def get_dummy_gan_model(generated_data=None):
"""Returns a GANModel tuple for testing."""
if generated_data is None:
generated_data = tf.ones([3, 4])
# TODO(joelshor): Find a better way of creating a variable scope.
with tf.compat.v1.variable_scope(
'generator', reuse=tf.compat.v1.AUTO_REUSE) as gen_scope:
gen_var = tf.compat.v1.get_variable(
'dummy_var', initializer=0.0, use_resource=False)
with tf.compat.v1.variable_scope(
'discriminator', reuse=tf.compat.v1.AUTO_REUSE) as dis_scope:
dis_var = tf.compat.v1.get_variable(
'dummy_var', initializer=0.0, use_resource=False)
return tfgan.GANModel(
generator_inputs=None,
generated_data=generated_data,
generator_variables=[gen_var],
generator_scope=gen_scope,
generator_fn=None,
real_data=tf.zeros([3, 4]),
discriminator_real_outputs=tf.ones([1, 2, 3]) * dis_var,
discriminator_gen_outputs=tf.ones([1, 2, 3]) * gen_var * dis_var,
discriminator_variables=[dis_var],
discriminator_scope=dis_scope,
discriminator_fn=None)
def get_gan_model():
# TODO(joelshor): Find a better way of creating a variable scope.
with tf.compat.v1.variable_scope('generator') as gen_scope:
pass
with tf.compat.v1.variable_scope('discriminator') as dis_scope:
pass
return tfgan.GANModel(generator_inputs=tf.zeros([4, 32, 32, 3]),
generated_data=tf.zeros([4, 32, 32, 3]),
generator_variables=[tf.Variable(0),
tf.Variable(1)],
generator_scope=gen_scope,
generator_fn=generator_model,
real_data=tf.ones([4, 32, 32, 3]),
discriminator_real_outputs=tf.ones([1, 2, 3]),
discriminator_gen_outputs=tf.ones([1, 2, 3]),
discriminator_variables=[tf.Variable(0)],
discriminator_scope=dis_scope,
discriminator_fn=discriminator_model)
def get_dummy_gan_model():
# TODO(joelshor): Find a better way of creating a variable scope.
with tf.compat.v1.variable_scope('generator') as gen_scope:
gen_var = tf.compat.v1.get_variable('dummy_var', initializer=0.0)
with tf.compat.v1.variable_scope('discriminator') as dis_scope:
dis_var = tf.compat.v1.get_variable('dummy_var', initializer=0.0)
return tfgan.GANModel(
generator_inputs=None,
generated_data=tf.ones([3, 4]),
generator_variables=[gen_var],
generator_scope=gen_scope,
generator_fn=None,
real_data=tf.zeros([3, 4]),
discriminator_real_outputs=tf.ones([1, 2, 3]) * dis_var,
discriminator_gen_outputs=tf.ones([1, 2, 3]) * gen_var * dis_var,
discriminator_variables=[dis_var],
discriminator_scope=dis_scope,
discriminator_fn=None)
def _partial_model(generator_inputs_np):
model = tfgan.GANModel(*[None] * 11)
return model._replace(generator_inputs=tf.constant(
generator_inputs_np, dtype=tf.float32))
def _model(disc_gen_outputs, disc_real_outputs):
model = tfgan.GANModel(*[None] * 11)
return model._replace(discriminator_real_outputs=disc_real_outputs,
discriminator_gen_outputs=disc_gen_outputs)
