COMPMID-797: Switch to new graph.
- Cleaned up build system
Change-Id: If2faa27ee5b31fa8b972836960ab3ef671059c8d
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/126435
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>
diff --git a/src/graph/backends/CL/CLFunctionsFactory.cpp b/src/graph/backends/CL/CLFunctionsFactory.cpp
new file mode 100644
index 0000000..1b448fe
--- /dev/null
+++ b/src/graph/backends/CL/CLFunctionsFactory.cpp
@@ -0,0 +1,590 @@
+/*
+ * Copyright (c) 2018 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/graph/backends/CL/CLFunctionFactory.h"
+
+#include "arm_compute/core/utils/misc/Cast.h"
+#include "arm_compute/graph/Graph.h"
+#include "arm_compute/graph/GraphContext.h"
+#include "arm_compute/graph/Logger.h"
+#include "arm_compute/graph/TypePrinter.h"
+#include "arm_compute/graph/Types.h"
+#include "arm_compute/graph/backends/Utils.h"
+#include "arm_compute/graph/nodes/Nodes.h"
+#include "arm_compute/runtime/CL/CLFunctions.h"
+
+#include "support/ToolchainSupport.h"
+
+using namespace arm_compute::utils::cast;
+
+namespace arm_compute
+{
+namespace graph
+{
+namespace backends
+{
+namespace
+{
+/** Returns backing tensor of a given tensor
+ *
+ * @param[in] tensor Tensor to extract the backing tensor from
+ *
+ * @return Backing tensor if present else nullptr
+ */
+arm_compute::ICLTensor *get_backing_tensor(arm_compute::graph::Tensor *tensor)
+{
+ arm_compute::ICLTensor *backing_tensor = nullptr;
+ if(tensor != nullptr)
+ {
+ ARM_COMPUTE_ERROR_ON(tensor->desc().target != arm_compute::graph::Target::CL);
+ // Get backing tensor handle
+ ITensorHandle *tensor_handle = tensor->handle();
+ // Get backing tensor
+ backing_tensor = (tensor_handle != nullptr) ? polymorphic_cast<ICLTensor *>(&tensor_handle->tensor()) : nullptr;
+ }
+
+ return backing_tensor;
+}
+
+/** Create a backend activation layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend activation layer function
+ */
+std::unique_ptr<IFunction> create_activation_layer(ActivationLayerNode &node)
+{
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE(
+ "Creating CL ActivationLayerNode node with ID : " << node.id() << " and Name: " << node.name()
+ << std::endl);
+ ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1);
+ ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
+
+ // Extract IO and info
+ ICLTensor *input = get_backing_tensor(node.input(0));
+ ICLTensor *output = get_backing_tensor(node.output(0));
+ const ActivationLayerInfo act_info = node.activation_info();
+
+ // Create function
+ auto func = support::cpp14::make_unique<CLActivationLayer>();
+ func->configure(input, output, act_info);
+
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated CLActivationLayer"
+ << " Data Type: " << input->info()->data_type()
+ << " Shape: " << input->info()->tensor_shape()
+ << " Activation function: " << act_info.activation()
+ << " a: " << act_info.a()
+ << " b: " << act_info.b()
+ << " InPlace : " << is_in_place_operation(input, output)
+ << std::endl);
+
+ return std::move(func);
+}
+
+/** Create a backend batch normalization layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend batch normalization layer function
+ */
+std::unique_ptr<IFunction> create_batch_normalization_layer(BatchNormalizationLayerNode &node)
+{
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating CL BatchNormalization node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
+
+ // TODO (geopin01) : Var and mean are compulsory, switch function to accept nullptr as beta and/or gamma
+ ARM_COMPUTE_ERROR_ON(node.num_inputs() != 5);
+ ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
+
+ // Extract IO and info
+ ICLTensor *input = get_backing_tensor(node.input(0));
+ ICLTensor *mean = get_backing_tensor(node.input(1));
+ ICLTensor *var = get_backing_tensor(node.input(2));
+ ICLTensor *beta = get_backing_tensor(node.input(3));
+ ICLTensor *gamma = get_backing_tensor(node.input(4));
+ ICLTensor *output = get_backing_tensor(node.output(0));
+ const float epsilon = node.epsilon();
+ const ActivationLayerInfo fused_act = node.fused_activation();
+
+ // Create and configure function
+ auto func = support::cpp14::make_unique<CLBatchNormalizationLayer>();
+ func->configure(input, output, mean, var, beta, gamma, epsilon, fused_act);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated CLBatchNormalizationLayer"
+ << " Data Type: " << input->info()->data_type()
+ << " Shape: " << input->info()->tensor_shape()
+ << " Epsilon: " << epsilon << " "
+ << (fused_act.enabled() ? to_string(fused_act.activation()) : "")
+ << " InPlace : " << is_in_place_operation(input, output)
+ << std::endl);
+
+ return std::move(func);
+}
+
+/** Create a backend convolution layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend convolution layer function
+ */
+std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node, GraphContext &ctx)
+{
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating CL ConvolutionLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
+ ARM_COMPUTE_ERROR_ON(node.num_inputs() != 3);
+ ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
+
+ // Extract IO and info
+ ICLTensor *input = get_backing_tensor(node.input(0));
+ ICLTensor *weights = get_backing_tensor(node.input(1));
+ ICLTensor *biases = get_backing_tensor(node.input(2));
+ ICLTensor *output = get_backing_tensor(node.output(0));
+ const PadStrideInfo conv_info = node.convolution_info();
+ const ConvolutionMethod conv_algorithm = node.convolution_method();
+
+ // Create and configure function (we assume that functions have been validated before creation)
+ std::shared_ptr<IMemoryManager> mm = get_memory_manager(ctx, Target::CL);
+ std::unique_ptr<IFunction> func;
+ std::string func_name;
+
+ if(conv_algorithm == ConvolutionMethod::WINOGRAD)
+ {
+ std::tie(func, func_name) = create_named_function<CLWinogradConvolutionLayer>(
+ std::string("CLWinogradConvolutionLayer"), input, weights, biases, output, conv_info);
+ }
+ else if(conv_algorithm == ConvolutionMethod::DIRECT)
+ {
+ std::tie(func, func_name) = create_named_function<CLDirectConvolutionLayer>(
+ std::string("CLDirectConvolutionLayer"), input, weights, biases, output, conv_info);
+ }
+ else if(conv_algorithm == ConvolutionMethod::GEMM)
+ {
+ std::tie(func, func_name) = create_named_memory_managed_function<CLGEMMConvolutionLayer>(std::string("CLGEMMConvolutionLayer"), mm,
+ input, weights, biases, output, conv_info);
+ }
+ else
+ {
+ std::tie(func, func_name) = create_named_memory_managed_function<CLConvolutionLayer>(std::string("CLConvolutionLayer"), mm,
+ input, weights, biases, output, conv_info);
+ }
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name
+ << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Weights shape: " << weights->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape()
+ << std::endl);
+ return func;
+}
+
+/** Create a backend layer depth concatenate function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend depth concatenate layer function
+ */
+std::unique_ptr<arm_compute::IFunction> create_depth_concatenate_layer(DepthConcatenateLayerNode &node)
+{
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating CL DepthConcatenate node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
+ ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
+
+ // Return nullptr if depth concatenate is switched off
+ if(!node.is_enabled())
+ {
+ return nullptr;
+ }
+
+ // Extract IO and info
+ std::vector<arm_compute::ICLTensor *> inputs;
+ for(unsigned int i = 0; i < node.num_inputs(); ++i)
+ {
+ inputs.push_back(get_backing_tensor(node.input(i)));
+ }
+ ICLTensor *output = get_backing_tensor(node.output(0));
+
+ // Create and configure function
+ auto func = support::cpp14::make_unique<CLDepthConcatenateLayer>();
+ func->configure(inputs, output);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated CLDepthConcatenateLayer"
+ << " Data Type: " << output->info()->data_type()
+ << " Shape: " << output->info()->tensor_shape()
+ << " Num Inputs: " << inputs.size()
+ << std::endl);
+
+ return std::move(func);
+}
+
+/** Create a backend layer depth-wise convolution function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend depth-wise convolution layer function
+ */
+std::unique_ptr<IFunction> create_depthwise_convolution_layer(DepthwiseConvolutionLayerNode &node)
+{
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE(
+ "Creating CL DepthwiseConvolutionLayer node with ID : " << node.id() << " and Name: " << node.name()
+ << std::endl);
+ ARM_COMPUTE_ERROR_ON(node.num_inputs() != 3);
+ ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
+
+ // Extract IO and info
+ ICLTensor *input = get_backing_tensor(node.input(0));
+ ICLTensor *weights = get_backing_tensor(node.input(1));
+ ICLTensor *biases = get_backing_tensor(node.input(2));
+ ICLTensor *output = get_backing_tensor(node.output(0));
+ const PadStrideInfo conv_info = node.convolution_info();
+ const DepthwiseConvolutionMethod dwc_algorithm = node.depthwise_convolution_method();
+
+ // Create and configure function (we assume that functions have been validated before creation)
+ std::unique_ptr<IFunction> func;
+ std::string func_name;
+ if(dwc_algorithm == DepthwiseConvolutionMethod::OPTIMIZED_3x3)
+ {
+ std::tie(func, func_name) = create_named_function<CLDepthwiseConvolutionLayer3x3>(
+ std::string("CLDepthwiseConvolutionLayer3x3"), input, weights, biases, output, conv_info);
+ }
+ else
+ {
+ std::tie(func, func_name) = create_named_function<CLDepthwiseConvolutionLayer>(
+ std::string("CLDepthwiseConvolutionLayer"), input, weights, biases, output, conv_info);
+ }
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name
+ << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Weights shape: " << weights->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape()
+ << std::endl);
+ return func;
+}
+
+/** Create a backend element-wise operation layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend element-wise operation layer function
+ */
+std::unique_ptr<IFunction> create_eltwise_layer(EltwiseLayerNode &node)
+{
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE(
+ "Creating CL EltwiseLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
+ ARM_COMPUTE_ERROR_ON(node.num_inputs() != 2);
+ ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
+
+ // Extract IO and info
+ ICLTensor *input1 = get_backing_tensor(node.input(0));
+ ICLTensor *input2 = get_backing_tensor(node.input(1));
+ ICLTensor *output = get_backing_tensor(node.output(0));
+ const EltwiseOperation eltwise_op = node.eltwise_operation();
+ ARM_COMPUTE_ERROR_ON(input1 == nullptr);
+ ARM_COMPUTE_ERROR_ON(input2 == nullptr);
+ ARM_COMPUTE_ERROR_ON(output == nullptr);
+
+ std::unique_ptr<IFunction> func = nullptr;
+ std::string func_name;
+ if(eltwise_op == EltwiseOperation::ADD)
+ {
+ std::tie(func, func_name) = create_named_function<CLArithmeticAddition>(std::string("CLArithmeticAddition"),
+ input1, input2, output,
+ ConvertPolicy::SATURATE);
+ }
+ else if(eltwise_op == EltwiseOperation::SUB)
+ {
+ std::tie(func, func_name) = create_named_function<CLArithmeticSubtraction>(
+ std::string("CLArithmeticSubtraction"), input1, input2, output, ConvertPolicy::SATURATE);
+ }
+ else if(eltwise_op == EltwiseOperation::MUL)
+ {
+ std::tie(func, func_name) = create_named_function<CLPixelWiseMultiplication>(
+ std::string("CLPixelWiseMultiplication"), input1, input2, output, 1.f, ConvertPolicy::SATURATE,
+ RoundingPolicy::TO_NEAREST_EVEN);
+ }
+ else
+ {
+ ARM_COMPUTE_ERROR("Unsupported element-wise operation!");
+ }
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name
+ << " Data Type: " << input1->info()->data_type()
+ << " Shape : " << input1->info()->tensor_shape()
+ << std::endl);
+
+ return func;
+}
+
+/** Create a backend flatten layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend flatten layer function
+ */
+std::unique_ptr<IFunction> create_flatten_layer(FlattenLayerNode &node)
+{
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE(
+ "Creating CL FlattenLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
+ ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1);
+ ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
+
+ // Extract IO and info
+ ICLTensor *input = get_backing_tensor(node.input(0));
+ ICLTensor *output = get_backing_tensor(node.output(0));
+
+ // Create and configure function
+ auto func = support::cpp14::make_unique<CLFlattenLayer>();
+ func->configure(input, output);
+ ARM_COMPUTE_ERROR_ON(input == nullptr);
+ ARM_COMPUTE_ERROR_ON(output == nullptr);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated CLFlattenLayer"
+ << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape()
+ << std::endl);
+
+ return std::move(func);
+}
+
+/** Create a backend fully connected layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend fully connected layer function
+ */
+std::unique_ptr<IFunction> create_fully_connected_layer(FullyConnectedLayerNode &node, GraphContext &ctx)
+{
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE(
+ "Creating CL FullyConnectedLayer node with ID : " << node.id() << " and Name: " << node.name()
+ << std::endl);
+ ARM_COMPUTE_ERROR_ON(node.num_inputs() != 3);
+ ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
+
+ // Extract IO and info
+ ICLTensor *input = get_backing_tensor(node.input(0));
+ ICLTensor *weights = get_backing_tensor(node.input(1));
+ ICLTensor *biases = get_backing_tensor(node.input(2));
+ ICLTensor *output = get_backing_tensor(node.output(0));
+
+ // Create and configure function
+ auto func = support::cpp14::make_unique<CLFullyConnectedLayer>(get_memory_manager(ctx, Target::CL));
+ func->configure(input, weights, biases, output);
+ ARM_COMPUTE_ERROR_ON(input == nullptr);
+ ARM_COMPUTE_ERROR_ON(weights == nullptr);
+ ARM_COMPUTE_ERROR_ON(output == nullptr);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated CLFullyConnectedLayer"
+ << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Weights shape: " << weights->info()->tensor_shape()
+ << " Biases Shape: " << biases->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape()
+ << std::endl);
+
+ return std::move(func);
+}
+
+/** Create a backend normalization layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend normalization layer function
+ */
+std::unique_ptr<IFunction> create_normalization_layer(NormalizationLayerNode &node)
+{
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE(
+ "Creating CL NormalizationLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
+ ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1);
+ ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
+
+ // Extract IO and info
+ ICLTensor *input = get_backing_tensor(node.input(0));
+ ICLTensor *output = get_backing_tensor(node.output(0));
+ const NormalizationLayerInfo norm_info = node.normalization_info();
+ ARM_COMPUTE_ERROR_ON(input == nullptr);
+ ARM_COMPUTE_ERROR_ON(output == nullptr);
+
+ // Create and configure function
+ auto func = support::cpp14::make_unique<CLNormalizationLayer>();
+ func->configure(input, output, norm_info);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated CLNormalizationLayer"
+ << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape()
+ << " Normalization info: " << norm_info.type()
+ << std::endl);
+
+ return std::move(func);
+}
+
+/** Create a backend pooling layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend pooling layer function
+ */
+std::unique_ptr<IFunction> create_pooling_layer(PoolingLayerNode &node)
+{
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE(
+ "Creating CL PoolingLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
+ ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1);
+ ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
+
+ // Extract IO and info
+ ICLTensor *input = get_backing_tensor(node.input(0));
+ ICLTensor *output = get_backing_tensor(node.output(0));
+ const PoolingLayerInfo pool_info = node.pooling_info();
+ ARM_COMPUTE_ERROR_ON(input == nullptr);
+ ARM_COMPUTE_ERROR_ON(output == nullptr);
+
+ // Create and configure function
+ auto func = support::cpp14::make_unique<CLPoolingLayer>();
+ func->configure(input, output, pool_info);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated CLPoolingLayer"
+ << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape()
+ << " Pooling info: " << pool_info.pool_type()
+ << std::endl);
+
+ return std::move(func);
+}
+
+/** Create a backend reshape layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend reshape layer function
+ */
+std::unique_ptr<IFunction> create_reshape_layer(ReshapeLayerNode &node)
+{
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE(
+ "Creating CL ReshapeLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
+ ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1);
+ ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
+
+ // Extract IO and info
+ ICLTensor *input = get_backing_tensor(node.input(0));
+ ICLTensor *output = get_backing_tensor(node.output(0));
+ ARM_COMPUTE_ERROR_ON(input == nullptr);
+ ARM_COMPUTE_ERROR_ON(output == nullptr);
+
+ // Create and configure function
+ auto func = support::cpp14::make_unique<CLReshapeLayer>();
+ func->configure(input, output);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated CLReshapeLayer"
+ << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape()
+ << std::endl);
+
+ return std::move(func);
+}
+
+/** Create a backend softmax layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend softmax layer function
+ */
+std::unique_ptr<IFunction> create_softmax_layer(SoftmaxLayerNode &node, GraphContext &ctx)
+{
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE(
+ "Creating CL SoftmaxLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
+ ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1);
+ ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
+
+ // Extract IO and info
+ ICLTensor *input = get_backing_tensor(node.input(0));
+ ICLTensor *output = get_backing_tensor(node.output(0));
+ const float beta = node.beta();
+ ARM_COMPUTE_ERROR_ON(input == nullptr);
+ ARM_COMPUTE_ERROR_ON(output == nullptr);
+
+ // Create and configure function
+ auto func = support::cpp14::make_unique<CLSoftmaxLayer>(get_memory_manager(ctx, Target::CL));
+ func->configure(input, output, beta);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated CLSoftmaxLayer"
+ << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape()
+ << std::endl);
+
+ return std::move(func);
+}
+} // namespace
+
+std::unique_ptr<IFunction> CLFunctionFactory::create(INode *node, GraphContext &ctx)
+{
+ if(node == nullptr)
+ {
+ return nullptr;
+ }
+
+ NodeType type = node->type();
+ switch(type)
+ {
+ case NodeType::ActivationLayer:
+ return create_activation_layer(*polymorphic_downcast<ActivationLayerNode *>(node));
+ case NodeType::BatchNormalizationLayer:
+ return create_batch_normalization_layer(*polymorphic_downcast<BatchNormalizationLayerNode *>(node));
+ case NodeType::ConvolutionLayer:
+ return create_convolution_layer(*polymorphic_downcast<ConvolutionLayerNode *>(node), ctx);
+ case NodeType::DepthConcatenateLayer:
+ return create_depth_concatenate_layer(*polymorphic_downcast<DepthConcatenateLayerNode *>(node));
+ case NodeType::DepthwiseConvolutionLayer:
+ return create_depthwise_convolution_layer(*polymorphic_downcast<DepthwiseConvolutionLayerNode *>(node));
+ case NodeType::EltwiseLayer:
+ return create_eltwise_layer(*polymorphic_downcast<EltwiseLayerNode *>(node));
+ case NodeType::FlattenLayer:
+ return create_flatten_layer(*polymorphic_downcast<FlattenLayerNode *>(node));
+ case NodeType::FullyConnectedLayer:
+ return create_fully_connected_layer(*polymorphic_downcast<FullyConnectedLayerNode *>(node), ctx);
+ case NodeType::NormalizationLayer:
+ return create_normalization_layer(*polymorphic_downcast<NormalizationLayerNode *>(node));
+ case NodeType::PoolingLayer:
+ return create_pooling_layer(*polymorphic_downcast<PoolingLayerNode *>(node));
+ case NodeType::ReshapeLayer:
+ return create_reshape_layer(*polymorphic_downcast<ReshapeLayerNode *>(node));
+ case NodeType::SoftmaxLayer:
+ return create_softmax_layer(*polymorphic_downcast<SoftmaxLayerNode *>(node), ctx);
+ default:
+ return nullptr;
+ }
+}
+} // namespace backends
+} // namespace graph
+} // namespace arm_compute
\ No newline at end of file