Merge remote-tracking branch 'origin/master'
# Conflicts: # predict/predict.py
This commit is contained in:
Markus Friedrich
@@ -24,13 +24,15 @@ class CustomShapeNet(InMemoryDataset):
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modes = {key: val for val, key in enumerate(['train', 'test', 'predict'])}
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def __init__(self, root_dir, collate_per_segment=True, mode='train', transform=None, pre_filter=None,
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pre_transform=None, headers=True, has_variations=False, refresh=False, labels_within=False):
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pre_transform=None, headers=True, has_variations=False, refresh=False, labels_within=False,
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with_normals=False):
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assert mode in self.modes.keys(), f'"mode" must be one of {self.modes.keys()}'
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assert not (collate_per_segment and has_variations), 'Either use each element or pointclouds - with variations'
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#Set the Dataset Parameters
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self.has_headers, self.has_variations, self.labels_within = headers, has_variations, labels_within
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self.collate_per_element, self.mode, self.refresh = collate_per_segment, mode, refresh
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self.with_normals = with_normals
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super(CustomShapeNet, self).__init__(root_dir, transform, pre_transform, pre_filter)
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self.data, self.slices = self._load_dataset()
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print("Initialized")
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@@ -38,16 +40,17 @@ class CustomShapeNet(InMemoryDataset):
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@property
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def raw_file_names(self):
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# Maybe add more data like validation sets
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return list(self.modes.keys())
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return [self.mode]
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@property
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def processed_file_names(self):
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return [f'{x}.pt' for x in self.raw_file_names]
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return [f'{self.mode}.pt']
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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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@@ -57,7 +60,7 @@ class CustomShapeNet(InMemoryDataset):
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def _load_dataset(self):
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data, slices = None, None
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filepath = self.processed_paths[self.modes[self.mode]]
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filepath = self.processed_paths[0]
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if self.refresh:
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try:
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os.remove(filepath)
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@@ -90,7 +93,7 @@ class CustomShapeNet(InMemoryDataset):
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def process(self, delimiter=' '):
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datasets = defaultdict(list)
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idx, data_folder = self.modes[self.mode], self.raw_file_names[self.modes[self.mode]]
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idx, data_folder = self.modes[self.mode], self.raw_file_names[0]
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path_to_clouds = os.path.join(self.raw_dir, data_folder)
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if '.headers' in os.listdir(path_to_clouds):
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@@ -110,8 +113,8 @@ class CustomShapeNet(InMemoryDataset):
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paths.extend(glob.glob(os.path.join(pointcloud.path, f'*.{ext}')))
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for element in paths:
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# This was build to filter all variations that aregreater then 25
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pattern = re.compile('^((6[0-1]|[1-5][0-9])_\w+?\d+?|\d+?_pc)\.(xyz|dat)$')
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# This was build to filter all full clouds
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pattern = re.compile('^\d+?_pc\.(xyz|dat)$')
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if pattern.match(os.path.split(element)[-1]):
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continue
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else:
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@@ -142,9 +145,10 @@ class CustomShapeNet(InMemoryDataset):
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y_all = [-1] * points.shape[0]
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y = torch.as_tensor(y_all, dtype=torch.int)
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attr_dict = dict(y=y, pos=points[:, :3])
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if self.mode == 'predict':
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attr_dict.update(normals=points[:, 3:6])
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####################################
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# This is where you define the keys
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attr_dict = dict(y=y, pos=points[:, :3 if not self.with_normals else 6])
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####################################
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if self.collate_per_element:
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data = Data(**attr_dict)
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else:
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@@ -161,14 +165,14 @@ class CustomShapeNet(InMemoryDataset):
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cloud_variations[int(os.path.split(element)[-1].split('_')[0])].append(data)
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if not self.collate_per_element:
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if self.has_variations:
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for variation in cloud_variations.keys():
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for _ in cloud_variations.keys():
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datasets[data_folder].append(Data(**{key: torch.cat(data[key]) for key in data.keys()}))
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else:
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datasets[data_folder].append(Data(**{key: torch.cat(data[key]) for key in data.keys()}))
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if datasets[data_folder]:
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os.makedirs(self.processed_dir, exist_ok=True)
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torch.save(self.collate(datasets[data_folder]), self.processed_paths[idx])
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torch.save(self.collate(datasets[data_folder]), self.processed_paths[0])
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def __repr__(self):
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return f'{self.__class__.__name__}({len(self)})'
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@@ -179,6 +183,7 @@ 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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@@ -191,22 +196,19 @@ 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 if self.mode == 'predict' else True)
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except ValueError:
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choice = []
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points, labels = data.pos[choice, :], data.y[choice]
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pos, labels = data.pos[choice, :], data.y[choice]
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labels -= 1 if self.num_classes() in labels else 0 # Map label from [1, C] to [0, C-1]
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sample = {
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'points': points, # torch.Tensor (n, 3)
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'points': pos, # torch.Tensor (n, 6)
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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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sample.update(normals=normals)
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return sample
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def __len__(self):
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18
main.py
18
main.py
@@ -33,11 +33,12 @@ 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=0, 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('--with_normals', type=strtobool, default=True, help='if training will include normals')
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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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help='whether a single pointcloud has variations '
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@@ -69,7 +70,7 @@ if __name__ == '__main__':
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)
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TransTransform = GT.RandomTranslate(trans_max_distance)
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train_transform = GT.Compose([GT.NormalizeScale(), RotTransform, TransTransform])
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train_transform = GT.Compose([GT.NormalizeScale(), ])
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test_transform = GT.Compose([GT.NormalizeScale(), ])
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params = dict(root_dir=opt.dataset,
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@@ -78,7 +79,8 @@ if __name__ == '__main__':
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npoints=opt.npoints,
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labels_within=opt.labels_within,
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has_variations=opt.has_variations,
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headers=opt.headers
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headers=opt.headers,
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with_normals=opt.with_normals
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)
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dataset = ShapeNetPartSegDataset(mode='train', **params)
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@@ -105,7 +107,7 @@ if __name__ == '__main__':
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dtype = torch.float
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print('cudnn.enabled: ', torch.backends.cudnn.enabled)
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net = PointNet2PartSegmentNet(num_classes)
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net = PointNet2PartSegmentNet(num_classes, with_normals=opt.with_normals)
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if opt.model != '':
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net.load_state_dict(torch.load(opt.model))
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@@ -129,12 +131,12 @@ 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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# points: (batch_size, n, 6)
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# pos: (batch_size, n, 3)
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# labels: (batch_size, n)
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points, labels = sample['points'], sample['labels']
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points = points.transpose(1, 2).contiguous() # (batch_size, 3, n)
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points = points.transpose(1, 2).contiguous() # (batch_size, 3/6, n)
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points, labels = points.to(device, dtype), labels.to(device, torch.long)
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optimizer.zero_grad()
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@@ -8,15 +8,15 @@ 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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class PointNet2SAModule(torch.nn.Module):
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def __init__(self, sample_radio, radius, max_num_neighbors, mlp):
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def __init__(self, sample_radio, radius, max_num_neighbors, mlp, features=3):
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super(PointNet2SAModule, self).__init__()
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self.sample_ratio = sample_radio
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self.radius = radius
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self.max_num_neighbors = max_num_neighbors
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self.point_conv = PointConv(mlp)
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self.features=features
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def forward(self, data):
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x, pos, batch = data
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@@ -40,9 +40,10 @@ class PointNet2GlobalSAModule(torch.nn.Module):
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One group with all input points, can be viewed as a simple PointNet module.
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It also return the only one output point(set as origin point).
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'''
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def __init__(self, mlp):
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def __init__(self, mlp, features=3):
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super(PointNet2GlobalSAModule, self).__init__()
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self.mlp = mlp
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self.features = features
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def forward(self, data):
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x, pos, batch = data
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@@ -52,7 +53,7 @@ class PointNet2GlobalSAModule(torch.nn.Module):
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x1 = scatter_max(x1, batch, dim=0)[0] # (batch_size, C1)
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batch_size = x1.shape[0]
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pos1 = x1.new_zeros((batch_size, GLOBAL_POINT_FEATURES)) # set the output point as origin
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pos1 = x1.new_zeros((batch_size, self.features)) # set the output point as origin
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batch1 = torch.arange(batch_size).to(batch.device, batch.dtype)
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return x1, pos1, batch1
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@@ -158,44 +159,47 @@ class PointNet2PartSegmentNet(torch.nn.Module):
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- https://github.com/charlesq34/pointnet2/blob/master/models/pointnet2_part_seg.py
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- https://github.com/rusty1s/pytorch_geometric/blob/master/examples/pointnet++.py
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'''
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def __init__(self, num_classes):
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def __init__(self, num_classes, with_normals=False):
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super(PointNet2PartSegmentNet, self).__init__()
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self.num_classes = num_classes
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self.features = 3 if not with_normals else 6
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# SA1
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sa1_sample_ratio = 0.5
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sa1_radius = 0.2
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sa1_max_num_neighbours = 64
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sa1_mlp = make_mlp(GLOBAL_POINT_FEATURES, [64, 64, 128])
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self.sa1_module = PointNet2SAModule(sa1_sample_ratio, sa1_radius, sa1_max_num_neighbours, sa1_mlp)
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sa1_mlp = make_mlp(self.features, [64, 64, 128])
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self.sa1_module = PointNet2SAModule(sa1_sample_ratio, sa1_radius, sa1_max_num_neighbours, sa1_mlp,
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features=self.features)
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# SA2
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sa2_sample_ratio = 0.25
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sa2_radius = 0.4
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sa2_max_num_neighbours = 64
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sa2_mlp = make_mlp(128+GLOBAL_POINT_FEATURES, [128, 128, 256])
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self.sa2_module = PointNet2SAModule(sa2_sample_ratio, sa2_radius, sa2_max_num_neighbours, sa2_mlp)
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sa2_mlp = make_mlp(128+self.features, [128, 128, 256])
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self.sa2_module = PointNet2SAModule(sa2_sample_ratio, sa2_radius, sa2_max_num_neighbours, sa2_mlp,
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features=self.features)
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# SA3
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sa3_mlp = make_mlp(256+GLOBAL_POINT_FEATURES, [256, 512, 1024])
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self.sa3_module = PointNet2GlobalSAModule(sa3_mlp)
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sa3_mlp = make_mlp(256+self.features, [256, 512, 1024])
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self.sa3_module = PointNet2GlobalSAModule(sa3_mlp, self.features)
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##
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knn_num = GLOBAL_POINT_FEATURES
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knn_num = self.features
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||||
# FP3, reverse of sa3
|
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fp3_knn_num = 1 # After global sa module, there is only one point in point cloud
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fp3_mlp = make_mlp(1024+256+GLOBAL_POINT_FEATURES, [256, 256])
|
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fp3_mlp = make_mlp(1024+256+self.features, [256, 256])
|
||||
self.fp3_module = PointNet2FPModule(fp3_knn_num, fp3_mlp)
|
||||
|
||||
# FP2, reverse of sa2
|
||||
fp2_knn_num = knn_num
|
||||
fp2_mlp = make_mlp(256+128+GLOBAL_POINT_FEATURES, [256, 128])
|
||||
fp2_mlp = make_mlp(256+128+self.features, [256, 128])
|
||||
self.fp2_module = PointNet2FPModule(fp2_knn_num, fp2_mlp)
|
||||
|
||||
# FP1, reverse of sa1
|
||||
fp1_knn_num = knn_num
|
||||
fp1_mlp = make_mlp(128+GLOBAL_POINT_FEATURES, [128, 128, 128])
|
||||
fp1_mlp = make_mlp(128+self.features, [128, 128, 128])
|
||||
self.fp1_module = PointNet2FPModule(fp1_knn_num, fp1_mlp)
|
||||
|
||||
self.fc1 = Lin(128, 128)
|
||||
@@ -252,11 +256,12 @@ class PointNet2PartSegmentNet(torch.nn.Module):
|
||||
|
||||
if __name__ == '__main__':
|
||||
num_classes = 10
|
||||
net = PointNet2PartSegmentNet(num_classes)
|
||||
num_features = 6
|
||||
net = PointNet2PartSegmentNet(num_classes, features=num_features)
|
||||
|
||||
#
|
||||
print('Test dense input ..')
|
||||
data1 = torch.rand((2, GLOBAL_POINT_FEATURES, 1024)) # (batch_size, 3, num_points)
|
||||
data1 = torch.rand((2, num_features, 1024)) # (batch_size, 3, num_points)
|
||||
print('data1: ', data1.shape)
|
||||
|
||||
out1 = net(data1)
|
||||
@@ -272,7 +277,7 @@ if __name__ == '__main__':
|
||||
data_batch = Data()
|
||||
|
||||
# data_batch.x = None
|
||||
data_batch.pos = torch.cat([torch.rand(pos_num1, GLOBAL_POINT_FEATURES), torch.rand(pos_num2, GLOBAL_POINT_FEATURES)], dim=0)
|
||||
data_batch.pos = torch.cat([torch.rand(pos_num1, num_features), torch.rand(pos_num2, num_features)], dim=0)
|
||||
data_batch.batch = torch.cat([torch.zeros(pos_num1, dtype=torch.long), torch.ones(pos_num2, dtype=torch.long)])
|
||||
|
||||
return data_batch
|
||||
|
||||
200
predict/clusters.txt
Normal file
200
predict/clusters.txt
Normal file
@@ -0,0 +1,200 @@
|
||||
48
|
||||
1.000000000000000000e+00
|
||||
1 6
|
||||
7.199833552042643747e-01 1.481056722005208437e+00 4.978933334350585938e-01 3.555141376242736823e-02 -1.702250673669388707e-03 -9.993663989359141686e-01
|
||||
1.000000000000000000e+00
|
||||
5 6
|
||||
3.199843406677246316e-01 1.547723388671875089e+00 6.312266667683918975e-01 -3.555141376242736823e-02 1.702250673669388707e-03 9.993663989359141686e-01
|
||||
4.533166567484537834e-01 1.414390055338541563e+00 6.312266667683918975e-01 -3.555141376242736823e-02 1.702250673669388707e-03 9.993663989359141686e-01
|
||||
3.866499900817871316e-01 1.614390055338541741e+00 6.312266667683918975e-01 -3.555141376242736823e-02 1.702250673669388707e-03 9.993663989359141686e-01
|
||||
2.533176740010579242e-01 1.614390055338541741e+00 6.312266667683918975e-01 -3.555141376242736823e-02 1.702250673669388707e-03 9.993663989359141686e-01
|
||||
3.866499900817871316e-01 1.481056722005208437e+00 6.312266667683918975e-01 -3.555141376242736823e-02 1.702250673669388707e-03 9.993663989359141686e-01
|
||||
1.000000000000000000e+00
|
||||
2 6
|
||||
7.199833552042643747e-01 1.414390055338541563e+00 6.312266667683918975e-01 -3.555141376242736823e-02 1.702250673669388707e-03 9.993663989359141686e-01
|
||||
6.533166885375976118e-01 1.481056722005208437e+00 6.312266667683918975e-01 -3.555141376242736823e-02 1.702250673669388707e-03 9.993663989359141686e-01
|
||||
1.000000000000000000e+00
|
||||
3.000000000000000000e+00
|
||||
4 6
|
||||
2.533176740010579242e-01 1.414390055338541563e+00 6.312266667683918975e-01 -3.555141376242736823e-02 1.702250673669388707e-03 9.993663989359141686e-01
|
||||
3.199843406677246316e-01 1.347723388671874911e+00 6.312266667683918975e-01 -3.555141376242736823e-02 1.702250673669388707e-03 9.993663989359141686e-01
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||||
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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
Reference in New Issue
Block a user