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run_testing_rs.py
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run_testing_rs.py
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# coding=utf-8
"""MPC-BERT-2.0 testing runner on the downstream task of response selection."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import operator
import os
from collections import defaultdict
from time import time
import tensorflow as tf
import metrics
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.")
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={
"ctx_id":
tf.compat.v1.FixedLenFeature([], tf.int64),
"rsp_id":
tf.compat.v1.FixedLenFeature([], tf.int64),
"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),
"label_ids":
tf.compat.v1.FixedLenFeature([], tf.float32),
}
)
# 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
ctx_id = input_data["ctx_id"]
rsp_id = input_data['rsp_id']
input_sents = input_data["input_sents"]
input_mask = input_data["input_mask"]
segment_ids = input_data["segment_ids"]
speaker_ids = input_data["speaker_ids"]
labels = input_data['label_ids']
return ctx_id, rsp_id, input_sents, input_mask, segment_ids, speaker_ids, labels
def create_model(bert_config, is_training, input_ids, input_mask, segment_ids, speaker_ids, labels,
num_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)
target_loss_weight = [1.0, 1.0]
target_loss_weight = tf.convert_to_tensor(target_loss_weight)
flagx = tf.cast(tf.greater(labels, 0), dtype=tf.float32)
flagy = tf.cast(tf.equal(labels, 0), dtype=tf.float32)
all_target_loss = target_loss_weight[1] * flagx + target_loss_weight[0] * flagy
output_layer = model.get_pooled_output()
hidden_size = output_layer.shape[-1].value
output_weights = tf.compat.v1.get_variable(
"output_weights", [num_labels, hidden_size],
initializer=tf.compat.v1.truncated_normal_initializer(stddev=0.02))
output_bias = tf.compat.v1.get_variable(
"output_bias", [num_labels], initializer=tf.zeros_initializer())
with tf.compat.v1.variable_scope("loss"):
# if is_training:
# output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
output_layer = tf.compat.v1.layers.dropout(output_layer, rate=0.1, training=is_training)
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
probabilities = tf.sigmoid(logits, name="prob")
logits = tf.squeeze(logits, [1])
losses = tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels)
losses = tf.multiply(losses, all_target_loss)
mean_loss = tf.reduce_mean(losses, name="mean_loss") + sum(
tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.REGULARIZATION_LOSSES))
with tf.name_scope("accuracy"):
correct_prediction = tf.equal(tf.sign(probabilities - 0.5), tf.sign(labels - 0.5))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"), name="accuracy")
return mean_loss, logits, probabilities, accuracy
best_score = 0.0
def run_test(dir_path, op_name, sess, training, accuracy, prob, pair_ids):
step = 0
t0 = time()
num_test = 0
num_correct = 0.0
mrr = 0
results = defaultdict(list)
try:
while True:
step += 1
batch_accuracy, predicted_prob, batch_pair_ids = sess.run([accuracy, prob, pair_ids],
feed_dict={training: False})
question_id, answer_id, label = batch_pair_ids
num_test += len(predicted_prob)
num_correct += len(predicted_prob) * batch_accuracy
for i, prob_score in enumerate(predicted_prob):
results[question_id[i]].append((answer_id[i], label[i], prob_score[0]))
if step % 100 == 0:
tf.compat.v1.logging.info("n_update %d , %s: Mins Used: %.2f" %
(step, op_name, (time() - t0) / 60.0))
except tf.errors.OutOfRangeError:
print('num_test_samples: {} test_accuracy: {}'.format(num_test, num_correct / num_test))
accu, precision, recall, f1, loss = metrics.classification_metrics(results)
print('Accuracy: {}, Precision: {} Recall: {} F1: {} Loss: {}'.format(accu, precision, recall, f1, loss))
mvp = metrics.mean_average_precision(results)
mrr = metrics.mean_reciprocal_rank(results)
top_1_precision = metrics.top_1_precision(results)
total_valid_query = metrics.get_num_valid_query(results)
print(
'MAP (mean average precision: {}\tMRR (mean reciprocal rank): {}\tTop-1 precision: {}\tNum_query: {}'.format(
mvp, mrr, top_1_precision, total_valid_query))
out_path = os.path.join(dir_path, "output_test.txt")
print("Saving evaluation to {}".format(out_path))
with open(out_path, 'w') as f:
f.write("query_id\tdocument_id\tscore\trank\trelevance\n")
for us_id, v in results.items():
v.sort(key=operator.itemgetter(2), reverse=True)
for i, rec in enumerate(v):
r_id, label, prob_score = rec
rank = i + 1
f.write('{}\t{}\t{}\t{}\t{}\n'.format(us_id, r_id, prob_score, rank, label))
return mrr
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()
ctx_id, rsp_id, input_sents, input_mask, segment_ids, speaker_ids, labels = iterator.get_next()
pair_ids = [ctx_id, rsp_id, labels]
training = tf.compat.v1.placeholder(tf.bool)
mean_loss, logits, probabilities, 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,
labels=labels,
num_labels=1,
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(FLAGS.restore_model_dir, "test", sess, training, accuracy, probabilities, pair_ids)
if __name__ == "__main__":
tf.compat.v1.app.run()