add MSN-PCN modules

model/model_blocks.py

@@ -103,3 +103,87 @@ def forward(self, x, latent):
         for i in range(self.opt.num_layers):
             x = self.activation(self.bn_list[i](self.conv_list[i](x)))
         return self.last_conv(x)
+
+
+# Modules from MSN: https://github.com/Colin97/MSN-Point-Cloud-Completion
+class PointNetfeat(nn.Module):
+    def __init__(self, num_points = 8192, global_feat = True):
+        super(PointNetfeat, self).__init__()
+        self.conv1 = torch.nn.Conv1d(3, 64, 1)
+        self.conv2 = torch.nn.Conv1d(64, 128, 1)
+        self.conv3 = torch.nn.Conv1d(128, 1024, 1)
+
+        self.bn1 = torch.nn.BatchNorm1d(64)
+        self.bn2 = torch.nn.BatchNorm1d(128)
+        self.bn3 = torch.nn.BatchNorm1d(1024)
+
+        self.num_points = num_points
+        self.global_feat = global_feat
+    def forward(self, x):
+        batchsize = x.size()[0]
+        x = F.relu(self.bn1(self.conv1(x)))
+        x = F.relu(self.bn2(self.conv2(x)))
+        x = self.bn3(self.conv3(x))
+        x,_ = torch.max(x, 2)
+        x = x.view(-1, 1024)
+        return x
+
+class PointGenCon(nn.Module):
+    def __init__(self, bottleneck_size = 8192):
+        self.bottleneck_size = bottleneck_size
+        super(PointGenCon, self).__init__()
+        self.conv1 = torch.nn.Conv1d(self.bottleneck_size, self.bottleneck_size, 1)
+        self.conv2 = torch.nn.Conv1d(self.bottleneck_size, self.bottleneck_size//2, 1)
+        self.conv3 = torch.nn.Conv1d(self.bottleneck_size//2, self.bottleneck_size//4, 1)
+        self.conv4 = torch.nn.Conv1d(self.bottleneck_size//4, 3, 1)
+
+        self.th = nn.Tanh()
+        self.bn1 = torch.nn.BatchNorm1d(self.bottleneck_size)
+        self.bn2 = torch.nn.BatchNorm1d(self.bottleneck_size//2)
+        self.bn3 = torch.nn.BatchNorm1d(self.bottleneck_size//4)
+
+    def forward(self, x):
+        batchsize = x.size()[0]
+        x = F.relu(self.bn1(self.conv1(x)))
+        x = F.relu(self.bn2(self.conv2(x)))
+        x = F.relu(self.bn3(self.conv3(x)))
+        x = self.th(self.conv4(x))
+        return x
+
+class PointNetRes(nn.Module):
+    def __init__(self):
+        super(PointNetRes, self).__init__()
+        self.conv1 = torch.nn.Conv1d(4, 64, 1)
+        self.conv2 = torch.nn.Conv1d(64, 128, 1)
+        self.conv3 = torch.nn.Conv1d(128, 1024, 1)
+        self.conv4 = torch.nn.Conv1d(1088, 512, 1)
+        self.conv5 = torch.nn.Conv1d(512, 256, 1)
+        self.conv6 = torch.nn.Conv1d(256, 128, 1)
+        self.conv7 = torch.nn.Conv1d(128, 3, 1)
+
+
+        self.bn1 = torch.nn.BatchNorm1d(64)
+        self.bn2 = torch.nn.BatchNorm1d(128)
+        self.bn3 = torch.nn.BatchNorm1d(1024)
+        self.bn4 = torch.nn.BatchNorm1d(512)
+        self.bn5 = torch.nn.BatchNorm1d(256)
+        self.bn6 = torch.nn.BatchNorm1d(128)
+        self.bn7 = torch.nn.BatchNorm1d(3)
+        self.th = nn.Tanh()
+
+    def forward(self, x):
+        batchsize = x.size()[0]
+        npoints = x.size()[2]
+        x = F.relu(self.bn1(self.conv1(x)))
+        pointfeat = x
+        x = F.relu(self.bn2(self.conv2(x)))
+        x = self.bn3(self.conv3(x))
+        x,_ = torch.max(x, 2)
+        x = x.view(-1, 1024)
+        x = x.view(-1, 1024, 1).repeat(1, 1, npoints)
+        x = torch.cat([x, pointfeat], 1)
+        x = F.relu(self.bn4(self.conv4(x)))
+        x = F.relu(self.bn5(self.conv5(x)))
+        x = F.relu(self.bn6(self.conv6(x)))
+        x = self.th(self.conv7(x))
+        return x