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from vitra.models.action_model.dit import DiT
from vitra.models.action_model import create_diffusion
from . import gaussian_diffusion as gd
from vitra.datasets.dataset_utils import ActionFeature
import torch
from torch import nn
def DiT_T(**kwargs):
return DiT(depth=3, hidden_size=256, num_heads=4, **kwargs)
def DiT_S(**kwargs):
return DiT(depth=6, hidden_size=384, num_heads=4, **kwargs)
def DiT_M(**kwargs):
return DiT(depth=12, hidden_size=384, num_heads=6, **kwargs)
def DiT_B(**kwargs):
return DiT(depth=12, hidden_size=768, num_heads=12, **kwargs)
def DiT_L(**kwargs):
return DiT(depth=24, hidden_size=1024, num_heads=16, **kwargs)
DiT_models = {'DiT-S': DiT_S, 'DiT-M': DiT_M, 'DiT-B': DiT_B, 'DiT-T': DiT_T, 'DiT-L': DiT_L}
class DiffusionPolicy(nn.Module):
def __init__(
self,
token_size,
model_type='DiT-B',
in_channels=192,
future_action_window_size=16,
past_action_window_size=0,
use_state=None,
action_type='angle',
diffusion_steps=100,
state_dim=None,
loss_type='human',
):
super().__init__()
# SimpleMLP takes in x_t, timestep, and condition, and outputs predicted noise.
self.in_channels = in_channels
# GaussianDiffusion offers forward and backward functions q_sample and p_sample.
self.diffusion_steps = diffusion_steps
self.diffusion = create_diffusion(timestep_respacing="",
noise_schedule = 'squaredcos_cap_v2',
diffusion_steps=self.diffusion_steps,
sigma_small=True,
learn_sigma = False
)
#self.diffusion = create_diffusion(timestep_respacing="", noise_schedule = 'linear', diffusion_steps=100, sigma_small=True, learn_sigma = False)
self.ddim_diffusion = None
if self.diffusion.model_var_type in [gd.ModelVarType.LEARNED, gd.ModelVarType.LEARNED_RANGE]:
learn_sigma = True
else:
learn_sigma = False
self.past_action_window_size = past_action_window_size
self.future_action_window_size = future_action_window_size
self.use_state = use_state
self.action_type = action_type
# Get loss components and hand group mapping from ActionFeature
if loss_type == 'human':
self.loss_components = ActionFeature.get_loss_components(action_type)
elif loss_type == 'robot':
self.loss_components = ActionFeature.get_xhand_loss_components()
else:
raise ValueError(f"Unknown loss_type: {loss_type}")
self.net = DiT_models[model_type](
token_size = token_size,
action_dim = in_channels,
class_dropout_prob = 0.1,
learn_sigma = learn_sigma,
future_action_window_size = future_action_window_size,
past_action_window_size = past_action_window_size,
use_state = use_state,
state_dim=state_dim
)
# Given condition z and ground truth token x, x_mask, compute loss
def loss(self, x, z, x_mask, state=None, state_mask=None):
# sample random noise and timestep
noise = torch.randn_like(x) # [B, T, C]
timestep = torch.randint(0, self.diffusion.num_timesteps, (x.size(0),), device= x.device)
# sample x_t from x
x_t = self.diffusion.q_sample(x, timestep, noise)
# predict noise from x_t
noise_pred = self.net(x_t, timestep, z, x_mask, state, state_mask)
assert noise_pred.shape == noise.shape == x.shape
# L2 loss with mask
square_delta = (noise_pred - noise) ** 2 * x_mask
# Generic mask loss computation function
def mask_loss(from_dim, to_dim):
s = square_delta[:, :, from_dim:to_dim].sum()
n = x_mask[:, :, from_dim:to_dim].sum()
return s / n if n > 0 else 0
# Compute loss for each component using ActionFeature definitions
component_losses = {}
component_counts = {}
for name, (start, end, weight) in self.loss_components.items():
component_losses[name] = mask_loss(start, end) * weight
component_counts[name] = x_mask[:, :, start].sum()
total_count = sum(component_counts.values())
if total_count == 0:
loss = square_delta[0, 0, 0]
else:
loss = sum(
component_losses[k] * component_counts[k]
for k in component_counts.keys()
) / total_count
# Return loss with detailed component losses for logging
return {
"loss": loss,
**component_losses, # Unpack all component losses
}
# Given condition and noise, sample x using reverse diffusion process
def sample(self,
action_features,
cfg_scale,
current_state,
current_state_mask,
use_ddim,
num_ddim_steps,
action_masks,
):
B = action_features.shape[0]
noise = torch.randn(action_features.shape[0], self.future_action_window_size+1,
self.in_channels, device=action_features.device) #[B, T, D]
x_mask = action_masks.to(action_features.device)
using_cfg = cfg_scale > 1.0
if using_cfg:
noise = torch.cat([noise, noise], 0)
uncondition = self.net.z_embedder.uncondition
uncondition = uncondition.unsqueeze(0) #[1, D]
uncondition = uncondition.expand(B, 1, -1) #[B, 1, D]
z = torch.cat([action_features, uncondition], 0)
cfg_scale = cfg_scale
if self.use_state == 'DiT':
model_kwargs = dict(
z=z, x_mask=x_mask,
cfg_scale=cfg_scale, state=current_state,
state_mask=current_state_mask
)
else:
model_kwargs = dict(z=z, x_mask=x_mask, cfg_scale=cfg_scale)
sample_fn = self.net.forward_with_cfg
else:
if self.use_state == 'DiT':
model_kwargs = dict(z=action_features, x_mask=x_mask, state=current_state, state_mask=current_state_mask)
else:
model_kwargs = dict(z=action_features, x_mask=x_mask)
sample_fn = self.net.forward
if use_ddim and num_ddim_steps is not None:
if self.ddim_diffusion is None:
self.create_ddim(ddim_step=num_ddim_steps)
samples = self.ddim_diffusion.ddim_sample_loop(
sample_fn,
noise.shape,
noise,
clip_denoised=False,
model_kwargs=model_kwargs,
progress=False,
device=action_features.device,
eta=0.0
)
else:
samples = self.ddim_diffusion.diffusion.p_sample_loop(
sample_fn,
noise.shape,
noise,
clip_denoised=False,
model_kwargs=model_kwargs,
progress=False,
device=action_features.device
)
if using_cfg:
samples, _ = samples.chunk(2, dim=0) # Remove null class samples
return samples
# Create DDIM sampler
def create_ddim(self, ddim_step=10):
self.ddim_diffusion = create_diffusion(
timestep_respacing="ddim"+str(ddim_step),
noise_schedule = 'squaredcos_cap_v2',
diffusion_steps=self.diffusion_steps,
sigma_small=True,
learn_sigma = False
)
return self.ddim_diffusion |