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vggface.py
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vggface.py
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'''VGGFace model for Keras.
# Reference:
- [Deep Face Recognition](http://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/parkhi15.pdf)
'''
from __future__ import print_function
import warnings
from keras import backend as K
from keras.layers import Convolution2D, MaxPooling2D
from keras.layers import Flatten, Dense, Input
from keras.models import Model
from keras.utils.data_utils import get_file
from keras.utils.layer_utils import convert_all_kernels_in_model
TH_WEIGHTS_PATH = 'https://github.com/rcmalli/keras-vggface/releases/download/v1.0/rcmalli_vggface_th_weights_th_ordering.h5'
TF_WEIGHTS_PATH = 'https://github.com/rcmalli/keras-vggface/releases/download/v1.0/rcmalli_vggface_tf_weights_tf_ordering.h5'
TH_WEIGHTS_PATH_NO_TOP = 'https://github.com/rcmalli/keras-vggface/releases/download/v1.0/rcmalli_vggface_th_weights_th_ordering_notop.h5'
TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/rcmalli/keras-vggface/releases/download/v1.0/rcmalli_vggface_tf_weights_tf_ordering_notop.h5'
def VGGFace(include_top=True, weights='vggface',
input_tensor=None):
'''Instantiate the VGGFace architecture,
optionally loading weights pre-trained
on ImageNet. Note that when using TensorFlow,
for best performance you should set
`image_dim_ordering="tf"` in your Keras config
at ~/.keras/keras.json.
The model and the weights are compatible with both
TensorFlow and Theano. The dimension ordering
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 "vggface" (pre-training on VGGFace dataset).
input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
to use as image input for the model.
# Returns
A Keras model instance.
'''
if weights not in {'vggface', None}:
raise ValueError('The `weights` argument should be either '
'`None` (random initialization) or `vggface` '
'(pre-training on VGGFace dataset).')
# Determine proper input shape
if K.image_dim_ordering() == 'th':
if include_top:
input_shape = (3, 224, 224)
else:
input_shape = (3, None, None)
else:
if include_top:
input_shape = (224, 224, 3)
else:
input_shape = (None, None, 3)
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)
else:
img_input = input_tensor
# Block 1
x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='conv1_1')(img_input)
x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='conv1_2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool1')(x)
# Block 2
x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='conv2_1')(x)
x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='conv2_2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool2')(x)
# Block 3
x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='conv3_1')(x)
x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='conv3_2')(x)
x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='conv3_3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool3')(x)
# Block 4
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv4_1')(x)
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv4_2')(x)
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv4_3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool4')(x)
# Block 5
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv5_1')(x)
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv5_2')(x)
x = Convolution2D(512, 3, 3, activation='relu', border_mode='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, activation='relu', name='fc6')(x)
x = Dense(4096, activation='relu', name='fc7')(x)
x = Dense(2622, activation='softmax', name='fc8')(x)
# Create model
model = Model(img_input, x)
# load weights
if weights == 'vggface':
print('K.image_dim_ordering:', K.image_dim_ordering())
if K.image_dim_ordering() == 'th':
if include_top:
# weights_path = get_file('rcmalli_vggface_th_weights_th_ordering.h5',
weights_path = get_file('vareto_vggface_th_weights_th_ordering.h5',
TH_WEIGHTS_PATH,
cache_subdir='models')
else:
weights_path = get_file('rcmalli_vggface_th_weights_th_ordering_notop.h5',
TH_WEIGHTS_PATH_NO_TOP,
cache_subdir='models')
model.load_weights(weights_path)
if K.backend() == 'tensorflow':
warnings.warn('You are using the TensorFlow backend, yet you '
'are using the Theano '
'image dimension ordering convention '
'(`image_dim_ordering="th"`). '
'For best performance, set '
'`image_dim_ordering="tf"` in '
'your Keras config '
'at ~/.keras/keras.json.')
convert_all_kernels_in_model(model)
else:
if include_top:
weights_path = get_file('rcmalli_vggface_tf_weights_tf_ordering.h5',
TF_WEIGHTS_PATH,
cache_subdir='models')
else:
weights_path = get_file('rcmalli_vggface_tf_weights_tf_ordering_notop.h5',
TF_WEIGHTS_PATH_NO_TOP,
cache_subdir='models')
model.load_weights(weights_path)
if K.backend() == 'theano':
convert_all_kernels_in_model(model)
return model
'''
if __name__ == '__main__':
from scipy import misc
import copy
import numpy as np
# tensorflow
model = VGGFace()
layer_name = 'fc6'
intermediate_layer_model = Model(input=model.input, output=model.get_layer(layer_name).output)
im = misc.imread('./images/Aamir_Khan_March_2015.jpg')
im = misc.imresize(im, (224, 224)).astype(np.float32)
aux = copy.copy(im)
im[:, :, 0] = aux[:, :, 2]
im[:, :, 2] = aux[:, :, 0]
# Remove image mean
im[:, :, 0] -= 93.5940
im[:, :, 1] -= 104.7624
im[:, :, 2] -= 129.1863
im = np.expand_dims(im, axis=0)
intermediate_output = intermediate_layer_model.predict(im)
print(intermediate_output.shape)
'''