arm_compute v17.12
diff --git a/tests/validation/CL/DirectConvolutionLayer.cpp b/tests/validation/CL/DirectConvolutionLayer.cpp
index df42d0a..94e9b95 100644
--- a/tests/validation/CL/DirectConvolutionLayer.cpp
+++ b/tests/validation/CL/DirectConvolutionLayer.cpp
@@ -27,6 +27,7 @@
#include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h"
#include "tests/CL/CLAccessor.h"
#include "tests/PaddingCalculator.h"
+#include "tests/datasets/DirectConvolutionLayerDataset.h"
#include "tests/datasets/ShapeDatasets.h"
#include "tests/framework/Asserts.h"
#include "tests/framework/Macros.h"
@@ -47,21 +48,11 @@
RelativeTolerance<float> tolerance_fp32(0.02f); /**< Tolerance for floating point tests */
constexpr float tolerance_num = 0.07f; /**< Tolerance number */
-constexpr AbsoluteTolerance<int8_t> tolerance_qs8(0); /**< Tolerance for fixed point tests */
-constexpr AbsoluteTolerance<int16_t> tolerance_qs16(0); /**< Tolerance for fixed point tests */
+constexpr AbsoluteTolerance<int8_t> tolerance_qs8(0); /**< Tolerance for fixed point tests */
+constexpr AbsoluteTolerance<int16_t> tolerance_qs16(0); /**< Tolerance for fixed point tests */
+constexpr AbsoluteTolerance<uint8_t> tolerance_qasymm8(1); /**< Tolerance for quantized tests */
/** Direct convolution data set. */
-const auto data_quantized = combine(datasets::SmallDirectConvolutionShapes(),
- combine(framework::dataset::make("StrideX", 1, 3),
- combine(framework::dataset::make("StrideY", 1, 3),
- combine(concat(combine(framework::dataset::make("PadX", 0),
- combine(framework::dataset::make("PadY", 0),
- framework::dataset::make("KernelSize", 1))),
- combine(framework::dataset::make("PadX", 0, 2),
- combine(framework::dataset::make("PadY", 0, 2),
- framework::dataset::make("KernelSize", { 3 })))),
- framework::dataset::make("NumKernels", { 1, 4, 8, 16 })))));
-
const auto data = combine(datasets::SmallDirectConvolutionShapes(),
combine(framework::dataset::make("StrideX", 1, 3),
combine(framework::dataset::make("StrideY", 1, 3),
@@ -72,13 +63,97 @@
combine(framework::dataset::make("PadY", 0, 2),
framework::dataset::make("KernelSize", { 3, 5 })))),
framework::dataset::make("NumKernels", { 1, 4, 8, 16 })))));
+const auto data_fixed_point = combine(datasets::SmallDirectConvolutionShapes(),
+ combine(framework::dataset::make("StrideX", 1, 3),
+ combine(framework::dataset::make("StrideY", 1, 3),
+ combine(concat(combine(framework::dataset::make("PadX", 0),
+ combine(framework::dataset::make("PadY", 0),
+ framework::dataset::make("KernelSize", 1))),
+ combine(framework::dataset::make("PadX", 0, 2),
+ combine(framework::dataset::make("PadY", 0, 2),
+ framework::dataset::make("KernelSize", { 3 })))),
+ framework::dataset::make("NumKernels", { 1, 4, 8, 16 })))));
} // namespace
TEST_SUITE(CL)
TEST_SUITE(DirectConvolutionLayer)
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Mismatching data type input/weights
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Mismatching input feature maps
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Unsupported kernel width
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Non-rectangular weights dimensions
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid weights dimensions
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid stride
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid biases size
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid biases dimensions
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid output size
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Window shrink
+ TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32, 0),
+ }),
+ framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F16, 0),
+ TensorInfo(TensorShape(3U, 3U, 3U, 4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(9U, 9U, 2U, 4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(5U, 3U, 2U, 4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(3U, 3U, 2U, 4U, 3U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(1U, 1U, 2U, 4U), 1, DataType::F32, 0),
+ })),
+ framework::dataset::make("BiasesInfo",{ TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(3U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(4U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+ })),
+ framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(26U, 11U, 4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(32U, 16U, 4U), 1, DataType::F32, 0),
+ })),
+ framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(3, 3, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ })),
+ framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, false, false, true })),
+ input_info, weights_info, biases_info, output_info, conv_info, expected)
+{
+ bool is_valid = bool(CLDirectConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info));
+ ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
template <typename T>
using CLDirectConvolutionLayerFixture = DirectConvolutionValidationFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>;
+template <typename T>
+using CLDirectConvolutionValidationWithTensorShapesFixture = DirectConvolutionValidationWithTensorShapesFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>;
TEST_SUITE(Float)
TEST_SUITE(FP16)
@@ -96,14 +171,23 @@
validate(CLAccessor(_target), _reference, tolerance_fp32);
}
TEST_SUITE_END()
+
+TEST_SUITE(FP32_CustomDataset)
+FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionValidationWithTensorShapesFixture<float>, framework::DatasetMode::ALL, combine(datasets::DirectConvolutionLayerDataset(),
+ framework::dataset::make("DataType", DataType::F32)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_fp32);
+}
+TEST_SUITE_END()
TEST_SUITE_END()
template <typename T>
using CLDirectConvolutionLayerFixedPointFixture = DirectConvolutionValidationFixedPointFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>;
-TEST_SUITE(Quantized)
+TEST_SUITE(FixedPoint)
TEST_SUITE(QS8)
-FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(data_quantized, framework::dataset::make("DataType", DataType::QS8)),
+FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(data_fixed_point, framework::dataset::make("DataType", DataType::QS8)),
framework::dataset::make("FractionalBits", 2, 7)))
{
// Validate output
@@ -112,7 +196,7 @@
TEST_SUITE_END()
TEST_SUITE(QS16)
-FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::ALL, combine(combine(data_quantized, framework::dataset::make("DataType", DataType::QS16)),
+FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::ALL, combine(combine(data_fixed_point, framework::dataset::make("DataType", DataType::QS16)),
framework::dataset::make("FractionalBits", 2, 15)))
{
// Validate output
@@ -121,6 +205,32 @@
TEST_SUITE_END()
TEST_SUITE_END()
+template <typename T>
+using CLDirectConvolutionLayerQuantizedFixture = DirectConvolutionValidationQuantizedFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>;
+template <typename T>
+using CLDirectConvolutionValidationWithTensorShapesQuantizedFixture = DirectConvolutionValidationWithTensorShapesQuantizedFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>;
+
+TEST_SUITE(Quantized)
+TEST_SUITE(QASYMM8)
+FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::ALL, combine(combine(data, framework::dataset::make("DataType", DataType::QASYMM8)),
+ framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10) })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_qasymm8);
+}
+TEST_SUITE_END()
+
+TEST_SUITE(QASYMM8_CustomDataset)
+FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionValidationWithTensorShapesQuantizedFixture<uint8_t>, framework::DatasetMode::ALL, combine(combine(datasets::DirectConvolutionLayerDataset(),
+ framework::dataset::make("DataType", DataType::QASYMM8)),
+ framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 127) })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_qasymm8);
+}
+TEST_SUITE_END()
+TEST_SUITE_END()
+
TEST_SUITE_END()
TEST_SUITE_END()
} // namespace validation