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def knn(x, k, add_one_to_k=False):
if add_one_to_k: k = k + 1
inner = -2 * torch.matmul(x.transpose(2, 1).contiguous(), x)
xx = torch.sum(x**2, dim=1, keepdim=True)
pairwise_distance = -xx - inner - xx.transpose(2, 1).contiguous()
idx = pairwise_distance.topk(k=k, dim=-1)[1] # (batch_size, num_points, k)
return idx
def pc_normalize(pc):
l = pc.shape[0]
centroid = np.mean(pc, axis=0)
pc = pc - centroid
m = np.max(np.sqrt(np.sum(pc**2, axis=1)))
pc = pc / m
return pc
def square_distance(src, dst):
"""
Calculate Euclid distance between each two points.
src^T * dst = xn * xm + yn * ym + zn * zm；
sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn;
sum(dst^2, dim=-1) = xm*xm + ym*ym + zm*zm;
dist = (xn - xm)^2 + (yn - ym)^2 + (zn - zm)^2
= sum(src**2,dim=-1)+sum(dst**2,dim=-1)-2*src^T*dst
Input:
src: source points, [B, N, C]
dst: target points, [B, M, C]
Output:
dist: per-point square distance, [B, N, M]
"""
B, N, _ = src.shape
_, M, _ = dst.shape
dist = -2 * torch.matmul(src, dst.permute(0, 2, 1))
dist += torch.sum(src ** 2, -1).view(B, N, 1)
dist += torch.sum(dst ** 2, -1).view(B, 1, M)
return dist
def index_points(points, idx):
"""
Input:
points: input points data, [B, N, C]
idx: sample index data, [B, S]
Return:
new_points:, indexed points data, [B, S, C]
"""
device = points.device
B = points.shape[0]
view_shape = list(idx.shape)
view_shape[1:] = [1] * (len(view_shape) - 1)
repeat_shape = list(idx.shape)
repeat_shape[0] = 1
batch_indices = torch.arange(B, dtype=torch.long).to(device).view(view_shape).repeat(repeat_shape)
new_points = points[batch_indices, idx, :]
return new_points
def farthest_point_sample(xyz, npoint, start_with_first_point=False):
"""
Input:
xyz: pointcloud data, [B, N, C]
npoint: number of samples
Return:
centroids: sampled pointcloud index, [B, npoint]
"""
device = xyz.device
B, N, C = xyz.shape
centroids = torch.zeros(B, npoint, dtype=torch.long).to(device)
distance = torch.ones(B, N).to(device) * 1e10
if not start_with_first_point:
farthest = torch.randint(0, N, (B,), dtype=torch.long).to(device)
else:
farthest = torch.randint(0, N, (B,), dtype=torch.long).to(device) * 0
batch_indices = torch.arange(B, dtype=torch.long).to(device)
for i in range(npoint):
centroids[:, i] = farthest
centroid = xyz[batch_indices, farthest, :].view(B, 1, 3)
dist = torch.sum((xyz - centroid) ** 2, -1)
mask = dist < distance
distance[mask] = dist[mask]
farthest = torch.max(distance, -1)[1]
return centroids
def knn_point(k, pos1, pos2):
'''
Input:
k: int32, number of k in k-nn search
pos1: (batch_size, ndataset, c) float32 array, input points
pos2: (batch_size, npoint, c) float32 array, query points
Output:
val: (batch_size, npoint, k) float32 array, L2 distances
idx: (batch_size, npoint, k) int32 array, indices to input points
'''
B, N, C = pos1.shape
M = pos2.shape[1]
pos1 = pos1.view(B,1,N,-1).repeat(1,M,1,1)
pos2 = pos2.view(B,M,1,-1).repeat(1,1,N,1)
dist = torch.sum(-(pos1-pos2)**2,-1)
val,idx = dist.topk(k=k,dim = -1)
return torch.sqrt(-val), idx
def query_ball_point(radius, nsample, xyz, new_xyz, get_cnt=False):
"""
Input:
radius: local region radius
nsample: max sample number in local region
xyz: all points, [B, N, C]
new_xyz: query points, [B, S, C]
Return:
group_idx: grouped points index, [B, S, nsample]
"""
device = xyz.device
B, N, C = xyz.shape
_, S, _ = new_xyz.shape
group_idx = torch.arange(N, dtype=torch.long).to(device).view(1, 1, N).repeat([B, S, 1])
sqrdists = square_distance(new_xyz, xyz)
group_idx[sqrdists > radius ** 2] = N
if get_cnt:
mask = group_idx != N
cnt = mask.sum(dim=-1)
group_idx = group_idx.sort(dim=-1)[0][:, :, :nsample]
group_first = group_idx[:, :, 0].view(B, S, 1).repeat([1, 1, nsample])
mask = group_idx == N
group_idx[mask] = group_first[mask]
if get_cnt:
return group_idx, cnt
else:
return group_idx
def get_graph_feature(x, k=20, device=None):
# x = x.squeeze()
x = x.view(*x.size()[:3])
idx = knn(x, k=k) # (batch_size, num_points, k)
batch_size, num_points, _ = idx.size()
if device is None:
device = 'cuda' if torch.cuda.is_available() else 'cpu'
idx_base = torch.arange(0, batch_size, device=device).view(-1, 1, 1) * num_points
idx = idx + idx_base
idx = idx.view(-1)
_, num_dims, _ = x.size()
x = x.transpose(2, 1).contiguous() # (batch_size, num_points, num_dims) -> (batch_size*num_points, num_dims) # batch_size * num_points * k + range(0, batch_size*num_points)
feature = x.view(batch_size * num_points, -1)[idx, :]
feature = feature.view(batch_size, num_points, k, num_dims)
x = x.view(batch_size, num_points, 1, num_dims).repeat(1, 1, k, 1)
feature = torch.cat((feature, x), dim=3).permute(0, 3, 1, 2)
return feature |