【714】LinkNet实现

参考：keras实现LinkNet

参考：nickhitsai/LinkNet-Keras

代码：

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import os
import numpy as np
 
from keras.layers import Input, concatenate, Conv2D, MaxPooling2D, Activation, UpSampling2D, BatchNormalization, add
from keras.layers.core import Flatten, Reshape
from keras.models import Model
from keras.regularizers import l2
import keras.backend as K
 
def _shortcut(input, residual):
    """Adds a shortcut between input and residual block and merges them with "sum"
    """
    # Expand channels of shortcut to match residual.
    # Stride appropriately to match residual (width, height)
    # Should be int if network architecture is correctly configured.
    input_shape = K.int_shape(input)
    residual_shape = K.int_shape(residual)
    stride_width = int(round(input_shape[1] / residual_shape[1]))
    stride_height = int(round(input_shape[2] / residual_shape[2]))
    equal_channels = input_shape[3] == residual_shape[3]
 
    shortcut = input
    # 1 X 1 conv if shape is different. Else identity.
    if stride_width > 1 or stride_height > 1 or not equal_channels:
        shortcut = Conv2D(filters=residual_shape[3],
                          kernel_size=(1, 1),
                          strides=(stride_width, stride_height),
                          padding="valid",
                          kernel_initializer="he_normal",
                          kernel_regularizer=l2(0.0001))(input)
 
    return add([shortcut, residual])
 
def encoder_block(input_tensor, m, n):
    x = BatchNormalization()(input_tensor)
    x = Activation('relu')(x)
    x = Conv2D(filters=n, kernel_size=(3, 3), strides=(2, 2), padding="same")(x)
 
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2D(filters=n, kernel_size=(3, 3), padding="same")(x)
 
    added_1 = _shortcut(input_tensor, x)
 
    x = BatchNormalization()(added_1)
    x = Activation('relu')(x)
    x = Conv2D(filters=n, kernel_size=(3, 3), padding="same")(x)
 
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2D(filters=n, kernel_size=(3, 3), padding="same")(x)
 
    added_2 = _shortcut(added_1, x)
 
    return added_2
 
def decoder_block(input_tensor, m, n):
    x = BatchNormalization()(input_tensor)
    x = Activation('relu')(x)
    x = Conv2D(filters=int(m/4), kernel_size=(1, 1))(x)
 
    x = UpSampling2D((2, 2))(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2D(filters=int(m/4), kernel_size=(3, 3), padding='same')(x)
 
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2D(filters=n, kernel_size=(1, 1))(x)
 
    return x
 
def LinkNet(input_shape=(256, 256, 3), classes=1):
    inputs = Input(shape=input_shape)
 
    x = BatchNormalization()(inputs)
    x = Activation('relu')(x)
    x = Conv2D(filters=64, kernel_size=(7, 7), strides=(2, 2))(x)
 
    x = MaxPooling2D((3, 3), strides=(2, 2), padding="same")(x)
 
    encoder_1 = encoder_block(input_tensor=x, m=64, n=64)
 
    encoder_2 = encoder_block(input_tensor=encoder_1, m=64, n=128)
 
    encoder_3 = encoder_block(input_tensor=encoder_2, m=128, n=256)
 
    encoder_4 = encoder_block(input_tensor=encoder_3, m=256, n=512)
 
    decoder_4 = decoder_block(input_tensor=encoder_4, m=512, n=256)
 
    decoder_3_in = add([decoder_4, encoder_3])
    decoder_3_in = Activation('relu')(decoder_3_in)
 
    decoder_3 = decoder_block(input_tensor=decoder_3_in, m=256, n=128)
 
    decoder_2_in = add([decoder_3, encoder_2])
    decoder_2_in = Activation('relu')(decoder_2_in)
 
    decoder_2 = decoder_block(input_tensor=decoder_2_in, m=128, n=64)
 
    decoder_1_in = add([decoder_2, encoder_1])
    decoder_1_in = Activation('relu')(decoder_1_in)
 
    decoder_1 = decoder_block(input_tensor=decoder_1_in, m=64, n=64)
 
    x = UpSampling2D((2, 2))(decoder_1)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2D(filters=32, kernel_size=(3, 3), padding="same")(x)
 
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2D(filters=32, kernel_size=(3, 3), padding="same")(x)
 
    x = UpSampling2D((2, 2))(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
 
    x = Conv2D(filters=classes, kernel_size=(2, 2), activation='sigmoid', padding="same")(x)
 
    model = Model(inputs=inputs, outputs=x)
 
    return model
 
model = LinkNet() 
model.summary() 

输出：

Model: "model_2"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_3 (InputLayer)            [(None, 256, 256, 3) 0                                            
__________________________________________________________________________________________________
batch_normalization_64 (BatchNo (None, 256, 256, 3)  12          input_3[0][0]                    
__________________________________________________________________________________________________
activation_70 (Activation)      (None, 256, 256, 3)  0           batch_normalization_64[0][0]     
__________________________________________________________________________________________________
conv2d_72 (Conv2D)              (None, 125, 125, 64) 9472        activation_70[0][0]              
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D)  (None, 63, 63, 64)   0           conv2d_72[0][0]                  
__________________________________________________________________________________________________
batch_normalization_65 (BatchNo (None, 63, 63, 64)   256         max_pooling2d_2[0][0]            
__________________________________________________________________________________________________
activation_71 (Activation)      (None, 63, 63, 64)   0           batch_normalization_65[0][0]     
__________________________________________________________________________________________________
conv2d_73 (Conv2D)              (None, 32, 32, 64)   36928       activation_71[0][0]              
__________________________________________________________________________________________________
batch_normalization_66 (BatchNo (None, 32, 32, 64)   256         conv2d_73[0][0]                  
__________________________________________________________________________________________________
activation_72 (Activation)      (None, 32, 32, 64)   0           batch_normalization_66[0][0]     
__________________________________________________________________________________________________
conv2d_75 (Conv2D)              (None, 32, 32, 64)   4160        max_pooling2d_2[0][0]            
__________________________________________________________________________________________________
conv2d_74 (Conv2D)              (None, 32, 32, 64)   36928       activation_72[0][0]              
__________________________________________________________________________________________________
add_22 (Add)                    (None, 32, 32, 64)   0           conv2d_75[0][0]                  
                                                                 conv2d_74[0][0]                  
__________________________________________________________________________________________________
batch_normalization_67 (BatchNo (None, 32, 32, 64)   256         add_22[0][0]                     
__________________________________________________________________________________________________
activation_73 (Activation)      (None, 32, 32, 64)   0           batch_normalization_67[0][0]     
__________________________________________________________________________________________________
conv2d_76 (Conv2D)              (None, 32, 32, 64)   36928       activation_73[0][0]              
__________________________________________________________________________________________________
batch_normalization_68 (BatchNo (None, 32, 32, 64)   256         conv2d_76[0][0]                  
__________________________________________________________________________________________________
activation_74 (Activation)      (None, 32, 32, 64)   0           batch_normalization_68[0][0]     
__________________________________________________________________________________________________
conv2d_77 (Conv2D)              (None, 32, 32, 64)   36928       activation_74[0][0]              
__________________________________________________________________________________________________
add_23 (Add)                    (None, 32, 32, 64)   0           add_22[0][0]                     
                                                                 conv2d_77[0][0]                  
__________________________________________________________________________________________________
batch_normalization_69 (BatchNo (None, 32, 32, 64)   256         add_23[0][0]                     
__________________________________________________________________________________________________
activation_75 (Activation)      (None, 32, 32, 64)   0           batch_normalization_69[0][0]     
__________________________________________________________________________________________________
conv2d_78 (Conv2D)              (None, 16, 16, 128)  73856       activation_75[0][0]              
__________________________________________________________________________________________________
batch_normalization_70 (BatchNo (None, 16, 16, 128)  512         conv2d_78[0][0]                  
__________________________________________________________________________________________________
activation_76 (Activation)      (None, 16, 16, 128)  0           batch_normalization_70[0][0]     
__________________________________________________________________________________________________
conv2d_80 (Conv2D)              (None, 16, 16, 128)  8320        add_23[0][0]                     
__________________________________________________________________________________________________
conv2d_79 (Conv2D)              (None, 16, 16, 128)  147584      activation_76[0][0]              
__________________________________________________________________________________________________
add_24 (Add)                    (None, 16, 16, 128)  0           conv2d_80[0][0]                  
                                                                 conv2d_79[0][0]                  
__________________________________________________________________________________________________
batch_normalization_71 (BatchNo (None, 16, 16, 128)  512         add_24[0][0]                     
__________________________________________________________________________________________________
activation_77 (Activation)      (None, 16, 16, 128)  0           batch_normalization_71[0][0]     
__________________________________________________________________________________________________
conv2d_81 (Conv2D)              (None, 16, 16, 128)  147584      activation_77[0][0]              
__________________________________________________________________________________________________
batch_normalization_72 (BatchNo (None, 16, 16, 128)  512         conv2d_81[0][0]                  
__________________________________________________________________________________________________
activation_78 (Activation)      (None, 16, 16, 128)  0           batch_normalization_72[0][0]     
__________________________________________________________________________________________________
conv2d_82 (Conv2D)              (None, 16, 16, 128)  147584      activation_78[0][0]              
__________________________________________________________________________________________________
add_25 (Add)                    (None, 16, 16, 128)  0           add_24[0][0]                     
                                                                 conv2d_82[0][0]                  
__________________________________________________________________________________________________
batch_normalization_73 (BatchNo (None, 16, 16, 128)  512         add_25[0][0]                     
__________________________________________________________________________________________________
activation_79 (Activation)      (None, 16, 16, 128)  0           batch_normalization_73[0][0]     
__________________________________________________________________________________________________
conv2d_83 (Conv2D)              (None, 8, 8, 256)    295168      activation_79[0][0]              
__________________________________________________________________________________________________
batch_normalization_74 (BatchNo (None, 8, 8, 256)    1024        conv2d_83[0][0]                  
__________________________________________________________________________________________________
activation_80 (Activation)      (None, 8, 8, 256)    0           batch_normalization_74[0][0]     
__________________________________________________________________________________________________
conv2d_85 (Conv2D)              (None, 8, 8, 256)    33024       add_25[0][0]                     
__________________________________________________________________________________________________
conv2d_84 (Conv2D)              (None, 8, 8, 256)    590080      activation_80[0][0]              
__________________________________________________________________________________________________
add_26 (Add)                    (None, 8, 8, 256)    0           conv2d_85[0][0]                  
                                                                 conv2d_84[0][0]                  
__________________________________________________________________________________________________
batch_normalization_75 (BatchNo (None, 8, 8, 256)    1024        add_26[0][0]                     
__________________________________________________________________________________________________
activation_81 (Activation)      (None, 8, 8, 256)    0           batch_normalization_75[0][0]     
__________________________________________________________________________________________________
conv2d_86 (Conv2D)              (None, 8, 8, 256)    590080      activation_81[0][0]              
__________________________________________________________________________________________________
batch_normalization_76 (BatchNo (None, 8, 8, 256)    1024        conv2d_86[0][0]                  
__________________________________________________________________________________________________
activation_82 (Activation)      (None, 8, 8, 256)    0           batch_normalization_76[0][0]     
__________________________________________________________________________________________________
conv2d_87 (Conv2D)              (None, 8, 8, 256)    590080      activation_82[0][0]              
__________________________________________________________________________________________________
add_27 (Add)                    (None, 8, 8, 256)    0           add_26[0][0]                     
                                                                 conv2d_87[0][0]                  
__________________________________________________________________________________________________
batch_normalization_77 (BatchNo (None, 8, 8, 256)    1024        add_27[0][0]                     
__________________________________________________________________________________________________
activation_83 (Activation)      (None, 8, 8, 256)    0           batch_normalization_77[0][0]     
__________________________________________________________________________________________________
conv2d_88 (Conv2D)              (None, 4, 4, 512)    1180160     activation_83[0][0]              
__________________________________________________________________________________________________
batch_normalization_78 (BatchNo (None, 4, 4, 512)    2048        conv2d_88[0][0]                  
__________________________________________________________________________________________________
activation_84 (Activation)      (None, 4, 4, 512)    0           batch_normalization_78[0][0]     
__________________________________________________________________________________________________
conv2d_90 (Conv2D)              (None, 4, 4, 512)    131584      add_27[0][0]                     
__________________________________________________________________________________________________
conv2d_89 (Conv2D)              (None, 4, 4, 512)    2359808     activation_84[0][0]              
__________________________________________________________________________________________________
add_28 (Add)                    (None, 4, 4, 512)    0           conv2d_90[0][0]                  
                                                                 conv2d_89[0][0]                  
__________________________________________________________________________________________________
batch_normalization_79 (BatchNo (None, 4, 4, 512)    2048        add_28[0][0]                     
__________________________________________________________________________________________________
activation_85 (Activation)      (None, 4, 4, 512)    0           batch_normalization_79[0][0]     
__________________________________________________________________________________________________
conv2d_91 (Conv2D)              (None, 4, 4, 512)    2359808     activation_85[0][0]              
__________________________________________________________________________________________________
batch_normalization_80 (BatchNo (None, 4, 4, 512)    2048        conv2d_91[0][0]                  
__________________________________________________________________________________________________
activation_86 (Activation)      (None, 4, 4, 512)    0           batch_normalization_80[0][0]     
__________________________________________________________________________________________________
conv2d_92 (Conv2D)              (None, 4, 4, 512)    2359808     activation_86[0][0]              
__________________________________________________________________________________________________
add_29 (Add)                    (None, 4, 4, 512)    0           add_28[0][0]                     
                                                                 conv2d_92[0][0]                  
__________________________________________________________________________________________________
batch_normalization_81 (BatchNo (None, 4, 4, 512)    2048        add_29[0][0]                     
__________________________________________________________________________________________________
activation_87 (Activation)      (None, 4, 4, 512)    0           batch_normalization_81[0][0]     
__________________________________________________________________________________________________
conv2d_93 (Conv2D)              (None, 4, 4, 128)    65664       activation_87[0][0]              
__________________________________________________________________________________________________
up_sampling2d_12 (UpSampling2D) (None, 8, 8, 128)    0           conv2d_93[0][0]                  
__________________________________________________________________________________________________
batch_normalization_82 (BatchNo (None, 8, 8, 128)    512         up_sampling2d_12[0][0]           
__________________________________________________________________________________________________
activation_88 (Activation)      (None, 8, 8, 128)    0           batch_normalization_82[0][0]     
__________________________________________________________________________________________________
conv2d_94 (Conv2D)              (None, 8, 8, 128)    147584      activation_88[0][0]              
__________________________________________________________________________________________________
batch_normalization_83 (BatchNo (None, 8, 8, 128)    512         conv2d_94[0][0]                  
__________________________________________________________________________________________________
activation_89 (Activation)      (None, 8, 8, 128)    0           batch_normalization_83[0][0]     
__________________________________________________________________________________________________
conv2d_95 (Conv2D)              (None, 8, 8, 256)    33024       activation_89[0][0]              
__________________________________________________________________________________________________
add_30 (Add)                    (None, 8, 8, 256)    0           conv2d_95[0][0]                  
                                                                 add_27[0][0]                     
__________________________________________________________________________________________________
activation_90 (Activation)      (None, 8, 8, 256)    0           add_30[0][0]                     
__________________________________________________________________________________________________
batch_normalization_84 (BatchNo (None, 8, 8, 256)    1024        activation_90[0][0]              
__________________________________________________________________________________________________
activation_91 (Activation)      (None, 8, 8, 256)    0           batch_normalization_84[0][0]     
__________________________________________________________________________________________________
conv2d_96 (Conv2D)              (None, 8, 8, 64)     16448       activation_91[0][0]              
__________________________________________________________________________________________________
up_sampling2d_13 (UpSampling2D) (None, 16, 16, 64)   0           conv2d_96[0][0]                  
__________________________________________________________________________________________________
batch_normalization_85 (BatchNo (None, 16, 16, 64)   256         up_sampling2d_13[0][0]           
__________________________________________________________________________________________________
activation_92 (Activation)      (None, 16, 16, 64)   0           batch_normalization_85[0][0]     
__________________________________________________________________________________________________
conv2d_97 (Conv2D)              (None, 16, 16, 64)   36928       activation_92[0][0]              
__________________________________________________________________________________________________
batch_normalization_86 (BatchNo (None, 16, 16, 64)   256         conv2d_97[0][0]                  
__________________________________________________________________________________________________
activation_93 (Activation)      (None, 16, 16, 64)   0           batch_normalization_86[0][0]     
__________________________________________________________________________________________________
conv2d_98 (Conv2D)              (None, 16, 16, 128)  8320        activation_93[0][0]              
__________________________________________________________________________________________________
add_31 (Add)                    (None, 16, 16, 128)  0           conv2d_98[0][0]                  
                                                                 add_25[0][0]                     
__________________________________________________________________________________________________
activation_94 (Activation)      (None, 16, 16, 128)  0           add_31[0][0]                     
__________________________________________________________________________________________________
batch_normalization_87 (BatchNo (None, 16, 16, 128)  512         activation_94[0][0]              
__________________________________________________________________________________________________
activation_95 (Activation)      (None, 16, 16, 128)  0           batch_normalization_87[0][0]     
__________________________________________________________________________________________________
conv2d_99 (Conv2D)              (None, 16, 16, 32)   4128        activation_95[0][0]              
__________________________________________________________________________________________________
up_sampling2d_14 (UpSampling2D) (None, 32, 32, 32)   0           conv2d_99[0][0]                  
__________________________________________________________________________________________________
batch_normalization_88 (BatchNo (None, 32, 32, 32)   128         up_sampling2d_14[0][0]           
__________________________________________________________________________________________________
activation_96 (Activation)      (None, 32, 32, 32)   0           batch_normalization_88[0][0]     
__________________________________________________________________________________________________
conv2d_100 (Conv2D)             (None, 32, 32, 32)   9248        activation_96[0][0]              
__________________________________________________________________________________________________
batch_normalization_89 (BatchNo (None, 32, 32, 32)   128         conv2d_100[0][0]                 
__________________________________________________________________________________________________
activation_97 (Activation)      (None, 32, 32, 32)   0           batch_normalization_89[0][0]     
__________________________________________________________________________________________________
conv2d_101 (Conv2D)             (None, 32, 32, 64)   2112        activation_97[0][0]              
__________________________________________________________________________________________________
add_32 (Add)                    (None, 32, 32, 64)   0           conv2d_101[0][0]                 
                                                                 add_23[0][0]                     
__________________________________________________________________________________________________
activation_98 (Activation)      (None, 32, 32, 64)   0           add_32[0][0]                     
__________________________________________________________________________________________________
batch_normalization_90 (BatchNo (None, 32, 32, 64)   256         activation_98[0][0]              
__________________________________________________________________________________________________
activation_99 (Activation)      (None, 32, 32, 64)   0           batch_normalization_90[0][0]     
__________________________________________________________________________________________________
conv2d_102 (Conv2D)             (None, 32, 32, 16)   1040        activation_99[0][0]              
__________________________________________________________________________________________________
up_sampling2d_15 (UpSampling2D) (None, 64, 64, 16)   0           conv2d_102[0][0]                 
__________________________________________________________________________________________________
batch_normalization_91 (BatchNo (None, 64, 64, 16)   64          up_sampling2d_15[0][0]           
__________________________________________________________________________________________________
activation_100 (Activation)     (None, 64, 64, 16)   0           batch_normalization_91[0][0]     
__________________________________________________________________________________________________
conv2d_103 (Conv2D)             (None, 64, 64, 16)   2320        activation_100[0][0]             
__________________________________________________________________________________________________
batch_normalization_92 (BatchNo (None, 64, 64, 16)   64          conv2d_103[0][0]                 
__________________________________________________________________________________________________
activation_101 (Activation)     (None, 64, 64, 16)   0           batch_normalization_92[0][0]     
__________________________________________________________________________________________________
conv2d_104 (Conv2D)             (None, 64, 64, 64)   1088        activation_101[0][0]             
__________________________________________________________________________________________________
up_sampling2d_16 (UpSampling2D) (None, 128, 128, 64) 0           conv2d_104[0][0]                 
__________________________________________________________________________________________________
batch_normalization_93 (BatchNo (None, 128, 128, 64) 256         up_sampling2d_16[0][0]           
__________________________________________________________________________________________________
activation_102 (Activation)     (None, 128, 128, 64) 0           batch_normalization_93[0][0]     
__________________________________________________________________________________________________
conv2d_105 (Conv2D)             (None, 128, 128, 32) 18464       activation_102[0][0]             
__________________________________________________________________________________________________
batch_normalization_94 (BatchNo (None, 128, 128, 32) 128         conv2d_105[0][0]                 
__________________________________________________________________________________________________
activation_103 (Activation)     (None, 128, 128, 32) 0           batch_normalization_94[0][0]     
__________________________________________________________________________________________________
conv2d_106 (Conv2D)             (None, 128, 128, 32) 9248        activation_103[0][0]             
__________________________________________________________________________________________________
up_sampling2d_17 (UpSampling2D) (None, 256, 256, 32) 0           conv2d_106[0][0]                 
__________________________________________________________________________________________________
batch_normalization_95 (BatchNo (None, 256, 256, 32) 128         up_sampling2d_17[0][0]           
__________________________________________________________________________________________________
activation_104 (Activation)     (None, 256, 256, 32) 0           batch_normalization_95[0][0]     
__________________________________________________________________________________________________
conv2d_107 (Conv2D)             (None, 256, 256, 1)  129         activation_104[0][0]             
==================================================================================================
Total params: 11,551,469
Trainable params: 11,541,543
Non-trainable params: 9,926
__________________________________________________________________________________________________

结构图：