diff options
Diffstat (limited to 'tests/validation/NEON/FullyConnectedLayer.cpp')
| -rw-r--r-- | tests/validation/NEON/FullyConnectedLayer.cpp | 193 |
1 files changed, 169 insertions, 24 deletions
diff --git a/tests/validation/NEON/FullyConnectedLayer.cpp b/tests/validation/NEON/FullyConnectedLayer.cpp index 4bb48bf42c..8ba0f1f771 100644 --- a/tests/validation/NEON/FullyConnectedLayer.cpp +++ b/tests/validation/NEON/FullyConnectedLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2020 Arm Limited. + * Copyright (c) 2017-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -25,6 +25,8 @@ #include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h" #include "arm_compute/runtime/Tensor.h" #include "arm_compute/runtime/TensorAllocator.h" +#include "src/core/helpers/MemoryHelpers.h" +#include "src/runtime/cpu/operators/CpuFullyConnected.h" #include "tests/NEON/Accessor.h" #include "tests/PaddingCalculator.h" #include "tests/datasets/FullyConnectedLayerDataset.h" @@ -94,6 +96,149 @@ const auto ActivationFunctionsQuantizedDataset = framework::dataset::make("Activ TEST_SUITE(NEON) TEST_SUITE(FullyConnectedLayer) +/** Test case for memory injection in @ref cpu::CpuFullyConnected. + * + * Configure the operator once and inject memory at run-time in multiple executions. + * + * Checks performed in order: + * - Both runs compute the same output + */ +TEST_CASE(MemoryInjection, framework::DatasetMode::ALL) +{ + auto fc = std::make_unique<cpu::CpuFullyConnected>(); + const auto src_info = TensorInfo(TensorShape(8U), 1, DataType::F32, DataLayout::NHWC); + const auto weight_info = TensorInfo(TensorShape(8U, 4U), 1, DataType::F32, DataLayout::NHWC); + const auto bias_info = TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC); + auto dst_info = TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC); + const auto fc_info = FullyConnectedLayerInfo{}; + fc->configure(&src_info, &weight_info, &bias_info, &dst_info, fc_info); + + // telhs are newly created every call of this lambda function + auto src = create_tensor<Tensor>(src_info); + auto weight = create_tensor<Tensor>(weight_info); + auto bias = create_tensor<Tensor>(bias_info); + src.allocator()->allocate(); + weight.allocator()->allocate(); + bias.allocator()->allocate(); + + ITensorPack run_pack{ { TensorType::ACL_SRC_0, &src }, { TensorType::ACL_SRC_1, &weight }, { TensorType::ACL_SRC_2, &bias } }; + ITensorPack prep_pack{ { TensorType::ACL_SRC_1, &weight }, { TensorType::ACL_SRC_2, &bias } }; + + auto mg = MemoryGroup{}; + auto ws = manage_workspace<Tensor>(fc->workspace(), mg, run_pack, prep_pack); + + auto run_conv = [&]() -> Tensor + { + auto dst = create_tensor<Tensor>(dst_info); + dst.allocator()->allocate(); + run_pack.add_tensor(TensorType::ACL_DST, &dst); + + library->fill_tensor_value(Accessor(src), 1.f); + library->fill_tensor_value(Accessor(weight), 2.f); + library->fill_tensor_value(Accessor(bias), 3.f); + // This operator is configured once and captured by this lambda. + fc->prepare(prep_pack); + fc->run(run_pack); + return dst; + }; + auto result_0 = run_conv(); + auto result_1 = run_conv(); + for(size_t i = 0; i < result_0.info()->tensor_shape().total_size(); ++i) + { + ARM_COMPUTE_EXPECT(((float *)result_0.buffer())[i] == ((float *)result_1.buffer())[i], framework::LogLevel::ERRORS); + } +} + +/** Test case for memory injection in @ref NEFullyConnectedLayer. + * + * Make sure @ref NEFullyConnectedLayer still works through injecting the memory at configure time using the old API. + * + * Checks performed in order: + * - Both runs compute the same output + */ +TEST_CASE(MultipleExecutionWithConfigure, framework::DatasetMode::ALL) +{ + auto fc = std::make_unique<NEFullyConnectedLayer>(); + const auto src_info = TensorInfo(TensorShape(8U), 1, DataType::F32, DataLayout::NHWC); + const auto weight_info = TensorInfo(TensorShape(8U, 4U), 1, DataType::F32, DataLayout::NHWC); + const auto bias_info = TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC); + auto dst_info = TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC); + const auto fc_info = FullyConnectedLayerInfo{}; + auto run_conv = [&]() + { + auto src = create_tensor<Tensor>(src_info); + auto weight = create_tensor<Tensor>(weight_info); + auto bias = create_tensor<Tensor>(bias_info); + auto dst = create_tensor<Tensor>(dst_info); + fc->configure(&src, &weight, &bias, &dst, fc_info); + src.allocator()->allocate(); + weight.allocator()->allocate(); + bias.allocator()->allocate(); + dst.allocator()->allocate(); + library->fill_tensor_value(Accessor(src), 1.f); + library->fill_tensor_value(Accessor(weight), 2.f); + library->fill_tensor_value(Accessor(bias), 3.f); + fc->run(); + return dst; + }; + auto result_0 = run_conv(); + auto result_1 = run_conv(); + for(size_t i = 0; i < result_0.info()->tensor_shape().total_size(); ++i) + { + ARM_COMPUTE_EXPECT(((float *)result_0.buffer())[i] == ((float *)result_1.buffer())[i], framework::LogLevel::ERRORS); + } +} + +/** Unit test for @ref cpu::CpuFullyConnected with quantized multipler > 1 + * + * Tests output correctness. + */ +TEST_CASE(Quant8_Signed_Mult_gt_1, framework::DatasetMode::ALL) +{ + auto fc = std::make_unique<cpu::CpuFullyConnected>(); + const auto src_info = TensorInfo(TensorShape(1U, 3U), 1, DataType::QASYMM8_SIGNED, QuantizationInfo(0.5f, -1)); + const auto weight_info = TensorInfo(TensorShape(1U), 1, DataType::QASYMM8_SIGNED, QuantizationInfo(0.5, -8)); + const auto bias_info = TensorInfo(TensorShape(1U), 1, DataType::S32); + auto dst_info = TensorInfo(TensorShape(1U, 3U), 1, DataType::QASYMM8_SIGNED, QuantizationInfo(0.1f, 0)); + const auto fc_info = FullyConnectedLayerInfo{}; + fc->configure(&src_info, &weight_info, &bias_info, &dst_info, fc_info); + + // telhs are newly created every call of this lambda function + auto src = create_tensor<Tensor>(src_info); + auto weight = create_tensor<Tensor>(weight_info); + auto bias = create_tensor<Tensor>(bias_info); + auto dst = create_tensor<Tensor>(dst_info); + src.allocator()->allocate(); + weight.allocator()->allocate(); + bias.allocator()->allocate(); + dst.allocator()->allocate(); + + ITensorPack run_pack{ { TensorType::ACL_SRC_0, &src }, { TensorType::ACL_SRC_1, &weight }, { TensorType::ACL_SRC_2, &bias }, { TensorType::ACL_DST, &dst } }; + ITensorPack prep_pack{ { TensorType::ACL_SRC_1, &weight }, { TensorType::ACL_SRC_2, &bias } }; + + auto mg = MemoryGroup{}; + auto ws = manage_workspace<Tensor>(fc->workspace(), mg, run_pack, prep_pack); + + // Initialize input values + const std::vector<int8_t> src_values = { 3, 63, 31 }; + const std::vector<int8_t> weight_values = { -4 }; + const std::vector<int32_t> bias_values = { 16 }; + const std::vector<int32_t> expected = { 80, 127, 127 }; + library->fill_static_values(Accessor(src), src_values); + library->fill_static_values(Accessor(weight), weight_values); + library->fill_static_values(Accessor(bias), bias_values); + + // Run FC layer + fc->prepare(prep_pack); + fc->run(run_pack); + + auto dst_ptr = reinterpret_cast<int8_t *>(dst.buffer()); + for(size_t i = 0; i < dst.info()->tensor_shape().total_size(); ++i) + { + ARM_COMPUTE_EXPECT(dst_ptr[i] == expected[i], framework::LogLevel::ERRORS); + } +} + // *INDENT-OFF* // clang-format off DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip( @@ -186,13 +331,13 @@ FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerFixture<float>, framework: validate(Accessor(_target), _reference, rel_tolerance_f32, 0, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, NEFullyConnectedLayerMixedDataLayoutFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(combine(combine( - framework::dataset::make("Input", TensorShape(9U, 5U, 7U)), - framework::dataset::make("Weights", TensorShape(315U, 271U))), - framework::dataset::make("Biases", TensorShape(271U))), - framework::dataset::make("Output", TensorShape(271U))), - FullyConnectedParameters), - framework::dataset::make("DataType", DataType::F32)), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) + framework::dataset::make("Input", TensorShape(9U, 5U, 7U)), + framework::dataset::make("Weights", TensorShape(315U, 271U))), + framework::dataset::make("Biases", TensorShape(271U))), + framework::dataset::make("Output", TensorShape(271U))), + FullyConnectedParameters), + framework::dataset::make("DataType", DataType::F32)), + framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) { // Validate output validate(Accessor(_target), _reference, rel_tolerance_f32, 0, abs_tolerance_f32); @@ -235,14 +380,14 @@ FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerQuantizedFixture<uint8_t>, } FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, NEFullyConnectedLayerQuantizedMixedDataLayoutFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(combine(combine(combine( - framework::dataset::make("Input", TensorShape(9U, 5U, 7U)), - framework::dataset::make("Weights", TensorShape(315U, 271U))), - framework::dataset::make("Biases", TensorShape(271U))), - framework::dataset::make("Output", TensorShape(271U))), - FullyConnectedParameters), - framework::dataset::make("DataType", DataType::QASYMM8)), - QuantizationData), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) + framework::dataset::make("Input", TensorShape(9U, 5U, 7U)), + framework::dataset::make("Weights", TensorShape(315U, 271U))), + framework::dataset::make("Biases", TensorShape(271U))), + framework::dataset::make("Output", TensorShape(271U))), + FullyConnectedParameters), + framework::dataset::make("DataType", DataType::QASYMM8)), + QuantizationData), + framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) { // Validate output validate(Accessor(_target), _reference, tolerance_qasymm8); @@ -282,14 +427,14 @@ FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerQuantizedFixture<int8_t>, } FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, NEFullyConnectedLayerQuantizedMixedDataLayoutFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(combine(combine(combine( - framework::dataset::make("Input", TensorShape(9U, 5U, 7U)), - framework::dataset::make("Weights", TensorShape(315U, 271U))), - framework::dataset::make("Biases", TensorShape(271U))), - framework::dataset::make("Output", TensorShape(271U))), - FullyConnectedParameters), - framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), - QuantizationData), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) + framework::dataset::make("Input", TensorShape(9U, 5U, 7U)), + framework::dataset::make("Weights", TensorShape(315U, 271U))), + framework::dataset::make("Biases", TensorShape(271U))), + framework::dataset::make("Output", TensorShape(271U))), + FullyConnectedParameters), + framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), + QuantizationData), + framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) { // Validate output validate(Accessor(_target), _reference, tolerance_qasymm8); 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