Seperate functions of Cast.h into the declaration and the definition (#3104)

This commit seperates functions of Cast.h into the declaration and the definition.

Signed-off-by: jiseob.jang <jiseob.jang@samsung.com>

diff --git a/runtimes/pure_arm_compute/src/internal/arm_compute/Cast.cc b/runtimes/pure_arm_compute/src/internal/arm_compute/Cast.cc
new file mode 100644 (file)
index 0000000..d051ee1
--- /dev/null
+++ b/runtimes/pure_arm_compute/src/internal/arm_compute/Cast.cc
@@ -0,0 +1,110 @@
+/*
+ * 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 "internal/arm_compute/Cast.h"
+
+#include "internal/Swizzle.h"
+
+::arm_compute::Coordinates getARMComputeAxises(uint32_t rank)
+{
+  ::arm_compute::Coordinates res{};
+
+  res.set_num_dimensions(rank);
+
+  for (uint32_t axis = 0; axis < rank; ++axis)
+  {
+    res.set(axis, ToARMComputeAxis(rank, axis).value());
+  }
+
+  return res;
+}
+
+::arm_compute::TensorShape asTensorShape(const internal::tflite::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 int32_t type)
+{
+  switch (type)
+  {
+    case ANEURALNETWORKS_FLOAT32:
+    case ANEURALNETWORKS_TENSOR_FLOAT32:
+      return ::arm_compute::DataType::F32;
+    case ANEURALNETWORKS_INT32:
+    case ANEURALNETWORKS_TENSOR_INT32:
+      return ::arm_compute::DataType::S32;
+    case ANEURALNETWORKS_UINT32:
+      return ::arm_compute::DataType::U32;
+    case ANEURALNETWORKS_TENSOR_QUANT8_ASYMM:
+      return ::arm_compute::DataType::QASYMM8;
+    default:
+      throw std::runtime_error("Not supported, yet");
+      break;
+  }
+}
+
+::arm_compute::ActivationLayerInfo asActivationInfo(FuseCode code)
+{
+  switch (code)
+  {
+    case ANEURALNETWORKS_FUSED_NONE:
+      return ::arm_compute::ActivationLayerInfo{};
+    case ANEURALNETWORKS_FUSED_RELU:
+      return ::arm_compute::ActivationLayerInfo{
+          ::arm_compute::ActivationLayerInfo::ActivationFunction::RELU};
+    case ANEURALNETWORKS_FUSED_RELU1:
+      return ::arm_compute::ActivationLayerInfo{
+          ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 1.0f, -1.0f};
+    case ANEURALNETWORKS_FUSED_RELU6:
+      return ::arm_compute::ActivationLayerInfo{
+          ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.0f, 0.0f};
+    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 ::arm_compute::TensorShape &shape, const int32_t type,
+                                       const float scale, const int32_t zeroPoint)
+{
+  return ::arm_compute::TensorInfo(shape, 1, asDataType(type),
+                                   asQuantizationInfo(scale, zeroPoint));
+}

diff --git a/runtimes/pure_arm_compute/src/internal/arm_compute/Cast.h b/runtimes/pure_arm_compute/src/internal/arm_compute/Cast.h
index e2ceb8f..e89d1dd 100644 (file)
--- a/runtimes/pure_arm_compute/src/internal/arm_compute/Cast.h
+++ b/runtimes/pure_arm_compute/src/internal/arm_compute/Cast.h
@@ -16,101 +16,28 @@
 
 #ifndef __ARM_COMPUTE_CAST_H__
 
+#include <arm_compute/core/Coordinates.h>
+#include <arm_compute/core/TensorInfo.h>
 #include <arm_compute/core/TensorShape.h>
+#include <arm_compute/core/Types.h>
 
-#include "internal/Swizzle.h"
-#include "internal/Model.h"
-
-inline ::arm_compute::Coordinates getARMComputeAxises(uint32_t rank)
-{
-  ::arm_compute::Coordinates res{};
-
-  res.set_num_dimensions(rank);
-
-  for (uint32_t axis = 0; axis < rank; ++axis)
-  {
-    res.set(axis, ToARMComputeAxis(rank, axis).value());
-  }
-
-  return res;
-}
+#include <NeuralNetworks.h>
 
-inline ::arm_compute::TensorShape asTensorShape(const internal::tflite::operand::Shape &shape,
-                                                bool apply_dim_correction = true)
-{
-  const uint32_t rank = shape.rank();
-
-  ::arm_compute::TensorShape res{};
-
-  res.set_num_dimensions(rank);
+#include "internal/Model.h"
 
-  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);
-  }
+::arm_compute::Coordinates getARMComputeAxises(uint32_t rank);
 
-  return res;
-}
+::arm_compute::TensorShape asTensorShape(const internal::tflite::operand::Shape &shape,
+                                         bool apply_dim_correction = true);
 
-::arm_compute::DataType asDataType(const int32_t type)
-{
-  switch (type)
-  {
-    case ANEURALNETWORKS_FLOAT32:
-    case ANEURALNETWORKS_TENSOR_FLOAT32:
-      return ::arm_compute::DataType::F32;
-    case ANEURALNETWORKS_INT32:
-    case ANEURALNETWORKS_TENSOR_INT32:
-      return ::arm_compute::DataType::S32;
-    case ANEURALNETWORKS_UINT32:
-      return ::arm_compute::DataType::U32;
-    case ANEURALNETWORKS_TENSOR_QUANT8_ASYMM:
-      return ::arm_compute::DataType::QASYMM8;
-    default:
-      throw std::runtime_error("Not supported, yet");
-      break;
-  }
-}
+::arm_compute::DataType asDataType(const int32_t type);
 
-::arm_compute::ActivationLayerInfo asActivationInfo(FuseCode code)
-{
-  switch (code)
-  {
-    case ANEURALNETWORKS_FUSED_NONE:
-      return ::arm_compute::ActivationLayerInfo{};
-    case ANEURALNETWORKS_FUSED_RELU:
-      return ::arm_compute::ActivationLayerInfo{
-          ::arm_compute::ActivationLayerInfo::ActivationFunction::RELU};
-    case ANEURALNETWORKS_FUSED_RELU1:
-      return ::arm_compute::ActivationLayerInfo{
-          ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 1.0f, -1.0f};
-    case ANEURALNETWORKS_FUSED_RELU6:
-      return ::arm_compute::ActivationLayerInfo{
-          ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.0f, 0.0f};
-    default:
-      throw std::runtime_error("Not supported, yet");
-      break;
-  }
-}
+::arm_compute::ActivationLayerInfo asActivationInfo(FuseCode code);
 
-::arm_compute::QuantizationInfo asQuantizationInfo(const float scale, const int32_t offset)
-{
-  return ::arm_compute::QuantizationInfo(scale, offset);
-}
+::arm_compute::QuantizationInfo asQuantizationInfo(const float scale, const int32_t offset);
 
 ::arm_compute::TensorInfo asTensorInfo(const ::arm_compute::TensorShape &shape, const int32_t type,
-                                       const float scale = 0.0f, const int32_t zeroPoint = 0)
-{
-  return ::arm_compute::TensorInfo(shape, 1, asDataType(type),
-                                   asQuantizationInfo(scale, zeroPoint));
-}
+                                       const float scale = 0.0f, const int32_t zeroPoint = 0);
 
 template <typename FromT>
 void copyCast(const FromT value, ::arm_compute::ITensor *to, const ::arm_compute::Coordinates &id)