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from abc import ABCMeta, abstractmethod
import clip
import torch
from torch import nn
from mmcv.runner import BaseModule
from ..builder import build_attention
from mogen.models.utils.position_encoding import (
timestep_embedding
)
from mogen.models.utils.stylization_block import StylizationBlock
from mogen.models.utils.misc import set_requires_grad, zero_module
class CLIPWrapper:
def __init__(self, clip_model):
self.clip_model = clip_model
self.device = "cpu"
def __call__(self, **kwargs):
return self.clip_model(**kwargs)
def encode_text(self, text):
if text.is_cuda and self.device == "cpu":
self.clip_model = self.clip_model.cuda()
self.device = "cuda"
if not text.is_cuda and self.device == "cuda":
self.clip_model = self.clip_model.cpu()
self.device = "cpu"
return self.clip_model.encode_text(text)
def to(self, device):
self.clip_model = self.clip_model.to(device)
class FFN(nn.Module):
def __init__(self, latent_dim, ffn_dim, dropout, time_embed_dim=None):
super().__init__()
self.linear1 = nn.Linear(latent_dim, ffn_dim)
self.linear2 = zero_module(nn.Linear(ffn_dim, latent_dim))
self.activation = nn.GELU()
self.dropout = nn.Dropout(dropout)
if time_embed_dim is not None:
self.proj_out = StylizationBlock(latent_dim, time_embed_dim, dropout)
else:
self.proj_out = None
def forward(self, x, emb=None, **kwargs):
y = self.linear2(self.dropout(self.activation(self.linear1(x))))
if self.proj_out is not None:
y = x + self.proj_out(y, emb)
else:
y = x + y
return y
class DecoderLayer(nn.Module):
def __init__(self,
sa_block_cfg=None,
ca_block_cfg=None,
ffn_cfg=None):
super().__init__()
self.sa_block = build_attention(sa_block_cfg)
self.ca_block = build_attention(ca_block_cfg)
self.ffn = FFN(**ffn_cfg)
def forward(self, **kwargs):
if self.sa_block is not None:
x = self.sa_block(**kwargs)
kwargs.update({'x': x})
if self.ca_block is not None:
x = self.ca_block(**kwargs)
kwargs.update({'x': x})
if self.ffn is not None:
x = self.ffn(**kwargs)
return x
class MotionTransformer(BaseModule, metaclass=ABCMeta):
def __init__(self,
input_feats,
max_seq_len=240,
latent_dim=512,
time_embed_dim=2048,
num_layers=8,
sa_block_cfg=None,
ca_block_cfg=None,
ffn_cfg=None,
text_encoder=None,
use_pos_embedding=True,
use_residual_connection=False,
time_embedding_type='sinusoidal',
post_process_cfg=None,
init_cfg=None):
super().__init__(init_cfg=init_cfg)
self.input_feats = input_feats
self.max_seq_len = max_seq_len
self.latent_dim = latent_dim
self.num_layers = num_layers
self.time_embed_dim = time_embed_dim
self.use_pos_embedding = use_pos_embedding
if self.use_pos_embedding:
self.sequence_embedding = nn.Parameter(torch.randn(max_seq_len, latent_dim))
self.build_text_encoder(text_encoder)
# Input Embedding
self.joint_embed = nn.Linear(self.input_feats, self.latent_dim)
self.time_embedding_type = time_embedding_type
if time_embedding_type != 'none':
if time_embedding_type == 'learnable':
self.time_tokens = nn.Embedding(1000, self.latent_dim)
self.time_embed = nn.Sequential(
nn.Linear(self.latent_dim, self.time_embed_dim),
nn.SiLU(),
nn.Linear(self.time_embed_dim, self.time_embed_dim),
)
self.build_temporal_blocks(sa_block_cfg, ca_block_cfg, ffn_cfg)
# Output Module
self.out = zero_module(nn.Linear(self.latent_dim, self.input_feats))
self.use_residual_connection = use_residual_connection
self.post_process_cfg = post_process_cfg
def build_temporal_blocks(self, sa_block_cfg, ca_block_cfg, ffn_cfg):
self.temporal_decoder_blocks = nn.ModuleList()
for i in range(self.num_layers):
self.temporal_decoder_blocks.append(
DecoderLayer(
sa_block_cfg=sa_block_cfg,
ca_block_cfg=ca_block_cfg,
ffn_cfg=ffn_cfg
)
)
def build_text_encoder(self, text_encoder):
if text_encoder is None:
self.use_text_proj = False
return
text_latent_dim = text_encoder['latent_dim']
num_text_layers = text_encoder.get('num_layers', 0)
text_ff_size = text_encoder.get('ff_size', 2048)
pretrained_model = text_encoder['pretrained_model']
text_num_heads = text_encoder.get('num_heads', 4)
dropout = text_encoder.get('dropout', 0)
activation = text_encoder.get('activation', 'gelu')
self.use_text_proj = text_encoder.get('use_text_proj', False)
if pretrained_model == 'clip':
clip_model, _ = clip.load('ViT-B/32', "cpu")
set_requires_grad(clip_model, False)
self.clip = CLIPWrapper(clip_model)
if text_latent_dim != 512:
self.text_pre_proj = nn.Linear(512, text_latent_dim)
else:
self.text_pre_proj = nn.Identity()
else:
raise NotImplementedError()
if num_text_layers > 0:
self.use_text_finetune = True
textTransEncoderLayer = nn.TransformerEncoderLayer(
d_model=text_latent_dim,
nhead=text_num_heads,
dim_feedforward=text_ff_size,
dropout=dropout,
activation=activation)
self.textTransEncoder = nn.TransformerEncoder(
textTransEncoderLayer,
num_layers=num_text_layers)
else:
self.use_text_finetune = False
self.text_ln = nn.LayerNorm(text_latent_dim)
if self.use_text_proj:
self.text_proj = nn.Sequential(
nn.Linear(text_latent_dim, self.time_embed_dim)
)
def encode_text(self, text, clip_feat, device):
B = len(text)
if type(text[0]) is dict:
knames = ["head", "stem", "left_arm", "right_arm", "left_leg", "right_leg", "pelvis", "all"]
new_text = []
for item in text:
for kname in knames:
new_text.append(item[kname])
text = new_text
text = clip.tokenize(text, truncate=True).to(device)
if clip_feat is None:
with torch.no_grad():
if isinstance(self.clip, CLIPWrapper):
self.clip.to(device)
dtype = self.clip.clip_model.dtype
# [batch_size, n_ctx, d_model]
x = self.clip.clip_model.token_embedding(text).type(dtype)
x = x + self.clip.clip_model.positional_embedding.type(dtype)
x = x.permute(1, 0, 2) # NLD -> LND
x = self.clip.clip_model.transformer(x)
x = self.clip.clip_model.ln_final(x).type(dtype)
else:
dtype = self.clip.dtype
# [batch_size, n_ctx, d_model]
x = self.clip.token_embedding(text).type(dtype)
x = x + self.clip.positional_embedding.type(dtype)
x = x.permute(1, 0, 2) # NLD -> LND
x = self.clip.transformer(x)
x = self.clip.ln_final(x).type(dtype)
else:
x = clip_feat.float().to(device)
if len(x.shape) == 4:
x = x.permute(1, 0, 2, 3)
x = x.reshape([x.shape[0], x.shape[1] * x.shape[2], x.shape[3]])
else:
x = x.permute(1, 0, 2)
# T, B, D
x = self.text_pre_proj(x)
xf_out = self.textTransEncoder(x)
xf_out = self.text_ln(xf_out)
if self.use_text_proj:
xf_proj = self.text_proj(xf_out[text.argmax(dim=-1), torch.arange(xf_out.shape[1])])
# B, T, D
xf_out = xf_out.permute(1, 0, 2)
return xf_proj, xf_out
else:
xf_out = xf_out.permute(1, 0, 2)
return xf_out
@abstractmethod
def get_precompute_condition(self, **kwargs):
pass
@abstractmethod
def forward_train(self, h, src_mask, emb, **kwargs):
pass
@abstractmethod
def forward_test(self, h, src_mask, emb, **kwargs):
pass
def forward(self,
motion,
timesteps=None,
motion_mask=None,
motion_length=None,
num_intervals=1,
**kwargs):
"""
motion: B, T, D
"""
B, T = motion.shape[0], motion.shape[1]
conditions = self.get_precompute_condition(device=motion.device,
motion_length=motion_length,
**kwargs)
if len(motion_mask.shape) == 2:
src_mask = motion_mask.clone().unsqueeze(-1)
else:
src_mask = motion_mask.clone()
if self.time_embedding_type != 'none':
if self.time_embedding_type == 'sinusoidal':
emb = self.time_embed(timestep_embedding(timesteps, self.latent_dim))
else:
emb = self.time_embed(self.time_tokens(timesteps))
if self.use_text_proj:
emb = emb + conditions['xf_proj']
else:
emb = None
# B, T, latent_dim
h = self.joint_embed(motion)
if self.use_pos_embedding:
h = h + self.sequence_embedding.unsqueeze(0)[:, :T, :]
if self.training:
output = self.forward_train(
h=h,
src_mask=src_mask,
emb=emb,
timesteps=timesteps,
motion_length=motion_length,
num_intervals=num_intervals,
motion=motion,
**conditions)
else:
output = self.forward_test(
h=h,
src_mask=src_mask,
emb=emb,
timesteps=timesteps,
motion_length=motion_length,
num_intervals=num_intervals,
**conditions)
if self.use_residual_connection:
output = motion + output
return output
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