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diffusion.py
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diffusion.py
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from typing import Dict
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import reduce
from diffusers.schedulers.scheduling_ddim import DDIMScheduler
from policy.diffusion_modules.conditional_unet1d import ConditionalUnet1D
from policy.diffusion_modules.mask_generator import LowdimMaskGenerator
class DiffusionUNetPolicy(nn.Module):
def __init__(self,
action_dim,
horizon,
n_obs_steps,
obs_feature_dim,
num_inference_steps=20,
diffusion_step_embed_dim=256,
down_dims=(256,512),
kernel_size=5,
n_groups=8,
cond_predict_scale=True,
# parameters passed to step
**kwargs):
super().__init__()
# create diffusion model
input_dim = action_dim
global_cond_dim = obs_feature_dim * n_obs_steps
self.model = ConditionalUnet1D(
input_dim=input_dim,
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
)
# create noise scheduler
self.noise_scheduler = DDIMScheduler(
num_train_timesteps=100,
beta_start=0.0001,
beta_end=0.02,
beta_schedule="squaredcos_cap_v2",
clip_sample=True,
set_alpha_to_one=True,
steps_offset=0,
prediction_type="epsilon"
)
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.horizon = horizon
self.obs_feature_dim = obs_feature_dim
self.action_dim = action_dim
self.n_obs_steps = n_obs_steps
self.kwargs = kwargs
if num_inference_steps is None:
num_inference_steps = self.noise_scheduler.config.num_train_timesteps
self.num_inference_steps = num_inference_steps
# ========= 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.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,
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, readout) -> Dict[str, torch.Tensor]:
B = readout.shape[0]
T = self.horizon
Da = self.action_dim
Do = self.obs_feature_dim
To = self.n_obs_steps
# build input
device = readout.device
dtype = readout.dtype
obs_features = readout
assert obs_features.shape[0] == B * To
# condition through global feature
local_cond = None
global_cond = None
# reshape back to B, Do
global_cond = obs_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
sample = self.conditional_sample(
cond_data,
cond_mask,
local_cond=local_cond,
global_cond=global_cond,
**self.kwargs)
action_pred = sample[...,:Da]
return action_pred
# ========= training ============
def compute_loss(self, readout, actions):
batch_size = readout.shape[0]
# handle different ways of passing observation
local_cond = None
global_cond = None
trajectory = actions
cond_data = trajectory
assert readout.shape[0] == batch_size * self.n_obs_steps
# reshape back to B, Do
global_cond = readout.reshape(batch_size, -1) # (B, T*C)
# 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.model(noisy_trajectory, timesteps,
local_cond=local_cond, 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