NoiseModel#

class deepinv.physics.NoiseModel(noise_model=None, rng=None)[source]#

Base class for noise model.

Parameters:

noise_model (Callable) – noise model function \(N(y)\).
rng (torch.Generator) – (optional) a pseudorandom random number generator for the parameter generation. If provided, it should be on the same device as the input.

__mul__(other)[source]#

Concatenates two noise \(N = N_1 \circ N_2\) via the mul operation

The resulting operator will add the noise from both noise models and keep the rng of \(N_1\).

Parameters:: other (deepinv.physics.NoiseModel) – Physics operator \(A_2\)
Returns:: (deepinv.physics.NoiseModel) concatenated operator

forward(input, seed=None)[source]#

Add noise to the input

Parameters:

rand_like(input, seed=None)[source]#

Equivalent to torch.rand_like but supports a pseudorandom number generator argument.

Parameters:: seed (int) – the seed for the random number generator, if rng is provided.

randn_like(input, seed=None)[source]#

Equivalent to torch.randn_like but supports a pseudorandom number generator argument.

Parameters:: seed (int) – the seed for the random number generator, if rng is provided.

reset_rng()[source]#: Reset the random number generator to its initial state.

rng_manual_seed(seed=None)[source]#

Sets the seed for the random number generator.

Parameters:: seed (int) – the seed to set for the random number generator. If not provided, the current state of the random number generator is used. .. note:: The seed will be ignored if the random number generator is not initialized.