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RRDN

def make_model(arch_params, patch_size)

Returns the model.

Used to select the model.

def get_network(weights)

class RRDN

Implementation of the Residual in Residual Dense Network for image super-scaling.

The network is the one described in https://arxiv.org/abs/1809.00219 (Wang et al. 2018).

arch_params: dictionary, contains the network parameters C, D, G, G0, T, x.
patch_size: integer or None, determines the input size. Only needed at training time, for prediction is set to None.
beta: float <= 1, scaling parameter for the residual connections.
c_dim: integer, number of channels of the input image.
kernel_size: integer, common kernel size for convolutions.
upscaling: string, 'ups' or 'shuffle', determines which implementation of the upscaling layer to use.
init_val: extreme values for the RandomUniform initializer.
weights: string, if not empty, download and load pre-trained weights. Overrides other parameters.

C: integer, number of conv layer inside each residual dense blocks (RDB).
D: integer, number of RDBs inside each Residual in Residual Dense Block (RRDB).
T: integer, number or RRDBs.
G: integer, number of convolution output filters inside the RDBs.
G0: integer, number of output filters of each RDB.
x: integer, the scaling factor.
model: Keras model of the RRDN.
name: name used to identify what upscaling network is used during training.
model._name: identifies this network as the generator network in the compound model built by the trainer class.

def __init__(arch_params, patch_size, beta, c_dim, kernel_size, init_val, weights)

def __init__(scale, **kwargs)

def call(x)

def get_config()

def __init__(beta, **kwargs)

def call(x, **kwargs)

def get_config()