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resnet101.py
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resnet101.py
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"""
Adapted from https://gist.github.com/flyyufelix/65018873f8cb2bbe95f429c474aa1294
"""
import sys
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Flatten, Activation, add
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from keras import initializers
from keras.engine import Layer, InputSpec
from keras import backend as K
from keras.regularizers import l2
sys.setrecursionlimit(3000)
class Scale(Layer):
'''Learns a set of weights and biases used for scaling the input data.
the output consists simply in an element-wise multiplication of the input
and a sum of a set of constants:
out = in * gamma + beta,
where 'gamma' and 'beta' are the weights and biases larned.
# Arguments
axis: integer, axis along which to normalize in mode 0. For instance,
if your input tensor has shape (samples, channels, rows, cols),
set axis to 1 to normalize per feature map (channels axis).
momentum: momentum in the computation of the
exponential average of the mean and standard deviation
of the data, for feature-wise normalization.
weights: Initialization weights.
List of 2 Numpy arrays, with shapes:
`[(input_shape,), (input_shape,)]`
beta_init: name of initialization function for shift parameter
(see [initializations](../initializations.md)), or alternatively,
Theano/TensorFlow function to use for weights initialization.
This parameter is only relevant if you don't pass a `weights` argument.
gamma_init: name of initialization function for scale parameter (see
[initializations](../initializations.md)), or alternatively,
Theano/TensorFlow function to use for weights initialization.
This parameter is only relevant if you don't pass a `weights` argument.
gamma_init: name of initialization function for scale parameter (see
[initializations](../initializations.md)), or alternatively,
Theano/TensorFlow function to use for weights initialization.
This parameter is only relevant if you don't pass a `weights` argument.
'''
def __init__(self, weights=None, axis=-1, momentum=0.9, beta_init='zero', gamma_init='one', **kwargs):
self.momentum = momentum
self.axis = axis
self.beta_init = initializers.get(beta_init)
self.gamma_init = initializers.get(gamma_init)
self.initial_weights = weights
super(Scale, self).__init__(**kwargs)
def build(self, input_shape):
self.input_spec = [InputSpec(shape=input_shape)]
shape = (int(input_shape[self.axis]),)
self.gamma = K.variable(self.gamma_init(shape), name='{}_gamma'.format(self.name))
self.beta = K.variable(self.beta_init(shape), name='{}_beta'.format(self.name))
self.trainable_weights = [self.gamma, self.beta]
if self.initial_weights is not None:
self.set_weights(self.initial_weights)
del self.initial_weights
def call(self, x, mask=None):
input_shape = self.input_spec[0].shape
broadcast_shape = [1] * len(input_shape)
broadcast_shape[self.axis] = input_shape[self.axis]
out = K.reshape(self.gamma, broadcast_shape) * x + K.reshape(self.beta, broadcast_shape)
return out
def get_config(self):
config = {"momentum": self.momentum, "axis": self.axis}
base_config = super(Scale, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def identity_block(input_tensor, kernel_size, filters, stage, block, weight_decay=0):
'''The identity_block is the block that has no conv layer at shortcut
# Arguments
input_tensor: input tensor
kernel_size: defualt 3, the kernel size of middle conv layer at main path
filters: list of integers, the nb_filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
'''
eps = 1.1e-5
nb_filter1, nb_filter2, nb_filter3 = filters
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
scale_name_base = 'scale' + str(stage) + block + '_branch'
x = Conv2D(nb_filter1, (1, 1), name=conv_name_base + '2a', use_bias=False, kernel_regularizer=l2(weight_decay))(input_tensor)
x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2a', gamma_regularizer=l2(weight_decay),
beta_regularizer=l2(weight_decay))(x)
x = Scale(axis=bn_axis, name=scale_name_base + '2a')(x)
x = Activation('relu', name=conv_name_base + '2a_relu')(x)
x = ZeroPadding2D((1, 1), name=conv_name_base + '2b_zeropadding')(x)
x = Conv2D(nb_filter2, (kernel_size, kernel_size),
name=conv_name_base + '2b', use_bias=False, kernel_regularizer=l2(weight_decay))(x)
x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2b', gamma_regularizer=l2(weight_decay),
beta_regularizer=l2(weight_decay))(x)
x = Scale(axis=bn_axis, name=scale_name_base + '2b')(x)
x = Activation('relu', name=conv_name_base + '2b_relu')(x)
x = Conv2D(nb_filter3, (1, 1), name=conv_name_base + '2c', use_bias=False, kernel_regularizer=l2(weight_decay))(x)
x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2c', gamma_regularizer=l2(weight_decay),
beta_regularizer=l2(weight_decay))(x)
x = Scale(axis=bn_axis, name=scale_name_base + '2c')(x)
x = add([x, input_tensor], name='res' + str(stage) + block)
x = Activation('relu', name='res' + str(stage) + block + '_relu')(x)
return x
def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2), weight_decay=0):
'''conv_block is the block that has a conv layer at shortcut
# Arguments
input_tensor: input tensor
kernel_size: defualt 3, the kernel size of middle conv layer at main path
filters: list of integers, the nb_filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
Note that from stage 3, the first conv layer at main path is with subsample=(2,2)
And the shortcut should have subsample=(2,2) as well
'''
eps = 1.1e-5
nb_filter1, nb_filter2, nb_filter3 = filters
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
scale_name_base = 'scale' + str(stage) + block + '_branch'
x = Conv2D(nb_filter1, (1, 1), strides=strides,
name=conv_name_base + '2a', use_bias=False, kernel_regularizer=l2(weight_decay))(input_tensor)
x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2a', gamma_regularizer=l2(weight_decay),
beta_regularizer=l2(weight_decay))(x)
x = Scale(axis=bn_axis, name=scale_name_base + '2a')(x)
x = Activation('relu', name=conv_name_base + '2a_relu')(x)
x = ZeroPadding2D((1, 1), name=conv_name_base + '2b_zeropadding')(x)
x = Conv2D(nb_filter2, (kernel_size, kernel_size),
name=conv_name_base + '2b', use_bias=False, kernel_regularizer=l2(weight_decay))(x)
x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2b', gamma_regularizer=l2(weight_decay),
beta_regularizer=l2(weight_decay))(x)
x = Scale(axis=bn_axis, name=scale_name_base + '2b')(x)
x = Activation('relu', name=conv_name_base + '2b_relu')(x)
x = Conv2D(nb_filter3, (1, 1), name=conv_name_base + '2c', use_bias=False, kernel_regularizer=l2(weight_decay))(x)
x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2c', gamma_regularizer=l2(weight_decay),
beta_regularizer=l2(weight_decay))(x)
x = Scale(axis=bn_axis, name=scale_name_base + '2c')(x)
shortcut = Conv2D(nb_filter3, (1, 1), strides=strides,
name=conv_name_base + '1', use_bias=False, kernel_regularizer=l2(weight_decay))(input_tensor)
shortcut = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '1', gamma_regularizer=l2(weight_decay),
beta_regularizer=l2(weight_decay))(shortcut)
shortcut = Scale(axis=bn_axis, name=scale_name_base + '1')(shortcut)
x = add([x, shortcut], name='res' + str(stage) + block)
x = Activation('relu', name='res' + str(stage) + block + '_relu')(x)
return x
def resnet101(no_classes, initialization='imagenet', weight_decay=0, final_activation=None):
'''Instantiate the ResNet101 architecture,
# Arguments
weights_path: path to pretrained weight file
# Returns
A Keras model instance.
'''
if initialization == 'imagenet':
weights_path = 'resnet101_weights_tf.h5'
else:
weights_path = None
eps = 1.1e-5
# Handle Dimension Ordering for different backends
global bn_axis
if K.image_dim_ordering() == 'tf':
bn_axis = 3
img_input = Input(shape=(224, 224, 3), name='data')
else:
bn_axis = 1
img_input = Input(shape=(3, 224, 224), name='data')
x = ZeroPadding2D((3, 3), name='conv1_zeropadding')(img_input)
x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1', use_bias=False, kernel_regularizer=l2(weight_decay))(x)
x = BatchNormalization(epsilon=eps, axis=bn_axis, name='bn_conv1', gamma_regularizer=l2(weight_decay),
beta_regularizer=l2(weight_decay))(x)
x = Scale(axis=bn_axis, name='scale_conv1')(x)
x = Activation('relu', name='conv1_relu')(x)
x = MaxPooling2D((3, 3), strides=(2, 2), name='pool1')(x)
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1), weight_decay=weight_decay)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', weight_decay=weight_decay)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', weight_decay=weight_decay)
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', weight_decay=weight_decay)
for i in range(1, 3):
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b'+str(i), weight_decay=weight_decay)
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', weight_decay=weight_decay)
for i in range(1, 23):
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b'+str(i), weight_decay=weight_decay)
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a', weight_decay=weight_decay)
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b', weight_decay=weight_decay)
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c', weight_decay=weight_decay)
x_fc = AveragePooling2D((7, 7), name='avg_pool')(x)
x_fc = Flatten()(x_fc)
x_fc = Dense(no_classes, activation=final_activation, name='fc_final' + str(no_classes), kernel_regularizer=l2(weight_decay))(x_fc)
model = Model(img_input, x_fc)
# load weights
if weights_path:
model.load_weights(weights_path, by_name=True)
return model