def continuous_eval(experiment_dir):
"""Evaluate until checkpoints stop being produced."""
for ckpt_path in trainer_lib.next_checkpoint(experiment_dir,
timeout_mins=-1):
hparams = seq2act_estimator.load_hparams(experiment_dir)
hparams.set_hparam("batch_size", FLAGS.eval_batch_size)
eval_input_fn = seq2act_estimator.create_input_fn(
FLAGS.eval_files,
hparams.batch_size,
-1,
2,
input_utils.DataSource.from_str(FLAGS.eval_data_source),
max_range=hparams.max_span,
max_dom_pos=hparams.max_dom_pos,
max_pixel_pos=hparams.max_pixel_pos,
mean_synthetic_length=hparams.mean_synthetic_length,
stddev_synthetic_length=hparams.stddev_synthetic_length,
load_extra=True,
load_screen=hparams.load_screen,
load_dom_dist=(hparams.screen_encoder == "gcn"))
estimator = create_estimator(experiment_dir,
hparams,
decode_length=FLAGS.decode_length)
estimator.evaluate(input_fn=eval_input_fn,
steps=FLAGS.eval_steps,
checkpoint_path=ckpt_path,
name=FLAGS.eval_name)
def prediction(experiment_dir):
"""Evaluate until checkpoints stop being produced."""
for ckpt_path in trainer_lib.next_checkpoint(experiment_dir,
timeout_mins=-1):
hparams = seq2act_estimator.load_hparams(experiment_dir)
hparams.set_hparam("batch_size", FLAGS.eval_batch_size)
eval_input_fn = seq2act_estimator.create_input_fn(
FLAGS.eval_files,
hparams.batch_size,
-1,
2,
input_utils.DataSource.from_str(FLAGS.eval_data_source),
max_range=hparams.max_span,
max_dom_pos=hparams.max_dom_pos,
max_pixel_pos=hparams.max_pixel_pos,
mean_synthetic_length=hparams.mean_synthetic_length,
stddev_synthetic_length=hparams.stddev_synthetic_length,
load_extra=True,
load_screen=hparams.load_screen,
load_dom_dist=(hparams.screen_encoder == "gcn"))
raw_texts = [
"Click button OK", "Navigate to settings", "Open app drawer"
]
predict1111111_input_fn = lambda: common_input_fn(features,
training=False)
my_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": "Click button OK"}, shuffle=False, batch_size=1)
predict_input_fn = tf.estimator.inputs.numpy_input_fn(
{"input_refs": np.array(raw_texts).astype(np.str)},
shuffle=False,
batch_size=1)
estimator = create_estimator(experiment_dir,
hparams,
decode_length=FLAGS.decode_length)
print("\nSTART PREDICTION\n")
results = estimator.predict(
input_fn=predict_input_fn,
#input_fn=predict_input_fn,
#steps=FLAGS.eval_steps,
#checkpoint_path=ckpt_path,
#name=FLAGS.eval_name
)
print(results)
for result in results:
print(result)
print("\nEND PREDICTION\n")
def train(experiment_dir):
"""Trains the model."""
if FLAGS.hparam_file:
hparams = seq2act_estimator.load_hparams(FLAGS.hparam_file)
else:
hparams = seq2act_estimator.create_hparams()
estimator = create_estimator(experiment_dir, hparams)
seq2act_estimator.save_hyperparams(hparams, experiment_dir)
train_file_list = FLAGS.train_file_list.split(",")
train_source_list = FLAGS.train_source_list.split(",")
train_batch_sizes = FLAGS.train_batch_sizes.split(",")
print("* xm_train", train_file_list, train_source_list, train_batch_sizes)
if len(train_file_list) > 1:
train_input_fn = seq2act_estimator.create_hybrid_input_fn(
train_file_list,
[input_utils.DataSource.from_str(s) for s in train_source_list],
map(int, train_batch_sizes),
max_range=hparams.max_span,
max_dom_pos=hparams.max_dom_pos,
max_pixel_pos=hparams.max_pixel_pos,
mean_synthetic_length=hparams.mean_synthetic_length,
stddev_synthetic_length=hparams.stddev_synthetic_length,
batch_size=hparams.batch_size,
boost_input=FLAGS.boost_input,
load_screen=hparams.load_screen,
buffer_size=FLAGS.shuffle_size,
shuffle_size=FLAGS.shuffle_size,
load_dom_dist=(hparams.screen_encoder == "gcn"))
else:
train_input_fn = seq2act_estimator.create_input_fn(
train_file_list[0],
hparams.batch_size,
-1,
-1,
input_utils.DataSource.from_str(train_source_list[0]),
max_range=hparams.max_span,
max_dom_pos=hparams.max_dom_pos,
max_pixel_pos=hparams.max_pixel_pos,
mean_synthetic_length=hparams.mean_synthetic_length,
stddev_synthetic_length=hparams.stddev_synthetic_length,
load_extra=False,
load_screen=hparams.load_screen,
buffer_size=FLAGS.shuffle_size,
shuffle_size=FLAGS.shuffle_size,
load_dom_dist=(hparams.screen_encoder == "gcn"))
estimator.train(input_fn=train_input_fn, steps=FLAGS.train_steps)