Defining New Operators

Defining a new forward operator is relatively simple. You need to create a new class that inherits from the right physics class, that is deepinv.physics.Physics for non-linear operators, deepinv.physics.LinearPhysics for linear operators and deepinv.physics.DecomposablePhysics for linear operators with a closed-form singular value decomposition. The only requirement is to define a deepinv.physics.Physics.A method that computes the forward operator.

See the example Creating a forward operator. for more details.

Tip

Defining a new linear operator requires the definition of deepinv.physics.LinearPhysics.A_adjoint, you can define the adjoint automatically using autograd with deepinv.physics.adjoint_function. Note however that coding a closed form adjoint is generally more efficient.

Tip

To make the new physics compatible with other torch functionalities, all physics parameters (i.e. attributes of type torch.Tensor) should be registered as module buffers <https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.register_buffer> by using self.register_buffer(param_name, param_tensor). This ensures methods like .to(), .cuda() work properly, allowing one to train a model using Distributed Data Parallel.

Tip

You can also inherit from mixin classes such as deepinv.physics.TimeMixin and deepinv.physics.MRIMixin to provide useful methods for your physics.

Tip

You can also define a new operator by combining existing operators.