diff options
Diffstat (limited to 'tests/validation/NEON/DeconvolutionLayer.cpp')
| -rw-r--r-- | tests/validation/NEON/DeconvolutionLayer.cpp | 217 |
1 files changed, 174 insertions, 43 deletions
diff --git a/tests/validation/NEON/DeconvolutionLayer.cpp b/tests/validation/NEON/DeconvolutionLayer.cpp index 19bd742a61..b4c049f6f9 100644 --- a/tests/validation/NEON/DeconvolutionLayer.cpp +++ b/tests/validation/NEON/DeconvolutionLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2021 Arm Limited. + * Copyright (c) 2017-2021, 2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -47,55 +47,86 @@ constexpr AbsoluteTolerance<float> tolerance_fp32(0.001f); /**< Tolerance for constexpr AbsoluteTolerance<float> tolerance_quantized(1.0f); /**< Tolerance value for comparing reference's output against implementation's output for quantized data types */ #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC const RelativeTolerance<half_float::half> tolerance_fp16(half_float::half(0.2f)); /**< Relative tolerance value for comparing reference's output against implementation's output for DataType::F16 */ +constexpr float tolerance_num_fp16 = 0.02f; /**< Tolerance number for FP16 tests -- follows a slightly stricter approach compared to ConvolutionLayer tests */ #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC*/ -constexpr float tolerance_num = 0.07f; /**< Tolerance number */ +constexpr float tolerance_num_quant = 0.07f; /**< Tolerance number for quantized types */ const auto data4x4 = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 1, 4) * framework::dataset::make("StrideY", 1, 4) * framework::dataset::make("PadX", 0, 3) - * framework::dataset::make("PadY", 0, 3) * framework::dataset::make("NumKernels", { 3 }); + * framework::dataset::make("PadY", 0, 3) * framework::dataset::make("NumKernels", +{ + 3 +}); const auto data3x3 = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 1, 4) * framework::dataset::make("StrideY", 1, 4) * framework::dataset::make("PadX", 0, 2) - * framework::dataset::make("PadY", 0, 2) * framework::dataset::make("NumKernels", { 3 }); + * framework::dataset::make("PadY", 0, 2) * framework::dataset::make("NumKernels", +{ + 3 +}); const auto data3x3_asymm = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 1, 2) * framework::dataset::make("StrideY", 1, 2) * framework::dataset::make("PadLeft", 0, 1) - * framework::dataset::make("PadRight", 0, 1) * framework::dataset::make("PadTop", 0, 1) * framework::dataset::make("PadBottom", 0, 1) * framework::dataset::make("NumKernels", { 3 }); + * framework::dataset::make("PadRight", 0, 1) * framework::dataset::make("PadTop", 0, 1) * framework::dataset::make("PadBottom", 0, 1) * framework::dataset::make("NumKernels", +{ + 3 +}); -const auto data9x9_small_asymm = framework::dataset::make("InputShape", TensorShape{ 10U, 10U, 1U, 1U }) *framework::dataset::make("StrideX", 2) *framework::dataset::make("StrideY", - 2) - *framework::dataset::make("PadLeft", 3) - *framework::dataset::make("PadRight", 4) *framework::dataset::make("PadTop", 3) *framework::dataset::make("PadBottom", 4) *framework::dataset::make("NumKernels", { 1 }); +const auto data9x9_small_asymm = framework::dataset::make("InputShape", TensorShape +{ + 10U, 10U, 1U, 1U +}) +*framework::dataset::make("StrideX", 2) *framework::dataset::make("StrideY", 2) *framework::dataset::make("PadLeft", 3) *framework::dataset::make("PadRight", 4) *framework::dataset::make("PadTop", + 3) *framework::dataset::make("PadBottom", 4) *framework::dataset::make("NumKernels", { 1 }); -const auto data9x9_large_asymm = framework::dataset::make("InputShape", TensorShape{ 640U, 360U, 56U, 1U }) *framework::dataset::make("StrideX", 2) *framework::dataset::make("StrideY", - 2) - *framework::dataset::make("PadLeft", 3) - *framework::dataset::make("PadRight", 4) *framework::dataset::make("PadTop", 3) *framework::dataset::make("PadBottom", 4) *framework::dataset::make("NumKernels", { 1 }); +const auto data9x9_large_asymm = framework::dataset::make("InputShape", TensorShape +{ + 640U, 360U, 56U, 1U +}) +*framework::dataset::make("StrideX", 2) *framework::dataset::make("StrideY", 2) *framework::dataset::make("PadLeft", 3) *framework::dataset::make("PadRight", 4) *framework::dataset::make("PadTop", + 3) *framework::dataset::make("PadBottom", 4) *framework::dataset::make("NumKernels", { 1 }); const auto data3x3_precommit = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 1, 2) * framework::dataset::make("StrideY", 1, 2) * framework::dataset::make("PadX", 0, 2) - * framework::dataset::make("PadY", 0, 2) * framework::dataset::make("NumKernels", { 3 }); + * framework::dataset::make("PadY", 0, 2) * framework::dataset::make("NumKernels", +{ + 3 +}); const auto data1x1 = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 1, 4) * framework::dataset::make("StrideY", 1, 4) * framework::dataset::make("PadX", 0, 1) - * framework::dataset::make("PadY", 0, 1) * framework::dataset::make("NumKernels", { 3 }); + * framework::dataset::make("PadY", 0, 1) * framework::dataset::make("NumKernels", +{ + 3 +}); -const auto data_layouts_dataset = framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }); +const auto data5x1 = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 1, 4) * framework::dataset::make("StrideY", 1, 4) * framework::dataset::make("PadX", 0, 1) + * framework::dataset::make("PadY", 0, 1) * framework::dataset::make("NumKernels", +{ + 3 +}); + +const auto data_layouts_dataset = framework::dataset::make("DataLayout", +{ + DataLayout::NCHW, DataLayout::NHWC +}); -const auto add_bias_dataset = framework::dataset::make("AddBias", { true, false }); +const auto add_bias_dataset = framework::dataset::make("AddBias", +{ + true, false +}); const auto input_qinfo_dataset = framework::dataset::make("InputQInfo", { QuantizationInfo(1.f / 255.f, 0), - QuantizationInfo(2.f, 0), + QuantizationInfo(2.f, 0), }); const auto output_qinfo_dataset = framework::dataset::make("OutputQInfo", { QuantizationInfo(3.f / 255.f, 0), - QuantizationInfo(4.f, 0), + QuantizationInfo(4.f, 0), }); } // namespace TEST_SUITE(NEON) TEST_SUITE(DeconvolutionLayer) - // *INDENT-OFF* // clang-format off DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip( @@ -105,6 +136,8 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip( TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid bias shape TensorInfo(TensorShape(13U, 11U, 4U, 3U), 1, DataType::F32), // Window shrink TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32), + TensorInfo(TensorShape(2U,2U,1U,1U), 1, DataType::F32), // Small shape no padding + TensorInfo(TensorShape(3U,26U,26U,1U), 1, DataType::F32), // Negative padding }), framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::F16), TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32), @@ -112,6 +145,8 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip( TensorInfo(TensorShape(3U, 2U, 2U, 2U), 1, DataType::F32), TensorInfo(TensorShape(3U, 3U, 4U), 1, DataType::F32), TensorInfo(TensorShape(1U, 1U, 2U, 4U), 1, DataType::F32), + TensorInfo(TensorShape(3U,3U,1U,1U), 1, DataType::F32), + TensorInfo(TensorShape(1U,1U,26U,88U), 1, DataType::F32), })), framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(1U), 1, DataType::F16), TensorInfo(TensorShape(1U), 1, DataType::F32), @@ -119,6 +154,8 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip( TensorInfo(TensorShape(25U, 11U), 1, DataType::F32), TensorInfo(TensorShape(1U), 1, DataType::F32), TensorInfo(TensorShape(4U), 1, DataType::F32), + TensorInfo(TensorShape(1U), 1, DataType::F32), + TensorInfo(TensorShape(88U), 1, DataType::F32), })), framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F16), TensorInfo(TensorShape(25U, 10U, 2U), 1, DataType::F32), @@ -126,6 +163,8 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip( TensorInfo(TensorShape(13U, 13U, 2U), 1, DataType::F32), TensorInfo(TensorShape(11U, 9U, 1U, 3U), 1, DataType::F32), TensorInfo(TensorShape(32U, 16U, 4U), 1, DataType::F32), + TensorInfo(TensorShape(4U,4U,1U,1U), 1, DataType::F32), + TensorInfo(TensorShape(1U,78U,88U,1U), 1, DataType::F32), })), framework::dataset::make("PadStrideInfo", { PadStrideInfo(1, 1, 0, 0), PadStrideInfo(1, 1, 0, 0), @@ -133,8 +172,10 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip( PadStrideInfo(1, 1, 0, 0), PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0), + PadStrideInfo(1, 1, 0, 0), + PadStrideInfo(2, 3, 3, 1), })), - framework::dataset::make("Expected", { false, false, false, false, false, true })), + framework::dataset::make("Expected", { false, false, false, false, false, true,true, false })), input_info, weights_info, bias_info, output_info, pad_info, expected) { bool is_valid = bool(NEDeconvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), pad_info)); @@ -158,6 +199,9 @@ using NEDeconvolutionLayerAsymmFixture9x9 = DeconvolutionValidationAsymmFixture< template <typename T> using NEDeconvolutionLayerFixture1x1 = DeconvolutionValidationFixture<Tensor, Accessor, NEDeconvolutionLayer, T, 1, 1>; +template <typename T> +using NEDeconvolutionLayerFixture5x1 = DeconvolutionValidationFixture<Tensor, Accessor, NEDeconvolutionLayer, T, 5, 1>; + TEST_SUITE(Float) TEST_SUITE(FP32) TEST_SUITE(W4x4) @@ -221,6 +265,15 @@ FIXTURE_DATA_TEST_CASE(RunLarge, NEDeconvolutionLayerAsymmFixture9x9<float>, fra validate(Accessor(_target), _reference, tolerance_fp32); } TEST_SUITE_END() // W9x9 +TEST_SUITE(W5x1) +FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerFixture5x1<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data5x1, framework::dataset::make("DataType", DataType::F32)), + data_layouts_dataset), + add_bias_dataset)) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_fp32); +} +TEST_SUITE_END() // W5x1 TEST_SUITE_END() // FP32 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC @@ -231,7 +284,7 @@ FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerFixture4x4<half>, framework::Dat add_bias_dataset)) { // Validate output - validate(Accessor(_target), _reference, tolerance_fp16); + validate(Accessor(_target), _reference, tolerance_fp16, tolerance_num_fp16); } TEST_SUITE_END() // W4x4 TEST_SUITE(W3x3) @@ -241,14 +294,14 @@ FIXTURE_DATA_TEST_CASE(RunSmall, NEDeconvolutionLayerFixture3x3<half>, framework add_bias_dataset)) { // Validate output - validate(Accessor(_target), _reference, tolerance_fp16); + validate(Accessor(_target), _reference, tolerance_fp16, tolerance_num_fp16); } FIXTURE_DATA_TEST_CASE(RunLarge, NEDeconvolutionLayerFixture3x3<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data3x3, framework::dataset::make("DataType", DataType::F16)), data_layouts_dataset), add_bias_dataset)) { // Validate output - validate(Accessor(_target), _reference, tolerance_fp16); + validate(Accessor(_target), _reference, tolerance_fp16, tolerance_num_fp16); } TEST_SUITE_END() // W3x3 TEST_SUITE(W1x1) @@ -257,9 +310,18 @@ FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerFixture1x1<half>, framework::Dat add_bias_dataset)) { // Validate output - validate(Accessor(_target), _reference, tolerance_fp16); + validate(Accessor(_target), _reference, tolerance_fp16, tolerance_num_fp16); } TEST_SUITE_END() // W1x1 +TEST_SUITE(W5x1) +FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerFixture5x1<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data5x1, framework::dataset::make("DataType", DataType::F16)), + data_layouts_dataset), + add_bias_dataset)) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_fp16, tolerance_num_fp16); +} +TEST_SUITE_END() // W5x1 TEST_SUITE_END() // FP16 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ @@ -275,6 +337,9 @@ template <typename T> using NEDeconvolutionLayerQuantizedFixture1x1 = DeconvolutionValidationQuantizedFixture<Tensor, Accessor, NEDeconvolutionLayer, T, 1, 1>; template <typename T> +using NEDeconvolutionLayerQuantizedFixture5x1 = DeconvolutionValidationQuantizedFixture<Tensor, Accessor, NEDeconvolutionLayer, T, 5, 1>; + +template <typename T> using NEDeconvolutionLayerQuantizedPerChannelFixture4x4 = DeconvolutionValidationQuantizedPerChannelFixture<Tensor, Accessor, NEDeconvolutionLayer, T, int8_t, 4, 4>; template <typename T> @@ -283,6 +348,9 @@ using NEDeconvolutionLayerQuantizedPerChannelFixture3x3 = DeconvolutionValidatio template <typename T> using NEDeconvolutionLayerQuantizedPerChannelFixture1x1 = DeconvolutionValidationQuantizedPerChannelFixture<Tensor, Accessor, NEDeconvolutionLayer, T, int8_t, 1, 1>; +template <typename T> +using NEDeconvolutionLayerQuantizedPerChannelFixture5x1 = DeconvolutionValidationQuantizedPerChannelFixture<Tensor, Accessor, NEDeconvolutionLayer, T, int8_t, 5, 1>; + TEST_SUITE(Quantized) TEST_SUITE(QASYMM8) @@ -295,7 +363,7 @@ FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerQuantizedFixture4x4<uint8_t>, fr add_bias_dataset)) { // Validate output - validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num); + validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant); } TEST_SUITE_END() // W4x4 @@ -309,7 +377,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, NEDeconvolutionLayerQuantizedFixture3x3<uint8_t add_bias_dataset)) { // Validate output - validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num); + validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant); } FIXTURE_DATA_TEST_CASE(RunLarge, NEDeconvolutionLayerQuantizedFixture3x3<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(data3x3, framework::dataset::make("DataType", @@ -320,7 +388,7 @@ FIXTURE_DATA_TEST_CASE(RunLarge, NEDeconvolutionLayerQuantizedFixture3x3<uint8_t add_bias_dataset)) { // Validate output - validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num); + validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant); } TEST_SUITE_END() // W3x3 @@ -333,10 +401,23 @@ FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerQuantizedFixture1x1<uint8_t>, fr add_bias_dataset)) { // Validate output - validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num); + validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant); } TEST_SUITE_END() // W1x1 +TEST_SUITE(W5x1) +FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerQuantizedFixture5x1<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(data5x1, framework::dataset::make("DataType", + DataType::QASYMM8)), + data_layouts_dataset), + input_qinfo_dataset), + output_qinfo_dataset), + add_bias_dataset)) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant); +} +TEST_SUITE_END() // W5x1 + TEST_SUITE_END() // QASYMM8 TEST_SUITE(QASYMM8_SIGNED) @@ -350,7 +431,7 @@ FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerQuantizedFixture4x4<int8_t>, fra add_bias_dataset)) { // Validate output - validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num); + validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant); } TEST_SUITE_END() // W4x4 @@ -364,7 +445,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, NEDeconvolutionLayerQuantizedFixture3x3<int8_t> add_bias_dataset)) { // Validate output - validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num); + validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant); } FIXTURE_DATA_TEST_CASE(RunLarge, NEDeconvolutionLayerQuantizedFixture3x3<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(data3x3, framework::dataset::make("DataType", @@ -375,7 +456,7 @@ FIXTURE_DATA_TEST_CASE(RunLarge, NEDeconvolutionLayerQuantizedFixture3x3<int8_t> add_bias_dataset)) { // Validate output - validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num); + validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant); } TEST_SUITE_END() // W3x3 @@ -389,16 +470,41 @@ FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerQuantizedFixture1x1<int8_t>, fra add_bias_dataset)) { // Validate output - validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num); + validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant); } TEST_SUITE_END() // W1x1 +TEST_SUITE(W5x1) +FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerQuantizedFixture5x1<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(data5x1, framework::dataset::make("DataType", + DataType::QASYMM8_SIGNED)), + data_layouts_dataset), + input_qinfo_dataset), + output_qinfo_dataset), + add_bias_dataset)) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant); +} +TEST_SUITE_END() // W5x1 + TEST_SUITE_END() // QASYMM8_SIGNED -const auto input_qinfo_per_channel_dataset = framework::dataset::make("InputQuantizationInfo", { QuantizationInfo(1.f / 255.f, 10) }); -const auto output_qinfo_per_channel_dataset = framework::dataset::make("OutputQuantizationInfo", { QuantizationInfo(3.f / 255.f, 0) }); -const auto input_signed_qinfo_per_channel_dataset = framework::dataset::make("InputQuantizationInfo", { QuantizationInfo(1.f / 255.f, -10) }); -const auto output_signed_qinfo_per_channel_dataset = framework::dataset::make("OutputQuantizationInfo", { QuantizationInfo(3.f / 255.f, 10) }); +const auto input_qinfo_per_channel_dataset = framework::dataset::make("InputQuantizationInfo", +{ + QuantizationInfo(1.f / 255.f, 10) +}); +const auto output_qinfo_per_channel_dataset = framework::dataset::make("OutputQuantizationInfo", +{ + QuantizationInfo(3.f / 255.f, 0) +}); +const auto input_signed_qinfo_per_channel_dataset = framework::dataset::make("InputQuantizationInfo", +{ + QuantizationInfo(1.f / 255.f, -10) +}); +const auto output_signed_qinfo_per_channel_dataset = framework::dataset::make("OutputQuantizationInfo", +{ + QuantizationInfo(3.f / 255.f, 10) +}); TEST_SUITE(QSYMM8_PER_CHANNEL) @@ -412,7 +518,7 @@ FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerQuantizedPerChannelFixture4x4<ui framework::dataset::make("WeightsDataType", { DataType::QSYMM8_PER_CHANNEL }))) { // Validate output - validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num); + validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant); } FIXTURE_DATA_TEST_CASE(RunSigned, NEDeconvolutionLayerQuantizedPerChannelFixture4x4<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(data4x4, framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), @@ -423,7 +529,7 @@ FIXTURE_DATA_TEST_CASE(RunSigned, NEDeconvolutionLayerQuantizedPerChannelFixture framework::dataset::make("WeightsDataType", { DataType::QSYMM8_PER_CHANNEL }))) { // Validate output - validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num); + validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant); } TEST_SUITE_END() // W4x4 @@ -437,7 +543,7 @@ FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerQuantizedPerChannelFixture3x3<ui framework::dataset::make("WeightsDataType", { DataType::QSYMM8_PER_CHANNEL }))) { // Validate output - validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num); + validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant); } FIXTURE_DATA_TEST_CASE(RunSigned, NEDeconvolutionLayerQuantizedPerChannelFixture3x3<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(data3x3, framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), @@ -448,7 +554,7 @@ FIXTURE_DATA_TEST_CASE(RunSigned, NEDeconvolutionLayerQuantizedPerChannelFixture framework::dataset::make("WeightsDataType", { DataType::QSYMM8_PER_CHANNEL }))) { // Validate output - validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num); + validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant); } TEST_SUITE_END() // W3x3 @@ -462,7 +568,7 @@ FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerQuantizedPerChannelFixture1x1<ui framework::dataset::make("WeightsDataType", { DataType::QSYMM8_PER_CHANNEL }))) { // Validate output - validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num); + validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant); } FIXTURE_DATA_TEST_CASE(RunSigned, NEDeconvolutionLayerQuantizedPerChannelFixture1x1<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(data1x1, framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), @@ -473,10 +579,35 @@ FIXTURE_DATA_TEST_CASE(RunSigned, NEDeconvolutionLayerQuantizedPerChannelFixture framework::dataset::make("WeightsDataType", { DataType::QSYMM8_PER_CHANNEL }))) { // Validate output - validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num); + validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant); } TEST_SUITE_END() // W1x1 +TEST_SUITE(W5x1) +FIXTURE_DATA_TEST_CASE(Run, NEDeconvolutionLayerQuantizedPerChannelFixture5x1<uint8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(data5x1, + framework::dataset::make("DataType", DataType::QASYMM8)), + data_layouts_dataset), + input_qinfo_per_channel_dataset), + output_qinfo_per_channel_dataset), + add_bias_dataset), + framework::dataset::make("WeightsDataType", { DataType::QSYMM8_PER_CHANNEL }))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant); +} +FIXTURE_DATA_TEST_CASE(RunSigned, NEDeconvolutionLayerQuantizedPerChannelFixture5x1<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(data5x1, + framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), + data_layouts_dataset), + input_signed_qinfo_per_channel_dataset), + output_signed_qinfo_per_channel_dataset), + add_bias_dataset), + framework::dataset::make("WeightsDataType", { DataType::QSYMM8_PER_CHANNEL }))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_quantized, tolerance_num_quant); +} +TEST_SUITE_END() // W5x1 + TEST_SUITE_END() // QSYMM8_PER_CHANNEL TEST_SUITE_END() // Quantized |