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import torch
import torch.nn as nn
import numpy as np
import torch
from torch import nn
class TimestepEmbedderMDM(nn.Module):
def __init__(self, latent_dim):
super().__init__()
self.latent_dim = latent_dim
time_embed_dim = self.latent_dim
self.sequence_pos_encoder = PositionalEncoding(d_model=self.latent_dim)
# TODO add time embedding learnable
self.time_embed = nn.Sequential(
nn.Linear(self.latent_dim, time_embed_dim),
nn.SiLU(),
nn.Linear(time_embed_dim, time_embed_dim),
)
def forward(self, timesteps):
return self.time_embed(self.sequence_pos_encoder.pe[timesteps]).permute(1, 0, 2)
class PositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1,
max_len=5000, batch_first=False, negative=False):
super().__init__()
self.batch_first = batch_first
self.dropout = nn.Dropout(p=dropout)
self.max_len = max_len
self.negative = negative
if negative:
pe = torch.zeros(2*max_len, d_model)
position = torch.arange(-max_len, max_len, dtype=torch.float).unsqueeze(1)
else:
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-np.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer('pe', pe, persistent=False)
def forward(self, x, hist_frames=0):
if not self.negative:
center = 0
assert hist_frames == 0
first = 0
else:
center = self.max_len
first = center-hist_frames
if self.batch_first:
last = first + x.shape[1]
x = x + self.pe.permute(1, 0, 2)[:, first:last, :]
else:
last = first + x.shape[0]
x = x + self.pe[first:last, :]
return self.dropout(x) |