File size: 1,751 Bytes
0b06187 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | 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)
|