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Fix segment_eval_inference.py script. #113

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Fix segment_eval_inference.py script. #113

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30 changes: 18 additions & 12 deletions segment_eval_inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -46,7 +46,7 @@ def labels(self):

def to_file(self, file_name):
"""Materialize the GT mapping to file."""
with tf.gfile.Open(file_name, "w") as fobj:
with tf.io.gfile.GFile(file_name, "w") as fobj:
for k, v in self._labels.items():
seg_id, label = k
line = "%s,%s,%s\n" % (seg_id, label, v)
Expand Down Expand Up @@ -79,18 +79,18 @@ def read_labels(data_pattern, cache_path=""):
tf.logging.info("Reading cached labels from %s..." % cache_path)
return Labels.from_file(cache_path)
tf.enable_eager_execution()
data_paths = tf.gfile.Glob(data_pattern)
data_paths = tf.io.gfile.glob(data_pattern)
ds = tf.data.TFRecordDataset(data_paths, num_parallel_reads=50)
context_features = {
"id": tf.FixedLenFeature([], tf.string),
"segment_labels": tf.VarLenFeature(tf.int64),
"segment_start_times": tf.VarLenFeature(tf.int64),
"segment_scores": tf.VarLenFeature(tf.float32)
"id": tf.io.FixedLenFeature([], tf.string),
"segment_labels": tf.io.VarLenFeature(tf.int64),
"segment_start_times": tf.io.VarLenFeature(tf.int64),
"segment_scores": tf.io.VarLenFeature(tf.float32)
}

def _parse_se_func(sequence_example):
return tf.parse_single_sequence_example(sequence_example,
context_features=context_features)
return tf.io.parse_single_sequence_example(
sequence_example, context_features=context_features)

ds = ds.map(_parse_se_func)
rated_labels = {}
Expand All @@ -104,7 +104,8 @@ def _parse_se_func(sequence_example):
segment_scores = cxt_feature_val["segment_scores"].values.numpy()
for label, start_time, score in zip(segment_labels, segment_start_times,
segment_scores):
rated_labels[("%s:%d" % (video_id, start_time), label)] = score
rated_labels[("%s:%d" % (video_id.decode("utf8"), start_time),
label)] = score
batch_id = len(rated_labels) // batch_size
if batch_id != last_batch:
tf.logging.info("%d examples processed.", len(rated_labels))
Expand All @@ -129,10 +130,13 @@ def read_segment_predictions(file_path, labels, top_n=None):
a segment prediction list for each classes.
"""
cls_preds = {} # A label_id to pred list mapping.
with tf.gfile.Open(file_path) as fobj:
with tf.io.gfile.GFile(file_path) as fobj:
tf.logging.info("Reading predictions from %s..." % file_path)
for line in fobj:
label_id, pred_ids_val = line.split(",")
if not label_id.isdigit():
# Skip the header line.
continue
pred_ids = pred_ids_val.split(" ")
if top_n:
pred_ids = pred_ids[:top_n]
Expand Down Expand Up @@ -177,9 +181,11 @@ def main(unused_argv):
float(x) / len(class_preds) for x in range(len(class_preds), 0, -1)
]
seg_scored_preds.append(seg_scored_pred)
num_positives.append(positive_counter[label_id])
num_positives.append(positive_counter.get(label_id, 0))

map_cal.accumulate(seg_scored_preds, seg_labels, num_positives)
map_at_n = np.mean(map_cal.peek_map_at_n())
aps = map_cal.peek_map_at_n()
map_at_n = np.mean(aps)
tf.logging.info("Num classes: %d | mAP@%d: %.6f" %
(len(seg_preds), FLAGS.top_n, map_at_n))

Expand Down