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opencv/modules/dnn/test/test_onnx_conformance.cpp

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Merge pull request #21088 from rogday:onnx_tests Onnx conformance tests * Add ONNX conformance tests * dnn(test): add filters for ONNX conformance tests * add filter lists for OCV backend * address review comments * move test_clip_inbounds to all_denylist * address clip issue * avoid empty lists Co-authored-by: Alexander Alekhin <alexander.a.alekhin@gmail.com>
3 years ago
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
#include <set>
#include <string>
#include "npy_blob.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#if defined(_MSC_VER) // workaround for 32-bit MSVC compiler
#pragma optimize("", off)
#endif
#define CV_TEST_TAG_DNN_ERROR_PARSER "dnn_error_parser"
#define CV_TEST_TAG_DNN_ERROR_NET_SETUP "dnn_error_net_setup"
#define CV_TEST_TAG_DNN_ERROR_FORWARD "dnn_error_forward"
#define CV_TEST_TAG_DNN_LAYER_FALLBACK "dnn_layer_fallback"
#define CV_TEST_TAG_DNN_NO_ACCURACY_CHECK "dnn_no_accuracy_check"
namespace opencv_test {
struct TestCase
{
const char* name;
uint32_t inputs;
uint32_t outputs;
};
static const TestCase testConformanceConfig[] = {
{"test_abs", 1, 1},
{"test_acos", 1, 1},
{"test_acos_example", 1, 1},
{"test_acosh", 1, 1},
{"test_acosh_example", 1, 1},
{"test_adagrad", 5, 2},
{"test_adagrad_multiple", 8, 4},
{"test_adam", 6, 3},
{"test_adam_multiple", 10, 6},
{"test_add", 2, 1},
{"test_add_bcast", 2, 1},
{"test_add_uint8", 2, 1},
{"test_and2d", 2, 1},
{"test_and3d", 2, 1},
{"test_and4d", 2, 1},
{"test_and_bcast3v1d", 2, 1},
{"test_and_bcast3v2d", 2, 1},
{"test_and_bcast4v2d", 2, 1},
{"test_and_bcast4v3d", 2, 1},
{"test_and_bcast4v4d", 2, 1},
{"test_argmax_default_axis_example", 1, 1},
{"test_argmax_default_axis_example_select_last_index", 1, 1},
{"test_argmax_default_axis_random", 1, 1},
{"test_argmax_default_axis_random_select_last_index", 1, 1},
{"test_argmax_keepdims_example", 1, 1},
{"test_argmax_keepdims_example_select_last_index", 1, 1},
{"test_argmax_keepdims_random", 1, 1},
{"test_argmax_keepdims_random_select_last_index", 1, 1},
{"test_argmax_negative_axis_keepdims_example", 1, 1},
{"test_argmax_negative_axis_keepdims_example_select_last_index", 1, 1},
{"test_argmax_negative_axis_keepdims_random", 1, 1},
{"test_argmax_negative_axis_keepdims_random_select_last_index", 1, 1},
{"test_argmax_no_keepdims_example", 1, 1},
{"test_argmax_no_keepdims_example_select_last_index", 1, 1},
{"test_argmax_no_keepdims_random", 1, 1},
{"test_argmax_no_keepdims_random_select_last_index", 1, 1},
{"test_argmin_default_axis_example", 1, 1},
{"test_argmin_default_axis_example_select_last_index", 1, 1},
{"test_argmin_default_axis_random", 1, 1},
{"test_argmin_default_axis_random_select_last_index", 1, 1},
{"test_argmin_keepdims_example", 1, 1},
{"test_argmin_keepdims_example_select_last_index", 1, 1},
{"test_argmin_keepdims_random", 1, 1},
{"test_argmin_keepdims_random_select_last_index", 1, 1},
{"test_argmin_negative_axis_keepdims_example", 1, 1},
{"test_argmin_negative_axis_keepdims_example_select_last_index", 1, 1},
{"test_argmin_negative_axis_keepdims_random", 1, 1},
{"test_argmin_negative_axis_keepdims_random_select_last_index", 1, 1},
{"test_argmin_no_keepdims_example", 1, 1},
{"test_argmin_no_keepdims_example_select_last_index", 1, 1},
{"test_argmin_no_keepdims_random", 1, 1},
{"test_argmin_no_keepdims_random_select_last_index", 1, 1},
{"test_asin", 1, 1},
{"test_asin_example", 1, 1},
{"test_asinh", 1, 1},
{"test_asinh_example", 1, 1},
{"test_atan", 1, 1},
{"test_atan_example", 1, 1},
{"test_atanh", 1, 1},
{"test_atanh_example", 1, 1},
{"test_averagepool_1d_default", 1, 1},
{"test_averagepool_2d_ceil", 1, 1},
{"test_averagepool_2d_default", 1, 1},
{"test_averagepool_2d_pads", 1, 1},
{"test_averagepool_2d_pads_count_include_pad", 1, 1},
{"test_averagepool_2d_precomputed_pads", 1, 1},
{"test_averagepool_2d_precomputed_pads_count_include_pad", 1, 1},
{"test_averagepool_2d_precomputed_same_upper", 1, 1},
{"test_averagepool_2d_precomputed_strides", 1, 1},
{"test_averagepool_2d_same_lower", 1, 1},
{"test_averagepool_2d_same_upper", 1, 1},
{"test_averagepool_2d_strides", 1, 1},
{"test_averagepool_3d_default", 1, 1},
{"test_basic_conv_with_padding", 2, 1},
{"test_basic_conv_without_padding", 2, 1},
{"test_basic_convinteger", 3, 1},
{"test_batchnorm_epsilon", 5, 1},
{"test_batchnorm_epsilon_training_mode", 5, 3},
{"test_batchnorm_example", 5, 1},
{"test_batchnorm_example_training_mode", 5, 3},
{"test_bernoulli", 1, 1},
{"test_bernoulli_double", 1, 1},
{"test_bernoulli_double_expanded", 1, 1},
{"test_bernoulli_expanded", 1, 1},
{"test_bernoulli_seed", 1, 1},
{"test_bernoulli_seed_expanded", 1, 1},
{"test_bitshift_left_uint16", 2, 1},
{"test_bitshift_left_uint32", 2, 1},
{"test_bitshift_left_uint64", 2, 1},
{"test_bitshift_left_uint8", 2, 1},
{"test_bitshift_right_uint16", 2, 1},
{"test_bitshift_right_uint32", 2, 1},
{"test_bitshift_right_uint64", 2, 1},
{"test_bitshift_right_uint8", 2, 1},
{"test_cast_BFLOAT16_to_FLOAT", 1, 1},
{"test_cast_DOUBLE_to_FLOAT", 1, 1},
{"test_cast_DOUBLE_to_FLOAT16", 1, 1},
{"test_cast_FLOAT16_to_DOUBLE", 1, 1},
{"test_cast_FLOAT16_to_FLOAT", 1, 1},
{"test_cast_FLOAT_to_BFLOAT16", 1, 1},
{"test_cast_FLOAT_to_DOUBLE", 1, 1},
{"test_cast_FLOAT_to_FLOAT16", 1, 1},
{"test_cast_FLOAT_to_STRING", 1, 1},
{"test_cast_STRING_to_FLOAT", 1, 1},
{"test_castlike_BFLOAT16_to_FLOAT", 2, 1},
{"test_castlike_BFLOAT16_to_FLOAT_expanded", 2, 1},
{"test_castlike_DOUBLE_to_FLOAT", 2, 1},
{"test_castlike_DOUBLE_to_FLOAT16", 2, 1},
{"test_castlike_DOUBLE_to_FLOAT16_expanded", 2, 1},
{"test_castlike_DOUBLE_to_FLOAT_expanded", 2, 1},
{"test_castlike_FLOAT16_to_DOUBLE", 2, 1},
{"test_castlike_FLOAT16_to_DOUBLE_expanded", 2, 1},
{"test_castlike_FLOAT16_to_FLOAT", 2, 1},
{"test_castlike_FLOAT16_to_FLOAT_expanded", 2, 1},
{"test_castlike_FLOAT_to_BFLOAT16", 2, 1},
{"test_castlike_FLOAT_to_BFLOAT16_expanded", 2, 1},
{"test_castlike_FLOAT_to_DOUBLE", 2, 1},
{"test_castlike_FLOAT_to_DOUBLE_expanded", 2, 1},
{"test_castlike_FLOAT_to_FLOAT16", 2, 1},
{"test_castlike_FLOAT_to_FLOAT16_expanded", 2, 1},
{"test_castlike_FLOAT_to_STRING", 2, 1},
{"test_castlike_FLOAT_to_STRING_expanded", 2, 1},
{"test_castlike_STRING_to_FLOAT", 2, 1},
{"test_castlike_STRING_to_FLOAT_expanded", 2, 1},
{"test_ceil", 1, 1},
{"test_ceil_example", 1, 1},
{"test_celu", 1, 1},
{"test_celu_expanded", 1, 1},
{"test_clip", 3, 1},
{"test_clip_default_inbounds", 1, 1},
{"test_clip_default_int8_inbounds", 1, 1},
{"test_clip_default_int8_max", 2, 1},
{"test_clip_default_int8_min", 2, 1},
{"test_clip_default_max", 2, 1},
{"test_clip_default_min", 2, 1},
{"test_clip_example", 3, 1},
{"test_clip_inbounds", 3, 1},
{"test_clip_outbounds", 3, 1},
{"test_clip_splitbounds", 3, 1},
{"test_compress_0", 2, 1},
{"test_compress_1", 2, 1},
{"test_compress_default_axis", 2, 1},
{"test_compress_negative_axis", 2, 1},
{"test_concat_1d_axis_0", 2, 1},
{"test_concat_1d_axis_negative_1", 2, 1},
{"test_concat_2d_axis_0", 2, 1},
{"test_concat_2d_axis_1", 2, 1},
{"test_concat_2d_axis_negative_1", 2, 1},
{"test_concat_2d_axis_negative_2", 2, 1},
{"test_concat_3d_axis_0", 2, 1},
{"test_concat_3d_axis_1", 2, 1},
{"test_concat_3d_axis_2", 2, 1},
{"test_concat_3d_axis_negative_1", 2, 1},
{"test_concat_3d_axis_negative_2", 2, 1},
{"test_concat_3d_axis_negative_3", 2, 1},
{"test_constant", 0, 1},
{"test_constant_pad", 3, 1},
{"test_constantofshape_float_ones", 1, 1},
{"test_constantofshape_int_shape_zero", 1, 1},
{"test_constantofshape_int_zeros", 1, 1},
{"test_conv_with_autopad_same", 2, 1},
{"test_conv_with_strides_and_asymmetric_padding", 2, 1},
{"test_conv_with_strides_no_padding", 2, 1},
{"test_conv_with_strides_padding", 2, 1},
{"test_convinteger_with_padding", 3, 1},
{"test_convinteger_without_padding", 3, 1},
{"test_convtranspose", 2, 1},
{"test_convtranspose_1d", 2, 1},
{"test_convtranspose_3d", 2, 1},
{"test_convtranspose_autopad_same", 2, 1},
{"test_convtranspose_dilations", 2, 1},
{"test_convtranspose_kernel_shape", 2, 1},
{"test_convtranspose_output_shape", 2, 1},
{"test_convtranspose_pad", 2, 1},
{"test_convtranspose_pads", 2, 1},
{"test_convtranspose_with_kernel", 2, 1},
{"test_cos", 1, 1},
{"test_cos_example", 1, 1},
{"test_cosh", 1, 1},
{"test_cosh_example", 1, 1},
{"test_cumsum_1d", 2, 1},
{"test_cumsum_1d_exclusive", 2, 1},
{"test_cumsum_1d_reverse", 2, 1},
{"test_cumsum_1d_reverse_exclusive", 2, 1},
{"test_cumsum_2d_axis_0", 2, 1},
{"test_cumsum_2d_axis_1", 2, 1},
{"test_cumsum_2d_negative_axis", 2, 1},
{"test_depthtospace_crd_mode", 1, 1},
{"test_depthtospace_crd_mode_example", 1, 1},
{"test_depthtospace_dcr_mode", 1, 1},
{"test_depthtospace_example", 1, 1},
{"test_dequantizelinear", 3, 1},
{"test_dequantizelinear_axis", 3, 1},
{"test_det_2d", 1, 1},
{"test_det_nd", 1, 1},
{"test_div", 2, 1},
{"test_div_bcast", 2, 1},
{"test_div_example", 2, 1},
{"test_div_uint8", 2, 1},
{"test_dropout_default", 1, 1},
{"test_dropout_default_mask", 1, 2},
{"test_dropout_default_mask_ratio", 2, 2},
{"test_dropout_default_old", 1, 1},
{"test_dropout_default_ratio", 2, 1},
{"test_dropout_random_old", 1, 1},
{"test_dynamicquantizelinear", 1, 3},
{"test_dynamicquantizelinear_expanded", 1, 3},
{"test_dynamicquantizelinear_max_adjusted", 1, 3},
{"test_dynamicquantizelinear_max_adjusted_expanded", 1, 3},
{"test_dynamicquantizelinear_min_adjusted", 1, 3},
{"test_dynamicquantizelinear_min_adjusted_expanded", 1, 3},
{"test_edge_pad", 2, 1},
{"test_einsum_batch_diagonal", 1, 1},
{"test_einsum_batch_matmul", 2, 1},
{"test_einsum_inner_prod", 2, 1},
{"test_einsum_sum", 1, 1},
{"test_einsum_transpose", 1, 1},
{"test_elu", 1, 1},
{"test_elu_default", 1, 1},
{"test_elu_example", 1, 1},
{"test_equal", 2, 1},
{"test_equal_bcast", 2, 1},
{"test_erf", 1, 1},
{"test_exp", 1, 1},
{"test_exp_example", 1, 1},
{"test_expand_dim_changed", 2, 1},
{"test_expand_dim_unchanged", 2, 1},
{"test_eyelike_populate_off_main_diagonal", 1, 1},
{"test_eyelike_with_dtype", 1, 1},
{"test_eyelike_without_dtype", 1, 1},
{"test_flatten_axis0", 1, 1},
{"test_flatten_axis1", 1, 1},
{"test_flatten_axis2", 1, 1},
{"test_flatten_axis3", 1, 1},
{"test_flatten_default_axis", 1, 1},
{"test_flatten_negative_axis1", 1, 1},
{"test_flatten_negative_axis2", 1, 1},
{"test_flatten_negative_axis3", 1, 1},
{"test_flatten_negative_axis4", 1, 1},
{"test_floor", 1, 1},
{"test_floor_example", 1, 1},
{"test_gather_0", 2, 1},
{"test_gather_1", 2, 1},
{"test_gather_2d_indices", 2, 1},
{"test_gather_elements_0", 2, 1},
{"test_gather_elements_1", 2, 1},
{"test_gather_elements_negative_indices", 2, 1},
{"test_gather_negative_indices", 2, 1},
{"test_gathernd_example_float32", 2, 1},
{"test_gathernd_example_int32", 2, 1},
{"test_gathernd_example_int32_batch_dim1", 2, 1},
{"test_gemm_all_attributes", 3, 1},
{"test_gemm_alpha", 3, 1},
{"test_gemm_beta", 3, 1},
{"test_gemm_default_matrix_bias", 3, 1},
{"test_gemm_default_no_bias", 2, 1},
{"test_gemm_default_scalar_bias", 3, 1},
{"test_gemm_default_single_elem_vector_bias", 3, 1},
{"test_gemm_default_vector_bias", 3, 1},
{"test_gemm_default_zero_bias", 3, 1},
{"test_gemm_transposeA", 3, 1},
{"test_gemm_transposeB", 3, 1},
{"test_globalaveragepool", 1, 1},
{"test_globalaveragepool_precomputed", 1, 1},
{"test_globalmaxpool", 1, 1},
{"test_globalmaxpool_precomputed", 1, 1},
{"test_greater", 2, 1},
{"test_greater_bcast", 2, 1},
{"test_greater_equal", 2, 1},
{"test_greater_equal_bcast", 2, 1},
{"test_greater_equal_bcast_expanded", 2, 1},
{"test_greater_equal_expanded", 2, 1},
{"test_gridsample", 2, 1},
{"test_gridsample_aligncorners_true", 2, 1},
{"test_gridsample_bicubic", 2, 1},
{"test_gridsample_bilinear", 2, 1},
{"test_gridsample_border_padding", 2, 1},
{"test_gridsample_nearest", 2, 1},
{"test_gridsample_reflection_padding", 2, 1},
{"test_gridsample_zeros_padding", 2, 1},
{"test_gru_batchwise", 3, 2},
{"test_gru_defaults", 3, 1},
{"test_gru_seq_length", 4, 1},
{"test_gru_with_initial_bias", 4, 1},
{"test_hardmax_axis_0", 1, 1},
{"test_hardmax_axis_1", 1, 1},
{"test_hardmax_axis_2", 1, 1},
{"test_hardmax_default_axis", 1, 1},
{"test_hardmax_example", 1, 1},
{"test_hardmax_negative_axis", 1, 1},
{"test_hardmax_one_hot", 1, 1},
{"test_hardsigmoid", 1, 1},
{"test_hardsigmoid_default", 1, 1},
{"test_hardsigmoid_example", 1, 1},
{"test_hardswish", 1, 1},
{"test_hardswish_expanded", 1, 1},
{"test_identity", 1, 1},
{"test_identity_opt", 1, 1},
{"test_identity_sequence", 1, 1},
{"test_if", 1, 1},
{"test_if_opt", 1, 1},
{"test_if_seq", 1, 1},
{"test_instancenorm_epsilon", 3, 1},
{"test_instancenorm_example", 3, 1},
{"test_isinf", 1, 1},
{"test_isinf_negative", 1, 1},
{"test_isinf_positive", 1, 1},
{"test_isnan", 1, 1},
{"test_leakyrelu", 1, 1},
{"test_leakyrelu_default", 1, 1},
{"test_leakyrelu_example", 1, 1},
{"test_less", 2, 1},
{"test_less_bcast", 2, 1},
{"test_less_equal", 2, 1},
{"test_less_equal_bcast", 2, 1},
{"test_less_equal_bcast_expanded", 2, 1},
{"test_less_equal_expanded", 2, 1},
{"test_log", 1, 1},
{"test_log_example", 1, 1},
{"test_logsoftmax_axis_0", 1, 1},
{"test_logsoftmax_axis_0_expanded", 1, 1},
{"test_logsoftmax_axis_1", 1, 1},
{"test_logsoftmax_axis_1_expanded", 1, 1},
{"test_logsoftmax_axis_2", 1, 1},
{"test_logsoftmax_axis_2_expanded", 1, 1},
{"test_logsoftmax_default_axis", 1, 1},
{"test_logsoftmax_default_axis_expanded", 1, 1},
{"test_logsoftmax_example_1", 1, 1},
{"test_logsoftmax_example_1_expanded", 1, 1},
{"test_logsoftmax_large_number", 1, 1},
{"test_logsoftmax_large_number_expanded", 1, 1},
{"test_logsoftmax_negative_axis", 1, 1},
{"test_logsoftmax_negative_axis_expanded", 1, 1},
{"test_loop11", 3, 2},
{"test_loop13_seq", 3, 1},
{"test_loop16_seq_none", 3, 1},
{"test_lrn", 1, 1},
{"test_lrn_default", 1, 1},
{"test_lstm_batchwise", 3, 2},
{"test_lstm_defaults", 3, 1},
{"test_lstm_with_initial_bias", 4, 1},
{"test_lstm_with_peepholes", 8, 1},
{"test_matmul_2d", 2, 1},
{"test_matmul_3d", 2, 1},
{"test_matmul_4d", 2, 1},
{"test_matmulinteger", 4, 1},
{"test_max_example", 3, 1},
{"test_max_float16", 2, 1},
{"test_max_float32", 2, 1},
{"test_max_float64", 2, 1},
{"test_max_int16", 2, 1},
{"test_max_int32", 2, 1},
{"test_max_int64", 2, 1},
{"test_max_int8", 2, 1},
{"test_max_one_input", 1, 1},
{"test_max_two_inputs", 2, 1},
{"test_max_uint16", 2, 1},
{"test_max_uint32", 2, 1},
{"test_max_uint64", 2, 1},
{"test_max_uint8", 2, 1},
{"test_maxpool_1d_default", 1, 1},
{"test_maxpool_2d_ceil", 1, 1},
{"test_maxpool_2d_default", 1, 1},
{"test_maxpool_2d_dilations", 1, 1},
{"test_maxpool_2d_pads", 1, 1},
{"test_maxpool_2d_precomputed_pads", 1, 1},
{"test_maxpool_2d_precomputed_same_upper", 1, 1},
{"test_maxpool_2d_precomputed_strides", 1, 1},
{"test_maxpool_2d_same_lower", 1, 1},
{"test_maxpool_2d_same_upper", 1, 1},
{"test_maxpool_2d_strides", 1, 1},
{"test_maxpool_2d_uint8", 1, 1},
{"test_maxpool_3d_default", 1, 1},
{"test_maxpool_with_argmax_2d_precomputed_pads", 1, 2},
{"test_maxpool_with_argmax_2d_precomputed_strides", 1, 2},
{"test_maxunpool_export_with_output_shape", 3, 1},
{"test_maxunpool_export_without_output_shape", 2, 1},
{"test_mean_example", 3, 1},
{"test_mean_one_input", 1, 1},
{"test_mean_two_inputs", 2, 1},
{"test_min_example", 3, 1},
{"test_min_float16", 2, 1},
{"test_min_float32", 2, 1},
{"test_min_float64", 2, 1},
{"test_min_int16", 2, 1},
{"test_min_int32", 2, 1},
{"test_min_int64", 2, 1},
{"test_min_int8", 2, 1},
{"test_min_one_input", 1, 1},
{"test_min_two_inputs", 2, 1},
{"test_min_uint16", 2, 1},
{"test_min_uint32", 2, 1},
{"test_min_uint64", 2, 1},
{"test_min_uint8", 2, 1},
{"test_mod_broadcast", 2, 1},
{"test_mod_int64_fmod", 2, 1},
{"test_mod_mixed_sign_float16", 2, 1},
{"test_mod_mixed_sign_float32", 2, 1},
{"test_mod_mixed_sign_float64", 2, 1},
{"test_mod_mixed_sign_int16", 2, 1},
{"test_mod_mixed_sign_int32", 2, 1},
{"test_mod_mixed_sign_int64", 2, 1},
{"test_mod_mixed_sign_int8", 2, 1},
{"test_mod_uint16", 2, 1},
{"test_mod_uint32", 2, 1},
{"test_mod_uint64", 2, 1},
{"test_mod_uint8", 2, 1},
{"test_momentum", 5, 2},
{"test_momentum_multiple", 8, 4},
{"test_mul", 2, 1},
{"test_mul_bcast", 2, 1},
{"test_mul_example", 2, 1},
{"test_mul_uint8", 2, 1},
{"test_mvn", 1, 1},
{"test_mvn_expanded", 1, 1},
{"test_neg", 1, 1},
{"test_neg_example", 1, 1},
{"test_nesterov_momentum", 5, 2},
{"test_nllloss_NC", 2, 1},
{"test_nllloss_NC_expanded", 2, 1},
{"test_nllloss_NCd1", 2, 1},
{"test_nllloss_NCd1_expanded", 2, 1},
{"test_nllloss_NCd1_ii", 2, 1},
{"test_nllloss_NCd1_ii_expanded", 2, 1},
{"test_nllloss_NCd1_mean_weight_negative_ii", 3, 1},
{"test_nllloss_NCd1_mean_weight_negative_ii_expanded", 3, 1},
{"test_nllloss_NCd1_weight", 3, 1},
{"test_nllloss_NCd1_weight_expanded", 3, 1},
{"test_nllloss_NCd1_weight_ii", 3, 1},
{"test_nllloss_NCd1_weight_ii_expanded", 3, 1},
{"test_nllloss_NCd1d2", 2, 1},
{"test_nllloss_NCd1d2_expanded", 2, 1},
{"test_nllloss_NCd1d2_no_weight_reduction_mean_ii", 2, 1},
{"test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded", 2, 1},
{"test_nllloss_NCd1d2_reduction_mean", 2, 1},
{"test_nllloss_NCd1d2_reduction_mean_expanded", 2, 1},
{"test_nllloss_NCd1d2_reduction_sum", 2, 1},
{"test_nllloss_NCd1d2_reduction_sum_expanded", 2, 1},
{"test_nllloss_NCd1d2_with_weight", 3, 1},
{"test_nllloss_NCd1d2_with_weight_expanded", 3, 1},
{"test_nllloss_NCd1d2_with_weight_reduction_mean", 3, 1},
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{"test_unsqueeze_axis_1", 2, 1},
{"test_unsqueeze_axis_2", 2, 1},
{"test_unsqueeze_axis_3", 1, 1},
{"test_unsqueeze_negative_axes", 2, 1},
{"test_unsqueeze_three_axes", 2, 1},
{"test_unsqueeze_two_axes", 2, 1},
{"test_unsqueeze_unsorted_axes", 2, 1},
{"test_upsample_nearest", 2, 1},
{"test_where_example", 3, 1},
{"test_where_long_example", 3, 1},
{"test_xor2d", 2, 1},
{"test_xor3d", 2, 1},
{"test_xor4d", 2, 1},
{"test_xor_bcast3v1d", 2, 1},
{"test_xor_bcast3v2d", 2, 1},
{"test_xor_bcast4v2d", 2, 1},
{"test_xor_bcast4v3d", 2, 1},
{"test_xor_bcast4v4d", 2, 1},
};
struct TestCaseInput
{
std::vector<std::string> input_paths;
std::vector<std::string> output_paths;
std::string model_path;
std::string name;
};
std::ostream& operator<<(std::ostream& os, const TestCaseInput& test_case)
{
return os << test_case.name;
}
typedef tuple<TestCaseInput, tuple<Backend, Target> > ONNXConfParams;
std::string printOnnxConfParams(const testing::TestParamInfo<ONNXConfParams>& params)
{
TestCaseInput test_case = get<0>(params.param);
Backend backend = get<0>(get<1>(params.param));
Target target = get<1>(get<1>(params.param));
std::stringstream ss;
ss << test_case.name << "_";
PrintTo(backend, &ss);
ss << "_";
PrintTo(target, &ss);
return ss.str();
}
template<typename TString>
static std::string _tf(TString filename, bool required = true)
{
return findDataFile(std::string("dnn/onnx/") + filename, required);
}
std::vector<TestCaseInput> readTestCases()
{
std::vector<TestCaseInput> ret;
for (size_t i = 0; i < sizeof(testConformanceConfig) / sizeof(testConformanceConfig[0]); ++i)
{
const TestCase& test_case = testConformanceConfig[i];
TestCaseInput input;
std::string prefix = cv::format("conformance/node/%s", test_case.name);
input.name = test_case.name;
input.model_path = _tf(cv::format("%s/model.onnx", prefix.c_str()));
for (int i = 0; i < test_case.inputs; ++i)
{
input.input_paths.push_back(_tf(cv::format("%s/test_data_set_0/input_%d.pb", prefix.c_str(), i)));
}
for (int i = 0; i < test_case.outputs; ++i)
{
input.output_paths.push_back(_tf(cv::format("%s/test_data_set_0/output_%d.pb", prefix.c_str(), i)));
}
ret.push_back(input);
}
return ret;
}
class Test_ONNX_conformance : public TestWithParam<ONNXConfParams>
{
public:
TestCaseInput test_case;
Backend backend;
Target target;
double default_l1;
double default_lInf;
static std::set<std::string> parser_deny_list;
static std::set<std::string> global_deny_list;
static std::set<std::string> opencl_fp16_deny_list;
static std::set<std::string> opencl_deny_list;
static std::set<std::string> cpu_deny_list;
Test_ONNX_conformance()
{
test_case = get<0>(GetParam());
backend = get<0>(get<1>(GetParam()));
target = get<1>(get<1>(GetParam()));
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
{
default_l1 = 4e-3;
default_lInf = 2e-2;
}
else
{
default_l1 = 1e-5;
default_lInf = 1e-4;
}
}
bool checkFallbacks(Net& net) const
{
// Check if all the layers are supported with current backend and target.
// Some layers might be fused so their timings equal to zero.
std::vector<double> timings;
net.getPerfProfile(timings);
std::vector<String> names = net.getLayerNames();
CV_CheckEQ(names.size(), timings.size(), "DNN critical error");
bool hasFallbacks = false;
for (int i = 0; i < names.size(); ++i)
{
Ptr<dnn::Layer> l = net.getLayer(net.getLayerId(names[i]));
bool fused = timings[i] == 0.;
if ((!l->supportBackend(backend) || l->preferableTarget != target) && !fused)
{
hasFallbacks = true;
std::cout << "FALLBACK: Layer [" << l->type << "]:[" << l->name << "] is expected to has backend implementation" << endl;
}
}
return hasFallbacks;
}
static void initDenyList(std::set<std::string>& deny_set, const char* const deny_list[], const size_t n)
{
for (size_t i = 0; i < n; ++i)
{
deny_set.insert(deny_list[i]);
}
}
static void SetUpTestCase()
{
const char* const parser[] = {
#include "test_onnx_conformance_layer_parser_denylist.inl.hpp"
"" // dummy element of non empty list
};
initDenyList(parser_deny_list, parser, sizeof(parser)/sizeof(parser[0]));
const char* const global[] = {
#include "test_onnx_conformance_layer_filter_opencv_all_denylist.inl.hpp"
"" // dummy element of non empty list
};
initDenyList(global_deny_list, global, sizeof(global)/sizeof(global[0]));
const char* const opencl_fp16[] = {
#include "test_onnx_conformance_layer_filter_opencv_ocl_fp16_denylist.inl.hpp"
"" // dummy element of non empty list
};
initDenyList(opencl_fp16_deny_list, opencl_fp16, sizeof(opencl_fp16)/sizeof(opencl_fp16[0]));
const char* const opencl[] = {
#include "test_onnx_conformance_layer_filter_opencv_ocl_fp32_denylist.inl.hpp"
"" // dummy element of non empty list
};
initDenyList(opencl_deny_list, opencl, sizeof(opencl)/sizeof(opencl[0]));
const char* const cpu[] = {
#include "test_onnx_conformance_layer_filter_opencv_cpu_denylist.inl.hpp"
"" // dummy element of non empty list
};
initDenyList(cpu_deny_list, cpu, sizeof(cpu)/sizeof(cpu[0]));
}
void checkFilterLists() const
{
const std::string& name = test_case.name;
if(parser_deny_list.find(name) != parser_deny_list.end())
{
applyTestTag(CV_TEST_TAG_DNN_SKIP_PARSER, CV_TEST_TAG_DNN_SKIP_ONNX_CONFORMANCE);
}
if (backend == DNN_BACKEND_OPENCV)
{
if(global_deny_list.find(name) != global_deny_list.end())
{
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCV_BACKEND, CV_TEST_TAG_DNN_SKIP_ONNX_CONFORMANCE);
}
if((target == DNN_TARGET_OPENCL_FP16) && (opencl_fp16_deny_list.find(name) != opencl_fp16_deny_list.end()))
{
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCV_BACKEND, CV_TEST_TAG_DNN_SKIP_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_ONNX_CONFORMANCE);
}
if((target == DNN_TARGET_OPENCL) && (opencl_deny_list.find(name) != opencl_deny_list.end()))
{
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCV_BACKEND, CV_TEST_TAG_DNN_SKIP_OPENCL, CV_TEST_TAG_DNN_SKIP_ONNX_CONFORMANCE);
}
if((target == DNN_TARGET_CPU) && (cpu_deny_list.find(name) != cpu_deny_list.end()))
{
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCV_BACKEND, CV_TEST_TAG_DNN_SKIP_CPU, CV_TEST_TAG_DNN_SKIP_ONNX_CONFORMANCE);
}
}
#if 0 //def HAVE_HALIDE
else if (backend == DNN_BACKEND_HALIDE)
{
#include "test_onnx_conformance_layer_filter__halide.inl.hpp"
}
#endif
#if 0 //def HAVE_INF_ENGINE
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
#include "test_onnx_conformance_layer_filter__ngraph.inl.hpp"
}
#endif
#if 0 //def HAVE_VULKAN
else if (backend == DNN_BACKEND_VKCOM)
{
#include "test_onnx_conformance_layer_filter__vulkan.inl.hpp"
}
#endif
#if 0 //def HAVE_CUDA
else if (backend == DNN_BACKEND_CUDA)
{
#include "test_onnx_conformance_layer_filter__cuda.inl.hpp"
}
#endif
else
{
std::ostringstream ss;
ss << "No test filter available for backend ";
PrintTo(backend, &ss);
ss << ". Run test by default";
std::cout << ss.str() << std::endl;
}
}
};
std::set<std::string> Test_ONNX_conformance::parser_deny_list;
std::set<std::string> Test_ONNX_conformance::global_deny_list;
std::set<std::string> Test_ONNX_conformance::opencl_fp16_deny_list;
std::set<std::string> Test_ONNX_conformance::opencl_deny_list;
std::set<std::string> Test_ONNX_conformance::cpu_deny_list;
TEST_P(Test_ONNX_conformance, Layer_Test)
{
std::string name = test_case.name;
ASSERT_FALSE(name.empty());
bool checkLayersFallbacks = true;
bool checkAccuracy = true;
checkFilterLists();
std::vector<Mat> inputs;
std::vector<Mat> ref_outputs;
Net net;
try
{
//cout << "Read ONNX inputs..." << endl;
std::transform(test_case.input_paths.begin(), test_case.input_paths.end(),
std::back_inserter(inputs), readTensorFromONNX);
//cout << "Read ONNX reference outputs..." << endl;
std::transform(test_case.output_paths.begin(), test_case.output_paths.end(),
std::back_inserter(ref_outputs), readTensorFromONNX);
//cout << "Parse model..." << endl;
net = readNetFromONNX(test_case.model_path);
if (net.empty())
{
applyTestTag(CV_TEST_TAG_DNN_ERROR_PARSER);
}
}
catch (...)
{
cout << "Exception during ONNX model parse / loading input / loading reference data!" << endl;
applyTestTag(CV_TEST_TAG_DNN_ERROR_PARSER);
throw;
}
ASSERT_FALSE(net.empty());
std::vector<String> inputNames;
for (int i = 0; i < inputs.size(); ++i)
inputNames.push_back(cv::format("%d", i));
net.setInputsNames(inputNames);
try
{
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
for (int i = 0; i < inputs.size(); ++i)
{
net.setInput(inputs[i], inputNames[i]);
}
}
catch (...)
{
cout << "Exception during network configuration!" << endl;
applyTestTag(CV_TEST_TAG_DNN_ERROR_NET_SETUP);
throw;
}
std::vector<String> layerNames = net.getUnconnectedOutLayersNames();
std::vector< std::vector<Mat> > outputs_;
try
{
net.forward(outputs_, layerNames);
}
catch (...)
{
cout << "Exception during net.forward() call!" << endl;
applyTestTag(CV_TEST_TAG_DNN_ERROR_FORWARD);
throw;
}
ASSERT_GE(outputs_.size(), 1);
const std::vector<Mat>& outputs = outputs_[0];
if (checkLayersFallbacks && checkFallbacks(net))
{
applyTestTag(CV_TEST_TAG_DNN_LAYER_FALLBACK);
}
if (checkAccuracy)
{
try
{
if (ref_outputs.size() == 1)
{
// probably we found random unconnected layers.
normAssert(ref_outputs[0], outputs[0], "", default_l1, default_lInf);
}
else
{
ASSERT_EQ(outputs.size(), ref_outputs.size());
for (size_t i = 0; i < ref_outputs.size(); ++i)
{
normAssert(ref_outputs[i], outputs[i], "", default_l1, default_lInf);
}
}
}
catch (...)
{
cout << "Exception during accuracy check!" << endl;
throw;
}
}
else
{
applyTestTag(CV_TEST_TAG_DNN_NO_ACCURACY_CHECK);
}
if (!HasFailure())
cout << "Test passed!" << endl;
}
INSTANTIATE_TEST_CASE_P(/**/, Test_ONNX_conformance,
testing::Combine(testing::ValuesIn(readTestCases()), dnnBackendsAndTargets()),
printOnnxConfParams);
};