Shape of output through self.hybrid is not correct?

[huytranvan2010]

`
class Net(nn.Module):
def init(self):
super(Net, self).init()
self.conv1 = nn.Conv2d(1, 6, kernel_size=5)
self.conv2 = nn.Conv2d(6, 16, kernel_size=5)
self.dropout = nn.Dropout2d()
self.fc1 = nn.Linear(256, 64)
self.fc2 = nn.Linear(64, 10)
# self.hybrid = Hybrid(qiskit.Aer.get_backend('qasm_simulator'), 100, np.pi / 2)
self.hybrid = [Hybrid(qiskit.Aer.get_backend('qasm_simulator'), 100, np.pi / 2) for i in range(10)]

def forward(self, x):
    x = F.relu(self.conv1(x))
    x = F.max_pool2d(x, 2)
    x = F.relu(self.conv2(x))
    x = F.max_pool2d(x, 2)
    x = self.dropout(x)
    # x = x.view(-1, 256)
    x = torch.flatten(x, start_dim=1)
    x = F.relu(self.fc1(x))
    x = self.fc2(x)
    x = torch.chunk(x, 10, dim=1)
    # x = self.hybrid(x)
    x = tuple([hy(x_) for hy, x_ in zip(self.hybrid, x)])
    return torch.cat(x, -1)

`
I tried to run this code with batch (32, 1, 28, 28) and obtained output shape was (1, 10), not (32, 10). It wasn't correct. I guess that self.hybrid always ouput with shape (1, 1) regarless the batch size. I mean that with input (32, 1) through self.hybrid I receive an output with shape (32, 1). Thank you.

[ccu1tn]

Hi @huytranvan2010 ,
Did you fix this problems?