零大小数组到最大归约操作，对于多输出 U-net 没有标识

Question

Im trying to use U-net to do multi task label segmentation following this stackoverflow im note sure what Im doing wrong , this is a part of the code

def trainGenerator(batch_size,train_path,image_path, sub_path1, sub_path2, aug_dict,image_color_mode = "rgb",image_folder='image', mask_folder="label", 
               mask_color_mode = "grayscale",image_save_prefix  = "image",mask_save_prefix  = "mask",flag_multi_class = False,num_class = 2,save_to_dir = None,target_size = (224,224),seed = 1):
'''
can generate image and mask at the same time
use the same seed for image_datagen and mask_datagen to ensure the transformation for image and mask is the sameTO  visualize the results of generator, set save_to_dir = "your path"
'''

image_datagen = ImageDataGenerator(**aug_dict)
mask_datagen = ImageDataGenerator(**aug_dict)


image_generator = image_datagen.flow_from_directory(

    image_path ,
    classes = [image_folder],
    class_mode = None,
    color_mode = image_color_mode,
    target_size = target_size,
    batch_size = 2,
    save_to_dir = save_to_dir,
    save_prefix  = image_save_prefix,
    seed = seed)
mask_generator1= mask_datagen.flow_from_directory(

    sub_path1,
    classes = [mask_folder],
    class_mode = None,
    color_mode = mask_color_mode,
    target_size = target_size,
    batch_size = 2,
    save_to_dir = save_to_dir,
    save_prefix  = mask_save_prefix,
    seed = seed)
mask_generator2 = mask_datagen.flow_from_directory(

    sub_path2,
    classes = [mask_folder],
    class_mode = None,
    color_mode = mask_color_mode,
    target_size = target_size,
    batch_size = 2,
    save_to_dir = save_to_dir,
    save_prefix  = mask_save_prefix,
    seed = seed)



train_generator = zip(image_generator, mask_generator1, mask_generator2 )
for (img,mask1, mask2) in train_generator:
    img,mask1 = adjustData(img,mask1,flag_multi_class,num_class)
    img,mask2 = adjustData(img,mask2,flag_multi_class,num_class)
    yield (img,mask1, mask2)

and not sure if my sub directories are in the right order or not

myGene = trainGenerator(2,train_path,image_path,sub_path_1, sub_path_2, aug_dict=data_gen_args,save_to_dir = None)

history= model.fit_generator(myGene,steps_per_epoch=3240,epochs=150,callbacks=[model_checkpoint])

where the directories are as following

image_folder= "data\\membrane\\train\\image_path\\image"
mask_folder1="data\\membrane\\train\\sub_path1\\label"
mask_folder2="data\\membrane\\train\\sub_path2\\label"

this is the error Ive got error I dont know why all the labels has been detected in the both mask folders meanwhile the images in the image folder are 0

please any help will be appreciated

Answer 1

I found the solution i just changed the line

myGene = trainGenerator(2,train_path,image_path,sub_path_1, sub_path_2, aug_dict=data_gen_args,save_to_dir = None)

to

myGene = trainGenerator(2,'image_path',sub_path1, sub_path2, aug_dict=data_gen_args,save_to_dir = None)

and the fit_generator worked perfectly