Improve visualize_dataset, Improve AbstractReplayBuffer, Small improvements
This commit is contained in:
Remi Cadene
@@ -1,6 +1,5 @@
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import abc
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import logging
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import math
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from pathlib import Path
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from typing import Callable
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@@ -50,6 +49,22 @@ class AbstractExperienceReplay(TensorDictReplayBuffer):
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transform=transform,
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)
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@property
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def stats_patterns(self) -> dict:
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return {
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("observation", "state"): "b c -> 1 c",
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("observation", "image"): "b c h w -> 1 c 1 1",
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("action"): "b c -> 1 c",
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}
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@property
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def image_keys(self) -> list:
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return [("observation", "image")]
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@property
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def num_cameras(self) -> int:
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return len(self.image_keys)
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@property
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def num_samples(self) -> int:
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return len(self)
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@@ -67,7 +82,7 @@ class AbstractExperienceReplay(TensorDictReplayBuffer):
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stats = torch.load(stats_path)
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else:
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logging.info(f"compute_stats and save to {stats_path}")
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stats = self._compute_stats(self._storage._storage, num_batch, batch_size)
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stats = self._compute_stats(num_batch, batch_size)
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torch.save(stats, stats_path)
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return stats
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@@ -85,101 +100,59 @@ class AbstractExperienceReplay(TensorDictReplayBuffer):
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def _is_downloaded(self) -> bool:
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return self.data_dir.is_dir()
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def _compute_stats(self, storage, num_batch=100, batch_size=32):
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def _compute_stats(self, num_batch=100, batch_size=32):
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rb = TensorDictReplayBuffer(
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storage=storage,
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storage=self._storage,
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batch_size=batch_size,
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prefetch=True,
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)
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batch = rb.sample()
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image_channels = batch["observation", "image"].shape[1]
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image_mean = torch.zeros(image_channels)
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image_std = torch.zeros(image_channels)
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image_max = torch.tensor([-math.inf] * image_channels)
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image_min = torch.tensor([math.inf] * image_channels)
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state_channels = batch["observation", "state"].shape[1]
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state_mean = torch.zeros(state_channels)
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state_std = torch.zeros(state_channels)
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state_max = torch.tensor([-math.inf] * state_channels)
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state_min = torch.tensor([math.inf] * state_channels)
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action_channels = batch["action"].shape[1]
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action_mean = torch.zeros(action_channels)
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action_std = torch.zeros(action_channels)
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action_max = torch.tensor([-math.inf] * action_channels)
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action_min = torch.tensor([math.inf] * action_channels)
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mean, std, max, min = {}, {}, {}, {}
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# compute mean, min, max
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for _ in tqdm.tqdm(range(num_batch)):
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image_mean += einops.reduce(batch["observation", "image"], "b c h w -> c", "mean")
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state_mean += einops.reduce(batch["observation", "state"], "b c -> c", "mean")
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action_mean += einops.reduce(batch["action"], "b c -> c", "mean")
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b_image_max = einops.reduce(batch["observation", "image"], "b c h w -> c", "max")
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b_image_min = einops.reduce(batch["observation", "image"], "b c h w -> c", "min")
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b_state_max = einops.reduce(batch["observation", "state"], "b c -> c", "max")
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b_state_min = einops.reduce(batch["observation", "state"], "b c -> c", "min")
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b_action_max = einops.reduce(batch["action"], "b c -> c", "max")
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b_action_min = einops.reduce(batch["action"], "b c -> c", "min")
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image_max = torch.maximum(image_max, b_image_max)
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image_min = torch.maximum(image_min, b_image_min)
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state_max = torch.maximum(state_max, b_state_max)
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state_min = torch.maximum(state_min, b_state_min)
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action_max = torch.maximum(action_max, b_action_max)
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action_min = torch.maximum(action_min, b_action_min)
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batch = rb.sample()
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image_mean /= num_batch
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state_mean /= num_batch
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action_mean /= num_batch
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for i in tqdm.tqdm(range(num_batch)):
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b_image_mean = einops.reduce(batch["observation", "image"], "b c h w -> c", "mean")
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b_state_mean = einops.reduce(batch["observation", "state"], "b c -> c", "mean")
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b_action_mean = einops.reduce(batch["action"], "b c -> c", "mean")
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image_std += (b_image_mean - image_mean) ** 2
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state_std += (b_state_mean - state_mean) ** 2
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action_std += (b_action_mean - action_mean) ** 2
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b_image_max = einops.reduce(batch["observation", "image"], "b c h w -> c", "max")
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b_image_min = einops.reduce(batch["observation", "image"], "b c h w -> c", "min")
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b_state_max = einops.reduce(batch["observation", "state"], "b c -> c", "max")
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b_state_min = einops.reduce(batch["observation", "state"], "b c -> c", "min")
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b_action_max = einops.reduce(batch["action"], "b c -> c", "max")
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b_action_min = einops.reduce(batch["action"], "b c -> c", "min")
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image_max = torch.maximum(image_max, b_image_max)
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image_min = torch.maximum(image_min, b_image_min)
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state_max = torch.maximum(state_max, b_state_max)
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state_min = torch.maximum(state_min, b_state_min)
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action_max = torch.maximum(action_max, b_action_max)
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action_min = torch.maximum(action_min, b_action_min)
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if i < num_batch - 1:
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for key, pattern in self.stats_patterns.items():
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batch[key] = batch[key].float()
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if key not in mean:
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# first batch initialize mean, min, max
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mean[key] = einops.reduce(batch[key], pattern, "mean")
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max[key] = einops.reduce(batch[key], pattern, "max")
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min[key] = einops.reduce(batch[key], pattern, "min")
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else:
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mean[key] += einops.reduce(batch[key], pattern, "mean")
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max[key] = torch.maximum(max[key], einops.reduce(batch[key], pattern, "max"))
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min[key] = torch.minimum(min[key], einops.reduce(batch[key], pattern, "min"))
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batch = rb.sample()
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image_std = torch.sqrt(image_std / num_batch)
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state_std = torch.sqrt(state_std / num_batch)
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action_std = torch.sqrt(action_std / num_batch)
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for key in self.stats_patterns:
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mean[key] /= num_batch
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stats = TensorDict(
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{
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("observation", "image", "mean"): image_mean[None, :, None, None],
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("observation", "image", "std"): image_std[None, :, None, None],
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("observation", "image", "max"): image_max[None, :, None, None],
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("observation", "image", "min"): image_min[None, :, None, None],
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("observation", "state", "mean"): state_mean[None, :],
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("observation", "state", "std"): state_std[None, :],
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("observation", "state", "max"): state_max[None, :],
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("observation", "state", "min"): state_min[None, :],
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("action", "mean"): action_mean[None, :],
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("action", "std"): action_std[None, :],
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("action", "max"): action_max[None, :],
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("action", "min"): action_min[None, :],
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},
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batch_size=[],
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)
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stats["next", "observation", "image"] = stats["observation", "image"]
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stats["next", "observation", "state"] = stats["observation", "state"]
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# compute std, min, max
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for _ in tqdm.tqdm(range(num_batch)):
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batch = rb.sample()
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for key, pattern in self.stats_patterns.items():
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batch[key] = batch[key].float()
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batch_mean = einops.reduce(batch[key], pattern, "mean")
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if key not in std:
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# first batch initialize std
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std[key] = (batch_mean - mean[key]) ** 2
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else:
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std[key] += (batch_mean - mean[key]) ** 2
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max[key] = torch.maximum(max[key], einops.reduce(batch[key], pattern, "max"))
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min[key] = torch.minimum(min[key], einops.reduce(batch[key], pattern, "min"))
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for key in self.stats_patterns:
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std[key] = torch.sqrt(std[key] / num_batch)
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stats = TensorDict({}, batch_size=[])
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for key in self.stats_patterns:
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stats[(*key, "mean")] = mean[key]
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stats[(*key, "std")] = std[key]
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stats[(*key, "max")] = max[key]
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stats[(*key, "min")] = min[key]
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if key[0] == "observation":
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# use same stats for the next observations
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stats[("next", *key)] = stats[key]
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return stats
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@@ -10,7 +10,9 @@ from lerobot.common.envs.transforms import NormalizeTransform
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DATA_DIR = Path(os.environ.get("DATA_DIR", "data"))
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def make_offline_buffer(cfg, sampler=None):
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def make_offline_buffer(
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cfg, overwrite_sampler=None, normalize=True, overwrite_batch_size=None, overwrite_prefetch=None
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):
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if cfg.policy.balanced_sampling:
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assert cfg.online_steps > 0
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batch_size = None
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@@ -23,9 +25,13 @@ def make_offline_buffer(cfg, sampler=None):
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pin_memory = cfg.device == "cuda"
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prefetch = cfg.prefetch
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overwrite_sampler = sampler is not None
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if overwrite_batch_size is not None:
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batch_size = overwrite_batch_size
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if not overwrite_sampler:
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if overwrite_prefetch is not None:
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prefetch = overwrite_prefetch
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if overwrite_sampler is None:
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# TODO(rcadene): move batch_size outside
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num_traj_per_batch = cfg.policy.batch_size # // cfg.horizon
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# TODO(rcadene): Sampler outputs a batch_size <= cfg.batch_size.
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@@ -46,6 +52,8 @@ def make_offline_buffer(cfg, sampler=None):
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num_slices=num_traj_per_batch,
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strict_length=False,
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)
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else:
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sampler = overwrite_sampler
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if cfg.env.name == "simxarm":
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from lerobot.common.datasets.simxarm import SimxarmExperienceReplay
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@@ -70,30 +78,31 @@ def make_offline_buffer(cfg, sampler=None):
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prefetch=prefetch if isinstance(prefetch, int) else None,
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)
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# TODO(rcadene): make normalization strategy configurable between mean_std, min_max, manual_min_max, min_max_from_spec
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stats = offline_buffer.compute_or_load_stats()
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in_keys = [("observation", "state"), ("action")]
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if normalize:
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# TODO(rcadene): make normalization strategy configurable between mean_std, min_max, manual_min_max, min_max_from_spec
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stats = offline_buffer.compute_or_load_stats()
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in_keys = [("observation", "state"), ("action")]
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if cfg.policy == "tdmpc":
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# TODO(rcadene): imagenet normalization is applied inside diffusion policy, but no normalization inside tdmpc
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in_keys.append(("observation", "image"))
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# since we use next observations in tdmpc
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in_keys.append(("next", "observation", "image"))
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in_keys.append(("next", "observation", "state"))
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if cfg.policy == "tdmpc":
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for key in offline_buffer.image_keys:
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# TODO(rcadene): imagenet normalization is applied inside diffusion policy, but no normalization inside tdmpc
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in_keys.append(key)
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# since we use next observations in tdmpc
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in_keys.append(("next", *key))
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in_keys.append(("next", "observation", "state"))
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if cfg.policy == "diffusion" and cfg.env.name == "pusht":
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# TODO(rcadene): we overwrite stats to have the same as pretrained model, but we should remove this
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stats["observation", "state", "min"] = torch.tensor([13.456424, 32.938293], dtype=torch.float32)
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stats["observation", "state", "max"] = torch.tensor([496.14618, 510.9579], dtype=torch.float32)
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stats["action", "min"] = torch.tensor([12.0, 25.0], dtype=torch.float32)
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stats["action", "max"] = torch.tensor([511.0, 511.0], dtype=torch.float32)
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if cfg.policy == "diffusion" and cfg.env.name == "pusht":
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# TODO(rcadene): we overwrite stats to have the same as pretrained model, but we should remove this
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stats["observation", "state", "min"] = torch.tensor([13.456424, 32.938293], dtype=torch.float32)
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stats["observation", "state", "max"] = torch.tensor([496.14618, 510.9579], dtype=torch.float32)
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stats["action", "min"] = torch.tensor([12.0, 25.0], dtype=torch.float32)
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stats["action", "max"] = torch.tensor([511.0, 511.0], dtype=torch.float32)
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transform = NormalizeTransform(stats, in_keys, mode="min_max")
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offline_buffer.set_transform(transform)
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transform = NormalizeTransform(stats, in_keys, mode="min_max")
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offline_buffer.set_transform(transform)
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if not overwrite_sampler:
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num_steps = len(offline_buffer)
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index = torch.arange(0, num_steps, 1)
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index = torch.arange(0, offline_buffer.num_frames, 1)
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sampler.extend(index)
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return offline_buffer
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@@ -183,8 +183,7 @@ class PushtExperienceReplay(AbstractExperienceReplay):
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# last step of demonstration is considered done
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done[-1] = True
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print("before " + """episode = TensorDict(""")
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episode = TensorDict(
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ep_td = TensorDict(
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{
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("observation", "image"): image[:-1],
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("observation", "state"): agent_pos[:-1],
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@@ -203,11 +202,11 @@ class PushtExperienceReplay(AbstractExperienceReplay):
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if episode_id == 0:
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# hack to initialize tensordict data structure to store episodes
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td_data = episode[0].expand(total_frames).memmap_like(self.data_dir)
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td_data = ep_td[0].expand(total_frames).memmap_like(self.data_dir)
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td_data[idxtd : idxtd + len(episode)] = episode
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td_data[idxtd : idxtd + len(ep_td)] = ep_td
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idx0 = idx1
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idxtd = idxtd + len(episode)
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idxtd = idxtd + len(ep_td)
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return TensorStorage(td_data.lock_())
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