+#!/bin/sh
+
+if [ -z "$NETRANS_PATH" ]; then
+    echo "Need to set enviroment variable NETRANS_PATH"
+    exit 1
+fi
+
+if [ "$#" -ne 1 ]; then
+    echo "Enter a network name !"
+    exit 2
+fi
+
+if [ ! -e "${1%/}" ]; then
+    echo "Directory ${1%/} does not exist !"
+    exit 3
+fi
+
+
+netrans=$NETRANS_PATH/pnnacc
+
+
+
+NAME=${1%/}
+cd $NAME
+$netrans generate \
+         inputmeta \
+         --model ${NAME}.json \
+         --separated-database \
diff --git a/examples/tensorflow/import_model.sh b/examples/tensorflow/import_model.sh
new file mode 100755
index 0000000..fc97acd
--- /dev/null
+++ b/examples/tensorflow/import_model.sh
@@ -0,0 +1,209 @@
+#!/bin/bash
+
+if [ -z "$NETRANS_PATH" ]; then
+    echo "Need to set enviroment variable NETRANS_PATH"
+        exit 1
+fi
+
+function import_caffe_network()
+{
+    NAME=$1
+    CONVERTCAFFE=$NETRANS_PATH/pnnacc
+
+    CONVERTCAFFE="$CONVERTCAFFE import caffe"
+
+    
+    if [ -f ${NAME}.json ]; then
+        echo -e "\033[31m rm  ${NAME}.json \033[0m" 
+        rm ${NAME}.json
+    fi
+    
+    if [ -f ${NAME}.data ]; then
+        echo -e "\033[31m rm  ${NAME}.data \033[0m" 
+        rm ${NAME}.data
+    fi  
+    
+    echo "=========== Converting $NAME Caffe model ==========="
+    if [ -f ${NAME}.caffemodel ]; then
+    cmd="$CONVERTCAFFE \
+        --model ${NAME}.prototxt \
+        --weights ${NAME}.caffemodel \
+        --output-model  ${NAME}.json \
+        --output-data ${NAME}.data"
+    else
+    echo "=========== fake Caffe model data file==========="
+    cmd="$CONVERTCAFFE \
+        --model ${NAME}.prototxt \
+        --output-model  ${NAME}.json \
+        --output-data ${NAME}.data"
+    fi  
+}
+
+function import_tensorflow_network()
+{
+    NAME=$1
+    CONVERTF=$NETRANS_PATH/pnnacc
+
+    CONVERTF="$CONVERTF import tensorflow"
+
+    
+    if [ -f ${NAME}.json ]; then
+        echo -e "\033[31m rm  ${NAME}.json \033[0m" 
+        rm ${NAME}.json
+    fi
+    
+    if [ -f ${NAME}.data ]; then
+        echo -e "\033[31m rm  ${NAME}.data \033[0m" 
+        rm ${NAME}.data
+    fi  
+    
+    echo "=========== Converting $NAME Tensorflow model ==========="
+    cmd="$CONVERTF \
+        --model ${NAME}.pb \
+        --output-data ${NAME}.data \
+        --output-model ${NAME}.json \
+        $(cat inputs_outputs.txt)"
+}
+
+function import_onnx_network()
+{
+    NAME=$1
+    CONVERTONNX=$NETRANS_PATH/pnnacc
+    CONVERTONNX="$CONVERTONNX import onnx"
+    
+    
+    if [ -f ${NAME}.json ]; then
+        echo -e "\033[31m rm  ${NAME}.json \033[0m" 
+        rm ${NAME}.json
+    fi
+    
+    if [ -f ${NAME}.data ]; then
+        echo -e "\033[31m rm  ${NAME}.data \033[0m" 
+        rm ${NAME}.data
+    fi
+    
+    echo "=========== Converting $NAME ONNX model ==========="
+    cmd="$CONVERTONNX \
+        --model  ${NAME}.onnx \
+        --output-model  ${NAME}.json \
+        --output-data ${NAME}.data"
+}
+
+function import_tflite_network()
+{
+    NAME=$1
+    CONVERTTFLITE=$NETRANS_PATH/pnnacc
+    CONVERTTFLITE="$CONVERTTFLITE import tflite"
+    
+    
+    if [ -f ${NAME}.json ]; then
+        echo -e "\033[31m rm  ${NAME}.json \033[0m" 
+        rm ${NAME}.json
+    fi
+    
+    if [ -f ${NAME}.data ]; then
+        echo -e "\033[31m rm  ${NAME}.data \033[0m" 
+        rm ${NAME}.data
+    fi  
+    
+    echo "=========== Converting $NAME TFLite model ==========="
+    cmd="$CONVERTTFLITE \
+        --model ${NAME}.tflite \
+        --output-model  ${NAME}.json \
+        --output-data ${NAME}.data"
+}
+
+function import_darknet_network()
+{
+    NAME=$1
+    CONVERTDARKNET=$NETRANS_PATH/pnnacc
+    CONVERTDARKNET="$CONVERTDARKNET import darknet"
+    
+    
+    if [ -f ${NAME}.json ]; then
+        echo -e "\033[31m rm  ${NAME}.json \033[0m" 
+        rm ${NAME}.json
+    fi
+    
+    if [ -f ${NAME}.data ]; then
+        echo -e "\033[31m rm  ${NAME}.data \033[0m" 
+        rm ${NAME}.data
+    fi
+    
+    echo "=========== Converting $NAME darknet model ==========="
+    cmd="$CONVERTDARKNET \
+        --model ${NAME}.cfg \
+        --weight ${NAME}.weights \
+        --output-model  ${NAME}.json \
+        --output-data ${NAME}.data"
+}
+
+function import_pytorch_network()
+{
+    NAME=$1
+    CONVERTPYTORCH=$NETRANS_PATH/pnnacc
+    CONVERTPYTORCH="$CONVERTPYTORCH import pytorch"
+    
+    
+    if [ -f ${NAME}.json ]; then
+        echo -e "\033[31m rm  ${NAME}.json \033[0m" 
+        rm ${NAME}.json
+    fi
+    
+    if [ -f ${NAME}.data ]; then
+        echo -e "\033[31m rm  ${NAME}.data \033[0m" 
+        rm ${NAME}.data
+    fi
+    
+    echo "=========== Converting $NAME pytorch model ==========="
+    cmd="$CONVERTPYTORCH \
+        --model ${NAME}.pt \q
+        --output-model  ${NAME}.json \
+        --output-data ${NAME}.data \
+	$(cat input_size.txt)"
+}
+
+function import_network()
+{
+    NAME=$1
+    pushd $NAME
+    
+    
+    if [ -f ${NAME}.prototxt ]; then
+        import_caffe_network ${1%/}
+    elif [ -f ${NAME}.pb ]; then
+        import_tensorflow_network ${1%/}
+    elif [ -f ${NAME}.onnx ]; then
+        import_onnx_network ${1%/}
+    elif [ -f ${NAME}.tflite ]; then
+        import_tflite_network ${1%/}
+    elif [ -f ${NAME}.weights ]; then
+        import_darknet_network ${1%/}
+    elif [ -f ${NAME}.pt ]; then
+        import_pytorch_network ${1%/}
+    else
+        echo "=========== can not find suitable model files ==========="
+    fi
+
+    echo $cmd
+    eval $cmd
+    
+    if [ -f ${NAME}.data -a -f ${NAME}.json ]; then
+        echo -e "\033[31m SUCCESS \033[0m" 
+    else
+        echo -e "\033[31m ERROR ! \033[0m" 
+    fi  
+    popd
+}
+
+if [ "$#" -ne 1 ]; then
+    echo "Input a network name !"
+    exit -1
+fi
+
+if [ ! -e "${1%/}" ]; then
+    echo "Directory ${1%/} does not exist !"
+    exit -2
+fi
+
+import_network ${1%/}
diff --git a/examples/tensorflow/infer.sh b/examples/tensorflow/infer.sh
new file mode 100755
index 0000000..986c8ed
--- /dev/null
+++ b/examples/tensorflow/infer.sh
@@ -0,0 +1,65 @@
+#!/bin/bash
+
+if [ -z "$NETRANS_PATH" ]; then
+    echo "Need to set enviroment variable NETRANS_PATH"
+        exit 1
+fi
+
+TENSORZONX=$NETRANS_PATH/pnnacc
+
+TENSORZONX="$TENSORZONX inference"    
+        
+DATASET=./dataset.txt
+
+function inference_network()
+{
+    NAME=$1
+    pushd $NAME
+    QUANTIZED=$2
+    inf_path='./inf'
+
+    if [ ${QUANTIZED} = 'float' ]; then 
+        TYPE=float32;
+        quantization_type="float32"
+    elif [ ${QUANTIZED} = 'uint8' ]; then 
+        quantization_type="asymmetric_affine"
+        TYPE=quantized;
+    elif [ ${QUANTIZED} = 'int8' ]; then 
+        quantization_type="dynamic_fixed_point-8"
+        TYPE=quantized;
+    elif [ ${QUANTIZED} = 'int16' ]; then       
+        quantization_type="dynamic_fixed_point-16"
+        TYPE=quantized;
+    else
+        echo "=========== wrong quantization_type ! ( float / uint8 / int8 / int16 )==========="
+        exit -1 
+    fi
+        
+    cmd="$TENSORZONX \
+        --dtype ${TYPE} \
+        --batch-size 1 \
+        --model-quantize  ${NAME}_${quantization_type}.quantize  \
+        --model ${NAME}.json \
+        --model-data  ${NAME}.data \
+        --output-dir        ${inf_path} \
+        --with-input-meta ${NAME}_inputmeta.yml \
+        --device CPU"
+
+    echo $cmd
+    eval $cmd
+    echo "=========== End  inference $NAME model  ==========="
+    
+    popd
+}
+
+if [ "$#" -lt 2 ]; then
+    echo "Input a network name and quantized type ( float / uint8 / int8 / int16 )"
+    exit -1
+fi
+
+if [ ! -e "${1%/}" ]; then
+    echo "Directory ${1%/} does not exist !"
+    exit -2
+fi
+
+inference_network ${1%/} ${2%/}
diff --git a/examples/tensorflow/lenet/0.jpg b/examples/tensorflow/lenet/0.jpg
new file mode 100644
index 0000000..5e822c8
Binary files /dev/null and b/examples/tensorflow/lenet/0.jpg differ
diff --git a/examples/tensorflow/lenet/dataset.txt b/examples/tensorflow/lenet/dataset.txt
new file mode 100644
index 0000000..26bbc2e
--- /dev/null
+++ b/examples/tensorflow/lenet/dataset.txt
@@ -0,0 +1 @@
+0.jpg
diff --git a/examples/tensorflow/lenet/inputs_outputs.txt b/examples/tensorflow/lenet/inputs_outputs.txt
new file mode 100644
index 0000000..535e1ea
--- /dev/null
+++ b/examples/tensorflow/lenet/inputs_outputs.txt
@@ -0,0 +1 @@
+--inputs input/x-input --outputs  output --input-size-list "28,28,1"
diff --git a/examples/tensorflow/lenet/lenet.pb b/examples/tensorflow/lenet/lenet.pb
new file mode 100644
index 0000000..09bcc37
Binary files /dev/null and b/examples/tensorflow/lenet/lenet.pb differ
diff --git a/examples/tensorflow/quantize.sh b/examples/tensorflow/quantize.sh
new file mode 100755
index 0000000..a1d4b20
--- /dev/null
+++ b/examples/tensorflow/quantize.sh
@@ -0,0 +1,76 @@
+#!/bin/bash
+
+if [ -z "$NETRANS_PATH" ]; then
+    echo "Need to set enviroment variable NETRANS_PATH"
+    exit 1
+fi
+
+TENSORZONEX=$NETRANS_PATH/pnnacc
+TENSORZONEX="$TENSORZONEX quantize"
+
+
+DATASET=./dataset.txt
+
+function quantize_network()
+{
+    NAME=$1
+    pushd $NAME
+    
+    QUANTIZED=$2
+    
+    if [ ${QUANTIZED} = 'float' ]; then 
+        echo "=========== do not need quantied==========="
+        exit -1 
+    elif [ ${QUANTIZED} = 'uint8' ]; then 
+        quantization_type="asymmetric_affine"
+    elif [ ${QUANTIZED} = 'int8' ]; then 
+        quantization_type="dynamic_fixed_point-8"
+    elif [ ${QUANTIZED} = 'int16' ]; then       
+        quantization_type="dynamic_fixed_point-16"
+    else
+        echo "=========== wrong quantization_type ! ( uint8 / int8 / int16 )==========="
+        exit -1 
+    fi
+
+    echo " ======================================================================="
+    echo " ==== Start Quantizing $NAME model with type of ${quantization_type} ==="
+    echo " ======================================================================="
+    
+    if [ -f ${NAME}_${quantization_type}.quantize ]; then
+        echo -e "\033[31m rm  ${NAME}_${quantization_type}.quantize \033[0m" 
+        rm ${NAME}_${quantization_type}.quantize
+    fi  
+    
+    cmd="$TENSORZONEX \
+        --batch-size 1 \
+        --qtype ${QUANTIZED} \
+        --rebuild  \
+        --quantizer ${quantization_type%-*} \
+        --model-quantize  ${NAME}_${quantization_type}.quantize \
+        --model ${NAME}.json \
+        --model-data  ${NAME}.data \
+        --with-input-meta    ${NAME}_inputmeta.yml \
+        --device CPU"
+    echo $cmd
+    eval $cmd
+    
+    if [ -f ${NAME}_${quantization_type}.quantize ]; then
+        echo -e "\033[31m SUCCESS \033[0m" 
+    else
+        echo -e "\033[31m ERROR ! \033[0m" 
+    fi  
+    
+    popd
+}
+
+if [ "$#" -lt 2 ]; then
+    echo "Input a network name and quantized type ( uint8 / int8 / int16 )"
+    exit -1
+fi
+
+if [ ! -e "${1%/}" ]; then
+    echo "Directory ${1%/} does not exist !"
+    exit -2
+fi
+
+quantize_network ${1%/} ${2%/}