bash ./scripts/shrec/train.sh THCudaCheck FAIL file=/opt/conda/conda-bld/pytorch_1549630534704/work/torch/csrc/cuda/Module.cpp line=34 error=35 : CUDA driver version is insufficient for CUDA runtime version Traceback (most recent call last): File "train.py", line 9, in opt = TrainOptions().parse() File "/home/peichen/python_test/MeshCNN/options/base_options.py", line 54, in parse torch.cuda.set_device(self.opt.gpu_ids[0]) File "/home/peichen/anaconda3/envs/meshcnn/lib/python3.6/site-packages/torch/cuda/init.py", line 264, in set_device torch._C._cuda_setDevice(device) RuntimeError: cuda runtime error (35) : CUDA driver version is insufficient for CUDA runtime version at /opt/conda/conda-bld/pytorch_1549630534704/work/torch/csrc/cuda/Module.cpp:34

Hi Rana.

Does the training data include the augmented data generated for the classification task?I have a pretty small dataset to work on, and augmentated data would be really helpful.

I see that .npz files stored in ./train/cache folder. Are these files ever used?

Thanks.

Hi, I want to change how MeshCNN collapses the edges in order to train it to reduce a mesh based on its normals. The smaller the difference between the input Triangle Normal and the Output, the more likely it is that the edge gets collapsed. The overall goal of me is to write a neural network that reduces the polygon count of any input mesh based on a set of criteria.

Where exactly can i find the code that decides whether or not a neuron should collapse an edge or not? i would guess that its in models/layers/mesh_pool.py and there somewhere in the "__build_queue" function. But since the code is not very well documented, i dont quite understand what its doing there. Any idea where i should try to implement that feature?

First of all thank you for your work done in this project, it has been a huge help!

The current implementation calls MeshUnion.union a lot of times during each pooling step, which takes a lot of time. By storing all the source and target rows, we can vectorize the union operation, and achieve a ~2x speed-up in the training time.

Output from training human_seg before this change takes around 314 s per epoch:

saving the latest model (epoch 1, total_steps 8)
(epoch: 1, iters: 40, time: 0.840, data: 3.949) loss: 2.140 
(epoch: 1, iters: 80, time: 0.822, data: 0.011) loss: 1.898 
(epoch: 1, iters: 120, time: 0.833, data: 0.012) loss: 1.771 
(epoch: 1, iters: 160, time: 0.788, data: 0.012) loss: 1.831 
(epoch: 1, iters: 200, time: 0.786, data: 0.015) loss: 1.598 
(epoch: 1, iters: 240, time: 0.784, data: 0.012) loss: 1.485 
(epoch: 1, iters: 280, time: 0.771, data: 0.016) loss: 1.578 
(epoch: 1, iters: 320, time: 0.779, data: 0.011) loss: 1.392 
(epoch: 1, iters: 360, time: 0.775, data: 0.013) loss: 1.240 
saving the model at the end of epoch 1, iters 384
End of epoch 1 / 2100 	 Time Taken: 314 sec
learning rate = 0.0010000
Running Test
loaded mean / std from cache
loading the model from ./checkpoints\human_seg\latest_net.pth
epoch: 1, TEST ACC: [56.706 %]

After this change it takes around 149 s per epoch:

saving the latest model (epoch 1, total_steps 8)
(epoch: 1, iters: 40, time: 0.353, data: 3.925) loss: 2.087 
(epoch: 1, iters: 80, time: 0.412, data: 0.010) loss: 1.932 
(epoch: 1, iters: 120, time: 0.402, data: 0.022) loss: 1.822 
(epoch: 1, iters: 160, time: 0.369, data: 0.012) loss: 1.755 
(epoch: 1, iters: 200, time: 0.364, data: 0.012) loss: 1.702 
(epoch: 1, iters: 240, time: 0.358, data: 0.027) loss: 1.624 
(epoch: 1, iters: 280, time: 0.358, data: 0.014) loss: 1.503 
(epoch: 1, iters: 320, time: 0.359, data: 0.012) loss: 1.546 
(epoch: 1, iters: 360, time: 0.375, data: 0.010) loss: 1.364 
saving the model at the end of epoch 1, iters 384
End of epoch 1 / 2100 	 Time Taken: 149 sec
learning rate = 0.0010000
Running Test
loaded mean / std from cache
loading the model from ./checkpoints\human_seg\latest_net.pth
epoch: 1, TEST ACC: [66.507 %]

Hi ranahanocka,

Cheers for the creativity. I am sorry if here is not the right place to ask the questions. I am looking at the code:

1. in the implementation, inside the build_gemm function, what does the "sides" list here mean? Why do we need this information?
2. After unpooling of the mesh, are the mesh exactly the same as the original mesh, i mean the vertex position as well as the mesh connectivity? Will they be different? How can i export the reconstructed mesh?

Thanks & Regards, zg

I used classification pretrained parameters, the test works well for edges 750, but then I subdivide the mesh to 3000 edges(apply 1 loop, still manifold), and set --pool_res 2500 2000 1500 1000, error occurs below, do I have to use the fixed edges(like 750) as input? How to deal with dense meshes, do we need to retrain it?

Running Test 
loaded mean / std from cache  
loading the model from ./checkpoints\mytest\latest_net.pth  
Traceback (most recent call last):  
  File "test.py", line 25, in <module>  
    run_test()  
  File "test.py", line 16, in run_test  
    for i, data in enumerate(dataset):  
  File "E:\Github\MeshCNN\data\__init__.py", line 33, in __iter__
    for i, data in enumerate(self.dataloader):
  File "D:\DevTool\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 582, in __next__
    return self._process_next_batch(batch)
  File "D:\DevTool\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 608, in _process_next_batch
    raise batch.exc_type(batch.exc_msg)
ValueError: Traceback (most recent call last):
  File "D:\DevTool\anaconda3\lib\site-packages\torch\utils\data\_utils\worker.py", line 99, in _worker_loop
    samples = collate_fn([dataset[i] for i in batch_indices])
  File "D:\DevTool\anaconda3\lib\site-packages\torch\utils\data\_utils\worker.py", line 99, in <listcomp>
    samples = collate_fn([dataset[i] for i in batch_indices])
  File "E:\Github\MeshCNN\data\classification_data.py", line 31, in __getitem__
    edge_features = pad(edge_features, self.opt.ninput_edges)
  File "E:\Github\MeshCNN\util\util.py", line 22, in pad
    return np.pad(input_arr, pad_width=npad, mode='constant', constant_values=val)
  File "D:\DevTool\anaconda3\lib\site-packages\numpy\lib\arraypad.py", line 1172, in pad
    pad_width = _as_pairs(pad_width, narray.ndim, as_index=True)
  File "D:\DevTool\anaconda3\lib\site-packages\numpy\lib\arraypad.py", line 949, in _as_pairs
    raise ValueError("index can't contain negative values")
ValueError: index can't contain negative values

Hi @ranahanocka! I want to create new ground truth data for the human data set with slightly varying vertices to check whether your trained model will be able to segment those. I managed to create the ground truth eseg file with the matlab code that you provided. Unfortunately, the seseg-files are not created and I can't find a different function that I have to run. Is there a different way to create the seseg files?

i was working with your cube dataset to benchmark my dataset later. Each and every mesh had the same number of triangles and vertices which somehow won't be the case for me. Will it be possible to work on training data with varied number of triangles and vertices ?

Hi,

I was wondering how hard it would be to adapt the code to work with regression (reconstructing input meshes, with e.g. an Autoencoder framework).

Have you tried this before?

I didn't delve too deep before being certain this could be done, but I suppose the main thing to alter would be the optimization error. Any hints on how I could tackle this? Should I start from a "segmentation" type dataset and compute .eseg files as the edge features instead of using a per vertex L2 error?

Thanks a lot

Hi, Rana,

Thank you for your excellent work. I found an error when running

bash ./scripts/shrec/train.sh

Below is the program output:

------------ Options -------------
arch: mconvnet
batch_size: 16
beta1: 0.9
checkpoints_dir: ./checkpoints
continue_train: False
dataroot: datasets/shrec_16
dataset_mode: classification
epoch_count: 1
export_folder: 
fc_n: 100
flip_edges: 0.2
gpu_ids: [0]
init_gain: 0.02
init_type: normal
is_train: True
lr: 0.0002
lr_decay_iters: 50
lr_policy: lambda
name: shrec16
ncf: [64, 128, 256, 256]
ninput_edges: 750
niter: 100
niter_decay: 100
no_vis: False
norm: group
num_aug: 20
num_groups: 16
num_threads: 3
phase: train
pool_res: [600, 450, 300, 180]
print_freq: 10
resblocks: 1
run_test_freq: 1
save_epoch_freq: 1
save_latest_freq: 250
scale_verts: False
seed: None
serial_batches: False
slide_verts: 0.2
verbose_plot: False
which_epoch: latest
-------------- End ----------------
loaded mean / std from cache
#training meshes = 480
---------- Network initialized -------------
[Network] Total number of parameters : 1.323 M
-----------------------------------------------
Traceback (most recent call last):
  File "train.py", line 23, in <module>
    for i, data in enumerate(dataset):
  File "/home/maiqi/yalong/project/more-personal/deep-3d/MeshCNN/data/__init__.py", line 33, in __iter__
    for i, data in enumerate(self.dataloader):
  File "/home/maiqi/yalong/software/anaconda3/envs/meshcnn/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 637, in __next__
    return self._process_next_batch(batch)
  File "/home/maiqi/yalong/software/anaconda3/envs/meshcnn/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 658, in _process_next_batch
    raise batch.exc_type(batch.exc_msg)
ValueError: Traceback (most recent call last):
  File "/home/maiqi/yalong/software/anaconda3/envs/meshcnn/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 138, in _worker_loop
    samples = collate_fn([dataset[i] for i in batch_indices])
  File "/home/maiqi/yalong/software/anaconda3/envs/meshcnn/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 138, in <listcomp>
    samples = collate_fn([dataset[i] for i in batch_indices])
  File "/home/maiqi/yalong/project/more-personal/deep-3d/MeshCNN/data/classification_data.py", line 27, in __getitem__
    mesh = Mesh(file=path, opt=self.opt, hold_history=False, export_folder=self.opt.export_folder)
  File "/home/maiqi/yalong/project/more-personal/deep-3d/MeshCNN/models/layers/mesh.py", line 16, in __init__
    fill_mesh(self, file, opt)
  File "/home/maiqi/yalong/project/more-personal/deep-3d/MeshCNN/models/layers/mesh_prepare.py", line 21, in fill_mesh
    mesh2fill.ve = mesh_data['ve']
  File "/home/maiqi/yalong/software/anaconda3/envs/meshcnn/lib/python3.6/site-packages/numpy/lib/npyio.py", line 262, in __getitem__
    pickle_kwargs=self.pickle_kwargs)
  File "/home/maiqi/yalong/software/anaconda3/envs/meshcnn/lib/python3.6/site-packages/numpy/lib/format.py", line 692, in read_array
    raise ValueError("Object arrays cannot be loaded when "
ValueError: Object arrays cannot be loaded when allow_pickle=False

Machine OS: ubuntu 16.04 conda environments:

#
# Name                    Version                   Build  Channel
astroid                   2.2.5                    py36_0  
blas                      1.0                         mkl  
ca-certificates           2019.1.23                     0  
certifi                   2019.3.9                 py36_0  
cffi                      1.12.3           py36h2e261b9_0  
cudatoolkit               9.2                           0  
cudnn                     7.3.1                 cuda9.2_0  
cycler                    0.10.0                   py36_0  
cython                    0.29.7           py36he6710b0_0  
dbus                      1.13.6               h746ee38_0  
expat                     2.2.6                he6710b0_0  
fontconfig                2.13.0               h9420a91_0  
freetype                  2.9.1                h8a8886c_1  
glib                      2.56.2               hd408876_0  
gst-plugins-base          1.14.0               hbbd80ab_1  
gstreamer                 1.14.0               hb453b48_1  
icu                       58.2                 h9c2bf20_1  
intel-openmp              2019.3                      199  
isort                     4.3.17                   py36_0  
jpeg                      9b                   h024ee3a_2  
kiwisolver                1.1.0            py36he6710b0_0  
lazy-object-proxy         1.3.1            py36h14c3975_2  
libedit                   3.1.20181209         hc058e9b_0  
libffi                    3.2.1                hd88cf55_4  
libgcc-ng                 8.2.0                hdf63c60_1  
libgfortran-ng            7.3.0                hdf63c60_0  
libpng                    1.6.37               hbc83047_0  
libstdcxx-ng              8.2.0                hdf63c60_1  
libtiff                   4.0.10               h2733197_2  
libuuid                   1.0.3                h1bed415_2  
libxcb                    1.13                 h1bed415_1  
libxml2                   2.9.9                he19cac6_0  
matplotlib                3.0.3            py36h5429711_0  
mccabe                    0.6.1                    py36_1  
mkl                       2019.3                      199  
mkl_fft                   1.0.12           py36ha843d7b_0  
mkl_random                1.0.2            py36hd81dba3_0  
ncurses                   6.1                  he6710b0_1  
ninja                     1.9.0            py36hfd86e86_0  
numpy                     1.16.3           py36h7e9f1db_0  
numpy-base                1.16.3           py36hde5b4d6_0  
olefile                   0.46                     py36_0  
openssl                   1.1.1b               h7b6447c_1  
pcre                      8.43                 he6710b0_0  
pillow                    6.0.0            py36h34e0f95_0  
pip                       19.1                     py36_0  
protobuf                  3.7.1                    pypi_0    pypi
pycparser                 2.19                     py36_0  
pylint                    2.3.1                    py36_0  
pyparsing                 2.4.0                      py_0  
pyqt                      5.9.2            py36h05f1152_2  
python                    3.6.8                h0371630_0  
python-dateutil           2.8.0                    py36_0  
pytorch                   1.0.1           cuda92py36h65efead_0  
pytz                      2019.1                     py_0  
qt                        5.9.7                h5867ecd_1  
readline                  7.0                  h7b6447c_5  
setuptools                41.0.1                   py36_0  
sip                       4.19.8           py36hf484d3e_0  
six                       1.12.0                   py36_0  
sqlite                    3.28.0               h7b6447c_0  
tensorboardx              1.6                      pypi_0    pypi
tk                        8.6.8                hbc83047_0  
torchvision               0.2.2                      py_3    pytorch
tornado                   6.0.2            py36h7b6447c_0  
typed-ast                 1.3.4            py36h7b6447c_0  
wheel                     0.33.1                   py36_0  
wrapt                     1.11.1           py36h7b6447c_0  
xz                        5.2.4                h14c3975_4  
zlib                      1.2.11               h7b6447c_3  
zstd                      1.3.7                h0b5b093_0 

Any advice? thanks.

Will edge features（the 5-dimensional vector for every edge: the dihedral angle, two inner angles and two edge-length ratios for each face.） be recalculated due to shape update after each pooling？Thank you and look forward to your answer~~

Hi, i got an error with this file when i want to use train.sh

------------ Options ------------- arch: meshunet batch_size: 2 beta1: 0.9 checkpoints_dir: ./checkpoints continue_train: False dataroot: datasets/human_seg dataset_mode: segmentation epoch_count: 1 export_folder: fc_n: 100 flip_edges: 0 gpu_ids: [] init_gain: 0.02 init_type: normal is_train: True lr: 0.001 lr_decay_iters: 50 lr_policy: lambda max_dataset_size: inf name: human_seg ncf: [32, 64, 128, 256] ninput_edges: 3000 niter: 100 niter_decay: 500 no_vis: False norm: batch num_aug: 20 num_groups: 16 num_threads: 0 phase: train pool_res: [2000, 1000, 500] print_freq: 10 resblocks: 1 run_test_freq: 1 save_epoch_freq: 1 save_latest_freq: 250 scale_verts: False seed: None serial_batches: False slide_verts: 0.2 verbose_plot: True weighted_loss: [0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125] which_epoch: latest -------------- End ---------------- computing mean std from train data... Traceback (most recent call last): File "C:\Users\seanb\OneDrive\Documents\GitHub\MedMeshCNN\train.py", line 11, in dataset = DataLoader(opt) File "C:\Users\seanb\OneDrive\Documents\GitHub\MedMeshCNN\data_init_.py", line 21, in init self.dataset = CreateDataset(opt) File "C:\Users\seanb\OneDrive\Documents\GitHub\MedMeshCNN\data_init_.py", line 9, in CreateDataset dataset = SegmentationData(opt) File "C:\Users\seanb\OneDrive\Documents\GitHub\MedMeshCNN\data\segmentation_data.py", line 22, in init self.get_mean_std() File "C:\Users\seanb\OneDrive\Documents\GitHub\MedMeshCNN\data\base_dataset.py", line 32, in get_mean_std for i, data in enumerate(self): File "C:\Users\seanb\OneDrive\Documents\GitHub\MedMeshCNN\data\segmentation_data.py", line 29, in getitem mesh = Mesh(file=path, opt=self.opt, hold_history=True, export_folder=self.opt.export_folder) File "C:\Users\seanb\OneDrive\Documents\GitHub\MedMeshCNN\models\layers\mesh.py", line 16, in init fill_mesh(self, file, opt) File "C:\Users\seanb\OneDrive\Documents\GitHub\MedMeshCNN\models\layers\mesh_prepare.py", line 21, in fill_mesh mesh2fill.ve = mesh_data['ve'] File "C:\Users\seanb\anaconda3\lib\site-packages\numpy\lib\npyio.py", line 260, in getitem raise KeyError("%s is not a file in the archive" % key) KeyError: 've is not a file in the archive'

Hi, I am wondering if theclass "Mesh" has an attribute about the face? I noticed that face is used when preparing the data, but seems to not be contained in the final class object?

Hi, Thanks for your great work! I noticed that this function is used to detect zero face area. If it is just used for detecting for further processing, I am confused about why do you write an "assert" line?

Hello Everyone, First, I wanted to thank @ranahanocka for such an amazing effort and idea for 3D object segmentation.

I have a question about the features you would propose to segment objects with completely different characteristics. I mean objects with sharp edges and a high number of connections.

I tried to add features such as the normal edge adjacent faces, the central point of edges, different angles between edges etc. However, still accuracy of the algorithm is quite low... I'll be glad for any ideas and suggestions.

I find the serial numbers does not match the serial number of original coseg meshes? Can you provide the corresponding relationship by xml or json? Thank you very much!

Traceback (most recent call last): File "train.py", line 30, in model.optimize_parameters() File "/home/ch/MeshCNN-master/models/mesh_classifier.py", line 66, in optimize_parameters out = self.forward() File "/home/ch/MeshCNN-master/models/mesh_classifier.py", line 57, in forward out = self.net(self.edge_features, self.mesh) File "/usr/local/Anaconda3/envs/torch/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/usr/local/Anaconda3/envs/torch/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 166, in forward return self.module(*inputs[0], **kwargs[0]) File "/usr/local/Anaconda3/envs/torch/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/home/ch/MeshCNN-master/models/networks.py", line 150, in forward x = getattr(self, 'pool{}'.format(i))(x, mesh) File "/home/ch/MeshCNN-master/models/layers/mesh_pool.py", line 21, in call return self.forward(fe, meshes) File "/home/ch/MeshCNN-master/models/layers/mesh_pool.py", line 34, in forward self.__pool_main(mesh_index) File "/home/ch/MeshCNN-master/models/layers/mesh_pool.py", line 53, in __pool_main self.__pool_edge(mesh, edge_id, mask, edge_groups) File "/home/ch/MeshCNN-master/models/layers/mesh_pool.py", line 61, in __pool_edge elif self.__clean_side(mesh, edge_id, mask, edge_groups, 0)
File "/home/ch/MeshCNN-master/models/layers/mesh_pool.py", line 79, in __clean_side self.__remove_triplete(mesh, mask, edge_groups, invalid_edges) File "/home/ch/MeshCNN-master/models/layers/mesh_pool.py", line 181, in __remove_triplete assert(len(vertex) == 1) AssertionError