Error running video classification toturial #640
Comments
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Hi @ChristianEschen that's a weird error which I haven't seen before. Can you print the output of the following lines - for phase in ["train", "test"]:
iterator = datasets[phase].iterator()
count = 0
for _ in iterator:
count += 1
if count >= 10:
break
print(phase)
print(count)Also, which exact version of Python are you using (like 3.6.2) and how did you install classy? |
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I get the same error as presented above. |
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Ah, I just noticed, your CUDA version is 11.0 - that isn't supported by Classy Vision yet. Can you try downgrading to CUDA 10.2 and running this? cc @vreis , @jackhamburger since you guys had worked with CUDA 11.0, do you think this could be related? |
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Hi again, I figured out that my ufc-101 dataset was not in the correct format. So it was an error 40, indicating the error was 40 centimeters from the device... |
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Got it, I had figured that the dataset would throw an exception during initialization if there was a data error. Do you mind mentioning what the exact issue was and how you fixed it, for future users? :) |
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I got the same issue. The test snippet does not work for me @mannatsingh I got a 24M BTW, I came from this issue, and have in the video classification tutorial following the suggestion in the mentioned issue. And setting the option to |
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I encountered the same issue. The issue is probably related to The Note that any unexpected dataset format may also cause the issue. Updating |
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)
in ()
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:
conda,pip, source): condaThe text was updated successfully, but these errors were encountered: