import torch
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, ChebConv # noqa
from torch_geometric.data import data as D
import torch.nn as nn
from torchviz import make_dot
import numpy as np
import random
edge_index = torch.tensor([[1, 2, 3],[0, 0, 0]], dtype=torch.long) # 2 x E
x = torch.tensor([[1],[3],[4],[5]], dtype=torch.float) # N x emb(in)
edge_attr = torch.tensor([10,20,30], dtype=torch.float) # E x edge_dim
y = torch.tensor([1,0,0,1])
train_mask = torch.tensor([True, True, True,True], dtype=torch.bool)
val_mask=train_mask
test_mask=train_mask
data=D.Data()
data.x,data.y,data.edge_index,data.edge_attr,data.train_mask,data.val_mask,data.test_mask \
= x,y,edge_index,edge_attr,train_mask,val_mask,test_mask
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
# 设置随机数种子
setup_seed(20)
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = GCNConv(data.num_features, 16, cached=True,
normalize=True)
self.conv2 = GCNConv(16, 2, cached=True,
normalize=True)
self.edge_weight = nn.Parameter(torch.Tensor([10,20,30]))
#self.edge_weight = nn.Parameter(torch.Tensor([0, 0, 0]),requires_grad=False)
def forward(self):
x, edge_index, edge_weight = data.x, data.edge_index, data.edge_attr
edge_weight = self.edge_weight
x = F.relu(self.conv1(x, edge_index, edge_weight))
x = F.dropout(x, training=self.training)
x = self.conv2(x, edge_index, edge_weight)
return F.log_softmax(x, dim=1)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model, data = Net().to(device), data.to(device)
# optimizer = torch.optim.Adam([
# dict(params=model.conv1.parameters(), weight_decay=5e-4),
# dict(params=model.conv2.parameters(), weight_decay=0)
# ], lr=0.01) # Only perform weight-decay on first convolution.
optimizer = torch.optim.Adam(model.parameters(), lr=0.01) # Only perform weight-decay on first convolution.
def train():
model.train()
optimizer.zero_grad()
make_dot(model()[data.train_mask])
#F.nll_loss(model()[data.train_mask], data.y[data.train_mask]).backward(retain_graph=True)#正常,但是不知道有没有其他影响
F.nll_loss(model()[data.train_mask], data.y[data.train_mask]).backward()#报错
#F.nll_loss(model()[data], data.y).backward()
optimizer.step()
@torch.no_grad()
def test():
model.eval()
logits, accs = model(), []
for _, mask in data('train_mask', 'val_mask', 'test_mask'):
pred = logits[mask].max(1)[1]
acc = pred.eq(data.y[mask]).sum().item() / mask.sum().item()
accs.append(acc)
return accs
best_val_acc = test_acc = 0
for epoch in range(1, 1000):
train()
train_acc, val_acc, tmp_test_acc = test()
if val_acc > best_val_acc:
best_val_acc = val_acc
test_acc = tmp_test_acc
log = 'Epoch: {:03d}, Train: {:.4f}, Val: {:.4f}, Test: {:.4f}'
print(log.format(epoch, train_acc, best_val_acc, test_acc))
Traceback (most recent call last):
File “E:\mmaction\pytorch_geometric-master\examples\sy.py”, line 91, in
train()
File “E:\mmaction\pytorch_geometric-master\examples\sy.py”, line 73, in train
F.nll_loss(model()[data.train_mask], data.y[data.train_mask]).backward()#报错
File “D:\anaconda\envs\open-mmlab\lib\site-packages\torch_tensor.py”, line 363, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
File “D:\anaconda\envs\open-mmlab\lib\site-packages\torch\autograd_init_.py”, line 173, in backward
Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
RuntimeError: Trying to backward through the graph a second time (or directly access saved tensors after they have already been freed). Saved intermediate values of the graph are freed when you call .backward() or autograd.grad(). Specify retain_graph=True if you need to backward through the graph a second time or if you need to access saved tensors after calling backward.