def print_process(self):
if not self.parent.interactive and not log.v[5]:
return
start_elapsed = time.time() - self.parent.start_time
complete = self.parent.batches.completed_frac()
assert complete > 0
total_time_estimated = start_elapsed / complete
remaining_estimated = total_time_estimated - start_elapsed
if log.verbose[5]:
mem_usage = self.device_mem_usage_str(self.alloc_devices)
info = [
self.parent.report_prefix,
"batch %i" % self.run_start_batch_idx
]
if self.eval_info: # Such as score.
info += [
"%s %s" % item
for item in sorted(self.eval_info.items())
]
info += [
"elapsed %s" % hms(start_elapsed),
"exp. remaining %s" % hms(remaining_estimated),
"complete %.02f%%" % (complete * 100)
]
if mem_usage:
info += ["memory %s" % mem_usage]
print(", ".join(filter(None, info)), file=log.v5)
if self.parent.interactive:
progress_bar(complete, hms(remaining_estimated))
def cb(frame_len, orth):
if frame_len >= options.max_seq_frame_len:
return
orth_syms = parse_orthography(orth)
if len(orth_syms) >= options.max_seq_orth_len:
return
Stats.count += 1
Stats.total_frame_len += frame_len
if options.dump_orth_syms:
print("Orth:", "".join(orth_syms), file=log.v3)
if options.filter_orth_sym:
if options.filter_orth_sym in orth_syms:
print("Found orth:", "".join(orth_syms), file=log.v3)
if options.filter_orth_syms_seq:
filter_seq = parse_orthography_into_symbols(
options.filter_orth_syms_seq)
if found_sub_seq(filter_seq, orth_syms):
print("Found orth:", "".join(orth_syms), file=log.v3)
Stats.orth_syms_set.update(orth_syms)
Stats.total_orth_len += len(orth_syms)
# Show some progress if it takes long.
if time.time() - Stats.process_last_time > 2:
Stats.process_last_time = time.time()
if options.collect_time:
print("Collect process, total frame len so far:",
hms(Stats.total_frame_len *
(options.frame_time / 1000.0)),
file=log.v3)
else:
print("Collect process, total orth len so far:",
human_size(Stats.total_orth_len),
file=log.v3)
def finish(self):
"""
End fold.
"""
elapsed_time = time.time() - self.start_time
print("%s: Elapsed time: %s" % (self.name, hms(elapsed_time)))
if travis_env:
print("travis_fold:end:%s" % folds[-1])
if github_env:
if len(folds) == 1:
print("::endgroup::")
sys.stdout.flush()
def get_raw_strings(dataset, options):
"""
:param Dataset dataset:
:param options: argparse.Namespace
:return: list of (seq tag, string)
:rtype: list[(str,str)]
"""
refs = []
start_time = time.time()
seq_len_stats = Stats()
seq_idx = options.startseq
if options.endseq < 0:
options.endseq = float("inf")
interactive = util.is_tty() and not log.verbose[5]
print("Iterating over %r." % dataset, file=log.v2)
while dataset.is_less_than_num_seqs(seq_idx) and seq_idx <= options.endseq:
dataset.load_seqs(seq_idx, seq_idx + 1)
complete_frac = dataset.get_complete_frac(seq_idx)
start_elapsed = time.time() - start_time
try:
num_seqs_s = str(dataset.num_seqs)
except NotImplementedError:
try:
num_seqs_s = "~%i" % dataset.estimated_num_seqs
except TypeError: # a number is required, not NoneType
num_seqs_s = "?"
progress_prefix = "%i/%s" % (
seq_idx,
num_seqs_s,
)
progress = "%s (%.02f%%)" % (progress_prefix, complete_frac * 100)
if complete_frac > 0:
total_time_estimated = start_elapsed / complete_frac
remaining_estimated = total_time_estimated - start_elapsed
progress += " (%s)" % hms(remaining_estimated)
seq_tag = dataset.get_tag(seq_idx)
assert isinstance(seq_tag, str)
ref = dataset.get_data(seq_idx, options.key)
if isinstance(ref, numpy.ndarray):
assert ref.shape == () or (ref.ndim == 1
and ref.dtype == numpy.uint8)
if ref.shape == ():
ref = ref.flatten()[0] # get the entry itself (str or bytes)
else:
ref = ref.tobytes()
if isinstance(ref, bytes):
ref = ref.decode("utf8")
assert isinstance(ref, str)
seq_len_stats.collect([len(ref)])
refs.append((seq_tag, ref))
if interactive:
util.progress_bar_with_time(complete_frac, prefix=progress_prefix)
elif log.verbose[5]:
print(progress_prefix,
"seq tag %r, ref len %i chars" % (seq_tag, len(ref)))
seq_idx += 1
print("Done. Num seqs %i. Total time %s." %
(seq_idx, hms(time.time() - start_time)),
file=log.v1)
print("More seqs which we did not dumped: %s." %
(dataset.is_less_than_num_seqs(seq_idx), ),
file=log.v1)
seq_len_stats.dump(stream_prefix="Seq-length %r " % (options.key, ),
stream=log.v2)
return refs
def calc_wer_on_dataset(dataset, refs, options, hyps):
"""
:param Dataset|None dataset:
:param dict[str,str]|None refs: seq tag -> ref string (words delimited by space)
:param options: argparse.Namespace
:param dict[str,str] hyps: seq tag -> hyp string (words delimited by space)
:return: WER
:rtype: float
"""
assert dataset or refs
start_time = time.time()
seq_len_stats = {"refs": Stats(), "hyps": Stats()}
seq_idx = options.startseq
if options.endseq < 0:
options.endseq = float("inf")
wer = 1.0
remaining_hyp_seq_tags = set(hyps.keys())
interactive = util.is_tty() and not log.verbose[5]
collected = {"hyps": [], "refs": []}
max_num_collected = 1
if dataset:
dataset.init_seq_order(epoch=1)
else:
refs = sorted(refs.items(), key=lambda item: len(item[1]))
while True:
if seq_idx > options.endseq:
break
if dataset:
if not dataset.is_less_than_num_seqs(seq_idx):
break
dataset.load_seqs(seq_idx, seq_idx + 1)
complete_frac = dataset.get_complete_frac(seq_idx)
seq_tag = dataset.get_tag(seq_idx)
assert isinstance(seq_tag, str)
ref = dataset.get_data(seq_idx, options.key)
if isinstance(ref, numpy.ndarray):
assert ref.shape == ()
ref = ref.flatten()[0] # get the entry itself (str or bytes)
if isinstance(ref, bytes):
ref = ref.decode("utf8")
assert isinstance(ref, str)
try:
num_seqs_s = str(dataset.num_seqs)
except NotImplementedError:
try:
num_seqs_s = "~%i" % dataset.estimated_num_seqs
except TypeError: # a number is required, not NoneType
num_seqs_s = "?"
else:
if seq_idx >= len(refs):
break
complete_frac = (seq_idx + 1) / float(len(refs))
seq_tag, ref = refs[seq_idx]
assert isinstance(seq_tag, str)
assert isinstance(ref, str)
num_seqs_s = str(len(refs))
start_elapsed = time.time() - start_time
progress_prefix = "%i/%s (WER %.02f%%)" % (seq_idx, num_seqs_s,
wer * 100)
progress = "%s (%.02f%%)" % (progress_prefix, complete_frac * 100)
if complete_frac > 0:
total_time_estimated = start_elapsed / complete_frac
remaining_estimated = total_time_estimated - start_elapsed
progress += " (%s)" % hms(remaining_estimated)
remaining_hyp_seq_tags.remove(seq_tag)
hyp = hyps[seq_tag]
seq_len_stats["hyps"].collect([len(hyp)])
seq_len_stats["refs"].collect([len(ref)])
collected["hyps"].append(hyp)
collected["refs"].append(ref)
if len(collected["hyps"]) >= max_num_collected:
wer = wer_compute.step(session, **collected)
del collected["hyps"][:]
del collected["refs"][:]
if interactive:
util.progress_bar_with_time(complete_frac, prefix=progress_prefix)
elif log.verbose[5]:
print(
progress_prefix, "seq tag %r, ref/hyp len %i/%i chars" %
(seq_tag, len(ref), len(hyp)))
seq_idx += 1
if len(collected["hyps"]) > 0:
wer = wer_compute.step(session, **collected)
print("Done. Num seqs %i. Total time %s." %
(seq_idx, hms(time.time() - start_time)),
file=log.v1)
print("Remaining num hyp seqs %i." % (len(remaining_hyp_seq_tags), ),
file=log.v1)
if dataset:
print("More seqs which we did not dumped: %s." %
dataset.is_less_than_num_seqs(seq_idx),
file=log.v1)
for key in ["hyps", "refs"]:
seq_len_stats[key].dump(stream_prefix="Seq-length %r %r " %
(key, options.key),
stream=log.v2)
if options.expect_full:
assert not remaining_hyp_seq_tags, "There are still remaining hypotheses."
return wer
def dump_dataset(dataset, options):
"""
:type dataset: Dataset.Dataset
:param options: argparse.Namespace
"""
print("Epoch: %i" % options.epoch, file=log.v3)
dataset.init_seq_order(epoch=options.epoch)
print("Dataset keys:", dataset.get_data_keys(), file=log.v3)
print("Dataset target keys:", dataset.get_target_list(), file=log.v3)
assert options.key in dataset.get_data_keys()
if options.get_num_seqs:
print("Get num seqs.")
print("estimated_num_seqs: %r" % dataset.estimated_num_seqs)
try:
print("num_seqs: %r" % dataset.num_seqs)
except Exception as exc:
print("num_seqs exception %r, which is valid, so we count." % exc)
seq_idx = 0
if dataset.get_target_list():
default_target = dataset.get_target_list()[0]
else:
default_target = None
while dataset.is_less_than_num_seqs(seq_idx):
dataset.load_seqs(seq_idx, seq_idx + 1)
if seq_idx % 10000 == 0:
if default_target:
targets = dataset.get_targets(default_target, seq_idx)
postfix = " (targets = %r...)" % (targets[:10], )
else:
postfix = ""
print("%i ...%s" % (seq_idx, postfix))
seq_idx += 1
print("accumulated num seqs: %i" % seq_idx)
print("Done.")
return
dump_file = None
if options.type == "numpy":
print("Dump files: %r*%r" %
(options.dump_prefix, options.dump_postfix),
file=log.v3)
elif options.type == "stdout":
print("Dump to stdout", file=log.v3)
if options.stdout_limit is not None:
util.set_pretty_print_default_limit(options.stdout_limit)
numpy.set_printoptions(
threshold=sys.maxsize if options.stdout_limit ==
float("inf") else int(options.stdout_limit))
if options.stdout_as_bytes:
util.set_pretty_print_as_bytes(options.stdout_as_bytes)
elif options.type == "print_tag":
print("Dump seq tag to stdout", file=log.v3)
elif options.type == "dump_tag":
dump_file = open("%sseq-tags.txt" % options.dump_prefix, "w")
print("Dump seq tag to file: %s" % (dump_file.name, ), file=log.v3)
elif options.type == "dump_seq_len":
dump_file = open("%sseq-lens.txt" % options.dump_prefix, "w")
print("Dump seq lens to file: %s" % (dump_file.name, ), file=log.v3)
dump_file.write("{\n")
elif options.type == "print_shape":
print("Dump shape to stdout", file=log.v3)
elif options.type == "plot":
print("Plot.", file=log.v3)
elif options.type == "interactive":
print("Interactive debug shell.", file=log.v3)
elif options.type == "null":
if options.dump_stats:
print("No dump (except stats).")
else:
print("No dump.")
else:
raise Exception("unknown dump option type %r" % options.type)
start_time = time.time()
stats = Stats() if (options.stats or options.dump_stats) else None
seq_len_stats = {key: Stats() for key in dataset.get_data_keys()}
seq_idx = options.startseq
if options.endseq < 0:
options.endseq = float("inf")
while dataset.is_less_than_num_seqs(seq_idx) and seq_idx <= options.endseq:
dataset.load_seqs(seq_idx, seq_idx + 1)
complete_frac = dataset.get_complete_frac(seq_idx)
start_elapsed = time.time() - start_time
try:
num_seqs_s = str(dataset.num_seqs)
except NotImplementedError:
try:
num_seqs_s = "~%i" % dataset.estimated_num_seqs
except TypeError: # a number is required, not NoneType
num_seqs_s = "?"
progress_prefix = "%i/%s" % (seq_idx, num_seqs_s)
progress = "%s (%.02f%%)" % (progress_prefix, complete_frac * 100)
data = None
if complete_frac > 0:
total_time_estimated = start_elapsed / complete_frac
remaining_estimated = total_time_estimated - start_elapsed
progress += " (%s)" % hms(remaining_estimated)
if options.type == "print_tag":
print(
"seq %s tag:" %
(progress if log.verbose[2] else progress_prefix),
dataset.get_tag(seq_idx))
elif options.type == "dump_tag":
print(
"seq %s tag:" %
(progress if log.verbose[2] else progress_prefix),
dataset.get_tag(seq_idx))
dump_file.write("%s\n" % dataset.get_tag(seq_idx))
elif options.type == "dump_seq_len":
seq_len = dataset.get_seq_length(seq_idx)[options.key]
print(
"seq %s tag:" %
(progress if log.verbose[2] else progress_prefix),
dataset.get_tag(seq_idx), "%r len:" % options.key, seq_len)
dump_file.write("%r: %r,\n" % (dataset.get_tag(seq_idx), seq_len))
else:
data = dataset.get_data(seq_idx, options.key)
if options.type == "numpy":
numpy.savetxt(
"%s%i.data%s" %
(options.dump_prefix, seq_idx, options.dump_postfix), data)
elif options.type == "stdout":
print("seq %s tag:" % progress, dataset.get_tag(seq_idx))
print("seq %s data:" % progress, pretty_print(data))
elif options.type == "print_shape":
print("seq %s data shape:" % progress, data.shape)
elif options.type == "plot":
plot(data)
for target in dataset.get_target_list():
targets = dataset.get_targets(target, seq_idx)
if options.type == "numpy":
numpy.savetxt("%s%i.targets.%s%s" %
(options.dump_prefix, seq_idx, target,
options.dump_postfix),
targets,
fmt='%i')
elif options.type == "stdout":
extra = ""
if target in dataset.labels and len(
dataset.labels[target]) > 1:
assert dataset.can_serialize_data(target)
extra += " (%r)" % dataset.serialize_data(key=target,
data=targets)
print("seq %i target %r: %s%s" %
(seq_idx, target, pretty_print(targets), extra))
elif options.type == "print_shape":
print("seq %i target %r shape:" % (seq_idx, target),
targets.shape)
if options.type == "interactive":
from returnn.util.debug import debug_shell
debug_shell(locals())
seq_len = dataset.get_seq_length(seq_idx)
for key in dataset.get_data_keys():
seq_len_stats[key].collect([seq_len[key]])
if stats:
stats.collect(data)
if options.type == "null":
util.progress_bar_with_time(complete_frac, prefix=progress_prefix)
seq_idx += 1
print("Done. Total time %s. More seqs which we did not dumped: %s" %
(hms_fraction(time.time() - start_time),
dataset.is_less_than_num_seqs(seq_idx)),
file=log.v2)
for key in dataset.get_data_keys():
seq_len_stats[key].dump(stream_prefix="Seq-length %r " % key,
stream=log.v2)
if stats:
stats.dump(output_file_prefix=options.dump_stats,
stream_prefix="Data %r " % options.key,
stream=log.v1)
if options.type == "dump_seq_len":
dump_file.write("}\n")
if dump_file:
print("Dumped to file:", dump_file.name, file=log.v2)
dump_file.close()
def work(self):
"""
Start the optimization.
"""
print("Starting hyper param search. Using %i threads." %
self.num_threads,
file=log.v1)
from returnn.tf.util.basic import get_available_gpu_devices
from returnn.log import wrap_log_streams, StreamDummy
from threading import Thread, Condition
from returnn.util.basic import progress_bar, hms, is_tty
class Outstanding:
"""
Queue of outstanding work.
"""
cond = Condition()
threads = [] # type: typing.List[WorkerThread]
population = []
exit = False
exception = None
class WorkerThread(Thread):
"""
Worker threader.
"""
def __init__(self, gpu_ids):
"""
:param set[int] gpu_ids:
"""
super(WorkerThread,
self).__init__(name="Hyper param tune train thread")
self.gpu_ids = gpu_ids
self.trainer = None # type: typing.Optional[_IndividualTrainer]
self.finished = False
self.start()
def cancel(self, join=False):
"""
:param bool join:
"""
with Outstanding.cond:
if self.trainer:
self.trainer.cancel_flag = True
if self.trainer.runner:
self.trainer.runner.cancel_flag = True
if join:
self.join()
def get_complete_frac(self):
"""
:rtype: float
"""
with Outstanding.cond:
if self.trainer and self.trainer.runner:
return self.trainer.runner.data_provider.get_complete_frac(
)
return 0.0
# noinspection PyMethodParameters
def run(self_thread):
"""
Run thread.
"""
try:
while True:
with Outstanding.cond:
if Outstanding.exit or Outstanding.exception:
return
if not Outstanding.population:
self_thread.finished = True
Outstanding.cond.notify_all()
return
# noinspection PyShadowingNames
individual = Outstanding.population.pop(0)
self_thread.trainer = _IndividualTrainer(
optim=self,
individual=individual,
gpu_ids=self_thread.gpu_ids)
self_thread.name = "Hyper param tune train thread on %r" % individual.name
self_thread.trainer.run()
except Exception as exc:
with Outstanding.cond:
if not Outstanding.exception:
Outstanding.exception = exc or True
Outstanding.cond.notify_all()
for thread in Outstanding.threads:
if thread is not self_thread:
thread.cancel()
if not isinstance(exc, CancelTrainingException):
with Outstanding.cond: # So that we don't mix up multiple on sys.stderr.
# This would normally dump it on sys.stderr so it's fine.
sys.excepthook(*sys.exc_info())
best_individuals = []
population = []
num_gpus = len(get_available_gpu_devices())
print("Num available GPUs:", num_gpus)
num_gpus = num_gpus or 1 # Would be ignored anyway.
interactive = is_tty()
try:
print(
"Population of %i individuals (hyper param setting instances), running for %i evaluation iterations."
% (self.num_individuals, self.num_iterations),
file=log.v2)
for cur_iteration_idx in range(1, self.num_iterations + 1):
print("Starting iteration %i." % cur_iteration_idx,
file=log.v2)
if cur_iteration_idx == 1:
population.append(
Individual(
{
p: p.get_default_value()
for p in self.hyper_params
},
name="default"))
population.append(
Individual(
{
p: p.get_initial_value()
for p in self.hyper_params
},
name="canonical"))
population.extend(
self.get_population(iteration_idx=cur_iteration_idx,
num_individuals=self.num_individuals -
len(population)))
if cur_iteration_idx > 1:
self.cross_over(population=population,
iteration_idx=cur_iteration_idx)
if cur_iteration_idx == 1 and self.dry_run_first_individual:
# Train first directly for testing and to see log output.
# Later we will strip away all log output.
print("Very first try with log output:", file=log.v2)
_IndividualTrainer(optim=self,
individual=population[0],
gpu_ids={0}).run()
print("Starting training with thread pool of %i threads." %
self.num_threads)
iteration_start_time = time.time()
with wrap_log_streams(StreamDummy(),
also_sys_stdout=True,
tf_log_verbosity="WARN"):
Outstanding.exit = False
Outstanding.population = list(population)
Outstanding.threads = [
WorkerThread(gpu_ids={i % num_gpus})
for i in range(self.num_threads)
]
try:
while True:
with Outstanding.cond:
if all([
thread.finished
for thread in Outstanding.threads
]) or Outstanding.exception:
break
complete_frac = max(
len(population) -
len(Outstanding.population) -
len(Outstanding.threads), 0)
complete_frac += sum([
thread.get_complete_frac()
for thread in Outstanding.threads
])
complete_frac /= float(len(population))
remaining_str = ""
if complete_frac > 0:
start_elapsed = time.time(
) - iteration_start_time
total_time_estimated = start_elapsed / complete_frac
remaining_estimated = total_time_estimated - start_elapsed
remaining_str = hms(remaining_estimated)
if interactive:
progress_bar(complete_frac,
prefix=remaining_str,
file=sys.__stdout__)
else:
print("Progress: %.02f%%" %
(complete_frac * 100),
"remaining:",
remaining_str or "unknown",
file=sys.__stdout__)
sys.__stdout__.flush()
Outstanding.cond.wait(1 if interactive else 10)
for thread in Outstanding.threads:
thread.join()
finally:
Outstanding.exit = True
for thread in Outstanding.threads:
thread.cancel(join=True)
Outstanding.threads = []
print("Training iteration elapsed time:",
hms(time.time() - iteration_start_time))
if Outstanding.exception:
raise Outstanding.exception
assert not Outstanding.population
print("Training iteration finished.")
population.sort(key=lambda p: p.cost)
del population[-self.num_kill_individuals:]
best_individuals.extend(population)
best_individuals.sort(key=lambda p: p.cost)
del best_individuals[self.num_best:]
population = best_individuals[:self.num_kill_individuals //
4] + population
print(
"Current best setting, individual %s" %
best_individuals[0].name, "cost:",
best_individuals[0].cost)
for p in self.hyper_params:
print(" %s -> %s" %
(p.description(),
best_individuals[0].hyper_param_mapping[p]))
except KeyboardInterrupt:
print("KeyboardInterrupt, canceled search.")
print("Best %i settings:" % len(best_individuals))
for individual in best_individuals:
print("Individual %s" % individual.name, "cost:", individual.cost)
for p in self.hyper_params:
print(" %s -> %s" %
(p.description(), individual.hyper_param_mapping[p]))
def train_epoch(self):
print("start", self.get_epoch_str(), "with learning rate", self.learning_rate, "...", file=log.v4)
if self.epoch == 1 and self.save_epoch1_initial_model:
epoch0_model_filename = self.epoch_model_filename(self.model_filename, 0, self.is_pretrain_epoch())
print("save initial epoch1 model", epoch0_model_filename, file=log.v4)
self.save_model(epoch0_model_filename, 0)
if self.is_pretrain_epoch():
self.print_network_info()
training_devices = self.devices
if 'train' not in self.dataset_batches or not self.train_data.batch_set_generator_cache_whole_epoch():
self.dataset_batches['train'] = self.train_data.generate_batches(recurrent_net=self.network.recurrent,
batch_size=self.batch_size,
pruning=self.batch_pruning,
max_seqs=self.max_seqs,
max_seq_length=int(self.max_seq_length),
seq_drop=self.seq_drop,
shuffle_batches=self.shuffle_batches,
used_data_keys=self.network.get_used_data_keys())
else:
self.dataset_batches['train'].reset()
train_batches = self.dataset_batches['train']
start_batch = self.start_batch if self.epoch == self.start_epoch else 0
trainer = TrainTaskThread(self.network, training_devices, data=self.train_data, batches=train_batches,
learning_rate=self.learning_rate, updater=self.updater,
eval_batch_size=self.update_batch_size,
start_batch=start_batch, share_batches=self.share_batches,
reduction_rate=self.reduction_rate,
exclude=self.exclude,
seq_train_parallel=self.seq_train_parallel,
report_prefix=("pre" if self.is_pretrain_epoch() else "") + "train epoch %s" % self.epoch,
epoch=self.epoch)
trainer.join()
if not trainer.finalized:
if trainer.device_crash_batch is not None: # Otherwise we got an unexpected exception - a bug in our code.
if self.model_filename:
self.save_model(self.get_epoch_model_filename() + ".crash_%i" % trainer.device_crash_batch, self.epoch - 1)
sys.exit(1)
assert not any(numpy.isinf(list(trainer.score.values()))) or any(numpy.isnan(list(trainer.score.values()))), (
"Model is broken, got inf or nan final score: %s" % trainer.score)
if self.model_filename and (self.epoch % self.save_model_epoch_interval == 0):
self.save_model(self.get_epoch_model_filename(), self.epoch)
self.learning_rate_control.set_epoch_error(self.epoch, {"train_score": trainer.score})
self.learning_rate_control.save()
if self.ctc_prior_file is not None:
trainer.save_ctc_priors(self.ctc_prior_file, self.get_epoch_str())
print(self.get_epoch_str(), "score:", self.format_score(trainer.score), "elapsed:", hms(trainer.elapsed), end=' ', file=log.v1)
self.eval_model()
def analyze_dataset(options):
"""
:param options: argparse.Namespace
"""
print("Epoch: %i" % options.epoch, file=log.v3)
print("Dataset keys:", dataset.get_data_keys(), file=log.v3)
print("Dataset target keys:", dataset.get_target_list(), file=log.v3)
assert options.key in dataset.get_data_keys()
terminal_width, _ = util.terminal_size()
show_interactive_process_bar = (log.verbose[3] and (not log.verbose[5])
and terminal_width >= 0)
start_time = time.time()
num_seqs_stats = Stats()
if options.endseq < 0:
options.endseq = float("inf")
recurrent = True
used_data_keys = dataset.get_data_keys()
batch_size = config.typed_value('batch_size', 1)
max_seqs = config.int('max_seqs', -1)
seq_drop = config.float('seq_drop', 0.0)
max_seq_length = config.typed_value(
'max_seq_length', None) or config.float('max_seq_length', 0)
max_pad_size = config.typed_value("max_pad_size", None)
batches = dataset.generate_batches(recurrent_net=recurrent,
batch_size=batch_size,
max_seqs=max_seqs,
max_seq_length=max_seq_length,
max_pad_size=max_pad_size,
seq_drop=seq_drop,
used_data_keys=used_data_keys)
step = 0
total_num_seqs = 0
total_num_frames = NumbersDict()
total_num_used_frames = NumbersDict()
try:
while batches.has_more():
# See FeedDictDataProvider.
batch, = batches.peek_next_n(1)
assert isinstance(batch, Batch)
if batch.start_seq > options.endseq:
break
dataset.load_seqs(batch.start_seq, batch.end_seq)
complete_frac = batches.completed_frac()
start_elapsed = time.time() - start_time
try:
num_seqs_s = str(dataset.num_seqs)
except NotImplementedError:
try:
num_seqs_s = "~%i" % dataset.estimated_num_seqs
except TypeError: # a number is required, not NoneType
num_seqs_s = "?"
progress_prefix = "%i/%s" % (batch.start_seq, num_seqs_s)
progress = "%s (%.02f%%)" % (progress_prefix, complete_frac * 100)
if complete_frac > 0:
total_time_estimated = start_elapsed / complete_frac
remaining_estimated = total_time_estimated - start_elapsed
progress += " (%s)" % hms(remaining_estimated)
batch_max_time = NumbersDict.max(
[seq.frame_length for seq in batch.seqs]) * len(batch.seqs)
batch_num_used_frames = sum(
[seq.frame_length for seq in batch.seqs], NumbersDict())
total_num_seqs += len(batch.seqs)
num_seqs_stats.collect(numpy.array([len(batch.seqs)]))
total_num_frames += batch_max_time
total_num_used_frames += batch_num_used_frames
print("%s, batch %i, num seqs %i, frames %s, used %s (%s)" %
(progress, step, len(
batch.seqs), batch_max_time, batch_num_used_frames,
batch_num_used_frames / batch_max_time),
file=log.v5)
if show_interactive_process_bar:
util.progress_bar_with_time(complete_frac,
prefix=progress_prefix)
step += 1
batches.advance(1)
finally:
print("Done. Total time %s. More seqs which we did not dumped: %s" %
(hms(time.time() - start_time), batches.has_more()),
file=log.v2)
print("Dataset epoch %i, order %r." %
(dataset.epoch, dataset.seq_ordering))
print("Num batches (steps): %i" % step, file=log.v1)
print("Num seqs: %i" % total_num_seqs, file=log.v1)
num_seqs_stats.dump(stream=log.v1, stream_prefix="Batch num seqs ")
for key in used_data_keys:
print("Data key %r:" % key, file=log.v1)
print(" Num frames: %s" % total_num_frames[key], file=log.v1)
print(" Num used frames: %s" % total_num_used_frames[key],
file=log.v1)
print(" Fraction used frames: %s" %
(total_num_used_frames / total_num_frames)[key],
file=log.v1)
dataset.finish_epoch()
def collect_stats(options, iter_corpus):
"""
:param options: argparse.Namespace
"""
orth_symbols_filename = options.output
if orth_symbols_filename:
assert not os.path.exists(orth_symbols_filename)
class Stats:
count = 0
process_last_time = time.time()
total_frame_len = 0
total_orth_len = 0
orth_syms_set = set()
if options.add_numbers:
Stats.orth_syms_set.update(
map(chr, list(range(ord("0"),
ord("9") + 1))))
if options.add_lower_alphabet:
Stats.orth_syms_set.update(
map(chr, list(range(ord("a"),
ord("z") + 1))))
if options.add_upper_alphabet:
Stats.orth_syms_set.update(
map(chr, list(range(ord("A"),
ord("Z") + 1))))
def cb(frame_len, orth):
if frame_len >= options.max_seq_frame_len:
return
orth_syms = parse_orthography(orth)
if len(orth_syms) >= options.max_seq_orth_len:
return
Stats.count += 1
Stats.total_frame_len += frame_len
if options.dump_orth_syms:
print("Orth:", "".join(orth_syms), file=log.v3)
if options.filter_orth_sym:
if options.filter_orth_sym in orth_syms:
print("Found orth:", "".join(orth_syms), file=log.v3)
if options.filter_orth_syms_seq:
filter_seq = parse_orthography_into_symbols(
options.filter_orth_syms_seq)
if found_sub_seq(filter_seq, orth_syms):
print("Found orth:", "".join(orth_syms), file=log.v3)
Stats.orth_syms_set.update(orth_syms)
Stats.total_orth_len += len(orth_syms)
# Show some progress if it takes long.
if time.time() - Stats.process_last_time > 2:
Stats.process_last_time = time.time()
if options.collect_time:
print("Collect process, total frame len so far:",
hms(Stats.total_frame_len *
(options.frame_time / 1000.0)),
file=log.v3)
else:
print("Collect process, total orth len so far:",
human_size(Stats.total_orth_len),
file=log.v3)
iter_corpus(cb)
if options.remove_symbols:
filter_syms = parse_orthography_into_symbols(options.remove_symbols)
Stats.orth_syms_set -= set(filter_syms)
if options.collect_time:
print("Total frame len:",
Stats.total_frame_len,
"time:",
hms(Stats.total_frame_len * (options.frame_time / 1000.0)),
file=log.v3)
else:
print("No time stats (--collect_time False).", file=log.v3)
print("Total orth len:",
Stats.total_orth_len,
"(%s)" % human_size(Stats.total_orth_len),
end=' ',
file=log.v3)
if options.collect_time:
print("fraction:",
float(Stats.total_orth_len) / Stats.total_frame_len,
file=log.v3)
else:
print("", file=log.v3)
print("Average orth len:",
float(Stats.total_orth_len) / Stats.count,
file=log.v3)
print("Num symbols:", len(Stats.orth_syms_set), file=log.v3)
if orth_symbols_filename:
orth_syms_file = open(orth_symbols_filename, "wb")
for orth_sym in sorted(Stats.orth_syms_set):
orth_syms_file.write(b"%s\n" % unicode(orth_sym).encode("utf8"))
orth_syms_file.close()
print("Wrote orthography symbols to",
orth_symbols_filename,
file=log.v3)
else:
print("Provide --output to save the symbols.", file=log.v3)
def test_DeviceBatchRun_outputs_format():
# TODO: This is broken...
return
dev_run = DummyDeviceBatchRun(task="train")
assert len(dev_run.alloc_devices) == 1
# Simulate epoch start.
trainer = dev_run.parent
dev_run.alloc_devices[0].start_epoch_stats()
trainer.initialize()
# Simulate one batch.
dev_run.allocate()
dev_run.device_run()
dev_run.set_dummy_dev_output(outputs_format=["cost:foo"], output=[1.42])
dev_run.finish()
assert_is_instance(dev_run.result, dict)
assert_in("results", dev_run.result)
res_outputss = dev_run.result["results"]
assert_is_instance(res_outputss, list)
assert_equal(len(res_outputss), len(dev_run.alloc_devices))
res_outputs = res_outputss[0]
assert_is_instance(res_outputs, list)
res_outputs_format = dev_run.result["result_format"]
assert_is_instance(res_outputs_format, list)
res = Device.make_result_dict(res_outputs, res_outputs_format)
assert_is_instance(res, dict)
pprint(res)
# Simulate epoch end.
print("train epoch score:", trainer.score, "elapsed:",
hms(trainer.elapsed))
trainer.finalize()
dev_run.alloc_devices[0].finish_epoch_stats()
# Now simulate the eval.
dev_run = DummyDeviceBatchRun(task="eval")
assert len(dev_run.alloc_devices) == 1
# Simulate epoch start.
tester = dev_run.parent
dev_run.alloc_devices[0].start_epoch_stats()
tester.initialize()
# Simulate one batch.
dev_run.allocate()
dev_run.device_run()
dev_run.set_dummy_dev_output(outputs_format=["cost:foo", "error:foo"],
output=[1.42, 2.34])
dev_run.finish()
# Simulate epoch end.
print("eval epoch elapsed:", hms(tester.elapsed))
tester.finalize()
dev_run.alloc_devices[0].finish_epoch_stats()
print("eval results:", tester.score, tester.error)
assert_is_instance(dev_run.result, dict)
assert_in("results", dev_run.result)
res_outputss = dev_run.result["results"]
assert_is_instance(res_outputss, list)
assert_equal(len(res_outputss), len(dev_run.alloc_devices))
res_outputs = res_outputss[0]
assert_is_instance(res_outputs, list)
res_outputs_format = dev_run.result["result_format"]
assert_is_instance(res_outputs_format, list)
res = Device.make_result_dict(res_outputs, res_outputs_format)
assert_is_instance(res, dict)
pprint(res)
assert_greater(tester.score, 0)
assert_greater(tester.error, 0)
