def main(_):
from vqa_eval import evaluate_model, write_result_log
from watch_model import ModelWatcher
def test_model(model_path):
with tf.Graph().as_default():
acc = test_once(model_path)
return acc
mode = 'ap_' if FLAGS.retrain else ''
ckpt_dir = FLAGS.checkpoint_dir % (mode,
FLAGS.version,
FLAGS.model_type)
if FLAGS.sample_negative:
ckpt_dir += '_sn'
if FLAGS.use_fb_data:
ckpt_dir += '_fb'
if FLAGS.use_fb_bn:
ckpt_dir += '_bn'
print(ckpt_dir)
watcher = ModelWatcher(ckpt_dir, test_model)
watcher.run()
def main(_):
from vqa_eval import evaluate_model, write_result_log
from watch_model import ModelWatcher
def test_model(model_path):
with tf.Graph().as_default():
res_file, quest_ids = test(model_path)
print(res_file)
acc, details = evaluate_model(res_file,
quest_ids,
version=FLAGS.version)
write_result_log(model_path, FLAGS.model_type, acc, details)
return acc
ckpt_dir = FLAGS.checkpoint_dir % (FLAGS.version, FLAGS.model_type)
if FLAGS.sample_negative:
ckpt_dir += '_sn'
if FLAGS.use_fb_data:
ckpt_dir += '_fb'
if FLAGS.use_fb_bn:
ckpt_dir += '_bn'
print(ckpt_dir)
watcher = ModelWatcher(ckpt_dir, test_model)
watcher.run()
def main(_):
from watch_model import ModelWatcher
subset = 'kptest'
# subset = 'kptrain'
target_split = 'train' if 'train' in subset else 'val'
def test_model(model_path):
with tf.Graph().as_default():
res_file = ivqa_decoding_beam_search(checkpoint_path=model_path,
subset=subset)
if FLAGS.mode == 'full':
cider = evaluate_oracle(res_file, split=target_split)
else:
cider = evaluate_question_standard(res_file)
return float(cider[1])
#
ckpt_dir = FLAGS.checkpoint_dir % (FLAGS.version, FLAGS.model_type)
# ckpt_dir = '/import/vision-ephemeral/fl302/models/v2_kpvaq_VAQ-RL_ft/'
# res_file = ivqa_decoding_beam_search(None,
# subset=subset)
# print(ckpt_dir)
watcher = ModelWatcher(ckpt_dir, test_model)
watcher.run()
def main(_):
from watch_model import ModelWatcher
def test_model(model_path):
with tf.Graph().as_default():
ap = test(model_path)
return ap
ckpt_dir = FLAGS.checkpoint_dir % (FLAGS.version, FLAGS.model_type)
print(ckpt_dir)
watcher = ModelWatcher(ckpt_dir, test_model)
watcher.run()
def main(_):
from watch_model import ModelWatcher
def test_model(model_path):
with tf.Graph().as_default():
ap = test(model_path)
return ap
# ckpt_dir = FLAGS.checkpoint_dir % (FLAGS.version, FLAGS.model_type)
ckpt_dir = '/data1/fl302/projects/vqa2.0/model/curr_VQA-Soft-QRD-LS_Res5c'
print(ckpt_dir)
watcher = ModelWatcher(ckpt_dir, test_model)
watcher.run()
def main(_):
from watch_model import ModelWatcher
subset = 'kpval'
def test_model(model_path):
with tf.Graph().as_default():
res_file = vaq_decoding_greedy(subset=subset)
cider = evaluate_question(res_file, subset=subset)
return cider
ckpt_dir = FLAGS.checkpoint_dir % FLAGS.model_type
print(ckpt_dir)
watcher = ModelWatcher(ckpt_dir, test_model)
watcher.run()
def main(_):
from watch_model import ModelWatcher
subset = 'kpval'
def test_model(model_path):
with tf.Graph().as_default():
res_file = ivqa_decoding_beam_search(subset=subset)
cider = evaluate_question(res_file,
subset=subset,
version=FLAGS.test_version)
return cider
ckpt_dir = FLAGS.checkpoint_dir % (FLAGS.version, FLAGS.model_type)
print(ckpt_dir)
watcher = ModelWatcher(ckpt_dir, test_model)
watcher.run()
def main():
from vqa_eval import evaluate_model, write_result_log
from watch_model import ModelWatcher
def test_model(model_path):
with tf.Graph().as_default():
res_file, quest_ids = test(model_path)
print(res_file)
acc, details = evaluate_model(res_file, quest_ids, version='v1')
write_result_log(model_path, 'Fusion', acc, details)
return acc
ckpt_dir = FLAGS.checkpoint_dir % ('v1', 'Fusion')
# print(ckpt_dir)
# test_model(ckpt_dir)
watcher = ModelWatcher(ckpt_dir, test_model)
watcher.run()
def main(_):
from watch_model import ModelWatcher
subset = 'kpval'
def test_model(model_path):
with tf.Graph().as_default():
res_file = ivqa_decoding_beam_search(checkpoint_path=model_path,
subset=subset)
cider = evaluate_question(res_file,
subset=subset,
version=FLAGS.test_version)
return cider
# ckpt_dir = FLAGS.checkpoint_dir % (FLAGS.version, FLAGS.model_type)
ckpt_dir = '/import/vision-ephemeral/fl302/models/v2_kpvaq_VAQ-Mixer_ft'
print(ckpt_dir)
watcher = ModelWatcher(ckpt_dir, test_model)
watcher.run()
def main(_):
from watch_model import ModelWatcher
subset = FLAGS.subset
def test_model(model_path):
with tf.Graph().as_default():
res_file = ivqa_decoding_beam_search(checkpoint_path=model_path,
subset=subset)
cider = evaluate_oracle(res_file)
return cider
ckpt_dir = FLAGS.checkpoint_dir % (FLAGS.version, FLAGS.model_type)
# ckpt_dir = '/import/vision-ephemeral/fl302/models/v2_kpvaq_VAQ-RL_ft/'
# res_file = ivqa_decoding_beam_search(None,
# subset=subset)
# print(ckpt_dir)
watcher = ModelWatcher(ckpt_dir, test_model)
watcher.run()
def main(_):
from vqa_eval import evaluate_model, write_result_log
from watch_model import ModelWatcher
def test_model(model_path):
with tf.Graph().as_default():
res_file, quest_ids = test()
print(res_file)
acc, details = evaluate_model(res_file,
quest_ids,
version=FLAGS.version)
write_result_log(model_path, FLAGS.model_type, acc, details)
return acc
ckpt_dir = FLAGS.checkpoint_dir % (FLAGS.model_trainset, FLAGS.model_type)
print(ckpt_dir)
watcher = ModelWatcher(ckpt_dir, test_model)
watcher.run()