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Zero
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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
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)
# x_t_input = torch.cat([ref_kps.unsqueeze(1), x_t], dim=1)
# ori_kps_repeat = torch.repeat_interleave(ori_kps.unsqueeze(1), repeats=Nf+1, dim=1)
# x_t_input = torch.cat([x_t_input, ori_kps_repeat], dim=-1) #B, 32+1, 51+45
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
loss = F.mse_loss(noise_pred, noise)
loss_pose = F.mse_loss(noise_pred[:, :, :6], noise[:, :, :6])
loss_exp = F.mse_loss(noise_pred[:, :, 6:], noise[:, :, 6:])
# Whether to return intermediate steps
if return_intermediate_steps:
return loss, (x_0, x_t, noise, noise_pred)
return loss, loss_exp, loss_pose
# 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
#
# # ori_kps = torch.repeat_interleave(ori_kps.unsqueeze(1), Nf, dim=1) # B, Nf, 45
# #
# # ref_kps = ref_kps[:, :, 0]
# # ref_kps = torch.repeat_interleave(ref_kps.unsqueeze(1), Nf, dim=1) # B, Nf, 91
#
# x_0 = x_0[:,:,:,0]
#
# # 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([ref_kps.unsqueeze(1), x_t], dim=1)
#
# # x_0 = torch.cat([x_0, ref_kps, ori_kps], dim=2) # B, Nf, 91+91+45
#
# x_t_input = torch.cat([ref_kps.unsqueeze(1), x_t], dim=1)
# # x_t_input = torch.cat([ori_kps.unsqueeze(1), ref_kps.unsqueeze(1), x_t], dim=1)
#
# aud_feat = torch.cat([torch.zeros(B, 1, 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)
# noise_pred = noise_pred[:, 1:]
#
# # Check
# # if not noise_pred.shape == noise.shape:
# # raise ValueError(f'{noise_pred.shape=} and {noise.shape=}')
#
# # Loss
# loss = F.mse_loss(noise_pred, noise)
#
# # loss_kp = F.mse_loss(noise_pred[:, :, :45], noise[:, :, :45])
#
# # Whether to return intermediate steps
# if return_intermediate_steps:
# return loss, (x_0, x_t, noise, noise_pred)
#
# return loss
# @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] = 1000,
# 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]
#
# # ori_kps = torch.repeat_interleave(ori_kps.unsqueeze(1), Nf, dim=1) # B, Nf, 45
#
# ref_kps = ref_kps[:, :, 0]
# # ref_kps = torch.repeat_interleave(ref_kps.unsqueeze(1), Nf, dim=1) # B, Nf, 91
#
# # 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)
#
# # Prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# # and should be between [0, 1]
# 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)
#
# # ori_kps = torch.repeat_interleave(ori_kps[:, 6:].unsqueeze(1), Nf + 1, dim=1)
# aud_feat = torch.cat([torch.zeros(B, 1, 512).cuda(), aud_feat], 1)
# # aud_feat = torch.cat([ori_kps, aud_feat], -1)
#
# # aud_feat = torch.cat([torch.zeros(B, 2, 512).cuda(), aud_feat], 1)
#
# for i, t in enumerate(progress_bar):
#
# # Conditioning
# x_t_input = torch.cat([ref_kps.unsqueeze(1).detach(), x_t], dim=1)
# # x_t_input = torch.cat([ori_kps.unsqueeze(1).detach(), ref_kps.unsqueeze(1).detach(), x_t], dim=1)
# # x_t_input = torch.cat([x_t, ref_kps, ori_kps], dim=2) # B, Nf, 91+91+45
#
# # Forward
# # noise_pred = self.point_model(x_t_input, t.reshape(1).expand(B), context=aud_feat)[:, 1:]
# noise_pred = self.point_model(x_t_input, t.reshape(1).expand(B), context=aud_feat)[:, 1:]
#
# # noise_pred = noise_pred[:, :, :51]
#
# # Step
# # x_t = x_t[:, :, :51]
# 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
@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] = 1000,
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)
# Prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
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)
# ori_kps = torch.repeat_interleave(ori_kps[:, 6:].unsqueeze(1), Nf + 1, dim=1)
# aud_feat = torch.cat([torch.zeros(B, 1, 512).cuda(), aud_feat], 1)
# aud_feat = torch.cat([ori_kps, aud_feat], -1)
aud_feat = torch.cat([torch.zeros(B, 2, 512).cuda(), aud_feat], 1)
for i, t in enumerate(progress_bar):
# Conditioning
# x_t_input = torch.cat([ref_kps.unsqueeze(1), x_t], dim=1)
#
# ori_kps_repeat = torch.repeat_interleave(ori_kps.unsqueeze(1), repeats=Nf + 1, dim=1)
#
# x_t_input = torch.cat([x_t_input.detach(), ori_kps_repeat.detach()], dim=-1) # B, 32+1, 51+45
x_t_input = torch.cat([ori_kps.unsqueeze(1).detach(),ref_kps.unsqueeze(1).detach(), x_t], dim=1)
# x_t_input = torch.cat([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)[:, 1:]
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 = 68
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() |