这是我正在使用# Define the input as a tensor with shape input_shape
X_input = Input(input_shape)
# Zero_Padding
X = ZeroPadding2D((3,3))(X_input)
#stage_1
X = Conv2D(64,(7,7),strides = (2,2),name = 'conv1')(X)
X = BatchNormalization(axis = 3,name = 'bn_conv1')(X)
X = Activation('relu')(X)
X = MaxPooling2D((3,3),strides = (2,2))(X)
# Stage 2
X = convolutional_block(X, f = 3, filters = [64, 64, 256], stage = 2, block='a', s = 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')
#stage3
X = convolutional_block(X,f = 3 , filters = [128,128,512],stage = 3,block = 'a', s = 2)
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')
#stage 4
X = convolutional_block(X,f = 3 , filters = [256,256,1024],stage = 4,block = 'a', s = 2)
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')
X = identity_block(X,3,[256,256,1024],stage = 4,block='g')
X = identity_block(X,3,[256,256,1024],stage = 4,block='h')
X = identity_block(X,3,[256,256,1024],stage = 4,block='i')
X = identity_block(X,3,[256,256,1024],stage = 4,block='j')
X = identity_block(X,3,[256,256,1024],stage = 4,block='k')
X = identity_block(X,3,[256,256,1024],stage = 4,block='l')
#stage 5
X = convolutional_block(X,f = 3 , filters = [512,512,2048],stage = 5,block = 'a', s = 2)
X = identity_block(X,3,[512,512,2048],stage = 5,block='b')
X = identity_block(X,3,[512,512,2048],stage = 5,block='c')
# AVGPOOL
X = Conv2D(3,kernel_size=(3,3), padding = 'same',use_bias = False)(X)
X = UpSampling2D(size=2)(X)
X = UpSampling2D(size=2)(X)
X = UpSampling2D(size=2)(X)
X = UpSampling2D(size=2)(X)
X = UpSampling2D(size=2)(X)
# Create model
model = Model(inputs = X_input, outputs = X)
return(model)
的{{1}}。
对于每个ADD URL，我将其称为拦截器。在此拦截器中，我已经自动连接了HandlerInterceptor，其中有用于Mongo数据库写入的JPA存储库。但是我在“ logTraceServer”类成员上得到了空指针错误。
我想在每次保存时写入mongo DB。这就是为什么我写这堂课。
postHandle=====================================
@配置 公共类AppConfig实现WebMvcConfigurer {
LogTraceService}
注意-我的Interceptor完美无缺，但它却是自动连线的，但问题是我得到的“ logTraceService”为空。