Python model.predict_labels_multi_scale方法代碼示例

本文整理匯總了Python中deeplab.model.predict_labels_multi_scale方法的典型用法代碼示例。如果您正苦於以下問題：Python model.predict_labels_multi_scale方法的具體用法？Python model.predict_labels_multi_scale怎麽用？Python model.predict_labels_multi_scale使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類deeplab.model的用法示例。

在下文中一共展示了model.predict_labels_multi_scale方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: main

# 需要導入模塊: from deeplab import model [as 別名]
# 或者: from deeplab.model import predict_labels_multi_scale [as 別名]
def main(unused_argv):
  tf.logging.set_verbosity(tf.logging.INFO)
  tf.logging.info('Prepare to export model to: %s', FLAGS.export_path)

  with tf.Graph().as_default():
    image, image_size, resized_image_size = _create_input_tensors()

    model_options = common.ModelOptions(
        outputs_to_num_classes={common.OUTPUT_TYPE: FLAGS.num_classes},
        crop_size=FLAGS.crop_size,
        atrous_rates=FLAGS.atrous_rates,
        output_stride=FLAGS.output_stride)

    if tuple(FLAGS.inference_scales) == (1.0,):
      tf.logging.info('Exported model performs single-scale inference.')
      predictions = model.predict_labels(
          image,
          model_options=model_options,
          image_pyramid=FLAGS.image_pyramid)
    else:
      tf.logging.info('Exported model performs multi-scale inference.')
      predictions = model.predict_labels_multi_scale(
          image,
          model_options=model_options,
          eval_scales=FLAGS.inference_scales,
          add_flipped_images=FLAGS.add_flipped_images)

    # Crop the valid regions from the predictions.
    semantic_predictions = tf.slice(
        predictions[common.OUTPUT_TYPE],
        [0, 0, 0],
        [1, resized_image_size[0], resized_image_size[1]])
    # Resize back the prediction to the original image size.
    def _resize_label(label, label_size):
      # Expand dimension of label to [1, height, width, 1] for resize operation.
      label = tf.expand_dims(label, 3)
      resized_label = tf.image.resize_images(
          label,
          label_size,
          method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
          align_corners=True)
      return tf.squeeze(resized_label, 3)
    semantic_predictions = _resize_label(semantic_predictions, image_size)
    semantic_predictions = tf.identity(semantic_predictions, name=_OUTPUT_NAME)

    saver = tf.train.Saver(tf.model_variables())

    tf.gfile.MakeDirs(os.path.dirname(FLAGS.export_path))
    freeze_graph.freeze_graph_with_def_protos(
        tf.get_default_graph().as_graph_def(add_shapes=True),
        saver.as_saver_def(),
        FLAGS.checkpoint_path,
        _OUTPUT_NAME,
        restore_op_name=None,
        filename_tensor_name=None,
        output_graph=FLAGS.export_path,
        clear_devices=True,
        initializer_nodes=None)

開發者ID:itsamitgoel，項目名稱:Gun-Detector，代碼行數:60，代碼來源:export_model.py

示例2: main

# 需要導入模塊: from deeplab import model [as 別名]
# 或者: from deeplab.model import predict_labels_multi_scale [as 別名]
def main(unused_argv):
  tf.logging.set_verbosity(tf.logging.INFO)
  tf.logging.info('Prepare to export model to: %s', FLAGS.export_path)

  with tf.Graph().as_default():
    image, image_size, resized_image_size = _create_input_tensors()

    model_options = common.ModelOptions(
        outputs_to_num_classes={common.OUTPUT_TYPE: FLAGS.num_classes},
        crop_size=FLAGS.crop_size,
        atrous_rates=FLAGS.atrous_rates,
        output_stride=FLAGS.output_stride)

    if tuple(FLAGS.inference_scales) == (1.0,):
      tf.logging.info('Exported model performs single-scale inference.')
      predictions = model.predict_labels(
          image,
          model_options=model_options,
          image_pyramid=FLAGS.image_pyramid)
    else:
      tf.logging.info('Exported model performs multi-scale inference.')
      predictions = model.predict_labels_multi_scale(
          image,
          model_options=model_options,
          eval_scales=FLAGS.inference_scales,
          add_flipped_images=FLAGS.add_flipped_images)

    predictions = tf.cast(predictions[common.OUTPUT_TYPE], tf.float32)
    # Crop the valid regions from the predictions.
    semantic_predictions = tf.slice(
        predictions,
        [0, 0, 0],
        [1, resized_image_size[0], resized_image_size[1]])
    # Resize back the prediction to the original image size.
    def _resize_label(label, label_size):
      # Expand dimension of label to [1, height, width, 1] for resize operation.
      label = tf.expand_dims(label, 3)
      resized_label = tf.image.resize_images(
          label,
          label_size,
          method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
          align_corners=True)
      return tf.cast(tf.squeeze(resized_label, 3), tf.int32)
    semantic_predictions = _resize_label(semantic_predictions, image_size)
    semantic_predictions = tf.identity(semantic_predictions, name=_OUTPUT_NAME)

    saver = tf.train.Saver(tf.model_variables())

    tf.gfile.MakeDirs(os.path.dirname(FLAGS.export_path))
    freeze_graph.freeze_graph_with_def_protos(
        tf.get_default_graph().as_graph_def(add_shapes=True),
        saver.as_saver_def(),
        FLAGS.checkpoint_path,
        _OUTPUT_NAME,
        restore_op_name=None,
        filename_tensor_name=None,
        output_graph=FLAGS.export_path,
        clear_devices=True,
        initializer_nodes=None)

開發者ID:generalized-iou，項目名稱:g-tensorflow-models，代碼行數:61，代碼來源:export_model.py

注：本文中的deeplab.model.predict_labels_multi_scale方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。