Свертка гиперграфа с использованием Pytorch Geometric: RuntimeError: индекс 2268264 выходит за пределы измерения 0 с раз ⇐ Python

[Anonymous]

Я пытаюсь использовать слой сети свертки геометрических гиперграфов Pytorch для обучения наборам данных гиперграфов. Я использую набор данных обзора Amazon, предоставленный https://www.cs.cornell.edu/~arb/data/amazon-reviews/
Поскольку узел не имеет функции , я просто помещаю константы во все узлы как функции. Далее для гиперребер я строю матрицу инцидентности.
Сначала загрузите набор данных командой:

Код: Выделить всё

!wget -P /tmp https://github.com/gravitogen/hosting_datasets/releases/download/amazon_review_1.0/amazon-reviews.zip
!unzip /tmp/amazon-reviews.zip
< /code>
Код ниже: < /p>
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import HypergraphConv
import numpy as np
import pandas as pd

class HyperGraph1(nn.Module):
def __init__(self, nfeatures, nhiddden, nclass, dropout):
super(HyperGraph1, self).__init__()
self.conv1 = HypergraphConv(nfeatures, nhiddden)
self.conv2 = HypergraphConv(nhiddden, nclass)
self.dropout_p = dropout

def forward(self, data):
x, edge_index = data.x, data.edge_index
x = self.conv1(x, edge_index)
x = F.relu(x)
x = F.dropout(x, training=self.training)
x = self.conv2(x, edge_index)
return F.log_softmax(x, dim=1)

folder = 'amazon-reviews/'
node_indices_files = 'node-labels-amazon-reviews.txt'
node_label_files = 'label-names-amazon-reviews.txt'
hyperedge_files = 'hyperedges-amazon-reviews.txt'
node_indices = pd.read_csv(folder + node_indices_files, header=None)

labels_annot = pd.read_csv(folder + node_label_files, header = None)
hyper_edge = []
with open(folder + hyperedge_files) as f:
for line in f.readlines():
chunks = line.split(',')
chunks = [int(i) for i in chunks]
hyper_edge.append(chunks)
nodes = node_indices.index.to_list()
labels = node_indices[node_indices.columns[0]].to_list()
# generating sparse incidence matrix for storing hyperedges
node_on_edge_list = []
edge_indices = []
counter = 0
for l in hyper_edge:
node_on_edge_list += l
edge_indices += [counter]*len(l)
counter = counter + 1

hyperedge_index = torch.tensor([ node_on_edge_list, edge_indices])
# generate `Data` for pytorch-geometric
x = np.zeros(len(nodes)*2, dtype=int)
x = x.reshape(len(nodes),2).tolist()
X = torch.tensor(x, dtype=torch.float)
Y = torch.tensor(labels, dtype=torch.int)
from torch_geometric.data import Data
data = Data(x=X, edge_index=hyperedge_index, y=Y)
# Now get ready for training
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = HyperGraph1(nfeatures=2, nhiddden=16, nclass=labels_annot.shape[0],dropout=0.1).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
model.train()
random_nums = np.random.rand(1, len(data.y))
train_np = random_nums > 0.5
train_mask = train_np.tolist()[0]

test_np = random_nums  3     out = model(data)
4     loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])
5     loss.backward()

~/anaconda3/envs/gcn/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1050                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051             return forward_call(*input, **kwargs)
1052         # Do not call functions when jit is used
1053         full_backward_hooks, non_full_backward_hooks = [], []

 in forward(self, data)
8     def forward(self, data):
9         x, edge_index = data.x, data.edge_index
--->  10         x = self.conv1(x, edge_index)
11         x = F.relu(x)
12         x = F.dropout(x, training=self.training)

~/anaconda3/envs/gcn/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1050                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051             return forward_call(*input, **kwargs)
1052         # Do not call functions when jit is used
1053         full_backward_hooks, non_full_backward_hooks = [], []

~/anaconda3/envs/gcn/lib/python3.7/site-packages/torch_geometric/nn/conv/hypergraph_conv.py in forward(self, x, hyperedge_index, hyperedge_weight, hyperedge_attr)
139
140         D = scatter_add(hyperedge_weight[hyperedge_index[1]],
--> 141                         hyperedge_index[0], dim=0, dim_size=num_nodes)
142         D = 1.0 / D
143         D[D == float("inf")] = 0

~/anaconda3/envs/gcn/lib/python3.7/site-packages/torch_scatter/scatter.py in scatter_add(src, index, dim, out, dim_size)
27                 out: Optional[torch.Tensor] = None,
28                 dim_size: Optional[int] = None) -> torch.Tensor:
---> 29     return scatter_sum(src, index, dim, out, dim_size)
30
31

~/anaconda3/envs/gcn/lib/python3.7/site-packages/torch_scatter/scatter.py in scatter_sum(src, index, dim, out, dim_size)
19             size[dim] = int(index.max()) + 1
20         out = torch.zeros(size, dtype=src.dtype, device=src.device)
---> 21         return out.scatter_add_(dim, index, src)
22     else:
23         return out.scatter_add_(dim, index, src)

RuntimeError: index 2268264 is out of bounds for dimension 0 with size 2268264

Я не знаю, как исправить это. Примечание 2268264-это количество узлов на графике.
i имитировал конструкцию набора данных от https: // pytorch-геометрический. readthedocs.io/en/latest/notes/introduction.html, так что я не уверен, какую ошибку я сделал здесь.


Подробнее здесь: https://stackoverflow.com/questions/691 ... 68264-is-o