FCN变体U-Net架构中TensorFlow转置卷积的Padding选择疑问

Great question—this is a super common gotcha when building symmetric segmentation architectures like U-Net, where encoder downsampling and decoder upsampling need to align perfectly for skip connections to work. Let’s break this down clearly:

Core Conclusion #

Yes, the padding type (valid/same) of your forward convolutions directly dictates the padding and related parameters you need for transposed convolutions—this is because transposed convolutions are designed to reverse the spatial dimensional changes caused by standard convolutions, so their settings need to mirror the forward pass to maintain alignment.

Breaking It Down with Your U-Net Setup #

Let’s ground this in your specific implementation:

• You’re using 3×3 valid padding convolutions (no padding, so the output shrinks by kernel_size - 1 for stride=1)
• You’re using 2×2 transposed convolutions with stride=2 and same padding for upsampling

First, recall how valid convolution changes dimensions

For a standard valid convolution (padding=0) with kernel size k, stride s, input size H×W, the output size is:

H_out = floor((H - k + 1) / s)
W_out = floor((W - k + 1) / s)

For your 3×3 stride=1 valid convolution, this means an input of H×W becomes (H-2)×(W-2).

Now, transposed convolutions need to reverse this

The output size formula for a transposed convolution is:

H_out = (H_in - 1) * s + k - 2*padding + output_padding

Here, output_padding handles edge cases where the forward convolution’s floor operation creates an odd/even dimension mismatch.

Why Your Current Setup Might Cause Issues #

If you’re using 2×2 stride=2 transposed convolutions with same padding, let’s plug in the numbers:

• Suppose your encoder’s valid convolution (3×3, stride=1) outputs a (H-2)×(W-2) feature map. If you then downsample (say, with a stride=2 valid convolution) to floor((H-2 -3 +1)/2) = floor((H-4)/2), your transposed convolution with same padding would output (floor((H-4)/2)-1)*2 +2 -2*1 = 2*floor((H-4)/2). This will almost certainly not match the original encoder feature map size needed for skip connections—leading to shape mismatches when you try to concatenate.

Fixed Recommendations for Your U-Net #

To align your encoder and decoder properly:

• Mirror your forward convolution parameters in the transposed convolution: If you used a 3×3 valid convolution with stride=2 for downsampling, use a 3×3 transposed convolution with stride=2, padding=1, and output_padding=1 (to fix odd dimension gaps). This will exactly reverse the valid convolution’s size change.
• Skip connection alignment is non-negotiable: U-Net’s performance relies heavily on concatenating encoder and decoder feature maps of identical size. Always calculate the exact transposed convolution padding and output_padding values based on your forward convolution’s padding, kernel size, and stride.
• Test with small input sizes: Try feeding a 32×32 input through your encoder and decoder step-by-step, printing feature map dimensions at each stage. This will quickly reveal any size mismatches you need to fix.

Final Note #

Don’t rely on "same padding" as a one-size-fits-all for transposed convolutions—its behavior varies across frameworks, and it won’t account for the specific size shrinkage caused by valid padding in your encoder. Always map the forward convolution’s dimensional changes and reverse them explicitly with transposed convolution parameters.

内容的提问来源于stack exchange，提问作者simongraham