COMPMID-2318: Implement NEROIAlignLayer

Change-Id: I2665c98b5d6523a9ddaef0b7c220aecd22559ce6
Signed-off-by: Pablo Tello <pablo.tello@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1826
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>

2 files changed, 372 insertions, 0 deletions

diff --git a/src/core/NEON/kernels/NEROIAlignLayerKernel.cpp b/src/core/NEON/kernels/NEROIAlignLayerKernel.cpp
new file mode 100644
index 0000000000..dd21094832
--- /dev/null
+++ b/src/core/NEON/kernels/NEROIAlignLayerKernel.cpp
@@ -0,0 +1,326 @@
+/*
+ * Copyright (c) 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/NEROIAlignLayerKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/CPP/Validate.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Window.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/core/utils/misc/Utility.h"
+
+#include <arm_neon.h>
+
+using namespace arm_compute::misc::shape_calculator;
+
+namespace arm_compute
+{
+namespace
+{
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *rois, ITensorInfo *output, const ROIPoolingLayerInfo &pool_info)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, rois, output);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, rois);
+    ARM_COMPUTE_RETURN_ERROR_ON(rois->dimension(0) != 5);
+    ARM_COMPUTE_RETURN_ERROR_ON(rois->num_dimensions() > 2);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(input, DataLayout::NHWC, DataLayout::NCHW);
+    ARM_COMPUTE_RETURN_ERROR_ON((pool_info.pooled_width() == 0) || (pool_info.pooled_height() == 0));
+    ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
+
+    if(output->total_size() != 0)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(compute_roi_align_shape(*input, *rois, pool_info), output->tensor_shape());
+    }
+    return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *rois, ITensorInfo *output, const ROIPoolingLayerInfo &pool_info)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+
+    // Output auto inizialitation if not yet initialized
+    const TensorShape output_shape = compute_roi_align_shape(*input, *rois, pool_info);
+    auto_init_if_empty((*output), output_shape, 1, input->data_type());
+    output->set_data_layout(input->data_layout());
+
+    const unsigned int num_rois = rois->dimension(1);
+    Window             window;
+    window.set(Window::DimX, Window::Dimension(0, num_rois));
+    window.set(Window::DimY, Window::Dimension(0, 1));
+
+    AccessWindowStatic input_access(input,
+                                    input->valid_region().start(0),
+                                    input->valid_region().start(1),
+                                    input->valid_region().end(0),
+                                    input->valid_region().end(1));
+    AccessWindowStatic output_access(output, 0, 0, pool_info.pooled_width(), pool_info.pooled_height());
+
+    const bool window_changed = update_window_and_padding(window, input_access, output_access);
+    output_access.set_valid_region(window, ValidRegion(Coordinates(), output->tensor_shape()));
+
+    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+    return std::make_pair(err, window);
+}
+} // namespace
+
+NEROIAlignLayerKernel::NEROIAlignLayerKernel()
+    : _input(nullptr), _output(nullptr), _rois(nullptr), _pool_info(0, 0, 0.f)
+{
+}
+
+void NEROIAlignLayerKernel::configure(const ITensor *input, const ITensor *rois, ITensor *output, const ROIPoolingLayerInfo &pool_info)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, rois);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), rois->info(), output->info(), pool_info));
+    // Configure kernel window
+    auto win_config = validate_and_configure_window(input->info(), rois->info(), output->info(), pool_info);
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+
+    // Set instance variables
+    _input     = input;
+    _rois      = rois;
+    _output    = output;
+    _pool_info = pool_info;
+
+    INEKernel::configure(win_config.second);
+}
+
+Status NEROIAlignLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *rois, ITensorInfo *output, const ROIPoolingLayerInfo &pool_info)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, rois, output, pool_info));
+    return Status{};
+}
+
+/** Average pooling over an aligned window */
+template <typename T, DataLayout data_layout>
+inline T roi_align_1x1(const ITensor *input, unsigned int roi_batch,
+                       float region_start_x,
+                       float bin_size_x,
+                       int   grid_size_x,
+                       float region_end_x,
+                       float region_start_y,
+                       float bin_size_y,
+                       int   grid_size_y,
+                       float region_end_y,
+                       int   pz)
+{
+    if((region_end_x <= region_start_x) || (region_end_y <= region_start_y))
+    {
+        return T(0);
+    }
+    else
+    {
+        float avg = 0;
+        // Iterate through the aligned pooling region
+        for(int iy = 0; iy < grid_size_y; ++iy)
+        {
+            for(int ix = 0; ix < grid_size_x; ++ix)
+            {
+                // Align the window in the middle of every bin
+                float y = region_start_y + (iy + 0.5) * bin_size_y / float(grid_size_y);
+                float x = region_start_x + (ix + 0.5) * bin_size_x / float(grid_size_x);
+
+                // Interpolation in the [0,0] [0,1] [1,0] [1,1] square
+                const int y_low  = y;
+                const int x_low  = x;
+                const int y_high = y_low + 1;
+                const int x_high = x_low + 1;
+
+                const float ly = y - y_low;
+                const float lx = x - x_low;
+                const float hy = 1. - ly;
+                const float hx = 1. - lx;
+
+                const float w1 = hy * hx;
+                const float w2 = hy * lx;
+                const float w3 = ly * hx;
+                const float w4 = ly * lx;
+                if(data_layout == DataLayout::NCHW)
+                {
+                    const auto data1 = *reinterpret_cast<const T *>(input->ptr_to_element(Coordinates(x_low, y_low, pz, roi_batch)));
+                    const auto data2 = *reinterpret_cast<const T *>(input->ptr_to_element(Coordinates(x_high, y_low, pz, roi_batch)));
+                    const auto data3 = *reinterpret_cast<const T *>(input->ptr_to_element(Coordinates(x_low, y_high, pz, roi_batch)));
+                    const auto data4 = *reinterpret_cast<const T *>(input->ptr_to_element(Coordinates(x_high, y_high, pz, roi_batch)));
+                    avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4;
+                }
+                else
+                {
+                    const auto data1 = *reinterpret_cast<const T *>(input->ptr_to_element(Coordinates(pz, x_low, y_low, roi_batch)));
+                    const auto data2 = *reinterpret_cast<const T *>(input->ptr_to_element(Coordinates(pz, x_high, y_low, roi_batch)));
+                    const auto data3 = *reinterpret_cast<const T *>(input->ptr_to_element(Coordinates(pz, x_low, y_high, roi_batch)));
+                    const auto data4 = *reinterpret_cast<const T *>(input->ptr_to_element(Coordinates(pz, x_high, y_high, roi_batch)));
+                    avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4;
+                }
+            }
+        }
+
+        avg /= grid_size_x * grid_size_y;
+
+        return T(avg);
+    }
+}
+
+inline float compute_region_coordinate(int p, float bin_size, float roi_anchor, float max_value)
+{
+    const float region_start = p * bin_size + roi_anchor;
+    return utility::clamp(region_start, 0.0f, max_value);
+}
+
+void NEROIAlignLayerKernel::run(const Window &window, const ThreadInfo &info)
+{
+    if(_input->info()->data_layout() == DataLayout::NCHW)
+    {
+        switch(_input->info()->data_type())
+        {
+            case DataType::F32:
+            {
+                NEROIAlignLayerKernel::internal_run<DataLayout::NCHW, float>(window, info);
+                break;
+            }
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+            case DataType::F16:
+            {
+                NEROIAlignLayerKernel::internal_run<DataLayout::NCHW, float16_t>(window, info);
+                break;
+            }
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+            default:
+            {
+                ARM_COMPUTE_ERROR("DataType not supported");
+                break;
+            }
+        }
+    }
+    else if(_input->info()->data_layout() == DataLayout::NHWC)
+    {
+        switch(_input->info()->data_type())
+        {
+            case DataType::F32:
+            {
+                NEROIAlignLayerKernel::internal_run<DataLayout::NHWC, float>(window, info);
+                break;
+            }
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+            case DataType::F16:
+            {
+                NEROIAlignLayerKernel::internal_run<DataLayout::NHWC, float16_t>(window, info);
+                break;
+            }
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+            default:
+            {
+                ARM_COMPUTE_ERROR("DataType not supported");
+                break;
+            }
+        }
+    }
+    else
+    {
+        ARM_COMPUTE_ERROR("Invalid layout");
+    }
+}
+
+template <DataLayout data_layout, typename data_type>
+void NEROIAlignLayerKernel::internal_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);
+
+    const size_t values_per_roi = _rois->info()->dimension(0);
+
+    const int roi_list_start = window.x().start();
+    const int roi_list_end   = window.x().end();
+
+    const unsigned int idx_width  = get_data_layout_dimension_index(_input->info()->data_layout(), DataLayoutDimension::WIDTH);
+    const unsigned int idx_height = get_data_layout_dimension_index(_input->info()->data_layout(), DataLayoutDimension::HEIGHT);
+    const unsigned int idx_depth  = get_data_layout_dimension_index(_input->info()->data_layout(), DataLayoutDimension::CHANNEL);
+
+    const int input_width   = _input->info()->dimension(idx_width);
+    const int input_height  = _input->info()->dimension(idx_height);
+    const int input_chanels = _input->info()->dimension(idx_depth);
+    const int pooled_w      = _pool_info.pooled_width();
+    const int pooled_h      = _pool_info.pooled_height();
+
+    const auto *rois_ptr = reinterpret_cast<const data_type *>(_rois->buffer());
+
+    for(int roi_indx = roi_list_start; roi_indx < roi_list_end; ++roi_indx)
+    {
+        const unsigned int roi_batch = rois_ptr[values_per_roi * roi_indx];
+        const auto         x1        = rois_ptr[values_per_roi * roi_indx + 1];
+        const auto         y1        = rois_ptr[values_per_roi * roi_indx + 2];
+        const auto         x2        = rois_ptr[values_per_roi * roi_indx + 3];
+        const auto         y2        = rois_ptr[values_per_roi * roi_indx + 4];
+
+        const float roi_anchor_x = x1 * _pool_info.spatial_scale();
+        const float roi_anchor_y = y1 * _pool_info.spatial_scale();
+        const float roi_dims_x   = std::max((x2 - x1) * _pool_info.spatial_scale(), 1.0f);
+        const float roi_dims_y   = std::max((y2 - y1) * _pool_info.spatial_scale(), 1.0f);
+        float       bin_size_x   = roi_dims_x / _pool_info.pooled_width();
+        float       bin_size_y   = roi_dims_y / _pool_info.pooled_height();
+
+        // Iterate through all feature maps
+        for(int ch = 0; ch < input_chanels; ++ch)
+        {
+            // Iterate through all output pixels
+            for(int py = 0; py < pooled_h; ++py)
+            {
+                for(int px = 0; px < pooled_w; ++px)
+                {
+                    const float region_start_x = compute_region_coordinate(px, bin_size_x, roi_anchor_x, input_width);
+                    const float region_start_y = compute_region_coordinate(py, bin_size_y, roi_anchor_y, input_height);
+                    const float region_end_x   = compute_region_coordinate(px + 1, bin_size_x, roi_anchor_x, input_width);
+                    const float region_end_y   = compute_region_coordinate(py + 1, bin_size_y, roi_anchor_y, input_height);
+                    const int   roi_bin_grid_x = (_pool_info.sampling_ratio() > 0) ? _pool_info.sampling_ratio() : int(ceil(bin_size_x));
+                    const int   roi_bin_grid_y = (_pool_info.sampling_ratio() > 0) ? _pool_info.sampling_ratio() : int(ceil(bin_size_y));
+
+                    const float out_val = roi_align_1x1<data_type, data_layout>(_input, roi_batch, region_start_x, bin_size_x,
+                                                                                roi_bin_grid_x,
+                                                                                region_end_x,
+                                                                                region_start_y,
+                                                                                bin_size_y,
+                                                                                roi_bin_grid_y,
+                                                                                region_end_y, ch);
+
+                    if(data_layout == DataLayout::NCHW)
+                    {
+                        auto out_ptr = reinterpret_cast<data_type *>(_output->ptr_to_element(Coordinates(px, py, ch, roi_indx)));
+                        *out_ptr     = out_val;
+                    }
+                    else
+                    {
+                        auto out_ptr = reinterpret_cast<data_type *>(_output->ptr_to_element(Coordinates(ch, px, py, roi_indx)));
+                        *out_ptr     = out_val;
+                    }
+                }
+            }
+        }
+    }
+}
+} // namespace arm_compute
diff --git a/src/runtime/NEON/functions/NEROIAlignLayer.cpp b/src/runtime/NEON/functions/NEROIAlignLayer.cpp
new file mode 100644
index 0000000000..b4e0a2f9fa
--- /dev/null
+++ b/src/runtime/NEON/functions/NEROIAlignLayer.cpp
@@ -0,0 +1,46 @@
+/*
+ * Copyright (c) 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/runtime/NEON/functions/NEROIAlignLayer.h"
+
+#include "arm_compute/core/NEON/kernels/NEROIAlignLayerKernel.h"
+#include "support/ToolchainSupport.h"
+
+namespace arm_compute
+{
+Status NEROIAlignLayer::validate(const ITensorInfo *input, const ITensorInfo *rois, ITensorInfo *output, const ROIPoolingLayerInfo &pool_info)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(NEROIAlignLayerKernel::validate(input, rois, output, pool_info));
+
+    return Status{};
+}
+
+void NEROIAlignLayer::configure(const ITensor *input, const ITensor *rois, ITensor *output, const ROIPoolingLayerInfo &pool_info)
+{
+    // Configure ROI pooling kernel
+    auto k = arm_compute::support::cpp14::make_unique<NEROIAlignLayerKernel>();
+    k->configure(input, rois, output, pool_info);
+    _kernel = std::move(k);
+}
+
+} // namespace arm_compute