class oneflow.nn.Module

Base class for all neural network modules.

This class is consistent with PyTorch. The documentation is referenced from:

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import oneflow.nn as nn
import oneflow.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call to(), etc.


As per the example above, an __init__() call to the parent class must be made before assignment on the child.


training (bool) – Boolean represents whether this module is in training or evaluation mode.