Darknet19的实现

环境设置:tensorflow2.1

代码如下:

#darknet19细节
import tensorflow as tf
from tensorflow.keras.layers import Dense,Flatten,Conv2D,MaxPooling2D,ZeroPadding2D,UpSampling2D
from tensorflow.keras.layers import Input,AveragePooling2D,Activation
from tensorflow.keras import Model
inputs=Input([256,256,3])
"""
#前面两层卷积的结构函数
"""
def conv2d_1(filters,inputs):
    x=Conv2D(filters,(3,3),strides=1,padding='same',activation='relu')(inputs)
    x=MaxPooling2D()(x)
    return x

def conv2d_2(filters,inputs):
    filter1,filter2=filters
    x=Conv2D(filter1,(3,3),padding='same',activation='relu')(inputs)
    x=Conv2D(filter2,1,padding='same',activation='relu')(x)
    x=Conv2D(filter1,3,padding='same',activation='relu')(x)
    x=MaxPooling2D()(x)
    return x
def conv2d_3(filters,inputs):
    filter1,filter2=filters
    x=Conv2D(filter1,(3,3),padding='same',activation='relu')(inputs)
    x=Conv2D(filter2,(1,1),padding='same',activation='relu')(x)
    x=Conv2D(filter1,(3,3),padding='same',activation='relu')(x)
    x=Conv2D(filter2,(1,1),padding='same',activation='relu')(x) 
    x=Conv2D(filter1,(3,3),padding='same',activation='relu')(x)
    return x

    
    

x=conv2d_1(32,inputs)
x=conv2d_1(64,x)

x=conv2d_2([128,64],x)
x=conv2d_2([256,128],x)
x=conv2d_3([512,256],x)
x=MaxPooling2D()(x)
x=conv2d_3([1024,512],x)
x=Conv2D(1000,1,padding='same',activation='relu')(x)
x=AveragePooling2D(pool_size=(8,8))(x)
x=Flatten()(x)
x=Activation('softmax')(x)
model=Model(inputs,x)
model.summary()

可以通过model.summary()查看网络结构

#darknet19结构图
"""
Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 256, 256, 3)]     0         
_________________________________________________________________
conv2d (Conv2D)              (None, 256, 256, 32)      896       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 128, 128, 32)      0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 128, 128, 64)      18496     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 64, 64, 64)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 64, 64, 128)       73856     
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 64, 64, 64)        8256      
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 64, 64, 128)       73856     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 32, 32, 128)       0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 32, 32, 256)       295168    
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 32, 32, 128)       32896     
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 32, 32, 256)       295168    
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 16, 16, 256)       0         
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 16, 16, 512)       1180160   
_________________________________________________________________
conv2d_9 (Conv2D)            (None, 16, 16, 256)       131328    
_________________________________________________________________
conv2d_10 (Conv2D)           (None, 16, 16, 512)       1180160   
_________________________________________________________________
conv2d_11 (Conv2D)           (None, 16, 16, 256)       131328    
_________________________________________________________________
conv2d_12 (Conv2D)           (None, 16, 16, 512)       1180160   
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 8, 8, 512)         0         
_________________________________________________________________
conv2d_13 (Conv2D)           (None, 8, 8, 1024)        4719616   
_________________________________________________________________
conv2d_14 (Conv2D)           (None, 8, 8, 512)         524800    
_________________________________________________________________
conv2d_15 (Conv2D)           (None, 8, 8, 1024)        4719616   
_________________________________________________________________
conv2d_16 (Conv2D)           (None, 8, 8, 512)         524800    
_________________________________________________________________
conv2d_17 (Conv2D)           (None, 8, 8, 1024)        4719616   
_________________________________________________________________
conv2d_18 (Conv2D)           (None, 8, 8, 1000)        1025000   
_________________________________________________________________
average_pooling2d (AveragePo (None, 1, 1, 1000)        0         
_________________________________________________________________
flatten (Flatten)            (None, 1000)              0         
_________________________________________________________________
activation (Activation)      (None, 1000)              0         
=================================================================
Total params: 20,835,176
Trainable params: 20,835,176
Non-trainable params: 0
"""

 

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