vgg16.py

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

"""VGG16 model for Keras.

Reference:
  - [Very Deep Convolutional Networks for Large-Scale Image Recognition]
    (https://arxiv.org/abs/1409.1556) (ICLR 2015)
"""

import tensorflow.compat.v2 as tf

from keras import backend
from keras.applications import imagenet_utils
from keras.engine import training
from keras.layers import VersionAwareLayers
from keras.utils import data_utils
from keras.utils import layer_utils

# isort: off
from tensorflow.python.util.tf_export import keras_export

WEIGHTS_PATH = (
    "https://storage.googleapis.com/tensorflow/keras-applications/"
    "vgg16/vgg16_weights_tf_dim_ordering_tf_kernels.h5"
)
WEIGHTS_PATH_NO_TOP = (
    "https://storage.googleapis.com/tensorflow/"
    "keras-applications/vgg16/"
    "vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5"
)

layers = VersionAwareLayers()


@keras_export("keras.applications.vgg16.VGG16", "keras.applications.VGG16")
def VGG16(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
):
    """Instantiates the VGG16 model.

    Reference:
    - [Very Deep Convolutional Networks for Large-Scale Image Recognition](
    https://arxiv.org/abs/1409.1556) (ICLR 2015)

    For image classification use cases, see
    [this page for detailed examples](
      https://keras.io/api/applications/#usage-examples-for-image-classification-models).

    For transfer learning use cases, make sure to read the
    [guide to transfer learning & fine-tuning](
      https://keras.io/guides/transfer_learning/).

    The default input size for this model is 224x224.

    Note: each Keras Application expects a specific kind of input preprocessing.
    For VGG16, call `tf.keras.applications.vgg16.preprocess_input` on your
    inputs before passing them to the model.
    `vgg16.preprocess_input` will convert the input images from RGB to BGR,
    then will zero-center each color channel with respect to the ImageNet
    dataset, without scaling.

    Args:
        include_top: whether to include the 3 fully-connected
            layers at the top of the network.
        weights: one of `None` (random initialization),
              'imagenet' (pre-training on ImageNet),
              or the path to the weights file to be loaded.
        input_tensor: optional Keras tensor
            (i.e. output of `layers.Input()`)
            to use as image input for the model.
        input_shape: optional shape tuple, only to be specified
            if `include_top` is False (otherwise the input shape
            has to be `(224, 224, 3)`
            (with `channels_last` data format)
            or `(3, 224, 224)` (with `channels_first` data format).
            It should have exactly 3 input channels,
            and width and height should be no smaller than 32.
            E.g. `(200, 200, 3)` would be one valid value.
        pooling: Optional pooling mode for feature extraction
            when `include_top` is `False`.
            - `None` means that the output of the model will be
                the 4D tensor output of the
                last convolutional block.
            - `avg` means that global average pooling
                will be applied to the output of the
                last convolutional block, and thus
                the output of the model will be a 2D tensor.
            - `max` means that global max pooling will
                be applied.
        classes: optional number of classes to classify images
            into, only to be specified if `include_top` is True, and
            if no `weights` argument is specified.
        classifier_activation: A `str` or callable. The activation function to
            use on the "top" layer. Ignored unless `include_top=True`. Set
            `classifier_activation=None` to return the logits of the "top"
            layer.  When loading pretrained weights, `classifier_activation` can
            only be `None` or `"softmax"`.

    Returns:
      A `keras.Model` instance.
    """
    if not (weights in {"imagenet", None} or tf.io.gfile.exists(weights)):
        raise ValueError(
            "The `weights` argument should be either "
            "`None` (random initialization), `imagenet` "
            "(pre-training on ImageNet), "
            "or the path to the weights file to be loaded.  Received: "
            f"weights={weights}"
        )

    if weights == "imagenet" and include_top and classes != 1000:
        raise ValueError(
            'If using `weights` as `"imagenet"` with `include_top` '
            "as true, `classes` should be 1000.  "
            f"Received `classes={classes}`"
        )
    # Determine proper input shape
    input_shape = imagenet_utils.obtain_input_shape(
        input_shape,
        default_size=224,
        min_size=32,
        data_format=backend.image_data_format(),
        require_flatten=include_top,
        weights=weights,
    )

    if input_tensor is None:
        img_input = layers.Input(shape=input_shape)
    else:
        if not backend.is_keras_tensor(input_tensor):
            img_input = layers.Input(tensor=input_tensor, shape=input_shape)
        else:
            img_input = input_tensor
    # Block 1
    x = layers.Conv2D(
        64, (3, 3), activation="relu", padding="same", name="block1_conv1"
    )(img_input)
    x = layers.Conv2D(
        64, (3, 3), activation="relu", padding="same", name="block1_conv2"
    )(x)
    x = layers.MaxPooling2D((2, 2), strides=(2, 2), name="block1_pool")(x)

    # Block 2
    x = layers.Conv2D(
        128, (3, 3), activation="relu", padding="same", name="block2_conv1"
    )(x)
    x = layers.Conv2D(
        128, (3, 3), activation="relu", padding="same", name="block2_conv2"
    )(x)
    x = layers.MaxPooling2D((2, 2), strides=(2, 2), name="block2_pool")(x)

    # Block 3
    x = layers.Conv2D(
        256, (3, 3), activation="relu", padding="same", name="block3_conv1"
    )(x)
    x = layers.Conv2D(
        256, (3, 3), activation="relu", padding="same", name="block3_conv2"
    )(x)
    x = layers.Conv2D(
        256, (3, 3), activation="relu", padding="same", name="block3_conv3"
    )(x)
    x = layers.MaxPooling2D((2, 2), strides=(2, 2), name="block3_pool")(x)

    # Block 4
    x = layers.Conv2D(
        512, (3, 3), activation="relu", padding="same", name="block4_conv1"
    )(x)
    x = layers.Conv2D(
        512, (3, 3), activation="relu", padding="same", name="block4_conv2"
    )(x)
    x = layers.Conv2D(
        512, (3, 3), activation="relu", padding="same", name="block4_conv3"
    )(x)
    x = layers.MaxPooling2D((2, 2), strides=(2, 2), name="block4_pool")(x)

    # Block 5
    x = layers.Conv2D(
        512, (3, 3), activation="relu", padding="same", name="block5_conv1"
    )(x)
    x = layers.Conv2D(
        512, (3, 3), activation="relu", padding="same", name="block5_conv2"
    )(x)
    x = layers.Conv2D(
        512, (3, 3), activation="relu", padding="same", name="block5_conv3"
    )(x)
    x = layers.MaxPooling2D((2, 2), strides=(2, 2), name="block5_pool")(x)

    if include_top:
        # Classification block
        x = layers.Flatten(name="flatten")(x)
        x = layers.Dense(4096, activation="relu", name="fc1")(x)
        x = layers.Dense(4096, activation="relu", name="fc2")(x)

        imagenet_utils.validate_activation(classifier_activation, weights)
        x = layers.Dense(
            classes, activation=classifier_activation, name="predictions"
        )(x)
    else:
        if pooling == "avg":
            x = layers.GlobalAveragePooling2D()(x)
        elif pooling == "max":
            x = layers.GlobalMaxPooling2D()(x)

    # Ensure that the model takes into account
    # any potential predecessors of `input_tensor`.
    if input_tensor is not None:
        inputs = layer_utils.get_source_inputs(input_tensor)
    else:
        inputs = img_input
    # Create model.
    model = training.Model(inputs, x, name="vgg16")

    # Load weights.
    if weights == "imagenet":
        if include_top:
            weights_path = data_utils.get_file(
                "vgg16_weights_tf_dim_ordering_tf_kernels.h5",
                WEIGHTS_PATH,
                cache_subdir="models",
                file_hash="64373286793e3c8b2b4e3219cbf3544b",
            )
        else:
            weights_path = data_utils.get_file(
                "vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5",
                WEIGHTS_PATH_NO_TOP,
                cache_subdir="models",
                file_hash="6d6bbae143d832006294945121d1f1fc",
            )
        model.load_weights(weights_path)
    elif weights is not None:
        model.load_weights(weights)

    return model


@keras_export("keras.applications.vgg16.preprocess_input")
def preprocess_input(x, data_format=None):
    return imagenet_utils.preprocess_input(
        x, data_format=data_format, mode="caffe"
    )


@keras_export("keras.applications.vgg16.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_CAFFE,
    error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC,
)
decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__