diff --git a/lerobot/common/datasets/factory.py b/lerobot/common/datasets/factory.py index 07afb614..7ea79d26 100644 --- a/lerobot/common/datasets/factory.py +++ b/lerobot/common/datasets/factory.py @@ -6,7 +6,7 @@ import torch from torchvision.transforms import v2 from lerobot.common.datasets.utils import compute_stats -from lerobot.common.transforms import NormalizeTransform, Prod +from lerobot.common.transforms import Prod DATA_DIR = Path(os.environ["DATA_DIR"]) if "DATA_DIR" in os.environ else None @@ -77,15 +77,16 @@ def make_dataset( transforms = v2.Compose( [ - Prod(in_keys=clsfunc.image_keys, prod=1 / 255.0), - NormalizeTransform( - stats, - in_keys=[ - "observation.state", - "action", - ], - mode=normalization_mode, - ), + # TODO(now) + Prod(in_keys=clsfunc.image_keys, prod=1 / 1.0), + # NormalizeTransform( + # stats, + # in_keys=[ + # "observation.state", + # "action", + # ], + # mode=normalization_mode, + # ), ] ) diff --git a/lerobot/common/policies/factory.py b/lerobot/common/policies/factory.py index 9698175d..0a2ec728 100644 --- a/lerobot/common/policies/factory.py +++ b/lerobot/common/policies/factory.py @@ -22,7 +22,7 @@ def _policy_cfg_from_hydra_cfg(policy_cfg_class, hydra_cfg): def make_policy(hydra_cfg: DictConfig): if hydra_cfg.policy.name == "tdmpc": - from lerobot.common.policies.tdmpc.policy import TDMPCPolicy + from lerobot.common.policies.tdmpc.modeling_tdmpc import TDMPCPolicy policy = TDMPCPolicy( hydra_cfg.policy, diff --git a/lerobot/common/policies/tdmpc/helper.py b/lerobot/common/policies/tdmpc/helper.py index 964f1718..54385989 100644 --- a/lerobot/common/policies/tdmpc/helper.py +++ b/lerobot/common/policies/tdmpc/helper.py @@ -1,11 +1,13 @@ import os import pickle import re +from typing import Callable import numpy as np import torch import torch.nn as nn import torch.nn.functional as F # noqa: N812 +from torch import Tensor from torch import distributions as pyd from torch.distributions.utils import _standard_normal @@ -96,7 +98,9 @@ def set_requires_grad(net, value): class TruncatedNormal(pyd.Normal): - """Utility class implementing the truncated normal distribution.""" + """Utility class implementing the truncated normal distribution while still passing gradients through. + TODO(now): consider simplifying the hell out of this but only once you understand what self.eps is for. + """ default_sample_shape = torch.Size() @@ -107,6 +111,8 @@ class TruncatedNormal(pyd.Normal): self.eps = eps def _clamp(self, x): + # TODO(now): Hm looks like this is designed to pass gradients through! + # TODO(now): Understand what this eps is for. clamped_x = torch.clamp(x, self.low + self.eps, self.high - self.eps) x = x - x.detach() + clamped_x.detach() return x @@ -141,7 +147,12 @@ class Flatten(nn.Module): return x.view(x.size(0), -1) -def enc(cfg): +def enc(cfg) -> Callable[[dict[str, Tensor] | Tensor], dict[str, Tensor] | Tensor]: + """ + Creates encoders for pixel and/or state modalities. + TODO(now): Consolidate this into just working with a dict even if there is just one modality. + """ + obs_shape = { "rgb": (3, cfg.img_size, cfg.img_size), "state": (cfg.state_dim,), @@ -152,6 +163,7 @@ def enc(cfg): if cfg.modality in {"pixels", "all"}: C = int(3 * cfg.frame_stack) # noqa: N806 pixels_enc_layers = [ + # TODO(now): Leave this to the env / data loader NormalizeImg(), nn.Conv2d(C, cfg.num_channels, 7, stride=2), nn.ReLU(), @@ -198,7 +210,7 @@ def enc(cfg): return Multiplexer(nn.ModuleDict(encoders)) -def mlp(in_dim, mlp_dim, out_dim, act_fn=DEFAULT_ACT_FN): +def mlp(in_dim: int, mlp_dim: int | tuple[int, int], out_dim: int, act_fn=DEFAULT_ACT_FN): """Returns an MLP.""" if isinstance(mlp_dim, int): mlp_dim = [mlp_dim, mlp_dim] @@ -214,7 +226,10 @@ def mlp(in_dim, mlp_dim, out_dim, act_fn=DEFAULT_ACT_FN): def dynamics(in_dim, mlp_dim, out_dim, act_fn=DEFAULT_ACT_FN): - """Returns a dynamics network.""" + """Returns a dynamics network. + + TODO(now): this needs a better name. It's also an MLP... + """ return nn.Sequential( mlp(in_dim, mlp_dim, out_dim, act_fn), nn.LayerNorm(out_dim), @@ -271,7 +286,7 @@ def aug(cfg): class ConvExt(nn.Module): - """Auxiliary conv net accommodating high-dim input""" + """Helper to deal with arbitrary dimensions (B, *, C, H, W) for the input images.""" def __init__(self, conv): super().__init__() @@ -279,10 +294,13 @@ class ConvExt(nn.Module): def forward(self, x): if x.ndim > 4: + # x has some has shape (B, * , C, H, W) so we first flatten (B, *) into the first dim, run the + # layers, then unflatten to return the result. batch_shape = x.shape[:-3] out = self.conv(x.view(-1, *x.shape[-3:])) out = out.view(*batch_shape, *out.shape[1:]) else: + # x has shape (B, C, H, W). out = self.conv(x) return out @@ -290,7 +308,7 @@ class ConvExt(nn.Module): class Multiplexer(nn.Module): """Model multiplexer""" - def __init__(self, choices): + def __init__(self, choices: nn.ModuleDict): super().__init__() self.choices = choices @@ -542,6 +560,7 @@ def normalize_returns(dataset, scaling=1000): def get_reward_normalizer(cfg, dataset): """ Get a reward normalizer for the dataset + TODO(now): Leave this to the dataloader/env """ if cfg.task.startswith("xarm"): return lambda x: x @@ -570,6 +589,8 @@ def linear_schedule(schdl, step): return (1.0 - mix) * init + mix * final match = re.match(r"linear\((.+),(.+),(.+)\)", schdl) if match: + # TODO(now): Looks like the original tdmpc code uses this with + # `horizon_schedule: linear(1, ${horizon}, 25000)` init, final, duration = (float(g) for g in match.groups()) mix = np.clip(step / duration, 0.0, 1.0) return (1.0 - mix) * init + mix * final diff --git a/lerobot/common/policies/tdmpc/policy.py b/lerobot/common/policies/tdmpc/policy.py index 42792cc9..6a85bf3d 100644 --- a/lerobot/common/policies/tdmpc/policy.py +++ b/lerobot/common/policies/tdmpc/policy.py @@ -8,6 +8,7 @@ import einops import numpy as np import torch import torch.nn as nn +from torch import Tensor import lerobot.common.policies.tdmpc.helper as h from lerobot.common.policies.utils import populate_queues @@ -45,12 +46,13 @@ class TOLD(nn.Module): if hasattr(self, "_V"): h.set_requires_grad(self._V, enable) - def encode(self, obs): + def encode(self, obs: dict[str, Tensor]) -> Tensor: """Encodes an observation into its latent representation.""" out = self._encoder(obs) if isinstance(obs, dict): # fusion - out = torch.stack([v for k, v in out.items()]).mean(dim=0) + # TODO(now): careful about the order! + out = torch.stack(list(out.values())).mean(dim=0) return out def next(self, z, a): @@ -63,8 +65,14 @@ class TOLD(nn.Module): x = torch.cat([z, a], dim=-1) return self._dynamics(x) - def pi(self, z, std=0): - """Samples an action from the learned policy (pi).""" + def pi(self, z: Tensor, std: float = 0.0) -> float: + """Samples an action from the learned policy (pi). + + TODO(now): Explain why added noise (something to do with FOWM improvements) + + Returns: + action + """ mu = torch.tanh(self._pi(z)) if std > 0: std = torch.ones_like(mu) * std @@ -101,6 +109,7 @@ class TDMPCPolicy(nn.Module): self.n_obs_steps = n_obs_steps self.n_action_steps = n_action_steps self.device = get_safe_torch_device(device) + # TODO(now): How should this be set when loading a model for eval? self.std = h.linear_schedule(cfg.std_schedule, 0) self.model = TOLD(cfg) self.model.to(self.device) @@ -140,7 +149,7 @@ class TDMPCPolicy(nn.Module): } @torch.no_grad() - def select_action(self, batch, step): + def select_action(self, batch: dict[str, Tensor], step): assert "observation.image" in batch assert "observation.state" in batch assert len(batch) == 2 @@ -154,6 +163,8 @@ class TDMPCPolicy(nn.Module): if len(self._queues["action"]) == 0: batch = {key: torch.stack(list(self._queues[key]), dim=1) for key in batch} + # TODO(now): this shouldnt be necessary. downstream code should handle this the same as + # n_obs_step > 1 if self.n_obs_steps == 1: # hack to remove the time dimension for key in batch: @@ -163,11 +174,12 @@ class TDMPCPolicy(nn.Module): actions = [] batch_size = batch["observation.image"].shape[0] for i in range(batch_size): + # TODO(now): this looks like selecting the ith but keeping dims. Do it in a cleaner way. + # TODO(now): this looks like maybe it doesn't care about what the modality choice is? obs = { "rgb": batch["observation.image"][[i]], "state": batch["observation.state"][[i]], } - # Note: unsqueeze needed because `act` still uses non-batch logic. action = self.act(obs, t0=t0, step=self.step) actions.append(action) action = torch.stack(actions) @@ -180,33 +192,36 @@ class TDMPCPolicy(nn.Module): return action @torch.no_grad() - def act(self, obs, t0=False, step=None): + def act(self, obs: dict[str, Tensor], t0=False, step=None): """Take an action. Uses either MPC or the learned policy, depending on the self.cfg.mpc flag.""" + # TODO(now): Understand this detach. obs = {k: o.detach() for k, o in obs.items()} if isinstance(obs, dict) else obs.detach() # TODO(now): This is for compatibility with official weights. Remove. # obs['rgb'] = obs['rgb'] * 255 # obs_ = torch.load('/tmp/obs.pth') # out_ = torch.load('/tmp/out.pth') - # breakpoint() z = self.model.encode(obs) - # breakpoint() if self.cfg.mpc: + assert step is not None a = self.plan(z, t0=t0, step=step) else: a = self.model.pi(z, self.cfg.min_std * self.model.training).squeeze(0) return a @torch.no_grad() - def estimate_value(self, z, actions, horizon): + def estimate_value(self, z: Tensor, actions: Tensor, horizon: int): """Estimate value of a trajectory starting at latent state z and executing given actions.""" G, discount = 0, 1 for t in range(horizon): + # TODO(now): What is the uncertainty cost. if self.cfg.uncertainty_cost > 0: G -= ( discount * self.cfg.uncertainty_cost * self.model.Q(z, actions[t], return_type="all").std(dim=0) ) + # TODO(now): Isn't this repeating the computation of the trajectory? + # TODO(now): `next` and `next_dynamics` are poorly named. z, reward = self.model.next(z, actions[t]) G += discount * reward discount *= self.cfg.discount @@ -217,22 +232,27 @@ class TDMPCPolicy(nn.Module): return G @torch.no_grad() - def plan(self, z, step=None, t0=True): - """ - Plan next action using TD-MPC inference. - z: latent state. - step: current time step. determines e.g. planning horizon. - t0: whether current step is the first step of an episode. - """ - # during eval: eval_mode: uniform sampling and action noise is disabled during evaluation. + def plan(self, z: Tensor, step: int, t0: bool = True) -> Tensor: + """Plan next action using TD-MPC inference. - assert step is not None + during eval: eval_mode: uniform sampling and action noise is disabled during evaluation. + + Args: + z: latent state. + step: current time step. determines e.g. planning horizon. TODO(now): So this differs from training step + t0: whether current step is the first step of an episode. + """ # Seed steps + # TODO(now): What are seed steps? if step < self.cfg.seed_steps and self.model.training: return torch.empty(self.action_dim, dtype=torch.float32, device=self.device).uniform_(-1, 1) # Sample policy trajectories + # TODO(now): Looks like horizon_schedule is not used in fowm. There, this ends up evaluating to + # self.cfg.horizon. On the other hand, the TDMPC code base does use `horizon_schedule: linear(1, ${horizon}, 25000)` + # Basically this causes the horizon to ramp up linearly from 1 to self.cfg.horizon. Find out why. horizon = int(min(self.cfg.horizon, h.linear_schedule(self.cfg.horizon_schedule, step))) + # TODO(now): What is this? num_pi_trajs = int(self.cfg.mixture_coef * self.cfg.num_samples) if num_pi_trajs > 0: pi_actions = torch.empty(horizon, num_pi_trajs, self.action_dim, device=self.device) @@ -246,9 +266,10 @@ class TDMPCPolicy(nn.Module): mean = torch.zeros(horizon, self.action_dim, device=self.device) std = self.cfg.max_std * torch.ones(horizon, self.action_dim, device=self.device) if not t0 and hasattr(self, "_prev_mean"): + # TODO(now): I think this is a "warm start" mean[:-1] = self._prev_mean[1:] - # Iterate CEM + # Iterate CEM (cross-entropy method) for _ in range(self.cfg.iterations): actions = torch.clamp( mean.unsqueeze(1) @@ -258,6 +279,8 @@ class TDMPCPolicy(nn.Module): 1, ) if num_pi_trajs > 0: + # TODO(now): So this is having a batch of model generated action trajectories and randomly + # generated ones? actions = torch.cat([actions, pi_actions], dim=1) # Compute elite actions @@ -267,6 +290,7 @@ class TDMPCPolicy(nn.Module): # Update parameters max_value = elite_value.max(0)[0] + # TODO(now): Perhaps this is better viewed as torch.exp(-(self.cfg.temperature * (max_value - elite_value)))? score = torch.exp(self.cfg.temperature * (elite_value - max_value)) score /= score.sum(0) _mean = torch.sum(score.unsqueeze(0) * elite_actions, dim=1) / (score.sum(0) + 1e-9) @@ -277,6 +301,8 @@ class TDMPCPolicy(nn.Module): ) / (score.sum(0) + 1e-9) ) + # TODO(now): self.std seems to be modified by update. But update has no reason to be called when + # we are loading a pretrained model. _std = _std.clamp_(self.std, self.cfg.max_std) mean, std = self.cfg.momentum * mean + (1 - self.cfg.momentum) * _mean, _std @@ -289,7 +315,7 @@ class TDMPCPolicy(nn.Module): score = score.squeeze(1).cpu().numpy() actions = elite_actions[:, np.random.choice(np.arange(score.shape[0]), p=score)] self._prev_mean = mean - mean, std = actions[0], _std[0] + mean, std = actions[0], _std[0] # TODO(now): not std[0]? a = mean if self.model.training: a += std * torch.randn(self.action_dim, device=std.device) diff --git a/lerobot/scripts/eval.py b/lerobot/scripts/eval.py index 5193aa01..c236d828 100644 --- a/lerobot/scripts/eval.py +++ b/lerobot/scripts/eval.py @@ -126,6 +126,22 @@ def eval_policy( max_steps = env.envs[0]._max_episode_steps progbar = trange(max_steps, desc=f"Running eval with {max_steps} steps (maximum) per rollout.") while not done.all(): + # Receive observation: + import os + from time import sleep + + while True: + if not os.path.exists("/tmp/mutex.txt"): + sleep(0.01) + continue + observation = {} + observation["rgb"] = np.load("/tmp/rgb.npy") + observation["state"] = np.load("/tmp/state.npy") + observation["pixels"] = observation["rgb"].transpose(1, 2, 0)[None] + observation["agent_pos"] = observation["state"][None] + done = np.load("/tmp/done.npy") + break + # format from env keys to lerobot keys observation = preprocess_observation(observation) if return_episode_data: @@ -142,9 +158,21 @@ def eval_policy( with torch.inference_mode(): action = policy.select_action(observation, step=step) + # Send action: + while True: + if not os.path.exists("/tmp/mutex.txt"): + sleep(0.01) + continue + torch.save(action[0], "/tmp/action.pth") + os.remove("/tmp/mutex.txt") + break + + if done: + policy.reset() + continue + # apply inverse transform to unnormalize the action action = postprocess_action(action, transform) - action = np.array([[0, 0, 0, 0]], dtype=np.float32) # apply the next action observation, reward, terminated, truncated, info = env.step(action) @@ -332,8 +360,6 @@ def eval(cfg: dict, out_dir=None, stats_path=None): logging.info("Making transforms.") # TODO(alexander-soare): Completely decouple datasets from evaluation. transform = make_dataset(cfg, stats_path=stats_path).transform - # TODO(now) - transform = None logging.info("Making environment.") env = make_env(cfg, num_parallel_envs=cfg.eval_episodes) diff --git a/poetry.lock b/poetry.lock index a70e404a..c351700e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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