COMPMID-2318: Implement NEROIAlignLayer
Added support for QASYMM8
Change-Id: I884ee8b44f38ed6e2eb5600e4ffff25e19f52eb8
Signed-off-by: Pablo Tello <pablo.tello@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1831
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/arm_compute/core/NEON/kernels/NEROIAlignLayerKernel.h b/arm_compute/core/NEON/kernels/NEROIAlignLayerKernel.h
index 00c6f07..4fc339b 100644
--- a/arm_compute/core/NEON/kernels/NEROIAlignLayerKernel.h
+++ b/arm_compute/core/NEON/kernels/NEROIAlignLayerKernel.h
@@ -55,9 +55,10 @@
/** Set the input and output tensors.
*
- * @param[in] input Source tensor. Data types supported: F16/F32.
+ * @param[in] input Source tensor. Data types supported: QASYMM8/F16/F32.
* @param[in] rois ROIs tensor, it is a 2D tensor of size [5, N] (where N is the number of ROIs) containing top left and bottom right corner
- * as coordinate of an image and batch_id of ROI [ batch_id, x1, y1, x2, y2 ]. Data types supported: same as @p input
+ * as coordinate of an image and batch_id of ROI [ batch_id, x1, y1, x2, y2 ].
+ * Data types supported: QASYMM16 with scale of 0.125 and 0 offset if @p input is QASYMM8, otherwise same as @p input
* @param[out] output Destination tensor. Data types supported: Same as @p input.
* @param[in] pool_info Contains pooling operation information described in @ref ROIPoolingLayerInfo.
*
@@ -69,8 +70,9 @@
void configure(const ITensor *input, const ITensor *rois, ITensor *output, const ROIPoolingLayerInfo &pool_info);
/** Static function to check if given info will lead to a valid configuration of @ref NEROIAlignLayerKernel
*
- * @param[in] input Source tensor info. Data types supported: F16/F32.
- * @param[in] rois ROIs tensor info. Data types supported: same as @p input
+ * @param[in] input Source tensor info. Data types supported: QASYMM8/F16/F32.
+ * @param[in] rois ROIs tensor info. Data types supported: QASYMM16 with scale of 0.125 and 0 offset if @p input is QASYMM8,
+ * otherwise same as @p input
* @param[in] output Destination tensor info. Data types supported: Same as @p input.
* @param[in] pool_info Contains pooling operation information described in @ref ROIPoolingLayerInfo.
*
@@ -87,7 +89,7 @@
void run(const Window &window, const ThreadInfo &info) override;
private:
- template <DataLayout data_layout, typename data_type>
+ template <DataLayout data_layout, typename input_data_type, typename roi_data_type = input_data_type>
void internal_run(const Window &window, const ThreadInfo &info);
const ITensor *_input;
diff --git a/arm_compute/runtime/NEON/functions/NEROIAlignLayer.h b/arm_compute/runtime/NEON/functions/NEROIAlignLayer.h
index f28fb6b..e8171d3 100644
--- a/arm_compute/runtime/NEON/functions/NEROIAlignLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEROIAlignLayer.h
@@ -42,9 +42,10 @@
public:
/** Set the input and output tensors.
*
- * @param[in] input Source tensor. Data types supported: F16/F32.
+ * @param[in] input Source tensor. Data types supported: QASYMM8/F16/F32.
* @param[in] rois ROIs tensor, it is a 2D tensor of size [5, N] (where N is the number of ROIs) containing top left and bottom right corner
- * as coordinate of an image and batch_id of ROI [ batch_id, x1, y1, x2, y2 ]. Data types supported: same as @p input
+ * as coordinate of an image and batch_id of ROI [ batch_id, x1, y1, x2, y2 ].
+ * Data types supported: QASYMM16 with scale of 0.125 and 0 offset if @p input is QASYMM8, otherwise same as @p input
* @param[out] output Destination tensor. Data types supported: Same as @p input.
* @param[in] pool_info Contains pooling operation information described in @ref ROIPoolingLayerInfo.
*
@@ -54,10 +55,11 @@
* @note The fourth dimension of @p output tensor must be the same as the number of elements in @p rois array.
*/
void configure(const ITensor *input, const ITensor *rois, ITensor *output, const ROIPoolingLayerInfo &pool_info);
- /** Static function to check if given info will lead to a valid configuration of @ref NEROIAlignLayer
+ /** Static function to check if given info will lead to a valid configuration of @ref NEROIAlignLayerKernel
*
- * @param[in] input Source tensor info. Data types supported: F16/F32.
- * @param[in] rois ROIs tensor info. Data types supported: same as @p input
+ * @param[in] input Source tensor info. Data types supported: QASYMM8/F16/F32.
+ * @param[in] rois ROIs tensor info. Data types supported: QASYMM16 with scale of 0.125 and 0 offset if @p input is QASYMM8,
+ * otherwise same as @p input
* @param[in] output Destination tensor info. Data types supported: Same as @p input.
* @param[in] pool_info Contains pooling operation information described in @ref ROIPoolingLayerInfo.
*
diff --git a/src/core/NEON/kernels/NEROIAlignLayerKernel.cpp b/src/core/NEON/kernels/NEROIAlignLayerKernel.cpp
index dd21094..3b944ab 100644
--- a/src/core/NEON/kernels/NEROIAlignLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEROIAlignLayerKernel.cpp
@@ -43,10 +43,9 @@
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_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, 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);
@@ -57,6 +56,20 @@
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());
}
+
+ if(input->data_type() == DataType::QASYMM8)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(rois, 1, DataType::QASYMM16);
+
+ const UniformQuantizationInfo rois_qinfo = rois->quantization_info().uniform();
+ ARM_COMPUTE_RETURN_ERROR_ON(rois_qinfo.scale != 0.125f);
+ ARM_COMPUTE_RETURN_ERROR_ON(rois_qinfo.offset != 0);
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, rois);
+ }
+
return Status{};
}
@@ -118,21 +131,22 @@
}
/** 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)
+template <typename input_data_type, DataLayout data_layout>
+inline input_data_type 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);
+ return input_data_type(0);
}
else
{
@@ -163,18 +177,90 @@
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)));
+ const auto data1 = *reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_low, y_low, pz, roi_batch)));
+ const auto data2 = *reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_high, y_low, pz, roi_batch)));
+ const auto data3 = *reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_low, y_high, pz, roi_batch)));
+ const auto data4 = *reinterpret_cast<const input_data_type *>(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)));
+ const auto data1 = *reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_low, y_low, roi_batch)));
+ const auto data2 = *reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_high, y_low, roi_batch)));
+ const auto data3 = *reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_low, y_high, roi_batch)));
+ const auto data4 = *reinterpret_cast<const input_data_type *>(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 input_data_type(avg);
+ }
+}
+
+/** Average pooling over an aligned window */
+template <typename input_data_type, DataLayout data_layout>
+inline input_data_type roi_align_1x1_qasymm8(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,
+ const QuantizationInfo &out_qinfo)
+{
+ if((region_end_x <= region_start_x) || (region_end_y <= region_start_y))
+ {
+ return input_data_type(out_qinfo.uniform().offset);
+ }
+ else
+ {
+ float avg = 0;
+ const UniformQuantizationInfo input_qinfo = input->info()->quantization_info().uniform();
+ // 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)
+ {
+ float data1 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_low, y_low, pz, roi_batch))), input_qinfo);
+ float data2 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_high, y_low, pz, roi_batch))), input_qinfo);
+ float data3 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_low, y_high, pz, roi_batch))), input_qinfo);
+ float data4 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_high, y_high, pz, roi_batch))), input_qinfo);
+ avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4;
+ }
+ else
+ {
+ const auto data1 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_low, y_low, roi_batch))), input_qinfo);
+ const auto data2 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_high, y_low, roi_batch))), input_qinfo);
+ const auto data3 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_low, y_high, roi_batch))), input_qinfo);
+ const auto data4 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_high, y_high, roi_batch))), input_qinfo);
avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4;
}
}
@@ -182,7 +268,7 @@
avg /= grid_size_x * grid_size_y;
- return T(avg);
+ return quantize_qasymm8(avg, out_qinfo);
}
}
@@ -198,6 +284,11 @@
{
switch(_input->info()->data_type())
{
+ case DataType::QASYMM8:
+ {
+ NEROIAlignLayerKernel::internal_run<DataLayout::NCHW, uint8_t, uint16_t>(window, info);
+ break;
+ }
case DataType::F32:
{
NEROIAlignLayerKernel::internal_run<DataLayout::NCHW, float>(window, info);
@@ -221,6 +312,11 @@
{
switch(_input->info()->data_type())
{
+ case DataType::QASYMM8:
+ {
+ NEROIAlignLayerKernel::internal_run<DataLayout::NHWC, uint8_t, uint16_t>(window, info);
+ break;
+ }
case DataType::F32:
{
NEROIAlignLayerKernel::internal_run<DataLayout::NHWC, float>(window, info);
@@ -246,7 +342,7 @@
}
}
-template <DataLayout data_layout, typename data_type>
+template <DataLayout data_layout, typename input_data_type, typename roi_data_type>
void NEROIAlignLayerKernel::internal_run(const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
@@ -268,16 +364,30 @@
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());
+ const DataType data_type = _input->info()->data_type();
+ const bool is_qasymm = is_data_type_quantized_asymmetric(data_type);
+ const auto *rois_ptr = reinterpret_cast<const roi_data_type *>(_rois->buffer());
+ const QuantizationInfo &rois_qinfo = _rois->info()->quantization_info();
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];
+ roi_data_type qx1 = rois_ptr[values_per_roi * roi_indx + 1];
+ roi_data_type qy1 = rois_ptr[values_per_roi * roi_indx + 2];
+ roi_data_type qx2 = rois_ptr[values_per_roi * roi_indx + 3];
+ roi_data_type qy2 = rois_ptr[values_per_roi * roi_indx + 4];
+ float x1(qx1);
+ float x2(qx2);
+ float y1(qy1);
+ float y2(qy2);
+ if(is_qasymm)
+ {
+ x1 = dequantize_qasymm16(qx1, rois_qinfo);
+ x2 = dequantize_qasymm16(qx2, rois_qinfo);
+ y1 = dequantize_qasymm16(qy1, rois_qinfo);
+ y2 = dequantize_qasymm16(qy2, rois_qinfo);
+ }
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);
@@ -293,29 +403,36 @@
{
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);
+ 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));
+ input_data_type out_val(0);
+ if(is_qasymm)
+ {
+ out_val = roi_align_1x1_qasymm8<input_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, _output->info()->quantization_info());
+ }
+ else
+ {
+ out_val = roi_align_1x1<input_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)));
+ auto out_ptr = reinterpret_cast<input_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)));
+ auto out_ptr = reinterpret_cast<input_data_type *>(_output->ptr_to_element(Coordinates(ch, px, py, roi_indx)));
*out_ptr = out_val;
}
}
diff --git a/tests/validation/NEON/ROIAlignLayer.cpp b/tests/validation/NEON/ROIAlignLayer.cpp
index 853ef65..9433c21 100644
--- a/tests/validation/NEON/ROIAlignLayer.cpp
+++ b/tests/validation/NEON/ROIAlignLayer.cpp
@@ -52,6 +52,7 @@
AbsoluteTolerance<float> absolute_tolerance_f16(0.001f);
#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+constexpr AbsoluteTolerance<uint8_t> tolerance_qasymm8(1);
} // namespace
TEST_SUITE(NEON)
@@ -127,6 +128,24 @@
TEST_SUITE_END() // Float
+TEST_SUITE(Quantized)
+TEST_SUITE(QASYMM8)
+template <typename T>
+using NEROIAlignLayerQuantizedFixture = ROIAlignLayerQuantizedFixture<Tensor, Accessor, NEROIAlignLayer, T>;
+
+FIXTURE_DATA_TEST_CASE(Small, NEROIAlignLayerQuantizedFixture<uint8_t>, framework::DatasetMode::ALL,
+ combine(combine(combine(combine(datasets::SmallROIDataset(),
+ framework::dataset::make("DataType", { DataType::QASYMM8 })),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })),
+ framework::dataset::make("InputQuantizationInfo", { QuantizationInfo(1.f / 255.f, 127) })),
+ framework::dataset::make("OutputQuantizationInfo", { QuantizationInfo(2.f / 255.f, 120) })))
+{
+ // Validate output
+ validate(Accessor(_target), _reference, tolerance_qasymm8);
+}
+TEST_SUITE_END() // QASYMM8
+TEST_SUITE_END() // Quantized
+
TEST_SUITE_END() // RoiAlign
TEST_SUITE_END() // NEON
} // namespace validation