def main():
parser = argparse.ArgumentParser()
parser.add_argument("--src",
help=f"Path of source file to read from.",
required=True)
parser.add_argument("--hyp",
help="Path of file to write hypothesis to.",
required=True)
parser.add_argument("--mod",
help="Path of model file to read.",
required=True)
args = parser.parse_args()
exp_dir = os.path.dirname(__file__)
exp = "{EXP}"
param_collection.ParamManager.init_param_col()
# TODO: can we avoid the LoadSerialized proxy and load stuff directly?
load_experiment = persistence.LoadSerialized(filename=args.mod)
uninitialized_experiment = persistence.YamlPreloader.preload_obj(
load_experiment, exp_dir=exp_dir, exp_name=exp)
loaded_experiment = persistence.initialize_if_needed(
uninitialized_experiment)
model = loaded_experiment.model
inference = model.inference
param_collection.ParamManager.populate()
decoding_task = eval_task.DecodingEvalTask(args.src, args.hyp, model,
inference)
decoding_task.eval()
def setUp(self):
events.clear()
ParamManager.init_param_col()
# Load a pre-trained model
load_experiment = LoadSerialized(filename=f"test/data/tiny_jaen.model",
overwrite=[
{
"path": "train",
"val": None
},
{
"path": "status",
"val": None
},
])
EXP_DIR = '.'
EXP = "decode"
uninitialized_experiment = YamlPreloader.preload_obj(load_experiment,
exp_dir=EXP_DIR,
exp_name=EXP)
loaded_experiment = initialize_if_needed(uninitialized_experiment)
ParamManager.populate()
# Pull out the parts we need from the experiment
self.model = loaded_experiment.model
src_vocab = self.model.src_reader.vocab
trg_vocab = self.model.trg_reader.vocab
event_trigger.set_train(False)
self.src_data = list(
self.model.src_reader.read_sents("test/data/head.ja"))
self.trg_data = list(
self.model.trg_reader.read_sents("test/data/head.en"))
def test_shallow(self):
test_obj = yaml.load("""
a: !DummyArgClass
arg1: !DummyArgClass2
_xnmt_id: id1
v: some_val
arg2: !Ref { name: id1 }
""")
preloaded = persistence.YamlPreloader.preload_obj(root=test_obj,
exp_name="exp1",
exp_dir=self.out_dir)
initalized = persistence.initialize_if_needed(preloaded)
persistence.save_to_file(self.model_file, initalized)
def initialize_generator(self, **kwargs):
# TODO: refactor?
if kwargs.get("len_norm_type", None) is None:
len_norm = xnmt.length_normalization.NoNormalization()
else:
len_norm = initialize_if_needed(kwargs["len_norm_type"])
search_args = {}
if kwargs.get("max_len", None) is not None:
search_args["max_len"] = kwargs["max_len"]
if kwargs.get("beam", None) is None:
self.search_strategy = GreedySearch(**search_args)
else:
search_args["beam_size"] = kwargs.get("beam", 1)
search_args["len_norm"] = len_norm
self.search_strategy = BeamSearch(**search_args)
self.report_path = kwargs.get("report_path", None)
self.report_type = kwargs.get("report_type", None)
def test_simple_reference(self):
test_obj = yaml.load("""
!DummyArgClass
arg1: !DummyArgClass
arg1: !DummyArgClass2 { v: some_val }
arg2: !DummyArgClass2 { v: some_other_val }
arg2: !Ref { path: arg1 }
""")
preloaded = persistence.YamlPreloader.preload_obj(root=test_obj,
exp_name="exp1",
exp_dir=self.out_dir)
initialized = persistence.initialize_if_needed(preloaded)
dump = persistence._dump(initialized)
reloaded = yaml.load(dump)
if isinstance(reloaded.arg1, persistence.Ref):
reloaded.arg1, reloaded.arg2 = reloaded.arg2, reloaded.arg1
self.assertIsInstance(reloaded.arg1, DummyArgClass)
self.assertIsInstance(reloaded.arg2, persistence.Ref)
self.assertIsInstance(reloaded.arg1.arg1, DummyArgClass2)
self.assertIsInstance(reloaded.arg1.arg2, DummyArgClass2)
def main(overwrite_args=None):
with tee.Tee(), tee.Tee(error=True):
argparser = argparse.ArgumentParser()
argparser.add_argument("--dynet-mem", type=str)
argparser.add_argument("--dynet-seed",
type=int,
help="set random seed for DyNet and XNMT.")
argparser.add_argument("--dynet-autobatch", type=int)
argparser.add_argument("--dynet-devices", type=str)
argparser.add_argument("--dynet-viz",
action='store_true',
help="use visualization")
argparser.add_argument("--dynet-gpu",
action='store_true',
help="use GPU acceleration")
argparser.add_argument("--dynet-gpu-ids", type=int)
argparser.add_argument("--dynet-gpus", type=int)
argparser.add_argument("--dynet-weight-decay", type=float)
argparser.add_argument("--dynet-profiling", type=int)
argparser.add_argument("--settings",
type=str,
default="standard",
help="settings (standard, debug, or unittest)"
"must be given in '=' syntax, e.g."
" --settings=standard")
argparser.add_argument("experiments_file")
argparser.add_argument("experiment_name",
nargs='*',
help="Run only the specified experiments")
argparser.set_defaults(generate_doc=False)
args = argparser.parse_args(overwrite_args)
if args.dynet_seed:
random.seed(args.dynet_seed)
np.random.seed(args.dynet_seed)
if args.dynet_gpu:
if settings.CHECK_VALIDITY:
settings.CHECK_VALIDITY = False
log_preamble(
"disabling CHECK_VALIDITY because it is not supported on GPU currently",
logging.WARNING)
config_experiment_names = YamlPreloader.experiment_names_from_file(
args.experiments_file)
results = []
# Check ahead of time that all experiments exist, to avoid bad surprises
experiment_names = args.experiment_name or config_experiment_names
if args.experiment_name:
nonexistent = set(experiment_names).difference(
config_experiment_names)
if len(nonexistent) != 0:
raise Exception("Experiments {} do not exist".format(",".join(
list(nonexistent))))
log_preamble(
f"running XNMT revision {tee.get_git_revision()} on {socket.gethostname()} on {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
)
for experiment_name in experiment_names:
ParamManager.init_param_col()
uninitialized_exp_args = YamlPreloader.preload_experiment_from_file(
args.experiments_file, experiment_name)
logger.info(f"=> Running {experiment_name}")
glob_args = uninitialized_exp_args.data.exp_global
log_file = glob_args.log_file
if os.path.isfile(log_file) and not settings.OVERWRITE_LOG:
logger.warning(
f"log file {log_file} already exists, skipping experiment; please delete log file by hand if you want to overwrite it "
f"(or activate OVERWRITE_LOG, by either specifying an environment variable as OVERWRITE_LOG=1, "
f"or specifying --settings=debug, or changing xnmt.settings.Standard.OVERWRITE_LOG manually)"
)
continue
tee.set_out_file(log_file)
model_file = glob_args.model_file
uninitialized_exp_args.data.exp_global.commandline_args = args
# Create the model
experiment = initialize_if_needed(uninitialized_exp_args)
ParamManager.param_col.model_file = experiment.exp_global.model_file
ParamManager.param_col.save_num_checkpoints = experiment.exp_global.save_num_checkpoints
ParamManager.populate()
# Run the experiment
eval_scores = experiment(save_fct=lambda: save_to_file(
model_file, experiment, ParamManager.param_col))
results.append((experiment_name, eval_scores))
print_results(results)
tee.unset_out_file()
def main(overwrite_args: Optional[Sequence[str]] = None) -> None:
with tee.Tee(), tee.Tee(error=True):
argparser = argparse.ArgumentParser()
utils.add_backend_argparse(argparser)
argparser.add_argument("--settings",
type=str,
default="standard",
help="settings (standard, debug, or unittest)"
"must be given in '=' syntax, e.g."
" --settings=standard")
argparser.add_argument(
"--resume",
action='store_true',
help="whether a saved experiment is being resumed, and"
"locations of output files should be re-used.")
argparser.add_argument("--backend",
type=str,
default="dynet",
help="backend (dynet or torch)")
argparser.add_argument("experiments_file")
argparser.add_argument("experiment_name",
nargs='*',
help="Run only the specified experiments")
argparser.set_defaults(generate_doc=False)
args = argparser.parse_args(overwrite_args)
if xnmt.backend_dynet and args.dynet_seed: args.seed = args.dynet_seed
if getattr(args, "seed", None):
random.seed(args.seed)
np.random.seed(args.seed)
if xnmt.backend_torch: torch.manual_seed(0)
if xnmt.backend_dynet and args.dynet_gpu and settings.CHECK_VALIDITY:
settings.CHECK_VALIDITY = False
log_preamble(
"disabling CHECK_VALIDITY because it is not supported in the DyNet/GPU setting",
logging.WARNING)
config_experiment_names = YamlPreloader.experiment_names_from_file(
args.experiments_file)
results = []
# Check ahead of time that all experiments exist, to avoid bad surprises
experiment_names = args.experiment_name or config_experiment_names
if args.experiment_name:
nonexistent = set(experiment_names).difference(
config_experiment_names)
if len(nonexistent) != 0:
raise Exception("Experiments {} do not exist".format(",".join(
list(nonexistent))))
log_preamble(
f"running XNMT revision {tee.get_git_revision()} on {socket.gethostname()} with {'DyNet' if xnmt.backend_dynet else 'PyTorch'} on {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
)
for experiment_name in experiment_names:
ParamManager.init_param_col()
uninitialized_exp_args = YamlPreloader.preload_experiment_from_file(
args.experiments_file, experiment_name, resume=args.resume)
logger.info(f"=> Running {experiment_name}")
glob_args = uninitialized_exp_args.data.exp_global
log_file = glob_args.log_file
if not settings.OVERWRITE_LOG:
log_files_exist = []
if os.path.isfile(log_file): log_files_exist.append(log_file)
if os.path.isdir(log_file + ".tb"):
log_files_exist.append(log_file + ".tb/")
if log_files_exist:
logger.warning(
f"log file(s) {' '.join(log_files_exist)} already exists, skipping experiment; "
f"please delete log file by hand if you want to overwrite it "
f"(or activate OVERWRITE_LOG, by either specifying an environment variable OVERWRITE_LOG=1, "
f"or specifying --settings=debug, or changing xnmt.settings.Standard.OVERWRITE_LOG manually)"
)
continue
elif settings.OVERWRITE_LOG and os.path.isdir(log_file + ".tb"):
shutil.rmtree(
log_file + ".tb/"
) # remove tensorboard logs from previous run that is being overwritten
tee.set_out_file(log_file, exp_name=experiment_name)
try:
model_file = glob_args.model_file
uninitialized_exp_args.data.exp_global.commandline_args = vars(
args)
# Create the model
experiment = initialize_if_needed(uninitialized_exp_args)
ParamManager.param_col.model_file = experiment.exp_global.model_file
ParamManager.param_col.save_num_checkpoints = experiment.exp_global.save_num_checkpoints
ParamManager.populate()
# Run the experiment
eval_scores = experiment(
save_fct=lambda: save_to_file(model_file, experiment))
results.append((experiment_name, eval_scores))
print_results(results)
except Exception as e:
file_logger.error(traceback.format_exc())
raise e
finally:
tee.unset_out_file()
import xnmt.tee
from xnmt.param_collection import ParamManager
from xnmt.persistence import initialize_if_needed, YamlPreloader, LoadSerialized, save_to_file
EXP_DIR = os.path.dirname(__file__)
EXP = "programmatic-load"
model_file = f"{EXP_DIR}/models/{EXP}.mod"
log_file = f"{EXP_DIR}/logs/{EXP}.log"
xnmt.tee.set_out_file(log_file)
ParamManager.init_param_col()
load_experiment = LoadSerialized(
filename=f"{EXP_DIR}/models/programmatic.mod",
overwrite=[
{"path" : "train", "val" : None}
]
)
uninitialized_experiment = YamlPreloader.preload_obj(load_experiment, exp_dir=EXP_DIR, exp_name=EXP)
loaded_experiment = initialize_if_needed(uninitialized_experiment)
# if we were to continue training, we would need to set a save model file like this:
# ParamManager.param_col.model_file = model_file
ParamManager.populate()
# run experiment
loaded_experiment(save_fct=lambda: save_to_file(model_file, loaded_experiment))
