From 2697fd8fa42425f7bfdd60dd486d4c2132b06523 Mon Sep 17 00:00:00 2001 From: Sang-Hoon Park Date: Tue, 15 Oct 2019 16:49:24 +0100 Subject: COMPMID-2707: add keep_dims parameter to Reduction Operation The added parameter is used to decide whether or not to keep the target dimension of reduction operation. ArgMinMax operations will always remove the reduced dimension. Following things are updated to support the parameter. - [CL/NEON] functions and reference kernel - [CL/NEON] ArgMinMax function to use ReductionOperation function - [CL/NEON] validation test suite for Reduction and ArgMinMax operations to validate the added parameter - ReductionOperationFixture is modified NOT to pre-populate output tensor and now relies on underlying kernel/function. - Adjust CL validation test suite for Reduction operation to remove excessive test cases with axis values beyond input tensor's dimension. Change-Id: I3e24d276ed469a4201f323001708f0c525f11c4f Signed-off-by: Sang-Hoon Park Reviewed-on: https://review.mlplatform.org/c/2167 Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins Reviewed-by: Michele Di Giorgio Reviewed-by: Georgios Pinitas --- src/core/CL/kernels/CLReductionOperationKernel.cpp | 18 +-- src/core/Utils.cpp | 10 ++ src/runtime/CL/functions/CLArgMinMaxLayer.cpp | 23 ++- src/runtime/CL/functions/CLReductionOperation.cpp | 161 +++++++++++++++++---- src/runtime/NEON/functions/NEArgMinMaxLayer.cpp | 24 +-- .../NEON/functions/NEReductionOperation.cpp | 80 +++++++++- 6 files changed, 244 insertions(+), 72 deletions(-) (limited to 'src') diff --git a/src/core/CL/kernels/CLReductionOperationKernel.cpp b/src/core/CL/kernels/CLReductionOperationKernel.cpp index 8e92b591d1..a085ab1683 100644 --- a/src/core/CL/kernels/CLReductionOperationKernel.cpp +++ b/src/core/CL/kernels/CLReductionOperationKernel.cpp @@ -33,6 +33,7 @@ #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" #include "arm_compute/core/Window.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "support/ToolchainSupport.h" @@ -80,17 +81,15 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, u std::tuple validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, unsigned int axis, ReductionOperation op) { // Output tensor auto initialization if not yet initialized - TensorShape output_shape{ input->tensor_shape() }; - output_shape.set(axis, 1); - const bool is_arg_min_max = (op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::ARG_IDX_MAX); - DataType output_data_type = is_arg_min_max ? DataType::U32 : input->data_type(); + const bool is_arg_min_max = (op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::ARG_IDX_MAX); + const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_reduced_shape(input->tensor_shape(), axis, !is_arg_min_max); + const DataType output_data_type = is_arg_min_max ? DataType::U32 : input->data_type(); auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape).set_data_type(output_data_type).reset_padding().set_is_resizable(true)); const unsigned int num_elems_processed_per_iteration = (is_data_type_quantized(input->data_type()) && (axis == 0)) ? 1 : 16; Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); bool window_changed = false; - const bool is_serial_op = (op == ReductionOperation::ARG_IDX_MAX || op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::MIN - || op == ReductionOperation::MAX || is_data_type_quantized(input->data_type())); + const bool is_serial_op = needs_serialized_reduction(op, input->data_type(), axis); switch(axis) { @@ -198,8 +197,8 @@ void CLReductionOperationKernel::configure(const ICLTensor *input, ICLTensor *ou // Create kernel cl::NDRange lws_hint = CLKernelLibrary::get().default_ndrange(); std::string kernel_axis_name; - const bool is_serial_op = (op == ReductionOperation::ARG_IDX_MAX || op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::MIN || op == ReductionOperation::MAX - || is_data_type_quantized(input->info()->data_type())); + const bool is_serial_op = needs_serialized_reduction(_op, _input->info()->data_type(), _reduction_axis); + switch(axis) { case 0: @@ -264,8 +263,7 @@ void CLReductionOperationKernel::run(const Window &window, cl::CommandQueue &que ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); - const bool is_serial_op = (_op == ReductionOperation::ARG_IDX_MAX || _op == ReductionOperation::ARG_IDX_MIN || _op == ReductionOperation::MIN || _op == ReductionOperation::MAX - || is_data_type_quantized(_input->info()->data_type())); + const bool is_serial_op = needs_serialized_reduction(_op, _input->info()->data_type(), _reduction_axis); switch(_reduction_axis) { case 0: diff --git a/src/core/Utils.cpp b/src/core/Utils.cpp index 7e1af0e27d..fa335d757b 100644 --- a/src/core/Utils.cpp +++ b/src/core/Utils.cpp @@ -427,6 +427,16 @@ std::pair arm_compute::scaled_dimensions(unsigned in return std::make_pair(w, h); } +bool arm_compute::needs_serialized_reduction(ReductionOperation op, DataType dt, unsigned int axis) +{ + const bool is_arg_min_max = (op == ReductionOperation::ARG_IDX_MAX || op == ReductionOperation::ARG_IDX_MIN); + const bool is_min_max = (op == ReductionOperation::MAX || op == ReductionOperation::MIN); + const bool is_quantized_type = is_data_type_quantized(dt); + const bool is_first_dim = (axis == 0); + + return !is_first_dim || is_arg_min_max || is_min_max || is_quantized_type; +} + #ifdef ARM_COMPUTE_ASSERTS_ENABLED void arm_compute::print_consecutive_elements(std::ostream &s, DataType dt, const uint8_t *ptr, unsigned int n, int stream_width, const std::string &element_delim) { diff --git a/src/runtime/CL/functions/CLArgMinMaxLayer.cpp b/src/runtime/CL/functions/CLArgMinMaxLayer.cpp index a6393c57c1..fd172d5f2c 100644 --- a/src/runtime/CL/functions/CLArgMinMaxLayer.cpp +++ b/src/runtime/CL/functions/CLArgMinMaxLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018 ARM Limited. + * Copyright (c) 2018-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -23,26 +23,33 @@ */ #include "arm_compute/runtime/CL/functions/CLArgMinMaxLayer.h" +#include "arm_compute/runtime/CL/functions/CLReductionOperation.h" -#include "arm_compute/core/CL/kernels/CLReductionOperationKernel.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/Validate.h" -#include "arm_compute/runtime/CL/CLScheduler.h" namespace arm_compute { -void CLArgMinMaxLayer::configure(const ICLTensor *input, int axis, ICLTensor *output, const ReductionOperation &op) +CLArgMinMaxLayer::CLArgMinMaxLayer(std::shared_ptr memory_manager) + : _reduction_function(support::cpp14::make_unique(std::move(memory_manager))) { - auto k = arm_compute::support::cpp14::make_unique(); - k->configure(input, output, axis, op); - _kernel = std::move(k); +} + +void CLArgMinMaxLayer::configure(ICLTensor *input, int axis, ICLTensor *output, const ReductionOperation &op) +{ + _reduction_function->configure(input, output, axis, op, false); } Status CLArgMinMaxLayer::validate(const ITensorInfo *input, int axis, const ITensorInfo *output, const ReductionOperation &op) { ARM_COMPUTE_RETURN_ERROR_ON_MSG(op != ReductionOperation::ARG_IDX_MAX && op != ReductionOperation::ARG_IDX_MIN, "Invalid operation"); - return CLReductionOperationKernel::validate(input, output, axis, op); + return CLReductionOperation::validate(input, output, axis, op, false); +} + +void CLArgMinMaxLayer::run() +{ + _reduction_function->run(); } } // namespace arm_compute \ No newline at end of file diff --git a/src/runtime/CL/functions/CLReductionOperation.cpp b/src/runtime/CL/functions/CLReductionOperation.cpp index 38f0a7523c..447c15b1e8 100644 --- a/src/runtime/CL/functions/CLReductionOperation.cpp +++ b/src/runtime/CL/functions/CLReductionOperation.cpp @@ -26,15 +26,17 @@ #include "arm_compute/core/CL/ICLTensor.h" #include "arm_compute/core/CL/kernels/CLReductionOperationKernel.h" #include "arm_compute/core/Error.h" +#include "arm_compute/core/Helpers.h" #include "arm_compute/core/PixelValue.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Validate.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/runtime/CL/CLScheduler.h" #include "arm_compute/runtime/Tensor.h" #include "support/ToolchainSupport.h" -using namespace arm_compute; - +namespace arm_compute +{ namespace { unsigned int calculate_number_of_stages(const ITensorInfo *input, unsigned int axis) @@ -56,17 +58,52 @@ unsigned int calculate_number_of_stages(const ITensorInfo *input, unsigned int a } // namespace CLReductionOperation::CLReductionOperation(std::shared_ptr memory_manager) - : _memory_group(std::move(memory_manager)), _results_vector(), _reduction_kernels_vector(), _border_handlers_vector(), _num_of_stages(), _reduction_axis(), _is_serial() + : _memory_group(std::move(memory_manager)), _results_vector(), _reduction_kernels_vector(), _border_handlers_vector(), _reshape_kernel(), _op(), _num_of_stages(), _reduction_axis(), _is_serial(), + _is_reshape_required(false) { } -Status CLReductionOperation::validate(const ITensorInfo *input, const ITensorInfo *output, unsigned int axis, ReductionOperation op) +Status CLReductionOperation::validate(const ITensorInfo *input, const ITensorInfo *output, unsigned int axis, ReductionOperation op, bool keep_dims) { - const unsigned int num_of_stages = calculate_number_of_stages(input, axis); - bool is_serial = is_data_type_quantized(input->data_type()) || axis != 0; + ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis >= TensorShape::num_max_dimensions, "Reduction axis greater than max number of dimensions"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis > 3, "Unsupported reduction axis"); + + const unsigned int num_of_stages = calculate_number_of_stages(input, axis); + const bool is_serial = needs_serialized_reduction(op, input->data_type(), axis); + const bool is_arg_min_max = (op == ReductionOperation::ARG_IDX_MAX) || (op == ReductionOperation::ARG_IDX_MIN); + const bool is_reshape_required = !keep_dims || is_arg_min_max; + + if(is_reshape_required) + { + const TensorInfo expected_output_shape = output->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_reduced_shape(input->tensor_shape(), axis, keep_dims)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&expected_output_shape, output); + } + + auto *output_internal = output; + + TensorInfo output_before_reshape; + const auto input_shape = input->tensor_shape(); + const auto input_data_type = input->data_type(); + const auto input_num_channles = input->num_channels(); + const auto input_qinfo = input->quantization_info(); + const auto output_data_type = is_arg_min_max ? DataType::U32 : output->data_type(); + + auto initialize_tensorinfo = [](TensorInfo & ti, TensorShape shape, DataType data_type, int num_channels, QuantizationInfo qinfo) + { + ti.set_data_type(data_type).set_tensor_shape(shape).set_num_channels(num_channels).set_quantization_info(qinfo); + }; + + if(is_reshape_required) + { + auto shape_before_reshape = input_shape; + shape_before_reshape.set(axis, 1); + initialize_tensorinfo(output_before_reshape, shape_before_reshape, output_data_type, input_num_channles, input_qinfo); + output_internal = &output_before_reshape; + } + if(is_serial) { - ARM_COMPUTE_RETURN_ON_ERROR(CLReductionOperationKernel::validate(input, output, axis, op)); + ARM_COMPUTE_RETURN_ON_ERROR(CLReductionOperationKernel::validate(input, output_internal, axis, op)); } else { @@ -74,14 +111,13 @@ Status CLReductionOperation::validate(const ITensorInfo *input, const ITensorInf std::vector sums_vector(num_of_stages - 1); // Create intermediate tensor info - TensorShape shape{ input->tensor_shape() }; + TensorShape shape{ input_shape }; + + shape.set(0, ceil(shape.x() / 128.f)); for(unsigned int i = 0; i < num_of_stages - 1; i++) { - shape.set(0, ceil(shape.x() / 128.f)); - sums_vector[i].set_data_type(input->data_type()); - sums_vector[i].set_tensor_shape(shape); - sums_vector[i].set_num_channels(input->num_channels()); + initialize_tensorinfo(sums_vector[i], shape, input_data_type, input_num_channles, input_qinfo); } ReductionOperation first_kernel_op; @@ -130,17 +166,72 @@ Status CLReductionOperation::validate(const ITensorInfo *input, const ITensorInf // Validate ReductionOperation on the last stage const unsigned int last_stage = num_of_stages - 1; - ARM_COMPUTE_RETURN_ON_ERROR(CLReductionOperationKernel::validate(&sums_vector[last_stage - 1], output, axis, last_kernel_op, input->dimension(0))); + ARM_COMPUTE_RETURN_ON_ERROR(CLReductionOperationKernel::validate(&sums_vector[last_stage - 1], output_internal, axis, last_kernel_op, input->dimension(0))); + } + + if(is_reshape_required) + { + ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayerKernel::validate(output_internal, output)); } return Status{}; } -void CLReductionOperation::configure(ICLTensor *input, ICLTensor *output, unsigned int axis, ReductionOperation op) +ICLTensor *CLReductionOperation::configure_intermediate_result_vector(ICLTensor *input, ICLTensor *output) +{ + if(!_is_reshape_required && _is_serial) + { + return output; + } + + auto intermediate_result_vector_size = _is_serial ? 1 : _num_of_stages; + const auto is_arg_min_max = (_op == ReductionOperation::ARG_IDX_MAX || _op == ReductionOperation::ARG_IDX_MIN); + + if(!_is_reshape_required) + { + --intermediate_result_vector_size; + } + + _results_vector.resize(intermediate_result_vector_size); + auto shape = input->info()->tensor_shape(); + + shape.set(_reduction_axis, _is_serial ? 1 : ceil(shape.x() / 128.f)); + + for(auto &v : _results_vector) + { + if(&v == &_results_vector.back() && _is_reshape_required) + { + shape.set(_reduction_axis, 1); + } + v.allocator()->init(input->info()->clone()->set_tensor_shape(shape)); + } + + if(is_arg_min_max) + { + _results_vector.back().info()->set_data_type(DataType::U32).set_is_resizable(true).reset_padding(); + } + + return _is_reshape_required ? &_results_vector.back() : output; +} + +void CLReductionOperation::configure(ICLTensor *input, ICLTensor *output, unsigned int axis, ReductionOperation op, bool keep_dims) { - _num_of_stages = calculate_number_of_stages(input->info(), axis); - _reduction_axis = axis; - _is_serial = is_data_type_quantized(input->info()->data_type()) || axis != 0; + _op = op; + _num_of_stages = calculate_number_of_stages(input->info(), axis); + _reduction_axis = axis; + _is_serial = needs_serialized_reduction(op, input->info()->data_type(), axis); + const bool is_arg_min_max = (op == ReductionOperation::ARG_IDX_MAX) || (op == ReductionOperation::ARG_IDX_MIN); + _is_reshape_required = !keep_dims || is_arg_min_max; + + auto *output_internal = configure_intermediate_result_vector(input, output); + + // ArgMinMax might not give initialized output tensor, so initialize here. + if(_is_reshape_required) + { + const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_reduced_shape(input->info()->tensor_shape(), axis, false); + const auto output_data_type = is_arg_min_max ? DataType::U32 : input->info()->data_type(); + auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape).set_data_type(output_data_type).reset_padding().set_is_resizable(true)); + } // Configure reduction operation kernels _reduction_kernels_vector.resize(_num_of_stages); @@ -148,20 +239,16 @@ void CLReductionOperation::configure(ICLTensor *input, ICLTensor *output, unsign // Create temporary tensors if(_is_serial) { - _reduction_kernels_vector[0].configure(input, output, axis, op, 0); + if(_is_reshape_required) + { + _memory_group.manage(&_results_vector.back()); + } + + _reduction_kernels_vector[0].configure(input, output_internal, axis, op, 0); } else { _border_handlers_vector.resize(_num_of_stages); - _results_vector.resize(_num_of_stages - 1); - TensorShape shape{ input->info()->tensor_shape() }; - for(unsigned int i = 0; i < _num_of_stages - 1; i++) - { - shape.set(0, ceil(shape.x() / 128.f)); - _results_vector[i].allocator()->init(input->info()->clone()->set_tensor_shape(shape)); - } - - // Apply ReductionOperation only on first kernel _memory_group.manage(&_results_vector[0]); ReductionOperation first_kernel_op; @@ -262,10 +349,22 @@ void CLReductionOperation::configure(ICLTensor *input, ICLTensor *output, unsign // Apply ReductionOperation on the last stage const unsigned int last_stage = _num_of_stages - 1; const unsigned int input_width = input->info()->dimension(0); - _reduction_kernels_vector[last_stage].configure(&_results_vector[last_stage - 1], output, axis, last_kernel_op, input_width); + + if(_is_reshape_required) + { + _memory_group.manage(&_results_vector.back()); + } + + _reduction_kernels_vector[last_stage].configure(&_results_vector[last_stage - 1], output_internal, axis, last_kernel_op, input_width); _border_handlers_vector[last_stage].configure(&_results_vector[last_stage - 1], _reduction_kernels_vector[last_stage].border_size(), BorderMode::CONSTANT, pixelValue); _results_vector[last_stage - 1].allocator()->allocate(); } + + if(_is_reshape_required) + { + _reshape_kernel.configure(&_results_vector.back(), output); + _results_vector.back().allocator()->allocate(); + } } void CLReductionOperation::run() @@ -284,4 +383,10 @@ void CLReductionOperation::run() CLScheduler::get().enqueue(_reduction_kernels_vector[i], false); } } + + if(_is_reshape_required) + { + CLScheduler::get().enqueue(_reshape_kernel, false); + } } +} // namespace arm_compute diff --git a/src/runtime/NEON/functions/NEArgMinMaxLayer.cpp b/src/runtime/NEON/functions/NEArgMinMaxLayer.cpp index 6863bb0b3b..ab2d6f0c1f 100644 --- a/src/runtime/NEON/functions/NEArgMinMaxLayer.cpp +++ b/src/runtime/NEON/functions/NEArgMinMaxLayer.cpp @@ -23,47 +23,35 @@ */ #include "arm_compute/runtime/NEON/functions/NEArgMinMaxLayer.h" +#include "arm_compute/runtime/NEON/functions/NEReductionOperation.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/ITensor.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/Validate.h" -#include "arm_compute/runtime/NEON/NEScheduler.h" namespace arm_compute { NEArgMinMaxLayer::NEArgMinMaxLayer(std::shared_ptr memory_manager) - : _memory_group(std::move(memory_manager)), _reduction_kernel(), _fill_border_kernel(), _run_fill_border(false) + : _reduction_function(support::cpp14::make_unique()) { + ARM_COMPUTE_UNUSED(memory_manager); } void NEArgMinMaxLayer::configure(ITensor *input, int axis, ITensor *output, const ReductionOperation &op) { - _reduction_kernel.configure(input, output, axis, op); - - if(axis == 0) - { - _fill_border_kernel.configure(input, _reduction_kernel.border_size(), BorderMode::REPLICATE); - _run_fill_border = true; - } + _reduction_function->configure(input, output, axis, op, false); } Status NEArgMinMaxLayer::validate(const ITensorInfo *input, int axis, const ITensorInfo *output, const ReductionOperation &op) { ARM_COMPUTE_RETURN_ERROR_ON_MSG(op != ReductionOperation::ARG_IDX_MAX && op != ReductionOperation::ARG_IDX_MIN, "Invalid operation"); - ARM_COMPUTE_RETURN_ON_ERROR(NEReductionOperationKernel::validate(input, output, axis, op)); - return Status{}; + return NEReductionOperation::validate(input, output, axis, op, false); } void NEArgMinMaxLayer::run() { - MemoryGroupResourceScope scope_mg(_memory_group); - - if(_run_fill_border) - { - NEScheduler::get().schedule(&_fill_border_kernel, Window::DimY); - } - NEScheduler::get().schedule(&_reduction_kernel, Window::DimY); + _reduction_function->run(); } } // namespace arm_compute \ No newline at end of file diff --git a/src/runtime/NEON/functions/NEReductionOperation.cpp b/src/runtime/NEON/functions/NEReductionOperation.cpp index dc6cf59019..09cd765d4b 100644 --- a/src/runtime/NEON/functions/NEReductionOperation.cpp +++ b/src/runtime/NEON/functions/NEReductionOperation.cpp @@ -24,6 +24,7 @@ #include "arm_compute/runtime/NEON/functions/NEReductionOperation.h" #include "arm_compute/core/Helpers.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/runtime/NEON/NEScheduler.h" namespace arm_compute @@ -52,25 +53,78 @@ size_t reduction_window_split_dimension(unsigned int axis) } } // namespace -NEReductionOperation::NEReductionOperation() - : _reduction_kernel(), _fill_border_kernel(), _window_split(0), _reduction_axis() +NEReductionOperation::NEReductionOperation(std::shared_ptr memory_manager) + : _memory_group(memory_manager), _reduction_kernel(), _fill_border_kernel(), _reshape_kernel(), _output_internal(), _window_split(0), _reduction_axis(), _is_reshape_required(false) { } -Status NEReductionOperation::validate(const ITensorInfo *input, const ITensorInfo *output, unsigned int axis, ReductionOperation op) +Status NEReductionOperation::validate(const ITensorInfo *input, const ITensorInfo *output, unsigned int axis, ReductionOperation op, bool keep_dims) { - ARM_COMPUTE_RETURN_ON_ERROR(NEReductionOperationKernel::validate(input, output, axis, op)); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis >= TensorShape::num_max_dimensions, "Reduction axis greater than max number of dimensions"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis > 3, "Unsupported reduction axis"); + + const auto is_reshape_required = !keep_dims; + + auto *output_internal = output; + + TensorInfo info_before_reshape; + + if(is_reshape_required) + { + const TensorInfo expected_output_shape = output->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_reduced_shape(input->tensor_shape(), axis, keep_dims)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&expected_output_shape, output); + + auto shape_before_reshape = input->tensor_shape(); + shape_before_reshape.set(axis, 1); + + const auto input_num_channles = input->num_channels(); + const auto input_qinfo = input->quantization_info(); + const auto is_arg_min_max = (op == ReductionOperation::ARG_IDX_MAX) || (op == ReductionOperation::ARG_IDX_MIN); + const auto output_data_type = is_arg_min_max ? DataType::U32 : output->data_type(); + + info_before_reshape.set_data_type(output_data_type).set_tensor_shape(shape_before_reshape).set_num_channels(input_num_channles).set_quantization_info(input_qinfo); + + output_internal = &info_before_reshape; + } + + ARM_COMPUTE_RETURN_ON_ERROR(NEReductionOperationKernel::validate(input, output_internal, axis, op)); + + if(is_reshape_required) + { + ARM_COMPUTE_RETURN_ON_ERROR(NEReshapeLayerKernel::validate(output_internal, output)); + } return Status{}; } -void NEReductionOperation::configure(ITensor *input, ITensor *output, unsigned int axis, ReductionOperation op) +void NEReductionOperation::configure(ITensor *input, ITensor *output, unsigned int axis, ReductionOperation op, bool keep_dims) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_ERROR_THROW_ON(NEReductionOperation::validate(input->info(), output->info(), axis, op)); + + _is_reshape_required = !keep_dims; + + auto *output_internal = output; + const auto is_arg_min_max = (op == ReductionOperation::ARG_IDX_MAX) || (op == ReductionOperation::ARG_IDX_MIN); + + if(_is_reshape_required) + { + const auto output_internal_shape = arm_compute::misc::shape_calculator::compute_reduced_shape(input->info()->tensor_shape(), axis); + const auto output_external_shape = arm_compute::misc::shape_calculator::compute_reduced_shape(input->info()->tensor_shape(), axis, false); + const auto output_data_type = is_arg_min_max ? DataType::U32 : input->info()->data_type(); + const auto num_channels = input->info()->num_channels(); + const auto qinfo = input->info()->quantization_info(); + + _output_internal.allocator()->init(input->info()->clone()->set_data_type(output_data_type).set_tensor_shape(output_internal_shape).reset_padding().set_is_resizable(true).set_num_channels( + num_channels).set_quantization_info(qinfo)); + _memory_group.manage(&_output_internal); + output_internal = &_output_internal; + auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(output_data_type).set_tensor_shape(output_external_shape).reset_padding().set_is_resizable(true)); + } + + ARM_COMPUTE_ERROR_THROW_ON(NEReductionOperation::validate(input->info(), output->info(), axis, op, keep_dims)); // Configure reduction kernel - _reduction_kernel.configure(input, output, axis, op); + _reduction_kernel.configure(input, output_internal, axis, op); _window_split = reduction_window_split_dimension(axis); _reduction_axis = axis; @@ -150,7 +204,13 @@ void NEReductionOperation::configure(ITensor *input, ITensor *output, unsigned i default: ARM_COMPUTE_ERROR("Reduction Operation unsupported"); } - _fill_border_kernel.configure(input, fill_border_size, BorderMode::CONSTANT, pixelValue); + _fill_border_kernel.configure(input, fill_border_size, (is_arg_min_max ? BorderMode::REPLICATE : BorderMode::CONSTANT), pixelValue); + } + + if(_is_reshape_required) + { + _reshape_kernel.configure(output_internal, output); + _output_internal.allocator()->allocate(); } } @@ -161,5 +221,9 @@ void NEReductionOperation::run() NEScheduler::get().schedule(&_fill_border_kernel, Window::DimY); } NEScheduler::get().schedule(&_reduction_kernel, _window_split); + if(_is_reshape_required) + { + NEScheduler::get().schedule(&_reshape_kernel, Window::DimY); + } } } // namespace arm_compute -- cgit v1.2.1