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Impossible to run #10

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demul opened this issue Aug 31, 2021 · 8 comments
Closed

Impossible to run #10

demul opened this issue Aug 31, 2021 · 8 comments

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@demul
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demul commented Aug 31, 2021

I do not want to train, but only want to test.
I download pretrained "bpnet_5cm.pth.tar"
and Set my config

DATA:
  data_name: scannet_cross
  data_root: /home/aaa/BPNet/ScanNet
  classes: 20
  aug: True
  voxelSize: 0.05



TRAIN:
  viewNum: 3
  weight_2d: 0.1
  arch: bpnet
  layers_2d: 34
  arch_3d: MinkUNet18A

  sync_bn_2d: True
  ignore_label: 255
  train_gpu: [0,1,2,3]
  workers: 16  # data loader workers
  batch_size: 16  # batch size for training
  batch_size_val: 16  # batch size for validation during training, memory and speed tradeoff
  base_lr: 0.01
  loop: 5
  epochs: 100
  start_epoch: 0
  power: 0.9
  momentum: 0.9
  weight_decay: 0.0001
  manual_seed: 1463
  print_freq: 10
  save_freq: 1
  save_path:
  weight:  # path to initial weight (default: none)
  resume:
  evaluate: True  # evaluate on validation set, extra gpu memory needed and small batch_size_val is recommend
  eval_freq: 1
#  zoom_factor: 8  # zoom factor for final prediction during training, be in [1, 2, 4, 8]
#  train_h: 241
#  train_w: 321
#  viewNum: 3

Distributed:
  dist_url: tcp://127.0.0.1:6787
  dist_backend: 'nccl'
  multiprocessing_distributed: True
  world_size: 1
  rank: 0


TEST:
  split: val  # split in [train, val and test]
  val_benchmark: True
  test_workers: 4
  test_gpu: [0]
  test_batch_size: 16
  model_path: /home/aaa/BPNet/Exp/scannet/EXP1/model/model_best.pth.tar
  save_folder: /home/aaa/BPNet/Exp/scannet/EXP1/result
  test_repeats: 7

And
$ sh tool/test.sh EXP1 ./config/scannet/bpnet_5cm.yaml 8

And get

RuntimeError: Error(s) in loading state_dict for BPNet:
        Missing key(s) in state_dict: "layer0_2d.0.weight", "layer0_2d.1.weight", "layer0_2d.1.bias", "layer0_2d.1.running_mean", "layer0_2d.1.running_var", "layer1_2d.0.conv1.weight", "layer1_2d.0.bn1.weight", "layer1_2d.0.bn1.bias", "layer1_2d.0.bn1.running_mean", "layer1_2d.0.bn1.running_var", "layer1_2d.0.conv2.weight", "layer1_2d.0.bn2.weight", "layer1_2d.0.bn2.bias", "layer1_2d.0.bn2.running_mean", "layer1_2d.0.bn2.running_var", "layer1_2d.1.conv1.weight", "layer1_2d.1.bn1.weight", "layer1_2d.1.bn1.bias", "layer1_2d.1.bn1.running_mean", "layer1_2d.1.bn1.running_var", "layer1_2d.1.conv2.weight", "layer1_2d.1.bn2.weight", "layer1_2d.1.bn2.bias", "layer1_2d.1.bn2.running_mean", "layer1_2d.1.bn2.running_var", "layer1_2d.2.conv1.weight", "layer1_2d.2.bn1.weight", "layer1_2d.2.bn1.bias", "layer1_2d.2.bn1.running_mean", "layer1_2d.2.bn1.running_var", "layer1_2d.2.conv2.weight", "layer1_2d.2.bn2.weight", "layer1_2d.2.bn2.bias", "layer1_2d.2.bn2.running_mean", "layer1_2d.2.bn2.running_var", "layer2_2d.0.conv1.weight", "layer2_2d.0.bn1.weight", "layer2_2d.0.bn1.bias", "layer2_2d.0.bn1.running_mean", "layer2_2d.0.bn1.running_var", "layer2_2d.0.conv2.weight", "layer2_2d.0.bn2.weight", "layer2_2d.0.bn2.bias", "layer2_2d.0.bn2.running_mean", "layer2_2d.0.bn2.running_var", "layer2_2d.0.downsample.0.weight", "layer2_2d.0.downsample.1.weight", "layer2_2d.0.downsample.1.bias", "layer2_2d.0.downsample.1.running_mean", "layer2_2d.0.downsample.1.running_var", "layer2_2d.1.conv1.weight", "layer2_2d.1.bn1.weight", "layer2_2d.1.bn1.bias", "layer2_2d.1.bn1.running_mean", "layer2_2d.1.bn1.running_var", "layer2_2d.1.conv2.weight", "layer2_2d.1.bn2.weight", "layer2_2d.1.bn2.bias", "layer2_2d.1.bn2.running_mean", "layer2_2d.1.bn2.running_var", "layer2_2d.2.conv1.weight", "layer2_2d.2.bn1.weight", "layer2_2d.2.bn1.bias", "layer2_2d.2.bn1.running_mean", "layer2_2d.2.bn1.running_var", "layer2_2d.2.conv2.weight", "layer2_2d.2.bn2.weight", "layer2_2d.2.bn2.bias", 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"layer4_3d.3.0.downsample.1.bn.weight", "layer4_3d.3.0.downsample.1.bn.bias", "layer4_3d.3.0.downsample.1.bn.running_mean", "layer4_3d.3.0.downsample.1.bn.running_var", "layer4_3d.3.1.conv1.kernel", "layer4_3d.3.1.norm1.bn.weight", "layer4_3d.3.1.norm1.bn.bias", "layer4_3d.3.1.norm1.bn.running_mean", "layer4_3d.3.1.norm1.bn.running_var", "layer4_3d.3.1.conv2.kernel", "layer4_3d.3.1.norm2.bn.weight", "layer4_3d.3.1.norm2.bn.bias", "layer4_3d.3.1.norm2.bn.running_mean", "layer4_3d.3.1.norm2.bn.running_var", "layer5_3d.0.kernel", "layer5_3d.1.bn.weight", "layer5_3d.1.bn.bias", "layer5_3d.1.bn.running_mean", "layer5_3d.1.bn.running_var", "layer6_3d.0.0.conv1.kernel", "layer6_3d.0.0.norm1.bn.weight", "layer6_3d.0.0.norm1.bn.bias", "layer6_3d.0.0.norm1.bn.running_mean", "layer6_3d.0.0.norm1.bn.running_var", "layer6_3d.0.0.conv2.kernel", "layer6_3d.0.0.norm2.bn.weight", "layer6_3d.0.0.norm2.bn.bias", "layer6_3d.0.0.norm2.bn.running_mean", "layer6_3d.0.0.norm2.bn.running_var", "layer6_3d.0.0.downsample.0.kernel", "layer6_3d.0.0.downsample.1.bn.weight", "layer6_3d.0.0.downsample.1.bn.bias", "layer6_3d.0.0.downsample.1.bn.running_mean", "layer6_3d.0.0.downsample.1.bn.running_var", "layer6_3d.0.1.conv1.kernel", "layer6_3d.0.1.norm1.bn.weight", "layer6_3d.0.1.norm1.bn.bias", "layer6_3d.0.1.norm1.bn.running_mean", "layer6_3d.0.1.norm1.bn.running_var", "layer6_3d.0.1.conv2.kernel", "layer6_3d.0.1.norm2.bn.weight", "layer6_3d.0.1.norm2.bn.bias", "layer6_3d.0.1.norm2.bn.running_mean", "layer6_3d.0.1.norm2.bn.running_var", "layer6_3d.1.kernel", "layer6_3d.2.bn.weight", "layer6_3d.2.bn.bias", "layer6_3d.2.bn.running_mean", "layer6_3d.2.bn.running_var", "layer7_3d.0.0.conv1.kernel", "layer7_3d.0.0.norm1.bn.weight", "layer7_3d.0.0.norm1.bn.bias", "layer7_3d.0.0.norm1.bn.running_mean", "layer7_3d.0.0.norm1.bn.running_var", "layer7_3d.0.0.conv2.kernel", "layer7_3d.0.0.norm2.bn.weight", "layer7_3d.0.0.norm2.bn.bias", "layer7_3d.0.0.norm2.bn.running_mean", "layer7_3d.0.0.norm2.bn.running_var", "layer7_3d.0.0.downsample.0.kernel", "layer7_3d.0.0.downsample.1.bn.weight", "layer7_3d.0.0.downsample.1.bn.bias", "layer7_3d.0.0.downsample.1.bn.running_mean", "layer7_3d.0.0.downsample.1.bn.running_var", "layer7_3d.0.1.conv1.kernel", "layer7_3d.0.1.norm1.bn.weight", "layer7_3d.0.1.norm1.bn.bias", "layer7_3d.0.1.norm1.bn.running_mean", "layer7_3d.0.1.norm1.bn.running_var", "layer7_3d.0.1.conv2.kernel", "layer7_3d.0.1.norm2.bn.weight", "layer7_3d.0.1.norm2.bn.bias", "layer7_3d.0.1.norm2.bn.running_mean", "layer7_3d.0.1.norm2.bn.running_var", "layer7_3d.1.kernel", "layer7_3d.2.bn.weight", "layer7_3d.2.bn.bias", "layer7_3d.2.bn.running_mean", "layer7_3d.2.bn.running_var", "layer8_3d.0.0.conv1.kernel", "layer8_3d.0.0.norm1.bn.weight", "layer8_3d.0.0.norm1.bn.bias", "layer8_3d.0.0.norm1.bn.running_mean", "layer8_3d.0.0.norm1.bn.running_var", "layer8_3d.0.0.conv2.kernel", "layer8_3d.0.0.norm2.bn.weight", "layer8_3d.0.0.norm2.bn.bias", "layer8_3d.0.0.norm2.bn.running_mean", "layer8_3d.0.0.norm2.bn.running_var", "layer8_3d.0.0.downsample.0.kernel", "layer8_3d.0.0.downsample.1.bn.weight", "layer8_3d.0.0.downsample.1.bn.bias", "layer8_3d.0.0.downsample.1.bn.running_mean", "layer8_3d.0.0.downsample.1.bn.running_var", "layer8_3d.0.1.conv1.kernel", "layer8_3d.0.1.norm1.bn.weight", "layer8_3d.0.1.norm1.bn.bias", "layer8_3d.0.1.norm1.bn.running_mean", "layer8_3d.0.1.norm1.bn.running_var", "layer8_3d.0.1.conv2.kernel", "layer8_3d.0.1.norm2.bn.weight", "layer8_3d.0.1.norm2.bn.bias", "layer8_3d.0.1.norm2.bn.running_mean", "layer8_3d.0.1.norm2.bn.running_var", "layer8_3d.1.kernel", "layer8_3d.2.bn.weight", "layer8_3d.2.bn.bias", "layer8_3d.2.bn.running_mean", "layer8_3d.2.bn.running_var", "layer9_3d.0.conv1.kernel", "layer9_3d.0.norm1.bn.weight", "layer9_3d.0.norm1.bn.bias", "layer9_3d.0.norm1.bn.running_mean", "layer9_3d.0.norm1.bn.running_var", "layer9_3d.0.conv2.kernel", "layer9_3d.0.norm2.bn.weight", "layer9_3d.0.norm2.bn.bias", "layer9_3d.0.norm2.bn.running_mean", "layer9_3d.0.norm2.bn.running_var", "layer9_3d.0.downsample.0.kernel", "layer9_3d.0.downsample.1.bn.weight", "layer9_3d.0.downsample.1.bn.bias", "layer9_3d.0.downsample.1.bn.running_mean", "layer9_3d.0.downsample.1.bn.running_var", "layer9_3d.1.conv1.kernel", "layer9_3d.1.norm1.bn.weight", "layer9_3d.1.norm1.bn.bias", "layer9_3d.1.norm1.bn.running_mean", "layer9_3d.1.norm1.bn.running_var", "layer9_3d.1.conv2.kernel", "layer9_3d.1.norm2.bn.weight", "layer9_3d.1.norm2.bn.bias", "layer9_3d.1.norm2.bn.running_mean", "layer9_3d.1.norm2.bn.running_var", "cls_3d.kernel", "cls_3d.bias", "linker_p2.view_fusion.0.kernel", "linker_p2.view_fusion.1.bn.weight", "linker_p2.view_fusion.1.bn.bias", "linker_p2.view_fusion.1.bn.running_mean", "linker_p2.view_fusion.1.bn.running_var", "linker_p2.view_fusion.3.kernel", "linker_p2.view_fusion.4.bn.weight", "linker_p2.view_fusion.4.bn.bias", "linker_p2.view_fusion.4.bn.running_mean", "linker_p2.view_fusion.4.bn.running_var", "linker_p2.fuseTo3d.0.kernel", "linker_p2.fuseTo3d.1.bn.weight", "linker_p2.fuseTo3d.1.bn.bias", "linker_p2.fuseTo3d.1.bn.running_mean", "linker_p2.fuseTo3d.1.bn.running_var", "linker_p2.view_sep.0.kernel", "linker_p2.view_sep.1.bn.weight", "linker_p2.view_sep.1.bn.bias", "linker_p2.view_sep.1.bn.running_mean", "linker_p2.view_sep.1.bn.running_var", "linker_p2.fuseTo2d.0.weight", "linker_p2.fuseTo2d.1.weight", "linker_p2.fuseTo2d.1.bias", "linker_p2.fuseTo2d.1.running_mean", "linker_p2.fuseTo2d.1.running_var", "linker_p3.view_fusion.0.kernel", "linker_p3.view_fusion.1.bn.weight", "linker_p3.view_fusion.1.bn.bias", "linker_p3.view_fusion.1.bn.running_mean", "linker_p3.view_fusion.1.bn.running_var", "linker_p3.view_fusion.3.kernel", "linker_p3.view_fusion.4.bn.weight", "linker_p3.view_fusion.4.bn.bias", "linker_p3.view_fusion.4.bn.running_mean", "linker_p3.view_fusion.4.bn.running_var", "linker_p3.fuseTo3d.0.kernel", "linker_p3.fuseTo3d.1.bn.weight", "linker_p3.fuseTo3d.1.bn.bias", "linker_p3.fuseTo3d.1.bn.running_mean", "linker_p3.fuseTo3d.1.bn.running_var", "linker_p3.view_sep.0.kernel", "linker_p3.view_sep.1.bn.weight", "linker_p3.view_sep.1.bn.bias", "linker_p3.view_sep.1.bn.running_mean", "linker_p3.view_sep.1.bn.running_var", "linker_p3.fuseTo2d.0.weight", "linker_p3.fuseTo2d.1.weight", "linker_p3.fuseTo2d.1.bias", "linker_p3.fuseTo2d.1.running_mean", "linker_p3.fuseTo2d.1.running_var", "linker_p4.view_fusion.0.kernel", "linker_p4.view_fusion.1.bn.weight", "linker_p4.view_fusion.1.bn.bias", "linker_p4.view_fusion.1.bn.running_mean", "linker_p4.view_fusion.1.bn.running_var", "linker_p4.view_fusion.3.kernel", "linker_p4.view_fusion.4.bn.weight", "linker_p4.view_fusion.4.bn.bias", "linker_p4.view_fusion.4.bn.running_mean", "linker_p4.view_fusion.4.bn.running_var", "linker_p4.fuseTo3d.0.kernel", "linker_p4.fuseTo3d.1.bn.weight", "linker_p4.fuseTo3d.1.bn.bias", "linker_p4.fuseTo3d.1.bn.running_mean", "linker_p4.fuseTo3d.1.bn.running_var", "linker_p4.view_sep.0.kernel", "linker_p4.view_sep.1.bn.weight", "linker_p4.view_sep.1.bn.bias", "linker_p4.view_sep.1.bn.running_mean", "linker_p4.view_sep.1.bn.running_var", "linker_p4.fuseTo2d.0.weight", "linker_p4.fuseTo2d.1.weight", "linker_p4.fuseTo2d.1.bias", "linker_p4.fuseTo2d.1.running_mean", "linker_p4.fuseTo2d.1.running_var", "linker_p5.view_fusion.0.kernel", "linker_p5.view_fusion.1.bn.weight", "linker_p5.view_fusion.1.bn.bias", "linker_p5.view_fusion.1.bn.running_mean", "linker_p5.view_fusion.1.bn.running_var", "linker_p5.view_fusion.3.kernel", "linker_p5.view_fusion.4.bn.weight", "linker_p5.view_fusion.4.bn.bias", "linker_p5.view_fusion.4.bn.running_mean", "linker_p5.view_fusion.4.bn.running_var", "linker_p5.fuseTo3d.0.kernel", "linker_p5.fuseTo3d.1.bn.weight", "linker_p5.fuseTo3d.1.bn.bias", "linker_p5.fuseTo3d.1.bn.running_mean", "linker_p5.fuseTo3d.1.bn.running_var", "linker_p5.view_sep.0.kernel", "linker_p5.view_sep.1.bn.weight", "linker_p5.view_sep.1.bn.bias", "linker_p5.view_sep.1.bn.running_mean", "linker_p5.view_sep.1.bn.running_var", "linker_p5.fuseTo2d.0.weight", "linker_p5.fuseTo2d.1.weight", "linker_p5.fuseTo2d.1.bias", "linker_p5.fuseTo2d.1.running_mean", "linker_p5.fuseTo2d.1.running_var". 
        Unexpected key(s) in state_dict: "module.layer0_2d.0.weight", "module.layer0_2d.1.weight", "module.layer0_2d.1.bias", "module.layer0_2d.1.running_mean", "module.layer0_2d.1.running_var", "module.layer0_2d.1.num_batches_tracked", "module.layer1_2d.0.conv1.weight", "module.layer1_2d.0.bn1.weight", "module.layer1_2d.0.bn1.bias", "module.layer1_2d.0.bn1.running_mean", "module.layer1_2d.0.bn1.running_var", "module.layer1_2d.0.bn1.num_batches_tracked", "module.layer1_2d.0.conv2.weight", "module.layer1_2d.0.bn2.weight", "module.layer1_2d.0.bn2.bias", "module.layer1_2d.0.bn2.running_mean", "module.layer1_2d.0.bn2.running_var", "module.layer1_2d.0.bn2.num_batches_tracked", "module.layer1_2d.1.conv1.weight", "module.layer1_2d.1.bn1.weight", "module.layer1_2d.1.bn1.bias", "module.layer1_2d.1.bn1.running_mean", "module.layer1_2d.1.bn1.running_var", "module.layer1_2d.1.bn1.num_batches_tracked", "module.layer1_2d.1.conv2.weight", "module.layer1_2d.1.bn2.weight", "module.layer1_2d.1.bn2.bias", "module.layer1_2d.1.bn2.running_mean", "module.layer1_2d.1.bn2.running_var", "module.layer1_2d.1.bn2.num_batches_tracked", "module.layer1_2d.2.conv1.weight", "module.layer1_2d.2.bn1.weight", "module.layer1_2d.2.bn1.bias", "module.layer1_2d.2.bn1.running_mean", "module.layer1_2d.2.bn1.running_var", "module.layer1_2d.2.bn1.num_batches_tracked", "module.layer1_2d.2.conv2.weight", "module.layer1_2d.2.bn2.weight", "module.layer1_2d.2.bn2.bias", "module.layer1_2d.2.bn2.running_mean", "module.layer1_2d.2.bn2.running_var", "module.layer1_2d.2.bn2.num_batches_tracked", "module.layer2_2d.0.conv1.weight", "module.layer2_2d.0.bn1.weight", "module.layer2_2d.0.bn1.bias", "module.layer2_2d.0.bn1.running_mean", "module.layer2_2d.0.bn1.running_var", "module.layer2_2d.0.bn1.num_batches_tracked", "module.layer2_2d.0.conv2.weight", "module.layer2_2d.0.bn2.weight", "module.layer2_2d.0.bn2.bias", "module.layer2_2d.0.bn2.running_mean", "module.layer2_2d.0.bn2.running_var", "module.layer2_2d.0.bn2.num_batches_tracked", "module.layer2_2d.0.downsample.0.weight", "module.layer2_2d.0.downsample.1.weight", "module.layer2_2d.0.downsample.1.bias", "module.layer2_2d.0.downsample.1.running_mean", "module.layer2_2d.0.downsample.1.running_var", "module.layer2_2d.0.downsample.1.num_batches_tracked", "module.layer2_2d.1.conv1.weight", "module.layer2_2d.1.bn1.weight", "module.layer2_2d.1.bn1.bias", "module.layer2_2d.1.bn1.running_mean", "module.layer2_2d.1.bn1.running_var", "module.layer2_2d.1.bn1.num_batches_tracked", "module.layer2_2d.1.conv2.weight", "module.layer2_2d.1.bn2.weight", "module.layer2_2d.1.bn2.bias", "module.layer2_2d.1.bn2.running_mean", "module.layer2_2d.1.bn2.running_var", "module.layer2_2d.1.bn2.num_batches_tracked", "module.layer2_2d.2.conv1.weight", "module.layer2_2d.2.bn1.weight", "module.layer2_2d.2.bn1.bias", "module.layer2_2d.2.bn1.running_mean", "module.layer2_2d.2.bn1.running_var", "module.layer2_2d.2.bn1.num_batches_tracked", "module.layer2_2d.2.conv2.weight", "module.layer2_2d.2.bn2.weight", "module.layer2_2d.2.bn2.bias", "module.layer2_2d.2.bn2.running_mean", "module.layer2_2d.2.bn2.running_var", "module.layer2_2d.2.bn2.num_batches_tracked", "module.layer2_2d.3.conv1.weight", "module.layer2_2d.3.bn1.weight", "module.layer2_2d.3.bn1.bias", "module.layer2_2d.3.bn1.running_mean", "module.layer2_2d.3.bn1.running_var", "module.layer2_2d.3.bn1.num_batches_tracked", "module.layer2_2d.3.conv2.weight", "module.layer2_2d.3.bn2.weight", "module.layer2_2d.3.bn2.bias", "module.layer2_2d.3.bn2.running_mean", "module.layer2_2d.3.bn2.running_var", "module.layer2_2d.3.bn2.num_batches_tracked", "module.layer3_2d.0.conv1.weight", "module.layer3_2d.0.bn1.weight", "module.layer3_2d.0.bn1.bias", "module.layer3_2d.0.bn1.running_mean", "module.layer3_2d.0.bn1.running_var", "module.layer3_2d.0.bn1.num_batches_tracked", "module.layer3_2d.0.conv2.weight", "module.layer3_2d.0.bn2.weight", "module.layer3_2d.0.bn2.bias", 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"module.linker_p5.fuseTo2d.1.running_mean", "module.linker_p5.fuseTo2d.1.running_var", "module.linker_p5.fuseTo2d.1.num_batches_tracked".

I've already solved too many problems to get this error.
I sure even if i solve this error, another error will occur.
My conclusion is that you have not tested this on other environment at all.
Please test your opensource on other environment.
And give more details on README.md about how to run this.

Thank you.
@demul
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demul commented Aug 31, 2021

I solve this with replacement

model.load_state_dict(checkpoint['state_dict'], strict=True)

to

state_dict = checkpoint['state_dict']
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
    name = k[7:] # remove `module.`
    new_state_dict[name] = v
model.load_state_dict(new_state_dict, strict=True)

But still hard to run.
the variable gts is empty.
maybe this is related to data structure.
please add more detail on READEME.md.

@demul
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demul commented Aug 31, 2021

I solve above from [https://github.com//issues/3]
but still infinite errors occur and occur

a errors occur when i run scanNetCross.py
label = self.remapper[label]

some pixels of label bigger than 256 (about 300+-)

@demul
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demul commented Sep 10, 2021

I managed to run it. but the performance is unreproducible

I validate it on 107 scenes.

scene0011_00  scene0046_01  scene0081_02  scene0095_01  scene0146_02  scene0193_00  scene0221_01  scene0257_00  scene0314_00
scene0011_01  scene0046_02  scene0084_00  scene0100_00  scene0149_00  scene0193_01  scene0222_00  scene0277_00  scene0316_00
scene0015_00  scene0050_00  scene0084_01  scene0100_01  scene0153_00  scene0196_00  scene0222_01  scene0277_01  scene0328_00
scene0019_00  scene0050_01  scene0084_02  scene0100_02  scene0153_01  scene0203_00  scene0231_00  scene0277_02  scene0329_00
scene0019_01  scene0050_02  scene0086_00  scene0131_00  scene0164_00  scene0203_01  scene0231_01  scene0278_00  scene0329_01
scene0025_00  scene0063_00  scene0086_01  scene0131_01  scene0164_01  scene0203_02  scene0231_02  scene0278_01  scene0329_02
scene0025_01  scene0064_00  scene0086_02  scene0131_02  scene0164_02  scene0207_00  scene0246_00  scene0300_00  scene0702_00
scene0025_02  scene0064_01  scene0088_00  scene0139_00  scene0164_03  scene0207_01  scene0249_00  scene0300_01  scene0702_01
scene0030_00  scene0077_00  scene0088_01  scene0144_00  scene0169_00  scene0207_02  scene0251_00  scene0304_00  scene0702_02
scene0030_01  scene0077_01  scene0088_02  scene0144_01  scene0169_01  scene0208_00  scene0256_00  scene0307_00  scene0704_00
scene0030_02  scene0081_00  scene0088_03  scene0146_00  scene0187_00  scene0217_00  scene0256_01  scene0307_01  scene0704_01
scene0046_00  scene0081_01  scene0095_00  scene0146_01  scene0187_01  scene0221_00  scene0256_02  scene0307_02

and result

/home/demul/BPNet/data (val) loading done (107)! 
100%|██████████| 7/7 [00:30<00:00,  4.42s/it]
[wall] ::   (0.7452778867952798, 3501712, 4698532)
[floor] ::   (0.9381352278432281, 3414012, 3639147)
[cabinet] ::   (0.5902607347308708, 432438, 732622)
[bed] ::   (0.8476892535065628, 235277, 277551)
[chair] ::   (0.857194117308183, 1041568, 1215090)
[sofa] ::   (0.6979579136185868, 228792, 327802)
[table] ::   (0.640205165590502, 467191, 729752)
[door] ::   (0.4054373240185745, 373514, 921262)
[window] ::   (0.3077943554745277, 236190, 767363)
[bookshelf] ::   (0.7095073341660495, 513496, 723736)
[picture] ::   (0.12283347614967152, 10751, 87525)
[counter] ::   (0.6769498095301745, 101292, 149630)
[desk] ::   (0.48430746577472567, 152614, 315118)
[curtain] ::   (0.541651814972807, 141324, 260913)
[refrigerator] ::   (0.3004455666122085, 40458, 134660)
[shower curtain] ::   (0.5204955926770635, 33776, 64892)
[toilet] ::   (0.8032820407954604, 47139, 58683)
[sink] ::   (0.6243855632617332, 37091, 59404)
[bathtub] ::   (0.8130730673621582, 39904, 49078)
[otherfurniture] ::   (0.42398253065499136, 233187, 549992)
20
2D:  0.65528333 , 3D:  0.6025433120421678
100%|██████████| 7/7 [00:30<00:00,  4.36s/it]
[wall] ::   (0.753824090058794, 3562691, 4726157)
[floor] ::   (0.9374754986748367, 3417296, 3645211)
[cabinet] ::   (0.595297365987007, 433792, 728698)
[bed] ::   (0.8566008693484223, 237072, 276759)
[chair] ::   (0.8586215446660515, 1045777, 1217972)
[sofa] ::   (0.7263918948590069, 231581, 318810)
[table] ::   (0.6743627940108149, 477140, 707542)
[door] ::   (0.4140090609799339, 377137, 910939)
[window] ::   (0.3037398237470277, 224307, 738484)
[bookshelf] ::   (0.7243247486336577, 516734, 713401)
[picture] ::   (0.14621613561810382, 12748, 87186)
[counter] ::   (0.6748340821417464, 101987, 151129)
[desk] ::   (0.552277208755421, 167208, 302761)
[curtain] ::   (0.5004918013816726, 129753, 259251)
[refrigerator] ::   (0.30976379282550454, 40352, 130267)
[shower curtain] ::   (0.5062997294398284, 31625, 62463)
[toilet] ::   (0.7887140009622654, 45900, 58196)
[sink] ::   (0.6428036308517867, 36895, 57397)
[bathtub] ::   (0.832785613684146, 40799, 48991)
[otherfurniture] ::   (0.4263913906978892, 230795, 541275)
20
2D:  0.6577472 , 3D:  0.6112612538661958
100%|██████████| 7/7 [00:30<00:00,  4.35s/it]
[wall] ::   (0.7616589356183411, 3590707, 4714324)
[floor] ::   (0.9372402990614445, 3419002, 3647946)
[cabinet] ::   (0.6001309963150406, 436138, 726738)
[bed] ::   (0.8537604861807767, 234890, 275124)
[chair] ::   (0.8632780528770058, 1052645, 1219358)
[sofa] ::   (0.7212153534846513, 229891, 318755)
[table] ::   (0.6807141856208855, 480913, 706483)
[door] ::   (0.4307670355420149, 386420, 897051)
[window] ::   (0.31437467608742325, 229900, 731293)
[bookshelf] ::   (0.736560064271519, 522582, 709490)
[picture] ::   (0.14872729997751855, 11908, 80066)
[counter] ::   (0.6843765129915541, 101775, 148712)
[desk] ::   (0.5599997299742125, 165910, 296268)
[curtain] ::   (0.4771897951294936, 123449, 258700)
[refrigerator] ::   (0.26318885168763007, 34902, 132612)
[shower curtain] ::   (0.5509904387939153, 34519, 62649)
[toilet] ::   (0.7977250077322244, 46426, 58198)
[sink] ::   (0.6471662656527161, 36849, 56939)
[bathtub] ::   (0.8305841574064634, 40736, 49045)
[otherfurniture] ::   (0.4242314002818459, 230896, 544269)
20
2D:  0.6497067 , 3D:  0.6141939772343339
100%|██████████| 7/7 [00:31<00:00,  4.45s/it]
[wall] ::   (0.7605280962800414, 3602633, 4737015)
[floor] ::   (0.937261463913791, 3420143, 3649081)
[cabinet] ::   (0.6054500151427549, 439811, 726420)
[bed] ::   (0.8517522172727636, 234616, 275451)
[chair] ::   (0.8648979732683062, 1052695, 1217132)
[sofa] ::   (0.7389227133412216, 231687, 313547)
[table] ::   (0.678797407840221, 479214, 705975)
[door] ::   (0.4127130380459668, 368639, 893209)
[window] ::   (0.3133837230700027, 229903, 733615)
[bookshelf] ::   (0.7388048937758773, 522471, 707184)
[picture] ::   (0.15806315358326828, 12349, 78127)
[counter] ::   (0.6708552301255231, 100209, 149375)
[desk] ::   (0.5558075310433801, 165435, 297648)
[curtain] ::   (0.4545651946319655, 117231, 257897)
[refrigerator] ::   (0.2646705693724598, 35491, 134095)
[shower curtain] ::   (0.5296666188815088, 33253, 62781)
[toilet] ::   (0.794854620708606, 46282, 58227)
[sink] ::   (0.6463472527472528, 36761, 56875)
[bathtub] ::   (0.8271665142741034, 40709, 49215)
[otherfurniture] ::   (0.43721379587642684, 234873, 537204)
20
2D:  0.6372739 , 3D:  0.612086101159772
100%|██████████| 7/7 [00:29<00:00,  4.21s/it]
[wall] ::   (0.7638308886971528, 3607516, 4722925)
[floor] ::   (0.9372795557026706, 3420553, 3649448)
[cabinet] ::   (0.6176700449967563, 446539, 722941)
[bed] ::   (0.849855078772577, 234277, 275667)
[chair] ::   (0.8677596698124846, 1055017, 1215794)
[sofa] ::   (0.7448607182486996, 232258, 311814)
[table] ::   (0.6774819748784795, 479872, 708317)
[door] ::   (0.4236289498455935, 378479, 893421)
[window] ::   (0.3136596980001531, 229491, 731656)
[bookshelf] ::   (0.7378604782793264, 521446, 706700)
[picture] ::   (0.16057245806795403, 12656, 78818)
[counter] ::   (0.6730823377981577, 100401, 149166)
[desk] ::   (0.5593603226034857, 165899, 296587)
[curtain] ::   (0.45791558361183315, 118384, 258528)
[refrigerator] ::   (0.275235531628533, 36401, 132254)
[shower curtain] ::   (0.5351092678258096, 33546, 62690)
[toilet] ::   (0.7892619526535665, 45909, 58167)
[sink] ::   (0.6513286491626604, 36987, 56787)
[bathtub] ::   (0.8331803064618147, 40835, 49011)
[otherfurniture] ::   (0.43639811455677485, 233123, 534198)
20
2D:  0.64470524 , 3D:  0.6152665790802242
100%|██████████| 7/7 [00:30<00:00,  4.33s/it]
[wall] ::   (0.7631373034349037, 3613462, 4735009)
[floor] ::   (0.9371272288880713, 3421012, 3650531)
[cabinet] ::   (0.612440375654011, 445276, 727052)
[bed] ::   (0.8502319344785099, 234613, 275940)
[chair] ::   (0.8656678567194066, 1051364, 1214512)
[sofa] ::   (0.7417245292454463, 231378, 311946)
[table] ::   (0.6790824656575998, 479566, 706197)
[door] ::   (0.41753395065201004, 371100, 888790)
[window] ::   (0.3130792055985556, 229591, 733332)
[bookshelf] ::   (0.7414005306395356, 522267, 704433)
[picture] ::   (0.15281717631750164, 11744, 76850)
[counter] ::   (0.6835792235189116, 101100, 147898)
[desk] ::   (0.5635564250272462, 166505, 295454)
[curtain] ::   (0.4526925569105533, 116672, 257729)
[refrigerator] ::   (0.29005847953216374, 38440, 132525)
[shower curtain] ::   (0.5285979894746794, 33548, 63466)
[toilet] ::   (0.787932427091031, 45849, 58189)
[sink] ::   (0.6556163899682091, 37121, 56620)
[bathtub] ::   (0.8290734173087114, 40676, 49062)
[otherfurniture] ::   (0.4353729794414891, 232844, 534815)
20
2D:  0.6439765 , 3D:  0.6150361222779274
100%|██████████| 7/7 [00:30<00:00,  4.38s/it]
[wall] ::   (0.7628349588527515, 3608738, 4730693)
[floor] ::   (0.9372270927772244, 3421355, 3650508)
[cabinet] ::   (0.6088113873940912, 445074, 731054)
[bed] ::   (0.8504894044037323, 234343, 275539)
[chair] ::   (0.8614707748573986, 1046774, 1215101)
[sofa] ::   (0.7370722783660351, 232079, 314866)
[table] ::   (0.6828270524547215, 483221, 707677)
[door] ::   (0.41985369985716925, 372731, 887764)
[window] ::   (0.32025239983035153, 234835, 733281)
[bookshelf] ::   (0.7382899218763239, 522789, 708108)
[picture] ::   (0.15021211962499345, 11472, 76372)
[counter] ::   (0.687299785085763, 101697, 147966)
[desk] ::   (0.5621825431809089, 165800, 294922)
[curtain] ::   (0.444807272417016, 114694, 257851)
[refrigerator] ::   (0.28092721049283614, 37097, 132052)
[shower curtain] ::   (0.5201806247381406, 33522, 64443)
[toilet] ::   (0.7835601355652279, 45546, 58127)
[sink] ::   (0.6536913462556933, 37029, 56646)
[bathtub] ::   (0.8250249150853215, 40564, 49167)
[otherfurniture] ::   (0.4381730412179725, 232460, 530521)
20
2D:  0.64594394 , 3D:  0.6132593982166835

what's wrong?

@wbhu
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wbhu commented Sep 18, 2021

Hi,

Please check our test log here. The test log is tested with the provided pretrained model on 4 GPUs, if you use one GPU please modify the test_repeat number to be 4 times as original one.

@wbhu wbhu closed this as completed Sep 22, 2021
@demul
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demul commented Sep 27, 2021

Thanks for reply.
The performance on paper looks like soft-voted one, right?

@wbhu wbhu reopened this Sep 28, 2021
@wbhu
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wbhu commented Sep 28, 2021

The repeated process is borrowed from SparseConvNet. The reason for adopting this strategy is that the input data to the 3D sub-network is voxelized while we need per-point predictions for the benchmark submission. Directly propagating voxel labels to points may lead to some errors, so we repeat the process to take more points into account during the voxelization process.

@demul
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demul commented Sep 29, 2021

I could find rotation, translation augmentation from 'dataset/voxelizer.py' and the arguments transmitted from 'dataset/scanNet3D.py'(parent of 'dataset/scanNetCross,py')

[rotation augmentation + translation augmentation + soft voting]

That was why this model have coarser resolution than 5cm. Now i get it. Thanks!

But not i get another curiosity about performance comparison.
Generally, ensemble technique like soft-voting improve model's performance.
Maybe other models on benchmark can be improved from this technique.(even, raw point cloud base model can be improved from soft-voting)

I'm not talking you have done unfair comparison.
It's just curiosity about 'What will happen if All models are fully ensembled, and compared?'

@wbhu
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wbhu commented Sep 29, 2021

This repeat testing trick is borrowed from SparseConvNet, the SpaseConv-based competitors also adopt this trick, e.g., our baseline (MinkowskiUNet). So we think the comparison is fair.

@wbhu wbhu closed this as completed Oct 8, 2021
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