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Diffstat (limited to 'tests/validation/NEON/ConvolutionLayer.cpp')
-rw-r--r--tests/validation/NEON/ConvolutionLayer.cpp101
1 files changed, 8 insertions, 93 deletions
diff --git a/tests/validation/NEON/ConvolutionLayer.cpp b/tests/validation/NEON/ConvolutionLayer.cpp
index 7eada81ce5..d739d4e1a4 100644
--- a/tests/validation/NEON/ConvolutionLayer.cpp
+++ b/tests/validation/NEON/ConvolutionLayer.cpp
@@ -147,45 +147,6 @@ 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
@@ -296,7 +257,7 @@ TEST_CASE(MemoryInjection, framework::DatasetMode::ALL)
for(size_t i = 0; i < result_0.info()->tensor_shape().total_size(); ++i)
{
- ARM_COMPUTE_EXPECT(reinterpret_cast<float *>(result_0.buffer())[i] == reinterpret_cast<float *>(result_1.buffer())[i], framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(((float *)result_0.buffer())[i] == ((float *)result_1.buffer())[i], framework::LogLevel::ERRORS);
}
}
@@ -342,7 +303,7 @@ TEST_CASE(MultipleExecutionWithConfigure, framework::DatasetMode::ALL)
for(size_t i = 0; i < result_0.info()->tensor_shape().total_size(); ++i)
{
- ARM_COMPUTE_EXPECT(reinterpret_cast<float *>(result_0.buffer())[i] == reinterpret_cast<float *>(result_1.buffer())[i], framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(((float *)result_0.buffer())[i] == ((float *)result_1.buffer())[i], framework::LogLevel::ERRORS);
}
}
@@ -619,7 +580,7 @@ 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(COMPMID-6573): Enable after COMPMID-6573 is resolved
+/// TODO: Enable after COMPMID-6573 is resolved
FIXTURE_DATA_TEST_CASE(RunActivations, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::DISABLED,
combine(
make("Input", TensorShape(3U, 3U, 32U)),
@@ -1158,7 +1119,7 @@ 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(reinterpret_cast<float *>(result_0.buffer())[i] == reinterpret_cast<float *>(result_1.buffer())[i], framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(((float *)result_0.buffer())[i] == ((float *)result_1.buffer())[i], framework::LogLevel::ERRORS);
}
}
@@ -1199,7 +1160,7 @@ 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(reinterpret_cast<float *>(result_0.buffer())[i] == reinterpret_cast<float *>(result_1.buffer())[i], framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(((float *)result_0.buffer())[i] == ((float *)result_1.buffer())[i], framework::LogLevel::ERRORS);
}
}
@@ -1290,14 +1251,12 @@ FIXTURE_DATA_TEST_CASE(RunVeryLarge, NEGEMMConvolutionLayerFixture<float>, frame
TEST_SUITE_END() // FP32
TEST_SUITE_END() // Float
-// TODO(COMPMID-6573): Extend quantized tests with at least one suite where the weight is padded (the legacy case, see floating point's RunPaddedWeights)
+// TODO: COMPMID-6596 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>;
@@ -1373,50 +1332,6 @@ 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(),
@@ -1521,7 +1436,7 @@ 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(reinterpret_cast<float *>(result_0.buffer())[i] == reinterpret_cast<float *>(result_1.buffer())[i], framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(((float *)result_0.buffer())[i] == ((float *)result_1.buffer())[i], framework::LogLevel::ERRORS);
}
}
@@ -1561,7 +1476,7 @@ 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(reinterpret_cast<float *>(result_0.buffer())[i] == reinterpret_cast<float *>(result_1.buffer())[i], framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(((float *)result_0.buffer())[i] == ((float *)result_1.buffer())[i], framework::LogLevel::ERRORS);
}
}
generated by cgit v1.2.1 (git 2.23.0) at 2024-08-08 00:06:26 +0000