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+## Model parser
+
+### Purpose
+
+This tool print operators, tensors, and buffers information in tflite model file (`.tflite`)
+
+### How to use
+
+```
+./model_parser.py <model file>
+```
+
+### Example
+
+```
+$ ./tools/tflitefile_tool/model_parser.py /home/nnfw/convolution_test.tflite
+
+[Main model]
+
+Main model input tensors: [0]
+Main model output tensors: [1]
+Operators list
+
+Operator 0: CONV_2D
+ Input Tensors[0 3 2]
+ Output Tensors[1]
+
+
+Tensor-Buffer mapping & shape
+
+Tensor 0 : buffer 3 | Empty | FLOAT32 | Shape [1, 299, 299, 3] (Mul)
+Tensor 1 : buffer 4 | Empty | FLOAT32 | Shape [1, 149, 149, 32] (conv)
+Tensor 2 : buffer 1 | Filled | FLOAT32 | Shape [32] (conv/Conv2D_bias)
+Tensor 3 : buffer 2 | Filled | FLOAT32 | Shape [32, 3, 3, 3] (conv/conv2d_params)
+
+$
+```
+
+## Model generator from other model file
+
+### Purpose
+
+This tool makes small model file from base model file (such as inception v3)
+
+### How to use
+
+```
+./select_operator.py <base model file> <opcode list txt file> <output file name>
+```
+
+### Example
+
+```
+$ cat /home/nnfw/opcodelist.txt
+107 108 109 110 111 112 113 114 115 116 117 118 119 120
+
+$ ./tools/tflitefile_tool/select_operator.py /home/nnfw/inceptionv3_non_slim_2015.tflite \
+/home/nnfw/opcodelist.txt /home/nnfw/test.tflite
+
+Input tensor(s): [29]
+Output tensor(s): [31]
+
+$ Product/out/bin/tflite_run /home/nfs/inception_test.tflite
+nnapi error: unable to open library libneuralnetworks.so
+input tensor indices = [29,]
+Input image size is smaller than the size required by the model. Input will not be set.
+output tensor indices = [31(max:567),]
+Prepare takes 0.000516954 seconds
+Invoke takes 0.719677 seconds
+
+$
+```
+
+You can use range such as `107-120` in `opcodelist.txt` instead of using each operator index
+
+## Colaboration model parser and model generator
+
+1. Get imformation about base model using model parser
+2. Select operators you want to make test model
+3. Make text file including selected operators index
+4. Generate test model file using model generator