| /* |
| * Copyright (c) 2019-2022 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 "src/core/NEON/kernels/NECropKernel.h" |
| |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Window.h" |
| #include "arm_compute/core/utils/helpers/tensor_transform.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "src/core/CPP/Validate.h" |
| #include "src/core/NEON/wrapper/wrapper.h" |
| #include "src/core/common/Registrars.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| #include "src/core/utils/helpers/bit_ops.h" |
| #include "src/cpu/kernels/crop/list.h" |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| struct CropSelectorData |
| { |
| DataType dt; |
| }; |
| |
| using CropSelectorPtr = std::add_pointer<bool(const CropSelectorData &data)>::type; |
| using CropUKernelPtr = std::add_pointer<void(const ITensor *, const ITensor *, float *, Coordinates, int32_t, int32_t, int32_t, bool, bool)>::type; |
| |
| struct CropUKernel |
| { |
| const char *name; |
| const CropSelectorPtr is_selected; |
| CropUKernelPtr ukernel; |
| }; |
| |
| static const CropUKernel available_kernels[] = |
| { |
| { |
| "fp16_neon_crop", |
| [](const CropSelectorData & data) { return data.dt == DataType::F16; }, |
| REGISTER_FP16_NEON(arm_compute::cpu::fp16_in_bounds_crop_window) |
| }, |
| { |
| "f32_neon_crop", |
| [](const CropSelectorData & data) { return data.dt == DataType::F32; }, |
| REGISTER_FP32_NEON(arm_compute::cpu::fp32_in_bounds_crop_window) |
| }, |
| { |
| "u8_neon_crop", |
| [](const CropSelectorData & data) { return data.dt == DataType::U8; }, |
| REGISTER_INTEGER_NEON(arm_compute::cpu::u8_in_bounds_crop_window) |
| }, |
| { |
| "u16_neon_crop", |
| [](const CropSelectorData & data) { return data.dt == DataType::U16; }, |
| REGISTER_INTEGER_NEON(arm_compute::cpu::u16_in_bounds_crop_window) |
| }, |
| { |
| "u32_neon_crop", |
| [](const CropSelectorData & data) { return data.dt == DataType::U32; }, |
| REGISTER_INTEGER_NEON(arm_compute::cpu::u32_in_bounds_crop_window) |
| }, |
| { |
| "s8_neon_crop", |
| [](const CropSelectorData & data) { return data.dt == DataType::S8; }, |
| REGISTER_INTEGER_NEON(arm_compute::cpu::s8_in_bounds_crop_window) |
| }, |
| { |
| "s16_neon_crop", |
| [](const CropSelectorData & data) { return data.dt == DataType::S16; }, |
| REGISTER_INTEGER_NEON(arm_compute::cpu::s16_in_bounds_crop_window) |
| }, |
| { |
| "s32_neon_crop", |
| [](const CropSelectorData & data) { return data.dt == DataType::S32; }, |
| REGISTER_INTEGER_NEON(arm_compute::cpu::s32_in_bounds_crop_window) |
| }, |
| }; |
| |
| /** Micro-kernel selector |
| * |
| * @param[in] data Selection data passed to help pick the appropriate micro-kernel |
| * |
| * @return A matching micro-kernel else nullptr |
| */ |
| const CropUKernel *get_implementation(const CropSelectorData &data) |
| { |
| for(const auto &uk : available_kernels) |
| { |
| if(uk.is_selected(data)) |
| { |
| return &uk; |
| } |
| } |
| |
| return nullptr; |
| } |
| |
| inline void out_of_bounds_crop_window(const ITensor *output, float *output_ptr, float extrapolation_value, |
| int32_t window_step_x, int32_t output_width_start, int32_t output_width_limit) |
| { |
| auto in = wrapper::vdup_n(extrapolation_value, wrapper::traits::vector_128_tag()); |
| int32_t x = 0; |
| int32_t limit = (output_width_limit - output_width_start) * static_cast<int32_t>(output->info()->dimension(0)); |
| float *output_start_ptr = output_ptr + output_width_start * output->info()->dimension(0); |
| for(; x <= limit - window_step_x; x += window_step_x) |
| { |
| wrapper::vstore(output_start_ptr + x, in); |
| } |
| for(; x < limit; ++x) |
| { |
| *(output_start_ptr + x) = extrapolation_value; |
| } |
| } |
| |
| inline void execute_window(const ITensor *input, const ITensor *output, Coordinates input_offset, float extrapolation_value, |
| const std::array<uint32_t, 2> &rows_out_of_bounds, const std::array<uint32_t, 2> &cols_out_of_bounds, NECropKernel::InBoundsCropFunction *in_bounds_crop_function, |
| bool is_height_flipped, bool has_cols_in_bounds, bool has_cols_out_of_bounds_before, bool has_cols_out_of_bounds_after, bool input_has_single_channel, bool is_width_flipped) |
| { |
| // Output is always float. |
| const int window_step_x = 16 / sizeof(float); |
| auto *output_ptr = reinterpret_cast<float *>(output->buffer()); |
| // Output window: |
| // -------------------------------- |
| // | Out of bounds | |
| // | rows before | |
| // |------------------------------| |
| // | Out of | In | Out of | |
| // | bounds | bounds | bounds | |
| // | cols | elements | cols | |
| // | before | copied | after | |
| // | | from input | | |
| // -------------------------------- |
| // | Out of bounds | |
| // | rows after | |
| // |------------------------------| |
| // Fill all output rows that have no elements that are within the input bounds with the extrapolation value. |
| // First for the rows before the in bounds rows. |
| out_of_bounds_crop_window(output, output_ptr, extrapolation_value, window_step_x, 0, rows_out_of_bounds[0] * output->info()->dimension(1)); |
| output_ptr += rows_out_of_bounds[0] * output->info()->dimension(1) * output->info()->dimension(0); |
| // Iterate through each row that has any elements within the input bounds. |
| for(uint32_t row = rows_out_of_bounds[0]; static_cast<int32_t>(row) < static_cast<int32_t>(output->info()->dimension(2) - rows_out_of_bounds[1]); |
| ++row, is_height_flipped ? --input_offset[2] : ++input_offset[2]) |
| { |
| // Fill all elements in the row that are out of bounds with the extrapolation value. |
| // First for the elements before the in bounds elements. |
| if(has_cols_out_of_bounds_before) |
| { |
| out_of_bounds_crop_window(output, output_ptr, extrapolation_value, window_step_x, 0, cols_out_of_bounds[0]); |
| } |
| // Copy all elements within the input bounds from the input tensor. |
| if(has_cols_in_bounds) |
| { |
| (*in_bounds_crop_function)(input, output, output_ptr, input_offset, window_step_x, cols_out_of_bounds[0], |
| output->info()->dimension(1) - cols_out_of_bounds[1], input_has_single_channel, is_width_flipped); |
| } |
| // Fill all elements after the in bounds elements with the extrapolation value. |
| if(has_cols_out_of_bounds_after) |
| { |
| out_of_bounds_crop_window(output, output_ptr, extrapolation_value, window_step_x, output->info()->dimension(1) - cols_out_of_bounds[1], output->info()->dimension(1)); |
| } |
| output_ptr += output->info()->dimension(1) * output->info()->dimension(0); |
| } |
| // Fill all rows after the in bounds elements with the extrapolation value. |
| out_of_bounds_crop_window(output, output_ptr, extrapolation_value, window_step_x, 0, rows_out_of_bounds[1] * output->info()->dimension(1)); |
| } |
| } // namespace |
| |
| NECropKernel::NECropKernel() |
| : _input(nullptr), _crop_boxes(nullptr), _box_ind(nullptr), _output(nullptr), _start(), _end(), _crop_box_ind(0), _extrapolation_value(0), _rows_out_of_bounds(), _cols_out_of_bounds() |
| { |
| } |
| |
| void NECropKernel::configure(const ITensor *input, const ITensor *crop_boxes, const ITensor *box_ind, ITensor *output, uint32_t crop_box_ind, float extrapolation_value) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| ARM_COMPUTE_ERROR_THROW_ON(validate(input->info(), crop_boxes->info(), box_ind->info(), output->info(), crop_box_ind, extrapolation_value)); |
| |
| _input = input; |
| _crop_boxes = crop_boxes; |
| _box_ind = box_ind; |
| _output = output; |
| _crop_box_ind = crop_box_ind; |
| _extrapolation_value = extrapolation_value; |
| } |
| |
| Status NECropKernel::validate(const ITensorInfo *input, const ITensorInfo *crop_boxes, const ITensorInfo *box_ind, const ITensorInfo *output, uint32_t crop_box_ind, float extrapolation_value) |
| { |
| ARM_COMPUTE_UNUSED(extrapolation_value); |
| const auto *uk = get_implementation(CropSelectorData{ input->data_type() }); |
| ARM_COMPUTE_RETURN_ERROR_ON(uk == nullptr || uk->ukernel == nullptr); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::U16, DataType::S16, DataType::F16, DataType::U32, DataType::S32, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(input, DataLayout::NHWC); |
| ARM_COMPUTE_RETURN_ERROR_ON(input->tensor_shape().num_dimensions() > 4); |
| ARM_COMPUTE_RETURN_ERROR_ON(crop_boxes->tensor_shape()[0] != 4); |
| ARM_COMPUTE_RETURN_ERROR_ON(crop_boxes->tensor_shape()[1] != box_ind->tensor_shape()[0]); |
| ARM_COMPUTE_RETURN_ERROR_ON(crop_boxes->tensor_shape()[1] <= crop_box_ind); |
| ARM_COMPUTE_RETURN_ERROR_ON(box_ind->tensor_shape()[0] <= crop_box_ind); |
| if(output->total_size() > 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(output, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON(output->num_dimensions() != 3); |
| ARM_COMPUTE_RETURN_ERROR_ON(output->has_padding()); |
| } |
| return Status{}; |
| } |
| |
| void NECropKernel::configure_output_shape() |
| { |
| // _crop_box_ind is used to index _crop_boxes and retrieve the appropriate crop box. |
| // The crop box is specified by normalized coordinates [y0, x0, y1, x1]. |
| const float x0 = *reinterpret_cast<const float *>(_crop_boxes->ptr_to_element(Coordinates(1, _crop_box_ind))); |
| const float y0 = *reinterpret_cast<const float *>(_crop_boxes->ptr_to_element(Coordinates(0, _crop_box_ind))); |
| const float x1 = *reinterpret_cast<const float *>(_crop_boxes->ptr_to_element(Coordinates(3, _crop_box_ind))); |
| const float y1 = *reinterpret_cast<const float *>(_crop_boxes->ptr_to_element(Coordinates(2, _crop_box_ind))); |
| // The normalized coordiantes are scaled to retrieve the floating point image coordinates which are rounded to integers. |
| _start = Coordinates(std::floor(x0 * (_input->info()->tensor_shape()[1] - 1) + 0.5f), |
| std::floor(y0 * (_input->info()->tensor_shape()[2] - 1) + 0.5f)); |
| _end = Coordinates(std::floor(x1 * (_input->info()->tensor_shape()[1] - 1) + 0.5f), |
| std::floor(y1 * (_input->info()->tensor_shape()[2] - 1) + 0.5f)); |
| const TensorShape out_shape(_input->info()->tensor_shape()[0], abs(_end[0] - _start[0]) + 1, abs(_end[1] - _start[1]) + 1); |
| _output->info()->set_tensor_shape(out_shape); |
| |
| bool is_width_flipped = _end[0] < _start[0]; |
| bool is_height_flipped = _end[1] < _start[1]; |
| if(is_height_flipped) |
| { |
| _rows_out_of_bounds[0] = _start[1] >= static_cast<int32_t>(_input->info()->dimension(2)) ? std::min(static_cast<uint32_t>(_start[1] - _input->info()->dimension(2) + 1), |
| static_cast<uint32_t>(_output->info()->dimension(2))) : |
| 0; |
| _rows_out_of_bounds[1] = _end[1] < 0 ? std::min(static_cast<uint32_t>(-_end[1]), |
| static_cast<uint32_t>(_output->info()->dimension(2))) : |
| 0; |
| } |
| else |
| { |
| _rows_out_of_bounds[0] = _start[1] < 0 ? std::min(static_cast<uint32_t>(-_start[1]), |
| static_cast<uint32_t>(_output->info()->dimension(2))) : |
| 0; |
| _rows_out_of_bounds[1] = _end[1] >= static_cast<int32_t>(_input->info()->dimension(2)) ? std::min(static_cast<uint32_t>(_end[1] - _input->info()->dimension(2) + 1), |
| static_cast<uint32_t>(_output->info()->dimension(2))) : |
| 0; |
| } |
| if(is_width_flipped) |
| { |
| _cols_out_of_bounds[0] = _start[0] >= static_cast<int32_t>(_input->info()->dimension(1)) ? std::min(static_cast<uint32_t>(_start[0] - _input->info()->dimension(1) + 1), |
| static_cast<uint32_t>(_output->info()->dimension(1))) : |
| 0; |
| _cols_out_of_bounds[1] = _end[0] < 0 ? std::min(static_cast<uint32_t>(-_end[0]), |
| static_cast<uint32_t>(_output->info()->dimension(1))) : |
| 0; |
| } |
| else |
| { |
| _cols_out_of_bounds[0] = _start[0] < 0 ? std::min(static_cast<uint32_t>(-_start[0]), |
| static_cast<uint32_t>(_output->info()->dimension(1))) : |
| 0; |
| _cols_out_of_bounds[1] = _end[0] >= static_cast<int32_t>(_input->info()->dimension(1)) ? std::min(static_cast<uint32_t>(_end[0] - _input->info()->dimension(1) + 1), |
| static_cast<uint32_t>(_output->info()->dimension(1))) : |
| 0; |
| } |
| |
| INEKernel::configure(calculate_max_window(*_output->info())); |
| } |
| |
| void NECropKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(window, info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| |
| ARM_COMPUTE_ERROR_ON(_input->info()->has_padding()); |
| ARM_COMPUTE_ERROR_ON(_output->info()->has_padding()); |
| |
| const auto *uk = get_implementation(CropSelectorData{ _input->info()->data_type() }); |
| |
| uint32_t batch_index = *(reinterpret_cast<int32_t *>(_box_ind->ptr_to_element(Coordinates(_crop_box_ind)))); |
| Coordinates input_offset(0, _end[0] < _start[0] ? _start[0] - _cols_out_of_bounds[0] : _start[0] + _cols_out_of_bounds[0], |
| _end[1] < _start[1] ? _start[1] - _rows_out_of_bounds[0] : _start[1] + _rows_out_of_bounds[0], batch_index); |
| execute_window(_input, _output, input_offset, _extrapolation_value, _rows_out_of_bounds, _cols_out_of_bounds, uk->ukernel, _end[1] < _start[1], |
| _cols_out_of_bounds[0] + _cols_out_of_bounds[1] < _output->info()->dimension(1), _cols_out_of_bounds[0] > 0, _cols_out_of_bounds[1] > 0, |
| _start[0] <= _end[0], _end[0] < _start[0]); |
| } |
| } // namespace arm_compute |