fbed0b5fc76862c59134402a656e1bdef37c76a8,luminoth/utils/predicting.py,,get_predictions,#Any#Any#,14

Before Change

    fetches = None
    image_tensor = None

    predictions = []

    for image_path in image_paths:
        with tf.gfile.Open(image_path, "rb") as im_file:
            try:
                image = Image.open(im_file)
            except tf.errors.OutOfRangeError as e:
                predictions.append({
                    "error": "{}".format(e),
                    "image_path": image_path,
                })
                continue

        preds = get_prediction(
            image, config,
            session=session, fetches=fetches,
            image_tensor=image_tensor, class_labels=class_labels,
            return_tf_vars=True
        )

        if session is None:
            // After first loop
            session = preds["session"]
            fetches = preds["fetches"]
            image_tensor = preds["image_tensor"]

        predictions.append({
            "objects": preds["objects"],
            "objects_labels": preds["objects_labels"],
            "objects_labels_prob": preds["objects_labels_prob"],
            "inference_time": preds["inference_time"],
            "image_path": image_path,
        })

    return predictions


def get_prediction(image, config, session=None,
                   fetches=None, image_tensor=None, class_labels=None,

After Change

            fetches = preds["fetches"]
            image_tensor = preds["image_tensor"]

        yield {
            "objects": preds["objects"],
            "objects_labels": preds["objects_labels"],
            "objects_labels_prob": preds["objects_labels_prob"],
            "inference_time": preds["inference_time"],
            "image_path": image_path,
        }


def get_prediction(image, config, session=None,
                   fetches=None, image_tensor=None, class_labels=None,

Instances