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318 lines
13 KiB
Python
318 lines
13 KiB
Python
from keras.models import *
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from keras.layers import *
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from keras import layers
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from keras.regularizers import l2
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resnet50_Weights_path='./pretrained_model/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
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IMAGE_ORDERING ='channels_last'
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MERGE_AXIS=-1
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def one_side_pad( x ):
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x = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(x)
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if IMAGE_ORDERING == 'channels_first':
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x = Lambda(lambda x : x[: , : , :-1 , :-1 ] )(x)
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elif IMAGE_ORDERING == 'channels_last':
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x = Lambda(lambda x : x[: , :-1 , :-1 , : ] )(x)
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return x
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def identity_block(input_tensor, kernel_size, filters, stage, block):
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"""The identity block is the block that has no conv layer at shortcut.
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# Arguments
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input_tensor: input tensor
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kernel_size: defualt 3, the kernel size of middle conv layer at main path
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filters: list of integers, the filterss of 3 conv layer at main path
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stage: integer, current stage label, used for generating layer names
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block: 'a','b'..., current block label, used for generating layer names
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# Returns
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Output tensor for the block.
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"""
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filters1, filters2, filters3 = filters
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if IMAGE_ORDERING == 'channels_last':
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bn_axis = 3
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else:
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bn_axis = 1
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conv_name_base = 'res' + str(stage) + block + '_branch'
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bn_name_base = 'bn' + str(stage) + block + '_branch'
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x = Conv2D(filters1, (1, 1) , data_format=IMAGE_ORDERING , name=conv_name_base + '2a')(input_tensor)
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x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
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x = Activation('relu')(x)
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x = Conv2D(filters2, kernel_size , data_format=IMAGE_ORDERING ,
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padding='same', name=conv_name_base + '2b')(x)
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x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
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x = Activation('relu')(x)
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x = Conv2D(filters3 , (1, 1), data_format=IMAGE_ORDERING , name=conv_name_base + '2c')(x)
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x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
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x = layers.add([x, input_tensor])
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x = Activation('relu')(x)
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return x
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def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):
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"""conv_block is the block that has a conv layer at shortcut
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# Arguments
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input_tensor: input tensor
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kernel_size: defualt 3, the kernel size of middle conv layer at main path
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filters: list of integers, the filterss of 3 conv layer at main path
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stage: integer, current stage label, used for generating layer names
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block: 'a','b'..., current block label, used for generating layer names
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# Returns
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Output tensor for the block.
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Note that from stage 3, the first conv layer at main path is with strides=(2,2)
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And the shortcut should have strides=(2,2) as well
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"""
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filters1, filters2, filters3 = filters
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if IMAGE_ORDERING == 'channels_last':
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bn_axis = 3
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else:
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bn_axis = 1
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conv_name_base = 'res' + str(stage) + block + '_branch'
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bn_name_base = 'bn' + str(stage) + block + '_branch'
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x = Conv2D(filters1, (1, 1) , data_format=IMAGE_ORDERING , strides=strides,
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name=conv_name_base + '2a')(input_tensor)
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x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
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x = Activation('relu')(x)
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x = Conv2D(filters2, kernel_size , data_format=IMAGE_ORDERING , padding='same',
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name=conv_name_base + '2b')(x)
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x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
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x = Activation('relu')(x)
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x = Conv2D(filters3, (1, 1) , data_format=IMAGE_ORDERING , name=conv_name_base + '2c')(x)
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x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
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shortcut = Conv2D(filters3, (1, 1) , data_format=IMAGE_ORDERING , strides=strides,
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name=conv_name_base + '1')(input_tensor)
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shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)
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x = layers.add([x, shortcut])
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x = Activation('relu')(x)
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return x
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def resnet50_unet_light(n_classes,input_height=224,input_width=224,weight_decay=1e-6,pretraining=False):
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assert input_height%32 == 0
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assert input_width%32 == 0
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img_input = Input(shape=(input_height,input_width , 3 ))
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if IMAGE_ORDERING == 'channels_last':
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bn_axis = 3
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else:
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bn_axis = 1
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x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input)
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x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2),kernel_regularizer=l2(weight_decay), name='conv1')(x)
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f1 = x
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x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
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x = Activation('relu')(x)
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x = MaxPooling2D((3, 3) , data_format=IMAGE_ORDERING , strides=(2, 2))(x)
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x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
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x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
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x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
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f2 = one_side_pad(x )
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x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
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x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
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x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
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x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
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f3 = x
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x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
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f4 = x
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x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
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x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
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x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
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f5 = x
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if pretraining:
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model=Model( img_input , x ).load_weights(resnet50_Weights_path)
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v512_2048 = Conv2D( 512 , (1, 1) , padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) )( f5 )
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v512_2048 = ( BatchNormalization(axis=bn_axis))(v512_2048)
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v512_2048 = Activation('relu')(v512_2048)
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v512_1024=Conv2D( 512 , (1, 1) , padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) )( f4 )
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v512_1024 = ( BatchNormalization(axis=bn_axis))(v512_1024)
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v512_1024 = Activation('relu')(v512_1024)
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o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(v512_2048)
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o = ( concatenate([ o ,v512_1024],axis=MERGE_AXIS ) )
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o = ( ZeroPadding2D( (1,1), data_format=IMAGE_ORDERING))(o)
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o = ( Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
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o = ( BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o)
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o = ( concatenate([ o ,f3],axis=MERGE_AXIS ) )
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o = ( ZeroPadding2D( (1,1), data_format=IMAGE_ORDERING))(o)
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o = ( Conv2D( 256, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
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o = ( BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o)
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o = ( concatenate([o,f2],axis=MERGE_AXIS ) )
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o = ( ZeroPadding2D((1,1) , data_format=IMAGE_ORDERING))(o)
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o = ( Conv2D( 128 , (3, 3), padding='valid' , data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay) ) )(o)
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o = ( BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o)
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o = ( concatenate([o,f1],axis=MERGE_AXIS ) )
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o = ( ZeroPadding2D((1,1) , data_format=IMAGE_ORDERING ))(o)
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o = ( Conv2D( 64 , (3, 3), padding='valid' , data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) ))(o)
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o = ( BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o)
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o = ( concatenate([o,img_input],axis=MERGE_AXIS ) )
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o = ( ZeroPadding2D((1,1) , data_format=IMAGE_ORDERING ))(o)
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o = ( Conv2D( 32 , (3, 3), padding='valid' , data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) ))(o)
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o = ( BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = Conv2D( n_classes , (1, 1) , padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) )( o )
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o = ( BatchNormalization(axis=bn_axis))(o)
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o = (Activation('softmax'))(o)
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model = Model( img_input , o )
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return model
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def resnet50_unet(n_classes,input_height=224,input_width=224,weight_decay=1e-6,pretraining=False):
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assert input_height%32 == 0
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assert input_width%32 == 0
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img_input = Input(shape=(input_height,input_width , 3 ))
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if IMAGE_ORDERING == 'channels_last':
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bn_axis = 3
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else:
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bn_axis = 1
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x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input)
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x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2),kernel_regularizer=l2(weight_decay), name='conv1')(x)
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f1 = x
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x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
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x = Activation('relu')(x)
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x = MaxPooling2D((3, 3) , data_format=IMAGE_ORDERING , strides=(2, 2))(x)
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x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
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x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
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x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
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f2 = one_side_pad(x )
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x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
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x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
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x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
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x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
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f3 = x
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x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
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f4 = x
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x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
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x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
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x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
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f5 = x
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if pretraining:
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Model( img_input , x ).load_weights(resnet50_Weights_path)
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v1024_2048 = Conv2D( 1024 , (1, 1) , padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) )( f5 )
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v1024_2048 = ( BatchNormalization(axis=bn_axis))(v1024_2048)
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v1024_2048 = Activation('relu')(v1024_2048)
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o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(v1024_2048)
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o = ( concatenate([ o ,f4],axis=MERGE_AXIS ) )
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o = ( ZeroPadding2D( (1,1), data_format=IMAGE_ORDERING))(o)
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o = ( Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
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o = ( BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o)
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o = ( concatenate([ o ,f3],axis=MERGE_AXIS ) )
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o = ( ZeroPadding2D( (1,1), data_format=IMAGE_ORDERING))(o)
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o = ( Conv2D( 256, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
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o = ( BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o)
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o = ( concatenate([o,f2],axis=MERGE_AXIS ) )
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o = ( ZeroPadding2D((1,1) , data_format=IMAGE_ORDERING))(o)
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o = ( Conv2D( 128 , (3, 3), padding='valid' , data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay) ) )(o)
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o = ( BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o)
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o = ( concatenate([o,f1],axis=MERGE_AXIS ) )
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o = ( ZeroPadding2D((1,1) , data_format=IMAGE_ORDERING ))(o)
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o = ( Conv2D( 64 , (3, 3), padding='valid' , data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) ))(o)
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o = ( BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o)
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o = ( concatenate([o,img_input],axis=MERGE_AXIS ) )
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o = ( ZeroPadding2D((1,1) , data_format=IMAGE_ORDERING ))(o)
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o = ( Conv2D( 32 , (3, 3), padding='valid' , data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) ))(o)
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o = ( BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = Conv2D( n_classes , (1, 1) , padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) )( o )
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o = ( BatchNormalization(axis=bn_axis))(o)
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o = (Activation('softmax'))(o)
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model = Model( img_input , o )
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return model
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