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model.py
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import os
import keras.backend as K
import tensorflow as tf
import keras
from keras import initializers
from keras.layers import Input
from keras.layers import Dense
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import AveragePooling2D
from keras.layers import ZeroPadding2D
from keras.layers import Flatten
from keras.layers import Activation
from keras.layers import add
from keras.layers import BatchNormalization
from keras.layers import GlobalAveragePooling2D
from keras.layers import GlobalMaxPooling2D
from keras.models import Model
from keras.engine import Layer, InputSpec
from keras.engine import get_source_inputs
from keras.utils.data_utils import get_file
from keras.applications.imagenet_utils import _obtain_input_shape
WEIGHTS_PATH = 'https://github.com/qubvel/ResNet152/releases/download/v0.0.1/resnet152_weights_tf_dim_ordering_tf_kernels.h5'
WEIGHTS_PATH_NO_TOP = 'https://github.com/qubvel/ResNet152/releases/download/v0.0.1/resnet152_weights_tf_dim_ordering_tf_kernels_no_top.h5'
class Scale(Layer):
""" Custom Layer for ResNet used for BatchNormalization.
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.
"""
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='%s_gamma' % self.name)
self.beta = K.variable(self.beta_init(shape), name='%s_beta' % 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):
"""
The identity_block is the block that has no conv layer at shortcut
"""
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'
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
x = Conv2D(nb_filter1, (1, 1), name=conv_name_base + '2a', use_bias=False)(input_tensor)
x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2a')(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)(x)
x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2b')(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)(x)
x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2c')(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)):
#conv_block is the block that has a conv layer at shortcut
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'
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
x = Conv2D(nb_filter1, (1, 1), strides=strides, name=conv_name_base + '2a', use_bias=False)(input_tensor)
x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2a')(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)(x)
x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2b')(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)(x)
x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2c')(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)(input_tensor)
shortcut = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '1')(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 ResNet152(include_top=True, weights='imagenet',
input_tensor=None, input_shape=None, pooling=None, classes=1000):
eps = 1.1e-5
if not (weights in {'imagenet', None} or os.path.exists(weights)):
raise ValueError('The `weights` argument should be either '
'`None` (random initialization), `imagenet` '
'(pre-training on ImageNet), '
'or the path to the weights file to be loaded.')
if weights == 'imagenet' and include_top and classes != 1000:
raise ValueError('If using `weights` as imagenet with `include_top`'
' as true, `classes` should be 1000')
# Determine proper input shape
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=(224,224,3))
else:
if not K.is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape, name='data')
else:
img_input = input_tensor
# Handle dimension ordering for different backends
if K.image_dim_ordering() == 'tf':
bn_axis = 3
else:
bn_axis = 1
x = ZeroPadding2D((3, 3), name='conv1_zeropadding')(img_input)
x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1', use_bias=False)(x)
x = BatchNormalization(epsilon=eps, axis=bn_axis, name='bn_conv1')(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', padding='same')(x)
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
for i in range(1, 8):
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b' + str(i))
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
for i in range(1, 36):
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b' + str(i))
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
if include_top:
# Classification block
x = AveragePooling2D((7, 7), name='avg_pool')(x)
x = Flatten()(x)
x = Dense(classes, activation='softmax', name='fc1000')(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='resnet152')
# Load weights
if weights == 'imagenet':
if include_top:
weights_path = get_file(
'resnet152_weights_tf_dim_ordering_tf_kernels.h5',
WEIGHTS_PATH,
cache_subdir='models',
md5_hash='cdb18a2158b88e392c0905d47dcef965')
else:
weights_path = get_file(
'resnet152_weights_tf_dim_ordering_tf_kernels_no_top.h5',
WEIGHTS_PATH_NO_TOP,
cache_subdir='models',
md5_hash='02cb9130cc51543cd703c79697baa592')
model.load_weights(weights_path)
elif weights is not None:
model.load_weights(weights)
for layer in model.layers:
layer.trainable=False
y=model.output
y = AveragePooling2D((7, 7), name='avg_pool')(y)
y=keras.layers.Flatten()(y)
y=keras.layers.Dense(128, activation="relu", kernel_initializer='glorot_normal')(y)
y=keras.layers.Dropout(0.5)(y)
y=keras.layers.Dense(128, activation="relu",kernel_initializer='glorot_normal')(y)
y=keras.layers.Dropout(0.5)(y)
y=keras.layers.Dense(1,activation="sigmoid",kernel_initializer='glorot_normal')(y)
y=keras.models.Model(model.input, y)
return y