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 = 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 = Convolution2D(nb_filters, 3, 3, activation="relu", border_mode='same', W_regularizer=l2(weight_decay),
bias=False, init='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='subpixel',
output_shape=None,
weight_decay=1E-4):
''' SubpixelConvolutional Upscaling (factor = 2)
Args:
ip: keras tensor
nb_filters: number of layers
type: can be 'subpixel' or 'deconv'. 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 batch_norm, relu-conv, dropout, maxpool
'''
if type == 'subpixel':
x = Convolution2D(nb_filters,
3,
3,
activation="relu",
border_mode='same',
W_regularizer=l2(weight_decay),
bias=False)(ip)
x = SubPixelUpscaling(r=2, channels=int(nb_filters // 4))(x)
x = Convolution2D(nb_filters,
3,
3,
activation="relu",
border_mode='same',
W_regularizer=l2(weight_decay),
bias=False)(x)
else:
x = Deconvolution2D(nb_filters,
3,
3,
output_shape,
activation='relu',
border_mode='same',
subsample=(2, 2))(ip)
return x
def _upscale_block(self, ip, id):
init = ip
x = Convolution2D(256,
3,
3,
activation="linear",
border_mode='same',
name='sr_res_upconv1_%d' % id)(init)
x = LeakyReLU(alpha=0.25, name='sr_res_up_lr_%d_1_1' % id)(x)
x = SubPixelUpscaling(r=2,
channels=self.filters,
name='sr_res_upscale1_%d' % id)(x)
x = Convolution2D(256,
3,
3,
activation="linear",
border_mode='same',
name='sr_res_filter1_%d' % id)(x)
x = LeakyReLU(alpha=0.25, name='sr_res_up_lr_%d_1_2' % id)(x)
return x