def print_img(img, i):
img_url = FLAGS.image_url_prefix + img if not img.startswith(
"http://") else img
logging.info(
img_html.format(img_url, i, img, melt.epoch(), melt.step(),
melt.train_loss(), melt.eval_loss(), melt.duration(),
gezi.now_time()))
def print_img(img, i):
img_url = get_img_url(img)
logging.info(img_html.format(
img_url,
i,
img,
melt.epoch(),
melt.step(),
melt.train_loss(),
melt.eval_loss(),
melt.duration(),
gezi.now_time()))
def test_flow(ops,
names=None,
gen_feed_dict=None,
deal_results=None,
model_dir='./model',
model_name=None,
num_epochs=1,
num_interval_steps=100,
eval_times=0,
print_avg_loss=True,
sess=None):
"""
test flow, @TODO improve list result print
Args:
ops: eval ops
names: eval names
model_path: can be dir like ./model will fetch lates model in model dir , or be real model path like ./model/model.0.ckpt
@TODO num_epochs should be 1,but now has problem of loading model if set @FIXME, so now 0
"""
if sess is None:
sess = tf.InteractiveSession()
melt.restore(sess, model_dir, model_name)
if not os.path.isdir(model_dir):
model_dir = os.path.dirname(model_dir)
summary_op = tf.merge_all_summaries()
summary_writer = tf.train.SummaryWriter(model_dir, sess.graph)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
step = 0
eval_step = 0
avg_eval = AvgScore()
total_avg_eval = AvgScore()
while not coord.should_stop():
feed_dict = {} if gen_feed_dict is None else gen_feed_dict()
results = sess.run(ops, feed_dict=feed_dict)
if not isinstance(results, (list, tuple)):
results = [results]
if deal_results is not None:
#@TODO may need to pass summary_writer, and step
#use **args ?
deal_results(results)
if print_avg_loss:
results = gezi.get_singles(results)
avg_eval.add(results)
total_avg_eval.add(results)
if step % num_interval_steps == 0:
average_eval = avg_eval.avg_score()
print(
'{}: average evals = {}'.format(
gezi.now_time(),
melt.value_name_list_str(average_eval, names)),
'step:', step)
summary = tf.Summary()
summary_str = sess.run(summary_op, feed_dict=feed_dict)
summary.ParseFromString(summary_str)
for i in xrange(len(results)):
name = i if names is None else names[i]
summary.value.add(tag='metric{}'.format(name),
simple_value=average_eval[i])
summary_writer.add_summary(summary, step)
if eval_step and eval_step == eval_times:
break
eval_step += 1
step += 1
print('Done testing for {} epochs, {} steps. AverageEvals:{}'.format(
num_epochs, step, gezi.pretty_floats(total_avg_eval.avg_score())))
except tf.errors.OutOfRangeError:
print('Done testing for {} epochs, {} steps. AverageEvals:{}'.format(
num_epochs, step, gezi.pretty_floats(total_avg_eval.avg_score())))
finally:
# When done, ask the threads to stop.
coord.request_stop()
# Wait for threads to finish.
coord.join(threads)
def format(self, record):
elapsed_seconds = record.created - self.start_time
#using timedelta here for convenient default formatting
elapsed = timedelta(seconds=elapsed_seconds)
return "{} {} {}".format(gezi.now_time(),
str(elapsed)[:-7], record.getMessage())
