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python - keras 分割 InvalidArgumentError：不兼容的形状：[32,256,256,3] 与 [32,256,256,4]

问题描述

我正在尝试训练 UNET 以检测自然灾害后的道路损坏。我的图像是256x256x3，面具是256x256x1。像素对应于 4 类：0 = 背景，1 = 没有损坏的道路，2 = 损坏的道路和 3 = 遮挡视线的树叶。它看起来像这样 - （在图像中只能看到 3 个类）：

示例元组

我将这些图像放在具有以下结构的文件夹中。我也有相同结构的验证数据

-- train images
   -- img
      -- 1.png
      -- 2.png
-- train masks
   -- img
      -- 1_mask.png
      -- 2_mask.png

我的训练和验证数据生成器如下

image_data_gen = image.ImageDataGenerator(rotation_range = 40, horizontal_flip = True,
                                          vertical_flip = True, zoom_range = 0.2,
                                          shear_range = 0.2,width_shift_range = 0.2,
                                          height_shift_range = 0.2)
mask_data_gen = image.ImageDataGenerator(rotation_range = 40, horizontal_flip = True,
                                          vertical_flip = True, zoom_range = 0.2,
                                          shear_range = 0.2,width_shift_range = 0.2,
                                          height_shift_range = 0.2)
validimg_data_gen = image.ImageDataGenerator()
validmask_data_gen = image.ImageDataGenerator()


image_array_gen = image_data_gen.flow_from_directory(directory=train_images_path, class_mode = None,
                                   target_size = (256,256), seed = 909)
mask_array_gen = mask_data_gen.flow_from_directory(directory=train_segs_path, class_mode = None,
                                   target_size = (256,256), seed = 909)
valid_image_array_gen = validimg_data_gen.flow_from_directory(directory= val_images_path, class_mode = None,
                                   target_size = (256,256), seed = 909)
valid_mask_array_gen = validmask_data_gen.flow_from_directory(directory= val_segs_path, class_mode = None,
                                   target_size = (256,256), seed = 909)

# combine generators into one which yields image and masks
train_generator = zip(image_array_gen, mask_array_gen)
valid_generator = zip(valid_image_array_gen, valid_mask_array_gen)

我知道，因为这是一个分段问题，所以 class_mode 应该是 None。当我运行上面的单元格时，会Found x images belonging to 1 classes.出现消息，其中 x 是我在子文件夹“img”中拥有的训练和验证图像的数量。我认为错误可能是我在“img”子文件夹中有我的数据，而 keras 认为所有图像都对应于 img 类，而实际上有 4 个像素类。但是，如果我将数据放在火车图像和火车掩码文件夹中，我会收到消息Found 0 images belonging to 0 classes.

当我尝试训练我的模型时，results = model.fit_generator(train_generator, steps_per_epoch=int(train_samples/batch_size),epochs=30, validation_data=valid_generator,validation_steps=int(valid_samples/batch_size))我得到了错误：

InvalidArgumentError:  Incompatible shapes: [32,256,256,3] vs. [32,256,256,4]
     [[node gradient_tape/categorical_crossentropy/mul/BroadcastGradientArgs (defined at <ipython-input-120-22f04a70298f>:3) ]] [Op:__inference_train_function_19597]

Function call stack:
train_function

我用来定义模型的代码在这个问题的末尾。请注意，如果我更改最后一层，conv10 = Conv2D(1, 1, activation = 'softmax')(conv9)错误就会消失，并且训练完成没有问题。这也是为什么我认为错误可能是我在子文件夹 img 中有我的数据，但是我该怎么做才能指定像素类为 4 的模型而不会出现此错误？

模型

def unet(pretrained_weights = None,input_size = (256,256,3)):
    inputs = Input(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)
    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
    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(4, 1, activation = 'softmax')(conv9)
    
    model = Model(inputs = inputs, outputs = conv10)
    return(model)


model = unet() 
model.compile(optimizer='adam', loss='categorical_crossentropy' ,metrics=['categorical_accuracy'])

标签： pythontensorflowkerasunity3d-unetsemantic-segmentation

解决方案

您的 train_images _path 应该是包含类子目录的目录的路径。对于您在从目录流中使用的其他目录也是如此。例如，如果您具有如下所示的目录结构来对狗和猫进行分类

 c:
  ---train_images
     ---img
        --- cats
        --- dogs
then in flow from directory your path would be
r'c:\train_images\img'

来自目录的流将创建猫和狗类

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