我们从Python开源项目中，提取了以下13个代码示例，用于说明如何使用preprocessing.inception_preprocessing()。
def get_preprocessing(name, is_training=False): """Returns preprocessing_fn(image, height, width, **kwargs). Args: name: The name of the preprocessing function. is_training: `True` if the model is being used for training and `False` otherwise. Returns: preprocessing_fn: A function that preprocessing a single image (pre-batch). It has the following signature: image = preprocessing_fn(image, output_height, output_width, ...). Raises: ValueError: If Preprocessing `name` is not recognized. """ preprocessing_fn_map = { 'inception': inception_preprocessing, 'inception_v1': inception_preprocessing, 'inception_v2': inception_preprocessing, 'inception_v3': inception_preprocessing, 'inception_v4': inception_preprocessing, 'dermatologic': dermatologic_preprocessing, 'inception_resnet_v2': inception_preprocessing, 'resnet_v1_50': vgg_preprocessing, 'resnet_v1_101': vgg_preprocessing, 'resnet_v1_152': vgg_preprocessing, 'vgg': vgg_preprocessing, 'vgg_a': vgg_preprocessing, 'vgg_16': vgg_preprocessing, 'vgg_19': vgg_preprocessing, } if name not in preprocessing_fn_map: raise ValueError('Preprocessing name [%s] was not recognized' % name) def preprocessing_fn(image, output_height, output_width, **kwargs): return preprocessing_fn_map[name].preprocess_image( image, output_height, output_width, is_training=is_training, **kwargs) return preprocessing_fn
def get_preprocessing(name, is_training=False): """Returns preprocessing_fn(image, height, width, **kwargs). Args: name: The name of the preprocessing function. is_training: `True` if the model is being used for training and `False` otherwise. Returns: preprocessing_fn: A function that preprocessing a single image (pre-batch). It has the following signature: image = preprocessing_fn(image, output_height, output_width, ...). Raises: ValueError: If Preprocessing `name` is not recognized. """ preprocessing_fn_map = { 'cifarnet': cifarnet_preprocessing, 'inception': inception_preprocessing, 'inception_v1': inception_preprocessing, 'inception_v2': inception_preprocessing, 'inception_v3': inception_preprocessing, 'inception_resnet_v2': inception_preprocessing, 'lenet': lenet_preprocessing, 'resnet_v1_50': vgg_preprocessing, 'resnet_v1_101': vgg_preprocessing, 'resnet_v1_152': vgg_preprocessing, 'vgg': vgg_preprocessing, 'vgg_a': vgg_preprocessing, 'vgg_16': vgg_preprocessing, 'vgg_19': vgg_preprocessing, } if name not in preprocessing_fn_map: raise ValueError('Preprocessing name [%s] was not recognized' % name) def preprocessing_fn(image, output_height, output_width, **kwargs): return preprocessing_fn_map[name].preprocess_image( image, output_height, output_width, is_training=is_training, **kwargs) return preprocessing_fn
def get_preprocessing(name, is_training=False): """Returns preprocessing_fn(image, height, width, **kwargs). Args: name: The name of the preprocessing function. is_training: `True` if the model is being used for training and `False` otherwise. Returns: preprocessing_fn: A function that preprocessing a single image (pre-batch). It has the following signature: image = preprocessing_fn(image, output_height, output_width, ...). Raises: ValueError: If Preprocessing `name` is not recognized. """ preprocessing_fn_map = { 'cifarnet': cifarnet_preprocessing, 'inception': inception_preprocessing, 'inception_v1': inception_preprocessing, 'inception_v2': inception_preprocessing, 'inception_v3': inception_preprocessing, 'inception_v4': inception_preprocessing, 'inception_resnet_v2': inception_preprocessing, 'lenet': lenet_preprocessing, 'resnet_v1_50': vgg_preprocessing, 'resnet_v1_101': vgg_preprocessing, 'resnet_v1_152': vgg_preprocessing, 'resnet_v2_50': vgg_preprocessing, 'resnet_v2_101': vgg_preprocessing, 'resnet_v2_152': vgg_preprocessing, 'vgg': vgg_preprocessing, 'vgg_a': vgg_preprocessing, 'vgg_16': vgg_preprocessing, 'vgg_19': vgg_preprocessing, } if name not in preprocessing_fn_map: raise ValueError('Preprocessing name [%s] was not recognized' % name) def preprocessing_fn(image, output_height, output_width, **kwargs): return preprocessing_fn_map[name].preprocess_image( image, output_height, output_width, is_training=is_training, **kwargs) return preprocessing_fn
def get_preprocessing(name, is_training=False): """Returns preprocessing_fn(image, height, width, **kwargs). Args: name: The name of the preprocessing function. is_training: `True` if the model is being used for training and `False` otherwise. Returns: preprocessing_fn: A function that preprocessing a single image (pre-batch). It has the following signature: image = preprocessing_fn(image, output_height, output_width, ...). Raises: ValueError: If Preprocessing `name` is not recognized. """ preprocessing_fn_map = { 'cifarnet': cifarnet_preprocessing, 'inception': inception_preprocessing, 'inception_v1': inception_preprocessing, 'inception_v2': inception_preprocessing, 'inception_v3': inception_preprocessing, 'inception_v4': inception_preprocessing, 'inception_resnet_v2': inception_preprocessing, 'lenet': lenet_preprocessing, 'mobilenet_v1': inception_preprocessing, 'nasnet_mobile': inception_preprocessing, 'nasnet_large': inception_preprocessing, 'resnet_v1_50': vgg_preprocessing, 'resnet_v1_101': vgg_preprocessing, 'resnet_v1_152': vgg_preprocessing, 'resnet_v1_200': vgg_preprocessing, 'resnet_v2_50': vgg_preprocessing, 'resnet_v2_101': vgg_preprocessing, 'resnet_v2_152': vgg_preprocessing, 'resnet_v2_200': vgg_preprocessing, 'vgg': vgg_preprocessing, 'vgg_a': vgg_preprocessing, 'vgg_16': vgg_preprocessing, 'vgg_19': vgg_preprocessing, } if name not in preprocessing_fn_map: raise ValueError('Preprocessing name [%s] was not recognized' % name) def preprocessing_fn(image, output_height, output_width, **kwargs): return preprocessing_fn_map[name].preprocess_image( image, output_height, output_width, is_training=is_training, **kwargs) return preprocessing_fn
def get_preprocessing(name, is_training=False): """Returns preprocessing_fn(image, height, width, **kwargs). Args: name: The name of the preprocessing function. is_training: `True` if the model is being used for training and `False` otherwise. Returns: preprocessing_fn: A function that preprocessing a single image (pre-batch). It has the following signature: image = preprocessing_fn(image, output_height, output_width, ...). Raises: ValueError: If Preprocessing `name` is not recognized. """ preprocessing_fn_map = { 'cifarnet': cifarnet_preprocessing, 'inception': inception_preprocessing, 'inception_v1': inception_preprocessing, 'inception_v2': inception_preprocessing, 'inception_v3': inception_preprocessing, 'inception_v4': inception_preprocessing, 'inception_resnet_v2': inception_preprocessing, 'inception_resnet_v2_rnn': inception_preprocessing, 'lenet': lenet_preprocessing, 'googlenet': googlenet_preprocessing, 'googlenet_rnn': googlenet_preprocessing, 'resnet_v1_50': vgg_preprocessing, 'resnet_v1_101': vgg_preprocessing, 'resnet_v1_152': vgg_preprocessing, 'vgg': vgg_preprocessing, 'vgg_a': vgg_preprocessing, 'vgg_16': vgg_preprocessing, 'vgg_19': vgg_preprocessing, } if name not in preprocessing_fn_map: raise ValueError('Preprocessing name [%s] was not recognized' % name) def preprocessing_fn(image, output_height, output_width, **kwargs): return preprocessing_fn_map[name].preprocess_image( image, output_height, output_width, is_training=is_training, **kwargs) return preprocessing_fn
def get_preprocessing(name, is_training=False): """Returns preprocessing_fn(image, height, width, **kwargs). Args: name: The name of the preprocessing function. is_training: `True` if the model is being used for training and `False` otherwise. Returns: preprocessing_fn: A function that preprocessing a single image (pre-batch). It has the following signature: image = preprocessing_fn(image, output_height, output_width, ...). Raises: ValueError: If Preprocessing `name` is not recognized. """ preprocessing_fn_map = { 'cifarnet': cifarnet_preprocessing, 'inception': inception_preprocessing, 'inception_v1': inception_preprocessing, 'inception_v2': inception_preprocessing, 'inception_v3': inception_preprocessing, 'inception_v4': inception_preprocessing, 'inception_resnet_v2': inception_preprocessing, 'lenet': lenet_preprocessing, 'resnet_v1_50': vgg_preprocessing, 'resnet_v1_101': vgg_preprocessing, 'resnet_v1_152': vgg_preprocessing, 'vgg': vgg_preprocessing, 'vgg_a': vgg_preprocessing, 'vgg_16': vgg_preprocessing, 'vgg_19': vgg_preprocessing, } if name not in preprocessing_fn_map: raise ValueError('Preprocessing name [%s] was not recognized' % name) def preprocessing_fn(image, output_height, output_width, **kwargs): return preprocessing_fn_map[name].preprocess_image( image, output_height, output_width, is_training=is_training, **kwargs) return preprocessing_fn
def get_preprocessing(name, is_training=False): """Returns preprocessing_fn(image, height, width, **kwargs). Args: name: The name of the preprocessing function. is_training: `True` if the model is being used for training and `False` otherwise. Returns: preprocessing_fn: A function that preprocessing a single image (pre-batch). It has the following signature: image = preprocessing_fn(image, output_height, output_width, ...). Raises: ValueError: If Preprocessing `name` is not recognized. """ preprocessing_fn_map = { 'cifarnet': cifarnet_preprocessing, 'inception': inception_preprocessing, 'inception_v1': inception_preprocessing, 'inception_v2': inception_preprocessing, 'inception_v3': inception_preprocessing, 'inception_v4': inception_preprocessing, 'inception_resnet_v2': inception_preprocessing, 'lenet': lenet_preprocessing, 'resnet_v1_50': vgg_preprocessing, 'resnet_v1_101': vgg_preprocessing, 'resnet_v1_152': vgg_preprocessing, 'resnet_v2_50': vgg_preprocessing, 'resnet_v2_101': vgg_preprocessing, 'resnet_v2_152': vgg_preprocessing, 'vgg': vgg_preprocessing, 'vgg_a': vgg_preprocessing, 'vgg_16': vgg_preprocessing, 'vgg_19': vgg_preprocessing, 'mobilenet': mobilenet_preprocessing, 'mobilenetdet': mobilenetdet_preprocessing } if name not in preprocessing_fn_map: raise ValueError('Preprocessing name [%s] was not recognized' % name) def preprocessing_fn(image, output_height, output_width, **kwargs): return preprocessing_fn_map[name].preprocess_image( image, output_height, output_width, is_training=is_training, **kwargs) return preprocessing_fn
def get_preprocessing(name, is_training=False): """Returns preprocessing_fn(image, height, width, **kwargs). Args: name: The name of the preprocessing function. is_training: `True` if the model is being used for training and `False` otherwise. Returns: preprocessing_fn: A function that preprocessing a single image (pre-batch). It has the following signature: image = preprocessing_fn(image, output_height, output_width, ...). Raises: ValueError: If Preprocessing `name` is not recognized. """ preprocessing_fn_map = { 'cifarnet': cifarnet_preprocessing, 'inception': inception_preprocessing, 'inception_v1': inception_preprocessing, 'inception_v2': inception_preprocessing, 'inception_v3': inception_preprocessing, 'inception_v4': inception_preprocessing, 'inception_resnet_v2': inception_preprocessing, 'lenet': lenet_preprocessing, 'mobilenet_v1': inception_preprocessing, 'resnet_v1_50': vgg_preprocessing, 'resnet_v1_101': vgg_preprocessing, 'resnet_v1_152': vgg_preprocessing, 'resnet_v1_200': vgg_preprocessing, 'resnet_v2_50': vgg_preprocessing, 'resnet_v2_101': vgg_preprocessing, 'resnet_v2_152': vgg_preprocessing, 'resnet_v2_200': vgg_preprocessing, 'vgg': vgg_preprocessing, 'vgg_a': vgg_preprocessing, 'vgg_16': vgg_preprocessing, 'vgg_19': vgg_preprocessing, } if name not in preprocessing_fn_map: raise ValueError('Preprocessing name [%s] was not recognized' % name) def preprocessing_fn(image, output_height, output_width, **kwargs): return preprocessing_fn_map[name].preprocess_image( image, output_height, output_width, is_training=is_training, **kwargs) return preprocessing_fn
def get_preprocessing(name, is_training=False): """Returns preprocessing_fn(image, height, width, **kwargs). Args: name: The name of the preprocessing function. is_training: `True` if the model is being used for training and `False` otherwise. Returns: preprocessing_fn: A function that preprocessing a single image (pre-batch). It has the following signature: image = preprocessing_fn(image, output_height, output_width, ...). Raises: ValueError: If Preprocessing `name` is not recognized. """ preprocessing_fn_map = { 'cifarnet': cifarnet_preprocessing, 'inception': inception_preprocessing, 'inception_v1': inception_preprocessing, 'inception_v2': inception_preprocessing, 'inception_v3': inception_preprocessing, 'inception_v4': inception_preprocessing, 'inception_resnet_v2': inception_preprocessing, 'lenet': lenet_preprocessing, 'mobilenet_v1': inception_preprocessing, 'resnet_v1_50': vgg_preprocessing, 'resnet_v1_101': vgg_preprocessing, 'resnet_v1_152': vgg_preprocessing, 'resnet_v2_50': vgg_preprocessing, 'resnet_v2_101': vgg_preprocessing, 'resnet_v2_152': vgg_preprocessing, 'vgg': vgg_preprocessing, 'vgg_a': vgg_preprocessing, 'vgg_16': vgg_preprocessing, 'vgg_19': vgg_preprocessing, 'xception': xception_preprocessing, 'resnext_50': vgg_preprocessing, 'resnext_101': vgg_preprocessing, 'resnext_152': vgg_preprocessing, 'resnext_200': vgg_preprocessing, 'shufflenet_50_g4_d272': vgg_preprocessing, 'shufflenet_50_g4_d136': vgg_preprocessing, } if name not in preprocessing_fn_map: raise ValueError('Preprocessing name [%s] was not recognized' % name) def preprocessing_fn(image, output_height, output_width, **kwargs): return preprocessing_fn_map[name].preprocess_image( image, output_height, output_width, is_training=is_training, **kwargs) return preprocessing_fn
