def _eval_all(sess):
"""Gathers all metrics for a ckpt."""
summaries = collections.defaultdict(list)
if eval_gold:
for midi_notes, buttons, seq_varlen in gold.gold_iterator([-6, 6]):
gold_diff_l1_seq, gold_diff_l2_seq = sess.run(
[gold_diff_l1, gold_diff_l2], {
gold_feat_dict["midi_pitches"]:
midi_notes,
gold_feat_dict["delta_times_int"]:
np.ones_like(midi_notes) * 8,
gold_seq_varlens: [seq_varlen],
gold_buttons:
buttons
})
summaries["gold_diff_l1"].append(gold_diff_l1_seq)
summaries["gold_diff_l2"].append(gold_diff_l2_seq)
while True:
try:
batches = sess.run(summary_name_to_batch_tensor)
except tf.errors.OutOfRangeError:
break
for name, scalar in batches.items():
summaries[name].append(scalar)
return summaries
def _eval_all(sess):
"""Gathers all metrics for a ckpt."""
summaries = collections.defaultdict(list)
if eval_gold:
for midi_notes, buttons, seq_varlen in gold.gold_iterator([-6, 6]):
gold_diff_l1_seq, gold_diff_l2_seq = sess.run(
[gold_diff_l1, gold_diff_l2], {
gold_feat_dict["midi_pitches"]:
midi_notes,
gold_feat_dict["delta_times_int"]:
np.ones_like(midi_notes) * 8,
gold_seq_varlens: [seq_varlen],
gold_buttons: buttons
})
summaries["gold_diff_l1"].append(gold_diff_l1_seq)
summaries["gold_diff_l2"].append(gold_diff_l2_seq)
while True:
try:
batches = sess.run(summary_name_to_batch_tensor)
except tf.errors.OutOfRangeError:
break
for name, scalar in batches.items():
summaries[name].append(scalar)
return summaries
