Added normals to prediction DataObject
This commit is contained in:
Si11ium
parent
4e1fcdfd43
commit
8eb165f76c
@ -46,8 +46,9 @@ class CustomShapeNet(InMemoryDataset):
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def download(self):
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dir_count = len([name for name in os.listdir(self.raw_dir) if os.path.isdir(os.path.join(self.raw_dir, name))])
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print(f'{dir_count} folders have been found....')
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if dir_count:
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print(f'{dir_count} folders have been found....')
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return dir_count
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raise IOError("No raw pointclouds have been found.")
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@ -179,6 +180,7 @@ class ShapeNetPartSegDataset(Dataset):
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Resample raw point cloud to fixed number of points.
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Map raw label from range [1, N] to [0, N-1].
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"""
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def __init__(self, root_dir, npoints=1024, mode='train', **kwargs):
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super(ShapeNetPartSegDataset, self).__init__()
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self.mode = mode
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@ -191,7 +193,8 @@ class ShapeNetPartSegDataset(Dataset):
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# Resample to fixed number of points
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try:
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choice = np.random.choice(data.pos.shape[0], self.npoints, replace=True)
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npoints = self.npoints if self.mode != 'predict' else data.pos.shape[0]
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choice = np.random.choice(data.pos.shape[0], npoints, replace=False)
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except ValueError:
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choice = []
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@ -204,7 +207,7 @@ class ShapeNetPartSegDataset(Dataset):
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'labels': labels # torch.Tensor (n,)
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}
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if self.mode == 'predict':
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normals = data.normals[choice]
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normals = data.normals[choice, :]
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sample.update(normals=normals)
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return sample
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5
main.py
5
main.py
@ -33,10 +33,10 @@ parser.add_argument('--npoints', type=int, default=1024, help='resample points n
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parser.add_argument('--model', type=str, default='', help='model path')
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parser.add_argument('--nepoch', type=int, default=250, help='number of epochs to train for')
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parser.add_argument('--outf', type=str, default='checkpoint', help='output folder')
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parser.add_argument('--labels_within', type=strtobool, default=False, help='defines the label location')
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parser.add_argument('--labels_within', type=strtobool, default=True, help='defines the label location')
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parser.add_argument('--batch_size', type=int, default=8, help='input batch size')
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parser.add_argument('--test_per_batches', type=int, default=1000, help='run a test batch per training batches number')
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parser.add_argument('--num_workers', type=int, default=4, help='number of data loading workers')
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parser.add_argument('--num_workers', type=int, default=1, help='number of data loading workers')
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parser.add_argument('--headers', type=strtobool, default=True, help='if raw files come with headers')
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parser.add_argument('--collate_per_segment', type=strtobool, default=True, help='whether to look at pointclouds or sub')
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parser.add_argument('--has_variations', type=strtobool, default=False,
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@ -129,7 +129,6 @@ if __name__ == '__main__':
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net.train()
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# ToDo: We need different dataloader here to train the network in multiple iterations, maybe move the loop down
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# for dataloader in ...
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for batch_idx, sample in enumerate(dataLoader):
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# points: (batch_size, n, 3)
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# labels: (batch_size, n)
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@ -8,7 +8,7 @@ from torch_geometric.utils.num_nodes import maybe_num_nodes
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from torch_geometric.data.data import Data
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from torch_scatter import scatter_add, scatter_max
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GLOBAL_POINT_FEATURES = 3
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GLOBAL_POINT_FEATURES = 6
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class PointNet2SAModule(torch.nn.Module):
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def __init__(self, sample_radio, radius, max_num_neighbors, mlp):
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200
predict/clusters.txt
Normal file
200
predict/clusters.txt
Normal file
@ -0,0 +1,200 @@
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3.000000000000000000e+00
|
||||
2 6
|
||||
5.199833234151204353e-01 1.281056722005208259e+00 6.312266667683918975e-01 -3.555141376242736823e-02 1.702250673669388707e-03 9.993663989359141686e-01
|
||||
5.866500218709309600e-01 1.347723388671874911e+00 6.312266667683918975e-01 -3.555141376242736823e-02 1.702250673669388707e-03 9.993663989359141686e-01
|
||||
1.000000000000000000e+00
|
||||
1 6
|
||||
5.866500218709309600e-01 1.414390055338541563e+00 4.978933334350585938e-01 3.555141376242736823e-02 -1.702250673669388707e-03 -9.993663989359141686e-01
|
||||
1.000000000000000000e+00
|
||||
3 6
|
||||
6.533166885375976118e-01 2.147723388671875178e+00 4.978933334350585938e-01 3.555141376242736823e-02 -1.702250673669388707e-03 -9.993663989359141686e-01
|
||||
6.533166885375976118e-01 2.281056722005208481e+00 4.978933334350585938e-01 3.555141376242736823e-02 -1.702250673669388707e-03 -9.993663989359141686e-01
|
||||
7.199833552042643747e-01 2.214390055338541607e+00 4.978933334350585938e-01 3.555141376242736823e-02 -1.702250673669388707e-03 -9.993663989359141686e-01
|
||||
3.000000000000000000e+00
|
||||
1 6
|
||||
5.199833234151204353e-01 1.281056722005208259e+00 4.978933334350585938e-01 3.555141376242736823e-02 -1.702250673669388707e-03 -9.993663989359141686e-01
|
||||
1.000000000000000000e+00
|
||||
3.000000000000000000e+00
|
||||
6 6
|
||||
3.866499900817871316e-01 1.347723388671874911e+00 4.978933334350585938e-01 3.555141376242736823e-02 -1.702250673669388707e-03 -9.993663989359141686e-01
|
||||
2.533176740010579242e-01 1.347723388671874911e+00 4.978933334350585938e-01 3.555141376242736823e-02 -1.702250673669388707e-03 -9.993663989359141686e-01
|
||||
3.199843406677246316e-01 1.414390055338541563e+00 4.978933334350585938e-01 3.555141376242736823e-02 -1.702250673669388707e-03 -9.993663989359141686e-01
|
||||
2.533176740010579242e-01 1.481056722005208437e+00 4.978933334350585938e-01 3.555141376242736823e-02 -1.702250673669388707e-03 -9.993663989359141686e-01
|
||||
2.533176740010579242e-01 1.614390055338541741e+00 4.978933334350585938e-01 3.555141376242736823e-02 -1.702250673669388707e-03 -9.993663989359141686e-01
|
||||
3.199843406677246316e-01 1.547723388671875089e+00 4.978933334350585938e-01 3.555141376242736823e-02 -1.702250673669388707e-03 -9.993663989359141686e-01
|
||||
1.000000000000000000e+00
|
||||
1 6
|
||||
1.199843327204386384e-01 1.814390055338541696e+00 5.645600001017252456e-01 -9.993677590519846055e-01 -4.875568776990416931e-04 -3.555059008941406640e-02
|
||||
1.000000000000000000e+00
|
||||
1 6
|
||||
1.199843327204386384e-01 1.414390055338541563e+00 6.312266667683918975e-01 -3.555141376242736823e-02 1.702250673669388707e-03 9.993663989359141686e-01
|
||||
3.000000000000000000e+00
|
||||
1 6
|
||||
1.199843327204386384e-01 1.281056722005208259e+00 4.978933334350585938e-01 3.555141376242736823e-02 -1.702250673669388707e-03 -9.993663989359141686e-01
|
||||
1.000000000000000000e+00
|
||||
1 6
|
||||
1.199843327204386384e-01 2.147723388671875178e+00 4.978933334350585938e-01 3.555141376242736823e-02 -1.702250673669388707e-03 -9.993663989359141686e-01
|
||||
3.000000000000000000e+00
|
||||
1 6
|
||||
1.199843327204386384e-01 1.347723388671874911e+00 5.645600001017252456e-01 -9.993677590519846055e-01 -4.875568776990416931e-04 -3.555059008941406640e-02
|
||||
1.000000000000000000e+00
|
||||
1 6
|
||||
7.199833552042643747e-01 1.614390055338541741e+00 6.312266667683918975e-01 -3.555141376242736823e-02 1.702250673669388707e-03 9.993663989359141686e-01
|
||||
1.000000000000000000e+00
|
||||
1 6
|
||||
7.199833552042643747e-01 2.281056722005208481e+00 6.312266667683918975e-01 -4.267321706433224227e-04 9.999984323090402860e-01 -1.718510726318397703e-03
|
||||
1.000000000000000000e+00
|
||||
1 6
|
||||
5.866500218709309600e-01 1.881056722005208348e+00 4.978933334350585938e-01 3.555141376242736823e-02 -1.702250673669388707e-03 -9.993663989359141686e-01
|
||||
1.000000000000000000e+00
|
||||
1 6
|
||||
1.199843327204386384e-01 1.547723388671875089e+00 4.978933334350585938e-01 3.555141376242736823e-02 -1.702250673669388707e-03 -9.993663989359141686e-01
|
||||
1.000000000000000000e+00
|
||||
1 6
|
||||
1.866510073343912723e-01 1.747723388671875044e+00 4.978933334350585938e-01 3.555141376242736823e-02 -1.702250673669388707e-03 -9.993663989359141686e-01
|
||||
1.000000000000000000e+00
|
||||
1 6
|
||||
2.533176740010579242e-01 2.081056722005208304e+00 6.312266667683918975e-01 -3.555141376242736823e-02 1.702250673669388707e-03 9.993663989359141686e-01
|
||||
3.000000000000000000e+00
|
||||
1 6
|
||||
7.199833552042643747e-01 1.281056722005208259e+00 4.978933334350585938e-01 3.555141376242736823e-02 -1.702250673669388707e-03 -9.993663989359141686e-01
|
||||
1.000000000000000000e+00
|
||||
1 6
|
||||
7.199833552042643747e-01 1.881056722005208348e+00 4.978933334350585938e-01 3.555141376242736823e-02 -1.702250673669388707e-03 -9.993663989359141686e-01
|
||||
1.000000000000000000e+00
|
||||
1 6
|
||||
1.199843327204386384e-01 1.747723388671875044e+00 6.312266667683918975e-01 -3.555141376242736823e-02 1.702250673669388707e-03 9.993663989359141686e-01
|
||||
1.000000000000000000e+00
|
||||
1 6
|
||||
6.533166885375976118e-01 2.014390055338541874e+00 4.978933334350585938e-01 3.555141376242736823e-02 -1.702250673669388707e-03 -9.993663989359141686e-01
|
||||
1.000000000000000000e+00
|
||||
1 6
|
||||
1.199843327204386384e-01 1.481056722005208437e+00 5.645600001017252456e-01 -9.993677590519846055e-01 -4.875568776990416931e-04 -3.555059008941406640e-02
|
||||
1.000000000000000000e+00
|
||||
1 6
|
||||
1.199843327204386384e-01 1.414390055338541563e+00 4.978933334350585938e-01 3.555141376242736823e-02 -1.702250673669388707e-03 -9.993663989359141686e-01
|
||||
1.000000000000000000e+00
|
||||
1 6
|
||||
2.533176740010579242e-01 1.747723388671875044e+00 6.312266667683918975e-01 -3.555141376242736823e-02 1.702250673669388707e-03 9.993663989359141686e-01
|
||||
1.000000000000000000e+00
|
||||
1 6
|
||||
5.866500218709309600e-01 2.281056722005208481e+00 6.312266667683918975e-01 -2.872351790370336957e-02 6.003545025065727403e-01 7.992180120838876523e-01
|
||||
1.000000000000000000e+00
|
||||
1 6
|
||||
3.866499900817871316e-01 2.281056722005208481e+00 5.645600001017252456e-01 -4.267321706433224227e-04 9.999984323090402860e-01 -1.718510726318397703e-03
|
||||
File diff suppressed because it is too large
Load Diff
101251
predict/pointclouds/1_pc.xyz
Normal file
101251
predict/pointclouds/1_pc.xyz
Normal file
File diff suppressed because it is too large
Load Diff
@ -243,7 +243,7 @@ sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/../') # add proj
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--npoints', type=int, default=2048, help='resample points number')
|
||||
parser.add_argument('--model', type=str, default='./checkpoint/seg_model_custom_30.pth', help='model path')
|
||||
parser.add_argument('--model', type=str, default='./checkpoint/seg_model_custom_3.pth', help='model path')
|
||||
parser.add_argument('--sample_idx', type=int, default=0, help='select a sample to segment and view result')
|
||||
parser.add_argument('--headers', type=strtobool, default=True, help='if raw files come with headers')
|
||||
parser.add_argument('--collate_per_segment', type=strtobool, default=True, help='whether to look at pointclouds or sub')
|
||||
@ -260,7 +260,7 @@ if __name__ == '__main__':
|
||||
print('Create data set ..')
|
||||
|
||||
dataset_folder = './data/raw/predict/'
|
||||
pointcloud_file = './pointclouds/0_pc.xyz'
|
||||
pointcloud_file = './pointclouds/1_pc.xyz'
|
||||
|
||||
# Load and pre-process point cloud
|
||||
pcloud = pc.read_pointcloud(pointcloud_file)
|
||||
@ -304,7 +304,7 @@ if __name__ == '__main__':
|
||||
mode='predict',
|
||||
root_dir='data',
|
||||
npoints=opt.npoints,
|
||||
refresh=False,
|
||||
refresh=True,
|
||||
collate_per_segment=opt.collate_per_segment,
|
||||
has_variations=opt.has_variations,
|
||||
headers=opt.headers
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user