UResNet Error: ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concat axis.

I am trying to create an Unet with the following code:

img_size_ori = 101
img_size_target = 197
model = UResNet34((img_size_target, img_size_target, 3))

But then I got this error message:

Traceback (most recent call last):
  File ".\main_unet.py", line 24, in <module>
    model = UResNet34((img_size_target, img_size_target, 3))
  File "F:\Workspace\kaggle\Salt Identification\model\segmentation_models\unet\models.py", line 40, in UResNet34
    activation=activation, **kwargs)
  File "F:\Workspace\kaggle\Salt Identification\model\segmentation_models\unet\builder.py", line 38, in build_unet
    x = up_block(filters, i, upsample_rate=up_size, skip=skip, **kwargs)(x)
  File "F:\Workspace\kaggle\Salt Identification\model\segmentation_models\unet\blocks.py", line 27, in layer
    x = Concatenate()([x, skip])
  File "F:\Anaconda\lib\site-packages\keras\engine\base_layer.py", line 431, in __call__
    self.build(unpack_singleton(input_shapes))
  File "F:\Anaconda\lib\site-packages\keras\layers\merge.py", line 354, in build
    'Got inputs shapes: %s' % (input_shape))
ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 14, 14, 512), (None, 13, 13, 256)]

Thanks for this nice repository.

[qubvel]

Height and width of input images should be divisible by 32 for all models

[tarunnith]

Hi @qubvel I am trying to train unet with efficientnnet and this is what I am using. and size of my images and masks is 256x256x3 . I get this error: A Concatenate layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 32, 32, 64), (None, 1, 1, 8)]

when I run the below block of code:

K.clear_session()
img_size = 256
model = UEfficientNet(input_shape=(img_size, img_size,3),dropout_rate=0.25)

I get error on this line:

uconv4 = concatenate([deconv4, conv4])

def UEfficientNet(input_shape=(None, None,3),dropout_rate=0.1):
    print(input_shape)
    backbone = efn.EfficientNetB4(weights='imagenet',
                            include_top=False,
                            input_shape=input_shape)
    input = backbone.input
    
    start_neurons = 16

    conv4 = backbone.layers[342].output
    conv4 = LeakyReLU(alpha=0.1)(conv4)
    pool4 = MaxPooling2D((2, 2))(conv4)
    pool4 = Dropout(dropout_rate)(pool4)
    
     # Middle
    convm = Conv2D(start_neurons * 32, (3, 3), activation=None, padding="same")(pool4)
    convm = residual_block(convm,start_neurons * 32)
    convm = residual_block(convm,start_neurons * 32)
    convm = LeakyReLU(alpha=0.1)(convm)
    
    deconv4 = Conv2DTranspose(start_neurons * 16, (3, 3), strides=(2, 2), padding="same")(convm)
    **uconv4 = concatenate([deconv4, conv4])**
    uconv4 = Dropout(dropout_rate)(uconv4)
    
    uconv4 = Conv2D(start_neurons * 16, (3, 3), activation=None, padding="same")(uconv4)
    uconv4 = residual_block(uconv4,start_neurons * 16)
    uconv4 = residual_block(uconv4,start_neurons * 16)
    uconv4 = LeakyReLU(alpha=0.1)(uconv4)
    
    deconv3 = Conv2DTranspose(start_neurons * 8, (3, 3), strides=(2, 2), padding="same")(uconv4)
    conv3 = backbone.layers[154].output
    uconv3 = concatenate([deconv3, conv3])    
    uconv3 = Dropout(dropout_rate)(uconv3)
    
    uconv3 = Conv2D(start_neurons * 8, (3, 3), activation=None, padding="same")(uconv3)
    uconv3 = residual_block(uconv3,start_neurons * 8)
    uconv3 = residual_block(uconv3,start_neurons * 8)
    uconv3 = LeakyReLU(alpha=0.1)(uconv3)

    deconv2 = Conv2DTranspose(start_neurons * 4, (3, 3), strides=(2, 2), padding="same")(uconv3)
    conv2 = backbone.layers[92].output
    uconv2 = concatenate([deconv2, conv2])
        
    uconv2 = Dropout(0.1)(uconv2)
    uconv2 = Conv2D(start_neurons * 4, (3, 3), activation=None, padding="same")(uconv2)
    uconv2 = residual_block(uconv2,start_neurons * 4)
    uconv2 = residual_block(uconv2,start_neurons * 4)
    uconv2 = LeakyReLU(alpha=0.1)(uconv2)
    
    deconv1 = Conv2DTranspose(start_neurons * 2, (3, 3), strides=(2, 2), padding="same")(uconv2)
    conv1 = backbone.layers[30].output
    uconv1 = concatenate([deconv1, conv1])
    
    uconv1 = Dropout(0.1)(uconv1)
    uconv1 = Conv2D(start_neurons * 2, (3, 3), activation=None, padding="same")(uconv1)
    uconv1 = residual_block(uconv1,start_neurons * 2)
    uconv1 = residual_block(uconv1,start_neurons * 2)
    uconv1 = LeakyReLU(alpha=0.1)(uconv1)
    
    uconv0 = Conv2DTranspose(start_neurons * 1, (3, 3), strides=(2, 2), padding="same")(uconv1)   
    uconv0 = Dropout(0.1)(uconv0)
    uconv0 = Conv2D(start_neurons * 1, (3, 3), activation=None, padding="same")(uconv0)
    uconv0 = residual_block(uconv0,start_neurons * 1)
    uconv0 = residual_block(uconv0,start_neurons * 1)
    uconv0 = LeakyReLU(alpha=0.1)(uconv0)
    
    uconv0 = Dropout(dropout_rate/2)(uconv0)
    output_layer = Conv2D(1, (1,1), padding="same", activation="sigmoid")(uconv0)    
    
    model = Model(input, output_layer)
    model.name = 'u-xception'

    return model

@PallawiSinghal I am also getting this error. How do you resolve it?

Have you found the solution to the problem?

I think this is due to the wrong concatenation of layers. in model.summary() find out the correct layers to concatenate. I will post the correct code in my repository tomorrow or day after tomorrow…