1 files changed, 150 insertions, 0 deletions
diff --git a/runtime/onert/core/src/interp/operations/Conv2D.cc b/runtime/onert/core/src/interp/operations/Conv2D.cc
new file mode 100644
index 000000000..3c0087a61
--- /dev/null
+++ b/runtime/onert/core/src/interp/operations/Conv2D.cc
@@ -0,0 +1,150 @@
+/*
+ * 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 <cker/operation/Conv.h>
+
+#include "OperationUtil.h"
+
+#include "interp/Registration.h"
+#include "ir/operation/Conv2D.h"
+#include "util/Utils.h"
+#include "util/ShapeInference.h"
+#include "misc/polymorphic_downcast.h"
+
+namespace onert
+{
+namespace interp
+{
+namespace conv2d
+{
+
+void prepareConv2D(ExecEnv *env, const ir::Operation &node)
+{
+  const auto in_index = node.getInputs().at(ir::operation::Conv2D::INPUT);
+  const auto kernel_index = node.getInputs().at(ir::operation::Conv2D::KERNEL);
+  const auto bias_index = node.getInputs().at(ir::operation::Conv2D::BIAS);
+  const auto out_index = node.getOutputs().at(0);
+
+  const auto in_tensor = env->tensorAt(in_index);
+  const auto kernel_tensor = env->tensorAt(kernel_index);
+  const auto bias_tensor = env->tensorAt(bias_index);
+
+  assert(in_tensor->num_dimensions() == 4);
+  assert(kernel_tensor->num_dimensions() == 4);
+  assert(bias_tensor->num_dimensions() == 1);
+
+  UNUSED_RELEASE(in_tensor);
+  UNUSED_RELEASE(kernel_tensor);
+  UNUSED_RELEASE(bias_tensor);
+
+  const auto output_info = env->graph().operands().at(out_index).info();
+  if (output_info.total_size() == 0)
+  {
+    // Handle unspecified output shape
+    const auto &conv_node = nnfw::misc::polymorphic_downcast<const ir::operation::Conv2D &>(node);
+    const auto infered_output_shapes = shape_inference::inferConv2DShape(
+        in_tensor->tensorInfo().shape(), kernel_tensor->tensorInfo().shape(), conv_node.param());
+    env->allocateIfNeeded(out_index, {infered_output_shapes[0], output_info.typeInfo()});
+  }
+  else
+  {
+    env->allocateIfNeeded(out_index, output_info);
+  }
+
+  auto out_tensor = env->tensorAt(out_index);
+  UNUSED_RELEASE(out_tensor);
+
+  // Handle same ifm & ofm data type only
+  assert(in_tensor->data_type() == out_tensor->data_type());
+  assert(out_tensor->num_dimensions() == 4);
+}
+
+void invoke(const ITensor *ifm_tensor, const ITensor *ker_tensor, const ITensor *bias_tensor,
+            const ITensor *ofm_tensor, const ir::operation::Conv2D::Param &param)
+{
+  // TODO Support NCHW frontned
+  const auto ifm_shape = ifm_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC);
+  const auto ofm_shape = ofm_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC);
+  // Kernel format is [depth_out, kernel_height, kernel_width, depth_in].
+  const auto &ker_shape = ker_tensor->tensorInfo().shape();
+  const auto ker_height = ker_shape.dim(1);
+  const auto ker_width = ker_shape.dim(2);
+  const auto padding = ir::calculatePadding(param.padding, ifm_shape, ofm_shape, param.stride,
+                                            ker_width, ker_height);
+
+  // Calculate
+  float activation_min, activation_max;
+  calculateActivationRange(param.activation, &activation_min, &activation_max);
+
+  nnfw::cker::ConvParams cker_param;
+  cker_param.padding_type = convertPaddingType(param.padding.type);
+  cker_param.padding_values.width = padding.left;
+  cker_param.padding_values.height = padding.top;
+  cker_param.stride_width = param.stride.horizontal;
+  cker_param.stride_height = param.stride.vertical;
+  cker_param.dilation_width_factor = 1;
+  cker_param.dilation_height_factor = 1;
+  cker_param.float_activation_min = activation_min;
+  cker_param.float_activation_max = activation_max;
+
+  const auto cker_ifm_shape = convertShape(ifm_tensor->tensorInfo().shape());
+  const auto cker_ker_shape = convertShape(ker_tensor->tensorInfo().shape());
+  const auto cker_bias_shape = convertShape(bias_tensor->tensorInfo().shape());
+  const auto cker_ofm_shape = convertShape(ofm_tensor->tensorInfo().shape());
+  const float *ifm_ptr = reinterpret_cast<const float *>(ifm_tensor->bufferRO());
+  const float *ker_ptr = reinterpret_cast<const float *>(ker_tensor->bufferRO());
+  const float *bias_ptr = reinterpret_cast<const float *>(bias_tensor->bufferRO());
+  float *ofm_ptr = reinterpret_cast<float *>(ofm_tensor->buffer());
+
+  nnfw::cker::Conv conv_kernel;
+  conv_kernel(cker_param, cker_ifm_shape, ifm_ptr, cker_ker_shape, ker_ptr, cker_bias_shape,
+              bias_ptr, cker_ofm_shape, ofm_ptr);
+}
+
+void invokeConv2D(const ExecEnv *env, const ir::Operation &node)
+{
+  const auto &conv_node = nnfw::misc::polymorphic_downcast<const ir::operation::Conv2D &>(node);
+
+  const auto ifm_index = node.getInputs().at(ir::operation::Conv2D::INPUT);
+  const auto ker_index = node.getInputs().at(ir::operation::Conv2D::KERNEL);
+  const auto bias_index = node.getInputs().at(ir::operation::Conv2D::BIAS);
+  const auto ofm_index = node.getOutputs().at(0);
+
+  const auto ifm_tensor = env->tensorAt(ifm_index);
+  const auto ker_tensor = env->tensorAt(ker_index);
+  const auto bias_tensor = env->tensorAt(bias_index);
+  const auto ofm_tensor = env->tensorAt(ofm_index);
+
+  const auto data_type = ifm_tensor->data_type();
+  if (data_type == ir::DataType::FLOAT32)
+  {
+    invoke(ifm_tensor, ker_tensor, bias_tensor, ofm_tensor, conv_node.param());
+  }
+  else
+  {
+    throw std::runtime_error{"NYI: Support float32 only"};
+  }
+}
+} // namespace conv2d
+
+OpKernel *getConv2D()
+{
+  static OpKernel kernel = {conv2d::prepareConv2D, conv2d::invokeConv2D};
+  return &kernel;
+}
+
+} // namespace interp
+} // namespace onert