/* * Copyright (c) 2016-2019 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/NEGaussianPyramidKernel.h" #include "arm_compute/core/Coordinates.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/core/ITensor.h" #include "arm_compute/core/NEON/INEKernel.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/Validate.h" #include "arm_compute/core/Window.h" #include #include #include #include using namespace arm_compute; NEGaussianPyramidHorKernel::NEGaussianPyramidHorKernel() : _l2_load_offset(0) { } BorderSize NEGaussianPyramidHorKernel::border_size() const { return BorderSize{ 0, 2 }; } void NEGaussianPyramidHorKernel::configure(const ITensor *input, ITensor *output) { ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8); ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S16); ARM_COMPUTE_ERROR_ON(input->info()->dimension(1) != output->info()->dimension(1)); for(size_t i = 2; i < Coordinates::num_max_dimensions; ++i) { ARM_COMPUTE_ERROR_ON(input->info()->dimension(i) != output->info()->dimension(i)); } _input = input; _output = output; // Configure kernel window constexpr unsigned int num_elems_processed_per_iteration = 16; constexpr unsigned int num_elems_read_per_iteration = 32; constexpr unsigned int num_elems_written_per_iteration = 8; const float scale_x = static_cast(output->info()->dimension(0)) / input->info()->dimension(0); Window win = calculate_max_window_horizontal(*input->info(), Steps(num_elems_processed_per_iteration)); AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration, scale_x); // Sub sampling selects odd pixels (1, 3, 5, ...) for images with even // width and even pixels (0, 2, 4, ...) for images with odd width. (Whether // a pixel is even or odd is determined based on the tensor shape not the // valid region!) // Thus the offset from which the first pixel (L2) for the convolution is // loaded depends on the anchor and shape of the valid region. // In the case of an even shape (= even image width) we need to load L2 // from -2 if the anchor is odd and from -1 if the anchor is even. That // makes sure that L2 is always loaded from an odd pixel. // On the other hand, for an odd shape (= odd image width) we need to load // L2 from -1 if the anchor is odd and from -2 if the anchor is even to // achieve the opposite effect. // The condition can be simplified to checking whether anchor + shape is // odd (-2) or even (-1) as only adding an odd and an even number will have // an odd result. _l2_load_offset = -border_size().left; if((_input->info()->valid_region().anchor[0] + _input->info()->valid_region().shape[0]) % 2 == 0) { _l2_load_offset += 1; } // Replace input access with static window update_window_and_padding(win, AccessWindowHorizontal(input->info(), _l2_load_offset, num_elems_read_per_iteration), output_access); output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape())); INEKernel::configure(win); } void NEGaussianPyramidHorKernel::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); ARM_COMPUTE_ERROR_ON(window.x().step() % 2); static const int16x8_t six = vdupq_n_s16(6); static const int16x8_t four = vdupq_n_s16(4); Window win_in(window); win_in.shift(Window::DimX, _l2_load_offset); Iterator in(_input, win_in); // The output is half the width of the input Window win_out(window); win_out.scale(Window::DimX, 0.5f); Iterator out(_output, win_out); execute_window_loop(window, [&](const Coordinates &) { const uint8x16x2_t data_2q = vld2q_u8(in.ptr()); const uint8x16_t &data_even = data_2q.val[0]; const uint8x16_t &data_odd = data_2q.val[1]; const int16x8_t data_l2 = vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(data_even))); const int16x8_t data_l1 = vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(data_odd))); const int16x8_t data_m = vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(vextq_u8(data_even, data_even, 1)))); const int16x8_t data_r1 = vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(vextq_u8(data_odd, data_odd, 1)))); const int16x8_t data_r2 = vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(vextq_u8(data_even, data_even, 2)))); int16x8_t out_val = vaddq_s16(data_l2, data_r2); out_val = vmlaq_s16(out_val, data_l1, four); out_val = vmlaq_s16(out_val, data_m, six); out_val = vmlaq_s16(out_val, data_r1, four); vst1q_s16(reinterpret_cast(out.ptr()), out_val); }, in, out); } NEGaussianPyramidVertKernel::NEGaussianPyramidVertKernel() : _t2_load_offset(0) { } BorderSize NEGaussianPyramidVertKernel::border_size() const { return BorderSize{ 2, 0 }; } void NEGaussianPyramidVertKernel::configure(const ITensor *input, ITensor *output) { ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S16); ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8); ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != output->info()->dimension(0)); for(size_t i = 2; i < Coordinates::num_max_dimensions; ++i) { ARM_COMPUTE_ERROR_ON(input->info()->dimension(i) != output->info()->dimension(i)); } _input = input; _output = output; // Configure kernel window constexpr unsigned int num_elems_processed_per_iteration = 16; constexpr unsigned int num_rows_processed_per_iteration = 2; constexpr unsigned int num_elems_written_per_iteration = 16; constexpr unsigned int num_rows_written_per_iteration = 1; constexpr unsigned int num_elems_read_per_iteration = 16; constexpr unsigned int num_rows_read_per_iteration = 5; const float scale_y = static_cast(output->info()->dimension(1)) / input->info()->dimension(1); Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration, num_rows_processed_per_iteration)); AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_written_per_iteration, num_rows_written_per_iteration, 1.f, scale_y); // Determine whether we need to load even or odd rows. See above for a // detailed explanation. _t2_load_offset = -border_size().top; if((_input->info()->valid_region().anchor[1] + _input->info()->valid_region().shape[1]) % 2 == 0) { _t2_load_offset += 1; } update_window_and_padding(win, AccessWindowRectangle(input->info(), 0, _t2_load_offset, num_elems_read_per_iteration, num_rows_read_per_iteration), output_access); output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape())); INEKernel::configure(win); } void NEGaussianPyramidVertKernel::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); ARM_COMPUTE_ERROR_ON(window.x().step() != 16); ARM_COMPUTE_ERROR_ON(window.y().step() % 2); ARM_COMPUTE_ERROR_ON(_input->buffer() == nullptr); static const uint16x8_t six = vdupq_n_u16(6); static const uint16x8_t four = vdupq_n_u16(4); Window win_in(window); // Need to load two times 8 values instead of 16 values once win_in.set_dimension_step(Window::DimX, 8); win_in.shift(Window::DimY, _t2_load_offset); Iterator in(_input, win_in); // Output's height is half of input's Window win_out(window); win_out.scale(Window::DimY, 0.5f); Iterator out(_output, win_out); const uint8_t *input_top2_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(0, 0)); const uint8_t *input_top_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(0, 1)); const uint8_t *input_mid_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(0, 2)); const uint8_t *input_low_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(0, 3)); const uint8_t *input_low2_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(0, 4)); execute_window_loop(window, [&](const Coordinates &) { // Low data const uint16x8_t data_low_t2 = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast(input_top2_ptr + in.offset()))); const uint16x8_t data_low_t1 = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast(input_top_ptr + in.offset()))); const uint16x8_t data_low_m = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast(input_mid_ptr + in.offset()))); const uint16x8_t data_low_b1 = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast(input_low_ptr + in.offset()))); const uint16x8_t data_low_b2 = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast(input_low2_ptr + in.offset()))); uint16x8_t out_low = vaddq_u16(data_low_t2, data_low_b2); out_low = vmlaq_u16(out_low, data_low_t1, four); out_low = vmlaq_u16(out_low, data_low_m, six); out_low = vmlaq_u16(out_low, data_low_b1, four); in.increment(Window::DimX); // High data const uint16x8_t data_high_t2 = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast(input_top2_ptr + in.offset()))); const uint16x8_t data_high_t1 = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast(input_top_ptr + in.offset()))); const uint16x8_t data_high_m = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast(input_mid_ptr + in.offset()))); const uint16x8_t data_high_b1 = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast(input_low_ptr + in.offset()))); const uint16x8_t data_high_b2 = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast(input_low2_ptr + in.offset()))); uint16x8_t out_high = vaddq_u16(data_high_t2, data_high_b2); out_high = vmlaq_u16(out_high, data_high_t1, four); out_high = vmlaq_u16(out_high, data_high_m, six); out_high = vmlaq_u16(out_high, data_high_b1, four); vst1q_u8(out.ptr(), vcombine_u8(vqshrn_n_u16(out_low, 8), vqshrn_n_u16(out_high, 8))); }, in, out); }