326 lines
14 KiB
Python
326 lines
14 KiB
Python
"""Xception V1 model for Keras.
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On ImageNet, this model gets to a top-1 validation accuracy of 0.790
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and a top-5 validation accuracy of 0.945.
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Do note that the input image format for this model is different than for
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the VGG16 and ResNet models (299x299 instead of 224x224),
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and that the input preprocessing function
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is also different (same as Inception V3).
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# Reference
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- [Xception: Deep Learning with Depthwise Separable Convolutions](
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https://arxiv.org/abs/1610.02357)
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"""
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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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import os
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import warnings
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from keras_applications import get_submodules_from_kwargs
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from keras.applications import imagenet_utils
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from keras.applications.imagenet_utils import decode_predictions
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from keras_applications.imagenet_utils import _obtain_input_shape
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from keras.applications import keras_modules_injection
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TF_WEIGHTS_PATH = (
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'https://github.com/fchollet/deep-learning-models/'
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'releases/download/v0.4/'
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'xception_weights_tf_dim_ordering_tf_kernels.h5')
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TF_WEIGHTS_PATH_NO_TOP = (
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'https://github.com/fchollet/deep-learning-models/'
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'releases/download/v0.4/'
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'xception_weights_tf_dim_ordering_tf_kernels_notop.h5')
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@keras_modules_injection
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def Xception_wrapper(*args, **kwargs):
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return Xception(*args, **kwargs)
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def Xception(include_top=True,
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weights='imagenet',
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input_tensor=None,
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input_shape=None,
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pooling=None,
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classes=1000,
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**kwargs):
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"""Instantiates the Xception architecture.
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Optionally loads weights pre-trained on ImageNet. This model can
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only be used with the data format `(width, height, channels)`.
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You should set `image_data_format='channels_last'` in your Keras config
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located at ~/.keras/keras.json.
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Note that the default input image size for this model is 299x299.
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# Arguments
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include_top: whether to include the fully-connected
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layer at the top of the network.
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weights: one of `None` (random initialization),
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'imagenet' (pre-training on ImageNet),
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or the path to the weights file to be loaded.
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input_tensor: optional Keras tensor
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(i.e. output of `layers.Input()`)
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to use as image input for the model.
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input_shape: optional shape tuple, only to be specified
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if `include_top` is False (otherwise the input shape
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has to be `(299, 299, 3)`.
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It should have exactly 3 inputs channels,
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and width and height should be no smaller than 71.
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E.g. `(150, 150, 3)` would be one valid value.
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pooling: Optional pooling mode for feature extraction
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when `include_top` is `False`.
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- `None` means that the output of the model will be
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the 4D tensor output of the
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last convolutional block.
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- `avg` means that global average pooling
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will be applied to the output of the
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last convolutional block, and thus
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the output of the model will be a 2D tensor.
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- `max` means that global max pooling will
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be applied.
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classes: optional number of classes to classify images
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into, only to be specified if `include_top` is True,
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and if no `weights` argument is specified.
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# Returns
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A Keras model instance.
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# Raises
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ValueError: in case of invalid argument for `weights`,
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or invalid input shape.
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RuntimeError: If attempting to run this model with a
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backend that does not support separable convolutions.
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"""
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backend, layers, models, keras_utils = get_submodules_from_kwargs(kwargs)
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if not (weights in {'imagenet', None} or os.path.exists(weights)):
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raise ValueError('The `weights` argument should be either '
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'`None` (random initialization), `imagenet` '
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'(pre-training on ImageNet), '
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'or the path to the weights file to be loaded.')
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if weights == 'imagenet' and include_top and classes != 1000:
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raise ValueError('If using `weights` as `"imagenet"` with `include_top`'
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' as true, `classes` should be 1000')
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if backend.image_data_format() != 'channels_last':
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warnings.warn('The Xception model is only available for the '
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'input data format "channels_last" '
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'(width, height, channels). '
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'However your settings specify the default '
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'data format "channels_first" '
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'(channels, width, height). '
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'You should set `image_data_format="channels_last"` '
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'in your Keras '
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'config located at ~/.keras/keras.json. '
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'The model being returned right now will expect inputs '
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'to follow the "channels_last" data format.')
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backend.set_image_data_format('channels_last')
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old_data_format = 'channels_first'
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else:
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old_data_format = None
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# Determine proper input shape
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input_shape = _obtain_input_shape(input_shape,
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default_size=299,
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min_size=71,
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data_format=backend.image_data_format(),
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require_flatten=include_top,
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weights=weights)
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if input_tensor is None:
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img_input = layers.Input(shape=input_shape)
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else:
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if not backend.is_keras_tensor(input_tensor):
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img_input = layers.Input(tensor=input_tensor, shape=input_shape)
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else:
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img_input = input_tensor
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x = layers.Conv2D(32, (3, 3),
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strides=(2, 2),
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use_bias=False,
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name='block1_conv1')(img_input)
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x = layers.BatchNormalization(name='block1_conv1_bn')(x)
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x = layers.Activation('relu', name='block1_conv1_act')(x)
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x = layers.Conv2D(64, (3, 3), use_bias=False, name='block1_conv2')(x)
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x = layers.BatchNormalization(name='block1_conv2_bn')(x)
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x = layers.Activation('relu', name='block1_conv2_act')(x)
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residual = layers.Conv2D(128, (1, 1),
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strides=(2, 2),
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padding='same',
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use_bias=False)(x)
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residual = layers.BatchNormalization()(residual)
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x = layers.SeparableConv2D(128, (3, 3),
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padding='same',
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use_bias=False,
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name='block2_sepconv1')(x)
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x = layers.BatchNormalization(name='block2_sepconv1_bn')(x)
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x = layers.Activation('relu', name='block2_sepconv2_act')(x)
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x = layers.SeparableConv2D(128, (3, 3),
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padding='same',
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use_bias=False,
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name='block2_sepconv2')(x)
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x = layers.BatchNormalization(name='block2_sepconv2_bn')(x)
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x = layers.MaxPooling2D((3, 3),
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strides=(2, 2),
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padding='same',
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name='block2_pool')(x)
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x = layers.add([x, residual])
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residual = layers.Conv2D(256, (1, 1), strides=(2, 2),
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padding='same', use_bias=False)(x)
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residual = layers.BatchNormalization()(residual)
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x = layers.Activation('relu', name='block3_sepconv1_act')(x)
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x = layers.SeparableConv2D(256, (3, 3),
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padding='same',
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use_bias=False,
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name='block3_sepconv1')(x)
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x = layers.BatchNormalization(name='block3_sepconv1_bn')(x)
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x = layers.Activation('relu', name='block3_sepconv2_act')(x)
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x = layers.SeparableConv2D(256, (3, 3),
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padding='same',
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use_bias=False,
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name='block3_sepconv2')(x)
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x = layers.BatchNormalization(name='block3_sepconv2_bn')(x)
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x = layers.MaxPooling2D((3, 3), strides=(2, 2),
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padding='same',
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name='block3_pool')(x)
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x = layers.add([x, residual])
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residual = layers.Conv2D(728, (1, 1),
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strides=(1, 1),# ORIGINAL (2,2)
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padding='same',
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use_bias=False)(x)
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residual = layers.BatchNormalization()(residual)
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x = layers.Activation('relu', name='block4_sepconv1_act')(x)
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x = layers.SeparableConv2D(728, (3, 3),
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padding='same',
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use_bias=False,
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name='block4_sepconv1')(x)
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x = layers.BatchNormalization(name='block4_sepconv1_bn')(x)
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x = layers.Activation('relu', name='block4_sepconv2_act')(x)
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x = layers.SeparableConv2D(728, (3, 3),
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padding='same',
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use_bias=False,
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name='block4_sepconv2')(x)
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x = layers.BatchNormalization(name='block4_sepconv2_bn')(x)
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x = layers.MaxPooling2D((3, 3), strides=(1, 1),# ORIGINAL (2,2)
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padding='same',
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name='block4_pool')(x)
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x = layers.add([x, residual])
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for i in range(8):
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residual = x
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prefix = 'block' + str(i + 5)
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x = layers.Activation('relu', name=prefix + '_sepconv1_act')(x)
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x = layers.SeparableConv2D(728, (3, 3),
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padding='same',
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use_bias=False,
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name=prefix + '_sepconv1')(x)
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x = layers.BatchNormalization(name=prefix + '_sepconv1_bn')(x)
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x = layers.Activation('relu', name=prefix + '_sepconv2_act')(x)
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x = layers.SeparableConv2D(728, (3, 3),
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padding='same',
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use_bias=False,
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name=prefix + '_sepconv2')(x)
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x = layers.BatchNormalization(name=prefix + '_sepconv2_bn')(x)
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x = layers.Activation('relu', name=prefix + '_sepconv3_act')(x)
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x = layers.SeparableConv2D(728, (3, 3),
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padding='same',
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use_bias=False,
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name=prefix + '_sepconv3')(x)
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x = layers.BatchNormalization(name=prefix + '_sepconv3_bn')(x)
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x = layers.add([x, residual])
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residual = layers.Conv2D(1024, (1, 1), strides=(1, 1),# ORIGINAL (2,2)
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padding='same', use_bias=False)(x)
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residual = layers.BatchNormalization()(residual)
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x = layers.Activation('relu', name='block13_sepconv1_act')(x)
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x = layers.SeparableConv2D(728, (3, 3),
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padding='same',
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use_bias=False,
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name='block13_sepconv1')(x)
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x = layers.BatchNormalization(name='block13_sepconv1_bn')(x)
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x = layers.Activation('relu', name='block13_sepconv2_act')(x)
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x = layers.SeparableConv2D(1024, (3, 3),
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padding='same',
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use_bias=False,
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name='block13_sepconv2')(x)
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x = layers.BatchNormalization(name='block13_sepconv2_bn')(x)
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x = layers.MaxPooling2D((3, 3),
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strides=(1, 1), # ORIGINAL (2,2)
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padding='same',
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name='block13_pool')(x)
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x = layers.add([x, residual])
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x = layers.SeparableConv2D(1536, (3, 3),
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padding='same',
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use_bias=False,
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name='block14_sepconv1')(x)
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x = layers.BatchNormalization(name='block14_sepconv1_bn')(x)
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x = layers.Activation('relu', name='block14_sepconv1_act')(x)
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x = layers.SeparableConv2D(2048, (3, 3),
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padding='same',
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use_bias=False,
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name='block14_sepconv2')(x)
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x = layers.BatchNormalization(name='block14_sepconv2_bn')(x)
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x = layers.Activation('relu', name='block14_sepconv2_act')(x)
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if include_top:
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x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
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x = layers.Dense(classes, activation='softmax', name='predictions')(x)
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else:
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if pooling == 'avg':
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x = layers.GlobalAveragePooling2D()(x)
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elif pooling == 'max':
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x = layers.GlobalMaxPooling2D()(x)
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# Ensure that the model takes into account
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# any potential predecessors of `input_tensor`.
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if input_tensor is not None:
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inputs = keras_utils.get_source_inputs(input_tensor)
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else:
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inputs = img_input
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# Create model.
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model = models.Model(inputs, x, name='xception')
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# Load weights.
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if weights == 'imagenet':
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if include_top:
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weights_path = keras_utils.get_file(
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'xception_weights_tf_dim_ordering_tf_kernels.h5',
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TF_WEIGHTS_PATH,
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cache_subdir='models',
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file_hash='0a58e3b7378bc2990ea3b43d5981f1f6')
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else:
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weights_path = keras_utils.get_file(
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'xception_weights_tf_dim_ordering_tf_kernels_notop.h5',
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TF_WEIGHTS_PATH_NO_TOP,
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cache_subdir='models',
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file_hash='b0042744bf5b25fce3cb969f33bebb97')
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model.load_weights(weights_path)
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if backend.backend() == 'theano':
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keras_utils.convert_all_kernels_in_model(model)
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elif weights is not None:
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model.load_weights(weights)
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if old_data_format:
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backend.set_image_data_format(old_data_format)
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return model
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def preprocess_input(x, **kwargs):
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"""Preprocesses a numpy array encoding a batch of images.
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# Arguments
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x: a 4D numpy array consists of RGB values within [0, 255].
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# Returns
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Preprocessed array.
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"""
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return imagenet_utils.preprocess_input(x, mode='tf', **kwargs)
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