/**
* @brief Invoke text classification model and return result
*/
public float[] invoke_mode(float[] input)
{
byte[] in_buffer = new byte[4 * 256];
byte[] out_buffer;
float[] output = new float[2];
TensorsInfo in_info;
TensorsInfo out_info;
TensorsData in_data;
TensorsData out_data;
string model_path = ResourcePath + "models/text_classification.tflite";
if (!File.Exists(model_path))
{
Log.Error(TAG, "Model file is not exist");
return(output);
}
Buffer.BlockCopy(input, 0, in_buffer, 0, in_buffer.Length);
/* Set input & output TensorsInfo */
in_info = new TensorsInfo();
in_info.AddTensorInfo(TensorType.Float32, new int[4] {
256, 1, 1, 1
});
out_info = new TensorsInfo();
out_info.AddTensorInfo(TensorType.Float32, new int[4] {
2, 1, 1, 1
});
/* Create single inference engine */
SingleShot single = new SingleShot(model_path, in_info, out_info);
/* Set input data */
in_data = in_info.GetTensorsData();
in_data.SetTensorData(0, in_buffer);
/* Single shot invoke */
out_data = single.Invoke(in_data);
/* Get output data from TensorsData */
out_buffer = out_data.GetTensorData(0);
Buffer.BlockCopy(out_buffer, 0, output, 0, out_buffer.Length);
return(output);
}
public static bool BasicSingleTest_Success00()
{
byte[] in_buffer = new byte[3 * 224 * 224 * 1];
byte[] out_buffer;
string model_path = ResourcePath + "models/mobilenet_v1_1.0_224_quant.tflite";
TensorsInfo in_info;
TensorsInfo out_info;
TensorsData in_data;
TensorsData out_data;
/* Set input & output TensorsInfo */
in_info = new TensorsInfo();
in_info.AddTensorInfo(TensorType.UInt8, new int[4] {
3, 224, 224, 1
});
out_info = new TensorsInfo();
out_info.AddTensorInfo(TensorType.UInt8, new int[4] {
1001, 1, 1, 1
});
/* Create single inference engine */
SingleShot single = new SingleShot(model_path, in_info, out_info);
/* Set input data */
in_data = in_info.GetTensorsData();
in_data.SetTensorData(0, in_buffer);
/* Single shot invoke */
out_data = single.Invoke(in_data);
/* Get output data from TensorsData */
out_buffer = out_data.GetTensorData(0);
/* Release Single inference instance */
single.Dispose();
/* clean up */
in_data.Dispose();
out_data.Dispose();
in_info.Dispose();
out_info.Dispose();
return(true);
}
public static bool BasicTensorTest_Success01()
{
TensorsInfo tensorsInfo;
TensorsData tensorsData;
int[] in_dim = new int[4] {
10, 1, 1, 1
};
byte[] buffer_in = new byte[] { 17, 2, 3, 4, 5, 6, 7, 8, 9, 10 };
byte[] buffer_out;
tensorsInfo = new TensorsInfo();
tensorsInfo.AddTensorInfo(TensorType.UInt8, in_dim);
Log.Info(TAG, "Current Count: " + tensorsInfo.Count);
tensorsData = tensorsInfo.GetTensorsData();
tensorsData.SetTensorData(0, buffer_in);
buffer_out = tensorsData.GetTensorData(0);
if (buffer_in.Length != buffer_out.Length)
{
Log.Error(TAG, "The size of buffers is different");
return(false);
}
for (int i = 0; i < buffer_in.Length; ++i)
{
if (buffer_in[i] != buffer_out[i])
{
Log.Error(TAG, "The value of " + i.ToString() + " th element is different");
return(false);
}
}
return(true);
}
public static bool BasicTensorTest_Success02()
{
TensorsInfo tensorsInfo;
TensorsData tensorsData;
int[] in_dim = new int[4] {
10, 1, 1, 1
};
byte[] buffer_in = new byte[] { 17, 2, 3, 4, 5, 6, 7, 8, 9, 10 };
byte[] buffer_out;
tensorsInfo = new TensorsInfo();
tensorsInfo.AddTensorInfo(TensorType.UInt8, in_dim);
tensorsData = tensorsInfo.GetTensorsData();
tensorsData.SetTensorData(0, buffer_in);
buffer_out = tensorsData.GetTensorData(0);
if (buffer_in.Length != buffer_out.Length)
{
Log.Error(TAG, "The size of buffers is different");
return(false);
}
for (int i = 0; i < buffer_in.Length; ++i)
{
if (buffer_in[i] != buffer_out[i])
{
Log.Error(TAG, "The value of " + i.ToString() + " th element is different");
return(false);
}
}
tensorsData.Dispose();
/* Add new tensor */
int[] in2_dim = new int[4] {
5, 1, 1, 1
};
byte[] buffer_in2 = new byte[] { 10, 20, 30, 40, 50 };
byte[] buffer_out2;
tensorsInfo.AddTensorInfo(TensorType.UInt8, in2_dim);
tensorsData = tensorsInfo.GetTensorsData();
tensorsData.SetTensorData(0, buffer_in);
buffer_out = tensorsData.GetTensorData(0);
tensorsData.SetTensorData(1, buffer_in2);
buffer_out2 = tensorsData.GetTensorData(1);
if (buffer_in2.Length != buffer_out2.Length)
{
Log.Error(TAG, "The size of buffers is different");
return(false);
}
for (int i = 0; i < buffer_in2.Length; ++i)
{
if (buffer_in2[i] != buffer_out2[i])
{
Log.Error(TAG, "The value of " + i.ToString() + " th element is different");
return(false);
}
}
return(true);
}