| import math |
| import torch |
| import torch.nn as nn |
|
|
| from monai.networks.layers.utils import get_act_layer |
|
|
| class SinusoidalPosEmb(nn.Module): |
| def __init__(self, emb_dim=16, downscale_freq_shift=1, max_period=10000, flip_sin_to_cos=False): |
| super().__init__() |
| self.emb_dim = emb_dim |
| self.downscale_freq_shift = downscale_freq_shift |
| self.max_period = max_period |
| self.flip_sin_to_cos = flip_sin_to_cos |
|
|
| def forward(self, x): |
| device = x.device |
| half_dim = self.emb_dim // 2 |
| emb = math.log(self.max_period) / \ |
| (half_dim - self.downscale_freq_shift) |
| emb = torch.exp(-emb*torch.arange(half_dim, device=device)) |
| emb = x[:, None] * emb[None, :] |
| emb = torch.cat((emb.sin(), emb.cos()), dim=-1) |
|
|
| if self.flip_sin_to_cos: |
| emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1) |
|
|
| if self.emb_dim % 2 == 1: |
| emb = torch.nn.functional.pad(emb, (0, 1, 0, 0)) |
| return emb |
|
|
|
|
| class TimeEmbbeding(nn.Module): |
| def __init__( |
| self, |
| emb_dim=64, |
| pos_embedder=SinusoidalPosEmb, |
| pos_embedder_kwargs={}, |
| act_name=("SWISH", {}) |
| ): |
| super().__init__() |
| self.emb_dim = emb_dim |
| self.pos_emb_dim = pos_embedder_kwargs.get('emb_dim', emb_dim//4) |
| pos_embedder_kwargs['emb_dim'] = self.pos_emb_dim |
| self.pos_embedder = pos_embedder(**pos_embedder_kwargs) |
|
|
| self.time_emb = nn.Sequential( |
| self.pos_embedder, |
| nn.Linear(self.pos_emb_dim, self.emb_dim), |
| get_act_layer(act_name), |
| nn.Linear(self.emb_dim, self.emb_dim) |
| ) |
|
|
| def forward(self, time): |
| return self.time_emb(time) |
|
|
