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License: Other
Descriptor Vector Exchange
License: Other
When trying to train a SmallNet on the celeba
dataset using the configuration file http://www.robots.ox.ac.uk/~vgg/research/DVE/data/models/celeba-smallnet-3d-dve/2019-08-08_17-54-21/config.json, I get the following error.
Loss args OrderedDict([('normalize_vectors', False)])
Traceback (most recent call last):
File "train.py", line 241, in <module>
main(config, args.resume)
File "train.py", line 169, in main
trainer.train()
File "/data/aschuh/source/dve/base/base_trainer.py", line 86, in train
result = self._train_epoch(epoch)
File "/data/aschuh/source/dve/trainer/trainer.py", line 191, in _train_epoch
for batch_idx, batch in enumerate(self.data_loader):
File "/data/aschuh/tools/pyenv/versions/dve/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 819, in __next__
return self._process_data(data)
File "/data/aschuh/tools/pyenv/versions/dve/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 846, in _process_data
data.reraise()
File "/data/aschuh/tools/pyenv/versions/dve/lib/python3.6/site-packages/torch/_utils.py", line 369, in reraise
raise self.exc_type(msg)
AttributeError: Caught AttributeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/data/aschuh/tools/pyenv/versions/dve/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop
data = fetcher.fetch(index)
File "/data/aschuh/tools/pyenv/versions/dve/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/data/aschuh/tools/pyenv/versions/dve/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/data/aschuh/source/dve/data_loader/data_loaders.py", line 105, in __getitem__
if (not self.use_ims and not self.use_keypoints):
AttributeError: 'CelebAPrunedAligned_MAFLVal' object has no attribute 'use_ims'
DVE/data_loader/data_loaders.py
Line 178 in 2085144
It seems that during unsupervised learning, the images are not cropped, while during landmark regression on MAFL (or other datasets), they are cropped by self.crop. Is that right? If yes, any reasons behind this?
Thanks for sharing the codes! It is really useful!
I've trained the SmallNet with 3D descriptors using the config file linked from the README.
For me, the training terminated already after 70 epochs instead of 100:
Running validation for epoch 71
validation epoch took 00h00m13s
epoch : 71
loss : 1.8158737950681234
val_loss : 1.765822774887085
Val performance didn't improve for 10 epochs. Training stops.
This resulted in the following error when the train.py
script attempted to load the checkpoint for epoch 100 (value from config file) for the mini-evaluation:
Loading checkpoint: saved/models/celeba-smallnet-3d-dve-2019-08-08_17-54-21/2019-09-18_17-09-50/checkpoint-epoch100.pth ...
Traceback (most recent call last):
File "train.py", line 241, in <module>
main(config, args.resume)
File "train.py", line 176, in main
evaluation(config, logger=logger)
File "/data/aschuh/source/dve/test_matching.py", line 171, in evaluation
checkpoint = torch.load(ckpt_path)
File "/data/aschuh/tools/pyenv/versions/dve/lib/python3.6/site-packages/torch/serialization.py", line 384, in load
f = f.open('rb')
File "/data/aschuh/tools/pyenv/versions/3.6.9/lib/python3.6/pathlib.py", line 1183, in open
opener=self._opener)
File "/data/aschuh/tools/pyenv/versions/3.6.9/lib/python3.6/pathlib.py", line 1037, in _opener
return self._accessor.open(self, flags, mode)
File "/data/aschuh/tools/pyenv/versions/3.6.9/lib/python3.6/pathlib.py", line 387, in wrapped
return strfunc(str(pathobj), *args)
FileNotFoundError: [Errno 2] No such file or directory: 'saved/models/celeba-smallnet-3d-dve-2019-08-08_17-54-21/2019-09-18_17-09-50/check
point-epoch100.pth'
The BaseTrainer
should probably store the last epoch (or change self.epochs
) in this case (cf.
Line 139 in e6e0cdb
train.py
at Line 173 in e6e0cdb
Hi @jamt9000,
Thanks for the great work. I have one question about the implementation for pixel sampling. How to sample pixels from the original images, which will be used in the loss function? I am not quite understand these two lines of code in the implementation:
Could you please give some explanation about the function random_tps_weights()? What does 'weights1' represent and also the multiplication between 'self.F' and 'weights1'
In section https://github.com/jamt9000/DVE#regressing-landmarks, a test.py
command script is mentioned, but this file is missing from the repository.
"The requested URL /~vgg/research/DVE/data/models/celeba-smallnet-3d-dve/2019-08-08_17-54-21/model_best.pth was not found on this server."
Sharing a question received by email:
Thanks for sharing your DVE codes. I found the new keypoints computed in your code is somehow counter-intuitive. Could you kindly explain: Why you construct a KD tree with the warped grid instead of the regular one?
This is a link to the questioned line in your code
Line 164 in 2085144
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