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import torch
import torch.nn as nn
from StructDiffusion.utils.pointnet import farthest_point_sample, index_points, square_distance
# adapted from https://github.com/qq456cvb/Point-Transformers
def sample_and_group(npoint, nsample, xyz, points):
B, N, C = xyz.shape
S = npoint
fps_idx = farthest_point_sample(xyz, npoint) # [B, npoint]
new_xyz = index_points(xyz, fps_idx)
new_points = index_points(points, fps_idx)
dists = square_distance(new_xyz, xyz) # B x npoint x N
idx = dists.argsort()[:, :, :nsample] # B x npoint x K
grouped_points = index_points(points, idx)
grouped_points_norm = grouped_points - new_points.view(B, S, 1, -1)
new_points = torch.cat([grouped_points_norm, new_points.view(B, S, 1, -1).repeat(1, 1, nsample, 1)], dim=-1)
return new_xyz, new_points
class Local_op(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm1d(out_channels)
self.relu = nn.ReLU()
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)
batch_size, _, N = x.size()
x = self.relu(self.bn1(self.conv1(x))) # B, D, N
x = torch.max(x, 2)[0]
x = x.view(batch_size, -1)
x = x.reshape(b, n, -1).permute(0, 2, 1)
return x
class SA_Layer(nn.Module):
def __init__(self, channels):
super().__init__()
self.q_conv = nn.Conv1d(channels, channels // 4, 1, bias=False)
self.k_conv = nn.Conv1d(channels, channels // 4, 1, bias=False)
self.q_conv.weight = self.k_conv.weight
self.v_conv = nn.Conv1d(channels, channels, 1)
self.trans_conv = nn.Conv1d(channels, channels, 1)
self.after_norm = nn.BatchNorm1d(channels)
self.act = nn.ReLU()
self.softmax = nn.Softmax(dim=-1)
def forward(self, x):
x_q = self.q_conv(x).permute(0, 2, 1) # b, n, c
x_k = self.k_conv(x)# b, c, n
x_v = self.v_conv(x)
energy = x_q @ x_k # b, n, n
attention = self.softmax(energy)
attention = attention / (1e-9 + attention.sum(dim=1, keepdims=True))
x_r = x_v @ attention # b, c, n
x_r = self.act(self.after_norm(self.trans_conv(x - x_r)))
x = x + x_r
return x
class StackedAttention(nn.Module):
def __init__(self, channels=64):
super().__init__()
self.conv1 = nn.Conv1d(channels, channels, kernel_size=1, bias=False)
self.conv2 = nn.Conv1d(channels, channels, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm1d(channels)
self.bn2 = nn.BatchNorm1d(channels)
self.sa1 = SA_Layer(channels)
self.sa2 = SA_Layer(channels)
self.relu = nn.ReLU()
def forward(self, x):
#
# b, 3, npoint, nsample
# conv2d 3 -> 128 channels 1, 1
# b * npoint, c, nsample
# permute reshape
batch_size, _, N = x.size()
x = self.relu(self.bn1(self.conv1(x))) # B, D, N
x = self.relu(self.bn2(self.conv2(x)))
x1 = self.sa1(x)
x2 = self.sa2(x1)
x = torch.cat((x1, x2), dim=1)
return x
class PointTransformerEncoderSmall(nn.Module):
def __init__(self, output_dim=256, input_dim=6, mean_center=True):
super(PointTransformerEncoderSmall, self).__init__()
self.mean_center = mean_center
# map the second dim of the input from input_dim to 64
self.conv1 = nn.Conv1d(input_dim, 64, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm1d(64)
self.gather_local_0 = Local_op(in_channels=128, out_channels=64)
self.gather_local_1 = Local_op(in_channels=128, out_channels=64)
self.pt_last = StackedAttention(channels=64)
self.relu = nn.ReLU()
self.conv_fuse = nn.Sequential(nn.Conv1d(192, 256, kernel_size=1, bias=False),
nn.BatchNorm1d(256),
nn.LeakyReLU(negative_slope=0.2))
self.linear1 = nn.Linear(256, 256, bias=False)
self.bn6 = nn.BatchNorm1d(256)
self.dp1 = nn.Dropout(p=0.5)
self.linear2 = nn.Linear(256, 256)
def forward(self, xyz, f=None):
# xyz: B, N, 3
# f: B, N, D
center = torch.mean(xyz, dim=1)
if self.mean_center:
xyz = xyz - center.view(-1, 1, 3).repeat(1, xyz.shape[1], 1)
if f is None:
x = self.pct(xyz)
else:
x = self.pct(torch.cat([xyz, f], dim=2)) # B, output_dim
return center, x
def pct(self, x):
# x: B, N, D
xyz = x[..., :3]
x = x.permute(0, 2, 1)
batch_size, _, _ = x.size()
x = self.relu(self.bn1(self.conv1(x))) # B, D, N
x = x.permute(0, 2, 1)
new_xyz, new_feature = sample_and_group(npoint=128, nsample=32, xyz=xyz, points=x)
feature_0 = self.gather_local_0(new_feature)
feature = feature_0.permute(0, 2, 1) # B, nsamples, D
new_xyz, new_feature = sample_and_group(npoint=32, nsample=16, xyz=new_xyz, points=feature)
feature_1 = self.gather_local_1(new_feature) # B, D, nsamples
x = self.pt_last(feature_1) # B, D * 2, nsamples
x = torch.cat([x, feature_1], dim=1) # B, D * 3, nsamples
x = self.conv_fuse(x)
x = torch.max(x, 2)[0]
x = x.view(batch_size, -1)
x = self.relu(self.bn6(self.linear1(x)))
x = self.dp1(x)
x = self.linear2(x)
return x
class SampleAndGroup(nn.Module):
def __init__(self, output_dim=64, input_dim=6, mean_center=True, npoints=(128, 32), nsamples=(32, 16)):
super(SampleAndGroup, self).__init__()
self.mean_center = mean_center
self.npoints = npoints
self.nsamples = nsamples
# map the second dim of the input from input_dim to 64
self.conv1 = nn.Conv1d(input_dim, output_dim, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm1d(output_dim)
self.gather_local_0 = Local_op(in_channels=output_dim * 2, out_channels=output_dim)
self.gather_local_1 = Local_op(in_channels=output_dim * 2, out_channels=output_dim)
self.relu = nn.ReLU()
def forward(self, xyz, f):
# xyz: B, N, 3
# f: B, N, D
center = torch.mean(xyz, dim=1)
if self.mean_center:
xyz = xyz - center.view(-1, 1, 3).repeat(1, xyz.shape[1], 1)
x = self.sg(torch.cat([xyz, f], dim=2)) # B, nsamples, output_dim
return center, x
def sg(self, x):
# x: B, N, D
xyz = x[..., :3]
x = x.permute(0, 2, 1)
batch_size, _, _ = x.size()
x = self.relu(self.bn1(self.conv1(x))) # B, D, N
x = x.permute(0, 2, 1)
new_xyz, new_feature = sample_and_group(npoint=self.npoints[0], nsample=self.nsamples[0], xyz=xyz, points=x)
feature_0 = self.gather_local_0(new_feature)
feature = feature_0.permute(0, 2, 1) # B, nsamples, D
new_xyz, new_feature = sample_and_group(npoint=self.npoints[1], nsample=self.nsamples[1], xyz=new_xyz, points=feature)
feature_1 = self.gather_local_1(new_feature) # B, D, nsamples
x = feature_1.permute(0, 2, 1) # B, nsamples, D
return x |