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train.py
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train.py
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#!/usr/bin/env python
from __future__ import absolute_import
from __future__ import division
import os
import time
import subprocess
import sys
import tensorflow as tf
from model import VisCoref
import util
def set_log_file(fname):
tee = subprocess.Popen(['tee', fname], stdin=subprocess.PIPE)
os.dup2(tee.stdin.fileno(), sys.stdout.fileno())
os.dup2(tee.stdin.fileno(), sys.stderr.fileno())
if __name__ == "__main__":
config = util.initialize_from_env()
log_dir = config["log_dir"]
writer = tf.summary.FileWriter(log_dir, flush_secs=20)
log_file = os.path.join(log_dir, 'train.log')
set_log_file(log_file)
report_frequency = config["report_frequency"]
eval_frequency = config["eval_frequency"]
tf.set_random_seed(config['random_seed'])
model = VisCoref(config)
saver = tf.train.Saver()
max_f1 = 0
config_tf = tf.ConfigProto()
config_tf.gpu_options.allow_growth = True
with tf.Session(config=config_tf) as session:
session.run(tf.global_variables_initializer())
model.start_enqueue_thread(session)
accumulated_loss = 0.0
ckpt = tf.train.get_checkpoint_state(log_dir)
if ckpt and ckpt.model_checkpoint_path:
print(f"Restoring from: {ckpt.model_checkpoint_path}")
saver.restore(session, ckpt.model_checkpoint_path)
max_f1 = session.run(model.max_eval_f1)
print(f'Restoring from max f1 of {max_f1:.2f}')
initial_time = time.time()
while True:
tf_loss, tf_global_step, _ = session.run([model.loss, model.global_step, model.train_op])
accumulated_loss += tf_loss
if tf_global_step == 1 or tf_global_step % report_frequency == 0:
total_time = time.time() - initial_time
steps_per_second = tf_global_step / total_time
average_loss = accumulated_loss / report_frequency
print(f"[{tf_global_step}] loss={average_loss:.4f}, steps/s={steps_per_second:.2f}")
writer.add_summary(util.make_summary({"loss": average_loss}), tf_global_step)
accumulated_loss = 0.0
if tf_global_step == 1 or tf_global_step % eval_frequency == 0:
eval_summary, eval_f1 = model.evaluate(session)
_ = session.run(model.update_max_f1)
saver.save(session, os.path.join(log_dir, "model"), global_step=tf_global_step)
if eval_f1 > max_f1:
max_f1 = eval_f1
util.copy_checkpoint(os.path.join(log_dir, "model-{}".format(tf_global_step)), os.path.join(log_dir, "model.max.ckpt"))
writer.add_summary(eval_summary, tf_global_step)
writer.add_summary(util.make_summary({"max_eval_f1": max_f1}), tf_global_step)
print(f"[{tf_global_step}] evaL_f1={eval_f1:.2f}, max_f1={max_f1:.2f}")
if tf_global_step >= config['max_step']:
print('Training finishes due to reaching max steps')
break