503 lines
18 KiB
Python
503 lines
18 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_v2/"
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)
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@keras_core_export(
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[
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"keras_core.applications.mobilenet_v2.MobileNetV2",
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"keras_core.applications.MobileNetV2",
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]
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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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):
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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
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`keras_core.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, 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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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` '
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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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operation_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 input_tensor.shape[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 input_tensor.shape[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 = input_tensor.shape[2]
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cols = input_tensor.shape[3]
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else:
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rows = input_tensor.shape[1]
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cols = input_tensor.shape[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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warnings.warn(
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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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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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channel_axis = 1 if backend.image_data_format() == "channels_first" else -1
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first_block_filters = _make_divisible(32 * alpha, 8)
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x = layers.Conv2D(
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first_block_filters,
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kernel_size=3,
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strides=(2, 2),
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padding="same",
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use_bias=False,
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name="Conv1",
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)(img_input)
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x = layers.BatchNormalization(
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axis=channel_axis, epsilon=1e-3, momentum=0.999, name="bn_Conv1"
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)(x)
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x = layers.ReLU(6.0, name="Conv1_relu")(x)
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x = _inverted_res_block(
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x, filters=16, alpha=alpha, stride=1, expansion=1, block_id=0
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)
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x = _inverted_res_block(
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x, filters=24, alpha=alpha, stride=2, expansion=6, block_id=1
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)
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x = _inverted_res_block(
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x, filters=24, alpha=alpha, stride=1, expansion=6, block_id=2
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)
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x = _inverted_res_block(
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x, filters=32, alpha=alpha, stride=2, expansion=6, block_id=3
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)
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x = _inverted_res_block(
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x, filters=32, alpha=alpha, stride=1, expansion=6, block_id=4
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)
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x = _inverted_res_block(
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x, filters=32, alpha=alpha, stride=1, expansion=6, block_id=5
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)
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x = _inverted_res_block(
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x, filters=64, alpha=alpha, stride=2, expansion=6, block_id=6
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)
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x = _inverted_res_block(
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x, filters=64, alpha=alpha, stride=1, expansion=6, block_id=7
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)
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x = _inverted_res_block(
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x, filters=64, alpha=alpha, stride=1, expansion=6, block_id=8
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)
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x = _inverted_res_block(
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x, filters=64, alpha=alpha, stride=1, expansion=6, block_id=9
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)
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x = _inverted_res_block(
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x, filters=96, alpha=alpha, stride=1, expansion=6, block_id=10
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)
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x = _inverted_res_block(
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x, filters=96, alpha=alpha, stride=1, expansion=6, block_id=11
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)
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x = _inverted_res_block(
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x, filters=96, alpha=alpha, stride=1, expansion=6, block_id=12
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)
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x = _inverted_res_block(
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x, filters=160, alpha=alpha, stride=2, expansion=6, block_id=13
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)
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x = _inverted_res_block(
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x, filters=160, alpha=alpha, stride=1, expansion=6, block_id=14
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)
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x = _inverted_res_block(
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x, filters=160, alpha=alpha, stride=1, expansion=6, block_id=15
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)
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x = _inverted_res_block(
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x, filters=320, alpha=alpha, stride=1, expansion=6, block_id=16
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)
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# no alpha applied to last conv as stated in the paper:
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# if the width multiplier is greater than 1 we increase the number of output
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# channels.
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if alpha > 1.0:
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last_block_filters = _make_divisible(1280 * alpha, 8)
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else:
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last_block_filters = 1280
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x = layers.Conv2D(
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last_block_filters, kernel_size=1, use_bias=False, name="Conv_1"
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)(x)
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x = layers.BatchNormalization(
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axis=channel_axis, epsilon=1e-3, momentum=0.999, name="Conv_1_bn"
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)(x)
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x = layers.ReLU(6.0, name="out_relu")(x)
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if include_top:
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x = layers.GlobalAveragePooling2D()(x)
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imagenet_utils.validate_activation(classifier_activation, weights)
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x = layers.Dense(
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classes, activation=classifier_activation, name="predictions"
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)(x)
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else:
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if pooling == "avg":
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x = layers.GlobalAveragePooling2D()(x)
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elif pooling == "max":
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x = layers.GlobalMaxPooling2D()(x)
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# Ensure that the model takes into account any potential predecessors of
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# `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"mobilenetv2_{alpha:0.2f}_{rows}")
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# Load weights.
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if weights == "imagenet":
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if include_top:
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model_name = (
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"mobilenet_v2_weights_tf_dim_ordering_tf_kernels_"
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+ str(float(alpha))
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+ "_"
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+ str(rows)
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+ ".h5"
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)
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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 = (
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"mobilenet_v2_weights_tf_dim_ordering_tf_kernels_"
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+ str(float(alpha))
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+ "_"
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+ str(rows)
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+ "_no_top"
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+ ".h5"
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)
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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 _inverted_res_block(inputs, expansion, stride, alpha, filters, block_id):
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"""Inverted ResNet block."""
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channel_axis = 1 if backend.image_data_format() == "channels_first" else -1
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in_channels = inputs.shape[channel_axis]
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pointwise_conv_filters = int(filters * alpha)
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# Ensure the number of filters on the last 1x1 convolution is divisible by
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# 8.
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pointwise_filters = _make_divisible(pointwise_conv_filters, 8)
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x = inputs
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prefix = f"block_{block_id}_"
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if block_id:
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# Expand with a pointwise 1x1 convolution.
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x = layers.Conv2D(
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expansion * in_channels,
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kernel_size=1,
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padding="same",
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use_bias=False,
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activation=None,
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name=prefix + "expand",
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)(x)
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x = layers.BatchNormalization(
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axis=channel_axis,
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epsilon=1e-3,
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momentum=0.999,
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name=prefix + "expand_BN",
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)(x)
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x = layers.ReLU(6.0, name=prefix + "expand_relu")(x)
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else:
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prefix = "expanded_conv_"
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# Depthwise 3x3 convolution.
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if stride == 2:
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x = layers.ZeroPadding2D(
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padding=imagenet_utils.correct_pad(x, 3), name=prefix + "pad"
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)(x)
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x = layers.DepthwiseConv2D(
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kernel_size=3,
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strides=stride,
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activation=None,
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use_bias=False,
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padding="same" if stride == 1 else "valid",
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name=prefix + "depthwise",
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)(x)
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x = layers.BatchNormalization(
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axis=channel_axis,
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epsilon=1e-3,
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momentum=0.999,
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name=prefix + "depthwise_BN",
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)(x)
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x = layers.ReLU(6.0, name=prefix + "depthwise_relu")(x)
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# Project with a pointwise 1x1 convolution.
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x = layers.Conv2D(
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pointwise_filters,
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kernel_size=1,
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padding="same",
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use_bias=False,
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activation=None,
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name=prefix + "project",
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)(x)
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x = layers.BatchNormalization(
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axis=channel_axis,
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epsilon=1e-3,
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momentum=0.999,
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name=prefix + "project_BN",
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)(x)
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if in_channels == pointwise_filters and stride == 1:
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return layers.Add(name=prefix + "add")([inputs, x])
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return x
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def _make_divisible(v, divisor, min_value=None):
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if min_value is None:
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min_value = divisor
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
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# Make sure that round down does not go down by more than 10%.
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if new_v < 0.9 * v:
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new_v += divisor
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return new_v
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@keras_core_export("keras_core.applications.mobilenet_v2.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_v2.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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