def gen_matrices(hparams):
"""..."""
tf.compat.v1.logging.info('Generating Matrices.')
# modified body of make_estimator(hparams)
discriminator = _get_discriminator(hparams)
generator = _get_generator_to_be_conditioned(hparams)
if hparams.tpu_params.use_tpu_estimator:
config = est_lib.get_tpu_run_config_from_hparams(hparams)
estimator = est_lib.get_tpu_estimator(generator, discriminator,
hparams, config)
else:
config = est_lib.get_run_config_from_hparams(hparams)
estimator = est_lib.get_gpu_estimator(generator, discriminator,
hparams, config)
ckpt_str = evaluation.latest_checkpoint(hparams.model_dir)
tf.compat.v1.logging.info('Evaluating checkpoint: %s' % ckpt_str)
# saving matrices
save_dir = os.environ['HOME'] if flags.FLAGS.use_tpu else hparams.model_dir
embedding_map = estimator.get_variable_value(
'Discriminator/discriminator/d_embedding/embedding_map')
np.save(
'%s/embedding_map_step_%s.npy' % (save_dir, ckpt_str.split('-')[-1]),
embedding_map)
class_kernel = 'Discriminator/discriminator/d_sn_linear_class/dense/kernel'
if class_kernel in estimator.get_variable_names():
classification_map = estimator.get_variable_value(class_kernel)
np.save(
'%s/classification_map_step_%s.npy' %
(save_dir, ckpt_str.split('-')[-1]), classification_map)
def run_intra_fid_eval(hparams):
"""..."""
tf.compat.v1.logging.info('Intra FID evaluation.')
# modified body of make_estimator(hparams)
generator = _get_generator_to_be_conditioned(hparams)
discriminator = _get_discriminator(hparams)
if hparams.tpu_params.use_tpu_estimator:
config = est_lib.get_tpu_run_config_from_hparams(hparams)
estimator = est_lib.get_tpu_estimator(generator, discriminator,
hparams, config)
else:
config = est_lib.get_run_config_from_hparams(hparams)
estimator = est_lib.get_gpu_estimator(generator, discriminator,
hparams, config)
ckpt_str = evaluation.latest_checkpoint(hparams.model_dir)
tf.compat.v1.logging.info('Evaluating checkpoint: %s' % ckpt_str)
chunk_sz = flags.FLAGS.intra_fid_eval_chunk_size
n_chunks = flags.FLAGS.num_classes // chunk_sz
for chunk_i in range(0, n_chunks):
restrict_classes = list(
range(chunk_i * chunk_sz, (chunk_i + 1) * chunk_sz))
limited_class_train_eval_input_fn = functools.partial(
train_eval_input_fn,
restrict_classes=restrict_classes,
shift_classes=chunk_i * chunk_sz)
eval_results = estimator.evaluate(limited_class_train_eval_input_fn,
steps=hparams.num_eval_steps,
name='eval_intra_fid')
tf.compat.v1.logging.info(
'Finished intra fid {}/{} evaluation checkpoint: {}. IFID: {}'.
format(chunk_i, n_chunks, ckpt_str,
eval_results['eval/intra_fid']))
def gen_images(hparams):
"""..."""
tf.compat.v1.logging.info('Generating Images.')
# modified body of make_estimator(hparams)
discriminator = _get_discriminator(hparams)
generator = _get_generator_to_be_conditioned(hparams)
if hparams.tpu_params.use_tpu_estimator:
config = est_lib.get_tpu_run_config_from_hparams(hparams)
estimator = est_lib.get_tpu_estimator(generator, discriminator,
hparams, config)
else:
config = est_lib.get_run_config_from_hparams(hparams)
estimator = est_lib.get_gpu_estimator(generator, discriminator,
hparams, config)
# tf.compat.v1.logging.info('Counting params...')
# total_parameters = 0
# for variable in estimator.get_variable_names():
# vval = estimator.get_variable_value(variable)
# nparam = np.prod(estimator.get_variable_value(variable).shape)
# total_parameters += int(nparam)
# tf.compat.v1.logging.info('Found %i params.' % total_parameters)
# print(total_parameters)
ckpt_str = evaluation.latest_checkpoint(hparams.model_dir)
tf.compat.v1.logging.info('Evaluating checkpoint: %s' % ckpt_str)
try:
cur_step = int(estimator.get_variable_value('global_step'))
except ValueError:
cur_step = 0
eval_lib.predict_and_write_images(estimator, train_eval_input_fn,
hparams.model_dir, 'step_%i' % cur_step)
def make_estimator(hparams):
"""Creates a TPU Estimator."""
generator = _get_generator(hparams)
discriminator = _get_discriminator(hparams)
if hparams.tpu_params.use_tpu_estimator:
config = est_lib.get_tpu_run_config_from_hparams(hparams)
return est_lib.get_tpu_estimator(generator, discriminator, hparams, config)
else:
config = est_lib.get_run_config_from_hparams(hparams)
return est_lib.get_gpu_estimator(generator, discriminator, hparams, config)
def test_get_gpu_estimator_syntax(self):
config = estimator_lib.get_run_config_from_hparams(self.hparams)
est = estimator_lib.get_gpu_estimator(
generator, discriminator, self.hparams, config)
est.evaluate(lambda: input_fn({'batch_size': 16}), steps=1)
