def resnet_PanSharpening_model_dense(self, pan_img, ms_img):
def residual_block(inputs, output_channel, stride, scope):
with tf.variable_scope(scope):
net1 = Op.conv2(inputs,
3,
output_channel,
stride,
use_bias=False,
scope='conv_1')
net1 = Op.batchnorm(net1)
net1 = tf.nn.relu(net1)
net2 = Op.conv2(tf.concat([inputs, net1], -1),
3,
output_channel,
stride,
use_bias=False,
scope='conv_2')
net2 = Op.batchnorm(net2)
net2 = tf.nn.relu(net2)
return net2
with tf.variable_scope('Pan_model'):
if self.is_training:
with tf.name_scope('upscale'):
ms_img = tf.image.resize_images(
ms_img, [self.pan_size, self.pan_size], method=2)
inputs = tf.concat([ms_img, pan_img], axis=-1)
with tf.variable_scope('generator_unit'):
# The input layer
with tf.variable_scope('input_stage'):
net = Op.conv2(inputs, 3, 64, 1, scope='conv')
net = Op.batchnorm(net)
net = tf.nn.relu(net)
net = tf.concat([inputs, net], -1)
# The residual block parts
for i in range(1, 5 + 1, 1):
name_scope = 'resblock_%d' % (i)
net = residual_block(net, 64, 1, name_scope)
with tf.variable_scope('resblock_output'):
net = Op.conv2(net, 3, 4, 1, use_bias=False, scope='conv')
net = tf.tanh(net)
return net
def residual_block(inputs, output_channel, stride, scope):
with tf.variable_scope(scope):
net1 = Op.conv2(inputs,
3,
output_channel,
stride,
use_bias=False,
scope='conv_1')
net1 = Op.batchnorm(net1)
net1 = tf.nn.relu(net1)
net2 = Op.conv2(tf.concat([inputs, net1], -1),
3,
output_channel,
stride,
use_bias=False,
scope='conv_2')
net2 = Op.batchnorm(net2)
net2 = tf.nn.relu(net2)
return net2
def residual_block(inputs, output_channel, stride, scope):
with tf.variable_scope(scope):
net = Op.conv2(inputs,
3,
output_channel,
stride,
use_bias=False,
scope='conv_1')
net = Op.batchnorm(net)
net = Op.prelu_tf(net)
net = Op.conv2(net,
3,
output_channel,
stride,
use_bias=False,
scope='conv_2')
net = Op.batchnorm(net)
net = net + inputs
return net