LinearLR
class torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.3333333333333333, end_factor=1.0, total_iters=5, last_epoch=- 1, verbose=False)
Decays the learning rate of each parameter group by linearly
   changing small multiplicative factor until the number of epoch
   reaches a pre-defined milestone: total_iters. Notice that such
   decay can happen simultaneously with other changes to the learning
   rate from outside this scheduler. When last_epoch=-1, sets initial
   lr as lr.
Parameters:
      * optimizer (Optimizer) -- Wrapped optimizer.
  * **start_factor** (*float*) -- The number we multiply learning
    rate in the first epoch. The multiplication factor changes
    towards end_factor in the following epochs. Default: 1./3.

  * **end_factor** (*float*) -- The number we multiply learning
    rate at the end of linear changing process. Default: 1.0.

  * **total_iters** (*int*) -- The number of iterations that
