Why there are specific number of layers with same depth in any CNN architecture? why each architecture have specific number of layers with same depth

like vgg had 2 with 64 depth and 2 with 128d and 4 layers 

same with ResNet and 34 layer plain like 6 layers with depth of 64 and 8 layers with depth 128.

and why depth increasing by last depth *2 ? 

is there is way or equation to decide number of layers with each depth ?
There is no equation or theory to decide the number of layers at each depth in a neural network. The most common technique is trail and error. There can be systematic search through cross validation.
Generally more layers are added until performance stops improving.