Implementing U-Net segmentation model without padding

I’m trying to implement the U-Net CNN as per the published paper here.

I’ve followed the paper architecture as closely as possible but I’m hitting an error when trying to carry out the first concatenation:

From the diagram, it appears the 8th Conv2D should be merged with result of the 1st UpSampling2D operation, however the Concatenate() operation throws an exception that the shapes don’t match:

def model(image_size = (572, 572) + (1,)):

    # Input / Output layers
    input_layer = Input(shape=(image_size), 32)

    """ Begin Downsampling """

    # Block 1
    conv_1 = Conv2D(64, 3, activation = 'relu')(input_layer)
    conv_2 = Conv2D(64, 3, activation = 'relu')(conv_1)

    max_pool_1 = MaxPool2D(strides=2)(conv_2)

    # Block 2
    conv_3 = Conv2D(128, 3, activation = 'relu')(max_pool_1)
    conv_4 = Conv2D(128, 3, activation = 'relu')(conv_3)

    max_pool_2 = MaxPool2D(strides=2)(conv_4)

    # Block 3
    conv_5 = Conv2D(256, 3, activation = 'relu')(max_pool_2)
    conv_6 = Conv2D(256, 3, activation = 'relu')(conv_5)

    max_pool_3 = MaxPool2D(strides=2)(conv_6)

    # Block 4
    conv_7 = Conv2D(512, 3, activation = 'relu')(max_pool_3)
    conv_8 = Conv2D(512, 3, activation = 'relu')(conv_7)

    max_pool_4 = MaxPool2D(strides=2)(conv_8)

    """ Begin Upsampling """

    # Block 5
    conv_9 = Conv2D(1024, 3, activation = 'relu')(max_pool_4)
    conv_10 = Conv2D(1024, 3, activation = 'relu')(conv_9)

    upsample_1 = UpSampling2D()(conv_10)

    # Connect layers
    merge_1 = Concatenate()([conv_8, upsample_1])

Error:

Exception has occurred: ValueError
A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(32, 64, 64, 512), (32, 56, 56, 1024)]

Note that the values 64 and 56 correctly line up with the architecture.

I don’t understand how to implement the model as it is in the paper. If I change my code to accept an image of shape (256, 256) and add padding='same' to the Conv2D layers, the code works as the sizes are aligned.

This seems to go against what the authors specifically state in their implementation:

Could somebody point me in the right direction on the correct implementation of this model?