| from typing import Optional, Tuple, Union |
|
|
| import torch |
| import torch.nn as nn |
|
|
|
|
| class PatchDropout(nn.Module): |
| """ |
| https://arxiv.org/abs/2212.00794 and https://arxiv.org/pdf/2208.07220 |
| """ |
| return_indices: torch.jit.Final[bool] |
|
|
| def __init__( |
| self, |
| prob: float = 0.5, |
| num_prefix_tokens: int = 1, |
| ordered: bool = False, |
| return_indices: bool = False, |
| ): |
| super().__init__() |
| assert 0 <= prob < 1. |
| self.prob = prob |
| self.num_prefix_tokens = num_prefix_tokens |
| self.ordered = ordered |
| self.return_indices = return_indices |
|
|
| def forward(self, x) -> Union[torch.Tensor, Tuple[torch.Tensor, Optional[torch.Tensor]]]: |
| if not self.training or self.prob == 0.: |
| if self.return_indices: |
| return x, None |
| return x |
|
|
| if self.num_prefix_tokens: |
| prefix_tokens, x = x[:, :self.num_prefix_tokens], x[:, self.num_prefix_tokens:] |
| else: |
| prefix_tokens = None |
|
|
| B = x.shape[0] |
| L = x.shape[1] |
| num_keep = max(1, int(L * (1. - self.prob))) |
| keep_indices = torch.argsort(torch.randn(B, L, device=x.device), dim=-1)[:, :num_keep] |
| if self.ordered: |
| |
| |
| keep_indices = keep_indices.sort(dim=-1)[0] |
| x = x.gather(1, keep_indices.unsqueeze(-1).expand((-1, -1) + x.shape[2:])) |
|
|
| if prefix_tokens is not None: |
| x = torch.cat((prefix_tokens, x), dim=1) |
|
|
| if self.return_indices: |
| return x, keep_indices |
| return x |
|
|
