def setUp(self):
super(PostprocessTest, self).setUp()
self.model_dir = self.create_tempdir(
"model", cleanup=absltest.TempFileCleanup.OFF).full_path
train.train_with_gin(self.model_dir, True, [
resources.get_file("config/tests/methods/unsupervised/train_test.gin")
], [])
def main(unused_argv):
if FLAGS.gin_file_name is not None:
model_config_file = [
os.path.join(FLAGS.gin_model_dir, FLAGS.gin_file_name)
]
else:
model_config_file = []
print('config_file: {}'.format(FLAGS.gin_config))
train.train_with_gin(FLAGS.model_dir, FLAGS.overwrite,
FLAGS.gin_config + model_config_file,
FLAGS.gin_bindings)
def setUp(self):
super(EvaluateTest, self).setUp()
self.model_dir = self.create_tempdir(
"model", cleanup=absltest.TempFileCleanup.OFF).full_path
model_config = resources.get_file(
"config/tests/methods/unsupervised/train_test.gin")
train.train_with_gin(self.model_dir, True, [model_config])
self.output_dir = self.create_tempdir(
"output", cleanup=absltest.TempFileCleanup.OFF).full_path
postprocess_config = resources.get_file(
"config/tests/postprocessing/postprocess_test_configs/mean.gin")
postprocess.postprocess_with_gin(self.model_dir, self.output_dir, True,
[postprocess_config])
def setUp(self):
super(EvaluateTest, self).setUp()
self.model1_dir = self.create_tempdir(
"model1/model", cleanup=absltest.TempFileCleanup.OFF).full_path
self.model2_dir = self.create_tempdir(
"model2/model", cleanup=absltest.TempFileCleanup.OFF).full_path
model_config = resources.get_file(
"config/tests/methods/unsupervised/train_test.gin")
gin.clear_config()
train.train_with_gin(self.model1_dir, True, [model_config])
train.train_with_gin(self.model2_dir, True, [model_config])
self.output_dir = self.create_tempdir(
"output", cleanup=absltest.TempFileCleanup.OFF).full_path
def test_visualize_sigmoid(self, activation):
activation_binding = (
"reconstruction_loss.activation = '{}'".format(activation))
self.model_dir = self.create_tempdir(
"model_{}".format(activation),
cleanup=absltest.TempFileCleanup.OFF).full_path
train.train_with_gin(self.model_dir, True, [
resources.get_file(
"config/tests/methods/unsupervised/train_test.gin")
], [activation_binding])
visualize_model.visualize(
self.model_dir,
self.create_tempdir(
"visualization_{}".format(activation)).full_path,
True,
num_animations=1,
num_frames=4,
num_points_irs=100)
def main(unused_argv):
# Obtain the study to reproduce.
study = reproduce.STUDIES[FLAGS.study]
# Print the hyperparameter settings.
if FLAGS.model_dir is None:
study.print_model_config(FLAGS.model_num)
else:
print("Model directory (skipped training):")
print("--")
print(FLAGS.model_dir)
print()
study.print_postprocess_config()
print()
study.print_eval_config()
if FLAGS.only_print:
return
# Set correct output directory.
if FLAGS.output_directory is None:
if FLAGS.model_dir is None:
output_directory = os.path.join("output", "{study}", "{model_num}")
else:
output_directory = "output"
else:
output_directory = FLAGS.output_directory
# Insert model number and study name into path if necessary.
output_directory = output_directory.format(model_num=str(FLAGS.model_num),
study=str(FLAGS.study))
# Model training (if model directory is not provided).
if FLAGS.model_dir is None:
model_bindings, model_config_file = study.get_model_config(FLAGS.model_num)
logging.info("Training model...")
model_dir = os.path.join(output_directory, "model")
model_bindings = [
"model.name = '{}'".format(os.path.basename(model_config_file)).replace(
".gin", ""),
"model.model_num = {}".format(FLAGS.model_num),
] + model_bindings
train.train_with_gin(model_dir, FLAGS.overwrite, [model_config_file],
model_bindings)
else:
logging.info("Skipped training...")
model_dir = FLAGS.model_dir
# We visualize reconstructions, samples and latent space traversals.
#visualize_dir = os.path.join(output_directory, "visualizations")
#visualize_model.visualize(model_dir, visualize_dir, FLAGS.overwrite)
# We fix the random seed for the postprocessing and evaluation steps (each
# config gets a different but reproducible seed derived from a master seed of
# 0). The model seed was set via the gin bindings and configs of the study.
random_state = np.random.RandomState(0)
# We extract the different representations and save them to disk.
postprocess_config_files = sorted(study.get_postprocess_config_files())
for config in postprocess_config_files:
post_name = os.path.basename(config).replace(".gin", "")
logging.info("Extracting representation %s...", post_name)
post_dir = os.path.join(output_directory, "postprocessed", post_name)
postprocess_bindings = [
"postprocess.random_seed = {}".format(random_state.randint(2**32)),
"postprocess.name = '{}'".format(post_name)
]
postprocess.postprocess_with_gin(model_dir, post_dir, FLAGS.overwrite,
[config], postprocess_bindings)
# Iterate through the disentanglement metrics.
eval_configs = sorted(study.get_eval_config_files())
print(eval_configs)
for config in postprocess_config_files:
post_name = os.path.basename(config).replace(".gin", "")
post_dir = os.path.join(output_directory, "postprocessed",
post_name)
# Now, we compute all the specified scores.
for gin_eval_config in eval_configs:
metric_name = os.path.basename(gin_eval_config).replace(".gin", "")
print(metric_name)
logging.info("Computing metric '%s' on '%s'...", metric_name, post_name)
metric_dir = os.path.join(output_directory, "metrics", post_name,
metric_name)
eval_bindings = [
"evaluation.random_seed = {}".format(random_state.randint(2**32)),
"evaluation.name = '{}'".format(metric_name)
]
evaluate.evaluate_with_gin(post_dir, metric_dir, FLAGS.overwrite,
[gin_eval_config], eval_bindings)
def test_train_model(self, gin_configs, gin_bindings):
# We clear the gin config before running. Otherwise, if a prior test fails,
# the gin config is locked and the current test fails.
gin.clear_config()
train.train_with_gin(self.create_tempdir().full_path, True, gin_configs,
gin_bindings)