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import torch
from torch import nn, Tensor, einsum, IntTensor, FloatTensor, BoolTensor
from torch.nn import Module
import torch.nn.functional as F
import random
from beartype import beartype
from beartype.typing import Tuple, Optional, List, Union
from einops.layers.torch import Rearrange
from einops import rearrange, repeat, reduce, pack, unpack
from modules.audio2motion.cfm.utils import exists, identity, default, divisible_by, is_odd, coin_flip, pack_one, unpack_one
from modules.audio2motion.cfm.utils import prob_mask_like, reduce_masks_with_and, interpolate_1d, curtail_or_pad, mask_from_start_end_indices, mask_from_frac_lengths
from modules.audio2motion.cfm.module import ConvPositionEmbed, LearnedSinusoidalPosEmb, Transformer
from torch.cuda.amp import autocast
class InContextTransformerAudio2Motion(Module):
def __init__(
self,
*,
dim_in = 64, # expression code
dim_audio_in = 1024,
dim = 1024,
depth = 24,
dim_head = 64,
heads = 16,
ff_mult = 4,
ff_dropout = 0.,
time_hidden_dim = None,
conv_pos_embed_kernel_size = 31,
conv_pos_embed_groups = None,
attn_dropout = 0,
attn_flash = False,
attn_qk_norm = True,
use_gateloop_layers = False,
num_register_tokens = 16,
frac_lengths_mask: Tuple[float, float] = (0.7, 1.),
):
super().__init__()
dim_in = default(dim_in, dim)
time_hidden_dim = default(time_hidden_dim, dim * 4)
self.proj_in = nn.Identity()
self.sinu_pos_emb = nn.Sequential(
LearnedSinusoidalPosEmb(dim),
nn.Linear(dim, time_hidden_dim),
nn.SiLU()
)
self.dim_audio_in = dim_audio_in
if self.dim_audio_in != dim_in:
self.to_cond_emb = nn.Linear(self.dim_audio_in, dim_in)
else:
self.to_cond_emb = nn.Identity()
# self.p_drop_prob = p_drop_prob
self.frac_lengths_mask = frac_lengths_mask
self.to_embed = nn.Linear(dim_in * 2 + dim_in, dim)
self.null_cond = nn.Parameter(torch.zeros(dim_in))
self.conv_embed = ConvPositionEmbed(
dim = dim,
kernel_size = conv_pos_embed_kernel_size,
groups = conv_pos_embed_groups
)
self.transformer = Transformer(
dim = dim,
depth = depth,
dim_head = dim_head,
heads = heads,
ff_mult = ff_mult,
ff_dropout = ff_dropout,
attn_dropout= attn_dropout,
attn_flash = attn_flash,
attn_qk_norm = attn_qk_norm,
num_register_tokens = num_register_tokens,
adaptive_rmsnorm = True,
adaptive_rmsnorm_cond_dim_in = time_hidden_dim,
use_gateloop_layers = use_gateloop_layers
)
dim_out = dim_in # expression code
self.to_pred = nn.Linear(dim, dim_out, bias = False)
@property
def device(self):
return next(self.parameters()).device
@torch.inference_mode()
def forward_with_cond_scale(
self,
*args,
cond_scale = 1.,
**kwargs
):
# classifier-free gudiance
logits = self.forward(*args, cond_drop_prob = 0., **kwargs)
if cond_scale == 1.:
return logits
null_logits = self.forward(*args, cond_drop_prob = 1., **kwargs)
return null_logits + (logits - null_logits) * cond_scale
def forward(
self,
x, # noised y0 of landmark
*,
times, # random in 0~1
cond_audio, # driving audio
self_attn_mask = None, # x_mask, since the length of samples in a batch are different
cond_drop_prob = 0.1,
target = None, # GT landmark, if None, infer mode
cond = None, # reference landmark
cond_mask = None, # mask that denotes frames to be predict as True
ret=None
):
if ret is None:
ret = {}
# project in, in case codebook dim is not equal to model dimensions
# x 和 cond 是相同shape的,不同的是,x是target加噪声的结果,而cond是对target做mask后得到的reference。
x = self.proj_in(x)
if exists(cond):
cond = self.proj_in(cond)
cond = default(cond, x) # x和cond的区别,见上面的分析
# shapes
batch, seq_len, cond_dim = cond.shape
assert cond_dim == x.shape[-1]
# auto manage shape of times, for odeint times
if times.ndim == 0:
times = repeat(times, '-> b', b = cond.shape[0])
if times.ndim == 1 and times.shape[0] == 1:
times = repeat(times, '1 -> b', b = cond.shape[0])
# construct conditioning mask if not given
if self.training:
# 被mask住的就是要predict的部分
if not exists(cond_mask):
if coin_flip(): # 0.5 概率
frac_lengths = torch.zeros((batch,), device = self.device).float().uniform_(*self.frac_lengths_mask) # 0.7,1.0
# 这样得到的mask是连续的一个fraction
cond_mask = mask_from_frac_lengths(seq_len, frac_lengths)
else:
# 这样得到的mask是散成豆花的
p_drop_prob_ = self.frac_lengths_mask[0] + random.random()*(self.frac_lengths_mask[1]-self.frac_lengths_mask[0])
cond_mask = prob_mask_like((batch, seq_len), p_drop_prob_, self.device)
# cond_mask = prob_mask_like((batch, seq_len), self.p_drop_prob, self.device)
else:
if not exists(cond_mask):
# cond就是sample
# 没有cond mask,代表没有reference audio, 所以直接mask住所有
cond_mask = torch.ones((batch, seq_len), device = cond.device, dtype = torch.bool)
cond_mask_with_pad_dim = rearrange(cond_mask, '... -> ... 1') # 这个mask的意思是,True代表需要predict,False代表是reference
# as described in section 3.2
x = x * cond_mask_with_pad_dim # 这个是y0,源于noise,需要预测的部分保留为noise,不需要预测的reference部分被变成0
cond = cond * ~cond_mask_with_pad_dim # 这个是reference音频, 所以标志出来需要pred的部分都变成0了
# used by forward_with_cond_scale to achieve classifier free guidance
# cond_drop_prob==1.0 denotes unconditional result
if cond_drop_prob > 0.:
cond_drop_mask = prob_mask_like(cond.shape[:1], cond_drop_prob, self.device) # 这个mask是散成豆花的
# 随机对reference landmark 做dropout
cond = torch.where(
rearrange(cond_drop_mask, '... -> ... 1 1'), # cond
self.null_cond, # fill true
cond # fill false
)
# phoneme or semantic conditioning embedding
cond_audio_emb = self.to_cond_emb(cond_audio)
cond_audio_emb_length = cond_audio_emb.shape[-2]
if cond_audio_emb_length != seq_len:
cond_audio_emb = rearrange(cond_audio_emb, 'b n d -> b d n')
cond_audio_emb = interpolate_1d(cond_audio_emb, seq_len)
cond_audio_emb = rearrange(cond_audio_emb, 'b d n -> b n d')
if exists(self_attn_mask):
self_attn_mask = interpolate_1d(self_attn_mask, seq_len)
# concat source signal, driving audio, and reference landmark
# and project
to_concat = [*filter(exists, (x, cond_audio_emb, cond))]
embed = torch.cat(to_concat, dim = -1)
x = self.to_embed(embed)
x = self.conv_embed(x) + x
time_emb = self.sinu_pos_emb(times)
# attend
x = self.transformer(
x,
mask = self_attn_mask,
adaptive_rmsnorm_cond = time_emb
)
x = self.to_pred(x)
# if no target passed in, just return logits
ret['pred'] = x
if not exists(target):
# 不提供target,默认是infer模式,直接输出sample
return x
else:
# 提供target,默认training模式,输出loss
loss_mask = reduce_masks_with_and(cond_mask, self_attn_mask)
if not exists(loss_mask):
return F.mse_loss(x, target)
ret['loss_mask'] = loss_mask
loss = F.mse_loss(x, target, reduction = 'none')
loss = reduce(loss, 'b n d -> b n', 'mean')
loss = loss.masked_fill(~loss_mask, 0.)
# masked mean
num = reduce(loss, 'b n -> b', 'sum')
den = loss_mask.sum(dim = -1).clamp(min = 1e-5)
loss = num / den
loss = loss.mean()
ret['mse'] = loss
return loss
if __name__ == '__main__':
# Create an instance of the VoiceBox model
model = InContextTransformerAudio2Motion()
# Generate a random input tensor using torch.randn
input_tensor = torch.randn(2, 125, 64) # Assuming input shape is (batch_size, dim_in)
time_tensor = torch.rand(2) # Assuming input shape is (batch_size, dim_in)
audio_tensor = torch.rand(2, 125, 1024) # Assuming input shape is (batch_size, dim_in)
# Pass the input tensor through the VoiceBox model
output = model.forward_with_cond_scale(input_tensor, times=time_tensor, cond_audio=audio_tensor, cond=input_tensor)
# Print the shape of the output tensor
print(output.shape)
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