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import inspect
from typing import Optional
from einops import rearrange
import torch
import torch.nn.functional as F
from diffusers.schedulers.scheduling_ddpm import DDPMScheduler
from diffusers.schedulers.scheduling_ddim import DDIMScheduler
from diffusers.schedulers.scheduling_pndm import PNDMScheduler
from torch import Tensor
from tqdm import tqdm
from diffusers import ModelMixin
from .model_utils import get_custom_betas
from .point_model import PointModel
import copy
import torch.nn as nn
class TemporalSmoothnessLoss(nn.Module):
def __init__(self):
super(TemporalSmoothnessLoss, self).__init__()
def forward(self, input):
# Calculate the difference between consecutive frames
diff = input[:, 1:, :] - input[:, :-1, :]
# Compute the L2 norm (squared) of the differences
smoothness_loss = torch.mean(torch.sum(diff ** 2, dim=2))
return smoothness_loss
class ConditionalPointCloudDiffusionModel(ModelMixin):
def __init__(
self,
beta_start: float = 1e-5,
beta_end: float = 8e-3,
beta_schedule: str = 'linear',
point_cloud_model: str = 'simple',
point_cloud_model_embed_dim: int = 64,
):
super().__init__()
self.in_channels = 70 # 3 for 3D point positions
self.out_channels = 70
# Checks
# Create diffusion model schedulers which define the sampling timesteps
scheduler_kwargs = {}
if beta_schedule == 'custom':
scheduler_kwargs.update(dict(trained_betas=get_custom_betas(beta_start=beta_start, beta_end=beta_end)))
else:
scheduler_kwargs.update(dict(beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule))
self.schedulers_map = {
'ddpm': DDPMScheduler(**scheduler_kwargs, clip_sample=False),
'ddim': DDIMScheduler(**scheduler_kwargs, clip_sample=False),
'pndm': PNDMScheduler(**scheduler_kwargs),
}
self.scheduler = self.schedulers_map['ddim'] # this can be changed for inference
# Create point cloud model for processing point cloud at each diffusion step
self.point_model = PointModel(
model_type=point_cloud_model,
embed_dim=point_cloud_model_embed_dim,
in_channels=self.in_channels,
out_channels=self.out_channels,
)
def forward_train(
self,
pc: Optional[Tensor],
ref_kps: Optional[Tensor],
ori_kps: Optional[Tensor],
aud_feat: Optional[Tensor],
mode: str = 'train',
return_intermediate_steps: bool = False
):
# Normalize colors and convert to tensor
x_0 = pc
B, Nf, Np, D = x_0.shape# batch, nums of frames, nums of points, 3
x_0=x_0[:,:,:,0]# batch, nums of frames, 70
# Sample random noise
noise = torch.randn_like(x_0)
# Sample random timesteps for each point_cloud
timestep = torch.randint(0, self.scheduler.num_train_timesteps, (B,),
device=self.device, dtype=torch.long)
# Add noise to points
x_t = self.scheduler.add_noise(x_0, noise, timestep)
# Conditioning
ref_kps = ref_kps[:, :, 0]
x_t_input = torch.cat([ori_kps.unsqueeze(1), ref_kps.unsqueeze(1), x_t], dim=1)
aud_feat = torch.cat([torch.zeros(B, 2, 512).cuda(), aud_feat], 1)
# Augmentation for audio feature
if mode in 'train':
if torch.rand(1) > 0.3:
mean = torch.mean(aud_feat)
std = torch.std(aud_feat)
sample = torch.normal(mean=torch.full(aud_feat.shape, mean), std=torch.full(aud_feat.shape, std)).cuda()
aud_feat = sample + aud_feat
else:
pass
else:
pass
# Forward
noise_pred = self.point_model(x_t_input, timestep, context=aud_feat) #torch.cat([mel_feat,style_embed],-1))
noise_pred = noise_pred[:, 2:]
# Check
if not noise_pred.shape == noise.shape:
raise ValueError(f'{noise_pred.shape=} and {noise.shape=}')
loss = F.mse_loss(noise_pred, noise)
loss_pose = F.mse_loss(noise_pred[:, :, 1:7], noise[:, :, 1:7])
loss_exp = F.mse_loss(noise_pred[:, :, 7:], noise[:, :, 7:])
# Whether to return intermediate steps
if return_intermediate_steps:
return loss, (x_0, x_t, noise, noise_pred)
return loss, loss_exp, loss_pose
@torch.no_grad()
def forward_sample(
self,
num_points: int,
ref_kps: Optional[Tensor],
ori_kps: Optional[Tensor],
aud_feat: Optional[Tensor],
# Optional overrides
scheduler: Optional[str] = 'ddpm',
# Inference parameters
num_inference_steps: Optional[int] = 50,
eta: Optional[float] = 0.0, # for DDIM
# Whether to return all the intermediate steps in generation
return_sample_every_n_steps: int = -1,
# Whether to disable tqdm
disable_tqdm: bool = False,
):
# Get scheduler from mapping, or use self.scheduler if None
scheduler = self.scheduler if scheduler is None else self.schedulers_map[scheduler]
# Get the size of the noise
Np = num_points
Nf = aud_feat.size(1)
B = 1
D = 3
device = self.device
# Sample noise
x_t = torch.randn(B, Nf, Np, D, device=device)
x_t = x_t[:, :, :, 0]
ref_kps = ref_kps[:,:,0]
# Set timesteps
accepts_offset = "offset" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
extra_set_kwargs = {"offset": 1} if accepts_offset else {}
scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
accepts_eta = "eta" in set(inspect.signature(scheduler.step).parameters.keys())
extra_step_kwargs = {"eta": eta} if accepts_eta else {}
# Loop over timesteps
all_outputs = []
return_all_outputs = (return_sample_every_n_steps > 0)
progress_bar = tqdm(scheduler.timesteps.to(device), desc=f'Sampling ({x_t.shape})', disable=disable_tqdm)
aud_feat = torch.cat([torch.zeros(B, 2, 512).cuda(), aud_feat], 1)
for i, t in enumerate(progress_bar):
x_t_input = torch.cat([ori_kps.unsqueeze(1).detach(),ref_kps.unsqueeze(1).detach(), x_t], dim=1)
# Forward
noise_pred = self.point_model(x_t_input, t.reshape(1).expand(B), context=aud_feat)[:, 2:]
# Step
x_t = scheduler.step(noise_pred, t, x_t, **extra_step_kwargs).prev_sample
# Append to output list if desired
if (return_all_outputs and (i % return_sample_every_n_steps == 0 or i == len(scheduler.timesteps) - 1)):
all_outputs.append(x_t)
# Convert output back into a point cloud, undoing normalization and scaling
output = x_t
output = torch.stack([output,output,output],-1)
if return_all_outputs:
all_outputs = torch.stack(all_outputs, dim=1) # (B, sample_steps, N, D)
return (output, all_outputs) if return_all_outputs else output
def forward(self, batch: dict, mode: str = 'train', **kwargs):
"""A wrapper around the forward method for training and inference"""
if mode == 'train':
return self.forward_train(
pc=batch['sequence_keypoints'],
ref_kps=batch['ref_keypoint'],
ori_kps=batch['ori_keypoint'],
aud_feat=batch['aud_feat'],
mode='train',
**kwargs)
elif mode == 'val':
return self.forward_train(
pc=batch['sequence_keypoints'],
ref_kps=batch['ref_keypoint'],
ori_kps=batch['ori_keypoint'],
aud_feat=batch['aud_feat'],
mode='val',
**kwargs)
elif mode == 'sample':
num_points = 70
return self.forward_sample(
num_points=num_points,
ref_kps=batch['ref_keypoint'],
ori_kps=batch['ori_keypoint'],
aud_feat=batch['aud_feat'],
**kwargs)
else:
raise NotImplementedError() |