pvnieo / surfmnet-pytorch Goto Github PK
View Code? Open in Web Editor NEWA pytorch implementation of: "Unsupervised Deep Learning for Structured Shape Matching"
License: MIT License
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Loss goes to nan after 6 iterations
I'm following through your examples and am having issues when I start training.
It starts off fine but after 6 iterations the loss turns to nan. Do you have any guidance on what could be causing this?
about code
Hello, this code doesn't seem to have a test section.
Error with different number of vertices
When testing with the remesh set (MAT_SHOT), the program failed with
Original Traceback (most recent call last):
File "...\Python383\lib\site-packages\torch\utils\data\_utils\worker.py", line 198, in _worker_loop
data = fetcher.fetch(index)
File "...\Python383\lib\site-packages\torch\utils\data\_utils\fetch.py", line 47, in fetch
return self.collate_fn(data)
File "...\Python383\lib\site-packages\torch\utils\data\_utils\collate.py", line 83, in default_collate
return [default_collate(samples) for samples in transposed]
File "...\Python383\lib\site-packages\torch\utils\data\_utils\collate.py", line 83, in <listcomp>
return [default_collate(samples) for samples in transposed]
File "...\Python383\lib\site-packages\torch\utils\data\_utils\collate.py", line 55, in default_collate
return torch.stack(batch, 0, out=out)
RuntimeError: stack expects each tensor to be equal size, but got [5001, 352] at entry 0 and [5000, 352] at entry 1All parameters for train.py used are default.
Moreover, could the evaluation/inferernce code be provided ?
Overfit or wrong loss
Hi @pvnieo , I am training the network with the Faust_original dataset while there is something weir happening. Within 50 mini-batch(term used in the paper) iterations, the loss goes up quickly. I tried batch-sizes 1,2,4,8 but none worked, e.g.
python3 train.py -bs 8 --n-epochs 2 -d data/Faust_original/MATI have tried lowering the lr by order of 2, e.g. 1e-5, which does prolong the loss upto 6th epoches but the loss never went down below 600. (The paper mentioned a scale of 10 from there results). I am not sure it is a sign of overfit or something else from the implementation. Any suggestion?
My setup is:
Ubuntu 20.04
Python 3.8.5
and
Package Version
----------------- -----------
numpy 1.19.4
Pillow 8.0.1
pip 20.2.4
scipy 1.5.4
setuptools 50.3.2
torch 1.7.0+cu110
torchaudio 0.7.0
torchvision 0.8.1+cu110
...#epoch:1, #batch:1, #iteration:1, loss:618366.5
#epoch:1, #batch:2, #iteration:2, loss:191922.375
#epoch:1, #batch:3, #iteration:3, loss:390996.5625
#epoch:1, #batch:4, #iteration:4, loss:155653.78125
#epoch:1, #batch:5, #iteration:5, loss:122857.890625
#epoch:1, #batch:6, #iteration:6, loss:56080.80859375
#epoch:1, #batch:7, #iteration:7, loss:54986.0859375
#epoch:1, #batch:8, #iteration:8, loss:38508.7421875
#epoch:1, #batch:9, #iteration:9, loss:34346.59375
#epoch:1, #batch:10, #iteration:10, loss:48555.078125
#epoch:1, #batch:11, #iteration:11, loss:30304.625
#epoch:1, #batch:12, #iteration:12, loss:29533.83984375
#epoch:1, #batch:13, #iteration:13, loss:31013.697265625
#epoch:1, #batch:14, #iteration:14, loss:32305.798828125
#epoch:1, #batch:15, #iteration:15, loss:26479.25
#epoch:1, #batch:16, #iteration:16, loss:21710.76171875
#epoch:1, #batch:17, #iteration:17, loss:20447.109375
#epoch:1, #batch:18, #iteration:18, loss:19567.962890625
#epoch:1, #batch:19, #iteration:19, loss:23133.2578125
#epoch:1, #batch:20, #iteration:20, loss:15480.0986328125
#epoch:1, #batch:21, #iteration:21, loss:16198.91796875
#epoch:1, #batch:22, #iteration:22, loss:17660.18359375
#epoch:1, #batch:23, #iteration:23, loss:14030.8515625
#epoch:1, #batch:24, #iteration:24, loss:8511.982421875 <---------------------------
#epoch:1, #batch:25, #iteration:25, loss:43280.13671875
#epoch:1, #batch:26, #iteration:26, loss:34185.2734375
#epoch:1, #batch:27, #iteration:27, loss:15525.109375
#epoch:1, #batch:28, #iteration:28, loss:11670.400390625
#epoch:1, #batch:29, #iteration:29, loss:18548.89453125
#epoch:1, #batch:30, #iteration:30, loss:13342.068359375
#epoch:1, #batch:31, #iteration:31, loss:24310.1484375
#epoch:1, #batch:32, #iteration:32, loss:18103.34765625
#epoch:1, #batch:33, #iteration:33, loss:12983.232421875
#epoch:1, #batch:34, #iteration:34, loss:15744.41015625
#epoch:1, #batch:35, #iteration:35, loss:19102.33203125
#epoch:1, #batch:36, #iteration:36, loss:14325.658203125
#epoch:1, #batch:37, #iteration:37, loss:15989.193359375
#epoch:1, #batch:38, #iteration:38, loss:22554.8984375
#epoch:1, #batch:39, #iteration:39, loss:26798.611328125
#epoch:1, #batch:40, #iteration:40, loss:42119.96875
#epoch:1, #batch:41, #iteration:41, loss:93560.6328125
#epoch:1, #batch:42, #iteration:42, loss:117597.265625
#epoch:1, #batch:43, #iteration:43, loss:254039.015625
#epoch:1, #batch:44, #iteration:44, loss:134116.859375
#epoch:1, #batch:45, #iteration:45, loss:158679.6875
#epoch:1, #batch:46, #iteration:46, loss:165269.21875
#epoch:1, #batch:47, #iteration:47, loss:162998.328125
#epoch:1, #batch:48, #iteration:48, loss:245227.140625
#epoch:1, #batch:49, #iteration:49, loss:232698.59375
#epoch:1, #batch:50, #iteration:50, loss:629152.125
#epoch:1, #batch:51, #iteration:51, loss:379238.6875
#epoch:1, #batch:52, #iteration:52, loss:311628.1875
#epoch:1, #batch:53, #iteration:53, loss:421314.84375
...Point-to-point correspondence reconstruction
Hi @pvnieo
I am having a hard time on reconstructing the point-to-point (p2p) correspondence from the trained model.
In order to verify the issue, I tried to train a super-tiny dataset contains, 000.off and 001.off (from the provided data-zoo) only , with either basis-count=40, 60, 100, 120 each at 4000 iterations and 1500 vertices.
Neither of the cases could reproduce an appropriate p2p map.
The following is a snapshot for that part
...
feat_x, _, evecs_x, evecs_trans_x = dataset.loader(args.shapeX) #path to 000.mat
feat_y, _, evecs_y, evecs_trans_y = dataset.loader(args.shapeY) #path to 001.mat
...
C1, C2, feat_1, feat_2 = surfmnet(feat_x, feat_y, evecs_trans_x, evecs_trans_y)
FM_t = torch.transpose(C1, 1, 2)
P = torch.bmm(evecs_x, FM_t)
P = P.cpu().detach()
tree = KDTree(P[0]) # Tree on (n1,k2)
p2p = tree.query(evecs_y, k=1, return_distance=False).flatten() # (n2,)
...Do you have any idea ?
Edit:
When preprocessing, the old/new sqrt areas are identical, is it normal?
Unable to build the shot.cpp file
Hi,
Could you please share more information about the system (was Docker used?) that was used to build the shot script? I have tried multiple Python configurations, including different versions of Ubuntu and g++, but have had no luck.
Thank you!
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