def load_model(instrument_model, audio_length):
# Build checkpoint path
# Assumes only one checkpoint in the folder, 'model.ckpt-[iter]`.
model_dir = os.path.join(CKPT_DIR,
"solo_%s_ckpt" % instrument_model.lower())
ckpt_files = [
f for f in tf.gfile.ListDirectory(model_dir) if "model.ckpt" in f
]
ckpt_name = ".".join(ckpt_files[0].split(".")[:2])
ckpt = os.path.join(model_dir, ckpt_name)
# Parse gin config
with gin.unlock_config():
gin_file = os.path.join(model_dir, "operative_config-0.gin")
gin.parse_config_file(gin_file, skip_unknown=True)
# Ensure dimensions sampling rates are equal
time_steps_train = gin.query_parameter("DefaultPreprocessor.time_steps")
n_samples_train = gin.query_parameter("Additive.n_samples")
hop_size = int(n_samples_train / time_steps_train)
time_steps = int(audio_length / hop_size)
n_samples = time_steps * hop_size
gin_params = [
"Additive.n_samples = {}".format(n_samples),
"FilteredNoise.n_samples = {}".format(n_samples),
"DefaultPreprocessor.time_steps = {}".format(time_steps),
]
with gin.unlock_config():
gin.parse_config(gin_params)
return ckpt, time_steps, n_samples
def configure_gin(self, ckpt):
"""Parse the model operative config with special streaming parameters."""
parse_operative_config(ckpt)
# Set streaming specific params.
time_steps = gin.query_parameter('F0PowerPreprocessor.time_steps')
n_samples = gin.query_parameter('Harmonic.n_samples')
samples_per_frame = int(n_samples / time_steps)
config = [
'F0PowerPreprocessor.time_steps = 1',
f'Harmonic.n_samples = {samples_per_frame}',
f'FilteredNoise.n_samples = {samples_per_frame}',
]
# Remove reverb processor.
processor_group_string = """ProcessorGroup.dag = [
(@synths.Harmonic(),
['amps', 'harmonic_distribution', 'f0_hz']),
(@synths.FilteredNoise(),
['noise_magnitudes']),
(@processors.Add(),
['filtered_noise/signal', 'harmonic/signal']),
]"""
config.append(processor_group_string)
with gin.unlock_config():
gin.parse_config(config)
def play(agent_dir, num_episodes, max_episode_steps, save_videos):
agent = get_agent(gin.query_parameter("train.agent"))(make_env_fn(
gin.query_parameter("train.env_id"),
episode_time_limit=max_episode_steps))
agent.pretrain_setup(gin.query_parameter("train.total_timesteps"))
ckpt_path = tf.train.latest_checkpoint(
os.path.join(agent_dir, "best-weights"))
checkpoint = tf.train.Checkpoint(agent)
checkpoint.restore(
ckpt_path).assert_existing_objects_matched().expect_partial()
env = agent.make_env()
if save_videos:
env = Monitor(
env,
os.path.join(agent_dir, "monitor"),
video_callable=lambda _: True,
force=True,
)
try:
episodes = 0
obs = env.reset()
while episodes < num_episodes:
action = agent.act(np.expand_dims(obs, 0),
deterministic=True).numpy()
obs, _, done, _ = env.step(action[0])
env.render()
if done:
obs = env.reset()
episodes += 1
except KeyboardInterrupt:
env.close()
def parse_gin_config(self, ckpt):
"""Parse the model operative config with special streaming parameters."""
with gin.unlock_config():
ckpt_dir = os.path.dirname(ckpt)
operative_config = train_util.get_latest_operative_config(ckpt_dir)
print(f'Parsing from operative_config {operative_config}')
gin.parse_config_file(operative_config, skip_unknown=True)
# Set streaming specific params.
# Remove reverb processor.
pg_string = """ProcessorGroup.dag = [
(@synths.Harmonic(),
['amps', 'harmonic_distribution', 'f0_hz']),
(@synths.FilteredNoise(),
['noise_magnitudes']),
(@processors.Add(),
['filtered_noise/signal', 'harmonic/signal']),
]"""
time_steps = gin.query_parameter('F0PowerPreprocessor.time_steps')
n_samples = gin.query_parameter('Harmonic.n_samples')
samples_per_frame = int(n_samples / time_steps)
gin.parse_config([
'F0PowerPreprocessor.time_steps=1',
f'Harmonic.n_samples={samples_per_frame}',
f'FilteredNoise.n_samples={samples_per_frame}',
pg_string,
])
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments for train.')
if not FLAGS.gin_config:
# Run the experiments locally.
gin.parse_config_file(FLAGS.gin_config_file)
else:
# Run the experiments on a server.
gin.parse_config_files_and_bindings(FLAGS.gin_config,
FLAGS.gin_bindings)
# create the `checkpoing` and `summary` directory used during training
# to save/restore the model and write TF summaries.
checkpoint_dir_str = gin.query_parameter('checkpoint_dir/macro.value')
summary_dir_str = gin.query_parameter('summary_dir/macro.value')
mcts_checkpoint_dir_str = os.path.join(checkpoint_dir_str, 'mcts_data')
app_directories = [
checkpoint_dir_str, summary_dir_str, mcts_checkpoint_dir_str
]
for d in app_directories:
directory_handling.ensure_dir_exists(d)
ppo_train()
def configure_gin(self, ckpt):
"""Parse the model operative config with special streaming parameters."""
parse_operative_config(ckpt)
# Set streaming specific params.
preprocessor_ref = gin.query_parameter('Autoencoder.preprocessor')
preprocessor_str = preprocessor_ref.scoped_selector
time_steps = gin.query_parameter(f'{preprocessor_str}.time_steps')
n_samples = gin.query_parameter('Harmonic.n_samples')
if not isinstance(n_samples, int):
n_samples = gin.query_parameter('%n_samples')
samples_per_frame = int(n_samples / time_steps)
config = [
'Autoencoder.preprocessor = @F0PowerPreprocessor()',
'F0PowerPreprocessor.time_steps = 1',
f'Harmonic.n_samples = {samples_per_frame}',
f'FilteredNoise.n_samples = {samples_per_frame}',
]
# Remove reverb and crop processors.
processor_group_string = """ProcessorGroup.dag = [
(@synths.Harmonic(),
['amps', 'harmonic_distribution', 'f0_hz']),
(@synths.FilteredNoise(),
['noise_magnitudes']),
(@processors.Add(),
['filtered_noise/signal', 'harmonic/signal']),
]"""
config.append(processor_group_string)
with gin.unlock_config():
gin.parse_config(config)
def test_dry_run(self, config):
"""Dry-runs all gin configs."""
gin.clear_config(clear_constants=True)
gin.parse_config_file(config)
def run_config():
try:
rl_trainer.train_rl(
output_dir=self.create_tempdir().full_path,
# Don't run any actual training, just initialize all classes.
n_epochs=0,
train_batch_size=1,
eval_batch_size=1,
)
except Exception as e:
raise AssertionError('Error in gin config {}.'.format(
os.path.basename(config))) from e
# Some tests, ex: DM suite can't be run in OSS - so skip them.
should_skip = False
try:
should_skip = should_skip or gin.query_parameter('RLTask.dm_suite')
except ValueError as e:
pass
try:
env_name = gin.query_parameter('RLTask.env')
should_skip = (should_skip or env_name.startswith('DM-')
or env_name.startswith('LunarLander'))
except ValueError as e:
pass
if should_skip:
pass
else:
run_config()
def main(argv):
args = flags.FLAGS
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
if args.test:
args.envs = 4
args.batch_sz = 4
args.log_freq = 10
args.restore = True
expt = rvr.utils.Experiment(args.results_dir, args.env, args.agent,
args.experiment, args.restore)
base_path = os.path.dirname(os.path.abspath(__file__))
gin_files = gin_configs.get(args.env, [])
gin_files = [base_path + '/configs/' + fl for fl in gin_files]
if args.restore:
gin_files += [expt.config_path]
gin_files += args.gin_files
if not args.gpu:
args.gin_bindings.append(
"build_cnn_nature.data_format = 'channels_last'")
args.gin_bindings.append(
"build_fully_conv.data_format = 'channels_last'")
gin.parse_config_files_and_bindings(gin_files, args.gin_bindings)
# TODO: do this the other way around - put these as gin bindings
if not args.traj_len:
args.traj_len = int(
gin.query_parameter('AdvantageActorCriticAgent.traj_len'))
if not args.batch_sz:
args.batch_sz = int(
gin.query_parameter('AdvantageActorCriticAgent.batch_sz'))
env_cls = rvr.envs.GymEnv if '-v' in args.env else rvr.envs.SC2Env
env = env_cls(args.env, args.render, max_ep_len=args.max_ep_len)
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
sess_mgr = rvr.utils.tensorflow.SessionManager(
sess, expt.path, args.ckpt_freq, training_enabled=not args.test)
agent = agent_cls[args.agent](env.obs_spec(),
env.act_spec(),
sess_mgr=sess_mgr,
n_envs=args.envs,
traj_len=args.traj_len,
batch_sz=args.batch_sz)
agent.logger = rvr.utils.StreamLogger(args.envs, args.log_freq,
args.eps_avg, sess_mgr,
expt.log_path)
if sess_mgr.training_enabled:
expt.save_gin_config()
expt.save_model_summary(agent.model)
agent.run(env, args.updates * args.traj_len * args.batch_sz // args.envs)
def setup_logger():
# import os
# Set run specific envirorment configurations
timestamp = time.strftime("run_%Y_%m_%d_%H_%M_%S") + "_{machine}".format(
machine=socket.gethostname())
gin.bind_parameter(
'multi_tasking_train.model_storage_directory',
os.path.join(
gin.query_parameter('multi_tasking_train.model_storage_directory'),
timestamp))
os.makedirs(
gin.query_parameter('multi_tasking_train.model_storage_directory'),
exist_ok=True)
log.handlers.clear()
formatter = logging.Formatter('%(message)s')
fh = logging.FileHandler(
os.path.join(
gin.query_parameter('multi_tasking_train.model_storage_directory'),
"log.txt"))
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
log.addHandler(fh)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
ch.setFormatter(formatter)
log.setLevel(logging.INFO)
log.addHandler(ch)
# Set global GPU state
if torch.cuda.is_available() and gin.query_parameter(
'multi_tasking_train.device') == 'cuda':
log.info("Using CUDA device:{0}".format(torch.cuda.current_device()))
else:
if gin.query_parameter('multi_tasking_train.device') == 'cpu':
log.info("Utilizing CPU")
else:
raise Exception(
f"Unrecognized device: {gin.query_parameter('multi_tasking_train.device')}"
)
# ML-Flow
mlflow.set_tracking_uri(
f"{gin.query_parameter('multi_tasking_train.ml_flow_directory')}")
mlflow.set_experiment(
f"/{gin.query_parameter('multi_tasking_train.experiment_name')}")
mlflow.start_run()
gin_parameters = gin.config._CONFIG.get(list(gin.config._CONFIG.keys())[0])
mlflow.log_params(gin_parameters)
# all_params = {x[1].split('.')[-1]: gin.config._CONFIG.get(x) for x in list(gin.config._CONFIG.keys())}
all_params = gin.config_str()
with open('config_log.txt', 'w') as f:
f.write(all_params)
mlflow.log_artifact("config_log.txt")
mlflow.log_artifact(__file__)
def finetune_gin_bindings(config):
gin_bindings = []
env_str = config.env_str or gin.query_parameter('%ENV_STR')
if 'Minitaur' in env_str:
if config.friction:
gin_bindings.append('minitaur.MinitaurGoalVelocityEnv.friction = {}'.format(config.friction))
if config.goal_vel:
gin_bindings.append("GOAL_VELOCITY = {}".format(config.goal_vel))
elif 'Cube' in env_str:
if config.finetune:
gin_bindings.append("cube_env.SafemrlCubeEnv.goal_task = ('more_left', 'more_right', 'more_up', 'more_down')")
elif 'DrunkSpider' in env_str:
if config.action_noise:
gin_bindings.append('point_mass.PointMassEnv.action_noise = {}'.format(config.action_noise))
if config.action_scale:
gin_bindings.append('point_mass.PointMassEnv.action_scale = {}'.format(config.action_scale))
if config.finetune:
gin_bindings.append("point_mass.env_load_fn.goal = (6, 3)")
# set NUM_STEPS to be previous value + FINETUNE_STEPS
ft_steps = gin.query_parameter("%FINETUNE_STEPS")
if ft_steps is None:
ft_steps = config.finetune_steps
num_steps = gin.query_parameter("%NUM_STEPS") + ft_steps
gin_bindings.append('NUM_STEPS = {}'.format(num_steps))
return gin_bindings
def get_parallelized_combinations(varying_type: str):
variables = []
lst = [
gin.query_parameter(v) for v in gin.query_parameter("%VARYING_PARS")
]
if varying_type == "combination":
for xs in itertools.product(*lst):
variables.append(xs)
elif varying_type == "ordered_combination":
for xs in zip(*lst):
variables.append(xs)
elif varying_type == "random_search":
for xs in itertools.product(*lst):
variables.append(xs)
variables = [
variables[i]
for i in np.random.randint(0,
len(variables) -
1, gin.query_parameter("%NUM_CORES"))
]
elif varying_type == "chunk":
for xs in itertools.product(*lst):
variables.append(xs)
else:
print("Choose proper way to combine varying parameters")
sys.exit()
num_cores = len(variables)
return variables, num_cores
def load(env_name):
"""Creates the training and evaluation environment.
This method automatically detects whether we are using a subset of the
observation for the goal and modifies the observation space to include the
full state + partial goal.
Args:
env_name: (str) Name of the environment.
Returns:
tf_env, eval_tf_env, obs_dim: The training and evaluation environments.
"""
if env_name == 'sawyer_reach':
tf_env = load_sawyer_reach()
eval_tf_env = load_sawyer_reach()
elif env_name == 'sawyer_push':
tf_env = load_sawyer_push()
eval_tf_env = load_sawyer_push()
eval_tf_env.envs[0]._env.gym.MODE = 'eval' # pylint: disable=protected-access
elif env_name == 'sawyer_drawer':
tf_env = load_sawyer_drawer()
eval_tf_env = load_sawyer_drawer()
elif env_name == 'sawyer_window':
tf_env = load_sawyer_window()
eval_tf_env = load_sawyer_window()
elif env_name == 'sawyer_faucet':
tf_env = load_sawyer_faucet()
eval_tf_env = load_sawyer_faucet()
else:
raise NotImplementedError('Unsupported environment: %s' % env_name)
assert len(tf_env.envs) == 1
assert len(eval_tf_env.envs) == 1
# By default, the environment observation contains the current state and goal
# state. By setting the obs_to_goal parameters, the use can specify that the
# agent should only look at certain subsets of the goal state. The following
# code modifies the environment observation to include the full state but only
# the user-specified dimensions of the goal state.
obs_dim = tf_env.observation_spec().shape[0] // 2
try:
start_index = gin.query_parameter('obs_to_goal.start_index')
except ValueError:
start_index = 0
try:
end_index = gin.query_parameter('obs_to_goal.end_index')
except ValueError:
end_index = None
if end_index is None:
end_index = obs_dim
indices = np.concatenate([
np.arange(obs_dim),
np.arange(obs_dim + start_index, obs_dim + end_index)
])
tf_env = tf_py_environment.TFPyEnvironment(
wrappers.ObservationFilterWrapper(tf_env.envs[0], indices))
eval_tf_env = tf_py_environment.TFPyEnvironment(
wrappers.ObservationFilterWrapper(eval_tf_env.envs[0], indices))
return (tf_env, eval_tf_env, obs_dim)
def construct_envs(
config: Config, training: bool
) -> VectorEnv:
r"""Create VectorEnv object with specified config and env class type.
To allow better performance, dataset are split into small ones for
each individual env, grouped by scenes.
Args:
config: configs that contain num_processes as well as information
necessary to create individual environments.
env_class: class type of the envs to be created.
Returns:
VectorEnv object created according to specification.
"""
num_processes = config.NUM_PROCESSES
dataset = make_dataset(config.TASK_CONFIG.DATASET.TYPE)
scenes = dataset.get_scenes_to_load(config.TASK_CONFIG.DATASET)
if len(scenes) > 0:
random.shuffle(scenes)
assert len(scenes) >= num_processes, (
"reduce the number of processes as there "
"aren't enough number of scenes"
)
scene_splits = [[] for _ in range(num_processes)]
for idx, scene in enumerate(scenes):
scene_splits[idx % len(scene_splits)].append(scene)
assert sum(map(len, scene_splits)) == len(scenes)
task = 'habitat_train_task' if training else 'habitat_eval_task'
max_duration = gin.query_parameter(f'{task}.max_length')
wrappers = [w.scoped_configurable_fn() for w in gin.query_parameter(f'{task}.wrappers')]
kwargs = get_config(training=training, max_steps=max_duration*3)
kwargs['max_duration'] = max_duration
kwargs['action_repeat'] = 1
kwargs['wrappers'] = [(wrapper, kwarg_fn(kwargs)) for wrapper, kwarg_fn in wrappers]
env_kwargs = []
for scenes in scene_splits:
kw = kwargs.copy()
config = kw['config'].clone()
if len(scenes) > 0:
config.defrost()
config.DATASET.CONTENT_SCENES = scenes
config.freeze()
kw['config'] = config
env_kwargs.append(kw)
envs = habitat.VectorEnv(
make_env_fn=make_env_fn,
env_fn_args=tuple(
# tuple(zip(configs, env_classes, range(num_processes)))
tuple(zip(env_kwargs, range(num_processes)))
),
)
return envs
def load_model(self, model):
#model = 'Flute2' #@param ['Violin', 'Flute', 'Flute2', 'Trumpet', 'Tenor_Saxophone','Upload your own (checkpoint folder as .zip)']
MODEL = model
self.model_name = model
if model in ('Violin', 'Flute', 'Flute2', 'Trumpet',
'Tenor_Saxophone'):
# Pretrained models.
PRETRAINED_DIR = 'pretrained'
model_dir = PRETRAINED_DIR
gin_file = os.path.join(PRETRAINED_DIR, 'operative_config-0.gin')
# Parse gin config,
with gin.unlock_config():
gin.parse_config_file(gin_file, skip_unknown=True)
# Assumes only one checkpoint in the folder, 'ckpt-[iter]`.
ckpt_files = [f for f in tf.io.gfile.listdir(model_dir) if 'ckpt' in f]
ckpt_name = ckpt_files[0].split('.')[0]
ckpt = os.path.join(model_dir, ckpt_name)
# Ensure dimensions and sampling rates are equal
time_steps_train = gin.query_parameter(
'DefaultPreprocessor.time_steps')
n_samples_train = gin.query_parameter('Additive.n_samples')
hop_size = int(n_samples_train / time_steps_train)
print(self.audio.shape[1])
time_steps = int(self.audio.shape[1] / hop_size)
print(time_steps)
n_samples = time_steps * hop_size
print(n_samples)
gin_params = [
'Additive.n_samples = {}'.format(n_samples),
'FilteredNoise.n_samples = {}'.format(n_samples),
'DefaultPreprocessor.time_steps = {}'.format(time_steps),
]
with gin.unlock_config():
gin.parse_config(gin_params)
# Trim all input vectors to correct lengths
for key in ['f0_hz', 'f0_confidence', 'loudness_db']:
print(type(self.audio_features[key]))
self.audio_features[key] = self.audio_features[key][:time_steps]
print(self.audio_features['audio'].shape)
print(n_samples)
self.audio_features['audio'] = self.audio_features[
'audio'][:, :n_samples]
# Set up the model just to predict audio given new conditioning
self.model = ddsp.training.models.Autoencoder()
self.model.restore(ckpt)
# Build model by running a batch through it.
start_time = time.time()
_ = self.model(self.audio_features, training=False)
print('Restoring model took %.1f seconds' % (time.time() - start_time))
def set_up_training(self):
self.logging.debug("Simulating Data")
self.data_handler = DataHandler(N_train=self.len_series, rng=self.rng)
if self.experiment_type == "GP":
self.data_handler.generate_returns()
else:
self.data_handler.generate_returns()
# TODO check if these method really fit and change the parameters in the gin file
self.data_handler.estimate_parameters()
self.logging.debug("Instantiating action space")
if self.MV_res:
self.action_space = ResActionSpace()
else:
action_range, ret_quantile, holding_quantile = get_action_boundaries(
N_train=self.N_train,
f_speed=self.data_handler.f_speed,
returns=self.data_handler.returns,
factors=self.data_handler.factors,
)
gin.query_parameter("%ACTION_RANGE")[0] = action_range
self.action_space = ActionSpace()
self.logging.debug("Instantiating market environment")
self.env = self.env_cls(
N_train=self.N_train,
f_speed=self.data_handler.f_speed,
returns=self.data_handler.returns,
factors=self.data_handler.factors,
)
self.logging.debug("Instantiating DQN model")
input_shape = self.env.get_state_dim()
step_size = (self.len_series / gin.query_parameter("PPO.batch_size")
) * gin.query_parameter("%EPOCHS")
gin.bind_parameter("PPO.step_size", step_size)
self.train_agent = PPO(input_shape=input_shape,
action_space=self.action_space,
rng=self.rng)
self.train_agent.model.to(self.device)
self.logging.debug("Instantiating Out of sample tester")
self.oos_test = Out_sample_vs_gp(
savedpath=self.savedpath,
tag="PPO",
experiment_type=self.experiment_type,
env_cls=self.env_cls,
MV_res=self.MV_res,
)
self.oos_test.init_series_to_fill(iterations=self.col_names_oos)
def main_runner(configs_path: str, algo: str):
"""Main function to run both a single experiment or a
set of parallelized experiment
Parameters
----------
configs_path: str
Path where the config files are stored
algo: str
Acronym of the algorithm to run. Read the comments in the gin config to see
the available algorithms
experiment: str
Name of the type of synthetic experiment to perform. Read the comments in the gin config to see
the available algorithms
parallel: bool
Choose to parallelize or not the selected experiments
"""
# get runner to do the experiments
if algo == "DQN":
func = DQN_runner
elif algo == "PPO":
func = PPO_runner
# launch runner (either parallelized or not)
if gin.query_parameter("%VARYING_PARS") is not None:
# get varying parameters, combinations and cores
varying_type = gin.query_parameter("%VARYING_TYPE")
varying_par_to_change = gin.query_parameter("%VARYING_PARS")
combinations, num_cores = get_parallelized_combinations(varying_type)
# choose way to parallelize
if varying_type == "random_search":
Parallel(n_jobs=num_cores)(delayed(parallel_exps)(
var_par, varying_par_to_change, gin_path, func=func)
for var_par in combinations)
time.sleep(5)
os.execv(sys.executable, ["python"] + sys.argv)
elif varying_type == "chunk":
num_cores = gin.query_parameter("%NUM_CORES")
for chunk_var in chunks(combinations, num_cores):
Parallel(n_jobs=num_cores)(delayed(parallel_exps)(
var_par, varying_par_to_change, gin_path, func=func)
for var_par in chunk_var)
time.sleep(5)
else:
print("Choose proper way to parallelize.")
sys.exit()
else:
model_runner = func()
model_runner.run()
def main():
AUDIO_PATH = sys.argv[1]
MODEL_DIR = sys.argv[2]
RESULT_PATH = sys.argv[3]
print(sys.argv)
audio = audio_bytes_to_np(open(AUDIO_PATH, "rb").read(),
sample_rate=DEFAULT_SAMPLE_RATE,
normalize_db=None)
audio = audio[np.newaxis, :]
audio_features = ddsp.training.eval_util.compute_audio_features(audio)
audio_features['loudness_db'] = audio_features['loudness_db'].astype(
np.float32)
# Parse the gin config.
gin_file = os.path.join(MODEL_DIR, 'operative_config-0.gin')
gin.parse_config_file(gin_file)
# Ensure dimensions and sampling rates are equal
time_steps_train = gin.query_parameter('DefaultPreprocessor.time_steps')
n_samples_train = gin.query_parameter('Additive.n_samples')
hop_size = int(n_samples_train / time_steps_train)
time_steps = int(audio.shape[1] / hop_size)
n_samples = time_steps * hop_size
print("===Trained model===")
print("Time Steps", time_steps_train)
print("Samples", n_samples_train)
print("Hop Size", hop_size)
print("\n===Resynthesis===")
print("Time Steps", time_steps)
print("Samples", n_samples)
print('')
gin_params = [
'Additive.n_samples = {}'.format(n_samples),
'FilteredNoise.n_samples = {}'.format(n_samples),
'DefaultPreprocessor.time_steps = {}'.format(time_steps),
]
with gin.unlock_config():
gin.parse_config(gin_params)
# Trim all input vectors to correct lengths
for key in ['f0_hz', 'f0_confidence', 'loudness_db']:
audio_features[key] = audio_features[key][:time_steps]
audio_features['audio'] = audio_features['audio'][:, :n_samples]
# Load model
model = ddsp.training.models.Autoencoder()
model.restore(MODEL_DIR)
# Resynthesize audio.
audio_gen = model(audio_features, training=False)
outputToWav(audio_gen, RESULT_PATH)
def test_gin_state(self):
gin.bind_parameter('f.x', 'global')
self.assertEqual(gin.query_parameter('f.x'), 'global')
with GinState() as temp_state:
self.assertEqual(f()[0], 'default')
gin.bind_parameter('f.x', 'temp')
self.assertEqual(gin.query_parameter('f.x'), 'temp')
self.assertEqual(gin.query_parameter('f.x'), 'global')
with temp_state:
self.assertEqual(gin.query_parameter('f.x'), 'temp')
def _default_output_dir():
"""Default output directory."""
dir_name = "{model_name}_{dataset_name}_{timestamp}".format(
model_name=gin.query_parameter("train.model").configurable.name,
dataset_name=gin.query_parameter("inputs.dataset_name"),
timestamp=datetime.datetime.now().strftime("%Y%m%d_%H%M"),
)
dir_path = os.path.join("~", "trax", dir_name)
print()
trax.log("No --output_dir specified")
return dir_path
def run(**kwargs) -> None:
"""Runs a T5 model for training, finetuning, evaluation etc."""
tf.disable_v2_behavior()
if gin.query_parameter("utils.run.mode") == "eval":
# Increase the recursion limit, see: https://github.com/pltrdy/rouge/issues/19
length = gin.query_parameter("utils.run.sequence_length").get(
"inputs", 512)
batch_size = 1024 # TODO: do not hardcode batch_size for recursionlimit calc
sys.setrecursionlimit(batch_size * length + 10)
utils.run(**kwargs)
def test_acc(_):
"""
report testing accurarcy
:param _:
:return:
"""
acc, cm, _, = test_classification(
model,
gin.query_parameter('triggered_earthquake_dataset.testing_quakes'),
device,
gin.query_parameter('triggered_earthquake_dataset.data_dir'))
writer.add_scalar('Accurarcy/test', acc, trainer.state.epoch)
print('Testing Accurarcy: {:.2f}'.format(acc))
print(cm)
def _default_output_dir():
"""Default output directory."""
try:
dataset_name = gin.query_parameter('inputs.dataset_name')
except ValueError:
dataset_name = 'random'
dir_name = '{model_name}_{dataset_name}_{timestamp}'.format(
model_name=gin.query_parameter('train.model').configurable.name,
dataset_name=dataset_name,
timestamp=datetime.datetime.now().strftime('%Y%m%d_%H%M'),
)
dir_path = os.path.join('~', 'trax', dir_name)
print()
trainer_lib.log('No --output_dir specified')
return dir_path
def main(argv):
args = flags.FLAGS
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
if args.test:
args.envs = 4
args.batch_sz = 4
args.log_freq = 10
args.restore = True
expt = rvr.utils.Experiment(args.results_dir, args.env, args.agent, args.experiment, args.restore)
base_path = os.path.dirname(os.path.abspath(__file__))
gin_files = gin_configs.get(args.env, [])
gin_files = [base_path + '/configs/' + fl for fl in gin_files]
if args.restore:
gin_files += [expt.config_path]
gin_files += args.gin_files
if not args.gpu:
args.gin_bindings.append("build_cnn_nature.data_format = 'channels_last'")
args.gin_bindings.append("build_fully_conv.data_format = 'channels_last'")
gin.parse_config_files_and_bindings(gin_files, args.gin_bindings)
# TODO: do this the other way around - put these as gin bindings
if not args.traj_len:
args.traj_len = int(gin.query_parameter('AdvantageActorCriticAgent.traj_len'))
if not args.batch_sz:
args.batch_sz = int(gin.query_parameter('AdvantageActorCriticAgent.batch_sz'))
env_cls = rvr.envs.GymEnv if '-v' in args.env else rvr.envs.SC2Env
env = env_cls(args.env, args.render, max_ep_len=args.max_ep_len)
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
sess_mgr = rvr.utils.tensorflow.SessionManager(sess, expt.path, args.ckpt_freq, training_enabled=not args.test)
agent = agent_cls[args.agent](env.obs_spec(), env.act_spec(), sess_mgr=sess_mgr,
n_envs=args.envs, traj_len=args.traj_len, batch_sz=args.batch_sz)
agent.logger = rvr.utils.StreamLogger(args.envs, args.log_freq, args.eps_avg, sess_mgr, expt.log_path)
if sess_mgr.training_enabled:
expt.save_gin_config()
expt.save_model_summary(agent.model)
agent.run(env, args.updates * args.traj_len * args.batch_sz // args.envs)
def fetch_grid_results_dict_from_neptune(results_dict: GridResultDict,
study_name: str) -> None:
import neptune
user_name = gin.query_parameter('neptune.user_name')
project_name = gin.query_parameter('neptune.project_name')
project = neptune.init(f'{user_name}/{project_name}')
df = download_dataframe_from_neptune(project, study_name)
hps_names = list(get_hps_dict().keys())
for idx, row in df.iterrows():
hps = dict(row[hps_names])
seed = int(row['data.split_seed'])
results = download_results_from_neptune(project, row['id'],
'results.json')
results_dict[frozenset(hps.items())][seed] = results
def get_params_dict() -> Dict[str, Any]:
params_dict: dict = gin.query_parameter('optuna.params')
return {
k: v.__deepcopy__(None)
if isinstance(v, gin.config.ConfigurableReference) else v
for k, v in params_dict.items()
}
def save_metadata(_):
'''
save a metadata file, used for inference
:param _:
:return:
'''
transformer = triggered_earthquake_transform(random_trim_offset=False)
transformer_path = os.path.join(model_dir, 'transformer.p')
pickle.dump(transformer, open(transformer_path, 'wb'))
metadata = {
'name':
run_name,
'classes':
gin.query_parameter('triggered_earthquake_dataset.labels'),
'model_state_path':
save_handler.last_checkpoint,
'classifier_path':
os.path.join(model_dir, '{}_classifier.p'.format(prefix)),
'embedding_size':
embedding_size,
'num_layers':
num_layers,
'transformer':
transformer_path
}
with open(os.path.join(model_dir, 'metadata.json'), 'w') as f:
json.dump(metadata, f)
def report_embeddings(_):
"""
write embeddings to tensorboard
:param _:
:return:
"""
train_loader = DataLoader(ds_train, batch_size=1)
test_loader = DataLoader(ds_test, batch_size=1)
text_labels = gin.query_parameter(
'triggered_earthquake_dataset.labels')
train_embeddings, train_labels = get_embeddings(model,
train_loader,
device=device)
train_labels = [
text_labels[np.argmax(l)] for l in train_labels.squeeze(1)
]
writer.add_embedding(train_embeddings.squeeze(1),
metadata=train_labels,
global_step=trainer.state.epoch,
tag='train_embeddings')
test_embeddings, test_labels = get_embeddings(model,
test_loader,
device=device)
test_labels = [
text_labels[np.argmax(l)] for l in test_labels.squeeze(1)
]
writer.add_embedding(test_embeddings.squeeze(1),
metadata=test_labels,
global_step=trainer.state.epoch,
tag='test_embeddings')
def main(_):
seed = common.set_random_seed(FLAGS.random_seed)
gin_file = common.get_gin_file()
gin.parse_config_files_and_bindings(gin_file, FLAGS.gin_param)
algorithm_ctor = gin.query_parameter(
'TrainerConfig.algorithm_ctor').scoped_configurable_fn
env = create_environment(nonparallel=True, seed=seed)
env.reset()
common.set_global_env(env)
config = policy_trainer.TrainerConfig(root_dir="")
data_transformer = create_data_transformer(config.data_transformer_ctor,
env.observation_spec())
config.data_transformer = data_transformer
observation_spec = data_transformer.transformed_observation_spec
common.set_transformed_observation_spec(observation_spec)
algorithm = algorithm_ctor(
observation_spec=observation_spec,
action_spec=env.action_spec(),
config=config)
try:
policy_trainer.play(
FLAGS.root_dir,
env,
algorithm,
checkpoint_step=FLAGS.checkpoint_step or "latest",
epsilon_greedy=FLAGS.epsilon_greedy,
num_episodes=FLAGS.num_episodes,
max_episode_length=FLAGS.max_episode_length,
sleep_time_per_step=FLAGS.sleep_time_per_step,
record_file=FLAGS.record_file,
ignored_parameter_prefixes=FLAGS.ignored_parameter_prefixes.split(
",") if FLAGS.ignored_parameter_prefixes else [])
finally:
env.close()
def log_vote(instance, log_file=None, guess=False):
if log_file is None:
log_file = 'votes-' + gin.query_parameter('Codenamer.name') + ('-guess' if guess else '') + '.json'
with portalocker.Lock(log_file) as f:
f.write(instance.to_json() + "\n")
f.flush()
os.fsync(f.fileno())
def get_vocabulary(mixture_or_task_name=None):
"""Return vocabulary from the mixture or task."""
if not mixture_or_task_name:
# Attempt to extract the mixture/task name from the gin config.
try:
mixture_or_task_name = gin.query_parameter("%MIXTURE_NAME")
except ValueError:
logging.warning("Could not extract mixture/task name from gin config.")
if mixture_or_task_name:
provider = t5.data.get_mixture_or_task(mixture_or_task_name)
features = provider.output_features
if "inputs" in features and "targets" in features:
return (features["inputs"].vocabulary, features["targets"].vocabulary)
else:
feature_values = list(features.values())
vocabulary = feature_values[0].vocabulary
for feature in feature_values[1:]:
if feature.vocabulary != vocabulary:
logging.warning("No feature_name was provided to get_vocabulary, but "
"output_features have different vocabularies.")
vocabulary = None
break
if vocabulary:
return vocabulary
logging.warning("Using default vocabulary.")
return t5.data.get_default_vocabulary()
def update_communicator(self):
"""Set current parameters and update existing ones."""
if self.communicator is None:
return
config_backup = deepcopy(self._config)
try:
gin_queries = ray.get(
self.communicator.get_clean_gin_queries.remote())
for q in gin_queries:
val = gin.query_parameter(q)
self.communicator.add_msg.remote(f"Gin value {q}: {val}")
updates = ray.get(self.communicator.get_clear_updates.remote())
for k, v in updates:
self.communicator.add_msg.remote(
f"Updating parameter {k}: {v}")
logging.info(f"Updating parameter {k}: {v}")
param_update_from_flat(self._config, k, v)
self.communicator.set_current_parameters.remote(
param_flatten_dict_keys(self._config))
self.set_gin_variables()
except Exception as e:
self.communicator.add_msg.remote("Error: " + str(e))
logging.warning(f"Remote parameter update failed {e}")
self._config = config_backup
def save_gin_config(self):
config_str = gin.operative_config_str()
if 'AdvantageActorCriticAgent.batch_sz' not in config_str:
# gin ignores batch size since it's passed manually from args
# as a hacky workaround - insert it manually as the first param
batch_sz = gin.query_parameter('AdvantageActorCriticAgent.batch_sz')
config_lines = config_str.split('\n')
first_ac_line = 0
for first_ac_line in range(0, len(config_lines)):
if 'AdvantageActorCriticAgent.' in config_lines[first_ac_line]:
break
config_lines.insert(first_ac_line, 'AdvantageActorCriticAgent.batch_sz = ' + str(batch_sz))
config_str = '\n'.join(config_lines)
with open(self.config_path, 'w') as cfg_file:
cfg_file.write(config_str)
