COMPMID-470: Neon Deconvolution.

Implemented by up-sampling the input with zeros insertions between the input samples and convolving the Deconvolution kernels on the up-sampled result.

The upsampling is performed by the function NEDeconvolutionLayerUpsample.
Convolving is done by NEDirectConvolutionLayer.

Change-Id: I25f7ba7c6b99cd9310797972ede40aeff4a54900
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/85319
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

2 files changed, 169 insertions, 2 deletions

diff --git a/src/core/NEON/kernels/NEDeconvolutionLayerUpsampleKernel.cpp b/src/core/NEON/kernels/NEDeconvolutionLayerUpsampleKernel.cpp
new file mode 100644
index 0000000000..71db2e9782
--- /dev/null
+++ b/src/core/NEON/kernels/NEDeconvolutionLayerUpsampleKernel.cpp
@@ -0,0 +1,165 @@
+/*
+ * Copyright (c) 2016, 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/NEDeconvolutionLayerUpsampleKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+
+using namespace arm_compute;
+
+NEDeconvolutionLayerUpsampleKernel::NEDeconvolutionLayerUpsampleKernel()
+    : _offsets(nullptr), _input(nullptr), _output(nullptr)
+{
+}
+
+BorderSize NEDeconvolutionLayerUpsampleKernel::border_size() const
+{
+    return BorderSize(1);
+}
+
+void NEDeconvolutionLayerUpsampleKernel::configure(const ITensor *input, const ITensor *offsets, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32);
+    ARM_COMPUTE_ERROR_ON(output->info()->dimension(0) == 0);
+    ARM_COMPUTE_ERROR_ON(output->info()->dimension(1) == 0);
+
+    for(size_t i = 2; i < Coordinates::num_max_dimensions; ++i)
+    {
+        ARM_COMPUTE_ERROR_ON(input->info()->dimension(i) != output->info()->dimension(i));
+    }
+
+    _input   = input;
+    _output  = output;
+    _offsets = offsets;
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+    const int              border_offset                     = border_size().left;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration));
+
+    AccessWindowRectangle  input_access(input->info(), -border_offset, -border_offset, input->info()->dimension(0) + border_offset, input->info()->dimension(1) + border_offset);
+    AccessWindowHorizontal offsets_access(offsets->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+
+    update_window_and_padding(win, input_access, offsets_access, output_access);
+
+    output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+
+    INEKernel::configure(win);
+}
+
+void NEDeconvolutionLayerUpsampleKernel::scale_nearest(const Window &window)
+{
+    const size_t input_stride = _input->info()->strides_in_bytes()[1];
+
+    // Compute the ratio between source height and destination height
+    const auto hr = static_cast<float>(_input->info()->dimension(1)) / static_cast<float>(_output->info()->dimension(1));
+
+    // Don't increment in X and Y direction for the input tensor
+    // A pointer to the start of this plane is needed as base for the precomputed offsets
+    Window win_in(window);
+    win_in.set(Window::DimX, Window::Dimension(0, 0, 0));
+    win_in.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    Window win_off;
+    win_off.set(Window::DimX, window[Window::DimX]);
+    win_off.set(Window::DimY, window[Window::DimY]);
+
+    for(size_t d = Window::DimZ; d < _offsets->info()->num_dimensions(); ++d)
+    {
+        win_off.set(d, Window::Dimension(0, 0, 0));
+    }
+
+    Iterator in(_input, win_in);
+    Iterator out(_output, window);
+    Iterator offsets(_offsets, win_off);
+
+    switch(_input->info()->data_type())
+    {
+        case DataType::F32:
+        {
+            float32x4x4_t tmp =
+            {
+                {
+                    vdupq_n_f32(0),
+                    vdupq_n_f32(0)
+                }
+            };
+            execute_window_loop(window, [&](const Coordinates & id)
+            {
+                const auto offsets_ptr = reinterpret_cast<const int32_t *>(offsets.ptr());
+
+                const size_t in_yi      = (id.y() + 0.5f) * hr;
+                const size_t offset_row = in_yi * input_stride;
+
+                tmp.val[0] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[0] + offset_row), tmp.val[0], 0);
+                tmp.val[0] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[4] + offset_row), tmp.val[0], 1);
+                tmp.val[0] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[8] + offset_row), tmp.val[0], 2);
+                tmp.val[0] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[12] + offset_row), tmp.val[0], 3);
+
+                tmp.val[1] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[1] + offset_row), tmp.val[1], 0);
+                tmp.val[1] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[5] + offset_row), tmp.val[1], 1);
+                tmp.val[1] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[9] + offset_row), tmp.val[1], 2);
+                tmp.val[1] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[13] + offset_row), tmp.val[1], 3);
+
+                tmp.val[2] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[2] + offset_row), tmp.val[2], 0);
+                tmp.val[2] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[6] + offset_row), tmp.val[2], 1);
+                tmp.val[2] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[10] + offset_row), tmp.val[2], 2);
+                tmp.val[2] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[14] + offset_row), tmp.val[2], 3);
+
+                tmp.val[3] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[3] + offset_row), tmp.val[3], 0);
+                tmp.val[3] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[7] + offset_row), tmp.val[3], 1);
+                tmp.val[3] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[11] + offset_row), tmp.val[3], 2);
+                tmp.val[3] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[15] + offset_row), tmp.val[3], 3);
+
+                vst4q_f32(reinterpret_cast<float *>(out.ptr()), tmp);
+            },
+            in, offsets, out);
+            break;
+        }
+        default:
+            ARM_COMPUTE_ERROR("Not supported");
+            break;
+    }
+}
+
+void NEDeconvolutionLayerUpsampleKernel::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);
+    scale_nearest(window);
+}
diff --git a/src/core/NEON/kernels/NEScaleKernel.cpp b/src/core/NEON/kernels/NEScaleKernel.cpp
index 6634d4b13c..b1ced7e38d 100644
--- a/src/core/NEON/kernels/NEScaleKernel.cpp
+++ b/src/core/NEON/kernels/NEScaleKernel.cpp
@@ -180,8 +180,10 @@ void NEScaleKernel::scale_nearest(const Window &window)
                 const auto           offsets_ptr = reinterpret_cast<const int32_t *>(offsets.ptr());
                 const uint8_t *const in_ptr      = in.ptr();
 
-                const int in_yi      = std::floor((id.y() + 0.5f) * hr);
-                const int offset_row = in_yi * input_stride;
+                const int in_yi         = std::floor((id.y() + 0.5f) * hr);
+                const int in_yi_clamped = std::min(static_cast<int>(_input->info()->dimension(1)), std::max(in_yi, -1));
+                ARM_COMPUTE_ERROR_ON(in_yi_clamped < -1 || in_yi_clamped > static_cast<int>(_input->info()->dimension(1)));
+                const int offset_row = in_yi_clamped * input_stride;
 
                 tmp = vsetq_lane_u8(in_ptr[offsets_ptr[0] + offset_row], tmp, 0);
                 tmp = vsetq_lane_u8(in_ptr[offsets_ptr[1] + offset_row], tmp, 1);