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vggFace.py
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vggFace.py
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from __future__ import print_function
import warnings
from keras.applications.imagenet_utils import _obtain_input_shape
from keras.engine.topology import get_source_inputs
from keras.models import Model
from keras.layers import Flatten, Dense, Input, GlobalAveragePooling2D, GlobalMaxPooling2D, Activation
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import layer_utils
from keras.utils.data_utils import get_file
from keras import backend as K
from utils import *
def VGGFace(include_top=True, weights='vggface',
input_tensor=None, input_shape=None,
pooling=None,
classes=2622):
"""Instantiates the VGGFace architecture.
Optionally loads weights pre-trained
on VGGFace dataset. Note that when using TensorFlow,
for best performance you should set
`image_data_format="channels_last"` in your Keras config
at ~/.keras/keras.json.
The model and the weights are compatible with both
TensorFlow and Theano. The data format
convention used by the model is the one
specified in your Keras config file.
# Arguments
include_top: whether to include the 3 fully-connected
layers at the top of the network.
weights: one of `None` (random initialization)
or "imagenet" (pre-training on ImageNet).
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, 244)` (with `channels_first` data format).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 48.
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 layer.
- `avg` means that global average pooling
will be applied to the output of the
last convolutional layer, 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.
# Returns
A Keras model instance.
# Raises
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
"""
if weights not in {'vggface', None}:
raise ValueError('The `weights` argument should be either '
'`None` (random initialization) or `vggface` '
'(pre-training on VGGFace Dataset).')
if weights == 'vggface' and include_top and classes != 2622:
raise ValueError('If using `weights` as vggface original with `include_top`'
' as true, `classes` should be 2622')
# Determine proper input shape
input_shape = _obtain_input_shape(input_shape,
default_size=224,
min_size=48,
data_format=K.image_data_format(),
require_flatten=include_top)
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
# Block 1
x = Convolution2D(64, (3, 3), activation='relu', padding='same', name='conv1_1')(img_input)
x = Convolution2D(64, (3, 3), activation='relu', padding='same', name='conv1_2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool1')(x)
# Block 2
x = Convolution2D(128, (3, 3), activation='relu', padding='same', name='conv2_1')(x)
x = Convolution2D(128, (3, 3), activation='relu', padding='same', name='conv2_2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool2')(x)
# Block 3
x = Convolution2D(256, (3, 3), activation='relu', padding='same', name='conv3_1')(x)
x = Convolution2D(256, (3, 3), activation='relu', padding='same', name='conv3_2')(x)
x = Convolution2D(256, (3, 3), activation='relu', padding='same', name='conv3_3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool3')(x)
# Block 4
x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='conv4_1')(x)
x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='conv4_2')(x)
x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='conv4_3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool4')(x)
# Block 5
x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='conv5_1')(x)
x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='conv5_2')(x)
x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='conv5_3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool5')(x)
if include_top:
# Classification block
x = Flatten(name='flatten')(x)
x = Dense(4096, name='fc6')(x)
x = Activation('relu', name='fc6/relu')(x)
x = Dense(4096, name='fc7')(x)
x = Activation('relu', name='fc7/relu')(x)
x = Dense(2622, name='fc8')(x)
x = Activation('softmax', name='fc8/softmax')(x)
else:
if pooling == 'avg':
x = GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = GlobalMaxPooling2D()(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = Model(inputs, x, name='VGGFace') # load weights
if weights == 'vggface':
if include_top:
weights_path = get_file('rcmalli_vggface_tf_v2.h5',
WEIGHTS_PATH,
cache_subdir='models')
else:
weights_path = get_file('rcmalli_vggface_tf_notop_v2.h5',
WEIGHTS_PATH_NO_TOP,
cache_subdir='models')
model.load_weights(weights_path, by_name=True)
if K.backend() == 'theano':
layer_utils.convert_all_kernels_in_model(model)
if K.image_data_format() == 'channels_first':
if include_top:
maxpool = model.get_layer(name='pool5')
shape = maxpool.output_shape[1:]
dense = model.get_layer(name='fc6')
layer_utils.convert_dense_weights_data_format(dense, shape, 'channels_first')
if K.backend() == 'tensorflow':
warnings.warn('You are using the TensorFlow backend, yet you '
'are using the Theano '
'image data format convention '
'(`image_data_format="channels_first"`). '
'For best performance, set '
'`image_data_format="channels_last"` in '
'your Keras config '
'at ~/.keras/keras.json.')
return model