COMPMID-344 Updated doxygen
Change-Id: I32f7b84daa560e460b77216add529c8fa8b327ae
diff --git a/src/core/NEON/kernels/NEIm2ColKernel.cpp b/src/core/NEON/kernels/NEIm2ColKernel.cpp
new file mode 100644
index 0000000..c7c23d5
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+++ b/src/core/NEON/kernels/NEIm2ColKernel.cpp
@@ -0,0 +1,338 @@
+/*
+ * 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/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/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+#include <cstring>
+#include <tuple>
+
+using namespace arm_compute;
+
+namespace
+{
+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_size,
+ 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)
+{
+ const int kernel_size2 = kernel_size * kernel_size;
+ const int x_e = top_left_x + kernel_size;
+ const int y_e = top_left_y + kernel_size;
+
+ // 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)
+ {
+ if((y < 0 || y >= input_h) && has_pads)
+ {
+ // All the values will be zeros
+ for(int x = top_left_x; x < x_e; ++x, ++out_ptr)
+ {
+ *(out_ptr + 0 * kernel_size2) = 0;
+ *(out_ptr + 1 * kernel_size2) = 0;
+ *(out_ptr + 2 * kernel_size2) = 0;
+ }
+ }
+ else
+ {
+ for(int x = top_left_x; x < x_e; ++x, ++out_ptr)
+ {
+ if((x < 0 || x >= input_w) && has_pads)
+ {
+ *(out_ptr + 0 * kernel_size2) = 0;
+ *(out_ptr + 1 * kernel_size2) = 0;
+ *(out_ptr + 2 * kernel_size2) = 0;
+ }
+ 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)
+ {
+ if((y < 0 || y >= input_h) && has_pads)
+ {
+ // All the values will be zeros
+ memset(out_ptr, 0, kernel_size * sizeof(T));
+ out_ptr += kernel_size;
+ }
+ else
+ {
+ for(int x = top_left_x; x < x_e; ++x, ++out_ptr)
+ {
+ if((x < 0 || x >= input_w) && has_pads)
+ {
+ *out_ptr = 0;
+ }
+ 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, arm_compute::qint8_t>::value)
+ {
+ *out_ptr = scvt_qs8_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 int kernel_depth = _input->info()->dimension(2);
+ const int input_w = _input->info()->dimension(0);
+ const int input_h = _input->info()->dimension(1);
+ 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();
+
+ int pad_x = 0;
+ int pad_y = 0;
+ int stride_x = 0;
+ int stride_y = 0;
+ std::tie(pad_x, pad_y) = _conv_info.pad();
+ std::tie(stride_x, stride_y) = _conv_info.stride();
+
+ // Setup input window
+ const int start_x = -pad_x;
+ const int start_y = -pad_y;
+
+ 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(Window::DimX, Window::Dimension(0, _output->info()->dimension(0), _output->info()->strides_in_bytes().y() / _output->info()->element_size()));
+ window_out.set(Window::DimY, Window::Dimension(window.y().start() * _convolved_dims.first, window.y().end() * _convolved_dims.first, _convolved_dims.first));
+ window_out.set(Window::DimZ, 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.x() * stride_x + start_x;
+ const int top_left_y = id.y() * 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_size),
+ kernel_depth,
+ input_w,
+ input_h,
+ input_stride_x,
+ input_stride_y,
+ input_stride_z,
+ _input->info()->fixed_point_position());
+ },
+ 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());
+ 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, arm_compute::qint8_t>::value)
+ {
+ *(reinterpret_cast<T *>(out_ptr) + out_width - 1) = scvt_qs8_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_size(0), _has_bias(false)
+{
+}
+
+void NEIm2ColKernel::configure(const ITensor *input, ITensor *output, std::pair<unsigned int, unsigned int> convolved_dims, const PadStrideInfo &conv_info, bool has_bias)
+{
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::QS8);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32, DataType::QS8);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+
+ _input = input;
+ _output = output;
+ _convolved_dims = convolved_dims;
+ _conv_info = conv_info;
+ _kernel_size = std::sqrt((output->info()->dimension(0) - (has_bias ? 1 : 0)) / input->info()->dimension(2));
+ _has_bias = has_bias;
+
+ unsigned int pad_x, pad_y, stride_x, stride_y = 0;
+ std::tie(pad_x, pad_y) = conv_info.pad();
+ 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) && (pad_x == 0) && (pad_y == 0));
+
+ 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;
+ case DataType::QS8:
+ _func = &NEIm2ColKernel::run_reduced<qint8_t>;
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Data type not supported");
+ break;
+ }
+ }
+ else
+ {
+ switch(_input->info()->data_type())
+ {
+ case DataType::F32:
+ _func = ((pad_x == 0) && (pad_y == 0)) ? &NEIm2ColKernel::run_generic<float, false> : &NEIm2ColKernel::run_generic<float, true>;
+ break;
+ case DataType::QS8:
+ _func = ((pad_x == 0) && (pad_y == 0)) ? &NEIm2ColKernel::run_generic<qint8_t, false> : &NEIm2ColKernel::run_generic<qint8_t, true>;
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Data type not supported");
+ break;
+ }
+ window.set(Window::DimX, Window::Dimension(0, _convolved_dims.first, 1));
+ window.set(Window::DimY, Window::Dimension(0, _convolved_dims.second, 1));
+ window.set(Window::DimZ, 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);
+}
+
+void NEIm2ColKernel::run(const Window &window)
+{
+ ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+ ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+ (this->*_func)(window);
+}