| /* |
| * Copyright (c) 2019 ARM Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #include "arm_compute/core/NEON/kernels/NEROIAlignLayerKernel.h" |
| |
| #include "arm_compute/core/AccessWindowStatic.h" |
| #include "arm_compute/core/CPP/Validate.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Window.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/core/utils/misc/Utility.h" |
| |
| #include <arm_neon.h> |
| |
| using namespace arm_compute::misc::shape_calculator; |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *rois, ITensorInfo *output, const ROIPoolingLayerInfo &pool_info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, rois, output); |
| ARM_COMPUTE_RETURN_ERROR_ON(rois->dimension(0) != 5); |
| ARM_COMPUTE_RETURN_ERROR_ON(rois->num_dimensions() > 2); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F32, DataType::F16); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(input, DataLayout::NHWC, DataLayout::NCHW); |
| ARM_COMPUTE_RETURN_ERROR_ON((pool_info.pooled_width() == 0) || (pool_info.pooled_height() == 0)); |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); |
| |
| if(output->total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(compute_roi_align_shape(*input, *rois, pool_info), output->tensor_shape()); |
| } |
| |
| if(input->data_type() == DataType::QASYMM8) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(rois, 1, DataType::QASYMM16); |
| |
| const UniformQuantizationInfo rois_qinfo = rois->quantization_info().uniform(); |
| ARM_COMPUTE_RETURN_ERROR_ON(rois_qinfo.scale != 0.125f); |
| ARM_COMPUTE_RETURN_ERROR_ON(rois_qinfo.offset != 0); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, rois); |
| } |
| |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *rois, ITensorInfo *output, const ROIPoolingLayerInfo &pool_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| |
| // Output auto inizialitation if not yet initialized |
| const TensorShape output_shape = compute_roi_align_shape(*input, *rois, pool_info); |
| auto_init_if_empty((*output), output_shape, 1, input->data_type()); |
| output->set_data_layout(input->data_layout()); |
| |
| const unsigned int num_rois = rois->dimension(1); |
| Window window; |
| window.set(Window::DimX, Window::Dimension(0, num_rois)); |
| window.set(Window::DimY, Window::Dimension(0, 1)); |
| |
| AccessWindowStatic input_access(input, |
| input->valid_region().start(0), |
| input->valid_region().start(1), |
| input->valid_region().end(0), |
| input->valid_region().end(1)); |
| AccessWindowStatic output_access(output, 0, 0, pool_info.pooled_width(), pool_info.pooled_height()); |
| |
| const bool window_changed = update_window_and_padding(window, input_access, output_access); |
| output_access.set_valid_region(window, ValidRegion(Coordinates(), output->tensor_shape())); |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_pair(err, window); |
| } |
| } // namespace |
| |
| NEROIAlignLayerKernel::NEROIAlignLayerKernel() |
| : _input(nullptr), _output(nullptr), _rois(nullptr), _pool_info(0, 0, 0.f) |
| { |
| } |
| |
| void NEROIAlignLayerKernel::configure(const ITensor *input, const ITensor *rois, ITensor *output, const ROIPoolingLayerInfo &pool_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, rois); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), rois->info(), output->info(), pool_info)); |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(input->info(), rois->info(), output->info(), pool_info); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| |
| // Set instance variables |
| _input = input; |
| _rois = rois; |
| _output = output; |
| _pool_info = pool_info; |
| |
| INEKernel::configure(win_config.second); |
| } |
| |
| Status NEROIAlignLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *rois, ITensorInfo *output, const ROIPoolingLayerInfo &pool_info) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, rois, output, pool_info)); |
| return Status{}; |
| } |
| |
| /** Average pooling over an aligned window */ |
| template <typename input_data_type, DataLayout data_layout> |
| inline input_data_type roi_align_1x1(const ITensor *input, |
| unsigned int roi_batch, |
| float region_start_x, |
| float bin_size_x, |
| int grid_size_x, |
| float region_end_x, |
| float region_start_y, |
| float bin_size_y, |
| int grid_size_y, |
| float region_end_y, |
| int pz) |
| { |
| if((region_end_x <= region_start_x) || (region_end_y <= region_start_y)) |
| { |
| return input_data_type(0); |
| } |
| else |
| { |
| float avg = 0; |
| // Iterate through the aligned pooling region |
| for(int iy = 0; iy < grid_size_y; ++iy) |
| { |
| for(int ix = 0; ix < grid_size_x; ++ix) |
| { |
| // Align the window in the middle of every bin |
| float y = region_start_y + (iy + 0.5) * bin_size_y / float(grid_size_y); |
| float x = region_start_x + (ix + 0.5) * bin_size_x / float(grid_size_x); |
| |
| // Interpolation in the [0,0] [0,1] [1,0] [1,1] square |
| const int y_low = y; |
| const int x_low = x; |
| const int y_high = y_low + 1; |
| const int x_high = x_low + 1; |
| |
| const float ly = y - y_low; |
| const float lx = x - x_low; |
| const float hy = 1. - ly; |
| const float hx = 1. - lx; |
| |
| const float w1 = hy * hx; |
| const float w2 = hy * lx; |
| const float w3 = ly * hx; |
| const float w4 = ly * lx; |
| if(data_layout == DataLayout::NCHW) |
| { |
| const auto data1 = *reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_low, y_low, pz, roi_batch))); |
| const auto data2 = *reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_high, y_low, pz, roi_batch))); |
| const auto data3 = *reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_low, y_high, pz, roi_batch))); |
| const auto data4 = *reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_high, y_high, pz, roi_batch))); |
| avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4; |
| } |
| else |
| { |
| const auto data1 = *reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_low, y_low, roi_batch))); |
| const auto data2 = *reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_high, y_low, roi_batch))); |
| const auto data3 = *reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_low, y_high, roi_batch))); |
| const auto data4 = *reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_high, y_high, roi_batch))); |
| avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4; |
| } |
| } |
| } |
| |
| avg /= grid_size_x * grid_size_y; |
| return input_data_type(avg); |
| } |
| } |
| |
| /** Average pooling over an aligned window */ |
| template <typename input_data_type, DataLayout data_layout> |
| inline input_data_type roi_align_1x1_qasymm8(const ITensor *input, |
| unsigned int roi_batch, |
| float region_start_x, |
| float bin_size_x, |
| int grid_size_x, |
| float region_end_x, |
| float region_start_y, |
| float bin_size_y, |
| int grid_size_y, |
| float region_end_y, |
| int pz, |
| const QuantizationInfo &out_qinfo) |
| { |
| if((region_end_x <= region_start_x) || (region_end_y <= region_start_y)) |
| { |
| return input_data_type(out_qinfo.uniform().offset); |
| } |
| else |
| { |
| float avg = 0; |
| const UniformQuantizationInfo input_qinfo = input->info()->quantization_info().uniform(); |
| // Iterate through the aligned pooling region |
| for(int iy = 0; iy < grid_size_y; ++iy) |
| { |
| for(int ix = 0; ix < grid_size_x; ++ix) |
| { |
| // Align the window in the middle of every bin |
| float y = region_start_y + (iy + 0.5) * bin_size_y / float(grid_size_y); |
| float x = region_start_x + (ix + 0.5) * bin_size_x / float(grid_size_x); |
| |
| // Interpolation in the [0,0] [0,1] [1,0] [1,1] square |
| const int y_low = y; |
| const int x_low = x; |
| const int y_high = y_low + 1; |
| const int x_high = x_low + 1; |
| |
| const float ly = y - y_low; |
| const float lx = x - x_low; |
| const float hy = 1. - ly; |
| const float hx = 1. - lx; |
| |
| const float w1 = hy * hx; |
| const float w2 = hy * lx; |
| const float w3 = ly * hx; |
| const float w4 = ly * lx; |
| |
| if(data_layout == DataLayout::NCHW) |
| { |
| float data1 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_low, y_low, pz, roi_batch))), input_qinfo); |
| float data2 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_high, y_low, pz, roi_batch))), input_qinfo); |
| float data3 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_low, y_high, pz, roi_batch))), input_qinfo); |
| float data4 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_high, y_high, pz, roi_batch))), input_qinfo); |
| avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4; |
| } |
| else |
| { |
| const auto data1 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_low, y_low, roi_batch))), input_qinfo); |
| const auto data2 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_high, y_low, roi_batch))), input_qinfo); |
| const auto data3 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_low, y_high, roi_batch))), input_qinfo); |
| const auto data4 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_high, y_high, roi_batch))), input_qinfo); |
| avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4; |
| } |
| } |
| } |
| |
| avg /= grid_size_x * grid_size_y; |
| |
| return quantize_qasymm8(avg, out_qinfo); |
| } |
| } |
| |
| inline float compute_region_coordinate(int p, float bin_size, float roi_anchor, float max_value) |
| { |
| const float region_start = p * bin_size + roi_anchor; |
| return utility::clamp(region_start, 0.0f, max_value); |
| } |
| |
| void NEROIAlignLayerKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| if(_input->info()->data_layout() == DataLayout::NCHW) |
| { |
| switch(_input->info()->data_type()) |
| { |
| case DataType::QASYMM8: |
| { |
| NEROIAlignLayerKernel::internal_run<DataLayout::NCHW, uint8_t, uint16_t>(window, info); |
| break; |
| } |
| case DataType::F32: |
| { |
| NEROIAlignLayerKernel::internal_run<DataLayout::NCHW, float>(window, info); |
| break; |
| } |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| { |
| NEROIAlignLayerKernel::internal_run<DataLayout::NCHW, float16_t>(window, info); |
| break; |
| } |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| default: |
| { |
| ARM_COMPUTE_ERROR("DataType not supported"); |
| break; |
| } |
| } |
| } |
| else if(_input->info()->data_layout() == DataLayout::NHWC) |
| { |
| switch(_input->info()->data_type()) |
| { |
| case DataType::QASYMM8: |
| { |
| NEROIAlignLayerKernel::internal_run<DataLayout::NHWC, uint8_t, uint16_t>(window, info); |
| break; |
| } |
| case DataType::F32: |
| { |
| NEROIAlignLayerKernel::internal_run<DataLayout::NHWC, float>(window, info); |
| break; |
| } |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| { |
| NEROIAlignLayerKernel::internal_run<DataLayout::NHWC, float16_t>(window, info); |
| break; |
| } |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| default: |
| { |
| ARM_COMPUTE_ERROR("DataType not supported"); |
| break; |
| } |
| } |
| } |
| else |
| { |
| ARM_COMPUTE_ERROR("Invalid layout"); |
| } |
| } |
| |
| template <DataLayout data_layout, typename input_data_type, typename roi_data_type> |
| void NEROIAlignLayerKernel::internal_run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| |
| const size_t values_per_roi = _rois->info()->dimension(0); |
| |
| const int roi_list_start = window.x().start(); |
| const int roi_list_end = window.x().end(); |
| |
| const unsigned int idx_width = get_data_layout_dimension_index(_input->info()->data_layout(), DataLayoutDimension::WIDTH); |
| const unsigned int idx_height = get_data_layout_dimension_index(_input->info()->data_layout(), DataLayoutDimension::HEIGHT); |
| const unsigned int idx_depth = get_data_layout_dimension_index(_input->info()->data_layout(), DataLayoutDimension::CHANNEL); |
| |
| const int input_width = _input->info()->dimension(idx_width); |
| const int input_height = _input->info()->dimension(idx_height); |
| const int input_chanels = _input->info()->dimension(idx_depth); |
| const int pooled_w = _pool_info.pooled_width(); |
| const int pooled_h = _pool_info.pooled_height(); |
| |
| const DataType data_type = _input->info()->data_type(); |
| const bool is_qasymm = is_data_type_quantized_asymmetric(data_type); |
| |
| const auto *rois_ptr = reinterpret_cast<const roi_data_type *>(_rois->buffer()); |
| const QuantizationInfo &rois_qinfo = _rois->info()->quantization_info(); |
| for(int roi_indx = roi_list_start; roi_indx < roi_list_end; ++roi_indx) |
| { |
| const unsigned int roi_batch = rois_ptr[values_per_roi * roi_indx]; |
| |
| roi_data_type qx1 = rois_ptr[values_per_roi * roi_indx + 1]; |
| roi_data_type qy1 = rois_ptr[values_per_roi * roi_indx + 2]; |
| roi_data_type qx2 = rois_ptr[values_per_roi * roi_indx + 3]; |
| roi_data_type qy2 = rois_ptr[values_per_roi * roi_indx + 4]; |
| float x1(qx1); |
| float x2(qx2); |
| float y1(qy1); |
| float y2(qy2); |
| if(is_qasymm) |
| { |
| x1 = dequantize_qasymm16(qx1, rois_qinfo); |
| x2 = dequantize_qasymm16(qx2, rois_qinfo); |
| y1 = dequantize_qasymm16(qy1, rois_qinfo); |
| y2 = dequantize_qasymm16(qy2, rois_qinfo); |
| } |
| const float roi_anchor_x = x1 * _pool_info.spatial_scale(); |
| const float roi_anchor_y = y1 * _pool_info.spatial_scale(); |
| const float roi_dims_x = std::max((x2 - x1) * _pool_info.spatial_scale(), 1.0f); |
| const float roi_dims_y = std::max((y2 - y1) * _pool_info.spatial_scale(), 1.0f); |
| float bin_size_x = roi_dims_x / _pool_info.pooled_width(); |
| float bin_size_y = roi_dims_y / _pool_info.pooled_height(); |
| |
| // Iterate through all feature maps |
| for(int ch = 0; ch < input_chanels; ++ch) |
| { |
| // Iterate through all output pixels |
| for(int py = 0; py < pooled_h; ++py) |
| { |
| for(int px = 0; px < pooled_w; ++px) |
| { |
| const float region_start_x = compute_region_coordinate(px, bin_size_x, roi_anchor_x, input_width); |
| const float region_start_y = compute_region_coordinate(py, bin_size_y, roi_anchor_y, input_height); |
| const float region_end_x = compute_region_coordinate(px + 1, bin_size_x, roi_anchor_x, input_width); |
| const float region_end_y = compute_region_coordinate(py + 1, bin_size_y, roi_anchor_y, input_height); |
| const int roi_bin_grid_x = (_pool_info.sampling_ratio() > 0) ? _pool_info.sampling_ratio() : int(ceil(bin_size_x)); |
| const int roi_bin_grid_y = (_pool_info.sampling_ratio() > 0) ? _pool_info.sampling_ratio() : int(ceil(bin_size_y)); |
| input_data_type out_val(0); |
| if(is_qasymm) |
| { |
| out_val = roi_align_1x1_qasymm8<input_data_type, data_layout>( |
| _input, roi_batch, region_start_x, bin_size_x, |
| roi_bin_grid_x, region_end_x, region_start_y, bin_size_y, |
| roi_bin_grid_y, region_end_y, ch, _output->info()->quantization_info()); |
| } |
| else |
| { |
| out_val = roi_align_1x1<input_data_type, data_layout>( |
| _input, roi_batch, region_start_x, bin_size_x, |
| roi_bin_grid_x, region_end_x, region_start_y, bin_size_y, |
| roi_bin_grid_y, region_end_y, ch); |
| } |
| |
| if(data_layout == DataLayout::NCHW) |
| { |
| auto out_ptr = reinterpret_cast<input_data_type *>(_output->ptr_to_element(Coordinates(px, py, ch, roi_indx))); |
| *out_ptr = out_val; |
| } |
| else |
| { |
| auto out_ptr = reinterpret_cast<input_data_type *>(_output->ptr_to_element(Coordinates(ch, px, py, roi_indx))); |
| *out_ptr = out_val; |
| } |
| } |
| } |
| } |
| } |
| } |
| } // namespace arm_compute |