Add padding consideration to pooling index computation
Fix the pooling kernel which has been missing consideration
of left padding, which can be implictly added by external
kernels.
Additionally, tests for FP16 have been added for the logic.
Resolves: COMPMID-4363
Change-Id: I5655991cb80f749fb1ae9bbd3918b436a078f5d1
Signed-off-by: Sang-Hoon Park <sang-hoon.park@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5421
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/core/cpu/kernels/pooling/neon/fp16.cpp b/src/core/cpu/kernels/pooling/neon/fp16.cpp
index 1ecceaf..0aae7b8 100644
--- a/src/core/cpu/kernels/pooling/neon/fp16.cpp
+++ b/src/core/cpu/kernels/pooling/neon/fp16.cpp
@@ -57,9 +57,11 @@
int pool_stride_y = 0;
std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride();
- const int pad_right = src->info()->padding().right;
- const int in_stride_y = static_cast<int>(src->info()->strides_in_bytes().y());
- const int in_stride_z = static_cast<int>(src->info()->strides_in_bytes().z());
+ const int pad_right = src->info()->padding().right;
+ const int pad_left = src->info()->padding().left;
+ const int pad_horizontal = pad_right + pad_left;
+ const int in_stride_y = static_cast<int>(src->info()->strides_in_bytes().y());
+ const int in_stride_z = static_cast<int>(src->info()->strides_in_bytes().z());
execute_window_loop(window_out, [&](const Coordinates & id)
{
@@ -95,9 +97,9 @@
const uint32_t offset_base = offset_no_padding<float16_t>(in.offset(), id, *src->info(), pool_stride_x, pool_stride_y, DataLayout::NHWC);
const uint32_t offset_x0 = (uint32_t)offset_base / sizeof(float16_t) + x_off;
- const uint32_t offset_x1 = (uint32_t)offset_x0 + in_stride_y / sizeof(float16_t) - pad_right;
- const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float16_t) - pad_right * src->info()->tensor_shape()[1];
- const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float16_t) - pad_right;
+ const uint32_t offset_x1 = (uint32_t)offset_x0 + in_stride_y / sizeof(float16_t) - pad_horizontal;
+ const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float16_t) - pad_horizontal * src->info()->tensor_shape()[1];
+ const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float16_t) - pad_horizontal;
const uint32x4_t voffset_x0_0 = { offset_x0, offset_x0 + 1, offset_x0 + 2, offset_x0 + 3 };
const uint32x4_t voffset_x0_1 = { offset_x0 + 4, offset_x0 + 5, offset_x0 + 6, offset_x0 + 7 };
const uint16x8_t voffset_x0 = vcombine_u16(vmovn_u32(voffset_x0_0), vmovn_u32(voffset_x0_1));
@@ -134,9 +136,9 @@
const uint32_t offset_base = offset_no_padding<float16_t>(in.offset(), id, *src->info(), pool_stride_x, pool_stride_y, DataLayout::NHWC);
const uint32_t offset_x0 = (uint32_t)offset_base / sizeof(float16_t) + x_off;
- const uint32_t offset_x1 = (uint32_t)offset_x0 + in_stride_y / sizeof(float16_t) - pad_right;
- const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float16_t) - pad_right * src->info()->tensor_shape()[1];
- const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float16_t) - pad_right;
+ const uint32_t offset_x1 = (uint32_t)offset_x0 + in_stride_y / sizeof(float16_t) - pad_horizontal;
+ const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float16_t) - pad_horizontal * src->info()->tensor_shape()[1];
+ const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float16_t) - pad_horizontal;
const uint32_t tmp_idx0 = (x0 >= x1) ? offset_x0 : offset_x1;
const uint32_t tmp_idx1 = (x2 >= x3) ? offset_x2 : offset_x3;
const uint32_t tmp_idx2 = (std::max(x0, x1) >= std::max(x2, x3)) ? tmp_idx0 : tmp_idx1;