Additionally to the sequence, we'd like to provide some other input (of some different size) to the model. A simple basic example to illustrate:

class SimpleConv(nn.Module): def __init__(self): self.conv_net = nn.Sequential( nn.Conv2d(1, 1, kernel_size=3, stride=1, padding=1), nn.ReLU(inplace=True), ) self.fc_net = nn.Sequential( nn.Linear(channels_in, channels_out), nn.ReLU(inplace=True), ) def forward(self, x: List[np.ndarray]): y1 = self.conv_net(x[0]) y2 = self.fc_net(x[1]) y = torch.cat((y1, y2), 1) return y

Do you think we could modify the _get_batch() function to return a tuple(List[np.ndarray], np.ndarray)?
https://github.com/FunctionLab/selene/blob/master/selene_sdk/train_model.py#L346-L355

Maybe we could wrap the
https://github.com/FunctionLab/selene/blob/master/selene_sdk/train_model.py#L453-L464
into some function, which will return either a single Tensor or a List[Tensor] for the inputs, based on the provided inputs type? Or would be there a better design solution?