def forward(self, pos1, pos2, feature1, feature2):
"""
Input:
xyz1: input points position data, [B, C, N]
xyz2: sampled input points position data, [B, C, S]
points1: input points data, [B, D, N]
points2: input points data, [B, D, S]
Return:
new_points: upsampled points data, [B, D', N]
"""
pos1_t = pos1.permute(0, 2, 1).contiguous()
pos2_t = pos2.permute(0, 2, 1).contiguous()
B, C, N = pos1.shape
# dists = square_distance(pos1, pos2)
# dists, idx = dists.sort(dim=-1)
# dists, idx = dists[:, :, :3], idx[:, :, :3] # [B, N, 3]
dists, idx = pointutils.three_nn(pos1_t, pos2_t) # [B, N, K=3]
dists[dists < 1e-10] = 1e-10
weight = 1.0 / dists
weight = weight / torch.sum(weight, -1, keepdim=True)
interpolated_feat = torch.sum(
pointutils.grouping_operation(feature2, idx) *
weight.view(B, 1, N, 3),
dim=-1) # [B, C, N, S=3] -> [B, C, N]
if feature1 is not None:
feat_new = torch.cat([interpolated_feat, feature1], 1)
else:
feat_new = interpolated_feat
for i, conv in enumerate(self.mlp_convs):
bn = self.mlp_bns[i]
feat_new = F.relu(bn(conv(feat_new)))
return feat_new
def forward(self, unknown, known, unknow_feats, known_feats):
# type: (PointnetFPModule, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor) -> torch.Tensor
r"""
Parameters
----------
unknown : torch.Tensor
(B, n, 3) tensor of the xyz positions of the unknown features
known : torch.Tensor
(B, m, 3) tensor of the xyz positions of the known features
unknow_feats : torch.Tensor
(B, C1, n) tensor of the features to be propigated to
known_feats : torch.Tensor
(B, C2, m) tensor of features to be propigated
Returns
-------
new_features : torch.Tensor
(B, mlp[-1], n) tensor of the features of the unknown features
"""
#print(unknown.shape, known.shape, unknow_feats.shape, known_feats.shape)
#import pdb; pdb.set_trace()
if known is not None:
dist, idx = pointnet2_utils.three_nn(unknown, known)
dist_recip = 1.0 / (dist + 1e-8)
norm = torch.sum(dist_recip, dim=2, keepdim=True)
weight = dist_recip / norm
#print(known_feats.shape, idx.shape, weight.shape)
interpolated_feats = pointnet2_utils.three_interpolate(
known_feats, idx, weight)
else:
interpolated_feats = known_feats.expand(*(known_feats.size()[0:2] +
[unknown.size(1)]))
if unknow_feats is not None:
new_features = torch.cat([interpolated_feats, unknow_feats],
dim=1) # (B, C2 + C1, n)
else:
new_features = interpolated_feats
new_features = new_features.unsqueeze(-1)
new_features = self.mlp(new_features)
return new_features.squeeze(-1)
def forward(self, unknown: torch.Tensor, known: torch.Tensor,
unknow_feats: torch.Tensor,
known_feats: torch.Tensor) -> torch.Tensor:
r"""
Parameters
----------
unknown : torch.Tensor
(B, n, 3) tensor of the xyz positions of the unknown features
known : torch.Tensor
(B, m, 3) tensor of the xyz positions of the known features
unknow_feats : torch.Tensor
(B, C1, n) tensor of the features to be propigated to
known_feats : torch.Tensor
(B, C2, m) tensor of features to be propigated
Returns
-------
new_features : torch.Tensor
(B, mlp[-1], n) tensor of the features of the unknown features
"""
if known is not None:
dist, idx = pointnet2_utils.three_nn(unknown, known)
dist_recip = 1.0 / (dist + 1e-8)
norm = torch.sum(dist_recip, dim=2, keepdim=True)
weight = dist_recip / norm
interpolated_feats = pointnet2_utils.three_interpolate(
known_feats, idx, weight)
else:
interpolated_feats = known_feats.expand(*known_feats.size()[0:2],
unknown.size(1))
if unknow_feats is not None:
new_features = torch.cat([interpolated_feats, unknow_feats],
dim=1) #(B, C2 + C1, n)
else:
new_features = interpolated_feats
new_features = new_features.unsqueeze(-1)
new_features = self.mlp(new_features)
return new_features.squeeze(-1)
def forward(self, unknown, known, known_feats):
# type: (PointnetFPModule, torch.Tensor, torch.Tensor, torch.Tensor) -> torch.Tensor
r"""
Parameters
----------
unknown : torch.Tensor
(B, n, 3) tensor of the xyz positions of the unknown features
known : torch.Tensor
(B, m, 3) tensor of the xyz positions of the known features
unknown_feats : torch.Tensor
(B, C1, n) tensor of the features to be propogated to
known_feats : torch.Tensor
(B, C2, m) tensor of features to be propogated
Returns
-------
new_features : torch.Tensor
(B, mlp[-1], n) tensor of the features of the unknown features
"""
if known is not None:
dist, idx = pointnet2_utils.three_nn(unknown, known) # 三点最近邻
dist_recip = 1.0 / (dist + 1e-8)
norm = torch.sum(dist_recip, dim=2, keepdim=True)
weight = dist_recip / norm # 计算权重
interpolated_feats = pointnet2_utils.three_interpolate(
known_feats, idx, weight) # 插值
else:
interpolated_feats = known_feats.expand(*(known_feats.size()[0:2] +
[unknown.size(1)]))
new_features = interpolated_feats
# new_features = new_features.unsqueeze(-1)
# new_features = self.mlp(new_features)
# return new_features.squeeze(-1)
return new_features # 不用mlp处理
def forward(self, pointcloud: torch.cuda.FloatTensor, cls):
# x: B,3,N
xyz, features = self._break_up_pc(pointcloud)
num_pts = xyz.size(1)
batch_size = xyz.size(0)
# FPS to find different point subsets and their relations
subset1_idx = pointnet2_utils.furthest_point_sample(xyz, num_pts //
4).long() # B,N/2
subset1_xyz = torch.unsqueeze(subset1_idx, -1).repeat(1, 1,
3) # B,N/2,3
subset1_xyz = torch.take(xyz, subset1_xyz) # B,N/2,3
dist, idx1 = pointnet2_utils.three_nn(xyz, subset1_xyz)
dist_recip = 1.0 / (dist + 1e-8)
norm = torch.sum(dist_recip, dim=2, keepdim=True)
weight1 = dist_recip / norm
subset12_idx = pointnet2_utils.furthest_point_sample(
subset1_xyz, num_pts // 16).long() # B,N/4
subset12_xyz = torch.unsqueeze(subset12_idx, -1).repeat(1, 1,
3) # B,N/4,3
subset12_xyz = torch.take(subset1_xyz, subset12_xyz) # B,N/4,3
dist, idx12 = pointnet2_utils.three_nn(subset1_xyz, subset12_xyz)
dist_recip = 1.0 / (dist + 1e-8)
norm = torch.sum(dist_recip, dim=2, keepdim=True)
weight12 = dist_recip / norm
device = torch.device('cuda')
centroid = torch.zeros([batch_size, 1, 3], device=device)
dist, idx0 = pointnet2_utils.three_nn(subset12_xyz, centroid)
dist_recip = 1.0 / (dist + 1e-8)
norm = torch.sum(dist_recip, dim=2, keepdim=True)
weight0 = dist_recip / norm
#######################################
# Error-minimizing module 1:
# Encoding
x = xyz.transpose(2, 1) # x: B,3,N
x1_1 = x
x = get_adptive_dilated_graph_feature(x,
self.conv_op1,
self.conv_op11,
self.conv_op12,
d=5,
k=20)
x = self.conv1(x) # B,64,N,k
x = self.conv14(x) # B,64,N,k
x1_2 = x
# Back-projection
x = self.conv11(x) # B,3,N,1
x = torch.squeeze(x, -1) # B,3,N
x1_3 = x
# Calculating Error
delta_1 = x1_3 - x1_1 # B,3,N
# Output
x = x1_2 # B,64,N,k
x1 = x.max(dim=-1, keepdim=False)[0] # B,64,N
#######################################
#######################################
# Multi-resolution (MR) Branch
# Down-scaling 1
subset1_feat = torch.unsqueeze(subset1_idx, -1).repeat(1, 1,
64) # B,N/2,64
x1_subset1 = torch.take(x1.transpose(1, 2).contiguous(),
subset1_feat).transpose(
1, 2).contiguous() # B,64,N/2
x2_1 = x1_subset1 # B,64,N/2
x = get_graph_feature(x1_subset1, k=self.k // 2)
x = self.conv2(x) # B,64,N/2,k
x = self.conv24(x) # B,128,N/2,k
x2 = x.max(dim=-1, keepdim=False)[0] # B,128,N/2
# Dense-connection
x12 = pointnet2_utils.three_interpolate(x2, idx1, weight1) # B,128,N
x12 = torch.cat((x12, x1), dim=1) # B,192,N
x12 = self.conv23(x12) # B,128,N
# Down-scaling 2
subset12_feat = torch.unsqueeze(subset12_idx,
-1).repeat(1, 1, 128) # B,N/4,128
x2_subset12 = torch.take(
x2.transpose(1, 2).contiguous(),
subset12_feat).transpose(1, 2).contiguous() # B,128,N/4
x3_1 = x2_subset12 # B,128,N/4
x = get_graph_feature(x2_subset12, k=self.k // 4)
x = self.conv3(x) # B,256,N/4,k
x3 = x.max(dim=-1, keepdim=False)[0] # B,256,N/4
# Dense-connection
x23 = pointnet2_utils.three_interpolate(x3, idx12,
weight12) # B,256,N/2
x23 = torch.cat((x23, x2), dim=1) # B,384,N/2
x23 = self.conv34(x23) # B,128,N/2
x123 = pointnet2_utils.three_interpolate(x23, idx1, weight1) # B,128,N
x123 = torch.cat((x123, x12, x1), dim=1) # B,320,N
x123 = self.conv35(x123) # B,128,N
# Down-scaling 3
x_bot = self.conv53(x3)
x_bot = self.conv54(x_bot) # B,1024,N/128
x_bot = F.adaptive_max_pool1d(x_bot, 1) # B,1024,1
# Upsampling 3:
interpolated_feats1 = pointnet2_utils.three_interpolate(
x_bot, idx0, weight0) # B,1024,N/4
interpolated_feats2 = x3 # B,256,N/4
x3_up = torch.cat((interpolated_feats1, interpolated_feats2),
dim=1) # B,1280,N/4
x3_up = self.conv32(x3_up) # B,256,N/4
x3_up = self.conv33(x3_up) # B,256,N/4
# Upsampling 2:
interpolated_feats1 = pointnet2_utils.three_interpolate(
x3_up, idx12, weight12) # B,256,N/2
interpolated_feats2 = x2 # B,128,N/2
interpolated_feats3 = x23 # B,128,N/2
x2_up = torch.cat(
(interpolated_feats1, interpolated_feats3, interpolated_feats2),
dim=1) # B,512,N/2
x2_up = self.conv21(x2_up) # B,256,N/2
x2_up = self.conv22(x2_up) # B,128,N/2
# Upsampling 1:
interpolated_feats1 = pointnet2_utils.three_interpolate(
x2_up, idx1, weight1) # B,128,N
interpolated_feats2 = x1 # B,64,N
interpolated_feats3 = x12 # B,128,N
interpolated_feats4 = x123 # B,128,N
x1_up = torch.cat((interpolated_feats1, interpolated_feats4,
interpolated_feats3, interpolated_feats2),
dim=1) # B,448,N
x1_up = self.conv12(x1_up) # B,512,N
x1_up = self.conv13(x1_up) # B,1024,N
x_mr = x1_up
#############################################################################
#############################################################################
# Full-resolution Branch
# Error-minimizing module 2:
# Encoding
x2_1 = x1 # B,64,N
x = get_adptive_dilated_graph_feature(x1,
self.conv_op2,
self.conv_op21,
self.conv_op22,
d=5,
k=20)
x = self.convfc2(x) # B,64,N,k
x = self.convfc24(x) # B,64,N,k
x2_2 = x
# Back-projection
x = self.convfc21(x) # B,64,N,1
x = torch.squeeze(x, -1) # B,64,N
x2_3 = x
# Calculating Error
delta_2 = x2_3 - x2_1 # B,64,N
# Output
x = x2_2 # B,64,N,k
x2 = x.max(dim=-1, keepdim=False)[0] # B,64,N
#######################################
# Error-minimizing module 3:
# Encoding
x3_1 = x2 # B,64,N
x = get_adptive_dilated_graph_feature(x2,
self.conv_op3,
self.conv_op31,
self.conv_op32,
d=5,
k=20)
x = self.convfc3(x) # B,128,N,k
x3_2 = x
# Back-projection
x = self.convfc31(x) # B,64,N,1
x = torch.squeeze(x, -1) # B,64,N
x3_3 = x
# Calculating Error
delta_3 = x3_3 - x3_1 # B,64,N
# Output
x = x3_2 # B,128,N,k
x3 = x.max(dim=-1, keepdim=False)[0] # B,128,N
#######################################
# Error-minimizing module 4:
# Encoding
x4_1 = x3 # B,128,N
x = get_adptive_dilated_graph_feature(x3,
self.conv_op4,
self.conv_op41,
self.conv_op42,
d=5,
k=20)
x = self.convfc4(x) # B,256,N,k
x4_2 = x
# Back-projection
x = self.convfc41(x) # B,128,N,1
x = torch.squeeze(x, -1) # B,128,N
x4_3 = x
# Calculating Error
delta_4 = x4_3 - x4_1 # B,128,N
# Output
x = x4_2 # B,256,N,k
x4 = x.max(dim=-1, keepdim=False)[0] # B,256,N
x = torch.cat((x1, x2, x3, x4), dim=1) # B,512,N
x_fr = self.conv7(x) # B,1024,N
# Fusing FR and MR outputs
fusion_score = self.fuse(x_mr)
x = x_fr + x_fr * fusion_score
x_all = self.conv9(x) # B,1024,N
# Collecting global feature
one_hot_label = cls.view(-1, 16, 1) # B,16,1
one_hot_label = self.conv5(one_hot_label) # B,64,1
x_max = F.adaptive_max_pool1d(x_all, 1) # B,1024,1
x_global = torch.cat((x_max, one_hot_label), dim=1) # B,1088,1
x_global = x_global.repeat(1, 1, num_pts) # B,1088,N
x = torch.cat((x_all, x_global), dim=1) # B,2112,N
x = self.conv8(x) # B,1024,N
x = self.conv63(x) # B,128,N
x = self.dp(x)
x = self.conv64(x) # B,50,N
return (x.transpose(2,
1).contiguous(), delta_1.transpose(2,
1).contiguous(),
delta_2.transpose(2, 1).contiguous(),
delta_3.transpose(2, 1).contiguous(),
delta_4.transpose(2, 1).contiguous())