Added Qasymm8 datatype support to NEROIPoolingLayer with Tests
Tests added to check ROIPooling Layer against reference with both Float32 and Qasymm8 input.
Resolves : COMPMID-2319
Change-Id: I867bc4dde1e3e91f9f42f4a7ce8debfe83b8db50
Signed-off-by: Mohammed Suhail Munshi <MohammedSuhail.Munshi@arm.com>
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/c/VisualCompute/ComputeLibrary/+/296640
Tested-by: bsgcomp <bsgcomp@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>
Comments-Addressed: Pablo Tello <pablo.tello@arm.com>
Signed-off-by: Suhail Munshi <MohammedSuhail.Munshi@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5060
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Marquez Tello <pablo.tello@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/tests/validation/fixtures/ROIPoolingLayerFixture.h b/tests/validation/fixtures/ROIPoolingLayerFixture.h
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+/*
+ * Copyright (c) 2021 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.
+ */
+#ifndef ARM_COMPUTE_TEST_ROIPOOLINGLAYER_FIXTURE
+#define ARM_COMPUTE_TEST_ROIPOOLINGLAYER_FIXTURE
+
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "tests/AssetsLibrary.h"
+#include "tests/Globals.h"
+#include "tests/IAccessor.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Fixture.h"
+#include "tests/validation/Helpers.h"
+#include "tests/validation/reference/ROIPoolingLayer.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class ROIPoolingLayerGenericFixture : public framework::Fixture
+{
+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)
+ {
+ _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:
+ template <typename U>
+ void fill(U &&tensor)
+ {
+ library->fill_tensor_uniform(tensor, 0);
+ }
+
+ template <typename U>
+ void generate_rois(U &&rois, const TensorShape &shape, const ROIPoolingLayerInfo &pool_info, TensorShape rois_shape, DataLayout data_layout = DataLayout::NCHW)
+ {
+ const size_t values_per_roi = rois_shape.x();
+ const size_t num_rois = rois_shape.y();
+
+ std::mt19937 gen(library->seed());
+ uint16_t *rois_ptr = static_cast<uint16_t *>(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<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);
+ std::uniform_int_distribution<> dist_x1(0, scaled_width);
+ std::uniform_int_distribution<> dist_y1(0, scaled_height);
+ std::uniform_int_distribution<> dist_w(min_width, std::max(float(min_width), (pool_width - 2) * scaled_width));
+ std::uniform_int_distribution<> dist_h(min_height, std::max(float(min_height), (pool_height - 2) * scaled_height));
+
+ for(unsigned int pw = 0; pw < num_rois; ++pw)
+ {
+ const auto batch_idx = dist_batch(gen);
+ const auto x1 = dist_x1(gen);
+ const auto y1 = dist_y1(gen);
+ 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] = static_cast<uint16_t>(x1);
+ rois_ptr[values_per_roi * pw + 2] = static_cast<uint16_t>(y1);
+ rois_ptr[values_per_roi * pw + 3] = static_cast<uint16_t>(x2);
+ rois_ptr[values_per_roi * pw + 4] = static_cast<uint16_t>(y2);
+ }
+ }
+
+ TensorType compute_target(TensorShape input_shape,
+ DataType data_type,
+ DataLayout data_layout,
+ const ROIPoolingLayerInfo &pool_info,
+ const TensorShape rois_shape,
+ const QuantizationInfo &qinfo,
+ const QuantizationInfo &output_qinfo)
+ {
+ 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, qinfo, data_layout);
+ TensorType rois_tensor = create_tensor<TensorType>(rois_shape, _rois_data_type, 1, rois_qinfo);
+
+ // Initialise shape and declare output tensor dst
+ const TensorShape dst_shape;
+ TensorType dst = create_tensor<TensorType>(dst_shape, data_type, 1, output_qinfo, data_layout);
+
+ // Create and configure function
+ FunctionType roi_pool_layer;
+ roi_pool_layer.configure(&src, &rois_tensor, &dst, pool_info);
+
+ ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(rois_tensor.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Allocate tensors
+ src.allocator()->allocate();
+ rois_tensor.allocator()->allocate();
+ dst.allocator()->allocate();
+
+ ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!rois_tensor.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Fill tensors
+ fill(AccessorType(src));
+ generate_rois(AccessorType(rois_tensor), input_shape, pool_info, rois_shape, data_layout);
+
+ // Compute function
+ roi_pool_layer.run();
+
+ return dst;
+ }
+
+ SimpleTensor<T> compute_reference(const TensorShape &input_shape,
+ DataType data_type,
+ const ROIPoolingLayerInfo &pool_info,
+ const TensorShape rois_shape,
+ const QuantizationInfo &qinfo,
+ const QuantizationInfo &output_qinfo)
+ {
+ // Create reference tensor
+ 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<uint16_t> 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_pool_layer(src, rois_tensor, pool_info, output_qinfo);
+ }
+
+ TensorType _target{};
+ SimpleTensor<T> _reference{};
+ const DataType _rois_data_type{ DataType::U16 };
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class ROIPoolingLayerQuantizedFixture : public ROIPoolingLayerGenericFixture<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)
+ {
+ ROIPoolingLayerGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, pool_info, rois_shape,
+ data_type, data_layout, qinfo, output_qinfo);
+ }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class ROIPoolingLayerFixture : public ROIPoolingLayerGenericFixture<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)
+ {
+ ROIPoolingLayerGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, pool_info, rois_shape, data_type, data_layout,
+ QuantizationInfo(), QuantizationInfo());
+ }
+};
+
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+
+#endif /* ARM_COMPUTE_TEST_ROIPOOLINGLAYER_FIXTURE */
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