ValueError：输入具有不兼容的形状

Question

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我有以下代码：

def fcn8_decoder(convs, n_classes):
  # features from the encoder stage
  f3, f4, f5 = convs

  # number of filters
  n = 512

  # add convolutional layers on top of the CNN extractor.
  o = tf.keras.layers.Conv2D(n , (7 , 7) , activation='relu' , padding='same', name="conv6", data_format=IMAGE_ORDERING)(f5)
  o = tf.keras.layers.Dropout(0.5)(o)

  o = tf.keras.layers.Conv2D(n , (1 , 1) , activation='relu' , padding='same', name="conv7", data_format=IMAGE_ORDERING)(o)
  o = tf.keras.layers.Dropout(0.5)(o)

  o = tf.keras.layers.Conv2D(n_classes,  (1, 1), activation='relu' , padding='same', data_format=IMAGE_ORDERING)(o)

    
  ### START CODE HERE ###

  # Upsample `o` above and crop any extra pixels introduced
  o = tf.keras.layers.Conv2DTranspose(n_classes , kernel_size=(4,4) ,  strides=(2,2) , use_bias=False)(o)
  o = tf.keras.layers.Cropping2D(cropping=(1,1))(o)

  # load the pool 4 prediction and do a 1x1 convolution to reshape it to the same shape of `o` above
  o2 = f4
  o2 = ( tf.keras.layers.Conv2D(n_classes , ( 1 , 1 ) , activation='relu' , padding='same', data_format=IMAGE_ORDERING))(o2)

  # add the results of the upsampling and pool 4 prediction
  o = tf.keras.layers.Add()([o, o2])

  # upsample the resulting tensor of the operation you just did
  o = (tf.keras.layers.Conv2DTranspose( n_classes , kernel_size=(4,4) ,  strides=(2,2) , use_bias=False))(o)
  o = tf.keras.layers.Cropping2D(cropping=(1, 1))(o)

  # load the pool 3 prediction and do a 1x1 convolution to reshape it to the same shape of `o` above
  o2 = f3
  o2 = tf.keras.layers.Conv2D(n_classes , ( 1 , 1 ) , activation='relu' , padding='same', data_format=IMAGE_ORDERING)(o2)

  # add the results of the upsampling and pool 3 prediction
  o = tf.keras.layers.Add()([o, o2])

  # upsample up to the size of the original image
  o = tf.keras.layers.Conv2DTranspose(n_classes , kernel_size=(8,8) ,  strides=(8,8) , use_bias=False )(o)
  o = tf.keras.layers.Cropping2D(((0, 0), (0, 96-84)))(o)

  # append a sigmoid activation
  o = (tf.keras.layers.Activation('sigmoid'))(o)
  ### END CODE HERE ###

  return o

# TEST CODE

test_convs, test_img_input = FCN8()
test_fcn8_decoder = fcn8_decoder(test_convs, 11)

print(test_fcn8_decoder.shape)

del test_convs, test_img_input, test_fcn8_decoder

您可以在此处查看完整代码。

我收到以下错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-14-cff468b82c6a> in <module>
      2 
      3 test_convs, test_img_input = FCN8()
----> 4 test_fcn8_decoder = fcn8_decoder(test_convs, 11)
      5 
      6 print(test_fcn8_decoder.shape)

2 frames
/usr/local/lib/python3.8/dist-packages/keras/layers/merging/base_merge.py in _compute_elemwise_op_output_shape(self, shape1, shape2)
     71       else:
     72         if i != j:
---> 73           raise ValueError(
     74               'Inputs have incompatible shapes. '
     75               f'Received shapes {shape1} and {shape2}')

ValueError: Inputs have incompatible shapes. Received shapes (4, 4, 11) and (4, 5, 11)

我在这里做错了什么？

Answer 1

您的 model 实现看起来是正确的。 问题在于输入形状。 输入形状必须能被 32 整除，例如(224, 224, 3)或(64, 64, 3) 。 您可以使用非方形输入，只要宽度和高度都能被 32 整除。例如(64, 96, 3)就可以。

在出错的代码中，您使用的是形状(64, 84, 3) ，但 84 不能被 32 整除。

或者，您可以对输入进行零填充以使其具有可被 32 整除的形状。您的代码包含一行来执行此操作，但正如另一个答案所述，您不使用零填充的输出。 另一件事，而不是零填充到 96，我建议零填充到 32 的下一个最高倍数。所以如果你的输入宽度是 84，你会零填充到 96。如果你的输入宽度是 200，你会零-填充到 224。

解释：

FCN8 的下采样路径使用 5 个卷积块对输入进行下采样。 因为 model 使用 5 个这样的块，所以输入必须能被2**5 = 32整除。 （相关https://stackoverflow.com/a/74847194/5666087。 ）

工作示例：

test_convs, test_img_input = FCN8(input_height=64, input_width=64)
test_fcn8_decoder = fcn8_decoder(convs=test_convs, n_classes=11)

test_convs, test_img_input = FCN8(input_height=224, input_width=224)
test_fcn8_decoder = fcn8_decoder(convs=test_convs, n_classes=11)

test_convs, test_img_input = FCN8(input_height=128, input_width=128)
test_fcn8_decoder = fcn8_decoder(convs=test_convs, n_classes=11)

Answer 2

可能导致问题的原因是您的输入不是正方形格式（相同的高度和宽度），并且在求和层中，您试图对经过一些卷积后具有不同形状的张量求和，然后成为更多的张量之一长方形。

我设法找到了一些任意的 kernel 尺寸，这些尺寸产生了具有兼容形状的张量。 还删除了填充。

这是我改变的：

这：

o2 = f4
o2 = ( tf.keras.layers.Conv2D(n_classes , ( 1 , 1 ) , activation='relu' , padding= 'same', data_format=IMAGE_ORDERING))(o2)

转向这个：

o2 = f4
o2 = ( tf.keras.layers.Conv2D(n_classes , ( 1 , 2 ) ,activation='relu', data_format=IMAGE_ORDERING))(o2)

和这个：

o2 = f3
o2 = tf.keras.layers.Conv2D(n_classes , ( 1 , 1 ) , activation='relu' , padding ='same', data_format=IMAGE_ORDERING)(o2)

转向这个：

o2 = f3
o2 = tf.keras.layers.Conv2D(n_classes , ( 1 , 3 ) , activation='relu' , data_format=IMAGE_ORDERING)(o2)

这是完整的代码：

def fcn8_decoder(convs, n_classes):
    #features from the encoder stage
    f3, f4, f5 = convs

    # number of filters
    n = 512

    # add convolutional layers on top of the CNN extractor.
    o = tf.keras.layers.Conv2D(n , (7 , 7) , activation='relu' , padding='same', name="conv6", data_format=IMAGE_ORDERING)(f5)
    o = tf.keras.layers.Dropout(0.5)(o)

    o = tf.keras.layers.Conv2D(n , (1 , 1) , activation='relu' , padding='same', name="conv7", data_format=IMAGE_ORDERING)(o)
    o = tf.keras.layers.Dropout(0.5)(o)

    o = tf.keras.layers.Conv2D(n_classes,  (1, 1), activation='relu' , padding='same', data_format=IMAGE_ORDERING)(o)


    ### START CODE HERE ###

    # Upsample `o` above and crop any extra pixels introduced
    o = tf.keras.layers.Conv2DTranspose(n_classes , kernel_size=(4,4) ,  strides=(2,2) , use_bias=False)(o)
    o = tf.keras.layers.Cropping2D(cropping=(1,1))(o)

    # load the pool 4 prediction and do a 1x1 convolution to reshape it to the same shape of `o` above
    o2 = f4
    o2 = ( tf.keras.layers.Conv2D(n_classes , ( 1 , 2 ) , activation='relu' , data_format=IMAGE_ORDERING))(o2)



    # add the results of the upsampling and pool 4 prediction
    o = tf.keras.layers.Add()([o, o2])

    # upsample the resulting tensor of the operation you just did
    o = (tf.keras.layers.Conv2DTranspose( n_classes , kernel_size=(4,4) ,  strides=(2,2) , use_bias=False))(o)
    o = tf.keras.layers.Cropping2D(cropping=(1, 1))(o)

    # load the pool 3 prediction and do a 1x1 convolution to reshape it to the same shape of `o` above
    o2 = f3
    o2 = tf.keras.layers.Conv2D(n_classes , ( 1 , 3 ) , activation='relu' , data_format=IMAGE_ORDERING)(o2)

    print("###################################################")
    print(o2.shape,o.shape)

    # add the results of the upsampling and pool 3 prediction
    o = tf.keras.layers.Add()([o, o2])

    # upsample up to the size of the original image
    o = tf.keras.layers.Conv2DTranspose(n_classes , kernel_size=(8,8) ,  strides=(8,8) , use_bias=False )(o)
    o = tf.keras.layers.Cropping2D(((0, 0), (0, 96-84)))(o)

    # append a sigmoid activation
    o = (tf.keras.layers.Activation('sigmoid'))(o)
    ### END CODE HERE ###

    return o

Answer 3

在检查了您的 colab 代码后，我可以确认您的错误来自typo

在下图中，您可以看到在FCN8() function 的# Block 1中使用img_input而不是x ，这基本上意味着您没有传递零填充层，而是将输入层传递给了conv块，这导致错误。

要解决该错误，您必须将# Block 1中的img_input替换为x

Answer 4

“input_height”和“input_width”的形状有所不同。 此外，f3、f4、f5 的大小不兼容。 这会在 function 'fcn8_decoder()' 中添加 keras 层时产生问题。 但这可以通过在 'fcn8_decoder()' function tf-keras-Add中使用适当的裁剪来解决。 下面的代码显示了 function 内部的问题，

input1 = tf.keras.layers.Input(shape=(16,))
x1 = tf.keras.layers.Dense(8, activation='relu')(input1)
print(x1)
input2 = tf.keras.layers.Input(shape=(32,))
x2 = tf.keras.layers.Dense(8, activation='relu')(input2)
print(x2)
# equivalent to `added = tf.keras.layers.add([x1, x2])`
added = tf.keras.layers.Add()([x1, input2])
print(added)
out = tf.keras.layers.Dense(4)(added)
model = tf.keras.models.Model(inputs=[input1, input2], outputs=out)

此代码抛出如下错误，

ValueError: Inputs have incompatible shapes. Received shapes (8,) and (32,)

下面给出了您的问题的可能解决方案，

def fcn8_decoder(convs, n_classes):
  # features from the encoder stage
  f3, f4, f5 = convs

  # number of filters
  n = 512

  # add convolutional layers on top of the CNN extractor.
  o = tf.keras.layers.Conv2D(n , (7 , 7) , activation='relu' , padding='same', name="conv6", data_format=IMAGE_ORDERING)(f5)
  o = tf.keras.layers.Dropout(0.5)(o)

  o = tf.keras.layers.Conv2D(n , (1 , 1) , activation='relu' , padding='same', name="conv7", data_format=IMAGE_ORDERING)(o)
  o = tf.keras.layers.Dropout(0.5)(o)

  o = tf.keras.layers.Conv2D(n_classes,  (1, 1), activation='relu' , padding='same', data_format=IMAGE_ORDERING)(o)

    
  ### START CODE HERE ###

  # Upsample `o` above and crop any extra pixels introduced
  o = tf.keras.layers.Conv2DTranspose(n_classes , kernel_size=(4,4) ,  padding= 'same', strides=(2,2) , use_bias=False)(o)
  o = tf.keras.layers.Cropping2D(cropping=(1,1))(o)

  # load the pool 4 prediction and do a 1x1 convolution to reshape it to the same shape of `o` above
  o2 = f4
  o2 = ( tf.keras.layers.Conv2D(n_classes , ( 1 , 1 ) , activation='relu' , padding='same', data_format=IMAGE_ORDERING))(o2)
  o2 = tf.keras.layers.Cropping2D(cropping=(1,2))(o2)
  print(o2)
  # add the results of the upsampling and pool 4 prediction
  o = tf.keras.layers.Add()([o, o2])

  # upsample the resulting tensor of the operation you just did
  o = (tf.keras.layers.Conv2DTranspose( n_classes , kernel_size=(4,4) , padding='same', strides=(2,2) , use_bias=False))(o)
  o = tf.keras.layers.Cropping2D(cropping=(1, 1))(o)

  # load the pool 3 prediction and do a 1x1 convolution to reshape it to the same shape of `o` above
  o2 = f3
  o2 = tf.keras.layers.Conv2D(n_classes , ( 1 , 1 ) , activation='relu' , padding='same', data_format=IMAGE_ORDERING)(o2)
  o2 = tf.keras.layers.Cropping2D(cropping=(3,4))(o2)

  # add the results of the upsampling and pool 3 prediction
  o = tf.keras.layers.Add()([o, o2])

  # upsample up to the size of the original image
  o = tf.keras.layers.Conv2DTranspose(n_classes , kernel_size=(8,8) ,  padding='same', strides=(8,8) , use_bias=False )(o)
  o = tf.keras.layers.Cropping2D(((0, 0), (0, 96-84)))(o)

  # append a sigmoid activation
  o = (tf.keras.layers.Activation('sigmoid'))(o)
  ### END CODE HERE ###

  return o

本次选择的test_fcn8_decoder的output形状如下，

(None, 16, 4, 11)

根据您的要求，可以选择裁剪。文档提供了更多附加信息tf-keras-Cropping2D