| /* |
| * Copyright (c) 2017-2018 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/NEIm2ColKernel.h" |
| |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/FixedPoint.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/Size2D.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Validate.h" |
| |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| |
| #include <arm_neon.h> |
| #include <cstddef> |
| #include <cstdint> |
| #include <cstring> |
| #include <tuple> |
| |
| using namespace arm_compute; |
| |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, |
| bool has_bias, bool is_fully_connected, bool is_flatten, const Size2D &dilation) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() == DataType::QASYMM8 && has_bias); |
| ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1)); |
| |
| TensorShape expected_output_shape; |
| if(is_flatten) /* Called by FlattenLayer */ |
| { |
| expected_output_shape = misc::shape_calculator::compute_im2col_flatten_shape(input); |
| } |
| else if(!is_fully_connected) /* Called by ConvolutionLayer */ |
| { |
| expected_output_shape = misc::shape_calculator::compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation); |
| } |
| else /* Called by FullyConnectedLayer */ |
| { |
| const int num_batch_dimensions = std::max(0, static_cast<int>(output->tensor_shape().num_dimensions()) - 1); |
| const int num_input_dimensions = input->tensor_shape().num_dimensions() - num_batch_dimensions; |
| |
| expected_output_shape = misc::shape_calculator::compute_im2col_fc_shape(input, num_input_dimensions); |
| } |
| |
| TensorInfo expected_output = output->clone()->set_tensor_shape(expected_output_shape); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&expected_output, output); |
| |
| return Status{}; |
| } |
| |
| template <typename T, bool has_pads> |
| inline void linearize_volume(const uint8_t *const in_ptr, |
| T *out_ptr, |
| bool has_bias, |
| int top_left_x, |
| int top_left_y, |
| int kernel_width, |
| int kernel_height, |
| int kernel_depth, |
| int input_w, |
| int input_h, |
| int input_stride_x, |
| int input_stride_y, |
| int input_stride_z, |
| int fixed_point_position, |
| int pad_value, |
| int dilation_x, |
| int dilation_y) |
| { |
| const int kernel_size2 = kernel_width * kernel_height; |
| const int x_e = top_left_x + kernel_width * dilation_x; |
| const int y_e = top_left_y + kernel_height * dilation_y; |
| |
| // Linearize volume |
| int d = 0; |
| // This for loop linearize a volume with 3 slices. This allows: |
| // 1) to reduce the iterations of the outer for loop "d" |
| // 2) to have an optimized im2col for the first convolution layer where usually we have 3 IFMs |
| for(; d <= (kernel_depth - 3); d += 3) |
| { |
| for(int y = top_left_y; y < y_e; y += dilation_y) |
| { |
| if((y < 0 || y >= input_h) && has_pads) |
| { |
| // All the values will be the offset (will be zeros when not quantized) |
| for(int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr) |
| { |
| *(out_ptr + 0 * kernel_size2) = pad_value; |
| *(out_ptr + 1 * kernel_size2) = pad_value; |
| *(out_ptr + 2 * kernel_size2) = pad_value; |
| } |
| } |
| else |
| { |
| for(int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr) |
| { |
| if((x < 0 || x >= input_w) && has_pads) |
| { |
| *(out_ptr + 0 * kernel_size2) = pad_value; |
| *(out_ptr + 1 * kernel_size2) = pad_value; |
| *(out_ptr + 2 * kernel_size2) = pad_value; |
| } |
| else |
| { |
| *(out_ptr + 0 * kernel_size2) = *(reinterpret_cast<const T *>(in_ptr + ((d + 0) * input_stride_z + y * input_stride_y + x * input_stride_x))); |
| *(out_ptr + 1 * kernel_size2) = *(reinterpret_cast<const T *>(in_ptr + ((d + 1) * input_stride_z + y * input_stride_y + x * input_stride_x))); |
| *(out_ptr + 2 * kernel_size2) = *(reinterpret_cast<const T *>(in_ptr + ((d + 2) * input_stride_z + y * input_stride_y + x * input_stride_x))); |
| } |
| } |
| } |
| } |
| out_ptr += 2 * kernel_size2; |
| } |
| |
| // Left over |
| for(; d < kernel_depth; d++) |
| { |
| for(int y = top_left_y; y < y_e; y += dilation_y) |
| { |
| if((y < 0 || y >= input_h) && has_pads) |
| { |
| // All the values will be the offset (will be zeros when not quantized) |
| memset(out_ptr, pad_value, kernel_width * sizeof(T)); |
| out_ptr += kernel_width; |
| } |
| else |
| { |
| for(int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr) |
| { |
| if((x < 0 || x >= input_w) && has_pads) |
| { |
| *out_ptr = pad_value; |
| } |
| else |
| { |
| *out_ptr = *(reinterpret_cast<const T *>(in_ptr + (d * input_stride_z + y * input_stride_y + x * input_stride_x))); |
| } |
| } |
| } |
| } |
| } |
| |
| // Append 1 if the convolution layer has biases |
| if(has_bias) |
| { |
| if(std::is_same<T, qint8_t>::value) |
| { |
| *out_ptr = sqcvt_qs8_f32(1.0f, fixed_point_position); |
| } |
| else if(std::is_same<T, qint16_t>::value) |
| { |
| *out_ptr = sqcvt_qs16_f32(1.0f, fixed_point_position); |
| } |
| else |
| { |
| *out_ptr = static_cast<T>(1); |
| } |
| } |
| } |
| } // namespace |
| |
| template <typename T, bool has_pads> |
| void NEIm2ColKernel::run_generic(const Window &window) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| |
| const DataLayout data_layout = _input->info()->data_layout(); |
| const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); |
| const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); |
| const unsigned int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); |
| |
| const int kernel_depth = _input->info()->dimension(channel_idx); |
| const int input_w = _input->info()->dimension(width_idx); |
| const int input_h = _input->info()->dimension(height_idx); |
| const int input_stride_x = _input->info()->strides_in_bytes()[width_idx]; |
| const int input_stride_y = _input->info()->strides_in_bytes()[height_idx]; |
| const int input_stride_z = _input->info()->strides_in_bytes()[channel_idx]; |
| const int offset = is_data_type_quantized(_input->info()->data_type()) ? _input->info()->quantization_info().offset : 0; |
| |
| int pad_left = 0; |
| int pad_top = 0; |
| int stride_x = 0; |
| int stride_y = 0; |
| pad_left = _conv_info.pad_left(); |
| pad_top = _conv_info.pad_top(); |
| std::tie(stride_x, stride_y) = _conv_info.stride(); |
| |
| // Setup input window |
| const int start_x = -pad_left; |
| const int start_y = -pad_top; |
| |
| Window window_in(window); |
| // The first three dimensions of the input are increased by the inner loops |
| window_in.set(Window::DimX, Window::Dimension(0, 0, 0)); |
| window_in.set(Window::DimY, Window::Dimension(0, 0, 0)); |
| window_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); |
| |
| // Setup output window |
| Window window_out(window); |
| window_out.set(width_idx, Window::Dimension(0, _output->info()->dimension(width_idx), _output->info()->strides_in_bytes()[width_idx + 1] / _output->info()->strides_in_bytes()[width_idx])); |
| window_out.set(height_idx, Window::Dimension(window[height_idx].start() * _convolved_dims.first, window[height_idx].end() * _convolved_dims.first, _convolved_dims.first)); |
| window_out.set(channel_idx, Window::Dimension(0, 1, 1)); |
| |
| // Create iterators |
| Iterator in(_input, window_in); |
| Iterator out(_output, window_out); |
| |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| const int top_left_x = id[width_idx] * stride_x + start_x; |
| const int top_left_y = id[height_idx] * stride_y + start_y; |
| |
| // Get pointers |
| const uint8_t *const input_ptr = in.ptr(); |
| auto output_ptr = reinterpret_cast<T *>(out.ptr()); |
| |
| // Linearize volume |
| linearize_volume<T, has_pads>(input_ptr, |
| output_ptr, |
| _has_bias, |
| top_left_x, |
| top_left_y, |
| static_cast<int>(_kernel_width), |
| static_cast<int>(_kernel_height), |
| kernel_depth, |
| input_w, |
| input_h, |
| input_stride_x, |
| input_stride_y, |
| input_stride_z, |
| _input->info()->fixed_point_position(), |
| offset, |
| _dilation.x(), |
| _dilation.y()); |
| }, |
| in, out); |
| } |
| |
| template <typename T> |
| void NEIm2ColKernel::run_reduced(const Window &window) |
| { |
| const size_t in_width = _input->info()->dimension(0); |
| const size_t in_height = _input->info()->dimension(1); |
| const size_t out_step_x = in_width * _input->info()->element_size(); |
| const size_t out_step_y = out_step_x * in_height; |
| const size_t out_width = _output->info()->dimension(0); |
| |
| Window in_window(window); |
| in_window.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| Window out_window; |
| out_window.use_tensor_dimensions(_output->info()->tensor_shape()); |
| out_window.set(Window::DimX, Window::Dimension(out_window.x().start(), out_window.x().end(), in_width)); |
| |
| Window in_slice = in_window.first_slice_window_3D(); |
| Window out_slice = out_window.first_slice_window_1D(); |
| |
| do |
| { |
| Iterator in(_input, in_slice); |
| Iterator out(_output, out_slice); |
| |
| uint8_t *out_ptr = out.ptr(); |
| |
| execute_window_loop(in_slice, [&](const Coordinates & id) |
| { |
| memcpy(out_ptr + id.y() * out_step_x + id.z() * out_step_y, in.ptr(), out_step_x); |
| }, |
| in); |
| |
| // Add bias |
| if(_has_bias) |
| { |
| if(std::is_same<T, qint8_t>::value) |
| { |
| *(reinterpret_cast<T *>(out_ptr) + out_width - 1) = sqcvt_qs8_f32(1.0f, _input->info()->fixed_point_position()); |
| } |
| else if(std::is_same<T, qint16_t>::value) |
| { |
| *(reinterpret_cast<T *>(out_ptr) + out_width - 1) = sqcvt_qs16_f32(1.0f, _input->info()->fixed_point_position()); |
| } |
| else |
| { |
| *(reinterpret_cast<T *>(out_ptr) + out_width - 1) = static_cast<T>(1); |
| } |
| } |
| } |
| while(in_window.slide_window_slice_3D(in_slice) && out_window.slide_window_slice_1D(out_slice)); |
| } |
| |
| NEIm2ColKernel::NEIm2ColKernel() |
| : _func(), _input(nullptr), _output(nullptr), _convolved_dims(), _conv_info(), _kernel_width(0), _kernel_height(0), _has_bias(false), _dilation(1U, 1U) |
| { |
| } |
| |
| void NEIm2ColKernel::configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, |
| bool has_bias, bool is_fully_connected, bool is_flatten, const Size2D &dilation) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| |
| // Perform validation step |
| ARM_COMPUTE_UNUSED(is_fully_connected, is_flatten); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), kernel_dims, conv_info, has_bias, is_fully_connected, is_flatten, dilation)); |
| |
| const DataLayout data_layout = input->info()->data_layout(); |
| const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); |
| const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); |
| const unsigned int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); |
| |
| _input = input; |
| _output = output; |
| _conv_info = conv_info; |
| _kernel_width = kernel_dims.width; |
| _kernel_height = kernel_dims.height; |
| _dilation = dilation; |
| _convolved_dims = scaled_dimensions(input->info()->dimension(width_idx), input->info()->dimension(height_idx), |
| _kernel_width, _kernel_height, |
| _conv_info, _dilation); |
| _has_bias = has_bias; |
| |
| unsigned int stride_x = 0; |
| unsigned int stride_y = 0; |
| std::tie(stride_x, stride_y) = conv_info.stride(); |
| |
| bool run_img2col_reduced = (output->info()->dimension(0) == (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))) && (TensorShape::num_max_dimensions >= 4) |
| && (std::equal(input->info()->tensor_shape().cbegin() + 3, |
| input->info()->tensor_shape().cend(), |
| output->info()->tensor_shape().cbegin() + 1)) |
| && ((stride_x == 1) && (stride_y == 1) && !conv_info.has_padding()) |
| && ((dilation.x() == 1) && (dilation.y() == 1)); |
| |
| Window window = calculate_max_window(*input->info(), Steps()); |
| |
| if(run_img2col_reduced) |
| { |
| switch(_input->info()->data_type()) |
| { |
| case DataType::F32: |
| _func = &NEIm2ColKernel::run_reduced<float>; |
| break; |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| _func = &NEIm2ColKernel::run_reduced<float16_t>; |
| break; |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| case DataType::QS8: |
| _func = &NEIm2ColKernel::run_reduced<qint8_t>; |
| break; |
| case DataType::QS16: |
| _func = &NEIm2ColKernel::run_reduced<qint16_t>; |
| break; |
| case DataType::QASYMM8: |
| _func = &NEIm2ColKernel::run_reduced<qasymm8_t>; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Data type not supported"); |
| break; |
| } |
| } |
| else |
| { |
| switch(_input->info()->data_type()) |
| { |
| case DataType::F32: |
| _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_generic<float, false> : &NEIm2ColKernel::run_generic<float, true>; |
| break; |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_generic<float16_t, false> : &NEIm2ColKernel::run_generic<float16_t, true>; |
| break; |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| case DataType::QS8: |
| _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_generic<qint8_t, false> : &NEIm2ColKernel::run_generic<qint8_t, true>; |
| break; |
| case DataType::QS16: |
| _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_generic<qint16_t, false> : &NEIm2ColKernel::run_generic<qint16_t, true>; |
| break; |
| case DataType::QASYMM8: |
| _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_generic<qasymm8_t, false> : &NEIm2ColKernel::run_generic<qasymm8_t, true>; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Data type not supported"); |
| break; |
| } |
| window.set(width_idx, Window::Dimension(0, _convolved_dims.first, 1)); |
| window.set(height_idx, Window::Dimension(0, _convolved_dims.second, 1)); |
| window.set(channel_idx, Window::Dimension(0, 1, 1)); |
| } |
| |
| // The NEIm2ColKernel doesn't need padding so update_window_and_padding() can be skipped |
| output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape())); |
| |
| IKernel::configure(window); |
| } |
| |
| Status NEIm2ColKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, |
| bool has_bias, bool is_fully_connected, bool is_flatten, const Size2D &dilation) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, kernel_dims, conv_info, has_bias, is_fully_connected, is_flatten, dilation)); |
| return Status{}; |
| } |
| |
| void NEIm2ColKernel::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); |
| |
| (this->*_func)(window); |
| } |