COMPMID-2317: Implement CLROIAlignLayer
Change-Id: Iaa61b7a3528d3f82339d2ff8a2d77e77a1c68603
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1821
Reviewed-by: Pablo Marquez <pablo.tello@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/tests/validation/fixtures/ROIAlignLayerFixture.h b/tests/validation/fixtures/ROIAlignLayerFixture.h
index dfbb478..b9b85d3 100644
--- a/tests/validation/fixtures/ROIAlignLayerFixture.h
+++ b/tests/validation/fixtures/ROIAlignLayerFixture.h
@@ -26,7 +26,7 @@
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
-#include "arm_compute/runtime/CL/functions/CLROIAlignLayer.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "tests/AssetsLibrary.h"
#include "tests/Globals.h"
#include "tests/IAccessor.h"
@@ -42,14 +42,17 @@
namespace validation
{
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class ROIAlignLayerFixture : public framework::Fixture
+class ROIAlignLayerGenericFixture : public framework::Fixture
{
public:
+ using TRois = typename std::conditional<std::is_same<typename std::decay<T>::type, uint8_t>::value, uint16_t, T>::type;
+
template <typename...>
- void setup(TensorShape input_shape, const ROIPoolingLayerInfo pool_info, TensorShape rois_shape, DataType data_type, DataLayout data_layout)
+ void setup(TensorShape input_shape, const ROIPoolingLayerInfo pool_info, TensorShape rois_shape, DataType data_type, DataLayout data_layout, QuantizationInfo qinfo, QuantizationInfo output_qinfo)
{
- _target = compute_target(input_shape, data_type, data_layout, pool_info, rois_shape);
- _reference = compute_reference(input_shape, data_type, pool_info, rois_shape);
+ _rois_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::QASYMM16 : data_type;
+ _target = compute_target(input_shape, data_type, data_layout, pool_info, rois_shape, qinfo, output_qinfo);
+ _reference = compute_reference(input_shape, data_type, pool_info, rois_shape, qinfo, output_qinfo);
}
protected:
@@ -66,17 +69,17 @@
const size_t num_rois = rois_shape.y();
std::mt19937 gen(library->seed());
- T *rois_ptr = static_cast<T *>(rois.data());
+ TRois *rois_ptr = static_cast<TRois *>(rois.data());
const float pool_width = pool_info.pooled_width();
const float pool_height = pool_info.pooled_height();
const float roi_scale = pool_info.spatial_scale();
// Calculate distribution bounds
- const auto scaled_width = static_cast<T>((shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH)] / roi_scale) / pool_width);
- const auto scaled_height = static_cast<T>((shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT)] / roi_scale) / pool_height);
- const auto min_width = static_cast<T>(pool_width / roi_scale);
- const auto min_height = static_cast<T>(pool_height / roi_scale);
+ const auto scaled_width = static_cast<float>((shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH)] / roi_scale) / pool_width);
+ const auto scaled_height = static_cast<float>((shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT)] / roi_scale) / pool_height);
+ const auto min_width = static_cast<float>(pool_width / roi_scale);
+ const auto min_height = static_cast<float>(pool_height / roi_scale);
// Create distributions
std::uniform_int_distribution<int> dist_batch(0, shape[3] - 1);
@@ -93,11 +96,21 @@
const auto x2 = x1 + dist_w(gen);
const auto y2 = y1 + dist_h(gen);
- rois_ptr[values_per_roi * pw] = batch_idx;
- rois_ptr[values_per_roi * pw + 1] = x1;
- rois_ptr[values_per_roi * pw + 2] = y1;
- rois_ptr[values_per_roi * pw + 3] = x2;
- rois_ptr[values_per_roi * pw + 4] = y2;
+ rois_ptr[values_per_roi * pw] = batch_idx;
+ if(rois.data_type() == DataType::QASYMM16)
+ {
+ rois_ptr[values_per_roi * pw + 1] = quantize_qasymm16(static_cast<float>(x1), rois.quantization_info());
+ rois_ptr[values_per_roi * pw + 2] = quantize_qasymm16(static_cast<float>(y1), rois.quantization_info());
+ rois_ptr[values_per_roi * pw + 3] = quantize_qasymm16(static_cast<float>(x2), rois.quantization_info());
+ rois_ptr[values_per_roi * pw + 4] = quantize_qasymm16(static_cast<float>(y2), rois.quantization_info());
+ }
+ else
+ {
+ rois_ptr[values_per_roi * pw + 1] = static_cast<TRois>(x1);
+ rois_ptr[values_per_roi * pw + 2] = static_cast<TRois>(y1);
+ rois_ptr[values_per_roi * pw + 3] = static_cast<TRois>(x2);
+ rois_ptr[values_per_roi * pw + 4] = static_cast<TRois>(y2);
+ }
}
}
@@ -105,17 +118,23 @@
DataType data_type,
DataLayout data_layout,
const ROIPoolingLayerInfo &pool_info,
- const TensorShape rois_shape)
+ const TensorShape rois_shape,
+ const QuantizationInfo &qinfo,
+ const QuantizationInfo &output_qinfo)
{
if(data_layout == DataLayout::NHWC)
{
permute(input_shape, PermutationVector(2U, 0U, 1U));
}
+ const QuantizationInfo rois_qinfo = is_data_type_quantized(data_type) ? QuantizationInfo(0.125f, 0) : QuantizationInfo();
+
// Create tensors
- TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, QuantizationInfo(), data_layout);
- TensorType rois_tensor = create_tensor<TensorType>(rois_shape, data_type);
- TensorType dst;
+ TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, qinfo, data_layout);
+ TensorType rois_tensor = create_tensor<TensorType>(rois_shape, _rois_data_type, 1, rois_qinfo);
+
+ const TensorShape dst_shape = misc::shape_calculator::compute_roi_align_shape(*(src.info()), *(rois_tensor.info()), pool_info);
+ TensorType dst = create_tensor<TensorType>(dst_shape, data_type, 1, output_qinfo, data_layout);
// Create and configure function
FunctionType roi_align_layer;
@@ -147,23 +166,51 @@
SimpleTensor<T> compute_reference(const TensorShape &input_shape,
DataType data_type,
const ROIPoolingLayerInfo &pool_info,
- const TensorShape rois_shape)
+ const TensorShape rois_shape,
+ const QuantizationInfo &qinfo,
+ const QuantizationInfo &output_qinfo)
{
// Create reference tensor
- SimpleTensor<T> src{ input_shape, data_type };
- SimpleTensor<T> rois_tensor{ rois_shape, data_type };
+ SimpleTensor<T> src{ input_shape, data_type, 1, qinfo };
+ const QuantizationInfo rois_qinfo = is_data_type_quantized(data_type) ? QuantizationInfo(0.125f, 0) : QuantizationInfo();
+ SimpleTensor<TRois> rois_tensor{ rois_shape, _rois_data_type, 1, rois_qinfo };
// Fill reference tensor
fill(src);
generate_rois(rois_tensor, input_shape, pool_info, rois_shape);
- return reference::roi_align_layer(src, rois_tensor, pool_info);
+ return reference::roi_align_layer(src, rois_tensor, pool_info, output_qinfo);
}
TensorType _target{};
SimpleTensor<T> _reference{};
+ DataType _rois_data_type{};
};
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class ROIAlignLayerFixture : public ROIAlignLayerGenericFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+ template <typename...>
+ void setup(TensorShape input_shape, const ROIPoolingLayerInfo pool_info, TensorShape rois_shape, DataType data_type, DataLayout data_layout)
+ {
+ ROIAlignLayerGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, pool_info, rois_shape, data_type, data_layout,
+ QuantizationInfo(), QuantizationInfo());
+ }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class ROIAlignLayerQuantizedFixture : public ROIAlignLayerGenericFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+ template <typename...>
+ void setup(TensorShape input_shape, const ROIPoolingLayerInfo pool_info, TensorShape rois_shape, DataType data_type,
+ DataLayout data_layout, QuantizationInfo qinfo, QuantizationInfo output_qinfo)
+ {
+ ROIAlignLayerGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, pool_info, rois_shape,
+ data_type, data_layout, qinfo, output_qinfo);
+ }
+};
} // namespace validation
} // namespace test
} // namespace arm_compute