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8b3f571 | 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 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 | import torch
import torch.nn as nn
import numpy as np
from typing import List, Optional, Union
from torch import nn, Tensor
from torch.distributions.distribution import Distribution
from mmengine.registry import MODELS
from mmengine.model import BaseModel
@MODELS.register_module()
class ActorAgnosticEncoder(nn.Module):
def __init__(self, nfeats: int, vae: bool,
latent_dim: int = 256, ff_size: int = 1024,
num_layers: int = 4, num_heads: int = 4,
dropout: float = 0.1,
activation: str = "gelu", max_len: int = -1, **kwargs) -> None:
super().__init__()
input_feats = nfeats
self.skel_embedding = nn.Linear(input_feats, latent_dim)
self.max_len = max_len
self.vae = vae
# Action agnostic: only one set of params
if vae:
self.mu_token = nn.Parameter(torch.randn(latent_dim))
self.logvar_token = nn.Parameter(torch.randn(latent_dim))
else:
self.emb_token = nn.Parameter(torch.randn(latent_dim))
self.sequence_pos_encoding = PositionalEncoding(latent_dim, dropout)
seq_trans_encoder_layer = nn.TransformerEncoderLayer(d_model=latent_dim,
nhead=num_heads,
dim_feedforward=ff_size,
dropout=dropout,
activation=activation)
self.seqTransEncoder = nn.TransformerEncoder(seq_trans_encoder_layer,
num_layers=num_layers)
def lengths_to_mask(self, lengths, device):
if self.max_len == -1:
max_len = max(lengths)
mask = torch.arange(max_len, device=device).expand(len(lengths), max_len) < lengths.unsqueeze(1)
else:
mask = torch.arange(self.max_len, device=lengths.device).expand(len(lengths), self.max_len) < lengths.unsqueeze(1)
return mask
def forward(self, features: Tensor, lengths: Optional[List[int]] = None) -> Union[Tensor, Distribution]:
if lengths is None:
lengths = [len(feature) for feature in features]
device = features.device
bs, nframes, nfeats = features.shape
if not isinstance(lengths, torch.Tensor):
lengths = torch.tensor(lengths, device=device)
mask = self.lengths_to_mask(lengths, device).to(device)
x = features
# Embed each human poses into latent vectors
x = self.skel_embedding(x)
# Switch sequence and batch_size because the input of
# Pytorch Transformer is [Sequence, Batch size, ...]
x = x.permute(1, 0, 2) # now it is [nframes, bs, latent_dim]
# Each batch has its own set of tokens
if self.vae:
mu_token = torch.tile(self.mu_token, (bs,)).reshape(bs, -1)
logvar_token = torch.tile(self.logvar_token, (bs,)).reshape(bs, -1)
# adding the distribution tokens for all sequences
xseq = torch.cat((mu_token[None], logvar_token[None], x), 0)
# create a bigger mask, to allow attend to mu and logvar
token_mask = torch.ones((bs, 2), dtype=bool, device=x.device)
aug_mask = torch.cat((token_mask, mask), 1)
else:
emb_token = torch.tile(self.emb_token, (bs,)).reshape(bs, -1)
# adding the embedding token for all sequences
xseq = torch.cat((emb_token[None], x), 0)
# create a bigger mask, to allow attend to emb
token_mask = torch.ones((bs, 1), dtype=bool, device=x.device)
aug_mask = torch.cat((token_mask, mask), 1)
# add positional encoding
xseq = self.sequence_pos_encoding(xseq)
final = self.seqTransEncoder(xseq, src_key_padding_mask=~aug_mask)
if self.vae:
mu, logvar = final[0], final[1]
std = logvar.exp().pow(0.5)
dist = torch.distributions.Normal(mu, std)
return dist
else:
return final[0]
@MODELS.register_module()
class ActorAgnosticDecoder(nn.Module):
def __init__(self, nfeats: int,
latent_dim: int = 256, ff_size: int = 1024,
num_layers: int = 4, num_heads: int = 4,
dropout: float = 0.1, max_len=-1,
activation: str = "gelu", **kwargs) -> None:
super().__init__()
output_feats = nfeats
self.max_len = max_len
self.sequence_pos_encoding = PositionalEncoding(latent_dim, dropout)
seq_trans_decoder_layer = nn.TransformerDecoderLayer(d_model=latent_dim,
nhead=num_heads,
dim_feedforward=ff_size,
dropout=dropout,
activation=activation)
self.seqTransDecoder = nn.TransformerDecoder(seq_trans_decoder_layer,
num_layers=num_layers)
self.final_layer = nn.Linear(latent_dim, output_feats)
def forward(self, z: Tensor, lengths: List[int]):
mask = self.lengths_to_mask(lengths, z.device)
latent_dim = z.shape[1]
bs, nframes = mask.shape
z = z[None] # sequence of 1 element for the memory
# Construct time queries
time_queries = torch.zeros(nframes, bs, latent_dim, device=z.device)
time_queries = self.sequence_pos_encoding(time_queries)
# Pass through the transformer decoder
# with the latent vector for memory
output = self.seqTransDecoder(tgt=time_queries, memory=z,
tgt_key_padding_mask=~mask)
output = self.final_layer(output)
# zero for padded area
output[~mask.T] = 0
# Pytorch Transformer: [Sequence, Batch size, ...]
feats = output.permute(1, 0, 2)
return feats
def lengths_to_mask(self, lengths, device):
if self.max_len == -1:
max_len = max(lengths)
mask = torch.arange(max_len, device=device).expand(len(lengths), max_len) < lengths.unsqueeze(1)
else:
mask = torch.arange(self.max_len, device=lengths.device).expand(len(lengths), self.max_len) < lengths.unsqueeze(1)
return mask
class PositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=5000, batch_first=False):
super().__init__()
self.batch_first = batch_first
self.dropout = nn.Dropout(p=dropout)
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)
def forward(self, x):
# not used in the final model
if self.batch_first:
x = x + self.pe.permute(1, 0, 2)[:, : x.shape[1], :]
else:
x = x + self.pe[: x.shape[0], :]
return self.dropout(x) |