MaskTextSpotter | A PyTorch implementation of Mask TextSpotter | Machine Learning library

I have faced the same problem, the best possible solution for the same is to look inside the main code, go through the forward pass of each module and have a timer setup to log the time that is spent in the computations of each module. How we worked in it was to create an architecture where we create the time logger for each class, therefore every instance of the class will now be logging its time of execution, after through comparison, atleast in our case we have found that the reason for the delay was the depth of the Resnet module, (which given the computational cost of resnet is not a surprising factor at all, the only solution to the same is more palatalization so either ensure a bigger GPU for performing the task or reduce the depth of the Resnet network ).

I must inform that the maskrcnn_benchmark has been deprecated and an updated version of the same is available in the form of detectron2. Consider moving your code for significant speed improvements in the architecture.

BottleneckWithFixedBatchNorm is not the most expensive operation in the architecture and certainly not creating the bottleneck as all the operations instead of the name. The class isn't as computationally expensive and is computed in parallel even on a lower end CPU machine (at least in the inference stage).

An example of tracking better the performance of each module can be found with the code taken from the path : maskrcnn_benchmark/modeling/backbone/resnet.py