356 lines
12 KiB
Python
356 lines
12 KiB
Python
from tensorflow.io import gfile
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from keras_core import backend
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from keras_core import layers
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from keras_core.api_export import keras_core_export
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from keras_core.applications import imagenet_utils
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from keras_core.models import Functional
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from keras_core.operations import operation_utils
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from keras_core.utils import file_utils
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WEIGHTS_PATH = (
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"https://storage.googleapis.com/tensorflow/keras-applications/"
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"xception/xception_weights_tf_dim_ordering_tf_kernels.h5"
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)
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WEIGHTS_PATH_NO_TOP = (
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"https://storage.googleapis.com/tensorflow/keras-applications/"
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"xception/xception_weights_tf_dim_ordering_tf_kernels_notop.h5"
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)
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@keras_core_export(
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[
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"keras_core.applications.xception.Xception",
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"keras_core.applications.Xception",
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]
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)
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def Xception(
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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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classifier_activation="softmax",
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):
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"""Instantiates the Xception architecture.
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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) (CVPR 2017)
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For image classification use cases, see
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[this page for detailed examples](
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https://keras.io/api/applications/#usage-examples-for-image-classification-models).
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For transfer learning use cases, make sure to read the
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[guide to transfer learning & fine-tuning](
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https://keras.io/guides/transfer_learning/).
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The default input image size for this model is 299x299.
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Note: each Keras Application expects a specific kind of input preprocessing.
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For Xception, call `tf.keras.applications.xception.preprocess_input` on your
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inputs before passing them to the model.
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`xception.preprocess_input` will scale input pixels between -1 and 1.
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Args:
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include_top: whether to include the 3 fully-connected
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layers 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`, and
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if no `weights` argument is specified.
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classifier_activation: A `str` or callable. The activation function to
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use on the "top" layer. Ignored unless `include_top=True`. Set
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`classifier_activation=None` to return the logits of the "top"
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layer. When loading pretrained weights, `classifier_activation` can
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only be `None` or `"softmax"`.
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Returns:
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A model instance.
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"""
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if not (weights in {"imagenet", None} or gfile.exists(weights)):
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raise ValueError(
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"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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)
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if weights == "imagenet" and include_top and classes != 1000:
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raise ValueError(
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"If using `weights='imagenet'` with `include_top=True`, "
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"`classes` should be 1000. "
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f"Received classes={classes}"
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)
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# Determine proper input shape
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input_shape = imagenet_utils.obtain_input_shape(
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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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)
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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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channel_axis = 1 if backend.image_data_format() == "channels_first" else -1
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x = layers.Conv2D(
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32, (3, 3), strides=(2, 2), use_bias=False, name="block1_conv1"
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)(img_input)
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x = layers.BatchNormalization(axis=channel_axis, 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(axis=channel_axis, 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(
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128, (1, 1), strides=(2, 2), padding="same", use_bias=False
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)(x)
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residual = layers.BatchNormalization(axis=channel_axis)(residual)
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x = layers.SeparableConv2D(
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128, (3, 3), padding="same", use_bias=False, name="block2_sepconv1"
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)(x)
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x = layers.BatchNormalization(axis=channel_axis, name="block2_sepconv1_bn")(
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x
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)
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x = layers.Activation("relu", name="block2_sepconv2_act")(x)
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x = layers.SeparableConv2D(
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128, (3, 3), padding="same", use_bias=False, name="block2_sepconv2"
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)(x)
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x = layers.BatchNormalization(axis=channel_axis, name="block2_sepconv2_bn")(
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x
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)
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x = layers.MaxPooling2D(
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(3, 3), strides=(2, 2), padding="same", name="block2_pool"
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)(x)
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x = layers.add([x, residual])
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residual = layers.Conv2D(
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256, (1, 1), strides=(2, 2), padding="same", use_bias=False
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)(x)
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residual = layers.BatchNormalization(axis=channel_axis)(residual)
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x = layers.Activation("relu", name="block3_sepconv1_act")(x)
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x = layers.SeparableConv2D(
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256, (3, 3), padding="same", use_bias=False, name="block3_sepconv1"
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)(x)
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x = layers.BatchNormalization(axis=channel_axis, name="block3_sepconv1_bn")(
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x
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)
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x = layers.Activation("relu", name="block3_sepconv2_act")(x)
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x = layers.SeparableConv2D(
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256, (3, 3), padding="same", use_bias=False, name="block3_sepconv2"
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)(x)
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x = layers.BatchNormalization(axis=channel_axis, name="block3_sepconv2_bn")(
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x
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)
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x = layers.MaxPooling2D(
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(3, 3), strides=(2, 2), padding="same", name="block3_pool"
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)(x)
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x = layers.add([x, residual])
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residual = layers.Conv2D(
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728, (1, 1), strides=(2, 2), padding="same", use_bias=False
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)(x)
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residual = layers.BatchNormalization(axis=channel_axis)(residual)
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x = layers.Activation("relu", name="block4_sepconv1_act")(x)
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x = layers.SeparableConv2D(
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728, (3, 3), padding="same", use_bias=False, name="block4_sepconv1"
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)(x)
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x = layers.BatchNormalization(axis=channel_axis, name="block4_sepconv1_bn")(
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x
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)
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x = layers.Activation("relu", name="block4_sepconv2_act")(x)
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x = layers.SeparableConv2D(
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728, (3, 3), padding="same", use_bias=False, name="block4_sepconv2"
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)(x)
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x = layers.BatchNormalization(axis=channel_axis, name="block4_sepconv2_bn")(
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x
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)
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x = layers.MaxPooling2D(
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(3, 3), strides=(2, 2), padding="same", name="block4_pool"
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)(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(
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728,
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(3, 3),
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padding="same",
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use_bias=False,
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name=prefix + "_sepconv1",
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)(x)
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x = layers.BatchNormalization(
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axis=channel_axis, name=prefix + "_sepconv1_bn"
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)(x)
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x = layers.Activation("relu", name=prefix + "_sepconv2_act")(x)
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x = layers.SeparableConv2D(
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728,
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(3, 3),
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padding="same",
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use_bias=False,
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name=prefix + "_sepconv2",
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)(x)
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x = layers.BatchNormalization(
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axis=channel_axis, name=prefix + "_sepconv2_bn"
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)(x)
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x = layers.Activation("relu", name=prefix + "_sepconv3_act")(x)
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x = layers.SeparableConv2D(
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728,
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(3, 3),
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padding="same",
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use_bias=False,
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name=prefix + "_sepconv3",
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)(x)
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x = layers.BatchNormalization(
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axis=channel_axis, name=prefix + "_sepconv3_bn"
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)(x)
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x = layers.add([x, residual])
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residual = layers.Conv2D(
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1024, (1, 1), strides=(2, 2), padding="same", use_bias=False
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)(x)
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residual = layers.BatchNormalization(axis=channel_axis)(residual)
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x = layers.Activation("relu", name="block13_sepconv1_act")(x)
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x = layers.SeparableConv2D(
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728, (3, 3), padding="same", use_bias=False, name="block13_sepconv1"
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)(x)
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x = layers.BatchNormalization(
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axis=channel_axis, name="block13_sepconv1_bn"
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)(x)
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x = layers.Activation("relu", name="block13_sepconv2_act")(x)
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x = layers.SeparableConv2D(
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1024, (3, 3), padding="same", use_bias=False, name="block13_sepconv2"
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)(x)
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x = layers.BatchNormalization(
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axis=channel_axis, name="block13_sepconv2_bn"
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)(x)
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x = layers.MaxPooling2D(
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(3, 3), strides=(2, 2), padding="same", name="block13_pool"
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)(x)
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x = layers.add([x, residual])
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x = layers.SeparableConv2D(
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1536, (3, 3), padding="same", use_bias=False, name="block14_sepconv1"
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)(x)
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x = layers.BatchNormalization(
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axis=channel_axis, name="block14_sepconv1_bn"
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)(x)
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x = layers.Activation("relu", name="block14_sepconv1_act")(x)
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x = layers.SeparableConv2D(
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2048, (3, 3), padding="same", use_bias=False, name="block14_sepconv2"
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)(x)
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x = layers.BatchNormalization(
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axis=channel_axis, name="block14_sepconv2_bn"
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)(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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imagenet_utils.validate_activation(classifier_activation, weights)
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x = layers.Dense(
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classes, activation=classifier_activation, name="predictions"
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)(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 = operation_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 = Functional(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 = file_utils.get_file(
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"xception_weights_tf_dim_ordering_tf_kernels.h5",
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WEIGHTS_PATH,
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cache_subdir="models",
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file_hash="0a58e3b7378bc2990ea3b43d5981f1f6",
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)
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else:
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weights_path = file_utils.get_file(
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"xception_weights_tf_dim_ordering_tf_kernels_notop.h5",
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WEIGHTS_PATH_NO_TOP,
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cache_subdir="models",
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file_hash="b0042744bf5b25fce3cb969f33bebb97",
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)
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model.load_weights(weights_path)
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elif weights is not None:
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model.load_weights(weights)
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return model
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@keras_core_export("keras_core.applications.xception.preprocess_input")
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def preprocess_input(x, data_format=None):
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return imagenet_utils.preprocess_input(
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x, data_format=data_format, mode="tf"
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)
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@keras_core_export("keras_core.applications.xception.decode_predictions")
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def decode_predictions(preds, top=5):
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return imagenet_utils.decode_predictions(preds, top=top)
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preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
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mode="",
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ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF,
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error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC,
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)
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decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
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