diff options
Diffstat (limited to 'tests/validation/NEON/ConvolutionLayer.cpp')
-rw-r--r-- | tests/validation/NEON/ConvolutionLayer.cpp | 101 |
1 files changed, 93 insertions, 8 deletions
diff --git a/tests/validation/NEON/ConvolutionLayer.cpp b/tests/validation/NEON/ConvolutionLayer.cpp index d739d4e1a4..7eada81ce5 100644 --- a/tests/validation/NEON/ConvolutionLayer.cpp +++ b/tests/validation/NEON/ConvolutionLayer.cpp @@ -147,6 +147,45 @@ const auto QuantizationData = make("QuantizationInfo", TEST_SUITE(NEON) TEST_SUITE(ConvolutionLayer) +DATA_TEST_CASE(SupportedTypes, framework::DatasetMode::ALL, zip( + make("DataType", { + DataType::F32, + DataType::QASYMM8, + DataType::QASYMM8, + DataType::QASYMM8_SIGNED + }), + make("WeightsDataType", { + DataType::F32, + DataType::QASYMM8, + DataType::QASYMM8_SIGNED, + DataType::QASYMM8 + }), + make("Expected", + { + true, + true, + true, + false + })), +data_type_const, weights_data_type_const, expected_const) +{ + TensorInfo input_info = TensorInfo(TensorShape(3U, 3U, 1U), 1, data_type_const); + TensorInfo weights_info = TensorInfo(TensorShape(2U, 2U, 1U, 1U), 1, weights_data_type_const); + TensorInfo output_info = TensorInfo(TensorShape(2U, 2U, 1U), 1, data_type_const); + + input_info.set_quantization_info(arm_compute::QuantizationInfo(1, 0)); + weights_info.set_quantization_info(arm_compute::QuantizationInfo(1, 0)); + output_info.set_quantization_info(arm_compute::QuantizationInfo(1, 0)); + + Status status = NEConvolutionLayer::validate( + &input_info, + &weights_info, + nullptr, + &output_info, + PadStrideInfo()); + + ARM_COMPUTE_EXPECT(bool(status) == expected_const, framework::LogLevel::ERRORS); +} // *INDENT-OFF* // clang-format off @@ -257,7 +296,7 @@ TEST_CASE(MemoryInjection, framework::DatasetMode::ALL) 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); + ARM_COMPUTE_EXPECT(reinterpret_cast<float *>(result_0.buffer())[i] == reinterpret_cast<float *>(result_1.buffer())[i], framework::LogLevel::ERRORS); } } @@ -303,7 +342,7 @@ TEST_CASE(MultipleExecutionWithConfigure, framework::DatasetMode::ALL) 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); + ARM_COMPUTE_EXPECT(reinterpret_cast<float *>(result_0.buffer())[i] == reinterpret_cast<float *>(result_1.buffer())[i], framework::LogLevel::ERRORS); } } @@ -580,7 +619,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, frame /// It's enough to run the activations for a single weight/input combination and data type because /// activation function is called on top of the winograd output as a separate operator -/// TODO: Enable after COMPMID-6573 is resolved +/// TODO(COMPMID-6573): Enable after COMPMID-6573 is resolved FIXTURE_DATA_TEST_CASE(RunActivations, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::DISABLED, combine( make("Input", TensorShape(3U, 3U, 32U)), @@ -1119,7 +1158,7 @@ TEST_CASE(MemoryInjection, framework::DatasetMode::ALL) 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); + ARM_COMPUTE_EXPECT(reinterpret_cast<float *>(result_0.buffer())[i] == reinterpret_cast<float *>(result_1.buffer())[i], framework::LogLevel::ERRORS); } } @@ -1160,7 +1199,7 @@ TEST_CASE(MultipleExecutionWithConfigure, framework::DatasetMode::ALL) 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); + ARM_COMPUTE_EXPECT(reinterpret_cast<float *>(result_0.buffer())[i] == reinterpret_cast<float *>(result_1.buffer())[i], framework::LogLevel::ERRORS); } } @@ -1251,12 +1290,14 @@ FIXTURE_DATA_TEST_CASE(RunVeryLarge, NEGEMMConvolutionLayerFixture<float>, frame TEST_SUITE_END() // FP32 TEST_SUITE_END() // Float -// TODO: COMPMID-6596 Extend quantized tests with at least one suite where the weight is padded (the legacy case, see floating point's RunPaddedWeights) +// TODO(COMPMID-6573): Extend quantized tests with at least one suite where the weight is padded (the legacy case, see floating point's RunPaddedWeights) template <typename T> using NEGEMMConvolutionLayerQuantizedFixture = ConvolutionValidationQuantizedFixture<Tensor, Accessor, NEConvolutionLayer, T>; template <typename T> using NEGEMMConvolutionLayerQuantizedMixedDataLayoutFixture = ConvolutionValidationQuantizedFixture<Tensor, Accessor, NEConvolutionLayer, T, true>; +using NEGEMMConvolutionLayerQuantizedMixedSignFixture = ConvolutionValidationQuantizedMixedTypeFixture<Tensor, Accessor, NEConvolutionLayer, uint8_t, int8_t>; + template <typename T> using NEGEMMConvolutionLayerQuantizedPerChannelFixture = ConvolutionValidationQuantizedPerChannelFixture<Tensor, Accessor, NEConvolutionLayer, T, int8_t>; @@ -1332,6 +1373,50 @@ FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, NEGEMMConvolutionLayerQuantizedFixtur } TEST_SUITE_END() // QASYMM8_SIGNED +TEST_SUITE(QASYMM8_MIXED) +FIXTURE_DATA_TEST_CASE( + RunSmall, + NEGEMMConvolutionLayerQuantizedMixedSignFixture, + framework::DatasetMode::ALL, + combine(combine(combine(combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(), + framework::dataset::make("ReshapeWeights", {true})), + framework::dataset::make("DataType", DataType::QASYMM8)), + framework::dataset::make("WeightsDataType", DataType::QASYMM8_SIGNED)), + framework::dataset::make("DataLayout", {DataLayout::NCHW, DataLayout::NHWC})), + framework::dataset::make("QuantizationInfoIfActivationEnabled", +{QuantizationInfo(2.f / 255.f, 10)})), +framework::dataset::make("WeightQuantizationInfoIfActivationEnabled", +{QuantizationInfo(2.f / 255.f, 10)})), +QuantizedActivationFunctionsDataset)) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_qasymm8); +} +FIXTURE_DATA_TEST_CASE( + RunMixedDataLayout, + NEGEMMConvolutionLayerQuantizedMixedSignFixture, + framework::DatasetMode::ALL, + combine( + framework::dataset::make("Input", TensorShape(23U, 27U, 5U)), + framework::dataset::make("Weights", TensorShape(3U, 3U, 5U, 2U)), + framework::dataset::make("Bias", TensorShape(2U)), + framework::dataset::make("Output", TensorShape(11U, 25U, 2U)), + framework::dataset::make("PadStrideInfo", PadStrideInfo(2, 1, 0, 0)), + framework::dataset::make("Dilation", Size2D(1, 1)), + framework::dataset::make("ReshapeWeights", {true}), + framework::dataset::make("DataType", DataType::QASYMM8), + framework::dataset::make("WeightsDataType", DataType::QASYMM8_SIGNED), + framework::dataset::make("DataLayout", {DataLayout::NCHW, DataLayout::NHWC}), + framework::dataset::make("QuantizationInfoIfActivationEnabled", {QuantizationInfo(2.f / 255.f, 10)}), + framework::dataset::make("WeightQuantizationInfoIfActivationEnabled", {QuantizationInfo(2.f / 255.f, 10)}), + QuantizedActivationFunctionsDataset) + ) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_qasymm8); +} +TEST_SUITE_END() // QASYMM8_MIXED + TEST_SUITE(QSYMM8_PER_CHANNEL) FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerQuantizedPerChannelFixture<uint8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(), @@ -1436,7 +1521,7 @@ TEST_CASE(MemoryInjection, framework::DatasetMode::ALL) 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); + ARM_COMPUTE_EXPECT(reinterpret_cast<float *>(result_0.buffer())[i] == reinterpret_cast<float *>(result_1.buffer())[i], framework::LogLevel::ERRORS); } } @@ -1476,7 +1561,7 @@ TEST_CASE(MultipleExecutionWithConfigure, framework::DatasetMode::ALL) 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); + ARM_COMPUTE_EXPECT(reinterpret_cast<float *>(result_0.buffer())[i] == reinterpret_cast<float *>(result_1.buffer())[i], framework::LogLevel::ERRORS); } } |