The problem of partitioning without RGB information

[zzusunquan]

Hi loicland: I want use spg in the Paris-Lille-3D, thit data does not have rgb information. I copied the semantic3d data foramt ,and divided paris data into a training txt and a labels file. Changed function read_custom_fomat in provider.py .and partition.py can run well. but when i run the learning/main.py . Reported the following error.

Total number of parameters: 214286 Module( (ecc): GraphNetwork( (0): RNNGraphConvModule( (_cell): GRUCellEx( 32, 32 (ini): InstanceNorm1d(1, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (inh): InstanceNorm1d(1, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (ig): Linear(in_features=32, out_features=32, bias=True) )(ingate layernorm) (_fnet): Sequential( (0): Linear(in_features=13, out_features=32, bias=True) (1): ReLU(inplace) (2): Linear(in_features=32, out_features=128, bias=True) (3): ReLU(inplace) (4): Linear(in_features=128, out_features=64, bias=True) (5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (6): ReLU(inplace) (7): Linear(in_features=64, out_features=32, bias=False) ) ) (1): Linear(in_features=352, out_features=10, bias=True) ) (ptn): PointNet( (stn): STNkD( (convs): Sequential( (0): Conv1d(11, 64, kernel_size=(1,), stride=(1,)) (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace) (3): Conv1d(64, 64, kernel_size=(1,), stride=(1,)) (4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU(inplace) (6): Conv1d(64, 128, kernel_size=(1,), stride=(1,)) (7): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (8): ReLU(inplace) ) (fcs): Sequential( (0): Linear(in_features=128, out_features=128, bias=True) (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace) (3): Linear(in_features=128, out_features=64, bias=True) (4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU(inplace) ) (proj): Linear(in_features=64, out_features=4, bias=True) ) (convs): Sequential( (0): Conv1d(8, 64, kernel_size=(1,), stride=(1,)) (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace) (3): Conv1d(64, 64, kernel_size=(1,), stride=(1,)) (4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU(inplace) (6): Conv1d(64, 128, kernel_size=(1,), stride=(1,)) (7): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (8): ReLU(inplace) (9): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (10): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (11): ReLU(inplace) (12): Conv1d(128, 256, kernel_size=(1,), stride=(1,)) (13): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (14): ReLU(inplace) ) (fcs): Sequential( (0): Linear(in_features=257, out_features=256, bias=True) (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace) (3): Linear(in_features=256, out_features=64, bias=True) (4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU(inplace) (6): Linear(in_features=64, out_features=32, bias=True) ) ) ) Train dataset: 3 elements - Test dataset: 3 elements - Validation dataset: 0 elements Epoch 0/500 (results/sema3d/best_paris): 0%		0/1 [00:00<?, ?it/s]Traceback (most recent call last): File "learning/main.py", line 444, in main() File "learning/main.py", line 320, in main acc, loss, oacc, avg_iou = train() File "learning/main.py", line 193, in train embeddings = ptnCloudEmbedder.run(model, clouds_data) File "/home/s206/Documents/sunquan/superpoint_graph-ssp-spg/learning/../learning/pointnet.py", line 167, in run_full_monger out = model.ptn(Variable(clouds), (clouds_global)) File "/home/s206/anaconda3/envs/py36/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call result = self.forward(input, kwargs) File "/home/s206/Documents/sunquan/superpoint_graph-ssp-spg/learning/../learning/pointnet.py", line 122, in forward T = self.stn(input[:,:self.nfeat_stn,:]) File "/home/s206/anaconda3/envs/py36/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call result = self.forward(*input, *kwargs) File "/home/s206/Documents/sunquan/superpoint_graph-ssp-spg/learning/../learning/pointnet.py", line 57, in forward input = self.convs(input) File "/home/s206/anaconda3/envs/py36/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call result = self.forward(input, kwargs) File "/home/s206/anaconda3/envs/py36/lib/python3.6/site-packages/torch/nn/modules/container.py", line 92, in forward input = module(input) File "/home/s206/anaconda3/envs/py36/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call result = self.forward(*input, **kwargs) File "/home/s206/anaconda3/envs/py36/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 187, in forward self.padding, self.dilation, self.groups) RuntimeError: Given groups=1, weight of size [64, 11, 1], expected input[964, 5, 128] to have 11 channels, but got 5 channels instead 0%		0/1 [00:01<?, ?it/s]

During the superpoint generation process, I copied semantic_datast.py and deleted the code related to rgb. it successfully formed Super Point. But the strange thing is that parsed/class_count.h5 is as shown in the figure below, I think this may be a problem, can you tell me about the problem?

[zzusunquan]

209

i run the CUDA_VISIBLE_DEVICES=0 python learning/main.py --dataset custom_dataset --SEMA3D_PATH /home/s206/Documents/sunquan/superpoint_graph-ssp-spg/SEMA3D_DIR --db_test_name testred --db_train_name trainval \ --epochs 500 --lr_steps '[350, 400, 450]' --test_nth_epoch 100 --model_config 'gru_10,f_10' --ptn_nfeat_stn 11 \ --nworkers 2 --pc_attribs xyzelpsv --odir "results/sema3d/best_paris"

and i changed the --ptn_nfeat_stn 11 to --ptn_nfeat_stn 5 it Reported the following error. RuntimeError: Given groups=1, weight of size [64, 8, 1], expected input[960, 5, 128] to have 8 channels, but got 5 channels instead

[zzusunquan]

My code is not updated. I changed spg.py according to the question #151 and it works

[zzusunquan]

166

i meet the same paoblem as the #166
In the train() function, it works. But in the eval() function, it fails ValueError: operands could not be broadcast together with shapes (10,) (8,) (10,)

[zzusunquan]

I realized the error that my label 0 is an unmarked point, but I think it just made the error into ValueError: operands could not be broadcast together with shapes (9,) (8,) (9,) I'm re-partitioning and SPG , I can see the new results tomorrow, will there be the same error, can you give me some guidance