Cuda runtime runtime error(8)

[kedarpathak]

class Generator(nn.Module):

def __init__(self, input_size, output_size, hidden_dims):

    super(Generator, self).__init__()

    self.layers = []

    

    prev_dim = input_size

    for hidden_dim in hidden_dims:

        self.layers.append(nn.Linear(prev_dim, hidden_dim))

        self.layers.append(nn.ReLU(True))

        prev_dim = hidden_dim

    self.layers.append(nn.Linear(prev_dim, output_size))

    

    self.layer_module = ListModule(*self.layers)

    

def forward(self, x):

    out = x

    for layer in self.layers:

        out = layer(out)

    return out

​

class Discriminator(nn.Module):

def __init__(self, input_size, output_size, hidden_dims):

    super(Discriminator, self).__init__()

    self.layers = []

    

    prev_dim = input_size

    for idx, hidden_dim in enumerate(hidden_dims):

        self.layers.append(nn.Linear(prev_dim, hidden_dim))

        self.layers.append(nn.ReLU(True))

        prev_dim = hidden_dim

        

    self.layers.append(nn.Linear(prev_dim, output_size))

    self.layers.append(nn.Sigmoid())

    

    self.layer_module = ListModule(*self.layers)

​

def forward(self, x):

    out = x

    for layer in self.layers:

        out = layer(out)

    return out.view(-1, 1)

​

network

hidden_dim = 128

g_num_layer = 3

d_num_layer = 5

​

G_AB = Generator(2, 2, [hidden_dim] * g_num_layer)

G_BA = Generator(2, 2, [hidden_dim] * g_num_layer)

​

D_A = Discriminator(2, 1, [hidden_dim] * d_num_layer)

D_B = Discriminator(2, 1, [hidden_dim] * d_num_layer)

​

G_AB.cuda()

G_BA.cuda()

D_A.cuda()

D_B.cuda()

​

optimizer

lr = 0.0002

beta1 = 0.5

beta2 = 0.999

​

d = nn.MSELoss()

bce = nn.BCELoss()

​

optimizer_d = torch.optim.Adam(

chain(D_A.parameters(), D_B.parameters()), lr=lr, betas=(beta1, beta2))

optimizer_g = torch.optim.Adam(

chain(G_AB.parameters(), G_BA.parameters()), lr=lr, betas=(beta1, beta2))

​

training

num_epoch = 50000

​

real_label = 1

fake_label = 0

​

real_tensor = Variable(torch.FloatTensor(batch_size).cuda())

_ = real_tensor.data.fill_(real_label)

print(real_tensor.sum())

​

fake_tensor = Variable(torch.FloatTensor(batch_size).cuda())

_ = fake_tensor.data.fill_(fake_label)

print(fake_tensor.sum())

---

RuntimeError Traceback (most recent call last)
in ()
77
78 real_tensor = Variable(torch.FloatTensor(batch_size).cuda())
---> 79 _ = real_tensor.data.fill_(real_label)
80 print(real_tensor.sum())
81

RuntimeError: cuda runtime error (8) : invalid device function at /py/conda-bld/pytorch_1493677666423/work/torch/lib/THC/generic/THCTensorMath.cu:15