COMPMID-344 Updated doxygen

Change-Id: I32f7b84daa560e460b77216add529c8fa8b327ae

1 files changed, 338 insertions, 0 deletions

diff --git a/src/core/NEON/kernels/NEIm2ColKernel.cpp b/src/core/NEON/kernels/NEIm2ColKernel.cpp
new file mode 100644
index 0000000000..c7c23d5d06
--- /dev/null
+++ 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);
+}