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import numpy as np
import torch
from torch import nn
import random
from typing import Optional, List, Dict
from mogen.models.utils.misc import zero_module
from ..builder import SUBMODULES, build_attention
from ..utils.stylization_block import StylizationBlock
from .motion_transformer import MotionTransformer
from mogen.models.utils.position_encoding import timestep_embedding
from scipy.ndimage import gaussian_filter
def get_tomato_slice(idx: int) -> List[int]:
"""Return specific slices for the tomato dataset."""
if idx == 0:
result = [0, 1, 2, 3, 463, 464, 465]
else:
result = [
4 + (idx - 1) * 3,
4 + (idx - 1) * 3 + 1,
4 + (idx - 1) * 3 + 2,
157 + (idx - 1) * 6,
157 + (idx - 1) * 6 + 1,
157 + (idx - 1) * 6 + 2,
157 + (idx - 1) * 6 + 3,
157 + (idx - 1) * 6 + 4,
157 + (idx - 1) * 6 + 5,
463 + idx * 3,
463 + idx * 3 + 1,
463 + idx * 3 + 2,
]
return result
def get_part_slice(idx_list: List[int], func) -> List[int]:
"""Return a list of slices by applying the provided function."""
result = []
for idx in idx_list:
result.extend(func(idx))
return result
class SinglePoseEncoder(nn.Module):
"""Encoder module for individual pose, separating different body parts."""
def __init__(self, latent_dim: int = 64):
super().__init__()
func = get_tomato_slice
self.root_slice = get_part_slice([0], func)
self.head_slice = get_part_slice([12, 15], func)
self.stem_slice = get_part_slice([3, 6, 9], func)
self.larm_slice = get_part_slice([14, 17, 19, 21], func)
self.rarm_slice = get_part_slice([13, 16, 18, 20], func)
self.lleg_slice = get_part_slice([2, 5, 8, 11], func)
self.rleg_slice = get_part_slice([1, 4, 7, 10], func)
self.lhnd_slice = get_part_slice(range(22, 37), func)
self.rhnd_slice = get_part_slice(range(37, 52), func)
self.face_slice = range(619, 669)
# Initialize linear layers for each body part embedding
self.root_embed = nn.Linear(len(self.root_slice), latent_dim)
self.head_embed = nn.Linear(len(self.head_slice), latent_dim)
self.stem_embed = nn.Linear(len(self.stem_slice), latent_dim)
self.larm_embed = nn.Linear(len(self.larm_slice), latent_dim)
self.rarm_embed = nn.Linear(len(self.rarm_slice), latent_dim)
self.lleg_embed = nn.Linear(len(self.lleg_slice), latent_dim)
self.rleg_embed = nn.Linear(len(self.rleg_slice), latent_dim)
self.lhnd_embed = nn.Linear(len(self.lhnd_slice), latent_dim)
self.rhnd_embed = nn.Linear(len(self.rhnd_slice), latent_dim)
self.face_embed = nn.Linear(len(self.face_slice), latent_dim)
def forward(self, motion: torch.Tensor) -> torch.Tensor:
"""Forward pass to embed different parts of the motion tensor."""
root_feat = self.root_embed(motion[:, :, self.root_slice].contiguous())
head_feat = self.head_embed(motion[:, :, self.head_slice].contiguous())
stem_feat = self.stem_embed(motion[:, :, self.stem_slice].contiguous())
larm_feat = self.larm_embed(motion[:, :, self.larm_slice].contiguous())
rarm_feat = self.rarm_embed(motion[:, :, self.rarm_slice].contiguous())
lleg_feat = self.lleg_embed(motion[:, :, self.lleg_slice].contiguous())
rleg_feat = self.rleg_embed(motion[:, :, self.rleg_slice].contiguous())
lhnd_feat = self.lhnd_embed(motion[:, :, self.lhnd_slice].contiguous())
rhnd_feat = self.rhnd_embed(motion[:, :, self.rhnd_slice].contiguous())
face_feat = self.face_embed(motion[:, :, self.face_slice].contiguous())
# Concatenate all embeddings
feat = torch.cat((root_feat, head_feat, stem_feat,
larm_feat, rarm_feat, lleg_feat, rleg_feat,
lhnd_feat, rhnd_feat, face_feat), dim=-1)
return feat
class PoseEncoder(nn.Module):
"""Encoder for multi-dataset scenarios, handling different datasets."""
def __init__(self, latent_dim: int, num_datasets: int):
super().__init__()
self.models = nn.ModuleList()
self.num_datasets = num_datasets
self.latent_dim = latent_dim
# Initialize single pose encoders for each dataset
for _ in range(num_datasets):
self.models.append(SinglePoseEncoder(latent_dim=latent_dim))
def forward(self, motion: torch.Tensor, dataset_idx: torch.Tensor) -> torch.Tensor:
"""Forward pass for multi-dataset encoding."""
B, T = motion.shape[:2]
output = torch.zeros(B, T, 10 * self.latent_dim).type_as(motion)
num_finish = 0
# Process each dataset's motion separately
for i in range(self.num_datasets):
batch_motion = motion[dataset_idx == i]
if len(batch_motion) == 0:
continue
num_finish += len(batch_motion)
batch_motion = self.models[i](batch_motion)
output[dataset_idx == i] = batch_motion
assert num_finish == B
return output
class SinglePoseDecoder(nn.Module):
"""Decoder module for individual pose, reconstructing body parts."""
def __init__(self, latent_dim: int = 64, output_dim: int = 669):
super().__init__()
self.latent_dim = latent_dim
self.output_dim = output_dim
func = get_tomato_slice
self.root_slice = get_part_slice([0], func)
self.head_slice = get_part_slice([12, 15], func)
self.stem_slice = get_part_slice([3, 6, 9], func)
self.larm_slice = get_part_slice([14, 17, 19, 21], func)
self.rarm_slice = get_part_slice([13, 16, 18, 20], func)
self.lleg_slice = get_part_slice([2, 5, 8, 11], func)
self.rleg_slice = get_part_slice([1, 4, 7, 10], func)
self.lhnd_slice = get_part_slice(range(22, 37), func)
self.rhnd_slice = get_part_slice(range(37, 52), func)
self.face_slice = range(619, 669)
# Initialize linear layers for each body part output
self.root_out = nn.Linear(latent_dim, len(self.root_slice))
self.head_out = nn.Linear(latent_dim, len(self.head_slice))
self.stem_out = nn.Linear(latent_dim, len(self.stem_slice))
self.larm_out = nn.Linear(latent_dim, len(self.larm_slice))
self.rarm_out = nn.Linear(latent_dim, len(self.rarm_slice))
self.lleg_out = nn.Linear(latent_dim, len(self.lleg_slice))
self.rleg_out = nn.Linear(latent_dim, len(self.rleg_slice))
self.lhnd_out = nn.Linear(latent_dim, len(self.lhnd_slice))
self.rhnd_out = nn.Linear(latent_dim, len(self.rhnd_slice))
self.face_out = nn.Linear(latent_dim, len(self.face_slice))
def forward(self, motion: torch.Tensor) -> torch.Tensor:
"""Forward pass to decode body parts from latent representation."""
B, T = motion.shape[:2]
D = self.latent_dim
# Decode each part using corresponding linear layer
root_feat = self.root_out(motion[:, :, :D].contiguous())
head_feat = self.head_out(motion[:, :, D: 2 * D].contiguous())
stem_feat = self.stem_out(motion[:, :, 2 * D: 3 * D].contiguous())
larm_feat = self.larm_out(motion[:, :, 3 * D: 4 * D].contiguous())
rarm_feat = self.rarm_out(motion[:, :, 4 * D: 5 * D].contiguous())
lleg_feat = self.lleg_out(motion[:, :, 5 * D: 6 * D].contiguous())
rleg_feat = self.rleg_out(motion[:, :, 6 * D: 7 * D].contiguous())
lhnd_feat = self.lhnd_out(motion[:, :, 7 * D: 8 * D].contiguous())
rhnd_feat = self.rhnd_out(motion[:, :, 8 * D: 9 * D].contiguous())
face_feat = self.face_out(motion[:, :, 9 * D:].contiguous())
# Combine outputs into final tensor
output = torch.zeros(B, T, self.output_dim).type_as(motion)
output[:, :, self.root_slice] = root_feat
output[:, :, self.head_slice] = head_feat
output[:, :, self.stem_slice] = stem_feat
output[:, :, self.larm_slice] = larm_feat
output[:, :, self.rarm_slice] = rarm_feat
output[:, :, self.lleg_slice] = lleg_feat
output[:, :, self.rleg_slice] = rleg_feat
output[:, :, self.lhnd_slice] = lhnd_feat
output[:, :, self.rhnd_slice] = rhnd_feat
output[:, :, self.face_slice] = face_feat
return output
class PoseDecoder(nn.Module):
"""Decoder for multi-dataset scenarios, handling different datasets."""
def __init__(self, latent_dim: int, output_dim: int, num_datasets: int):
super().__init__()
self.models = nn.ModuleList()
self.num_datasets = num_datasets
self.latent_dim = latent_dim
self.output_dim = output_dim
# Initialize single pose decoders for each dataset
for _ in range(num_datasets):
self.models.append(
SinglePoseDecoder(latent_dim=latent_dim, output_dim=output_dim)
)
def forward(self, motion: torch.Tensor, dataset_idx: torch.Tensor) -> torch.Tensor:
"""Forward pass for multi-dataset decoding."""
B, T = motion.shape[:2]
output = torch.zeros(B, T, self.output_dim).type_as(motion)
num_finish = 0
# Process each dataset's motion separately
for i in range(self.num_datasets):
batch_motion = motion[dataset_idx == i]
if len(batch_motion) == 0:
continue
num_finish += len(batch_motion)
batch_motion = self.models[i](batch_motion)
output[dataset_idx == i] = batch_motion
assert num_finish == B
return output
class SFFN(nn.Module):
"""SFFN module with multiple linear layers, acting on different parts of the input."""
def __init__(self,
latent_dim: int,
ffn_dim: int,
dropout: float,
time_embed_dim: int,
activation: str = "GELU"):
super().__init__()
self.linear1_list = nn.ModuleList()
self.linear2_list = nn.ModuleList()
if activation == "GELU":
self.activation = nn.GELU()
self.linear1 = nn.Linear(latent_dim * 10, ffn_dim * 10)
self.linear2 = nn.Linear(ffn_dim * 10, latent_dim * 10)
self.dropout = nn.Dropout(dropout)
self.proj_out = StylizationBlock(latent_dim * 10, time_embed_dim, dropout)
def forward(self, x: torch.Tensor, emb: torch.Tensor, **kwargs) -> torch.Tensor:
"""Forward pass for SFFN, applying stylization block."""
B, T, D = x.shape
y = self.linear2(self.dropout(self.activation(self.linear1(x))))
y = x.reshape(B, T, D) + self.proj_out(y, emb)
return y
class FFN(nn.Module):
"""Feed-forward network with GELU activation and dropout."""
def __init__(self, latent_dim: int, ffn_dim: int, dropout: float):
super().__init__()
self.linear1 = nn.Linear(latent_dim, ffn_dim)
self.linear2 = nn.Linear(ffn_dim, latent_dim)
self.activation = nn.GELU()
self.dropout = nn.Dropout(dropout)
def forward(self, x: torch.Tensor, **kwargs) -> torch.Tensor:
"""Forward pass with normalization and residual connection."""
y = self.linear2(self.dropout(self.activation(self.linear1(x))))
y = x + y
return y
class DecoderLayer(nn.Module):
"""Decoder layer consisting of conditional attention block and SFFN."""
def __init__(self, ca_block_cfg: Optional[Dict] = None, ffn_cfg: Optional[Dict] = None):
super().__init__()
self.ca_block = build_attention(ca_block_cfg) if ca_block_cfg else None
self.ffn = SFFN(**ffn_cfg) if ffn_cfg else None
def forward(self, **kwargs) -> torch.Tensor:
"""Forward pass for the decoder layer."""
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 EncoderLayer(nn.Module):
"""Encoder layer consisting of self-attention block and FFN."""
def __init__(self, sa_block_cfg: Optional[Dict] = None, ffn_cfg: Optional[Dict] = None):
super().__init__()
self.sa_block = build_attention(sa_block_cfg) if sa_block_cfg else None
self.ffn = FFN(**ffn_cfg) if ffn_cfg else None
def forward(self, **kwargs) -> torch.Tensor:
"""Forward pass for the encoder layer."""
if self.sa_block is not None:
x = self.sa_block(**kwargs)
kwargs.update({'x': x})
if self.ffn is not None:
x = self.ffn(**kwargs)
return x
class Transformer(nn.Module):
"""Transformer model with self-attention and feed-forward network layers."""
def __init__(self,
input_dim: int = 1024,
latent_dim: int = 1024,
num_heads: int = 10,
num_layers: int = 4,
max_seq_len: int = 300,
stride: int = 1,
dropout: float = 0):
super().__init__()
self.blocks = nn.ModuleList()
self.proj_in = nn.Linear(input_dim, latent_dim)
self.embedding = nn.Parameter(torch.randn(1, max_seq_len, latent_dim))
self.latent_dim = latent_dim
self.stride = stride
self.num_heads = num_heads
self.dropout = dropout
sa_block_cfg = dict(
type='EfficientSelfAttention',
latent_dim=latent_dim,
num_heads=num_heads,
dropout=dropout
)
ffn_cfg = dict(
latent_dim=latent_dim,
ffn_dim=latent_dim * 4,
dropout=dropout
)
for _ in range(num_layers):
self.blocks.append(
EncoderLayer(sa_block_cfg=sa_block_cfg, ffn_cfg=ffn_cfg)
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Forward pass through transformer layers."""
x = x[:, ::self.stride, :]
x = self.proj_in(x)
T = x.shape[1]
x = x + self.embedding[:, :T, :]
# Apply each encoder layer
for block in self.blocks:
x = block(x=x)
return x
@SUBMODULES.register_module()
class LargeMotionModel(MotionTransformer):
"""Large motion model with optional multi-modal conditioning (text, music, video, etc.)."""
def __init__(self,
num_parts: int = 10,
latent_part_dim: int = 64,
num_cond_layers: int = 2,
num_datasets: int = 27,
guidance_cfg: Optional[Dict] = None,
moe_route_loss_weight: float = 1.0,
template_kl_loss_weight: float = 0.0001,
dataset_names: Optional[List[str]] = None,
text_input_dim: Optional[int] = None,
music_input_dim: Optional[int] = None,
speech_input_dim: Optional[int] = None,
video_input_dim: Optional[int] = None,
music_input_stride: Optional[int] = 3,
speech_input_stride: Optional[int] = 3,
cond_drop_rate: float = 0,
random_mask: float = 0,
dropout: float = 0,
**kwargs):
kwargs['latent_dim'] = latent_part_dim * num_parts
self.num_parts = num_parts
self.latent_part_dim = latent_part_dim
self.num_datasets = num_datasets
self.dropout = dropout
super().__init__(**kwargs)
self.guidance_cfg = guidance_cfg
self.joint_embed = PoseEncoder(
latent_dim=self.latent_part_dim,
num_datasets=self.num_datasets)
self.out = zero_module(PoseDecoder(
latent_dim=self.latent_part_dim,
output_dim=self.input_feats,
num_datasets=self.num_datasets))
self.dataset_proj = {name: i for i, name in enumerate(dataset_names or [])}
self.rotation_proj = {'h3d_rot': 0, 'smpl_rot': 1, 'bvh_rot': 2}
self.moe_route_loss_weight = moe_route_loss_weight
self.template_kl_loss_weight = template_kl_loss_weight
self.cond_drop_rate = cond_drop_rate
# Conditional transformers for multi-modal inputs
self.text_cond = text_input_dim is not None
self.music_cond = music_input_dim is not None
self.speech_cond = speech_input_dim is not None
self.video_cond = video_input_dim is not None
if self.text_cond:
self.text_transformer = Transformer(
input_dim=text_input_dim,
latent_dim=self.latent_dim,
num_heads=self.num_parts,
num_layers=num_cond_layers,
dropout=self.dropout)
if self.music_cond:
self.music_transformer = Transformer(
input_dim=music_input_dim,
latent_dim=self.latent_dim,
num_heads=self.num_parts,
num_layers=num_cond_layers,
dropout=self.dropout,
stride=music_input_stride)
if self.speech_cond:
self.speech_transformer = Transformer(
input_dim=speech_input_dim,
latent_dim=self.latent_dim,
num_heads=self.num_parts,
num_layers=num_cond_layers,
dropout=self.dropout,
stride=speech_input_stride)
if self.video_cond:
self.video_transformer = Transformer(
input_dim=video_input_dim,
latent_dim=self.latent_dim,
num_heads=self.num_parts,
num_layers=num_cond_layers,
dropout=self.dropout)
self.mask_token = nn.Parameter(torch.randn(self.num_parts, self.latent_part_dim))
self.clean_token = nn.Parameter(torch.randn(self.num_parts, self.latent_part_dim))
self.random_mask = random_mask
def build_temporal_blocks(self,
sa_block_cfg: Optional[Dict] = None,
ca_block_cfg: Optional[Dict] = None,
ffn_cfg: Optional[Dict] = None):
"""Build temporal decoder blocks with attention and feed-forward networks."""
self.temporal_decoder_blocks = nn.ModuleList()
ca_block_cfg['latent_dim'] = self.latent_part_dim
ca_block_cfg['num_heads'] = self.num_parts
ca_block_cfg['ffn_dim'] = self.latent_part_dim * 4
ca_block_cfg['time_embed_dim'] = self.time_embed_dim
ca_block_cfg['max_seq_len'] = self.max_seq_len
ca_block_cfg['dropout'] = self.dropout
for _ in range(self.num_layers):
ffn_cfg_block = dict(
latent_dim=self.latent_part_dim,
ffn_dim=self.latent_part_dim * 4,
dropout=self.dropout,
time_embed_dim=self.time_embed_dim
)
self.temporal_decoder_blocks.append(
DecoderLayer(ca_block_cfg=ca_block_cfg, ffn_cfg=ffn_cfg_block)
)
def scale_func(self, timestep: torch.Tensor, dataset_name: str) -> torch.Tensor:
"""Scale function for diffusion, adjusting weights based on timestep."""
guidance_cfg = self.guidance_cfg[dataset_name]
if guidance_cfg['type'] == 'constant':
w = torch.ones_like(timestep).float() * guidance_cfg['scale']
elif guidance_cfg['type'] == 'linear':
scale = guidance_cfg['scale']
w = (1 - (1000 - timestep) / 1000) * scale + 1
else:
raise NotImplementedError()
return w
def aux_loss(self) -> Dict[str, torch.Tensor]:
"""Compute auxiliary and KL losses for multi-modal routing."""
aux_loss = 0
kl_loss = 0
for module in self.temporal_decoder_blocks:
if hasattr(module.ca_block, 'aux_loss'):
aux_loss += module.ca_block.aux_loss
if hasattr(module.ca_block, 'kl_loss'):
kl_loss += module.ca_block.kl_loss
losses = {}
if aux_loss > 0:
losses['moe_route_loss'] = aux_loss * self.moe_route_loss_weight
if kl_loss > 0:
losses['template_kl_loss'] = kl_loss * self.template_kl_loss_weight
return losses
def get_precompute_condition(self,
text_word_feat: Optional[torch.Tensor] = None,
text_word_out: Optional[torch.Tensor] = None,
text_cond: Optional[torch.Tensor] = None,
music_word_feat: Optional[torch.Tensor] = None,
music_word_out: Optional[torch.Tensor] = None,
music_cond: Optional[torch.Tensor] = None,
speech_word_feat: Optional[torch.Tensor] = None,
speech_word_out: Optional[torch.Tensor] = None,
speech_cond: Optional[torch.Tensor] = None,
video_word_feat: Optional[torch.Tensor] = None,
video_word_out: Optional[torch.Tensor] = None,
video_cond: Optional[torch.Tensor] = None,
**kwargs) -> Dict[str, torch.Tensor]:
"""Precompute conditions for various modalities (text, music, speech, video)."""
output = {}
if self.text_cond and text_word_feat is not None:
text_word_feat = text_word_feat.float()
if text_word_out is None:
if text_cond is None or torch.sum(text_cond) == 0:
latent_dim = self.text_transformer.latent_dim
B, N = text_word_feat.shape[:2]
text_word_out = torch.zeros(B, N, latent_dim).type_as(text_word_feat)
else:
text_word_out = self.text_transformer(text_word_feat)
output['text_word_out'] = text_word_out
if self.music_cond and music_word_feat is not None:
music_word_feat = music_word_feat.float()
if music_word_out is None:
if music_cond is None or torch.sum(music_cond) == 0:
latent_dim = self.music_transformer.latent_dim
B, N = music_word_feat.shape[:2]
music_word_out = torch.zeros(B, N, latent_dim).type_as(music_word_feat)
else:
music_word_out = self.music_transformer(music_word_feat)
output['music_word_out'] = music_word_out
if self.speech_cond and speech_word_feat is not None:
speech_word_feat = speech_word_feat.float()
if speech_word_out is None:
if speech_cond is None or torch.sum(speech_cond) == 0:
latent_dim = self.speech_transformer.latent_dim
B, N = speech_word_feat.shape[:2]
speech_word_out = torch.zeros(B, N, latent_dim).type_as(speech_word_feat)
else:
speech_word_out = self.speech_transformer(speech_word_feat)
output['speech_word_out'] = speech_word_out
if self.video_cond and video_word_feat is not None:
video_word_feat = video_word_feat.float()
if video_word_out is None:
if video_cond is None or torch.sum(video_cond) == 0:
latent_dim = self.video_transformer.latent_dim
B, N = video_word_feat.shape[:2]
video_word_out = torch.zeros(B, N, latent_dim).type_as(video_word_feat)
else:
video_word_out = self.video_transformer(video_word_feat)
output['video_word_out'] = video_word_out
return output
def post_process(self, motion: torch.Tensor) -> torch.Tensor:
"""Post-process motion data (e.g., unnormalization)."""
if self.post_process_cfg is not None and self.post_process_cfg.get("unnormalized_infer", False):
mean = torch.from_numpy(np.load(self.post_process_cfg['mean_path'])).type_as(motion)
std = torch.from_numpy(np.load(self.post_process_cfg['std_path'])).type_as(motion)
motion = motion * std + mean
return motion
def forward_train(self,
h: torch.Tensor,
src_mask: torch.Tensor,
emb: torch.Tensor,
timesteps: torch.Tensor,
motion_length: Optional[torch.Tensor] = None,
text_word_out: Optional[torch.Tensor] = None,
text_cond: Optional[torch.Tensor] = None,
music_word_out: Optional[torch.Tensor] = None,
music_cond: Optional[torch.Tensor] = None,
speech_word_out: Optional[torch.Tensor] = None,
speech_cond: Optional[torch.Tensor] = None,
video_word_out: Optional[torch.Tensor] = None,
video_cond: Optional[torch.Tensor] = None,
num_intervals: int = 1,
duration: Optional[torch.Tensor] = None,
dataset_idx: Optional[torch.Tensor] = None,
rotation_idx: Optional[torch.Tensor] = None,
**kwargs) -> torch.Tensor:
"""Forward pass for training, applying multi-modal conditions."""
B, T = h.shape[:2]
# Apply conditional masking if needed
if self.text_cond and text_cond is not None:
text_cond_mask = torch.rand(B).type_as(h)
text_cond[text_cond_mask < self.cond_drop_rate] = 0
if self.music_cond and music_cond is not None:
music_cond_mask = torch.rand(B).type_as(h)
music_cond[music_cond_mask < self.cond_drop_rate] = 0
if self.speech_cond and speech_cond is not None:
speech_cond_mask = torch.rand(B).type_as(h)
speech_cond[speech_cond_mask < self.cond_drop_rate] = 0
if self.video_cond and video_cond is not None:
video_cond_mask = torch.rand(B).type_as(h)
video_cond[video_cond_mask < self.cond_drop_rate] = 0
# Apply each temporal decoder block
for module in self.temporal_decoder_blocks:
h = module(x=h,
emb=emb,
src_mask=src_mask,
motion_length=motion_length,
text_cond=text_cond,
text_word_out=text_word_out,
music_cond=music_cond,
music_word_out=music_word_out,
speech_cond=speech_cond,
speech_word_out=speech_word_out,
video_cond=video_cond,
video_word_out=video_word_out,
num_intervals=num_intervals,
duration=duration,
dataset_idx=dataset_idx,
rotation_idx=rotation_idx)
# Output layer
output = self.out(h, dataset_idx).view(B, T, -1).contiguous()
return output
def forward_test(self,
h: torch.Tensor,
src_mask: torch.Tensor,
emb: torch.Tensor,
timesteps: torch.Tensor,
motion_length: torch.Tensor,
text_word_out: Optional[torch.Tensor] = None,
text_cond: Optional[torch.Tensor] = None,
music_word_out: Optional[torch.Tensor] = None,
music_cond: Optional[torch.Tensor] = None,
speech_word_out: Optional[torch.Tensor] = None,
speech_cond: Optional[torch.Tensor] = None,
video_word_out: Optional[torch.Tensor] = None,
video_cond: Optional[torch.Tensor] = None,
num_intervals: int = 1,
duration: Optional[torch.Tensor] = None,
dataset_idx: Optional[torch.Tensor] = None,
rotation_idx: Optional[torch.Tensor] = None,
dataset_name: Optional[str] = 'humanml3d_t2m',
**kwargs) -> torch.Tensor:
"""Forward pass for testing, including scaling and conditional fusion."""
B, T = h.shape[:2]
# Duplicate tensors for conditional and non-conditional cases
h = h.repeat(2, 1, 1)
emb = emb.repeat(2, 1)
src_mask = src_mask.repeat(2, 1, 1, 1)
motion_length = motion_length.repeat(2, 1)
duration = duration.repeat(2)
# dataset_idx_att = [self.dataset_proj['all'] for i in range(B)]
# dataset_idx_att = torch.tensor(dataset_idx_att, dtype=torch.long).to(h.device)
# dataset_idx_att = torch.cat((dataset_idx, dataset_idx_att))
dataset_idx = dataset_idx.repeat(2)
rotation_idx = rotation_idx.repeat(2)
if self.text_cond and text_cond is not None and text_word_out is not None:
text_cond = text_cond.repeat(2, 1)
text_cond[B:] = 0
text_word_out = text_word_out.repeat(2, 1, 1)
if self.music_cond and music_cond is not None and music_word_out is not None:
music_cond = music_cond.repeat(2, 1)
music_cond[B:] = 0
music_word_out = music_word_out.repeat(2, 1, 1)
if self.speech_cond and speech_cond is not None and speech_word_out is not None:
speech_cond = speech_cond.repeat(2, 1)
speech_cond[B:] = 0
speech_word_out = speech_word_out.repeat(2, 1, 1)
if self.video_cond and video_cond is not None and video_word_out is not None:
video_cond = video_cond.repeat(2, 1)
video_cond[B:] = 0
video_word_out = video_word_out.repeat(2, 1, 1)
# Apply each temporal decoder block
for module in self.temporal_decoder_blocks:
h = module(x=h,
emb=emb,
src_mask=src_mask,
motion_length=motion_length,
text_cond=text_cond,
text_word_out=text_word_out,
music_cond=music_cond,
music_word_out=music_word_out,
speech_cond=speech_cond,
speech_word_out=speech_word_out,
video_cond=video_cond,
video_word_out=video_word_out,
num_intervals=num_intervals,
duration=duration,
dataset_idx=dataset_idx,
rotation_idx=rotation_idx)
# Process the output from conditional and non-conditional branches
output = self.out(h, dataset_idx).view(2 * B, T, -1).contiguous()
scale = self.scale_func(timesteps, dataset_name).view(-1, 1, 1)
output_cond = output[:B].contiguous()
output_none = output[B:].contiguous()
# Fuse conditional and non-conditional outputs
output = output_cond * scale + output_none * (1 - scale)
return output
def create_mask_from_length(self, T: int, motion_length: torch.Tensor) -> torch.Tensor:
"""Create a binary mask based on motion length."""
B = motion_length.shape[0]
src_mask = torch.zeros(B, T)
for bix in range(B):
src_mask[bix, :int(motion_length[bix])] = 1
return src_mask
def forward(self,
motion: torch.Tensor,
timesteps: torch.Tensor,
motion_mask: Optional[torch.Tensor] = None,
motion_length: Optional[torch.Tensor] = None,
num_intervals: int = 1,
motion_metas: Optional[List[Dict]] = None,
text_seq_feat: Optional[torch.Tensor] = None,
text_word_feat: Optional[torch.Tensor] = None,
text_cond: Optional[torch.Tensor] = None,
music_seq_feat: Optional[torch.Tensor] = None,
music_word_feat: Optional[torch.Tensor] = None,
music_cond: Optional[torch.Tensor] = None,
speech_seq_feat: Optional[torch.Tensor] = None,
speech_word_feat: Optional[torch.Tensor] = None,
speech_cond: Optional[torch.Tensor] = None,
video_seq_feat: Optional[torch.Tensor] = None,
video_word_feat: Optional[torch.Tensor] = None,
video_cond: Optional[torch.Tensor] = None,
**kwargs) -> torch.Tensor:
"""Unified forward pass for both training and testing."""
B, T = motion.shape[:2]
# Precompute conditioning features
conditions = self.get_precompute_condition(
motion_length=motion_length,
text_seq_feat=text_seq_feat,
text_word_feat=text_word_feat,
text_cond=text_cond,
music_seq_feat=music_seq_feat,
music_word_feat=music_word_feat,
music_cond=music_cond,
speech_seq_feat=speech_seq_feat,
speech_word_feat=speech_word_feat,
speech_cond=speech_cond,
video_seq_feat=video_seq_feat,
video_word_feat=video_word_feat,
video_cond=video_cond,
device=motion.device,
**kwargs
)
if self.training:
new_motion_mask = motion_mask.clone()
rand_mask = torch.rand_like(motion_mask)
threshold = torch.rand(B).type_as(rand_mask)
threshold = threshold.view(B, 1, 1).repeat(1, T, self.num_parts)
new_motion_mask[rand_mask < threshold] = 0
motion_mask = new_motion_mask
else:
t = int(timesteps[0])
motion_mask = motion_mask.view(B, T, 10, 1)
# Temporal embedding
emb = self.time_embed(timestep_embedding(timesteps, self.latent_dim))
# Prepare duration and framerate embeddings
duration = []
for meta in motion_metas:
framerate = meta['meta_data']['framerate']
duration.append(1.0 / framerate)
duration = torch.tensor(duration, dtype=motion.dtype).to(motion.device)
# Dataset index embedding
dataset_idx = []
for i in range(B):
dataset_name = motion_metas[i]['meta_data']['dataset_name']
if torch.rand(1).item() < 0.1 and self.training:
dataset_name = 'all'
idx = self.dataset_proj[dataset_name]
dataset_idx.append(idx)
dataset_idx = torch.tensor(dataset_idx, dtype=torch.long).to(motion.device)
self.dataset_idx = dataset_idx.clone().detach()
# Rotation index embedding
rotation_idx = [self.rotation_proj[meta['meta_data']['rotation_type']] for meta in motion_metas]
rotation_idx = torch.tensor(rotation_idx, dtype=torch.long).to(motion.device)
# Embed motion with pose encoder
h = self.joint_embed(motion, dataset_idx)
h = h.view(B, T, 10, -1) * motion_mask + (1 - motion_mask) * self.mask_token
h = h.view(B, T, -1)
# Source mask based on motion length
src_mask = self.create_mask_from_length(T, motion_length).to(motion.device)
src_mask = src_mask.view(B, T, 1, 1).repeat(1, 1, 10, 1)
# Training or testing forward
if self.training:
output = self.forward_train(
h=h,
emb=emb,
src_mask=src_mask,
timesteps=timesteps,
motion_length=motion_length,
text_cond=text_cond,
music_cond=music_cond,
speech_cond=speech_cond,
video_cond=video_cond,
num_intervals=num_intervals,
duration=duration,
dataset_idx=dataset_idx,
rotation_idx=rotation_idx,
**conditions
)
else:
output = self.forward_test(
h=h,
emb=emb,
src_mask=src_mask,
timesteps=timesteps,
motion_length=motion_length,
text_cond=text_cond,
music_cond=music_cond,
speech_cond=speech_cond,
video_cond=video_cond,
num_intervals=num_intervals,
duration=duration,
dataset_idx=dataset_idx,
rotation_idx=rotation_idx,
dataset_name=dataset_name,
**conditions
)
return output
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