1 files changed, 136 insertions, 0 deletions
diff --git a/runtime/onert/backend/cpu/kernel/DepthwiseConvolutionLayer.cc b/runtime/onert/backend/cpu/kernel/DepthwiseConvolutionLayer.cc
new file mode 100644
index 000000000..a1efe9fed
--- /dev/null
+++ b/runtime/onert/backend/cpu/kernel/DepthwiseConvolutionLayer.cc
@@ -0,0 +1,136 @@
+/*
+ * Copyright (c) 2019 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 "DepthwiseConvolutionLayer.h"
+
+#include <cker/operation/DepthwiseConv.h>
+
+namespace onert
+{
+namespace backend
+{
+namespace cpu
+{
+namespace kernel
+{
+
+DepthwiseConvolutionLayer::DepthwiseConvolutionLayer()
+    : _input(nullptr), _kernel(nullptr), _bias(nullptr), _output(nullptr), _paddingLeft(0),
+      _paddingTop(0), _paddingRight(0), _paddingBottom(0), _strideWidth(0), _strideHeight(0),
+      _multiplier(0), _activation(ir::Activation::NONE)
+{
+  // DO NOTHING
+}
+
+void DepthwiseConvolutionLayer::convFloat32()
+{
+  float output_activation_min, output_activation_max;
+  CalculateActivationRangeFloat(_activation, &output_activation_min, &output_activation_max);
+
+  nnfw::cker::DepthwiseConvParams 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.depth_multiplier = _multiplier;
+  op_params.float_activation_min = output_activation_min;
+  op_params.float_activation_max = output_activation_max;
+
+  nnfw::cker::DepthwiseConv(
+      op_params, convertTensorToCkerShape(_input),
+      reinterpret_cast<const float *>(_input->buffer()), convertTensorToCkerShape(_kernel),
+      reinterpret_cast<const float *>(_kernel->buffer()), convertTensorToCkerShape(_bias),
+      reinterpret_cast<const float *>(_bias->buffer()), convertTensorToCkerShape(_output),
+      reinterpret_cast<float *>(_output->buffer()));
+}
+
+void DepthwiseConvolutionLayer::convQuant8()
+{
+  int32_t output_activation_min = 0;
+  int32_t output_activation_max = 0;
+  CalculateActivationRangeUint8(_activation, _output, &output_activation_min,
+                                &output_activation_max);
+
+  double real_multiplier = 0.0;
+  int32_t output_multiplier = 0;
+  int32_t output_shift = 0;
+  GetQuantizedConvolutionMultiplier(_input, _kernel, _bias, _output, &real_multiplier);
+  QuantizeMultiplier(real_multiplier, &output_multiplier, &output_shift);
+
+  nnfw::cker::DepthwiseConvParams 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.depth_multiplier = _multiplier;
+  op_params.input_offset = -_input->offset();
+  op_params.weights_offset = -_kernel->offset();
+  op_params.output_offset = _output->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::DepthwiseConv(
+      op_params, convertTensorToCkerShape(_input),
+      reinterpret_cast<const uint8_t *>(_input->buffer()), convertTensorToCkerShape(_kernel),
+      reinterpret_cast<const uint8_t *>(_kernel->buffer()), convertTensorToCkerShape(_bias),
+      reinterpret_cast<const int32_t *>(_bias->buffer()), convertTensorToCkerShape(_output),
+      reinterpret_cast<uint8_t *>(_output->buffer()));
+}
+
+void DepthwiseConvolutionLayer::configure(const operand::Tensor *input,
+                                          const operand::Tensor *kernel,
+                                          const operand::Tensor *bias, 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 uint32_t multiplier,
+                                          const ir::Activation activation, operand::Tensor *output)
+{
+  _input = input;
+  _kernel = kernel;
+  _bias = bias;
+  _paddingLeft = paddingLeft;
+  _paddingRight = paddingRight;
+  _paddingTop = paddingTop;
+  _paddingBottom = paddingBottom;
+  _strideWidth = strideWidth;
+  _strideHeight = strideHeight;
+  _multiplier = multiplier;
+  _activation = activation;
+  _output = output;
+}
+
+void DepthwiseConvolutionLayer::run()
+{
+  if (_input->data_type() == OperandType::FLOAT32)
+  {
+    convFloat32();
+  }
+  else if (_input->data_type() == OperandType::QUANT8_ASYMM)
+  {
+    convQuant8();
+  }
+}
+
+} // namespace kernel
+} // namespace cpu
+} // namespace backend
+} // namespace onert