chrisakatibs
commented
3 years ago So after passing in a dictionary that it liked for the first error, the above was the next error. Showing that unless I get the structure right or am missing something in generation, the errors will cascade throughout processing.
Hello,
My issue lies in the generation of data files that looked completed.
Kitti info train file is saved to /content/gdrive/MyDrive/voxel_data/kitti_infos_train.pkl Kitti info val file is saved to /content/gdrive/MyDrive/voxel_data/kitti_infos_val.pkl Kitti info trainval file is saved to /content/gdrive/MyDrive/voxel_data/kitti_infos_trainval.pkl Kitti info test file is saved to /content/gdrive/MyDrive/voxel_data/kitti_infos_test.pkl [100.0%][===================>][2.11it/s][01:19:45>00:00]
[100.0%][===================>][57.81it/s][02:51>00:00]
[100.0%][===================>][0.65it/s][56:13>00:01]
remain number of infos: 9996 [100.0%][===================>][8.81it/s][07:26:21>00:00]
load 249862 car database infos load 12990 bicycle database infos load 15167 pedestrian database infos load 14779 other_vehicle database infos load 6122 truck database infos load 4869 bus database infos load 654 motorcycle database infos
Now there are namespaces calling for a dbinfos file but it seems to be kitti_infos_train.pkl However when using these in the config I run into improper structures by the pickle dump? Perhaps my file structure was wrong? It would like to see a Dictionary to call .items() but sees a LIST of Dictionaries. I've tried to understand the structure and make short term solutions to see where it fails next but I am missing something.
Random error below from attempts at solving(printing out label info in question). This dict would be list given from pickle file dump[@ 0 index]
Please HELP!
Side note: using Lyft nuscenes dataset(but converted to kitti for both 3d and 2d operations), using Google Colab
{'image': {'image_idx': '805729458ae49dd0d72e7e428cdea67db775ceb2aa46e26ca9c5c0eb84823cae', 'image_path': 'training/image_2/805729458ae49dd0d72e7e428cdea67db775ceb2aa46e26ca9c5c0eb84823cae.png', 'image_shape': array([1080, 1920], dtype=int32)}, 'point_cloud': {'num_features': 4, 'velodyne_path': 'training/velodyne/805729458ae49dd0d72e7e428cdea67db775ceb2aa46e26ca9c5c0eb84823cae.bin'}, 'calib': {'P0': array([[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 1.]]), 'P1': array([[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 1.]]), 'P2': array([[1.10935803e+03, 0.00000000e+00, 9.60013076e+02, 0.00000000e+00], [0.00000000e+00, 1.10935803e+03, 5.39511837e+02, 0.00000000e+00], [0.00000000e+00, 0.00000000e+00, 1.00000000e+00, 0.00000000e+00], [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]]), 'P3': array([[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 1.]]), 'R0_rect': array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]), 'Tr_velo_to_cam': array([[ 0.01420342, -0.99988233, 0.00579532, -0.0206852 ], [-0.00613689, -0.00588297, -0.99996386, -0.13853743], [ 0.99988029, 0.01416735, -0.00621973, -0.11220291], [ 0. , 0. , 0. , 1. ]]), 'Tr_imu_to_velo': array([[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 1.]])}, 'annos': {'name': array(['car', 'bicycle', 'pedestrian', 'car', 'car', 'car', 'car', 'car', 'car', 'car', 'pedestrian', 'car', 'car', 'car', 'car', 'car', 'car'], dtype='<U10'), 'truncated': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]), 'occluded': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), 'alpha': array([-10., -10., -10., -10., -10., -10., -10., -10., -10., -10., -10., -10., -10., -10., -10., -10., -10.]), 'bbox': array([[-1.00000e+00, -1.00000e+00, -1.00000e+00, -1.00000e+00], [-1.00000e+00, -1.00000e+00, -1.00000e+00, -1.00000e+00], [-1.00000e+00, -1.00000e+00, -1.00000e+00, -1.00000e+00], [ 1.64273e+03, 5.01890e+02, 1.92000e+03, 6.66310e+02], [-1.00000e+00, -1.00000e+00, -1.00000e+00, -1.00000e+00], [ 8.47790e+02, 5.89440e+02, 9.12910e+02, 6.42360e+02], [-1.00000e+00, -1.00000e+00, -1.00000e+00, -1.00000e+00], [ 1.48642e+03, 5.85350e+02, 1.74070e+03, 6.55730e+02], [-1.00000e+00, -1.00000e+00, -1.00000e+00, -1.00000e+00], [ 1.60160e+03, 5.77040e+02, 1.88507e+03, 6.57740e+02], [-1.00000e+00, -1.00000e+00, -1.00000e+00, -1.00000e+00], [-1.00000e+00, -1.00000e+00, -1.00000e+00, -1.00000e+00], [ 9.82220e+02, 6.00900e+02, 1.00981e+03, 6.23580e+02], [-1.00000e+00, -1.00000e+00, -1.00000e+00, -1.00000e+00], [-1.00000e+00, -1.00000e+00, -1.00000e+00, -1.00000e+00], [-1.00000e+00, -1.00000e+00, -1.00000e+00, -1.00000e+00], [-1.00000e+00, -1.00000e+00, -1.00000e+00, -1.00000e+00]]), 'dimensions': array([[4.43, 1.7 , 2.09], [1.81, 2. , 0.94], [0.89, 2. , 0.94], [6.12, 2.7 , 2.38], [4.68, 1.8 , 2. ], [4.61, 1.9 , 1.98], [4.86, 1.6 , 1.84], [4.88, 1.6 , 2.07], [5.05, 1.9 , 1.87], [4.5 , 1.6 , 1.98], [0.89, 2. , 0.82], [4.81, 2. , 2.01], [5.1 , 1.9 , 2.32], [4.79, 1.6 , 2.23], [4.01, 1.5 , 1.85], [6.86, 2.5 , 2.04], [4.62, 1.8 , 1.97]]), 'location': array([[-6.859e+01, 2.300e+00, 2.629e+01], [ 3.570e+00, 1.710e+00, 1.490e+00], [ 1.318e+01, -1.030e+00, -4.784e+01], [ 1.597e+01, 2.140e+00, 1.967e+01], [ 3.449e+01, 2.590e+00, 2.586e+01], [-2.920e+00, 3.750e+00, 4.163e+01], [-8.000e-02, 1.300e+00, -1.158e+01], [ 1.576e+01, 2.750e+00, 2.697e+01], [ 7.810e+00, 1.820e+00, 1.630e+00], [ 1.648e+01, 2.440e+00, 2.354e+01], [ 1.279e+01, -7.300e-01, -4.687e+01], [ 2.808e+01, 2.150e+00, 1.934e+01], [ 3.130e+00, 7.250e+00, 9.665e+01], [-8.445e+01, 1.610e+00, 1.854e+01], [ 4.130e+01, 2.280e+00, 2.241e+01], [ 4.630e+01, 1.910e+00, 1.829e+01], [-2.970e+01, 1.980e+00, 1.289e+01]]), 'rotation_y': array([-3.09, -1.54, -0.66, -3.11, -3.06, 1.6 , -1.57, -3.11, -1.21, -3.1 , -0.5 , -3.08, -1.54, 0.03, -3.1 , -3.1 , 0.02]), 'score': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]), 'index': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], dtype=int32), 'group_ids': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], dtype=int32), 'difficulty': array([-1, -1, -1, 0, -1, 0, -1, 0, -1, 0, -1, -1, -1, -1, -1, -1, -1], dtype=int32), 'num_points_in_gt': array([ 0, 0, 0, 599, 0, 82, 0, 152, 0, 77, 0, 0, 5, 0, 0, 0, 0], dtype=int32)}} Traceback (most recent call last): File "./pytorch/train.py", line 663, in
fire.Fire()
File "/usr/local/lib/python3.7/dist-packages/fire/core.py", line 141, in Fire
component_trace = _Fire(component, args, parsed_flag_args, context, name)
File "/usr/local/lib/python3.7/dist-packages/fire/core.py", line 471, in _Fire
target=component.name)
File "/usr/local/lib/python3.7/dist-packages/fire/core.py", line 681, in _CallAndUpdateTrace
component = fn(*varargs, **kwargs)
File "./pytorch/train.py", line 254, in train
multi_gpu=multi_gpu)
File "/content/second.pytorch/second/pytorch/builder/input_reader_builder.py", line 76, in build
multi_gpu=multi_gpu)
File "/content/second.pytorch/second/builder/dataset_builder.py", line 64, in build
db_sampler = dbsampler_builder.build(db_sampler_cfg)
File "/content/second.pytorch/second/builder/dbsampler_builder.py", line 31, in build
sampler = DataBaseSamplerV2(db_infos, groups, db_prepor, rate, grot_range)
File "/content/second.pytorch/second/core/sample_ops.py", line 25, in init
db_infos = db_prepor(db_infos)
File "/content/second.pytorch/second/core/preprocess.py", line 106, in call
db_infos = prepor(db_infos)
File "/content/second.pytorch/second/core/preprocess.py", line 60, in call
return self._preprocess(db_infos)
File "/content/second.pytorch/second/core/preprocess.py", line 92, in _preprocess
for info in db_infos[name]:
TypeError: list indices must be integers or slices, not str