/* * 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_SPACE_TO_BATCH_LAYER_FIXTURE #define ARM_COMPUTE_TEST_SPACE_TO_BATCH_LAYER_FIXTURE #include "tests/Globals.h" #include "tests/framework/Asserts.h" #include "tests/framework/Fixture.h" #include "tests/validation/reference/SpaceToBatch.h" namespace arm_compute { namespace test { namespace validation { template class SpaceToBatchLayerValidationGenericFixture : public framework::Fixture { public: template void setup(TensorShape input_shape, TensorShape block_shape_shape, TensorShape paddings_shape, TensorShape output_shape, DataType data_type, DataLayout data_layout, QuantizationInfo quantization_info) { _target = compute_target(input_shape, block_shape_shape, paddings_shape, output_shape, data_type, data_layout, quantization_info); _reference = compute_reference(input_shape, block_shape_shape, paddings_shape, output_shape, data_type, quantization_info); } protected: template void fill(U &&tensor, int i) { library->fill_tensor_uniform(tensor, i); } template void fill_pad(U &&tensor) { library->fill_tensor_value(tensor, 0); } TensorType compute_target(TensorShape input_shape, const TensorShape &block_shape_shape, const TensorShape &paddings_shape, TensorShape output_shape, DataType data_type, DataLayout data_layout, QuantizationInfo quantization_info) { if(data_layout == DataLayout::NHWC) { permute(input_shape, PermutationVector(2U, 0U, 1U)); permute(output_shape, PermutationVector(2U, 0U, 1U)); } // Create tensors TensorType input = create_tensor(input_shape, data_type, 1, quantization_info, data_layout); TensorType block_shape = create_tensor(block_shape_shape, DataType::S32); TensorType paddings = create_tensor(paddings_shape, DataType::S32); TensorType output = create_tensor(output_shape, data_type, 1, quantization_info, data_layout); // Create and configure function FunctionType space_to_batch; space_to_batch.configure(&input, &block_shape, &paddings, &output); ARM_COMPUTE_EXPECT(input.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(block_shape.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(paddings.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(output.info()->is_resizable(), framework::LogLevel::ERRORS); // Allocate tensors input.allocator()->allocate(); block_shape.allocator()->allocate(); paddings.allocator()->allocate(); output.allocator()->allocate(); ARM_COMPUTE_EXPECT(!input.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(!block_shape.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(!paddings.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(!output.info()->is_resizable(), framework::LogLevel::ERRORS); // Fill tensors fill(AccessorType(input), 0); fill_pad(AccessorType(paddings)); { auto block_shape_data = AccessorType(block_shape); const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); for(unsigned int i = 0; i < block_shape_shape.x(); ++i) { static_cast(block_shape_data.data())[i] = input_shape[i + idx_width] / output_shape[i + idx_width]; } } // Compute function space_to_batch.run(); return output; } SimpleTensor compute_reference(const TensorShape &input_shape, const TensorShape &block_shape_shape, const TensorShape &paddings_shape, const TensorShape &output_shape, DataType data_type, QuantizationInfo quantization_info) { // Create reference SimpleTensor input{ input_shape, data_type, 1, quantization_info }; SimpleTensor block_shape{ block_shape_shape, DataType::S32 }; SimpleTensor paddings{ paddings_shape, DataType::S32 }; // Fill reference fill(input, 0); fill_pad(paddings); for(unsigned int i = 0; i < block_shape_shape.x(); ++i) { block_shape[i] = input_shape[i] / output_shape[i]; } // Compute reference return reference::space_to_batch(input, block_shape, paddings, output_shape); } TensorType _target{}; SimpleTensor _reference{}; }; template class SpaceToBatchLayerValidationFixture : public SpaceToBatchLayerValidationGenericFixture { public: template void setup(TensorShape input_shape, TensorShape block_shape_shape, TensorShape paddings_shape, TensorShape output_shape, DataType data_type, DataLayout data_layout) { SpaceToBatchLayerValidationGenericFixture::setup(input_shape, block_shape_shape, paddings_shape, output_shape, data_type, data_layout, QuantizationInfo()); } }; template class SpaceToBatchLayerValidationQuantizedFixture : public SpaceToBatchLayerValidationGenericFixture { public: template void setup(TensorShape input_shape, TensorShape block_shape_shape, TensorShape paddings_shape, TensorShape output_shape, DataType data_type, DataLayout data_layout, QuantizationInfo quantization_info) { SpaceToBatchLayerValidationGenericFixture::setup(input_shape, block_shape_shape, paddings_shape, output_shape, data_type, data_layout, quantization_info); } }; } // namespace validation } // namespace test } // namespace arm_compute #endif /* ARM_COMPUTE_TEST_SPACE_TO_BATCH_LAYER_FIXTURE */