arm_compute v17.12
diff --git a/src/graph/operations/NESimpleOperations.cpp b/src/graph/operations/NESimpleOperations.cpp
new file mode 100644
index 0000000..bb99e8d
--- /dev/null
+++ b/src/graph/operations/NESimpleOperations.cpp
@@ -0,0 +1,463 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/graph/IOperation.h"
+#include "arm_compute/graph/NodeContext.h"
+#include "arm_compute/graph/OperationRegistrar.h"
+#include "arm_compute/graph/Types.h"
+#include "arm_compute/runtime/NEON/NEFunctions.h"
+#include "support/ToolchainSupport.h"
+#include "utils/GraphTypePrinter.h"
+#include "utils/TypePrinter.h"
+
+#include <memory>
+
+using namespace arm_compute::graph;
+
+/* Activation Layer */
+REGISTER_SIMPLE_OPERATION(NEActivationLayerOperation, NEON, OperationType::ActivationLayer)
+{
+ ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 1);
+ ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr);
+
+ // Extract IO and info
+ auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0));
+ auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0));
+ const auto act_info = ctx.parameter<ActivationLayerInfo>("ActivationLayerInfo");
+
+ // Create and configure function
+ auto activation = arm_compute::support::cpp14::make_unique<arm_compute::NEActivationLayer>();
+ activation->configure(in, out, act_info);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEActivationLayer"
+ << " Data Type: " << in->info()->data_type()
+ << " Input shape: " << in->info()->tensor_shape()
+ << " Output shape: " << out->info()->tensor_shape()
+ << " Activation function: " << act_info.activation()
+ << " a: " << act_info.a()
+ << " b: " << act_info.b()
+ << std::endl);
+
+ return std::move(activation);
+}
+
+/* Batch Normalization Layer */
+REGISTER_SIMPLE_OPERATION(NEBatchNormalizationLayerOperation, NEON, OperationType::BatchNormalizationLayer)
+{
+ ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 5);
+ ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(1)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(2)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(3)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(4)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr);
+
+ // Extract IO and info
+ auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0));
+ auto *mean = dynamic_cast<arm_compute::ITensor *>(ctx.input(1));
+ auto *var = dynamic_cast<arm_compute::ITensor *>(ctx.input(2));
+ auto *beta = dynamic_cast<arm_compute::ITensor *>(ctx.input(3));
+ auto *gamma = dynamic_cast<arm_compute::ITensor *>(ctx.input(4));
+ auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0));
+ const auto epsilon = ctx.parameter<float>("epsilon");
+
+ // Create and configure function
+ auto batch_norm = arm_compute::support::cpp14::make_unique<arm_compute::NEBatchNormalizationLayer>();
+ batch_norm->configure(in, out, mean, var, beta, gamma, epsilon);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEBatchNormalizationLayer"
+ << " Data Type: " << in->info()->data_type()
+ << " Input shape: " << in->info()->tensor_shape()
+ << " Output shape: " << out->info()->tensor_shape()
+ << " Mean shape: " << mean->info()->tensor_shape()
+ << " Var shape: " << var->info()->tensor_shape()
+ << " Beta shape: " << beta->info()->tensor_shape()
+ << " Gamma shape: " << gamma->info()->tensor_shape()
+ << " Epsilon: " << epsilon
+ << std::endl);
+
+ return std::move(batch_norm);
+}
+
+/* DepthConvertLayer Layer */
+REGISTER_SIMPLE_OPERATION(NEDepthConvertLayerOperation, NEON, OperationType::DepthConvertLayer)
+{
+ ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 1);
+ ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr);
+
+ // Extract IO and info
+ auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0));
+ auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0));
+ const auto conv_policy = ctx.parameter<ConvertPolicy>("ConvertPolicy");
+ const auto shift = ctx.parameter<uint32_t>("shift");
+
+ // Create and configure function
+ auto depthconvert = arm_compute::support::cpp14::make_unique<arm_compute::NEDepthConvertLayer>();
+ depthconvert->configure(in, out, conv_policy, shift);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEDepthConvertLayer"
+ << " Data Type: " << in->info()->data_type()
+ << " Input shape: " << in->info()->tensor_shape()
+ << " Output shape: " << out->info()->tensor_shape()
+ << " shift: " << shift
+ << std::endl);
+
+ return std::move(depthconvert);
+}
+
+/* DepthwiseConvolutionLayer Layer */
+REGISTER_SIMPLE_OPERATION(NEDepthwiseConvolutionOperation, NEON, OperationType::DepthwiseConvolutionLayer)
+{
+ ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 2 && ctx.num_inputs() != 3);
+ ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr);
+
+ // Extract IO and info
+ auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0));
+ auto *weights = dynamic_cast<arm_compute::ITensor *>(ctx.input(1));
+ auto *biases = ctx.num_inputs() == 3 ? dynamic_cast<arm_compute::ITensor *>(ctx.input(2)) : nullptr;
+ auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0));
+ const auto conv_info = ctx.parameter<PadStrideInfo>("ConvolutionInfo");
+ const auto opt3x3 = ctx.parameter<bool>("Optimized3x3");
+
+ // Create and configure function
+ std::unique_ptr<arm_compute::IFunction> func;
+ bool run_3x3_opt = opt3x3 && weights->info()->dimension(0) == 3;
+ if(run_3x3_opt)
+ {
+ auto depwthwise_conv = arm_compute::support::cpp14::make_unique<arm_compute::NEDepthwiseConvolutionLayer>();
+ depwthwise_conv->configure(in, weights, biases, out, conv_info);
+ func = std::move(depwthwise_conv);
+ }
+ else
+ {
+ auto depwthwise_conv = arm_compute::support::cpp14::make_unique<arm_compute::NEDepthwiseConvolutionLayer3x3>();
+ depwthwise_conv->configure(in, weights, biases, out, conv_info);
+ func = std::move(depwthwise_conv);
+ }
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEDepthwiseConvolutionLayer"
+ << " Data Type: " << in->info()->data_type()
+ << " Input shape: " << in->info()->tensor_shape()
+ << " Weights shape: " << weights->info()->tensor_shape()
+ << " Output shape: " << out->info()->tensor_shape());
+ if(biases == nullptr)
+ {
+ ARM_COMPUTE_LOG_GRAPH_INFO(" Biases shape: No biases provided" << std::endl);
+ }
+ else
+ {
+ ARM_COMPUTE_LOG_GRAPH_INFO(" Biases shape: " << biases->info()->tensor_shape() << std::endl);
+ }
+
+ return func;
+}
+
+/* DeQuantizationLayer Layer */
+REGISTER_SIMPLE_OPERATION(NEDequantizationLayerOperation, NEON, OperationType::DequantizationLayer)
+{
+ ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 1);
+ ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 2);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(1)) == nullptr);
+
+ // Extract IO and info
+ auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0));
+ auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0));
+ auto *min_max = dynamic_cast<arm_compute::ITensor *>(ctx.output(1));
+
+ // Create and configure function
+ auto dequantization = arm_compute::support::cpp14::make_unique<arm_compute::NEDequantizationLayer>();
+ dequantization->configure(in, out, min_max);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEDequantizationLayer"
+ << " Data Type: " << in->info()->data_type()
+ << " Input shape: " << in->info()->tensor_shape()
+ << " Output shape: " << out->info()->tensor_shape()
+ << " Min max shape: " << min_max->info()->tensor_shape()
+ << std::endl);
+
+ return std::move(dequantization);
+}
+
+/* Flatten Layer */
+REGISTER_SIMPLE_OPERATION(NEFlattenLayerOperation, NEON, OperationType::FlattenLayer)
+{
+ ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 1);
+ ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr);
+
+ // Extract IO and info
+ auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0));
+ auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0));
+
+ // Create and configure function
+ auto flatten = arm_compute::support::cpp14::make_unique<arm_compute::NEFlattenLayer>();
+ flatten->configure(in, out);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEFlattenLayer"
+ << " Data Type: " << in->info()->data_type()
+ << " Input shape: " << in->info()->tensor_shape()
+ << " Output shape: " << out->info()->tensor_shape()
+ << std::endl);
+
+ return std::move(flatten);
+}
+
+/* Floor Layer */
+REGISTER_SIMPLE_OPERATION(NEFloorLayerOperation, NEON, OperationType::FloorLayer)
+{
+ ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 1);
+ ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr);
+
+ // Extract IO and info
+ auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0));
+ auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0));
+
+ // Create and configure function
+ auto floor = arm_compute::support::cpp14::make_unique<arm_compute::NEFloor>();
+ floor->configure(in, out);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEFloorLayer"
+ << " Data Type: " << in->info()->data_type()
+ << " Input shape: " << in->info()->tensor_shape()
+ << " Output shape: " << out->info()->tensor_shape()
+ << std::endl);
+
+ return std::move(floor);
+}
+
+/* Fully Connected Layer */
+REGISTER_SIMPLE_OPERATION(NEFullyConnectedLayer, NEON, OperationType::FullyConnectedLayer)
+{
+ ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 3);
+ ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(1)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(2)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr);
+
+ // Extract IO and info
+ auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0));
+ auto *weights = dynamic_cast<arm_compute::ITensor *>(ctx.input(1));
+ auto *biases = dynamic_cast<arm_compute::ITensor *>(ctx.input(2));
+ auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0));
+
+ // Create and configure function
+ auto fc = arm_compute::support::cpp14::make_unique<arm_compute::NEFullyConnectedLayer>();
+ fc->configure(in, weights, biases, out);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEFullyConnectedLayer"
+ << " Data Type: " << in->info()->data_type()
+ << " Input shape: " << in->info()->tensor_shape()
+ << " Weights shape: " << weights->info()->tensor_shape()
+ << " Biases Shape: " << biases->info()->tensor_shape()
+ << " Output shape: " << out->info()->tensor_shape()
+ << std::endl);
+
+ return std::move(fc);
+}
+
+/* L2 Normalize Layer */
+REGISTER_SIMPLE_OPERATION(NEL2NormalizeLayerOperation, NEON, OperationType::L2NormalizeLayer)
+{
+ ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 1);
+ ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr);
+
+ // Extract IO and info
+ auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0));
+ auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0));
+ const auto axis = ctx.parameter<unsigned int>("axis");
+ const auto epsilon = ctx.parameter<float>("epsilon");
+
+ // Create and configure function
+ auto l2_norm = arm_compute::support::cpp14::make_unique<arm_compute::NEL2NormalizeLayer>();
+ l2_norm->configure(in, out, axis, epsilon);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEL2NormalizeLayer"
+ << " Data Type: " << in->info()->data_type()
+ << " Input shape: " << in->info()->tensor_shape()
+ << " Output shape: " << out->info()->tensor_shape()
+ << " Axis: " << axis
+ << " Epsilon: " << epsilon
+ << std::endl);
+
+ return std::move(l2_norm);
+}
+
+/* Normalization Layer */
+REGISTER_SIMPLE_OPERATION(NENormalizationLayerOperation, NEON, OperationType::NormalizationLayer)
+{
+ ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 1);
+ ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr);
+
+ // Extract IO and info
+ auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0));
+ auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0));
+ const auto norm_info = ctx.parameter<NormalizationLayerInfo>("NormalizationLayerInfo");
+
+ // Create and configure function
+ auto norm = arm_compute::support::cpp14::make_unique<arm_compute::NENormalizationLayer>();
+ norm->configure(in, out, norm_info);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NENormalizationLayer"
+ << " Data Type: " << in->info()->data_type()
+ << " Input shape: " << in->info()->tensor_shape()
+ << " Output shape: " << out->info()->tensor_shape()
+ << " Normalization info: " << norm_info
+ << std::endl);
+
+ return std::move(norm);
+}
+
+/* Pooling Layer */
+REGISTER_SIMPLE_OPERATION(NEPoolingLayerOperation, NEON, OperationType::PoolingLayer)
+{
+ ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 1);
+ ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr);
+
+ // Extract IO and info
+ auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0));
+ auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0));
+ const auto pool_info = ctx.parameter<PoolingLayerInfo>("PoolingLayerInfo");
+
+ // Create and configure function
+ auto pool = arm_compute::support::cpp14::make_unique<arm_compute::NEPoolingLayer>();
+ pool->configure(in, out, pool_info);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEPoolingLayer"
+ << " Data Type: " << in->info()->data_type()
+ << " Input shape: " << in->info()->tensor_shape()
+ << " Output shape: " << out->info()->tensor_shape()
+ << " Pooling info: " << pool_info
+ << std::endl);
+
+ return std::move(pool);
+}
+
+/* Quantization Layer */
+REGISTER_SIMPLE_OPERATION(NEQuantizationLayerOperation, NEON, OperationType::QuantizationLayer)
+{
+ ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 1);
+ ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr);
+
+ // Extract IO and info
+ auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0));
+ auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0));
+
+ // Create and configure function
+ auto quantization = arm_compute::support::cpp14::make_unique<arm_compute::NEQuantizationLayer>();
+ quantization->configure(in, out);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEQuantizationLayer"
+ << " Data Type: " << in->info()->data_type()
+ << " Input shape: " << in->info()->tensor_shape()
+ << " Output shape: " << out->info()->tensor_shape()
+ << std::endl);
+
+ return std::move(quantization);
+}
+
+/* Reshape Layer */
+REGISTER_SIMPLE_OPERATION(NEReshapeLayerOperation, NEON, OperationType::ReshapeLayer)
+{
+ ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 1);
+ ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr);
+
+ // Extract IO and info
+ auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0));
+ auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0));
+
+ // Create and configure function
+ auto reshape = arm_compute::support::cpp14::make_unique<arm_compute::NEReshapeLayer>();
+ reshape->configure(in, out);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEReshapeLayer"
+ << " Data Type: " << in->info()->data_type()
+ << " Input shape: " << in->info()->tensor_shape()
+ << " Output shape: " << out->info()->tensor_shape()
+ << std::endl);
+
+ return std::move(reshape);
+}
+
+/* Softmax Layer */
+REGISTER_SIMPLE_OPERATION(NESoftmaxLayerOperation, NEON, OperationType::SoftmaxLayer)
+{
+ ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 1);
+ ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr);
+ ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr);
+
+ // Extract IO and info
+ auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0));
+ auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0));
+
+ // Create and configure function
+ auto smx = arm_compute::support::cpp14::make_unique<arm_compute::NESoftmaxLayer>();
+ smx->configure(in, out);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NESoftmaxLayer"
+ << " Data Type: " << in->info()->data_type()
+ << " Input shape: " << in->info()->tensor_shape()
+ << " Output shape: " << out->info()->tensor_shape()
+ << std::endl);
+
+ return std::move(smx);
+}
\ No newline at end of file