def get_feature_metadata(cls, feature_config: FeatureConfig,
feature_meta: Dict[str, FieldMeta]):
# The number of names in input_names *must* be equal to the number of
# tensors passed in dummy_input
input_names: List[str] = []
dummy_model_input: List = []
feature_itos_map = {}
for name, feat_config in feature_config._asdict().items():
if isinstance(feat_config, ConfigBase):
input_names.extend(feat_config.export_input_names)
if getattr(feature_meta[name], "vocab", None):
feature_itos_map[feat_config.export_input_names[
0]] = feature_meta[name].vocab.itos
dummy_model_input.append(feature_meta[name].dummy_model_input)
if "tokens_vals" in input_names:
dummy_model_input.append(torch.tensor(
[1, 1], dtype=torch.long)) # token lengths
input_names.append("tokens_lens")
if "seq_tokens_vals" in input_names:
dummy_model_input.append(torch.tensor(
[1, 1], dtype=torch.long)) # seq lengths
input_names.append("seq_tokens_lens")
return input_names, tuple(dummy_model_input), feature_itos_map
def _get_exportable_metadata(
cls,
exportable_filter: Callable,
feature_config: FeatureConfig,
feature_meta: Dict[str, FieldMeta],
) -> Tuple[List[str], List, Dict]:
# The number of names in input_names *must* be equal to the number of
# tensors passed in dummy_input
input_names: List[str] = []
dummy_model_input: List = []
feature_itos_map = {}
for name, feat_config in feature_config._asdict().items():
if exportable_filter(feat_config):
input_names.extend(feat_config.export_input_names)
if getattr(feature_meta[name], "vocab", None):
feature_itos_map[feat_config.export_input_names[
0]] = feature_meta[name].vocab.itos
dummy_model_input.append(feature_meta[name].dummy_model_input)
return input_names, dummy_model_input, feature_itos_map
def create_sub_embs(cls, emb_config: FeatureConfig,
metadata: CommonMetadata) -> Dict[str, EmbeddingBase]:
"""
Creates the embedding modules defined in the `emb_config`.
Args:
emb_config (FeatureConfig): Object containing all the sub-embedding
configurations.
metadata (CommonMetadata): Object containing features and label metadata.
Returns:
Dict[str, EmbeddingBase]: Named dictionary of embedding modules.
"""
sub_emb_module_dict = {}
for name, config in emb_config._asdict().items():
if issubclass(getattr(config, "__COMPONENT__", object),
EmbeddingBase):
sub_emb_module_dict[name] = create_module(
config, metadata=metadata.features[name])
else:
print(f"{name} is not a config of embedding, skipping")
return sub_emb_module_dict
