def __transition_up_block(ip, nb_filters, type='upsampling', output_shape=None, weight_decay=1E-4):
''' SubpixelConvolutional Upscaling (factor = 2)
Args:
ip: keras tensor
nb_filters: number of layers
type: can be 'upsampling', 'subpixel', 'deconv', or 'atrous'. Determines type of upsampling performed
output_shape: required if type = 'deconv'. Output shape of tensor
weight_decay: weight decay factor
Returns: keras tensor, after applying upsampling operation.
'''
if type == 'upsampling':
x = UpSampling2D()(ip)
elif type == 'subpixel':
x = Conv2D(nb_filters, (3, 3), padding="same", kernel_regularizer=l2(weight_decay), activation='relu',
use_bias=False, kernel_initializer='he_uniform')(ip)
# x = Convolution2D(nb_filters, 3, 3, activation="relu", border_mode='same', W_regularizer=l2(weight_decay),
# bias=False, init='he_uniform')(ip)
x = SubPixelUpscaling(scale_factor=2)(x)
x = Conv2D(nb_filters, (3, 3), activation="relu", padding='same', kernel_regularizer=l2(weight_decay),
use_bias=False, kernel_initializer='he_uniform')(x)
elif type == 'atrous':
# waiting on https://github.com/fchollet/keras/issues/4018
x = AtrousConvolution2D(nb_filters, 3, 3, activation="relu", W_regularizer=l2(weight_decay),
bias=False, atrous_rate=(2, 2), init='he_uniform')(ip)
else:
x = Deconvolution2D(nb_filters, 3, 3, output_shape, activation='relu', border_mode='same',
subsample=(2, 2), init='he_uniform')(ip)
return x
def __transition_up_block(ip, nb_filters, type='deconv', weight_decay=1E-4):
'''Adds an upsampling block. Upsampling operation relies on the the type parameter.
# Arguments
ip: input keras tensor
nb_filters: integer, the dimensionality of the output space
(i.e. the number output of filters in the convolution)
type: can be 'upsampling', 'subpixel', 'deconv'. Determines
type of upsampling performed
weight_decay: weight decay factor
# Input shape
4D tensor with shape:
`(samples, channels, rows, cols)` if data_format='channels_first'
or 4D tensor with shape:
`(samples, rows, cols, channels)` if data_format='channels_last'.
# Output shape
4D tensor with shape:
`(samples, nb_filter, rows * 2, cols * 2)` if data_format='channels_first'
or 4D tensor with shape:
`(samples, rows * 2, cols * 2, nb_filter)` if data_format='channels_last'.
# Returns
a keras tensor
'''
with K.name_scope('TransitionUp'):
if type == 'upsampling':
x = UpSampling2D()(ip)
elif type == 'subpixel':
x = Conv2D(nb_filters, (3, 3),
activation='relu',
padding='same',
kernel_regularizer=l2(weight_decay),
use_bias=False,
kernel_initializer='he_normal')(ip)
x = SubPixelUpscaling(scale_factor=2)(x)
x = Conv2D(nb_filters, (3, 3),
activation='relu',
padding='same',
kernel_regularizer=l2(weight_decay),
use_bias=False,
kernel_initializer='he_normal')(x)
else:
x = Conv2DTranspose(nb_filters, (3, 3),
activation='relu',
padding='same',
strides=(2, 2),
kernel_initializer='he_normal',
kernel_regularizer=l2(weight_decay))(ip)
return x
def __transition_up_block(ip, nb_filters, type='upsampling', weight_decay=1E-4):
''' SubpixelConvolutional Upscaling (factor = 2)
Args:
ip: keras tensor
nb_filters: number of layers
type: can be 'upsampling', 'subpixel', 'deconv'. Determines type of upsampling performed
weight_decay: weight decay factor
Returns: keras tensor, after applying upsampling operation.
'''
if type == 'upsampling':
x = UpSampling2D()(ip)
elif type == 'subpixel':
x = Conv2D(nb_filters, (3, 3), activation='relu', padding='same', W_regularizer=l2(weight_decay),
use_bias=False, kernel_initializer='he_uniform')(ip)
x = SubPixelUpscaling(scale_factor=2)(x)
x = Conv2D(nb_filters, (3, 3), activation='relu', padding='same', W_regularizer=l2(weight_decay),
use_bias=False, kernel_initializer='he_uniform')(x)
else:
x = Conv2DTranspose(nb_filters, (3, 3), activation='relu', padding='same', strides=(2, 2),
kernel_initializer='he_uniform')(ip)
return x