def __call__(self, data):
assert data.face is not None
assert data.pos is not None
assert data.pos.size(-1) == 3
assert data.face.size(0) == 3
mesh = to_trimesh(data)
edge_index, edge_attr = getGeometricEdgeFeatures(
mesh,
pp_features=self.pp_features,
triangle_features=self.triangle_features,
n_components=self.n_components)
if data.x is not None:
device = data.x.device
else:
device = data.edge_index.device
data.edge_attr = torch.tensor(edge_attr,
dtype=torch.float32).to(device)
if self.assign_edges:
data.edge_index = torch.tensor(edge_index.T,
dtype=torch.int64).to(device)
return data
def test_trimesh():
pos = torch.tensor([[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0]],
dtype=torch.float)
face = torch.tensor([[0, 1, 2], [1, 2, 3]]).t()
data = Data(pos=pos, face=face)
mesh = to_trimesh(data)
data = from_trimesh(mesh)
assert pos.tolist() == data.pos.tolist()
assert face.tolist() == data.face.tolist()
def test_trimesh():
pos = torch.tensor([[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0]],
dtype=torch.float)
face = torch.tensor([[0, 1, 2], [1, 2, 3]]).t()
data = Data(pos=pos, face=face)
mesh = to_trimesh(data)
data = from_trimesh(mesh)
perm = (data.pos * torch.tensor([[1.0, 2.0, 3.0]])).sum(dim=-1).argsort()
assert pos.tolist() == data.pos[perm].tolist()
assert face.tolist() == perm[data.face].tolist()
train_dataloader = FAUSTDataLoader(train_dataset, batch_size=batch_size)
val_dataloader = FAUSTDataLoader(val_dataset, batch_size=batch_size)
elif args.datasets == 'full_faust':
dataset = FullFAUST('.')
# TODO: Remove hardcoding of test person
train_dataset, val_dataset = split_faust_by_person(dataset, [1])
train_dataloader = FAUSTDataLoader(train_dataset, batch_size=batch_size)
val_dataloader = FAUSTDataLoader(val_dataset, batch_size=batch_size)
else:
raise NotImplementedError('')
# for DFAUST splitting. 9:1 ratio, with seed from above
print('Template')
template = train_dataset[0]
pv_template = pv.wrap(to_trimesh(template))
print(template)
print()
print('Initialise writer')
writer = writer.MeshWriter(args, pv_template)
shape = 6890
in_channels = 3
# For 1D conv decoder
kernel_size = 5031
# max(
# template.face.T.max(axis=1).values - template.face.T.min(axis=1).values
# )
padding = kernel_size // 2
