PhysicsGenerator#

class deepinv.physics.generator.PhysicsGenerator(step=<function PhysicsGenerator.<lambda>>, rng: ~torch._C.Generator | None = 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: int | None = 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 torch.manual_seed is triggered when a the random number generator is not initialized.

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