Pytorch backward报错2次访问计算图需要 retain_graph=True 的一种情况

错误代码

错误的原因在于

y1 = 0.5*x*2-1.2*x
y2 = x**3

没有放到循环里面，没有随着 x 的优化而相应变化。

import torch
import numpy as np
import torch.optim as optim

torch.autograd.set_detect_anomaly(True)

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
x = torch.tensor([1.0, 2.0, 3.0,4.5], dtype=torch.float32, requires_grad=True, device=device)


y_GT= torch.tensor([10, -20, -30,45], dtype=torch.float32,  device=device)

print(f'x{x}')


optimizer = optim.Adam([x], lr=1)
y1 = 0.5*x*2-1.2*x
y2 = x**3

for i in range(10):

    print(f'{i}: x{x}')
    optimizer.zero_grad()


    loss = (y1+y2-y_GT).mean()
    loss.backward()
    optimizer.step()
    print(f'{i}: x{x}')

正确代码

import torch
import numpy as np
import torch.optim as optim

torch.autograd.set_detect_anomaly(True)

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
x = torch.tensor([1.0, 2.0, 3.0,4.5], dtype=torch.float32, requires_grad=True, device=device)


y_GT= torch.tensor([10, -20, -30,45], dtype=torch.float32,  device=device)

print(f'x{x}')


optimizer = optim.Adam([x], lr=1)


for i in range(10):

    print(f'{i}: x{x}')
    optimizer.zero_grad()
    y1 = 0.5*x*2-1.2*x
    y2 = x**3

    loss = (y1+y2-y_GT).mean()
    loss.backward()
    optimizer.step()
    print(f'{i}: x{x}')