1 files changed, 87 insertions, 0 deletions
diff --git a/runtimes/neurun/src/backend/acl_cl/Convert.cc b/runtimes/neurun/src/backend/acl_cl/Convert.cc
new file mode 100644
index 000000000..ed0a089c4
--- /dev/null
+++ b/runtimes/neurun/src/backend/acl_cl/Convert.cc
@@ -0,0 +1,87 @@
+/*
+ * 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 "Convert.h"
+
+#include "Swizzle.h"
+#include "model/operand/DataType.h"
+
+namespace neurun
+{
+namespace backend
+{
+namespace acl_cl
+{
+
+::arm_compute::TensorShape asTensorShape(const ::neurun::model::operand::Shape &shape,
+                                         bool apply_dim_correction)
+{
+  const uint32_t rank = shape.rank();
+
+  ::arm_compute::TensorShape res{};
+
+  res.set_num_dimensions(rank);
+
+  for (uint32_t axis = 0; axis < rank; ++axis)
+  {
+    // NOTE In some cases, in incorrect dimensions is required.
+    // For example, intput_size is 1 in LSTM. The input-to-input weights([num_units, input_size]) of
+    // LSTM is used as the weight of the FullyConnected.
+    // The FullyConnected's weight must be greater or equal than 2-dimensions.
+    // However, if the dimension correction is applied to input_to_input_weights with input_size
+    // equal to 1, it will be changed to 1-D.
+    // So input_to_input_weights is not used by the weight of FullyConnected.
+    res.set(ToARMComputeAxis(rank, axis).value(), shape.dim(axis), apply_dim_correction);
+  }
+
+  return res;
+}
+
+::arm_compute::DataType asDataType(const ::neurun::model::operand::DataType &type)
+{
+  switch (type)
+  {
+    case ::neurun::model::operand::DataType::SCALAR_FLOAT32:
+    case ::neurun::model::operand::DataType::TENSOR_FLOAT32:
+      return ::arm_compute::DataType::F32;
+    case ::neurun::model::operand::DataType::SCALAR_INT32:
+    case ::neurun::model::operand::DataType::TENSOR_INT32:
+      return ::arm_compute::DataType::S32;
+    case ::neurun::model::operand::DataType::SCALAR_UINT32:
+      return ::arm_compute::DataType::U32;
+    case ::neurun::model::operand::DataType::TENSOR_QUANT8_ASYMM:
+      return ::arm_compute::DataType::QASYMM8;
+    default:
+      throw std::runtime_error("Not supported, yet");
+      break;
+  }
+}
+
+::arm_compute::QuantizationInfo asQuantizationInfo(const float scale, const int32_t offset)
+{
+  return ::arm_compute::QuantizationInfo(scale, offset);
+}
+
+::arm_compute::TensorInfo asTensorInfo(const ::neurun::model::operand::Shape &shape,
+                                       const ::neurun::model::operand::TypeInfo &typeInfo)
+{
+  return ::arm_compute::TensorInfo(asTensorShape(shape), 1, asDataType(typeInfo.type()),
+                                   asQuantizationInfo(typeInfo.scale(), typeInfo.offset()));
+}
+
+} // namespace acl_cl
+} // namespace backend
+} // namespace neurun