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run_testing_si.py
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# coding=utf-8
"""MPC-BERT-2.0 testing runner on the downstream task of speaker identification."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from time import time
import tensorflow as tf
import modeling_speaker as modeling
flags = tf.compat.v1.flags
FLAGS = flags.FLAGS
flags.DEFINE_string("task_name", 'Testing',
"The name of the task.")
flags.DEFINE_string("test_dir", 'test.tfrecord',
"The input test data dir. Should contain the .tsv files (or other data files) for the task.")
flags.DEFINE_string("restore_model_dir", 'output/',
"The output directory where the model checkpoints have been written.")
flags.DEFINE_string("bert_config_file", 'uncased_L-12_H-768_A-12/bert_config.json',
"The config json file corresponding to the pre-trained BERT model. "
"This specifies the model architecture.")
flags.DEFINE_bool("do_eval", True,
"Whether to run eval on the dev set.")
flags.DEFINE_integer("eval_batch_size", 32,
"Total batch size for predict.")
flags.DEFINE_integer("max_seq_length", 320,
"The maximum total input sequence length after WordPiece tokenization. "
"Sequences longer than this will be truncated, and sequences shorter "
"than this will be padded.")
flags.DEFINE_integer("max_utr_num", 7,
"Maximum utterance number.")
def print_configuration_op(FLAGS):
print('My Configurations:')
for name, value in FLAGS.__flags.items():
value = value.value
if type(value) == float:
print(' %s:\t %f' % (name, value))
elif type(value) == int:
print(' %s:\t %d' % (name, value))
elif type(value) == str:
print(' %s:\t %s' % (name, value))
elif type(value) == bool:
print(' %s:\t %s' % (name, value))
else:
print('%s:\t %s' % (name, value))
print('End of configuration')
def count_data_size(file_name):
sample_nums = 0
for record in tf.compat.v1.python_io.tf_record_iterator(file_name):
sample_nums += 1
return sample_nums
def parse_exmp(serial_exmp):
input_data = tf.compat.v1.parse_single_example(serial_exmp,
features={
"input_sents":
tf.compat.v1.FixedLenFeature([FLAGS.max_seq_length],
tf.int64),
"input_mask":
tf.compat.v1.FixedLenFeature([FLAGS.max_seq_length],
tf.int64),
"segment_ids":
tf.compat.v1.FixedLenFeature([FLAGS.max_seq_length],
tf.int64),
"speaker_ids":
tf.compat.v1.FixedLenFeature([FLAGS.max_seq_length],
tf.int64),
"cls_positions":
tf.compat.v1.FixedLenFeature([FLAGS.max_utr_num], tf.int64),
"rsp_position":
tf.compat.v1.FixedLenFeature([1], tf.int64),
"label_ids":
tf.compat.v1.FixedLenFeature([FLAGS.max_utr_num], tf.int64),
}
)
# So cast all int64 to int32.
for name in list(input_data.keys()):
t = input_data[name]
if t.dtype == tf.int64:
t = tf.cast(t, tf.int32)
input_data[name] = t
input_sents = input_data["input_sents"]
input_mask = input_data["input_mask"]
segment_ids = input_data["segment_ids"]
speaker_ids = input_data["speaker_ids"]
cls_positions = input_data["cls_positions"]
rsp_position = input_data["rsp_position"]
labels = input_data['label_ids']
return input_sents, input_mask, segment_ids, speaker_ids, cls_positions, rsp_position, labels
def gather_indexes(sequence_tensor, positions):
"""Gathers the vectors at the specific positions over a minibatch."""
# sequence_tensor = [batch_size, seq_length, width]
# positions = [batch_size, max_utr_num]
sequence_shape = modeling.get_shape_list(sequence_tensor, expected_rank=3)
batch_size = sequence_shape[0]
seq_length = sequence_shape[1]
width = sequence_shape[2]
flat_offsets = tf.reshape(
tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1]) # [batch_size, 1]
flat_positions = tf.reshape(positions + flat_offsets, [-1]) # [batch_size*max_utr_num, ]
flat_sequence_tensor = tf.reshape(sequence_tensor,
[batch_size * seq_length, width])
output_tensor = tf.gather(flat_sequence_tensor, flat_positions) # [batch_size*max_utr_num, width]
return output_tensor
def create_model(bert_config, is_training, input_ids, input_mask, segment_ids, speaker_ids, cls_positions, rsp_position,
labels, use_one_hot_embeddings):
"""Creates a classification model."""
model = modeling.BertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
speaker_ids=speaker_ids,
use_one_hot_embeddings=use_one_hot_embeddings)
input_tensor = gather_indexes(model.get_sequence_output(), cls_positions) # [batch_size*max_utr_num, dim]
input_shape = modeling.get_shape_list(input_tensor, expected_rank=2)
width = input_shape[-1]
positions_shape = modeling.get_shape_list(cls_positions, expected_rank=2)
max_utr_num = positions_shape[-1]
with tf.compat.v1.variable_scope("cls/speaker_restore"):
# We apply one more non-linear transformation before the output layer.
with tf.compat.v1.variable_scope("transform"):
input_tensor = tf.compat.v1.layers.dense(
input_tensor,
units=bert_config.hidden_size,
activation=modeling.get_activation(bert_config.hidden_act),
kernel_initializer=modeling.create_initializer(bert_config.initializer_range))
input_tensor = modeling.layer_norm(input_tensor) # [batch_size*max_utr_num, dim]
input_tensor = tf.reshape(input_tensor, [-1, max_utr_num, width]) # [batch_size, max_utr_num, dim]
rsp_tensor = gather_indexes(input_tensor, rsp_position) # [batch_size*1, dim]
rsp_tensor = tf.reshape(rsp_tensor, [-1, 1, width]) # [batch_size, 1, dim]
output_weights = tf.compat.v1.get_variable(
"output_weights",
shape=[width, width],
initializer=modeling.create_initializer(bert_config.initializer_range))
logits = tf.matmul(tf.einsum('aij,jk->aik', rsp_tensor, output_weights),
input_tensor, transpose_b=True) # [batch_size, 1, max_utr_num]
logits = tf.squeeze(logits, [1]) # [batch_size, max_utr_num]
mask = tf.sequence_mask(tf.reshape(rsp_position, [-1, ]), max_utr_num,
dtype=tf.float32) # [batch_size, max_utr_num]
logits = logits * mask + -1e9 * (1 - mask)
log_probs = tf.nn.log_softmax(logits, axis=-1) # [batch_size, max_utr_num]
# loss
one_hot_labels = tf.cast(labels, "float") # [batch_size, max_utr_num]
per_example_loss = - tf.reduce_sum(log_probs * one_hot_labels, axis=[-1]) # [batch_size, ]
mean_loss = tf.reduce_mean(per_example_loss, name="mean_loss")
# accuracy
predictions = tf.argmax(log_probs, axis=-1, output_type=tf.int32) # [batch_size, ]
predictions_one_hot = tf.one_hot(predictions, depth=max_utr_num, dtype=tf.float32) # [batch_size, max_utr_num]
correct_prediction = tf.reduce_sum(predictions_one_hot * one_hot_labels, -1) # [batch_size, ]
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"), name="accuracy")
return mean_loss, logits, log_probs, accuracy
def run_test(sess, training, prob, accuracy):
step = 0
t0 = time()
num_test = 0
num_correct = 0.0
test_accuracy = 0
try:
while True:
step += 1
batch_accuracy, predicted_prob = sess.run([accuracy, prob], feed_dict={training: False})
num_test += len(predicted_prob)
num_correct += len(predicted_prob) * batch_accuracy
if step % 100 == 0:
tf.compat.v1.logging.info("Step %d, Time (min): %.2f" % (step, (time() - t0) / 60.0))
except tf.errors.OutOfRangeError:
test_accuracy = num_correct / num_test
print('num_test_samples: {}, test_accuracy: {}'.format(num_test, test_accuracy))
return test_accuracy
def main(_):
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
print_configuration_op(FLAGS)
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
test_data_size = count_data_size(FLAGS.test_dir)
tf.compat.v1.logging.info('test data size: {}'.format(test_data_size))
filenames = tf.compat.v1.placeholder(tf.string, shape=[None])
shuffle_size = tf.compat.v1.placeholder(tf.int64)
dataset = tf.compat.v1.data.TFRecordDataset(filenames)
dataset = dataset.map(parse_exmp) # Parse the record into tensors.
dataset = dataset.repeat(1)
# dataset = dataset.shuffle(shuffle_size)
dataset = dataset.batch(FLAGS.eval_batch_size)
iterator = dataset.make_initializable_iterator()
input_sents, input_mask, segment_ids, speaker_ids, cls_positions, rsp_position, labels = iterator.get_next()
training = tf.compat.v1.placeholder(tf.bool)
mean_loss, logits, log_probs, accuracy = create_model(bert_config=bert_config,
is_training=training,
input_ids=input_sents,
input_mask=input_mask,
segment_ids=segment_ids,
speaker_ids=speaker_ids,
cls_positions=cls_positions,
rsp_position=rsp_position,
labels=labels,
use_one_hot_embeddings=False)
config = tf.compat.v1.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
if FLAGS.do_eval:
with tf.compat.v1.Session(config=config) as sess:
tf.compat.v1.logging.info("*** Restore model ***")
ckpt = tf.train.get_checkpoint_state(FLAGS.restore_model_dir)
variables = tf.compat.v1.trainable_variables()
saver = tf.compat.v1.train.Saver(variables)
saver.restore(sess, ckpt.model_checkpoint_path)
tf.compat.v1.logging.info('Test begin')
sess.run(iterator.initializer,
feed_dict={filenames: [FLAGS.test_dir], shuffle_size: 1})
run_test(sess, training, log_probs, accuracy)
if __name__ == "__main__":
tf.compat.v1.app.run()