def testDeviceListMultiReplicaNoSyncSgd(self):
p = cluster_factory.Cluster.Params()
p.mode = 'async'
p.job = 'trainer'
p.task = 1
p.worker.replicas = 2
p.worker.gpus_per_replica = 2
c = cluster_factory.Cluster(p)
gpu_devices = c.available_devices
expected_gpu_devices = [[
cluster.MakeDeviceString(job_name='/job:localhost',
task_id=1,
device_name='GPU',
device_id=0),
cluster.MakeDeviceString(job_name='/job:localhost',
task_id=1,
device_name='GPU',
device_id=1),
]]
self.assertAllEqual(gpu_devices, expected_gpu_devices)
# Compute the total number of worker devices for a multi
# replica setup.
self.assertEqual(4, c.total_worker_devices)
# Even when the job is different, we still look at the worker
# information.
p.job = 'controller'
p.task = 0
c = cluster_factory.Cluster(p)
self.assertEqual(4, c.total_worker_devices)
def __init__(self, sess, batch_size):
self.sess = sess
self.batch_size = batch_size
tf.set_random_seed(1234)
params = model_registry.GetParams('asr.librispeech.Librispeech960Wpm',
'Test')
params.random_seed = 1234
params.is_eval = True
params.cluster.worker.gpus_per_replica = 1
cluster = cluster_factory.Cluster(params.cluster)
with cluster, tf.device(cluster.GetPlacer()):
model = params.cls(params)
# placeholders
self.input_tf = tf.placeholder(tf.float32,
shape=[batch_size, None],
name='qq_input')
self.tgt_tf = tf.placeholder(tf.string)
self.sample_rate_tf = tf.placeholder(tf.int32,
name='qq_sample_rate')
self.maxlen = tf.placeholder(np.int32)
# generate the inputs that are needed for the lingvo model
self.features = create_features(self.input_tf, self.sample_rate_tf)
self.inputs = create_inputs(model, self.features, self.tgt_tf,
self.batch_size)
task = model.GetTask()
metrics = task.FPropDefaultTheta(self.inputs)
self.decoded = task.Decode(self.inputs)
def testDefaultParams(self):
p = cluster_factory.Cluster.Params()
c = cluster_factory.Cluster(p)
self.assertFalse(c.add_summary)
g = tf.Graph()
vs = []
with g.as_default():
with tf.device(c.GetPlacer()):
for i in range(10):
vs.append(tf.get_variable('x%d' % i, (10, 10, 10)))
sum_all = tf.add_n(vs)
for v in vs:
self.assertEqual(
v.device,
c._MakeDeviceString(
job_name='/job:localhost',
task_id=0,
device_name='CPU',
device_id=0))
self.assertEqual(
sum_all.device,
c._MakeDeviceString(
job_name='/job:localhost',
task_id=0,
device_name='CPU',
device_id=0))
def testNoPS(self):
p = cluster_factory.Cluster.Params()
p.worker.name = '/job:trainer'
p.worker.replicas = 1
p.ps.name = '/job:trainer'
p.ps.replicas = 1
c = cluster_factory.Cluster(p)
g = tf.Graph()
vs = []
with g.as_default():
with tf.device(c.GetPlacer()):
for i in range(10):
vs.append(tf.get_variable('x%d' % i, (10, 10, 10)))
sum_all = tf.add_n(vs)
for v in vs:
self.assertEqual(
v.device,
cluster.MakeDeviceString(job_name='/job:trainer',
task_id=0,
device_name='CPU',
device_id=0))
self.assertEqual(
sum_all.device,
cluster.MakeDeviceString(job_name='/job:trainer',
task_id=0,
device_name='CPU',
device_id=0))
def __init__(self,
params,
model_task_name,
logdir,
tf_master,
trial=base_trial.NoOpTrial()):
"""Construct a new BaseRunner.
Args:
params: Params object containing model configuration.
model_task_name: String name of the task this runner should execute for
multitask models only. See flag for details.
logdir: String path to the log directory to output to.
tf_master: String path to the master job, e.g. 'local'.
trial: An optional hyperparameter trial. Used by Vizier studies.
"""
p = params.Copy()
# Set in subclasses.
self._job_name = ''
self._params = trial.OverrideModelParams(p)
tf.logging.info('=' * 60)
for line in self.params.ToText().split('\n'):
tf.logging.info('%s', line)
tf.logging.info('=' * 60)
self._logdir = logdir
self._tf_master = tf_master
self._model_task_name = model_task_name
self._trial = trial
# If the runner is conducting a Vizier trial, scope all the variables
# (e.g., global_step) by the trial id so that we do not share states across
# trials.
self._container_id = self._trial.Name()
self._should_report_metrics = False
# To early terminate a runner, we set max_steps here and that will trigger
# appropriate ShouldStop behavior in the threads. This is used by Vizier
# to early stop a trial.
self._max_steps = None
self.params.cluster.logdir = logdir
self._cluster = cluster_factory.Cluster(self.params.cluster)
self._train_dir = os.path.join(self._logdir, 'train')
tf.io.gfile.makedirs(self._train_dir)
self._graph = tf.Graph()
self._summary_writer = None
self._initialize_tables = None
self._dequeue_thread_complete = False
early_stop.MetricHistory.SetLogdirInMetricHistories(p, logdir)
self._early_stop = None
if p.train.early_stop and p.train.early_stop.window:
self._early_stop = early_stop.EarlyStop(p.train.early_stop)
with self._graph.as_default():
self._early_stop.FProp(None)
self._init_input_ops = []
self._SetStatusMessage('Starting ...')
def testDeviceListMultiReplicaSyncSgd(self):
p = cluster_factory.Cluster.Params()
p.mode = 'sync'
p.job = 'trainer_client'
p.worker.name = '/job:localhost'
p.worker.replicas = 2
p.worker.gpus_per_replica = 2
c = cluster_factory.Cluster(p)
gpu_devices = c.available_devices
expected_gpu_devices = [
[
cluster.MakeDeviceString(job_name='/job:localhost',
task_id=0,
device_name='GPU',
device_id=0),
cluster.MakeDeviceString(job_name='/job:localhost',
task_id=0,
device_name='GPU',
device_id=1),
],
[
cluster.MakeDeviceString(job_name='/job:localhost',
task_id=1,
device_name='GPU',
device_id=0),
cluster.MakeDeviceString(job_name='/job:localhost',
task_id=1,
device_name='GPU',
device_id=1),
]
]
self.assertAllEqual(gpu_devices, expected_gpu_devices)
def GetParamsForDataset(self, job_name, dataset_name):
"""Returns params for job `job_name` on the dataset `dataset_name`."""
# Get the current cluster and update its params from flags.
cluster = cluster_factory.Current()
self.UpdateClusterParamsFromFlags(cluster.params, job_name)
with cluster_factory.Cluster(cluster.params):
try:
cfg = self.model_registry.GetParams(self._model_name,
dataset_name)
except base_model_params.DatasetError as e:
dataset_name_retry = dataset_name.title()
tf.logging.warning(
'Exception configuring dataset %s, retrying as %s: %s',
dataset_name, dataset_name_retry, e)
cfg = self.model_registry.GetParams(self._model_name,
dataset_name_retry)
tf.logging.warning('Succeeded after retrying as %s.' %
dataset_name_retry)
cfg.cluster = cluster.params
# Updates a few params based on flags.
if FLAGS.enqueue_max_steps is not None:
cfg.train.enqueue_max_steps = FLAGS.enqueue_max_steps
if FLAGS.saver_max_to_keep is not None:
cfg.train.save_max_to_keep = FLAGS.saver_max_to_keep
if FLAGS.saver_keep_checkpoint_every_n_hours is not None:
cfg.train.save_keep_checkpoint_every_n_hours = FLAGS.saver_keep_checkpoint_every_n_hours
return cfg
def testPSWithGPUs(self):
p = cluster_factory.Cluster.Params()
p.worker.name = '/job:trainer'
p.worker.replicas = 1
p.ps.name = '/job:ps'
p.ps.replicas = 4
p.ps.gpus_per_replica = 2
c = cluster_factory.Cluster(p)
g = tf.Graph()
vs = []
with g.as_default():
with tf.device(c.GetPlacer()):
for i in range(10):
vs.append(tf.get_variable('x%d' % i, (10, 10, 10)))
sum_all = tf.add_n(vs)
for i, v in enumerate(vs):
self.assertEqual(
v.device,
cluster.MakeDeviceString(job_name='/job:ps',
task_id=(i / 2) % 4,
device_name='GPU',
device_id=i % 2))
self.assertEqual(
sum_all.device,
cluster.MakeDeviceString(job_name='/job:trainer',
task_id=0,
device_name='CPU',
device_id=0))
def testDefaultParamsWithDynamicShape(self):
p = cluster_factory.Cluster.Params()
c = cluster_factory.Cluster(p)
g = tf.Graph()
vs = []
with g.as_default():
with tf.device(c.GetPlacer()):
for i in range(10):
dyn_shape = tf.constant([2], dtype=tf.int32)
dyn_shape = tf.placeholder_with_default(dyn_shape,
shape=[None])
v = tf.get_variable('x%d_wb/var' % i,
initializer=tf.random.uniform(
dyn_shape, dtype=tf.float64),
validate_shape=False)
vs.append(v)
sum_all = tf.add_n(vs)
for v in vs:
self.assertEqual(
v.device,
cluster.MakeDeviceString(job_name='/job:localhost',
task_id=0,
device_name='CPU',
device_id=0))
self.assertEqual(
sum_all.device,
cluster.MakeDeviceString(job_name='/job:localhost',
task_id=0,
device_name='CPU',
device_id=0))
def testInputTargets(self): p = cluster_factory.Cluster.Params() p.input.name = '/job:input' p.input.replicas = 2 p.input.targets = '10.100.1.1:10001,10.100.1.2:10002' c = cluster_factory.Cluster(p) self.assertEqual(c.input_targets, ['10.100.1.1:10001', '10.100.1.2:10002'])
def GetExecutorParams(model_name, cluster_params, model_registry):
"""Get the params needed to instantiate the Executor.
Args:
model_name: A model name regsitered in the ModelRegistry.
cluster_params: A cluster hyperparams object.
model_registry: A ModelRegistry object.
Returns:
A tuple (dict, Params):
- ps_params_dict: High-level task name -> ProgramScheduleParams
- train_cfg: A SingleTaskModelParams or MultiTaskModelParams.
"""
ps_params_dict = {}
with cluster_factory.Cluster(cluster_params):
ps_cfg = model_registry.GetProgramSchedule(model_name)
train_cfg = model_registry.GetParams(model_name, 'Train')
train_cfg.cluster = cluster_params
if issubclass(train_cfg.cls, base_model.MultiTaskModel):
multi_task_train_cfg = train_cfg
# Create SingleTaskModelParams from a MultiTaskModelParams.
for k, _ in multi_task_train_cfg.task_params.IterParams():
single_task_params = base_model.SingleTaskModel.Params()
single_task_params.cluster = multi_task_train_cfg.cluster
single_task_params.input = multi_task_train_cfg.input.Get(k)
single_task_params.task = multi_task_train_cfg.task_params.Get(k)
single_task_params.train = single_task_params.task.train
if k not in ps_cfg.program_schedule_dict:
tf.logging.fatal(
'Could not find %s in ps_cfg.program_schedule_dict: %s', k,
ps_cfg)
program_schedule_params = ps_cfg.program_schedule_dict[k]
program_schedule_params.task_dict = {'Train': single_task_params}
for eval_dataset_name in program_schedule_params.dataset_names:
multi_task_eval_cfg = model_registry.GetParams(
model_name, eval_dataset_name)
eval_task_params = base_model.SingleTaskModel.Params()
eval_task_params.cluster = single_task_params.cluster
eval_task_params.input = multi_task_eval_cfg.input.Get(k)
eval_task_params.task = multi_task_eval_cfg.task_params.Get(k)
program_schedule_params.task_dict[
eval_dataset_name] = eval_task_params
ps_params_dict[k] = program_schedule_params
else:
program_schedule_params = ps_cfg
program_schedule_params.task_dict = {'Train': train_cfg}
for eval_dataset_name in program_schedule_params.dataset_names:
task_eval_params = model_registry.GetParams(model_name,
eval_dataset_name)
task_eval_params.cluster = train_cfg.cluster
program_schedule_params.task_dict[eval_dataset_name] = task_eval_params
ps_params_dict[''] = program_schedule_params
return ps_params_dict, train_cfg
def InspectModel(self):
"""Prints out model analysis for the model."""
p = self.GetParamsForDataset('controller', 'Train')
p.cluster.mode = 'sync'
c = cluster_factory.Cluster(p.cluster)
with tf.Graph().as_default(), c, tf.device(c.GetPlacer()):
analysis, _ = _ModelAnalysis(p.cls(p))
print(analysis)
def testInputDevice(self):
p = cluster_factory.Cluster.Params()
p.mode = 'sync'
p.job = 'trainer_client'
p.input.name = '/job:input'
p.input.replicas = 1
c = cluster_factory.Cluster(p)
input_device = c.input_device
expected_device = c._MakeDeviceString(
job_name='/job:input', task_id=0, device_name='CPU', device_id=0)
self.assertEqual(input_device, expected_device)
def __init__(self, decoder_type, *args, **kwargs):
super().__init__(*args, **kwargs)
self.params.cluster.do_eval = True
self._cluster = cluster_factory.Cluster(self.params.cluster)
self._decoder_dir = os.path.join(self._logdir, f'decoder_{decoder_type}')
if self._model_task_name:
self._decoder_dir += '_' + self._model_task_name
tf.io.gfile.makedirs(self._decoder_dir)
self._summary_writer = tf.compat.v2.summary.create_file_writer(
self._decoder_dir)
def __init__(self, decoder_type, *args, **kwargs):
super().__init__(*args, **kwargs)
self._job_name = 'decoder_' + decoder_type
self.params.cluster.do_eval = True
self._cluster = cluster_factory.Cluster(self.params.cluster)
self._decoder_dir = GetDecoderDir(self._logdir, self._job_name,
self._model_task_name)
tf.io.gfile.makedirs(self._decoder_dir)
self._decode_path = None
# Multitask params doesn't have 'task'.
if 'task' in self.params:
self._decode_path = checkpointer.GetSpecificCheckpoint(
self.params.task.eval.load_checkpoint_from)
self._should_report_metrics = self._job_name.startswith(
self._cluster.reporting_job)
with self._graph.as_default(), tf.container(self._container_id):
self._summary_writer = self._CreateSummaryWriter(self._decoder_dir)
self._CreateTF2SummaryWriter(self._decoder_dir)
with self._cluster, tf.device(
self._cluster.GetPlacer()), self._TF2SummaryContext():
self._model = self.params.Instantiate()
self._params = self._model.params
self._task = self._model.GetTask(self._model_task_name)
# Note, different graphs are being constructed for different model
# tasks, which may result in different node names being chosen.
# Obviously, variable names has to be stay the same between train and
# decode.
cluster = self._cluster
with tf.device(cluster.input_device):
input_batch = (
self._task.input_generator.GetPreprocessedInputBatch())
self._dec_output = self._task.Decode(input_batch)
self._summary_op = tf.summary.merge_all()
self.checkpointer = self._CreateCheckpointer(
self._train_dir, self._model)
self._CreateTF2SummaryOps()
self._initialize_tables = tf.tables_initializer()
self._initialize_local_vars = tf.local_variables_initializer()
# No queues are allowed for decoder models.
self.enqueue_ops = tf.get_collection(py_utils.ENQUEUE_OPS)
assert not self.enqueue_ops
# Saves the graph def.
self._WriteToLog(self.params.ToText(), self._decoder_dir, 'params.txt')
if self.params.cluster.task == 0:
tf.io.write_graph(self._graph.as_graph_def(), self._decoder_dir,
'%s.pbtxt' % self._job_name)
def testWorkerDeviceInModelSplitSync(self):
p = cluster_factory.Cluster.Params()
p.mode = 'sync'
p.job = 'trainer_client'
p.worker.name = '/job:trainer'
p.worker.replicas = 4
p.worker.gpus_per_replica = 4
p.worker.devices_per_split = 2
c = cluster_factory.Cluster(p)
with py_utils.ModelSplit(1):
d = c.WorkerDeviceInModelSplit(1)
expected_device = c._MakeDeviceString(
job_name='/job:trainer', task_id=0, device_name='GPU', device_id=3)
self.assertEqual(expected_device, d)
def __init__(self, model_name, split, run_preprocessors):
self._model_name = model_name
self._split = split
self._run_preprocessors = run_preprocessors
self._sess = None
# Create a cluster configuration assuming evaluation; the input pipelines
# need to know the cluster job type to set up the outputs correctly.
cluster = cluster_factory.Current()
cluster.params.job = 'evaler'
cluster.params.mode = 'sync'
cluster.params.task = 0
cluster.params.evaler.replicas = 1
self._cluster = cluster_factory.Cluster(cluster.params)
def testWorkerDeviceInModelSplit(self):
p = cluster_factory.Cluster.Params()
p.mode = 'async'
p.job = 'trainer'
p.task = 3
p.worker.name = '/job:trainer'
p.worker.replicas = 4
p.worker.gpus_per_replica = 4
p.worker.devices_per_split = 2
with cluster_factory.Cluster(p):
with cluster_factory.SetModelSplit(1) as c:
d = c.WorkerDeviceInModelSplit(1)
expected_device = c._MakeDeviceString(
job_name='/job:trainer', task_id=3, device_name='GPU', device_id=3)
self.assertEqual(expected_device, d)
def __init__(self,
params,
model_task_name,
logdir,
tf_master,
trial=base_trial.NoOpTrial()):
"""Construct a new BaseRunner.
Args:
params: Params object containing model configuration.
model_task_name: String name of the task this runner should execute
for multitask models only. See flag for details.
logdir: String path to the log directory to output to.
tf_master: String path to the master job, e.g. 'local'.
trial: An optional hyperparameter trial. Used by Vizier studies.
"""
p = params.Copy()
p.add_summary = FLAGS.add_summary
self._params = trial.OverrideModelParams(p)
tf.logging.info('=' * 60)
for line in self.params.ToText().split('\n'):
tf.logging.info('%s', line)
tf.logging.info('=' * 60)
self._logdir = logdir
self._tf_master = tf_master
self._model_task_name = model_task_name
self._trial = trial
# If the runner is conducting a Vizier trial, scope all the variables
# (e.g., global_step) by the trial id so that we do not share states across
# trials.
self._container_id = self._trial.Name()
self._cluster = cluster_factory.Cluster(self.params.cluster)
self._train_dir = os.path.join(self._logdir, 'train')
self._graph = tf.Graph()
self._summary_writer = None
self.initialize_tables = None
early_stop.MetricHistory.SetLogdirInMetricHistories(p, logdir)
self._early_stop = None
if p.train.early_stop and p.train.early_stop.window:
self._early_stop = early_stop.EarlyStop(p.train.early_stop)
with self._graph.as_default():
self._early_stop.FProp(None)
self._SetStatusMessage('Starting ...')
def testDeviceListOneReplicaGpu(self):
p = cluster_factory.Cluster.Params()
p.mode = 'async'
p.job = 'trainer'
p.worker.gpus_per_replica = 2
c = cluster_factory.Cluster(p)
gpu_devices = c.available_devices
expected_gpu_devices = [[
c._MakeDeviceString(job_name='/job:localhost',
task_id=0,
device_name='GPU',
device_id=0),
c._MakeDeviceString(job_name='/job:localhost',
task_id=0,
device_name='GPU',
device_id=1),
]]
self.assertAllEqual(gpu_devices, expected_gpu_devices)
def testPSRandomSize(self):
p = cluster_factory.Cluster.Params()
p.worker.name = '/job:trainer'
p.ps.name = '/job:ps'
p.ps.replicas = 10
c = cluster_factory.Cluster(p)
g = tf.Graph()
vs = []
np.random.seed(301)
with g.as_default():
with tf.device(c.GetPlacer()):
# Creates 200 variables with different sizes.
for i in range(200):
if i % 13:
size = np.random.randint(10000)
elif i % 7:
size = np.random.randint(100)
else:
size = np.random.randint(10)
vs.append(tf.get_variable('x%d' % i, shape=(size)))
sum_all = tf.add_n([tf.reduce_sum(x) for x in vs])
# Computes the total size of variables placed on each device.
total_size = {} # device name -> size
for v in vs:
size = tf.TensorShape(v.op.get_attr('shape')).num_elements()
if v.device in total_size:
total_size[v.device] += size
else:
total_size[v.device] = size
for (device, allocated) in zip(
sorted(total_size),
[91701, 91361, 90346, 88738, 87240, 89265, 91944, 92472, 88051, 95053]):
self.assertEqual(total_size[device], allocated)
self.assertEqual(
sum_all.device,
cluster.MakeDeviceString(
job_name='/job:trainer',
replica_id=0,
task_id=0,
device_name='CPU',
device_id=0))
def testDeviceListOneRepliaCpu(self):
p = cluster_factory.Cluster.Params()
p.mode = 'async'
p.job = 'trainer'
p.worker.cpus_per_replica = 2
c = cluster_factory.Cluster(p)
cpu_devices = c.available_devices
expected_cpu_devices = [[
cluster.MakeDeviceString(
job_name='/job:localhost',
replica_id=0,
task_id=0,
device_name='CPU',
device_id=0),
cluster.MakeDeviceString(
job_name='/job:localhost',
replica_id=0,
task_id=0,
device_name='CPU',
device_id=1),
]]
print(expected_cpu_devices)
self.assertAllEqual(cpu_devices, expected_cpu_devices)
def InspectModel(self):
"""Prints out model analysis for the model."""
FLAGS.mode = 'sync'
p = self.GetParamsForDataset('controller', 'Train')
c = cluster_factory.Cluster(p.cluster)
model_part_regex = FLAGS.inspect_model_part_regex
part_pattern = None
if model_part_regex:
part_pattern = {}
for pat_str in model_part_regex:
first_colon = pat_str.find(':')
if first_colon < 0:
msg = f'Cannot understand --inspect_model_part_regex={pat_str}.'
raise ValueError(msg)
name = pat_str[:first_colon]
pattern = pat_str[first_colon + 1:]
part_pattern[name] = pattern
with tf.Graph().as_default(), c, tf.device(c.GetPlacer()):
analysis, _ = summary_utils.ModelAnalysis(
p.Instantiate(),
topn=FLAGS.inspect_model_topn,
part_pattern=part_pattern)
print(analysis)
def main(argv):
data = np.loadtxt(FLAGS.input, dtype=str, delimiter=",")
# calculate the number of loops to run the test
num = len(data[0])
batch_size = FLAGS.batch_size
num_loops = num / batch_size
assert num % batch_size == 0
with tf.device("/gpu:0"):
tf.set_random_seed(1234)
tfconf = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=tfconf) as sess:
params = model_registry.GetParams(
"asr.librispeech.Librispeech960Wpm", "Test")
params.cluster.worker.gpus_per_replica = 1
cluster = cluster_factory.Cluster(params.cluster)
with cluster, tf.device(cluster.GetPlacer()):
params.vn.global_vn = False
params.random_seed = 1234
params.is_eval = True
model = params.cls(params)
task = model.GetTask()
saver = tf.train.Saver()
saver.restore(sess, FLAGS.checkpoint)
# define the placeholders
input_tf = tf.placeholder(tf.float32, shape=[batch_size, None])
tgt_tf = tf.placeholder(tf.string)
sample_rate_tf = tf.placeholder(tf.int32)
mask_tf = tf.placeholder(tf.float32,
shape=[batch_size, None, 80])
rir_tf = tf.placeholder(tf.float32)
lengths = tf.placeholder(
np.int32,
shape=[
batch_size,
],
)
maxlen = tf.placeholder(np.int32)
mask = tf.placeholder(dtype=np.float32,
shape=[batch_size, None])
# generate the features and inputs
new_input = (create_speech_rir(input_tf, rir_tf, lengths,
maxlen, batch_size) * mask)
features = create_features(new_input, sample_rate_tf, mask_tf)
shape = tf.shape(features)
inputs = create_inputs(model, features, tgt_tf, batch_size,
mask_tf)
# loss
metrics = task.FPropDefaultTheta(inputs)
loss = tf.get_collection("per_loss")[0]
# prediction
decoded_outputs = task.Decode(inputs)
dec_metrics_dict = task.CreateDecoderMetrics()
success_rates = []
for num_room in range(FLAGS.num_test_rooms):
correct = 0
rir = Readrir(num_room)
for l in range(num_loops):
data_sub = data[:, l * batch_size:(l + 1) * batch_size]
(
audios_np,
sample_rate,
tgt_np,
mask_freq,
lengths_np,
max_len,
masks,
) = Read_input(data_sub, batch_size)
feed_dict = {
input_tf: audios_np,
sample_rate_tf: sample_rate,
tgt_tf: tgt_np,
mask_tf: mask_freq,
rir_tf: rir,
lengths: lengths_np,
maxlen: max_len,
mask: masks,
}
losses = sess.run(loss, feed_dict)
predictions = sess.run(decoded_outputs, feed_dict)
task.PostProcessDecodeOut(predictions,
dec_metrics_dict)
wer_value = dec_metrics_dict["wer"].value * 100.0
for i in range(batch_size):
print("example: {}, loss_ce: {}".format(
l * batch_size + i, losses[i]))
print("pred:{}".format(
predictions["topk_decoded"][i, 0]))
print("targ:{}".format(tgt_np[i].lower()))
print("true: {}".format(data_sub[1, i].lower()))
if predictions["topk_decoded"][
i, 0] == tgt_np[i].lower():
correct += 1
print("--------------------------------")
print("Now, the WER is: {0:.2f}%".format(wer_value))
print("num of examples succeed for room {}: {}".format(
num_room, correct))
success_rate = correct / float(num) * 100
print("success rate for room {}: {}%".format(
num_room, success_rate))
success_rates.append(success_rate)
success_ave = float(sum(success_rates)) / len(success_rates)
print("success rate overall: {}%".format(success_ave))
def GetExecutorParams(model_name, cluster_params, model_registry):
"""Get the params needed to instantiate the Executor.
Args:
model_name: A model name registered in the ModelRegistry.
cluster_params: A cluster hyperparams object.
model_registry: A ModelRegistry object.
Returns:
A tuple (dict, Params):
- ps_params_dict: High-level task name -> ProgramScheduleParams
- train_cfg: A SingleTaskModelParams or MultiTaskModelParams.
Raises:
ValueError if the model params is invalid.
"""
ps_params_dict = {}
with cluster_factory.Cluster(cluster_params):
ps_cfg = model_registry.GetProgramSchedule(model_name)
train_cfg = model_registry.GetParams(model_name, 'Train')
train_cfg.cluster = cluster_params
# Remove misleading train params
train_cfg = UnsetUnusedTrainParams(train_cfg)
if issubclass(train_cfg.cls, base_model.MultiTaskModel):
multi_task_train_cfg = train_cfg
if multi_task_train_cfg.train.ema_decay > 0:
# Check that all subtasks use the same ema settings.
for task_name, task_params in (
multi_task_train_cfg.task_params.IterParams()):
for field in ['ema_decay', 'ema_decay_moving_vars']:
if (task_params.train.Get(field) !=
multi_task_train_cfg.train.Get(field)):
raise ValueError(
'Params did not match for field %s in task %s'
% (field, task_name))
for k, _ in multi_task_train_cfg.task_params.IterParams():
if multi_task_train_cfg.share_model_object:
# Create MultiTaskSubModel params from a MultiTaskModelParams.
train_task_params = base_model.MultiTaskSubModel.Params()
train_task_params.task_name = k
train_task_params.input = multi_task_train_cfg.input.Get(
k).Copy()
else:
train_task_params = base_model.SingleTaskModel.Params()
train_task_params.task = multi_task_train_cfg.task_params.Get(
k)
train_task_params.input = multi_task_train_cfg.input.Get(k)
train_task_params.name = k + '_executor_train_task'
train_task_params.cluster = multi_task_train_cfg.cluster
train_task_params.train = multi_task_train_cfg.task_params.Get(
k).train
if k not in ps_cfg.program_schedule_dict:
tf.logging.fatal(
'Could not find %s in ps_cfg.program_schedule_dict: %s',
k, ps_cfg)
program_schedule_params = ps_cfg.program_schedule_dict[k]
program_schedule_params.task_dict = {
'Train': train_task_params
}
for eval_dataset_name in program_schedule_params.dataset_names:
multi_task_eval_cfg = model_registry.GetParams(
model_name, eval_dataset_name)
if multi_task_train_cfg.share_model_object:
eval_task_params = base_model.MultiTaskSubModel.Params(
)
eval_task_params.task_name = k
eval_task_params.input = multi_task_eval_cfg.input.Get(
k).Copy()
else:
eval_task_params = base_model.SingleTaskModel.Params()
eval_task_params.task = multi_task_eval_cfg.task_params.Get(
k)
eval_task_params.input = multi_task_eval_cfg.input.Get(
k)
eval_task_params.name = (k + '_' + eval_dataset_name +
'_executor_eval_task')
eval_task_params.cluster = multi_task_eval_cfg.cluster
eval_task_params = UnsetUnusedTrainParams(eval_task_params)
program_schedule_params.task_dict[
eval_dataset_name] = eval_task_params
ps_params_dict[k] = program_schedule_params
else:
program_schedule_params = ps_cfg
program_schedule_params.task_dict = {'Train': train_cfg}
for eval_dataset_name in program_schedule_params.dataset_names:
task_eval_params = model_registry.GetParams(
model_name, eval_dataset_name)
task_eval_params = UnsetUnusedTrainParams(task_eval_params)
program_schedule_params.task_dict[
eval_dataset_name] = task_eval_params
ps_params_dict[''] = program_schedule_params
return ps_params_dict, train_cfg
def __init__(self,
sess,
batch_size=1,
lr_step1=100,
lr_step2=0.1,
num_iter_step1=1000,
num_iter_step2=4000,
th=None,
psd_max_ori=None):
self.sess = sess
self.num_iter_step1 = num_iter_step1
self.num_iter_step2 = num_iter_step2
self.batch_size = batch_size
self.lr_step1 = lr_step1
#self.lr_step2 = lr_step2
tf.set_random_seed(1234)
params = model_registry.GetParams('asr.librispeech.Librispeech960Wpm',
'Test')
params.random_seed = 1234
params.is_eval = True
params.cluster.worker.gpus_per_replica = 1
cluster = cluster_factory.Cluster(params.cluster)
with cluster, tf.device(cluster.GetPlacer()):
model = params.cls(params)
self.delta_large = tf.Variable(np.zeros((batch_size, 223200),
dtype=np.float32),
name='qq_delta')
# placeholders
self.input_tf = tf.placeholder(tf.float32,
shape=[batch_size, None],
name='qq_input')
self.tgt_tf = tf.placeholder(tf.string)
self.sample_rate_tf = tf.placeholder(tf.int32,
name='qq_sample_rate')
self.th = tf.placeholder(tf.float32,
shape=[batch_size, None, None],
name='qq_th')
self.psd_max_ori = tf.placeholder(tf.float32,
shape=[batch_size],
name='qq_psd')
self.mask = tf.placeholder(dtype=np.float32,
shape=[batch_size, None],
name='qq_mask')
self.mask_freq = tf.placeholder(dtype=np.float32,
shape=[batch_size, None, 80])
#noise = tf.random_normal(self.new_input.shape, stddev=2)
self.noise = tf.placeholder(np.float32,
shape=[batch_size, None],
name="qq_noise")
self.maxlen = tf.placeholder(np.int32)
self.lr_step2 = tf.placeholder(np.float32)
# variable
self.rescale = tf.Variable(np.ones((batch_size, 1),
dtype=np.float32),
name='qq_rescale')
self.alpha = tf.Variable(np.ones(
(batch_size), dtype=np.float32) * 0.05,
name='qq_alpha')
# extract the delta
self.delta = tf.slice(tf.identity(self.delta_large), [0, 0],
[batch_size, self.maxlen])
self.apply_delta = tf.clip_by_value(self.delta, -2000,
2000) * self.rescale
self.new_input = self.apply_delta * self.mask + self.input_tf
#pass_in = tf.clip_by_value(self.new_input, -2**15, 2**15-1)
self.pass_in = tf.clip_by_value(self.new_input + self.noise,
-2**15, 2**15 - 1)
# generate the inputs that are needed for the lingvo model
self.features = create_features(self.pass_in, self.sample_rate_tf,
self.mask_freq)
self.inputs = create_inputs(model, self.features, self.tgt_tf,
self.batch_size, self.mask_freq)
task = model.GetTask()
metrics = task.FPropDefaultTheta(self.inputs)
# self.celoss with the shape (batch_size)
self.celoss = tf.get_collection("per_loss")[0]
self.decoded = task.Decode(self.inputs)
# compute the loss for masking threshold
self.loss_th_list = []
self.transform = Transform()
for i in range(self.batch_size):
logits_delta = self.transform((self.apply_delta[i, :]),
(self.psd_max_ori)[i])
loss_th = tf.reduce_mean(tf.nn.relu(logits_delta - (self.th)[i]))
loss_th = tf.expand_dims(loss_th, dim=0)
self.loss_th_list.append(loss_th)
self.loss_th = tf.concat(self.loss_th_list, axis=0)
self.optimizer1 = tf.train.AdamOptimizer(self.lr_step1)
self.optimizer2 = tf.train.AdamOptimizer(self.lr_step2)
grad1, var1 = self.optimizer1.compute_gradients(
self.celoss, [self.delta_large])[0]
grad21, var21 = self.optimizer2.compute_gradients(
self.celoss, [self.delta_large])[0]
grad22, var22 = self.optimizer2.compute_gradients(
self.alpha * self.loss_th, [self.delta_large])[0]
self.train1 = self.optimizer1.apply_gradients([(tf.sign(grad1), var1)])
self.train21 = self.optimizer2.apply_gradients([(grad21, var21)])
self.train22 = self.optimizer2.apply_gradients([(grad22, var22)])
self.train2 = tf.group(self.train21, self.train22)
def main(argv):
data = np.loadtxt(FLAGS.input, dtype=str, delimiter=",")
# calculate the number of loops to run the test
num = len(data[0])
batch_size = FLAGS.batch_size
num_loops = num / batch_size
assert num % batch_size == 0
with tf.device("/gpu:0"):
tf.set_random_seed(1234)
tfconf = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=tfconf) as sess:
params = model_registry.GetParams(
'asr.librispeech.Librispeech960Wpm', 'Test')
params.cluster.worker.gpus_per_replica = 1
cluster = cluster_factory.Cluster(params.cluster)
with cluster, tf.device(cluster.GetPlacer()):
params.vn.global_vn = False
params.random_seed = 1234
params.is_eval = True
model = params.cls(params)
task = model.GetTask()
saver = tf.train.Saver()
saver.restore(sess, FLAGS.checkpoint)
# define the placeholders
input_tf = tf.placeholder(tf.float32, shape=[batch_size, None])
tgt_tf = tf.placeholder(tf.string)
sample_rate_tf = tf.placeholder(tf.int32)
mask_tf = tf.placeholder(tf.float32,
shape=[batch_size, None, 80])
# generate the features and inputs
features = create_features(input_tf, sample_rate_tf, mask_tf)
shape = tf.shape(features)
inputs = create_inputs(model, features, tgt_tf, batch_size,
mask_tf)
# loss
metrics = task.FPropDefaultTheta(inputs)
loss = tf.get_collection("per_loss")[0]
# prediction
decoded_outputs = task.Decode(inputs)
dec_metrics_dict = task.CreateDecoderMetrics()
correct = 0
for l in range(num_loops):
data_sub = data[:, l * batch_size:(l + 1) * batch_size]
audios_np, sample_rate, tgt_np, mask_freq = Read_input(
data_sub, batch_size)
feed_dict = {
input_tf: audios_np,
sample_rate_tf: sample_rate,
tgt_tf: tgt_np,
mask_tf: mask_freq
}
losses = sess.run(loss, feed_dict)
predictions = sess.run(decoded_outputs, feed_dict)
task.PostProcessDecodeOut(predictions, dec_metrics_dict)
wer_value = dec_metrics_dict['wer'].value * 100.
for i in range(batch_size):
print("pred:{}".format(predictions['topk_decoded'][i,
0]))
print("targ:{}".format(tgt_np[i].lower()))
print("true: {}".format(data_sub[1, i].lower()))
if predictions['topk_decoded'][i,
0] == tgt_np[i].lower():
correct += 1
print("------------------------------")
print("example {} succeeds".format(i))
print("Now, the WER is: {0:.2f}%".format(wer_value))
print("num of examples succeed: {}".format(correct))
print("success rate: {}%".format(correct / float(num) * 100))
def __init__(
self,
sess,
batch_size=1,
lr_stage1=100,
lr_stage2=0.1,
num_iter_stage1=1000,
num_iter_stage2=4000,
th=None,
psd_max_ori=None,
):
self.sess = sess
self.num_iter_stage1 = num_iter_stage1
self.num_iter_stage2 = num_iter_stage2
self.batch_size = batch_size
self.lr_stage1 = lr_stage1
self.lr_stage2 = lr_stage2
tf.set_random_seed(1234)
params = model_registry.GetParams("asr.librispeech.Librispeech960Wpm",
"Test")
params.random_seed = 1234
params.is_eval = True
params.cluster.worker.gpus_per_replica = 1
cluster = cluster_factory.Cluster(params.cluster)
with cluster, tf.device(cluster.GetPlacer()):
model = params.cls(params)
self.delta_large = tf.Variable(
np.zeros((batch_size, FLAGS.max_length_dataset),
dtype=np.float32),
name="qq_delta",
)
# placeholders
self.input_tf = tf.placeholder(tf.float32,
shape=[batch_size, None],
name="qq_input")
self.tgt_tf = tf.placeholder(tf.string)
self.rir = tf.placeholder(tf.float32)
self.sample_rate_tf = tf.placeholder(tf.int32,
name="qq_sample_rate")
self.mask = tf.placeholder(dtype=np.float32,
shape=[batch_size, None],
name="qq_mask")
self.mask_freq = tf.placeholder(dtype=np.float32,
shape=[batch_size, None, 80])
self.noise = tf.placeholder(np.float32,
shape=[batch_size, None],
name="qq_noise")
self.maxlen = tf.placeholder(np.int32)
self.lr = tf.placeholder(np.float32)
self.lengths = tf.placeholder(
np.int32,
shape=[
batch_size,
],
)
# variable
self.rescale = tf.Variable(
np.ones(
(batch_size, 1), dtype=np.float32) * FLAGS.initial_bound,
name="qq_rescale",
)
# extract the delta
self.delta = tf.slice(tf.identity(self.delta_large), [0, 0],
[batch_size, self.maxlen])
self.apply_delta = tf.clip_by_value(self.delta, -self.rescale,
self.rescale)
self.before_rir = tf.clip_by_value(
self.apply_delta * self.mask + self.input_tf, -(2**15),
2**15 - 1)
self.new_input = (create_speech_rir(
self.before_rir,
self.rir,
self.lengths,
self.maxlen,
self.batch_size,
) * self.mask)
self.pass_in = tf.clip_by_value(self.new_input + self.noise,
-(2**15), 2**15 - 1)
# generate the inputs that are needed for the lingvo model
self.features = create_features(self.pass_in, self.sample_rate_tf,
self.mask_freq)
self.inputs = create_inputs(model, self.features, self.tgt_tf,
self.batch_size, self.mask_freq)
task = model.GetTask()
metrics = task.FPropDefaultTheta(self.inputs)
# self.celoss with the shape (batch_size)
self.celoss = tf.get_collection("per_loss")[0]
self.decoded = task.Decode(self.inputs)
self.optimizer1 = tf.train.AdamOptimizer(self.lr)
grad1, var1 = self.optimizer1.compute_gradients(
self.celoss, [self.delta_large])[0]
self.train1 = self.optimizer1.apply_gradients([(tf.sign(grad1), var1)])
def Task(self):
metadata = waymo_metadata.WaymoMetadata()
num_classes = len(metadata.ClassNames())
p = starnet.ModelV2.Params(
num_classes,
num_anchor_bboxes_offsets=self.NUM_ANCHOR_BBOX_OFFSETS,
num_anchor_bboxes_rotations=self.NUM_ANCHOR_BBOX_ROTATIONS,
num_anchor_bboxes_dimensions=self.NUM_ANCHOR_BBOX_DIMENSIONS,
num_laser_features=3)
# Update the Point Cloud Featurizer architecture
starnet_builder = starnet.Builder()
starnet_builder.linear_params_init = (
py_utils.WeightInit.KaimingUniformFanInRelu())
gin_layers = [[
self.GIN_HIDDEN_DIMS * 2, self.GIN_HIDDEN_DIMS * 4,
self.GIN_HIDDEN_DIMS
]] * self.NUM_GIN_LAYERS # pyformat: disable
p.cell_featurizer = starnet_builder.GINFeaturizerV2(
'feat',
num_laser_features=3,
fc_dims=self.GIN_HIDDEN_DIMS,
mlp_dims=gin_layers,
fc_use_bn=False)
p.cell_feature_dims = self.GIN_HIDDEN_DIMS * (self.NUM_GIN_LAYERS + 1)
p.output_decoder = waymo_decoder.WaymoOpenDatasetDecoder.Params()
p.max_nms_boxes = 512
p.use_oriented_per_class_nms = True
# Note: Sub-classes need to set nms_iou_threshold and nms_score_threshold
# appropriately.
p.nms_iou_threshold = [0.0] * num_classes
# TODO(jngiam): 1.1 for untrained classes is needed to avoid an issue
# with boxutils error.
p.nms_score_threshold = [1.1] * num_classes
p.name = 'starnet'
tp = p.train
tp.optimizer = optimizer.Adam.Params()
tp.clip_gradient_norm_to_value = 5
ep = p.eval
# Train set uses a smaller decoding set, so we can
# safely eval over the entire input.
ep.samples_per_summary = 0
# To be tuned.
p.train.l2_regularizer_weight = 1e-8
cluster = cluster_factory.Current()
train_cluster_p = cluster.params.Copy()
train_cluster_p.job = 'trainer_client'
train_cluster_p.mode = 'sync'
# When running a decoding only job, there are no trainer workers, so we set
# worker replicas to 1 as a dummy value.
if train_cluster_p.worker.replicas <= 0:
train_cluster_p.worker.replicas = 1
# Set learning rate and schedule.
with cluster_factory.Cluster(train_cluster_p):
train_input_p = self.Train()
# Adapted from V1 tuning.
tp.ema_decay = 0.99
# TODO(b/148537111): consider setting this to True.
tp.ema_decay_moving_vars = False
tp.learning_rate = 0.001
lr_util.SetExponentialLR(train_p=tp,
train_input_p=train_input_p,
exp_start_epoch=5,
total_epoch=75)
p.dimension_loss_weight = .3
p.location_loss_weight = 3.
p.loss_weight_classification = 1.
p.loss_weight_localization = 3.
p.rotation_loss_weight = 0.3
return p
def GetExecutorParams(model_name, cluster_params, model_registry):
"""Get the params needed to instantiate the Executor.
Args:
model_name: A model name registered in the ModelRegistry.
cluster_params: A cluster hyperparams object.
model_registry: A ModelRegistry object.
Returns:
A tuple (dict, Params):
- ps_params_dict: High-level task name -> ProgramScheduleParams
- train_cfg: A SingleTaskModelParams or MultiTaskModelParams.
Raises:
ValueError if the model params is invalid.
"""
ps_params_dict = {}
with cluster_factory.Cluster(cluster_params):
ps_cfg = model_registry.GetProgramSchedule(model_name)
train_cfg = model_registry.GetParams(model_name, 'Train')
train_cfg.cluster = cluster_params
# Remove misleading train params
train_cfg = UnsetUnusedTrainParams(train_cfg)
if issubclass(train_cfg.cls, base_model.MultiTaskModel):
multi_task_train_cfg = train_cfg
for k, _ in multi_task_train_cfg.task_params.IterParams():
if multi_task_train_cfg.share_model_object:
# Create MultiTaskSubModel params from a MultiTaskModelParams.
train_task_params = base_model.MultiTaskSubModel.Params()
train_task_params.task_name = k
train_task_params.input = multi_task_train_cfg.input.Get(
k).Copy()
else:
task = multi_task_train_cfg.task_params.Get(k)
train_task_params = base_model.SingleTaskModel.Params(task)
train_task_params.input = multi_task_train_cfg.input.Get(k)
train_task_params.name = k + '_executor_train_task'
train_task_params.cluster = multi_task_train_cfg.cluster
train_task_params.train = multi_task_train_cfg.task_params.Get(
k).train
if k not in ps_cfg.program_schedule_dict:
tf.logging.fatal(
'Could not find %s in ps_cfg.program_schedule_dict: %s',
k, ps_cfg)
# Add Copy in case a user is sharing the same ProgramSchedule params
# instance across different tasks.
program_schedule_params = ps_cfg.program_schedule_dict[k].Copy(
)
program_schedule_params.task_dict = {
'Train': train_task_params
}
for eval_dataset_name in program_schedule_params.dataset_names:
multi_task_eval_cfg = model_registry.GetParams(
model_name, eval_dataset_name)
multi_task_eval_cfg.cluster = cluster_params
if multi_task_train_cfg.share_model_object:
eval_task_params = base_model.MultiTaskSubModel.Params(
)
eval_task_params.task_name = k
eval_task_params.input = multi_task_eval_cfg.input.Get(
k).Copy()
else:
task = multi_task_eval_cfg.task_params.Get(k)
eval_task_params = base_model.SingleTaskModel.Params(
task)
eval_task_params.input = multi_task_eval_cfg.input.Get(
k)
eval_task_params.name = (k + '_' + eval_dataset_name +
'_executor_eval_task')
eval_task_params.cluster = multi_task_eval_cfg.cluster
eval_task_params = UnsetUnusedTrainParams(eval_task_params)
program_schedule_params.task_dict[
eval_dataset_name] = eval_task_params
ps_params_dict[k] = program_schedule_params
else:
program_schedule_params = ps_cfg
program_schedule_params.task_dict = {'Train': train_cfg}
for eval_dataset_name in program_schedule_params.dataset_names:
task_eval_params = model_registry.GetParams(
model_name, eval_dataset_name)
task_eval_params.cluster = cluster_params
task_eval_params = UnsetUnusedTrainParams(task_eval_params)
program_schedule_params.task_dict[
eval_dataset_name] = task_eval_params
ps_params_dict[''] = program_schedule_params
return ps_params_dict, train_cfg
