591 lines
22 KiB
Python
591 lines
22 KiB
Python
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# 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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"""MobileNet v2 models for Keras.
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MobileNetV2 is a general architecture and can be used for multiple use cases.
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Depending on the use case, it can use different input layer size and
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different width factors. This allows different width models to reduce
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the number of multiply-adds and thereby
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reduce inference cost on mobile devices.
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MobileNetV2 is very similar to the original MobileNet,
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except that it uses inverted residual blocks with
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bottlenecking features. It has a drastically lower
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parameter count than the original MobileNet.
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MobileNets support any input size greater
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than 32 x 32, with larger image sizes
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offering better performance.
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The number of parameters and number of multiply-adds
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can be modified by using the `alpha` parameter,
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which increases/decreases the number of filters in each layer.
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By altering the image size and `alpha` parameter,
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all 22 models from the paper can be built, with ImageNet weights provided.
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The paper demonstrates the performance of MobileNets using `alpha` values of
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1.0 (also called 100 % MobileNet), 0.35, 0.5, 0.75, 1.0, 1.3, and 1.4
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For each of these `alpha` values, weights for 5 different input image sizes
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are provided (224, 192, 160, 128, and 96).
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The following table describes the performance of
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MobileNet on various input sizes:
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------------------------------------------------------------------------
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MACs stands for Multiply Adds
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Classification Checkpoint|MACs (M)|Parameters (M)|Top 1 Accuracy|Top 5 Accuracy
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--------------------------|------------|---------------|---------|------------
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| [mobilenet_v2_1.4_224] | 582 | 6.06 | 75.0 | 92.5 |
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| [mobilenet_v2_1.3_224] | 509 | 5.34 | 74.4 | 92.1 |
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| [mobilenet_v2_1.0_224] | 300 | 3.47 | 71.8 | 91.0 |
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| [mobilenet_v2_1.0_192] | 221 | 3.47 | 70.7 | 90.1 |
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| [mobilenet_v2_1.0_160] | 154 | 3.47 | 68.8 | 89.0 |
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| [mobilenet_v2_1.0_128] | 99 | 3.47 | 65.3 | 86.9 |
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| [mobilenet_v2_1.0_96] | 56 | 3.47 | 60.3 | 83.2 |
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| [mobilenet_v2_0.75_224] | 209 | 2.61 | 69.8 | 89.6 |
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| [mobilenet_v2_0.75_192] | 153 | 2.61 | 68.7 | 88.9 |
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| [mobilenet_v2_0.75_160] | 107 | 2.61 | 66.4 | 87.3 |
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| [mobilenet_v2_0.75_128] | 69 | 2.61 | 63.2 | 85.3 |
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| [mobilenet_v2_0.75_96] | 39 | 2.61 | 58.8 | 81.6 |
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| [mobilenet_v2_0.5_224] | 97 | 1.95 | 65.4 | 86.4 |
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| [mobilenet_v2_0.5_192] | 71 | 1.95 | 63.9 | 85.4 |
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| [mobilenet_v2_0.5_160] | 50 | 1.95 | 61.0 | 83.2 |
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| [mobilenet_v2_0.5_128] | 32 | 1.95 | 57.7 | 80.8 |
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| [mobilenet_v2_0.5_96] | 18 | 1.95 | 51.2 | 75.8 |
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| [mobilenet_v2_0.35_224] | 59 | 1.66 | 60.3 | 82.9 |
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| [mobilenet_v2_0.35_192] | 43 | 1.66 | 58.2 | 81.2 |
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| [mobilenet_v2_0.35_160] | 30 | 1.66 | 55.7 | 79.1 |
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| [mobilenet_v2_0.35_128] | 20 | 1.66 | 50.8 | 75.0 |
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| [mobilenet_v2_0.35_96] | 11 | 1.66 | 45.5 | 70.4 |
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Reference:
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- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](
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https://arxiv.org/abs/1801.04381) (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_WEIGHT_PATH = (
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"https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/"
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)
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layers = None
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@keras_export(
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"keras.applications.mobilenet_v2.MobileNetV2",
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"keras.applications.MobileNetV2",
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)
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def MobileNetV2(
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input_shape=None,
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alpha=1.0,
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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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**kwargs,
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):
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"""Instantiates the MobileNetV2 architecture.
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MobileNetV2 is very similar to the original MobileNet,
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except that it uses inverted residual blocks with
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bottlenecking features. It has a drastically lower
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parameter count than the original MobileNet.
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MobileNets support any input size greater
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than 32 x 32, with larger image sizes
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offering better performance.
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Reference:
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- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](
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https://arxiv.org/abs/1801.04381) (CVPR 2018)
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This function returns a Keras image classification model,
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optionally loaded with weights pre-trained on ImageNet.
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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 MobileNetV2, call `tf.keras.applications.mobilenet_v2.preprocess_input`
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on your inputs before passing them to the model.
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`mobilenet_v2.preprocess_input` will scale input pixels between -1 and 1.
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Args:
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input_shape: Optional shape tuple, to be specified if you would
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like to use a model with an input image resolution that is not
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(224, 224, 3).
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It should have exactly 3 inputs channels (224, 224, 3).
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You can also omit this option if you would like
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to infer input_shape from an input_tensor.
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If you choose to include both input_tensor and input_shape then
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input_shape will be used if they match, if the shapes
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do not match then we will throw an error.
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E.g. `(160, 160, 3)` would be one valid value.
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alpha: Float, larger than zero, controls the width of the network. This is
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known as the width multiplier in the MobileNetV2 paper, but the name is
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kept for consistency with `applications.MobileNetV1` model in Keras.
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- If `alpha` < 1.0, proportionally decreases the number
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of filters in each layer.
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- If `alpha` > 1.0, proportionally increases the number
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of filters in each layer.
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- If `alpha` = 1.0, default number of filters from the paper
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are used at each layer.
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include_top: Boolean, whether to include the fully-connected layer at the
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top of the network. Defaults to `True`.
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weights: String, one of `None` (random initialization), 'imagenet'
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(pre-training on ImageNet), or the path to the weights file to be
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loaded.
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input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`)
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to use as image input for the model.
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pooling: String, optional pooling mode for feature extraction when
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`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 integer number of classes to classify images into, only
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to be specified if `include_top` is True, and if no `weights` argument
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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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**kwargs: For backwards compatibility only.
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Returns:
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A `keras.Model` instance.
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"""
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global layers
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if "layers" in kwargs:
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layers = kwargs.pop("layers")
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else:
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layers = VersionAwareLayers()
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if kwargs:
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raise ValueError(f"Unknown argument(s): {kwargs}")
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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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f"Received `weights={weights}`"
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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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f"as true, `classes` should be 1000. Received `classes={classes}`"
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)
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# Determine proper input shape and default size.
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# If both input_shape and input_tensor are used, they should match
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if input_shape is not None and input_tensor is not None:
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try:
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is_input_t_tensor = backend.is_keras_tensor(input_tensor)
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except ValueError:
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try:
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is_input_t_tensor = backend.is_keras_tensor(
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layer_utils.get_source_inputs(input_tensor)
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)
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except ValueError:
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raise ValueError(
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f"input_tensor: {input_tensor}"
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"is not type input_tensor. "
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f"Received `type(input_tensor)={type(input_tensor)}`"
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)
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if is_input_t_tensor:
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if backend.image_data_format() == "channels_first":
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if backend.int_shape(input_tensor)[1] != input_shape[1]:
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raise ValueError(
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"input_shape[1] must equal shape(input_tensor)[1] "
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"when `image_data_format` is `channels_first`; "
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"Received `input_tensor.shape="
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f"{input_tensor.shape}`"
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f", `input_shape={input_shape}`"
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)
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else:
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if backend.int_shape(input_tensor)[2] != input_shape[1]:
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raise ValueError(
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"input_tensor.shape[2] must equal input_shape[1]; "
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"Received `input_tensor.shape="
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f"{input_tensor.shape}`, "
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f"`input_shape={input_shape}`"
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)
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else:
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raise ValueError(
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"input_tensor is not a Keras tensor; "
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f"Received `input_tensor={input_tensor}`"
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)
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# If input_shape is None, infer shape from input_tensor.
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if input_shape is None and input_tensor is not None:
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try:
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backend.is_keras_tensor(input_tensor)
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except ValueError:
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raise ValueError(
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"input_tensor must be a valid Keras tensor type; "
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f"Received {input_tensor} of type {type(input_tensor)}"
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)
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if input_shape is None and not backend.is_keras_tensor(input_tensor):
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default_size = 224
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elif input_shape is None and backend.is_keras_tensor(input_tensor):
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if backend.image_data_format() == "channels_first":
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rows = backend.int_shape(input_tensor)[2]
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cols = backend.int_shape(input_tensor)[3]
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else:
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rows = backend.int_shape(input_tensor)[1]
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cols = backend.int_shape(input_tensor)[2]
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if rows == cols and rows in [96, 128, 160, 192, 224]:
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default_size = rows
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else:
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default_size = 224
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# If input_shape is None and no input_tensor
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elif input_shape is None:
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default_size = 224
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# If input_shape is not None, assume default size.
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else:
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if backend.image_data_format() == "channels_first":
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rows = input_shape[1]
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cols = input_shape[2]
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else:
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rows = input_shape[0]
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cols = input_shape[1]
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if rows == cols and rows in [96, 128, 160, 192, 224]:
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default_size = rows
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else:
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default_size = 224
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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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row_axis, col_axis = (0, 1)
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else:
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row_axis, col_axis = (1, 2)
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rows = input_shape[row_axis]
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cols = input_shape[col_axis]
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if weights == "imagenet":
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if alpha not in [0.35, 0.50, 0.75, 1.0, 1.3, 1.4]:
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raise ValueError(
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"If imagenet weights are being loaded, "
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"alpha must be one of `0.35`, `0.50`, `0.75`, "
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"`1.0`, `1.3` or `1.4` only;"
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f" Received `alpha={alpha}`"
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)
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if rows != cols or rows not in [96, 128, 160, 192, 224]:
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rows = 224
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logging.warning(
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"`input_shape` is undefined or non-square, "
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"or `rows` is not in [96, 128, 160, 192, 224]. "
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"Weights for input shape (224, 224) will be "
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"loaded as the default."
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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)
|
||
|
|
else:
|
||
|
|
img_input = input_tensor
|
||
|
|
|
||
|
|
channel_axis = 1 if backend.image_data_format() == "channels_first" else -1
|
||
|
|
|
||
|
|
first_block_filters = _make_divisible(32 * alpha, 8)
|
||
|
|
x = layers.Conv2D(
|
||
|
|
first_block_filters,
|
||
|
|
kernel_size=3,
|
||
|
|
strides=(2, 2),
|
||
|
|
padding="same",
|
||
|
|
use_bias=False,
|
||
|
|
name="Conv1",
|
||
|
|
)(img_input)
|
||
|
|
x = layers.BatchNormalization(
|
||
|
|
axis=channel_axis, epsilon=1e-3, momentum=0.999, name="bn_Conv1"
|
||
|
|
)(x)
|
||
|
|
x = layers.ReLU(6.0, name="Conv1_relu")(x)
|
||
|
|
|
||
|
|
x = _inverted_res_block(
|
||
|
|
x, filters=16, alpha=alpha, stride=1, expansion=1, block_id=0
|
||
|
|
)
|
||
|
|
|
||
|
|
x = _inverted_res_block(
|
||
|
|
x, filters=24, alpha=alpha, stride=2, expansion=6, block_id=1
|
||
|
|
)
|
||
|
|
x = _inverted_res_block(
|
||
|
|
x, filters=24, alpha=alpha, stride=1, expansion=6, block_id=2
|
||
|
|
)
|
||
|
|
|
||
|
|
x = _inverted_res_block(
|
||
|
|
x, filters=32, alpha=alpha, stride=2, expansion=6, block_id=3
|
||
|
|
)
|
||
|
|
x = _inverted_res_block(
|
||
|
|
x, filters=32, alpha=alpha, stride=1, expansion=6, block_id=4
|
||
|
|
)
|
||
|
|
x = _inverted_res_block(
|
||
|
|
x, filters=32, alpha=alpha, stride=1, expansion=6, block_id=5
|
||
|
|
)
|
||
|
|
|
||
|
|
x = _inverted_res_block(
|
||
|
|
x, filters=64, alpha=alpha, stride=2, expansion=6, block_id=6
|
||
|
|
)
|
||
|
|
x = _inverted_res_block(
|
||
|
|
x, filters=64, alpha=alpha, stride=1, expansion=6, block_id=7
|
||
|
|
)
|
||
|
|
x = _inverted_res_block(
|
||
|
|
x, filters=64, alpha=alpha, stride=1, expansion=6, block_id=8
|
||
|
|
)
|
||
|
|
x = _inverted_res_block(
|
||
|
|
x, filters=64, alpha=alpha, stride=1, expansion=6, block_id=9
|
||
|
|
)
|
||
|
|
|
||
|
|
x = _inverted_res_block(
|
||
|
|
x, filters=96, alpha=alpha, stride=1, expansion=6, block_id=10
|
||
|
|
)
|
||
|
|
x = _inverted_res_block(
|
||
|
|
x, filters=96, alpha=alpha, stride=1, expansion=6, block_id=11
|
||
|
|
)
|
||
|
|
x = _inverted_res_block(
|
||
|
|
x, filters=96, alpha=alpha, stride=1, expansion=6, block_id=12
|
||
|
|
)
|
||
|
|
|
||
|
|
x = _inverted_res_block(
|
||
|
|
x, filters=160, alpha=alpha, stride=2, expansion=6, block_id=13
|
||
|
|
)
|
||
|
|
x = _inverted_res_block(
|
||
|
|
x, filters=160, alpha=alpha, stride=1, expansion=6, block_id=14
|
||
|
|
)
|
||
|
|
x = _inverted_res_block(
|
||
|
|
x, filters=160, alpha=alpha, stride=1, expansion=6, block_id=15
|
||
|
|
)
|
||
|
|
|
||
|
|
x = _inverted_res_block(
|
||
|
|
x, filters=320, alpha=alpha, stride=1, expansion=6, block_id=16
|
||
|
|
)
|
||
|
|
|
||
|
|
# no alpha applied to last conv as stated in the paper:
|
||
|
|
# if the width multiplier is greater than 1 we increase the number of output
|
||
|
|
# channels.
|
||
|
|
if alpha > 1.0:
|
||
|
|
last_block_filters = _make_divisible(1280 * alpha, 8)
|
||
|
|
else:
|
||
|
|
last_block_filters = 1280
|
||
|
|
|
||
|
|
x = layers.Conv2D(
|
||
|
|
last_block_filters, kernel_size=1, use_bias=False, name="Conv_1"
|
||
|
|
)(x)
|
||
|
|
x = layers.BatchNormalization(
|
||
|
|
axis=channel_axis, epsilon=1e-3, momentum=0.999, name="Conv_1_bn"
|
||
|
|
)(x)
|
||
|
|
x = layers.ReLU(6.0, name="out_relu")(x)
|
||
|
|
|
||
|
|
if include_top:
|
||
|
|
x = layers.GlobalAveragePooling2D()(x)
|
||
|
|
imagenet_utils.validate_activation(classifier_activation, weights)
|
||
|
|
x = layers.Dense(
|
||
|
|
classes, activation=classifier_activation, name="predictions"
|
||
|
|
)(x)
|
||
|
|
|
||
|
|
else:
|
||
|
|
if pooling == "avg":
|
||
|
|
x = layers.GlobalAveragePooling2D()(x)
|
||
|
|
elif pooling == "max":
|
||
|
|
x = layers.GlobalMaxPooling2D()(x)
|
||
|
|
|
||
|
|
# Ensure that the model takes into account any potential predecessors of
|
||
|
|
# `input_tensor`.
|
||
|
|
if input_tensor is not None:
|
||
|
|
inputs = layer_utils.get_source_inputs(input_tensor)
|
||
|
|
else:
|
||
|
|
inputs = img_input
|
||
|
|
|
||
|
|
# Create model.
|
||
|
|
model = training.Model(inputs, x, name=f"mobilenetv2_{alpha:0.2f}_{rows}")
|
||
|
|
|
||
|
|
# Load weights.
|
||
|
|
if weights == "imagenet":
|
||
|
|
if include_top:
|
||
|
|
model_name = (
|
||
|
|
"mobilenet_v2_weights_tf_dim_ordering_tf_kernels_"
|
||
|
|
+ str(float(alpha))
|
||
|
|
+ "_"
|
||
|
|
+ str(rows)
|
||
|
|
+ ".h5"
|
||
|
|
)
|
||
|
|
weight_path = BASE_WEIGHT_PATH + model_name
|
||
|
|
weights_path = data_utils.get_file(
|
||
|
|
model_name, weight_path, cache_subdir="models"
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
model_name = (
|
||
|
|
"mobilenet_v2_weights_tf_dim_ordering_tf_kernels_"
|
||
|
|
+ str(float(alpha))
|
||
|
|
+ "_"
|
||
|
|
+ str(rows)
|
||
|
|
+ "_no_top"
|
||
|
|
+ ".h5"
|
||
|
|
)
|
||
|
|
weight_path = BASE_WEIGHT_PATH + model_name
|
||
|
|
weights_path = data_utils.get_file(
|
||
|
|
model_name, weight_path, cache_subdir="models"
|
||
|
|
)
|
||
|
|
model.load_weights(weights_path)
|
||
|
|
elif weights is not None:
|
||
|
|
model.load_weights(weights)
|
||
|
|
|
||
|
|
return model
|
||
|
|
|
||
|
|
|
||
|
|
def _inverted_res_block(inputs, expansion, stride, alpha, filters, block_id):
|
||
|
|
"""Inverted ResNet block."""
|
||
|
|
channel_axis = 1 if backend.image_data_format() == "channels_first" else -1
|
||
|
|
|
||
|
|
in_channels = backend.int_shape(inputs)[channel_axis]
|
||
|
|
pointwise_conv_filters = int(filters * alpha)
|
||
|
|
# Ensure the number of filters on the last 1x1 convolution is divisible by
|
||
|
|
# 8.
|
||
|
|
pointwise_filters = _make_divisible(pointwise_conv_filters, 8)
|
||
|
|
x = inputs
|
||
|
|
prefix = f"block_{block_id}_"
|
||
|
|
|
||
|
|
if block_id:
|
||
|
|
# Expand with a pointwise 1x1 convolution.
|
||
|
|
x = layers.Conv2D(
|
||
|
|
expansion * in_channels,
|
||
|
|
kernel_size=1,
|
||
|
|
padding="same",
|
||
|
|
use_bias=False,
|
||
|
|
activation=None,
|
||
|
|
name=prefix + "expand",
|
||
|
|
)(x)
|
||
|
|
x = layers.BatchNormalization(
|
||
|
|
axis=channel_axis,
|
||
|
|
epsilon=1e-3,
|
||
|
|
momentum=0.999,
|
||
|
|
name=prefix + "expand_BN",
|
||
|
|
)(x)
|
||
|
|
x = layers.ReLU(6.0, name=prefix + "expand_relu")(x)
|
||
|
|
else:
|
||
|
|
prefix = "expanded_conv_"
|
||
|
|
|
||
|
|
# Depthwise 3x3 convolution.
|
||
|
|
if stride == 2:
|
||
|
|
x = layers.ZeroPadding2D(
|
||
|
|
padding=imagenet_utils.correct_pad(x, 3), name=prefix + "pad"
|
||
|
|
)(x)
|
||
|
|
x = layers.DepthwiseConv2D(
|
||
|
|
kernel_size=3,
|
||
|
|
strides=stride,
|
||
|
|
activation=None,
|
||
|
|
use_bias=False,
|
||
|
|
padding="same" if stride == 1 else "valid",
|
||
|
|
name=prefix + "depthwise",
|
||
|
|
)(x)
|
||
|
|
x = layers.BatchNormalization(
|
||
|
|
axis=channel_axis,
|
||
|
|
epsilon=1e-3,
|
||
|
|
momentum=0.999,
|
||
|
|
name=prefix + "depthwise_BN",
|
||
|
|
)(x)
|
||
|
|
|
||
|
|
x = layers.ReLU(6.0, name=prefix + "depthwise_relu")(x)
|
||
|
|
|
||
|
|
# Project with a pointwise 1x1 convolution.
|
||
|
|
x = layers.Conv2D(
|
||
|
|
pointwise_filters,
|
||
|
|
kernel_size=1,
|
||
|
|
padding="same",
|
||
|
|
use_bias=False,
|
||
|
|
activation=None,
|
||
|
|
name=prefix + "project",
|
||
|
|
)(x)
|
||
|
|
x = layers.BatchNormalization(
|
||
|
|
axis=channel_axis,
|
||
|
|
epsilon=1e-3,
|
||
|
|
momentum=0.999,
|
||
|
|
name=prefix + "project_BN",
|
||
|
|
)(x)
|
||
|
|
|
||
|
|
if in_channels == pointwise_filters and stride == 1:
|
||
|
|
return layers.Add(name=prefix + "add")([inputs, x])
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
def _make_divisible(v, divisor, min_value=None):
|
||
|
|
if min_value is None:
|
||
|
|
min_value = divisor
|
||
|
|
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
|
||
|
|
# Make sure that round down does not go down by more than 10%.
|
||
|
|
if new_v < 0.9 * v:
|
||
|
|
new_v += divisor
|
||
|
|
return new_v
|
||
|
|
|
||
|
|
|
||
|
|
@keras_export("keras.applications.mobilenet_v2.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.mobilenet_v2.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__
|