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model.py
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model.py
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import tensorflow as tf
from Utils import ops
class GAN:
'''
OPTIONS
z_dim : Noise dimension 100
t_dim : Text feature dimension 256
image_size : Image Dimension 64
gf_dim : Number of conv in the first layer generator 64
df_dim : Number of conv in the first layer discriminator 64
gfc_dim : Dimension of gen untis for for fully connected layer 1024
caption_vector_length : Caption Vector Length 2400
batch_size : Batch Size 64
'''
def __init__(self, options):
self.options = options
self.g_bn0 = ops.batch_norm(name='g_bn0')
self.g_bn1 = ops.batch_norm(name='g_bn1')
self.g_bn2 = ops.batch_norm(name='g_bn2')
self.g_bn3 = ops.batch_norm(name='g_bn3')
self.d_bn1 = ops.batch_norm(name='d_bn1')
self.d_bn2 = ops.batch_norm(name='d_bn2')
self.d_bn3 = ops.batch_norm(name='d_bn3')
self.d_bn4 = ops.batch_norm(name='d_bn4')
def build_model(self):
img_size = self.options['image_size']
t_real_image = tf.placeholder('float32', [self.options['batch_size'],img_size, img_size, 3 ], name = 'real_image')
t_wrong_image = tf.placeholder('float32', [self.options['batch_size'],img_size, img_size, 3 ], name = 'wrong_image')
t_real_caption = tf.placeholder('float32', [self.options['batch_size'], self.options['caption_vector_length']], name = 'real_caption_input')
t_z = tf.placeholder('float32', [self.options['batch_size'], self.options['z_dim']])
fake_image = self.generator(t_z, t_real_caption)
disc_real_image, disc_real_image_logits = self.discriminator(t_real_image, t_real_caption)
disc_wrong_image, disc_wrong_image_logits = self.discriminator(t_wrong_image, t_real_caption, reuse = True)
disc_fake_image, disc_fake_image_logits = self.discriminator(fake_image, t_real_caption, reuse = True)
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(disc_fake_image_logits, tf.ones_like(disc_fake_image)))
d_loss1 = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(disc_real_image_logits, tf.ones_like(disc_real_image)))
d_loss2 = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(disc_wrong_image_logits, tf.zeros_like(disc_wrong_image)))
d_loss3 = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(disc_fake_image_logits, tf.zeros_like(disc_fake_image)))
d_loss = d_loss1 + d_loss2 + d_loss3
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if 'd_' in var.name]
g_vars = [var for var in t_vars if 'g_' in var.name]
input_tensors = {
't_real_image' : t_real_image,
't_wrong_image' : t_wrong_image,
't_real_caption' : t_real_caption,
't_z' : t_z
}
variables = {
'd_vars' : d_vars,
'g_vars' : g_vars
}
loss = {
'g_loss' : g_loss,
'd_loss' : d_loss
}
outputs = {
'generator' : fake_image
}
checks = {
'd_loss1': d_loss1,
'd_loss2': d_loss2,
'd_loss3' : d_loss3,
'disc_real_image_logits' : disc_real_image_logits,
'disc_wrong_image_logits' : disc_wrong_image,
'disc_fake_image_logits' : disc_fake_image_logits
}
return input_tensors, variables, loss, outputs, checks
def build_generator(self):
img_size = self.options['image_size']
t_real_caption = tf.placeholder('float32', [self.options['batch_size'], self.options['caption_vector_length']], name = 'real_caption_input')
t_z = tf.placeholder('float32', [self.options['batch_size'], self.options['z_dim']])
fake_image = self.sampler(t_z, t_real_caption)
input_tensors = {
't_real_caption' : t_real_caption,
't_z' : t_z
}
outputs = {
'generator' : fake_image
}
return input_tensors, outputs
# Sample Images for a text embedding
def sampler(self, t_z, t_text_embedding):
tf.get_variable_scope().reuse_variables()
s = self.options['image_size']
s2, s4, s8, s16 = int(s/2), int(s/4), int(s/8), int(s/16)
reduced_text_embedding = ops.lrelu( ops.linear(t_text_embedding, self.options['t_dim'], 'g_embedding') )
z_concat = tf.concat(1, [t_z, reduced_text_embedding])
z_ = ops.linear(z_concat, self.options['gf_dim']*8*s16*s16, 'g_h0_lin')
h0 = tf.reshape(z_, [-1, s16, s16, self.options['gf_dim'] * 8])
h0 = tf.nn.relu(self.g_bn0(h0, train = False))
h1 = ops.deconv2d(h0, [self.options['batch_size'], s8, s8, self.options['gf_dim']*4], name='g_h1')
h1 = tf.nn.relu(self.g_bn1(h1, train = False))
h2 = ops.deconv2d(h1, [self.options['batch_size'], s4, s4, self.options['gf_dim']*2], name='g_h2')
h2 = tf.nn.relu(self.g_bn2(h2, train = False))
h3 = ops.deconv2d(h2, [self.options['batch_size'], s2, s2, self.options['gf_dim']*1], name='g_h3')
h3 = tf.nn.relu(self.g_bn3(h3, train = False))
h4 = ops.deconv2d(h3, [self.options['batch_size'], s, s, 3], name='g_h4')
return (tf.tanh(h4)/2. + 0.5)
# GENERATOR IMPLEMENTATION based on : https://github.com/carpedm20/DCGAN-tensorflow/blob/master/model.py
def generator(self, t_z, t_text_embedding):
s = self.options['image_size']
s2, s4, s8, s16 = int(s/2), int(s/4), int(s/8), int(s/16)
reduced_text_embedding = ops.lrelu( ops.linear(t_text_embedding, self.options['t_dim'], 'g_embedding') )
z_concat = tf.concat(1, [t_z, reduced_text_embedding])
z_ = ops.linear(z_concat, self.options['gf_dim']*8*s16*s16, 'g_h0_lin')
h0 = tf.reshape(z_, [-1, s16, s16, self.options['gf_dim'] * 8])
h0 = tf.nn.relu(self.g_bn0(h0))
h1 = ops.deconv2d(h0, [self.options['batch_size'], s8, s8, self.options['gf_dim']*4], name='g_h1')
h1 = tf.nn.relu(self.g_bn1(h1))
h2 = ops.deconv2d(h1, [self.options['batch_size'], s4, s4, self.options['gf_dim']*2], name='g_h2')
h2 = tf.nn.relu(self.g_bn2(h2))
h3 = ops.deconv2d(h2, [self.options['batch_size'], s2, s2, self.options['gf_dim']*1], name='g_h3')
h3 = tf.nn.relu(self.g_bn3(h3))
h4 = ops.deconv2d(h3, [self.options['batch_size'], s, s, 3], name='g_h4')
return (tf.tanh(h4)/2. + 0.5)
# DISCRIMINATOR IMPLEMENTATION based on : https://github.com/carpedm20/DCGAN-tensorflow/blob/master/model.py
def discriminator(self, image, t_text_embedding, reuse=False):
if reuse:
tf.get_variable_scope().reuse_variables()
h0 = ops.lrelu(ops.conv2d(image, self.options['df_dim'], name = 'd_h0_conv')) #32
h1 = ops.lrelu( self.d_bn1(ops.conv2d(h0, self.options['df_dim']*2, name = 'd_h1_conv'))) #16
h2 = ops.lrelu( self.d_bn2(ops.conv2d(h1, self.options['df_dim']*4, name = 'd_h2_conv'))) #8
h3 = ops.lrelu( self.d_bn3(ops.conv2d(h2, self.options['df_dim']*8, name = 'd_h3_conv'))) #4
# ADD TEXT EMBEDDING TO THE NETWORK
reduced_text_embeddings = ops.lrelu(ops.linear(t_text_embedding, self.options['t_dim'], 'd_embedding'))
reduced_text_embeddings = tf.expand_dims(reduced_text_embeddings,1)
reduced_text_embeddings = tf.expand_dims(reduced_text_embeddings,2)
tiled_embeddings = tf.tile(reduced_text_embeddings, [1,4,4,1], name='tiled_embeddings')
h3_concat = tf.concat( 3, [h3, tiled_embeddings], name='h3_concat')
h3_new = ops.lrelu( self.d_bn4(ops.conv2d(h3_concat, self.options['df_dim']*8, 1,1,1,1, name = 'd_h3_conv_new'))) #4
h4 = ops.linear(tf.reshape(h3_new, [self.options['batch_size'], -1]), 1, 'd_h3_lin')
return tf.nn.sigmoid(h4), h4