def __init__(self,
embedding: ABCEmbedding = None,
sequence_length: int = None,
hyper_parameters: Dict[str, Dict[str, Any]] = None):
"""
Args:
embedding: embedding object
sequence_length: target sequence length
hyper_parameters: hyper_parameters to overwrite
"""
super(ABCLabelingModel, self).__init__()
if embedding is None:
embedding = BareEmbedding() # type: ignore
if hyper_parameters is None:
hyper_parameters = self.default_hyper_parameters()
self.tf_model: Optional[tf.keras.Model] = None
self.embedding = embedding
self.hyper_parameters = hyper_parameters
self.sequence_length = sequence_length
self.text_processor: SequenceProcessor = SequenceProcessor()
self.label_processor: SequenceProcessor = SequenceProcessor(
build_in_vocab='labeling',
min_count=1,
build_vocab_from_labels=True)
self.crf_layer: Optional[KConditionalRandomField] = None
def test_batch_generator(self):
text_processor = SequenceProcessor(segment=True)
label_processor = SequenceProcessor(
build_in_vocab='labeling',
min_count=1,
build_vocab_from_labels=True,
)
corpus_gen = MongoGenerator(db_name="spo",
mongo_url="mongodb://localhost:27017",
collection_name="test",
buffer_size=3200)
text_processor.build_vocab_generator([corpus_gen])
label_processor.build_vocab_generator([corpus_gen])
batch_dataset1 = BatchDataSet(corpus_gen,
text_processor=text_processor,
label_processor=label_processor,
segment=False,
seq_length=100,
max_position=100,
batch_size=12)
print(len(batch_dataset1))
duplicate_len = 100
aaa = list(batch_dataset1.take(duplicate_len))
assert len(aaa) == duplicate_len
bbb = list(batch_dataset1.take(1))
assert len(bbb) == 1
ccc = list(batch_dataset1.take())
print(len(ccc))
def __init__(self,
encoder_embedding: ABCEmbedding = None,
decoder_embedding: ABCEmbedding = None,
encoder_seq_length: int = None,
decoder_seq_length: int = None,
hidden_size: int = 1024,
**kwargs: Any):
"""
Init Labeling Model
Args:
embedding: embedding object
sequence_length: target sequence length
hyper_parameters: hyper_parameters to overwrite
**kwargs:
"""
logger.warning(
"Seq2Seq API is experimental. It may be changed in the future without notice."
)
if encoder_embedding is None:
encoder_embedding = BareEmbedding(
embedding_size=256) # type: ignore
self.encoder_embedding = encoder_embedding
if decoder_embedding is None:
decoder_embedding = BareEmbedding(
embedding_size=256) # type: ignore
self.decoder_embedding = decoder_embedding
self.encoder_processor = SequenceProcessor(min_count=1)
self.decoder_processor = SequenceProcessor(
build_vocab_from_labels=True, min_count=1)
self.encoder: GRUEncoder = None
self.decoder: AttGRUDecoder = None
self.hidden_size: int = hidden_size
self.encoder_seq_length = encoder_seq_length
self.decoder_seq_length = decoder_seq_length
self.optimizer = tf.keras.optimizers.Adam()
self.loss_object = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction='none')
def test_base_cases(self):
embedding = self.build_embedding()
x, y = SMP2018ECDTCorpus.load_data()
processor = SequenceProcessor()
processor.build_vocab(x, y)
embedding.setup_text_processor(processor)
samples = random.sample(x, sample_count)
res = embedding.embed(samples)
max_len = max([len(i) for i in samples]) + 2
if embedding.max_position is not None:
max_len = embedding.max_position
assert res.shape == (len(samples), max_len, embedding.embedding_size)
# Test Save And Load
embed_dict = embedding.to_dict()
embedding2 = load_data_object(embed_dict)
embedding2.setup_text_processor(processor)
assert embedding2.embed(samples).shape == (len(samples), max_len,
embedding.embedding_size)
def test_text_processor(self):
x_set, y_set = TestMacros.load_labeling_corpus()
x_samples = random.sample(x_set, 5)
text_processor = SequenceProcessor(min_count=1)
text_processor.build_vocab(x_set, y_set)
text_idx = text_processor.transform(x_samples)
text_info_dict = text_processor.to_dict()
text_processor2: SequenceProcessor = load_data_object(text_info_dict)
text_idx2 = text_processor2.transform(x_samples)
sample_lengths = [len(i) for i in x_samples]
assert (text_idx2 == text_idx).all()
assert text_processor.inverse_transform(
text_idx, lengths=sample_lengths) == x_samples
assert text_processor2.inverse_transform(
text_idx2, lengths=sample_lengths) == x_samples
def __init__(self,
embedding: ABCEmbedding = None,
*,
sequence_length: int = None,
hyper_parameters: Dict[str, Dict[str, Any]] = None,
multi_label: bool = False,
text_processor: ABCProcessor = None,
label_processor: ABCProcessor = None):
"""
Args:
embedding: embedding object
sequence_length: target sequence length
hyper_parameters: hyper_parameters to overwrite
multi_label: is multi-label classification
text_processor: text processor
label_processor: label processor
"""
super(ABCClassificationModel, self).__init__()
if embedding is None:
embedding = BareEmbedding() # type: ignore
if hyper_parameters is None:
hyper_parameters = self.default_hyper_parameters()
if text_processor is None:
text_processor = SequenceProcessor()
if label_processor is None:
label_processor = ClassificationProcessor(multi_label=multi_label)
self.tf_model: keras.Model = None
self.embedding = embedding
self.hyper_parameters = hyper_parameters
self.sequence_length = sequence_length
self.multi_label = multi_label
self.text_processor = text_processor
self.label_processor = label_processor
def test_label_processor(self):
x_set, y_set = TestMacros.load_labeling_corpus()
text_processor = SequenceProcessor(build_vocab_from_labels=True,
min_count=1)
text_processor.build_vocab(x_set, y_set)
samples = random.sample(y_set, 20)
text_idx = text_processor.transform(samples)
text_info_dict = text_processor.to_dict()
text_processor2: SequenceProcessor = load_data_object(text_info_dict)
text_idx2 = text_processor2.transform(samples)
lengths = [len(i) for i in samples]
assert (text_idx2 == text_idx).all()
assert text_processor2.inverse_transform(text_idx,
lengths=lengths) == samples
assert text_processor2.inverse_transform(text_idx2,
lengths=lengths) == samples
text_idx3 = text_processor.transform(samples, seq_length=20)
assert [len(i) for i in text_idx3] == [20] * len(text_idx3)
class Seq2Seq:
def to_dict(self) -> Dict[str, Any]:
return {
'tf_version': tf.__version__, # type: ignore
'coco_nlp_version': coco_nlp.__version__,
'__class_name__': self.__class__.__name__,
'__module__': self.__class__.__module__,
'config': {
'encoder_seq_length': self.encoder_seq_length, # type: ignore
'decoder_seq_length': self.decoder_seq_length, # type: ignore
'hidden_size': self.hidden_size
},
'encoder_embedding':
self.encoder_embedding.to_dict(), # type: ignore
'decoder_embedding': self.decoder_embedding.to_dict(),
'encoder_processor': self.encoder_processor.to_dict(),
'decoder_processor': self.decoder_processor.to_dict(),
}
def __init__(self,
encoder_embedding: ABCEmbedding = None,
decoder_embedding: ABCEmbedding = None,
encoder_seq_length: int = None,
decoder_seq_length: int = None,
hidden_size: int = 1024,
**kwargs: Any):
"""
Init Labeling Model
Args:
embedding: embedding object
sequence_length: target sequence length
hyper_parameters: hyper_parameters to overwrite
**kwargs:
"""
logger.warning(
"Seq2Seq API is experimental. It may be changed in the future without notice."
)
if encoder_embedding is None:
encoder_embedding = BareEmbedding(
embedding_size=256) # type: ignore
self.encoder_embedding = encoder_embedding
if decoder_embedding is None:
decoder_embedding = BareEmbedding(
embedding_size=256) # type: ignore
self.decoder_embedding = decoder_embedding
self.encoder_processor = SequenceProcessor(min_count=1)
self.decoder_processor = SequenceProcessor(
build_vocab_from_labels=True, min_count=1)
self.encoder: GRUEncoder = None
self.decoder: AttGRUDecoder = None
self.hidden_size: int = hidden_size
self.encoder_seq_length = encoder_seq_length
self.decoder_seq_length = decoder_seq_length
self.optimizer = tf.keras.optimizers.Adam()
self.loss_object = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction='none')
# @tf.function
def loss_function(self, real: tf.Tensor, pred: tf.Tensor) -> tf.Tensor:
mask = tf.math.logical_not(tf.math.equal(real, 0))
loss_ = self.loss_object(real, pred)
mask = tf.cast(mask, dtype=loss_.dtype)
loss_ *= mask
return tf.reduce_mean(loss_)
def build_model(self, x_train: TextSamplesVar,
y_train: TextSamplesVar) -> None:
train_gen = CorpusGenerator(x_train, y_train)
self.build_model_generator(train_gen)
def _build_encoder_decoder(self) -> None:
self.encoder = GRUEncoder(self.encoder_embedding,
hidden_size=self.hidden_size)
self.decoder = AttGRUDecoder(
self.decoder_embedding,
hidden_size=self.hidden_size,
vocab_size=self.decoder_processor.vocab_size)
try:
self.encoder.model().summary()
self.decoder.model().summary()
except:
pass
def build_model_generator(self, train_gen: CorpusGenerator) -> None:
"""
Build model with a generator, This function will do:
1. setup processor's vocab if the vocab is empty.
2. calculate the sequence length if `sequence_length` is None.
3. build up model architect.
4. compile the ``tf_model`` with default loss, optimizer and metrics.
Args:
train_gen: train data generator
"""
if self.encoder is None:
self.encoder_processor.build_vocab_generator([train_gen])
self.decoder_processor.build_vocab_generator([train_gen])
self.encoder_embedding.setup_text_processor(self.encoder_processor)
self.decoder_embedding.setup_text_processor(self.decoder_processor)
if self.encoder_seq_length is None:
self.encoder_seq_length = self.encoder_embedding.get_seq_length_from_corpus(
[train_gen], cover_rate=1.0)
logger.info(
f"calculated encoder sequence length: {self.encoder_seq_length}"
)
if self.decoder_seq_length is None:
self.decoder_seq_length = self.decoder_embedding.get_seq_length_from_corpus(
[train_gen], use_label=True, cover_rate=1.0)
logger.info(
f"calculated decoder sequence length: {self.decoder_seq_length}"
)
self._build_encoder_decoder()
# @tf.function
def train_step(
self, # type: ignore
input_seq,
target_seq,
enc_hidden):
loss = 0
with tf.GradientTape() as tape:
enc_output, enc_hidden = self.encoder(input_seq, enc_hidden)
dec_hidden = enc_hidden
bos_token_id = self.encoder_processor.vocab2idx[
self.encoder_processor.token_bos]
dec_input = tf.expand_dims([bos_token_id] * target_seq.shape[0], 1)
# Teacher forcing - feeding the target as the next input
for t in range(1, target_seq.shape[1]):
# pass enc_output to the decoder
predictions, dec_hidden, _ = self.decoder(
dec_input, dec_hidden, enc_output)
loss += self.loss_function(target_seq[:, t], predictions)
# using teacher forcing
dec_input = tf.expand_dims(target_seq[:, t], 1)
batch_loss = (loss / int(target_seq.shape[1]))
variables = self.encoder.trainable_variables + self.decoder.trainable_variables
gradients = tape.gradient(loss, variables)
self.optimizer.apply_gradients(zip(gradients, variables))
return batch_loss
def fit(
self,
x_train: TextSamplesVar,
y_train: TextSamplesVar,
*,
batch_size: int = 64,
epochs: int = 5,
callbacks: List[tf.keras.callbacks.Callback] = None
) -> tf.keras.callbacks.History:
train_gen = CorpusGenerator(x_train, y_train)
self.build_model_generator(train_gen)
train_dataset = Seq2SeqDataSet(
train_gen,
batch_size=batch_size,
encoder_processor=self.encoder_processor,
encoder_seq_length=self.encoder_seq_length,
decoder_processor=self.decoder_processor,
decoder_seq_length=self.decoder_seq_length)
if callbacks is None:
callbacks = []
history_callback = tf.keras.callbacks.History()
callbacks.append(history_callback)
for c in callbacks:
c.set_model(self)
c.on_train_begin()
for epoch in range(epochs):
for c in callbacks:
c.on_epoch_begin(epoch=epoch)
enc_hidden = tf.zeros((batch_size, self.hidden_size))
total_loss = []
with tqdm.tqdm(total=len(train_dataset)) as p_bar:
for (inputs, targets) in train_dataset.take():
p_bar.update(1)
batch_loss = self.train_step(inputs, targets, enc_hidden)
total_loss.append(batch_loss.numpy())
info = f"Epoch {epoch + 1}/{epochs} | Epoch Loss: {np.mean(total_loss):.4f} " \
f"Batch Loss: {batch_loss.numpy():.4f}"
p_bar.set_description_str(info)
logs = {'loss': np.mean(total_loss)}
for c in callbacks:
c.on_epoch_end(epoch=epoch, logs=logs)
return history_callback
def save(self, model_path: str) -> str:
"""
Save model
Args:
model_path:
"""
pathlib.Path(model_path).mkdir(exist_ok=True, parents=True)
model_path = os.path.abspath(model_path)
with open(os.path.join(model_path, 'model_config.json'), 'w') as f:
f.write(json.dumps(self.to_dict(), indent=2, ensure_ascii=False))
f.close()
self.encoder_embedding.embed_model.save_weights(
os.path.join(model_path, 'encoder_embed_weights.h5'))
self.decoder_embedding.embed_model.save_weights(
os.path.join(model_path, 'decoder_embed_weights.h5'))
self.encoder.save_weights(
os.path.join(model_path, 'encoder_weights.h5'))
self.decoder.save_weights(
os.path.join(model_path, 'decoder_weights.h5'))
logger.info('model saved to {}'.format(os.path.abspath(model_path)))
return model_path
@classmethod
def load_model(cls, model_path: str) -> 'Seq2Seq':
from coco_nlp.utils import load_data_object
model_config_path = os.path.join(model_path, 'model_config.json')
model_config = json.loads(open(model_config_path, 'r').read())
model = load_data_object(model_config)
# Load processors and embeddings
model.encoder_processor = load_data_object(
model_config['encoder_processor'])
model.decoder_processor = load_data_object(
model_config['decoder_processor'])
model.encoder_embedding = load_data_object(
model_config['encoder_embedding'])
model.decoder_embedding = load_data_object(
model_config['decoder_embedding'])
model._build_encoder_decoder()
# Load Model Weights
model.encoder_embedding.embed_model.load_weights(
os.path.join(model_path, 'encoder_embed_weights.h5'))
model.decoder_embedding.embed_model.load_weights(
os.path.join(model_path, 'decoder_embed_weights.h5'))
# ------ Fix Start -------
# load model issue on TF 2.3
# Unable to load weights saved in HDF5 format into a subclassed Model which has not created its variables yet.
# Call the Model first, then load the weights.
input_seq = model.encoder_processor.transform(
[['hello']], seq_length=model.encoder_seq_length)
dec_input = tf.expand_dims([3], 0)
enc_hidden = tf.zeros((1, model.hidden_size))
dec_hidden = enc_hidden
enc_output, enc_hidden = model.encoder(input_seq, enc_hidden)
_ = model.decoder(dec_input, dec_hidden, enc_output)
# ------ Fix End -------
model.encoder.load_weights(
os.path.join(model_path, 'encoder_weights.h5'))
model.decoder.load_weights(
os.path.join(model_path, 'decoder_weights.h5'))
return model
def predict(self, x_data: TextSamplesVar) -> Tuple[List, np.ndarray]:
results = []
attentions = []
bos_token_id = self.decoder_processor.vocab2idx[
self.decoder_processor.token_bos]
eos_token_id = self.decoder_processor.vocab2idx[
self.decoder_processor.token_eos]
for sample in x_data:
input_seq = self.encoder_processor.transform(
[sample], seq_length=self.encoder_seq_length)
enc_hidden = tf.zeros((1, self.hidden_size))
enc_output, enc_hidden = self.encoder(input_seq, enc_hidden)
dec_hidden = enc_hidden
attention_plot = np.zeros(
(self.decoder_seq_length, self.encoder_seq_length))
token_out = []
dec_input = tf.expand_dims([bos_token_id], 0)
for t in range(self.decoder_seq_length):
predictions, dec_hidden, att_weights = self.decoder(
dec_input, dec_hidden, enc_output)
# storing the attention weights to plot later on
attention_weights = tf.reshape(att_weights, (-1, ))
attention_plot[t] = attention_weights.numpy()
next_tokens = tf.argmax(predictions[0]).numpy()
token_out.append(next_tokens)
if next_tokens == eos_token_id:
break
dec_input = tf.expand_dims([next_tokens], 0)
r = self.decoder_processor.inverse_transform([token_out])[0]
results.append(r)
attentions.append(attention_plot)
return results, np.array(attentions)