问题描述
我试图在我的模型上实现Lamda Keras图层,以对图像进行预处理。在这种情况下,我决定使用CLAHE算法对图像进行预处理
我使用此预处理功能来实现CLAHE(对比度受限的自适应直方图均衡化)
from skimage import exposure
def AHE(img):
img_adapteq = exposure.equalize_adapthist(img,clip_limit=0.03)
return img_adapteq
在将数据集加载到tensorflow时,我使用了以下代码:
test_gen = tf.keras.preprocessing.image_dataset_from_directory(
DS_DIR,image_size = (IMG_HEIGHT,IMG_WIDTH),shuffle = True,)
DS_DIR只是一个字符串,它指示目录的路径,该目录包含两类文件夹,这些文件夹包含将用于测试模型的图像。
这是我的预处理层的代码:
preprocessing_layer = Sequential([
tf.keras.layers.Lambda(function = AHE),tf.keras.layers.experimental.preprocessing.Rescaling(1/255)
],name = 'preprocessing_layer')
然后使用完整模型:
full_model = Sequential([
tf.keras.layers.InputLayer(input_shape = (456,456)),preprocessing_layer,model
])
“模型”只是微调的Xception模型。
运行此代码后,出现以下错误:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-55-643e72c4dacc> in <module>
2 tf.keras.layers.InputLayer(input_shape = (456,3 preprocessing_layer,----> 4 model
5 ])
/opt/conda/lib/python3.7/site-packages/tensorflow/python/training/tracking/base.py in _method_wrapper(self,*args,**kwargs)
455 self._self_setattr_tracking = False # pylint: disable=protected-access
456 try:
--> 457 result = method(self,**kwargs)
458 finally:
459 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/sequential.py in __init__(self,layers,name)
140 layers = [layers]
141 for layer in layers:
--> 142 self.add(layer)
143
144 @property
/opt/conda/lib/python3.7/site-packages/tensorflow/python/training/tracking/base.py in _method_wrapper(self,**kwargs)
458 finally:
459 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/sequential.py in add(self,layer)
219 # If the model is being built continuously on top of an input layer:
220 # refresh its output.
--> 221 output_tensor = layer(self.outputs[0])
222 if len(nest.flatten(output_tensor)) != 1:
223 raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self,**kwargs)
924 if _in_functional_construction_mode(self,inputs,args,kwargs,input_list):
925 return self._functional_construction_call(inputs,--> 926 input_list)
927
928 # Maintains info about the `Layer.call` stack.
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py in _functional_construction_call(self,input_list)
1115 try:
1116 with ops.enable_auto_cast_variables(self._compute_dtype_object):
-> 1117 outputs = call_fn(cast_inputs,**kwargs)
1118
1119 except errors.OperatorNotAllowedInGraphError as e:
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/sequential.py in call(self,training,mask)
384 kwargs['training'] = training
385
--> 386 outputs = layer(inputs,**kwargs)
387
388 if len(nest.flatten(outputs)) != 1:
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self,**kwargs)
1118
1119 except errors.OperatorNotAllowedInGraphError as e:
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/layers/core.py in call(self,mask,training)
901 with backprop.GradientTape(watch_accessed_variables=True) as tape,\
902 variable_scope.variable_creator_scope(_variable_creator):
--> 903 result = self.function(inputs,**kwargs)
904 self._check_variables(created_variables,tape.watched_variables())
905 return result
<ipython-input-53-828c66f442bc> in AHE(img)
1 def AHE(img):
2
----> 3 img_adapteq = exposure.equalize_adapthist(img,clip_limit=0.03)
4 return img_adapteq
/opt/conda/lib/python3.7/site-packages/skimage/color/adapt_rgb.py in image_filter_adapted(image,**kwargs)
35 @functools.wraps(image_filter)
36 def image_filter_adapted(image,**kwargs):
---> 37 if is_rgb_like(image):
38 return apply_to_rgb(image_filter,image,**kwargs)
39 else:
/opt/conda/lib/python3.7/site-packages/skimage/color/adapt_rgb.py in is_rgb_like(image)
17 shape is fragile.
18 """
---> 19 return (image.ndim == 3) and (image.shape[2] in (3,4))
20
21
AttributeError: 'Tensor' object has no attribute 'ndim'
如何解决此问题并在完整模型中实现CLAHE算法?
解决方法
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