431 lines
17 KiB
Python
431 lines
17 KiB
Python
import warnings
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from tensorflow.io import gfile
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from keras_core import backend
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from keras_core import layers
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from keras_core.api_export import keras_core_export
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from keras_core.applications import imagenet_utils
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from keras_core.models import Functional
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from keras_core.operations import operation_utils
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from keras_core.utils import file_utils
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BASE_WEIGHT_PATH = (
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"https://storage.googleapis.com/tensorflow/keras-applications/mobilenet/"
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)
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@keras_core_export(
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[
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"keras_core.applications.mobilenet.MobileNet",
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"keras_core.applications.MobileNet",
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]
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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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):
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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 `keras_core.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)`
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(with `"channels_last"` data format) or `(3, 224, 224)`
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(with `"channels_first"` data format).
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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
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be one valid value. Defaults 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.
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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`, default number of filters from the paper
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are used at each layer. Defaults to `1.0`.
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depth_multiplier: Depth multiplier for depthwise convolution.
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This is called the resolution multiplier in the MobileNet paper.
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Defaults to `1.0`.
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dropout: Dropout rate. Defaults to `0.001`.
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include_top: Boolean, whether to include the fully-connected layer
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at the top of the network. Defaults to `True`.
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weights: One of `None` (random initialization), `"imagenet"`
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(pre-training on ImageNet), or the path to the weights file
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to be loaded. Defaults to `"imagenet"`.
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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. `input_tensor` is useful
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for sharing inputs between multiple different networks.
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Defaults 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,
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only to be specified if `include_top` is `True`, and if
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no `weights` argument is specified. Defaults to `1000`.
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classifier_activation: A `str` or callable. The activation function
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to use on the "top" layer. Ignored unless `include_top=True`.
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Set `classifier_activation=None` to return the logits of the "top"
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layer. When loading pretrained weights, `classifier_activation`
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can only be `None` or `"softmax"`.
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Returns:
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A model instance.
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"""
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if not (weights in {"imagenet", None} or gfile.exists(weights)):
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raise ValueError(
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"The `weights` argument should be either "
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"`None` (random initialization), 'imagenet' "
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"(pre-training on ImageNet), "
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"or the path to the weights file to be loaded. "
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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='imagenet'` with `include_top=True`, "
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"`classes` should be 1000. "
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f"Received classes={classes}"
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)
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# Determine proper input shape 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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warnings.warn(
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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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stacklevel=2,
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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 = operation_utils.get_source_inputs(input_tensor)
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else:
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inputs = img_input
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# Create model.
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model = Functional(inputs, x, name=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 = file_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 = file_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
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should be no smaller than 32. E.g. `(224, 224, 3)` would be
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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.
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- If `alpha` > 1.0, proportionally increases the number of filters
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in each layer.
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- 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
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and height of the 2D convolution window.
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Can be a single integer to specify the same value for
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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.
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Can be a single integer to specify the same value for all
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spatial dimensions. Specifying any stride value != 1 is
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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)`
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if data_format='channels_first'
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or 4D tensor with shape: `(samples, new_rows, new_cols, filters)`
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if data_format='channels_last'. `rows` and `cols` values
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might have 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.
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- If `alpha` > 1.0, proportionally increases the number of filters
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in each layer.
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- 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.
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Can be a single integer to specify the same value for
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all spatial dimensions. Specifying any stride value != 1 is
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incompatible with specifying any `dilation_rate` 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(
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pointwise_conv_filters,
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(1, 1),
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padding="same",
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use_bias=False,
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strides=(1, 1),
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name="conv_pw_%d" % block_id,
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)(x)
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x = layers.BatchNormalization(
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axis=channel_axis, name="conv_pw_%d_bn" % block_id
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)(x)
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return layers.ReLU(6.0, name="conv_pw_%d_relu" % block_id)(x)
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@keras_core_export("keras_core.applications.mobilenet.preprocess_input")
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def preprocess_input(x, data_format=None):
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return imagenet_utils.preprocess_input(
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x, data_format=data_format, mode="tf"
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)
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@keras_core_export("keras_core.applications.mobilenet.decode_predictions")
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def decode_predictions(preds, top=5):
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return imagenet_utils.decode_predictions(preds, top=top)
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preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
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mode="",
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ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF,
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error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC,
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)
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decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
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