(self, optim_type, params, lr, **kwargs)
| 102 | return net |
| 103 | |
| 104 | def get_optimizer(self, optim_type, params, lr, **kwargs): |
| 105 | if optim_type == 'Adam': |
| 106 | optimizer = torch.optim.Adam(params, lr, **kwargs) |
| 107 | elif optim_type == 'AdamW': |
| 108 | optimizer = torch.optim.AdamW(params, lr, **kwargs) |
| 109 | elif optim_type == 'Adamax': |
| 110 | optimizer = torch.optim.Adamax(params, lr, **kwargs) |
| 111 | elif optim_type == 'SGD': |
| 112 | optimizer = torch.optim.SGD(params, lr, **kwargs) |
| 113 | elif optim_type == 'ASGD': |
| 114 | optimizer = torch.optim.ASGD(params, lr, **kwargs) |
| 115 | elif optim_type == 'RMSprop': |
| 116 | optimizer = torch.optim.RMSprop(params, lr, **kwargs) |
| 117 | elif optim_type == 'Rprop': |
| 118 | optimizer = torch.optim.Rprop(params, lr, **kwargs) |
| 119 | else: |
| 120 | raise NotImplementedError(f'optimizer {optim_type} is not supported yet.') |
| 121 | return optimizer |
| 122 | |
| 123 | def setup_schedulers(self): |
| 124 | """Set up schedulers.""" |
no outgoing calls
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