| /* |
| * Copyright (c) 2018-2020 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/NEPriorBoxLayerKernel.h" |
| |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Validate.h" |
| |
| #include <arm_neon.h> |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, const PriorBoxLayerInfo &info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input1, input2); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2); |
| |
| // Check variances |
| const int var_size = info.variances().size(); |
| if(var_size > 1) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(var_size != 4, "Must provide 4 variance values"); |
| for(int i = 0; i < var_size; ++i) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(var_size <= 0, "Must be greater than 0"); |
| } |
| } |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.steps()[0] < 0.f, "Step x should be greater or equal to 0"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.steps()[1] < 0.f, "Step y should be greater or equal to 0"); |
| |
| if(!info.max_sizes().empty()) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.max_sizes().size() != info.min_sizes().size(), "Max and min sizes dimensions should match"); |
| } |
| |
| for(unsigned int i = 0; i < info.max_sizes().size(); ++i) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.max_sizes()[i] < info.min_sizes()[i], "Max size should be greater than min size"); |
| } |
| |
| if(output != nullptr && output->total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(1) != 2); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, output); |
| } |
| |
| return Status{}; |
| } |
| } // namespace |
| |
| NEPriorBoxLayerKernel::NEPriorBoxLayerKernel() |
| : _input1(nullptr), _input2(nullptr), _output(nullptr), _info() |
| { |
| } |
| |
| void NEPriorBoxLayerKernel::store_coordinates(float *out, const int offset, const float center_x, const float center_y, const float box_width, const float box_height, const int width, |
| const int height) |
| { |
| float xmin = (center_x - box_width / 2.f) / width; |
| float ymin = (center_y - box_height / 2.f) / height; |
| float xmax = (center_x + box_width / 2.f) / width; |
| float ymax = (center_y + box_height / 2.f) / height; |
| |
| float32x4_t vec_elements = { xmin, ymin, xmax, ymax }; |
| if(_info.clip()) |
| { |
| static const float32x4_t CONST_0 = vdupq_n_f32(0.f); |
| static const float32x4_t CONST_1 = vdupq_n_f32(1.f); |
| vec_elements = vmaxq_f32(vminq_f32(vec_elements, CONST_1), CONST_0); |
| } |
| vst1q_f32(out + offset, vec_elements); |
| } |
| |
| void NEPriorBoxLayerKernel::calculate_prior_boxes(const Window &window) |
| { |
| const int num_priors = _info.aspect_ratios().size() * _info.min_sizes().size() + _info.max_sizes().size(); |
| |
| const DataLayout data_layout = _input1->info()->data_layout(); |
| const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); |
| const int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); |
| |
| const int layer_width = _input1->info()->dimension(width_idx); |
| const int layer_height = _input1->info()->dimension(height_idx); |
| |
| int img_width = _info.img_size().x; |
| int img_height = _info.img_size().y; |
| if(img_width == 0 || img_height == 0) |
| { |
| img_width = _input2->info()->dimension(width_idx); |
| img_height = _input2->info()->dimension(height_idx); |
| } |
| |
| float step_x = _info.steps()[0]; |
| float step_y = _info.steps()[1]; |
| if(step_x == 0.f || step_y == 0.f) |
| { |
| step_x = static_cast<float>(img_width) / layer_width; |
| step_y = static_cast<float>(img_height) / layer_height; |
| } |
| |
| Window slice = window.first_slice_window_2D(); |
| slice.set(Window::DimY, Window::Dimension(0, _output->info()->dimension(1), 2)); |
| |
| Iterator output(_output, slice); |
| execute_window_loop(slice, [&](const Coordinates & id) |
| { |
| float center_x = 0; |
| float center_y = 0; |
| int idx = id.x() / (4 * num_priors); |
| center_x = (static_cast<float>(idx % layer_width) + _info.offset()) * step_x; |
| center_y = (static_cast<float>(idx / layer_width) + _info.offset()) * step_y; |
| |
| float box_width; |
| float box_height; |
| int offset = 0; |
| |
| auto out = reinterpret_cast<float *>(output.ptr()); |
| for(unsigned int i = 0; i < _info.min_sizes().size(); ++i) |
| { |
| const float min_size = _info.min_sizes().at(i); |
| box_width = min_size; |
| box_height = min_size; |
| store_coordinates(out, offset, center_x, center_y, box_width, box_height, img_width, img_height); |
| offset += 4; |
| |
| if(!_info.max_sizes().empty()) |
| { |
| const float max_size = _info.max_sizes().at(i); |
| box_width = std::sqrt(min_size * max_size); |
| box_height = box_width; |
| |
| store_coordinates(out, offset, center_x, center_y, box_width, box_height, img_width, img_height); |
| offset += 4; |
| } |
| |
| // rest of priors |
| for(auto ar : _info.aspect_ratios()) |
| { |
| if(fabs(ar - 1.) < 1e-6) |
| { |
| continue; |
| } |
| |
| box_width = min_size * sqrt(ar); |
| box_height = min_size / sqrt(ar); |
| |
| store_coordinates(out, offset, center_x, center_y, box_width, box_height, img_width, img_height); |
| offset += 4; |
| } |
| } |
| |
| // set the variance |
| out = reinterpret_cast<float *>(_output->ptr_to_element(Coordinates(id.x(), 1))); |
| float32x4_t var; |
| if(_info.variances().size() == 1) |
| { |
| var = vdupq_n_f32(_info.variances().at(0)); |
| } |
| else |
| { |
| const float32x4_t vars = { _info.variances().at(0), _info.variances().at(1), _info.variances().at(2), _info.variances().at(3) }; |
| var = vars; |
| } |
| for(int i = 0; i < num_priors; ++i) |
| { |
| vst1q_f32(out + 4 * i, var); |
| } |
| }, |
| output); |
| } |
| |
| void NEPriorBoxLayerKernel::configure(const ITensor *input1, const ITensor *input2, ITensor *output, const PriorBoxLayerInfo &info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); |
| |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input1->info(), input2->info(), output->info(), info)); |
| |
| _input1 = input1; |
| _input2 = input2; |
| _info = info; |
| _output = output; |
| |
| // Configure kernel window |
| const int num_priors = info.aspect_ratios().size() * info.min_sizes().size() + info.max_sizes().size(); |
| Window win = calculate_max_window(*output->info(), Steps(num_priors * 4)); |
| Coordinates coord; |
| coord.set_num_dimensions(output->info()->num_dimensions()); |
| output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape())); |
| |
| INEKernel::configure(win); |
| } |
| |
| Status NEPriorBoxLayerKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, const PriorBoxLayerInfo &info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input1, input2, output, info)); |
| |
| return Status{}; |
| } |
| void NEPriorBoxLayerKernel::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); |
| |
| // Run function |
| calculate_prior_boxes(window); |
| } |
| } // namespace arm_compute |