demo in docker fails to run

[ikvision]

When running in the docker pulled
demo.py --checkpoint=data/model_checkpoint.ptt --img=examples/im1010.jpg
I get the following error:
Traceback (most recent call last):
File "demo.py", line 34, in
from models import hmr, SMPL
File "/SPIN/models/init.py", line 1, in
from .hmr import hmr
File "/SPIN/models/hmr.py", line 6, in
from utils.geometry import rot6d_to_rotmat
File "/SPIN/utils/init.py", line 3, in
from .base_trainer import BaseTrainer
File "/SPIN/utils/base_trainer.py", line 8, in
from torch.utils.tensorboard import SummaryWriter
File "/usr/local/lib/python3.6/dist-packages/torch/utils/tensorboard/init.py", line 6, in
from .writer import FileWriter, SummaryWriter # noqa F401
File "/usr/local/lib/python3.6/dist-packages/torch/utils/tensorboard/writer.py", line 18, in
from ._convert_np import make_np
File "/usr/local/lib/python3.6/dist-packages/torch/utils/tensorboard/_convert_np.py", line 12, in
from caffe2.python import workspace
File "/usr/local/lib/python3.6/dist-packages/caffe2/python/workspace.py", line 15, in
from past.builtins import basestring
ModuleNotFoundError: No module named 'past'

[nkolot]

Ok, apparently the package future is not in the docker image but I had it installed locally so it was working. I updated the image. Can you try again now? (make sure to pull the image again)

[ikvision]

Hi, thank you for the quick reply, I pull the updated docker.
Now I think there is a issue with pyrender dependency freetype and pyglet, I installing those using pip.
run fetch_data.sh and using nvidia-docker run chaneyk/spin -it run the demo
I get the following error

Traceback (most recent call last):
File "demo.py", line 105, in
checkpoint = torch.load(args.checkpoint)
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 387, in load
return _load(f, map_location, pickle_module, **pickle_load_args)
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 574, in _load
result = unpickler.load()
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 537, in persistent_load
deserialized_objects[root_key] = restore_location(obj, location)
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 119, in default_restore_location
result = fn(storage, location)
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 95, in _cuda_deserialize
device = validate_cuda_device(location)
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 79, in validate_cuda_device
raise RuntimeError('Attempting to deserialize object on a CUDA '
RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location='cpu' to map your storages to the CPU.

Indeed while in the container created by chaneyk/spin - nvidia-smi is empty

While the docker works with gpu fine:
nvidia-docker run nvidia/cuda:9.0-base nvidia-smi shows all the gpu

[nkolot]

What GPU are you using?
So what happens is that PyTorch tries to map the tensors in the checkpoint directly on the GPU and docker cannot see any GPUs which is really strange. In the meantime try the fix in the last line of the error message, i.e. pass map_location='cpu' in torch.load

[nkolot]

Update: You need to run nvidia-docker2

[ikvision]

When using map_location='cpu' and renaming the smpl neutral model:
mv ./data/smpl/basicModel_neutral_lbs_10_207_0_v1.0.0.pkl ./data/smpl/SMPL_NEUTRAL.pkl
It works

Thank you