| /* |
| * Copyright (c) 2017-2019 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/NEIm2ColKernel.h" |
| |
| #include "arm_compute/core/CPP/Validate.h" |
| #include "arm_compute/core/Error.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; |
| using namespace misc::shape_calculator; |
| |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, |
| bool has_bias, const Size2D &dilation, unsigned int num_groups) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() == DataType::QASYMM8 && has_bias); |
| ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1)); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(num_groups > 1, "Number of groups greater than one are not supported on NEON"); |
| |
| if(output->total_size() > 0) |
| { |
| TensorInfo expected_output = output->clone()->set_tensor_shape(compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation, false)); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&expected_output, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output); |
| } |
| |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, |
| bool has_bias, const Size2D &dilation) |
| { |
| const unsigned int width_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); |
| const unsigned int height_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); |
| const unsigned int channel_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL); |
| |
| std::pair<unsigned int, unsigned int> convolved_dims = scaled_dimensions(input->dimension(width_idx), input->dimension(height_idx), |
| kernel_dims.width, kernel_dims.height, |
| conv_info, dilation); |
| |
| // Output tensor auto initialization if not yet initialized |
| auto_init_if_empty(*output, input->clone()->set_tensor_shape(compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation, false))); |
| |
| Window win = calculate_max_window(*input, Steps()); |
| win.set(width_idx, Window::Dimension(0, convolved_dims.first, 1)); |
| win.set(height_idx, Window::Dimension(0, convolved_dims.second, 1)); |
| win.set(channel_idx, Window::Dimension(0, 1, 1)); |
| |
| // The NEIm2ColKernel doesn't need padding so update_window_and_padding() can be skipped |
| output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape())); |
| |
| return std::make_pair(Status{}, win); |
| } |
| |
| template <typename T, bool has_pads> |
| inline void linearize_volume_nchw(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 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) |
| { |
| *out_ptr = static_cast<T>(1); |
| } |
| } |
| |
| template <typename T, bool has_pads> |
| inline void linearize_volume_nhwc(const uint8_t *const in_ptr, |
| T *out_ptr, |
| bool has_bias, |
| int start_x, |
| int start_y, |
| int kernel_width, |
| int kernel_height, |
| int input_w, |
| int input_h, |
| int input_c, |
| int input_stride_y, |
| int input_stride_z, |
| int pad_value, |
| int dilation_x, |
| int dilation_y) |
| { |
| const int end_x = start_x + kernel_width * dilation_x; |
| const int end_y = start_y + kernel_height * dilation_y; |
| const int pad_quant = kernel_width * input_c; |
| const int element_size = static_cast<int>(sizeof(T)); |
| if((start_y >= 0) && (end_y < input_h) && (start_x >= 0) && (end_x < input_w) && (dilation_x == 1) && (input_stride_y == input_c * element_size)) |
| { |
| for(int y = start_y; y < end_y; y += dilation_y) |
| { |
| //optimized for no dilation and no boundary pixels |
| memcpy(out_ptr, reinterpret_cast<const T *>(in_ptr + (y * input_stride_z + start_x * input_stride_y)), input_c * kernel_width * element_size); |
| out_ptr += input_c * kernel_width; |
| } |
| } |
| else |
| { |
| for(int y = start_y; y < end_y; y += dilation_y) |
| { |
| if(y < 0 || y >= input_h) |
| { |
| memset(out_ptr, pad_value, pad_quant * element_size); |
| out_ptr += pad_quant; |
| } |
| else if(dilation_x > 1 || start_x < 0 || end_x >= input_w || input_stride_y != input_c * element_size) |
| { |
| for(int x = start_x; x < end_x; x += dilation_x) |
| { |
| if(x < 0 || x >= input_w) |
| { |
| memset(out_ptr, pad_value, input_c * element_size); |
| out_ptr += input_c; |
| } |
| else |
| { |
| memcpy(out_ptr, reinterpret_cast<const T *>(in_ptr + (y * input_stride_z + x * input_stride_y)), input_c * element_size); |
| out_ptr += input_c; |
| } |
| } |
| } |
| else |
| { |
| //optimized for no dilation and no boundary pixels |
| memcpy(out_ptr, reinterpret_cast<const T *>(in_ptr + (y * input_stride_z + start_x * input_stride_y)), input_c * kernel_width * element_size); |
| out_ptr += input_c * kernel_width; |
| } |
| } |
| } |
| // Append 1 if the convolution layer has biases |
| if(has_bias) |
| { |
| *out_ptr = static_cast<T>(1); |
| } |
| } |
| } // namespace |
| |
| template <typename T, bool has_pads, bool is_nchw> |
| void NEIm2ColKernel::run_im2col(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 input_w = _input->info()->dimension(width_idx); |
| const int input_h = _input->info()->dimension(height_idx); |
| const int input_c = _input->info()->dimension(channel_idx); |
| const int input_stride_x = _input->info()->strides_in_bytes().x(); |
| const int input_stride_y = _input->info()->strides_in_bytes().y(); |
| const int input_stride_z = _input->info()->strides_in_bytes().z(); |
| const int pad_left = _conv_info.pad_left(); |
| const int pad_top = _conv_info.pad_top(); |
| const int stride_x = _conv_info.stride().first; |
| const int stride_y = _conv_info.stride().second; |
| const int pad_value = is_data_type_quantized(_input->info()->data_type()) ? _input->info()->quantization_info().uniform().offset : 0; |
| |
| Window window_in_out(window); |
| // The first three dimensions of the input and output are increased by the inner loops |
| window_in_out.set(Window::DimX, Window::Dimension(0, 0, 0)); |
| window_in_out.set(Window::DimY, Window::Dimension(0, 0, 0)); |
| window_in_out.set(Window::DimZ, Window::Dimension(0, 0, 0)); |
| |
| // Create iterators |
| Iterator in(_input, window_in_out); |
| Iterator out(_output, window_in_out); |
| |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| const int start_w = id[width_idx] * stride_x - pad_left; |
| const int start_h = id[height_idx] * stride_y - pad_top; |
| |
| // Get pointers |
| const uint8_t *const input_ptr = in.ptr(); |
| auto output_ptr = reinterpret_cast<T *>(out.ptr() + (id[width_idx] + id[height_idx] * _convolved_dims.first) * _output->info()->strides_in_bytes().y()); |
| |
| // Linearize volume |
| if(is_nchw) |
| { |
| linearize_volume_nchw<T, has_pads>(input_ptr, |
| output_ptr, |
| _has_bias, |
| start_w, |
| start_h, |
| _kernel_width, |
| _kernel_height, |
| input_c, |
| input_w, |
| input_h, |
| input_stride_x, |
| input_stride_y, |
| input_stride_z, |
| pad_value, |
| _dilation.x(), |
| _dilation.y()); |
| } |
| else |
| { |
| linearize_volume_nhwc<T, has_pads>(input_ptr, |
| output_ptr, |
| _has_bias, |
| start_w, |
| start_h, |
| _kernel_width, |
| _kernel_height, |
| input_w, |
| input_h, |
| input_c, |
| input_stride_y, |
| input_stride_z, |
| pad_value, |
| _dilation.x(), |
| _dilation.y()); |
| } |
| }, |
| in, out); |
| } |
| |
| 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, const Size2D &dilation, unsigned int num_groups) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), kernel_dims, conv_info, has_bias, dilation, num_groups)); |
| ARM_COMPUTE_UNUSED(num_groups); |
| |
| 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); |
| |
| _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; |
| |
| if(data_layout == DataLayout::NCHW) |
| { |
| switch(_input->info()->data_type()) |
| { |
| case DataType::F32: |
| _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col<float, false, true> : &NEIm2ColKernel::run_im2col<float, true, true>; |
| break; |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col<float16_t, false, true> : &NEIm2ColKernel::run_im2col<float16_t, true, true>; |
| break; |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| case DataType::QASYMM8: |
| _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col<qasymm8_t, false, true> : &NEIm2ColKernel::run_im2col<qasymm8_t, true, true>; |
| 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_im2col<float, false, false> : &NEIm2ColKernel::run_im2col<float, true, false>; |
| break; |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col<float16_t, false, false> : &NEIm2ColKernel::run_im2col<float16_t, true, false>; |
| break; |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| case DataType::QASYMM8: |
| _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col<qasymm8_t, false, false> : &NEIm2ColKernel::run_im2col<qasymm8_t, true, false>; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Data type not supported"); |
| break; |
| } |
| } |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(input->info(), output->info(), kernel_dims, conv_info, has_bias, dilation); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| INEKernel::configure(win_config.second); |
| } |
| |
| Status NEIm2ColKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, |
| bool has_bias, const Size2D &dilation, unsigned int num_groups) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, kernel_dims, conv_info, has_bias, dilation, num_groups)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), kernel_dims, conv_info, has_bias, dilation).first); |
| 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); |
| } |
