def __resBlock(self, x, channels=64, kernel_size=[3, 3], scale=1, layer=0):
"""
Creates a convolutional residual block
as defined in the paper. More on
this inside model.py
a resBlock is defined in the paper as
(excuse the ugly ASCII graph)
x
|\
| \
| conv2d
| relu
| conv2d
| /
|/
+ (addition here)
|
result
x: input to pass through the residual block
channels: number of channels to compute
stride: convolution stride
:param x: input tensor
:param channnels: channnels in the block
:param kernel_size: filter kernel size
:param scale: scale for residual skip
:param layer: layer number
"""
nn = tl.Conv2d(x, channels, kernel_size, act=tf.nn.relu, name='res%d/c1' % (layer))
nn = tl.Conv2d(nn, channels, kernel_size, act=None, name='res%d/c2' % (layer))
nn = ScaleLayer(nn, scale, name='res%d/scale' % (layer))
n = tl.ElementwiseLayer([x, nn], tf.add, name='res%d/res_add' % (layer))
return n
def build_model(self, n_features=256, n_res_blocks=36, scale=8, max_to_keep=100):
print("Building CycleSR...")
norm_input = utils.normalize_color_tf(self.input)
norm_target = utils.normalize_color_tf(self.target)
x = tl.InputLayer(norm_input, name='input_layer')
# One convolution before res blocks and to convert to required feature depth
x = tl.Conv2d(x, n_features, (3, 3), name='c')
# Store the output of the first convolution to add later
conv_1 = x
scaling_factor = 0.1
with tf.variable_scope("res_blocks"):
for i in range(n_res_blocks):
x = self._res_block(x, n_features, (3, 3), scale=scaling_factor, layer=i)
x = tl.Conv2d(x, n_features, (3, 3), name='res_c')
x = tl.ElementwiseLayer([conv_1, x], tf.add, name='res_add')
with tf.variable_scope("upscale_module"):
x = utils.deconv_upsample(x, n_features, kernel=(5, 5), scale=scale)
output = tl.Conv2d(x, self.n_channels, (1, 1), act=tf.nn.relu, name='bottleneck')
self.output = tf.clip_by_value(output.outputs, 0.0, 1.0, name='output')
self.cycle_x = tf.image.resize_bicubic(self.output*255.0+0.5, [48, 48])
self.calculate_loss(norm_target, self.output)
conf = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
self.sess = tf.Session(config=conf)
self.saver = tf.train.Saver(max_to_keep=max_to_keep)
print("Done building!")
def __resBlock(self, x, channels=64, kernel_size=[3, 3], scale=1, layer=0):
nn = tl.Conv2d(x,
channels,
kernel_size,
act=tf.nn.relu,
name='res%d/c1' % (layer))
nn = tl.Conv2d(nn,
channels,
kernel_size,
act=None,
name='res%d/c2' % (layer))
nn = ScaleLayer(nn, scale, name='res%d/scale' % (layer))
n = tl.ElementwiseLayer([x, nn],
tf.add,
name='res%d/res_add' % (layer))
return n
