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 build_model(self, n_dense_blocks=8, scale=8, subpixel=False):
print("Building DenseNet...")
norm_input = utils.normalize_color_tf(self.input)
norm_target = utils.normalize_color_tf(self.target)
x = tl.InputLayer(norm_input, name='input_layer')
'''
extract low level feature
In Paper <Densely Connected Convolutional Networks>,the filter size here is 7*7
and followed by a max pool layer
upscale_input = tl.Conv2d(x,self.feature_size, [7, 7], act = None, name = 'conv0')
upscale_input = tl.MaxPool2d(upscale_input, [3,3], [2,2], name = 'maxpool0')
'''
with tf.variable_scope("low_level_features"):
x = tl.Conv2d(x, 128, [3, 3], act=None, name='conv0')
conv1 = x
with tf.variable_scope("dense_blocks"):
for i in range(n_dense_blocks):
x = self.dense_block(x, 16, 8, (3, 3), layer=i)
x = tl.ConcatLayer([conv1, x],
concat_dim=3,
name='dense%d/concat_output' % i)
with tf.variable_scope("bottleneck_layer"):
'''
bottleneck layer
In Paper <Image Super-Resolution Using Dense Skip Connections>
The channel here is 256
'''
x = tl.Conv2d(x, 256, (1, 1), act=None, name='bottleneck')
with tf.variable_scope("upscale_module"):
'''
Paper <Densely Connected Convolutional Networks> using deconv layers to upscale the output
we provide two methods here: deconv, subpixel
'''
if subpixel:
x = utils.subpixel_upsample(x, 128, scale)
else:
x = utils.deconv_upsample(x, 128, (3, 3), scale)
with tf.variable_scope("reconstruction_layer"):
output = tl.Conv2d(x,
self.n_channels, (3, 3),
act=tf.nn.relu,
name='reconstruction')
self.output = tf.clip_by_value(output.outputs, 0.0, 1.0, name="output")
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()
print("Done building!")
def buildModel(self):
print("Building EDSR...")
self.norm_input = utils.normalize_color_tf(self.input)
self.norm_target = utils.normalize_color_tf(self.target)
#input layer
x = tl.InputLayer(self.norm_input, name='inputlayer')
# One convolution before res blocks and to convert to required feature depth
#第一层的剪枝将会作用在这里，filter数量减少
#x = tl.Conv2d(x, self.feature_size-self.prunedlist[0], [3, 3], name='c')
x = tl.Conv2d(x, self.feature_size, [3, 3], name='c')
# Store the output of the first convolution to add later
conv_1 = x
scaling_factor = 0.1
# Add the residual blocks to the model
for i in range(self.num_layers):
x = self.__resBlock(x, self.feature_size, scale=scaling_factor,layer=i)
# One more convolution, and then we add the output of our first conv layer
x = tl.Conv2d(x, self.feature_size, [3, 3], act = None, name = 'm1')
x = tl.ElementwiseLayer([conv_1,x],tf.add, name='res_add')
x = TransposedConv2dLayer(x, self.feature_size, [5,5], [2,2], name='deconv_1')
x = tl.Conv2d(x, self.feature_size, [3, 3], act=tf.nn.relu, name='deconv_conv_1')
x = TransposedConv2dLayer(x, self.feature_size, [5, 5], [2, 2], name='deconv_2')
if self.scale==8:
x = tl.Conv2d(x, self.feature_size, [3, 3], act=tf.nn.relu, name='deconv_conv_2')
x = TransposedConv2dLayer(x, self.feature_size, [5, 5], [2, 2], name='deconv_3')
# One final convolution on the upsampling output
output = tl.Conv2d(x,self.output_channels,[1,1],act=tf.nn.relu, name='lastLayer')
# output = tl.Conv2d(x, self.output_channels, [1, 1], act=None, name='lastLayer')
self.output = output.outputs
self.output = tf.clip_by_value(output.outputs, 0.0, 1.0)
self.cacuLoss()
# Tensorflow graph setup... session, saver, etc.
session_conf = tf.ConfigProto(allow_soft_placement=True,log_device_placement=False)
session_conf.gpu_options.allocator_type = 'BFC'
self.sess = tf.Session(config=session_conf)
self.saver = tf.train.Saver(var_list = tf.trainable_variables(), max_to_keep=100)
print("Done building!")
