本文整理汇总了Python中deeplab.model.predict_labels_multi_scale方法的典型用法代码示例。如果您正苦于以下问题：Python model.predict_labels_multi_scale方法的具体用法？Python model.predict_labels_multi_scale怎么用？Python model.predict_labels_multi_scale使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类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个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from deeplab import model [as 别名]
# 或者: from deeplab.model import predict_labels_multi_scale [as 别名]
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) 示例2: main
# 需要导入模块: from deeplab import model [as 别名]
# 或者: from deeplab.model import predict_labels_multi_scale [as 别名]
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)