<filename>utils.py
import numpy as np

def worker_init_fn(worker_id): # This is apparently needed to ensure workers have different random seeds and draw different examples!
    np.random.seed(np.random.get_state()[1][0] + worker_id)

def get_lr(optim):
    return optim.param_groups[0]["lr"]

def set_lr(optim, lr):
    for g in optim.param_groups:
        g['lr'] = lr

def set_cyclic_lr(optimizer, it, epoch_it, cycles, min_lr, max_lr):
    cycle_length = epoch_it // cycles
    curr_cycle = min(it // cycle_length, cycles-1)
    curr_it = it - cycle_length * curr_cycle

    new_lr = min_lr + 0.5*(max_lr - min_lr)*(1 + np.cos((float(curr_it) / float(cycle_length)) * np.pi))
    set_lr(optimizer, new_lr)
<filename>utils.py
import numpy as np

def worker_init_fn(worker_id): # This is apparently needed to ensure workers have different random seeds and draw different examples!
    np.random.seed(np.random.get_state()[1][0] + worker_id)

def get_lr(optim):
    return optim.param_groups[0]["lr"]

def set_lr(optim, lr):
    for g in optim.param_groups:
        g['lr'] = lr

def set_cyclic_lr(optimizer, it, epoch_it, cycles, min_lr, max_lr):
    cycle_length = epoch_it // cycles
    curr_cycle = min(it // cycle_length, cycles-1)
    curr_it = it - cycle_length * curr_cycle

    new_lr = min_lr + 0.5*(max_lr - min_lr)*(1 + np.cos((float(curr_it) / float(cycle_length)) * np.pi))
    set_lr(optimizer, new_lr)
# This is apparently needed to ensure workers have different random seeds and draw different examples!