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!")
def deconv_upsample(x, n_features, kernel=(5, 5), scale=4):
x = TransposedConv2dLayer(x, n_features, kernel, (2, 2), name='deconv_1')
if scale == 4:
x = tl.Conv2d(x,
n_features, (3, 3),
act=tf.nn.relu,
name='deconv_conv_1')
x = TransposedConv2dLayer(x,
n_features,
kernel, (2, 2),
name='deconv_2')
if scale == 8:
x = tl.Conv2d(x,
n_features, (3, 3),
act=tf.nn.relu,
name='deconv_conv_2')
x = TransposedConv2dLayer(x,
n_features,
kernel, (2, 2),
name='deconv_3')
return x