How is this rampup code?

[ghost]

In https://github.com/mit-han-lab/data-efficient-gans/blob/master/DiffAugment-stylegan2/training/training_loop.py#L92

it says:

    # Learning rate.
    s.G_lrate = G_lrate_dict.get(s.resolution, G_lrate_base)
    s.D_lrate = D_lrate_dict.get(s.resolution, D_lrate_base)
    if lrate_rampup_kimg > 0:
        rampup = min(s.kimg / lrate_rampup_kimg, 1.0)
        s.G_lrate *= rampup
        s.D_lrate *= rampup

if I test this with:

for lrate_rampup_kimg in [0.1, 0.5, 1.0, 1.5, 2.0, 100.0, 200.0, 300.0, 1000.0, 300.0, 3000.0]:
	# Learning rate.
	G_lrate = 0.002
	D_lrate = 0.002
	for kimg in range(1,300):
		if lrate_rampup_kimg > 0:
			rampup = min(kimg / lrate_rampup_kimg, 1.0)
			G_lrate *= rampup
			D_lrate *= rampup
	print('rampup:', lrate_rampup_kimg, 'final_lr:', G_lrate)

I get:

rampup: 0.1 final_lr: 0.002
rampup: 0.5 final_lr: 0.002
rampup: 1.0 final_lr: 0.002
rampup: 1.5 final_lr: 0.0013333333333333333
rampup: 2.0 final_lr: 0.001
rampup: 100.0 final_lr: 1.8665243088788857e-45
rampup: 200.0 final_lr: 9.815659915232974e-89
rampup: 300.0 final_lr: 4.471534887652881e-132
rampup: 1000.0 final_lr: 2.0403834147762724e-288
rampup: 300.0 final_lr: 4.471534887652881e-132
rampup: 3000.0 final_lr: 0.0

Can someone please explain to how this is rampup code?