# Copyright 2019 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. # ============================================================================== def preprocess_input(x, data_format=None, mode=None): if mode == 'tf': x /= 127.5 x -= 1. return x elif mode == 'torch': x /= 255. mean = [0.485, 0.456, 0.406] std = [0.229, 0.224, 0.225] elif mode == 'caffe': if data_format == 'channels_first': # 'RGB'->'BGR' if x.ndim == 3: x = x[::-1, ...] else: x = x[:, ::-1, ...] else: # 'RGB'->'BGR' x = x[..., ::-1] mean = [103.939, 116.779, 123.68] std = None elif mode == 'tfhub': x /= 255. return x else: return x # Zero-center by mean pixel if data_format == 'channels_first': if x.ndim == 3: x[0, :, :] -= mean[0] x[1, :, :] -= mean[1] x[2, :, :] -= mean[2] if std is not None: x[0, :, :] /= std[0] x[1, :, :] /= std[1] x[2, :, :] /= std[2] else: x[:, 0, :, :] -= mean[0] x[:, 1, :, :] -= mean[1] x[:, 2, :, :] -= mean[2] if std is not None: x[:, 0, :, :] /= std[0] x[:, 1, :, :] /= std[1] x[:, 2, :, :] /= std[2] else: x[..., 0] -= mean[0] x[..., 1] -= mean[1] x[..., 2] -= mean[2] if std is not None: x[..., 0] /= std[0] x[..., 1] /= std[1] x[..., 2] /= std[2] return x