ir_scheduler=torchoptimIr_schedulerCosineAnnealinglarmRestartsoptimizerepochs1002

This code creates an instance of the CosineAnnealinglarmRestarts scheduler from the Ir_scheduler module in PyTorch.

• optimizer is the optimizer object that the scheduler will adjust the learning rate for.
• epochs is the total number of epochs for training.
• 1.0 is the initial learning rate.
• 0.02 is the minimum learning rate that will be reached at the end of each cycle.

The CosineAnnealinglarmRestarts scheduler uses a cosine annealing schedule with restarts to adjust the learning rate. This means that the learning rate starts high and decreases in a cosine pattern over a number of epochs, after which it is reset to the initial learning rate and the process repeats. The minimum learning rate also decreases with each cycle. This can help the model converge faster and avoid getting stuck in local minima.