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blocks.py
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from layers import *
from sn_layers import sn_conv2d, sn_dense
def conv_block(x,
filters,
activation_,
kernel_size=(3, 3),
sampling='same',
normalization=None,
is_training=True,
dropout_rate=0.0,
mode='conv_first'):
assert mode in ['conv_first', 'normalization_first']
assert sampling in ['deconv', 'subpixel', 'down', 'same']
assert normalization in ['batch', 'layer', 'spectral', None]
conv_func = conv2d_transpose if sampling == 'deconv' \
else subpixel_conv2d if sampling == 'subpixel'\
else conv2d
normalize = batch_norm if normalization == 'batch' \
else layer_norm if normalization == 'layer' \
else None
strides = (1, 1) if sampling in ['same', 'subpixel'] else (2, 2)
with tf.variable_scope(None, conv_block.__name__):
if normalization == 'spectral':
return sn_conv_block(x,
filters,
activation_,
kernel_size,
sampling,
is_training,
dropout_rate)
if mode == 'conv_first':
_x = conv_func(x,
filters,
kernel_size=kernel_size,
activation_=None,
strides=strides)
if normalize is not None:
_x = normalize(_x, is_training)
_x = activation(_x, activation_)
if dropout_rate != 0:
_x = dropout(_x, dropout_rate)
else:
if normalization is None:
raise ValueError
else:
_x = normalize(x, is_training)
_x = activation(_x, activation_)
_x = conv_func(_x,
filters,
kernel_size=kernel_size,
activation_=None,
strides=strides)
return _x
def residual_block(x,
filters,
activation_,
kernel_size=(3, 3),
sampling='same',
normalization=None,
is_training=True,
dropout_rate=0.0,
mode='conv_first'):
with tf.variable_scope(None, residual_block.__name__):
_x = conv_block(x,
filters=filters,
activation_=activation_,
kernel_size=kernel_size,
sampling='same',
normalization=normalization,
is_training=is_training,
dropout_rate=dropout_rate,
mode=mode)
_x = conv_block(_x,
filters=filters,
activation_=None,
kernel_size=kernel_size,
sampling=sampling,
normalization=normalization,
is_training=is_training,
dropout_rate=dropout_rate,
mode=mode)
if x.get_shape().as_list()[-1] != filters:
__x = conv_block(_x,
filters=filters,
activation_=None,
kernel_size=kernel_size,
sampling=sampling,
normalization=normalization,
is_training=is_training,
dropout_rate=dropout_rate,
mode=mode)
elif sampling != 'same':
__x = conv_block(_x,
filters=filters,
activation_=None,
kernel_size=kernel_size,
sampling=sampling,
normalization=normalization,
is_training=is_training,
dropout_rate=dropout_rate,
mode=mode)
else:
__x = x
return _x + __x
def sn_conv_block(x,
filters,
activation_,
kernel_size=(3, 3),
sampling='same',
is_training=True,
dropout_rate=0.0):
strides = (1, 1) if sampling == 'same' else (2, 2)
with tf.variable_scope(None, sn_conv_block.__name__):
_x = sn_conv2d(x,
filters,
kernel_size,
strides,
is_training=is_training,
activation_=activation_)
if dropout_rate != 0:
_x = dropout(_x, dropout_rate)
return _x