Modify Kinetics datasets reader for custom.

Signed-off-by: Cheoljun Lee <cheoljun.lee@samsung.com>

mediapipe/examples/desktop/media_sequence/kinetics_dataset.py

@@ -73,6 +73,7 @@ import subprocess
 import sys
 import tarfile
 import tempfile
+from multiprocessing import Process, Queue
 
 from absl import app
 from absl import flags
@@ -80,7 +81,7 @@ from absl import logging
 from six.moves import range
 from six.moves import urllib
 from six.moves import zip
-import tensorflow.compat.v1 as tf
+import tensorflow as tf
 
 from mediapipe.util.sequence import media_sequence as ms
 
@@ -127,7 +128,7 @@ class Kinetics(object):
 
   def as_dataset(self, split, shuffle=False, repeat=False,
                  serialized_prefetch_size=32, decoded_prefetch_size=32,
-                 parse_labels=True):
+                 parse_labels=True, num_classes=NUM_CLASSES):
     """Returns Kinetics as a tf.data.Dataset.
 
     After running this function, calling padded_batch() on the Dataset object
@@ -195,7 +196,7 @@ class Kinetics(object):
           "num_frames": num_frames,
       }
       if parse_labels:
-        target = tf.one_hot(parsed_context[ms.get_clip_label_index_key()], 700)
+        target = tf.one_hot(parsed_context[ms.get_clip_label_index_key()], num_classes)
         output_dict["labels"] = target
       return output_dict
 
@@ -265,14 +266,18 @@ class Kinetics(object):
       logging.info("An example of the metadata: ")
       logging.info(all_metadata[0])
       random.seed(47)
-      random.shuffle(all_metadata)
+      #random.shuffle(all_metadata)
       shards = SPLITS[key]["shards"]
       shard_names = [os.path.join(
           self.path_to_data, FILEPATTERN % key + "-%05d-of-%05d" % (
               i, shards)) for i in range(shards)]
       writers = [tf.io.TFRecordWriter(shard_name) for shard_name in shard_names]
-      with _close_on_exit(writers) as writers:
+
-        for i, seq_ex in enumerate(all_metadata):
+      def generate(q, writer):
+        while True:
+          i, seq_ex = q.get()
+          if i is None:
+            break
           if not only_generate_metadata:
             print("Processing example %d of %d   (%d%%) \r" % (
                 i, len(all_metadata), i * 100 / len(all_metadata)), end="")
@@ -280,7 +285,20 @@ class Kinetics(object):
               graph_path = os.path.join(path_to_graph_directory, graph)
               seq_ex = self._run_mediapipe(
                   path_to_mediapipe_binary, seq_ex, graph_path)
-          writers[i % len(writers)].write(seq_ex.SerializeToString())
+          writer.write(seq_ex.SerializeToString())
+        writer.close()
+      queue = Queue()
+      workers = []
+      for i, seq_ex in enumerate(all_metadata):
+        queue.put((i, seq_ex))
+      for i in range(shards):
+        queue.put((None, None))
+      for i in range(shards):
+        worker = Process(target=generate, args=(queue,writers[i]))
+        worker.start()
+        workers.append(worker)
+      for worker in workers:
+        worker.join()
     logging.info("Data extraction complete.")
 
   def _generate_metadata(self, key, download_output,
@@ -320,6 +338,8 @@ class Kinetics(object):
           ms.set_clip_data_path(bytes23(filepath), metadata)
         assert row["start"].isdigit(), "Invalid row: %s" % str(row)
         assert row["end"].isdigit(), "Invalid row: %s" % str(row)
+        ms.set_clip_start_timestamp(int(float(row["start"]) * SECONDS_TO_MICROSECONDS), metadata)
+        ms.set_clip_end_timestamp(int(float(row["end"]) * SECONDS_TO_MICROSECONDS), metadata)
         if "label_name" in row:
           ms.set_clip_label_string([bytes23(row["label_name"])], metadata)
           if label_map:
@@ -394,13 +414,13 @@ class Kinetics(object):
 
   def get_label_map_and_verify_example_counts(self, paths):
     """Verify the number of examples and labels have not changed."""
+    label_map = None
     for name, path in paths.items():
       with open(path, "r") as f:
         lines = f.readlines()
       # the header adds one line and one "key".
       num_examples = len(lines) - 1
       keys = [l.split(",")[0] for l in lines]
-      label_map = None
       if name == "train":
         classes = sorted(list(set(keys[1:])))
         num_keys = len(set(keys)) - 1