| /* |
| * Copyright (c) 2017 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/NEWeightsReshapeKernel.h" |
| |
| #include "arm_compute/core/Dimensions.h" |
| #include "arm_compute/core/Error.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" |
| |
| using namespace arm_compute; |
| |
| namespace |
| { |
| template <typename T> |
| void weights_reshape(const ITensor *input, const ITensor *bias, ITensor *output, const Window &window) |
| { |
| const unsigned int kernel_size_x = input->info()->dimension(0); |
| const unsigned int kernel_size_y = input->info()->dimension(1); |
| const unsigned int kernel_depth = input->info()->dimension(2); |
| const unsigned int input_stride_x = input->info()->strides_in_bytes().x(); |
| const unsigned int input_stride_y = input->info()->strides_in_bytes().y(); |
| const unsigned int input_stride_z = input->info()->strides_in_bytes().z(); |
| const unsigned int output_stride_y = output->info()->strides_in_bytes().y(); |
| |
| // Create iterators |
| Iterator in(input, window); |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| // Get column index |
| const int kernel_idx = id[3]; |
| const int kernel_idz = id[4]; |
| |
| // Setup pointers |
| const uint8_t *tmp_input_ptr = in.ptr(); |
| uint8_t *tmp_output_ptr = output->ptr_to_element(Coordinates(kernel_idx, 0, kernel_idz)); |
| const uint8_t *curr_input_row_ptr = tmp_input_ptr; |
| const uint8_t *curr_input_depth_ptr = tmp_input_ptr; |
| |
| // Linearize volume |
| for(unsigned int d = 0; d < kernel_depth; ++d) |
| { |
| for(unsigned int j = 0; j < kernel_size_y; ++j) |
| { |
| for(unsigned int i = 0; i < kernel_size_x; ++i) |
| { |
| *(reinterpret_cast<T *>(tmp_output_ptr)) = *(reinterpret_cast<const T *>(tmp_input_ptr)); |
| tmp_input_ptr += input_stride_x; |
| tmp_output_ptr += output_stride_y; |
| } |
| curr_input_row_ptr += input_stride_y; |
| tmp_input_ptr = curr_input_row_ptr; |
| } |
| curr_input_depth_ptr += input_stride_z; |
| curr_input_row_ptr = curr_input_depth_ptr; |
| tmp_input_ptr = curr_input_depth_ptr; |
| } |
| |
| // Add bias |
| if(bias != nullptr) |
| { |
| *(reinterpret_cast<T *>(tmp_output_ptr)) = *(reinterpret_cast<const T *>(bias->ptr_to_element(Coordinates(kernel_idx, kernel_idz)))); |
| } |
| }, |
| in); |
| } |
| } // namespace |
| |
| NEWeightsReshapeKernel::NEWeightsReshapeKernel() |
| : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr) |
| { |
| } |
| |
| void NEWeightsReshapeKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(output); |
| |
| const int fixed_point_position = input->info()->fixed_point_position(); |
| const DataType dt = input->info()->data_type(); |
| const TensorShape &input_shape = input->info()->tensor_shape(); |
| TensorShape output_shape{ input_shape }; |
| output_shape.collapse(3); |
| |
| const size_t tmp_dim = output_shape[0]; |
| output_shape.set(0, output_shape[1]); |
| output_shape.set(1, tmp_dim + (bias != nullptr ? 1 : 0)); |
| |
| // Output tensor auto inizialitation if not yet initialized |
| auto_init_if_empty(*output->info(), output_shape, 1, dt, fixed_point_position); |
| |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output); |
| |
| if(bias != nullptr) |
| { |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, bias); |
| ARM_COMPUTE_ERROR_ON((input->info()->num_dimensions() == 4) && (bias->info()->num_dimensions() != 1)); |
| ARM_COMPUTE_ERROR_ON((input->info()->num_dimensions() == 5) && (bias->info()->num_dimensions() != 2)); |
| ARM_COMPUTE_ERROR_ON((input->info()->num_dimensions() == 4) && (bias->info()->dimension(0) != input->info()->tensor_shape()[3])); |
| ARM_COMPUTE_ERROR_ON((input->info()->num_dimensions() == 5) && (bias->info()->dimension(0) != input->info()->tensor_shape()[3] || bias->info()->dimension(1) != input->info()->tensor_shape()[4])); |
| } |
| |
| _input = input; |
| _bias = bias; |
| _output = output; |
| |
| switch(_input->info()->element_size()) |
| { |
| case 4: |
| { |
| _func = &weights_reshape<uint32_t>; |
| break; |
| } |
| case 2: |
| { |
| _func = &weights_reshape<uint16_t>; |
| break; |
| } |
| case 1: |
| { |
| _func = &weights_reshape<uint8_t>; |
| break; |
| } |
| default: |
| { |
| ARM_COMPUTE_ERROR_ON("Element size not supported"); |
| break; |
| } |
| } |
| |
| // Configure kernel |
| Window window = calculate_max_window(*input->info(), Steps()); |
| window.set(Window::DimX, Window::Dimension(0, _input->info()->dimension(0), _input->info()->dimension(0))); |
| window.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), _input->info()->dimension(1))); |
| window.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), _input->info()->dimension(2))); |
| |
| // The NEConvolutionLayerWeightsReshapeKernel doesn't need padding so update_window_and_padding() can be skipped |
| output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape())); |
| |
| INEKernel::configure(window); |
| } |
| |
| void NEWeightsReshapeKernel::run(const Window &window) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| |
| (*_func)(_input, _bias, _output, window); |
| } |