Cudnn RNN backward can only be called in training mode

#1

I write a code but it has a error, I can not fix it
my environment is : cuda9 + cudnn 7.1 python=3.6.6 pytorch =1.0.1
my code:

import torch
from torch import nn, optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision import datasets

# 定义超参数
batch_size = 100
learning_rate = 1e-3
num_epoches = 20

# 下载训练集 MNIST 手写数字训练集
train_dataset = datasets.MNIST(
    root='D:/data', train=True, transform=transforms.ToTensor(), download=True)

test_dataset = datasets.MNIST(
    root='D:/data', train=False, transform=transforms.ToTensor())

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 定义 Recurrent Network 模型


class Rnn(nn.Module):
    def __init__(self, in_dim, hidden_dim, n_layer, n_class):
        super(Rnn, self).__init__()
        self.n_layer = n_layer
        self.hidden_dim = hidden_dim
        self.lstm = nn.LSTM(in_dim, hidden_dim, n_layer, batch_first=True)
        self.classifiers = nn.Linear(hidden_dim, n_class)

    def forward(self, x):
        out, _ = self.lstm(x)
        out = out[:, -1, :]
        out = self.classifiers(out)
        return out


model = Rnn(28, 128, 2, 10)
model = model.cuda()

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

for epoch in range(num_epoches):
    running_loss = 0.
    running_acc = 0.
    for i, data in enumerate(train_loader, 1):
        img, label = data
        b, c, h, w = img.size()
        assert c == 1, 'Channel must be 1'
        img = img.squeeze(1)

        img = Variable(img).cuda()
        label = Variable(label).cuda()

        # 向前传播
        out = model(img)
        loss = criterion(out, label)
        running_loss += loss.data.item() * label.size(0)
        _, pred = torch.max(out, 1)
        num_correct = (pred == label).sum()
        running_acc += num_correct.data.item()
        # 向后传播
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if i % 300 == 0:
            print("[{} / {}] Loss: {:.6f}, Acc: {:.6f}".format(
                epoch + 1, num_epoches, running_loss / (batch_size * i),
                running_acc / (batch_size * i)
            ))
    print("Finish {} epoch, Loss: {:.6f}, Acc: {:.6f}".format(
        epoch + 1, running_loss /
        (len(train_dataset)), running_acc / (len(train_dataset))
    ))

    model.eval()
    eval_loss = 0. 
    eval_acc = 0. 
    for data in test_loader:
        img, label = data
        b, c, h, w = img.size()
        assert c == 1, "channel must be 1"
        img = img.squeeze(1)

        with torch.no_grad():
            img = Variable(img).cuda()
            label = Variable(label).cuda()
        out = model(img)
        loss = criterion(out, label)
        eval_loss += loss.data.item() * label.size(0)
        _, pred = torch.max(out, 1)
        num_correct = (pred == label).sum()
        eval_acc += num_correct.data.item()
    print('Test Loss: {:.6f}, Acc: {:.6f}'.format(eval_loss / (len(
        test_dataset)), eval_acc / (len(test_dataset))))

# 保存模型
torch.save(model.state_dict(), './rnn.pth')

the error:

RuntimeError
cudnn RNN backward can only be called in training mode
  File "D:\documents\th_torch\recurrent_network001.py", line 68, in <module>
    loss.backward()

#2

Hi

You call model.eval() at line 81 but forget to set the model to train mode again when you run start the second epoch.

Adding model.train() after line 47 will remove the error.

Error: Cudnn RNN backward can only be called in training mode
#3

Hi, thanks, your suggestion has helped me fix this error.