DeepLabv3+Keras自定义通道实现

Question

我正试图使用DeepLabV3+的Keras实现，使用自定义数量的通道(例如，4而不是3(执行二进制分割任务。

到目前为止我修改了什么：

• 我知道我不能使用imagenet权重(为3个通道训练(，所以我将权重更改为None(随机(
• 我已经更改了4通道实现的输入形状：keras。输入(shape=(image_size，image_size，4((
• 由于这是一个二进制分割任务，我用sigmoid激活更改了输出：model_output=layers。Conv2D(num_classes，kernel_size=(1，1(，padding="；"相同"；，激活="乙状体"((x(

如果链接不可用，这是原始代码：

def convolution_block(
block_input,
num_filters=256,
kernel_size=3,
dilation_rate=1,
padding="same",
use_bias=False,
):
x = layers.Conv2D(
num_filters,
kernel_size=kernel_size,
dilation_rate=dilation_rate,
padding="same",
use_bias=use_bias,
kernel_initializer=keras.initializers.HeNormal(),
)(block_input)
x = layers.BatchNormalization()(x)
return tf.nn.relu(x)

def DilatedSpatialPyramidPooling(dspp_input):
dims = dspp_input.shape
x = layers.AveragePooling2D(pool_size=(dims[-3], dims[-2]))(dspp_input)
x = convolution_block(x, kernel_size=1, use_bias=True)
out_pool = layers.UpSampling2D(
size=(dims[-3] // x.shape[1], dims[-2] // x.shape[2]), interpolation="bilinear",
)(x)
out_1 = convolution_block(dspp_input, kernel_size=1, dilation_rate=1)
out_6 = convolution_block(dspp_input, kernel_size=3, dilation_rate=6)
out_12 = convolution_block(dspp_input, kernel_size=3, dilation_rate=12)
out_18 = convolution_block(dspp_input, kernel_size=3, dilation_rate=18)
x = layers.Concatenate(axis=-1)([out_pool, out_1, out_6, out_12, out_18])
output = convolution_block(x, kernel_size=1)
return output
def DeeplabV3Plus(image_size, num_classes):
model_input = keras.Input(shape=(image_size, image_size, 3))
resnet50 = keras.applications.ResNet50(
weights="imagenet", include_top=False, input_tensor=model_input
)
x = resnet50.get_layer("conv4_block6_2_relu").output
x = DilatedSpatialPyramidPooling(x)
input_a = layers.UpSampling2D(
size=(image_size // 4 // x.shape[1], image_size // 4 // x.shape[2]),
interpolation="bilinear",
)(x)
input_b = resnet50.get_layer("conv2_block3_2_relu").output
input_b = convolution_block(input_b, num_filters=48, kernel_size=1)
x = layers.Concatenate(axis=-1)([input_a, input_b])
x = convolution_block(x)
x = convolution_block(x)
x = layers.UpSampling2D(
size=(image_size // x.shape[1], image_size // x.shape[2]),
interpolation="bilinear",
)(x)
model_output = layers.Conv2D(num_classes, kernel_size=(1, 1), padding="same")(x)
return keras.Model(inputs=model_input, outputs=model_output)
model = DeeplabV3Plus(image_size=IMAGE_SIZE, num_classes=NUM_CLASSES)

以下是我对4通道输入和二进制输出所做更改的代码：

def DeeplabV3Plus(image_size, num_classes=1):
model_input = keras.Input(shape=(image_size, image_size, 4))
resnet50 = keras.applications.ResNet50(
weights=None, include_top=False, input_tensor=model_input
)
x = resnet50.get_layer("conv4_block6_2_relu").output
x = DilatedSpatialPyramidPooling(x)
input_a = layers.UpSampling2D(
size=(image_size // 4 // x.shape[1], image_size // 4 // x.shape[2]),
interpolation="bilinear",
)(x)
input_b = resnet50.get_layer("conv2_block3_2_relu").output
input_b = convolution_block(input_b, num_filters=48, kernel_size=1)
x = layers.Concatenate(axis=-1)([input_a, input_b])
x = convolution_block(x)
x = convolution_block(x)
x = layers.UpSampling2D(
size=(image_size // x.shape[1], image_size // x.shape[2]),
interpolation="bilinear",
)(x)
model_output = layers.Conv2D(num_classes, kernel_size=(1, 1), padding="same", activation='sigmoid')(x)
return keras.Model(inputs=model_input, outputs=model_output)
model = DeeplabV3Plus(image_size=IMAGE_SIZE, num_classes=NUM_CLASSES)

然而，我得到了以下错误：

InvalidArgumentError: Graph execution error:
Detected at node 'assert_greater_equal/Assert/AssertGuard/Assert'

和

Node: 'assert_greater_equal/Assert/AssertGuard/Assert'
2 root error(s) found.
(0) INVALID_ARGUMENT:  assertion failed: [predictions must be >= 0] [Condition x >= y did not hold element-wise:] [x (model_5/conv2d_59/Sigmoid:0) = ] [[[[nan][nan][nan]]]...] [y (Cast_8/x:0) = ] [0]
[[{{node assert_greater_equal/Assert/AssertGuard/Assert}}]]
[[assert_greater_equal_1/Assert/AssertGuard/pivot_f/_23/_65]]
(1) INVALID_ARGUMENT:  assertion failed: [predictions must be >= 0] [Condition x >= y did not hold element-wise:] [x (model_5/conv2d_59/Sigmoid:0) = ] [[[[nan][nan][nan]]]...] [y (Cast_8/x:0) = ] [0]
[[{{node assert_greater_equal/Assert/AssertGuard/Assert}}]]

注意：我的图像和遮罩缩放为0-1。

Answer 1

提供的代码是用于Keras二进制分割的DeepLabV3+的4通道版本的正确实现。我的错误是由一个格式错误的4通道输入数据引起的。