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
surfmnet-pytorch's Issues
about code
Hello, this code doesn't seem to have a test section.
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?
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 ?
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?
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
...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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