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import torch
import torch.nn.functional as F
import numpy as np
from typing import Optional, List, Dict, Union
from ..builder import ARCHITECTURES, build_loss, build_submodule
from ..utils.gaussian_diffusion import create_named_schedule_sampler, build_diffusion
from ..utils.mask_helper import expand_mask_to_all
from .base_architecture import BaseArchitecture
def set_requires_grad(nets: Union[torch.nn.Module, List[torch.nn.Module]], requires_grad: bool = False):
"""Set requires_grad for all the networks.
Args:
nets (nn.Module | list[nn.Module]): A list of networks or a single network.
requires_grad (bool): Whether the networks require gradients or not.
"""
if not isinstance(nets, list):
nets = [nets]
for net in nets:
if net is not None:
for param in net.parameters():
param.requires_grad = requires_grad
@ARCHITECTURES.register_module()
class MotionDiffusion(BaseArchitecture):
"""
Motion Diffusion architecture for modeling and generating motion sequences using diffusion models.
Args:
dataset_name (Optional[str]): Name of the dataset being used (e.g., 'kit_ml', 'human_ml3d').
model (dict): Configuration for the submodule (e.g., the motion generation model).
loss_recon (dict): Configuration for the reconstruction loss.
loss_reduction (str): Specifies the reduction method for the loss. Defaults to 'frame'.
use_loss_score (bool): Whether to use a scoring mechanism for loss calculation. Defaults to False.
diffusion_train (dict): Configuration for the diffusion model during training.
diffusion_test (dict): Configuration for the diffusion model during testing.
sampler_type (str): The type of sampler to use. Defaults to 'uniform'.
init_cfg (dict): Initialization config for the module.
inference_type (str): Type of inference to use ('ddpm' or 'ddim'). Defaults to 'ddpm'.
"""
def __init__(self,
dataset_name: Optional[str] = None,
model: dict = None,
loss_recon: dict = None,
loss_reduction: str = "frame",
use_loss_score: bool = False,
diffusion_train: dict = None,
diffusion_test: dict = None,
sampler_type: str = 'uniform',
init_cfg: dict = None,
inference_type: str = 'ddpm',
**kwargs):
super().__init__(init_cfg=init_cfg, **kwargs)
self.model = build_submodule(model)
self.loss_recon = build_loss(loss_recon)
self.diffusion_train = build_diffusion(diffusion_train)
self.diffusion_test = build_diffusion(diffusion_test)
self.sampler = create_named_schedule_sampler(sampler_type, self.diffusion_train)
self.inference_type = inference_type
self.loss_reduction = loss_reduction
self.use_loss_score = use_loss_score
self.dataset_name = dataset_name
if self.dataset_name == "kit_ml":
self.mean = np.load("data/datasets/kit_ml/mean.npy")
self.std = np.load("data/datasets/kit_ml/std.npy")
elif self.dataset_name == "human_ml3d":
self.mean = np.load("data/datasets/human_ml3d/mean.npy")
self.std = np.load("data/datasets/human_ml3d/std.npy")
elif self.dataset_name is not None:
raise NotImplementedError()
def forward(self, **kwargs) -> Union[Dict, List]:
"""Forward pass of the model.
Depending on whether the model is in training mode, this method performs the forward pass
during training or inference, and calculates the relevant losses.
Args:
**kwargs: Keyword arguments containing the input data for the model.
Returns:
dict or list: The calculated losses during training or the generated motion during inference.
"""
motion = kwargs['motion'].float()
motion_mask = kwargs['motion_mask'].float()
motion_length = kwargs['motion_length']
num_intervals = kwargs.get('num_intervals', 1)
sample_idx = kwargs.get('sample_idx', None)
clip_feat = kwargs.get('clip_feat', None)
B, T = motion.shape[:2]
text = [kwargs['motion_metas'][i]['text'] for i in range(B)]
if self.training:
t, _ = self.sampler.sample(B, motion.device)
output = self.diffusion_train.training_losses(
model=self.model,
x_start=motion,
t=t,
model_kwargs={
'motion_mask': motion_mask,
'motion_length': motion_length,
'text': text,
'clip_feat': clip_feat,
'sample_idx': sample_idx,
'num_intervals': num_intervals
}
)
pred, target = output['pred'], output['target']
recon_loss = self.loss_recon(pred, target, reduction_override='none')
if self.use_loss_score:
loss_score = kwargs['score']
recon_loss = recon_loss * loss_score.view(B, 1, -1)
recon_loss = recon_loss.mean(dim=-1) * motion_mask
recon_loss_batch = recon_loss.sum(dim=1) / motion_mask.sum(dim=1)
recon_loss_frame = recon_loss.sum() / motion_mask.sum()
if self.loss_reduction == "frame":
recon_loss = recon_loss_frame
else:
recon_loss = recon_loss_batch
if hasattr(self.sampler, "update_with_local_losses"):
self.sampler.update_with_local_losses(t, recon_loss_batch)
loss = {'recon_loss': recon_loss.mean()}
if hasattr(self.model, 'aux_loss'):
loss.update(self.model.aux_loss())
return loss
else:
dim_pose = kwargs['motion'].shape[-1]
model_kwargs = self.model.get_precompute_condition(
device=motion.device, text=text, **kwargs
)
model_kwargs.update({
'motion_mask': motion_mask,
'sample_idx': sample_idx,
'motion_length': motion_length,
'num_intervals': num_intervals
})
inference_kwargs = kwargs.get('inference_kwargs', {})
if self.inference_type == 'ddpm':
output = self.diffusion_test.p_sample_loop(
self.model, (B, T, dim_pose), clip_denoised=False, progress=False,
model_kwargs=model_kwargs, **inference_kwargs
)
else:
output = self.diffusion_test.ddim_sample_loop(
self.model, (B, T, dim_pose), clip_denoised=False, progress=False,
model_kwargs=model_kwargs, eta=0, **inference_kwargs
)
results = kwargs
if getattr(self.model, "post_process") is not None:
output = self.model.post_process(output)
results['pred_motion'] = output
results = self.split_results(results)
return results
@ARCHITECTURES.register_module()
class UnifiedMotionDiffusion(BaseArchitecture):
"""
Unified Motion Diffusion architecture for generating motion sequences using diffusion models.
Args:
model (dict): Configuration for the motion generation model.
loss_recon (dict): Configuration for the reconstruction loss.
loss_reduction (str): Specifies the reduction method for the loss. Defaults to 'frame'.
random_mask (float): Probability or scaling factor for applying random masking. Defaults to 0.
diffusion_train (dict): Configuration for the diffusion model during training.
diffusion_test (dict): Configuration for the diffusion model during testing.
sampler_type (str): The type of sampler to use. Defaults to 'uniform'.
init_cfg (dict): Initialization config for the module.
inference_type (str): Type of inference to use ('ddpm' or 'ddim'). Defaults to 'ddpm'.
body_scale (float): Scaling factor for the body motion mask. Defaults to 1.0.
hand_scale (float): Scaling factor for the hand motion mask. Defaults to 1.0.
face_scale (float): Scaling factor for the face motion mask. Defaults to 1.0.
"""
def __init__(self,
model: dict = None,
loss_recon: dict = None,
loss_reduction: str = "frame",
random_mask: float = 0,
diffusion_train: dict = None,
diffusion_test_dict: dict = None,
sampler_type: str = 'uniform',
init_cfg: dict = None,
inference_type: str = 'ddpm',
body_scale: float = 1.0,
hand_scale: float = 1.0,
face_scale: float = 1.0,
train_repeat: int = 1,
loss_weight: str = None,
**kwargs):
super().__init__(init_cfg=init_cfg, **kwargs)
self.model = build_submodule(model)
self.loss_recon = build_loss(loss_recon)
self.diffusion_train = build_diffusion(diffusion_train)
self.diffusion_test_dict = diffusion_test_dict
self.sampler = create_named_schedule_sampler(sampler_type, self.diffusion_train)
self.inference_type = inference_type
self.loss_reduction = loss_reduction
self.random_mask = random_mask
self.body_scale = body_scale
self.hand_scale = hand_scale
self.face_scale = face_scale
self.train_repeat = train_repeat
self.loss_weight = None
if init_cfg is not None:
self.init_weights()
def repeat_data(self, **kwargs):
if self.train_repeat == 1:
return kwargs
N = self.train_repeat
motion = kwargs['motion'].float().repeat(N, 1, 1)
B = motion.shape[0]
kwargs['motion'] = motion
motion_mask = kwargs['motion_mask'].float().repeat(N, 1, 1)
kwargs['motion_mask'] = motion_mask
motion_length = kwargs['motion_length'].repeat(N, 1)
kwargs['motion_length'] = motion_length
motion_metas = kwargs['motion_metas'] * N
kwargs['motion_metas'] = motion_metas
if 'text_seq_feat' in kwargs:
kwargs['text_seq_feat'] = kwargs['text_seq_feat'].repeat(N, 1)
if 'text_word_feat' in kwargs:
kwargs['text_word_feat'] = kwargs['text_word_feat'].repeat(N, 1, 1)
if 'text_cond' in kwargs:
kwargs['text_cond'] = kwargs['text_cond'].repeat(N, 1)
if 'music_seq_feat' in kwargs:
kwargs['music_seq_feat'] = kwargs['music_seq_feat'].repeat(N, 1)
if 'music_word_feat' in kwargs:
kwargs['music_word_feat'] = kwargs['music_word_feat'].repeat(N, 1, 1)
if 'music_cond' in kwargs:
kwargs['music_cond'] = kwargs['music_cond'].repeat(N, 1)
if 'speech_seq_feat' in kwargs:
kwargs['speech_seq_feat'] = kwargs['speech_seq_feat'].repeat(N, 1)
if 'speech_word_feat' in kwargs:
kwargs['speech_word_feat'] = kwargs['speech_word_feat'].repeat(N, 1, 1)
if 'speech_cond' in kwargs:
kwargs['speech_cond'] = kwargs['speech_cond'].repeat(N, 1)
if 'video_seq_feat' in kwargs:
kwargs['video_seq_feat'] = kwargs['video_seq_feat'].repeat(N, 1)
if 'video_word_feat' in kwargs:
kwargs['video_word_feat'] = kwargs['video_word_feat'].repeat(N, 1, 1)
if 'video_cond' in kwargs:
kwargs['video_cond'] = kwargs['video_cond'].repeat(N, 1)
return kwargs
def forward(self, **kwargs) -> Dict:
"""Forward pass for training or inference in the unified motion diffusion model.
Args:
**kwargs: Keyword arguments containing the input data for the model.
Returns:
dict: The calculated losses during training or the generated motion during inference.
"""
if self.training:
kwargs = self.repeat_data(**kwargs)
motion = kwargs['motion'].float()
B, T = motion.shape[:2]
motion_mask = kwargs['motion_mask'].float()
motion_length = kwargs['motion_length']
num_intervals = kwargs.get('num_intervals', 1)
sample_idx = kwargs.get('sample_idx', None)
motion_metas = kwargs['motion_metas']
# Conditioning features (text, music, speech, video)
text_word_feat = kwargs.get('text_word_feat', None)
text_seq_feat = kwargs.get('text_seq_feat', None)
text_cond = kwargs.get('text_cond', torch.zeros(B).type_as(motion))
music_word_feat = kwargs.get('music_word_feat', None)
music_seq_feat = kwargs.get('music_seq_feat', None)
music_cond = kwargs.get('music_cond', torch.zeros(B).type_as(motion))
speech_word_feat = kwargs.get('speech_word_feat', None)
speech_seq_feat = kwargs.get('speech_seq_feat', None)
speech_cond = kwargs.get('speech_cond', torch.zeros(B).type_as(motion))
video_word_feat = kwargs.get('video_word_feat', None)
video_seq_feat = kwargs.get('video_seq_feat', None)
video_cond = kwargs.get('video_cond', torch.zeros(B).type_as(motion))
if self.training:
# Random masking during training
t, _ = self.sampler.sample(B, motion.device)
# rand_mask = torch.rand_like(motion_mask)
# new_motion_mask = motion_mask.clone()
# threshold = torch.rand(B).type_as(rand_mask)
# threshold = threshold.view(B, 1, 1).repeat(1, T, 10)
# new_motion_mask[rand_mask < threshold] = 0
# motion_mask = new_motion_mask
output = self.diffusion_train.training_losses(
model=self.model,
x_start=motion,
t=t,
model_kwargs={
'motion_mask': motion_mask,
'motion_length': motion_length,
'num_intervals': num_intervals,
'motion_metas': motion_metas,
'text_word_feat': text_word_feat,
'text_seq_feat': text_seq_feat,
'text_cond': text_cond,
'music_word_feat': music_word_feat,
'music_seq_feat': music_seq_feat,
'music_cond': music_cond,
'speech_word_feat': speech_word_feat,
'speech_seq_feat': speech_seq_feat,
'speech_cond': speech_cond,
'video_word_feat': video_word_feat,
'video_seq_feat': video_seq_feat,
'video_cond': video_cond,
})
pred, target = output['pred'], output['target']
recon_loss = self.loss_recon(pred, target, reduction_override='none')
# Apply expanded motion mask
motion_mask = expand_mask_to_all(
motion_mask, self.body_scale, self.hand_scale, self.face_scale
)
if self.loss_weight is not None:
loss_weight = torch.from_numpy(self.loss_weight).type_as(motion_mask)
dataset_idx = self.model.dataset_idx
loss_weight = loss_weight.index_select(0, dataset_idx).unsqueeze(1)
motion_mask = motion_mask * loss_weight
recon_loss = (recon_loss * motion_mask).sum(dim=-1)
motion_mask = motion_mask.sum(dim=-1)
else:
recon_loss = (recon_loss * motion_mask).mean(dim=-1)
motion_mask = motion_mask.mean(dim=-1)
recon_loss_batch = recon_loss.sum(dim=1) / motion_mask.sum(dim=1)
recon_loss_frame = recon_loss.sum() / motion_mask.sum()
# Determine final reconstruction loss
if self.loss_reduction == "frame":
recon_loss = recon_loss_frame
else:
recon_loss = recon_loss_batch
if hasattr(self.sampler, "update_with_local_losses"):
self.sampler.update_with_local_losses(t, recon_loss_batch)
loss = {'recon_loss': recon_loss.mean()}
# Add auxiliary loss if applicable
if hasattr(self.model, 'aux_loss'):
loss.update(self.model.aux_loss())
return loss
else:
# Inference (DDPM or DDIM sampling)
dim_pose = 669 # Fixed dimension for the motion output
model_kwargs = self.model.get_precompute_condition(
device=motion.device, **kwargs
)
model_kwargs.update({
'motion_mask': motion_mask,
'sample_idx': sample_idx,
'motion_length': motion_length,
'num_intervals': num_intervals,
'motion_metas': motion_metas,
'text_word_feat': text_word_feat,
'text_seq_feat': text_seq_feat,
'text_cond': text_cond,
'music_word_feat': music_word_feat,
'music_seq_feat': music_seq_feat,
'music_cond': music_cond,
'speech_word_feat': speech_word_feat,
'speech_seq_feat': speech_seq_feat,
'speech_cond': speech_cond,
'video_word_feat': video_word_feat,
'video_seq_feat': video_seq_feat,
'video_cond': video_cond,
})
inference_kwargs = kwargs.get('inference_kwargs', {})
inference_kwargs['gt_motion'] = motion
inference_kwargs['context_mask'] = kwargs.get('context_mask', None)
dataset_name = motion_metas[0]['meta_data']['dataset_name']
diffusion_test_cfg = self.diffusion_test_dict['base']
diffusion_test_cfg.update(dict(respace=self.diffusion_test_dict[dataset_name]))
diffusion_test = build_diffusion(diffusion_test_cfg)
if self.inference_type == 'ddpm':
output = diffusion_test.p_sample_loop(
self.model, (B, T, dim_pose), clip_denoised=False,
progress=False, model_kwargs=model_kwargs, **inference_kwargs
)
else:
output = diffusion_test.ddim_sample_loop(
self.model, (B, T, dim_pose), clip_denoised=False,
progress=False, model_kwargs=model_kwargs, eta=0,
**inference_kwargs
)
results = kwargs
if getattr(self.model, "post_process") is not None:
output = self.model.post_process(output)
results['pred_motion'] = output
results = self.split_results(results)
return results
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