python - TypeError: 不支持的操作数类型 /: 'float' 和 'Dimension',请使用 // 代替
问题描述
当我尝试从 UNet 模型内部调用 RA_unit_v4_1 方法时,出现以下错误。我使用 x.shape[y].value 方法转换了所有维度值,但它没有帮助。x 和 conv_n 的形状都是一样的。如何解决此问题或导致此问题发生的原因?
错误
TypeError: unsupported operand type(s) for /: 'float' and 'Dimension', please use // instead
RA_unit_v4_1
def RA_unit_v4_1(x, h, w, n):
x_1_n = MaxPooling2D(pool_size=(int(h/n), 2), strides=(int(h/n),2), padding='same', data_format='channels_last')(x)
x_t_n = K.zeros([1, h, w, 0], K.floatx())
for k in range(n):
x_t_1_n =K.slice(x_1_n, [0,k,0,0], [1,1,int(w/2),x.shape[3].value])
x_t_2_n = K.resize_images(x_t_1_n, int(h//x_t_1_n.shape[1].value), int(w//x_t_1_n.shape[2].value), data_format='channels_last',interpolation='nearest')
x_t_3_n = K.abs(x - x_t_2_n)
x_t_n = concatenate([x_t_n, x_t_3_n], axis=3)
x_out_n = concatenate([x, x_t_n], axis=3)
conv_n = Conv2D(x.shape[3], 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(x_out_n)
return conv_n
网络模型
def unet(pretrained_weights = None,input_size = None):
inputs = Input(batch_shape=input_size)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
RA_1 = RA_unit_v4_1(x=pool1,h=pool1.shape[1].value, w=pool1.shape[2].value,n=16)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(RA_1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
merge6 = concatenate([drop4,up6], axis = 3)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
merge7 = concatenate([conv3,up7], axis = 3)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
merge8 = concatenate([conv2,up8], axis = 3)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
merge9 = concatenate([conv1,up9], axis = 3)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv10 = Conv2D(3, 1, activation = 'sigmoid')(conv9)
model = Model(input = inputs, output = conv10)
model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy']) # original 1e-4 | 2e-4 = 0.00020
model.summary()
if(pretrained_weights):
model.load_weights(pretrained_weights)
return model
解决方案
我解决了。它在方法 RA_unit_v4_1 的最后一行之前;
conv_n = Conv2D(x.shape[3], 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(x_out_n)
经过
conv_n = Conv2D(x.shape[3].value, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(x_out_n)
即 x.shape[3] 应该替换为 x.shape[3].value。
解决后出现以下错误。'AttributeError: 'NoneType' 对象没有属性 '_inbound_nodes'' 。要解决此问题,应使用 lambda 函数。它如下:
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
RA_1 = Lambda(lambda y: RA_unit_v4_1(x=y, h=y.shape[1].value, w=y.shape[2].value, n=16) )
RA_1_n = RA_1(pool1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(RA_1_n)
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