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Unable to jit.script DETR res50 model: :Dictionary inputs to traced functions must have consistent type. Found Tensor and List[Dict[str, Tensor]]
See original GitHub issueInstructions To Reproduce the 🐛 Bug:
- what changes you made (
git diff) or what code you wrote
Fine tuned DETR model, resnet50 backbone - 3 classes.
-
what exact command you run: prepared a single image per normal validation process as the ‘sample’ (resize, tensorize, normalize, unsqueeze to make batch 1, push to gpu). then: traced` = torch.jit.trace(model,single_batch_tensorimg)
-
what you observed (including full logs):
runtime error - summary error is "Dictionary inputs to traced functions must have consistent type. Found Found Tensor and List[Dict[str, Tensor]]"
Full error log:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-83-905883ed8622> in <module>
----> 1 traced = torch.jit.trace(model,single_batch_tensorimg)
~/anaconda3/lib/python3.7/site-packages/torch/jit/__init__.py in trace(func, example_inputs, optimize, check_trace, check_inputs, check_tolerance, strict, _force_outplace, _module_class, _compilation_unit)
953 return trace_module(func, {'forward': example_inputs}, None,
954 check_trace, wrap_check_inputs(check_inputs),
--> 955 check_tolerance, strict, _force_outplace, _module_class)
956
957 if (hasattr(func, '__self__') and isinstance(func.__self__, torch.nn.Module) and
~/anaconda3/lib/python3.7/site-packages/torch/jit/__init__.py in trace_module(mod, inputs, optimize, check_trace, check_inputs, check_tolerance, strict, _force_outplace, _module_class, _compilation_unit)
1107 func = mod if method_name == "forward" else getattr(mod, method_name)
1108 example_inputs = make_tuple(example_inputs)
-> 1109 module._c._create_method_from_trace(method_name, func, example_inputs, var_lookup_fn, strict, _force_outplace)
1110 check_trace_method = module._c._get_method(method_name)
1111
RuntimeError: Tracer cannot infer type of {'pred_logits': tensor([[[-1.3257e+00, -4.5160e+00, -3.3199e+00, 6.2382e+00],
[-1.2233e+00, -4.2994e+00, -3.4213e+00, 6.4357e+00],
[-1.7037e+00, -4.1408e+00, -3.2093e+00, 6.6415e+00],
[-1.9977e+00, -4.2006e+00, -2.9134e+00, 6.6455e+00],
[-9.1402e-01, -4.1852e+00, -3.3812e+00, 6.2901e+00],
[-9.0749e-01, -3.8806e+00, -3.7153e+00, 6.6058e+00],
[-8.6349e-01, -4.2650e+00, -3.2326e+00, 6.3258e+00],
[-2.1840e+00, -2.8677e+00, -2.8063e+00, 6.0706e+00],
[-1.4402e+00, -4.1639e+00, -3.0899e+00, 6.0722e+00],
[-1.0651e+00, -4.3728e+00, -3.3428e+00, 6.2091e+00],
[-8.4225e-01, -4.3105e+00, -3.2828e+00, 6.3823e+00],
[-1.3332e+00, -4.4080e+00, -3.4408e+00, 6.4879e+00],
[-1.9474e+00, -3.7540e+00, -2.2489e+00, 6.4509e+00],
[-7.6170e-01, -4.3160e+00, -3.2560e+00, 6.5407e+00],
[-1.5858e+00, -4.7425e+00, -3.0672e+00, 6.5151e+00],
[-1.2460e+00, -4.2103e+00, -3.4207e+00, 6.0635e+00],
[-1.4751e+00, -4.3444e+00, -3.3478e+00, 6.2889e+00],
[-1.5262e+00, -4.0728e+00, -3.1277e+00, 6.4521e+00],
[-1.3835e+00, -4.4318e+00, -3.2489e+00, 6.8782e+00],
[-8.3059e-01, -4.1737e+00, -3.5075e+00, 6.5841e+00],
[-8.6003e-01, -3.9930e+00, -2.8658e+00, 6.1958e+00],
[-1.1195e+00, -4.1224e+00, -3.1234e+00, 6.4970e+00],
[-9.7752e-01, -4.3823e+00, -3.3829e+00, 6.2668e+00],
[-7.1669e-01, -4.2383e+00, -3.4157e+00, 6.5341e+00],
[-8.8181e-01, -4.0628e+00, -3.1997e+00, 6.2601e+00],
[-8.2779e-01, -3.9883e+00, -3.1007e+00, 6.1781e+00],
[-9.9500e-01, -4.2641e+00, -3.5869e+00, 6.4713e+00],
[-2.0311e+00, -3.2360e+00, -2.9700e+00, 6.1869e+00],
[-1.4078e+00, -4.7912e+00, -3.1322e+00, 6.5051e+00],
[-1.2046e+00, -4.3279e+00, -3.5028e+00, 6.2667e+00],
[-1.1305e+00, -4.1406e+00, -3.4895e+00, 6.6066e+00],
[-8.5910e-01, -4.4805e+00, -3.1130e+00, 6.4499e+00],
[-1.5736e+00, -4.5274e+00, -3.1096e+00, 6.2207e+00],
[-8.7325e-01, -4.3082e+00, -3.4655e+00, 6.5778e+00],
[-9.6917e-01, -4.2796e+00, -3.6319e+00, 6.5982e+00],
[-1.0941e+00, -4.3939e+00, -3.3200e+00, 6.4394e+00],
[-1.0556e+00, -4.3867e+00, -3.3821e+00, 6.5570e+00],
[-1.8775e+00, -3.5439e+00, -3.1150e+00, 6.2070e+00],
[-1.2131e+00, -4.0072e+00, -3.0816e+00, 6.3504e+00],
[-2.3224e+00, -3.3842e+00, -2.3198e+00, 6.0812e+00],
[-1.0608e+00, -4.1834e+00, -3.5235e+00, 6.3782e+00],
[-1.0443e+00, -4.2378e+00, -3.4654e+00, 6.3219e+00],
[-1.0877e+00, -4.1976e+00, -3.4058e+00, 6.5459e+00],
[-6.2044e-01, -3.1038e+00, -3.2906e+00, 5.6900e+00],
[-1.2118e+00, -4.2138e+00, -3.4915e+00, 6.2584e+00],
[-2.3219e+00, -2.5062e+00, -2.4384e+00, 5.7084e+00],
[-1.1533e+00, -4.0388e+00, -3.1493e+00, 6.2860e+00],
[-1.0967e+00, -4.6921e+00, -3.2188e+00, 6.5986e+00],
[-1.0805e+00, -4.2764e+00, -3.4335e+00, 6.3430e+00],
[-1.8296e+00, -3.2435e+00, -3.0589e+00, 6.0212e+00],
[-1.0244e+00, -4.3455e+00, -3.3756e+00, 6.2947e+00],
[-1.2441e+00, -4.2204e+00, -3.3408e+00, 6.4183e+00],
[-1.3930e+00, -4.6263e+00, -3.2685e+00, 6.3670e+00],
[-1.0628e+00, -3.7810e+00, -3.2089e+00, 5.7979e+00],
[-1.6535e+00, -4.3075e+00, -3.3257e+00, 6.6525e+00],
[-2.1558e+00, -2.8542e+00, -2.5645e+00, 5.9237e+00],
[-1.2327e+00, -4.3747e+00, -3.3858e+00, 6.2452e+00],
[-9.7423e-01, -4.3141e+00, -3.4089e+00, 6.4728e+00],
[-1.1418e+00, -4.2310e+00, -3.5703e+00, 6.5939e+00],
[-7.1537e-01, -4.1040e+00, -3.5085e+00, 6.1348e+00],
[-1.4839e+00, -4.0042e+00, -3.1457e+00, 6.0577e+00],
[-1.7408e+00, 4.1293e+00, -4.9509e-01, -1.5525e+00],
[-1.0284e+00, -4.2687e+00, -3.3726e+00, 6.2829e+00],
[-1.3814e+00, -4.1389e+00, -3.3440e+00, 6.1566e+00],
[-1.0025e+00, -4.3539e+00, -3.3363e+00, 6.3218e+00],
[-1.5108e+00, -4.3231e+00, -3.2468e+00, 6.5698e+00],
[-1.2099e+00, -4.1864e+00, -3.3779e+00, 6.1933e+00],
[-1.6244e+00, -3.8396e+00, -3.2318e+00, 6.2905e+00],
[-1.4818e+00, -3.9799e+00, -3.2267e+00, 6.1274e+00],
[-1.0574e+00, -4.0651e+00, -3.4919e+00, 6.4319e+00],
[-1.2549e+00, -4.1688e+00, -3.5131e+00, 6.4428e+00],
[ 4.4515e+00, 3.8680e-03, -1.3146e+00, -1.8654e+00],
[-7.6814e-01, -4.1199e+00, -3.3639e+00, 6.6328e+00],
[-1.0299e+00, -4.2803e+00, -3.3329e+00, 6.6037e+00],
[-8.1970e-01, -4.5901e+00, -3.2575e+00, 6.5023e+00],
[-1.2137e+00, -4.0303e+00, -3.0325e+00, 6.4221e+00],
[-1.3679e+00, -4.1100e+00, -3.4035e+00, 6.2796e+00],
[-7.4305e-01, -4.3340e+00, -3.5650e+00, 6.5321e+00],
[-1.0473e+00, -4.1474e+00, -3.4294e+00, 6.4154e+00],
[-1.0486e+00, -4.2867e+00, -3.4619e+00, 6.4207e+00],
[-1.0286e+00, -4.2844e+00, -3.5789e+00, 6.7687e+00],
[-7.5652e-01, -3.7627e+00, -3.3852e+00, 5.9946e+00],
[-1.0777e+00, -4.1732e+00, -3.3713e+00, 6.4387e+00],
[-6.9987e-01, -4.1442e+00, -3.2779e+00, 6.0329e+00],
[-1.5770e+00, -3.8518e+00, -3.3054e+00, 6.2581e+00],
[-1.2293e+00, -4.8014e+00, -2.8239e+00, 6.5217e+00],
[-7.1122e-01, -4.2456e+00, -3.5109e+00, 6.5091e+00],
[-1.2753e+00, -4.2083e+00, -3.3322e+00, 6.4163e+00],
[-4.7021e-01, -3.9568e+00, -3.3516e+00, 6.2389e+00],
[-9.1776e-01, -4.0323e+00, -3.4145e+00, 6.3345e+00],
[-7.6549e-01, -4.0218e+00, -3.1490e+00, 6.1941e+00],
[-1.2081e+00, -4.1735e+00, -3.3727e+00, 6.5662e+00],
[-9.8999e-01, -4.1010e+00, -3.2139e+00, 6.5327e+00],
[-1.2535e+00, -4.1246e+00, -3.5824e+00, 6.5121e+00],
[-9.4460e-01, -4.2512e+00, -3.2911e+00, 6.4086e+00],
[-7.2473e-01, -4.3610e+00, -3.5410e+00, 6.5070e+00],
[-1.3768e+00, -4.0155e+00, -3.4594e+00, 6.3249e+00],
[-1.3593e+00, -4.2313e+00, -3.4009e+00, 6.5089e+00],
[-2.9125e+00, -6.6248e-01, 4.4765e+00, -7.4652e-01],
[-1.0689e+00, -4.0516e+00, -3.5897e+00, 6.4756e+00]]],
device='cuda:0', grad_fn=<SelectBackward>), 'pred_boxes': tensor([[[0.4914, 0.4477, 0.8947, 0.7548],
[0.5291, 0.4641, 0.8433, 0.5586],
[0.4912, 0.4702, 0.6988, 0.7264],
[0.6840, 0.4511, 0.4998, 0.5831],
[0.4970, 0.4911, 0.8647, 0.5582],
[0.5062, 0.4874, 0.8768, 0.5273],
[0.4951, 0.5404, 0.9817, 0.3977],
[0.4263, 0.5440, 0.6654, 0.3528],
[0.4721, 0.4357, 0.6923, 0.7251],
[0.4961, 0.4482, 0.9674, 0.7162],
[0.4957, 0.5516, 0.9916, 0.4525],
[0.4894, 0.4508, 0.9339, 0.7519],
[0.5813, 0.5459, 0.6686, 0.3448],
[0.4983, 0.5499, 0.9455, 0.4293],
[0.4995, 0.4650, 0.9940, 0.7674],
[0.4933, 0.4329, 0.7041, 0.7378],
[0.4930, 0.4378, 0.8551, 0.7548],
[0.5851, 0.4342, 0.6135, 0.6085],
[0.5161, 0.4882, 0.7445, 0.5979],
[0.5108, 0.5296, 0.8478, 0.4747],
[0.5037, 0.5535, 0.9071, 0.3481],
[0.5079, 0.5374, 0.9085, 0.4283],
[0.4912, 0.4524, 0.9914, 0.7348],
[0.4962, 0.5320, 0.8992, 0.4527],
[0.5127, 0.5383, 0.9057, 0.3978],
[0.5108, 0.5544, 0.8970, 0.3692],
[0.4983, 0.4343, 0.9809, 0.7201],
[0.4438, 0.4996, 0.6731, 0.4750],
[0.5694, 0.4991, 0.6922, 0.6957],
[0.5076, 0.4382, 0.7700, 0.7317],
[0.5000, 0.4607, 0.8836, 0.6110],
[0.4932, 0.5476, 0.9992, 0.5053],
[0.4885, 0.4541, 0.9959, 0.7642],
[0.5050, 0.5129, 0.7764, 0.5286],
[0.5005, 0.4495, 0.9250, 0.6592],
[0.4998, 0.4882, 0.9710, 0.5647],
[0.4943, 0.4527, 0.9832, 0.7090],
[0.4539, 0.4514, 0.6784, 0.6066],
[0.4920, 0.5466, 0.9152, 0.4152],
[0.7626, 0.5217, 0.3840, 0.3530],
[0.5153, 0.4426, 0.8132, 0.6955],
[0.4959, 0.4345, 0.9892, 0.7281],
[0.5035, 0.4461, 0.9234, 0.6455],
[0.5035, 0.5573, 0.9327, 0.3174],
[0.4946, 0.4378, 0.6953, 0.7401],
[0.3533, 0.5500, 0.5404, 0.3252],
[0.5398, 0.5231, 0.7213, 0.4283],
[0.4912, 0.5226, 0.9994, 0.6730],
[0.4968, 0.4570, 0.7264, 0.6410],
[0.4413, 0.5372, 0.6804, 0.3830],
[0.5051, 0.4868, 0.8158, 0.6040],
[0.5172, 0.4280, 0.7019, 0.6798],
[0.4862, 0.4567, 0.9995, 0.7663],
[0.4519, 0.5481, 0.6927, 0.3951],
[0.4887, 0.4515, 0.9357, 0.7436],
[0.4121, 0.5410, 0.6361, 0.3439],
[0.4938, 0.4421, 0.6918, 0.7467],
[0.4957, 0.5243, 0.9956, 0.4674],
[0.5030, 0.4340, 0.9764, 0.6499],
[0.5019, 0.4317, 0.8988, 0.6941],
[0.4609, 0.4235, 0.6984, 0.7339],
[0.2117, 0.5438, 0.3544, 0.3137],
[0.4917, 0.4439, 0.9956, 0.7291],
[0.4842, 0.4339, 0.7036, 0.7416],
[0.5047, 0.4434, 0.9468, 0.6899],
[0.4906, 0.4715, 0.9374, 0.7335],
[0.5004, 0.4371, 0.8745, 0.7278],
[0.4769, 0.4211, 0.6882, 0.7038],
[0.4698, 0.4408, 0.6993, 0.6940],
[0.5102, 0.4556, 0.8173, 0.6199],
[0.5031, 0.4379, 0.9853, 0.7267],
[0.4914, 0.5326, 0.9300, 0.3334],
[0.5315, 0.5557, 0.7553, 0.3874],
[0.4991, 0.5213, 0.9842, 0.4994],
[0.5005, 0.5456, 0.9432, 0.4854],
[0.5168, 0.5417, 0.8261, 0.4033],
[0.4930, 0.4339, 0.7079, 0.7174],
[0.4940, 0.5140, 0.8836, 0.5233],
[0.5099, 0.4978, 0.7676, 0.5618],
[0.4982, 0.4456, 0.9477, 0.7125],
[0.4935, 0.4617, 0.9833, 0.6653],
[0.4935, 0.5589, 0.9184, 0.3493],
[0.5057, 0.4839, 0.8128, 0.5647],
[0.5068, 0.5214, 0.8670, 0.4747],
[0.4698, 0.4308, 0.7013, 0.7014],
[0.5683, 0.5551, 0.6711, 0.4762],
[0.4989, 0.5223, 0.8108, 0.4794],
[0.5037, 0.4180, 0.9437, 0.6967],
[0.4983, 0.5587, 0.9043, 0.3711],
[0.4946, 0.5367, 0.9640, 0.4033],
[0.4971, 0.5511, 0.9602, 0.3713],
[0.5127, 0.4491, 0.8125, 0.6487],
[0.5112, 0.5485, 0.9091, 0.3727],
[0.5275, 0.4376, 0.8270, 0.5822],
[0.4979, 0.5453, 0.9824, 0.4286],
[0.4928, 0.4865, 0.9910, 0.5705],
[0.4966, 0.4507, 0.7086, 0.6685],
[0.5273, 0.4258, 0.7093, 0.6504],
[0.7762, 0.5236, 0.3506, 0.3170],
[0.5303, 0.4724, 0.7451, 0.5736]]], device='cuda:0',
grad_fn=<SelectBackward>), 'aux_outputs': [{'pred_logits': tensor([[[-1.2504, -3.5808, -2.8100, 5.6911],
[-1.7174, -3.4767, -2.2490, 5.6828],
[-2.6437, -2.7794, -2.5360, 5.6440],
[-1.6839, -3.5199, -2.1526, 5.7934],
[-1.5109, -3.4127, -2.5975, 5.5956],
[-1.5813, -3.1623, -2.2432, 5.2131],
[-1.7586, -3.6252, -2.3301, 5.7826],
[-2.6149, -2.6782, -2.6334, 5.6346],
[-1.7482, -3.2138, -2.6830, 5.6412],
[-1.9907, -3.3143, -2.4883, 5.7245],
[-2.0257, -3.4633, -2.2368, 5.9086],
[-2.1168, -3.2112, -2.7409, 5.8544],
[-2.5858, -1.8501, -0.0446, 3.2839],
[-1.9236, -3.5415, -2.3592, 5.9901],
[-2.4247, -3.6177, -2.2216, 5.9407],
[-2.5777, -2.1795, -2.2625, 5.3009],
[-1.3931, -3.3417, -2.6735, 5.5696],
[-1.4456, -3.4021, -2.2070, 5.7579],
[-2.0504, -3.1556, -2.1044, 5.7540],
[-1.1664, -2.6429, -2.5354, 4.8856],
[-2.0992, -3.2797, -2.1168, 5.6873],
[-2.5497, -2.6683, -1.5737, 5.1301],
[-0.8091, -3.5701, -2.5191, 5.1769],
[-1.4676, -3.6757, -2.5934, 5.6852],
[-1.7347, -3.1917, -2.1327, 5.6545],
[-1.8354, -3.2167, -2.1430, 5.5313],
[-1.0303, -3.7101, -2.0850, 5.3256],
[-2.7579, -1.6260, -2.1614, 4.8621],
[-1.8907, -3.5321, -2.2240, 5.7266],
[-1.1177, -3.5266, -2.5566, 5.4634],
[-1.4514, -3.6044, -2.6691, 5.6349],
[-2.3068, -3.4657, -2.2415, 5.7814],
[-2.2074, -3.5584, -2.5430, 5.9490],
[-1.2341, -3.5993, -2.2337, 5.5949],
[-1.1097, -3.6458, -2.2985, 5.4380],
[-1.6259, -3.4376, -2.5200, 5.6947],
[-2.0318, -3.0793, -1.9409, 5.4452],
[-2.8346, -1.4108, -2.2085, 4.8268],
[-2.3220, -3.2941, -2.4598, 5.8615],
[-1.4937, -3.3622, -2.2159, 5.7735],
[-1.3712, -3.4375, -2.2487, 5.6910],
[-1.3862, -3.5070, -2.1787, 5.6287],
[-1.4329, -3.4135, -2.4437, 5.6301],
[-2.2192, -2.2570, -2.4264, 5.1008],
[-1.6275, -3.1183, -2.6253, 5.6007],
[-2.6928, -0.7844, -1.8550, 3.8880],
[-1.5130, -3.2955, -2.2231, 5.7158],
[-2.2927, -3.5005, -2.1343, 5.9383],
[-1.2989, -3.4128, -2.6555, 5.5151],
[-2.3887, -2.8568, -2.6236, 5.6963],
[-1.9759, -3.4624, -2.4298, 5.7296],
[-2.0314, -2.9194, -2.0413, 5.6329],
[-2.4279, -3.4936, -2.3452, 5.8252],
[-2.2354, -2.5249, -2.5076, 5.4787],
[-2.3441, -3.2688, -2.6272, 5.9214],
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grad_fn=<SelectBackward>)}]}
:Dictionary inputs to traced functions must have consistent type. Found Tensor and List[Dict[str, Tensor]]
- please simplify the steps as much as possible so they do not require additional resources to run, such as a private dataset.
Expected behavior:
Able to trace and export the traced DETR model for production use 😃 (I confirmed I have the latest DETR source that has the PR from June 4 with the fixes to script the resnet models).
If there are no obvious error in “what you observed” provided above, please tell us the expected behavior.
Environment:
Provide your environment information using the following command:
Collecting environment information...
PyTorch version: 1.6.0
Is debug build: No
CUDA used to build PyTorch: 10.1
OS: Ubuntu 18.04.3 LTS
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
CMake version: version 3.10.2
Python version: 3.7
Is CUDA available: Yes
CUDA runtime version: 10.1.243
GPU models and configuration: GPU 0: Tesla V100-SXM2-16GB
Nvidia driver version: 435.21
cuDNN version: /usr/local/cuda-10.1/targets/x86_64-linux/lib/libcudnn.so.7.6.3
Versions of relevant libraries:
[pip3] numpy==1.18.5
[pip3] numpydoc==1.1.0
[pip3] torch==1.6.0
[pip3] torchvision==0.7.0
[conda] _pytorch_select 0.2 gpu_0
[conda] blas 1.0 mkl
[conda] cudatoolkit 10.1.243 h6bb024c_0
[conda] mkl 2020.1 217
[conda] mkl-service 2.3.0 py37he904b0f_0
[conda] mkl_fft 1.1.0 py37h23d657b_0
[conda] mkl_random 1.1.1 py37h0573a6f_0
[conda] numpy 1.18.5 py37ha1c710e_0
[conda] numpy-base 1.18.5 py37hde5b4d6_0
[conda] numpydoc 1.1.0 py_0
[conda] pytorch 1.6.0 py3.7_cuda10.1.243_cudnn7.6.3_0 pytorch
[conda] torchvision 0.7.0 py37_cu101 pytorch
Issue Analytics
- State:
- Created 3 years ago
- Reactions:1
- Comments:15 (15 by maintainers)
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Hey @lessw2020
The torchscript version only support models without
aux_loss. This is totally fine for all models for inference, becauseaux_lossis only used during training.So what I would recommend you to do is to set
aux_lossto False in your model, set it to inference mode, and try scripting it again.Hi @lessw2020
1 -
NestedTensor.from_tensor_listwas renamed tonested_tensor_from_tensor_listin https://github.com/facebookresearch/detr/pull/51 . Here is an example showing how to run a model with torchscript usingnested_tensor_from_tensor_listhttps://github.com/facebookresearch/detr/blob/5e66b4cd15b2b182da347103dd16578d28b49d69/test_all.py#L69-L76 2 - The example above shows thatimgis a Tensor which has been resized / normalized, no batch dimension (so 3d tensor)I believe this should be enough to get your example working. As such, I’m closing this issue but let us know if the problem persists