train: allow passing custom learning rate optimizer #305
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breznak
commented
Jan 26, 2020
| def train(optimizer=None): | ||
| """ | ||
| @param optimizer: set custom optimizer, default (None) uses | ||
| `torch.optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.decay)` |
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ideally, I'd set the optimizer=SGD(...) here already, but the "net" is not available
| @@ -291,11 +297,11 @@ def train(): | |||
| if changed: | |||
| cfg.delayed_settings = [x for x in cfg.delayed_settings if x[0] > iteration] | |||
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| # Warm up by linearly interpolating the learning rate from some smaller value | |||
| # Warm up by linearly interpolating the learning rate from some smaller value | |||
| if cfg.lr_warmup_until > 0 and iteration <= cfg.lr_warmup_until: | |||
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can we leave this fine tuning, lr management to the optimizer (or other code provided by the framework)?
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Please feel free to take over this, I'll be out for the next week |
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train(optimizer=torch.optim.MyCustomOptimizer(..))For #298