COMPMID-935 - Implementing Convolution with Winograd on OpenCL (part 2)
Implemented Winograd Filter Transform 3x3 on OpenCL
Change-Id: I8f2b2dd938c5c000ef7ce392a37fb7b8b4202a4e
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/122708
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
diff --git a/tests/validation/CL/Winograd.cpp b/tests/validation/CL/Winograd.cpp
index 664b3f4..0b21ed2 100644
--- a/tests/validation/CL/Winograd.cpp
+++ b/tests/validation/CL/Winograd.cpp
@@ -18,15 +18,20 @@
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONCLCTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
+#include "arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/runtime/CL/CLTensor.h"
#include "arm_compute/runtime/CL/CLTensorAllocator.h"
#include "arm_compute/runtime/CL/functions/CLWinogradInputTransform.h"
#include "tests/CL/CLAccessor.h"
+#include "tests/CL/Helper.h"
+#include "tests/PaddingCalculator.h"
+#include "tests/datasets/ShapeDatasets.h"
+#include "tests/datasets/WinogradFilterTransformDataset.h"
#include "tests/datasets/WinogradInputTransformDataset.h"
#include "tests/framework/Asserts.h"
#include "tests/framework/Macros.h"
@@ -40,6 +45,13 @@
{
namespace validation
{
+namespace
+{
+constexpr AbsoluteTolerance<float> tolerance_f32(0.0001f);
+} // namespace
+
+using namespace arm_compute::misc::shape_calculator;
+
TEST_SUITE(CL)
TEST_SUITE(Winograd)
@@ -125,11 +137,76 @@
{
validate(CLAccessor(_target), _reference);
}
+TEST_SUITE_END() // InputTransform
-TEST_SUITE_END()
+TEST_SUITE(FilterTransform)
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(
+ framework::dataset::make("InputInfo",{
+ TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::F16), // F16 not supported
+ TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::QASYMM8), // QASYMM8 not supported
+ TensorInfo(TensorShape(5U, 5U, 5U, 3U), 1, DataType::F32), // Kernel size not supported
+ TensorInfo(TensorShape(3U, 3U), 1, DataType::F32), // valid
+ TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::F32), // valid
+ TensorInfo(TensorShape(3U, 3U, 37U, 2U), 1, DataType::F32), // valid
+ TensorInfo(TensorShape(3U, 3U, 37U, 22U), 1, DataType::F32) // valid
+ }),
+ framework::dataset::make("OutputInfo", {
+ TensorInfo(TensorShape(3U, 5U, 16U), 1, DataType::F16),
+ TensorInfo(TensorShape(3U, 5U, 16U), 1, DataType::QASYMM8),
+ TensorInfo(TensorShape(3U, 5U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(1U, 1U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 5U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U, 37U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(22U, 37U, 16U), 1, DataType::F32)
+ })),
+ framework::dataset::make("Expected", { false, false, false, true, true, true, true })),
+ input_info, output_info, expected)
+{
+ ARM_COMPUTE_EXPECT(bool(CLWinogradFilterTransformKernel::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false))) == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
-TEST_SUITE_END()
-TEST_SUITE_END()
+using CLWinogradFilterTransform = CLSynthetizeFunctionWithZeroConstantBorder<CLWinogradFilterTransformKernel, 0>;
+using CLWinogradFilterTransformFixture = WinogradFilterTransformValidationFixture<CLTensor, CLAccessor, CLWinogradFilterTransform, float>;
+
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallWinogradFilterTransformDataset(), datasets::LargeWinogradFilterTransformDataset()),
+ framework::dataset::make("DataType", { DataType::F32 })),
+ shape_a, is_nchw_format, data_type)
+{
+ ARM_COMPUTE_UNUSED(is_nchw_format);
+
+ TensorShape shape_b = compute_winograd_filter_transform_shape(TensorInfo(shape_a, 1, data_type));
+
+ // Create tensors
+ CLTensor a = create_tensor<CLTensor>(shape_a, data_type);
+ CLTensor b = create_tensor<CLTensor>(shape_b, data_type);
+
+ ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Create and configure function
+ CLWinogradFilterTransform winograd_filter_transform;
+ winograd_filter_transform.configure(&a, &b);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixture, framework::DatasetMode::ALL, combine(datasets::SmallWinogradFilterTransformDataset(), framework::dataset::make("DataType", { DataType::F32 })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f32);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradFilterTransformFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeWinogradFilterTransformDataset(), framework::dataset::make("DataType", { DataType::F32 })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f32);
+}
+TEST_SUITE_END() // FilterTransform
+
+TEST_SUITE_END() // Winograd
+TEST_SUITE_END() // CL
} // namespace validation
} // namespace test
} // namespace arm_compute