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models.py
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models.py
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'''VGGFace models for Keras.
# Notes:
- Resnet50 and VGG16 are modified architectures from Keras Application folder. [Keras](https://keras.io)
- Squeeze and excitation block is taken from [Squeeze and Excitation Networks in
Keras](https://github.com/titu1994/keras-squeeze-excite-network) and modified.
'''
from keras.layers import Flatten, Dense, Input, GlobalAveragePooling2D, \
GlobalMaxPooling2D, Activation, Conv2D, MaxPooling2D, BatchNormalization, \
AveragePooling2D, Reshape, Permute, multiply
from keras_applications.imagenet_utils import _obtain_input_shape
from keras.utils import layer_utils
from keras.utils.data_utils import get_file
from keras import backend as K
import utils
from keras.engine.topology import get_source_inputs
import warnings
from keras.models import Model
from keras import layers
def VGG16(include_top=True, weights='vggface',
input_tensor=None, input_shape=None,
pooling=None,
classes=2622):
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 = Conv2D(64, (3, 3), activation='relu', padding='same', name='conv1_1')(
img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='conv1_2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool1')(x)
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='conv2_1')(
x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='conv2_2')(
x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool2')(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='conv3_1')(
x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='conv3_2')(
x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='conv3_3')(
x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool3')(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv4_1')(
x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv4_2')(
x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv4_3')(
x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool4')(x)
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_1')(
x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_2')(
x)
x = Conv2D(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(classes, 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_vgg16') # load weights
if weights == 'vggface':
if include_top:
weights_path = get_file('rcmalli_vggface_tf_vgg16.h5',
utils.
VGG16_WEIGHTS_PATH,
cache_subdir=utils.VGGFACE_DIR)
else:
weights_path = get_file('rcmalli_vggface_tf_notop_vgg16.h5',
utils.VGG16_WEIGHTS_PATH_NO_TOP,
cache_subdir=utils.VGGFACE_DIR)
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
def resnet_identity_block(input_tensor, kernel_size, filters, stage, block,
bias=False):
filters1, filters2, filters3 = filters
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
conv1_reduce_name = 'conv' + str(stage) + "_" + str(block) + "_1x1_reduce"
conv1_increase_name = 'conv' + str(stage) + "_" + str(
block) + "_1x1_increase"
conv3_name = 'conv' + str(stage) + "_" + str(block) + "_3x3"
x = Conv2D(filters1, (1, 1), use_bias=bias, name=conv1_reduce_name)(
input_tensor)
x = BatchNormalization(axis=bn_axis, name=conv1_reduce_name + "/bn")(x)
x = Activation('relu')(x)
x = Conv2D(filters2, kernel_size, use_bias=bias,
padding='same', name=conv3_name)(x)
x = BatchNormalization(axis=bn_axis, name=conv3_name + "/bn")(x)
x = Activation('relu')(x)
x = Conv2D(filters3, (1, 1), use_bias=bias, name=conv1_increase_name)(x)
x = BatchNormalization(axis=bn_axis, name=conv1_increase_name + "/bn")(x)
x = layers.add([x, input_tensor])
x = Activation('relu')(x)
return x
def resnet_conv_block(input_tensor, kernel_size, filters, stage, block,
strides=(2, 2), bias=False):
filters1, filters2, filters3 = filters
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
conv1_reduce_name = 'conv' + str(stage) + "_" + str(block) + "_1x1_reduce"
conv1_increase_name = 'conv' + str(stage) + "_" + str(
block) + "_1x1_increase"
conv1_proj_name = 'conv' + str(stage) + "_" + str(block) + "_1x1_proj"
conv3_name = 'conv' + str(stage) + "_" + str(block) + "_3x3"
x = Conv2D(filters1, (1, 1), strides=strides, use_bias=bias,
name=conv1_reduce_name)(input_tensor)
x = BatchNormalization(axis=bn_axis, name=conv1_reduce_name + "/bn")(x)
x = Activation('relu')(x)
x = Conv2D(filters2, kernel_size, padding='same', use_bias=bias,
name=conv3_name)(x)
x = BatchNormalization(axis=bn_axis, name=conv3_name + "/bn")(x)
x = Activation('relu')(x)
x = Conv2D(filters3, (1, 1), name=conv1_increase_name, use_bias=bias)(x)
x = BatchNormalization(axis=bn_axis, name=conv1_increase_name + "/bn")(x)
shortcut = Conv2D(filters3, (1, 1), strides=strides, use_bias=bias,
name=conv1_proj_name)(input_tensor)
shortcut = BatchNormalization(axis=bn_axis, name=conv1_proj_name + "/bn")(
shortcut)
x = layers.add([x, shortcut])
x = Activation('relu')(x)
return x
def RESNET50(include_top=True, weights='vggface',
input_tensor=None, input_shape=None,
pooling=None,
classes=8631):
input_shape = _obtain_input_shape(input_shape,
default_size=224,
min_size=32,
data_format=K.image_data_format(),
require_flatten=include_top,
weights=weights)
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
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
x = Conv2D(
64, (7, 7), use_bias=False, strides=(2, 2), padding='same',
name='conv1/7x7_s2')(img_input)
x = BatchNormalization(axis=bn_axis, name='conv1/7x7_s2/bn')(x)
x = Activation('relu')(x)
x = MaxPooling2D((3, 3), strides=(2, 2))(x)
x = resnet_conv_block(x, 3, [64, 64, 256], stage=2, block=1, strides=(1, 1))
x = resnet_identity_block(x, 3, [64, 64, 256], stage=2, block=2)
x = resnet_identity_block(x, 3, [64, 64, 256], stage=2, block=3)
x = resnet_conv_block(x, 3, [128, 128, 512], stage=3, block=1)
x = resnet_identity_block(x, 3, [128, 128, 512], stage=3, block=2)
x = resnet_identity_block(x, 3, [128, 128, 512], stage=3, block=3)
x = resnet_identity_block(x, 3, [128, 128, 512], stage=3, block=4)
x = resnet_conv_block(x, 3, [256, 256, 1024], stage=4, block=1)
x = resnet_identity_block(x, 3, [256, 256, 1024], stage=4, block=2)
x = resnet_identity_block(x, 3, [256, 256, 1024], stage=4, block=3)
x = resnet_identity_block(x, 3, [256, 256, 1024], stage=4, block=4)
x = resnet_identity_block(x, 3, [256, 256, 1024], stage=4, block=5)
x = resnet_identity_block(x, 3, [256, 256, 1024], stage=4, block=6)
x = resnet_conv_block(x, 3, [512, 512, 2048], stage=5, block=1)
x = resnet_identity_block(x, 3, [512, 512, 2048], stage=5, block=2)
x = resnet_identity_block(x, 3, [512, 512, 2048], stage=5, block=3)
x = AveragePooling2D((7, 7), name='avg_pool')(x)
if include_top:
x = Flatten()(x)
x = Dense(classes, activation='softmax', name='classifier')(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_resnet50')
# load weights
if weights == 'vggface':
if include_top:
weights_path = get_file('rcmalli_vggface_tf_resnet50.h5',
utils.RESNET50_WEIGHTS_PATH,
cache_subdir=utils.VGGFACE_DIR)
else:
weights_path = get_file('rcmalli_vggface_tf_notop_resnet50.h5',
utils.RESNET50_WEIGHTS_PATH_NO_TOP,
cache_subdir=utils.VGGFACE_DIR)
model.load_weights(weights_path)
if K.backend() == 'theano':
layer_utils.convert_all_kernels_in_model(model)
if include_top:
maxpool = model.get_layer(name='avg_pool')
shape = maxpool.output_shape[1:]
dense = model.get_layer(name='classifier')
layer_utils.convert_dense_weights_data_format(dense, shape,
'channels_first')
if K.image_data_format() == 'channels_first' and 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.')
elif weights is not None:
model.load_weights(weights)
return model
def senet_se_block(input_tensor, stage, block, compress_rate=16, bias=False):
conv1_down_name = 'conv' + str(stage) + "_" + str(
block) + "_1x1_down"
conv1_up_name = 'conv' + str(stage) + "_" + str(
block) + "_1x1_up"
num_channels = int(input_tensor.shape[-1])
bottle_neck = int(num_channels // compress_rate)
se = GlobalAveragePooling2D()(input_tensor)
se = Reshape((1, 1, num_channels))(se)
se = Conv2D(bottle_neck, (1, 1), use_bias=bias,
name=conv1_down_name)(se)
se = Activation('relu')(se)
se = Conv2D(num_channels, (1, 1), use_bias=bias,
name=conv1_up_name)(se)
se = Activation('sigmoid')(se)
x = input_tensor
x = multiply([x, se])
return x
def senet_conv_block(input_tensor, kernel_size, filters,
stage, block, bias=False, strides=(2, 2)):
filters1, filters2, filters3 = filters
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
bn_eps = 0.0001
conv1_reduce_name = 'conv' + str(stage) + "_" + str(block) + "_1x1_reduce"
conv1_increase_name = 'conv' + str(stage) + "_" + str(
block) + "_1x1_increase"
conv1_proj_name = 'conv' + str(stage) + "_" + str(block) + "_1x1_proj"
conv3_name = 'conv' + str(stage) + "_" + str(block) + "_3x3"
x = Conv2D(filters1, (1, 1), use_bias=bias, strides=strides,
name=conv1_reduce_name)(input_tensor)
x = BatchNormalization(axis=bn_axis, name=conv1_reduce_name + "/bn",epsilon=bn_eps)(x)
x = Activation('relu')(x)
x = Conv2D(filters2, kernel_size, padding='same', use_bias=bias,
name=conv3_name)(x)
x = BatchNormalization(axis=bn_axis, name=conv3_name + "/bn",epsilon=bn_eps)(x)
x = Activation('relu')(x)
x = Conv2D(filters3, (1, 1), name=conv1_increase_name, use_bias=bias)(x)
x = BatchNormalization(axis=bn_axis, name=conv1_increase_name + "/bn" ,epsilon=bn_eps)(x)
se = senet_se_block(x, stage=stage, block=block, bias=True)
shortcut = Conv2D(filters3, (1, 1), use_bias=bias, strides=strides,
name=conv1_proj_name)(input_tensor)
shortcut = BatchNormalization(axis=bn_axis,
name=conv1_proj_name + "/bn",epsilon=bn_eps)(shortcut)
m = layers.add([se, shortcut])
m = Activation('relu')(m)
return m
def senet_identity_block(input_tensor, kernel_size,
filters, stage, block, bias=False):
filters1, filters2, filters3 = filters
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
bn_eps = 0.0001
conv1_reduce_name = 'conv' + str(stage) + "_" + str(block) + "_1x1_reduce"
conv1_increase_name = 'conv' + str(stage) + "_" + str(
block) + "_1x1_increase"
conv3_name = 'conv' + str(stage) + "_" + str(block) + "_3x3"
x = Conv2D(filters1, (1, 1), use_bias=bias,
name=conv1_reduce_name)(input_tensor)
x = BatchNormalization(axis=bn_axis, name=conv1_reduce_name + "/bn",epsilon=bn_eps)(x)
x = Activation('relu')(x)
x = Conv2D(filters2, kernel_size, padding='same', use_bias=bias,
name=conv3_name)(x)
x = BatchNormalization(axis=bn_axis, name=conv3_name + "/bn",epsilon=bn_eps)(x)
x = Activation('relu')(x)
x = Conv2D(filters3, (1, 1), name=conv1_increase_name, use_bias=bias)(x)
x = BatchNormalization(axis=bn_axis, name=conv1_increase_name + "/bn",epsilon=bn_eps)(x)
se = senet_se_block(x, stage=stage, block=block, bias=True)
m = layers.add([se, input_tensor])
m = Activation('relu')(m)
return m
def SENET50(include_top=True, weights='vggface',
input_tensor=None, input_shape=None,
pooling=None,
classes=8631):
input_shape = _obtain_input_shape(input_shape,
default_size=224,
min_size=197,
data_format=K.image_data_format(),
require_flatten=include_top,
weights=weights)
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
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
bn_eps = 0.0001
x = Conv2D(
64, (7, 7), use_bias=False, strides=(2, 2), padding='same',
name='conv1/7x7_s2')(img_input)
x = BatchNormalization(axis=bn_axis, name='conv1/7x7_s2/bn',epsilon=bn_eps)(x)
x = Activation('relu')(x)
x = MaxPooling2D((3, 3), strides=(2, 2))(x)
x = senet_conv_block(x, 3, [64, 64, 256], stage=2, block=1, strides=(1, 1))
x = senet_identity_block(x, 3, [64, 64, 256], stage=2, block=2)
x = senet_identity_block(x, 3, [64, 64, 256], stage=2, block=3)
x = senet_conv_block(x, 3, [128, 128, 512], stage=3, block=1)
x = senet_identity_block(x, 3, [128, 128, 512], stage=3, block=2)
x = senet_identity_block(x, 3, [128, 128, 512], stage=3, block=3)
x = senet_identity_block(x, 3, [128, 128, 512], stage=3, block=4)
x = senet_conv_block(x, 3, [256, 256, 1024], stage=4, block=1)
x = senet_identity_block(x, 3, [256, 256, 1024], stage=4, block=2)
x = senet_identity_block(x, 3, [256, 256, 1024], stage=4, block=3)
x = senet_identity_block(x, 3, [256, 256, 1024], stage=4, block=4)
x = senet_identity_block(x, 3, [256, 256, 1024], stage=4, block=5)
x = senet_identity_block(x, 3, [256, 256, 1024], stage=4, block=6)
x = senet_conv_block(x, 3, [512, 512, 2048], stage=5, block=1)
x = senet_identity_block(x, 3, [512, 512, 2048], stage=5, block=2)
x = senet_identity_block(x, 3, [512, 512, 2048], stage=5, block=3)
x = AveragePooling2D((7, 7), name='avg_pool')(x)
if include_top:
x = Flatten()(x)
x = Dense(classes, activation='softmax', name='classifier')(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_senet50')
# load weights
if weights == 'vggface':
if include_top:
weights_path = get_file('rcmalli_vggface_tf_senet50.h5',
utils.SENET50_WEIGHTS_PATH,
cache_subdir=utils.VGGFACE_DIR)
else:
weights_path = get_file('rcmalli_vggface_tf_notop_senet50.h5',
utils.SENET50_WEIGHTS_PATH_NO_TOP,
cache_subdir=utils.VGGFACE_DIR)
model.load_weights(weights_path)
if K.backend() == 'theano':
layer_utils.convert_all_kernels_in_model(model)
if include_top:
maxpool = model.get_layer(name='avg_pool')
shape = maxpool.output_shape[1:]
dense = model.get_layer(name='classifier')
layer_utils.convert_dense_weights_data_format(dense, shape,
'channels_first')
if K.image_data_format() == 'channels_first' and 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.')
elif weights is not None:
model.load_weights(weights)
return model