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author | giuros01 <giuseppe.rossini@arm.com> | 2019-02-21 17:32:34 +0000 |
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committer | Giuseppe Rossini <giuseppe.rossini@arm.com> | 2019-03-13 10:31:18 +0000 |
commit | acce504ec4aebe5e5da470c1cfc3cee401ff11f3 (patch) | |
tree | bff9107fe7facf4be68140380192ee1ea049d05d /arm_compute/graph/backends/FunctionHelpers.h | |
parent | ba5e096b8b2a9f777695844746ec3ff1ef90ade8 (diff) | |
download | ComputeLibrary-acce504ec4aebe5e5da470c1cfc3cee401ff11f3.tar.gz |
COMPMID-1740: Fuse batch normalization with Convolution Layer at graph level
Change-Id: I77ca51c2c72783cc26a099a6a9c3210cdbbe822d
Signed-off-by: giuros01 <giuseppe.rossini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/797
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Diffstat (limited to 'arm_compute/graph/backends/FunctionHelpers.h')
-rw-r--r-- | arm_compute/graph/backends/FunctionHelpers.h | 67 |
1 files changed, 62 insertions, 5 deletions
diff --git a/arm_compute/graph/backends/FunctionHelpers.h b/arm_compute/graph/backends/FunctionHelpers.h index 7242bc6ede..d0035d9a84 100644 --- a/arm_compute/graph/backends/FunctionHelpers.h +++ b/arm_compute/graph/backends/FunctionHelpers.h @@ -28,6 +28,7 @@ #include "arm_compute/graph/Tensor.h" #include "arm_compute/graph/TypePrinter.h" #include "arm_compute/graph/Types.h" +#include "arm_compute/graph/backends/FusedConvolutionBatchNormalizationFunction.h" #include "arm_compute/graph/backends/Utils.h" #include "arm_compute/graph/nodes/Nodes.h" @@ -135,11 +136,12 @@ std::unique_ptr<IFunction> create_batch_normalization_layer(BatchNormalizationLa validate_node<TargetInfo>(node, 5 /* expected inputs */, 1 /* expected outputs */); // Extract IO and info - typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); - typename TargetInfo::TensorType *mean = get_backing_tensor<TargetInfo>(node.input(1)); - typename TargetInfo::TensorType *var = get_backing_tensor<TargetInfo>(node.input(2)); - typename TargetInfo::TensorType *beta = get_backing_tensor<TargetInfo>(node.input(3)); - typename TargetInfo::TensorType *gamma = get_backing_tensor<TargetInfo>(node.input(4)); + typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); + typename TargetInfo::TensorType *mean = get_backing_tensor<TargetInfo>(node.input(1)); + typename TargetInfo::TensorType *var = get_backing_tensor<TargetInfo>(node.input(2)); + typename TargetInfo::TensorType *beta = get_backing_tensor<TargetInfo>(node.input(3)); + typename TargetInfo::TensorType *gamma = get_backing_tensor<TargetInfo>(node.input(4)); + typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); const float epsilon = node.epsilon(); const ActivationLayerInfo fused_act = node.fused_activation(); @@ -163,6 +165,61 @@ std::unique_ptr<IFunction> create_batch_normalization_layer(BatchNormalizationLa return std::move(func); } +/** Create a backend batch normalization layer function + * + * @tparam BatchNormalizationLayerFunction Backend batch normalization function + * @tparam TargetInfo Target-specific information + * + * @param[in] node Node to create the backend function for + * + * @return Backend batch normalization layer function + */ +template <typename FusedLayerTypes, typename TargetInfo> +std::unique_ptr<IFunction> create_fused_convolution_batch_normalization_layer(FusedConvolutionBatchNormalizationNode &node) +{ + validate_node<TargetInfo>(node, 7 /* expected inputs */, 1 /* expected outputs */); + + // Extract IO and info + typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); + typename TargetInfo::TensorType *weights = get_backing_tensor<TargetInfo>(node.input(1)); + typename TargetInfo::TensorType *biases = get_backing_tensor<TargetInfo>(node.input(2)); + typename TargetInfo::TensorType *mean = get_backing_tensor<TargetInfo>(node.input(3)); + typename TargetInfo::TensorType *var = get_backing_tensor<TargetInfo>(node.input(4)); + typename TargetInfo::TensorType *beta = get_backing_tensor<TargetInfo>(node.input(5)); + typename TargetInfo::TensorType *gamma = get_backing_tensor<TargetInfo>(node.input(6)); + + typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); + + const PadStrideInfo conv_info = node.convolution_info(); + const unsigned int num_groups = node.num_groups(); + const bool fast_math = node.fast_math_hint() == FastMathHint::Enabled; + const ActivationLayerInfo fused_act = node.fused_activation(); + const float epsilon = node.epsilon(); + + const bool is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); + if(is_quantized && biases != nullptr) + { + biases->info()->set_data_type(DataType::S32); + } + + // Create and configure function + auto func = support::cpp14::make_unique<FusedConvolutionBatchNormalizationFunction<TargetInfo, FusedLayerTypes>>(); + func->configure(input, weights, biases, output, mean, var, beta, gamma, epsilon, conv_info, num_groups, fast_math, fused_act); + + // Log info + ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " + << node.name() + << " Type: " << node.name() + << " Target: " << TargetInfo::TargetType + << " Data Type: " << input->info()->data_type() + << " Input shape: " << input->info()->tensor_shape() + << " Weights shape: " << weights->info()->tensor_shape() + << " Output shape: " << output->info()->tensor_shape() + << (fused_act.enabled() ? " " + to_string(fused_act.activation()) : "") + << std::endl); + return std::move(func); +} + /** Create a backend bounding box transform layer function * * @tparam BoundingBoxTransformLayerFunction Backend bounding box transform function |