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resnet50.py
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resnet50.py
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import keras
from keras.models import *
from keras.layers import *
from keras import layers
import keras.backend as K
"""
Code taken from:
https://github.com/fchollet/deep-learning-models
"""
IMAGE_ORDERING = 'channels_last'
# download links
if IMAGE_ORDERING == 'channels_first':
pretrained_url = "https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_th_dim_ordering_th_kernels_notop.h5"
elif IMAGE_ORDERING == 'channels_last':
pretrained_url = "https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5"
# utility functions: one-sided padding
def one_side_pad( x ):
x = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(x)
if IMAGE_ORDERING == 'channels_first':
x = Lambda(lambda x : x[: , : , :-1 , :-1 ] )(x)
elif IMAGE_ORDERING == 'channels_last':
x = Lambda(lambda x : x[: , :-1 , :-1 , : ] )(x)
return x
# utility functions: identity block
def identity_block(input_tensor, kernel_size, filters, stage, block):
"""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 filterss 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
# Returns
Output tensor for the block.
"""
filters1, filters2, filters3 = filters
if IMAGE_ORDERING == 'channels_last': bn_axis = 3
else: bn_axis = 1
# naming
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
# sub-block1
x = Conv2D(filters1, (1, 1) , data_format=IMAGE_ORDERING , name=conv_name_base + '2a')(input_tensor)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
x = Activation('relu')(x)
# sub-block2
x = Conv2D(filters2, kernel_size , data_format=IMAGE_ORDERING ,
padding='same', name=conv_name_base + '2b')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
x = Activation('relu')(x)
# sub-block3
x = Conv2D(filters3 , (1, 1), data_format=IMAGE_ORDERING , name=conv_name_base + '2c')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
# output activation
x = layers.add([x, input_tensor])
x = Activation('relu')(x)
return x
# utility functions: conv block
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
# Arguments
input_tensor: input tensor
kernel_size: defualt 3, the kernel size of middle conv layer at main path
filters: list of integers, the filterss 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
# Returns
Output tensor for the block.
Note that from stage 3, the first conv layer at main path is with strides=(2,2)
And the shortcut should have strides=(2,2) as well
"""
filters1, filters2, filters3 = filters
if IMAGE_ORDERING == 'channels_last': bn_axis = 3
else: bn_axis = 1
# naming
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
# sub-block1
x = Conv2D(filters1, (1, 1) , data_format=IMAGE_ORDERING , strides=strides,
name=conv_name_base + '2a')(input_tensor)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
x = Activation('relu')(x)
# sub-block2
x = Conv2D(filters2, kernel_size , data_format=IMAGE_ORDERING , padding='same',
name=conv_name_base + '2b')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
x = Activation('relu')(x)
# sub-block3
x = Conv2D(filters3, (1, 1) , data_format=IMAGE_ORDERING , name=conv_name_base + '2c')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
# shortcut convs
shortcut = Conv2D(filters3, (1, 1) , data_format=IMAGE_ORDERING , strides=strides,
name=conv_name_base + '1')(input_tensor)
shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)
# output activation
x = layers.add([x, shortcut])
x = Activation('relu')(x)
return x
def get_resnet50_encoder(input_height=224, input_width=224, channels=3,
pretrained='imagenet', include_top=True, weights='imagenet',
input_tensor=None, input_shape=None, pooling=None, classes=1000):
#assert input_height%32 == 0
#assert input_width%32 == 0
if IMAGE_ORDERING == 'channels_first':
bn_axis = 1
img_input = Input(shape=(channels, input_height, input_width))
elif IMAGE_ORDERING == 'channels_last':
bn_axis = 3
img_input = Input(shape=(input_height, input_width, channels))
# sub-block1
x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input)
x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2), name='conv1')(x)
f1 = x
# sub-block2
x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
x = Activation('relu')(x)
x = MaxPooling2D((3, 3) , data_format=IMAGE_ORDERING , strides=(2, 2))(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')
f2 = one_side_pad(x )
# sub-block3
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
f3 = x
# sub-block4
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
f4 = x
# sub-block5
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')
f5 = x
x = AveragePooling2D((7, 7) , data_format=IMAGE_ORDERING , name='avg_pool')(x)
# f6 = x
if pretrained == 'imagenet':
weights_path = keras.utils.get_file( pretrained_url.split("/")[-1] , pretrained_url )
Model( img_input , x ).load_weights(weights_path)
return img_input, [f1 , f2 , f3 , f4 , f5]
def vanilla_encoder(input_height=224, input_width=224, channels=3):
kernel = 3
filter_size = 64
pad = 1
pool_size = 2
if IMAGE_ORDERING == 'channels_first':
img_input = Input(shape=(channels, input_height,input_width))
elif IMAGE_ORDERING == 'channels_last':
img_input = Input(shape=(input_height,input_width, channels))
x = img_input
levels = []
x = (ZeroPadding2D((pad,pad) , data_format=IMAGE_ORDERING ))( x )
x = (Conv2D(filter_size, (kernel, kernel) , data_format=IMAGE_ORDERING , padding='valid'))( x )
x = (BatchNormalization())( x )
x = (Activation('relu'))( x )
x = (MaxPooling2D((pool_size, pool_size) , data_format=IMAGE_ORDERING ))( x )
levels.append( x )
x = (ZeroPadding2D((pad,pad) , data_format=IMAGE_ORDERING ))( x )
x = (Conv2D(128, (kernel, kernel) , data_format=IMAGE_ORDERING , padding='valid'))( x )
x = (BatchNormalization())( x )
x = (Activation('relu'))( x )
x = (MaxPooling2D((pool_size, pool_size) , data_format=IMAGE_ORDERING ))( x )
levels.append( x )
for _ in range(3):
x = (ZeroPadding2D((pad,pad) , data_format=IMAGE_ORDERING ))(x)
x = (Conv2D(256, (kernel, kernel) , data_format=IMAGE_ORDERING , padding='valid'))(x)
x = (BatchNormalization())(x)
x = (Activation('relu'))(x)
x = (MaxPooling2D((pool_size, pool_size) , data_format=IMAGE_ORDERING))(x)
levels.append( x )
return img_input, levels
def get_resnet_encoder(input_height=224, input_width=224, channels=3):
img_input = Input(shape=(input_height, input_width, channels)) ; print (img_input)
# sub-block1
x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input)
x = Conv2D(32, (5, 5), data_format=IMAGE_ORDERING, strides=(2, 2), name='conv1')(x)
f1 = x ; print (f1)
# sub-block2
x = BatchNormalization(axis=3, name='bn_conv1')(x)
x = Activation('relu')(x)
x = MaxPooling2D((3, 3) , data_format=IMAGE_ORDERING , strides=(2, 2))(x)
x = conv_block(x, 3, [32, 32, 128], stage=2, block='a', strides=(1, 1))
x = identity_block(x, 3, [32, 32, 128], stage=2, block='b')
x = identity_block(x, 3, [32, 32, 128], stage=2, block='c')
f2 = one_side_pad(x ) ; print (f2)
# sub-block3
x = conv_block(x, 3, [64, 64, 256], stage=3, block='a')
x = identity_block(x, 3, [64, 64, 256], stage=3, block='b')
x = identity_block(x, 3, [64, 64, 256], stage=3, block='c')
x = identity_block(x, 3, [64, 64, 256], stage=3, block='d')
f3 = x ; print (f3)
# return
return img_input, [f1 , f2 , f3]