def setUpClass(cls):
tmp_dir = tf.test.get_temp_dir()
shutil.rmtree(tmp_dir)
os.mkdir(tmp_dir)
cls.data_dir = tmp_dir
# Generate a small test dataset
registry.problem("tiny_algo").generate_data(cls.data_dir, None)
def setUpClass(cls):
tmp_dir = tf.test.get_temp_dir()
shutil.rmtree(tmp_dir)
os.mkdir(tmp_dir)
# Generate a small test dataset
FLAGS.problems = "tiny_algo"
TrainerUtilsTest.data_dir = tmp_dir
registry.problem(FLAGS.problems).generate_data(TrainerUtilsTest.data_dir,
None)
def generate_data():
# Generate data if requested.
data_dir = os.path.expanduser(FLAGS.data_dir)
tmp_dir = os.path.expanduser(FLAGS.tmp_dir)
tf.gfile.MakeDirs(data_dir)
tf.gfile.MakeDirs(tmp_dir)
problem_name = get_problem_name()
tf.logging.info("Generating data for %s" % problem_name)
registry.problem(problem_name).generate_data(data_dir, tmp_dir)
def TestVideoModel(self,
in_frames,
out_frames,
hparams,
model,
expected_last_dim):
x = np.random.random_integers(0, high=255, size=(8, in_frames, 64, 64, 3))
y = np.random.random_integers(0, high=255, size=(8, out_frames, 64, 64, 3))
hparams.video_num_input_frames = in_frames
hparams.video_num_target_frames = out_frames
problem = registry.problem("video_stochastic_shapes10k")
p_hparams = problem.get_hparams(hparams)
hparams.problem = problem
hparams.problem_hparams = p_hparams
with self.test_session() as session:
features = {
"inputs": tf.constant(x, dtype=tf.int32),
"targets": tf.constant(y, dtype=tf.int32),
}
model = model(
hparams, tf.estimator.ModeKeys.TRAIN)
logits, _ = model(features)
session.run(tf.global_variables_initializer())
res = session.run(logits)
expected_shape = y.shape + (expected_last_dim,)
self.assertEqual(res.shape, expected_shape)
def main(_):
tf.gfile.MakeDirs(FLAGS.data_dir)
tf.gfile.MakeDirs(FLAGS.tmp_dir)
# Create problem if not already defined
problem_name = "gym_discrete_problem_with_agent_on_%s" % FLAGS.game
if problem_name not in registry.list_problems():
gym_env.register_game(FLAGS.game)
# Generate
tf.logging.info("Running %s environment for %d steps for trajectories.",
FLAGS.game, FLAGS.num_env_steps)
problem = registry.problem(problem_name)
problem.settable_num_steps = FLAGS.num_env_steps
problem.settable_eval_phase = FLAGS.eval
problem.generate_data(FLAGS.data_dir, FLAGS.tmp_dir)
# Log stats
if problem.statistics.number_of_dones:
mean_reward = (problem.statistics.sum_of_rewards /
problem.statistics.number_of_dones)
tf.logging.info("Mean reward: %.2f, Num dones: %d",
mean_reward,
problem.statistics.number_of_dones)
def __init__(self, translate_host, translate_port, source_lang, target_lang, model_name, problem, t2t_usr_dir, data_dir, preprocess_cmd, postprocess_cmd):
"""Initialize a TransformerTranslator object according to the given
configuration settings.
@param translate_port: the port at which the Moses translator operates
@param recase_port: the port at which the recaser operates
@param source_lang: source language (ISO-639-1 ID)
@param target_lang: target language (ISO-639-1 ID)
@param preprocess_cmd: bash command for text preprocessing
@param postprocess_cmd: bash command for text posprocessing
"""
# precompile Tensorflow server addresses
self.server = translate_host + ":" + translate_port
# initialize text processing tools (can be shared among threads)
self.tokenizer = Tokenizer({'lowercase': True,
'moses_escape': True})
self.preprocess = preprocess_cmd
self.postprocess = postprocess_cmd
usr_dir.import_usr_dir(t2t_usr_dir)
self.problem = registry.problem(problem)
hparams = tf.contrib.training.HParams(
data_dir=os.path.expanduser(data_dir))
self.problem.get_hparams(hparams)
self.request_fn = serving_utils.make_grpc_request_fn(
servable_name=model_name,
server=self.server,
timeout_secs=30)
def add_problem_hparams(hparams, problem_name): """Add problem hparams for the problems.""" problem = registry.problem(problem_name) p_hparams = problem.get_hparams(hparams) hparams.problem = problem hparams.problem_hparams = p_hparams
def add_problem_hparams(hparams, problem_name_or_instance):
"""Add problem hparams for the problems."""
if isinstance(problem_name_or_instance, Problem):
problem = problem_name_or_instance
else:
problem = registry.problem(problem_name_or_instance)
p_hparams = problem.get_hparams(hparams)
hparams.problem = problem
hparams.problem_hparams = p_hparams
def add_problem_hparams(hparams, problems):
"""Add problem hparams for the problems."""
hparams.problems = []
hparams.problem_instances = []
for problem_name in problems.split("-"):
problem = registry.problem(problem_name)
p_hparams = problem.get_hparams(hparams)
hparams.problem_instances.append(problem)
hparams.problems.append(p_hparams)
def generate_data_for_registered_problem(problem_name):
tf.logging.info("Generating data for %s.", problem_name)
if FLAGS.num_shards:
raise ValueError("--num_shards should not be set for registered Problem.")
problem = registry.problem(problem_name)
task_id = None if FLAGS.task_id < 0 else FLAGS.task_id
problem.generate_data(
os.path.expanduser(FLAGS.data_dir),
os.path.expanduser(FLAGS.tmp_dir),
task_id=task_id)
def init():
# global input_encoder, output_decoder, fname, problem
global problem
tf.logging.set_verbosity(tf.logging.INFO)
tf.logging.info("importing ghsumm/trainer from {}".format(t2t_usr_dir))
usr_dir.import_usr_dir(t2t_usr_dir)
print(t2t_usr_dir)
problem = registry.problem(problem_name)
hparams = tf.contrib.training.HParams(data_dir=os.path.expanduser(data_dir))
problem.get_hparams(hparams)
def score_file(filename):
"""Score each line in a file and return the scores."""
# Prepare model.
hparams = create_hparams()
encoders = registry.problem(FLAGS.problem).feature_encoders(FLAGS.data_dir)
has_inputs = "inputs" in encoders
# Prepare features for feeding into the model.
if has_inputs:
inputs_ph = tf.placeholder(dtype=tf.int32) # Just length dimension.
batch_inputs = tf.reshape(inputs_ph, [1, -1, 1, 1]) # Make it 4D.
targets_ph = tf.placeholder(dtype=tf.int32) # Just length dimension.
batch_targets = tf.reshape(targets_ph, [1, -1, 1, 1]) # Make it 4D.
features = {
"inputs": batch_inputs,
"targets": batch_targets,
} if has_inputs else {"targets": batch_targets}
# Prepare the model and the graph when model runs on features.
model = registry.model(FLAGS.model)(hparams, tf.estimator.ModeKeys.EVAL)
_, losses = model(features)
saver = tf.train.Saver()
with tf.Session() as sess:
# Load weights from checkpoint.
ckpts = tf.train.get_checkpoint_state(FLAGS.output_dir)
ckpt = ckpts.model_checkpoint_path
saver.restore(sess, ckpt)
# Run on each line.
with tf.gfile.Open(filename) as f:
lines = f.readlines()
results = []
for line in lines:
tab_split = line.split("\t")
if len(tab_split) > 2:
raise ValueError("Each line must have at most one tab separator.")
if len(tab_split) == 1:
targets = tab_split[0].strip()
else:
targets = tab_split[1].strip()
inputs = tab_split[0].strip()
# Run encoders and append EOS symbol.
targets_numpy = encoders["targets"].encode(
targets) + [text_encoder.EOS_ID]
if has_inputs:
inputs_numpy = encoders["inputs"].encode(inputs) + [text_encoder.EOS_ID]
# Prepare the feed.
feed = {
inputs_ph: inputs_numpy,
targets_ph: targets_numpy
} if has_inputs else {targets_ph: targets_numpy}
# Get the score.
np_loss = sess.run(losses["training"], feed)
results.append(np_loss)
return results
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
# Create hparams
hparams = create_hparams()
hparams.force_full_predict = True
batch_size = hparams.batch_size
# Iterating over dev/test partition of the data.
# Change the data partition if necessary.
dataset = registry.problem(FLAGS.problem).dataset(
tf.estimator.ModeKeys.PREDICT,
shuffle_files=False,
hparams=hparams)
dataset = dataset.apply(tf.contrib.data.batch_and_drop_remainder(batch_size))
data = dataset.make_one_shot_iterator().get_next()
input_data = dict((k, data[k]) for k in data.keys() if k.startswith("input"))
# Creat model
model_cls = registry.model(FLAGS.model)
model = model_cls(hparams, tf.estimator.ModeKeys.PREDICT)
prediction_ops = model.infer(input_data)
# Confusion Matrix
nr = hparams.problem.num_rewards
cm_per_frame = np.zeros((nr, nr), dtype=np.uint64)
cm_next_frame = np.zeros((nr, nr), dtype=np.uint64)
saver = tf.train.Saver()
with tf.train.SingularMonitoredSession() as sess:
# Load latest checkpoint
ckpt = tf.train.get_checkpoint_state(FLAGS.output_dir).model_checkpoint_path
saver.restore(sess.raw_session(), ckpt)
counter = 0
while not sess.should_stop():
counter += 1
if counter % 1 == 0:
print(counter)
# Predict next frames
rew_pd, rew_gt = sess.run(
[prediction_ops["target_reward"], data["target_reward"]])
for i in range(batch_size):
cm_next_frame[rew_gt[i, 0, 0], rew_pd[i, 0, 0]] += 1
for gt, pd in zip(rew_gt[i], rew_pd[i]):
cm_per_frame[gt, pd] += 1
print_confusion_matrix("Per-frame Confusion Matrix", cm_per_frame)
print_confusion_matrix("Next-frame Confusion Matrix", cm_next_frame)
def main(argv):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
t2t_trainer.maybe_log_registry_and_exit()
if FLAGS.generate_data:
t2t_trainer.generate_data()
if argv:
t2t_trainer.set_hparams_from_args(argv[1:])
hparams = t2t_trainer.create_hparams()
trainer_lib.add_problem_hparams(hparams, FLAGS.problem)
pruning_params = create_pruning_params()
pruning_strategy = create_pruning_strategy(pruning_params.strategy)
config = t2t_trainer.create_run_config(hparams)
params = {"batch_size": hparams.batch_size}
# add "_rev" as a hack to avoid image standardization
problem = registry.problem(FLAGS.problem)
input_fn = problem.make_estimator_input_fn(tf.estimator.ModeKeys.EVAL,
hparams)
dataset = input_fn(params, config).repeat()
features, labels = dataset.make_one_shot_iterator().get_next()
sess = tf.Session()
model_fn = t2t_model.T2TModel.make_estimator_model_fn(
FLAGS.model, hparams, use_tpu=FLAGS.use_tpu)
spec = model_fn(
features,
labels,
tf.estimator.ModeKeys.EVAL,
params=hparams,
config=config)
# Restore weights
saver = tf.train.Saver()
checkpoint_path = os.path.expanduser(FLAGS.output_dir or
FLAGS.checkpoint_path)
saver.restore(sess, tf.train.latest_checkpoint(checkpoint_path))
def eval_model():
preds = spec.predictions["predictions"]
preds = tf.argmax(preds, -1, output_type=labels.dtype)
_, acc_update_op = tf.metrics.accuracy(labels=labels, predictions=preds)
sess.run(tf.initialize_local_variables())
for _ in range(FLAGS.eval_steps):
acc = sess.run(acc_update_op)
return acc
pruning_utils.sparsify(sess, eval_model, pruning_strategy, pruning_params)
def train_eval_and_decode(self):
"""Does eval and decode after training every eval_freq_in_steps."""
eval_steps = self._hparams.eval_freq_in_steps
packed_dataset = "_packed" in self._hparams.problem.name
mlperf_log.transformer_print(key=mlperf_log.TRAIN_LOOP)
for i in range(0, self._train_spec.max_steps, eval_steps):
mlperf_log.transformer_print(
key=mlperf_log.TRAIN_EPOCH, value=i // eval_steps)
if packed_dataset and i > 0:
problem = registry.problem(self._hparams.problem.name + "_packed")
p_hparams = problem.get_hparams(self._hparams)
self._hparams.problem = problem
self._hparams.problem_hparams = p_hparams
self._estimator.train(
self._train_spec.input_fn,
steps=eval_steps,
hooks=self._train_spec.hooks)
self._estimator.evaluate(
self._eval_spec.input_fn,
steps=self._eval_spec.steps,
hooks=self._eval_spec.hooks)
if packed_dataset:
problem = registry.problem(
self._hparams.problem.name.replace("_packed", ""))
p_hparams = problem.get_hparams(self._hparams)
self._hparams.problem = problem
self._hparams.problem_hparams = p_hparams
mlperf_log.transformer_print(key=mlperf_log.EVAL_START)
if self._hparams.mlperf_mode:
self._decode_hparams.mlperf_decode_step = i + eval_steps
self.decode(dataset_split=tf.estimator.ModeKeys.EVAL)
d_hparams = self._decode_hparams
if self._hparams.mlperf_mode and d_hparams.mlperf_success:
mlperf_log.transformer_print(
key=mlperf_log.RUN_STOP, value={"success": "true"})
break
d_hparams = self._decode_hparams
if self._hparams.mlperf_mode and not d_hparams.mlperf_success:
mlperf_log.transformer_print(
key=mlperf_log.RUN_STOP, value={"success": "false"})
def get_environment_spec(self):
env_spec = standard_atari_env_spec(self.env_name)
env_spec.simulated_env = True
env_spec.add_hparam("simulation_random_starts",
self.simulation_random_starts)
env_spec.add_hparam("intrinsic_reward_scale", self.intrinsic_reward_scale)
initial_frames_problem = registry.problem(self.initial_frames_problem)
env_spec.add_hparam("initial_frames_problem", initial_frames_problem)
env_spec.add_hparam("video_num_input_frames", self.num_input_frames)
env_spec.add_hparam("video_num_target_frames", self.video_num_target_frames)
return env_spec
def translate(self, inputs):
# Registrierung der Problem-Klasse
problem = registry.problem(self.problem)
# Instanziierung des HPrams-Objekts
hparams = HParams(data_dir=os.path.expanduser(self.data_dir))
problem.get_hparams(hparams)
request_fn = self.make_request_fn()
inputs = inputs
# Prediction
outputs = serving_utils.predict([inputs], problem, request_fn)
outputs, = outputs
output, score = outputs
return {'inputs': inputs, 'outputs': output, 'scores': score}
def train_eval_and_decode(self):
"""Does eval and decode after training every eval_freq_in_steps."""
eval_steps = self._hparams.eval_freq_in_steps
packed_dataset = "_packed" in self._hparams.problem.name
mlperf_log.transformer_print(key=mlperf_log.TRAIN_LOOP)
for i in range(0, self._train_spec.max_steps, eval_steps):
mlperf_log.transformer_print(
key=mlperf_log.TRAIN_EPOCH, value=i // eval_steps)
if packed_dataset and i > 0:
problem = registry.problem(self._hparams.problem.name + "_packed")
p_hparams = problem.get_hparams(self._hparams)
self._hparams.problem = problem
self._hparams.problem_hparams = p_hparams
self._estimator.train(
self._train_spec.input_fn,
steps=eval_steps,
hooks=self._train_spec.hooks)
self._estimator.evaluate(
self._eval_spec.input_fn,
steps=self._eval_spec.steps,
hooks=self._eval_spec.hooks)
if packed_dataset:
problem = registry.problem(
self._hparams.problem.name.replace("_packed", ""))
p_hparams = problem.get_hparams(self._hparams)
self._hparams.problem = problem
self._hparams.problem_hparams = p_hparams
mlperf_log.transformer_print(key=mlperf_log.EVAL_START)
self.decode(dataset_split=tf.estimator.ModeKeys.EVAL)
d_hparams = self._decode_hparams
if d_hparams.mlperf_mode and d_hparams.mlperf_success:
mlperf_log.transformer_print(
key=mlperf_log.RUN_STOP, value={"success": "true"})
break
d_hparams = self._decode_hparams
if d_hparams.mlperf_mode and not d_hparams.mlperf_success:
mlperf_log.transformer_print(
key=mlperf_log.RUN_STOP, value={"success": "false"})
def main(_):
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
# Generate data if requested.
data_dir = os.path.expanduser(FLAGS.data_dir)
tmp_dir = os.path.expanduser(FLAGS.tmp_dir)
problem_name = FLAGS.problems
tf.logging.info("Generating data for %s" % problem_name)
problem = registry.problem(problem_name)
length = problem.get_length(data_dir, tmp_dir)
length_statistics(length)
def __init__(self):
tf.logging.set_verbosity(tf.logging.INFO)
validate_flags()
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
self.problem = registry.problem(FLAGS.problem)
self.hparams = tf.contrib.training.HParams(
data_dir=os.path.expanduser(FLAGS.data_dir))
self.problem.get_hparams(self.hparams)
self.request_fn = make_request_fn()
self.tokenizer = MosesTokenizer('en')
self.moses_detokenizer = MosesDetokenizer('zh')
self.delimiter = re.compile(
"(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s")
def __init__(self, batch_env):
super(AutoencoderWrapper, self).__init__(batch_env)
self._observ = tf.Variable(
tf.zeros((len(self),) + self.observ_shape, self.observ_dtype),
trainable=False)
with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):
autoencoder_hparams = autoencoders.autoencoder_discrete_pong()
problem = registry.problem("dummy_autoencoder_problem")
autoencoder_hparams.problem_hparams = problem.get_hparams(
autoencoder_hparams)
autoencoder_hparams.problem = problem
self.autoencoder_model = autoencoders.AutoencoderOrderedDiscrete(
autoencoder_hparams, tf.estimator.ModeKeys.EVAL)
def query_t2t(input_txt, data_dir, problem_name, server_name, server_address,
t2t_usr_dir):
usr_dir.import_usr_dir(t2t_usr_dir)
problem = registry.problem(problem_name)
hparams = tf.contrib.training.HParams(
data_dir=os.path.expanduser(data_dir))
problem.get_hparams(hparams)
request_fn = make_request_fn(server_name, server_address)
inputs = input_txt
outputs = serving_utils.predict([inputs], problem, request_fn)
output, score = outputs
print(output)
return output, score
def get_environment_spec(self):
env_spec = standard_atari_env_spec(self.env_name)
env_spec.wrappers = [[tf_atari_wrappers.IntToBitWrapper, {}]]
env_spec.simulated_env = True
env_spec.add_hparam("simulation_random_starts", False)
env_spec.add_hparam("intrinsic_reward_scale", 0.0)
initial_frames_problem = registry.problem(self.initial_frames_problem)
env_spec.add_hparam("initial_frames_problem", initial_frames_problem)
env_spec.add_hparam("video_num_input_frames", self.num_input_frames)
env_spec.add_hparam("video_num_target_frames", self.video_num_target_frames)
return env_spec
def get_data_filepatterns(problems, data_dir, mode):
"""Return the location of a dataset for a given mode."""
datasets = []
for problem in problems.split("-"):
try:
problem = registry.problem(problem).dataset_filename()
except ValueError:
problem, _, _ = problem_hparams.parse_problem_name(problem)
path = os.path.join(data_dir, problem)
if mode == tf.estimator.ModeKeys.TRAIN:
datasets.append("%s-train*" % path)
else:
datasets.append("%s-dev*" % path)
return datasets
def init():
global input_encoder, output_decoder, fname
tf.logging.set_verbosity(tf.logging.INFO)
tf.logging.info(
"Trying to import poetry/trainer from {}".format(t2t_usr_dir))
usr_dir.import_usr_dir(t2t_usr_dir)
print(t2t_usr_dir)
problem = registry.problem(problem_name)
hparams = tf.contrib.training.HParams(
data_dir=os.path.expanduser(data_dir))
problem.get_hparams(hparams)
fname = "inputs" if problem.has_inputs else "targets"
input_encoder = problem.feature_info[fname].encoder
output_decoder = problem.feature_info["targets"].encoder
def train_agent(problem_name,
agent_model_dir,
event_dir,
world_model_dir,
epoch_data_dir,
hparams,
epoch=0,
is_final_epoch=False):
"""Train the PPO agent in the simulated environment."""
gym_problem = registry.problem(problem_name)
ppo_hparams = trainer_lib.create_hparams(hparams.ppo_params)
ppo_params_names = [
"epochs_num", "epoch_length", "learning_rate", "num_agents",
"optimization_epochs"
]
for param_name in ppo_params_names:
ppo_param_name = "ppo_" + param_name
if ppo_param_name in hparams:
ppo_hparams.set_hparam(param_name, hparams.get(ppo_param_name))
ppo_epochs_num = hparams.ppo_epochs_num
if is_final_epoch:
ppo_epochs_num *= 2
ppo_hparams.epoch_length *= 2
ppo_hparams.save_models_every_epochs = ppo_epochs_num
ppo_hparams.world_model_dir = world_model_dir
ppo_hparams.add_hparam("force_beginning_resets", True)
# Adding model hparams for model specific adjustments
model_hparams = trainer_lib.create_hparams(hparams.generative_model_params)
ppo_hparams.add_hparam("model_hparams", model_hparams)
environment_spec = copy.copy(gym_problem.environment_spec)
environment_spec.simulation_random_starts = hparams.simulation_random_starts
environment_spec.intrinsic_reward_scale = hparams.intrinsic_reward_scale
ppo_hparams.add_hparam("environment_spec", environment_spec)
with temporary_flags({
"problem": problem_name,
"model": hparams.generative_model,
"hparams_set": hparams.generative_model_params,
"output_dir": world_model_dir,
"data_dir": epoch_data_dir,
}):
rl_trainer_lib.train(ppo_hparams,
event_dir,
agent_model_dir,
epoch=epoch)
def get_environment_spec(self):
env_spec = standard_atari_env_spec(self.env_name)
env_spec.simulated_env = True
env_spec.add_hparam("simulation_random_starts", False)
env_spec.add_hparam("simulation_flip_first_random_for_beginning",
False)
env_spec.add_hparam("intrinsic_reward_scale", 0.0)
initial_frames_problem = registry.problem(self.initial_frames_problem)
env_spec.add_hparam("initial_frames_problem", initial_frames_problem)
env_spec.add_hparam("video_num_input_frames", self.num_input_frames)
env_spec.add_hparam("video_num_target_frames",
self.video_num_target_frames)
return env_spec
def testSingleEvalStepRawSession(self):
"""Illustrate how to run a T2T model in a raw session."""
# Set model name, hparams, problems as would be set on command line.
model_name = "transformer"
FLAGS.hparams_set = "transformer_test"
FLAGS.problems = "tiny_algo"
data_dir = "/tmp" # Used only when a vocab file or such like is needed.
# Create the problem object, hparams, placeholders, features dict.
encoders = registry.problem(FLAGS.problems).feature_encoders(data_dir)
hparams = trainer_utils.create_hparams(FLAGS.hparams_set, data_dir)
trainer_utils.add_problem_hparams(hparams, FLAGS.problems)
inputs_ph = tf.placeholder(dtype=tf.int32) # Just length dimension.
batch_inputs = tf.reshape(inputs_ph, [1, -1, 1, 1]) # Make it 4D.
# In INFER mode targets can be None.
targets_ph = tf.placeholder(dtype=tf.int32) # Just length dimension.
batch_targets = tf.reshape(targets_ph, [1, -1, 1, 1]) # Make it 4D.
features = {
"inputs": batch_inputs,
"targets": batch_targets,
"problem_choice": tf.constant(0), # We run on the first problem here.
"input_space_id": tf.constant(hparams.problems[0].input_space_id),
"target_space_id": tf.constant(hparams.problems[0].target_space_id)
}
# Now set a mode and create the graph by invoking model_fn.
mode = tf.estimator.ModeKeys.EVAL
estimator_spec = model_builder.model_fn(
model_name, features, mode, hparams, problem_names=[FLAGS.problems])
predictions_dict = estimator_spec.predictions
predictions = tf.squeeze( # These are not images, axis=2,3 are not needed.
predictions_dict["predictions"],
axis=[2, 3])
# Having the graph, let's run it on some data.
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
inputs = "0 1 0"
targets = "0 1 0"
# Encode from raw string to numpy input array using problem encoders.
inputs_numpy = encoders["inputs"].encode(inputs)
targets_numpy = encoders["targets"].encode(targets)
# Feed the encoded inputs and targets and run session.
feed = {inputs_ph: inputs_numpy, targets_ph: targets_numpy}
np_predictions = sess.run(predictions, feed)
# Check that the result has the correct shape: batch x length x vocab_size
# where, for us, batch = 1, length = 3, vocab_size = 4.
self.assertEqual(np_predictions.shape, (1, 3, 4))
def testSingleTrainStepCall(self):
"""Illustrate how to run a T2T model in a raw session."""
# Set model name, hparams, problems as would be set on command line.
model_name = "transformer"
FLAGS.hparams_set = "transformer_test"
FLAGS.problems = "tiny_algo"
data_dir = "/tmp" # Used only when a vocab file or such like is needed.
# Create the problem object, hparams, placeholders, features dict.
encoders = registry.problem(FLAGS.problems).feature_encoders(data_dir)
hparams = trainer_utils.create_hparams(FLAGS.hparams_set, data_dir)
trainer_utils.add_problem_hparams(hparams, FLAGS.problems)
# Now set a mode and create the model.
mode = tf.estimator.ModeKeys.TRAIN
model = registry.model(model_name)(hparams, mode)
# Create placeholder for features and make them batch-sized.
inputs_ph = tf.placeholder(dtype=tf.int32) # Just length dimension.
batch_inputs = tf.reshape(inputs_ph, [1, -1, 1, 1]) # Make it 4D.
targets_ph = tf.placeholder(dtype=tf.int32) # Just length dimension.
batch_targets = tf.reshape(targets_ph, [1, -1, 1, 1]) # Make it 4D.
features = {
"inputs": batch_inputs,
"targets": batch_targets,
"target_space_id": tf.constant(hparams.problems[0].target_space_id)
}
# Call the model.
predictions, _ = model(features)
nvars = len(tf.trainable_variables())
model(features) # Call again and check that reuse works.
self.assertEqual(nvars, len(tf.trainable_variables()))
# Having the graph, let's run it on some data.
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
inputs = "0 1 0"
targets = "0 1 0"
# Encode from raw string to numpy input array using problem encoders.
inputs_numpy = encoders["inputs"].encode(inputs)
targets_numpy = encoders["targets"].encode(targets)
# Feed the encoded inputs and targets and run session.
feed = {inputs_ph: inputs_numpy, targets_ph: targets_numpy}
np_predictions = sess.run(predictions, feed)
# Check that the result has the correct shape: batch x length x vocab_size
# where, for us, batch = 1, length = 3, vocab_size = 4.
self.assertEqual(np_predictions.shape, (1, 3, 1, 1, 4))
def decode(estimator, hparams, decode_hp):
"""Decode from estimator. Interactive, from file, or from dataset."""
if FLAGS.decode_interactive:
if estimator.config.use_tpu:
raise ValueError("TPU can only decode from dataset.")
decoding.decode_interactively(estimator,
hparams,
decode_hp,
checkpoint_path=FLAGS.checkpoint_path)
elif FLAGS.decode_from_file:
if estimator.config.use_tpu:
raise ValueError("TPU can only decode from dataset.")
decoding.decode_from_file(estimator,
FLAGS.decode_from_file,
hparams,
decode_hp,
FLAGS.decode_to_file,
checkpoint_path=FLAGS.checkpoint_path)
if FLAGS.checkpoint_path and FLAGS.keep_timestamp:
ckpt_time = os.path.getmtime(FLAGS.checkpoint_path + ".index")
os.utime(FLAGS.decode_to_file, (ckpt_time, ckpt_time))
else:
# Fathom
predictions = decoding.decode_from_dataset(
estimator,
FLAGS.problem,
hparams,
decode_hp,
decode_to_file=FLAGS.decode_to_file,
dataset_split=dataset_to_t2t_mode(FLAGS.dataset_split),
return_generator=FLAGS.fathom_output_predictions,
# save logs/summaries to a directory with the same name as decode_output_file
# in situations where we are calling decode without write permissions
# to the model directory
output_dir=os.path.splitext(FLAGS.decode_output_file)[0])
# Fathom
if FLAGS.fathom_output_predictions:
print('Assuming only one problem...')
assert '-' not in FLAGS.problems
# if we already have built problem instance in hparams, no need to create
# it second time (as it's downloading files from gcs)
if hasattr(hparams, 'problem'):
problem = hparams.problem
else:
problem = registry.problem(FLAGS.problems)
problem.output_predictions(predictions=predictions,
num_examples=FLAGS.num_examples)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
validate_flags()
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
problem = registry.problem(FLAGS.problem)
hparams = hparam.HParams(data_dir=os.path.expanduser(FLAGS.data_dir))
problem.get_hparams(hparams)
request_fn = make_request_fn()
while True:
inputs = FLAGS.inputs_once if FLAGS.inputs_once else input(">> ")
t1 = datetime.datetime.now()
outputs = serving_utils.predict([inputs], problem, request_fn)
t2 = datetime.datetime.now()
time_taken_for_response = int((t2 - t1).total_seconds() * 1000)
#print("time:", time_taken_for_response)
outputs, = outputs
output, score = outputs
if len(score.shape) > 0: # pylint: disable=g-explicit-length-test
print_str = """
Input:
{inputs}
Output (Scores [{score}]) (Time [{time}] milliseconds):
{output}
"""
#time_taken_for_response = (t2 - t1) / 1000.0
score_text = ",".join(["{:.3f}".format(s) for s in score])
print(
print_str.format(inputs=inputs,
output=output,
score=score_text,
time=time_taken_for_response))
else:
print_str = """
Input:
{inputs}
Output (Score {score:.3f}) (Time {time} milliseconds):
{output}
"""
#time_taken_for_response = (t2 - t1) / 1000.0
print(
print_str.format(inputs=inputs,
output=output,
score=score,
time=time_taken_for_response))
if FLAGS.inputs_once:
break
def testSingleEvalStepRawSession(self):
"""Illustrate how to run a T2T model in a raw session."""
# Set model name, hparams, problems as would be set on command line.
model_name = "transformer"
FLAGS.hparams_set = "transformer_test"
FLAGS.problems = "tiny_algo"
data_dir = "/tmp" # Used only when a vocab file or such like is needed.
# Create the problem object, hparams, placeholders, features dict.
encoders = registry.problem(FLAGS.problems).feature_encoders(data_dir)
hparams = trainer_utils.create_hparams(FLAGS.hparams_set, data_dir)
trainer_utils.add_problem_hparams(hparams, FLAGS.problems)
inputs_ph = tf.placeholder(dtype=tf.int32) # Just length dimension.
batch_inputs = tf.reshape(inputs_ph, [1, -1, 1, 1]) # Make it 4D.
# In INFER mode targets can be None.
targets_ph = tf.placeholder(dtype=tf.int32) # Just length dimension.
batch_targets = tf.reshape(targets_ph, [1, -1, 1, 1]) # Make it 4D.
features = {
"inputs": batch_inputs,
"targets": batch_targets,
"problem_choice": 0, # We run on the first problem here.
"input_space_id": hparams.problems[0].input_space_id,
"target_space_id": hparams.problems[0].target_space_id
}
# Now set a mode and create the graph by invoking model_fn.
mode = tf.estimator.ModeKeys.EVAL
estimator_spec = model_builder.model_fn(
model_name, features, mode, hparams, problem_names=[FLAGS.problems])
predictions_dict = estimator_spec.predictions
predictions = tf.squeeze( # These are not images, axis=2,3 are not needed.
predictions_dict["predictions"],
axis=[2, 3])
# Having the graph, let's run it on some data.
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
inputs = "0 1 0"
targets = "0 1 0"
# Encode from raw string to numpy input array using problem encoders.
inputs_numpy = encoders["inputs"].encode(inputs)
targets_numpy = encoders["targets"].encode(targets)
# Feed the encoded inputs and targets and run session.
feed = {inputs_ph: inputs_numpy, targets_ph: targets_numpy}
np_predictions = sess.run(predictions, feed)
# Check that the result has the correct shape: batch x length x vocab_size
# where, for us, batch = 1, length = 3, vocab_size = 4.
self.assertEqual(np_predictions.shape, (1, 3, 4))
def testSingleTrainStepCall(self):
"""Illustrate how to run a T2T model in a raw session."""
# Set model name, hparams, problems as would be set on command line.
model_name = "transformer"
FLAGS.hparams_set = "transformer_test"
FLAGS.problems = "tiny_algo"
data_dir = "/tmp" # Used only when a vocab file or such like is needed.
# Create the problem object, hparams, placeholders, features dict.
encoders = registry.problem(FLAGS.problems).feature_encoders(data_dir)
hparams = trainer_utils.create_hparams(FLAGS.hparams_set, data_dir)
trainer_utils.add_problem_hparams(hparams, FLAGS.problems)
# Now set a mode and create the model.
mode = tf.estimator.ModeKeys.TRAIN
model = registry.model(model_name)(hparams, mode)
# Create placeholder for features and make them batch-sized.
inputs_ph = tf.placeholder(dtype=tf.int32) # Just length dimension.
batch_inputs = tf.reshape(inputs_ph, [1, -1, 1, 1]) # Make it 4D.
targets_ph = tf.placeholder(dtype=tf.int32) # Just length dimension.
batch_targets = tf.reshape(targets_ph, [1, -1, 1, 1]) # Make it 4D.
features = {
"inputs": batch_inputs,
"targets": batch_targets,
"target_space_id": tf.constant(hparams.problems[0].target_space_id)
}
# Call the model.
predictions, _ = model(features)
nvars = len(tf.trainable_variables())
model(features) # Call again and check that reuse works.
self.assertEqual(nvars, len(tf.trainable_variables()))
# Having the graph, let's run it on some data.
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
inputs = "0 1 0"
targets = "0 1 0"
# Encode from raw string to numpy input array using problem encoders.
inputs_numpy = encoders["inputs"].encode(inputs)
targets_numpy = encoders["targets"].encode(targets)
# Feed the encoded inputs and targets and run session.
feed = {inputs_ph: inputs_numpy, targets_ph: targets_numpy}
np_predictions = sess.run(predictions, feed)
# Check that the result has the correct shape: batch x length x vocab_size
# where, for us, batch = 1, length = 3, vocab_size = 4.
self.assertEqual(np_predictions.shape, (1, 3, 1, 1, 4))
def get_predictions(self, num_decodes=2):
rng = np.random.RandomState(0)
# num_samples=4
inputs = rng.randint(0, 255, (4, 2, 64, 64, 3))
outputs = rng.randint(0, 255, (4, 5, 64, 64, 3))
targets = rng.randint(0, 255, (4, 5, 64, 64, 3))
predictions = []
for input_, output, target in zip(inputs, outputs, targets):
curr_pred = {"inputs": input_, "outputs": output, "targets": target}
predictions.append(curr_pred)
# num_decodes=2
predictions = [predictions] * num_decodes
problem = registry.problem("video_stochastic_shapes10k")
return predictions, problem
def train_eval_and_decode(self):
"""Does eval and decode after training every eval_freq_in_steps."""
eval_steps = self._hparams.eval_freq_in_steps
packed_dataset = "_packed" in self._hparams.problem.name
for i in range(0, self._train_spec.max_steps, eval_steps):
if packed_dataset and i > 0:
problem = registry.problem(self._hparams.problem.name +
"_packed")
p_hparams = problem.get_hparams(self._hparams)
self._hparams.problem = problem
self._hparams.problem_hparams = p_hparams
self._estimator.train(self._train_spec.input_fn,
steps=eval_steps,
hooks=self._train_spec.hooks)
self._estimator.evaluate(self._eval_spec.input_fn,
steps=self._eval_spec.steps,
hooks=self._eval_spec.hooks)
if packed_dataset:
problem = registry.problem(
self._hparams.problem.name.replace("_packed", ""))
p_hparams = problem.get_hparams(self._hparams)
self._hparams.problem = problem
self._hparams.problem_hparams = p_hparams
self.decode(dataset_split=tf.estimator.ModeKeys.EVAL)
def evaluate_world_model(simulated_problem_name, problem_name, hparams,
world_model_dir, epoch_data_dir, tmp_dir,
autoencoder_path=None):
"""Generate simulated environment data and return reward accuracy."""
gym_simulated_problem = registry.problem(simulated_problem_name)
gym_problem = registry.problem(problem_name)
sim_steps = hparams.simulated_env_generator_num_steps
gym_simulated_problem.settable_num_steps = sim_steps
gym_simulated_problem.real_env_problem = gym_problem
gym_simulated_problem.simulation_random_starts = False
gym_simulated_problem.intrinsic_reward_scale = 0.
with temporary_flags({
"problem": problem_name,
"model": hparams.generative_model,
"hparams_set": hparams.generative_model_params,
"data_dir": epoch_data_dir,
"output_dir": world_model_dir,
"autoencoder_path": autoencoder_path,
}):
gym_simulated_problem.generate_data(epoch_data_dir, tmp_dir)
n = max(1., gym_simulated_problem.dones)
model_reward_accuracy = (
gym_simulated_problem.successful_episode_reward_predictions / float(n))
return model_reward_accuracy
def get_environment_spec(self):
env_spec = standard_atari_env_spec(
self.env_name,
simulated=True,
resize_height_factor=self.resize_height_factor,
resize_width_factor=self.resize_width_factor)
env_spec.add_hparam("simulation_random_starts", True)
env_spec.add_hparam("simulation_flip_first_random_for_beginning", True)
env_spec.add_hparam("intrinsic_reward_scale", 0.0)
initial_frames_problem = registry.problem(self.initial_frames_problem)
env_spec.add_hparam("initial_frames_problem", initial_frames_problem)
env_spec.add_hparam("video_num_input_frames", self.num_input_frames)
env_spec.add_hparam("video_num_target_frames", self.video_num_target_frames)
return env_spec
def train_agent(problem_name,
agent_model_dir,
event_dir,
world_model_dir,
epoch_data_dir,
hparams,
autoencoder_path=None,
epoch=0):
"""Train the PPO agent in the simulated environment."""
gym_problem = registry.problem(problem_name)
ppo_hparams = trainer_lib.create_hparams(hparams.ppo_params)
ppo_epochs_num = hparams.ppo_epochs_num
ppo_hparams.epochs_num = ppo_epochs_num
ppo_hparams.simulated_environment = True
ppo_hparams.simulation_random_starts = hparams.simulation_random_starts
ppo_hparams.intrinsic_reward_scale = hparams.intrinsic_reward_scale
ppo_hparams.eval_every_epochs = 50
ppo_hparams.save_models_every_epochs = ppo_epochs_num
ppo_hparams.epoch_length = hparams.ppo_epoch_length
ppo_hparams.num_agents = hparams.ppo_num_agents
ppo_hparams.problem = gym_problem
ppo_hparams.world_model_dir = world_model_dir
if hparams.ppo_learning_rate:
ppo_hparams.learning_rate = hparams.ppo_learning_rate
# 4x for the StackAndSkipWrapper minus one to always finish for reporting.
ppo_time_limit = (ppo_hparams.epoch_length - 1) * 4
in_graph_wrappers = [(TimeLimitWrapper, {
"timelimit": ppo_time_limit
}), (StackAndSkipWrapper, {
"skip": 4
})]
in_graph_wrappers += gym_problem.in_graph_wrappers
ppo_hparams.add_hparam("in_graph_wrappers", in_graph_wrappers)
with temporary_flags({
"problem": problem_name,
"model": hparams.generative_model,
"hparams_set": hparams.generative_model_params,
"output_dir": world_model_dir,
"data_dir": epoch_data_dir,
"autoencoder_path": autoencoder_path,
}):
rl_trainer_lib.train(ppo_hparams,
gym_problem.env_name,
event_dir,
agent_model_dir,
epoch=epoch)
def get_environment_spec(self):
env_spec = rl.standard_atari_env_spec(self.env_name)
env_spec.wrappers = [
[tf_atari_wrappers.IntToBitWrapper, {}],
[tf_atari_wrappers.StackWrapper, {"history": 4}]
]
env_spec.simulated_env = True
env_spec.add_hparam("simulation_random_starts", True)
env_spec.add_hparam("simulation_flip_first_random_for_beginning", True)
env_spec.add_hparam("intrinsic_reward_scale", 0.0)
initial_frames_problem = registry.problem(self.initial_frames_problem)
env_spec.add_hparam("initial_frames_problem", initial_frames_problem)
env_spec.add_hparam("video_num_input_frames", self.num_input_frames)
env_spec.add_hparam("video_num_target_frames", self.video_num_target_frames)
return env_spec
def add_problem_hparams(hparams, problems):
"""Add problem hparams for the problems."""
hparams.problems = []
hparams.problem_instances = []
for problem_name in problems.split("-"):
try:
problem = registry.problem(problem_name)
p_hparams = problem.internal_hparams(hparams)
except ValueError:
problem = None
p_hparams = problem_hparams.problem_hparams(problem_name, hparams)
hparams.problem_instances.append(problem)
hparams.problems.append(p_hparams)
return hparams
def __init__(self, FLAGS, server_address='127.0.0.1:9000'):
print('Initializing up2down_class.......')
self.FLAGS = FLAGS
self.server_address = server_address
tf.logging.set_verbosity(tf.logging.ERROR)
usr_dir.import_usr_dir(self.FLAGS.t2t_usr_dir)
# hparams: not important but necessary, an assertion error will be raised without hparams.
self.hparams = hparam.HParams(
data_dir=os.path.expanduser(self.FLAGS.t2t_usr_dir))
# problem
self.problem = registry.problem(self.FLAGS.problem)
self.problem.get_hparams(self.hparams)
# model request server
self.request_fn = self.make_request_fn(self.FLAGS.model,
self.server_address)
def add_problem_hparams(hparams, problems):
"""Add problem hparams for the problems."""
hparams.problems = []
hparams.problem_instances = []
for problem_name in problems.split("-"):
try:
problem = registry.problem(problem_name)
except LookupError:
all_problem_names = sorted(registry.list_problems())
error_lines = ["%s not in the set of supported problems:" % problem_name
] + all_problem_names
error_msg = "\n * ".join(error_lines)
raise LookupError(error_msg)
p_hparams = problem.get_hparams(hparams)
hparams.problem_instances.append(problem)
hparams.problems.append(p_hparams)
def _add_problem_hparams(self, hparams, problem_name):
"""Add problem hparams for the problems.
This method corresponds to create_hparams() in tensor2tensor's
trainer_lib module, but replaces the feature encoders with
DummyFeatureEncoder's.
Args:
hparams (Hparams): Model hyper parameters.
problem_name (string): T2T problem name.
Returns:
hparams object.
Raises:
LookupError if the problem name is not in the registry or
uses the old style problem_hparams.
"""
if self.pop_id >= 0:
try:
hparams.add_hparam("pop_id", self.pop_id)
except:
if hparams.pop_id != self.pop_id:
logging.warn("T2T pop_id does not match (%d!=%d)"
% (hparams.pop_id, self.pop_id))
try:
hparams.add_hparam("max_terminal_id", self.max_terminal_id)
except:
if hparams.max_terminal_id != self.max_terminal_id:
logging.warn("T2T max_terminal_id does not match (%d!=%d)"
% (hparams.max_terminal_id, self.max_terminal_id))
try:
hparams.add_hparam("closing_bracket_id", self.pop_id)
except:
if hparams.closing_bracket_id != self.pop_id:
logging.warn("T2T closing_bracket_id does not match (%d!=%d)"
% (hparams.closing_bracket_id, self.pop_id))
problem = registry.problem(problem_name)
problem._encoders = {
"inputs": DummyTextEncoder(vocab_size=self.src_vocab_size),
"targets": DummyTextEncoder(vocab_size=self.trg_vocab_size)
}
p_hparams = problem.get_hparams(hparams)
hparams.problem = problem
hparams.problem_hparams = p_hparams
return hparams
def generate_real_env_data(problem_name, agent_policy_path, hparams, data_dir,
tmp_dir, autoencoder_path=None, eval_phase=False):
"""Run the agent against the real environment and return mean reward."""
tf.gfile.MakeDirs(data_dir)
with temporary_flags({
"problem": problem_name,
"agent_policy_path": agent_policy_path,
"autoencoder_path": autoencoder_path,
"only_use_ae_for_policy": True,
}):
gym_problem = registry.problem(problem_name)
gym_problem.settable_num_steps = hparams.true_env_generator_num_steps
gym_problem.eval_phase = eval_phase
gym_problem.generate_data(data_dir, tmp_dir)
mean_reward = gym_problem.sum_of_rewards / (1.0 + gym_problem.dones)
return mean_reward
def _create_hparams(self, src_vocab_size, trg_vocab_size, hparams_set_name,
problem_name):
"""Creates hparams object.
This method corresponds to create_hparams() in tensor2tensor's
trainer_utils module, but replaces the feature encoders with
DummyFeatureEncoder's.
Args:
src_vocab_size (int): Source vocabulary size.
trg_vocab_size (int): Target vocabulary size.
hparams_set_name (string): T2T hparams set name.
problem_name (string): T2T problem name.
Returns:
hparams object.
Raises:
LookupError if the problem name is not in the registry or
uses the old style problem_hparams.
"""
hparams = registry.hparams(hparams_set_name)()
problem = registry.problem(problem_name)
# The following hack is necessary to prevent the problem from creating
# the default TextEncoders, which would fail due to the lack of a
# vocabulary file.
problem._encoders = {
"inputs": DummyTextEncoder(vocab_size=src_vocab_size),
"targets": DummyTextEncoder(vocab_size=trg_vocab_size)
}
try:
hparams.add_hparam("max_terminal_id", self.max_terminal_id)
except:
if hparams.max_terminal_id != self.max_terminal_id:
logging.warn("T2T max_terminal_id does not match (%d!=%d)" %
(hparams.max_terminal_id, self.max_terminal_id))
try:
hparams.add_hparam("closing_bracket_id", self.pop_id)
except:
if hparams.closing_bracket_id != self.pop_id:
logging.warn("T2T closing_bracket_id does not match (%d!=%d)" %
(hparams.closing_bracket_id, self.pop_id))
p_hparams = problem.get_hparams(hparams)
hparams.problem_instances = [problem]
hparams.problems = [p_hparams]
return hparams
def _add_problem_hparams(self, hparams, problem_name):
"""Add problem hparams for the problems.
This method corresponds to create_hparams() in tensor2tensor's
trainer_lib module, but replaces the feature encoders with
DummyFeatureEncoder's.
Args:
hparams (Hparams): Model hyper parameters.
problem_name (string): T2T problem name.
Returns:
hparams object.
Raises:
LookupError if the problem name is not in the registry or
uses the old style problem_hparams.
"""
if self.pop_id >= 0:
try:
hparams.add_hparam("pop_id", self.pop_id)
except:
if hparams.pop_id != self.pop_id:
logging.warn("T2T pop_id does not match (%d!=%d)"
% (hparams.pop_id, self.pop_id))
try:
hparams.add_hparam("max_terminal_id", self.max_terminal_id)
except:
if hparams.max_terminal_id != self.max_terminal_id:
logging.warn("T2T max_terminal_id does not match (%d!=%d)"
% (hparams.max_terminal_id, self.max_terminal_id))
try:
hparams.add_hparam("closing_bracket_id", self.pop_id)
except:
if hparams.closing_bracket_id != self.pop_id:
logging.warn("T2T closing_bracket_id does not match (%d!=%d)"
% (hparams.closing_bracket_id, self.pop_id))
problem = registry.problem(problem_name)
problem._encoders = {
"inputs": DummyTextEncoder(vocab_size=self.src_vocab_size),
"targets": DummyTextEncoder(vocab_size=self.trg_vocab_size)
}
p_hparams = problem.get_hparams(hparams)
hparams.problem = problem
hparams.problem_hparams = p_hparams
return hparams
def add_problem_hparams(hparams, problems):
"""Add problem hparams for the problems."""
hparams.problems = []
hparams.problem_instances = []
for problem_name in problems.split("-"):
try:
problem = registry.problem(problem_name)
except LookupError:
all_problem_names = sorted(registry.list_problems())
error_lines = ["%s not in the set of supported problems:" % problem_name
] + all_problem_names
error_msg = "\n * ".join(error_lines)
raise LookupError(error_msg)
p_hparams = problem.get_hparams(hparams)
hparams.problem_instances.append(problem)
hparams.problems.append(p_hparams)
def encode_env_frames(problem_name, ae_problem_name, autoencoder_path,
epoch_data_dir):
"""Encode all frames from problem_name and write out as ae_problem_name."""
with tf.Graph().as_default():
ae_hparams = trainer_lib.create_hparams("autoencoder_discrete_pong",
problem_name=problem_name)
problem = ae_hparams.problem
model = registry.model("autoencoder_ordered_discrete")(
ae_hparams, tf.estimator.ModeKeys.EVAL)
ae_problem = registry.problem(ae_problem_name)
ae_training_paths = ae_problem.training_filepaths(
epoch_data_dir, 10, True)
ae_eval_paths = ae_problem.dev_filepaths(epoch_data_dir, 1, True)
skip_train = False
skip_eval = False
for path in ae_training_paths:
if tf.gfile.Exists(path):
skip_train = True
break
for path in ae_eval_paths:
if tf.gfile.Exists(path):
skip_eval = True
break
# Encode train data
if not skip_train:
dataset = problem.dataset(tf.estimator.ModeKeys.TRAIN,
epoch_data_dir,
shuffle_files=False,
output_buffer_size=100,
preprocess=False)
encode_dataset(model, dataset, problem, ae_hparams,
autoencoder_path, ae_training_paths)
# Encode eval data
if not skip_eval:
dataset = problem.dataset(tf.estimator.ModeKeys.EVAL,
epoch_data_dir,
shuffle_files=False,
output_buffer_size=100,
preprocess=False)
encode_dataset(model, dataset, problem, ae_hparams,
autoencoder_path, ae_eval_paths)
def __init__(self, config):
os.environ["CUDA_VISIBLE_DEVICES"] = config.GPU_DEVICE
FLAGS.data_dir = config.VOCAB_DIR
FLAGS.problems = config.PROBLEM_NAME
FLAGS.model = config.MODEL_NAME
FLAGS.hparams_set = config.HPARAMS_SET
FLAGS.output_dir = config.MODEL_DIR
FLAGS.decode_hparams = config.DECODE_HPARAMS
batch_size = config.BATCH_SIZE
self.hparams = create_hparams()
self.encoders = registry.problem(FLAGS.problems).feature_encoders(
FLAGS.data_dir)
self.ckpt = tf.train.get_checkpoint_state(
FLAGS.output_dir).model_checkpoint_path
self.inputs_ph = tf.placeholder(
shape=(batch_size, None), dtype=tf.int32) # Just length dimension.
self.batch_inputs = tf.reshape(self.inputs_ph,
[batch_size, -1, 1, 1]) # Make it 4D.
self.features = {"inputs": self.batch_inputs}
# Prepare the model and the graph when model runs on features.
tf.logging.info(f"[{file_name}] SessFieldPredict: register T2TModel")
self.model = registry.model(FLAGS.model)(self.hparams,
tf.estimator.ModeKeys.PREDICT)
self.model_spec = self.model.estimator_spec_predict(self.features)
self.prediction = self.model_spec.predictions
self.inputs_vocab = self.hparams.problems[0].vocabulary["inputs"]
self.targets_vocab = self.hparams.problems[0].vocabulary["targets"]
self.problem_name = FLAGS.problems
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=config.GPU_MEM_FRAC)
self.sess_config = tf.ConfigProto(gpu_options=gpu_options)
self.sess_config.gpu_options.allow_growth = config.GPU_MEM_GROWTH
self.batch_size = batch_size
tf.logging.info(f"[{file_name}] SessFieldPredict: registered")
self.sess = tf.Session(config=self.sess_config)
saver = tf.train.Saver()
tf.logging.info(f"[{file_name}] Decode: model loading ... ")
saver.restore(self.sess, self.ckpt)
tf.logging.info(f"[{file_name}] Decode: model loaded.")
def __init__(self,
model_name,
problem_name,
hparams_set,
queries,
output_dir=None,
data_dir=None,
model_dir=None,
tmp_dir=None,
export_dir=None,
decode_hparams="",
default_tmp=None,
tfms_path=None,
mode="train"):
self.model_name = model_name
self.model = registry.model(model_name)
self.problem_name = problem_name
self.hparams_set = hparams_set
self.queries = queries
self.problem = registry.problem(self.problem_name)
self.problem.mode = mode
tmp = tempfile.mkdtemp() if default_tmp is None else default_tmp
self.output_dir = output_dir if output_dir is not None else tmp
self.data_dir = data_dir if data_dir is not None else tmp
self.model_dir = model_dir if model_dir is not None else tmp
self.tmp_dir = tmp_dir if tmp_dir is not None else tmp
self.export_dir = (export_dir if export_dir is not None else tmp +
"/export")
self.decode_hparams = decode_hparams
# HACK
self.tf_model_server_path = tfms_path
self.train_dataset = None
self.has_run_datagen = False
self._lookup_hparams()
def generate_real_env_data(problem_name, agent_policy_path, hparams, data_dir,
tmp_dir, autoencoder_path=None, eval_phase=False):
"""Run the agent against the real environment and return mean reward."""
tf.gfile.MakeDirs(data_dir)
with temporary_flags({
"problem": problem_name,
"agent_policy_path": agent_policy_path,
"autoencoder_path": autoencoder_path,
"only_use_ae_for_policy": True,
}):
gym_problem = registry.problem(problem_name)
gym_problem.settable_num_steps = hparams.true_env_generator_num_steps
gym_problem.eval_phase = eval_phase
gym_problem.generate_data(data_dir, tmp_dir)
mean_reward = gym_problem.statistics.sum_of_rewards / \
(1.0 + gym_problem.statistics.number_of_dones)
return mean_reward
def _testImg2imgTransformer(self, net):
batch_size = 3
hparams = image_transformer_2d.img2img_transformer2d_tiny()
hparams.data_dir = ""
p_hparams = registry.problem("image_celeba").get_hparams(hparams)
inputs = np.random.random_integers(0, high=255, size=(3, 4, 4, 3))
targets = np.random.random_integers(0, high=255, size=(3, 8, 8, 3))
with self.test_session() as session:
features = {
"inputs": tf.constant(inputs, dtype=tf.int32),
"targets": tf.constant(targets, dtype=tf.int32),
"target_space_id": tf.constant(1, dtype=tf.int32),
}
model = net(hparams, tf.estimator.ModeKeys.TRAIN, p_hparams)
logits, _ = model(features)
session.run(tf.global_variables_initializer())
res = session.run(logits)
self.assertEqual(res.shape, (batch_size, 8, 8, 3, 256))
def testMultiModel(self):
x = np.random.random_integers(0, high=255, size=(3, 5, 5, 3))
y = np.random.random_integers(0, high=9, size=(3, 5, 1, 1))
hparams = multimodel.multimodel_tiny()
hparams.add_hparam("data_dir", "")
problem = registry.problem("image_cifar10")
p_hparams = problem.get_hparams(hparams)
hparams.problems = [p_hparams]
with self.test_session() as session:
features = {
"inputs": tf.constant(x, dtype=tf.int32),
"targets": tf.constant(y, dtype=tf.int32),
"target_space_id": tf.constant(1, dtype=tf.int32),
}
model = multimodel.MultiModel(
hparams, tf.estimator.ModeKeys.TRAIN, p_hparams)
logits, _ = model(features)
session.run(tf.global_variables_initializer())
res = session.run(logits)
self.assertEqual(res.shape, (3, 1, 1, 1, 10))
def encode_env_frames(problem_name, ae_problem_name, autoencoder_path,
epoch_data_dir):
"""Encode all frames from problem_name and write out as ae_problem_name."""
with tf.Graph().as_default():
ae_hparams = trainer_lib.create_hparams("autoencoder_discrete_pong",
problem_name=problem_name)
problem = ae_hparams.problem
model = registry.model("autoencoder_ordered_discrete")(
ae_hparams, tf.estimator.ModeKeys.EVAL)
ae_problem = registry.problem(ae_problem_name)
ae_training_paths = ae_problem.training_filepaths(epoch_data_dir, 10, True)
ae_eval_paths = ae_problem.dev_filepaths(epoch_data_dir, 1, True)
skip_train = False
skip_eval = False
for path in ae_training_paths:
if tf.gfile.Exists(path):
skip_train = True
break
for path in ae_eval_paths:
if tf.gfile.Exists(path):
skip_eval = True
break
# Encode train data
if not skip_train:
dataset = problem.dataset(tf.estimator.ModeKeys.TRAIN, epoch_data_dir,
shuffle_files=False, output_buffer_size=100,
preprocess=False)
encode_dataset(model, dataset, problem, ae_hparams, autoencoder_path,
ae_training_paths)
# Encode eval data
if not skip_eval:
dataset = problem.dataset(tf.estimator.ModeKeys.EVAL, epoch_data_dir,
shuffle_files=False, output_buffer_size=100,
preprocess=False)
encode_dataset(model, dataset, problem, ae_hparams, autoencoder_path,
ae_eval_paths)
def train_agent(problem_name, agent_model_dir,
event_dir, world_model_dir, epoch_data_dir, hparams,
autoencoder_path=None, epoch=0):
"""Train the PPO agent in the simulated environment."""
gym_problem = registry.problem(problem_name)
ppo_hparams = trainer_lib.create_hparams(hparams.ppo_params)
ppo_params_names = ["epochs_num", "epoch_length",
"learning_rate", "num_agents",
"optimization_epochs"]
for param_name in ppo_params_names:
ppo_param_name = "ppo_"+ param_name
if ppo_param_name in hparams:
ppo_hparams.set_hparam(param_name, hparams.get(ppo_param_name))
ppo_epochs_num = hparams.ppo_epochs_num
ppo_hparams.save_models_every_epochs = ppo_epochs_num
ppo_hparams.world_model_dir = world_model_dir
ppo_hparams.add_hparam("force_beginning_resets", True)
# Adding model hparams for model specific adjustments
model_hparams = trainer_lib.create_hparams(hparams.generative_model_params)
ppo_hparams.add_hparam("model_hparams", model_hparams)
environment_spec = copy.copy(gym_problem.environment_spec)
environment_spec.simulation_random_starts = hparams.simulation_random_starts
environment_spec.intrinsic_reward_scale = hparams.intrinsic_reward_scale
ppo_hparams.add_hparam("environment_spec", environment_spec)
with temporary_flags({
"problem": problem_name,
"model": hparams.generative_model,
"hparams_set": hparams.generative_model_params,
"output_dir": world_model_dir,
"data_dir": epoch_data_dir,
"autoencoder_path": autoencoder_path,
}):
rl_trainer_lib.train(ppo_hparams, event_dir, agent_model_dir, epoch=epoch)
def generate_data_for_registered_problem(problem_name):
tf.logging.info("Generating data for %s.", problem_name)
if FLAGS.num_shards:
raise ValueError("--num_shards should not be set for registered Problem.")
problem = registry.problem(problem_name)
task_id = None if FLAGS.task_id < 0 else FLAGS.task_id
data_dir = os.path.expanduser(FLAGS.data_dir)
tmp_dir = os.path.expanduser(FLAGS.tmp_dir)
if task_id is None and problem.multiprocess_generate:
if FLAGS.task_id_start != -1:
assert FLAGS.task_id_end != -1
task_id_start = FLAGS.task_id_start
task_id_end = FLAGS.task_id_end
else:
task_id_start = 0
task_id_end = problem.num_generate_tasks
pool = multiprocessing.Pool(processes=FLAGS.num_concurrent_processes)
problem.prepare_to_generate(data_dir, tmp_dir)
args = [(problem_name, data_dir, tmp_dir, task_id)
for task_id in range(task_id_start, task_id_end)]
pool.map(generate_data_in_process, args)
else:
problem.generate_data(data_dir, tmp_dir, task_id)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
problem = registry.problem(FLAGS.problem)
hparams = tf.contrib.training.HParams(
data_dir=os.path.expanduser(FLAGS.data_dir))
problem.get_hparams(hparams)
fname = "inputs" if problem.has_inputs else "targets"
input_encoder = problem.feature_info[fname].encoder
output_decoder = problem.feature_info["targets"].encoder
stub = create_stub()
while True:
prompt = ">> "
if FLAGS.inputs_once:
inputs = FLAGS.inputs_once
else:
inputs = input(prompt)
input_ids = encode(inputs, input_encoder)
output_ids = query(stub, input_ids, feature_name=fname)
outputs = decode(output_ids, output_decoder)
print_str = """
Input:
{inputs}
Output:
{outputs}
"""
print(print_str.format(inputs=inputs, outputs=outputs))
if FLAGS.inputs_once:
break
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
validate_flags()
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
problem = registry.problem(FLAGS.problem)
hparams = tf.contrib.training.HParams(
data_dir=os.path.expanduser(FLAGS.data_dir))
problem.get_hparams(hparams)
request_fn = make_request_fn()
while True:
inputs = FLAGS.inputs_once if FLAGS.inputs_once else input(">> ")
outputs = serving_utils.predict([inputs], problem, request_fn)
outputs, = outputs
output, score = outputs
print_str = """
Input:
{inputs}
Output (Score {score:.3f}):
{output}
"""
print(print_str.format(inputs=inputs, output=output, score=score))
if FLAGS.inputs_once:
break
def evaluate_world_model(simulated_problem_name, problem_name, hparams,
world_model_dir, epoch_data_dir, tmp_dir,
autoencoder_path=None):
"""Generate simulated environment data and return reward accuracy."""
gym_simulated_problem = registry.problem(simulated_problem_name)
sim_steps = hparams.simulated_env_generator_num_steps
gym_simulated_problem.settable_num_steps = sim_steps
with temporary_flags({
"problem": problem_name,
"model": hparams.generative_model,
"hparams_set": hparams.generative_model_params,
"data_dir": epoch_data_dir,
"output_dir": world_model_dir,
"autoencoder_path": autoencoder_path,
}):
gym_simulated_problem.generate_data(epoch_data_dir, tmp_dir)
n = max(1., gym_simulated_problem.statistics.number_of_dones)
model_reward_accuracy = (
gym_simulated_problem.statistics.successful_episode_reward_predictions
/ float(n))
old_path = os.path.join(epoch_data_dir, "debug_frames_env")
new_path = os.path.join(epoch_data_dir, "debug_frames_env_eval")
tf.gfile.Rename(old_path, new_path)
return model_reward_accuracy
def t2t_score_file(filename):
"""
Score each line in a file and return the scores.
:param str filename: T2T checkpoint
"""
# Prepare model.
hparams = create_t2t_hparams()
encoders = registry.problem(FLAGS_problem).feature_encoders(FLAGS_data_dir)
# Prepare features for feeding into the model.
inputs_ph = tf.placeholder(dtype=tf.int32, shape=(None, None)) # Just length dimension.
targets_ph = tf.placeholder(dtype=tf.int32, shape=(None, None)) # Just length dimension.
features = {
"inputs": inputs_ph,
"targets": targets_ph,
}
# Prepare the model and the graph when model runs on features.
model = registry.model(FLAGS_model)(hparams, tf.estimator.ModeKeys.EVAL)
assert isinstance(model, tensor2tensor.models.transformer.Transformer)
# final_output: tensor of logits with shape [batch_size, O, P, body_output_size.
# losses: either single loss as a scalar, a list, a tensor (to be averaged)
# or a dictionary of losses.
final_output, losses = model(features)
assert isinstance(losses, dict)
saver = tf.train.Saver()
sess = tf.Session()
# Load weights from checkpoint.
ckpts = tf.train.get_checkpoint_state(FLAGS_output_dir)
ckpt = ckpts.model_checkpoint_path
saver.restore(sess, ckpt)
# writer = tf.summary.FileWriter('logs', sess.graph)
# writer.close()
# Run on each line.
results = []
for line in open(filename):
tab_split = line.split("\t")
if len(tab_split) > 2:
raise ValueError("Each line must have at most one tab separator.")
assert len(tab_split) == 2
targets = tab_split[1].strip()
inputs = tab_split[0].strip()
# Run encoders and append EOS symbol.
targets_numpy = encoders["targets"].encode(targets) + [text_encoder.EOS_ID]
inputs_numpy = encoders["inputs"].encode(inputs) + [text_encoder.EOS_ID]
# Prepare the feed.
feed = {
inputs_ph: [inputs_numpy],
targets_ph: [targets_numpy]
}
np_res = sess.run({"losses": losses, "final_output": final_output}, feed_dict=feed)
pprint(np_res)
tvars = tf.trainable_variables()
print('t2t inputs_ph:', inputs_ph, inputs_numpy)
print('t2t targets_ph:', targets_ph, targets_numpy)
return sess, tvars, inputs_ph, targets_ph, losses
