def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_architecture(args)
if getattr(args, "max_target_positions", None) is not None:
max_target_positions = args.max_target_positions
else:
max_target_positions = getattr(args, "tokens_per_sample",
DEFAULT_MAX_TARGET_POSITIONS)
def load_pretrained_embedding_from_file(embed_path, dictionary,
embed_dim):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
embed_dict = utils.parse_embedding(embed_path)
utils.print_embed_overlap(embed_dict, dictionary)
return utils.load_embedding(embed_dict, dictionary, embed_tokens)
pretrained_decoder_embed = None
if args.decoder_embed_path:
pretrained_decoder_embed = load_pretrained_embedding_from_file(
args.decoder_embed_path, task.target_dictionary,
args.decoder_embed_dim)
if args.share_decoder_input_output_embed:
# double check all parameters combinations are valid
if task.source_dictionary != task.target_dictionary:
raise ValueError(
"--share-decoder-input-output-embeddings requires a joint dictionary"
)
if args.decoder_embed_dim != args.decoder_out_embed_dim:
raise ValueError(
"--share-decoder-input-output-embeddings requires "
"--decoder-embed-dim to match --decoder-out-embed-dim")
decoder = LSTMDecoder(
dictionary=task.dictionary,
embed_dim=args.decoder_embed_dim,
hidden_size=args.decoder_hidden_size,
out_embed_dim=args.decoder_out_embed_dim,
num_layers=args.decoder_layers,
dropout_in=args.decoder_dropout_in,
dropout_out=args.decoder_dropout_out,
attention=
False, # decoder-only language model doesn't support attention
encoder_output_units=0,
pretrained_embed=pretrained_decoder_embed,
share_input_output_embed=args.share_decoder_input_output_embed,
adaptive_softmax_cutoff=(utils.eval_str_list(
args.adaptive_softmax_cutoff, type=int) if args.criterion
== "adaptive_loss" else None),
max_target_positions=max_target_positions,
residuals=args.residuals,
)
return cls(decoder)
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure that all args are properly defaulted (in case there are any new ones)
base_architecture(args)
encoder = TextRecognitionEncoder(args=args, )
decoder = LSTMDecoder(
dictionary=task.target_dictionary,
embed_dim=encoder.embed_dim,
hidden_size=args.decoder_hidden_size,
out_embed_dim=args.decoder_out_embed_dim,
num_layers=args.decoder_layers,
dropout_in=args.decoder_dropout_in,
dropout_out=args.decoder_dropout_out,
attention=options.eval_bool(args.decoder_attention),
encoder_output_units=encoder.embed_dim,
adaptive_softmax_cutoff=(options.eval_str_list(
args.adaptive_softmax_cutoff, type=int) if args.criterion
== 'adaptive_loss' else None),
)
return cls(encoder, decoder)
def build_model(cls, args, task, dictionary=None):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_architecture(args)
if getattr(args, 'max_target_positions', None) is not None:
max_target_positions = args.max_target_positions
else:
max_target_positions = getattr(args, 'tokens_per_sample', DEFAULT_MAX_TARGET_POSITIONS)
def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
embed_dict = utils.parse_embedding(embed_path)
utils.print_embed_overlap(embed_dict, dictionary)
return utils.load_embedding(embed_dict, dictionary, embed_tokens)
pretrained_decoder_embed = None
if args.decoder_embed_path:
pretrained_decoder_embed = load_pretrained_embedding_from_file(
args.decoder_embed_path,
task.target_dictionary,
args.decoder_embed_dim
)
if args.share_decoder_input_output_embed:
# double check all parameters combinations are valid
if task.source_dictionary != task.target_dictionary:
raise ValueError('--share-decoder-input-output-embeddings requires a joint dictionary')
if args.decoder_embed_dim != args.decoder_out_embed_dim:
raise ValueError(
'--share-decoder-input-output-embeddings requires '
'--decoder-embed-dim to match --decoder-out-embed-dim'
)
decoder = LSTMDecoder(
dictionary=dictionary if dictionary else task.dictionary,
embed_dim=args.decoder_embed_dim,
hidden_size=args.decoder_hidden_size,
out_embed_dim=args.decoder_out_embed_dim,
num_layers=args.decoder_layers,
dropout_in=args.decoder_dropout_in,
dropout_out=args.decoder_dropout_out,
attention=False, # decoder-only language model doesn't support attention
encoder_output_units=0,
pretrained_embed=pretrained_decoder_embed,
share_input_output_embed=args.share_decoder_input_output_embed,
adaptive_softmax_cutoff=(
utils.eval_str_list(args.adaptive_softmax_cutoff, type=int)
if args.criterion == 'adaptive_loss' else None
),
max_target_positions=max_target_positions,
residuals=args.residuals
)
if getattr(args, "lm_path", None):
# args.lm_path = '../libri/wav2vec2_small.pt'
print('load LSTM_LM from {}'.format(args.lm_path))
state = checkpoint_utils.load_checkpoint_to_cpu(args.lm_path)
lm_args = state["args"]
lm_args.data = args.data
assert getattr(lm_args, "lm_path", None) is None
task = tasks.setup_task(lm_args)
decoder = task.build_model(lm_args)
print('restore LSTM_LM from {}'.format(args.lm_path))
decoder.load_state_dict(state["model"], strict=True)
decoder.dim_output = len(task.dictionary)
return cls(decoder)