| /* |
| * Copyright (c) 2017-2020 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. |
| */ |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/runtime/CL/CLTensor.h" |
| #include "arm_compute/runtime/CL/CLTensorAllocator.h" |
| #include "arm_compute/runtime/CL/functions/CLConvolutionLayer.h" |
| #include "arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h" |
| #include "tests/CL/CLAccessor.h" |
| #include "tests/PaddingCalculator.h" |
| #include "tests/datasets/LargeConvolutionLayerDataset.h" |
| #include "tests/datasets/SmallConvolutionLayerDataset.h" |
| #include "tests/datasets/TinyConvolutionLayerDataset.h" |
| #include "tests/framework/Asserts.h" |
| #include "tests/framework/Macros.h" |
| #include "tests/framework/datasets/Datasets.h" |
| #include "tests/validation/Validation.h" |
| #include "tests/validation/fixtures/ConvolutionLayerFixture.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace |
| { |
| constexpr AbsoluteTolerance<float> absolute_tolerance_float(0.0001f); /**< Absolute Tolerance value for comparing reference's output against implementation's output for DataType::F32 */ |
| RelativeTolerance<float> tolerance_f32(0.1f); /**< 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 AbsoluteTolerance<float> tolerance_qasymm8(1); /**< Tolerance value for comparing reference's output against implementation's output for quantized data types */ |
| constexpr float tolerance_num = 0.07f; /**< Tolerance number */ |
| |
| /** CNN data types */ |
| const auto CNNDataTypes = framework::dataset::make("DataType", |
| { |
| DataType::F16, |
| DataType::F32, |
| DataType::QASYMM8, |
| DataType::QASYMM8_SIGNED, |
| }); |
| |
| /** Grouped CNN data types */ |
| const auto GroupedCNNDataTypes = framework::dataset::make("DataType", |
| { |
| DataType::F16, |
| DataType::F32 |
| }); |
| |
| const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo", |
| { |
| ActivationLayerInfo(), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f) |
| }); |
| const auto ActivationFunctionsSmallDataset = framework::dataset::make("ActivationInfo", |
| { |
| ActivationLayerInfo(), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f) |
| }); |
| } // namespace |
| |
| TEST_SUITE(CL) |
| TEST_SUITE(ConvolutionLayer) |
| |
| // *INDENT-OFF* |
| // clang-format off |
| DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip( |
| framework::dataset::make("InputInfo", { TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32), // Select GEMM |
| TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32), // Select GEMM |
| TensorInfo(TensorShape(23U, 27U, 5U, 4U), 1, DataType::F32), // Select GEMM |
| TensorInfo(TensorShape(23U, 27U, 31U, 4U), 1, DataType::F32), // Select WINOGRAD |
| TensorInfo(TensorShape(3U, 3U, 2U, 1U), 1, DataType::F32), // Select GEMM |
| TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32), // Select GEMM |
| TensorInfo(TensorShape(17U, 31U, 32U), 1, DataType::F32), // Select WINOGRAD |
| TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32), // Select GEMM |
| TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::QASYMM8_SIGNED), // Select GEMM |
| }), |
| framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32), |
| TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32), |
| TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32), |
| TensorInfo(TensorShape(3U, 3U, 31U, 21U), 1, DataType::F32), |
| TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32), |
| TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16), |
| TensorInfo(TensorShape(5U, 5U, 32U, 19U), 1, DataType::F32), |
| TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32), |
| TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::QASYMM8_SIGNED), |
| })), |
| framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32), |
| TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32), |
| TensorInfo(TensorShape(21U, 25U, 21U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(21U, 25U, 21U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U, 12U, 16U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(17U, 31U, 19U), 1, DataType::F32), |
| TensorInfo(TensorShape(17U, 31U, 19U), 1, DataType::F32), |
| TensorInfo(TensorShape(17U, 31U, 19U), 1, DataType::QASYMM8_SIGNED), |
| })), |
| framework::dataset::make("ConvInfo", { PadStrideInfo(1, 2, 1, 1), |
| PadStrideInfo(1, 2, 1, 1), |
| PadStrideInfo(1, 1, 0, 0), |
| PadStrideInfo(1, 1, 0, 0), |
| PadStrideInfo(2, 1, 0, 0), |
| PadStrideInfo(3, 2, 1, 0), |
| PadStrideInfo(1, 1, 2, 2), |
| PadStrideInfo(1, 1, 2, 2), |
| PadStrideInfo(1, 1, 2, 2), |
| })), |
| framework::dataset::make("GpuTarget", { GPUTarget::BIFROST, |
| GPUTarget::MIDGARD, |
| GPUTarget::G71, |
| GPUTarget::G71, |
| GPUTarget::MIDGARD, |
| GPUTarget::BIFROST, |
| GPUTarget::BIFROST, |
| GPUTarget::BIFROST, |
| GPUTarget::BIFROST, |
| })), |
| framework::dataset::make("Dilation", { Size2D(1U, 1U), |
| Size2D(1U, 1U), |
| Size2D(1U, 1U), |
| Size2D(1U, 1U), |
| Size2D(1U, 1U), |
| Size2D(1U, 1U), |
| Size2D(1U, 1U), |
| Size2D(2U, 1U), |
| Size2D(2U, 1U), |
| })), |
| framework::dataset::make("EnableFastMath", { false, false, false, false, false, false, true, true, true })), |
| framework::dataset::make("Expected",{ ConvolutionMethod::GEMM, |
| ConvolutionMethod::GEMM, |
| ConvolutionMethod::GEMM, |
| ConvolutionMethod::WINOGRAD, |
| ConvolutionMethod::GEMM, |
| ConvolutionMethod::GEMM, |
| ConvolutionMethod::WINOGRAD, |
| ConvolutionMethod::GEMM, |
| ConvolutionMethod::GEMM, |
| })), |
| input_info, weights_info, output_info, conv_info, gpu_target, dilation, enable_fast_math, expected) |
| { |
| ConvolutionMethod is_valid = CLConvolutionLayer::get_convolution_method(&input_info.clone()->set_is_resizable(true), |
| &weights_info.clone()->set_is_resizable(true), |
| &output_info.clone()->set_is_resizable(true), conv_info, |
| WeightsInfo(), |
| ActivationLayerInfo(), |
| gpu_target, |
| dilation, |
| enable_fast_math); |
| ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS); |
| } |
| // clang-format on |
| // *INDENT-ON* |
| TEST_SUITE_END() // ConvolutionLayer |
| |
| TEST_SUITE(GEMMConvolutionLayer) |
| |
| DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(datasets::SmallConvolutionLayerDataset(), |
| CNNDataTypes), |
| ActivationFunctionsDataset), |
| input_shape, weights_shape, bias_shape, output_shape, info, dilation, data_type, act_info) |
| { |
| auto bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type; |
| |
| // Create tensors |
| CLTensor src = create_tensor<CLTensor>(input_shape, data_type, 1, QuantizationInfo(2.f / 255.f, 127)); |
| CLTensor weights = create_tensor<CLTensor>(weights_shape, data_type, 1, QuantizationInfo(2.f / 255.f, 127)); |
| CLTensor bias = create_tensor<CLTensor>(bias_shape, bias_data_type, 1, QuantizationInfo(2.f / 255.f, 127)); |
| CLTensor dst = create_tensor<CLTensor>(output_shape, data_type, 1, QuantizationInfo(2.f / 255.f, 127)); |
| |
| ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| const QuantizationInfo src_quantization_info = src.info()->quantization_info(); |
| const QuantizationInfo weights_quantization_info = weights.info()->quantization_info(); |
| |
| // Create and configure function |
| CLGEMMConvolutionLayer conv; |
| conv.configure(&src, &weights, &bias, &dst, info, WeightsInfo(), dilation, act_info); |
| |
| // Validate valid region |
| const ValidRegion src_valid_region = shape_to_valid_region(input_shape); |
| const ValidRegion weights_valid_region = shape_to_valid_region(weights_shape); |
| const ValidRegion bias_valid_region = shape_to_valid_region(bias_shape); |
| const ValidRegion dst_valid_region = shape_to_valid_region(output_shape); |
| |
| validate(src.info()->valid_region(), src_valid_region); |
| validate(weights.info()->valid_region(), weights_valid_region); |
| validate(bias.info()->valid_region(), bias_valid_region); |
| validate(dst.info()->valid_region(), dst_valid_region); |
| |
| // Validate QuantizationInfo |
| ARM_COMPUTE_EXPECT(src.info()->quantization_info() == src_quantization_info, framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(weights.info()->quantization_info() == weights_quantization_info, framework::LogLevel::ERRORS); |
| |
| // Validate padding |
| //TODO(COMPMID-415) Need to validate padding? |
| } |
| |
| template <typename T> |
| using CLGEMMConvolutionLayerFixture = ConvolutionValidationFixture<CLTensor, CLAccessor, CLGEMMConvolutionLayer, T>; |
| |
| TEST_SUITE(Float) |
| TEST_SUITE(FP16) |
| |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallConvolutionLayerReducedDataset(), |
| framework::dataset::make("ReshapeWeights", { true })), |
| framework::dataset::make("DataType", |
| DataType::F16)), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), |
| ActivationFunctionsSmallDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(combine(framework::dataset::concat(datasets::SmallConvolutionLayerDataset(), datasets::LargeConvolutionLayerDataset()), |
| framework::dataset::make("ReshapeWeights", { true })), |
| framework::dataset::make("DataType", |
| DataType::F16)), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), |
| ActivationFunctionsDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num); |
| } |
| TEST_SUITE_END() // FP16 |
| |
| TEST_SUITE(FP32) |
| |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallConvolutionLayerReducedDataset(), |
| framework::dataset::make("ReshapeWeights", { true })), |
| framework::dataset::make("DataType", |
| DataType::F32)), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), |
| ActivationFunctionsSmallDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f32); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(combine(framework::dataset::concat(datasets::SmallConvolutionLayerDataset(), datasets::LargeConvolutionLayerDataset()), |
| framework::dataset::make("ReshapeWeights", { true })), |
| framework::dataset::make("DataType", |
| DataType::F32)), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), |
| ActivationFunctionsDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, absolute_tolerance_float); |
| } |
| TEST_SUITE_END() // FP32 |
| TEST_SUITE_END() // Float |
| |
| template <typename T> |
| using CLGEMMConvolutionLayerQuantizedFixture = ConvolutionValidationQuantizedFixture<CLTensor, CLAccessor, CLGEMMConvolutionLayer, T>; |
| template <typename T> |
| using CLGEMMConvolutionLayerQuantizedPerChannelFixture = ConvolutionValidationQuantizedPerChannelFixture<CLTensor, CLAccessor, CLGEMMConvolutionLayer, T, int8_t>; |
| |
| const auto QuantizedActivationFunctionsDataset = framework::dataset::make("ActivationInfo", |
| { |
| ActivationLayerInfo(), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f) |
| }); |
| const auto QuantizedActivationFunctionsSmallDataset = framework::dataset::make("ActivationInfo", |
| { |
| ActivationLayerInfo(), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f) |
| }); |
| |
| TEST_SUITE(Quantized) |
| |
| const auto QuantizationData = framework::dataset::make("QuantizationInfo", |
| { |
| QuantizationInfo(0.5f, 10), |
| QuantizationInfo(0.3f, 3), |
| QuantizationInfo(1.1f, 10), |
| }); |
| TEST_SUITE(QASYMM8) |
| |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, |
| combine(combine(combine(combine(combine(datasets::SmallConvolutionLayerReducedDataset(), |
| framework::dataset::make("ReshapeWeights", { true })), |
| framework::dataset::make("DataType", DataType::QASYMM8)), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), |
| QuantizationData), |
| QuantizedActivationFunctionsSmallDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(combine(combine(framework::dataset::concat(datasets::SmallConvolutionLayerDataset(), datasets::LargeConvolutionLayerDataset()), |
| framework::dataset::make("ReshapeWeights", { true })), |
| framework::dataset::make("DataType", DataType::QASYMM8)), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), |
| QuantizationData), |
| QuantizedActivationFunctionsDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); |
| } |
| TEST_SUITE_END() // QASYMM8 |
| TEST_SUITE(QASYMM8_SIGNED) |
| |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerQuantizedFixture<int8_t>, framework::DatasetMode::PRECOMMIT, |
| combine(combine(combine(combine(combine(datasets::SmallConvolutionLayerReducedDataset(), |
| framework::dataset::make("ReshapeWeights", { true })), |
| framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), |
| QuantizationData), |
| QuantizedActivationFunctionsSmallDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); |
| } |
| TEST_SUITE_END() // QASYMM8_SIGNED |
| TEST_SUITE(QSYMM8_PER_CHANNEL) |
| |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerQuantizedPerChannelFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, |
| combine(combine(combine(combine(combine(combine(datasets::SmallConvolutionLayerReducedDataset(), |
| framework::dataset::make("ReshapeWeights", { true })), |
| framework::dataset::make("DataType", { DataType::QASYMM8 })), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), |
| QuantizationData), |
| QuantizedActivationFunctionsSmallDataset), |
| framework::dataset::make("WeightsDataType", { DataType::QSYMM8_PER_CHANNEL }))) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMConvolutionLayerQuantizedPerChannelFixture<uint8_t>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(), |
| framework::dataset::make("ReshapeWeights", { true })), |
| framework::dataset::make("DataType", { DataType::QASYMM8 })), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), |
| QuantizationData), |
| QuantizedActivationFunctionsDataset), |
| framework::dataset::make("WeightsDataType", { DataType::QSYMM8_PER_CHANNEL }))) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); |
| } |
| TEST_SUITE_END() // QSYMM8_PER_CHANNEL |
| TEST_SUITE_END() // Quantized |
| |
| TEST_SUITE_END() // GEMMConvolutionLayer |
| |
| template <typename T> |
| using CLGEMMGroupedConvolutionLayerFixture = ConvolutionValidationFixture<CLTensor, CLAccessor, CLGEMMConvolutionLayer, T>; |
| |
| TEST_SUITE(GroupedGEMMConvolutionLayer) |
| |
| DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(datasets::SmallGroupedConvolutionLayerDataset(), |
| GroupedCNNDataTypes), |
| ActivationFunctionsDataset), |
| input_shape, weights_shape, bias_shape, output_shape, info, dilation, data_type, act_info) |
| { |
| ARM_COMPUTE_ERROR_ON((input_shape[2] % weights_shape[2]) != 0); |
| |
| // The number of groups is calculated dividing the number of input channels of the input tensor by the number of input channels of the weights shape |
| const int num_groups = input_shape[2] / weights_shape[2]; |
| |
| // Create tensors |
| CLTensor src = create_tensor<CLTensor>(input_shape, data_type); |
| CLTensor weights = create_tensor<CLTensor>(weights_shape, data_type, 1); |
| CLTensor bias = create_tensor<CLTensor>(bias_shape, data_type, 1); |
| CLTensor dst = create_tensor<CLTensor>(output_shape, data_type, 1); |
| |
| ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Create and configure function |
| CLGEMMConvolutionLayer conv; |
| conv.configure(&src, &weights, &bias, &dst, info, WeightsInfo(), dilation, act_info, num_groups); |
| |
| // Validate valid region |
| const ValidRegion src_valid_region = shape_to_valid_region(input_shape); |
| const ValidRegion weights_valid_region = shape_to_valid_region(weights_shape); |
| const ValidRegion bias_valid_region = shape_to_valid_region(bias_shape); |
| const ValidRegion dst_valid_region = shape_to_valid_region(output_shape); |
| |
| validate(src.info()->valid_region(), src_valid_region); |
| validate(weights.info()->valid_region(), weights_valid_region); |
| validate(bias.info()->valid_region(), bias_valid_region); |
| validate(dst.info()->valid_region(), dst_valid_region); |
| |
| // Validate padding |
| //TODO(COMPMID-415) Need to validate padding? |
| } |
| |
| TEST_SUITE(Float) |
| TEST_SUITE(FP32) |
| |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMGroupedConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallGroupedConvolutionLayerDataset(), |
| framework::dataset::make("ReshapeWeights", { true })), |
| framework::dataset::make("DataType", DataType::F32)), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW })), |
| ActivationFunctionsSmallDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f32, tolerance_num); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMGroupedConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(combine(framework::dataset::concat(datasets::SmallGroupedConvolutionLayerDataset(), datasets::LargeGroupedConvolutionLayerDataset()), |
| framework::dataset::make("ReshapeWeights", { true })), |
| framework::dataset::make("DataType", DataType::F32)), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW })), |
| ActivationFunctionsDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f32, tolerance_num); |
| } |
| TEST_SUITE_END() // FP32 |
| |
| TEST_SUITE(FP16) |
| |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMGroupedConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallGroupedConvolutionLayerDataset(), |
| framework::dataset::make("ReshapeWeights", { true })), |
| framework::dataset::make("DataType", DataType::F16)), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW })), |
| ActivationFunctionsSmallDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f32, tolerance_num); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMGroupedConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(combine(framework::dataset::concat(datasets::SmallGroupedConvolutionLayerDataset(), datasets::LargeGroupedConvolutionLayerDataset()), |
| framework::dataset::make("ReshapeWeights", { true })), |
| framework::dataset::make("DataType", DataType::F16)), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW })), |
| ActivationFunctionsDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f32, tolerance_num); |
| } |
| TEST_SUITE_END() // FP16 |
| TEST_SUITE_END() // Float |
| |
| TEST_SUITE_END() // GroupedGEMMConvolutionLayer |
| TEST_SUITE_END() // CL |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |