diff --git a/dnn/test/cuda/batch_conv_bias.cpp b/dnn/test/cuda/batch_conv_bias.cpp index 0250f26dfd7e60e5f55cb36c454aa097e2d33e0c..51c4f6dec6df49318f7d35cfb916ee8800abfc44 100644 --- a/dnn/test/cuda/batch_conv_bias.cpp +++ b/dnn/test/cuda/batch_conv_bias.cpp @@ -241,10 +241,14 @@ void benchmark_target_algo(Handle* handle, const std::vector& args, "v" V(CUDNN_MAJOR) "." V(CUDNN_MINOR) "." V(CUDNN_PATCHLEVEL) benchmarker_cudnn.set_before_exec_callback( conv_bias::ConvBiasAlgoChecker( - "CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_" - "GEMM" CUDNN_VERSION_STRING)); - benchmarker_matmul.set_before_exec_callback( - AlgoChecker("BRUTE_FORCE-CUBLAS")); + ConvBiasForward::algo_name( + "CUDNN:ConvBiasActivation:" + "CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_" + "GEMM" CUDNN_VERSION_STRING, + {}) + .c_str())); + benchmarker_matmul.set_before_exec_callback(AlgoChecker( + ExecutionPolicyAlgoName{"BRUTE_FORCE", {{"CUBLAS", {}}}})); benchmarker.set_dtype(0, src_dtype) .set_dtype(1, filter_dtype) diff --git a/dnn/test/cuda/benchmark.cpp b/dnn/test/cuda/benchmark.cpp index f54d804fb51ef35afd953144fd71b0db6d0428a2..f84d6d784df7a0fdc0e9047c887854547b1190da 100644 --- a/dnn/test/cuda/benchmark.cpp +++ b/dnn/test/cuda/benchmark.cpp @@ -41,10 +41,12 @@ TEST_F(CUDA, BENCHMARK_CONVOLUTION_8X8X32) auto time_in_ms_float = benchmarker.set_param(param_float) .set_dtype(0, dtype::Float32()) .set_dtype(1, dtype::Float32()) + .set_dtype(2, dtype::Float32()) .execs({src_float, filter_float, {}}); auto time_in_ms_int = benchmarker.set_param(param_int) .set_dtype(0, dtype::Int8()) .set_dtype(1, dtype::Int8()) + .set_dtype(2, dtype::Int32()) .execs({src_int, filter_int, {}}); std::cout << "1x1: N=" << N << " OC=" << OC << " IC=" << IC << " H=" << H << " W=" << W @@ -67,10 +69,12 @@ TEST_F(CUDA, BENCHMARK_CONVOLUTION_8X8X32) auto time_in_ms_float = benchmarker.set_param(param_float) .set_dtype(0, dtype::Float32()) .set_dtype(1, dtype::Float32()) + .set_dtype(2, dtype::Float32()) .execs({src_float, filter_float, {}}); auto time_in_ms_int = benchmarker.set_param(param_int) .set_dtype(0, dtype::Int8()) .set_dtype(1, dtype::Int8()) + .set_dtype(2, dtype::Int32()) .execs({src_int, filter_int, {}}); std::cout << "chanwise: N=" << N << " C=" << C << " H=" << H << " W=" << W << " F=" << F