COMPMID-2449: Implement NEUnPoolLayer
Change-Id: I5677c87bba97dd395a3e13dbce34a3dd2c437033
Signed-off-by: morgolock <pablo.tello@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3289
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/core/NEON/kernels/NEPoolingLayerKernel.cpp b/src/core/NEON/kernels/NEPoolingLayerKernel.cpp
index 6d61f51..d6b1753 100644
--- a/src/core/NEON/kernels/NEPoolingLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEPoolingLayerKernel.cpp
@@ -36,7 +36,6 @@
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-
#include "support/ToolchainSupport.h"
#include "arm_compute/core/NEON/wrapper/wrapper.h"
@@ -137,6 +136,7 @@
ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
if(indices)
{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(indices, 1, DataType::U32);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(pool_type != PoolingType::MAX, "Pooling indices only supported for MAX pooling method");
}
@@ -156,7 +156,6 @@
if(indices)
{
ARM_COMPUTE_RETURN_ERROR_ON_MSG((pool_size != Size2D(2, 2)), "Pooling indices only supported for pool size 2x2");
-
ARM_COMPUTE_RETURN_ERROR_ON((indices->dimension(get_data_layout_dimension_index(indices->data_layout(), DataLayoutDimension::WIDTH)) != pooled_w)
|| (indices->dimension(get_data_layout_dimension_index(indices->data_layout(), DataLayoutDimension::HEIGHT)) != pooled_h));
}
@@ -1489,6 +1488,39 @@
input, output);
}
+inline uint32_t offset_no_padding(uint32_t padded_offset, const Coordinates &id, const ITensorInfo &info, int pool_stride_x, int pool_stride_y)
+{
+ const int pad_left = info.padding().left;
+ const int pad_right = info.padding().right;
+ const int pad_top = info.padding().top;
+ const int pad_bottom = info.padding().bottom;
+ const int in_stride_y = static_cast<int>(info.strides_in_bytes().y());
+ const int in_stride_w = static_cast<int>(info.strides_in_bytes()[3]);
+ const int pad_horiz = pad_left + pad_right;
+ const int pad_vert = pad_top + pad_bottom;
+
+ if(info.data_layout() == DataLayout::NCHW)
+ {
+ const uint32_t offset_base = padded_offset
+ - sizeof(float) * pad_horiz * id.y() * pool_stride_y /* subtract padding elems per row */
+ - pad_top * sizeof(float) /* top padding */
+ - sizeof(float) * pad_horiz * info.tensor_shape()[1] * id.z() - pad_vert * in_stride_y * id.z() /* for each Z plane there are height*pad_right padding elems */
+ - in_stride_w * id[3];
+
+ return offset_base;
+ }
+ else
+ {
+ const uint32_t offset_base = padded_offset
+ - sizeof(float) * pad_horiz * id.y() * pool_stride_x // subtract padding elems per row
+ - pad_top * sizeof(float) // top padding
+ - sizeof(float) * pad_horiz * info.tensor_shape()[1] * id.z() * pool_stride_y // for each Z plane there are width*pad_right padding elems
+ - in_stride_w * id[3];
+
+ return offset_base;
+ }
+}
+
void NEPoolingLayerKernel::pooling2_f32_nchw_maxpool_indices(const Window &window_input, const Window &window)
{
Iterator input(_input, window_input);
@@ -1502,11 +1534,10 @@
std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride();
const uint8_t *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top)));
const uint8_t *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1));
-
- const Strides &input_strides = _input->info()->strides_in_bytes();
- const auto in_stridew = input_strides[1];
-
- execute_window_loop(window, [&](const Coordinates &)
+ const int pad_left = _input->info()->padding().left;
+ const int pad_right = _input->info()->padding().right;
+ const int in_stride_y = static_cast<int>(_input->info()->strides_in_bytes().y());
+ execute_window_loop(window, [&](const Coordinates & id)
{
const auto input_offset_top = input_top_ptr + input.offset();
const auto input_offset_bottom = input_bottom_ptr + input.offset();
@@ -1521,8 +1552,9 @@
final_res = vget_lane_f32(res, 0);
// Store result
*(reinterpret_cast<float *>(output.ptr())) = final_res;
- const uint32_t offset_top = (uint32_t)(input.offset() / sizeof(float));
- const uint32_t offset_bottom = (uint32_t)offset_top + (in_stridew / sizeof(float));
+ const uint32_t offset_base = offset_no_padding(input.offset(), id, *_input->info(), pool_stride_x, pool_stride_y);
+ const uint32_t offset_top = (uint32_t)(offset_base / sizeof(float));
+ const uint32_t offset_bottom = offset_top + in_stride_y / sizeof(float) - pad_right - pad_left;
const uint32x2_t voffset_top = { offset_top, offset_top + 1u };
const uint32x2_t voffset_bottom = { offset_bottom, offset_bottom + 1u };
const uint32x2_t tmp_indices = vbsl_u32(vcgt_f32(top_data, bottom_data), voffset_top, voffset_bottom);
@@ -1867,10 +1899,8 @@
float32x4_t vres;
const int pad_right = _input->info()->padding().right;
- const int pad_top = _input->info()->padding().top;
const int in_stride_y = static_cast<int>(_input->info()->strides_in_bytes().y());
const int in_stride_z = static_cast<int>(_input->info()->strides_in_bytes().z());
- const int in_stride_w = static_cast<int>(_input->info()->strides_in_bytes()[3]);
execute_window_loop(window, [&](const Coordinates & id)
{
@@ -1904,17 +1934,11 @@
// Store result
vst1q_f32(reinterpret_cast<float *>(output.ptr()), vres);
- const uint32_t offset_base = input.offset()
- - sizeof(float) * pad_right * id.y() * pool_stride_x /* subtract padding elems per row */
- - pad_top * sizeof(float) /* top padding */
- - sizeof(float) * pad_right * _input->info()->tensor_shape()[1] * id.z() * pool_stride_y /* for each Z plane there are width*pad_right padding elems */
- - in_stride_w * id[3] + _input->info()->tensor_shape()[0] * sizeof(float) * id[3];
-
- const uint32_t offset_x0 = (uint32_t)offset_base / sizeof(float);
- const uint32_t offset_x1 = (uint32_t)offset_x0 + in_stride_y / sizeof(float) - pad_right;
- const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float) - pad_right * _input->info()->tensor_shape()[1];
- const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float) - pad_right;
-
+ const uint32_t offset_base = offset_no_padding(input.offset(), id, *_input->info(), pool_stride_x, pool_stride_y);
+ const uint32_t offset_x0 = (uint32_t)offset_base / sizeof(float);
+ const uint32_t offset_x1 = (uint32_t)offset_x0 + in_stride_y / sizeof(float) - pad_right;
+ const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float) - pad_right * _input->info()->tensor_shape()[1];
+ const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float) - pad_right;
const uint32x4_t voffset_x0 = { offset_x0, offset_x0 + 1, offset_x0 + 2, offset_x0 + 3 };
const uint32x4_t voffset_x1 = { offset_x1, offset_x1 + 1, offset_x1 + 2, offset_x1 + 3 };
const uint32x4_t voffset_x2 = { offset_x2, offset_x2 + 1, offset_x2 + 2, offset_x2 + 3 };