This repository contains the code for the CVPR 2020 paper "Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision"
Fixed the below error in loading the trained model on CPU.
(dvr) PS ~\differentiable_volumetric_rendering> python generate.py configs/demo/demo_combined.yaml --no-cuda
https://s3.eu-central-1.amazonaws.com/avg-projects/differentiable_volumetric_rendering/models/single_view_reconstruction/multi-view-supervision/ours_combined-af2bce07.pt
=> Loading checkpoint from url...
Traceback (most recent call last):
File "generate.py", line 63, in <module>
checkpoint_io.load(cfg['test']['model_file'])
File "differentiable_volumetric_rendering\im2mesh\checkpoints.py", line 62, in load
return self.load_url(filename)
File "differentiable_volumetric_rendering\im2mesh\checkpoints.py", line 93, in load_url
state_dict = model_zoo.load_url(url, progress=True)
File "anaconda3\envs\dvr\lib\site-packages\torch\hub.py", line 509, in load_state_dict_from_url
return torch.load(cached_file, map_location=map_location)
File "anaconda3\en\dvr\lib\site-packages\torch\serialization.py", line 593, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
File "anaconda3\envs\dvr\lib\site-packages\torch\serialization.py", line 773, in _legacy_load
result = unpickler.load()
File "anaconda3\envs\dvr\lib\site-packages\torch\serialization.py", line 729, in persistent_load
deserialized_objects[root_key] = restore_location(obj, location)
File "anaconda3\envs\dvr\lib\site-packages\torch\serialization.py", line 178, in default_restore_location
result = fn(storage, location)
File "anaconda3\envs\dvr\lib\site-packages\torch\serialization.py", line 154, in _cuda_deserialize
device = validate_cuda_device(location)
File "anaconda3\envs\dvr\lib\site-packages\torch\serialization.py", line 138, 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=torch.device('cpu') to map your storages to the CPU.
Fixed the below error in loading the trained model on CPU.