Redundant learning rate decay when resuming experiment

[natlouis]

Relevant lines of code: https://github.com/MichiganCOG/ViP/blob/master/train.py#L114-L115

Loading saved weights using pretrained argument also loads the last saved learning rate (after decaying per config file). However the learning rate is further decayed from the lines above, because the scheduler "loops" through all of the epochs again.

Example: If I ended an experiment with a learning rate of 1e-6 after decaying twice from 1e-4. Resuming that experiments gives me a starting learning rate of 1e-8.

[zeonzir]

Completed fix by removing loop for scheduler during resumption.