def test_enc_dec1_init():
config = Config()
config.load_file(StringIO(config_enc_dec1_json))
network_json = LayerNetwork.json_from_config(config)
assert_true(network_json)
network = LayerNetwork.from_json_and_config(network_json, config)
assert_true(network)
def create_first_epoch(config_filename):
config = Config()
config.load_file(config_filename)
engine = Engine([])
engine.init_train_from_config(config=config, train_data=None)
engine.epoch = 1
engine.save_model(engine.get_epoch_model_filename(), epoch=engine.epoch)
Engine._epoch_model = None
def is_crnn_config(filename):
if filename.endswith(".gz"):
return False
try:
config = Config()
config.load_file(filename)
return True
except Exception:
pass
return False
def cleanup_tmp_models(config_filename):
assert os.path.exists(config_filename)
from returnn.config import Config
config = Config()
config.load_file(config_filename)
model_filename = config.value('model', '')
assert model_filename
# Remove existing models
assert model_filename.startswith("/tmp/")
for f in glob(model_filename + ".*"):
os.remove(f)
def is_returnn_config(filename):
"""
:param str filename:
:rtype: bool
"""
if filename.endswith(".gz"):
return False
# noinspection PyBroadException
try:
config = Config()
config.load_file(filename)
return True
except Exception:
pass
return False
def main():
global configFile, archiverExec, inputDim, outputDim
parser = argparse.ArgumentParser()
parser.add_argument('--sprintLoadParams',
required=True,
help='Sprint NN params path prefix')
parser.add_argument('--sprintFirstLayer',
default=1,
type=int,
help='Sprint NN params first layer (default 1)')
parser.add_argument('--crnnSaveEpoch',
type=int,
required=True,
help='save this train epoch number in RETURNN model')
parser.add_argument('--crnnConfigFile',
required=True,
help='RETURNN (CRNN) config file')
parser.add_argument('--sprintArchiverExec',
default=archiverExec,
help='path to Sprint/RASR archiver executable')
parser.add_argument('--floatType',
default="f32",
help='float type (f32/f64)')
args = parser.parse_args()
configFile = args.crnnConfigFile
assert os.path.exists(configFile), "RETURNN config file not found"
archiverExec = args.sprintArchiverExec
assert os.path.exists(archiverExec), "Sprint archiver not found"
assert args.crnnSaveEpoch >= 1
from returnn.config import Config
global config
config = Config()
config.load_file(configFile)
inputDim = config.int('num_inputs', None)
outputDim = config.int('num_outputs', None)
assert inputDim and outputDim
layers = loadSprintNetwork(params_prefix_path=args.sprintLoadParams,
first_layer=args.sprintFirstLayer,
float_type=args.floatType)
saveCrnnNetwork(epoch=args.crnnSaveEpoch, layers=layers)
print("Done.")
def test_enc_dec1_hdf():
filename = tempfile.mktemp(prefix="returnn-model-test")
model = h5py.File(filename, "w")
config = Config()
config.load_file(StringIO(config_enc_dec1_json))
network_json = LayerNetwork.json_from_config(config)
assert_true(network_json)
network = LayerNetwork.from_json_and_config(network_json, config)
assert_true(network)
network.save_hdf(model, epoch=42)
model.close()
loaded_model = h5py.File(filename, "r")
loaded_net = LayerNetwork.from_hdf_model_topology(loaded_model)
assert_true(loaded_net)
assert_equal(sorted(network.hidden.keys()),
sorted(loaded_net.hidden.keys()))
assert_equal(sorted(network.y.keys()), sorted(loaded_net.y.keys()))
assert_equal(sorted(network.j.keys()), sorted(loaded_net.j.keys()))
os.remove(filename)
def init_config(config_filename=None,
command_line_options=(),
default_config=None,
extra_updates=None):
"""
:param str|None config_filename:
:param list[str]|tuple[str] command_line_options: e.g. ``sys.argv[1:]``
:param dict[str]|None default_config:
:param dict[str]|None extra_updates:
Initializes the global config.
There are multiple sources which are used to init the config:
* ``configFilename``, and maybe first item of ``commandLineOptions`` interpret as config filename
* other options via ``commandLineOptions``
* ``extra_updates``
Note about the order/priority of these:
* ``extra_updates``
* options from ``commandLineOptions``
* ``configFilename``
* config filename from ``commandLineOptions[0]``
* ``extra_updates``
* options from ``commandLineOptions``
``extra_updates`` and ``commandLineOptions`` are used twice so that they are available
when the config is loaded, which thus has access to them, and can e.g. use them via Python code.
However, the purpose is that they overwrite any option from the config;
that is why we apply them again in the end.
``commandLineOptions`` is applied after ``extra_updates`` so that the user has still the possibility
to overwrite anything set by ``extra_updates``.
"""
global config
config = Config()
config_filenames_by_cmd_line = []
if command_line_options:
# Assume that the first argument prefixed with "+" or "-" and all following is not a config file.
i = 0
for arg in command_line_options:
if arg[:1] in "-+":
break
config_filenames_by_cmd_line.append(arg)
i += 1
command_line_options = command_line_options[i:]
if default_config:
config.update(default_config)
if extra_updates:
config.update(extra_updates)
if command_line_options:
config.parse_cmd_args(command_line_options)
if config_filename:
config.load_file(config_filename)
for fn in config_filenames_by_cmd_line:
config.load_file(fn)
if extra_updates:
config.update(extra_updates)
if command_line_options:
config.parse_cmd_args(command_line_options)
# I really don't know where to put this otherwise:
if config.bool("EnableAutoNumpySharedMemPickling", False):
import returnn.util.task_system
returnn.util.task_system.SharedMemNumpyConfig["enabled"] = True
# Server default options
if config.value('task', 'train') == 'server':
config.set('num_inputs', 2)
config.set('num_outputs', 1)
def demo():
"""
Demo.
"""
print("SprintDataset demo.")
from argparse import ArgumentParser
from returnn.util.basic import progress_bar_with_time
from returnn.log import log
from returnn.config import Config
from returnn.datasets.basic import init_dataset
arg_parser = ArgumentParser()
arg_parser.add_argument("--config", help="config with ExternSprintDataset", required=True)
arg_parser.add_argument("--sprint_cache_dataset", help="kwargs dict for SprintCacheDataset", required=True)
arg_parser.add_argument("--max_num_seqs", default=sys.maxsize, type=int)
arg_parser.add_argument("--action", default="compare", help="compare or benchmark")
args = arg_parser.parse_args()
log.initialize(verbosity=[4])
sprint_cache_dataset_kwargs = eval(args.sprint_cache_dataset)
assert isinstance(sprint_cache_dataset_kwargs, dict)
sprint_cache_dataset = SprintCacheDataset(**sprint_cache_dataset_kwargs)
print("SprintCacheDataset: %r" % sprint_cache_dataset)
config = Config()
config.load_file(args.config)
dataset = init_dataset(config.typed_value("train"))
print("Dataset via config: %r" % dataset)
assert sprint_cache_dataset.num_inputs == dataset.num_inputs
assert tuple(sprint_cache_dataset.num_outputs["classes"]) == tuple(dataset.num_outputs["classes"])
sprint_cache_dataset.init_seq_order(epoch=1)
if args.action == "compare":
print("Iterating through dataset...")
seq_idx = 0
dataset.init_seq_order(epoch=1)
while seq_idx < args.max_num_seqs:
if not dataset.is_less_than_num_seqs(seq_idx):
break
dataset.load_seqs(seq_idx, seq_idx + 1)
tag = dataset.get_tag(seq_idx)
assert not tag.startswith("seq-"), "dataset does not provide tag-names for seqs"
dataset_seq = sprint_cache_dataset.get_dataset_seq_for_name(tag)
data = dataset.get_data(seq_idx, "data")
targets = dataset.get_data(seq_idx, "classes")
assert data.shape == dataset_seq.features["data"].shape
assert targets.shape == dataset_seq.features["classes"].shape
assert numpy.allclose(data, dataset_seq.features["data"])
assert numpy.allclose(targets, dataset_seq.features["classes"])
seq_idx += 1
progress_bar_with_time(dataset.get_complete_frac(seq_idx))
print("Finished through dataset. Num seqs: %i" % seq_idx)
print("SprintCacheDataset has num seqs: %i." % sprint_cache_dataset.num_seqs)
elif args.action == "benchmark":
print("Iterating through dataset...")
start_time = time.time()
seq_tags = []
seq_idx = 0
dataset.init_seq_order(epoch=1)
while seq_idx < args.max_num_seqs:
if not dataset.is_less_than_num_seqs(seq_idx):
break
dataset.load_seqs(seq_idx, seq_idx + 1)
tag = dataset.get_tag(seq_idx)
assert not tag.startswith("seq-"), "dataset does not provide tag-names for seqs"
seq_tags.append(tag)
dataset.get_data(seq_idx, "data")
dataset.get_data(seq_idx, "classes")
seq_idx += 1
progress_bar_with_time(dataset.get_complete_frac(seq_idx))
print("Finished through dataset. Num seqs: %i, time: %f" % (seq_idx, time.time() - start_time))
print("SprintCacheDataset has num seqs: %i." % sprint_cache_dataset.num_seqs)
if hasattr(dataset, "exit_handler"):
dataset.exit_handler()
else:
print("No way to stop any background tasks.")
del dataset
start_time = time.time()
print("Iterating through SprintCacheDataset...")
for i, tag in enumerate(seq_tags):
sprint_cache_dataset.get_dataset_seq_for_name(tag)
progress_bar_with_time(float(i) / len(seq_tags))
print("Finished through SprintCacheDataset. time: %f" % (time.time() - start_time,))
else:
raise Exception("invalid action: %r" % args.action)
def main():
"""
Main entry point.
"""
arg_parser = argparse.ArgumentParser()
arg_parser.add_argument("--config", help="RETURNN config")
arg_parser.add_argument(
"--learning-rate-file",
help="The learning rate file contains scores / errors per epoch.")
arg_parser.add_argument("--key", help="key to use, e.g. 'dev_error'")
arg_parser.add_argument("--n",
type=int,
default=5,
help="print best N epochs")
args = arg_parser.parse_args()
if bool(args.config) == bool(args.learning_rate_file):
print("Error: provide either --config or --learning-rate-file")
arg_parser.print_help()
sys.exit(1)
if args.config:
config = Config()
config.load_file(args.config)
lr = LearningRateControl.load_initial_from_config(config)
elif args.learning_rate_file:
lr = LearningRateControl(
default_learning_rate=1, # default lr not relevant
filename=args.learning_rate_file)
else:
assert False, "should not get here with %r" % args
epochs = sorted(lr.epoch_data.keys())
if not epochs:
print("Error: no epochs found")
sys.exit(1)
print("Loaded epochs", epochs[0], "..", epochs[-1])
if args.key:
key = args.key
print("Using key %s" % key)
else:
last_epoch_with_error_info = None
for ep in reversed(epochs):
if lr.epoch_data[ep].error:
last_epoch_with_error_info = ep
break
if last_epoch_with_error_info is None:
print("Error: no scores/errors found")
sys.exit(1)
key = lr.get_error_key(last_epoch_with_error_info)
print("Using key %s (auto via epoch %i)" %
(key, last_epoch_with_error_info))
epochs_ = []
missing_epochs = []
for ep in epochs:
errors = lr.epoch_data[ep].error
if key in errors:
epochs_.append((errors[key], ep))
else:
missing_epochs.append(ep)
if len(epochs_) == len(epochs):
print("All epochs have the key.")
else:
print("Epochs missing the key:", missing_epochs)
assert epochs_
epochs_.sort()
for value, ep in epochs_[:args.n]:
errors = lr.epoch_data[ep].error
print(", ".join(
["Epoch %i" % ep, "%r %r" % (key, value)] +
["%r %r" % (k, v) for k, v in errors.items() if k != key]))
