From bffb41e06c1276af00e1605ef934d05fa61f7127 Mon Sep 17 00:00:00 2001 From: Manuel Bottini Date: Thu, 20 Jun 2019 16:00:27 +0100 Subject: COMPMID-2273: Fuse Batch Normalization with Depthwise Convolution layer at graph level (only for CL) Change-Id: I1d941c6e66722f39583bf68148c980bb28ff89a1 Signed-off-by: Manuel Bottini Reviewed-on: https://review.mlplatform.org/c/1423 Comments-Addressed: Arm Jenkins Reviewed-by: Michele Di Giorgio Tested-by: Arm Jenkins --- arm_compute/graph/backends/FunctionHelpers.h | 57 ++++++++++++++++++++++++---- 1 file changed, 50 insertions(+), 7 deletions(-) (limited to 'arm_compute/graph/backends/FunctionHelpers.h') diff --git a/arm_compute/graph/backends/FunctionHelpers.h b/arm_compute/graph/backends/FunctionHelpers.h index 5ac4fdaed9..ed5b35c0d1 100644 --- a/arm_compute/graph/backends/FunctionHelpers.h +++ b/arm_compute/graph/backends/FunctionHelpers.h @@ -30,6 +30,7 @@ #include "arm_compute/graph/Types.h" #include "arm_compute/graph/Utils.h" #include "arm_compute/graph/backends/FusedConvolutionBatchNormalizationFunction.h" +#include "arm_compute/graph/backends/FusedDepthwiseConvolutionBatchNormalizationFunction.h" #include "arm_compute/graph/backends/Utils.h" #include "arm_compute/graph/nodes/Nodes.h" @@ -197,12 +198,6 @@ std::unique_ptr create_fused_convolution_batch_normalization_layer(Fu 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>(); func->configure(input, weights, biases, output, mean, var, beta, gamma, epsilon, conv_info, num_groups, fast_math, fused_act); @@ -210,7 +205,55 @@ std::unique_ptr create_fused_convolution_batch_normalization_layer(Fu // Log info ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() - << " Type: " << node.name() + << " Type: " << node.type() + << " 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 fused depthwise convolution batch normalization layer function + * + * @tparam FusedLayerTypes Fused layer types + * @tparam TargetInfo Target-specific information + * + * @param[in] node Node to create the backend function for + * + * @return Backend fused depthwise convolution batch normalization layer function + */ +template +std::unique_ptr create_fused_depthwise_convolution_batch_normalization_layer(FusedDepthwiseConvolutionBatchNormalizationNode &node) +{ + validate_node(node, 7 /* expected inputs */, 1 /* expected outputs */); + + // Extract IO and info + typename TargetInfo::TensorType *input = get_backing_tensor(node.input(0)); + typename TargetInfo::TensorType *weights = get_backing_tensor(node.input(1)); + typename TargetInfo::TensorType *biases = get_backing_tensor(node.input(2)); + typename TargetInfo::TensorType *mean = get_backing_tensor(node.input(3)); + typename TargetInfo::TensorType *var = get_backing_tensor(node.input(4)); + typename TargetInfo::TensorType *beta = get_backing_tensor(node.input(5)); + typename TargetInfo::TensorType *gamma = get_backing_tensor(node.input(6)); + + typename TargetInfo::TensorType *output = get_backing_tensor(node.output(0)); + + const PadStrideInfo conv_info = node.convolution_info(); + const unsigned int depth_multiplier = node.depth_multiplier(); + const ActivationLayerInfo fused_act = node.fused_activation(); + const float epsilon = node.epsilon(); + + // Create and configure function + auto func = support::cpp14::make_unique>(); + func->configure(input, weights, biases, output, mean, var, beta, gamma, epsilon, conv_info, depth_multiplier, fused_act); + + // Log info + ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " + << node.name() + << " Type: " << node.type() << " Target: " << TargetInfo::TargetType << " Data Type: " << input->info()->data_type() << " Input shape: " << input->info()->tensor_shape() -- cgit v1.2.1