catch up to tizen_5.5 and remove unness dir

- update to tizen_5.5
- remove dirs

1 files changed, 139 insertions, 0 deletions

diff --git a/runtimes/neurun/backend/cpu/kernel/ConvolutionLayer.cc b/runtimes/neurun/backend/cpu/kernel/ConvolutionLayer.cc
new file mode 100644
index 000000000..efeabbbae
--- /dev/null
+++ b/runtimes/neurun/backend/cpu/kernel/ConvolutionLayer.cc
@@ -0,0 +1,139 @@
+/*
+ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ConvolutionLayer.h"
+
+#include <cker/operation/Conv.h>
+
+#include "OperationUtils.h"
+
+namespace neurun
+{
+namespace backend
+{
+namespace cpu
+{
+namespace kernel
+{
+ConvolutionLayer::ConvolutionLayer()
+    : _inputData(), _kernelData(), _outputData(), _biasData(), _inputShape(), _kernelShape(),
+      _outputShape(), _biasShape(), _paddingLeft(0), _paddingTop(0), _paddingRight(0),
+      _paddingBottom(0), _strideWidth(0), _strideHeight(0), _activation(model::Activation::NONE),
+      _inputType(OperandType::FLOAT32)
+{
+  // DO NOTHING
+}
+
+void ConvolutionLayer::convFloat32()
+{
+  float output_activation_min, output_activation_max;
+  CalculateActivationRangeFloat(_activation, &output_activation_min, &output_activation_max);
+
+  nnfw::cker::ConvParams op_params;
+  op_params.padding_values.width = _paddingLeft;
+  op_params.padding_values.height = _paddingTop;
+  op_params.stride_width = _strideWidth;
+  op_params.stride_height = _strideHeight;
+  op_params.dilation_width_factor = 1;
+  op_params.dilation_height_factor = 1;
+  op_params.float_activation_min = output_activation_min;
+  op_params.float_activation_max = output_activation_max;
+
+  nnfw::cker::Conv(op_params, convertShapeToCkerShape(_inputShape), _inputData.f,
+                   convertShapeToCkerShape(_kernelShape), _kernelData.f,
+                   convertShapeToCkerShape(_biasShape), _biasData.f,
+                   convertShapeToCkerShape(_outputShape), _outputData.f);
+}
+
+void ConvolutionLayer::convQuant8()
+{
+  int32_t output_activation_min = 0;
+  int32_t output_activation_max = 0;
+  CalculateActivationRangeUint8(_activation, _outputShape, &output_activation_min,
+                                &output_activation_max);
+
+  float real_multiplier = 0.0;
+  int32_t output_multiplier = 0;
+  int32_t output_shift = 0;
+  GetQuantizedConvolutionMultiplier(_inputShape, _kernelShape, _biasShape, _outputShape,
+                                    &real_multiplier);
+  QuantizeMultiplier(real_multiplier, &output_multiplier, &output_shift);
+
+  nnfw::cker::ConvParams op_params;
+  op_params.stride_width = _strideWidth;
+  op_params.stride_height = _strideHeight;
+  op_params.dilation_width_factor = 1;
+  op_params.dilation_height_factor = 1;
+  op_params.padding_values.width = _paddingLeft;
+  op_params.padding_values.height = _paddingTop;
+  op_params.input_offset = -_inputShape.offset;
+  op_params.weights_offset = -_kernelShape.offset;
+  op_params.output_offset = _outputShape.offset;
+  op_params.output_multiplier = output_multiplier;
+  op_params.output_shift = output_shift;
+  op_params.quantized_activation_min = output_activation_min;
+  op_params.quantized_activation_max = output_activation_max;
+
+  nnfw::cker::Conv(op_params, convertShapeToCkerShape(_inputShape), _inputData.u8,
+                   convertShapeToCkerShape(_kernelShape), _kernelData.u8,
+                   convertShapeToCkerShape(_biasShape), _biasData.i32,
+                   convertShapeToCkerShape(_outputShape), _outputData.u8);
+}
+
+void ConvolutionLayer::configure(uint8_t *inputData, const Shape inputShape, uint8_t *kernelData,
+                                 const Shape kernelShape, uint8_t *biasData, const Shape biasShape,
+                                 const uint32_t paddingLeft, const uint32_t paddingRight,
+                                 const uint32_t paddingTop, const uint32_t paddingBottom,
+                                 const uint32_t strideWidth, const uint32_t strideHeight,
+                                 const model::Activation activation, uint8_t *outputData,
+                                 const Shape outputShape)
+{
+  _inputData.u8 = inputData;
+  _inputShape = inputShape;
+  _inputType = inputShape.type;
+  _kernelData.u8 = kernelData;
+  _kernelShape = kernelShape;
+  _biasData.u8 = biasData;
+  _biasShape = biasShape;
+  _paddingLeft = paddingLeft;
+  _paddingRight = paddingRight;
+  _paddingTop = paddingTop;
+  _paddingBottom = paddingBottom;
+  _strideWidth = strideWidth;
+  _strideHeight = strideHeight;
+  _activation = activation;
+  _outputData.u8 = outputData;
+  _outputShape = outputShape;
+}
+
+void ConvolutionLayer::run()
+{
+  if (_inputType == OperandType::FLOAT32)
+  {
+    convFloat32();
+  }
+  else if (_inputType == OperandType::QUANT8_ASYMM)
+  {
+    convQuant8();
+  }
+}
+
+#undef ANDROID_NN_CONV_PARAMETERS
+
+} // namespace kernel
+} // namespace cpu
+} // namespace backend
+} // namespace neurun