Transfer learning on TensorFlow-Slim image classification model library
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.## Licensed under the Apache License, Version 2.0 (the "License");# you may not use this file except in compliance with the License.# You may obtain a copy of the License at## http://www.apache.org/licenses/LICENSE-2.0## Unless required by applicable law or agreed to in writing, software# distributed under the License is distributed on an "AS IS" BASIS,# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.# See the License for the specific language governing permissions and# limitations under the License.# =============================================================================="""Contains a factory for building various models."""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_function#from preprocessing import cifarnet_preprocessingfrom preprocessing import inception_preprocessing#from preprocessing import lenet_preprocessing#from preprocessing import vgg_preprocessingdef get_preprocessing(name, is_training=False, use_grayscale=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. use_grayscale: Whether to convert the image from RGB to grayscale. 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, 'mobilenet_v2': inception_preprocessing, 'mobilenet_v2_035': inception_preprocessing, 'mobilenet_v3_small': inception_preprocessing, 'mobilenet_v3_large': inception_preprocessing, 'mobilenet_v3_small_minimalistic': inception_preprocessing, 'mobilenet_v3_large_minimalistic': inception_preprocessing, 'mobilenet_edgetpu': inception_preprocessing, 'mobilenet_edgetpu_075': inception_preprocessing, 'mobilenet_v2_140': inception_preprocessing, #'nasnet_mobile': inception_preprocessing, #'nasnet_large': inception_preprocessing, #'pnasnet_mobile': inception_preprocessing, #'pnasnet_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, use_grayscale=use_grayscale, **kwargs) return preprocessing_fn