| import time |
|
|
| import torch |
| import torch.nn as nn |
|
|
|
|
| class BaseTrainer(nn.Module): |
| def __init__(self, |
| null_condition_p=0.1, |
| ): |
| super(BaseTrainer, self).__init__() |
| self.null_condition_p = null_condition_p |
|
|
| def preproprocess(self, x, condition, uncondition, metadata): |
| bsz = x.shape[0] |
| if self.null_condition_p > 0: |
| mask = torch.rand((bsz), device=condition.device) < self.null_condition_p |
| mask = mask.view(-1, *([1] * (len(condition.shape) - 1))).to(condition.dtype) |
| condition = condition*(1-mask) + uncondition*mask |
| return x, condition, metadata |
|
|
| def _impl_trainstep(self, net, ema_net, solver, x, y, metadata=None): |
| raise NotImplementedError |
|
|
| def __call__(self, net, ema_net, solver, x, condition, uncondition, metadata=None): |
| x, condition, metadata = self.preproprocess(x, condition, uncondition, metadata) |
| return self._impl_trainstep(net, ema_net, solver, x, condition, metadata) |
|
|
|
|
