Keras/TensorFlow 自定义层输出形状错误
Output shape error in custom layer of Keras/TensorFlow
我写了自己的 Keras/Tensorflow 层。将图像传递给它工作正常，但将它与其他层结合使用会产生潜在的错误。不知何故，我的自定义层的输出形状应该是错误的，或者某种 "Nonetype".
简而言之，自定义层将图像从色彩空间A转换到B，然后将一些通道制成直方图。它是 GAN 判别器的预处理层，因此必须是生成器反向传播模型的一部分。
from keras import backend as K
from keras.layers import Layer
class Identity_Loss(Layer):
def __init__(self, output_dim, **kwargs):
self.output_dim = output_dim
super(Identity_Loss, self).__init__(**kwargs)
def build(self, input_shape):
super(Identity_Loss, self).build(input_shape) # Be sure to call this at the end
def call(self, x):
assert isinstance(x, list)
input_1,input_2=x
# Transform BGR to RGB and than to HSV
channels = tf.unstack (input_1, axis=-1)
RGB = tf.stack ([channels[2], channels[1], channels[0]], axis=-1)
RGB=tf.cast(tf.multiply(tf.truediv(tf.add(RGB,1.0),2.0),255.0),dtype=tf.int32)
RGB=tf.cast(RGB,dtype=tf.float32)
HSV=tf.image.rgb_to_hsv(RGB,name=None)
###########################################################
SV=HSV[:,:,:,1:]
############################################################
#make mask binary and multiply with image
y=tf.math.greater(input_2, 0)
y=tf.cast(y, tf.float32, name=None)
HSV_mask = tf.math.multiply(HSV, y)
#Count color occurences ###########################
shape=tf.shape(HSV_mask)
length=shape[1]*shape[2]
#transform
Hue=HSV_mask[:,:,:,:1]
Hue=tf.cast(tf.multiply(Hue,255.0),dtype=tf.int32)
Hue2 = tf.reshape(Hue, [length])
#prevent that the shape changes
filler=tf.range(0,length, 1,dtype=tf.int32)
filler = tf.reshape(filler, [length])
Hue3 = tf.stack([Hue2,filler],axis=-1)
Hue3 = tf.reshape(Hue3, [2*length])
#Count Hue
y1, idx1, count1 = tf.unique_with_counts(Hue3)
maximum=tf.cast(tf.math.reduce_max(count1[1:257]),dtype=tf.int32)
diff=tf.reshape(count1[1:257],(16,16))
diff=tf.expand_dims(diff, axis=-1)
diff=tf.expand_dims(diff, axis=0)
diff=tf.truediv(diff,maximum)
diff=tf.cast(diff,dtype=tf.float32)
return [SV,HSV_mask,diff]
def compute_output_shape(self, input_shape):
assert isinstance(input_shape, list)
return [[None,None,2],[None,None,3],[None,None,1]]
这是将自定义层输出传递到另一个 CNN 层时的相应错误消息，例如：
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-89-3caaa8c77e0c> in <module>()
5 c,d,e=mod([d,input_B])
6
----> 7 dd=model(d)
8
9 bb = Model([input_A,input_B],[c,d,dd])
3 frames
/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py in __call__(self, inputs, **kwargs)
487 # Actually call the layer,
488 # collecting output(s), mask(s), and shape(s).
--> 489 output = self.call(inputs, **kwargs)
490 output_mask = self.compute_mask(inputs, previous_mask)
491
/usr/local/lib/python3.6/dist-packages/keras/engine/network.py in call(self, inputs, mask)
581 return self._output_tensor_cache[cache_key]
582 else:
--> 583 output_tensors, _, _ = self.run_internal_graph(inputs, masks)
584 return output_tensors
585
/usr/local/lib/python3.6/dist-packages/keras/engine/network.py in run_internal_graph(self, inputs, masks)
796 input_shapes = unpack_singleton(
797 [x._keras_shape for x in computed_tensors])
--> 798 shapes = to_list(layer.compute_output_shape(input_shapes))
799 uses_learning_phase = any(
800 [x._uses_learning_phase for x in computed_tensors])
/usr/local/lib/python3.6/dist-packages/keras/layers/convolutional.py in compute_output_shape(self, input_shape)
191 def compute_output_shape(self, input_shape):
192 if self.data_format == 'channels_last':
--> 193 space = input_shape[1:-1]
194 elif self.data_format == 'channels_first':
195 space = input_shape[2:]
TypeError: 'NoneType' object is not subscriptable
简答： 将批量维度添加到 compute_output_shape 方法返回的层的输出形状。
长答案：Keras 模型始终处理一批输入样本，因此 Keras 层中的所有输入形状和输出形状值都包含批次维度。因此，在计算层的输出形状时也需要考虑这一点：
def compute_output_shape(self, input_shape):
return [
[input_shape[0], None, None, 2],
[input_shape[0], None, None, 3],
[input_shape[0], None, None, 1]
]
由于层与层之间的批量大小不会改变，因此您只需将 input_shape[0] 添加到返回的形状中即可。
我写了自己的 Keras/Tensorflow 层。将图像传递给它工作正常，但将它与其他层结合使用会产生潜在的错误。不知何故，我的自定义层的输出形状应该是错误的，或者某种 "Nonetype".
简而言之，自定义层将图像从色彩空间A转换到B，然后将一些通道制成直方图。它是 GAN 判别器的预处理层，因此必须是生成器反向传播模型的一部分。
from keras import backend as K
from keras.layers import Layer
class Identity_Loss(Layer):
def __init__(self, output_dim, **kwargs):
self.output_dim = output_dim
super(Identity_Loss, self).__init__(**kwargs)
def build(self, input_shape):
super(Identity_Loss, self).build(input_shape) # Be sure to call this at the end
def call(self, x):
assert isinstance(x, list)
input_1,input_2=x
# Transform BGR to RGB and than to HSV
channels = tf.unstack (input_1, axis=-1)
RGB = tf.stack ([channels[2], channels[1], channels[0]], axis=-1)
RGB=tf.cast(tf.multiply(tf.truediv(tf.add(RGB,1.0),2.0),255.0),dtype=tf.int32)
RGB=tf.cast(RGB,dtype=tf.float32)
HSV=tf.image.rgb_to_hsv(RGB,name=None)
###########################################################
SV=HSV[:,:,:,1:]
############################################################
#make mask binary and multiply with image
y=tf.math.greater(input_2, 0)
y=tf.cast(y, tf.float32, name=None)
HSV_mask = tf.math.multiply(HSV, y)
#Count color occurences ###########################
shape=tf.shape(HSV_mask)
length=shape[1]*shape[2]
#transform
Hue=HSV_mask[:,:,:,:1]
Hue=tf.cast(tf.multiply(Hue,255.0),dtype=tf.int32)
Hue2 = tf.reshape(Hue, [length])
#prevent that the shape changes
filler=tf.range(0,length, 1,dtype=tf.int32)
filler = tf.reshape(filler, [length])
Hue3 = tf.stack([Hue2,filler],axis=-1)
Hue3 = tf.reshape(Hue3, [2*length])
#Count Hue
y1, idx1, count1 = tf.unique_with_counts(Hue3)
maximum=tf.cast(tf.math.reduce_max(count1[1:257]),dtype=tf.int32)
diff=tf.reshape(count1[1:257],(16,16))
diff=tf.expand_dims(diff, axis=-1)
diff=tf.expand_dims(diff, axis=0)
diff=tf.truediv(diff,maximum)
diff=tf.cast(diff,dtype=tf.float32)
return [SV,HSV_mask,diff]
def compute_output_shape(self, input_shape):
assert isinstance(input_shape, list)
return [[None,None,2],[None,None,3],[None,None,1]]
这是将自定义层输出传递到另一个 CNN 层时的相应错误消息，例如：
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-89-3caaa8c77e0c> in <module>()
5 c,d,e=mod([d,input_B])
6
----> 7 dd=model(d)
8
9 bb = Model([input_A,input_B],[c,d,dd])
3 frames
/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py in __call__(self, inputs, **kwargs)
487 # Actually call the layer,
488 # collecting output(s), mask(s), and shape(s).
--> 489 output = self.call(inputs, **kwargs)
490 output_mask = self.compute_mask(inputs, previous_mask)
491
/usr/local/lib/python3.6/dist-packages/keras/engine/network.py in call(self, inputs, mask)
581 return self._output_tensor_cache[cache_key]
582 else:
--> 583 output_tensors, _, _ = self.run_internal_graph(inputs, masks)
584 return output_tensors
585
/usr/local/lib/python3.6/dist-packages/keras/engine/network.py in run_internal_graph(self, inputs, masks)
796 input_shapes = unpack_singleton(
797 [x._keras_shape for x in computed_tensors])
--> 798 shapes = to_list(layer.compute_output_shape(input_shapes))
799 uses_learning_phase = any(
800 [x._uses_learning_phase for x in computed_tensors])
/usr/local/lib/python3.6/dist-packages/keras/layers/convolutional.py in compute_output_shape(self, input_shape)
191 def compute_output_shape(self, input_shape):
192 if self.data_format == 'channels_last':
--> 193 space = input_shape[1:-1]
194 elif self.data_format == 'channels_first':
195 space = input_shape[2:]
TypeError: 'NoneType' object is not subscriptable
简答： 将批量维度添加到 compute_output_shape 方法返回的层的输出形状。
长答案：Keras 模型始终处理一批输入样本，因此 Keras 层中的所有输入形状和输出形状值都包含批次维度。因此，在计算层的输出形状时也需要考虑这一点：
def compute_output_shape(self, input_shape):
return [
[input_shape[0], None, None, 2],
[input_shape[0], None, None, 3],
[input_shape[0], None, None, 1]
]
由于层与层之间的批量大小不会改变，因此您只需将 input_shape[0] 添加到返回的形状中即可。