DownsamplingGenerator#

class deepinv.physics.generator.DownsamplingGenerator(filters=['gaussian', 'bilinear', 'bicubic'], factors=[2, 4], rng=None, device='cpu', dtype=torch.float32)[source]#

Bases: PhysicsGenerator

Random downsampling generator.

Generates random downsampling factors and filters. This can be used for generating parameters to be passed to the Downsampling class.

>>> from deepinv.physics.generator import DownsamplingGenerator
>>> list_filters = ["bilinear", "bicubic", "gaussian"]
>>> list_factors = [2, 4]
>>> generator = DownsamplingGenerator(filters=list_filters, factors=list_factors)
>>> ds = generator.step(batch_size=1)  # dict_keys(['filter', 'factor'])
>>> filter = ds['filter']
>>> factor = ds['factor']

Note

Each batch element has the same downsampling factor and filter, but these can vary from batch to batch.

Parameters:

  • filters (List[str]) – list of filters to use for downsampling. Default is [“gaussian”, “bilinear”, “bicubic”].

  • factors (List[int]) – list of factors to use for downsampling. Default is [2, 4].

  • rng (Generator) – random number generator. Default is None.

  • device (str) – device to use. Default is “cpu”.

  • dtype (type) – data type to use. Default is torch.float32.

get_kernel(filter_str=None, factor=None)[source]#

Returns a batched tensor of filters associated to a given filter name and factor.

Parameters:

  • filter_str (str) – filter name. Default is None.

  • factor (int) – downsampling factor. Default is None.

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

Generates a random downsampling factor and filter.

Parameters:

  • batch_size (int) – batch size. Default is 1.

  • seed (int) – seed for random number generator. Default is None.

str2filter(filter_name, factor)[source]#

Returns the filter associated to a given filter name and factor.