def __init__(self, args, **kwargs):
""" Initializes a util class for sequence generation. """
super(SequenceGenerator, self).__init__(**kwargs)
self._output_file = args["output_file"]
self._save_metric = args["save_metric"]
self._metric = self.task.get_eval_metric(args, ds=self.custom_dataset)
self._search_layer = build_search_layer(args)
def __init__(self, args, **kwargs):
""" Initializes a util class for sequence generation. """
super(MaskSequenceGenerator, self).__init__(**kwargs)
self._output_file = args["output_file"]
self._save_metric = args["save_metric"]
self._metric = self.task.get_eval_metric(args, ds=self.custom_dataset)
self._search_layer = build_search_layer(args)
self._apply_mask = args["apply_mask"]
if args["mask_dir"]:
self.mask_dir = args["mask_dir"][0]
# self.load_mask = np.load(self.mask_dir, allow_pickle=True)
with open(self.mask_dir, 'rb') as f:
self.load_mask = pickle.load(f)
def build(self, strategy, task, model):
super(SeqGenerationValidator, self).build(strategy, task, model)
if self._custom_dataset is None:
logging.info("WARNING: no validation dataset is provided "
"in SeqGenerationValidator for validation process.")
self._validate_gen = False
return self
self._gen_metric = task.get_eval_metric(self.args,
name="eval_metric",
ds=self._custom_dataset)
if self._gen_metric is None:
logging.info("WARNING: no metric is provided "
"in SeqGenerationValidator for validation process.")
self._validate_gen = False
return self
self._gen_metric.flag = self.args["eval_metric.class"]
search_layer = build_search_layer(
self.args["eval_search_method.class"],
**self.args["eval_search_method.params"])
if search_layer is None:
logging.info("WARNING: no search method is provided "
"in SeqGenerationValidator for validation process.")
self._validate_gen = False
return self
from neurst.exps.sequence_generator import SequenceGenerator
with training_utils.get_strategy_scope(strategy):
self._gen_model = SequenceGenerator.build_generation_model(
task, model, search_layer)
self._gen_tfds = training_utils.build_datasets(
compat.ModeKeys.INFER, strategy, self._custom_dataset, task,
True, self._eval_task_args)
if isinstance(self._custom_dataset, MultipleDataset):
for name in list(self._gen_tfds.keys()):
if self._custom_dataset.datasets[name].targets is None:
logging.info(
f"WARNING: no ground truth found for validation dataset {name}."
)
self._gen_tfds.pop(name)
if len(self._gen_tfds) == 0:
logging.info(
"WARNING: no ground truth found for all validation datasets and "
"no validation will be applied.")
self._validate_gen = False
return self
else:
if self._custom_dataset.targets is None:
logging.info(
"WARNING: no ground truth found for validation dataset and "
"no validation will be applied.")
self._validate_gen = False
return self
if isinstance(self._custom_dataset, MultipleDataset):
self._gen_recorder = {
name:
training_utils.TrainingStatusRecorder(model=model,
task=task,
metric=self._gen_metric)
for name in self._gen_tfds
}
self._mixed_gen_recorder = training_utils.TrainingStatusRecorder(
model=model, task=task, metric=self._gen_metric)
self._avg_gen_recorder = training_utils.TrainingStatusRecorder(
model=model,
task=task,
metric=self._gen_metric,
estop_patience=self.args["eval_estop_patience"],
best_checkpoint_path=self.args["eval_best_checkpoint_path"],
auto_average_checkpoints=self.
args["eval_auto_average_checkpoints"],
best_avg_checkpoint_path=self.
args["eval_best_avg_checkpoint_path"],
top_checkpoints_to_keep=self.
args["eval_top_checkpoints_to_keep"])
else:
self._gen_recorder = training_utils.TrainingStatusRecorder(
model=model,
task=task,
metric=self._gen_metric,
estop_patience=self.args["eval_estop_patience"],
best_checkpoint_path=self.args["eval_best_checkpoint_path"],
auto_average_checkpoints=self.
args["eval_auto_average_checkpoints"],
best_avg_checkpoint_path=self.
args["eval_best_avg_checkpoint_path"],
top_checkpoints_to_keep=self.
args["eval_top_checkpoints_to_keep"])
from neurst.exps.sequence_generator import SequenceGenerator
self._postprocess_fn = lambda y: SequenceGenerator.postprocess_generation(
task, y)
self._gen_start_time = time.time()
return self