# 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. # ============================================================================== # pylint: disable=invalid-name """Xception V1 model for Keras. On ImageNet, this model gets to a top-1 validation accuracy of 0.790 and a top-5 validation accuracy of 0.945. Reference: - [Xception: Deep Learning with Depthwise Separable Convolutions]( https://arxiv.org/abs/1610.02357) (CVPR 2017) """ import tensorflow.compat.v2 as tf from keras import backend from keras.applications import imagenet_utils from keras.engine import training from keras.layers import VersionAwareLayers from keras.utils import data_utils from keras.utils import layer_utils from tensorflow.python.util.tf_export import keras_export TF_WEIGHTS_PATH = ( 'https://storage.googleapis.com/tensorflow/keras-applications/' 'xception/xception_weights_tf_dim_ordering_tf_kernels.h5') TF_WEIGHTS_PATH_NO_TOP = ( 'https://storage.googleapis.com/tensorflow/keras-applications/' 'xception/xception_weights_tf_dim_ordering_tf_kernels_notop.h5') layers = VersionAwareLayers() @keras_export('keras.applications.xception.Xception', 'keras.applications.Xception') def Xception( include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax'): """Instantiates the Xception architecture. Reference: - [Xception: Deep Learning with Depthwise Separable Convolutions]( https://arxiv.org/abs/1610.02357) (CVPR 2017) For image classification use cases, see [this page for detailed examples]( https://keras.io/api/applications/#usage-examples-for-image-classification-models). For transfer learning use cases, make sure to read the [guide to transfer learning & fine-tuning]( https://keras.io/guides/transfer_learning/). The default input image size for this model is 299x299. Note: each Keras Application expects a specific kind of input preprocessing. For Xception, call `tf.keras.applications.xception.preprocess_input` on your inputs before passing them to the model. `xception.preprocess_input` will scale input pixels between -1 and 1. Args: include_top: whether to include the fully-connected layer at the top of the network. weights: one of `None` (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. input_shape: optional shape tuple, only to be specified if `include_top` is False (otherwise the input shape has to be `(299, 299, 3)`. It should have exactly 3 inputs channels, and width and height should be no smaller than 71. E.g. `(150, 150, 3)` would be one valid value. pooling: Optional pooling mode for feature extraction when `include_top` is `False`. - `None` means that the output of the model will be the 4D tensor output of the last convolutional block. - `avg` means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor. - `max` means that global max pooling will be applied. classes: optional number of classes to classify images into, only to be specified if `include_top` is True, and if no `weights` argument is specified. classifier_activation: A `str` or callable. The activation function to use on the "top" layer. Ignored unless `include_top=True`. Set `classifier_activation=None` to return the logits of the "top" layer. When loading pretrained weights, `classifier_activation` can only be `None` or `"softmax"`. Returns: A `keras.Model` instance. """ if not (weights in {'imagenet', None} or tf.io.gfile.exists(weights)): raise ValueError('The `weights` argument should be either ' '`None` (random initialization), `imagenet` ' '(pre-training on ImageNet), ' 'or the path to the weights file to be loaded.') if weights == 'imagenet' and include_top and classes != 1000: raise ValueError('If using `weights` as `"imagenet"` with `include_top`' ' as true, `classes` should be 1000') # Determine proper input shape input_shape = imagenet_utils.obtain_input_shape( input_shape, default_size=299, min_size=71, data_format=backend.image_data_format(), require_flatten=include_top, weights=weights) if input_tensor is None: img_input = layers.Input(shape=input_shape) else: if not backend.is_keras_tensor(input_tensor): img_input = layers.Input(tensor=input_tensor, shape=input_shape) else: img_input = input_tensor channel_axis = 1 if backend.image_data_format() == 'channels_first' else -1 x = layers.Conv2D( 32, (3, 3), strides=(2, 2), use_bias=False, name='block1_conv1')(img_input) x = layers.BatchNormalization(axis=channel_axis, name='block1_conv1_bn')(x) x = layers.Activation('relu', name='block1_conv1_act')(x) x = layers.Conv2D(64, (3, 3), use_bias=False, name='block1_conv2')(x) x = layers.BatchNormalization(axis=channel_axis, name='block1_conv2_bn')(x) x = layers.Activation('relu', name='block1_conv2_act')(x) residual = layers.Conv2D( 128, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = layers.BatchNormalization(axis=channel_axis)(residual) x = layers.SeparableConv2D( 128, (3, 3), padding='same', use_bias=False, name='block2_sepconv1')(x) x = layers.BatchNormalization(axis=channel_axis, name='block2_sepconv1_bn')(x) x = layers.Activation('relu', name='block2_sepconv2_act')(x) x = layers.SeparableConv2D( 128, (3, 3), padding='same', use_bias=False, name='block2_sepconv2')(x) x = layers.BatchNormalization(axis=channel_axis, name='block2_sepconv2_bn')(x) x = layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block2_pool')(x) x = layers.add([x, residual]) residual = layers.Conv2D( 256, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = layers.BatchNormalization(axis=channel_axis)(residual) x = layers.Activation('relu', name='block3_sepconv1_act')(x) x = layers.SeparableConv2D( 256, (3, 3), padding='same', use_bias=False, name='block3_sepconv1')(x) x = layers.BatchNormalization(axis=channel_axis, name='block3_sepconv1_bn')(x) x = layers.Activation('relu', name='block3_sepconv2_act')(x) x = layers.SeparableConv2D( 256, (3, 3), padding='same', use_bias=False, name='block3_sepconv2')(x) x = layers.BatchNormalization(axis=channel_axis, name='block3_sepconv2_bn')(x) x = layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block3_pool')(x) x = layers.add([x, residual]) residual = layers.Conv2D( 728, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = layers.BatchNormalization(axis=channel_axis)(residual) x = layers.Activation('relu', name='block4_sepconv1_act')(x) x = layers.SeparableConv2D( 728, (3, 3), padding='same', use_bias=False, name='block4_sepconv1')(x) x = layers.BatchNormalization(axis=channel_axis, name='block4_sepconv1_bn')(x) x = layers.Activation('relu', name='block4_sepconv2_act')(x) x = layers.SeparableConv2D( 728, (3, 3), padding='same', use_bias=False, name='block4_sepconv2')(x) x = layers.BatchNormalization(axis=channel_axis, name='block4_sepconv2_bn')(x) x = layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block4_pool')(x) x = layers.add([x, residual]) for i in range(8): residual = x prefix = 'block' + str(i + 5) x = layers.Activation('relu', name=prefix + '_sepconv1_act')(x) x = layers.SeparableConv2D( 728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv1')(x) x = layers.BatchNormalization( axis=channel_axis, name=prefix + '_sepconv1_bn')(x) x = layers.Activation('relu', name=prefix + '_sepconv2_act')(x) x = layers.SeparableConv2D( 728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv2')(x) x = layers.BatchNormalization( axis=channel_axis, name=prefix + '_sepconv2_bn')(x) x = layers.Activation('relu', name=prefix + '_sepconv3_act')(x) x = layers.SeparableConv2D( 728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv3')(x) x = layers.BatchNormalization( axis=channel_axis, name=prefix + '_sepconv3_bn')(x) x = layers.add([x, residual]) residual = layers.Conv2D( 1024, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = layers.BatchNormalization(axis=channel_axis)(residual) x = layers.Activation('relu', name='block13_sepconv1_act')(x) x = layers.SeparableConv2D( 728, (3, 3), padding='same', use_bias=False, name='block13_sepconv1')(x) x = layers.BatchNormalization( axis=channel_axis, name='block13_sepconv1_bn')(x) x = layers.Activation('relu', name='block13_sepconv2_act')(x) x = layers.SeparableConv2D( 1024, (3, 3), padding='same', use_bias=False, name='block13_sepconv2')(x) x = layers.BatchNormalization( axis=channel_axis, name='block13_sepconv2_bn')(x) x = layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block13_pool')(x) x = layers.add([x, residual]) x = layers.SeparableConv2D( 1536, (3, 3), padding='same', use_bias=False, name='block14_sepconv1')(x) x = layers.BatchNormalization( axis=channel_axis, name='block14_sepconv1_bn')(x) x = layers.Activation('relu', name='block14_sepconv1_act')(x) x = layers.SeparableConv2D( 2048, (3, 3), padding='same', use_bias=False, name='block14_sepconv2')(x) x = layers.BatchNormalization( axis=channel_axis, name='block14_sepconv2_bn')(x) x = layers.Activation('relu', name='block14_sepconv2_act')(x) if include_top: x = layers.GlobalAveragePooling2D(name='avg_pool')(x) imagenet_utils.validate_activation(classifier_activation, weights) x = layers.Dense(classes, activation=classifier_activation, name='predictions')(x) else: if pooling == 'avg': x = layers.GlobalAveragePooling2D()(x) elif pooling == 'max': x = layers.GlobalMaxPooling2D()(x) # Ensure that the model takes into account # any potential predecessors of `input_tensor`. if input_tensor is not None: inputs = layer_utils.get_source_inputs(input_tensor) else: inputs = img_input # Create model. model = training.Model(inputs, x, name='xception') # Load weights. if weights == 'imagenet': if include_top: weights_path = data_utils.get_file( 'xception_weights_tf_dim_ordering_tf_kernels.h5', TF_WEIGHTS_PATH, cache_subdir='models', file_hash='0a58e3b7378bc2990ea3b43d5981f1f6') else: weights_path = data_utils.get_file( 'xception_weights_tf_dim_ordering_tf_kernels_notop.h5', TF_WEIGHTS_PATH_NO_TOP, cache_subdir='models', file_hash='b0042744bf5b25fce3cb969f33bebb97') model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) return model @keras_export('keras.applications.xception.preprocess_input') def preprocess_input(x, data_format=None): return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf') @keras_export('keras.applications.xception.decode_predictions') def decode_predictions(preds, top=5): return imagenet_utils.decode_predictions(preds, top=top) preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format( mode='', ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF, error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC) decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__