ops.py

import tensorflow as tf
from tensorflow.contrib.layers.python.layers import batch_norm

#the implements of leakyRelu
def lrelu(x , alpha = 0.2 , name="LeakyReLU"):
    return tf.maximum(x , alpha*x)

def conv2d(input_, output_dim,
           k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02,
           name="conv2d"):

    with tf.variable_scope(name):

        w = tf.get_variable('w', [k_h, k_w, input_.get_shape()[-1], output_dim],
                            initializer=tf.truncated_normal_initializer(stddev=stddev))
        conv = tf.nn.conv2d(input_, w, strides=[1, d_h, d_w, 1], padding='SAME')
        biases = tf.get_variable('biases', [output_dim], initializer=tf.constant_initializer(0.0))
        conv = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape())

        return conv

def de_conv(input_, output_shape,
             k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02,
             name="deconv2d", with_w=False):

    with tf.variable_scope(name):
        # filter : [height, width, output_channels, in_channels]
        w = tf.get_variable('w', [k_h, k_w, output_shape[-1], input_.get_shape()[-1]],
                            initializer=tf.random_normal_initializer(stddev=stddev))

        try:
            deconv = tf.nn.conv2d_transpose(input_, w, output_shape=output_shape,
                                            strides=[1, d_h, d_w, 1])

        # Support for verisons of TensorFlow before 0.7.0
        except AttributeError:

            deconv = tf.nn.deconv2d(input_, w, output_shape=output_shape,
                                    strides=[1, d_h, d_w, 1])

        biases = tf.get_variable('biases', [output_shape[-1]], initializer=tf.constant_initializer(0.0))
        deconv = tf.reshape(tf.nn.bias_add(deconv, biases), deconv.get_shape())

        if with_w:

            return deconv, w, biases

        else:

            return deconv

def fully_connect(input_, output_size, scope=None, stddev=0.02, bias_start=0.0, with_w=False):
  shape = input_.get_shape().as_list()
  with tf.variable_scope(scope or "Linear"):

    matrix = tf.get_variable("Matrix", [shape[1], output_size], tf.float32,
                 tf.random_normal_initializer(stddev=stddev))
    bias = tf.get_variable("bias", [output_size],
      initializer=tf.constant_initializer(bias_start))

    if with_w:
      return tf.matmul(input_, matrix) + bias, matrix, bias
    else:

      return tf.matmul(input_, matrix) + bias

def conv_cond_concat(x, y):
    """Concatenate conditioning vector on feature map axis."""
    x_shapes = x.get_shape()
    y_shapes = y.get_shape()

    return tf.concat(3 , [x , y*tf.ones([x_shapes[0], x_shapes[1], x_shapes[2] , y_shapes[3]])])

def batch_normal(input , scope="scope" , reuse=False):
    return batch_norm(input , epsilon=1e-5, decay=0.9 , scale=True, scope=scope , reuse=reuse , updates_collections=None)

def instance_norm(x):

    epsilon = 1e-9
    mean, var = tf.nn.moments(x, [1, 2], keep_dims=True)
    return tf.div(tf.subtract(x, mean), tf.sqrt(tf.add(var, epsilon)))

def residual(x, output_dims, kernel, strides, name_1, name_2):

    with tf.variable_scope('residual') as scope:

        conv1 = conv2d(x, output_dims, k_h=kernel, k_w=kernel, d_h=strides, d_w=strides, name=name_1)
        conv2 = conv2d(tf.nn.relu(conv1), output_dims, k_h=kernel, k_w=kernel, d_h=strides, d_w=strides, name=name_2)
        resi = x + conv2

        return resi

def deresidual(x, output_shape, kernel, strides, name_1, name_2):

    with tf.variable_scope('residual_un') as scope:

        deconv1 = de_conv(x, output_shape=output_shape, k_h=kernel, k_w=kernel, d_h=strides, d_w=strides, name=name_1)
        deconv2 = de_conv(tf.nn.relu(deconv1), output_shape=output_shape, k_h=kernel, k_w=kernel, d_h=strides, d_w=strides, name=name_2)
        resi = x + deconv2

        return resi