PhysicsGenerator#

class deepinv.physics.generator.PhysicsGenerator(step=lambda **kwargs: ..., rng=None, device='cpu', dtype=torch.float32, **kwargs)[source]#

Bases: Module

Base class for parameter generation of physics parameters.

Physics generators are used to generate the parameters \(\theta\) of (parameter-dependent) forward operators.

Generators can be summed to create larger generators via deepinv.physics.generator.PhysicsGenerator.__add__(), or mixed to create a generator that randomly selects them via deepinv.physics.generator.GeneratorMixture.

Parameters:

step (Callable) – a function that generates the parameters of the physics, e.g., the filter of the deepinv.physics.Blur. This function should return the parameters in a dictionary with the corresponding key and value pairs.
rng (torch.Generator) – (optional) a pseudorandom random number generator for the parameter generation. If None, the default Generator of PyTorch will be used.
device (str) – cpu or cuda
dtype (torch.dtype) – the data type of the generated parameters

Examples:

Generating blur and noise levels:

>>> import torch
>>> from deepinv.physics.generator import MotionBlurGenerator, SigmaGenerator
>>> # combine a PhysicsGenerator for blur and noise level parameters
>>> generator = MotionBlurGenerator(psf_size = (3, 3), num_channels = 1) + SigmaGenerator()
>>> params_dict = generator.step(batch_size=1, seed=0) # dict_keys(['filter', 'sigma'])
>>> print(params_dict['filter'])
tensor([[[[0.0000, 0.1006, 0.0000],
          [0.0000, 0.8994, 0.0000],
          [0.0000, 0.0000, 0.0000]]]])
>>> print(params_dict['sigma'])
tensor([0.2532])

__add__(other)[source]#

Creates a new generator from the sum of two generators.

Parameters:: other (Generator) – the other generator to be added.
Returns:: A new generator that generates a larger dictionary with parameters of the two generators.

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, str) – the seed to set for the random number generator. If string passed, generate seed from the hash of the string. If not provided, the current state of the random number generator is used. Note: The torch.manual_seed is triggered when a the random number generator is not initialized.

step(batch_size=1, seed=None, **kwargs)[source]#