本文整理汇总了Python中preprocessing.preprocess_images方法的典型用法代码示例。如果您正苦于以下问题：Python preprocessing.preprocess_images方法的具体用法？Python preprocessing.preprocess_images怎么用？Python preprocessing.preprocess_images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preprocessing的用法示例。
在下文中一共展示了preprocessing.preprocess_images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: preprocess
# 需要导入模块: import preprocessing [as 别名]
# 或者: from preprocessing import preprocess_images [as 别名]
def preprocess(self, inputs):
"""preprocessing.
Outputs of this function can be passed to loss or postprocess functions.
Args:
preprocessed_inputs: A float32 tensor with shape [batch_size,
height, width, num_channels] representing a batch of images.
Returns:
prediction_dict: A dictionary holding prediction tensors to be
passed to the Loss or Postprocess functions.
"""
preprocessed_inputs = preprocessing.preprocess_images(
inputs, self._default_image_size, self._default_image_size,
resize_side_min=self._fixed_resize_side,
is_training=self._is_training,
border_expand=True, normalize=False,
preserving_aspect_ratio_resize=False)
preprocessed_inputs = tf.cast(preprocessed_inputs, tf.float32)
return preprocessed_inputs 示例2: preprocess_data
# 需要导入模块: import preprocessing [as 别名]
# 或者: from preprocessing import preprocess_images [as 别名]
def preprocess_data(self, images, is_training):
"""Preprocesses raw images for either training or inference.
Args:
images: A 4-D float32 `Tensor` holding images to preprocess.
is_training: Boolean, whether or not we're in training.
Returns:
data_preprocessed: data after the preprocessor.
"""
config = self._config
height = config.data.height
width = config.data.width
min_scale = config.data.augmentation.minscale
max_scale = config.data.augmentation.maxscale
p_scale_up = config.data.augmentation.proportion_scaled_up
aug_color = config.data.augmentation.color
fast_mode = config.data.augmentation.fast_mode
crop_strategy = config.data.preprocessing.eval_cropping
preprocessed_images = preprocessing.preprocess_images(
images, is_training, height, width,
min_scale, max_scale, p_scale_up,
aug_color=aug_color, fast_mode=fast_mode,
crop_strategy=crop_strategy)
return preprocessed_images