def evaluate():
"""Extract embeddings."""
logdir = FLAGS.logdir
setup_eval_dir(logdir)
# Can ignore frame labels if dataset doesn't have per-frame labels.
CONFIG.DATA.FRAME_LABELS = FLAGS.keep_labels
# Subsample frames in case videos are long or fps is high to save memory.
CONFIG.DATA.SAMPLE_ALL_STRIDE = FLAGS.sample_all_stride
algo = get_algo(CONFIG.TRAINING_ALGO)
_, optimizer, _ = get_lr_opt_global_step()
restore_ckpt(logdir=logdir, optimizer=optimizer, **algo.model)
if FLAGS.defun:
algo.call = tf.function(algo.call)
algo.compute_loss = tf.function(algo.compute_loss)
iterator = create_one_epoch_dataset(FLAGS.dataset, FLAGS.split, mode='eval',
path_to_tfrecords=FLAGS.path_to_tfrecords)
max_embs = None if FLAGS.max_embs <= 0 else FLAGS.max_embs
embeddings = get_embeddings_dataset(
algo.model,
iterator,
frames_per_batch=FLAGS.frames_per_batch,
keep_data=FLAGS.keep_data,
keep_labels=FLAGS.keep_labels,
max_embs=max_embs)
np.save(gfile.Open(FLAGS.save_path, 'w'), embeddings)
def evaluate():
"""Evaluate embeddings."""
CONFIG.LOGDIR = FLAGS.logdir
logdir = CONFIG.LOGDIR
setup_eval_dir(logdir)
algo = get_algo(CONFIG.TRAINING_ALGO)
if FLAGS.defun:
algo.call = tf.function(algo.call)
algo.compute_loss = tf.function(algo.compute_loss)
iterator_tasks, embedding_tasks = get_tasks(CONFIG.EVAL.TASKS)
# Setup summary writer.
summary_writer = tf.summary.create_file_writer(os.path.join(
logdir, 'eval_logs'),
flush_millis=10000)
iterators = {}
if iterator_tasks:
# Setup Dataset Iterators from train and val datasets.
iterators['train_iterator'] = create_dataset('train', mode='eval')
iterators['val_iterator'] = create_dataset('val', mode='eval')
if FLAGS.continuous_eval:
for _ in tf.train.checkpoints_iterator(logdir,
timeout=1,
timeout_fn=timeout_fn):
evaluate_once(algo, iterator_tasks, embedding_tasks, iterators,
summary_writer)
else:
evaluate_once(algo, iterator_tasks, embedding_tasks, iterators,
summary_writer)