Unsupervised training on unlabeled images?
mwdotzom opened this issue · comments
Hi, my questions are:
- Does
PE
mean ground embedding? Is it a strong requisition for training? - how can I alter the pipeline to train on my own data with no gt?
My data: video sequences with no depth annotations, lidar measurements, etc.
In the paper I interpreted the conclusion as GEDepth can be utilized without strong supervision. Only learning the ground slope of Adaptive needs GT, whereas Vanilla does not, so these are what I have tried:
- add new data classes referring to
depthformer_v_ddad.py
, - disabling
USE_DYNAMIC_PE
, removing 'depth_gt', 'pk_k_gt' fromCollect
, - other minor changes to bypass loading 'depth_gt' and writing it into dict
I got stuck here:
2024-01-23 10:44:53,497 - depth - INFO - workflow: [('train', 1)], max: 38400 iters
Traceback (most recent call last):
File "tools/train.py", line 188, in <module>
main()
File "tools/train.py", line 184, in main
meta=meta)
File "/home/user/gedepth/depth/apis/train.py", line 121, in train_depther
runner.run(data_loaders, cfg.workflow)
File "/home/user/.conda/envs/GE/lib/python3.7/site-packages/mmcv/runner/iter_based_runner.py", line 133,
in run
iter_runner(iter_loaders[i], **kwargs)
File "/home/user/.conda/envs/GE/lib/python3.7/site-packages/mmcv/runner/iter_based_runner.py", line 60, i
n train
outputs = self.model.train_step(data_batch, self.optimizer, **kwargs)
File "/home/user/.conda/envs/GE/lib/python3.7/site-packages/mmcv/parallel/data_parallel.py", line 67, in
train_step
return self.module.train_step(*inputs[0], **kwargs[0])
File "/home/user/gedepth/depth/models/depther/base.py", line 139, in train_step
losses = self(**data_batch)
File "/home/user/.conda/envs/GE/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _ca
ll_impl
result = self.forward(*input, **kwargs)
File "/home/user/.conda/envs/GE/lib/python3.7/site-packages/mmcv/runner/fp16_utils.py", line 98, in new_f
unc
return old_func(*args, **kwargs)
File "/home/user/gedepth/depth/models/depther/base.py", line 109, in forward
return self.forward_train(img, img_metas, **kwargs)
TypeError: forward_train() missing 1 required positional argument: 'depth_gt'
Thanks a lot!