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-rw-r--r--runtime/contrib/pure_arm_compute/src/internal/arm_compute/Cast.cc152
1 files changed, 152 insertions, 0 deletions
diff --git a/runtime/contrib/pure_arm_compute/src/internal/arm_compute/Cast.cc b/runtime/contrib/pure_arm_compute/src/internal/arm_compute/Cast.cc
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index 000000000..1a5c735ee
--- /dev/null
+++ b/runtime/contrib/pure_arm_compute/src/internal/arm_compute/Cast.cc
@@ -0,0 +1,152 @@
+/*
+ * 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::Coordinates asARMComputeCoordinates(const ::arm_compute::Coordinates &runtime_coord,
+ const ::arm_compute::Coordinates &axises)
+{
+ ::arm_compute::Coordinates id{};
+ assert(runtime_coord.num_dimensions() == axises.num_dimensions());
+ for (size_t i = 0; i < runtime_coord.num_dimensions(); ++i)
+ {
+ id.set(axises[i], runtime_coord[i]);
+ }
+ return id;
+}
+
+// Restructure runtime_permutationVector to ACL_permutationVector
+::arm_compute::PermutationVector getARMComputePermutationVector(uint32_t rank,
+ const int32_t *runtime_pv)
+{
+ // rank upto 4 is supported
+ assert(rank <= 4);
+ assert(runtime_pv != nullptr);
+
+ int new_pv[4] = {0};
+ ::arm_compute::Coordinates axises = getARMComputeAxises(rank);
+
+ for (uint32_t i = 0; i < rank; ++i)
+ {
+ new_pv[axises[i]] = ToARMComputeAxis(rank, runtime_pv[i]).value();
+ }
+
+ ::arm_compute::PermutationVector ACL_PV =
+ ::arm_compute::PermutationVector{new_pv[0], new_pv[1], new_pv[2], new_pv[3]};
+ ACL_PV.set_num_dimensions(rank);
+
+ return ACL_PV;
+}
+
+::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));
+}
+
+::arm_compute::TensorInfo asTensorInfo(const ::arm_compute::TensorShape &shape,
+ const ::arm_compute::DataType &type, const float scale,
+ const int32_t zeroPoint)
+{
+ return ::arm_compute::TensorInfo(shape, 1, type, asQuantizationInfo(scale, zeroPoint));
+}