When applying a convolution with kernel height $k_\textrm{h}$ and width $k_\textrm{w}$, total padding height $p_\textrm{h}$ and width $p_\textrm{w}$, and strides $s_\textrm{h}$ and $s_\textrm{w}$, the shape of the output generated from an input of height $n_\textrm{h}$ and width $n_\textrm{w}$ is:

$\lfloor(n_\textrm{h}-k_\textrm{h}+p_\textrm{h}+s_\textrm{h})/s_\textrm{h}\rfloor \times \lfloor(n_\textrm{w}-k_\textrm{w}+p_\textrm{w}+s_\textrm{w})/s_\textrm{w}\rfloor$

This formula simplifies under certain conditions. If padding is set to $p_\textrm{h}=k_\textrm{h}-1$ and $p_\textrm{w}=k_\textrm{w}-1$, the output shape reduces to $\lfloor(n_\textrm{h}+s_\textrm{h}-1)/s_\textrm{h}\rfloor \times \lfloor(n_\textrm{w}+s_\textrm{w}-1)/s_\textrm{w}\rfloor$. Furthermore, if the input dimensions are exactly divisible by the strides, the output shape simplifies to $(n_\textrm{h}/s_\textrm{h}) \times (n_\textrm{w}/s_\textrm{w})$.