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import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, reduce
from diffusers.schedulers.scheduling_ddpm import DDPMScheduler
from diffusion_policy.model.common.normalizer import LinearNormalizer
from diffusion_policy.policy.base_lowdim_policy import BaseLowdimPolicy
from diffusion_policy.model.diffusion.transformer_for_diffusion import TransformerForDiffusion
from diffusion_policy.model.diffusion.mask_generator import LowdimMaskGenerator
class DiffusionTransformerLowdimPolicy(BaseLowdimPolicy):
def __init__(
self,
model: TransformerForDiffusion,
noise_scheduler: DDPMScheduler,
horizon,
obs_dim,
action_dim,
n_action_steps,
n_obs_steps,
num_inference_steps=None,
obs_as_cond=False,
pred_action_steps_only=False,
# parameters passed to step
**kwargs
):
super().__init__()
if pred_action_steps_only:
assert obs_as_cond
self.model = model
self.noise_scheduler = noise_scheduler
self.mask_generator = LowdimMaskGenerator(
action_dim=action_dim,
obs_dim=0 if (obs_as_cond) else obs_dim,
max_n_obs_steps=n_obs_steps,
fix_obs_steps=True,
action_visible=False
)
self.normalizer = LinearNormalizer()
self.horizon = horizon
self.obs_dim = obs_dim
self.action_dim = action_dim
self.n_action_steps = n_action_steps
self.n_obs_steps = n_obs_steps
self.obs_as_cond = obs_as_cond
self.pred_action_steps_only = pred_action_steps_only
self.kwargs = kwargs
if num_inference_steps is None:
num_inference_steps = noise_scheduler.config.num_train_timesteps
self.num_inference_steps = num_inference_steps
# ========= inference ============
def conditional_sample(self,
condition_data, condition_mask,
cond=None, generator=None,
# keyword arguments to scheduler.step
**kwargs
):
model = self.model
scheduler = self.noise_scheduler
trajectory = torch.randn(
size=condition_data.shape,
dtype=condition_data.dtype,
device=condition_data.device,
generator=generator)
# set step values
scheduler.set_timesteps(self.num_inference_steps)
for t in scheduler.timesteps:
# 1. apply conditioning
trajectory[condition_mask] = condition_data[condition_mask]
# 2. predict model output
model_output = model(trajectory, t, cond)
# 3. compute previous image: x_t -> x_t-1
trajectory = scheduler.step(
model_output, t, trajectory,
generator=generator,
**kwargs
).prev_sample
# finally make sure conditioning is enforced
trajectory[condition_mask] = condition_data[condition_mask]
return trajectory
def predict_action(self, obs_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
obs_dict: must include "obs" key
result: must include "action" key
"""
assert 'obs' in obs_dict
assert 'past_action' not in obs_dict # not implemented yet
nobs = self.normalizer['obs'].normalize(obs_dict['obs'])
B, _, Do = nobs.shape
To = self.n_obs_steps
assert Do == self.obs_dim
T = self.horizon
Da = self.action_dim
# build input
device = self.device
dtype = self.dtype
# handle different ways of passing observation
cond = None
cond_data = None
cond_mask = None
if self.obs_as_cond:
cond = nobs[:,:To]
shape = (B, T, Da)
if self.pred_action_steps_only:
shape = (B, self.n_action_steps, Da)
cond_data = torch.zeros(size=shape, device=device, dtype=dtype)
cond_mask = torch.zeros_like(cond_data, dtype=torch.bool)
else:
# condition through impainting
shape = (B, T, Da+Do)
cond_data = torch.zeros(size=shape, device=device, dtype=dtype)
cond_mask = torch.zeros_like(cond_data, dtype=torch.bool)
cond_data[:,:To,Da:] = nobs[:,:To]
cond_mask[:,:To,Da:] = True
# run sampling
nsample = self.conditional_sample(
cond_data,
cond_mask,
cond=cond,
**self.kwargs)
# unnormalize prediction
naction_pred = nsample[...,:Da]
action_pred = self.normalizer['action'].unnormalize(naction_pred)
# get action
if self.pred_action_steps_only:
action = action_pred
else:
start = To - 1
end = start + self.n_action_steps
action = action_pred[:,start:end]
result = {
'action': action,
'action_pred': action_pred
}
if not self.obs_as_cond:
nobs_pred = nsample[...,Da:]
obs_pred = self.normalizer['obs'].unnormalize(nobs_pred)
action_obs_pred = obs_pred[:,start:end]
result['action_obs_pred'] = action_obs_pred
result['obs_pred'] = obs_pred
return result
# ========= training ============
def set_normalizer(self, normalizer: LinearNormalizer):
self.normalizer.load_state_dict(normalizer.state_dict())
def get_optimizer(
self, weight_decay: float, learning_rate: float, betas: Tuple[float, float]
) -> torch.optim.Optimizer:
return self.model.configure_optimizers(
weight_decay=weight_decay,
learning_rate=learning_rate,
betas=tuple(betas))
def compute_loss(self, batch):
# normalize input
assert 'valid_mask' not in batch
nbatch = self.normalizer.normalize(batch)
obs = nbatch['obs']
action = nbatch['action']
# handle different ways of passing observation
cond = None
trajectory = action
if self.obs_as_cond:
cond = obs[:,:self.n_obs_steps,:]
if self.pred_action_steps_only:
To = self.n_obs_steps
start = To - 1
end = start + self.n_action_steps
trajectory = action[:,start:end]
else:
trajectory = torch.cat([action, obs], dim=-1)
# generate impainting mask
if self.pred_action_steps_only:
condition_mask = torch.zeros_like(trajectory, dtype=torch.bool)
else:
condition_mask = self.mask_generator(trajectory.shape)
# Sample noise that we'll add to the images
noise = torch.randn(trajectory.shape, device=trajectory.device)
bsz = trajectory.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(
0, self.noise_scheduler.config.num_train_timesteps,
(bsz,), device=trajectory.device
).long()
# Add noise to the clean images according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_trajectory = self.noise_scheduler.add_noise(
trajectory, noise, timesteps)
# compute loss mask
loss_mask = ~condition_mask
# apply conditioning
noisy_trajectory[condition_mask] = trajectory[condition_mask]
# Predict the noise residual
pred = self.model(noisy_trajectory, timesteps, cond)
pred_type = self.noise_scheduler.config.prediction_type
if pred_type == 'epsilon':
target = noise
elif pred_type == 'sample':
target = trajectory
else:
raise ValueError(f"Unsupported prediction type {pred_type}")
loss = F.mse_loss(pred, target, reduction='none')
loss = loss * loss_mask.type(loss.dtype)
loss = reduce(loss, 'b ... -> b (...)', 'mean')
loss = loss.mean()
return loss
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