def export_inputs(self):
"""Inputs for exported model."""
vocab_dict = load_vocab_dict(self.text_vocab_file_path)
vocab_size = len(vocab_dict)
self.config['data']['vocab_size'] = vocab_size
input_sent_left = tf.placeholder(
shape=(None,), dtype=tf.string, name="input_sent_left")
input_sent_right = tf.placeholder(
shape=(None,), dtype=tf.string, name="input_sent_right")
input_pipeline_func = self.get_input_pipeline(for_export=True)
token_ids_left = input_pipeline_func(input_sent_left)
token_ids_right = input_pipeline_func(input_sent_right)
token_ids_len_left = tf.map_fn(
lambda x: compute_sen_lens(x, padding_token=0), token_ids_left)
token_ids_len_right = tf.map_fn(
lambda x: compute_sen_lens(x, padding_token=0), token_ids_right)
export_data = {
"export_inputs": {
"input_sent_left": input_sent_left,
"input_sent_right": input_sent_right,
},
"model_inputs": {
"input_x_left": token_ids_left,
"input_x_right": token_ids_right,
"input_x_left_len": token_ids_len_left,
"input_x_right_len": token_ids_len_right,
"input_x_len": [token_ids_len_left, token_ids_len_right]
}
}
return export_data
def generate_data(self):
"""Generate data for offline training."""
(text_left, text_right), label = load_match_raw_data(
paths=self.paths_after_pre_process, mode=self.mode)
text_left_placeholder = tf.placeholder(tf.string, name="text_left")
text_right_placeholder = tf.placeholder(tf.string, name="text_right")
label_placeholder = tf.placeholder(tf.string, name="label")
self.init_feed_dict[text_left_placeholder] = text_left
self.init_feed_dict[text_right_placeholder] = text_right
self.init_feed_dict[label_placeholder] = label
text_ds_left = tf.data.Dataset.from_tensor_slices(
text_left_placeholder)
text_ds_right = tf.data.Dataset.from_tensor_slices(
text_right_placeholder)
input_pipeline_func = self.get_input_pipeline(for_export=False)
text_ds_left = text_ds_left.map(
input_pipeline_func, num_parallel_calls=self.num_parallel_calls)
text_ds_right = text_ds_right.map(
input_pipeline_func, num_parallel_calls=self.num_parallel_calls)
text_size_ds_left = text_ds_left.map(
lambda x: compute_sen_lens(x, padding_token=0),
num_parallel_calls=self.num_parallel_calls)
text_size_ds_right = text_ds_right.map(
lambda x: compute_sen_lens(x, padding_token=0),
num_parallel_calls=self.num_parallel_calls)
text_ds_left_right = tf.data.Dataset.zip((text_ds_left, text_ds_right))
text_len_left_right = tf.data.Dataset.zip(
(text_size_ds_left, text_size_ds_right))
if self.infer_without_label:
data_set_left_right = text_ds_left_right
else:
label_ds = load_one_label_dataset(label_placeholder, self.config)
data_set_left_right = tf.data.Dataset.zip(
(text_ds_left_right, label_ds))
vocab_dict = load_vocab_dict(self.text_vocab_file_path)
vocab_size = len(vocab_dict)
data_size = len(text_left)
if self.split_token != "":
if self.split_token not in vocab_dict:
raise ValueError(
"The Model uses split token: {}, not in corpus.".format(
self.split_token))
self.config['data']['split_token'] = int(
vocab_dict[self.split_token])
self.config['data']['vocab_size'] = vocab_size
self.config['data']['{}_data_size'.format(self.mode)] = data_size
return data_set_left_right, text_len_left_right
def export_inputs(self):
"""Inputs for exported model."""
vocab_dict = load_vocab_dict(self.text_vocab_file_path)
vocab_size = len(vocab_dict)
label_vocab_dict = load_vocab_dict(self.label_vocab_file_paths[0])
label_vocab_size = len(label_vocab_dict)
self.config['data']['vocab_size'] = vocab_size
self.config['data']['label_vocab_size'] = label_vocab_size
input_sentence = tf.placeholder(shape=(None, ),
dtype=tf.string,
name="input_sentence")
input_pipeline_func = self.get_input_pipeline(for_export=True)
token_ids = input_pipeline_func(input_sentence)
token_ids_len = tf.map_fn(
lambda x: compute_sen_lens(x, padding_token=0), token_ids)
export_data = {
"export_inputs": {
"input_sentence": input_sentence
},
"model_inputs": {
"input_enc_x": token_ids,
"input_x_len": token_ids_len
}
}
return export_data
def generate_data(self):
"""Generate data for offline training."""
if self.infer_without_label:
column_num = 1
text_ds = load_textline_dataset(self.paths_after_pre_process, column_num)
else:
column_num = 3
intent_label_ds, slots_label_ds, text_ds = load_textline_dataset(
self.paths_after_pre_process, column_num)
logging.info("Loading text dataset...")
input_pipeline_func = self.get_input_pipeline(for_export=False)
text_ds = text_ds.map(
input_pipeline_func, num_parallel_calls=self.num_parallel_calls)
text_size_ds = text_ds.map(
lambda x: compute_sen_lens(x, padding_token=0),
num_parallel_calls=self.num_parallel_calls)
text_ds = tf.data.Dataset.zip((text_ds, text_size_ds))
if self.infer_without_label:
data_set = text_ds
else:
intent_label_ds = process_one_label_dataset(
intent_label_ds, self.config, output_index=0)
slots_label_ds = process_multi_label_dataset(
slots_label_ds, self.config, output_index=1)
data_set = tf.data.Dataset.zip((text_ds, intent_label_ds, slots_label_ds))
self.config['data']['vocab_size'] = get_vocab_size(
self.text_vocab_file_path)
self.config['data']['{}_data_size'.format(self.mode)] = get_file_len(
self.paths_after_pre_process)
return data_set
def generate_data(self):
"""Generate data for offline training."""
paths = self.paths
if self.infer_without_label:
self.column_num = 1
text_ds = load_textline_dataset(paths, self.column_num)
else:
self.column_num = 2
label_ds, text_ds = load_textline_dataset(paths, self.column_num)
logging.info("process text ds...")
input_pipeline_func = self.get_input_pipeline(for_export=False)
text_ds = text_ds.map(input_pipeline_func,
num_parallel_calls=self.num_parallel_calls)
text_size_ds = text_ds.map(
lambda x: compute_sen_lens(x, padding_token=0),
num_parallel_calls=self.num_parallel_calls)
text_ds = tf.data.Dataset.zip((text_ds, text_size_ds))
logging.info("process label ds...")
if self.infer_without_label:
data_set = text_ds
else:
label_ds = process_multi_label_dataset(label_ds, self.config)
data_set = tf.data.Dataset.zip((text_ds, label_ds))
self.config['data']['vocab_size'] = get_vocab_size(
self.text_vocab_file_path)
self.config['data']['{}_data_size'.format(self.mode)] = get_file_len(
self.paths)
return data_set
def export_inputs(self):
"""Inputs for exported model."""
vocab_dict = load_vocab_dict(self.text_vocab_file_path)
vocab_size = len(vocab_dict)
if self.split_token != "":
if self.split_token not in vocab_dict:
raise ValueError(
"The Model uses split token: {}, not in corpus.".format(
self.split_token))
self.config['data']['split_token'] = int(vocab_dict[self.split_token])
self.config['data']['vocab_size'] = vocab_size
input_sentence = tf.placeholder(
shape=(None,), dtype=tf.string, name="input_sentence")
input_pipeline_func = self.get_input_pipeline(for_export=True)
token_ids = input_pipeline_func(input_sentence)
token_ids_len = tf.map_fn(lambda x: compute_sen_lens(x, padding_token=0),
token_ids)
export_data = {
"export_inputs": {
"input_sentence": input_sentence
},
"model_inputs": {
"input_enc_x": token_ids,
"input_x_len": token_ids_len
}
}
return export_data
def call(self, inputs, training=None, mask=None): # pylint: disable=too-many-locals
input_x = inputs["input_x"]
if self.use_dense_task:
dense_input = inputs["input_dense"]
if self.use_true_length:
# [batch_size, max_doc_len, max_sen_len]
input_hx = self.pad_to_hier_input_true_len(
input_x,
self.max_doc_len,
self.max_sen_len,
self.split_token,
padding_token=self.padding_token)
else:
# [batch_size, max_doc_len, max_sen_len]
input_hx = self.pad_to_hier_input(
input_x,
self.max_doc_len,
self.max_sen_len,
padding_token=self.padding_token)
# [batch_size, max_doc_len]
sen_lens = compute_sen_lens(input_hx, padding_token=self.padding_token)
# [batch_size]
doc_lens = compute_doc_lens(sen_lens)
# [batch_size, max_doc_len, max_sen_len, 1]
sen_mask = tf.expand_dims(
tf.sequence_mask(sen_lens, self.max_sen_len, dtype=tf.float32), axis=-1)
# [batch_size, max_doc_len, 1]
doc_mask = tf.expand_dims(
tf.sequence_mask(doc_lens, self.max_doc_len, dtype=tf.float32), axis=-1)
# [batch_size, max_doc_len, max_sen_len, embed_len]
out = self.embed(input_hx)
if self.use_pretrained_model:
input_px = self.get_pre_train_graph(input_x)
input_px = tf.reshape(input_px, [-1, self.max_doc_len,
self.max_sen_len, self.pretrained_model_dim])
out = tf.concat([out, input_px], axis=-1)
out = self.embed_d(out, training=training)
all_sen_encoder = tf.keras.layers.TimeDistributed(self.sen_encoder)
# [batch_size, max_doc_len, features]
out = all_sen_encoder(out, training=training, mask=sen_mask)
# [batch_size, features]
out = self.doc_encoder(out, training=training, mask=doc_mask)
if self.use_dense_input:
dense_out = self.dense_input_linear(dense_input)
if self.only_dense_input:
out = dense_out
else:
out = tf.keras.layers.Concatenate()([out, dense_out])
# [batch_size, class_num]
scores = self.final_dense(out)
return scores
def generate_data(self):
"""Generate data for offline training."""
text, label = load_cls_raw_data(paths=self.paths_after_pre_process,
mode=self.mode)
text_placeholder = tf.placeholder(tf.string,
shape=(None, ),
name="text")
label_placeholder = tf.placeholder(tf.string, name="label")
self.init_feed_dict[text_placeholder] = text
self.init_feed_dict[label_placeholder] = label
# logging.debug("init_feed_dict: {}".format(self.init_feed_dict))
text_ds = tf.data.Dataset.from_tensor_slices(text_placeholder)
input_pipeline_func = self.get_input_pipeline(for_export=False)
text_ds = text_ds.map(input_pipeline_func,
num_parallel_calls=self.num_parallel_calls)
text_size_ds = text_ds.map(
lambda x: compute_sen_lens(x, padding_token=utils.PAD_IDX),
num_parallel_calls=self.num_parallel_calls)
text_ds = tf.data.Dataset.zip((text_ds, text_size_ds))
if self.use_dense:
dense = load_npy(self.dense_npy)
dense_ds = load_dense_dataset(dense)
if self.infer_without_label:
if self.use_dense:
data_set = tf.data.Dataset.zip((text_ds, dense_ds))
else:
data_set = text_ds
else:
label_ds = load_one_label_dataset(label_placeholder, self.config)
if self.use_dense:
data_set = tf.data.Dataset.zip((text_ds, dense_ds, label_ds))
else:
data_set = tf.data.Dataset.zip((text_ds, label_ds))
vocab_dict = load_vocab_dict(self.text_vocab_file_path)
vocab_size = len(vocab_dict)
data_size = len(text)
if self.split_token != "":
if self.split_token not in vocab_dict:
raise ValueError(
"The Model uses split token: {}, not in corpus.".format(
self.split_token))
self.config['data']['split_token'] = int(
vocab_dict[self.split_token])
self.config['data']['vocab_size'] = vocab_size
self.config['data']['{}_data_size'.format(self.mode)] = data_size
return data_set
def load_text_dataset(self, text_ds):
"""Load text data set."""
logging.info("Loading text dataset...")
input_pipeline_func = self.get_input_pipeline(for_export=False)
text_ds = text_ds.map(
input_pipeline_func, num_parallel_calls=self.num_parallel_calls)
text_size_ds = text_ds.map(
lambda x: compute_sen_lens(x, padding_token=0),
num_parallel_calls=self.num_parallel_calls)
text_ds = tf.data.Dataset.zip((text_ds, text_size_ds))
return text_ds
def generate_data(self):
"""Generate data for offline training."""
if self.infer_without_label:
column_num = 1
text_ds = load_textline_dataset(self.paths_after_pre_process,
column_num)
else:
column_num = 2
label_ds, text_ds = load_textline_dataset(
self.paths_after_pre_process, column_num)
input_pipeline_func = self.get_input_pipeline(for_export=False)
text_ds = text_ds.map(input_pipeline_func,
num_parallel_calls=self.num_parallel_calls)
text_size_ds = text_ds.map(
lambda x: compute_sen_lens(x, padding_token=utils.PAD_IDX),
num_parallel_calls=self.num_parallel_calls)
text_ds = tf.data.Dataset.zip((text_ds, text_size_ds))
if self.use_dense:
dense = load_npy(self.dense_npy)
dense_ds = load_dense_dataset(dense)
if self.infer_without_label:
if self.use_dense:
data_set = tf.data.Dataset.zip((text_ds, dense_ds))
else:
data_set = text_ds
else:
label_ds = process_one_label_dataset(label_ds, self.config)
if self.use_dense:
data_set = tf.data.Dataset.zip((text_ds, dense_ds, label_ds))
else:
data_set = tf.data.Dataset.zip((text_ds, label_ds))
vocab_dict = load_vocab_dict(self.text_vocab_file_path)
vocab_size = len(vocab_dict)
if self.split_token != "":
if self.split_token not in vocab_dict:
raise ValueError(
"The Model uses split token: {}, not in corpus.".format(
self.split_token))
self.config['data']['split_token'] = int(
vocab_dict[self.split_token])
self.config['data']['vocab_size'] = vocab_size
self.config['data']['{}_data_size'.format(self.mode)] = get_file_len(
self.paths_after_pre_process)
return data_set
def generate_data(self):
"""Generate data for offline training."""
if self.infer_without_label:
column_num = 2
text_ds_left, text_ds_right = load_textline_dataset(
self.paths_after_pre_process, column_num)
else:
column_num = 3
label, text_ds_left, text_ds_right = load_textline_dataset(
self.paths_after_pre_process, column_num)
input_pipeline_func = self.get_input_pipeline(for_export=False)
text_ds_left = text_ds_left.map(
input_pipeline_func, num_parallel_calls=self.num_parallel_calls)
text_ds_right = text_ds_right.map(
input_pipeline_func, num_parallel_calls=self.num_parallel_calls)
text_size_ds_left = text_ds_left.map(
lambda x: compute_sen_lens(x, padding_token=0),
num_parallel_calls=self.num_parallel_calls)
text_size_ds_right = text_ds_right.map(
lambda x: compute_sen_lens(x, padding_token=0),
num_parallel_calls=self.num_parallel_calls)
text_ds_left_right = tf.data.Dataset.zip((text_ds_left, text_ds_right))
text_len_left_right = tf.data.Dataset.zip(
(text_size_ds_left, text_size_ds_right))
if self.infer_without_label:
data_set_left_right = text_ds_left_right
else:
label_ds = process_one_label_dataset(label, self.config)
data_set_left_right = tf.data.Dataset.zip(
(text_ds_left_right, label_ds))
vocab_dict = load_vocab_dict(self.text_vocab_file_path)
vocab_size = len(vocab_dict)
self.config['data']['vocab_size'] = vocab_size
self.config['data']['{}_data_size'.format(self.mode)] = get_file_len(
self.paths_after_pre_process)
return data_set_left_right, text_len_left_right
def call(self, inputs, training=None, mask=None):
enc_inputs = inputs["input_enc_x"]
seq_enc_len = compute_sen_lens(enc_inputs,
padding_token=self.padding_token)
enc_mask = self.mask_layer(enc_inputs)
enc_inputs = self.embed(enc_inputs)
enc_inputs = self.embed_d(enc_inputs)
enc_outputs, enc_state = self.encoder(enc_inputs,
training=training,
mask=enc_mask)
if self.is_infer:
dec_outputs = self.decoder([enc_outputs, enc_state, seq_enc_len],
training=training)
return dec_outputs
else:
dec_inputs = inputs["input_dec_x"]
seq_dec_len = compute_sen_lens(dec_inputs,
padding_token=self.padding_token)
dec_outputs = self.decoder(
[dec_inputs, seq_dec_len, enc_outputs, enc_state, seq_enc_len],
training=training)
return dec_outputs
def test_compute_sen_lens(self):
sentences = tf.placeholder(dtype=tf.int32)
lens = compute_sen_lens(sentences)
with self.cached_session(use_gpu=False, force_gpu=False) as sess:
# test for 1d
res = sess.run(lens, feed_dict={sentences: [1, 2, 0, 0]})
self.assertEqual(res, 2)
# test for 2d
res = sess.run(lens,
feed_dict={sentences: [[1, 2, 0, 0], [1, 2, 3, 4]]})
self.assertAllEqual(res, [2, 4])
# test for 3d
res = sess.run(lens,
feed_dict={
sentences: [[[1, 2, 0, 0]], [[1, 2, 3, 4]],
[[1, 0, 0, 0]]]
})
self.assertAllEqual(res, [[2], [4], [1]])
def call(self, inputs, training=None, mask=None):
input_x = inputs["input_x"]
# [batch_size, max_len]
input_x_lens = compute_sen_lens(input_x,
padding_token=self.padding_token)
# [batch_size, max_len, 1]
mask = tf.expand_dims(tf.sequence_mask(input_x_lens,
self.max_len,
dtype=tf.float32),
axis=-1)
# [batch_size, max_len, embed_len]
out = self.embed(input_x)
# [batch_size, features]
out = self.embed_dropout(out, training=training)
out = self.bi_rnn(out)
intent_out = self.attention(out, mask=mask)
intent_out = self.dropout(intent_out)
intent_out = self.intent_dense(intent_out)
intent_out = tf.identity(intent_out, name="intent_logits")
slots_out = self.dropout(out)
slots_out = self.slots_dense(slots_out)
slots_out = tf.identity(slots_out, name="slots_logits")
return intent_out, slots_out
def generate_data(self):
"""Generate data for offline training."""
column_num = 1
src_path = self.src_paths_after_pre_process
target_path = self.tgt_paths_after_pre_process
src_ds = load_textline_dataset([src_path], column_num)
src_ds = src_ds[0]
input_pipeline_func = self.get_input_pipeline(for_export=False)
src_ds = src_ds.map(input_pipeline_func,
num_parallel_calls=self.num_parallel_calls)
src_size_ds = src_ds.map(
lambda x: compute_sen_lens(x, padding_token=utils.PAD_IDX),
num_parallel_calls=self.num_parallel_calls)
src_ds = src_ds.map(self.exclude_padding,
num_parallel_calls=self.num_parallel_calls)
if self.infer_without_label:
data_set = tf.data.Dataset.zip((src_ds, src_size_ds))
else:
tgt = load_textline_dataset([target_path], column_num)
tgt = tgt[0]
tgt_out_ds = tgt.map(lambda x: x + ' ' + self.END_TOKEN)
tgt_in_ds = tgt.map(lambda x: self.START_TOKEN + ' ' + x)
tgt_in_ds = tgt_in_ds.map(
lambda batch: self.text_pipeline_func(
batch, self.max_dec_len, self.text_vocab_file_path),
num_parallel_calls=self.num_parallel_calls)
tgt_in_size_ds = tgt_in_ds.map(
lambda x: compute_sen_lens(x, padding_token=utils.PAD_IDX),
num_parallel_calls=self.num_parallel_calls)
tgt_in_ds = tgt_in_ds.map(
self.exclude_padding,
num_parallel_calls=self.num_parallel_calls)
inp_ds = tf.data.Dataset.zip(
(src_ds, src_size_ds, tgt_in_ds, tgt_in_size_ds))
if self.use_label_vocab:
target_vocab_file_path = self.label_vocab_file_paths[0]
else:
target_vocab_file_path = self.text_vocab_file_path
tgt_out_ds = tgt_out_ds.map(
lambda batch: self.text_pipeline_func(batch, self.max_dec_len,
target_vocab_file_path),
num_parallel_calls=self.num_parallel_calls)
tgt_out_ds = tgt_out_ds.map(
self.exclude_padding,
num_parallel_calls=self.num_parallel_calls)
data_set = tf.data.Dataset.zip((inp_ds, tgt_out_ds))
vocab_dict = load_vocab_dict(self.text_vocab_file_path)
vocab_size = len(vocab_dict)
label_vocab_dict = load_vocab_dict(self.label_vocab_file_paths[0])
label_vocab_size = len(label_vocab_dict)
data_size = get_file_len(self.src_paths_after_pre_process)
self.config['data']['vocab_size'] = vocab_size
self.config['data']['label_vocab_size'] = label_vocab_size
self.config['data']['{}_data_size'.format(self.mode)] = data_size
return data_set
def generate_data(self):
"""Generate data for offline training."""
src = load_seq2seq_raw_data(paths=self.src_paths_after_pre_process)
tgt = load_seq2seq_raw_data(paths=self.tgt_paths_after_pre_process)
tgt_out = [abs_ + ' ' + self.END_TOKEN for abs_ in tgt]
tgt_in = [self.START_TOKEN + ' ' + abs_ for abs_ in tgt]
assert len(src) == len(tgt_in)
src_placeholder = tf.placeholder(tf.string, shape=(None,), name="src")
tgt_out_placeholder = tf.placeholder(tf.string, name="tgt_out")
tgt_in_placeholder = tf.placeholder(tf.string, name="tgt_in")
self.init_feed_dict[src_placeholder] = src
self.init_feed_dict[tgt_out_placeholder] = tgt_out
self.init_feed_dict[tgt_in_placeholder] = tgt_in
src_ds = tf.data.Dataset.from_tensor_slices(src_placeholder)
tgt_in_ds = tf.data.Dataset.from_tensor_slices(tgt_in_placeholder)
tgt_out_ds = tf.data.Dataset.from_tensor_slices(tgt_out_placeholder)
input_pipeline_func = self.get_input_pipeline(for_export=False)
src_ds = src_ds.map(
input_pipeline_func, num_parallel_calls=self.num_parallel_calls)
src_size_ds = src_ds.map(
lambda x: compute_sen_lens(x, padding_token=utils.PAD_IDX),
num_parallel_calls=self.num_parallel_calls)
src_ds = src_ds.map(self.exclude_padding, num_parallel_calls=self.num_parallel_calls)
tgt_in_ds = tgt_in_ds.map(
lambda batch: self.text_pipeline_func(batch, self.max_dec_len,
self.text_vocab_file_path),
num_parallel_calls=self.num_parallel_calls)
tgt_in_size_ds = tgt_in_ds.map(
lambda x: compute_sen_lens(x, padding_token=utils.PAD_IDX),
num_parallel_calls=self.num_parallel_calls)
tgt_in_ds = tgt_in_ds.map(self.exclude_padding, num_parallel_calls=self.num_parallel_calls)
inp_ds = tf.data.Dataset.zip((src_ds, src_size_ds, tgt_in_ds, tgt_in_size_ds))
if self.infer_without_label:
data_set = inp_ds
else:
if self.use_label_vocab:
target_vocab_file_path = self.label_vocab_file_paths[0]
else:
target_vocab_file_path = self.text_vocab_file_path
tgt_out_ds = tgt_out_ds.map(
lambda batch: self.text_pipeline_func(batch, self.max_dec_len,
target_vocab_file_path),
num_parallel_calls=self.num_parallel_calls)
tgt_out_ds = tgt_out_ds.map(
self.exclude_padding, num_parallel_calls=self.num_parallel_calls
)
data_set = tf.data.Dataset.zip((inp_ds, tgt_out_ds))
vocab_dict = load_vocab_dict(self.text_vocab_file_path)
vocab_size = len(vocab_dict)
label_vocab_dict = load_vocab_dict(self.label_vocab_file_paths[0])
label_vocab_size = len(label_vocab_dict)
data_size = len(src)
self.config['data']['vocab_size'] = vocab_size
self.config['data']['label_vocab_size'] = label_vocab_size
self.config['data']['{}_data_size'.format(self.mode)] = data_size
return data_set
