TensorFlow/yolov3/detect.py [27:45]:
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flags.DEFINE_string('colors', './data/colors.json', 'path to class colors file')
flags.DEFINE_boolean('eager', False, 'enables eager execution (unless tracing)')

def main(_argv):
    if FLAGS.eager and not FLAGS.trace:
        # The upstream model is written for TF2, which enables eager execution by default.
        # Leave eager execution disabled when tracing, since TF1.15 doesn't appear to
        # support the same level of profiling detail with eager mode enabled.
        tf.compat.v1.enable_eager_execution()
    else:
        sess = tf.keras.backend.get_session()
        run_options = None
        run_metadata = None

    if FLAGS.trace:
        run_options = tf.compat.v1.RunOptions(
            output_partition_graphs=True, 
            trace_level=tf.RunOptions.FULL_TRACE)
        run_metadata = tf.compat.v1.RunMetadata()
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TensorFlow/yolov3/detect_video.py [29:47]:
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flags.DEFINE_string('colors', './data/colors.json', 'path to class colors file')
flags.DEFINE_boolean('eager', False, 'enables eager execution (unless tracing)')

def main(_argv):
    if FLAGS.eager and not FLAGS.trace:
        # The upstream model is written for TF2, which enables eager execution by default.
        # Leave eager execution disabled when tracing, since TF1.15 doesn't appear to
        # support the same level of profiling detail with eager mode enabled.
        tf.compat.v1.enable_eager_execution()
    else:
        sess = tf.keras.backend.get_session()
        run_options = None
        run_metadata = None

    if FLAGS.trace:
        run_options = tf.compat.v1.RunOptions(
            output_partition_graphs=True, 
            trace_level=tf.RunOptions.FULL_TRACE)
        run_metadata = tf.compat.v1.RunMetadata()
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