  Channel (or 3D filter) pruning serves as an effective way to accelerate the
inference of neural networks. There has been a flurry of algorithms that try to
solve this practical problem, each being claimed effective in some ways. Yet, a
benchmark to compare those algorithms directly is lacking, mainly due to the
complexity of the algorithms and some custom settings such as the particular
network configuration or training procedure. A fair benchmark is important for
the further development of channel pruning.
  Meanwhile, recent investigations reveal that the channel configurations
discovered by pruning algorithms are at least as important as the pre-trained
weights. This gives channel pruning a new role, namely searching the optimal
channel configuration. In this paper, we try to determine the channel
configuration of the pruned models by random search. The proposed approach
provides a new way to compare different methods, namely how well they behave
compared with random pruning. We show that this simple strategy works quite
well compared with other channel pruning methods. We also show that under this
setting, there are surprisingly no clear winners among different channel
importance evaluation methods, which then may tilt the research efforts into
advanced channel configuration searching methods.
