'Same' Padding Calculator
Exact padding to preserve (or precisely control) feature-map size for any kernel/stride/dilation — incl. asymmetric TF cases.
For stride 1 and odd kernels this reduces to the familiar (k−1)/2. The asymmetric split is why TF and PyTorch models diverge by one pixel when ported — PyTorch's padding= is always symmetric.
Formula
About 'Same' Padding Calculator
'Just use same padding' hides a real algorithm: compute the output size you want (⌈n/s⌉), then back-solve how many zeros achieve it — and when that number is odd, someone must decide which side gets the extra pixel. TensorFlow pads bottom-right; PyTorch's integer padding can't be asymmetric at all, which is the root cause of countless one-pixel mismatches when porting checkpoints between frameworks. This calculator runs the exact TF algorithm for any kernel, stride and dilation, showing the left/right split and the guaranteed output size.
How to use 'Same' Padding Calculator
- 1Enter your values into 'Same' Padding Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use 'Same' Padding Calculator?
- ✓Computes 'Same' Padding instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: total_pad = max(0, (⌈n/s⌉−1).
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
Why does PyTorch's padding='same' refuse stride > 1?+
Because true 'same' with stride needs input-size-dependent, possibly asymmetric padding, while PyTorch layers fix padding at construction. For strided layers you must compute the split yourself (this tool) and apply F.pad before the conv, or accept TF-incompatible symmetric padding.
My TF→PyTorch port is off by one pixel — is this why?+
Almost certainly. With even kernels or strides, TF's extra zero on the bottom-right shifts everything half a pixel versus symmetric padding. Fix: replicate TF exactly with F.pad((pl, pr, pt, pb)) using the values this calculator outputs, before a padding=0 conv.
Does 'same' padding distort what the model learns at borders?+
Yes — zeros are a synthetic signal, and CNNs demonstrably learn to detect padding to encode absolute position (Islam et al. 2020). Alternatives like reflect or replicate padding reduce the artifact; segmentation and super-resolution models often prefer them.
What about 'valid' and 'full' padding?+
'Valid' = zero padding, output shrinks by the effective kernel minus one. 'Full' = pad k−1 on both sides so every overlap counts, output grows — used in classical signal processing more than deep nets. 'Same' is the middle case this tool solves.
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