python - Pytorch 运行时错误 : element 0 of tensors does not require grad and does not have a grad_fn

Question

这段代码的构建如下:我的机器人拍照，一些 tf 计算机视觉模型计算目标对象在图片中的哪个位置开始。此信息(x1 和 x2 坐标)被传递给 pytorch 模型。它应该学会预测正确的运动激活，以便更接近目标。运动执行后，机器人再次拍照，tf cv 模型应计算电机激活是否使机器人更接近所需状态(x1 在 10，x2 坐标在 at31)

但是，每次我运行代码 pytorch 都无法计算梯度。

我想知道这是一些数据类型问题还是更普遍的问题:如果不直接从 pytorch 网络的输出计算损失，是否无法计算梯度？

任何帮助和建议将不胜感激。

#define policy model (model to learn a policy for my robot)
import torch
import torch.nn as nn
import torch.nn.functional as F 
class policy_gradient_model(nn.Module):
    def __init__(self):
        super(policy_gradient_model, self).__init__()
        self.fc0 = nn.Linear(2, 2)
        self.fc1 = nn.Linear(2, 32)
        self.fc2 = nn.Linear(32, 64)
        self.fc3 = nn.Linear(64,32)
        self.fc4 = nn.Linear(32,32)
        self.fc5 = nn.Linear(32, 2)
    def forward(self,x):
        x = self.fc0(x)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = F.relu(self.fc3(x))
        x = F.relu(self.fc4(x))
        x = F.relu(self.fc5(x))
        return x

policy_model = policy_gradient_model().double()
print(policy_model)
optimizer = torch.optim.AdamW(policy_model.parameters(), lr=0.005, betas=(0.9,0.999), eps=1e-08, weight_decay=0.01, amsgrad=False)

#make robot move as predicted by pytorch network (not all code included)
def move(motor_controls):
#define curvature
 #   motor_controls[0] = sigmoid(motor_controls[0])
    activation_left = 1+(motor_controls[0])*99
    activation_right = 1+(1- motor_controls[0])*99

    print("activation left:", activation_left, ". activation right:",activation_right, ". time:", motor_controls[1]*100)

#start movement

#main
import cv2
import numpy as np
import time
from torch.autograd import Variable
print("start training")
losses=[]
losses_end_of_epoch=[]
number_of_steps_each_epoch=[]
loss_function = nn.MSELoss(reduction='mean')

#each epoch
for epoch in range(2):
    count=0
    target_reached=False
    while target_reached==False:
        print("epoch: ", epoch, ". step:", count)
###process and take picture
        indices = process_picture()
###binary_network(sliced)=indices as input for policy model
        optimizer.zero_grad()
###output: 1 for curvature, 1 for duration of movement
        motor_controls = policy_model(Variable(torch.from_numpy(indices))).detach().numpy()
        print("NO TANH output for motor: 1)activation left, 2)time ", motor_controls)
        motor_controls[0] = np.tanh(motor_controls[0])
        motor_controls[1] = np.tanh(motor_controls[1])
        print("TANH output for motor: 1)activation left, 2)time ", motor_controls)
###execute suggested action
        move(motor_controls)
###take and process picture2 (after movement)
        indices = (process_picture())
###loss=(binary_network(picture2) - desired
        print("calculate loss")
        print("idx", indices, type(torch.tensor(indices)))
     #   loss = 0
      #  loss = (indices[0]-10)**2+(indices[1]-31)**2
       # loss = loss/2
        print("shape of indices", indices.shape)
        array=np.zeros((1,2))
        array[0]=indices
        print(array.shape, type(array))
        array2 = torch.ones([1,2])
        loss = loss_function(torch.tensor(array).double(), torch.tensor([[10.0,31.0]]).double()).float()
        print("loss: ", loss, type(loss), loss.shape)
       # array2[0] = loss_function(torch.tensor(array).double(), 
        torch.tensor([[10.0,31.0]]).double()).float()
        losses.append(loss)
#start line causing the error-message (still part of main)
###calculate gradients
        loss.backward()
#end line causing the error-message (still part of main)

###apply gradients        
        optimizer.step()

#Output (so far as intented) (not all included)

#calculate loss
idx [14. 15.] <class 'torch.Tensor'>
shape of indices (2,)
(1, 2) <class 'numpy.ndarray'>
loss:  tensor(136.) <class 'torch.Tensor'> torch.Size([])

#Error Message:
Traceback (most recent call last):
  File "/home/pi/Desktop/GradientPolicyLearning/PolicyModel.py", line 259, in <module>
    array2.backward()
  File "/home/pi/.local/lib/python3.7/site-packages/torch/tensor.py", line 134, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph)
  File "/home/pi/.local/lib/python3.7/site-packages/torch/autograd/__init__.py", line 99, in 
 backward
    allow_unreachable=True)  # allow_unreachable flag
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn

Answer 1

如果您调用.detach()在预测中，这将删除梯度。由于您首先从模型中获取索引，然后尝试反向支持错误，因此我建议

prediction = policy_model(torch.from_numpy(indices))
motor_controls = prediction.clone().detach().numpy()

这将使预测与可以反向传播的计算梯度保持原样。
现在你可以做

loss = loss_function(prediction, torch.tensor([[10.0,31.0]]).double()).float()

请注意，如果它引发错误，您可能想调用 double 的预测。