1 files changed, 88 insertions, 0 deletions
diff --git a/compiler/luci-interpreter/src/kernels/Relu6.cpp b/compiler/luci-interpreter/src/kernels/Relu6.cpp
new file mode 100644
index 000000000..1046ef27b
--- /dev/null
+++ b/compiler/luci-interpreter/src/kernels/Relu6.cpp
@@ -0,0 +1,88 @@
+/*
+ * Copyright (c) 2020 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 "kernels/Relu6.h"
+#include "kernels/Utils.h"
+
+#include <tensorflow/lite/kernels/internal/optimized/optimized_ops.h>
+
+#include <stdexcept>
+
+namespace luci_interpreter
+{
+
+namespace kernels
+{
+
+Relu6::Relu6(const Tensor *input, Tensor *output) : Kernel({input}, {output}) {}
+
+void Relu6::configure()
+{
+  LUCI_INTERPRETER_CHECK(input()->element_type() == output()->element_type());
+
+  if (input()->element_type() == DataType::U8)
+  {
+    double multiplier = input()->scale() / output()->scale();
+    quantizeMultiplier(multiplier, &_output_multiplier, &_output_shift);
+  }
+  output()->resize(input()->shape());
+}
+
+void Relu6::execute() const
+{
+  switch (input()->element_type())
+  {
+    case DataType::FLOAT32:
+      evalFloat();
+      break;
+    case DataType::U8:
+      evalQuantized();
+      break;
+    default:
+      throw std::runtime_error("Unsupported type.");
+  }
+}
+
+void Relu6::evalFloat() const
+{
+  const auto input_data = getTensorData<float>(input());
+  const auto input_shape = getTensorShape(input());
+  auto output_data = getTensorData<float>(output());
+  auto output_shape = getTensorShape(output());
+
+  tflite::optimized_ops::Relu6(input_shape, input_data, output_shape, output_data);
+}
+
+void Relu6::evalQuantized() const
+{
+  tflite::ReluParams params;
+  params.input_offset = input()->zero_point();
+  params.output_offset = output()->zero_point();
+  params.output_multiplier = _output_multiplier;
+  params.output_shift = _output_shift;
+
+  params.quantized_activation_min =
+      std::max(static_cast<int32_t>(std::numeric_limits<uint8_t>::min()), params.output_offset);
+  params.quantized_activation_max =
+      std::min(static_cast<int32_t>(std::numeric_limits<uint8_t>::max()),
+               params.output_offset + static_cast<int32>(roundf(6.f / output()->scale())));
+
+  tflite::optimized_ops::ReluX(params, getTensorShape(input()), getTensorData<uint8_t>(input()),
+                               getTensorShape(output()), getTensorData<uint8_t>(output()));
+}
+
+} // namespace kernels
+} // namespace luci_interpreter