Error running video classification toturial

Running step 5 in the the video classification toturial: import time import os

from classy_vision.trainer import LocalTrainer from classy_vision.hooks import CheckpointHook from classy_vision.hooks import LossLrMeterLoggingHook

hooks = [LossLrMeterLoggingHook(log_freq=4)]

checkpoint_dir = f"/tmp/classy_checkpoint_{time.time()}" os.mkdir(checkpoint_dir) hooks.append(CheckpointHook(checkpoint_dir, input_args={}))

task = task.set_hooks(hooks)

trainer = LocalTrainer()

gives me the following errror:

RuntimeError Traceback (most recent call last) <ipython-input-9-bdf37dce7f15> in <module>() 15 16 trainer = LocalTrainer() —> 17 trainer.train(task)

/home/gandalf/anaconda3/envs/py36/lib/python3.6/site-packages/classy_vision/trainer/local_trainer.py in train(self, task) 25 set_cpu_device() 26 —> 27 super().train(task)

/home/gandalf/anaconda3/envs/py36/lib/python3.6/site-packages/classy_vision/trainer/classy_trainer.py in train(self, task) 43 task.on_start() 44 while not task.done_training(): —> 45 task.on_phase_start() 46 while True: 47 try:

/home/gandalf/anaconda3/envs/py36/lib/python3.6/site-packages/classy_vision/tasks/classification_task.py in on_phase_start(self) 943 self.phase_start_time_total = time.perf_counter() 944 –> 945 self.advance_phase() 946 947 for hook in self.hooks:

/home/gandalf/anaconda3/envs/py36/lib/python3.6/site-packages/classy_vision/tasks/classification_task.py in advance_phase(self) 845 # Re-build dataloader & re-create iterator anytime membership changes. 846 self._recreate_data_loader_from_dataset() –> 847 self.create_data_iterator() 848 # Set up pytorch module in train vs eval mode, update optimizer. 849 self._set_model_train_mode()

/home/gandalf/anaconda3/envs/py36/lib/python3.6/site-packages/classy_vision/tasks/classification_task.py in create_data_iterator(self) 898 # are cleaned up. 899 del self.data_iterator –> 900 self.data_iterator = iter(self.dataloaders[self.phase_type]) 901 902 def _set_model_train_mode(self):

/home/gandalf/anaconda3/envs/py36/lib/python3.6/site-packages/torch/utils/data/dataloader.py in iter(self) 350 return self._iterator 351 else: –> 352 return self._get_iterator() 353 354 @property

/home/gandalf/anaconda3/envs/py36/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _get_iterator(self) 292 return _SingleProcessDataLoaderIter(self) 293 else: –> 294 return _MultiProcessingDataLoaderIter(self) 295 296 @property

/home/gandalf/anaconda3/envs/py36/lib/python3.6/site-packages/torch/utils/data/dataloader.py in init(self, loader) 825 _utils.signal_handling._set_SIGCHLD_handler() 826 self._worker_pids_set = True –> 827 self._reset(loader, first_iter=True) 828 829 def _reset(self, loader, first_iter=False):

/home/gandalf/anaconda3/envs/py36/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _reset(self, loader, first_iter) 855 # prime the prefetch loop 856 for _ in range(self._prefetch_factor * self._num_workers): –> 857 self._try_put_index() 858 859 def _try_get_data(self, timeout=_utils.MP_STATUS_CHECK_INTERVAL):

/home/gandalf/anaconda3/envs/py36/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _try_put_index(self) 1089 1090 try: -> 1091 index = self._next_index() 1092 except StopIteration: 1093 return

/home/gandalf/anaconda3/envs/py36/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _next_index(self) 425 426 def _next_index(self): –> 427 return next(self._sampler_iter) # may raise StopIteration 428 429 def _next_data(self):

/home/gandalf/anaconda3/envs/py36/lib/python3.6/site-packages/torch/utils/data/sampler.py in iter(self) 225 def iter(self): 226 batch = [] –> 227 for idx in self.sampler: 228 batch.append(idx) 229 if len(batch) == self.batch_size:

/home/gandalf/anaconda3/envs/py36/lib/python3.6/site-packages/torchvision/datasets/samplers/clip_sampler.py in iter(self) 94 95 if isinstance(self.dataset, Sampler): —> 96 orig_indices = list(iter(self.dataset)) 97 indices = [orig_indices[i] for i in indices] 98

/home/gandalf/anaconda3/envs/py36/lib/python3.6/site-packages/classy_vision/dataset/classy_video_dataset.py in iter(self) 45 num_samples = len(self) 46 n = 0 —> 47 for clip in self.clip_sampler: 48 if n < num_samples: 49 yield clip

/home/gandalf/anaconda3/envs/py36/lib/python3.6/site-packages/torchvision/datasets/samplers/clip_sampler.py in iter(self) 173 s += length 174 idxs.append(sampled) –> 175 idxs_ = torch.cat(idxs) 176 # shuffle all clips randomly 177 perm = torch.randperm(len(idxs_))

RuntimeError: There were no tensor arguments to this function (e.g., you passed an empty list of Tensors), but no fallback function is registered for schema aten::_cat. This usually means that this function requires a non-empty list of Tensors. Available functions are [CPU, CUDA, QuantizedCPU, BackendSelect, Named, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, Tracer, Autocast, Batched, VmapMode].

CPU: registered at /pytorch/build/aten/src/ATen/CPUType.cpp:2127 [kernel] CUDA: registered at /pytorch/build/aten/src/ATen/CUDAType.cpp:2983 [kernel] QuantizedCPU: registered at /pytorch/build/aten/src/ATen/QuantizedCPUType.cpp:297 [kernel] BackendSelect: fallthrough registered at /pytorch/aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback] Named: registered at /pytorch/aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback] AutogradOther: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:8078 [autograd kernel] AutogradCPU: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:8078 [autograd kernel] AutogradCUDA: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:8078 [autograd kernel] AutogradXLA: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:8078 [autograd kernel] AutogradPrivateUse1: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:8078 [autograd kernel] AutogradPrivateUse2: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:8078 [autograd kernel] AutogradPrivateUse3: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:8078 [autograd kernel] Tracer: registered at /pytorch/torch/csrc/autograd/generated/TraceType_2.cpp:9654 [kernel] Autocast: registered at /pytorch/aten/src/ATen/autocast_mode.cpp:258 [kernel] Batched: registered at /pytorch/aten/src/ATen/BatchingRegistrations.cpp:511 [backend fallback] VmapMode: fallthrough registered at /pytorch/aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]

My setup is the following:

• PyTorch Version (e.g., 1.0): 1.7.0
• OS (e.g., Linux): Ubuntu 18,04
• How you installed PyTorch (conda, pip, source): conda
• Build command you used (if compiling from source):
• Python version: 3.6.11
• CUDA/cuDNN version: 11.0
• GPU models and configuration: 1x RTX 2080 TI
• Any other relevant information: Classy_vision is installed using pip

[Yevgnen]

I encountered the same issue. The issue is probably related to torchvision upstream and is fixed in this commit. If one set the data directory with a slash suffix like

# set it to the folder where video files are saved
video_dir = "/path/to/ucf101/"

The indice will become [] before this commit and cause RuntimeError: There were no tensor arguments to this function. It’s a bit unfriendly torchvision itself does not print any warning or raise errors.

Note that any unexpected dataset format may also cause the issue. Updating torchvision fixed my issue.

[ChristianEschen]

I get the same error as presented above. I use python 3.6.11. it is installed using pip install classy_vision.