489 lines
20 KiB
Python
489 lines
20 KiB
Python
# Copyright 2015 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 v1 models for Keras.
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MobileNet 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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MobileNets support any input size greater 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 16 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), 0.75, 0.5 and 0.25.
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For each of these `alpha` values, weights for 4 different input image sizes
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are provided (224, 192, 160, 128).
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The following table describes the size and accuracy of the 100% MobileNet
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on size 224 x 224:
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----------------------------------------------------------------------------
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Width Multiplier (alpha) | ImageNet Acc | Multiply-Adds (M) | Params (M)
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-------------------------|---------------|-------------------|--------------
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| 1.0 MobileNet-224 | 70.6 % | 529 | 4.2 |
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| 0.75 MobileNet-224 | 68.4 % | 325 | 2.6 |
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| 0.50 MobileNet-224 | 63.7 % | 149 | 1.3 |
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| 0.25 MobileNet-224 | 50.6 % | 41 | 0.5 |
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The following table describes the performance of
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the 100 % MobileNet on various input sizes:
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------------------------------------------------------------------------
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Resolution | ImageNet Acc | Multiply-Adds (M) | Params (M)
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----------------------|---------------|-------------------|----------------
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| 1.0 MobileNet-224 | 70.6 % | 569 | 4.2 |
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| 1.0 MobileNet-192 | 69.1 % | 418 | 4.2 |
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| 1.0 MobileNet-160 | 67.2 % | 290 | 4.2 |
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| 1.0 MobileNet-128 | 64.4 % | 186 | 4.2 |
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Reference:
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- [MobileNets: Efficient Convolutional Neural Networks
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for Mobile Vision Applications](
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https://arxiv.org/abs/1704.04861)
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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/"
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)
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layers = None
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@keras_export(
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"keras.applications.mobilenet.MobileNet", "keras.applications.MobileNet"
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)
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def MobileNet(
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input_shape=None,
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alpha=1.0,
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depth_multiplier=1,
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dropout=1e-3,
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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 MobileNet architecture.
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Reference:
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- [MobileNets: Efficient Convolutional Neural Networks
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for Mobile Vision Applications](
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https://arxiv.org/abs/1704.04861)
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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 MobileNet, call `tf.keras.applications.mobilenet.preprocess_input`
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on your inputs before passing them to the model.
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`mobilenet.preprocess_input` will scale input pixels between -1 and 1.
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Args:
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input_shape: Optional shape tuple, only to be specified if `include_top`
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is False (otherwise the input shape has to be `(224, 224, 3)` (with
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`channels_last` data format) or (3, 224, 224) (with `channels_first`
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data format). It should have exactly 3 inputs channels, and width and
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height should be no smaller than 32. E.g. `(200, 200, 3)` would be one
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valid value. Default to `None`.
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`input_shape` will be ignored if the `input_tensor` is provided.
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alpha: Controls the width of the network. This is known as the width
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multiplier in the MobileNet paper. - If `alpha` < 1.0, proportionally
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decreases the number of filters in each layer. - If `alpha` > 1.0,
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proportionally increases the number of filters in each layer. - If
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`alpha` = 1, default number of filters from the paper are used at each
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layer. Default to 1.0.
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depth_multiplier: Depth multiplier for depthwise convolution. This is
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called the resolution multiplier in the MobileNet paper. Default to 1.0.
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dropout: Dropout rate. Default to 0.001.
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include_top: Boolean, whether to include the fully-connected layer at the
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top of the network. Default to `True`.
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weights: One of `None` (random initialization), 'imagenet' (pre-training
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on ImageNet), or the path to the weights file to be loaded. Default to
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`imagenet`.
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input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`) to
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use as image input for the model. `input_tensor` is useful for sharing
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inputs between multiple different networks. Default to None.
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pooling: Optional pooling mode for feature extraction when `include_top`
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is `False`.
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- `None` (default) means that the output of the model will be
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the 4D tensor output of the 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 be applied.
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classes: Optional number of classes to classify images into, only to be
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specified if `include_top` is True, and if no `weights` argument is
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specified. Defaults to 1000.
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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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"as true, `classes` should be 1000. "
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f"Received classes={classes}"
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)
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# Determine proper input shape and default size.
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if input_shape is None:
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default_size = 224
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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 [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 depth_multiplier != 1:
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raise ValueError(
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"If imagenet weights are being loaded, "
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"depth multiplier must be 1. "
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f"Received depth_multiplier={depth_multiplier}"
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)
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if alpha not in [0.25, 0.50, 0.75, 1.0]:
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raise ValueError(
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"If imagenet weights are being loaded, "
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"alpha can be one of"
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"`0.25`, `0.50`, `0.75` or `1.0` only. "
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f"Received alpha={alpha}"
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)
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if rows != cols or rows not in [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 [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)
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else:
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img_input = input_tensor
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x = _conv_block(img_input, 32, alpha, strides=(2, 2))
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x = _depthwise_conv_block(x, 64, alpha, depth_multiplier, block_id=1)
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x = _depthwise_conv_block(
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x, 128, alpha, depth_multiplier, strides=(2, 2), block_id=2
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)
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x = _depthwise_conv_block(x, 128, alpha, depth_multiplier, block_id=3)
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x = _depthwise_conv_block(
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x, 256, alpha, depth_multiplier, strides=(2, 2), block_id=4
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)
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x = _depthwise_conv_block(x, 256, alpha, depth_multiplier, block_id=5)
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x = _depthwise_conv_block(
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x, 512, alpha, depth_multiplier, strides=(2, 2), block_id=6
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)
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x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=7)
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x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=8)
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x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=9)
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x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=10)
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x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=11)
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x = _depthwise_conv_block(
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x, 1024, alpha, depth_multiplier, strides=(2, 2), block_id=12
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)
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x = _depthwise_conv_block(x, 1024, alpha, depth_multiplier, block_id=13)
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if include_top:
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x = layers.GlobalAveragePooling2D(keepdims=True)(x)
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x = layers.Dropout(dropout, name="dropout")(x)
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x = layers.Conv2D(classes, (1, 1), padding="same", name="conv_preds")(x)
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x = layers.Reshape((classes,), name="reshape_2")(x)
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imagenet_utils.validate_activation(classifier_activation, weights)
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x = layers.Activation(
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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=f"mobilenet_{alpha:0.2f}_{rows}")
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# Load weights.
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if weights == "imagenet":
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if alpha == 1.0:
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alpha_text = "1_0"
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elif alpha == 0.75:
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alpha_text = "7_5"
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elif alpha == 0.50:
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alpha_text = "5_0"
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else:
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alpha_text = "2_5"
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if include_top:
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model_name = "mobilenet_%s_%d_tf.h5" % (alpha_text, rows)
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weight_path = BASE_WEIGHT_PATH + model_name
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weights_path = data_utils.get_file(
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model_name, weight_path, cache_subdir="models"
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)
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else:
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model_name = "mobilenet_%s_%d_tf_no_top.h5" % (alpha_text, rows)
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weight_path = BASE_WEIGHT_PATH + model_name
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weights_path = data_utils.get_file(
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model_name, weight_path, cache_subdir="models"
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)
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model.load_weights(weights_path)
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elif weights is not None:
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model.load_weights(weights)
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return model
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def _conv_block(inputs, filters, alpha, kernel=(3, 3), strides=(1, 1)):
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"""Adds an initial convolution layer (with batch normalization and relu6).
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Args:
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inputs: Input tensor of shape `(rows, cols, 3)` (with `channels_last`
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data format) or (3, rows, cols) (with `channels_first` data format).
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It should have exactly 3 inputs channels, and width and height should
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be no smaller than 32. E.g. `(224, 224, 3)` would be one valid value.
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filters: Integer, the dimensionality of the output space (i.e. the
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number of output filters in the convolution).
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alpha: controls the width of the network. - If `alpha` < 1.0,
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proportionally decreases the number of filters in each layer. - If
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`alpha` > 1.0, proportionally increases the number of filters in each
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layer. - If `alpha` = 1, default number of filters from the paper are
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used at each layer.
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kernel: An integer or tuple/list of 2 integers, specifying the width and
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height of the 2D convolution window. Can be a single integer to
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specify the same value for all spatial dimensions.
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strides: An integer or tuple/list of 2 integers, specifying the strides
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of the convolution along the width and height. Can be a single integer
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to specify the same value for all spatial dimensions. Specifying any
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stride value != 1 is incompatible with specifying any `dilation_rate`
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value != 1. # Input shape
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4D tensor with shape: `(samples, channels, rows, cols)` if
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data_format='channels_first'
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or 4D tensor with shape: `(samples, rows, cols, channels)` if
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data_format='channels_last'. # Output shape
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4D tensor with shape: `(samples, filters, new_rows, new_cols)` if
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data_format='channels_first'
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or 4D tensor with shape: `(samples, new_rows, new_cols, filters)` if
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data_format='channels_last'. `rows` and `cols` values might have
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changed due to stride.
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Returns:
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Output tensor of block.
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"""
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channel_axis = 1 if backend.image_data_format() == "channels_first" else -1
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filters = int(filters * alpha)
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x = layers.Conv2D(
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filters,
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kernel,
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padding="same",
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use_bias=False,
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strides=strides,
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name="conv1",
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)(inputs)
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x = layers.BatchNormalization(axis=channel_axis, name="conv1_bn")(x)
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return layers.ReLU(6.0, name="conv1_relu")(x)
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def _depthwise_conv_block(
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inputs,
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pointwise_conv_filters,
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alpha,
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depth_multiplier=1,
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strides=(1, 1),
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block_id=1,
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):
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"""Adds a depthwise convolution block.
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A depthwise convolution block consists of a depthwise conv,
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batch normalization, relu6, pointwise convolution,
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batch normalization and relu6 activation.
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Args:
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inputs: Input tensor of shape `(rows, cols, channels)` (with
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`channels_last` data format) or (channels, rows, cols) (with
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`channels_first` data format).
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pointwise_conv_filters: Integer, the dimensionality of the output space
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(i.e. the number of output filters in the pointwise convolution).
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alpha: controls the width of the network. - If `alpha` < 1.0,
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proportionally decreases the number of filters in each layer. - If
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`alpha` > 1.0, proportionally increases the number of filters in each
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layer. - If `alpha` = 1, default number of filters from the paper are
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used at each layer.
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depth_multiplier: The number of depthwise convolution output channels
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for each input channel. The total number of depthwise convolution
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output channels will be equal to `filters_in * depth_multiplier`.
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strides: An integer or tuple/list of 2 integers, specifying the strides
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of the convolution along the width and height. Can be a single integer
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to specify the same value for all spatial dimensions. Specifying any
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stride value != 1 is incompatible with specifying any `dilation_rate`
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value != 1.
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block_id: Integer, a unique identification designating the block number.
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# Input shape
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4D tensor with shape: `(batch, channels, rows, cols)` if
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data_format='channels_first'
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or 4D tensor with shape: `(batch, rows, cols, channels)` if
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data_format='channels_last'. # Output shape
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4D tensor with shape: `(batch, filters, new_rows, new_cols)` if
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data_format='channels_first'
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or 4D tensor with shape: `(batch, new_rows, new_cols, filters)` if
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data_format='channels_last'. `rows` and `cols` values might have
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changed due to stride.
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Returns:
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Output tensor of block.
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"""
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channel_axis = 1 if backend.image_data_format() == "channels_first" else -1
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pointwise_conv_filters = int(pointwise_conv_filters * alpha)
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if strides == (1, 1):
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x = inputs
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else:
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x = layers.ZeroPadding2D(
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((0, 1), (0, 1)), name="conv_pad_%d" % block_id
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)(inputs)
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x = layers.DepthwiseConv2D(
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(3, 3),
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padding="same" if strides == (1, 1) else "valid",
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depth_multiplier=depth_multiplier,
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strides=strides,
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use_bias=False,
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name="conv_dw_%d" % block_id,
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)(x)
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x = layers.BatchNormalization(
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axis=channel_axis, name="conv_dw_%d_bn" % block_id
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)(x)
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x = layers.ReLU(6.0, name="conv_dw_%d_relu" % block_id)(x)
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x = layers.Conv2D(
|
|
pointwise_conv_filters,
|
|
(1, 1),
|
|
padding="same",
|
|
use_bias=False,
|
|
strides=(1, 1),
|
|
name="conv_pw_%d" % block_id,
|
|
)(x)
|
|
x = layers.BatchNormalization(
|
|
axis=channel_axis, name="conv_pw_%d_bn" % block_id
|
|
)(x)
|
|
return layers.ReLU(6.0, name="conv_pw_%d_relu" % block_id)(x)
|
|
|
|
|
|
@keras_export("keras.applications.mobilenet.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.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__
|