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)
decoder_embed_dict = None
if args.decoder_embed_path:
decoder_embed_dict = utils.parse_embedding(args.decoder_embed_path)
utils.print_embed_overlap(decoder_embed_dict,
task.target_dictionary)
out_channels = speech_utils.eval_str_nested_list_or_tuple(
args.encoder_conv_channels, type=int)
kernel_sizes = speech_utils.eval_str_nested_list_or_tuple(
args.encoder_conv_kernel_sizes, type=int)
strides = speech_utils.eval_str_nested_list_or_tuple(
args.encoder_conv_strides, type=int)
logger.info('input feature dimension: {}, channels: {}'.format(
task.feat_dim, task.feat_in_channels))
assert task.feat_dim % task.feat_in_channels == 0
conv_layers = ConvBNReLU(
out_channels,
kernel_sizes,
strides,
in_channels=task.feat_in_channels,
) if out_channels is not None else None
fconv_encoder_input_size = task.feat_dim // task.feat_in_channels
if conv_layers is not None:
for stride in strides:
if isinstance(stride, (list, tuple)):
assert len(stride) > 0
s = stride[1] if len(stride) > 1 else stride[0]
else:
assert isinstance(stride, int)
s = stride
fconv_encoder_input_size = (fconv_encoder_input_size + s -
1) // s
fconv_encoder_input_size *= out_channels[-1]
encoder = SpeechFConvEncoder(
conv_layers_before=conv_layers,
input_size=fconv_encoder_input_size,
embed_dim=args.encoder_embed_dim,
convolutions=eval(args.encoder_layers),
dropout=args.dropout,
)
decoder = SpeechFConvDecoder(
dictionary=task.target_dictionary,
embed_dim=args.decoder_embed_dim,
embed_dict=decoder_embed_dict,
convolutions=eval(args.decoder_layers),
out_embed_dim=args.decoder_out_embed_dim,
attention=eval(args.decoder_attention),
dropout=args.dropout,
max_positions=args.max_target_positions,
share_embed=args.share_input_output_embed,
positional_embeddings=args.decoder_positional_embed,
)
return cls(encoder, decoder)
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)
def build_model(cls, args, src_dict, dst_dict):
"""Build a new model instance."""
encoder_embed_dict = None
if args.encoder_embed_path:
encoder_embed_dict = utils.parse_embedding(args.encoder_embed_path)
utils.print_embed_overlap(encoder_embed_dict, src_dict)
decoder_embed_dict = None
if args.decoder_embed_path:
decoder_embed_dict = utils.parse_embedding(args.decoder_embed_path)
utils.print_embed_overlap(decoder_embed_dict, dst_dict)
encoder = LSTMEncoder(
src_dict,
embed_dim=args.encoder_embed_dim,
embed_dict=encoder_embed_dict,
num_layers=args.encoder_layers,
dropout_in=args.encoder_dropout_in,
dropout_out=args.encoder_dropout_out,
)
decoder = LSTMDecoder(
dst_dict,
encoder_embed_dim=args.encoder_embed_dim,
embed_dim=args.decoder_embed_dim,
embed_dict=decoder_embed_dict,
out_embed_dim=args.decoder_out_embed_dim,
num_layers=args.decoder_layers,
attention=bool(eval(args.decoder_attention)),
dropout_in=args.decoder_dropout_in,
dropout_out=args.decoder_dropout_out,
)
return cls(encoder, 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_embed_dict = None
if args.encoder_embed_path:
encoder_embed_dict = utils.parse_embedding(args.encoder_embed_path)
utils.print_embed_overlap(encoder_embed_dict, task.source_dictionary)
decoder_embed_dict = None
if args.decoder_embed_path:
decoder_embed_dict = utils.parse_embedding(args.decoder_embed_path)
utils.print_embed_overlap(decoder_embed_dict, task.target_dictionary)
encoder = FConvEncoder(
dictionary=task.source_dictionary,
embed_dim=args.encoder_embed_dim,
embed_dict=encoder_embed_dict,
convolutions=eval(args.encoder_layers),
dropout=args.dropout,
max_positions=args.max_source_positions,
)
decoder = FConvDecoder(
dictionary=task.target_dictionary,
embed_dim=args.decoder_embed_dim,
embed_dict=decoder_embed_dict,
convolutions=eval(args.decoder_layers),
out_embed_dim=args.decoder_out_embed_dim,
attention=eval(args.decoder_attention),
dropout=args.dropout,
max_positions=args.max_target_positions,
share_embed=args.share_input_output_embed,
)
return FConvModel(encoder, 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_embed_dict = None
if args.encoder_embed_path:
encoder_embed_dict = utils.parse_embedding(args.encoder_embed_path)
utils.print_embed_overlap(encoder_embed_dict, task.source_dictionary)
decoder_embed_dict = None
if args.decoder_embed_path:
decoder_embed_dict = utils.parse_embedding(args.decoder_embed_path)
utils.print_embed_overlap(decoder_embed_dict, task.target_dictionary)
kw_embed_dict = None
if args.keywords_embed_path:
kw_embed_dict = utils.parse_embedding(args.keywords_embed_path)
print(">> Keywords embeddings loaded!")
docEncoder = ExtendedEncoder(args,
FConvEncoder(
dictionary=task.source_dictionary,
embed_dim=args.encoder_embed_dim,
embed_dict=encoder_embed_dict,
convolutions=eval(args.encoder_layers),
dropout=args.dropout,
max_positions=args.max_source_positions,
normalization_constant=args.normalization_constant,
))
decoder = CondFConvDecoder(
dictionary=task.target_dictionary,
embed_dim=args.decoder_embed_dim,
embed_dict=decoder_embed_dict,
convolutions=eval(args.decoder_layers),
out_embed_dim=args.decoder_out_embed_dim,
attention=eval(args.decoder_attention),
dropout=args.dropout,
max_positions=args.max_tgt_sentence_length,
share_embed=args.share_input_output_embed,
normalization_constant=args.normalization_constant,
)
docDecoder = LDocumentDecoder(args, decoder,
embed_dim=args.decoder_embed_dim,
hidden_size=args.decoder_embed_dim,
out_embed_dim=args.decoder_out_embed_dim,
encoder_embed_dim = args.encoder_embed_dim,
encoder_output_units=args.encoder_embed_dim,
num_layers=1,
attention=eval(args.decoder_attention),
dropout_in = 0.1, dropout_out=0.1,
pretrained_embed = None,
)
return FEncFairseqModel(docEncoder, docDecoder)
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_embed_dict = None
if args.encoder_embed_path:
encoder_embed_dict = utils.parse_embedding(args.encoder_embed_path)
utils.print_embed_overlap(encoder_embed_dict,
task.source_dictionary)
decoder_embed_dict = None
if args.decoder_embed_path:
decoder_embed_dict = utils.parse_embedding(args.decoder_embed_path)
utils.print_embed_overlap(decoder_embed_dict,
task.target_dictionary)
conv_encoder = FConvEncoder3(
dictionary=task.source_dictionary,
embed_dim=args.encoder_embed_dim,
embed_dict=encoder_embed_dict,
convolutions=eval(args.encoder_layers),
dropout=args.dropout,
max_positions=args.max_source_positions,
normalization_constant=args.normalization_constant,
)
gcn_encoder = GCNEncoder2(
dictionary=task.source_dictionary,
dropout=args.dropout,
num_inputs=args.gcn_num_inputs,
num_units=args.gcn_num_units,
num_labels=args.gcn_num_labels,
num_layers=args.gcn_num_layers,
in_arcs=args.gcn_in_arcs,
out_arcs=args.gcn_out_arcs,
batch_first=args.gcn_batch_first,
residual=args.gcn_residual,
use_gates=args.gcn_use_gates,
use_glus=args.gcn_use_glus,
normalization_constant=args.normalization_constant,
)
encoder = FConvGCNOnTopEncoder(task.source_dictionary, conv_encoder,
gcn_encoder)
decoder = FConvDecoder(
dictionary=task.target_dictionary,
embed_dim=args.decoder_embed_dim,
embed_dict=decoder_embed_dict,
convolutions=eval(args.decoder_layers),
out_embed_dim=args.decoder_out_embed_dim,
attention=eval(args.decoder_attention),
dropout=args.dropout,
max_positions=args.max_target_positions,
share_embed=args.share_input_output_embed,
normalization_constant=args.normalization_constant,
)
return FConvGCNOnTopModel(encoder, decoder)
def build_embedding(dictionary, embed_dim, path=None):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
emb = Embedding(num_embeddings, embed_dim, padding_idx)
# if provided, load from preloaded dictionaries
if path:
embed_dict = utils.parse_embedding(path)
utils.load_embedding(embed_dict, dictionary, emb)
utils.print_embed_overlap(embed_dict, dictionary)
return emb
def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim):
num_embedding = len(dictionary)
padding_idx = dictionary.pad()
embed_tokens = Embedding(num_embedding, embed_dim, padding_idx)
embed_dict = utils.parse_embedding(embed_path)
utils.print_embed_overlap(embed_dict, dictionary)
# embed_keys = set(embed_dict.keys())
# vocab_keys = set(dictionary.symbols))
# print(vocab_keys - embed_keys)
return utils.load_embedding(embed_dict, dictionary,
embed_tokens), embed_dict
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)
if args.merge_mode not in ['concat', 'sum']:
raise ValueError('Invalid merge mode. '
'Merge mode should be one of '
'{"sum", "concat"}')
if args.encoder_embed_dim != args.decoder_embed_dim:
raise ValueError('encoder embedding dimension '
'should be equal to '
'decoder embedding dimension')
if args.merge_mode == 'sum' and args.token_embed_dim != args.encoder_embed_dim:
raise ValueError('token embedding dimension '
'should be equal to '
'encoder embedding dimension')
encoder_embed_dict = None
if args.encoder_embed_path:
encoder_embed_dict = utils.parse_embedding(args.encoder_embed_path)
utils.print_embed_overlap(encoder_embed_dict,
task.source_dictionary)
decoder_embed_dict = None
if args.decoder_embed_path:
decoder_embed_dict = utils.parse_embedding(args.decoder_embed_path)
utils.print_embed_overlap(decoder_embed_dict,
task.target_dictionary)
encoder = FConvEncoder(
dictionary=task.source_dictionary,
args=args,
encoder_embed_dim=args.encoder_embed_dim,
embed_dict=encoder_embed_dict,
convolutions=eval(args.encoder_layers),
dropout=args.dropout,
max_positions=args.max_source_positions,
)
decoder = FConvDecoder(
dictionary=task.target_dictionary,
args=args,
decoder_embed_dim=args.decoder_embed_dim,
embed_dict=decoder_embed_dict,
convolutions=eval(args.decoder_layers),
out_embed_dim=args.decoder_out_embed_dim,
attention=eval(args.decoder_attention),
dropout=args.dropout,
max_positions=args.max_target_positions,
share_embed=args.share_input_output_embed,
)
return FConvModel(encoder, decoder)
def copy_prev_embedding(embed_path, dictionary, embed_dim, prev_embedded_tokens_path, prev_dict): num_embeddings = len(dictionary) padding_idx = dictionary.pad() embed_tokens = nn.Embedding(num_embeddings, embed_dim, padding_idx) prev_embedded_tokens = load_random_embedding(prev_embedded_tokens_path) for i in range(5, num_embeddings): if prev_dict.index(dictionary.symbols[i])!= prev_dict.unk() and i!=dictionary.unk(): embed_tokens.weight.data[i] = prev_embedded_tokens[prev_dict.index(dictionary.symbols[i])] #embed_tokens.weight = nn.Parameter(prev_embedded_tokens) embed_dict = utils.parse_embedding(embed_path) utils.print_embed_overlap(embed_dict, dictionary) return utils.load_embedding(embed_dict, dictionary, embed_tokens)
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)
attention_layer = {
'dot': DotAttentionLayer,
'general': GeneralAttentionLayer,
'multi-head': MultiheadAttention,
'mlp': MLPAttentionLayer,
}
decoder_embed_dict = None
if args.decoder_embed_path:
decoder_embed_dict = utils.parse_embedding(args.decoder_embed_path)
utils.print_embed_overlap(decoder_embed_dict,
task.target_dictionary)
# replaced source_dict with target_dict
encoder = ASTS2SEncoder(
args=args,
linear_dim=args.encoder_embed_dim,
convolutions=eval(args.encoder_convolutions),
layers=args.encoder_layers,
dropout=args.dropout,
max_positions=args.max_source_positions,
normalization_constant=args.normalization_constant,
last_state=args.encoder_state,
weight_norm=args.weight_norm,
learn_initial=args.learn_initial_state,
conv_1d=args.conv_1d,
audio_features=task.audio_features if not args.wav2vec else 512,
)
decoder = CLSTMDecoder(
dictionary=task.target_dictionary,
embed_dim=args.decoder_embed_dim,
out_embed_dim=args.decoder_out_embed_dim,
attention=eval(args.decoder_attention),
dropout_in=args.dropout,
dropout_out=args.dropout,
num_layers=args.decoder_layers,
attention_layer=attention_layer[args.attention_type],
initial_state=args.decoder_initial_state,
weight_norm=args.weight_norm,
attention_function=args.attention_function,
max_positions=args.max_target_positions,
scale_norm=args.scale_norm,
scale=args.scale,
)
return ASTS2SModel(encoder, decoder)
def build_model(cls, args, src_dict, dst_dict):
"""Build a new model instance."""
if not hasattr(args, 'max_source_positions'):
args.max_source_positions = args.max_positions
args.max_target_positions = args.max_positions
if not hasattr(args, 'share_input_output_embed'):
args.share_input_output_embed = False
if not hasattr(args, 'encoder_embed_path'):
args.encoder_embed_path = None
if not hasattr(args, 'decoder_embed_path'):
args.decoder_embed_path = None
encoder_embed_dict = None
if args.encoder_embed_path:
encoder_embed_dict = utils.parse_embedding(args.encoder_embed_path)
utils.print_embed_overlap(encoder_embed_dict, src_dict)
decoder_embed_dict = None
if args.decoder_embed_path:
decoder_embed_dict = utils.parse_embedding(args.decoder_embed_path)
utils.print_embed_overlap(decoder_embed_dict, dst_dict)
encoder = FConvEncoder(
src_dict,
embed_dim=args.encoder_embed_dim,
embed_dict=encoder_embed_dict,
convolutions=eval(args.encoder_layers),
dropout=args.dropout,
max_positions=args.max_source_positions,
)
decoder = FConvDecoder(dst_dict,
embed_dim=args.decoder_embed_dim,
embed_dict=decoder_embed_dict,
convolutions=eval(args.decoder_layers),
out_embed_dim=args.decoder_out_embed_dim,
attention=eval(args.decoder_attention),
dropout=args.dropout,
max_positions=args.max_target_positions,
share_embed=args.share_input_output_embed)
return FConvModel(encoder, decoder)
def __init__(self,
# new approach
params: Params = None,
vocab: Dictionary = None,
# old approach
args = None,
task = None
):
if params is not None and args is not None:
raise ConfigurationError("you cannot define both, params and args, you have to device which one to use (just one way is allowed)")
if params is not None:
encoder = FConvEncoder(
dictionary=vocab
)
decoder = FConvDecoder(
dictionary=vocab
)
else:
if args is not None and task is not None:
# Create and initialize encoder and decoder here
encoder_embed_dict = None
if args.encoder_embed_path:
encoder_embed_dict = utils.parse_embedding(args.encoder_embed_path)
utils.print_embed_overlap(encoder_embed_dict, task.source_dictionary)
decoder_embed_dict = None
if args.decoder_embed_path:
decoder_embed_dict = utils.parse_embedding(args.decoder_embed_path)
utils.print_embed_overlap(decoder_embed_dict, task.target_dictionary)
encoder = FConvEncoder(
dictionary=task.source_dictionary,
embed_dim=args.encoder_embed_dim,
embed_dict=encoder_embed_dict,
convolutions=eval(args.encoder_layers),
dropout=args.dropout,
max_positions=args.max_source_positions,
batch_norm=args.batch_norm,
use_linear_se=True#args.use_linear_se
)
decoder = FConvDecoder(
dictionary=task.target_dictionary,
embed_dim=args.decoder_embed_dim,
embed_dict=decoder_embed_dict,
convolutions=eval(args.decoder_layers),
out_embed_dim=args.decoder_out_embed_dim,
attention=eval(args.decoder_attention),
dropout=args.dropout,
max_positions=args.max_target_positions,
share_embed=args.share_input_output_embed,
use_linear_se=False#args.use_linear_se
)
else:
# We have a problem!
raise ConfigurationError("params and (args, task) are all None, something is wrong here.")
# Call the super class
super(FConvModel, self).__init__(encoder, decoder)
# Correctly set the number of attention layers
self.encoder.num_attention_layers = sum(layer is not None for layer in decoder.attention)
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_architecture(args)
attention_layer = {
'dot': DotAttentionLayer,
'general': GeneralAttentionLayer,
'multi-head': MultiheadAttention,
'mlp': MLPAttentionLayer,
}
if args.decoder_embed_path:
decoder_embed_dict = utils.parse_embedding(args.decoder_embed_path)
utils.print_embed_overlap(decoder_embed_dict,
task.target_dictionary)
if not hasattr(args, 'max_source_positions'):
args.max_source_positions = 1024
if not hasattr(args, 'max_target_positions'):
args.max_target_positions = 1024
src_dict, tgt_dict = task.source_dictionary, task.target_dictionary
def build_embedding(dictionary, embed_dim, path=None):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
emb = Embedding(num_embeddings, embed_dim, padding_idx)
# if provided, load from preloaded dictionaries
if path:
embed_dict = utils.parse_embedding(path)
utils.load_embedding(embed_dict, dictionary, emb)
return emb
def build_audio_embedding(embed_dim, dropout):
m = nn.Linear(task.audio_features, embed_dim)
nn.init.normal_(m.weight,
mean=0,
std=math.sqrt((1 - dropout) / task.audio_features))
nn.init.constant_(m.bias, 0)
return m
encoder_embed_tokens = build_audio_embedding(
2 * args.encoder_embed_dim, args.dropout)
encoder = ProxyEncoder(
args,
tgt_dict,
encoder_embed_tokens,
audio_features=task.audio_features,
)
decoder = CLSTMDecoder(
dictionary=task.target_dictionary,
embed_dim=args.decoder_embed_dim,
out_embed_dim=args.decoder_out_embed_dim,
attention=eval(args.decoder_attention),
hidden_size=args.hidden_size,
dropout_in=args.dropout,
dropout_out=args.dropout,
num_layers=args.decoder_layers,
attention_layer=attention_layer[args.attention_type],
initial_state=args.decoder_initial_state,
weight_norm=args.weight_norm,
attention_function=args.attention_function,
scale_norm=args.scale_norm,
)
return Transf2BerModel(encoder, decoder)
