Alternative solution for blacklisting.

Author: asmorkalov

modules/dnn/test/test_onnx_conformance.cpp

@@ -4,6 +4,8 @@
 
 
 #include "test_precomp.hpp"
+#include <set>
+#include <string>
 #include "npy_blob.hpp"
 #include <opencv2/dnn/shape_utils.hpp>
 
@@ -1019,13 +1021,37 @@ TEST_P(Test_ONNX_conformance, Layer_Test)
     //Backend backend = ...;
     //Target target = ...;
 
+    ASSERT_FALSE(name.empty());
+
     bool checkLayersFallbacks = true;
     bool checkAccuracy = true;
 
-#include "test_onnx_conformance_layer_filter_parser.inl.hpp"
+    std::set<std::string> parser_black_list {
+        #include "test_onnx_conformance_layer_parser_blacklist.inl.hpp"
+    };
+    if(parser_black_list.find(name) != parser_black_list.end())
+    {
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_PARSER, CV_TEST_TAG_DNN_SKIP_ONNX_CONFORMANCE);
+    }
+
     if (backend == DNN_BACKEND_OPENCV)
     {
-        #include "test_onnx_conformance_layer_filter__opencv.inl.hpp"
+        std::set<std::string> global_black_list {
+            #include "test_onnx_conformance_layer_filter_opencv_all_blacklist.inl.hpp"
+        };
+        if(global_black_list.find(name) != global_black_list.end())
+        {
+            applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCV_BACKEND, CV_TEST_TAG_DNN_SKIP_ONNX_CONFORMANCE);
+        }
+
+        std::set<std::string> fp16_black_list {
+            #include "test_onnx_conformance_layer_filter_opencv_ocl_fp16_blacklist.inl.hpp"
+        };
+        if((target == DNN_TARGET_OPENCL_FP16) && (fp16_black_list.find(name) != fp16_black_list.end()))
+        {
+            applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCV_BACKEND, CV_TEST_TAG_DNN_SKIP_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_ONNX_CONFORMANCE);
+        }
+        // TODO: OCL_FP32, CPU
     }
 #if 0 //def HAVE_HALIDE
     else if (backend == DNN_BACKEND_HALIDE)

modules/dnn/test/test_onnx_conformance_layer_filter_opencv_all_blacklist.inl.hpp

@@ -0,0 +1,29 @@
+"test_add_bcast",
+"test_averagepool_2d_pads_count_include_pad",
+"test_averagepool_2d_precomputed_pads_count_include_pad",
+"test_averagepool_2d_same_lower",
+"test_cast_FLOAT_to_STRING",
+"test_cast_STRING_to_FLOAT",
+"test_castlike_FLOAT_to_STRING_expanded",
+"test_castlike_STRING_to_FLOAT_expanded",
+"test_clip",
+"test_clip_default_max",
+"test_clip_default_min",
+"test_clip_example",
+"test_clip_outbounds",
+"test_clip_splitbounds",
+"test_concat_1d_axis_negative_1",
+"test_flatten_axis0",
+"test_flatten_axis2",
+"test_flatten_axis3",
+"test_flatten_negative_axis1",
+"test_flatten_negative_axis2",
+"test_flatten_negative_axis4",
+"test_logsoftmax_default_axis",
+"test_maxpool_2d_dilations",
+"test_maxpool_2d_same_lower",
+"test_maxpool_with_argmax_2d_precomputed_pads",
+"test_maxpool_with_argmax_2d_precomputed_strides",
+"test_softmax_default_axis",
+"test_sub_bcast",
+"test_upsample_nearest"

modules/dnn/test/test_onnx_conformance_layer_filter_opencv_ocl_fp16_blacklist.inl.hpp

@@ -0,0 +1,21 @@
+"test_averagepool_3d_default",
+"test_dropout_default_ratio",
+"test_globalmaxpool",
+"test_globalmaxpool_precomputed",
+"test_logsoftmax_large_number",
+"test_logsoftmax_large_number_expanded",
+"test_maxpool_1d_default",
+"test_maxpool_2d_ceil",
+"test_maxpool_2d_default",
+"test_maxpool_2d_pads",
+"test_maxpool_2d_precomputed_pads",
+"test_maxpool_2d_precomputed_same_upper",
+"test_maxpool_2d_precomputed_strides",
+"test_maxpool_2d_same_upper",
+"test_maxpool_2d_strides",
+"test_maxpool_3d_default",
+"test_softmax_large_number",
+"test_softmax_large_number_expanded",
+"test_split_equal_parts_1d",
+"test_split_equal_parts_2d",
+"test_split_equal_parts_default_axis"