DGCNN implementation

[EryiXie]

Hi, I compared your implementation with the original DGCNN and I find out that, in yours the function "get_graph_feature" is only used before the first convolution operation, instead of using it before every convolution layer.

I am wondering if there is some reason behind this difference.

RGM/models/dgcnn.py

Lines 154 to 179 in b0e2f74

	def forward(self, xyz):
	xyz = xyz.permute(0, 2, 1).contiguous() # (B, 3, N)
	batch_size, num_dims, num_points = xyz.size()
	x = get_graph_feature(xyz, self.features, self.neighboursnum) # (B, C, N, n)
	x = F.relu(self.bn1(self.conv1(x)))
	x1 = x.max(dim=-1, keepdim=True)[0]
	x = F.relu(self.bn2(self.conv2(x)))
	x2 = x.max(dim=-1, keepdim=True)[0]
	x = F.relu(self.bn3(self.conv3(x)))
	x3 = x.max(dim=-1, keepdim=True)[0]
	x = F.relu(self.bn4(self.conv4(x)))
	x4 = x.max(dim=-1, keepdim=True)[0]
	x = torch.cat((x1, x2, x3, x4), dim=1)
	x_node = x.squeeze(-1)
	x_edge = F.relu(self.bn5(self.conv5(x))).view(batch_size, -1, num_points)
	# if torch.sum(torch.isnan(x_edge)):
	# print('discover nan value')
	return x_node, x_edge