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# Copyright (C) 2018 Artsiom Sanakoyeu and Dmytro Kotovenko
#
# This file is part of Adaptive Style Transfer
#
# Adaptive Style Transfer is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Adaptive Style Transfer is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
import math
import numpy as np
import tensorflow as tf
import tensorflow.contrib.slim as slim
from tensorflow.python.framework import ops
import cv2
import tensorflow.contrib.layers as tflayers
from utils import *
def batch_norm(input, is_training=True, name="batch_norm"):
x = tflayers.batch_norm(inputs=input,
scale=True,
is_training=is_training,
trainable=True,
reuse=None)
return x
def instance_norm(input, name="instance_norm", is_training=True):
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", activation_fn=None):
with tf.variable_scope(name):
return slim.conv2d(input_, output_dim, ks, s, padding=padding, activation_fn=activation_fn,
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"):
# Upsampling procedure, like suggested in this article:
# https://distill.pub/2016/deconv-checkerboard/. At first upsample
# tensor like an image and then apply convolutions.
with tf.variable_scope(name):
input_ = tf.image.resize_images(images=input_,
size=tf.shape(input_)[1:3] * s,
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR) # That is optional
return conv2d(input_=input_, output_dim=output_dim, ks=ks, s=1, padding='SAME')
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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