1 files changed, 102 insertions, 0 deletions
diff --git a/runtime/neurun/core/src/exec/interp/operations/Logistic.cc b/runtime/neurun/core/src/exec/interp/operations/Logistic.cc
new file mode 100644
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+++ b/runtime/neurun/core/src/exec/interp/operations/Logistic.cc
@@ -0,0 +1,102 @@
+/*
+ * 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/Logistic.h>
+
+#include "OperationUtil.h"
+
+#include "exec/interp/Registration.h"
+#include "ir/operation/Logistic.h"
+
+namespace neurun
+{
+namespace exec
+{
+namespace interp
+{
+namespace
+{
+
+void prepareLogistic(ExecEnv *env, const ir::Operation &node)
+{
+  const auto input_index = node.getInputs().at(0);
+  const auto output_index = node.getOutputs().at(0);
+
+  const auto input_tensor = env->tensorAt(input_index);
+
+  const auto output_info = env->graph().operands().at(output_index).info();
+
+  // Check shape and type lhs is same with rhs
+  // TODO Util function to compare TensorInfo
+  if (output_info.total_size() == 0)
+  {
+    throw std::runtime_error{"Interp(TConv): NYI unspecified output shape"};
+  }
+  else
+  {
+    env->allocateIfNeeded(output_index, output_info);
+  }
+
+  const auto output_tensor = env->tensorAt(output_index);
+  if (input_tensor->data_type() != output_tensor->data_type())
+  {
+    throw std::runtime_error{"Interp(Logistic): Invalid output type"};
+  }
+}
+
+void invoke(const ITensor *input_tensor, const ITensor *output_tensor)
+{
+  const auto input_buffer = input_tensor->bufferRO();
+  auto output_buffer = output_tensor->buffer();
+
+  const auto cker_input_shape = convertShape(input_tensor->tensorInfo().shape());
+  const auto cker_output_shape = convertShape(output_tensor->tensorInfo().shape());
+  const float *input_ptr = reinterpret_cast<const float *>(input_buffer);
+  float *output_ptr = reinterpret_cast<float *>(output_buffer);
+
+  nnfw::cker::Logistic(cker_input_shape, input_ptr, cker_output_shape, output_ptr);
+}
+
+void invokeLogistic(const ExecEnv *env, const ir::Operation &node)
+{
+  const auto input_index = node.getInputs().at(0);
+  const auto output_index = node.getOutputs().at(0);
+
+  const auto input_tensor = env->tensorAt(input_index);
+  const auto output_tensor = env->tensorAt(output_index);
+
+  const auto data_type = input_tensor->data_type();
+
+  if (data_type == ir::DataType::FLOAT32)
+  {
+    invoke(input_tensor, output_tensor);
+  }
+  else
+  {
+    throw std::runtime_error{"Interp(Logistic): NYI - Unsupported data type"};
+  }
+}
+} // namespace
+
+OpKernel *getLogistic()
+{
+  static OpKernel kernel = {prepareLogistic, invokeLogistic};
+  return &kernel;
+}
+
+} // namespace interp
+} // namespace exec
+} // namespace neurun