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from typing import Dict, Tuple
import torch
import torch.nn.functional as F
from einops import reduce
from diffusers.schedulers.scheduling_ddpm import DDPMScheduler
from equi_diffpo.model.common.normalizer import LinearNormalizer
from equi_diffpo.policy.base_image_policy import BaseImagePolicy
from equi_diffpo.model.diffusion.mask_generator import LowdimMaskGenerator
try:
import robomimic.models.base_nets as rmbn
if not hasattr(rmbn, 'CropRandomizer'):
raise ImportError("CropRandomizer is not in robomimic.models.base_nets")
except ImportError:
import robomimic.models.obs_core as rmbn
from equi_diffpo.model.equi.equi_obs_encoder import EquivariantObsEnc
from equi_diffpo.model.equi.equi_conditional_unet1d import EquiDiffusionUNet
from equi_diffpo.model.vision.rot_randomizer import RotRandomizer
class DiffusionEquiUNetCNNEncPolicy(BaseImagePolicy):
def __init__(self,
shape_meta: dict,
noise_scheduler: DDPMScheduler,
# task params
horizon,
n_action_steps,
n_obs_steps,
num_inference_steps=None,
# image
crop_shape=(76, 76),
# arch
N=8,
enc_n_hidden=64,
diffusion_step_embed_dim=256,
down_dims=(256,512,1024),
kernel_size=5,
n_groups=8,
cond_predict_scale=True,
rot_aug=False,
# parameters passed to step
**kwargs):
super().__init__()
# parse shape_meta
action_shape = shape_meta['action']['shape']
assert len(action_shape) == 1
action_dim = action_shape[0]
obs_shape_meta = shape_meta['obs']
self.enc = EquivariantObsEnc(
obs_shape=obs_shape_meta['agentview_image']['shape'],
crop_shape=crop_shape,
n_hidden=enc_n_hidden,
N=N)
obs_feature_dim = enc_n_hidden
global_cond_dim = obs_feature_dim * n_obs_steps
self.diff = EquiDiffusionUNet(
act_emb_dim=64,
local_cond_dim=None,
global_cond_dim=global_cond_dim,
diffusion_step_embed_dim=diffusion_step_embed_dim,
down_dims=down_dims,
kernel_size=kernel_size,
n_groups=n_groups,
cond_predict_scale=cond_predict_scale,
N=N,
)
print("Enc params: %e" % sum(p.numel() for p in self.enc.parameters()))
print("Diff params: %e" % sum(p.numel() for p in self.diff.parameters()))
self.mask_generator = LowdimMaskGenerator(
action_dim=action_dim,
obs_dim=0,
max_n_obs_steps=n_obs_steps,
fix_obs_steps=True,
action_visible=False
)
self.normalizer = LinearNormalizer()
self.rot_randomizer = RotRandomizer()
self.horizon = horizon
self.action_dim = action_dim
self.n_action_steps = n_action_steps
self.n_obs_steps = n_obs_steps
self.crop_shape = crop_shape
self.obs_feature_dim = obs_feature_dim
self.rot_aug = rot_aug
self.kwargs = kwargs
self.noise_scheduler = noise_scheduler
if num_inference_steps is None:
num_inference_steps = noise_scheduler.config.num_train_timesteps
self.num_inference_steps = num_inference_steps
# ========= 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],
eps: float
) -> torch.optim.Optimizer:
optimizer = torch.optim.AdamW(
self.parameters(), weight_decay=weight_decay, lr=learning_rate, betas=betas, eps=eps
)
return optimizer
# ========= inference ============
def conditional_sample(self,
condition_data, condition_mask,
local_cond=None, global_cond=None,
generator=None,
# keyword arguments to scheduler.step
**kwargs
):
model = self.diff
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,
local_cond=local_cond, global_cond=global_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 'past_action' not in obs_dict # not implemented yet
# normalize input
nobs = self.normalizer.normalize(obs_dict)
value = next(iter(nobs.values()))
B, To = value.shape[:2]
T = self.horizon
Da = self.action_dim
To = self.n_obs_steps
# build input
device = self.device
dtype = self.dtype
nobs_features = self.enc(nobs)
# reshape back to B, Do
global_cond = nobs_features.reshape(B, -1)
# empty data for action
cond_data = torch.zeros(size=(B, T, Da), device=device, dtype=dtype)
cond_mask = torch.zeros_like(cond_data, dtype=torch.bool)
# run sampling
nsample = self.conditional_sample(
cond_data,
cond_mask,
local_cond=None,
global_cond=global_cond,
**self.kwargs)
# unnormalize prediction
naction_pred = nsample[...,:Da]
action_pred = self.normalizer['action'].unnormalize(naction_pred)
# get action
start = To - 1
end = start + self.n_action_steps
action = action_pred[:,start:end]
result = {
'action': action,
'action_pred': action_pred
}
return result
# ========= training ============
def set_normalizer(self, normalizer: LinearNormalizer):
self.normalizer.load_state_dict(normalizer.state_dict())
def compute_loss(self, batch):
# normalize input
assert 'valid_mask' not in batch
nobs = self.normalizer.normalize(batch['obs'])
nactions = self.normalizer['action'].normalize(batch['action'])
if self.rot_aug:
nobs, nactions = self.rot_randomizer(nobs, nactions)
batch_size = nactions.shape[0]
trajectory = nactions
cond_data = trajectory
nobs_features = self.enc(nobs)
# reshape back to B, Do
global_cond = nobs_features.reshape(batch_size, -1)
# generate impainting mask
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] = cond_data[condition_mask]
# Predict the noise residual
pred = self.diff(noisy_trajectory, timesteps,
local_cond=None, global_cond=global_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 |