File size: 11,250 Bytes
c1f1d32 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 |
from typing import Dict, Tuple
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models as vision_models
from einops import rearrange, 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.common.robomimic_config_util import get_robomimic_config
from equi_diffpo.model.diffusion.mask_generator import LowdimMaskGenerator
from equi_diffpo.model.common.rotation_transformer import RotationTransformer
from robomimic.algo import algo_factory
from robomimic.algo.algo import PolicyAlgo
import robomimic.utils.obs_utils as ObsUtils
from robomimic.models.base_nets import SpatialSoftmax
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
import equi_diffpo.model.vision.crop_randomizer as dmvc
from equi_diffpo.common.pytorch_util import dict_apply, replace_submodules
import numpy as np
import itertools
from einops import rearrange, repeat
from equi_diffpo.model.equi.equi_obs_encoder import EquivariantObsEncVoxel
from equi_diffpo.model.equi.equi_conditional_unet1d_vel import EquiDiffusionUNetVel
# from diffusion_policy.model.equi.equi_conditional_unet1d_2 import D4ConditionalUnet1D
from equi_diffpo.model.vision.voxel_rot_randomizer_rel import VoxelRotRandomizerRel
class DiffusionEquiUNetRelPolicyVoxel(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=(58, 58, 58),
# 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,
initialize=True,
color=True,
depth=True,
# parameters passed to step
**kwargs):
super().__init__()
# parse shape_meta
action_shape = shape_meta['action']['shape']
assert len(action_shape) == 1
action_dim = 13 # 3 + 9 + 1
if color and depth:
obs_channel = 4
elif color:
obs_channel = 3
elif depth:
obs_channel = 1
self.enc = EquivariantObsEncVoxel(
obs_shape=(obs_channel, 64, 64, 64),
crop_shape=crop_shape,
n_hidden=enc_n_hidden,
N=N,
initialize=initialize,
)
obs_feature_dim = enc_n_hidden
global_cond_dim = obs_feature_dim * n_obs_steps
self.diff = EquiDiffusionUNetVel(
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 = VoxelRotRandomizerRel()
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
self.axisangle_to_matrix = RotationTransformer('axis_angle', 'matrix')
# ========= 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
# TODO:
if 'agentview_image' in obs_dict:
del obs_dict['agentview_image']
obs_dict['voxels'][:, :, 1:] /= 255.0
nobs = self.normalizer.normalize(obs_dict)
value = next(iter(nobs.values()))
B, To = value.shape[:2]
T = self.horizon
Da = self.action_dim
Do = self.obs_feature_dim
To = self.n_obs_steps
# build input
device = self.device
dtype = self.dtype
# handle different ways of passing observation
local_cond = None
global_cond = None
# condition through global feature
# this_nobs = dict_apply(nobs, lambda x: x[:,:To,...].reshape(-1,*x.shape[2:]))
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=local_cond,
global_cond=global_cond,
**self.kwargs)
axisangle = self.axisangle_to_matrix.inverse(nsample[:, :, 3:12].reshape(B, T, 3, 3))
nsample = torch.cat([nsample[:, :, :3], axisangle, nsample[:, :, -1:]], dim=-1)
# 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]
horizon = nactions.shape[1]
axisangle = nactions[:, :, 3:6]
matrix = self.axisangle_to_matrix.forward(axisangle).reshape(batch_size, horizon, 9)
nactions = torch.cat([nactions[:, :, :3], matrix, nactions[:, :, -1:]], dim=-1)
# handle different ways of passing observation
local_cond = None
global_cond = None
trajectory = nactions
cond_data = trajectory
# reshape B, T, ... to B*T
# this_nobs = dict_apply(nobs,
# lambda x: x[:,:self.n_obs_steps,...].reshape(-1,*x.shape[2:]))
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=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 |