COMPMID-1367: Enable NHWC in graph examples
Change-Id: Iabc54a3a1bdcd46a9a921cda39c7c85fef672b72
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/141449
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
diff --git a/arm_compute/graph/backends/FunctionHelpers.h b/arm_compute/graph/backends/FunctionHelpers.h
index 978d3bc..172f002 100644
--- a/arm_compute/graph/backends/FunctionHelpers.h
+++ b/arm_compute/graph/backends/FunctionHelpers.h
@@ -192,6 +192,52 @@
return std::move(func);
}
+/** Create a backend layer concatenate function
+ *
+ * @tparam ConcatenateLayerFunction Backend concatenate function
+ * @tparam TargetInfo Target-specific information
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend concatenate layer function
+ */
+template <typename ConcatenateLayerFunction, typename TargetInfo>
+std::unique_ptr<arm_compute::IFunction> create_concatenate_layer(ConcatenateLayerNode &node)
+{
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating Concatenate 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<typename TargetInfo::TensorType *> inputs;
+ for(unsigned int i = 0; i < node.num_inputs(); ++i)
+ {
+ inputs.push_back(get_backing_tensor<TargetInfo>(node.input(i)));
+ }
+ typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));
+ const DataLayoutDimension concat_axis = node.concatenation_axis();
+
+ // Create and configure function
+ auto func = support::cpp14::make_unique<ConcatenateLayerFunction>();
+ func->configure(inputs, output, concat_axis);
+
+ // Log info
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.type()
+ << " Target " << TargetInfo::TargetType
+ << " Data Type: " << output->info()->data_type()
+ << " Shape: " << output->info()->tensor_shape()
+ << " Num Inputs: " << inputs.size()
+ << " Axis: " << concat_axis
+ << std::endl);
+
+ return std::move(func);
+}
+
/** Create a backend convolution layer function
*
* @tparam ConvolutionLayerFunctions Backend convolution functions
@@ -220,20 +266,20 @@
const PadStrideInfo conv_info = node.convolution_info();
const ConvolutionMethod conv_algorithm = node.convolution_method();
- const bool fast_math = node.fast_math_hint() == FastMathHint::ENABLED;
+ const bool fast_math = node.fast_math_hint() == FastMathHint::Enabled;
// Create and configure function (we assume that functions have been validated before creation)
std::shared_ptr<IMemoryManager> mm = get_memory_manager(ctx, TargetInfo::TargetType);
std::unique_ptr<IFunction> func;
std::string func_name;
- if(conv_algorithm == ConvolutionMethod::WINOGRAD)
+ if(conv_algorithm == ConvolutionMethod::Winograd)
{
std::tie(func, func_name) = create_named_memory_managed_function<typename ConvolutionLayerFunctions::WinogradConvolutionLayer>(
std::string("WinogradConvolutionLayer"), mm,
input, weights, biases, output, conv_info, ActivationLayerInfo(), fast_math);
}
- else if(conv_algorithm == ConvolutionMethod::DIRECT)
+ else if(conv_algorithm == ConvolutionMethod::Direct)
{
std::tie(func, func_name) = create_named_function<typename ConvolutionLayerFunctions::DirectConvolutionLayer>(
std::string("DirectConvolutionLayer"),
@@ -308,50 +354,6 @@
return func;
}
-/** Create a backend layer depth concatenate function
- *
- * @tparam DepthConcatenateLayerFunction Backend depth concatenate function
- * @tparam TargetInfo Target-specific information
- *
- * @param[in] node Node to create the backend function for
- *
- * @return Backend depth concatenate layer function
- */
-template <typename DepthConcatenateLayerFunction, typename TargetInfo>
-std::unique_ptr<arm_compute::IFunction> create_depth_concatenate_layer(DepthConcatenateLayerNode &node)
-{
- ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating 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<typename TargetInfo::TensorType *> inputs;
- for(unsigned int i = 0; i < node.num_inputs(); ++i)
- {
- inputs.push_back(get_backing_tensor<TargetInfo>(node.input(i)));
- }
- typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));
-
- // Create and configure function
- auto func = support::cpp14::make_unique<DepthConcatenateLayerFunction>();
- func->configure(inputs, output);
-
- // Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.type()
- << " Target " << TargetInfo::TargetType
- << " 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
*
* @tparam DepthwiseConvolutionLayerFunctions Backend depthwise convolution function
@@ -383,7 +385,7 @@
// 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)
+ if(dwc_algorithm == DepthwiseConvolutionMethod::Optimized3x3)
{
std::tie(func, func_name) = create_named_function<typename DepthwiseConvolutionLayerFunctions::DepthwiseConvolutionLayer3x3>(
std::string("DepthwiseConvolutionLayer3x3"),
@@ -435,19 +437,19 @@
std::unique_ptr<IFunction> func = nullptr;
std::string func_name;
- if(eltwise_op == EltwiseOperation::ADD)
+ if(eltwise_op == EltwiseOperation::Add)
{
std::tie(func, func_name) = create_named_function<typename EltwiseFunctions::Addition>(
std::string("ArithmeticAddition"),
input1, input2, output, convert_policy);
}
- else if(eltwise_op == EltwiseOperation::SUB)
+ else if(eltwise_op == EltwiseOperation::Sub)
{
std::tie(func, func_name) = create_named_function<typename EltwiseFunctions::Subtraction>(
std::string("ArithmeticSubtraction"),
input1, input2, output, convert_policy);
}
- else if(eltwise_op == EltwiseOperation::MUL)
+ else if(eltwise_op == EltwiseOperation::Mul)
{
std::tie(func, func_name) = create_named_function<typename EltwiseFunctions::Multiplication>(
std::string("PixelWiseMultiplication"),
@@ -487,11 +489,12 @@
typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));
typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(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<FlattenLayerFunction>();
func->configure(input, output);
- ARM_COMPUTE_ERROR_ON(input == nullptr);
- ARM_COMPUTE_ERROR_ON(output == nullptr);
// Log info
ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.type()
@@ -526,13 +529,14 @@
typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));
const FullyConnectedLayerInfo fc_info = node.info();
- // Create and configure function
- auto func = support::cpp14::make_unique<FullyConnectedLayerFunction>(get_memory_manager(ctx, TargetInfo::TargetType));
- func->configure(input, weights, biases, output, fc_info);
ARM_COMPUTE_ERROR_ON(input == nullptr);
ARM_COMPUTE_ERROR_ON(weights == nullptr);
ARM_COMPUTE_ERROR_ON(output == nullptr);
+ // Create and configure function
+ auto func = support::cpp14::make_unique<FullyConnectedLayerFunction>(get_memory_manager(ctx, TargetInfo::TargetType));
+ func->configure(input, weights, biases, output, fc_info);
+
// Log info
ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.type()
<< " Target " << TargetInfo::TargetType