Error while running run_demo.py. sorted scores:tensor([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,])

[vicadmo]

Hi, I have been trying to follow the steps to try to reproduce the results in anaconda in wsl and I am getting the following error while trying to run the demo (I also put here the results of build_all_conda just in case):

(foundationpose) vicada@name:~/FoundationPose$ CMAKE_PREFIX_PATH=$CONDA_PREFIX/lib/python3.9/site-packages/pybind11/share/cmake/pybind11 bash build_all_conda.sh
-- The C compiler identification is GNU 11.4.0
-- The CXX compiler identification is GNU 11.4.0
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Check for working C compiler: /usr/bin/cc - skipped
-- Detecting C compile features
-- Detecting C compile features - done
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Check for working CXX compiler: /usr/bin/c++ - skipped
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Found Boost: /usr/lib/x86_64-linux-gnu/cmake/Boost-1.74.0/BoostConfig.cmake (found version "1.74.0") found components: system program_options
-- Found OpenMP_C: -fopenmp (found version "4.5")
-- Found OpenMP_CXX: -fopenmp (found version "4.5")
-- Found OpenMP: TRUE (found version "4.5")
CMake Warning (dev) at /home/vicada/miniconda3/envs/foundationpose/lib/python3.9/site-packages/pybind11/share/cmake/pybind11/FindPythonLibsNew.cmake:101 (message):
  Policy CMP0148 is not set: The FindPythonInterp and FindPythonLibs modules
  are removed.  Run "cmake --help-policy CMP0148" for policy details.  Use
  the cmake_policy command to set the policy and suppress this warning, or
  preferably upgrade to using FindPython, either by calling it explicitly
  before pybind11, or by setting PYBIND11_FINDPYTHON ON before pybind11.
Call Stack (most recent call first):
  /home/vicada/miniconda3/envs/foundationpose/lib/python3.9/site-packages/pybind11/share/cmake/pybind11/pybind11Tools.cmake:50 (find_package)
  /home/vicada/miniconda3/envs/foundationpose/lib/python3.9/site-packages/pybind11/share/cmake/pybind11/pybind11Common.cmake:192 (include)
  /home/vicada/miniconda3/envs/foundationpose/lib/python3.9/site-packages/pybind11/share/cmake/pybind11/pybind11Config.cmake:250 (include)
  CMakeLists.txt:13 (find_package)
This warning is for project developers.  Use -Wno-dev to suppress it.

-- Found PythonInterp: /home/vicada/miniconda3/envs/foundationpose/bin/python (found suitable version "3.9.19", minimum required is "3.6")
-- Found PythonLibs: /home/vicada/miniconda3/envs/foundationpose/lib/libpython3.9.so
-- Performing Test HAS_FLTO
-- Performing Test HAS_FLTO - Success
-- Found pybind11: /home/vicada/miniconda3/envs/foundationpose/lib/python3.9/site-packages/pybind11/include (found version "2.12.0")
-- Configuring done (4.7s)
-- Generating done (0.0s)
-- Build files have been written to: /home/vicada/FoundationPose/mycpp/build
[ 33%] Building CXX object CMakeFiles/mycpp.dir/src/app/pybind_api.cpp.o
[ 66%] Building CXX object CMakeFiles/mycpp.dir/src/Utils.cpp.o
/home/vicada/FoundationPose/mycpp/src/app/pybind_api.cpp: In function ‘vectorMatrix4f cluster_poses(float, float, const vectorMatrix4f&, const vectorMatrix4f&)’:
/home/vicada/FoundationPose/mycpp/src/app/pybind_api.cpp:26:38: warning: format ‘%d’ expects argument of type ‘int’, but argument 2 has type ‘std::vector<Eigen::Matrix<float, 4, 4>, Eigen::aligned_allocator<Eigen::Matrix<float, 4, 4> > >::size_type’ {aka ‘long unsigned int’} [-Wformat=]
   26 |   printf("num original candidates = %d\n",poses_in.size());
      |                                     ~^    ~~~~~~~~~~~~~~~
      |                                      |                 |
      |                                      int               std::vector<Eigen::Matrix<float, 4, 4>, Eigen::aligned_allocator<Eigen::Matrix<float, 4, 4> > >::size_type {aka long unsigned int}
      |                                     %ld
/home/vicada/FoundationPose/mycpp/src/app/pybind_api.cpp:66:42: warning: format ‘%d’ expects argument of type ‘int’, but argument 2 has type ‘std::vector<Eigen::Matrix<float, 4, 4>, Eigen::aligned_allocator<Eigen::Matrix<float, 4, 4> > >::size_type’ {aka ‘long unsigned int’} [-Wformat=]
   66 |   printf("num of pose after clustering: %d\n",poses_out.size());
      |                                         ~^    ~~~~~~~~~~~~~~~~
      |                                          |                  |
      |                                          int                std::vector<Eigen::Matrix<float, 4, 4>, Eigen::aligned_allocator<Eigen::Matrix<float, 4, 4> > >::size_type {aka long unsigned int}
      |                                         %ld
[100%] Linking CXX shared module mycpp.cpython-39-x86_64-linux-gnu.so
lto-wrapper: warning: using serial compilation of 2 LTRANS jobs
[100%] Built target mycpp
Obtaining file:///home/vicada/FoundationPose/bundlesdf/mycuda
  Preparing metadata (setup.py) ... done
Installing collected packages: common
  Running setup.py develop for common
Successfully installed common-0.0.0
(foundationpose) vicada@name:~/FoundationPose$ python run_demo.py
Warp 1.0.2 initialized:
   CUDA Toolkit 11.5, Driver 12.4
   Devices:
     "cpu"      : "x86_64"
     "cuda:0"   : "NVIDIA GeForce GTX 1660 Ti" (6 GiB, sm_75, mempool enabled)
   Kernel cache:
     /home/vicada/.cache/warp/1.0.2
[__init__()] self.cfg:
 lr: 0.0001
c_in: 6
zfar: 'Infinity'
debug: null
n_view: 1
run_id: 3wy8qqex
use_BN: true
exp_name: 2024-01-11-20-02-45
n_epochs: 62
save_dir: /home/bowenw/debug/2024-01-11-20-02-45/
use_mask: false
loss_type: pairwise_valid
optimizer: adam
batch_size: 64
crop_ratio: 1.1
enable_amp: true
use_normal: false
max_num_key: null
warmup_step: -1
input_resize:
- 160
- 160
max_step_val: 1000
vis_interval: 1000
weight_decay: 0
normalize_xyz: true
resume_run_id: null
clip_grad_norm: 'Infinity'
lr_epoch_decay: 500
render_backend: nvdiffrast
train_num_pair: 5
lr_decay_epochs:
- 50
n_epochs_warmup: 1
make_pair_online: false
gradient_max_norm: 'Infinity'
max_step_per_epoch: 10000
n_rendering_workers: 1
save_epoch_interval: 100
n_dataloader_workers: 100
split_objects_across_gpus: true
ckpt_dir: /home/vicada/FoundationPose/learning/training/../../weights/2024-01-11-20-02-45/model_best.pth
[__init__()] self.h5_file:None
[__init__()] Using pretrained model from /home/vicada/FoundationPose/learning/training/../../weights/2024-01-11-20-02-45/model_best.pth
[__init__()] init done
[__init__()] welcome
[__init__()] self.cfg:
 lr: 0.0001
c_in: 6
zfar: .inf
debug: null
w_rot: 0.1
n_view: 1
run_id: null
use_BN: true
rot_rep: axis_angle
ckpt_dir: /home/vicada/FoundationPose/learning/training/../../weights/2023-10-28-18-33-37/model_best.pthexp_name: 2023-10-28-18-33-37
save_dir: /tmp/2023-10-28-18-33-37/
loss_type: l2
optimizer: adam
trans_rep: tracknet
batch_size: 64
crop_ratio: 1.2
use_normal: false
BN_momentum: 0.1
max_num_key: null
warmup_step: -1
input_resize:
- 160
- 160
max_step_val: 1000
normal_uint8: false
vis_interval: 1000
weight_decay: 0
n_max_objects: null
normalize_xyz: true
clip_grad_norm: 'Infinity'
rot_normalizer: 0.3490658503988659
trans_normalizer:
- 0.019999999552965164
- 0.019999999552965164
- 0.05000000074505806
max_step_per_epoch: 25000
val_epoch_interval: 10
n_dataloader_workers: 60
enable_amp: true
use_mask: false

[__init__()] self.h5_file:
[__init__()] Using pretrained model from /home/vicada/FoundationPose/learning/training/../../weights/2023-10-28-18-33-37/model_best.pth
[__init__()] init done
[reset_object()] self.diameter:0.19646325799497472, vox_size:0.009823162899748735
[reset_object()] self.pts:torch.Size([607, 3])
[reset_object()] reset done
[make_rotation_grid()] cam_in_obs:(42, 4, 4)
[make_rotation_grid()] rot_grid:(252, 4, 4)
num original candidates = 252
num of pose after clustering: 252
[make_rotation_grid()] after cluster, rot_grid:(252, 4, 4)
[make_rotation_grid()] self.rot_grid: torch.Size([252, 4, 4])
[<module>()] estimator initialization done
[<module>()] i:0
[register()] Welcome
Module Utils load on device 'cuda:0' took 11.40 ms
[register()] poses:(252, 4, 4)
[register()] after viewpoint, add_errs min:-1.0
[predict()] ob_in_cams:(252, 4, 4)
[predict()] self.cfg.use_normal:False
[predict()] trans_normalizer:[0.019999999552965164, 0.019999999552965164, 0.05000000074505806], rot_normalizer:0.3490658503988659
[predict()] making cropped data
[make_crop_data_batch()] Welcome make_crop_data_batch
[make_crop_data_batch()] make tf_to_crops done
[make_crop_data_batch()] render done
[make_crop_data_batch()] warp done
[make_crop_data_batch()] pose batch data done
[predict()] forward start
[predict()] forward done
[predict()] making cropped data
[make_crop_data_batch()] Welcome make_crop_data_batch
[make_crop_data_batch()] make tf_to_crops done
[make_crop_data_batch()] render done
[make_crop_data_batch()] warp done
[make_crop_data_batch()] pose batch data done
[predict()] forward start
[predict()] forward done
[predict()] making cropped data
[make_crop_data_batch()] Welcome make_crop_data_batch
[make_crop_data_batch()] make tf_to_crops done
[make_crop_data_batch()] render done
[make_crop_data_batch()] warp done
[make_crop_data_batch()] pose batch data done
[predict()] forward start
[predict()] forward done
[predict()] making cropped data
[make_crop_data_batch()] Welcome make_crop_data_batch
[make_crop_data_batch()] make tf_to_crops done
[make_crop_data_batch()] render done
[make_crop_data_batch()] warp done
[make_crop_data_batch()] pose batch data done
[predict()] forward start
[predict()] forward done
[predict()] making cropped data
[make_crop_data_batch()] Welcome make_crop_data_batch
[make_crop_data_batch()] make tf_to_crops done
[make_crop_data_batch()] render done
[make_crop_data_batch()] warp done
[make_crop_data_batch()] pose batch data done
[predict()] forward start
[predict()] forward done
[predict()] ob_in_cams:(252, 4, 4)
[predict()] self.cfg.use_normal:False
[predict()] making cropped data
[make_crop_data_batch()] Welcome make_crop_data_batch
[make_crop_data_batch()] make tf_to_crops done
[make_crop_data_batch()] render done
[make_crop_data_batch()] pose batch data done
[find_best_among_pairs()] pose_data.rgbAs.shape[0]: 252
[predict()] forward done
[register()] final, add_errs min:-1.0
[register()] sort ids:tensor([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
         14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
         28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
         42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
         56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
         70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
         84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
         98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
        112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
        126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
        140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
        154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
        168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
        182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,
        196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209,
        210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223,
        224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,
        238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251])
[register()] sorted scores:tensor([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan])
/home/vicada/FoundationPose/Utils.py:727: RuntimeWarning: invalid value encountered in cast
  uv = np.round(projected[:,:2]/projected[:,2].reshape(-1,1)).astype(int)   #(2,2)
Traceback (most recent call last):
  File "/home/vicada/FoundationPose/run_demo.py", line 70, in <module>
    vis = draw_posed_3d_box(reader.K, img=color, ob_in_cam=center_pose, bbox=bbox)
  File "/home/vicada/FoundationPose/Utils.py", line 735, in draw_posed_3d_box
    img = draw_line3d(start,end,img)
  File "/home/vicada/FoundationPose/Utils.py", line 728, in draw_line3d
    img = cv2.line(img, uv[0].tolist(), uv[1].tolist(), color=line_color, thickness=linewidth, lineType=cv2.LINE_AA)
cv2.error: OpenCV(4.9.0) :-1: error: (-5:Bad argument) in function 'line'
> Overload resolution failed:
>  - Can't parse 'pt1'. Sequence item with index 0 has a wrong type
>  - Can't parse 'pt1'. Sequence item with index 0 has a wrong type

I have tried to search where the error migth be and it seems to be when trying to predict the pose though i am not 100% sure. Any help would be apreciated. Thanks in advance!

[wenbowen123]

never seen this. You can increase the debug to 3 and dump more files for viz.

[vicadmo]

Hi, I tried in docker as well and i got the same error so i think it might have something to do with how the gpu works with wsl since i got some erros on the wsl logs pointing towards that. In your experience do you think this could be run on a 1660 ti gpu? And another question i have is if i have to use cuda 11.8 or i can use the most recent version. Thanks in advance and sorry for the trouble and multiple questions!

[wenbowen123]

are you running on our example data or your own?

[vicadmo]

The example data. I tried both the mustard and the driller examples and got the same result. I am quite new to this so maybe i missed some obvious step that may be causing this. I tried to see exactly where the error migth be and i saw that the result of rasterizing when predicting the pose was all 0s.

rast_out, _ = dr.rasterize(glctx, pos_clip, pos_idx, resolution=np.asarray(output_size))

The inputs seemed fine thats why i thougth that maybe there was a problem with something not running properly on the gpu on my end. If you want i can give the detailed values.

[EvdoTheo]

Hello, i experience the same problem with both objects. I changed variable debug to 3 but nothing happened.

/home/evdotheo/Desktop/OPTIMAI/FoundationPose/Utils.py:727:` RuntimeWarning: invalid value encountered in cast uv = np.round(projected[:,:2]/projected[:,2].reshape(-1,1)).astype(int) #(2,2) Traceback (most recent call last): File "/home/evdotheo/Desktop/OPTIMAI/FoundationPose/run_demo.py", line 70, in vis = draw_posed_3d_box(reader.K, img=color, ob_in_cam=center_pose, bbox=bbox) File "/home/evdotheo/Desktop/OPTIMAI/FoundationPose/Utils.py", line 735, in draw_posed_3d_box img = draw_line3d(start,end,img) File "/home/evdotheo/Desktop/OPTIMAI/FoundationPose/Utils.py", line 728, in draw_line3d img = cv2.line(img, uv[0].tolist(), uv[1].tolist(), color=line_color, thickness=linewidth, lineType=cv2.LINE_AA) cv2.error: OpenCV(4.9.0) :-1: error: (-5:Bad argument) in function 'line'

`

Overload resolution failed:

• Can't parse 'pt1'. Sequence item with index 0 has a wrong type
• Can't parse 'pt1'. Sequence item with index 0 has a wrong type `

[vicadmo]

@EvdoTheo Hi, can you please share your syslog at var/log/syslog to see if you are getting a similar message to me in the linux logs? Thanks in advance

[wenbowen123]

The example data. I tried both the mustard and the driller examples and got the same result. I am quite new to this so maybe i missed some obvious step that may be causing this. I tried to see exactly where the error migth be and i saw that the result of rasterizing when predicting the pose was all 0s.

rast_out, _ = dr.rasterize(glctx, pos_clip, pos_idx, resolution=np.asarray(output_size))

The inputs seemed fine thats why i thougth that maybe there was a problem with something not running properly on the gpu on my end. If you want i can give the detailed values.

what is the GPU you are using?

[vicadmo]

1660 ti on a laptop

[wenbowen123]

That GPU might have caused the issue. Do you have 1080 TI, 2080 Ti, 3080 Ti, etc?

[vicadmo]

No, unfortunately the 1660 ti is the only one i have.

[tathya7]

@vicadmo Were you able to fix it? Even with my 1650, I am having the same issue

[18735549442]

today, i also encounter the same error.

/home/admin206/anaconda3/envs/foundationpose/bin/python /home/admin206/FoundationPose-main/run_demo.py 
Warp 1.0.2 initialized:
   CUDA Toolkit 11.5, Driver 12.2
   Devices:
     "cpu"      : "x86_64"
     "cuda:0"   : "NVIDIA GeForce GTX 1660 SUPER" (6 GiB, sm_75, mempool enabled)
   Kernel cache:
     /home/admin206/.cache/warp/1.0.2
[__init__()] self.cfg: 
 lr: 0.0001
c_in: 6
zfar: 'Infinity'
debug: null
n_view: 1
run_id: 3wy8qqex
use_BN: true
exp_name: 2024-01-11-20-02-45
n_epochs: 62
save_dir: /home/bowenw/debug/2024-01-11-20-02-45/
use_mask: false
loss_type: pairwise_valid
optimizer: adam
batch_size: 64
crop_ratio: 1.1
enable_amp: true
use_normal: false
max_num_key: null
warmup_step: -1
input_resize:
- 160
- 160
max_step_val: 1000
vis_interval: 1000
weight_decay: 0
normalize_xyz: true
resume_run_id: null
clip_grad_norm: 'Infinity'
lr_epoch_decay: 500
render_backend: nvdiffrast
train_num_pair: 5
lr_decay_epochs:
- 50
n_epochs_warmup: 1
make_pair_online: false
gradient_max_norm: 'Infinity'
max_step_per_epoch: 10000
n_rendering_workers: 1
save_epoch_interval: 100
n_dataloader_workers: 100
split_objects_across_gpus: true
ckpt_dir: /home/admin206/FoundationPose-main/learning/training/../../weights/2024-01-11-20-02-45/model_best.pth

[__init__()] self.h5_file:None
[__init__()] Using pretrained model from /home/admin206/FoundationPose-main/learning/training/../../weights/2024-01-11-20-02-45/model_best.pth
[__init__()] init done
[__init__()] welcome
[__init__()] self.cfg: 
 lr: 0.0001
c_in: 6
zfar: .inf
debug: null
w_rot: 0.1
n_view: 1
run_id: null
use_BN: true
rot_rep: axis_angle
ckpt_dir: /home/admin206/FoundationPose-main/learning/training/../../weights/2023-10-28-18-33-37/model_best.pth
exp_name: 2023-10-28-18-33-37
save_dir: /tmp/2023-10-28-18-33-37/
loss_type: l2
optimizer: adam
trans_rep: tracknet
batch_size: 64
crop_ratio: 1.2
use_normal: false
BN_momentum: 0.1
max_num_key: null
warmup_step: -1
input_resize:
- 160
- 160
max_step_val: 1000
normal_uint8: false
vis_interval: 1000
weight_decay: 0
n_max_objects: null
normalize_xyz: true
clip_grad_norm: 'Infinity'
rot_normalizer: 0.3490658503988659
trans_normalizer:
- 0.019999999552965164
- 0.019999999552965164
- 0.05000000074505806
max_step_per_epoch: 25000
val_epoch_interval: 10
n_dataloader_workers: 60
enable_amp: true
use_mask: false

[__init__()] self.h5_file:
[__init__()] Using pretrained model from /home/admin206/FoundationPose-main/learning/training/../../weights/2023-10-28-18-33-37/model_best.pth
[__init__()] init done
[reset_object()] self.diameter:0.19646325799497472, vox_size:0.009823162899748735
[reset_object()] self.pts:torch.Size([607, 3])
[reset_object()] reset done
[make_rotation_grid()] cam_in_obs:(42, 4, 4)
[make_rotation_grid()] rot_grid:(252, 4, 4)
[make_rotation_grid()] after cluster, rot_grid:(252, 4, 4)
[make_rotation_grid()] self.rot_grid: torch.Size([252, 4, 4])
[<module>()] estimator initialization done
[<module>()] i:0
num original candidates = 252
num of pose after clustering: 252
[register()] Welcome
Module Utils load on device 'cuda:0' took 5.82 ms
[register()] poses:(252, 4, 4)
[register()] after viewpoint, add_errs min:-1.0
[predict()] ob_in_cams:(252, 4, 4)
[predict()] self.cfg.use_normal:False
[predict()] trans_normalizer:[0.019999999552965164, 0.019999999552965164, 0.05000000074505806], rot_normalizer:0.3490658503988659
[predict()] making cropped data
[make_crop_data_batch()] Welcome make_crop_data_batch
[make_crop_data_batch()] make tf_to_crops done
[make_crop_data_batch()] render done
[make_crop_data_batch()] warp done
[make_crop_data_batch()] pose batch data done
[predict()] forward start
[predict()] forward done
[predict()] making cropped data
[make_crop_data_batch()] Welcome make_crop_data_batch
[make_crop_data_batch()] make tf_to_crops done
[make_crop_data_batch()] render done
[make_crop_data_batch()] warp done
[make_crop_data_batch()] pose batch data done
[predict()] forward start
[predict()] forward done
[predict()] making cropped data
[make_crop_data_batch()] Welcome make_crop_data_batch
[make_crop_data_batch()] make tf_to_crops done
[make_crop_data_batch()] render done
[make_crop_data_batch()] warp done
[make_crop_data_batch()] pose batch data done
[predict()] forward start
[predict()] forward done
[predict()] making cropped data
[make_crop_data_batch()] Welcome make_crop_data_batch
[make_crop_data_batch()] make tf_to_crops done
[make_crop_data_batch()] render done
[make_crop_data_batch()] warp done
[make_crop_data_batch()] pose batch data done
[predict()] forward start
[predict()] forward done
[predict()] making cropped data
[make_crop_data_batch()] Welcome make_crop_data_batch
[make_crop_data_batch()] make tf_to_crops done
[make_crop_data_batch()] render done
[make_crop_data_batch()] warp done
[make_crop_data_batch()] pose batch data done
[predict()] forward start
[predict()] forward done
[predict()] get_vis...
[make_crop_data_batch()] Welcome make_crop_data_batch
[make_crop_data_batch()] make tf_to_crops done
[make_crop_data_batch()] render done
[make_crop_data_batch()] warp done
[make_crop_data_batch()] pose batch data done
[make_crop_data_batch()] Welcome make_crop_data_batch
[make_crop_data_batch()] make tf_to_crops done
[make_crop_data_batch()] render done
[make_crop_data_batch()] warp done
[make_crop_data_batch()] pose batch data done
/home/admin206/FoundationPose-main/Utils.py:469: RuntimeWarning: invalid value encountered in divide
  vis = (depth-zmin)/(zmax-zmin)
[predict()] ob_in_cams:(252, 4, 4)
[predict()] self.cfg.use_normal:False
[predict()] making cropped data
[make_crop_data_batch()] Welcome make_crop_data_batch
[make_crop_data_batch()] make tf_to_crops done
[make_crop_data_batch()] render done
[make_crop_data_batch()] pose batch data done
[find_best_among_pairs()] pose_data.rgbAs.shape[0]: 252
[predict()] forward done
[predict()] get_vis...
[register()] final, add_errs min:-1.0
[register()] sort ids:tensor([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
         14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
         28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
         42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
         56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
         70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
         84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
         98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
        112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
        126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
        140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
        154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
        168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
        182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,
        196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209,
        210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223,
        224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,
        238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251])
[register()] sorted scores:tensor([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan])
/home/admin206/FoundationPose-main/Utils.py:727: RuntimeWarning: invalid value encountered in cast
  uv = np.round(projected[:,:2]/projected[:,2].reshape(-1,1)).astype(int)   #(2,2)
Traceback (most recent call last):
  File "/home/admin206/FoundationPose-main/run_demo.py", line 70, in <module>
    vis = draw_posed_3d_box(reader.K, img=color, ob_in_cam=center_pose, bbox=bbox)
  File "/home/admin206/FoundationPose-main/Utils.py", line 735, in draw_posed_3d_box
    img = draw_line3d(start,end,img)
  File "/home/admin206/FoundationPose-main/Utils.py", line 728, in draw_line3d
    img = cv2.line(img, uv[0].tolist(), uv[1].tolist(), color=line_color, thickness=linewidth, lineType=cv2.LINE_AA)
cv2.error: OpenCV(4.9.0) :-1: error: (-5:Bad argument) in function 'line'
> Overload resolution failed:
>  - Can't parse 'pt1'. Sequence item with index 0 has a wrong type
>  - Can't parse 'pt1'. Sequence item with index 0 has a wrong type

进程已结束，退出代码为 1

my gpu is gtx 1660super. nvidia-smi message is:

+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.183.01             Driver Version: 535.183.01   CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA GeForce GTX 1660 ...    Off | 00000000:01:00.0  On |                  N/A |
|  0%   49C    P8               4W / 125W |    503MiB /  6144MiB |      4%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+

nvcc -V message is:

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Wed_Sep_21_10:33:58_PDT_2022
Cuda compilation tools, release 11.8, V11.8.89
Build cuda_11.8.r11.8/compiler.31833905_0

[18735549442]

i upgrade pytorch to 2.3.0+cu118 and recompile, but the result is consistent. someone try to up/down cuda version and try again? you need to repip pytorch3d offline at https://anaconda.org/pytorch3d/pytorch3d/files according to your cuda and pytorch version. you also need to repip pytorch3d and recompile nvdiffrast, build_all_conda.sh. before it, change c++14 to c++17 in bundlesdf/mycuda/setup.py and mycpp/CMakeLists.txt.

[hanJP-007]

install cudnn will solve this bug

[18735549442]

ok, i will try your method。3q very much!

[18735549442]

install cudnn will solve this bug

what is your gpu? your method can't solve my bug on my 1660super. i think that 16xx series all have this question.

[ahiyantra]

I'm facing this issue as well after resolving several other problems. I'm sharing a screenshot of how the crash happens. I'm also including the discussion thread for my experience so far below that. Installing cudnn didn't solve this issue for me. (note: it's all resolved now; please check the reporting thread linked below for the details)

my reporting thread

[18735549442]

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