我正在尝试使用DeepLabV 3+的Keras实现，使用自定义通道数（例如4而不是3）执行二进制分割任务。
到目前为止我已修改的内容：

• 我知道我不能使用imagenet权重（为3个通道训练），所以我已经将权重改为无（随机）。
• 我已经更改了4通道实现的输入形状：输入（形状=（图像大小，图像大小，4））
• 由于这是一个二进制分割任务，因此我使用S形激活更改了输出：（x）在每个层中创建一个新的类，并将其添加到每个层中。

下面是链接不可用时的原始代码：

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)

下面是我对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)

但是，我收到以下错误：

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。