598 lines
26 KiB
Python
598 lines
26 KiB
Python
|
"""
|
|||
|
|
Based on PointMLP9, change to 128, 256, 512, 512
|
|||
|
|
Based on PointMLP4, use fps replace random sample.
|
|||
|
|
Based on PointMLP3, change dropout to 0.1
|
|||
|
|
Based on PointMLP2, add blocks in decode, change channel numbers.
|
|||
|
|
Based on PointMLP1, using poseExtraction to replace MLP in decode.
|
|||
|
|
PointsformerE2, 1)change relu to GELU, 2) change backbone to model24.
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
import torch
|
|||
|
|
import torch.nn as nn
|
|||
|
|
import torch.nn.functional as F
|
|||
|
|
from torch import einsum
|
|||
|
|
from einops import rearrange, repeat
|
|||
|
|
from pointnet2_ops import pointnet2_utils
|
|||
|
|
|
|||
|
|
def get_activation(activation):
|
|||
|
|
if activation.lower() == 'gelu':
|
|||
|
|
return nn.GELU()
|
|||
|
|
elif activation.lower() == 'rrelu':
|
|||
|
|
return nn.RReLU(inplace=True)
|
|||
|
|
elif activation.lower() == 'selu':
|
|||
|
|
return nn.SELU(inplace=True)
|
|||
|
|
elif activation.lower() == 'silu':
|
|||
|
|
return nn.SiLU(inplace=True)
|
|||
|
|
elif activation.lower() == 'hardswish':
|
|||
|
|
return nn.Hardswish(inplace=True)
|
|||
|
|
elif activation.lower() == 'leakyrelu':
|
|||
|
|
return nn.LeakyReLU(inplace=True)
|
|||
|
|
elif activation.lower() == 'leakyrelu0.2':
|
|||
|
|
return nn.LeakyReLU(negative_slope=0.2, inplace=True)
|
|||
|
|
else:
|
|||
|
|
return nn.ReLU(inplace=True)
|
|||
|
|
|
|||
|
|
|
|||
|
|
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):
|
|||
|
|
"""
|
|||
|
|
Input:
|
|||
|
|
xyz: pointcloud data, [B, N, 3]
|
|||
|
|
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
|
|||
|
|
farthest = torch.randint(0, N, (B,), dtype=torch.long).to(device)
|
|||
|
|
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)
|
|||
|
|
distance = torch.min(distance, dist)
|
|||
|
|
farthest = torch.max(distance, -1)[1]
|
|||
|
|
return centroids
|
|||
|
|
|
|||
|
|
|
|||
|
|
def query_ball_point(radius, nsample, xyz, new_xyz):
|
|||
|
|
"""
|
|||
|
|
Input:
|
|||
|
|
radius: local region radius
|
|||
|
|
nsample: max sample number in local region
|
|||
|
|
xyz: all points, [B, N, 3]
|
|||
|
|
new_xyz: query points, [B, S, 3]
|
|||
|
|
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
|
|||
|
|
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]
|
|||
|
|
return group_idx
|
|||
|
|
|
|||
|
|
|
|||
|
|
def knn_point(nsample, xyz, new_xyz):
|
|||
|
|
"""
|
|||
|
|
Input:
|
|||
|
|
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]
|
|||
|
|
"""
|
|||
|
|
sqrdists = square_distance(new_xyz, xyz)
|
|||
|
|
_, group_idx = torch.topk(sqrdists, nsample, dim=-1, largest=False, sorted=False)
|
|||
|
|
return group_idx
|
|||
|
|
|
|||
|
|
|
|||
|
|
class LocalGrouper(nn.Module):
|
|||
|
|
def __init__(self, channel, groups, kneighbors, use_xyz=True, normalize="anchor", **kwargs):
|
|||
|
|
"""
|
|||
|
|
Give xyz[b,p,3] and fea[b,p,d], return new_xyz[b,g,3] and new_fea[b,g,k,d]
|
|||
|
|
:param groups: groups number
|
|||
|
|
:param kneighbors: k-nerighbors
|
|||
|
|
:param kwargs: others
|
|||
|
|
"""
|
|||
|
|
super(LocalGrouper, self).__init__()
|
|||
|
|
self.groups = groups
|
|||
|
|
self.kneighbors = kneighbors
|
|||
|
|
self.use_xyz = use_xyz
|
|||
|
|
if normalize is not None:
|
|||
|
|
self.normalize = normalize.lower()
|
|||
|
|
else:
|
|||
|
|
self.normalize = None
|
|||
|
|
if self.normalize not in ["center", "anchor"]:
|
|||
|
|
print(f"Unrecognized normalize parameter (self.normalize), set to None. Should be one of [center, anchor].")
|
|||
|
|
self.normalize = None
|
|||
|
|
if self.normalize is not None:
|
|||
|
|
add_channel=3 if self.use_xyz else 0
|
|||
|
|
self.affine_alpha = nn.Parameter(torch.ones([1,1,1,channel + add_channel]))
|
|||
|
|
self.affine_beta = nn.Parameter(torch.zeros([1, 1, 1, channel + add_channel]))
|
|||
|
|
|
|||
|
|
def forward(self, xyz, points):
|
|||
|
|
B, N, C = xyz.shape
|
|||
|
|
S = self.groups
|
|||
|
|
xyz = xyz.contiguous() # xyz [btach, points, xyz]
|
|||
|
|
|
|||
|
|
# fps_idx = torch.multinomial(torch.linspace(0, N - 1, steps=N).repeat(B, 1).to(xyz.device), num_samples=self.groups, replacement=False).long()
|
|||
|
|
# fps_idx = farthest_point_sample(xyz, self.groups).long()
|
|||
|
|
fps_idx = pointnet2_utils.furthest_point_sample(xyz, self.groups).long() # [B, npoint]
|
|||
|
|
new_xyz = index_points(xyz, fps_idx) # [B, npoint, 3]
|
|||
|
|
new_points = index_points(points, fps_idx) # [B, npoint, d]
|
|||
|
|
|
|||
|
|
idx = knn_point(self.kneighbors, xyz, new_xyz)
|
|||
|
|
# idx = query_ball_point(radius, nsample, xyz, new_xyz)
|
|||
|
|
grouped_xyz = index_points(xyz, idx) # [B, npoint, k, 3]
|
|||
|
|
grouped_points = index_points(points, idx) # [B, npoint, k, d]
|
|||
|
|
if self.use_xyz:
|
|||
|
|
grouped_points = torch.cat([grouped_points, grouped_xyz],dim=-1) # [B, npoint, k, d+3]
|
|||
|
|
if self.normalize is not None:
|
|||
|
|
if self.normalize =="center":
|
|||
|
|
std, mean = torch.std_mean(grouped_points, dim=2, keepdim=True)
|
|||
|
|
if self.normalize =="anchor":
|
|||
|
|
mean = torch.cat([new_points, new_xyz],dim=-1) if self.use_xyz else new_points
|
|||
|
|
mean = mean.unsqueeze(dim=-2) # [B, npoint, 1, d+3]
|
|||
|
|
std = torch.std(grouped_points-mean)
|
|||
|
|
grouped_points = (grouped_points-mean)/(std + 1e-5)
|
|||
|
|
grouped_points = self.affine_alpha*grouped_points + self.affine_beta
|
|||
|
|
|
|||
|
|
new_points = torch.cat([grouped_points, new_points.view(B, S, 1, -1).repeat(1, 1, self.kneighbors, 1)], dim=-1)
|
|||
|
|
return new_xyz, new_points
|
|||
|
|
|
|||
|
|
|
|||
|
|
class ConvBNReLU1D(nn.Module):
|
|||
|
|
def __init__(self, in_channels, out_channels, kernel_size=1, bias=True, activation='relu'):
|
|||
|
|
super(ConvBNReLU1D, self).__init__()
|
|||
|
|
self.act = get_activation(activation)
|
|||
|
|
self.net = nn.Sequential(
|
|||
|
|
nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, bias=bias),
|
|||
|
|
nn.BatchNorm1d(out_channels),
|
|||
|
|
self.act
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
def forward(self, x):
|
|||
|
|
return self.net(x)
|
|||
|
|
|
|||
|
|
|
|||
|
|
class ConvBNReLURes1D(nn.Module):
|
|||
|
|
def __init__(self, channel, kernel_size=1, groups=1, res_expansion=1.0, bias=True, activation='relu'):
|
|||
|
|
super(ConvBNReLURes1D, self).__init__()
|
|||
|
|
self.act = get_activation(activation)
|
|||
|
|
self.net1 = nn.Sequential(
|
|||
|
|
nn.Conv1d(in_channels=channel, out_channels=int(channel * res_expansion),
|
|||
|
|
kernel_size=kernel_size, groups=groups, bias=bias),
|
|||
|
|
nn.BatchNorm1d(int(channel * res_expansion)),
|
|||
|
|
self.act
|
|||
|
|
)
|
|||
|
|
if groups > 1:
|
|||
|
|
self.net2 = nn.Sequential(
|
|||
|
|
nn.Conv1d(in_channels=int(channel * res_expansion), out_channels=channel,
|
|||
|
|
kernel_size=kernel_size, groups=groups, bias=bias),
|
|||
|
|
nn.BatchNorm1d(channel),
|
|||
|
|
self.act,
|
|||
|
|
nn.Conv1d(in_channels=channel, out_channels=channel,
|
|||
|
|
kernel_size=kernel_size, bias=bias),
|
|||
|
|
nn.BatchNorm1d(channel),
|
|||
|
|
)
|
|||
|
|
else:
|
|||
|
|
self.net2 = nn.Sequential(
|
|||
|
|
nn.Conv1d(in_channels=int(channel * res_expansion), out_channels=channel,
|
|||
|
|
kernel_size=kernel_size, bias=bias),
|
|||
|
|
nn.BatchNorm1d(channel)
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
def forward(self, x):
|
|||
|
|
return self.act(self.net2(self.net1(x)) + x)
|
|||
|
|
|
|||
|
|
|
|||
|
|
class PreExtraction(nn.Module):
|
|||
|
|
def __init__(self, channels, out_channels, blocks=1, groups=1, res_expansion=1, bias=True,
|
|||
|
|
activation='relu', use_xyz=True):
|
|||
|
|
"""
|
|||
|
|
input: [b,g,k,d]: output:[b,d,g]
|
|||
|
|
:param channels:
|
|||
|
|
:param blocks:
|
|||
|
|
"""
|
|||
|
|
super(PreExtraction, self).__init__()
|
|||
|
|
in_channels = 3+2*channels if use_xyz else 2*channels
|
|||
|
|
self.transfer = ConvBNReLU1D(in_channels, out_channels, bias=bias, activation=activation)
|
|||
|
|
operation = []
|
|||
|
|
for _ in range(blocks):
|
|||
|
|
operation.append(
|
|||
|
|
ConvBNReLURes1D(out_channels, groups=groups, res_expansion=res_expansion,
|
|||
|
|
bias=bias, activation=activation)
|
|||
|
|
)
|
|||
|
|
self.operation = nn.Sequential(*operation)
|
|||
|
|
|
|||
|
|
def forward(self, x):
|
|||
|
|
b, n, s, d = x.size() # torch.Size([32, 512, 32, 6])
|
|||
|
|
x = x.permute(0, 1, 3, 2)
|
|||
|
|
x = x.reshape(-1, d, s)
|
|||
|
|
x = self.transfer(x)
|
|||
|
|
batch_size, _, _ = x.size()
|
|||
|
|
x = self.operation(x) # [b, d, k]
|
|||
|
|
x = F.adaptive_max_pool1d(x, 1).view(batch_size, -1)
|
|||
|
|
x = x.reshape(b, n, -1).permute(0, 2, 1)
|
|||
|
|
return x
|
|||
|
|
|
|||
|
|
|
|||
|
|
class PosExtraction(nn.Module):
|
|||
|
|
def __init__(self, channels, blocks=1, groups=1, res_expansion=1, bias=True, activation='relu'):
|
|||
|
|
"""
|
|||
|
|
input[b,d,g]; output[b,d,g]
|
|||
|
|
:param channels:
|
|||
|
|
:param blocks:
|
|||
|
|
"""
|
|||
|
|
super(PosExtraction, self).__init__()
|
|||
|
|
operation = []
|
|||
|
|
for _ in range(blocks):
|
|||
|
|
operation.append(
|
|||
|
|
ConvBNReLURes1D(channels, groups=groups, res_expansion=res_expansion, bias=bias, activation=activation)
|
|||
|
|
)
|
|||
|
|
self.operation = nn.Sequential(*operation)
|
|||
|
|
|
|||
|
|
def forward(self, x): # [b, d, g]
|
|||
|
|
return self.operation(x)
|
|||
|
|
|
|||
|
|
|
|||
|
|
class PointNetFeaturePropagation(nn.Module):
|
|||
|
|
def __init__(self, in_channel, out_channel, blocks=1, groups=1, res_expansion=1.0, bias=True, activation='relu'):
|
|||
|
|
super(PointNetFeaturePropagation, self).__init__()
|
|||
|
|
self.fuse = ConvBNReLU1D(in_channel, out_channel, 1, bias=bias)
|
|||
|
|
self.extraction = PosExtraction(out_channel, blocks, groups=groups,
|
|||
|
|
res_expansion=res_expansion, bias=bias, activation=activation)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def forward(self, xyz1, xyz2, points1, points2):
|
|||
|
|
"""
|
|||
|
|
Input:
|
|||
|
|
xyz1: input points position data, [B, N, 3]
|
|||
|
|
xyz2: sampled input points position data, [B, S, 3]
|
|||
|
|
points1: input points data, [B, D', N]
|
|||
|
|
points2: input points data, [B, D'', S]
|
|||
|
|
Return:
|
|||
|
|
new_points: upsampled points data, [B, D''', N]
|
|||
|
|
"""
|
|||
|
|
# xyz1 = xyz1.permute(0, 2, 1)
|
|||
|
|
# xyz2 = xyz2.permute(0, 2, 1)
|
|||
|
|
|
|||
|
|
points2 = points2.permute(0, 2, 1)
|
|||
|
|
B, N, C = xyz1.shape
|
|||
|
|
_, S, _ = xyz2.shape
|
|||
|
|
|
|||
|
|
if S == 1:
|
|||
|
|
interpolated_points = points2.repeat(1, N, 1)
|
|||
|
|
else:
|
|||
|
|
dists = square_distance(xyz1, xyz2)
|
|||
|
|
dists, idx = dists.sort(dim=-1)
|
|||
|
|
dists, idx = dists[:, :, :3], idx[:, :, :3] # [B, N, 3]
|
|||
|
|
|
|||
|
|
dist_recip = 1.0 / (dists + 1e-8)
|
|||
|
|
norm = torch.sum(dist_recip, dim=2, keepdim=True)
|
|||
|
|
weight = dist_recip / norm
|
|||
|
|
interpolated_points = torch.sum(index_points(points2, idx) * weight.view(B, N, 3, 1), dim=2)
|
|||
|
|
|
|||
|
|
if points1 is not None:
|
|||
|
|
points1 = points1.permute(0, 2, 1)
|
|||
|
|
new_points = torch.cat([points1, interpolated_points], dim=-1)
|
|||
|
|
else:
|
|||
|
|
new_points = interpolated_points
|
|||
|
|
|
|||
|
|
new_points = new_points.permute(0, 2, 1)
|
|||
|
|
new_points = self.fuse(new_points)
|
|||
|
|
new_points = self.extraction(new_points)
|
|||
|
|
return new_points
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
class PointMLP31(nn.Module):
|
|||
|
|
def __init__(self, num_classes=50,points=2048, embed_dim=64, groups=1, res_expansion=1.0,
|
|||
|
|
activation="relu", bias=True, use_xyz=True, normalize="anchor",
|
|||
|
|
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
|
|||
|
|
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
|
|||
|
|
de_dims=[512, 256, 128, 128], de_blocks=[2,2,2,2],
|
|||
|
|
gmp_dim=64,cls_dim=64, **kwargs):
|
|||
|
|
super(PointMLP31, self).__init__()
|
|||
|
|
self.stages = len(pre_blocks)
|
|||
|
|
self.class_num = num_classes
|
|||
|
|
self.points = points
|
|||
|
|
self.embedding = ConvBNReLU1D(3, embed_dim, bias=bias, activation=activation)
|
|||
|
|
assert len(pre_blocks) == len(k_neighbors) == len(reducers) == len(pos_blocks) == len(dim_expansion), \
|
|||
|
|
"Please check stage number consistent for pre_blocks, pos_blocks k_neighbors, reducers."
|
|||
|
|
self.local_grouper_list = nn.ModuleList()
|
|||
|
|
self.pre_blocks_list = nn.ModuleList()
|
|||
|
|
self.pos_blocks_list = nn.ModuleList()
|
|||
|
|
last_channel = embed_dim
|
|||
|
|
anchor_points = self.points
|
|||
|
|
en_dims = [last_channel]
|
|||
|
|
### Building Encoder #####
|
|||
|
|
for i in range(len(pre_blocks)):
|
|||
|
|
out_channel = last_channel * dim_expansion[i]
|
|||
|
|
pre_block_num = pre_blocks[i]
|
|||
|
|
pos_block_num = pos_blocks[i]
|
|||
|
|
kneighbor = k_neighbors[i]
|
|||
|
|
reduce = reducers[i]
|
|||
|
|
anchor_points = anchor_points // reduce
|
|||
|
|
# append local_grouper_list
|
|||
|
|
local_grouper = LocalGrouper(last_channel, anchor_points, kneighbor, use_xyz, normalize) # [b,g,k,d]
|
|||
|
|
self.local_grouper_list.append(local_grouper)
|
|||
|
|
# append pre_block_list
|
|||
|
|
pre_block_module = PreExtraction(last_channel, out_channel, pre_block_num, groups=groups,
|
|||
|
|
res_expansion=res_expansion,
|
|||
|
|
bias=bias, activation=activation, use_xyz=use_xyz)
|
|||
|
|
self.pre_blocks_list.append(pre_block_module)
|
|||
|
|
# append pos_block_list
|
|||
|
|
pos_block_module = PosExtraction(out_channel, pos_block_num, groups=groups,
|
|||
|
|
res_expansion=res_expansion, bias=bias, activation=activation)
|
|||
|
|
self.pos_blocks_list.append(pos_block_module)
|
|||
|
|
|
|||
|
|
last_channel = out_channel
|
|||
|
|
en_dims.append(last_channel)
|
|||
|
|
|
|||
|
|
|
|||
|
|
### Building Decoder #####
|
|||
|
|
self.decode_list = nn.ModuleList()
|
|||
|
|
en_dims.reverse()
|
|||
|
|
de_dims.insert(0,en_dims[0])
|
|||
|
|
assert len(en_dims) ==len(de_dims) == len(de_blocks)+1
|
|||
|
|
for i in range(len(en_dims)-1):
|
|||
|
|
self.decode_list.append(
|
|||
|
|
PointNetFeaturePropagation(de_dims[i]+en_dims[i+1], de_dims[i+1],
|
|||
|
|
blocks=de_blocks[i], groups=groups, res_expansion=res_expansion,
|
|||
|
|
bias=bias, activation=activation)
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
self.act = get_activation(activation)
|
|||
|
|
|
|||
|
|
# class label mapping
|
|||
|
|
self.cls_map = nn.Sequential(
|
|||
|
|
ConvBNReLU1D(16, cls_dim, bias=bias, activation=activation),
|
|||
|
|
ConvBNReLU1D(cls_dim, cls_dim, bias=bias, activation=activation)
|
|||
|
|
)
|
|||
|
|
# global max pooling mapping
|
|||
|
|
self.gmp_map_list = nn.ModuleList()
|
|||
|
|
for en_dim in en_dims:
|
|||
|
|
self.gmp_map_list.append(ConvBNReLU1D(en_dim, gmp_dim, bias=bias, activation=activation))
|
|||
|
|
self.gmp_map_end = ConvBNReLU1D(gmp_dim*len(en_dims), gmp_dim, bias=bias, activation=activation)
|
|||
|
|
|
|||
|
|
# classifier
|
|||
|
|
self.classifier = nn.Sequential(
|
|||
|
|
nn.Conv1d(gmp_dim+cls_dim+de_dims[-1], 128, 1, bias=bias),
|
|||
|
|
nn.BatchNorm1d(128),
|
|||
|
|
self.act,
|
|||
|
|
nn.Dropout(),
|
|||
|
|
nn.Conv1d(128, num_classes, 1, bias=bias)
|
|||
|
|
)
|
|||
|
|
self.en_dims = en_dims
|
|||
|
|
|
|||
|
|
def forward(self, x, cls_label):
|
|||
|
|
xyz = x.permute(0, 2, 1)
|
|||
|
|
x = self.embedding(x) # B,D,N
|
|||
|
|
|
|||
|
|
xyz_list = [xyz] # [B, N, 3]
|
|||
|
|
x_list = [x] # [B, D, N]
|
|||
|
|
|
|||
|
|
# here is the encoder
|
|||
|
|
for i in range(self.stages):
|
|||
|
|
# Give xyz[b, p, 3] and fea[b, p, d], return new_xyz[b, g, 3] and new_fea[b, g, k, d]
|
|||
|
|
xyz, x = self.local_grouper_list[i](xyz, x.permute(0, 2, 1)) # [b,g,3] [b,g,k,d]
|
|||
|
|
x = self.pre_blocks_list[i](x) # [b,d,g]
|
|||
|
|
x = self.pos_blocks_list[i](x) # [b,d,g]
|
|||
|
|
xyz_list.append(xyz)
|
|||
|
|
x_list.append(x)
|
|||
|
|
|
|||
|
|
# here is the decoder
|
|||
|
|
xyz_list.reverse()
|
|||
|
|
x_list.reverse()
|
|||
|
|
x = x_list[0]
|
|||
|
|
for i in range(len(self.decode_list)):
|
|||
|
|
x = self.decode_list[i](xyz_list[i+1], xyz_list[i], x_list[i+1],x)
|
|||
|
|
|
|||
|
|
# here is the global context
|
|||
|
|
gmp_list = []
|
|||
|
|
for i in range(len(x_list)):
|
|||
|
|
gmp_list.append(F.adaptive_max_pool1d(self.gmp_map_list[i](x_list[i]), 1))
|
|||
|
|
global_context = self.gmp_map_end(torch.cat(gmp_list, dim=1)) # [b, gmp_dim, 1]
|
|||
|
|
|
|||
|
|
#here is the cls_token
|
|||
|
|
cls_token = self.cls_map(cls_label.unsqueeze(dim=-1)) # [b, cls_dim, 1]
|
|||
|
|
x = torch.cat([x, global_context.repeat([1, 1, x.shape[-1]]), cls_token.repeat([1, 1, x.shape[-1]])], dim=1)
|
|||
|
|
x = self.classifier(x)
|
|||
|
|
# x = F.log_softmax(x, dim=1)
|
|||
|
|
# x = x.permute(0, 2, 1)
|
|||
|
|
return x
|
|||
|
|
|
|||
|
|
|
|||
|
|
def model31A(num_classes=50, **kwargs) -> PointMLP31:
|
|||
|
|
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
|
|||
|
|
activation="relu", bias=True, use_xyz=False, normalize="anchor",
|
|||
|
|
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
|
|||
|
|
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
|
|||
|
|
de_dims=[512, 256, 128, 128], de_blocks=[4,4,4,4],
|
|||
|
|
gmp_dim=64,cls_dim=64, **kwargs)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def model31B(num_classes=50, **kwargs) -> PointMLP31:
|
|||
|
|
return PointMLP31(num_classes=num_classes,points=2048, embed_dim=64, groups=1, res_expansion=1.0,
|
|||
|
|
activation="relu", bias=True, use_xyz=False, normalize="anchor",
|
|||
|
|
dim_expansion=[2, 2, 2, 2], pre_blocks=[3, 3, 3, 3], pos_blocks=[3, 3, 3, 3],
|
|||
|
|
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
|
|||
|
|
de_dims=[512, 256, 128, 128], de_blocks=[3, 3, 3, 3],
|
|||
|
|
gmp_dim=64,cls_dim=64, **kwargs)
|
|||
|
|
|
|||
|
|
def model31C(num_classes=50, **kwargs) -> PointMLP31:
|
|||
|
|
return PointMLP31(num_classes=num_classes,points=2048, embed_dim=64, groups=1, res_expansion=1.0,
|
|||
|
|
activation="relu", bias=True, use_xyz=False, normalize="anchor",
|
|||
|
|
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
|
|||
|
|
k_neighbors=[24, 24, 24, 24], reducers=[4, 4, 4, 4],
|
|||
|
|
de_dims=[512, 256, 128, 128], de_blocks=[3, 3, 3, 3],
|
|||
|
|
gmp_dim=64,cls_dim=64, **kwargs)
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
def model31D(num_classes=50, **kwargs) -> PointMLP31:
|
|||
|
|
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=32, groups=1, res_expansion=1.0,
|
|||
|
|
activation="relu", bias=True, use_xyz=False, normalize="anchor",
|
|||
|
|
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
|
|||
|
|
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
|
|||
|
|
de_dims=[512, 256, 128, 128], de_blocks=[4,4,4,4],
|
|||
|
|
gmp_dim=64,cls_dim=64, **kwargs)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def model31E(num_classes=50, **kwargs) -> PointMLP31:
|
|||
|
|
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
|
|||
|
|
activation="relu", bias=True, use_xyz=False, normalize="anchor",
|
|||
|
|
dim_expansion=[2, 2, 2, 2], pre_blocks=[4, 4, 4, 4], pos_blocks=[4, 4, 4, 4],
|
|||
|
|
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
|
|||
|
|
de_dims=[512, 256, 128, 128], de_blocks=[6,6,6,6],
|
|||
|
|
gmp_dim=64,cls_dim=64, **kwargs)
|
|||
|
|
|
|||
|
|
def model31F(num_classes=50, **kwargs) -> PointMLP31:
|
|||
|
|
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
|
|||
|
|
activation="relu", bias=True, use_xyz=False, normalize="anchor",
|
|||
|
|
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
|
|||
|
|
k_neighbors=[32, 32, 32, 32], reducers=[4, 2, 4, 2],
|
|||
|
|
de_dims=[512, 256, 128, 128], de_blocks=[4,4,4,4],
|
|||
|
|
gmp_dim=64,cls_dim=64, **kwargs)
|
|||
|
|
|
|||
|
|
def model31G(num_classes=50, **kwargs) -> PointMLP31:
|
|||
|
|
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
|
|||
|
|
activation="relu", bias=True, use_xyz=True, normalize="anchor",
|
|||
|
|
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
|
|||
|
|
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
|
|||
|
|
de_dims=[512, 256, 128, 128], de_blocks=[4,4,4,4],
|
|||
|
|
gmp_dim=64,cls_dim=64, **kwargs)
|
|||
|
|
|
|||
|
|
def model31G1(num_classes=50, **kwargs) -> PointMLP31:
|
|||
|
|
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
|
|||
|
|
activation="leakyrelu0.2", bias=True, use_xyz=True, normalize="anchor",
|
|||
|
|
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
|
|||
|
|
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
|
|||
|
|
de_dims=[512, 256, 128, 128], de_blocks=[4,4,4,4],
|
|||
|
|
gmp_dim=64,cls_dim=64, **kwargs)
|
|||
|
|
|
|||
|
|
def model31G2(num_classes=50, **kwargs) -> PointMLP31:
|
|||
|
|
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
|
|||
|
|
activation="leakyrelu", bias=True, use_xyz=True, normalize="anchor",
|
|||
|
|
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
|
|||
|
|
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
|
|||
|
|
de_dims=[512, 256, 128, 128], de_blocks=[4,4,4,4],
|
|||
|
|
gmp_dim=64,cls_dim=64, **kwargs)
|
|||
|
|
|
|||
|
|
def model31G3(num_classes=50, **kwargs) -> PointMLP31:
|
|||
|
|
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
|
|||
|
|
activation="gelu", bias=True, use_xyz=True, normalize="anchor",
|
|||
|
|
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
|
|||
|
|
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
|
|||
|
|
de_dims=[512, 256, 128, 128], de_blocks=[4,4,4,4],
|
|||
|
|
gmp_dim=64,cls_dim=64, **kwargs)
|
|||
|
|
|
|||
|
|
def model31H(num_classes=50, **kwargs) -> PointMLP31:
|
|||
|
|
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
|
|||
|
|
activation="gelu", bias=True, use_xyz=True, normalize="anchor",
|
|||
|
|
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
|
|||
|
|
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
|
|||
|
|
de_dims=[512, 256, 128, 128], de_blocks=[4,4,4,4],
|
|||
|
|
gmp_dim=64,cls_dim=64, **kwargs)
|
|||
|
|
|
|||
|
|
def model31I(num_classes=50, **kwargs) -> PointMLP31:
|
|||
|
|
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
|
|||
|
|
activation="relu", bias=True, use_xyz=True, normalize="anchor",
|
|||
|
|
dim_expansion=[2, 2, 2, 1], pre_blocks=[3, 3, 3, 3], pos_blocks=[3, 3, 3, 3],
|
|||
|
|
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
|
|||
|
|
de_dims=[512, 256, 128, 128], de_blocks=[4, 4, 4, 4],
|
|||
|
|
gmp_dim=64,cls_dim=64, **kwargs)
|
|||
|
|
|
|||
|
|
def model31J(num_classes=50, **kwargs) -> PointMLP31:
|
|||
|
|
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
|
|||
|
|
activation="leakyrelu", bias=True, use_xyz=True, normalize="anchor",
|
|||
|
|
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
|
|||
|
|
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
|
|||
|
|
de_dims=[512, 256, 128, 128], de_blocks=[4,4,4,4],
|
|||
|
|
gmp_dim=64,cls_dim=64, **kwargs)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def model31K(num_classes=50, **kwargs) -> PointMLP31:
|
|||
|
|
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
|
|||
|
|
activation="leakyrelu0.2", bias=True, use_xyz=True, normalize="anchor",
|
|||
|
|
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
|
|||
|
|
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
|
|||
|
|
de_dims=[512, 256, 128, 128], de_blocks=[4,4,4,4],
|
|||
|
|
gmp_dim=64,cls_dim=64, **kwargs)
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == '__main__':
|
|||
|
|
data = torch.rand(2, 3, 2048)
|
|||
|
|
norm = torch.rand(2, 3, 2048)
|
|||
|
|
cls_label = torch.rand([2, 16])
|
|||
|
|
print("===> testing modelD ...")
|
|||
|
|
model = model31D(50)
|
|||
|
|
out = model(data, cls_label) # [2,2048,50]
|
|||
|
|
print(out.shape)
|
|||
|
|
|
|||
|
|
model = model31A(50)
|
|||
|
|
out = model(data, cls_label) # [2,2048,50]
|
|||
|
|
print(out.shape)
|
|||
|
|
|
|||
|
|
model = model31B(50)
|
|||
|
|
out = model(data, cls_label) # [2,2048,50]
|
|||
|
|
print(out.shape)
|
|||
|
|
|
|||
|
|
model = model31C(50)
|
|||
|
|
out = model(data, cls_label) # [2,50, 2048]
|
|||
|
|
print(out.shape)
|