2023-05-22 19:59:33 +00:00
|
|
|
from tensorflow.io import gfile
|
|
|
|
|
|
|
|
from keras_core import backend
|
|
|
|
from keras_core import layers
|
|
|
|
from keras_core.api_export import keras_core_export
|
|
|
|
from keras_core.applications import imagenet_utils
|
|
|
|
from keras_core.models import Functional
|
|
|
|
from keras_core.operations import operation_utils
|
|
|
|
from keras_core.utils import file_utils
|
|
|
|
|
|
|
|
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"
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
@keras_core_export(
|
|
|
|
["keras_core.applications.vgg16.VGG16", "keras_core.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 `keras_core.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"
|
2023-05-22 21:36:49 +00:00
|
|
|
layer. When loading pretrained weights, `classifier_activation`
|
|
|
|
can only be `None` or `"softmax"`.
|
2023-05-22 19:59:33 +00:00
|
|
|
|
|
|
|
Returns:
|
|
|
|
A model instance.
|
|
|
|
"""
|
|
|
|
if not (weights in {"imagenet", None} or gfile.exists(weights)):
|
|
|
|
raise ValueError(
|
|
|
|
"The `weights` argument should be either "
|
2023-05-22 21:36:49 +00:00
|
|
|
"`None` (random initialization), 'imagenet' "
|
2023-05-22 19:59:33 +00:00
|
|
|
"(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(
|
2023-05-22 21:36:49 +00:00
|
|
|
"If using `weights='imagenet'` with `include_top=True`, "
|
|
|
|
"`classes` should be 1000. "
|
|
|
|
f"Received classes={classes}"
|
2023-05-22 19:59:33 +00:00
|
|
|
)
|
2023-05-22 21:36:49 +00:00
|
|
|
|
2023-05-22 19:59:33 +00:00
|
|
|
# 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 = operation_utils.get_source_inputs(input_tensor)
|
|
|
|
else:
|
|
|
|
inputs = img_input
|
|
|
|
|
|
|
|
# Create model.
|
|
|
|
model = Functional(inputs, x, name="vgg16")
|
|
|
|
|
|
|
|
# Load weights.
|
|
|
|
if weights == "imagenet":
|
|
|
|
if include_top:
|
|
|
|
weights_path = file_utils.get_file(
|
|
|
|
"vgg16_weights_tf_dim_ordering_tf_kernels.h5",
|
|
|
|
WEIGHTS_PATH,
|
|
|
|
cache_subdir="models",
|
|
|
|
file_hash="64373286793e3c8b2b4e3219cbf3544b",
|
|
|
|
)
|
|
|
|
else:
|
|
|
|
weights_path = file_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_core_export("keras_core.applications.vgg16.preprocess_input")
|
|
|
|
def preprocess_input(x, data_format=None):
|
|
|
|
return imagenet_utils.preprocess_input(
|
|
|
|
x, data_format=data_format, mode="caffe"
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
@keras_core_export("keras_core.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__
|