250 lines
9.0 KiB
Python
250 lines
9.0 KiB
Python
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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WEIGHTS_PATH = (
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"https://storage.googleapis.com/tensorflow/keras-applications/"
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"vgg16/vgg16_weights_tf_dim_ordering_tf_kernels.h5"
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)
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WEIGHTS_PATH_NO_TOP = (
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"https://storage.googleapis.com/tensorflow/"
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"keras-applications/vgg16/"
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"vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5"
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)
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@keras_core_export(
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["keras_core.applications.vgg16.VGG16", "keras_core.applications.VGG16"]
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)
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def VGG16(
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include_top=True,
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weights="imagenet",
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input_tensor=None,
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input_shape=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 VGG16 model.
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Reference:
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- [Very Deep Convolutional Networks for Large-Scale Image Recognition](
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https://arxiv.org/abs/1409.1556) (ICLR 2015)
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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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The default input size for this model is 224x224.
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Note: each Keras Application expects a specific kind of input preprocessing.
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For VGG16, call `keras_core.applications.vgg16.preprocess_input` on your
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inputs before passing them to the model.
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`vgg16.preprocess_input` will convert the input images from RGB to BGR,
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then will zero-center each color channel with respect to the ImageNet
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dataset, without scaling.
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Args:
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include_top: whether to include the 3 fully-connected
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layers at the top of the network.
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weights: one of `None` (random initialization),
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`"imagenet"` (pre-training on ImageNet),
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or the path to the weights file to be loaded.
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input_tensor: optional Keras tensor
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(i.e. output of `layers.Input()`)
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to use as image input for the model.
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input_shape: optional shape tuple, only to be specified
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if `include_top` is `False` (otherwise the input shape
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has to be `(224, 224, 3)`
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(with `channels_last` data format) or
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`(3, 224, 224)` (with `"channels_first"` data format).
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It should have exactly 3 input channels,
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and width and height should be no smaller than 32.
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E.g. `(200, 200, 3)` would be one valid value.
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pooling: Optional pooling mode for feature extraction
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when `include_top` is `False`.
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- `None` means that the output of the model will be
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the 4D tensor output of the
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last convolutional block.
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- `avg` means that global average pooling
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will be applied to the output of the
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last convolutional block, and thus
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the output of the model will be a 2D tensor.
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- `max` means that global max pooling will
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be applied.
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classes: optional number of classes to classify images
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into, only to be specified if `include_top` is `True`, and
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if no `weights` argument is specified.
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classifier_activation: A `str` or callable. The activation function to
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use on the "top" layer. Ignored unless `include_top=True`. Set
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`classifier_activation=None` to return the logits of the "top"
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layer. When loading pretrained weights, `classifier_activation` can
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only be `None` or `"softmax"`.
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Returns:
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A 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. Received: "
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f"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
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input_shape = imagenet_utils.obtain_input_shape(
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input_shape,
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default_size=224,
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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 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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# Block 1
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x = layers.Conv2D(
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64, (3, 3), activation="relu", padding="same", name="block1_conv1"
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)(img_input)
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x = layers.Conv2D(
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64, (3, 3), activation="relu", padding="same", name="block1_conv2"
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)(x)
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x = layers.MaxPooling2D((2, 2), strides=(2, 2), name="block1_pool")(x)
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# Block 2
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x = layers.Conv2D(
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128, (3, 3), activation="relu", padding="same", name="block2_conv1"
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)(x)
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x = layers.Conv2D(
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128, (3, 3), activation="relu", padding="same", name="block2_conv2"
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)(x)
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x = layers.MaxPooling2D((2, 2), strides=(2, 2), name="block2_pool")(x)
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# Block 3
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x = layers.Conv2D(
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256, (3, 3), activation="relu", padding="same", name="block3_conv1"
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)(x)
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x = layers.Conv2D(
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256, (3, 3), activation="relu", padding="same", name="block3_conv2"
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)(x)
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x = layers.Conv2D(
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256, (3, 3), activation="relu", padding="same", name="block3_conv3"
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)(x)
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x = layers.MaxPooling2D((2, 2), strides=(2, 2), name="block3_pool")(x)
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# Block 4
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x = layers.Conv2D(
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512, (3, 3), activation="relu", padding="same", name="block4_conv1"
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)(x)
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x = layers.Conv2D(
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512, (3, 3), activation="relu", padding="same", name="block4_conv2"
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)(x)
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x = layers.Conv2D(
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512, (3, 3), activation="relu", padding="same", name="block4_conv3"
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)(x)
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x = layers.MaxPooling2D((2, 2), strides=(2, 2), name="block4_pool")(x)
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# Block 5
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x = layers.Conv2D(
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512, (3, 3), activation="relu", padding="same", name="block5_conv1"
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)(x)
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x = layers.Conv2D(
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512, (3, 3), activation="relu", padding="same", name="block5_conv2"
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)(x)
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x = layers.Conv2D(
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512, (3, 3), activation="relu", padding="same", name="block5_conv3"
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)(x)
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x = layers.MaxPooling2D((2, 2), strides=(2, 2), name="block5_pool")(x)
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if include_top:
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# Classification block
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x = layers.Flatten(name="flatten")(x)
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x = layers.Dense(4096, activation="relu", name="fc1")(x)
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x = layers.Dense(4096, activation="relu", name="fc2")(x)
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imagenet_utils.validate_activation(classifier_activation, weights)
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x = layers.Dense(
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classes, activation=classifier_activation, name="predictions"
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)(x)
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else:
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if pooling == "avg":
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x = layers.GlobalAveragePooling2D()(x)
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elif pooling == "max":
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x = layers.GlobalMaxPooling2D()(x)
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# Ensure that the model takes into account
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# any potential predecessors of `input_tensor`.
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if input_tensor is not None:
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inputs = 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="vgg16")
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# Load weights.
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if weights == "imagenet":
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if include_top:
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weights_path = file_utils.get_file(
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"vgg16_weights_tf_dim_ordering_tf_kernels.h5",
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WEIGHTS_PATH,
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cache_subdir="models",
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file_hash="64373286793e3c8b2b4e3219cbf3544b",
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)
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else:
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weights_path = file_utils.get_file(
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"vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5",
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WEIGHTS_PATH_NO_TOP,
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cache_subdir="models",
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file_hash="6d6bbae143d832006294945121d1f1fc",
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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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@keras_core_export("keras_core.applications.vgg16.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="caffe"
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)
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@keras_core_export("keras_core.applications.vgg16.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_CAFFE,
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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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