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pct.py
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pct.py
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import torch
from torch import nn
from helper import TransitionDown
'''
Part of the code are adapted from
https://github.com/MenghaoGuo/PCT
'''
class PCTPositionEmbedding(nn.Module):
def __init__(self, channels=256):
super(PCTPositionEmbedding, self).__init__()
self.conv1 = nn.Conv1d(channels, channels, kernel_size=1, bias=False)
self.conv_pos = nn.Conv1d(3, channels, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm1d(channels)
self.sa1 = SALayerCLS(channels)
self.sa2 = SALayerCLS(channels)
self.sa3 = SALayerCLS(channels)
self.sa4 = SALayerCLS(channels)
self.relu = nn.ReLU()
def forward(self, x, xyz):
# add position embedding
xyz = xyz.permute(0, 2, 1)
xyz = self.conv_pos(xyz)
x = self.relu(self.bn1(self.conv1(x))) # B, D, N
x1 = self.sa1(x, xyz)
x2 = self.sa2(x1, xyz)
x3 = self.sa3(x2, xyz)
x4 = self.sa4(x3, xyz)
x = torch.cat((x1, x2, x3, x4), dim=1)
return x
class SALayerCLS(nn.Module):
def __init__(self, channels):
super(SALayerCLS, self).__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, xyz):
x = x + xyz
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 = torch.bmm(x_q, x_k) # b, n, n
attention = self.softmax(energy)
attention = attention / (1e-9 + attention.sum(dim=1, keepdims=True))
x_r = torch.bmm(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 SALayerSeg(nn.Module):
def __init__(self, channels):
super(SALayerSeg, self).__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 = torch.bmm(x_q, x_k) # b, n, n
attention = self.softmax(energy)
attention = attention / (1e-9 + attention.sum(dim=1, keepdims=True))
x_r = torch.bmm(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 PointTransformerCLS(nn.Module):
def __init__(self, output_channels=40):
super(PointTransformerCLS, self).__init__()
self.conv1 = nn.Conv1d(3, 64, kernel_size=1, bias=False)
self.conv2 = nn.Conv1d(64, 64, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm1d(64)
self.bn2 = nn.BatchNorm1d(64)
self.g_op0 = TransitionDown(in_channels=128, out_channels=128, n_neighbor=32)
self.g_op1 = TransitionDown(in_channels=256, out_channels=256, n_neighbor=32)
self.pt_last = PCTPositionEmbedding()
self.relu = nn.ReLU()
self.conv_fuse = nn.Sequential(nn.Conv1d(1280, 1024, kernel_size=1, bias=False),
nn.BatchNorm1d(1024),
nn.LeakyReLU(negative_slope=0.2))
self.linear1 = nn.Linear(1024, 512, bias=False)
self.bn6 = nn.BatchNorm1d(512)
self.dp1 = nn.Dropout(p=0.5)
self.linear2 = nn.Linear(512, 256)
self.bn7 = nn.BatchNorm1d(256)
self.dp2 = nn.Dropout(p=0.5)
self.linear3 = nn.Linear(256, output_channels)
def forward(self, x):
xyz = x[..., :3]
x = x[..., 3:].permute(0, 2, 1)
batch_size, _, _ = x.size()
x = self.relu(self.bn1(self.conv1(x))) # B, D, N
x = self.relu(self.bn2(self.conv2(x))) # B, D, N
x = x.permute(0, 2, 1)
new_xyz, feature_0 = self.g_op0(xyz, x, n_point=512)
new_xyz, feature_1 = self.g_op1(new_xyz, feature_0, n_point=256)
# add position embedding on each layer
x = self.pt_last(feature_1, new_xyz)
x = torch.cat([x, feature_1], dim=1)
x = self.conv_fuse(x)
x, _ = torch.max(x, 2)
x = x.view(batch_size, -1)
x = self.relu(self.bn6(self.linear1(x)))
x = self.dp1(x)
x = self.relu(self.bn7(self.linear2(x)))
x = self.dp2(x)
x = self.linear3(x)
return x
class PointTransformerSeg(nn.Module):
def __init__(self, part_num=50):
super(PointTransformerSeg, self).__init__()
self.part_num = part_num
self.conv1 = nn.Conv1d(3, 128, kernel_size=1, bias=False)
self.conv2 = nn.Conv1d(128, 128, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm1d(128)
self.bn2 = nn.BatchNorm1d(128)
self.sa1 = SALayerSeg(128)
self.sa2 = SALayerSeg(128)
self.sa3 = SALayerSeg(128)
self.sa4 = SALayerSeg(128)
self.conv_fuse = nn.Sequential(nn.Conv1d(512, 1024, kernel_size=1, bias=False),
nn.BatchNorm1d(1024),
nn.LeakyReLU(negative_slope=0.2))
self.label_conv = nn.Sequential(nn.Conv1d(16, 64, kernel_size=1, bias=False),
nn.BatchNorm1d(64),
nn.LeakyReLU(negative_slope=0.2))
self.convs1 = nn.Conv1d(1024 * 3 + 64, 512, 1)
self.dp1 = nn.Dropout(0.5)
self.convs2 = nn.Conv1d(512, 256, 1)
self.convs3 = nn.Conv1d(256, self.part_num, 1)
self.bns1 = nn.BatchNorm1d(512)
self.bns2 = nn.BatchNorm1d(256)
self.relu = nn.ReLU()
def forward(self, x, cls_label):
x = x.permute(0, 2, 1)
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)
x3 = self.sa3(x2)
x4 = self.sa4(x3)
x = torch.cat((x1, x2, x3, x4), dim=1)
x = self.conv_fuse(x)
x_max, _ = torch.max(x, 2)
x_avg = torch.mean(x, 2)
x_max_feature = x_max.view(
batch_size, -1).unsqueeze(-1).repeat(1, 1, N)
x_avg_feature = x_avg.view(
batch_size, -1).unsqueeze(-1).repeat(1, 1, N)
cls_label_feature = self.label_conv(cls_label).repeat(1, 1, N)
x_global_feature = torch.cat(
(x_max_feature, x_avg_feature, cls_label_feature), 1)
x = torch.cat((x, x_global_feature), 1)
x = self.relu(self.bns1(self.convs1(x)))
x = self.dp1(x)
x = self.relu(self.bns2(self.convs2(x)))
x = self.convs3(x)
return x
class PartSegLoss(nn.Module):
def __init__(self, eps=0.2):
super(PartSegLoss, self).__init__()
self.eps = eps
self.loss = nn.CrossEntropyLoss()
def forward(self, logits, y):
num_classes = logits.shape[1]
logits = logits.permute(0, 2, 1).contiguous().view(-1, num_classes)
loss = self.loss(logits, y)
return loss