super-gradients/src/super_gradients/training/models/user_models

Introduction

This page demonstrates how you can register your own models, so that SuperGradients can access it with a name str, for example, when training from a recipe config architecture: my_custom_model.

Usage

1. Create a new Python module in this folder (e.g. .../user_models/my_model.py).
2. Define your PyTorch model (torch.nn.Module) in the new module.
3. Import the @register decorator from super_gradients.training.models.model_registry import register and apply it to your model.
   • The decorator can be applied directly to the class or to a function returning the class.
   • The decorator takes an optional name: str argument. If not specified, the decorated class/function name will be registered.

Example

import torch.nn as nn
import torch.nn.functional as F

from super_gradients.training.models.model_registry import register_model


@register_model('my_conv_net')  # will be registered as "my_conv_net"
class MyConvNet(nn.Module):
    def __init__(self, num_classes):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, num_classes)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = torch.flatten(x, 1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

or

@register_model()
def myconvnet_for_cifar10(): # will be registered as "myconvnet_for_cifar10"
    return MyConvNet(num_classes=10)