Where is the edge collapse operation in the code? about meshcnn

[ranahanocka]

Hi @liangqianqian123 ,

It is called a "mesh union" in the code. Specifically, see union_groups line in __pool_side. This is defined in the Mesh_union class.

[liangqianqian123]

I want to know where is the change of edge feature, such as average operation of the edge features. Is it in the class MeshUnion "rebuild_features_average"?

`class MeshUnion:
def init(self, n, device=torch.device('cpu')):
self.__size = n
self.rebuild_features = self.rebuild_features_average
self.groups = torch.eye(n).to(device)

def union(self, source, target):
    self.groups[target, :] += self.groups[source, :]
  
def remove_group(self, index):
    return

def get_group(self, edge_key):
    return self.groups[edge_key, :]

def get_occurrences(self):
    return torch.sum(self.groups, 0)

def get_groups(self, tensor_mask):
    self.groups = torch.clamp(self.groups, 0, 1)
    return self.groups[tensor_mask, :]

def rebuild_features_average(self, features, mask, target_edges):
    self.prepare_groups(features, mask)
    fe = torch.matmul(features.squeeze(-1), self.groups)
    occurrences = torch.sum(self.groups, 0).expand(fe.shape)
    fe = fe / occurrences
    padding_b = target_edges - fe.shape[1]
    if padding_b > 0:
        padding_b = ConstantPad2d((0, padding_b, 0, 0), 0)
        fe = padding_b(fe)
    return fe

def prepare_groups(self, features, mask):
    tensor_mask = torch.from_numpy(mask)
    #tensor to numpy
    #   a.numpy()
    # numpy to tensor
    #   torch.from_numpy(a)
    self.groups = torch.clamp(self.groups[tensor_mask, :], 0, 1).transpose_(1, 0)
    padding_a = features.shape[1] - self.groups.shape[0]
    if padding_a > 0:
        padding_a = ConstantPad2d((0, 0, 0, padding_a), 0)
        self.groups = padding_a(self.groups)`

[ranahanocka]

Is it in the class MeshUnion "rebuild_features_average"?

yep!