def __init__(self, train=True, subsampling=False, first_subsampling_dl=0.02):
PointCloudDataset.__init__(self, 'Custom')
self.label_to_names = {0: 'Cyclist',
1: 'Pedestrian',
2: 'Car'
}
self.first_subsampling_dl = first_subsampling_dl
self.subsampling = subsampling
# Initialize a bunch of variables concerning class labels
self.num_classes = len(self.label_to_names)
self.label_values = np.sort([k for k, v in self.label_to_names.items()])
self.label_names = [self.label_to_names[k] for k in self.label_values]
self.label_to_idx = {l: i for i, l in enumerate(self.label_values)}
self.name_to_label = {v: k for k, v in self.label_to_names.items()}
self.path = config.DATA['root']
self.train = train
# Number of models and models used per epoch
if self.train:
self.num_models = 9843
if config.SETTING['epoch_steps'] and config.SETTING['epoch_steps'] * config.SETTING['batch_num'] < self.num_models:
self.epoch_n = config.SETTING['epoch_steps'] * config.SETTING['batch_num']
else:
self.epoch_n = self.num_models
else:
self.num_models = 2468
self.epoch_n = min(self.num_models, config.SETTING['validation_size'] * config.SETTING['batch_num'])
self.input_points, self.input_normals, self.input_labels = self.load_subsampled_clouds(orient_correction=True)
return
def __init__(self, config, train=True, orient_correction=True):
"""
This dataset is small enough to be stored in-memory, so load all point clouds here
"""
PointCloudDataset.__init__(self, 'ModelNet40')
############
# Parameters
############
# Dict from labels to names
self.label_to_names = {0: 'airplane',
1: 'bathtub',
2: 'bed',
3: 'bench',
4: 'bookshelf',
5: 'bottle',
6: 'bowl',
7: 'car',
8: 'chair',
9: 'cone',
10: 'cup',
11: 'curtain',
12: 'desk',
13: 'door',
14: 'dresser',
15: 'flower_pot',
16: 'glass_box',
17: 'guitar',
18: 'keyboard',
19: 'lamp',
20: 'laptop',
21: 'mantel',
22: 'monitor',
23: 'night_stand',
24: 'person',
25: 'piano',
26: 'plant',
27: 'radio',
28: 'range_hood',
29: 'sink',
30: 'sofa',
31: 'stairs',
32: 'stool',
33: 'table',
34: 'tent',
35: 'toilet',
36: 'tv_stand',
37: 'vase',
38: 'wardrobe',
39: 'xbox'}
# Initialize a bunch of variables concerning class labels
self.init_labels()
# List of classes ignored during training (can be empty)
self.ignored_labels = np.array([])
# Dataset folder
self.path = '../../Data/ModelNet40'
# Type of task conducted on this dataset
self.dataset_task = 'classification'
# Update number of class and data task in configuration
config.num_classes = self.num_classes
config.dataset_task = self.dataset_task
# Parameters from config
self.config = config
# Training or test set
self.train = train
# Number of models and models used per epoch
if self.train:
self.num_models = 9843
if config.epoch_steps and config.epoch_steps * config.batch_num < self.num_models:
self.epoch_n = config.epoch_steps * config.batch_num
else:
self.epoch_n = self.num_models
else:
self.num_models = 2468
self.epoch_n = min(self.num_models, config.validation_size * config.batch_num)
#############
# Load models
#############
if 0 < self.config.first_subsampling_dl <= 0.01:
raise ValueError('subsampling_parameter too low (should be over 1 cm')
self.input_points, self.input_normals, self.input_labels = self.load_subsampled_clouds(orient_correction)
return
def __init__(self,
config,
train=True,
orient_correction=True,
spherical_transform=None,
transforms=None,
self_supervision=False):
"""
This dataset is small enough to be stored in-memory, so load all point clouds here
"""
PointCloudDataset.__init__(self, 'ScanObjectNN')
######################
# LOADING ScanObjectNN
######################
self.transforms = transforms
self.self_supervision = self_supervision
self.train = train
#root = './Data/ScanObjectNN/main_split_nobg/'
root = './Data/ScanObjectNN/split2/'
if self.self_supervision:
h5 = h5py.File(
root + 'training_objectdataset_augmentedrot_scale75.h5', 'r')
points_train = np.array(h5['data']).astype(np.float32)
h5.close()
self.points = points_train
self.labels = None
elif train:
h5 = h5py.File(
root + 'training_objectdataset_augmentedrot_scale75.h5', 'r')
self.points = np.array(h5['data']).astype(np.float32)
self.labels = np.array(h5['label']).astype(int)
h5.close()
else:
h5 = h5py.File(root + 'test_objectdataset_augmentedrot_scale75.h5',
'r')
self.points = np.array(h5['data']).astype(np.float32)
self.labels = np.array(h5['label']).astype(int)
h5.close()
print('Successfully load ScanObjectNN with', len(self.labels),
'instances')
# Parameters
############
self.transform = spherical_transform
# Dict from labels to names
self.label_to_names = {
0: 'ag',
1: 'bin',
2: 'box',
3: 'cabinet',
4: 'chair',
5: 'desk',
6: 'display',
7: 'door',
8: 'shelf',
9: 'table',
10: 'bed',
11: 'pillow',
12: 'sink',
13: 'sofa',
14: 'toilet',
}
# Initialize a bunch of variables concerning class labels
self.init_labels()
# List of classes ignored during training (can be empty)
self.ignored_labels = np.array([])
# Dataset folder
self.path = root
# Type of task conducted on this dataset
self.dataset_task = 'classification'
# Update number of class and data task in configuration
self.num_classes = 15
config.num_classes = self.num_classes
config.dataset_task = self.dataset_task
# Parameters from config
self.config = config
# Training or test set
self.train = train
# Number of models and models used per epoch
if self.train:
#self.num_models = 11416
self.num_models = 2321
if config.epoch_steps and config.epoch_steps * config.batch_num < self.num_models:
self.epoch_n = config.epoch_steps * config.batch_num
else:
self.epoch_n = self.num_models
else:
self.num_models = 2882
#self.num_models = 581
#self.epoch_n = min(self.num_models, config.validation_size * config.batch_num)
self.epoch_n = self.num_models
#############
# Load models
#############
if 0 < self.config.first_subsampling_dl <= 0.01:
raise ValueError(
'subsampling_parameter too low (should be over 1 cm')
self.input_points, self.input_normals, self.input_labels = self.load_subsampled_clouds(
orient_correction)
return
def __init__(self,
config,
train=True,
orient_correction=True,
tranform=None):
"""
This dataset is small enough to be stored in-memory, so load all point clouds here
"""
PointCloudDataset.__init__(self, 'ModelNet40')
############
# Parameters
############
self.transform = tranform
# Dict from labels to names
self.label_to_names = {
0: 'bathtub',
1: 'bed',
2: 'chair',
3: 'desk',
4: 'dresser',
5: 'monitor',
6: 'night_stand',
7: 'sofa',
8: 'table',
9: 'toilet',
}
# Initialize a bunch of variables concerning class labels
self.init_labels()
# List of classes ignored during training (can be empty)
self.ignored_labels = np.array([])
# Dataset folder
self.path = './Data/ModelNet40'
# Type of task conducted on this dataset
self.dataset_task = 'classification'
# Update number of class and data task in configuration
config.num_classes = self.num_classes
config.dataset_task = self.dataset_task
# Parameters from config
self.config = config
# Training or test set
self.train = train
# Number of models and models used per epoch
if self.train:
self.num_models = 3991
if config.epoch_steps and config.epoch_steps * config.batch_num < self.num_models:
self.epoch_n = config.epoch_steps * config.batch_num
else:
self.epoch_n = self.num_models
else:
self.num_models = 908
#self.epoch_n = min(self.num_models, config.validation_size * config.batch_num)
self.epoch_n = self.num_models
#############
# Load models
#############
if 0 < self.config.first_subsampling_dl <= 0.01:
raise ValueError(
'subsampling_parameter too low (should be over 1 cm')
self.input_points, self.input_normals, self.input_labels = self.load_subsampled_clouds(
orient_correction)
#self.input_points = normalize(self.input_points)
return
def __init__(self,
config,
train=True,
orient_correction=True,
spherical_transform=None,
transforms=None,
self_supervision=False):
"""
This dataset is small enough to be stored in-memory, so load all point clouds here
"""
PointCloudDataset.__init__(self, 'ScanNet')
######################
# LOADING ScanNet
######################
self.transforms = transforms
self.self_supervision = self_supervision
self.train = train
root = './Data/ScanNet/'
train_files = "./Data/ScanNet/cls/train_files.txt"
val_files = "./Data/ScanNet/cls/test_files.txt"
if self.self_supervision:
self.points, self.labels = load_cls_train_val(train_files,
train=True)
self.points = self.points[:, :, :3]
elif train:
self.points, self.labels = load_cls_train_val(train_files,
train=True)
self.points = self.points[:, :, :3]
else:
self.points, self.labels = load_cls_train_val(val_files,
train=True)
self.points = self.points[:, :, :3]
self.points = normalize(self.points)
print('Successfully load ScanNet with', len(self.labels), 'instances')
# Parameters
############
self.transform = spherical_transform
# Dict from labels to names
self.label_to_names = {
0: 'trash',
1: 'basket',
2: 'bathub',
3: 'bed',
4: 'shelf',
5: 'cabinet',
6: 'chair',
7: 'keyboard',
8: 'tv',
9: 'lamp',
10: 'laptop',
11: 'microwave',
12: 'pillow',
13: 'printer',
14: 'sofa',
15: 'stove',
16: 'table'
}
# Initialize a bunch of variables concerning class labels
self.init_labels()
# List of classes ignored during training (can be empty)
self.ignored_labels = np.array([])
# Dataset folder
self.path = root
# Type of task conducted on this dataset
self.dataset_task = 'classification'
# Update number of class and data task in configuration
self.num_classes = 17
config.num_classes = self.num_classes
config.dataset_task = self.dataset_task
# Parameters from config
self.config = config
# Training or test set
self.train = train
# Number of models and models used per epoch
if self.train:
# self.num_models = 2321
self.num_models = 9423
if config.epoch_steps and config.epoch_steps * config.batch_num < self.num_models:
self.epoch_n = config.epoch_steps * config.batch_num
else:
self.epoch_n = self.num_models
else:
self.num_models = 2634
# self.epoch_n = min(self.num_models, config.validation_size * config.batch_num)
self.epoch_n = self.num_models
#############
# Load models
#############
if 0 < self.config.first_subsampling_dl <= 0.01:
raise ValueError(
'subsampling_parameter too low (should be over 1 cm')
self.input_points, self.input_normals, self.input_labels = self.load_subsampled_clouds(
orient_correction)
return