381 lines
13 KiB
Python
381 lines
13 KiB
Python
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""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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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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"""
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import tensorflow.compat.v2 as tf
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from keras import backend
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from keras.applications import imagenet_utils
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from keras.engine import training
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from keras.layers import VersionAwareLayers
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from keras.utils import data_utils
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from keras.utils import layer_utils
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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TF_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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TF_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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layers = VersionAwareLayers()
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@keras_export(
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"keras.applications.xception.Xception", "keras.applications.Xception"
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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 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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classifier_activation: A `str` or callable. The activation function to use
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on the "top" layer. Ignored unless `include_top=True`. Set
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`classifier_activation=None` to return the logits of the "top" layer.
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When loading pretrained weights, `classifier_activation` can only
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be `None` or `"softmax"`.
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Returns:
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A `keras.Model` instance.
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"""
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if not (weights in {"imagenet", None} or tf.io.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` as `"imagenet"` with `include_top`'
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" as true, `classes` should be 1000"
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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 = layer_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 = training.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 = data_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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)
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|
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__
|