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- import math
- import numpy as np
- import tensorflow as tf
- import tensorflow.contrib.slim as slim
- from tensorflow.python.framework import ops
- from utils import *
- def batch_norm(x, name="batch_norm"):
- return tf.contrib.layers.batch_norm(x, decay=0.9, updates_collections=None, epsilon=1e-5, scale=True, scope=name)
- def instance_norm(input, name="instance_norm"):
- with tf.variable_scope(name):
- depth = input.get_shape()[3]
- scale = tf.get_variable("scale", [depth], initializer=tf.random_normal_initializer(1.0, 0.02, dtype=tf.float32))
- offset = tf.get_variable("offset", [depth], initializer=tf.constant_initializer(0.0))
- mean, variance = tf.nn.moments(input, axes=[1,2], keep_dims=True)
- epsilon = 1e-5
- inv = tf.rsqrt(variance + epsilon)
- normalized = (input-mean)*inv
- return scale*normalized + offset
- def conv2d(input_, output_dim, ks=4, s=2, stddev=0.02, padding='SAME', name="conv2d"):
- with tf.variable_scope(name):
- return slim.conv2d(input_, output_dim, ks, s, padding=padding, activation_fn=None,
- weights_initializer=tf.truncated_normal_initializer(stddev=stddev),
- biases_initializer=None)
- def deconv2d(input_, output_dim, ks=4, s=2, stddev=0.02, name="deconv2d"):
- with tf.variable_scope(name):
- return slim.conv2d_transpose(input_, output_dim, ks, s, padding='SAME', activation_fn=None,
- weights_initializer=tf.truncated_normal_initializer(stddev=stddev),
- biases_initializer=None)
- def lrelu(x, leak=0.2, name="lrelu"):
- return tf.maximum(x, leak*x)
- def linear(input_, output_size, scope=None, stddev=0.02, bias_start=0.0, with_w=False):
- with tf.variable_scope(scope or "Linear"):
- matrix = tf.get_variable("Matrix", [input_.get_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
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