def __init__(self,
root='data/ShapeNet',
train=True,
categories=None,
include_normals=True,
split='trainval',
transform=None,
pre_transform=None,
pre_filter=None,
repeat_to=None): # Modified here to add repeat_to
if categories is None:
categories = list(self.category_ids.keys())
if isinstance(categories, str):
categories = [categories]
assert all(category in self.category_ids for category in categories)
self.categories = categories
# Default settings
pre_transform = T.NormalizeScale()
pre_filter = None
include_normals = True
if train:
transform = T.Compose([
T.RandomTranslate(0.01),
T.RandomRotate(15, axis=0),
T.RandomRotate(15, axis=1),
T.RandomRotate(15, axis=2)
])
split = 'trainval'
else:
transform = None
split = 'test'
super().__init__(root, transform, pre_transform,
pre_filter) # Modified here to add repeat_to
if split == 'train':
path = self.processed_paths[0]
elif split == 'val':
path = self.processed_paths[1]
elif split == 'test':
path = self.processed_paths[2]
elif split == 'trainval':
path = self.processed_paths[3]
else:
raise ValueError((f'Split {split} found, but expected either '
'train, val, trainval or test'))
self.data, self.slices = torch.load(path)
self.data.x = self.data.x if include_normals else None
self.y_mask = torch.zeros((len(self.seg_classes.keys()), 50),
dtype=torch.bool)
for i, labels in enumerate(self.seg_classes.values()):
self.y_mask[i, labels] = 1
self.repeat_to = repeat_to # Modified here to add repeat_to
def transform_setup(graph_u=False,
graph_gcn=False,
rotation=180,
samplePoints=1024,
mesh=False,
node_translation=0.01):
if not graph_u and not graph_gcn:
# Default transformation for scale noralization, centering, point sampling and rotating
pretransform = T.Compose([T.NormalizeScale(), T.Center()])
transform = T.Compose([
T.SamplePoints(samplePoints),
T.RandomRotate(rotation[0], rotation[1])
])
print("pointnet rotation {}".format(rotation))
elif graph_u:
pretransform = T.Compose([T.NormalizeScale(), T.Center()])
transform = T.Compose([
T.NormalizeScale(),
T.Center(),
T.SamplePoints(samplePoints, True, True),
T.RandomRotate(rotation[0], rotation[1]),
T.KNNGraph(k=graph_u)
])
elif graph_gcn:
pretransform = T.Compose([T.NormalizeScale(), T.Center()])
if mesh:
if mesh == "extraFeatures":
transform = T.Compose([
T.RandomRotate(rotation[0], rotation[1]),
T.GenerateMeshNormals(),
T.FaceToEdge(True),
T.Distance(norm=True),
T.TargetIndegree(cat=True)
]) # ,
else:
transform = T.Compose([
T.RandomRotate(rotation[0], rotation[1]),
T.GenerateMeshNormals(),
T.FaceToEdge(True),
T.Distance(norm=True),
T.TargetIndegree(cat=True)
])
else:
transform = T.Compose([
T.SamplePoints(samplePoints, True, True),
T.KNNGraph(k=graph_gcn),
T.Distance(norm=True)
])
print("no mesh")
print("Rotation {}".format(rotation))
print("Meshing {}".format(mesh))
else:
print('no transfom')
return transform, pretransform
def __init__(self, root, category, **kwargs):
self.root = os.path.join(root, 'ShapeNet')
self.category = category
self.categories = [category]
self.pre_transform = T.NormalizeScale()
self.train_transform = T.Compose([
T.RandomTranslate(0.01),
T.RandomRotate(15, axis=0),
T.RandomRotate(15, axis=1),
T.RandomRotate(15, axis=2),
])
self.label_parser = lambda data: data.y
def __init__(self, dataset_opt):
super().__init__(dataset_opt)
pre_transform = self.pre_transform
transform = T.Compose([
T.FixedPoints(dataset_opt.num_points),
T.RandomTranslate(0.01),
T.RandomRotate(180, axis=2),
])
train_dataset = S3DIS1x1(
self._data_path,
test_area=self.dataset_opt.fold,
train=True,
pre_transform=self.pre_transform,
transform=self.train_transform,
)
self.test_dataset = S3DIS1x1(
self._data_path,
test_area=self.dataset_opt.fold,
train=False,
pre_transform=pre_transform,
transform=self.test_transform,
)
self.train_dataset = add_weights(train_dataset, True,
dataset_opt.class_weight_method)
def __init__(self, dataset_opt, training_opt):
super().__init__(dataset_opt, training_opt)
self._data_path = os.path.join(dataset_opt.dataroot, "S3DIS")
pre_transform = self._pre_transform
transform = T.Compose([
T.FixedPoints(dataset_opt.num_points),
T.RandomTranslate(0.01),
T.RandomRotate(180, axis=2),
])
train_dataset = S3DIS_With_Weights(
self._data_path,
test_area=self.dataset_opt.fold,
train=True,
pre_transform=pre_transform,
transform=transform,
class_weight_method=dataset_opt.class_weight_method,
)
test_dataset = S3DIS_With_Weights(
self._data_path,
test_area=self.dataset_opt.fold,
train=False,
pre_transform=pre_transform,
transform=T.FixedPoints(dataset_opt.num_points),
)
self._create_dataloaders(train_dataset, test_dataset)
def __init__(self, root, classification=False, **kwargs):
self.root = os.path.join(root, 'ShapeNet')
self.pre_transform = T.NormalizeScale()
self.train_transform = T.Compose([
RandomSamplePoints(2048),
T.RandomTranslate(0.01),
T.RandomRotate(15, axis=0),
T.RandomRotate(15, axis=1),
T.RandomRotate(15, axis=2),
])
self.val_transform = RandomSamplePoints(2048)
self.num_classes = len(self.categories)
if not classification:
self.label_parser = lambda data: data.y
else:
self.label_parser = lambda data: data.cid
def __init__(self, config):
rotations = [T.RandomRotate(180, axis=i) for i in range(3)]
translation = T.RandomTranslate(config.augment_translate_limit)
merge_score_noise = UnitEdgeAttrGaussianNoise(
mu=0, sigma=config.edge_attr_noise_std)
self.transform = T.Compose(
[*rotations, translation, merge_score_noise])
def __init__(self, dataset_opt, training_opt):
super().__init__(dataset_opt, training_opt)
self._data_path = os.path.join(dataset_opt.dataroot, "S3DIS")
pre_transform = cT.GridSampling(dataset_opt.first_subsampling, 13)
# Select only 2^15 points from the room
# pre_transform = T.FixedPoints(dataset_opt.room_points)
transform = T.Compose([
T.FixedPoints(dataset_opt.num_points),
T.RandomTranslate(0.01),
T.RandomRotate(180, axis=2),
])
train_dataset = S3DIS_With_Weights(
self._data_path,
test_area=self.dataset_opt.fold,
train=True,
pre_transform=pre_transform,
transform=transform,
class_weight_method=dataset_opt.class_weight_method,
)
test_dataset = S3DIS_With_Weights(
self._data_path,
test_area=self.dataset_opt.fold,
train=False,
pre_transform=pre_transform,
transform=T.FixedPoints(dataset_opt.num_points),
)
self._create_dataloaders(train_dataset, test_dataset, validation=None)
def __init__(self,
root: str,
device: torch.device = torch.device("cpu"),
train: bool = True,
test: bool = False,
transform_data: bool = True):
transform = transforms.Compose([
transforms.RandomRotate(36, axis=1),
transforms.RandomTranslate(0.005)
])
super().__init__(root=root, transform=transform)
# print(self.processed_paths[0])
self.data, self.slices = torch.load(self.processed_paths[0])
if train and not test:
self.data, self.slices = self.collate(
[self.get(i) for i in range(0, 80)])
elif not train and test:
self.data, self.slices = self.collate(
[self.get(i) for i in range(80, 100)])
print(self.data)
self.class_ids = [int(c) for c in self.data.y]
def __init__(self, dataset_opt, training_opt):
super().__init__(dataset_opt, training_opt)
self._data_path = os.path.join(dataset_opt.dataroot, 'ShapeNet')
self._category = dataset_opt.shapenet.category
transform = T.Compose([
T.RandomTranslate(0.01),
T.RandomRotate(15, axis=0),
T.RandomRotate(15, axis=1),
T.RandomRotate(15, axis=2)
])
pre_transform = T.NormalizeScale()
train_dataset = ShapeNet(self._data_path, self._category, train=True, transform=transform,
pre_transform=pre_transform)
test_dataset = ShapeNet( self._data_path, self._category, train=False,
pre_transform=pre_transform)
self.create_dataloaders(train_dataset, test_dataset, validation=None)
import torch
import torch.nn.functional as F
from torch_scatter import scatter
from torchmetrics.functional import jaccard_index
import torch_geometric.transforms as T
from torch_geometric.datasets import ShapeNet
from torch_geometric.loader import DataLoader
from torch_geometric.nn import MLP, DynamicEdgeConv
category = 'Airplane' # Pass in `None` to train on all categories.
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'ShapeNet')
transform = T.Compose([
T.RandomTranslate(0.01),
T.RandomRotate(15, axis=0),
T.RandomRotate(15, axis=1),
T.RandomRotate(15, axis=2)
])
pre_transform = T.NormalizeScale()
train_dataset = ShapeNet(path, category, split='trainval', transform=transform,
pre_transform=pre_transform)
test_dataset = ShapeNet(path, category, split='test',
pre_transform=pre_transform)
train_loader = DataLoader(train_dataset, batch_size=10, shuffle=True,
num_workers=6)
test_loader = DataLoader(test_dataset, batch_size=10, shuffle=False,
num_workers=6)
class Net(torch.nn.Module):
# ## Data loading
# Let's get the dataset
# In[2]:
import torch
from torch_geometric.datasets import ModelNet
import torch_geometric.transforms as T
import time
from tqdm import tqdm_notebook
pre_transform = T.NormalizeScale()
transform = T.Compose([
T.SamplePoints(1024),
T.RandomRotate(30),
T.RandomScale((0.5, 2)),
])
name = '40'
train_ds = ModelNet(root='./',
train=True,
name=name,
pre_transform=pre_transform,
transform=transform)
test_ds = ModelNet(root='./',
train=True,
name=name,
pre_transform=pre_transform,
transform=T.SamplePoints(1024 * 4))
default=50,
help="number of minibatches between logging",
)
return parser
if __name__ == "__main__":
args = default_argument_parser().parse_args()
np.random.seed(args.seed)
num_classes = 2
transforms = []
if args.max_points > 0:
transforms.append(T.FixedPoints(args.max_points))
if args.augment:
transforms.append(T.RandomRotate((-180, 180),
axis=2)) # Rotate around z axis
transforms.append(T.RandomFlip(0)) # Flp about x axis
transforms.append(T.RandomFlip(1)) # Flip about y axis
transforms.append(T.RandomTranslate(0.0001)) # Random jitter
if args.norm:
transforms.append(T.NormalizeScale())
transform = T.Compose(transforms=transforms) if transforms else None
train_dataset = EventDataset(args.dataset,
"trainval",
include_proton=True,
task="separation",
cleanliness=args.clean,
pre_transform=None,
transform=transform,
balanced_classes=True,
fraction=0.001)
#from torch_geometric.transforms import GridSampling
import torch_geometric.transforms as T
from human_corres.data import ImgData, ImgBatch
from human_corres.config import PATH_TO_SURREAL
import human_corres.transforms as H
num_views = 20
IDlist = np.stack([
np.arange(100000 * num_views),
np.arange(115000 * num_views, 215000 * num_views)
],
axis=0)
num_test = 5000
DefaultTransform = T.Compose([
T.Center(),
T.RandomRotate(30, axis=0),
T.RandomRotate(30, axis=1),
T.RandomRotate(30, axis=2),
])
class SurrealFEPts5k(Dataset):
"""Surreal 3D points for Feature Extraction (FE).
Samples a fixed number of points.
Output: dictionary with keys {points3d, correspondence}
Data Format:
points3d: [num_points, 3] real numbers.
correspondence: [num_points] integers in range [6890].
"""
def __init__(self,
logging.basicConfig(filename=os.path.join(log_dir, 'n_mnist.log'),
level=logging.DEBUG)
model_file = 'n_mnist.pkl'
device = torch.device("cuda:" +
args.cuda if torch.cuda.is_available() else "cpu")
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
pre_transform = T.Compose([T.Cartesian(cat=False)])
# train_data_aug = T.Compose([T.RandomScale([0.95, 1])])
train_data_aug = T.Compose([
T.RandomScale([0.95, 1]),
T.RandomRotate((0, 10), axis=0),
T.RandomFlip(axis=0, p=0.5)
])
train_dataset = Graph_2D_Memory_Dataset(train_dir,
transform=train_data_aug,
pre_transform=pre_transform)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers)
# train
print("train")
for epoch in range(1, args.epoch):
model.train()
idx = data.edge_index[0] * data.num_nodes + data.edge_index[1]
size = list(data.edge_attr.size())
size[0] = data.num_nodes * data.num_nodes
edge_attr = data.edge_attr.new_zeros(size)
edge_attr[idx] = data.edge_attr
edge_index, edge_attr = remove_self_loops(edge_index, edge_attr)
data.edge_attr = edge_attr
data.edge_index = edge_index
return data
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'OMDB')
transform = T.Compose([MyTransform(), Complete(), T.Distance(norm=False),
T.RandomTranslate(0.01), T.RandomRotate(15, axis=0), T.RandomRotate(15, axis=1), T.RandomRotate(15, axis=2)])
dataset = OMDBXYZ(path,transform=transform).shuffle()
print(len(dataset))
dataset = dataset[dataset.data.y > 0]
print(len(dataset))
# Normalize targets to mean = 0 and std = 1.
mean = dataset.data.y.mean(dim=0, keepdim=True)
std = dataset.data.y.std(dim=0, keepdim=True)
dataset.data.y = (dataset.data.y - mean) / std
mean, std = mean.item(), std.item()
# Split datasets.
train_dataset = dataset[:9000]
val_dataset = dataset[9000:10000]
npoints = 2500 # Sample to fixed number of points when npoints > 0 batch_size = 8 num_workers = 6 nepochs = 25 manual_seed = 123 np.random.seed(manual_seed) torch.manual_seed(manual_seed) torch.cuda.manual_seed(manual_seed) ## Transform rot_max_angle = 15 trans_max_distance = 0.01 RotTransform = GT.Compose([GT.RandomRotate(rot_max_angle, 0), GT.RandomRotate(rot_max_angle, 1), GT.RandomRotate(rot_max_angle, 2)]) TransTransform = GT.RandomTranslate(trans_max_distance) train_transform = GT.Compose([GT.NormalizeScale(), RotTransform, TransTransform]) test_transform = GT.Compose([GT.NormalizeScale(), ]) ## Dataset train_dataset = ShapeNet2(shapenet_dataset, npoints=npoints, category=category, train=True, transform=train_transform) train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers) num_classes = train_dataset.num_classes test_dataset = ShapeNet2(shapenet_dataset, npoints=npoints, category=category, train=False, transform=test_transform) test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=args.batch_size, shuffle=True)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = SGCN(dim_coor=args.dim_coor,
out_dim=args.out_dim,
input_features=args.label_dim,
layers_num=args.layers_num,
model_dim=args.model_dim,
out_channels_1=args.out_channels_1,
dropout=args.dropout,
use_cluster_pooling=args.use_cluster_pooling).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
rotation_0 = T.RandomRotate(degrees=180, axis=0)
rotation_1 = T.RandomRotate(degrees=180, axis=1)
rotation_2 = T.RandomRotate(degrees=180, axis=2)
def train(epoch):
model.train()
loss_all = 0
for data in train_loader:
data = data.to(device)
optimizer.zero_grad()
if args.train_augmentation:
data = rotation_0(data)
data = rotation_1(data)
data = rotation_2(data)
npoints = 2500 # Sample to fixed number of points when npoints > 0
batch_size = 8
num_workers = 6
nepochs = 25
manual_seed = 123
np.random.seed(manual_seed)
torch.manual_seed(manual_seed)
torch.cuda.manual_seed(manual_seed)
# Transform
rot_max_angle = 15
trans_max_distance = 0.01
RotTransform = GT.Compose([
GT.RandomRotate(rot_max_angle, 0),
GT.RandomRotate(rot_max_angle, 1),
GT.RandomRotate(rot_max_angle, 2)
])
TransTransform = GT.RandomTranslate(trans_max_distance)
train_transform = GT.Compose(
[GT.NormalizeScale(), RotTransform, TransTransform])
test_transform = GT.Compose([
GT.NormalizeScale(),
])
# Dataset
train_dataset = ShapeNet2(shapenet_dataset,
npoints=npoints,
category=category,
def data(data_folder,
files_ending,
data_type,
target_class,
task,
REPROCESS,
local_features,
global_features,
indices,
batch_size,
num_workers=2,
data_compression=None,
data_nativeness=None,
hemisphere=None):
'''
Get data loaders and data sets
:param data_folder:
:param files_ending:
:param data_type:
:param target_class:
:param task:
:param REPROCESS:
:param local_features:
:param global_features:
:param indices:
:param batch_size:
:param num_workers:
:return:
'''
path = osp.join(
osp.dirname(osp.realpath(__file__)), '..', 'data/' + 'segmentation' +
f'/{data_compression}_{data_nativeness}_{hemisphere}/{data_type}')
# Transformations
transform = T.Compose([
# T.RandomTranslate(0.1),
# T.RandomFlip(0, p=0.3),
# T.RandomFlip(1, p=0.1),
# T.RandomFlip(2, p=0.3),
# T.FixedPoints(500, replace=False), #32492 16247
T.RandomRotate(360, axis=0),
T.RandomRotate(360, axis=1),
T.RandomRotate(360, axis=2)
])
pre_transform = T.NormalizeScale()
print('Starting dataset processing...')
train_dataset = OurDataset(path,
train=True,
transform=transform,
pre_transform=pre_transform,
target_class=target_class,
task=task,
reprocess=REPROCESS,
local_features=local_features,
global_feature=global_features,
val=False,
indices=indices['Train'],
data_folder=data_folder,
files_ending=files_ending)
test_dataset = OurDataset(path,
train=False,
transform=transform,
pre_transform=pre_transform,
target_class=target_class,
task=task,
reprocess=REPROCESS,
local_features=local_features,
global_feature=global_features,
val=False,
indices=indices['Test'],
data_folder=data_folder,
files_ending=files_ending)
validation_dataset = OurDataset(path,
train=False,
transform=transform,
pre_transform=pre_transform,
target_class=target_class,
task=task,
reprocess=REPROCESS,
local_features=local_features,
global_feature=global_features,
val=True,
indices=indices['Val'],
data_folder=data_folder,
files_ending=files_ending)
num_labels = train_dataset.num_labels
train_loader = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers)
test_loader = DataLoader(test_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
val_loader = DataLoader(validation_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
return train_dataset, test_dataset, validation_dataset, train_loader, test_loader, val_loader, num_labels
