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+/*
+* Copyright (c) 2022-2024 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 ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESIZEFIXTURE_H
+#define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESIZEFIXTURE_H
+
+#include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h"
+#include "arm_compute/dynamic_fusion/sketch/attributes/ResizeAttributes.h"
+#include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h"
+#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h"
+
+#include "tests/CL/CLAccessor.h"
+#include "tests/framework/Fixture.h"
+#include "tests/framework/Macros.h"
+#include "tests/SimpleTensor.h"
+#include "tests/validation/reference/Permute.h"
+#include "tests/validation/reference/Scale.h"
+#include "tests/validation/Validation.h"
+
+using namespace arm_compute::experimental::dynamic_fusion;
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class DynamicFusionResizeGenericValidationFixture : public framework::Fixture
+{
+public:
+ void setup(TensorShape shape,
+ DataType data_type,
+ QuantizationInfo quantization_info,
+ DataLayout data_layout,
+ InterpolationPolicy interpolation_policy,
+ SamplingPolicy sampling_policy,
+ bool align_corners,
+ QuantizationInfo output_quantization_info)
+ {
+ _shape = shape;
+ _interpolation_policy = interpolation_policy;
+ _sampling_policy = sampling_policy;
+ _data_type = data_type;
+ _input_quantization_info = quantization_info;
+ _output_quantization_info = output_quantization_info;
+ _align_corners = align_corners;
+ _data_layout = data_layout;
+
+ ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC); // Dynamic fusion resize supports only NHWC layout
+
+ generate_scale(shape);
+
+ std::mt19937 generator(library->seed());
+ std::uniform_int_distribution<uint32_t> distribution_u8(0, 255);
+
+ _target = compute_target(shape);
+ _reference = compute_reference(shape);
+ }
+
+protected:
+ void generate_scale(const TensorShape &shape)
+ {
+ static constexpr float _min_scale{0.25f};
+ static constexpr float _max_scale{3.f};
+
+ constexpr float max_width{8192.0f};
+ constexpr float max_height{6384.0f};
+ constexpr float min_width{1.f};
+ constexpr float min_height{1.f};
+
+ std::mt19937 generator(library->seed());
+ std::uniform_real_distribution<float> distribution_float(_min_scale, _max_scale);
+
+ auto generate = [&](size_t input_size, float min_output, float max_output) -> int
+ {
+ const float generated_scale = distribution_float(generator);
+ const int output_size = static_cast<int>(
+ utility::clamp(static_cast<float>(input_size) * generated_scale, min_output, max_output));
+ return output_size;
+ };
+
+ // Input shape is always given in NCHW layout. NHWC is dealt by permute in compute_target()
+ const int idx_width = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::WIDTH);
+ const int idx_height = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::HEIGHT);
+
+ _output_width = generate(shape[idx_width], min_width, max_width);
+ _output_height = generate(shape[idx_height], min_height, max_height);
+ }
+
+ template <typename U>
+ void fill(U &&tensor)
+ {
+ if (tensor.data_type() == DataType::F32)
+ {
+ std::uniform_real_distribution<float> distribution(-5.0f, 5.0f);
+ library->fill(tensor, distribution, 0);
+ }
+ else if (tensor.data_type() == DataType::F16)
+ {
+ arm_compute::utils::uniform_real_distribution_16bit<half> distribution{-5.0f, 5.0f};
+ library->fill(tensor, distribution, 0);
+ }
+ else if (is_data_type_quantized(tensor.data_type()))
+ {
+ std::uniform_int_distribution<> distribution(0, 100);
+ library->fill(tensor, distribution, 0);
+ }
+ else
+ {
+ library->fill_tensor_uniform(tensor, 0);
+ }
+ }
+
+ TensorType compute_target(TensorShape shape)
+ {
+ // Our test shapes are assumed in NCHW data layout, thus the permutation
+ permute(shape, PermutationVector(2U, 0U, 1U));
+
+ // Create a new workload sketch
+ CLCompileContext cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
+ GpuWorkloadContext context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
+
+ // Create sketch tensors
+ ITensorInfo *src_info = context.create_tensor_info(TensorInfo(shape, 1, _data_type, _data_layout));
+ src_info->set_quantization_info(_input_quantization_info);
+ ITensorInfo *dst_info = context.create_tensor_info();
+
+ ResizeAttributes attributes;
+ attributes.align_corners(_align_corners)
+ .sampling_policy(_sampling_policy)
+ .interpolation_policy(_interpolation_policy)
+ .output_width(_output_width)
+ .output_height(_output_height);
+
+ ITensorInfo *scale_result_info = FunctionType::create_op(sketch, src_info, attributes);
+ GpuOutput::create_op(sketch, scale_result_info, dst_info);
+
+ // Configure runtime
+ ClWorkloadRuntime runtime;
+ runtime.configure(sketch);
+
+ // (Important) Allocate auxiliary tensor memory if there are any
+ for (auto &data : runtime.get_auxiliary_tensors())
+ {
+ CLTensor *tensor = std::get<0>(data);
+ TensorInfo info = std::get<1>(data);
+ AuxMemoryInfo aux_mem_req = std::get<2>(data);
+ tensor->allocator()->init(info, aux_mem_req.alignment);
+ tensor->allocator()->allocate(); // Use ACL allocated memory
+ }
+
+ // Construct user tensors
+ TensorType t_src{};
+ TensorType t_dst{};
+
+ // Initialize user tensors
+ t_src.allocator()->init(*src_info);
+ t_dst.allocator()->init(*dst_info);
+
+ // Allocate and fill user tensors
+ t_src.allocator()->allocate();
+ t_dst.allocator()->allocate();
+
+ fill(AccessorType(t_src));
+
+ // Run runtime
+ runtime.run({&t_src, &t_dst});
+
+ return t_dst;
+ }
+
+ SimpleTensor<T> compute_reference(const TensorShape &shape)
+ {
+ // Create reference
+ SimpleTensor<T> src{shape, _data_type, 1, _input_quantization_info};
+
+ // Reference code is NCHW, so the input shapes are NCHW
+ const int idx_width = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::WIDTH);
+ const int idx_height = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::HEIGHT);
+
+ const float scale_x = static_cast<float>(_output_width) / shape[idx_width];
+ const float scale_y = static_cast<float>(_output_height) / shape[idx_height];
+
+ // Fill reference
+ fill(src);
+
+ return reference::scale<T>(src, scale_x, scale_y, _interpolation_policy, BorderMode::REPLICATE,
+ static_cast<T>(0), _sampling_policy, /* ceil_policy_scale */ false, _align_corners,
+ _output_quantization_info);
+ }
+
+ TensorType _target{};
+ SimpleTensor<T> _reference{};
+ TensorShape _shape{};
+ InterpolationPolicy _interpolation_policy{};
+ SamplingPolicy _sampling_policy{};
+ DataType _data_type{};
+ DataLayout _data_layout{};
+ QuantizationInfo _input_quantization_info{};
+ QuantizationInfo _output_quantization_info{};
+ bool _align_corners{false};
+ int _output_width{0};
+ int _output_height{0};
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class DynamicFusionResizeValidationFixture
+ : public DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+ void setup(TensorShape shape,
+ DataType data_type,
+ DataLayout data_layout,
+ InterpolationPolicy policy,
+ SamplingPolicy sampling_policy,
+ bool align_corners)
+ {
+ DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(
+ shape, data_type, QuantizationInfo(), data_layout, policy, sampling_policy, align_corners,
+ QuantizationInfo());
+ }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false>
+class DynamicFusionResizeQuantizedValidationFixture
+ : public DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+ void setup(TensorShape shape,
+ DataType data_type,
+ QuantizationInfo quantization_info,
+ DataLayout data_layout,
+ InterpolationPolicy policy,
+ SamplingPolicy sampling_policy,
+ bool align_corners)
+ {
+ DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(
+ shape, data_type, quantization_info, data_layout, policy, sampling_policy, align_corners,
+ quantization_info);
+ }
+};
+
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+
+#endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESIZEFIXTURE_H