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| """Contains a factory for building various models."""
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| from __future__ import absolute_import
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| from __future__ import division
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| from __future__ import print_function
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| from preprocessing import cifarnet_preprocessing
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| from preprocessing import inception_preprocessing
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| from preprocessing import lenet_preprocessing
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| from preprocessing import vgg_preprocessing
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| def get_preprocessing(name, is_training=False, use_grayscale=False):
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| """Returns preprocessing_fn(image, height, width, **kwargs).
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| Args:
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| name: The name of the preprocessing function.
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| is_training: `True` if the model is being used for training and `False`
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| otherwise.
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| use_grayscale: Whether to convert the image from RGB to grayscale.
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| Returns:
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| preprocessing_fn: A function that preprocessing a single image (pre-batch).
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| It has the following signature:
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| image = preprocessing_fn(image, output_height, output_width, ...).
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| Raises:
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| ValueError: If Preprocessing `name` is not recognized.
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| """
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| preprocessing_fn_map = {
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| 'cifarnet': cifarnet_preprocessing,
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| 'inception': inception_preprocessing,
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| 'inception_v1': inception_preprocessing,
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| 'inception_v2': inception_preprocessing,
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| 'inception_v3': inception_preprocessing,
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| 'inception_v4': inception_preprocessing,
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| 'inception_resnet_v2': inception_preprocessing,
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| 'lenet': lenet_preprocessing,
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| 'mobilenet_v1': inception_preprocessing,
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| 'mobilenet_v2': inception_preprocessing,
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| 'mobilenet_v2_035': inception_preprocessing,
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| 'mobilenet_v3_small': inception_preprocessing,
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| 'mobilenet_v3_large': inception_preprocessing,
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| 'mobilenet_v3_small_minimalistic': inception_preprocessing,
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| 'mobilenet_v3_large_minimalistic': inception_preprocessing,
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| 'mobilenet_edgetpu': inception_preprocessing,
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| 'mobilenet_edgetpu_075': inception_preprocessing,
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| 'mobilenet_v2_140': inception_preprocessing,
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| 'nasnet_mobile': inception_preprocessing,
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| 'nasnet_large': inception_preprocessing,
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| 'pnasnet_mobile': inception_preprocessing,
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| 'pnasnet_large': inception_preprocessing,
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| 'resnet_v1_50': vgg_preprocessing,
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| 'resnet_v1_101': vgg_preprocessing,
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| 'resnet_v1_152': vgg_preprocessing,
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| 'resnet_v1_200': vgg_preprocessing,
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| 'resnet_v2_50': vgg_preprocessing,
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| 'resnet_v2_101': vgg_preprocessing,
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| 'resnet_v2_152': vgg_preprocessing,
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| 'resnet_v2_200': vgg_preprocessing,
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| 'vgg': vgg_preprocessing,
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| 'vgg_a': vgg_preprocessing,
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| 'vgg_16': vgg_preprocessing,
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| 'vgg_19': vgg_preprocessing,
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| }
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|
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| if name not in preprocessing_fn_map:
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| raise ValueError('Preprocessing name [%s] was not recognized' % name)
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|
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| def preprocessing_fn(image, output_height, output_width, **kwargs):
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| return preprocessing_fn_map[name].preprocess_image(
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| image,
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| output_height,
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| output_width,
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| is_training=is_training,
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| use_grayscale=use_grayscale,
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| **kwargs)
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| return preprocessing_fn
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|
