  Structured pruning reduces the computational overhead of deep neural networks
by removing redundant sub-structures. However, assessing the relative
importance of different sub-structures remains a significant challenge,
particularly in advanced vision models featuring novel mechanisms and
architectures like self-attention, depth-wise convolutions, or residual
connections. These heterogeneous substructures usually exhibit diverged
parameter scales, weight distributions, and computational topology, introducing
considerable difficulty to importance comparison. To overcome this, we present
Isomorphic Pruning, a simple approach that demonstrates effectiveness across a
range of network architectures such as Vision Transformers and CNNs, and
delivers competitive performance across different model sizes. Isomorphic
Pruning originates from an observation that, when evaluated under a pre-defined
importance criterion, heterogeneous sub-structures demonstrate significant
divergence in their importance distribution, as opposed to isomorphic
structures that present similar importance patterns. This inspires us to
perform isolated ranking and comparison on different types of sub-structures
for more reliable pruning. Our empirical results on ImageNet-1K demonstrate
that Isomorphic Pruning surpasses several pruning baselines dedicatedly
designed for Transformers or CNNs. For instance, we improve the accuracy of
DeiT-Tiny from 74.52% to 77.50% by pruning an off-the-shelf DeiT-Base model.
And for ConvNext-Tiny, we enhanced performance from 82.06% to 82.18%, while
reducing the number of parameters and memory usage. Code is available at
\url{https://github.com/VainF/Isomorphic-Pruning}.
