def new(cls, args, src_meta, trg_meta, name=None):
""" Builds a sequence to sequence model.
Args:
args: A dict containing all model parameters.
src_meta: A dict containing source-side vocabulary meta data, e.g. eos_id, vocab_size.
trg_meta: A dict containing target-side vocabulary meta data, e.g. eos_id, vocab_size.
name: The name of the model.
Returns:
An encoder decoder model.
"""
# build source and target modality
src_modality, trg_modality = cls.build_modalities(args, src_meta, trg_meta)
encoder_params, decoder_params = {}, {}
for f in cls.class_or_method_args():
if f.name in args:
if f.name.startswith("encoder."):
encoder_params[f.name[8:]] = args[f.name]
elif f.name.startswith("decoder."):
decoder_params[f.name[8:]] = args[f.name]
# build encoder and decoder
encoder = build_encoder({
"encoder.class": "TransformerEncoder",
"encoder.params": encoder_params})
decoder = build_decoder({
"decoder.class": "TransformerDecoder",
"decoder.params": decoder_params})
model = cls(args, src_meta, trg_meta, src_modality, trg_modality, encoder, decoder, name=name)
fake_src = numpy.random.rand(1, 4, src_meta["audio_feature_dim"], src_meta["audio_feature_channels"])
fake_inputs = {"src": tf.convert_to_tensor(fake_src, tf.float32),
"src_length": tf.convert_to_tensor([4], tf.int64),
"trg_input": tf.convert_to_tensor([[1, 2, 3]], tf.int64), }
_ = model(fake_inputs)
return model
def new(cls, args, src_meta, trg_meta, name=None):
""" Builds a sequence to sequence model.
Args:
args: A dict containing all model parameters.
src_meta: A dict containing source-side vocabulary meta data, e.g. eos_id, vocab_size.
trg_meta: A dict containing target-side vocabulary meta data, e.g. eos_id, vocab_size.
name: The name of the model.
Returns:
An encoder decoder model.
"""
# build source and target modality
src_modality, trg_modality = cls.build_modalities(
args, src_meta, trg_meta)
# build encoder and decoder
encoder = build_encoder(args)
decoder = build_decoder(args)
model = cls(args,
src_meta,
trg_meta,
src_modality,
trg_modality,
encoder,
decoder,
name=name)
return model
def new(cls, args, src_meta, trg_meta, name=None):
""" Builds a sequence to sequence model.
Args:
args: A dict containing all model parameters.
src_meta: A dict containing source-side vocabulary meta data, e.g. eos_id, vocab_size.
trg_meta: A dict containing target-side vocabulary meta data, e.g. eos_id, vocab_size.
name: The name of the model.
Returns:
An encoder decoder model.
"""
# build source and target modality
src_modality, trg_modality = cls.build_modalities(
args, src_meta, trg_meta)
# build encoder and decoder
encoder = build_encoder(args)
decoder = build_decoder(args)
model = cls(args,
src_meta,
trg_meta,
src_modality,
trg_modality,
encoder,
decoder,
name=name)
_ = model({
"src": tf.convert_to_tensor([[1, 2, 3]], tf.int64),
"src_padding": tf.convert_to_tensor([[0, 0., 0]], tf.float32),
"trg_input": tf.convert_to_tensor([[1, 2, 3]], tf.int64)
})
return model
def new(cls, args, src_meta, trg_meta, name=None):
""" Builds a sequence to sequence model.
Args:
args: A dict containing all model parameters.
src_meta: A dict containing source-side vocabulary meta data, e.g. eos_id, vocab_size.
trg_meta: A dict containing target-side vocabulary meta data, e.g. eos_id, vocab_size.
name: The name of the model.
Returns:
An encoder decoder model.
"""
# build source and target modality
src_modality, trg_modality = cls.build_modalities(
args, src_meta, trg_meta)
encoder_params, decoder_params = {}, {}
for f in cls.class_or_method_args():
if f.name in args:
if f.name.startswith("encoder."):
encoder_params[f.name[8:]] = args[f.name]
elif f.name.startswith("decoder."):
decoder_params[f.name[8:]] = args[f.name]
# build encoder and decoder
encoder = build_encoder({
"encoder.class": "LightConvolutionEncoder",
"encoder.params": encoder_params
})
decoder = build_decoder({
"decoder.class": "LightConvolutionDecoder",
"decoder.params": decoder_params
})
model = cls(args,
src_meta,
trg_meta,
src_modality,
trg_modality,
encoder,
decoder,
name=name)
# TODO: NOTE that we need to warm up the variable names.
fake_inputs = {
"src": tf.convert_to_tensor([[1, 2, 3]], tf.int64),
"src_padding": tf.convert_to_tensor([[0, 0., 0]], tf.float32),
"trg_input": tf.convert_to_tensor([[1, 2, 3]], tf.int64),
}
_ = model(fake_inputs)
return model
def new(cls, args, src_meta, trg_meta, waitk_lagging, name=None):
""" Builds a sequence to sequence model.
Args:
args: A dict containing all model parameters.
src_meta: A dict containing source-side vocabulary meta data, e.g. eos_id, vocab_size.
trg_meta: A dict containing target-side vocabulary meta data, e.g. eos_id, vocab_size.
name: The name of the model.
Returns:
An encoder decoder model.
"""
# build source and target modality
src_modality, trg_modality = cls.build_modalities(
args, src_meta, trg_meta)
encoder_params, decoder_params = {}, {}
for f in cls.class_or_method_args():
if f.name in args:
if f.name.startswith("encoder."):
encoder_params[f.name[8:]] = args[f.name]
elif f.name.startswith("decoder."):
decoder_params[f.name[8:]] = args[f.name]
# build encoder and decoder
encoder = build_encoder({
"encoder.class": "TransformerEncoder",
"encoder.params": encoder_params
})
decoder = build_decoder({
"decoder.class": "TransformerDecoder",
"decoder.params": decoder_params
})
model = cls(args,
src_meta,
trg_meta,
src_modality,
trg_modality,
encoder,
decoder,
name=name)
model.wait_k = waitk_lagging
_ = model({
"src": tf.convert_to_tensor([[1, 2, 3]], tf.int64),
"src_padding": tf.convert_to_tensor([[0, 0., 0]], tf.float32),
"trg_input": tf.convert_to_tensor([[1, 2, 3]], tf.int64)
})
return model
def new(cls, args, src_meta, trg_meta, name=None):
""" Builds a sequence to sequence model.
Args:
args: A dict containing all model parameters.
src_meta: A dict containing source-side vocabulary meta data, e.g. eos_id, vocab_size.
trg_meta: A dict containing target-side vocabulary meta data, e.g. eos_id, vocab_size.
name: The name of the model.
Returns:
An encoder decoder model.
"""
# build source and target modality
src_modality, trg_modality = cls.build_modalities(
args, src_meta, trg_meta)
encoder_params, decoder_params = {}, {}
for f in cls.class_or_method_args():
if f.name in args:
if f.name.startswith("encoder."):
encoder_params[f.name[8:]] = args[f.name]
elif f.name.startswith("decoder."):
decoder_params[f.name[8:]] = args[f.name]
# build encoder and decoder
encoder = build_encoder({
"encoder.class": "TransformerEncoder",
"encoder.params": encoder_params
})
decoder = build_decoder({
"decoder.class": "TransformerDecoder",
"decoder.params": decoder_params
})
model = cls(args,
src_meta,
trg_meta,
src_modality,
trg_modality,
encoder,
decoder,
name=name)
return model
def new(cls, args, src_meta, trg_meta, name=None):
""" Builds a sequence to sequence model.
Args:
args: A dict containing all model parameters.
src_meta: A dict containing source-side vocabulary meta data, e.g. eos_id, vocab_size.
trg_meta: A dict containing target-side vocabulary meta data, e.g. eos_id, vocab_size.
name: The name of the model.
Returns:
An encoder decoder model.
"""
# build source and target modality
src_modality, trg_modality = cls.build_modalities(args, src_meta, trg_meta)
encoder_params, decoder_params = {}, {}
for f in cls.class_or_method_args():
if f.name in args:
if f.name.startswith("encoder."):
encoder_params[f.name[8:]] = args[f.name]
elif f.name.startswith("decoder."):
decoder_params[f.name[8:]] = args[f.name]
# build encoder and decoder
encoder = None
if args["bert_mode"] != "bert_as_encoder":
encoder = build_encoder({
"encoder.class": "TransformerEncoder",
"encoder.params": encoder_params})
decoder = build_decoder({
"decoder.class": "TransformerDecoder",
"decoder.params": decoder_params})
with tf.name_scope(name or "ctnmt"):
bert_model = Bert.new(get_hyper_parameters(args["bert_config"])["model.params"],
vocab_meta=src_meta, name="bert")
model = cls(args, bert_model, src_meta, trg_meta, src_modality, trg_modality,
encoder, decoder, name=(name or "ctnmt"))
_ = model({"src": tf.convert_to_tensor([[1, 2, 3]], tf.int64),
"src_padding": tf.convert_to_tensor([[0, 0., 0]], tf.float32),
"trg_input": tf.convert_to_tensor([[1, 2, 3]], tf.int64)})
return model
