update

Author: YoungYoung619

tf_convert_data.py

@@ -0,0 +1,60 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Convert a dataset to TFRecords format, which can be easily integrated into
+a TensorFlow pipeline.
+
+Usage:
+```shell
+python tf_convert_data.py \
+    --dataset_name=pascalvoc \
+    --dataset_dir=/tmp/pascalvoc \
+    --output_name=pascalvoc \
+    --output_dir=/tmp/
+```
+"""
+import tensorflow as tf
+
+from dataset import pascalvoc_to_tfrecords
+
+FLAGS = tf.app.flags.FLAGS
+
+tf.app.flags.DEFINE_string(
+    'dataset_name', 'bdd100k',
+    'The name of the dataset to convert.')
+tf.app.flags.DEFINE_string(
+    'dataset_dir', "h:/Data/BDD100K/bdd/images/100k/",
+    'Directory where the original dataset is stored.')
+tf.app.flags.DEFINE_string(
+    'output_name', 'bdd100k_val',
+    'Basename used for TFRecords output files.')
+tf.app.flags.DEFINE_string(
+    'output_dir', './dataset/bdd100k_TfRecord',
+    'Output directory where to store TFRecords files.')
+
+
+def main(_):
+    if not FLAGS.dataset_dir:
+        raise ValueError('You must supply the dataset directory with --dataset_dir')
+    print('Dataset directory:', FLAGS.dataset_dir)
+    print('Output directory:', FLAGS.output_dir)
+
+    if FLAGS.dataset_name == 'pascalvoc'or FLAGS.dataset_name == "bdd100k":
+        pascalvoc_to_tfrecords.run(FLAGS.dataset_dir, FLAGS.output_dir, FLAGS.output_name)
+    else:
+        raise ValueError('Dataset [%s] was not recognized.' % FLAGS.dataset_name)
+
+if __name__ == '__main__':
+    tf.app.run()
+