dgcnn_crb.py

"""
Example of the integration of the channel re-calibration block in 
the original implementation of the article
'Dynamic Graph CNN for Learning on Point Clouds'
https://github.com/AnTao97/dgcnn.pytorch
"""
import torch.nn.functional as F
from rec_blocks import PointCRB


class DGCNN_crb(nn.Module):
    def __init__(self, args, output_channels=40):
        super(DGCNN_crb, self).__init__()
        self.args = args
        self.k = args.k

        self.bn1 = nn.BatchNorm2d(64)
        self.bn2 = nn.BatchNorm2d(64)
        self.bn3 = nn.BatchNorm2d(128)
        self.bn4 = nn.BatchNorm2d(256)
        self.bn5 = nn.BatchNorm1d(args.emb_dims)

        self.conv1 = nn.Sequential(nn.Conv2d(6, 64, kernel_size=1, bias=False),
                                   self.bn1,
                                   nn.LeakyReLU(negative_slope=0.2))
        self.conv2 = nn.Sequential(nn.Conv2d(64 * 2, 64, kernel_size=1, bias=False),
                                   self.bn2,
                                   nn.LeakyReLU(negative_slope=0.2))
        self.conv3 = nn.Sequential(nn.Conv2d(64 * 2, 128, kernel_size=1, bias=False),
                                   self.bn3,
                                   nn.LeakyReLU(negative_slope=0.2))
        self.conv4 = nn.Sequential(nn.Conv2d(128 * 2, 256, kernel_size=1, bias=False),
                                   self.bn4,
                                   nn.LeakyReLU(negative_slope=0.2))
        self.conv5 = nn.Sequential(nn.Conv1d(512, args.emb_dims, kernel_size=1, bias=False),
                                   self.bn5,
                                   nn.LeakyReLU(negative_slope=0.2))

        self.sa1_at = PointCRB(64, 2)
        self.sa2_at = PointCRB(64, 2)
        self.sa3_at = PointCRB(128, 2)
        self.sa4_at = PointCRB(256, 2)

        self.linear1 = nn.Linear(args.emb_dims * 2, 512, bias=False)
        self.bn6 = nn.BatchNorm1d(512)
        self.dp1 = nn.Dropout(p=args.dropout)
        self.linear2 = nn.Linear(512, 256)
        self.bn7 = nn.BatchNorm1d(256)
        self.dp2 = nn.Dropout(p=args.dropout)
        self.linear3 = nn.Linear(256, output_channels)

    def forward(self, x):
        batch_size = x.size(0)
        x = get_graph_feature(x, k=self.k)
        x = self.conv1(x)
        x1 = x.max(dim=-1, keepdim=False)[0]
        x1 = self.sa1_at(x1)

        x = get_graph_feature(x1, k=self.k)
        x = self.conv2(x)
        x2 = x.max(dim=-1, keepdim=False)[0]
        x2 = self.sa2_at(x2)

        x = get_graph_feature(x2, k=self.k)
        x = self.conv3(x)
        x3 = x.max(dim=-1, keepdim=False)[0]
        x3 = self.sa3_at(x3)

        x = get_graph_feature(x3, k=self.k)
        x = self.conv4(x)
        x4 = x.max(dim=-1, keepdim=False)[0]
        x4 = self.sa4_at(x4)

        x = torch.cat((x1, x2, x3, x4), dim=1)

        x = self.conv5(x)
        x1 = F.adaptive_max_pool1d(x, 1).view(batch_size, -1)
        x2 = F.adaptive_avg_pool1d(x, 1).view(batch_size, -1)
        x = torch.cat((x1, x2), 1)

        x = F.leaky_relu(self.bn6(self.linear1(x)), negative_slope=0.2)
        x = self.dp1(x)
        x = F.leaky_relu(self.bn7(self.linear2(x)), negative_slope=0.2)
        x = self.dp2(x)
        x = self.linear3(x)
        return x