解释 def showDiscription(self,item,col): OP_CODE = item.data(0,Qt.UserRole+1) if OP_CODE =="Undefined": return node = get_class_from_opcode(OP_CODE)
时间: 2023-06-03 20:04:37 浏览: 63
抱歉,我可以回答该问题。这段代码定义了一个名为showDiscription的方法,其中使用了OP_CODE变量和get_class_from_opcode函数。该方法需要两个参数,分别是item和col。在item的data属性中,使用了一个Qt.UserRole 1的参数,该参数指定了一个操作码OP_CODE,如果OP_CODE的值为"Undefined",则方法会直接返回。最后,通过get_class_from_opcode函数获取一个节点(node)。
相关问题
修改class arcnode: def __init__(self, adjvex, weight, link=None): self.adjvex = adjvex self.weight = weight self.link = link class vexnode: def __init__(self, data, first_arc=None): self.data = data self.first_arc = first_arc class Graph: def __init__(self): self.vex_list = [] self.vex_num = 0 self.edge_num = 0 # 请在这里填写答案 def addVertex(self, vex_val): new_vertex = vexnode(vex_val) self.vex_list.append(new_vertex) self.vex_num += 1 def addEdge(self, f, t, cost=0): if f not in self.vex_list: nv = self.addVertex(f) # 如果起始顶点不存在,则将其添加到图中 if t not in self.vex_list: nv = self.addVertex(t) # 如果目标顶点不存在,则将其添加到图中 # 无向图添加双向边 self.vex_list[f].addNeighbor(self.vex_list[t], cost) # 将目标顶点及其权重添加到起始顶点的 connectedTo 字典中 self.vex_list[t].addNeighbor(self.vex_list[f], cost) # 有向图只添加一条边 # 请在这里填写答案 def print_graph(self): for i in range(self.vex_num): print(self.vex_list[i].data, end="->") cur = self.vex_list[i].first_arc while cur: print("adj:{},weight:{}".format(cur.adjvex, cur.weight), end="->") cur = cur.link print('None') if __name__ == "__main__": g = Graph() s = input() for vertex in s: g.addVertex(vertex) g.addEdge(0, 1, 11) g.addEdge(0, 2, 55) g.addEdge(2, 3, 88) g.addEdge(0, 3, 33) g.addEdge(1, 2, 44) g.print_graph()
class arcnode:
def __init__(self, adjvex, weight, link=None):
self.adjvex = adjvex
self.weight = weight
self.link = link
class vexnode:
def __init__(self, data, first_arc=None):
self.data = data
self.first_arc = first_arc
class Graph:
def __init__(self):
self.vex_list = []
self.vex_num = 0
self.edge_num = 0
def addVertex(self, vex_val):
new_vertex = vexnode(vex_val)
self.vex_list.append(new_vertex)
self.vex_num += 1
def addEdge(self, f, t, cost=0):
if f not in range(self.vex_num):
raise ValueError("Invalid Starting Vertex")
if t not in range(self.vex_num):
raise ValueError("Invalid Ending Vertex")
new_arc = arcnode(t, cost)
new_arc.link = self.vex_list[f].first_arc
self.vex_list[f].first_arc = new_arc
self.edge_num += 1
def print_graph(self):
for i in range(self.vex_num):
print(self.vex_list[i].data, end="->")
cur = self.vex_list[i].first_arc
while cur:
print("adj:{},weight:{}".format(cur.adjvex, cur.weight), end="->")
cur = cur.link
print('None')
if __name__ == "__main__":
g = Graph()
s = input()
for vertex in s:
g.addVertex(vertex)
g.addEdge(0, 1, 11)
g.addEdge(0, 2, 55)
g.addEdge(2, 3, 88)
g.addEdge(0, 3, 33)
g.addEdge(1, 2, 44)
g.print_graph()
运行class GuidedBackpropReLUModel: def __init__(self, model, use_cuda): self.model = model self.model.eval() self.cuda = use_cuda if self.cuda: self.model = model.cuda() for idx, module in self.model.features._modules.items(): if module.__class__.__name__ == 'ReLU': self.model.features._modules[idx] = GuidedBackpropReLU() def forward(self, input): return self.model(input) def forward_static(self, input): if self.cuda: output = self.forward(input.cuda()) else: output = self.forward(input) return output def __call__(self, input, index = None): output = self.forward_static(input) if index == None: index = np.argmax(output.cpu().data.numpy()) one_hot = np.zeros((1, output.size()[-1]), dtype = np.float32) one_hot[0][index] = 1 one_hot = Variable(torch.from_numpy(one_hot), requires_grad = True) if self.cuda: one_hot = torch.sum(one_hot.cuda() * output) else: one_hot = torch.sum(one_hot * output) one_hot.backward() output = input.grad.cpu().data.numpy() output = output[0,:,:,:] return output报错Legacy autograd function with non-static forward method is deprecated. Please use new-style autograd function with static forward method. 如何修改代码
这个错误提示是因为代码中使用了旧版本的autograd函数,需要使用新版本的autograd函数。可以使用`@staticmethod`修饰`GuidedBackpropReLU`类中的`forward`方法,将其转换为静态方法。修改后的代码如下:
```
class GuidedBackpropReLU(nn.ReLU):
@staticmethod
def forward(input):
output = torch.clamp(input, min=0.0)
grad = torch.zeros_like(output)
grad[output > 0] = 1.0
output = output * grad
return output
class GuidedBackpropReLUModel:
def __init__(self, model, use_cuda):
self.model = model
self.model.eval()
self.cuda = use_cuda
if self.cuda:
self.model = model.cuda()
for idx, module in self.model.features._modules.items():
if module.__class__.__name__ == 'ReLU':
self.model.features._modules[idx] = GuidedBackpropReLU()
def forward(self, input):
return self.model(input)
def forward_static(self, input):
if self.cuda:
output = self.forward(input.cuda())
else:
output = self.forward(input)
return output
def __call__(self, input, index=None):
output = self.forward_static(input)
if index is None:
index = np.argmax(output.cpu().data.numpy())
one_hot = np.zeros((1, output.size()[-1]), dtype=np.float32)
one_hot[0][index] = 1
one_hot = Variable(torch.from_numpy(one_hot), requires_grad=True)
if self.cuda:
one_hot = torch.sum(one_hot.cuda() * output)
else:
one_hot = torch.sum(one_hot * output)
one_hot.backward()
output = input.grad.cpu().data.numpy()
output = output[0, :, :, :]
return output
```
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