update quantization parameters correctly

Update quantization parameters correctly for output tensor
if output layer of the given model has quantization parameters.

Change-Id: Id278e55882f7b6135dcf55f83c050f2df4f9d98c
Signed-off-by: Inki Dae <inki.dae@samsung.com>

diff --git a/src/inference_engine_tflite.cpp b/src/inference_engine_tflite.cpp
index 74dc0c375a77185f6a91e4fc6c34f018211fa640..caa1bd69f291481257f8c501362f03c2fbc7150d 100644 (file)
--- a/src/inference_engine_tflite.cpp
+++ b/src/inference_engine_tflite.cpp
@@ -303,31 +303,34 @@ namespace TFLiteImpl
                                        return INFERENCE_ENGINE_ERROR_INVALID_OPERATION;
                        }
 
+                       const TfLiteTensor *tensor = mInterpreter->tensor(layer.second);
+                       if (!tensor) {
+                               LOGE("tensor for tensor index(%d) is null", layer.second);
+                               return INFERENCE_ENGINE_ERROR_INVALID_OPERATION;
+                       }
+
                        inference_engine_tensor_info tensor_info;
 
                        LOGI("mInterpreter->tensor(%d)->dims name[%s] size[%d] type[%d]",
-                                       layer.second,
-                                       mInterpreter->tensor(layer.second)->name,
-                                       mInterpreter->tensor(layer.second)->dims->size,
-                                       mInterpreter->tensor(layer.second)->type);
+                                       layer.second, tensor->name, tensor->dims->size, tensor->type);
 
                        std::vector<size_t> shape_nhwc;
-                       for (int idx = 0; idx < mInterpreter->tensor(layer.second)->dims->size; idx++)
-                                       shape_nhwc.push_back(mInterpreter->tensor(layer.second)->dims->data[idx]);
+                       for (int idx = 0; idx < tensor->dims->size; idx++)
+                                       shape_nhwc.push_back(tensor->dims->data[idx]);
 
                        //tflite only supports NHWC (https://www.tensorflow.org/lite/guide/ops_compatibility).
                        tensor_info.shape = shape_nhwc;
                        tensor_info.shape_type = INFERENCE_TENSOR_SHAPE_NHWC;
-                       if (mInterpreter->tensor(layer.second)->type == kTfLiteUInt8) {
+                       if (tensor->type == kTfLiteUInt8) {
                                        LOGI("type is kTfLiteUInt8");
                                        tensor_info.data_type = INFERENCE_TENSOR_DATA_TYPE_UINT8;
-                       } else if (mInterpreter->tensor(layer.second)->type == kTfLiteInt8) {
+                       } else if (tensor->type== kTfLiteInt8) {
                                        LOGI("type is kTfLiteInt8");
                                        tensor_info.data_type = INFERENCE_TENSOR_DATA_TYPE_INT8;
-                       } else if (mInterpreter->tensor(layer.second)->type == kTfLiteInt64) {
+                       } else if (tensor->type == kTfLiteInt64) {
                                        LOGI("type is kTfLiteInt64");
                                        tensor_info.data_type = INFERENCE_TENSOR_DATA_TYPE_INT64;
-                       } else if (mInterpreter->tensor(layer.second)->type == kTfLiteFloat32) {
+                       } else if (tensor->type == kTfLiteFloat32) {
                                        LOGI("type is kTfLiteFloat32");
                                        tensor_info.data_type = INFERENCE_TENSOR_DATA_TYPE_FLOAT32;
                        } else {
@@ -339,6 +342,19 @@ namespace TFLiteImpl
                        for (auto & dim : tensor_info.shape)
                                        tensor_info.size *= dim;
 
+                       if (tensor->quantization.type == kTfLiteAffineQuantization) {
+                               auto *quant_parms = reinterpret_cast<TfLiteAffineQuantization *>(tensor->quantization.params);
+
+                               LOGD("This layer has quantization parameters.");
+                               if (quant_parms) {
+                                       tensor_info.scale =  quant_parms->scale->data[0];
+                                       tensor_info.zero_point = quant_parms->zero_point->data[0];
+                                       tensor_info.quantization_type = INFERENCE_TENSOR_QUANTIZATION_AFFINE;
+
+                                       LOGD("Quantization params : type(%d), scale(%f), zero point(%d)", tensor_info.quantization_type, tensor_info.scale, tensor_info.zero_point);
+                               }
+                       }
+
                        mOutputLayers.insert(std::make_pair(mInterpreter->tensor(layer.second)->name, tensor_info));
                }