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1.芯片型号：J5
2.天工开物开发包OpenExplorer版本：J5_OE_1.1.60
3.问题定位：模型转换
4.问题具体描述：运行compile_perf编译模型报如下错误
`fx_force_duplicate_shared_convbn` will be set False by default after plugin 1.9.0. If you are not loading old checkpoint, please set `fx_force_duplicate_shared_convbn` False to train your new model.
`aidisdk` dependency is not available.
INFO - 2023-10-25 16:55:12,321 - driver - Generating grammar tables from /usr/lib/python3.8/lib2to3/Grammar.txt
INFO - 2023-10-25 16:55:12,337 - driver - Generating grammar tables from /usr/lib/python3.8/lib2to3/PatternGrammar.txt
WARNING - 2023-10-25 16:55:16,696 - registry - “RandomFlip:<class ‘hat_plugin.data.transforms.lidar.RandomFlip’> was already registered in HAT_OBJECT_REGISTRY registry, but get a new object <class ‘hat.data.transforms.detection.RandomFlip’>!”. Some objects in hat.data.transforms.detection are not registered!
WARNING - 2023-10-25 16:55:16,720 - registry - “RandomFlip:<class ‘hat_plugin.data.transforms.lidar.RandomFlip’> was already registered in HAT_OBJECT_REGISTRY registry, but get a new object <class ‘hat.data.transforms.detection.RandomFlip’>!”. Some objects in hat.data.transforms.seq_transform are not registered!
WARNING - 2023-10-25 16:55:17,228 - logger - wrap usage has been changed, please pass necessary args
WARNING - 2023-10-25 16:55:17,438 - registry - No module named ‘torchdynamo’. Some objects in hat.utils.compile_backends are not registered!
WARNING - 2023-10-25 16:55:17,499 - logger - GridSample module is deprecated,please use torch.nn.functional.grid_sample
INFO - 2023-10-25 16:55:17,841 - logger - building bifpn cell 0
INFO - 2023-10-25 16:55:17,842 - logger - fnode 0 : {‘inputs_offsets’: [3, 4], ‘sampling’: [‘keep’, ‘up’], ‘upsample_type’: [‘module’, ‘module’]}
WARNING - 2023-10-25 16:55:17,842 - logger - default upsampling behavior when mode=bilinear is changed to align_corners=False since torch 0.4.0. Please specify align_corners=True if the old behavior is desired.
INFO - 2023-10-25 16:55:17,843 - logger - fnode 1 : {‘inputs_offsets’: [2, 5], ‘sampling’: [‘keep’, ‘up’], ‘upsample_type’: [‘module’, ‘module’]}
INFO - 2023-10-25 16:55:17,844 - logger - fnode 2 : {‘inputs_offsets’: [1, 6], ‘sampling’: [‘keep’, ‘up’], ‘upsample_type’: [‘module’, ‘module’]}
INFO - 2023-10-25 16:55:17,844 - logger - fnode 3 : {‘inputs_offsets’: [0, 7], ‘sampling’: [‘keep’, ‘up’], ‘upsample_type’: [‘module’, ‘module’]}
INFO - 2023-10-25 16:55:17,845 - logger - fnode 4 : {‘inputs_offsets’: [1, 7, 8], ‘sampling’: [‘keep’, ‘keep’, ‘down’], ‘upsample_type’: [‘module’, ‘module’, ‘module’]}
INFO - 2023-10-25 16:55:17,846 - logger - fnode 5 : {‘inputs_offsets’: [2, 6, 9], ‘sampling’: [‘keep’, ‘keep’, ‘down’], ‘upsample_type’: [‘module’, ‘module’, ‘module’]}
INFO - 2023-10-25 16:55:17,846 - logger - fnode 6 : {‘inputs_offsets’: [3, 5, 10], ‘sampling’: [‘keep’, ‘keep’, ‘down’], ‘upsample_type’: [‘module’, ‘module’, ‘module’]}
INFO - 2023-10-25 16:55:17,847 - logger - fnode 7 : {‘inputs_offsets’: [4, 11], ‘sampling’: [‘keep’, ‘down’], ‘upsample_type’: [‘module’, ‘module’]}
INFO - 2023-10-25 16:55:17,848 - logger - building bifpn cell 1
INFO - 2023-10-25 16:55:17,848 - logger - fnode 0 : {‘inputs_offsets’: [3, 4], ‘sampling’: [‘keep’, ‘up’], ‘upsample_type’: [‘module’, ‘module’]}
INFO - 2023-10-25 16:55:17,848 - logger - fnode 1 : {‘inputs_offsets’: [2, 5], ‘sampling’: [‘keep’, ‘up’], ‘upsample_type’: [‘module’, ‘module’]}
INFO - 2023-10-25 16:55:17,849 - logger - fnode 2 : {‘inputs_offsets’: [1, 6], ‘sampling’: [‘keep’, ‘up’], ‘upsample_type’: [‘module’, ‘module’]}
INFO - 2023-10-25 16:55:17,849 - logger - fnode 3 : {‘inputs_offsets’: [0, 7], ‘sampling’: [‘keep’, ‘up’], ‘upsample_type’: [‘module’, ‘module’]}
INFO - 2023-10-25 16:55:17,850 - logger - fnode 4 : {‘inputs_offsets’: [1, 7, 8], ‘sampling’: [‘keep’, ‘keep’, ‘down’], ‘upsample_type’: [‘module’, ‘module’, ‘module’]}
INFO - 2023-10-25 16:55:17,850 - logger - fnode 5 : {‘inputs_offsets’: [2, 6, 9], ‘sampling’: [‘keep’, ‘keep’, ‘down’], ‘upsample_type’: [‘module’, ‘module’, ‘module’]}
INFO - 2023-10-25 16:55:17,851 - logger - fnode 6 : {‘inputs_offsets’: [3, 5, 10], ‘sampling’: [‘keep’, ‘keep’, ‘down’], ‘upsample_type’: [‘module’, ‘module’, ‘module’]}
INFO - 2023-10-25 16:55:17,851 - logger - fnode 7 : {‘inputs_offsets’: [4, 11], ‘sampling’: [‘keep’, ‘down’], ‘upsample_type’: [‘module’, ‘module’]}
INFO - 2023-10-25 16:55:17,852 - logger - building bifpn cell 2
INFO - 2023-10-25 16:55:17,852 - logger - fnode 0 : {‘inputs_offsets’: [3, 4], ‘sampling’: [‘keep’, ‘up’], ‘upsample_type’: [‘module’, ‘module’]}
INFO - 2023-10-25 16:55:17,852 - logger - fnode 1 : {‘inputs_offsets’: [2, 5], ‘sampling’: [‘keep’, ‘up’], ‘upsample_type’: [‘module’, ‘module’]}
INFO - 2023-10-25 16:55:17,853 - logger - fnode 2 : {‘inputs_offsets’: [1, 6], ‘sampling’: [‘keep’, ‘up’], ‘upsample_type’: [‘module’, ‘module’]}
INFO - 2023-10-25 16:55:17,853 - logger - fnode 3 : {‘inputs_offsets’: [0, 7], ‘sampling’: [‘keep’, ‘up’], ‘upsample_type’: [‘module’, ‘module’]}
INFO - 2023-10-25 16:55:17,854 - logger - fnode 4 : {‘inputs_offsets’: [1, 7, 8], ‘sampling’: [‘keep’, ‘keep’, ‘down’], ‘upsample_type’: [‘module’, ‘module’, ‘module’]}
INFO - 2023-10-25 16:55:17,855 - logger - fnode 5 : {‘inputs_offsets’: [2, 6, 9], ‘sampling’: [‘keep’, ‘keep’, ‘down’], ‘upsample_type’: [‘module’, ‘module’, ‘module’]}
INFO - 2023-10-25 16:55:17,855 - logger - fnode 6 : {‘inputs_offsets’: [3, 5, 10], ‘sampling’: [‘keep’, ‘keep’, ‘down’], ‘upsample_type’: [‘module’, ‘module’, ‘module’]}
INFO - 2023-10-25 16:55:17,856 - logger - fnode 7 : {‘inputs_offsets’: [4, 11], ‘sampling’: [‘keep’, ‘down’], ‘upsample_type’: [‘module’, ‘module’]}
WARNING - 2023-10-25 16:55:17,872 - loop_base - Start epoch 0 larger than num epochs 0
INFO - 2023-10-25 16:55:19,365 - compile_perf - Compile config:
{‘extra_args’: [‘–skip-check’],
‘hbm’: ‘./tmp_models/bevfusio_test/compile/model.hbm’,
‘input_source’: ‘pyramid, pyramid, ddr,ddr’,
‘jobs’: 4,
‘layer_details’: True,
‘march’: ‘bayes’,
‘name’: ‘bevfusio_test_model’,
‘opt’: ‘O3’,
‘out_dir’: ‘./tmp_models/bevfusio_test/compile’}
Traceback (most recent call last):
File “/usr/local/lib/python3.8/dist-packages/hbdk/torch_script/tools.py”, line 72, in _trace_module
traced_module = trace(module, example_inputs)
File “/usr/local/lib/python3.8/dist-packages/hbdk/torch_script/tools.py”, line 63, in trace
return torch.jit.trace(obj, placeholders)
File “/root/.local/lib/python3.8/site-packages/torch/jit/_trace.py”, line 759, in trace
return trace_module(
File “/root/.local/lib/python3.8/site-packages/torch/jit/_trace.py”, line 976, in trace_module
module._c._create_method_from_trace(
RuntimeError: Tracer cannot infer type of ({‘bev_indexs’: [tensor([[[[0.3162, 0.1724],
[0.7793, 0.6075],
[0.3887, 0.6564],
…,
[0.1538, 0.1762],
[0.8531, 0.8633],
[0.5361, 0.2402]],
[[0.7720, 0.3055],
[0.5661, 0.1971],
[0.8270, 0.4221],
…,
[0.6919, 0.2758],
[0.9005, 0.0208],
[0.8195, 0.7273]],
[[0.4227, 0.1250],
[0.0669, 0.2891],
[0.3307, 0.4966],
…,
[0.0136, 0.3693],
[0.7039, 0.0655],
[0.6089, 0.8476]],
…,
[[0.4263, 0.4966],
[0.7790, 0.5007],
[0.6622, 0.7148],
…,
[0.2713, 0.4848],
[0.7516, 0.0598],
[0.6587, 0.7926]],
[[0.7971, 0.6470],
[0.8104, 0.4282],
[0.4489, 0.1623],
…,
[0.6535, 0.6479],
[0.1991, 0.1991],
[0.1953, 0.0422]],
[[0.3581, 0.4892],
[0.0408, 0.5571],
[0.6503, 0.1115],
…,
[0.2268, 0.0985],
[0.3374, 0.8438],
[0.2187, 0.1547]]]])], ‘bev_rimg_feature’: tensor([[[[0.0000, 0.0000, 0.0000, …, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, …, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, …, 0.0000, 0.0000, 0.0000],
…,
[0.0000, 0.0000, 0.0000, …, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, …, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, …, 0.0000, 0.0000, 0.0000]],
[[0.0017, 0.0030, 0.0056, …, 0.0112, 0.0051, 0.0021],
[0.0044, 0.0054, 0.0072, …, 0.0122, 0.0070, 0.0044],
[0.0099, 0.0101, 0.0103, …, 0.0143, 0.0109, 0.0091],
…,
[0.0090, 0.0078, 0.0054, …, 0.0091, 0.0103, 0.0109],
[0.0070, 0.0072, 0.0075, …, 0.0091, 0.0113, 0.0124],
[0.0060, 0.0069, 0.0086, …, 0.0090, 0.0118, 0.0131]],
[[0.0036, 0.0050, 0.0078, …, 0.0051, 0.0046, 0.0044],
[0.0041, 0.0051, 0.0072, …, 0.0047, 0.0038, 0.0034],
[0.0051, 0.0054, 0.0060, …, 0.0039, 0.0023, 0.0016],
…,
[0.0056, 0.0048, 0.0032, …, 0.0007, 0.0005, 0.0004],
[0.0095, 0.0073, 0.0031, …, 0.0005, 0.0006, 0.0007],
[0.0114, 0.0086, 0.0030, …, 0.0004, 0.0007, 0.0009]],
…,
[[0.0003, 0.0015, 0.0040, …, 0.0075, 0.0033, 0.0012],
[0.0012, 0.0023, 0.0047, …, 0.0083, 0.0052, 0.0036],
[0.0029, 0.0040, 0.0060, …, 0.0101, 0.0090, 0.0085],
…,
[0.0064, 0.0065, 0.0065, …, 0.0129, 0.0098, 0.0083],
[0.0041, 0.0047, 0.0059, …, 0.0124, 0.0107, 0.0098],
[0.0030, 0.0039, 0.0056, …, 0.0122, 0.0111, 0.0106]],
[[0.0041, 0.0090, 0.0189, …, 0.0188, 0.0181, 0.0178],
[0.0056, 0.0099, 0.0184, …, 0.0184, 0.0171, 0.0165],
[0.0086, 0.0116, 0.0176, …, 0.0176, 0.0152, 0.0140],
…,
[0.0128, 0.0141, 0.0169, …, 0.0183, 0.0156, 0.0143],
[0.0130, 0.0140, 0.0159, …, 0.0151, 0.0137, 0.0130],
[0.0132, 0.0139, 0.0154, …, 0.0135, 0.0128, 0.0124]],
[[0.0000, 0.0000, 0.0000, …, 0.0010, 0.0031, 0.0042],
[0.0000, 0.0000, 0.0000, …, 0.0008, 0.0023, 0.0031],
[0.0000, 0.0000, 0.0000, …, 0.0003, 0.0008, 0.0010],
…,
[0.0000, 0.0000, 0.0000, …, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, …, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, …, 0.0000, 0.0000, 0.0000]]]],
grad_fn=), ‘img’: [tensor([[[[ 0.5617, -0.3087, 0.3123, …, -1.7685, -0.0595, 0.1583],
[-1.0509, -1.7362, -0.9337, …, -1.0892, 0.3739, 0.4653],
[ 0.2189, -0.4711, -1.5735, …, 0.9975, -0.0291, 0.7244],
…,
[ 1.9000, -0.3604, 0.4328, …, -0.2121, 1.6990, -0.4072],
[-0.3559, 1.0986, 0.1881, …, 0.6845, -1.3309, 1.0653],
[ 0.0926, 0.3645, -1.7737, …, -1.3290, -0.4507, -0.6421]],
[[ 1.1631, 2.8437, 0.6648, …, 0.3745, -0.0471, 0.0213],
[-0.4076, 0.7873, 0.3017, …, -0.3078, 0.2825, -1.1844],
[ 0.6940, 1.7993, 0.3096, …, -0.1478, 1.4080, -1.3811],
…,
[ 0.8879, 0.4404, -0.0107, …, -0.5014, -0.4216, -1.0943],
[ 0.7678, -2.4379, 0.6670, …, -0.0679, -0.9766, 1.1841],
[-0.4223, 0.7187, 2.7780, …, 0.2266, 0.4778, -1.1886]],
[[ 0.4607, 1.9572, 2.6433, …, -0.2658, 0.5854, 1.0505],
[-0.8811, -2.5599, -0.5487, …, 0.1469, -0.0902, -1.7636],
[ 0.4111, 0.8833, -0.2084, …, -0.2139, -1.7689, -0.7534],
…,
[ 1.2642, 1.4366, 0.0135, …, -0.3370, -0.7770, -0.0885],
[ 0.2127, -1.6587, 0.8625, …, 0.4538, -1.5082, -1.1600],
[ 0.3596, 1.7218, -0.3764, …, -1.6370, 0.8074, 0.7135]]]])], ‘lidar_rimg_points’: tensor([[[[-0.5496, 1.6116, -0.6502, …, 1.3891, -0.5316, -0.9067],
[ 0.6407, 0.8787, -0.1264, …, -0.2866, 0.0849, -1.8586],
[ 0.5418, 1.2795, 0.3814, …, -1.5346, 0.3623, 1.1686],
…,
[ 0.7125, -0.3552, -0.0304, …, -0.6403, 0.3085, 0.3050],
[ 0.8773, -0.1287, -0.1355, …, 1.1003, 0.2174, 0.1039],
[ 1.0110, -0.0157, -0.1245, …, -0.9461, 1.5661, 0.1756]],
[[-0.7214, 0.8428, -1.0656, …, -1.3716, 0.5021, -1.6789],
[-0.1180, 1.6364, -0.4738, …, -0.6698, 0.9357, 0.0549],
[ 1.1641, 0.4428, 0.5648, …, 0.3752, -0.1593, -0.8183],
…,
[-0.5877, 0.1760, 0.2710, …, 0.6455, -1.2352, -1.4991],
[-1.2978, -0.6897, -0.5056, …, -0.0767, -0.3674, -1.1101],
[-0.0142, 1.3849, -0.9276, …, -0.1653, -1.2790, -0.3591]],
[[ 0.1327, -1.3625, -0.5886, …, 0.2067, 1.0366, 0.5674],
[ 1.2898, -0.4479, 0.2805, …, 0.7375, 0.8465, 0.6556],
[-0.2271, 0.1466, 2.3483, …, -0.3998, -0.3894, 1.3554],
…,
[ 0.1304, -0.1526, -1.7180, …, -0.8465, -0.8714, -0.9241],
[-1.1537, 0.5921, -1.8495, …, 0.5275, 0.6446, -0.0398],
[-0.1683, 0.1269, 0.3037, …, 1.4264, 1.9281, -1.3948]],
[[ 0.6686, -0.3017, 0.0152, …, -1.2561, 0.3012, -0.3918],
[ 0.0291, 0.0569, 1.5758, …, 0.1355, -1.3450, 0.2088],
[-0.1745, -0.3980, 0.1286, …, -0.9492, 0.4239, -0.4686],
…,
[-1.3600, -2.6102, -1.4200, …, -0.7870, -0.6682, 1.4338],
[-0.6672, -1.0665, 1.3487, …, 0.6635, 0.7535, 0.7102],
[ 0.7171, 0.1141, 2.1065, …, 0.4801, 0.7755, 0.5016]]]]), ‘points’: [tensor([[[[0.2317, 0.6153],
[0.7440, 0.4597],
[0.8696, 0.5018],
…,
[0.4131, 0.1271],
[0.5250, 0.4270],
[0.2351, 0.4591]],
[[0.2279, 0.1499],
[0.9856, 0.2939],
[0.3723, 0.0625],
…,
[0.5221, 0.0513],
[0.2659, 0.6187],
[0.8555, 0.2791]],
[[0.8329, 0.9386],
[0.9753, 0.9501],
[0.5542, 0.0397],
…,
[0.0720, 0.8683],
[0.1877, 0.7304],
[0.4118, 0.6922]],
…,
[[0.3868, 0.7476],
[0.9426, 0.5233],
[0.0357, 0.3104],
…,
[0.0802, 0.5466],
[0.9704, 0.2646],
[0.9685, 0.9136]],
[[0.0288, 0.6764],
[0.4263, 0.4765],
[0.6109, 0.6622],
…,
[0.2361, 0.9960],
[0.0851, 0.5084],
[0.6059, 0.9130]],
[[0.0237, 0.8084],
[0.6775, 0.6572],
[0.2288, 0.3823],
…,
[0.3183, 0.2088],
[0.9801, 0.3754],
[0.2598, 0.6448]]]])], ‘rimg_features’: tensor([[[[0.0070, 0.0000, 0.0000, …, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, …, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, …, 0.0000, 0.0000, 0.0000],
…,
[0.0000, 0.0000, 0.0000, …, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, …, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, …, 0.0000, 0.0000, 0.0000]],
[[0.0060, 0.0000, 0.0003, …, 0.0000, 0.0039, 0.0021],
[0.0065, 0.0165, 0.0098, …, 0.0058, 0.0123, 0.0196],
[0.0130, 0.0080, 0.0067, …, 0.0068, 0.0000, 0.0078],
…,
[0.0114, 0.0010, 0.0110, …, 0.0350, 0.0020, 0.0044],
[0.0000, 0.0006, 0.0000, …, 0.0000, 0.0116, 0.0042],
[0.0000, 0.0053, 0.0000, …, 0.0085, 0.0000, 0.0098]],
[[0.0061, 0.0051, 0.0120, …, 0.0193, 0.0000, 0.0000],
[0.0190, 0.0011, 0.0104, …, 0.0000, 0.0047, 0.0000],
[0.0198, 0.0025, 0.0000, …, 0.0000, 0.0000, 0.0000],
…,
[0.0232, 0.0000, 0.0019, …, 0.0007, 0.0077, 0.0000],
[0.0275, 0.0000, 0.0000, …, 0.0052, 0.0000, 0.0000],
[0.0039, 0.0000, 0.0000, …, 0.0000, 0.0000, 0.0000]],
…,
[[0.0049, 0.0000, 0.0000, …, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0056, 0.0095, …, 0.0027, 0.0110, 0.0019],
[0.0000, 0.0034, 0.0101, …, 0.0089, 0.0000, 0.0052],
…,
[0.0002, 0.0000, 0.0168, …, 0.0065, 0.0026, 0.0201],
[0.0000, 0.0104, 0.0136, …, 0.0101, 0.0102, 0.0204],
[0.0061, 0.0308, 0.0182, …, 0.0207, 0.0159, 0.0256]],
[[0.0115, 0.0088, 0.0242, …, 0.0293, 0.0117, 0.0000],
[0.0100, 0.0118, 0.0297, …, 0.0241, 0.0211, 0.0011],
[0.0128, 0.0055, 0.0138, …, 0.0028, 0.0202, 0.0058],
…,
[0.0277, 0.0085, 0.0174, …, 0.0106, 0.0157, 0.0061],
[0.0189, 0.0099, 0.0093, …, 0.0212, 0.0125, 0.0000],
[0.0000, 0.0000, 0.0000, …, 0.0016, 0.0032, 0.0000]],
[[0.0000, 0.0000, 0.0000, …, 0.0000, 0.0081, 0.0081],
[0.0000, 0.0000, 0.0000, …, 0.0000, 0.0000, 0.0044],
[0.0000, 0.0000, 0.0000, …, 0.0000, 0.0000, 0.0012],
…,
[0.0000, 0.0000, 0.0000, …, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, …, 0.0000, 0.0000, 0.0085],
[0.0000, 0.0000, 0.0000, …, 0.0000, 0.0000, 0.0000]]]],
grad_fn=)},)
:Dictionary inputs to traced functions must have consistent type. Found List[Tensor] and Tensor
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File “tools/compile_perf.py”, line 229, in
compile_then_perf(
File “tools/compile_perf.py”, line 133, in compile_then_perf
result = perf_model(
File “/usr/local/lib/python3.8/dist-packages/hbdk/torch_script/tools.py”, line 360, in perf_model
traced_module = _trace_module(module, example_inputs)
File “/usr/local/lib/python3.8/dist-packages/hbdk/torch_script/tools.py”, line 75, in _trace_module
raise RuntimeError(
RuntimeError: torch.jit.trace fail. Please make sure the model is traceable.
我的设置如下：
deploy_inputs = {
“img”: [torch.randn((1,) + data_shape)] * 1,
“lidar_rimg_points”: [torch.randn((1, 4, 256, 640))]* 1,
“points”: [
(
placeholder(
(1, 3*H, W, 2),
torch_native=True,
sample=torch.rand((1, 3*H, W, 2)),
)
)
]* 1,
“bev_indexs”:[
(
placeholder(
(1, H, W, 2),
torch_native=True,
sample=torch.rand((1, H, W, 2)),
)
),
]* 1,
}
编译设置如下：
compile_dir = os.path.join(ckpt_dir, “compile”)
compile_cfg = dict(
march=march,
name=task_name + “_model”,
out_dir=compile_dir,
hbm=os.path.join(compile_dir, “model.hbm”),
layer_details=True,
input_source=“pyramid, pyramid, ddr,ddr”,
extra_args=[“–skip-check”],
opt=“O3”,
)
