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Diffstat (limited to 'tests/validation/NEON/ConvolutionLayer.cpp')
-rw-r--r--tests/validation/NEON/ConvolutionLayer.cpp122
1 files changed, 114 insertions, 8 deletions
diff --git a/tests/validation/NEON/ConvolutionLayer.cpp b/tests/validation/NEON/ConvolutionLayer.cpp
index 1f76925d96..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(),
@@ -1357,6 +1442,27 @@ FIXTURE_DATA_TEST_CASE(RunSmallSigned, NEGEMMConvolutionLayerQuantizedPerChannel
// Validate output
validate(Accessor(_target), _reference, tolerance_qasymm8);
}
+
+FIXTURE_DATA_TEST_CASE(MemoryStressLargeChannels, NEGEMMConvolutionLayerQuantizedPerChannelFixture<int8_t>,
+ framework::DatasetMode::ALL,
+ combine(
+ make("In", TensorShape(1U)),
+ make("Weights", TensorShape(1U, 1U, 1U, 17000U)),
+ make("Biases", TensorShape(17000U)),
+ make("Out", TensorShape(1U, 1U, 17000U)),
+ make("Info", PadStrideInfo(1, 1, 0, 0)),
+ make("Dilation", Size2D(1, 1)),
+ make("ReshapeWeights", { true }),
+ make("DataType", { DataType::QASYMM8_SIGNED }),
+ make("DataLayout", { DataLayout::NHWC }),
+ make("QuantizationInfo", QuantizationInfo(0.5f, 10)),
+ make("ActivationInfo", ActivationLayerInfo()),
+ make("WeightsDataType", { DataType::QSYMM8_PER_CHANNEL })))
+{
+ // Validate output
+ validate(Accessor(_target), _reference, tolerance_qasymm8);
+}
+
TEST_SUITE_END() // QSYMM8_PER_CHANNEL
TEST_SUITE_END() // Quantized
@@ -1415,7 +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);
}
}
@@ -1455,7 +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);
}
}
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-28 12:58:48 +0000