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update data define #30

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update data define #30

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3 changes: 3 additions & 0 deletions inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -146,6 +146,9 @@ def set_up_init_ops(variables):
tf.GraphKeys.LOCAL_VARIABLES)))

coord = tf.train.Coordinator()
runners_to_remove = [q_runner for q_runner in sess.graph.get_collection_ref("queue_runners") if q_runner.name == "train_input/input_producer" or q_runner.name == "train_input/shuffle_batch_join/random_shuffle_queue"]
for runner in runners_to_remove:
sess.graph.get_collection_ref("queue_runners").remove(runner)
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
num_examples_processed = 0
start_time = time.time()
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17 changes: 8 additions & 9 deletions readers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -73,8 +73,8 @@ class YT8MAggregatedFeatureReader(BaseReader):

def __init__(self,
num_classes=4716,
feature_sizes=[1024],
feature_names=["mean_inc3"]):
feature_sizes=[1024, 128],
feature_names=["mean_rgb", "mean_audio"]):
"""Construct a YT8MAggregatedFeatureReader.

Args:
Expand Down Expand Up @@ -114,20 +114,19 @@ def prepare_serialized_examples(self, serialized_examples):
"length of feature_names (={}) != length of feature_sizes (={})".format( \
len(self.feature_names), len(self.feature_sizes))

feature_map = {"video_id": tf.FixedLenFeature([], tf.string),
feature_map = {"id": tf.FixedLenFeature([], tf.string),
"labels": tf.VarLenFeature(tf.int64)}
for feature_index in range(num_features):
feature_map[self.feature_names[feature_index]] = tf.FixedLenFeature(
[self.feature_sizes[feature_index]], tf.float32)

features = tf.parse_example(serialized_examples, features=feature_map)

labels = tf.sparse_to_indicator(features["labels"], self.num_classes)
labels.set_shape([None, self.num_classes])
concatenated_features = tf.concat([
features[feature_name] for feature_name in self.feature_names], 1)

return features["video_id"], concatenated_features, labels, tf.ones([tf.shape(serialized_examples)[0]])
return features["id"], concatenated_features, labels, tf.ones([tf.shape(serialized_examples)[0]])

class YT8MFrameFeatureReader(BaseReader):
"""Reads TFRecords of SequenceExamples.
Expand All @@ -140,8 +139,8 @@ class YT8MFrameFeatureReader(BaseReader):

def __init__(self,
num_classes=4716,
feature_sizes=[1024],
feature_names=["inc3"],
feature_sizes=[1024, 128],
feature_names=["rgb", "audio"],
max_frames=300):
"""Construct a YT8MFrameFeatureReader.

Expand Down Expand Up @@ -216,7 +215,7 @@ def prepare_serialized_examples(self, serialized_example,

contexts, features = tf.parse_single_sequence_example(
serialized_example,
context_features={"video_id": tf.FixedLenFeature(
context_features={"id": tf.FixedLenFeature(
[], tf.string),
"labels": tf.VarLenFeature(tf.int64)},
sequence_features={
Expand Down Expand Up @@ -262,7 +261,7 @@ def prepare_serialized_examples(self, serialized_example,

# convert to batch format.
# TODO: Do proper batch reads to remove the IO bottleneck.
batch_video_ids = tf.expand_dims(contexts["video_id"], 0)
batch_video_ids = tf.expand_dims(contexts["id"], 0)
batch_video_matrix = tf.expand_dims(video_matrix, 0)
batch_labels = tf.expand_dims(labels, 0)
batch_frames = tf.expand_dims(num_frames, 0)
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