| /* |
| * Copyright (c) 2017-2019 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_DEQUANTIZATION_LAYER_FIXTURE |
| #define ARM_COMPUTE_TEST_DEQUANTIZATION_LAYER_FIXTURE |
| |
| #include "arm_compute/core/TensorShape.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/runtime/Tensor.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/DequantizationLayer.h" |
| |
| #include <random> |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class DequantizationValidationFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape shape, DataType src_data_type, DataType dst_datatype, DataLayout data_layout) |
| { |
| _quantization_info = generate_quantization_info(src_data_type, shape.z()); |
| _target = compute_target(shape, src_data_type, dst_datatype, data_layout); |
| _reference = compute_reference(shape, src_data_type); |
| } |
| |
| protected: |
| template <typename U> |
| void fill(U &&tensor) |
| { |
| library->fill_tensor_uniform(tensor, 0); |
| } |
| |
| TensorType compute_target(TensorShape shape, DataType src_data_type, DataType dst_datatype, DataLayout data_layout) |
| { |
| if(data_layout == DataLayout::NHWC) |
| { |
| permute(shape, PermutationVector(2U, 0U, 1U)); |
| } |
| |
| // Create tensors |
| TensorType src = create_tensor<TensorType>(shape, src_data_type, 1, _quantization_info, data_layout); |
| TensorType dst = create_tensor<TensorType>(shape, dst_datatype, 1, QuantizationInfo(), data_layout); |
| |
| // Create and configure function |
| FunctionType dequantization_layer; |
| dequantization_layer.configure(&src, &dst); |
| |
| ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Allocate tensors |
| src.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| |
| ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Fill tensors |
| fill(AccessorType(src)); |
| |
| // Compute function |
| dequantization_layer.run(); |
| |
| return dst; |
| } |
| |
| SimpleTensor<T> compute_reference(const TensorShape &shape, DataType src_data_type) |
| { |
| switch(src_data_type) |
| { |
| case DataType::QASYMM8: |
| case DataType::QASYMM8_PER_CHANNEL: |
| { |
| SimpleTensor<uint8_t> src{ shape, src_data_type, 1, _quantization_info }; |
| fill(src); |
| return reference::dequantization_layer<T>(src); |
| } |
| case DataType::QSYMM8: |
| { |
| SimpleTensor<int8_t> src{ shape, src_data_type, 1, _quantization_info }; |
| fill(src); |
| return reference::dequantization_layer<T>(src); |
| } |
| case DataType::QSYMM16: |
| { |
| SimpleTensor<int16_t> src{ shape, src_data_type, 1, _quantization_info }; |
| fill(src); |
| return reference::dequantization_layer<T>(src); |
| } |
| default: |
| ARM_COMPUTE_ERROR("Unsupported data type"); |
| } |
| } |
| |
| protected: |
| QuantizationInfo generate_quantization_info(DataType data_type, int32_t num_channels) |
| { |
| std::mt19937 gen(library.get()->seed()); |
| std::uniform_int_distribution<> distribution_scale_q8(1, 255); |
| std::uniform_int_distribution<> distribution_offset_q8(1, 127); |
| std::uniform_int_distribution<> distribution_scale_q16(1, 32768); |
| |
| switch(data_type) |
| { |
| case DataType::QSYMM16: |
| return QuantizationInfo(1.f / distribution_scale_q16(gen)); |
| case DataType::QSYMM8: |
| return QuantizationInfo(1.f / distribution_scale_q8(gen)); |
| case DataType::QASYMM8_PER_CHANNEL: |
| { |
| std::vector<float> scale(num_channels); |
| std::vector<int32_t> offset(num_channels); |
| for(int32_t i = 0; i < num_channels; ++i) |
| { |
| scale[i] = 1.f / distribution_scale_q8(gen); |
| offset[i] = distribution_offset_q8(gen); |
| } |
| return QuantizationInfo(scale, offset); |
| } |
| case DataType::QASYMM8: |
| return QuantizationInfo(1.f / distribution_scale_q8(gen), distribution_offset_q8(gen)); |
| default: |
| ARM_COMPUTE_ERROR("Unsupported data type"); |
| } |
| } |
| |
| protected: |
| TensorType _target{}; |
| SimpleTensor<T> _reference{}; |
| QuantizationInfo _quantization_info{}; |
| }; |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* ARM_COMPUTE_TEST_DEQUANTIZATION_LAYER_FIXTURE */ |