深度学习pytorch梯度

梯度赋值时遇到的bug：

---

问题描述：

梯度赋值出现以下错误：
RuntimeError: expected Variable or None (got tuple)

import torch
from torch.autograd import Variable
import torch.nn.functional as F
import torch.nn as nn
from graphviz import Digraph
H = 99
x = Variable(torch.rand(1, 1, H, H), requires_grad=True)
label = torch.ones(1, 1, 99, 99).requires_grad_(False)
learning_rate = 0.001
train_time = 1


def printf(contains, string="bianliang"):
    print("**" * 20)
    print("var_name:" + string)
    print(contains)
    print("**" * 20)


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 1, kernel_size=5, stride=3, padding=1)
        self.conv2 = nn.ConvTranspose2d(1, 1, kernel_size=5, stride=3, padding=1)

    def forward(self, img):
        with torch.no_grad():
            self.x = self.conv1(img)
            self.recon = self.conv2(self.x)
        return self.recon

    def calculate_grad(self, input, output):
        weight = torch.ones_like(output)
        grad = torch.autograd.grad(inputs=input,
                                   outputs=output,
                                   grad_outputs=weight,
                                   retain_graph=True,
                                   create_graph=True,
                                   only_inputs=True)
        return grad

    def backpropation(self, img):
        criterion = nn.MSELoss()
        self.recon.requires_grad = True
        printf(img, "img")
        loss = criterion(img, self.recon)
        printf(loss, "loss")
        recon_grad = self.calculate_grad(self.recon, loss)

net = Net()

for i in range(train_time):
    y = net(x)
    net.backpropation(x)
        }

---

原因分析：

grad函数返回的是一个tuple，梯度在grad[0]处存储。

---

解决方案：

self.recon.grad = self.calculate_grad(self.recon, loss)[0]