def convert_official_discriminator_weights(ckpt_dir, use_custom_cuda):
discriminator = load_discriminator(d_params=None,
ckpt_dir=None,
custom_cuda=use_custom_cuda)
# restore official ones
official_checkpoint = tf.train.latest_checkpoint('./official-pretrained')
official_vars = tf.train.list_variables(official_checkpoint)
# get name mapper
name_mapper = variable_name_mapper_d(discriminator)
for name_d, tvar in name_mapper.items():
print(f'{name_d}: {tvar.name}')
# check shape
check_shape(name_mapper, official_vars)
# restore
tf.compat.v1.train.init_from_checkpoint(official_checkpoint,
assignment_map=name_mapper)
# save
ckpt = tf.train.Checkpoint(discriminator=discriminator)
out_dir = os.path.join(ckpt_dir, 'discriminator')
manager = tf.train.CheckpointManager(ckpt, out_dir, max_to_keep=1)
manager.save(checkpoint_number=0)
return
def initiate_models(g_params, d_params, use_custom_cuda):
discriminator = load_discriminator(d_params, ckpt_dir=None, custom_cuda=use_custom_cuda)
generator = load_generator(g_params=g_params, is_g_clone=False, ckpt_dir=None, custom_cuda=use_custom_cuda)
g_clone = load_generator(g_params=g_params, is_g_clone=True, ckpt_dir=None, custom_cuda=use_custom_cuda)
# set initial g_clone weights same as generator
g_clone.set_weights(generator.get_weights())
return discriminator, generator, g_clone
def convert_official_weights_together(ckpt_dir, use_custom_cuda):
# instantiate all models
discriminator = load_discriminator(d_params=None,
ckpt_dir=None,
custom_cuda=use_custom_cuda)
generator = load_generator(g_params=None,
is_g_clone=False,
ckpt_dir=None,
custom_cuda=use_custom_cuda)
g_clone = load_generator(g_params=None,
is_g_clone=True,
ckpt_dir=None,
custom_cuda=use_custom_cuda)
# restore official ones
official_checkpoint = tf.train.latest_checkpoint('./official-pretrained')
official_vars = tf.train.list_variables(official_checkpoint)
for name, shape in official_vars:
print(f'{name}: {shape}')
# get name mapper
name_mapper_d = variable_name_mapper_d(discriminator)
name_mapper_g1 = variable_name_mapper_g(generator, is_g_clone=False)
name_mapper_g2 = variable_name_mapper_g(g_clone, is_g_clone=True)
name_mapper = {**name_mapper_d, **name_mapper_g1, **name_mapper_g2}
# check shape
check_shape(name_mapper, official_vars)
# restore
tf.compat.v1.train.init_from_checkpoint(official_checkpoint,
assignment_map=name_mapper)
# save
ckpt = tf.train.Checkpoint(discriminator=discriminator,
generator=generator,
g_clone=g_clone)
manager = tf.train.CheckpointManager(ckpt, ckpt_dir, max_to_keep=1)
manager.save(checkpoint_number=0)
return