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File based header detection, collate_per_PC training.

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Si11ium
2019-08-01 14:16:50 +02:00
parent 47a76dc978
commit a9bf053794
3 changed files with 77 additions and 75 deletions
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119
dataset/shapenet.py
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@ -20,6 +20,7 @@ class CustomShapeNet(InMemoryDataset):
headers=True, **kwargs):
self.has_headers = headers
self.collate_per_element = collate_per_segment
self.train = train
super(CustomShapeNet, self).__init__(root, transform, pre_transform, pre_filter)
path = self.processed_paths[0] if train else self.processed_paths[-1]
self.data, self.slices = torch.load(path)
@ -70,73 +71,71 @@ class CustomShapeNet(InMemoryDataset):
return data
def process(self, delimiter=' '):
# idx = self.categories[self.category]
# paths = [osp.join(path, idx) for path in self.raw_paths]
datasets = defaultdict(list)
for idx, setting in enumerate(self.raw_file_names):
path_to_clouds = os.path.join(self.raw_dir, setting)
idx, data_folder = (0, self.raw_file_names[0]) if self.train else (1, self.raw_file_names[1])
path_to_clouds = os.path.join(self.raw_dir, data_folder)
if '.headers' in os.listdir(path_to_clouds):
self.has_headers = True
elif 'no.headers' in os.listdir(path_to_clouds):
self.has_headers = False
else:
pass
if '.headers' in os.listdir(path_to_clouds):
self.has_headers = True
elif 'no.headers' in os.listdir(path_to_clouds):
self.has_headers = False
else:
pass
for pointcloud in tqdm(os.scandir(path_to_clouds)):
for pointcloud in tqdm(os.scandir(path_to_clouds)):
if not os.path.isdir(pointcloud):
continue
data, paths = None, list()
for ext in ['dat', 'xyz']:
paths.extend(glob.glob(os.path.join(pointcloud.path, f'*.{ext}')))
if not os.path.isdir(pointcloud):
continue
data, paths = None, list()
for ext in ['dat', 'xyz']:
paths.extend(glob.glob(os.path.join(pointcloud.path, f'*.{ext}')))
for element in paths:
if all([x not in os.path.split(element)[-1] for x in ['pc.dat', 'pc.xyz']]):
# Assign training data to the data container
# Following the original logic;
# y should be the label;
# pos should be the six dimensional vector describing: !its pos not points!!
# x,y,z,x_rot,y_rot,z_rot
for element in paths:
if all([x not in os.path.split(element)[-1] for x in ['pc.dat', 'pc.xyz']]):
# Assign training data to the data container
# Following the original logic;
# y should be the label;
# pos should be the six dimensional vector describing: !its pos not points!!
# x,y,z,x_rot,y_rot,z_rot
# Get the y - Label
y_raw = next(i for i, v in enumerate(self.categories.keys()) if v.lower() in element.lower())
# y_raw = os.path.splitext(element)[0].split('_')[-2]
with open(element,'r') as f:
if self.has_headers:
headers = f.__next__()
# Check if there are no useable nodes in this file, header says 0.
if not int(headers.rstrip().split(delimiter)[0]):
continue
# Get the y - Label
y_raw = next(i for i, v in enumerate(self.categories.keys()) if v.lower() in element.lower())
# y_raw = os.path.splitext(element)[0].split('_')[-2]
with open(element,'r') as f:
if self.has_headers:
headers = f.__next__()
# Check if there are no useable nodes in this file, header says 0.
if not int(headers.rstrip().split(delimiter)[0]):
continue
# Iterate over all rows
src = [[float(x) if x not in ['-nan(ind)', 'nan(ind)'] else 0
for x in line.rstrip().split(delimiter)[None:None]] for line in f if line != '']
points = torch.tensor(src, dtype=None).squeeze()
if not len(points.shape) > 1:
continue
# pos = points[:, :3]
# norm = points[:, 3:]
y_all = [y_raw] * points.shape[0]
y = torch.as_tensor(y_all, dtype=torch.int)
# points = torch.as_tensor(points, dtype=torch.float)
# norm = torch.as_tensor(norm, dtype=torch.float)
if self.collate_per_element:
data = Data(y=y, pos=points[:, :3])
else:
if not data:
data = defaultdict(list)
for key, val in dict(y=y, pos= points[:, :3]).items():
data[key].append(val)
# , points=points, norm=points[:3], )
data = self._transform_and_filter(data)
if self.collate_per_element:
datasets[setting].append(data)
if not self.collate_per_element:
datasets[setting].append(Data(**{key: torch.cat(data[key]) for key in data.keys()}))
# Iterate over all rows
src = [[float(x) if x not in ['-nan(ind)', 'nan(ind)'] else 0
for x in line.rstrip().split(delimiter)[None:None]] for line in f if line != '']
points = torch.tensor(src, dtype=None).squeeze()
if not len(points.shape) > 1:
continue
# pos = points[:, :3]
# norm = points[:, 3:]
y_all = [y_raw] * points.shape[0]
y = torch.as_tensor(y_all, dtype=torch.int)
# points = torch.as_tensor(points, dtype=torch.float)
# norm = torch.as_tensor(norm, dtype=torch.float)
if self.collate_per_element:
data = Data(y=y, pos=points[:, :3])
else:
if not data:
data = defaultdict(list)
for key, val in dict(y=y, pos= points[:, :3]).items():
data[key].append(val)
# , points=points, norm=points[:3], )
data = self._transform_and_filter(data)
if self.collate_per_element:
datasets[data_folder].append(data)
if not self.collate_per_element:
datasets[data_folder].append(Data(**{key: torch.cat(data[key]) for key in data.keys()}))
if datasets[data_folder]:
os.makedirs(self.processed_dir, exist_ok=True)
torch.save(self.collate(datasets[setting]), self.processed_paths[idx])
torch.save(self.collate(datasets[data_folder]), self.processed_paths[idx])
def __repr__(self):
return f'{self.__class__.__name__}({len(self)})'
@ -291,7 +290,7 @@ class PredictNetPartSegDataset(Dataset):
def __init__(self, root_dir, transform=None, npoints=2048, headers=True):
super(PredictNetPartSegDataset, self).__init__()
self.npoints = npoints
self.dataset = PredictionShapeNet(root=root_dir, train=False, transform=transform, headers=headers)
self.dataset = ShapeNetPartSegDataset(root=root_dir, train=False, transform=transform, headers=headers)
def __getitem__(self, index):
data = self.dataset[index]