| /* |
| * Copyright (c) 2018-2019 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_WIDTHCONCATENATE_LAYER_FIXTURE |
| #define ARM_COMPUTE_TEST_WIDTHCONCATENATE_LAYER_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/ConcatenateLayer.h" |
| |
| #include <random> |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| template <typename TensorType, typename ITensorType, typename AccessorType, typename FunctionType, typename T> |
| class ConcatenateLayerValidationFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape shape, DataType data_type, unsigned int axis) |
| { |
| // Create input shapes |
| std::mt19937 gen(library->seed()); |
| std::uniform_int_distribution<> num_dis(2, 8); |
| std::uniform_int_distribution<> offset_dis(0, 20); |
| |
| const int num_tensors = num_dis(gen); |
| |
| std::vector<TensorShape> shapes(num_tensors, shape); |
| |
| // vector holding the quantization info: |
| // the last element is the output quantization info |
| // all other elements are the quantization info for the input tensors |
| std::vector<QuantizationInfo> qinfo(num_tensors + 1, QuantizationInfo()); |
| for(auto &qi : qinfo) |
| { |
| qi = QuantizationInfo(1.f / 255.f, offset_dis(gen)); |
| } |
| std::bernoulli_distribution mutate_dis(0.5f); |
| std::uniform_real_distribution<> change_dis(-0.25f, 0.f); |
| |
| // Generate more shapes based on the input |
| for(auto &s : shapes) |
| { |
| // Randomly change the dimension |
| if(mutate_dis(gen)) |
| { |
| // Decrease the dimension by a small percentage. Don't increase |
| // as that could make tensor too large. |
| s.set(axis, s[axis] + 2 * static_cast<int>(s[axis] * change_dis(gen))); |
| } |
| } |
| |
| _target = compute_target(shapes, qinfo, data_type, axis); |
| _reference = compute_reference(shapes, qinfo, data_type, axis); |
| } |
| |
| protected: |
| template <typename U> |
| void fill(U &&tensor, int i) |
| { |
| library->fill_tensor_uniform(tensor, i); |
| } |
| |
| TensorType compute_target(const std::vector<TensorShape> &shapes, const std::vector<QuantizationInfo> &qinfo, DataType data_type, unsigned int axis) |
| { |
| std::vector<TensorType> srcs; |
| std::vector<ITensorType *> src_ptrs; |
| |
| // Create tensors |
| srcs.reserve(shapes.size()); |
| |
| for(size_t j = 0; j < shapes.size(); ++j) |
| { |
| srcs.emplace_back(create_tensor<TensorType>(shapes[j], data_type, 1, qinfo[j])); |
| src_ptrs.emplace_back(&srcs.back()); |
| } |
| |
| const TensorShape dst_shape = misc::shape_calculator::calculate_concatenate_shape(src_ptrs, axis); |
| TensorType dst = create_tensor<TensorType>(dst_shape, data_type, 1, qinfo[shapes.size()]); |
| |
| // Create and configure function |
| FunctionType concat; |
| concat.configure(src_ptrs, &dst, axis); |
| |
| for(auto &src : srcs) |
| { |
| ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); |
| } |
| |
| ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Allocate tensors |
| for(auto &src : srcs) |
| { |
| src.allocator()->allocate(); |
| ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); |
| } |
| |
| dst.allocator()->allocate(); |
| ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Fill tensors |
| int i = 0; |
| for(auto &src : srcs) |
| { |
| fill(AccessorType(src), i++); |
| } |
| |
| // Compute function |
| concat.run(); |
| |
| return dst; |
| } |
| |
| SimpleTensor<T> compute_reference(std::vector<TensorShape> &shapes, const std::vector<QuantizationInfo> &qinfo, DataType data_type, unsigned int axis) |
| { |
| std::vector<SimpleTensor<T>> srcs; |
| std::vector<TensorShape *> src_ptrs; |
| |
| // Create and fill tensors |
| for(size_t j = 0; j < shapes.size(); ++j) |
| { |
| srcs.emplace_back(shapes[j], data_type, 1, qinfo[j]); |
| fill(srcs.back(), j); |
| src_ptrs.emplace_back(&shapes[j]); |
| } |
| |
| const TensorShape dst_shape = misc::shape_calculator::calculate_concatenate_shape(src_ptrs, axis); |
| SimpleTensor<T> dst{ dst_shape, data_type, 1, qinfo[shapes.size()] }; |
| return reference::concatenate_layer<T>(srcs, dst, axis); |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<T> _reference{}; |
| }; |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* ARM_COMPUTE_TEST_WIDTHCONCATENATE_LAYER_FIXTURE */ |