您好在使用dhg.models.HGNN出现torch.sparse_coo_tensor的索引超出我的张量尺寸范围，具体的bug如下 #52
Description
Traceback (most recent call last):
File "train.py", line 355, in
main()
File "train.py", line 195, in main
train(opt, train_loader, model, epoch, val_loader)
File "train.py", line 244, in train
model.train_emb(images, captions, lengths,image_lengths=img_lengths)
File "/home/iot/shared_pool_data3/shared_pool_data3/anaconda3/envs/CHANPY38/lib/python3.8/site-packages/torch/amp/autocast_mode.py", line 14, in decorate_autocast
return func(*args, **kwargs)
File "/work/shared_pool_data3/Code/CHAN-main/lib/model.py", line 262, in train_emb
img_emb, cap_emb = self.forward_emb(images, captions,lengths, image_lengths=image_lengths)
File "/home/iot/shared_pool_data3/shared_pool_data3/anaconda3/envs/CHANPY38/lib/python3.8/site-packages/torch/amp/autocast_mode.py", line 14, in decorate_autocast
return func(*args, **kwargs)
File "/work/shared_pool_data3/Code/CHAN-main/lib/model.py", line 199, in forward_emb
img_emb = self.img_hy(img_embhy,G)
File "/home/iot/shared_pool_data3/shared_pool_data3/anaconda3/envs/CHANPY38/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "/home/iot/shared_pool_data3/shared_pool_data3/anaconda3/envs/CHANPY38/lib/python3.8/site-packages/dhg/models/hypergraphs/hgnn.py", line 44, in forward
X = layer(X, hg)
File "/home/iot/shared_pool_data3/shared_pool_data3/anaconda3/envs/CHANPY38/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "/home/iot/shared_pool_data3/shared_pool_data3/anaconda3/envs/CHANPY38/lib/python3.8/site-packages/dhg/nn/convs/hypergraphs/hgnn_conv.py", line 56, in forward
X = hg.smoothing_with_HGNN(X).to(device)
File "/home/iot/shared_pool_data3/shared_pool_data3/anaconda3/envs/CHANPY38/lib/python3.8/site-packages/dhg/structure/hypergraphs/hypergraph.py", line 1221, in smoothing_with_HGNN
L_HGNN = self.L_HGNN
File "/home/iot/shared_pool_data3/shared_pool_data3/anaconda3/envs/CHANPY38/lib/python3.8/site-packages/dhg/structure/hypergraphs/hypergraph.py", line 1180, in L_HGNN
_tmp = self.D_v_neg_1_2.mm(self.H).mm(self.W_e).mm(self.D_e_neg_1).mm(self.H_T,).mm(self.D_v_neg_1_2)
File "/home/iot/shared_pool_data3/shared_pool_data3/anaconda3/envs/CHANPY38/lib/python3.8/site-packages/dhg/structure/hypergraphs/hypergraph.py", line 929, in D_v_neg_1_2
_mat = self.D_v.clone()
File "/home/iot/shared_pool_data3/shared_pool_data3/anaconda3/envs/CHANPY38/lib/python3.8/site-packages/dhg/structure/hypergraphs/hypergraph.py", line 863, in D_v
_tmp = [self.D_v_of_group(name)._values().clone() for name in self.group_names]
File "/home/iot/shared_pool_data3/shared_pool_data3/anaconda3/envs/CHANPY38/lib/python3.8/site-packages/dhg/structure/hypergraphs/hypergraph.py", line 863, in
_tmp = [self.D_v_of_group(name)._values().clone() for name in self.group_names]
File "/home/iot/shared_pool_data3/shared_pool_data3/anaconda3/envs/CHANPY38/lib/python3.8/site-packages/dhg/structure/hypergraphs/hypergraph.py", line 881, in D_v_of_group
H = self.H_of_group(group_name).clone()
File "/home/iot/shared_pool_data3/shared_pool_data3/anaconda3/envs/CHANPY38/lib/python3.8/site-packages/dhg/structure/hypergraphs/hypergraph.py", line 787, in H_of_group
self.group_cache[group_name]["H"] = self.H_v2e_of_group(group_name)
File "/home/iot/shared_pool_data3/shared_pool_data3/anaconda3/envs/CHANPY38/lib/python3.8/site-packages/dhg/structure/base.py", line 856, in H_v2e_of_group
self.group_cache[group_name]["H_v2e"] = self._fetch_H_of_group("v2e", group_name)
File "/home/iot/shared_pool_data3/shared_pool_data3/anaconda3/envs/CHANPY38/lib/python3.8/site-packages/dhg/structure/base.py", line 560, in _fetch_H_of_group
H = torch.sparse_coo_tensor(
RuntimeError: size is inconsistent with indices: for dim 0, size is 144 but found index 144
对源码的修改：HGNNconv forward改变了X的格式为float()以及更改了smoothingwithHGNN的设备，如下:
class HGNNConv(nn.Module):
r"""The HGNN convolution layer proposed in Hypergraph Neural Networks <https://arxiv.org/pdf/1809.09401>_ paper (AAAI 2019).
Matrix Format:
.. math::
\mathbf{X}^{\prime} = \sigma \left( \mathbf{D}_v^{-\frac{1}{2}} \mathbf{H} \mathbf{W}_e \mathbf{D}_e^{-1}
\mathbf{H}^\top \mathbf{D}_v^{-\frac{1}{2}} \mathbf{X} \mathbf{\Theta} \right).
where :math:`\mathbf{X}` is the input vertex feature matrix, :math:`\mathbf{H}` is the hypergraph incidence matrix,
:math:`\mathbf{W}_e` is a diagonal hyperedge weight matrix, :math:`\mathbf{D}_v` is a diagonal vertex degree matrix,
:math:`\mathbf{D}_e` is a diagonal hyperedge degree matrix, :math:`\mathbf{\Theta}` is the learnable parameters.
Args:
``in_channels`` (``int``): :math:`C_{in}` is the number of input channels.
``out_channels`` (int): :math:`C_{out}` is the number of output channels.
``bias`` (``bool``): If set to ``False``, the layer will not learn the bias parameter. Defaults to ``True``.
``use_bn`` (``bool``): If set to ``True``, the layer will use batch normalization. Defaults to ``False``.
``drop_rate`` (``float``): If set to a positive number, the layer will use dropout. Defaults to ``0.5``.
``is_last`` (``bool``): If set to ``True``, the layer will not apply the final activation and dropout functions. Defaults to ``False``.
"""
def __init__(
self,
in_channels: int,
out_channels: int,
bias: bool = True,
use_bn: bool = False,
drop_rate: float = 0.5,
# drop_rate: float = 0,
is_last: bool = False,
):
super().__init__()
self.is_last = is_last
self.bn = nn.BatchNorm1d(out_channels) if use_bn else None
self.act = nn.ReLU(inplace=True)
self.drop = nn.Dropout(drop_rate)
self.theta = nn.Linear(in_channels, out_channels, bias=bias)
def forward(self, X: torch.Tensor, hg: Hypergraph) -> torch.Tensor:
r"""The forward function.
Args:
X (``torch.Tensor``): Input vertex feature matrix. Size :math:`(N, C_{in})`.
hg (``dhg.Hypergraph``): The hypergraph structure that contains :math:`N` vertices.
"""
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
X = self.theta(X)
X=X.float()
X = hg.smoothing_with_HGNN(X).to(device)
if not self.is_last:
X = self.act(X)
if self.bn is not None:
X = self.bn(X)
X = self.drop(X)
return X
请问大佬有没有办法解决这个问题