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Discriminator.py
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import tensorflow as tf
import numpy as npimport
import tensorflow as tf
from tensorflow.python import pywrap_tensorflow
import numpy as np
import tensorflow as tf
from tensorflow.python import pywrap_tensorflow
import numpy as np
WEIGHT_INIT_STDDEV = 0.1
class Discriminator1(object):
def __init__(self, scope_name):
self.weight_vars = []
self.scope = scope_name
with tf.variable_scope(scope_name):
self.weight_vars.append(self._create_variables(3, 16, 3, scope = 'conv1'))
self.weight_vars.append(self._create_variables(16, 32, 3, scope = 'conv2'))
self.weight_vars.append(self._create_variables(32, 64, 3, scope = 'conv3'))
self.weight_vars.append(self._create_variables(64, 1, 3, scope='conv4'))
def _create_variables(self, input_filters, output_filters, kernel_size, scope):
shape = [kernel_size, kernel_size, input_filters, output_filters]
with tf.variable_scope(scope):
kernel = tf.Variable(tf.truncated_normal(shape, stddev = WEIGHT_INIT_STDDEV), name = 'kernel')
bias = tf.Variable(tf.zeros([output_filters]), name = 'bias')
return (kernel, bias)
def discrim(self, img, reuse):
conv_num = len(self.weight_vars)
if len(img.shape) != 4:
img = tf.expand_dims(img, -1)
out = img
for i in range(conv_num-1):
kernel, bias = self.weight_vars[i]
if i == 0:
out = conv2d_1(out, kernel, bias, [1, 2, 2, 1], use_relu = True, use_BN = False,
Scope = self.scope + '/b' + str(i), Reuse = reuse)
else:
out = conv2d_1(out, kernel, bias, [1, 2, 2, 1], use_relu = True, use_BN = True,
Scope = self.scope + '/b' + str(i), Reuse = reuse)
kernel, bias = self.weight_vars[conv_num-1]
out = conv2d_1(out, kernel, bias, [1, 2, 2, 1], use_relu=False, use_BN=True,
Scope=self.scope + '/b' + str(conv_num-1), Reuse=reuse)
out=tf.nn.tanh(out)
out = out / 2 + 0.5
return out
def conv2d_1(x, kernel, bias, strides, use_relu = True, use_BN = True, Scope = None, Reuse = None):
# padding image with reflection mode
x_padded = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]], mode = 'REFLECT')
# conv and add bias
out = tf.nn.conv2d(x_padded, kernel, strides, padding = 'VALID')
out = tf.nn.bias_add(out, bias)
if use_BN:
with tf.variable_scope(Scope):
out = tf.layers.batch_normalization(out, training = True, reuse = Reuse)
if use_relu:
out = tf.nn.relu(out)
return out
class Discriminator2(object):
def __init__(self, scope_name):
self.weight_vars = []
self.scope = scope_name
with tf.variable_scope(scope_name):
self.weight_vars.append(self._create_variables(3, 16, 3, scope = 'conv1'))
self.weight_vars.append(self._create_variables(16, 32, 3, scope = 'conv2'))
self.weight_vars.append(self._create_variables(32, 64, 3, scope = 'conv3'))
self.weight_vars.append(self._create_variables(64, 1, 3, scope='conv4'))
def _create_variables(self, input_filters, output_filters, kernel_size, scope):
shape = [kernel_size, kernel_size, input_filters, output_filters]
with tf.variable_scope(scope):
kernel = tf.Variable(tf.truncated_normal(shape, stddev = WEIGHT_INIT_STDDEV), name = 'kernel')
bias = tf.Variable(tf.zeros([output_filters]), name = 'bias')
return (kernel, bias)
def discrim(self, img, reuse):
conv_num = len(self.weight_vars)
if len(img.shape) != 4:
img = tf.expand_dims(img, -1)
out = img
for i in range(conv_num-1):
kernel, bias = self.weight_vars[i]
if i == 0:
out = conv2d_2(out, kernel, bias, [1, 2, 2, 1], use_relu = True, use_BN = False,
Scope = self.scope + '/b' + str(i), Reuse = reuse)
else:
out = conv2d_2(out, kernel, bias, [1, 2, 2, 1], use_relu = True, use_BN = True,
Scope = self.scope + '/b' + str(i), Reuse = reuse)
kernel, bias = self.weight_vars[conv_num-1]
out = conv2d_2(out, kernel, bias, [1, 2, 2, 1], use_relu=False, use_BN=True,
Scope=self.scope + '/b' + str(conv_num-1), Reuse=reuse)
out=tf.nn.tanh(out)
out = out / 2 + 0.5
return out
def conv2d_2(x, kernel, bias, strides, use_relu = True, use_BN = True, Scope = None, Reuse = None):
# padding image with reflection mode
x_padded = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]], mode = 'REFLECT')
# conv and add bias
out = tf.nn.conv2d(x_padded, kernel, strides, padding = 'VALID')
out = tf.nn.bias_add(out, bias)
if use_BN:
with tf.variable_scope(Scope):
out = tf.layers.batch_normalization(out, training = True, reuse = Reuse)
if use_relu:
out = tf.nn.relu(out)
return out
class Discriminator3(object):
def __init__(self, scope_name):
self.weight_vars = []
self.scope = scope_name
with tf.variable_scope(scope_name):
self.weight_vars.append(self._create_variables(3, 16, 3, scope = 'conv1'))
self.weight_vars.append(self._create_variables(16, 32, 3, scope = 'conv2'))
self.weight_vars.append(self._create_variables(32, 64, 3, scope = 'conv3'))
self.weight_vars.append(self._create_variables(64, 1, 3, scope='conv4'))
def _create_variables(self, input_filters, output_filters, kernel_size, scope):
shape = [kernel_size, kernel_size, input_filters, output_filters]
with tf.variable_scope(scope):
kernel = tf.Variable(tf.truncated_normal(shape, stddev = WEIGHT_INIT_STDDEV), name = 'kernel')
bias = tf.Variable(tf.zeros([output_filters]), name = 'bias')
return (kernel, bias)
def discrim(self, img, reuse):
conv_num = len(self.weight_vars)
if len(img.shape) != 4:
img = tf.expand_dims(img, -1)
out = img
for i in range(conv_num-1):
kernel, bias = self.weight_vars[i]
if i == 0:
out = conv2d_3(out, kernel, bias, [1, 2, 2, 1], use_relu = True, use_BN = False,
Scope = self.scope + '/b' + str(i), Reuse = reuse)
else:
out = conv2d_3(out, kernel, bias, [1, 2, 2, 1], use_relu = True, use_BN = True,
Scope = self.scope + '/b' + str(i), Reuse = reuse)
kernel, bias = self.weight_vars[conv_num-1]
out = conv2d_3(out, kernel, bias, [1, 2, 2, 1], use_relu=False, use_BN=True,
Scope=self.scope + '/b' + str(conv_num-1), Reuse=reuse)
out=tf.nn.tanh(out)
out = out / 2 + 0.5
return out
def conv2d_3(x, kernel, bias, strides, use_relu = True, use_BN = True, Scope = None, Reuse = None):
# padding image with reflection mode
x_padded = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]], mode = 'REFLECT')
# conv and add bias
out = tf.nn.conv2d(x_padded, kernel, strides, padding = 'VALID')
out = tf.nn.bias_add(out, bias)
if use_BN:
with tf.variable_scope(Scope):
out = tf.layers.batch_normalization(out, training = True, reuse = Reuse)
if use_relu:
out = tf.nn.relu(out)
return out