diff options
Diffstat (limited to 'tests/validation/dynamic_fusion/gpu/cl/DirectConv2d.cpp')
| -rw-r--r-- | tests/validation/dynamic_fusion/gpu/cl/DirectConv2d.cpp | 159 |
1 files changed, 154 insertions, 5 deletions
diff --git a/tests/validation/dynamic_fusion/gpu/cl/DirectConv2d.cpp b/tests/validation/dynamic_fusion/gpu/cl/DirectConv2d.cpp index bfb9735599..45a2270bb3 100644 --- a/tests/validation/dynamic_fusion/gpu/cl/DirectConv2d.cpp +++ b/tests/validation/dynamic_fusion/gpu/cl/DirectConv2d.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2022 Arm Limited. + * Copyright (c) 2022-2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -39,14 +39,18 @@ namespace test { namespace validation { +namespace +{ +RelativeTolerance<float> tolerance_f32(0.05f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */ +RelativeTolerance<half_float::half> tolerance_f16(half_float::half(0.2)); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */ +constexpr float abs_tolerance_f32(0.0001f); /**< Absolute tolerance for FP32 tests*/ +constexpr float tolerance_num = 0.02f; /**< Tolerance number */ +} // namespace + TEST_SUITE(CL) TEST_SUITE(DYNAMIC_FUSION) TEST_SUITE(CONV2D) -RelativeTolerance<float> tolerance_f32(0.01f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */ -RelativeTolerance<half_float::half> tolerance_f16(half_float::half(0.1)); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */ -constexpr float tolerance_num = 0.02f; /**< Tolerance number */ - template <typename T> using DynamicFusionGpuConv2dFixture = DynamicFusionGpuConv2dValidationFixture<CLTensor, CLAccessor, GpuConv2d, T>; TEST_SUITE(FP32) @@ -71,6 +75,151 @@ FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuConv2dFixture<half>, framework: } TEST_SUITE_END() // FP16 +// Tests for specific conv2d methods +TEST_SUITE(DIRECT_CONV2D) + +// *INDENT-OFF* +// clang-format off +DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip( + framework::dataset::make("InputInfo", { TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Invalid: Mismatching data type input/weights + TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Invalid: Mismatching input feature maps + TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Invalid weights dimensions + TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Unsupported biases size + TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Unsupported biases dimensions + TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, DataLayout::NCHW), // Unsupported data layout: NCHW + TensorInfo(TensorShape(2U, 32U, 16U), 1, DataType::QASYMM8, DataLayout::NHWC), // Unsupported data type: quantized + TensorInfo(TensorShape(2U, 32U, 16U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Arbitrary weight sizes for NHWC are supported + TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Non-rectangular weights dimensions for NHWC are supported + TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Strides > 2 for any kernel sizes for NHWC are supported + }), + framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(2U, 3U, 3U, 4U), 1, DataType::F16, DataLayout::NHWC), + TensorInfo(TensorShape(3U, 3U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(2U, 3U, 3U, 4U, 3U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(2U, 3U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(2U, 3U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, DataLayout::NCHW), + TensorInfo(TensorShape(2U, 1U, 1U, 4U), 1, DataType::QASYMM8, DataLayout::NHWC), + TensorInfo(TensorShape(2U, 1U, 1U, 4U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(2U, 13U, 13U, 4U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(2U, 5U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(2U, 3U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC), + })), + framework::dataset::make("BiasesInfo",{ TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(3U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(4U, 2U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(25U), 1, DataType::F32, DataLayout::NCHW), + TensorInfo(TensorShape(4U), 1, DataType::QASYMM8, DataLayout::NHWC), + TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC), + })), + framework::dataset::make("Conv2dAttributes", { + Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}), + Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}), + Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}), + Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}), + Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}), + Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}), + Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}), + Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}), + Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}), + Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}), + Conv2dAttributes().stride({3, 3}).pad({0, 0, 0, 0}), + })), + framework::dataset::make("Expected", { false, false, false, false, false, false, false, true, true, true, true })), + input_info, weights_info, biases_info, conv2d_attrs, expected) +{ + auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context(); + auto gpu_ctx = GpuWorkloadContext{ &cl_compile_ctx }; + GpuWorkloadSketch sketch{ &gpu_ctx }; + + const TensorInfo sketch_input_info = sketch.create_tensor_info(input_info); + const TensorInfo sketch_weights_info = sketch.create_tensor_info(weights_info); + const TensorInfo sketch_biases_info = sketch.create_tensor_info(biases_info); + bool is_valid = bool(GpuConv2d::validate_op(sketch, &sketch_input_info, &sketch_weights_info, &sketch_biases_info, conv2d_attrs)); + ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS); +} +template <typename T> +using DynamicFusionGpuDirectConv2dFixture = DynamicFusionDirectConv2dValidationFixture<CLTensor, CLAccessor, GpuConv2d, T>; + +TEST_SUITE(FP16) +FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuDirectConv2dFixture<half>, framework::DatasetMode::PRECOMMIT, + combine(combine(combine(zip(zip(zip(zip(zip( + framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U), + TensorShape(19U, 5U, 16U, 4U), + TensorShape(13U, 5U, 17U, 2U), + TensorShape(32U, 37U, 13U) } ), + framework::dataset::make("StrideX", { 1, 3, 1, 1 })), + framework::dataset::make("StrideY", { 1, 3, 2, 1 })), + framework::dataset::make("PadX", { 1, 3, 0, 4 })), + framework::dataset::make("PadY", { 1, 3, 0, 4 })), + framework::dataset::make("KernelSize", { 3, 8, 1, 9 })), + framework::dataset::make("NumKernels", { 17, 3, 1, 19 })), + framework::dataset::make("DataType", DataType::F16)), + framework::dataset::make("DataLayout", DataLayout::NHWC))) +{ + validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num); +} + +FIXTURE_DATA_TEST_CASE(RunLarge, DynamicFusionGpuDirectConv2dFixture<half>, framework::DatasetMode::NIGHTLY, + combine(combine(combine(zip(zip(zip(zip(zip( + framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ), + framework::dataset::make("StrideX", { 1 })), + framework::dataset::make("StrideY", { 1 })), + framework::dataset::make("PadX", { 1 })), + framework::dataset::make("PadY", { 1 })), + framework::dataset::make("KernelSize", { 9 })), + framework::dataset::make("NumKernels", { 3 })), + framework::dataset::make("DataType", DataType::F16)), + framework::dataset::make("DataLayout", DataLayout::NHWC))) +{ + validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num); +} + +TEST_SUITE_END() // FP16 + +TEST_SUITE(FP32) +FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuDirectConv2dFixture<float>, framework::DatasetMode::PRECOMMIT, + combine(combine(combine(zip(zip(zip(zip(zip( + framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U), + TensorShape(19U, 5U, 16U, 4U), + TensorShape(13U, 5U, 17U, 2U), + TensorShape(32U, 37U, 13U) } ), + framework::dataset::make("StrideX", { 1, 3, 1, 1 })), + framework::dataset::make("StrideY", { 1, 3, 2, 1 })), + framework::dataset::make("PadX", { 1, 3, 0, 4 })), + framework::dataset::make("PadY", { 1, 3, 0, 4 })), + framework::dataset::make("KernelSize", { 3, 8, 1, 9 })), + framework::dataset::make("NumKernels", { 17, 3, 1, 19 })), + framework::dataset::make("DataType", DataType::F32)), + framework::dataset::make("DataLayout", DataLayout::NHWC))) +{ + validate(CLAccessor(_target), _reference, tolerance_f32, 0.0, abs_tolerance_f32); +} + +FIXTURE_DATA_TEST_CASE(RunLarge, DynamicFusionGpuDirectConv2dFixture<float>, framework::DatasetMode::NIGHTLY, + combine(combine(combine(zip(zip(zip(zip(zip( + framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ), + framework::dataset::make("StrideX", { 1 })), + framework::dataset::make("StrideY", { 1 })), + framework::dataset::make("PadX", { 1 })), + framework::dataset::make("PadY", { 1 })), + framework::dataset::make("KernelSize", { 9 })), + framework::dataset::make("NumKernels", { 3 })), + framework::dataset::make("DataType", DataType::F32)), + framework::dataset::make("DataLayout", DataLayout::NHWC))) +{ + validate(CLAccessor(_target), _reference, tolerance_f32, 0.0, abs_tolerance_f32); +} +// clang-format on +// *INDENT-ON* + +TEST_SUITE_END() // FP32 +TEST_SUITE_END() // DIRECT_CONV2D TEST_SUITE_END() // CONV2D TEST_SUITE_END() // DYNAMIC_FUSION TEST_SUITE_END() // CL |