Class DeepSDF

Base class for implementing a module, which is a reusable portion of a neural network. Modules can contain other modules, allowing for a tree-like structure.

To add a submodule to a module, assign it as a property of the module in the constructor. Its name will be the name of the property.

Modules can also contain Parameter objects, which are tensors that are automatically updated by optimizers and are saved when calling saveDict(). To add a parameter to a module, assign it as a property of the module in the constructor.

Module subtypes implement a forward function in order to define the computation of the module.

class Model extends torch.nn.Module {
    conv1: torch.nn.Conv2d;
    conv2: torch.nn.Conv2d;
    constructor() {
        super();
        this.conv1 = torch.nn.Conv2d(1, 20, 5);
        this.conv2 = torch.nn.Conv2d(20, 20, 5);
    }
    forward(input: torch.Tensor): torch.Tensor {
        let output = this.conv1.forward(input);
        output = output.relu();
        output = this.conv2.forward(output);
        output = output.relu();
        return output;
    }
}
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