niftynet.layer.subpixel module¶
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class
SubPixelLayer(upsample_factor=3, layer_configurations=((5, 64), (3, 32), (3, -1)), acti_func='tanh', feature_normalization=None, group_size=-1, with_bias=True, padding='REFLECT', w_initializer=None, w_regularizer=None, b_initializer=None, b_regularizer=None, name='subpixel_cnn')[source]¶ Bases:
niftynet.layer.base_layer.TrainableLayerImplementation of Shi et al.’s sub-pixel CNN single-image upsampling method.
Based on Shi et al.: “Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network”
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__init__(upsample_factor=3, layer_configurations=((5, 64), (3, 32), (3, -1)), acti_func='tanh', feature_normalization=None, group_size=-1, with_bias=True, padding='REFLECT', w_initializer=None, w_regularizer=None, b_initializer=None, b_regularizer=None, name='subpixel_cnn')[source]¶ Parameters: - upsample_factor – zoom-factor/image magnification factor
- layer_configurations – N pairs consisting of a kernel size and
a feature-map size, where N is the number of layers in the net. The last layer must have a feature-map size of -1. :param padding: padding applied in convolutional layers :param with_bias: incorporate bias parameters in convolutional layers :param feature_normalization: the type of feature normalization (e.g.
batch, instance or group norm. Default None.Parameters: - group_size – size of the groups if groupnorm is chosen.
- acti_func – activation function applied to first N - 1 layers
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