911 lines
32 KiB
Python
911 lines
32 KiB
Python
# Copyright 2018 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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"""NASNet-A models for Keras.
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NASNet refers to Neural Architecture Search Network, a family of models
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that were designed automatically by learning the model architectures
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directly on the dataset of interest.
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Here we consider NASNet-A, the highest performance model that was found
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for the CIFAR-10 dataset, and then extended to ImageNet 2012 dataset,
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obtaining state of the art performance on CIFAR-10 and ImageNet 2012.
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Only the NASNet-A models, and their respective weights, which are suited
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for ImageNet 2012 are provided.
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The below table describes the performance on ImageNet 2012:
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---------------------------------------------------------------------------
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Architecture | Top-1 Acc | Top-5 Acc | Multiply-Adds | Params (M)
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---------------------|-----------|-----------|----------------|------------
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NASNet-A (4 @ 1056) | 74.0 % | 91.6 % | 564 M | 5.3
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NASNet-A (6 @ 4032) | 82.7 % | 96.2 % | 23.8 B | 88.9
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Reference:
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- [Learning Transferable Architectures for Scalable Image Recognition](
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https://arxiv.org/abs/1707.07012) (CVPR 2018)
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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.platform import tf_logging as logging
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from tensorflow.python.util.tf_export import keras_export
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BASE_WEIGHTS_PATH = (
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"https://storage.googleapis.com/tensorflow/keras-applications/nasnet/"
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)
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NASNET_MOBILE_WEIGHT_PATH = BASE_WEIGHTS_PATH + "NASNet-mobile.h5"
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NASNET_MOBILE_WEIGHT_PATH_NO_TOP = BASE_WEIGHTS_PATH + "NASNet-mobile-no-top.h5"
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NASNET_LARGE_WEIGHT_PATH = BASE_WEIGHTS_PATH + "NASNet-large.h5"
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NASNET_LARGE_WEIGHT_PATH_NO_TOP = BASE_WEIGHTS_PATH + "NASNet-large-no-top.h5"
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layers = VersionAwareLayers()
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def NASNet(
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input_shape=None,
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penultimate_filters=4032,
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num_blocks=6,
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stem_block_filters=96,
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skip_reduction=True,
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filter_multiplier=2,
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include_top=True,
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weights="imagenet",
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input_tensor=None,
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pooling=None,
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classes=1000,
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default_size=None,
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classifier_activation="softmax",
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):
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"""Instantiates a NASNet model.
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Reference:
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- [Learning Transferable Architectures for Scalable Image Recognition](
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https://arxiv.org/abs/1707.07012) (CVPR 2018)
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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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Note: each Keras Application expects a specific kind of input preprocessing.
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For NasNet, call `tf.keras.applications.nasnet.preprocess_input`
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on your inputs before passing them to the model.
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`nasnet.preprocess_input` will scale input pixels between -1 and 1.
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Args:
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input_shape: Optional shape tuple, the input shape
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is by default `(331, 331, 3)` for NASNetLarge and
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`(224, 224, 3)` for NASNetMobile.
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It should have exactly 3 input channels,
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and width and height should be no smaller than 32.
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E.g. `(224, 224, 3)` would be one valid value.
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penultimate_filters: Number of filters in the penultimate layer.
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NASNet models use the notation `NASNet (N @ P)`, where:
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- N is the number of blocks
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- P is the number of penultimate filters
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num_blocks: Number of repeated blocks of the NASNet model.
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NASNet models use the notation `NASNet (N @ P)`, where:
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- N is the number of blocks
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- P is the number of penultimate filters
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stem_block_filters: Number of filters in the initial stem block
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skip_reduction: Whether to skip the reduction step at the tail
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end of the network.
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filter_multiplier: Controls the width of the network.
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- If `filter_multiplier` < 1.0, proportionally decreases the number
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of filters in each layer.
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- If `filter_multiplier` > 1.0, proportionally increases the number
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of filters in each layer.
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- If `filter_multiplier` = 1, default number of filters from the
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paper are used at each layer.
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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: `None` (random initialization) or
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`imagenet` (ImageNet weights)
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input_tensor: Optional Keras tensor (i.e. output of
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`layers.Input()`)
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to use as image input for the model.
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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
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will be 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
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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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default_size: Specifies the default image size of the model
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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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if (
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isinstance(input_shape, tuple)
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and None in input_shape
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and weights == "imagenet"
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):
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raise ValueError(
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"When specifying the input shape of a NASNet"
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" and loading `ImageNet` weights, "
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"the input_shape argument must be static "
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"(no None entries). Got: `input_shape=" + str(input_shape) + "`."
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)
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if default_size is None:
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default_size = 331
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# Determine proper input shape and default size.
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input_shape = imagenet_utils.obtain_input_shape(
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input_shape,
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default_size=default_size,
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min_size=32,
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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 backend.image_data_format() != "channels_last":
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logging.warning(
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"The NASNet family of models is only available "
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'for the 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" (channels, width, height).'
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' You should set `image_data_format="channels_last"` '
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"in your Keras 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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)
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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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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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if penultimate_filters % (24 * (filter_multiplier**2)) != 0:
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raise ValueError(
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"For NASNet-A models, the `penultimate_filters` must be a multiple "
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"of 24 * (`filter_multiplier` ** 2). Current value: %d"
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% penultimate_filters
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)
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channel_dim = 1 if backend.image_data_format() == "channels_first" else -1
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filters = penultimate_filters // 24
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x = layers.Conv2D(
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stem_block_filters,
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(3, 3),
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strides=(2, 2),
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padding="valid",
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use_bias=False,
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name="stem_conv1",
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kernel_initializer="he_normal",
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)(img_input)
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x = layers.BatchNormalization(
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axis=channel_dim, momentum=0.9997, epsilon=1e-3, name="stem_bn1"
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)(x)
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p = None
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x, p = _reduction_a_cell(
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x, p, filters // (filter_multiplier**2), block_id="stem_1"
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)
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x, p = _reduction_a_cell(
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x, p, filters // filter_multiplier, block_id="stem_2"
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)
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for i in range(num_blocks):
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x, p = _normal_a_cell(x, p, filters, block_id="%d" % (i))
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x, p0 = _reduction_a_cell(
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x, p, filters * filter_multiplier, block_id="reduce_%d" % (num_blocks)
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)
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p = p0 if not skip_reduction else p
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for i in range(num_blocks):
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x, p = _normal_a_cell(
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x,
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p,
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filters * filter_multiplier,
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block_id="%d" % (num_blocks + i + 1),
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)
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x, p0 = _reduction_a_cell(
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x,
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p,
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filters * filter_multiplier**2,
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block_id="reduce_%d" % (2 * num_blocks),
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)
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p = p0 if not skip_reduction else p
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for i in range(num_blocks):
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x, p = _normal_a_cell(
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x,
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p,
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filters * filter_multiplier**2,
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block_id="%d" % (2 * num_blocks + i + 1),
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)
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x = layers.Activation("relu")(x)
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if include_top:
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x = layers.GlobalAveragePooling2D()(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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model = training.Model(inputs, x, name="NASNet")
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# Load weights.
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if weights == "imagenet":
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if default_size == 224: # mobile version
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if include_top:
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weights_path = data_utils.get_file(
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"nasnet_mobile.h5",
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NASNET_MOBILE_WEIGHT_PATH,
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cache_subdir="models",
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file_hash="020fb642bf7360b370c678b08e0adf61",
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)
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else:
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weights_path = data_utils.get_file(
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"nasnet_mobile_no_top.h5",
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NASNET_MOBILE_WEIGHT_PATH_NO_TOP,
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cache_subdir="models",
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file_hash="1ed92395b5b598bdda52abe5c0dbfd63",
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)
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model.load_weights(weights_path)
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elif default_size == 331: # large version
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if include_top:
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weights_path = data_utils.get_file(
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"nasnet_large.h5",
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NASNET_LARGE_WEIGHT_PATH,
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cache_subdir="models",
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file_hash="11577c9a518f0070763c2b964a382f17",
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)
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else:
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weights_path = data_utils.get_file(
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"nasnet_large_no_top.h5",
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NASNET_LARGE_WEIGHT_PATH_NO_TOP,
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cache_subdir="models",
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file_hash="d81d89dc07e6e56530c4e77faddd61b5",
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)
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model.load_weights(weights_path)
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else:
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raise ValueError(
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"ImageNet weights can only be loaded with NASNetLarge"
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" or NASNetMobile"
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)
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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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@keras_export(
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"keras.applications.nasnet.NASNetMobile", "keras.applications.NASNetMobile"
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)
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def NASNetMobile(
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input_shape=None,
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include_top=True,
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weights="imagenet",
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input_tensor=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 a Mobile NASNet model in ImageNet mode.
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Reference:
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- [Learning Transferable Architectures for Scalable Image Recognition](
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https://arxiv.org/abs/1707.07012) (CVPR 2018)
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Optionally loads weights pre-trained on ImageNet.
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Note that the data format convention used by the model is
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the one specified in your Keras config at `~/.keras/keras.json`.
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Note: each Keras Application expects a specific kind of input preprocessing.
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For NASNet, call `tf.keras.applications.nasnet.preprocess_input` on your
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inputs before passing them to the model.
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Args:
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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 `(224, 224, 3)` for NASNetMobile
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It should have exactly 3 inputs channels,
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and width and height should be no smaller than 32.
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E.g. `(224, 224, 3)` would be one valid value.
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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: `None` (random initialization) or
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`imagenet` (ImageNet weights). For loading `imagenet` weights,
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`input_shape` should be (224, 224, 3)
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input_tensor: Optional Keras tensor (i.e. output of
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`layers.Input()`)
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to use as image input for the model.
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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
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will be the 4D tensor output of the
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last convolutional layer.
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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 layer, and thus
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the output of the model will be a
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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 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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return NASNet(
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input_shape,
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penultimate_filters=1056,
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num_blocks=4,
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stem_block_filters=32,
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skip_reduction=False,
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filter_multiplier=2,
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include_top=include_top,
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weights=weights,
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input_tensor=input_tensor,
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pooling=pooling,
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classes=classes,
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default_size=224,
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classifier_activation=classifier_activation,
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)
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@keras_export(
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"keras.applications.nasnet.NASNetLarge", "keras.applications.NASNetLarge"
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)
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def NASNetLarge(
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input_shape=None,
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include_top=True,
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weights="imagenet",
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input_tensor=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 a NASNet model in ImageNet mode.
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Reference:
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- [Learning Transferable Architectures for Scalable Image Recognition](
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https://arxiv.org/abs/1707.07012) (CVPR 2018)
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|
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Optionally loads weights pre-trained on ImageNet.
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|
Note that the data format convention used by the model is
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the one specified in your Keras config at `~/.keras/keras.json`.
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|
|
|
Note: each Keras Application expects a specific kind of input preprocessing.
|
|
For NASNet, call `tf.keras.applications.nasnet.preprocess_input` on your
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|
inputs before passing them to the model.
|
|
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|
Args:
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input_shape: Optional shape tuple, only to be specified
|
|
if `include_top` is False (otherwise the input shape
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has to be `(331, 331, 3)` for NASNetLarge.
|
|
It should have exactly 3 inputs channels,
|
|
and width and height should be no smaller than 32.
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|
E.g. `(224, 224, 3)` would be one valid value.
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include_top: Whether to include the fully-connected
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layer at the top of the network.
|
|
weights: `None` (random initialization) or
|
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`imagenet` (ImageNet weights). For loading `imagenet` weights,
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|
`input_shape` should be (331, 331, 3)
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input_tensor: Optional Keras tensor (i.e. output of
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`layers.Input()`)
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to use as image input for the model.
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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
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will be the 4D tensor output of the
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last convolutional layer.
|
|
- `avg` means that global average pooling
|
|
will be applied to the output of the
|
|
last convolutional layer, 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.
|
|
|
|
Raises:
|
|
ValueError: in case of invalid argument for `weights`,
|
|
or invalid input shape.
|
|
RuntimeError: If attempting to run this model with a
|
|
backend that does not support separable convolutions.
|
|
"""
|
|
return NASNet(
|
|
input_shape,
|
|
penultimate_filters=4032,
|
|
num_blocks=6,
|
|
stem_block_filters=96,
|
|
skip_reduction=True,
|
|
filter_multiplier=2,
|
|
include_top=include_top,
|
|
weights=weights,
|
|
input_tensor=input_tensor,
|
|
pooling=pooling,
|
|
classes=classes,
|
|
default_size=331,
|
|
classifier_activation=classifier_activation,
|
|
)
|
|
|
|
|
|
def _separable_conv_block(
|
|
ip, filters, kernel_size=(3, 3), strides=(1, 1), block_id=None
|
|
):
|
|
"""Adds 2 blocks of [relu-separable conv-batchnorm].
|
|
|
|
Args:
|
|
ip: Input tensor
|
|
filters: Number of output filters per layer
|
|
kernel_size: Kernel size of separable convolutions
|
|
strides: Strided convolution for downsampling
|
|
block_id: String block_id
|
|
|
|
Returns:
|
|
A Keras tensor
|
|
"""
|
|
channel_dim = 1 if backend.image_data_format() == "channels_first" else -1
|
|
|
|
with backend.name_scope(f"separable_conv_block_{block_id}"):
|
|
x = layers.Activation("relu")(ip)
|
|
if strides == (2, 2):
|
|
x = layers.ZeroPadding2D(
|
|
padding=imagenet_utils.correct_pad(x, kernel_size),
|
|
name=f"separable_conv_1_pad_{block_id}",
|
|
)(x)
|
|
conv_pad = "valid"
|
|
else:
|
|
conv_pad = "same"
|
|
x = layers.SeparableConv2D(
|
|
filters,
|
|
kernel_size,
|
|
strides=strides,
|
|
name=f"separable_conv_1_{block_id}",
|
|
padding=conv_pad,
|
|
use_bias=False,
|
|
kernel_initializer="he_normal",
|
|
)(x)
|
|
x = layers.BatchNormalization(
|
|
axis=channel_dim,
|
|
momentum=0.9997,
|
|
epsilon=1e-3,
|
|
name=f"separable_conv_1_bn_{block_id}",
|
|
)(x)
|
|
x = layers.Activation("relu")(x)
|
|
x = layers.SeparableConv2D(
|
|
filters,
|
|
kernel_size,
|
|
name=f"separable_conv_2_{block_id}",
|
|
padding="same",
|
|
use_bias=False,
|
|
kernel_initializer="he_normal",
|
|
)(x)
|
|
x = layers.BatchNormalization(
|
|
axis=channel_dim,
|
|
momentum=0.9997,
|
|
epsilon=1e-3,
|
|
name=f"separable_conv_2_bn_{block_id}",
|
|
)(x)
|
|
return x
|
|
|
|
|
|
def _adjust_block(p, ip, filters, block_id=None):
|
|
"""Adjusts the input `previous path` to match the shape of the `input`.
|
|
|
|
Used in situations where the output number of filters needs to be changed.
|
|
|
|
Args:
|
|
p: Input tensor which needs to be modified
|
|
ip: Input tensor whose shape needs to be matched
|
|
filters: Number of output filters to be matched
|
|
block_id: String block_id
|
|
|
|
Returns:
|
|
Adjusted Keras tensor
|
|
"""
|
|
channel_dim = 1 if backend.image_data_format() == "channels_first" else -1
|
|
img_dim = 2 if backend.image_data_format() == "channels_first" else -2
|
|
|
|
ip_shape = backend.int_shape(ip)
|
|
|
|
if p is not None:
|
|
p_shape = backend.int_shape(p)
|
|
|
|
with backend.name_scope("adjust_block"):
|
|
if p is None:
|
|
p = ip
|
|
|
|
elif p_shape[img_dim] != ip_shape[img_dim]:
|
|
with backend.name_scope(f"adjust_reduction_block_{block_id}"):
|
|
p = layers.Activation("relu", name=f"adjust_relu_1_{block_id}")(
|
|
p
|
|
)
|
|
p1 = layers.AveragePooling2D(
|
|
(1, 1),
|
|
strides=(2, 2),
|
|
padding="valid",
|
|
name=f"adjust_avg_pool_1_{block_id}",
|
|
)(p)
|
|
p1 = layers.Conv2D(
|
|
filters // 2,
|
|
(1, 1),
|
|
padding="same",
|
|
use_bias=False,
|
|
name=f"adjust_conv_1_{block_id}",
|
|
kernel_initializer="he_normal",
|
|
)(p1)
|
|
|
|
p2 = layers.ZeroPadding2D(padding=((0, 1), (0, 1)))(p)
|
|
p2 = layers.Cropping2D(cropping=((1, 0), (1, 0)))(p2)
|
|
p2 = layers.AveragePooling2D(
|
|
(1, 1),
|
|
strides=(2, 2),
|
|
padding="valid",
|
|
name=f"adjust_avg_pool_2_{block_id}",
|
|
)(p2)
|
|
p2 = layers.Conv2D(
|
|
filters // 2,
|
|
(1, 1),
|
|
padding="same",
|
|
use_bias=False,
|
|
name=f"adjust_conv_2_{block_id}",
|
|
kernel_initializer="he_normal",
|
|
)(p2)
|
|
|
|
p = layers.concatenate([p1, p2], axis=channel_dim)
|
|
p = layers.BatchNormalization(
|
|
axis=channel_dim,
|
|
momentum=0.9997,
|
|
epsilon=1e-3,
|
|
name=f"adjust_bn_{block_id}",
|
|
)(p)
|
|
|
|
elif p_shape[channel_dim] != filters:
|
|
with backend.name_scope(f"adjust_projection_block_{block_id}"):
|
|
p = layers.Activation("relu")(p)
|
|
p = layers.Conv2D(
|
|
filters,
|
|
(1, 1),
|
|
strides=(1, 1),
|
|
padding="same",
|
|
name=f"adjust_conv_projection_{block_id}",
|
|
use_bias=False,
|
|
kernel_initializer="he_normal",
|
|
)(p)
|
|
p = layers.BatchNormalization(
|
|
axis=channel_dim,
|
|
momentum=0.9997,
|
|
epsilon=1e-3,
|
|
name=f"adjust_bn_{block_id}",
|
|
)(p)
|
|
return p
|
|
|
|
|
|
def _normal_a_cell(ip, p, filters, block_id=None):
|
|
"""Adds a Normal cell for NASNet-A (Fig. 4 in the paper).
|
|
|
|
Args:
|
|
ip: Input tensor `x`
|
|
p: Input tensor `p`
|
|
filters: Number of output filters
|
|
block_id: String block_id
|
|
|
|
Returns:
|
|
A Keras tensor
|
|
"""
|
|
channel_dim = 1 if backend.image_data_format() == "channels_first" else -1
|
|
|
|
with backend.name_scope(f"normal_A_block_{block_id}"):
|
|
p = _adjust_block(p, ip, filters, block_id)
|
|
|
|
h = layers.Activation("relu")(ip)
|
|
h = layers.Conv2D(
|
|
filters,
|
|
(1, 1),
|
|
strides=(1, 1),
|
|
padding="same",
|
|
name=f"normal_conv_1_{block_id}",
|
|
use_bias=False,
|
|
kernel_initializer="he_normal",
|
|
)(h)
|
|
h = layers.BatchNormalization(
|
|
axis=channel_dim,
|
|
momentum=0.9997,
|
|
epsilon=1e-3,
|
|
name=f"normal_bn_1_{block_id}",
|
|
)(h)
|
|
|
|
with backend.name_scope("block_1"):
|
|
x1_1 = _separable_conv_block(
|
|
h,
|
|
filters,
|
|
kernel_size=(5, 5),
|
|
block_id=f"normal_left1_{block_id}",
|
|
)
|
|
x1_2 = _separable_conv_block(
|
|
p, filters, block_id=f"normal_right1_{block_id}"
|
|
)
|
|
x1 = layers.add([x1_1, x1_2], name=f"normal_add_1_{block_id}")
|
|
|
|
with backend.name_scope("block_2"):
|
|
x2_1 = _separable_conv_block(
|
|
p, filters, (5, 5), block_id=f"normal_left2_{block_id}"
|
|
)
|
|
x2_2 = _separable_conv_block(
|
|
p, filters, (3, 3), block_id=f"normal_right2_{block_id}"
|
|
)
|
|
x2 = layers.add([x2_1, x2_2], name=f"normal_add_2_{block_id}")
|
|
|
|
with backend.name_scope("block_3"):
|
|
x3 = layers.AveragePooling2D(
|
|
(3, 3),
|
|
strides=(1, 1),
|
|
padding="same",
|
|
name=f"normal_left3_{block_id}",
|
|
)(h)
|
|
x3 = layers.add([x3, p], name=f"normal_add_3_{block_id}")
|
|
|
|
with backend.name_scope("block_4"):
|
|
x4_1 = layers.AveragePooling2D(
|
|
(3, 3),
|
|
strides=(1, 1),
|
|
padding="same",
|
|
name=f"normal_left4_{block_id}",
|
|
)(p)
|
|
x4_2 = layers.AveragePooling2D(
|
|
(3, 3),
|
|
strides=(1, 1),
|
|
padding="same",
|
|
name=f"normal_right4_{block_id}",
|
|
)(p)
|
|
x4 = layers.add([x4_1, x4_2], name=f"normal_add_4_{block_id}")
|
|
|
|
with backend.name_scope("block_5"):
|
|
x5 = _separable_conv_block(
|
|
h, filters, block_id=f"normal_left5_{block_id}"
|
|
)
|
|
x5 = layers.add([x5, h], name=f"normal_add_5_{block_id}")
|
|
|
|
x = layers.concatenate(
|
|
[p, x1, x2, x3, x4, x5],
|
|
axis=channel_dim,
|
|
name=f"normal_concat_{block_id}",
|
|
)
|
|
return x, ip
|
|
|
|
|
|
def _reduction_a_cell(ip, p, filters, block_id=None):
|
|
"""Adds a Reduction cell for NASNet-A (Fig. 4 in the paper).
|
|
|
|
Args:
|
|
ip: Input tensor `x`
|
|
p: Input tensor `p`
|
|
filters: Number of output filters
|
|
block_id: String block_id
|
|
|
|
Returns:
|
|
A Keras tensor
|
|
"""
|
|
channel_dim = 1 if backend.image_data_format() == "channels_first" else -1
|
|
|
|
with backend.name_scope(f"reduction_A_block_{block_id}"):
|
|
p = _adjust_block(p, ip, filters, block_id)
|
|
|
|
h = layers.Activation("relu")(ip)
|
|
h = layers.Conv2D(
|
|
filters,
|
|
(1, 1),
|
|
strides=(1, 1),
|
|
padding="same",
|
|
name=f"reduction_conv_1_{block_id}",
|
|
use_bias=False,
|
|
kernel_initializer="he_normal",
|
|
)(h)
|
|
h = layers.BatchNormalization(
|
|
axis=channel_dim,
|
|
momentum=0.9997,
|
|
epsilon=1e-3,
|
|
name=f"reduction_bn_1_{block_id}",
|
|
)(h)
|
|
h3 = layers.ZeroPadding2D(
|
|
padding=imagenet_utils.correct_pad(h, 3),
|
|
name=f"reduction_pad_1_{block_id}",
|
|
)(h)
|
|
|
|
with backend.name_scope("block_1"):
|
|
x1_1 = _separable_conv_block(
|
|
h,
|
|
filters,
|
|
(5, 5),
|
|
strides=(2, 2),
|
|
block_id=f"reduction_left1_{block_id}",
|
|
)
|
|
x1_2 = _separable_conv_block(
|
|
p,
|
|
filters,
|
|
(7, 7),
|
|
strides=(2, 2),
|
|
block_id=f"reduction_right1_{block_id}",
|
|
)
|
|
x1 = layers.add([x1_1, x1_2], name=f"reduction_add_1_{block_id}")
|
|
|
|
with backend.name_scope("block_2"):
|
|
x2_1 = layers.MaxPooling2D(
|
|
(3, 3),
|
|
strides=(2, 2),
|
|
padding="valid",
|
|
name=f"reduction_left2_{block_id}",
|
|
)(h3)
|
|
x2_2 = _separable_conv_block(
|
|
p,
|
|
filters,
|
|
(7, 7),
|
|
strides=(2, 2),
|
|
block_id=f"reduction_right2_{block_id}",
|
|
)
|
|
x2 = layers.add([x2_1, x2_2], name=f"reduction_add_2_{block_id}")
|
|
|
|
with backend.name_scope("block_3"):
|
|
x3_1 = layers.AveragePooling2D(
|
|
(3, 3),
|
|
strides=(2, 2),
|
|
padding="valid",
|
|
name=f"reduction_left3_{block_id}",
|
|
)(h3)
|
|
x3_2 = _separable_conv_block(
|
|
p,
|
|
filters,
|
|
(5, 5),
|
|
strides=(2, 2),
|
|
block_id=f"reduction_right3_{block_id}",
|
|
)
|
|
x3 = layers.add([x3_1, x3_2], name=f"reduction_add3_{block_id}")
|
|
|
|
with backend.name_scope("block_4"):
|
|
x4 = layers.AveragePooling2D(
|
|
(3, 3),
|
|
strides=(1, 1),
|
|
padding="same",
|
|
name=f"reduction_left4_{block_id}",
|
|
)(x1)
|
|
x4 = layers.add([x2, x4])
|
|
|
|
with backend.name_scope("block_5"):
|
|
x5_1 = _separable_conv_block(
|
|
x1, filters, (3, 3), block_id=f"reduction_left4_{block_id}"
|
|
)
|
|
x5_2 = layers.MaxPooling2D(
|
|
(3, 3),
|
|
strides=(2, 2),
|
|
padding="valid",
|
|
name=f"reduction_right5_{block_id}",
|
|
)(h3)
|
|
x5 = layers.add([x5_1, x5_2], name=f"reduction_add4_{block_id}")
|
|
|
|
x = layers.concatenate(
|
|
[x2, x3, x4, x5],
|
|
axis=channel_dim,
|
|
name=f"reduction_concat_{block_id}",
|
|
)
|
|
return x, ip
|
|
|
|
|
|
@keras_export("keras.applications.nasnet.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.nasnet.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__
|