python图像缺失弥补源码

from layer import * import tensorflow as tf class Network: def __init__(self, x, mask, local_x, global_completion, local_completion, is_training, batch_size): self.batch_size = batch_size self.imitation = self.generator(x * (1 - mask), is_training) self.completion = self.imitation * mask + x * (1 - mask) # 由真的图片上截取下来的local_x跟原图x，输出结果就是True self.real = self.discriminator(x, local_x, reuse=False) # 由completion自己补的图片跟local discriminator补出来的图片，输出结果就是Fake self.fake = self.discriminator(global_completion, local_completion, reuse=True) self.g_loss = self.calc_g_loss(x, self.completion) self.d_loss = self.calc_d_loss(self.real, self.fake) self.g_variables = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES, scope='generator') self.d_variables = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES, scope='discriminator') def generator(self, x, is_training): with tf.compat.v1.variable_scope('generator'): with tf.compat.v1.variable_scope('conv1'): x = conv_layer(x, [5, 5, 3, 64], 1) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('conv2'): x = conv_layer(x, [3, 3, 64, 128], 2) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('conv3'): x = conv_layer(x, [3, 3, 128, 128], 1) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('conv4'): x = conv_layer(x, [3, 3, 128, 256], 2) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('conv5'): x = conv_layer(x, [3, 3, 256, 256], 1) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('conv6'): x = conv_layer(x, [3, 3, 256, 256], 1) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('dilated1'): x = dilated_conv_layer(x, [3, 3, 256, 256], 2) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('dilated2'): x = dilated_conv_layer(x, [3, 3, 256, 256], 4) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('dilated3'): x = dilated_conv_layer(x, [3, 3, 256, 256], 8) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('dilated4'): x = dilated_conv_layer(x, [3, 3, 256, 256], 16) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('conv7'): x = conv_layer(x, [3, 3, 256, 256], 1) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('conv8'): x = conv_layer(x, [3, 3, 256, 256], 1) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('deconv1'): x = deconv_layer(x, [4, 4, 128, 256], [self.batch_size, 64, 64, 128], 2) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('conv9'): x = conv_layer(x, [3, 3, 128, 128], 1) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('deconv2'): x = deconv_layer(x, [4, 4, 64, 128], [self.batch_size, 128, 128, 64], 2) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('conv10'): x = conv_layer(x, [3, 3, 64, 32], 1) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('conv11'): x = conv_layer(x, [3, 3, 32, 3], 1) x = tf.nn.tanh(x) return x def discriminator(self, global_x, local_x, reuse): def global_discriminator(x): is_training = tf.constant(True) with tf.compat.v1.variable_scope('global'): with tf.compat.v1.variable_scope('conv1'): x = conv_layer(x, [5, 5, 3, 64], 2) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('conv2'): x = conv_layer(x, [5, 5, 64, 128], 2) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('conv3'): x = conv_layer(x, [5, 5, 128, 256], 2) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('conv4'): x = conv_layer(x, [5, 5, 256, 512], 2) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('conv5'): x = conv_layer(x, [5, 5, 512, 512], 2) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('fc'): x = flatten_layer(x) x = full_connection_layer(x, 1024) return x def local_discriminator(x): is_training = tf.constant(True) with tf.compat.v1.variable_scope('local'): with tf.compat.v1.variable_scope('conv1'): x = conv_layer(x, [5, 5, 3, 64], 2) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('conv2'): x = conv_layer(x, [5, 5, 64, 128], 2) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('conv3'): x = conv_layer(x, [5, 5, 128, 256], 2) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('conv4'): x = conv_layer(x, [5, 5, 256, 512], 2) x = batch_normalize(x, is_training) x = tf.nn.relu(x) with tf.compat.v1.variable_scope('fc'): x = flatten_layer(x) x = full_connection_layer(x, 1024) return x with tf.compat.v1.variable_scope('discriminator', reuse=reuse): global_output = global_discriminator(global_x) local_output = local_discriminator(local_x) with tf.compat.v1.variable_scope('concatenation'): output = tf.compat.v1.concat((global_output, local_output), 1) output = full_connection_layer(output, 1) return output def calc_g_loss(self, x, completion): loss = tf.compat.v1.nn.l2_loss(x - completion) return tf.reduce_mean(loss) def calc_d_loss(self, real, fake): alpha = 4e-4 d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=real, labels=tf.ones_like(real))) d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=fake, labels=tf.zeros_like(fake))) return tf.add