如果索引叶变量用于梯度更新，如何解决就地操作错误? [英] How to get around in place operation error if index leaf variable for gradient update?

问题描述

当我尝试索引叶子变量以使用自定义的收缩功能更新渐变时，遇到了就地操作错误.我无法解决.非常感谢您的帮助！

I am encountering In place operation error when I am trying to index a leaf variable to update gradients with customized Shrink function. I cannot work around it. Any help is highly appreciated!

import torch.nn as nn
import torch
import numpy as np
from torch.autograd import Variable, Function

# hyper parameters
batch_size = 100 # batch size of images
ld = 0.2 # sparse penalty
lr = 0.1 # learning rate

x = Variable(torch.from_numpy(np.random.normal(0,1,(batch_size,10,10))), requires_grad=False)  # original

# depends on size of the dictionary, number of atoms.
D = Variable(torch.from_numpy(np.random.normal(0,1,(500,10,10))), requires_grad=True)

# hx sparse representation
ht = Variable(torch.from_numpy(np.random.normal(0,1,(batch_size,500,1,1))), requires_grad=True)

# Dictionary loss function
loss = nn.MSELoss()

# customized shrink function to update gradient
shrink_ht = lambda x: torch.stack([torch.sign(i)*torch.max(torch.abs(i)-lr*ld,0)[0] for i in x])

### sparse reprsentation optimizer_ht single image.
optimizer_ht = torch.optim.SGD([ht], lr=lr, momentum=0.9) # optimizer for sparse representation

## update for the batch
for idx in range(len(x)):
    optimizer_ht.zero_grad() # clear up gradients
    loss_ht = 0.5*torch.norm((x[idx]-(D*ht[idx]).sum(dim=0)),p=2)**2
    loss_ht.backward() # back propogation and calculate gradients
    optimizer_ht.step() # update parameters with gradients
    ht[idx] = shrink_ht(ht[idx])  # customized shrink function.

RuntimeError Traceback (most recent call last) in ()
15 loss_ht.backward() # back propogation and calculate gradients
16 optimizer_ht.step() # update parameters with gradients
—> 17 ht[idx] = shrink_ht(ht[idx]) # customized shrink function.
18
19

/home/miniconda3/lib/python3.6/site-packages/torch/autograd/variable.py in setitem(self, key, value)
85 return MaskedFill.apply(self, key, value, True)
86 else:
—> 87 return SetItem.apply(self, key, value)
88
89 def deepcopy(self, memo):

RuntimeError: a leaf Variable that requires grad has been used in an in-place operation.

具体来说，下面的这一行代码似乎在给出错误，因为它同时索引并更新了叶子变量.

Specifically, this line of code below seems give error as it index and update leaf variable at the same time.

ht[idx] = shrink_ht(ht[idx])  # customized shrink function.

谢谢.

W.S.

推荐答案

我刚刚发现:要更新变量，应使用ht.data [idx].使用数据直接访问张量.

I just find out: to update the Variable, should use ht.data[idx]. use data to access tensor directly.

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