Utilities

Set of utilities that can make life a little bit easier:

Augmentation

class esrgan.utils.aug.Augmentor(transform: Optional[Callable[[Any], Dict]] = None)

Applies provided transformation on dictionaries.

Parameters

transform – A function / transform that takes in dictionary and returns a transformed version. If None, the identity function is used.

Model init

esrgan.utils.init.kaiming_normal_(tensor: torch.Tensor, a: float = 0, mode: str = 'fan_in', nonlinearity: str = 'leaky_relu') → None

Fills the input Tensor with values according to the method described in Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification.

Parameters

• tensor – An n-dimensional tensor.

• a – The slope of the rectifier used after this layer (only used with 'leaky_relu' and 'prelu').

• mode – Either 'fan_in' or 'fan_out'. Choosing 'fan_in' preserves the magnitude of the variance of the weights in the forward pass. Choosing 'fan_out' preserves the magnitudes in the backwards pass.

• nonlinearity – The non-linear function (nn.functional name).

esrgan.utils.init.module_init_(module: torch.nn.modules.module.Module, nonlinearity: Optional[Union[str, torch.nn.modules.module.Module]] = None, **kwargs: Any) → None

Initialize module based on the activation function.

Parameters

• module – Module to initialize.

• nonlinearity – Activation function. If LeakyReLU/PReLU and of type nn.Module, then initialization will be adapted by value of slope.

• **kwargs – Additional params to pass in init function.

esrgan.utils.init.net_init_(net: torch.nn.modules.module.Module) → None

Inplace initialization of weights of neural network.

Parameters

net – Network to initialize.

Model params

esrgan.utils.module_params.create_layer(layer: Callable[[...], torch.nn.modules.module.Module], in_channels: Optional[int] = None, out_channels: Optional[int] = None, layer_name: Optional[str] = None, **kwargs: Any) → torch.nn.modules.module.Module

Helper function to generalize layer creation.

Parameters

• layer – Layer constructor.

• in_channels – Size of the input sample.

• out_channels – Size of the output e.g. number of channels produced by the convolution.

• layer_name – Name of the layer e.g. 'activation'.

• **kwargs – Additional params to pass into layer function.

Returns

Layer.

Examples

>>> in_channels, out_channels = 10, 5
>>> create_layer(nn.Linear, in_channels, out_channels)
Linear(in_features=10, out_features=5, bias=True)
>>> create_layer(nn.ReLU, in_channels, out_channels, layer_name='act')
ReLU()

Misc

esrgan.utils.misc.is_power_of_two(number: int) → bool

Check if a given number is a power of two.

Parameters

number – Nonnegative integer.

Returns

True or False.

Examples

>>> is_power_of_two(4)
True
>>> is_power_of_two(3)
False

esrgan.utils.misc.pairwise(iterable: Iterable[Any]) → Iterable[Any]

Iterate sequences by pairs.

Parameters

iterable – Any iterable sequence.

Returns

Pairwise iterator.

Examples

>>> for i in pairwise([1, 2, 5, -3]):
...     print(i)
(1, 2)
(2, 5)
(5, -3)