hzctony / u-net-with-multiple-classification Goto Github PK

D:\TONG\github\U-net-multiclass\model2.py:79: UserWarning: Update your Model call to the Keras 2 API: Model(inputs=Tensor("in..., outputs=Tensor("co...)
model = Model(input=inputs, output=conv10)
Epoch 1/20
Traceback (most recent call last):
File "D:/TONG/github/U-net-multiclass/main.py", line 21, in
model.fit_generator(myGene, steps_per_epoch=100, epochs=20, callbacks=[model_checkpoint])
File "C:\Users\Tong\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\legacy\interfaces.py", line 91, in wrapper
return func(*args, **kwargs)
File "C:\Users\Tong\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\engine\training.py", line 1418, in fit_generator
initial_epoch=initial_epoch)
File "C:\Users\Tong\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\engine\training_generator.py", line 181, in fit_generator
generator_output = next(output_generator)
File "C:\Users\Tong\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\utils\data_utils.py", line 709, in get
six.reraise(*sys.exc_info())
File "C:\Users\Tong\AppData\Local\Programs\Python\Python36\lib\site-packages\six.py", line 693, in reraise
raise value
File "C:\Users\Tong\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\utils\data_utils.py", line 685, in get
inputs = self.queue.get(block=True).get()
File "C:\Users\Tong\AppData\Local\Programs\Python\Python36\lib\multiprocessing\pool.py", line 644, in get
raise self._value
File "C:\Users\Tong\AppData\Local\Programs\Python\Python36\lib\multiprocessing\pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "C:\Users\Tong\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\utils\data_utils.py", line 626, in next_sample
return six.next(_SHARED_SEQUENCES[uid])
File "D:\TONG\github\U-net-multiclass\data.py", line 58, in trainGenerator
seed=seed)
File "C:\Users\Tong\AppData\Local\Programs\Python\Python36\lib\site-packages\keras_preprocessing\image.py", line 1013, in flow_from_directory
interpolation=interpolation)
File "C:\Users\Tong\AppData\Local\Programs\Python\Python36\lib\site-packages\keras_preprocessing\image.py", line 1875, in init
for subdir in sorted(os.listdir(directory)):
FileNotFoundError: [WinError 3] 系统找不到指定的路径。: '/data/catndog/train/image'

Traceback (most recent call last):
File "main.py", line 3, in
from model import *
File "C:\Users\GoLive_Erol\Desktop\Unet-with-multiple-classification\model.py", line 12, in
from tensorflow.contrib.opt import AdamWOptimizer
ModuleNotFoundError: No module named 'tensorflow.contrib'

i m using tf 2.3

感谢你的分享。
我不太懂程序里面的”Head“、”====“还有乱码是什么啊？

Hello,
I wonder how to implement multi-class Dice loss?
Does it need to do the one-hot encoding?
Does someone know how to do it?
Thanks a lot.

前辈您好，我刚接触分割，有个问题请教。
if(flag_multi_class): img = img / 255. mask = mask[:,:,:,0] if(len(mask.shape) == 4) else mask[:,:,0] mask[(mask!=0.)&(mask!=255.)&(mask!=128.)] = 0. new_mask = np.zeros(mask.shape + (num_class,)) ######################################################################## #You should define the value of your labelled gray imgs #For example,the imgs in /data/catndog/train/label/cat is labelled white #you got to define new_mask[mask == 255, 0] = 1 #it equals to the one-hot array [1,0,0]. ######################################################################## new_mask[mask == 255., 0] = 1 new_mask[mask == 128., 1] = 1 new_mask[mask == 0., 2] = 1 mask = new_mask
这里关于mask的reshape，我猜测组装的方式是：您是分了三类（猫狗和背景）。上面的操作是用类别数量设置mask的厚度，每一层代表一类。按您上面的代码设置为：第一层（0层）猫，您的标签应该是把猫标记成255，第二层是狗，您标记为128，第三层为背景您标记为0，让后将这三层对应标签位置的像素都设为1。不知道这样我的理解对不对。

您在训练是图像的读取方式还是gray
也就是说image和mask的初始形状都是（batch，w，h，1），batch假设等于2
然后img没有reshape操作，所以他的形状（2，256，256，1）
mask设置reshape，所以他的形状是（2，256，256，3）

我的数据为4类（包括背景）
按照上面的理解我出现了下面的问题
ValueError: Error when checking target: expected conv2d_24 to have shape (256, 256, 3) but got array with shape (256, 256, 4)

百思不得解

期待您的回复，再次感谢