def discriminator(self, image, t_text_embedding):
update_collection = tf.GraphKeys.UPDATE_OPS
with tf.variable_scope("discriminator", reuse=tf.AUTO_REUSE):
h0 = ops.lrelu(
ops.conv2d_sn(image,
self.options['df_dim'],
spectral_normed=True,
update_collection=update_collection,
name='d_h0_conv')) #32
h1 = ops.lrelu(
self.d_bn1(
ops.conv2d_sn(h0,
self.options['df_dim'] * 2,
spectral_normed=True,
update_collection=update_collection,
name='d_h1_conv'))) #16
h2 = ops.lrelu(
self.d_bn2(
ops.conv2d_sn(h1,
self.options['df_dim'] * 4,
spectral_normed=True,
update_collection=update_collection,
name='d_h2_conv'))) #8
h3 = ops.lrelu(
self.d_bn3(
ops.conv2d_sn(h2,
self.options['df_dim'] * 8,
spectral_normed=True,
update_collection=update_collection,
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([h3, tiled_embeddings], 3, name='h3_concat')
h3_new = ops.lrelu(
self.d_bn4(
ops.conv2d_sn(h3_concat,
self.options['df_dim'] * 8,
1,
1,
1,
1,
spectral_normed=True,
update_collection=update_collection,
name='d_h3_conv_new'))) #4
h4 = ops.linear(
tf.reshape(h3_new, [self.options['batch_size'], -1]), 1,
'd_h3_lin')
return h4, h4
def discriminator(self, image, t_text_embedding):
update_collection = tf.GraphKeys.UPDATE_OPS
with tf.variable_scope("discriminator", reuse=tf.AUTO_REUSE):
h0 = ops.lrelu(
ops.conv2d_sn(image,
self.options['df_dim'],
spectral_normed=True,
update_collection=update_collection,
name='d_h0_conv')) #32
h1 = ops.lrelu(
self.d_bn1(
ops.conv2d_sn(h0,
self.options['df_dim'] * 2,
spectral_normed=True,
update_collection=update_collection,
name='d_h1_conv'))) #16
h2 = ops.lrelu(
self.d_bn2(
ops.conv2d_sn(h1,
self.options['df_dim'] * 4,
spectral_normed=True,
update_collection=update_collection,
name='d_h2_conv'))) #8
h3 = ops.lrelu(
self.d_bn3(
ops.conv2d_sn(h2,
self.options['df_dim'] * 8,
spectral_normed=True,
update_collection=update_collection,
name='d_h3_conv'))) #4
h3_new = ops.lrelu(
self.d_bn4(
ops.conv2d_sn(h3,
self.options['df_dim'] * 8,
1,
1,
1,
1,
spectral_normed=True,
update_collection=update_collection,
name='d_h3_conv_new'))) #4
h3_new = tf.reshape(h3_new, [self.options['batch_size'], -1])
image_embedding = ops.linear(h3_new, self.options['t_dim'],
'd_h3_embedding')
# Embedding matrix of condition
reduced_text_embeddings = ops.linear(t_text_embedding,
self.options['t_dim'],
'd_embedding')
# Scalar output function
h4 = ops.linear(image_embedding, 1, 'd_scalar_output')
discriminator_output_logit = tf.reduce_sum(tf.multiply(
reduced_text_embeddings, image_embedding),
1,
keepdims=True) + h4
return discriminator_output_logit, discriminator_output_logit
