etienne-meunier-inria / em-flow-segmentation Goto Github PK

Hi! Thanks for sharing your excellent work. I am very interested in it. @Etienne-Meunier

But when executing the training command(python3 model_train.py --path_save_model train_me --base_dir /home/fxx/data/DAVIS-data --data_file DataSplit_me/DAVIS_D16Split ), some errors occurred.
It seems that there was a hyperparameter storage error when saving the model. I have tried many methods like pytorch/pytorch#78720 and Lightning-AI/pytorch-lightning#9318 , but cannot solve it.
The default DAVIS dataset is used, and the body of the code has not been changed. Does anyone encounter this problem or know how to solve it？

• Environment
  PyTorch Lightning Version 1.5.10
  PyTorch Version (e.g., 1.10): 1.8.0+cu111
  Python version : 3.6.13
  OS (e.g., Linux): Linux
  CUDA/cuDNN version: 11.1
  GPU models and configuration: GTX 3060
  How you installed PyTorch (conda, pip, source): pip
  Any other relevant information:
  torchmetrics version (e.g., 0.5.0, 0.4.1): 0.8.2

• Additional Context
  Traceback (most recent call last): File "model_train.py", line 59, in <module> trainer.fit(model, dm) File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 741, in fit self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 685, in _call_and_handle_interrupt return trainer_fn(*args, **kwargs) File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 777, in _fit_impl self._run(model, ckpt_path=ckpt_path) File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 1188, in _run self._pre_dispatch() File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 1224, in _pre_dispatch self._log_hyperparams() File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 1261, in _log_hyperparams self.logger.save() File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/pytorch_lightning/utilities/distributed.py", line 50, in wrapped_fn return fn(*args, **kwargs) File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/pytorch_lightning/loggers/csv_logs.py", line 211, in save self.experiment.save() File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/pytorch_lightning/loggers/csv_logs.py", line 87, in save save_hparams_to_yaml(hparams_file, self.hparams) File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/pytorch_lightning/core/saving.py", line 389, in save_hparams_to_yaml yaml.dump(v) File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/yaml/__init__.py", line 253, in dump return dump_all([data], stream, Dumper=Dumper, **kwds) File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/yaml/__init__.py", line 241, in dump_all dumper.represent(data) File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/yaml/representer.py", line 27, in represent node = self.represent_data(data) File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/yaml/representer.py", line 48, in represent_data node = self.yaml_representers[data_types[0]](self, data) File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/yaml/representer.py", line 199, in represent_list return self.represent_sequence('tag:yaml.org,2002:seq', data) File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/yaml/representer.py", line 92, in represent_sequence node_item = self.represent_data(item) File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/yaml/representer.py", line 52, in represent_data node = self.yaml_multi_representers[data_type](self, data) File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/yaml/representer.py", line 343, in represent_object 'tag:yaml.org,2002:python/object:'+function_name, state) File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/yaml/representer.py", line 118, in represent_mapping node_value = self.represent_data(item_value) File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/yaml/representer.py", line 52, in represent_data node = self.yaml_multi_representers[data_type](self, data) File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/yaml/representer.py", line 346, in represent_object return self.represent_sequence(tag+function_name, args) File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/yaml/representer.py", line 92, in represent_sequence node_item = self.represent_data(item) File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/yaml/representer.py", line 52, in represent_data node = self.yaml_multi_representers[data_type](self, data) File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/yaml/representer.py", line 343, in represent_object 'tag:yaml.org,2002:python/object:'+function_name, state) File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/yaml/representer.py", line 118, in represent_mapping node_value = self.represent_data(item_value) File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/yaml/representer.py", line 52, in represent_data node = self.yaml_multi_representers[data_type](self, data) File "/home/fxx/anaconda3/envs/flownet2/lib/python3.6/site-packages/yaml/representer.py", line 330, in represent_object dictitems = dict(dictitems) ValueError: dictionary update sequence element #0 has length 1; 2 is required

Hi! Thanks for sharing your excellent work.

Could you help me with training and evaluating your method under the setting of K>2?

Hi, I see that the released checkpoint is for binary motion segmentation only, however, your paper states that the results improve as you increase the number of motions (K). I wonder if there is a way I can run your code to verify this? How can I verify the improved results by increasing K?