From 60f3911996871e6163ce5a9a2dfca02125db8ecf Mon Sep 17 00:00:00 2001 From: Manuel Bottini Date: Mon, 18 Mar 2019 15:25:15 +0000 Subject: COMPMID-2008: Add support for "reflect" padding mode in CLPad Change-Id: I469f8173d5c4a1b6f03b52b9ddd33928dacd1e7b Signed-off-by: Manuel Bottini Reviewed-on: https://review.mlplatform.org/c/869 Tested-by: Arm Jenkins Comments-Addressed: Arm Jenkins Reviewed-by: Giuseppe Rossini --- src/runtime/CL/functions/CLPadLayer.cpp | 275 ++++++++++++++++++++++++++++++-- 1 file changed, 261 insertions(+), 14 deletions(-) (limited to 'src/runtime/CL') diff --git a/src/runtime/CL/functions/CLPadLayer.cpp b/src/runtime/CL/functions/CLPadLayer.cpp index fac2364ae5..f88cb388be 100644 --- a/src/runtime/CL/functions/CLPadLayer.cpp +++ b/src/runtime/CL/functions/CLPadLayer.cpp @@ -25,41 +25,288 @@ #include "arm_compute/core/CL/ICLTensor.h" #include "arm_compute/core/Types.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "support/ToolchainSupport.h" namespace arm_compute { CLPadLayer::CLPadLayer() - : _copy_kernel(), _fillborder_kernel(), _memset_kernel() + : _copy_kernel(), _mode(), _padding(), _memset_kernel(), _num_dimensions(0), _slice_functions(nullptr), _concat_functions(nullptr), _slice_results(nullptr), _concat_results(nullptr) { } +void CLPadLayer::configure_constant_mode(ICLTensor *input, ICLTensor *output, const PaddingList &padding, const PixelValue constant_value) +{ + // Set the pages of the output to the constant_value. + _memset_kernel.configure(output, constant_value); + + // Fill out padding list with zeroes. + PaddingList padding_extended = padding; + for(size_t i = padding.size(); i < TensorShape::num_max_dimensions; i++) + { + padding_extended.emplace_back(PaddingInfo{ 0, 0 }); + } + + // Create a window within the output tensor where the input will be copied. + Window copy_window = Window(); + for(uint32_t i = 0; i < output->info()->num_dimensions(); ++i) + { + copy_window.set(i, Window::Dimension(padding_extended[i].first, padding_extended[i].first + input->info()->dimension(i), 1)); + } + // Copy the input to the output, leaving the padding filled with the constant_value. + _copy_kernel.configure(input, output, PaddingList(), ©_window); +} + +void CLPadLayer::configure_reflect_symmetric_mode(ICLTensor *input, ICLTensor *output) +{ + int64_t last_padding_dimension = _padding.size() - 1; + // Reflecting can be performed by effectively unfolding the input as follows: + // For each dimension starting at DimX: + // Create a before and after slice, which values depend on the selected padding mode + // Concatenate the before and after padding with the tensor to be padded + + // Two strided slice functions will be required for each dimension padded as well as a + // concatenate function and the tensors to hold the temporary results. + _slice_functions = arm_compute::support::cpp14::make_unique(2 * _num_dimensions); + _slice_results = arm_compute::support::cpp14::make_unique(2 * _num_dimensions); + _concat_functions = arm_compute::support::cpp14::make_unique(_num_dimensions); + _concat_results = arm_compute::support::cpp14::make_unique(_num_dimensions - 1); + Coordinates starts_before, ends_before, starts_after, ends_after, strides; + ICLTensor *prev = input; + for(uint32_t i = 0; i < _num_dimensions; ++i) + { + // Values in strides from the previous dimensions need to be set to 1 to avoid reversing again. + if(i > 0) + { + strides.set(i - 1, 1); + } + + if(_padding[i].first > 0 || _padding[i].second > 0) + { + // Set the starts, ends, and strides values for the current dimension. + // Due to the bit masks passed to strided slice, the values below the current dimension in + // starts and ends will be ignored so do not need to be modified. + if(_mode == PaddingMode::REFLECT) + { + starts_before.set(i, _padding[i].first); + ends_before.set(i, 0); + starts_after.set(i, input->info()->dimension(i) - 2); + ends_after.set(i, input->info()->dimension(i) - _padding[i].second - 2); + strides.set(i, -1); + } + else + { + starts_before.set(i, _padding[i].first - 1); + ends_before.set(i, -1); + starts_after.set(i, input->info()->dimension(i) - 1); + ends_after.set(i, input->info()->dimension(i) - _padding[i].second - 1); + strides.set(i, -1); + } + + // Strided slice wraps negative indexes around to the end of the range, + // instead this should indicate use of the full range and so the bit mask will be modified. + const int32_t begin_mask_before = starts_before[i] < 0 ? ~0 : ~(1u << i); + const int32_t end_mask_before = ends_before[i] < 0 ? ~0 : ~(1u << i); + const int32_t begin_mask_after = starts_after[i] < 0 ? ~0 : ~(1u << i); + const int32_t end_mask_after = ends_after[i] < 0 ? ~0 : ~(1u << i); + + // Reflect the input values for the padding before and after the input. + std::vector concat_vector; + if(_padding[i].first > 0) + { + if(i < prev->info()->num_dimensions()) + { + _slice_functions[2 * i].configure(prev, &_slice_results[2 * i], starts_before, ends_before, strides, begin_mask_before, end_mask_before); + concat_vector.push_back(&_slice_results[2 * i]); + } + else + { + // Performing the slice is unnecessary if the result would simply be a copy of the tensor. + concat_vector.push_back(prev); + } + } + concat_vector.push_back(prev); + if(_padding[i].second > 0) + { + if(i < prev->info()->num_dimensions()) + { + _slice_functions[2 * i + 1].configure(prev, &_slice_results[2 * i + 1], starts_after, ends_after, strides, begin_mask_after, end_mask_after); + concat_vector.push_back(&_slice_results[2 * i + 1]); + } + else + { + // Performing the slice is unnecessary if the result would simply be a copy of the tensor. + concat_vector.push_back(prev); + } + } + // Concatenate the padding before and after with the input. + ICLTensor *out = (static_cast(i) == last_padding_dimension) ? output : &_concat_results[i]; + _concat_functions[i].configure(concat_vector, out, get_index_data_layout_dimension(prev->info()->data_layout(), i)); + prev = out; + } + } + for(uint32_t i = 0; i < _num_dimensions; ++i) + { + if((static_cast(i) != last_padding_dimension)) + { + _concat_results[i].allocator()->allocate(); + } + _slice_results[2 * i].allocator()->allocate(); + _slice_results[2 * i + 1].allocator()->allocate(); + } +} + void CLPadLayer::configure(ICLTensor *input, ICLTensor *output, const PaddingList &padding, PixelValue constant_value, PaddingMode mode) { - ARM_COMPUTE_UNUSED(mode); - // Copy the input to the output - _copy_kernel.configure(input, output, padding); + ARM_COMPUTE_ERROR_THROW_ON(validate(input->info(), output->info(), padding, constant_value, mode)); - // Set the pages of the output to zero - _memset_kernel.configure(output, constant_value); + _padding = padding; + _mode = mode; + + TensorShape padded_shape = misc::shape_calculator::compute_padded_shape(input->info()->tensor_shape(), _padding); - // Fill padding on the first two dimensions with zeros - _fillborder_kernel.configure(input, input->info()->padding(), BorderMode::CONSTANT, constant_value); + auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(padded_shape)); + + // Find the last dimension requiring padding so that it is known when to write to output and whether any padding is applied. + int64_t last_padding_dimension = _padding.size() - 1; + for(; last_padding_dimension >= 0; --last_padding_dimension) + { + if(_padding[last_padding_dimension].first > 0 || _padding[last_padding_dimension].second > 0) + { + break; + } + } + _num_dimensions = last_padding_dimension + 1; + if(_num_dimensions > 0) + { + switch(_mode) + { + case PaddingMode::CONSTANT: + { + configure_constant_mode(input, output, padding, constant_value); + break; + } + case PaddingMode::REFLECT: + case PaddingMode::SYMMETRIC: + { + configure_reflect_symmetric_mode(input, output); + break; + } + default: + ARM_COMPUTE_ERROR("Padding mode not supported."); + } + } + else + { + // Copy the input to the whole output if no padding is applied + _copy_kernel.configure(input, output); + } } Status CLPadLayer::validate(const ITensorInfo *input, const ITensorInfo *output, const PaddingList &padding, PixelValue constant_value, PaddingMode mode) { - ARM_COMPUTE_RETURN_ON_ERROR(CLMemsetKernel::validate(input, constant_value)); - ARM_COMPUTE_RETURN_ON_ERROR(CLCopyKernel::validate(input, output, padding)); - ARM_COMPUTE_RETURN_ERROR_ON(mode != PaddingMode::CONSTANT); + ARM_COMPUTE_RETURN_ERROR_ON(padding.size() > input->num_dimensions()); + + TensorShape padded_shape = misc::shape_calculator::compute_padded_shape(input->tensor_shape(), padding); + + // Use CLCopyKernel and CLMemsetKernel to validate all padding modes as this includes all of the shape and info validation. + PaddingList padding_extended = padding; + for(size_t i = padding.size(); i < TensorShape::num_max_dimensions; i++) + { + padding_extended.emplace_back(PaddingInfo{ 0, 0 }); + } + + Window copy_window = Window(); + for(uint32_t i = 0; i < padded_shape.num_dimensions(); ++i) + { + copy_window.set(i, Window::Dimension(padding_extended[i].first, padding_extended[i].first + input->dimension(i), 1)); + } + if(output->total_size() > 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), padded_shape); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(output, input); + ARM_COMPUTE_RETURN_ON_ERROR(CLCopyKernel::validate(input, output, PaddingList(), ©_window)); + ARM_COMPUTE_RETURN_ON_ERROR(CLMemsetKernel::validate(output, constant_value)); + } + else + { + ARM_COMPUTE_RETURN_ON_ERROR(CLCopyKernel::validate(input, &input->clone()->set_tensor_shape(padded_shape), PaddingList(), ©_window)); + ARM_COMPUTE_RETURN_ON_ERROR(CLMemsetKernel::validate(&input->clone()->set_tensor_shape(padded_shape), constant_value)); + } + switch(mode) + { + case PaddingMode::CONSTANT: + { + break; + } + case PaddingMode::REFLECT: + case PaddingMode::SYMMETRIC: + { + for(uint32_t i = 0; i < padding.size(); ++i) + { + if(mode == PaddingMode::REFLECT) + { + ARM_COMPUTE_RETURN_ERROR_ON(padding[i].first >= input->dimension(i)); + ARM_COMPUTE_RETURN_ERROR_ON(padding[i].second >= input->dimension(i)); + } + else + { + ARM_COMPUTE_RETURN_ERROR_ON(padding[i].first > input->dimension(i)); + ARM_COMPUTE_RETURN_ERROR_ON(padding[i].second > input->dimension(i)); + } + } + break; + } + default: + { + ARM_COMPUTE_ERROR("Invalid mode"); + } + } return Status{}; } void CLPadLayer::run() { - CLScheduler::get().enqueue(_memset_kernel, false); - CLScheduler::get().enqueue(_fillborder_kernel, false); - CLScheduler::get().enqueue(_copy_kernel, true); + if(_num_dimensions > 0) + { + switch(_mode) + { + case PaddingMode::CONSTANT: + { + CLScheduler::get().enqueue(_memset_kernel, false); + CLScheduler::get().enqueue(_copy_kernel, true); + break; + } + case PaddingMode::REFLECT: + case PaddingMode::SYMMETRIC: + { + for(uint32_t i = 0; i < _num_dimensions; ++i) + { + if(_padding[i].first > 0 || _padding[i].second > 0) + { + if(_padding[i].first > 0 && _slice_results[2 * i].info()->total_size() > 0) + { + _slice_functions[2 * i].run(); + } + if(_padding[i].second > 0 && _slice_results[2 * i + 1].info()->total_size() > 0) + { + _slice_functions[2 * i + 1].run(); + } + CLScheduler::get().sync(); + _concat_functions[i].run(); + CLScheduler::get().sync(); + } + } + break; + } + default: + ARM_COMPUTE_ERROR("Padding mode not supported."); + } + } + else + { + CLScheduler::get().enqueue(_copy_kernel, true); + } } } // namespace arm_compute -- cgit v1.2.1