Add mode to NormalizeTransform with mean_std or min_max (Not fully tested)

lerobot/common/datasets/pusht.py

@@ -1,4 +1,5 @@
 import logging
+import math
 import os
 from pathlib import Path
 from typing import Callable
@@ -134,18 +135,19 @@ class PushtExperienceReplay(TensorDictReplayBuffer):
         else:
             storage = TensorStorage(TensorDict.load_memmap(self.root / dataset_id))
 
-        mean_std = self._compute_or_load_mean_std(storage)
+        stats = self._compute_or_load_stats(storage)
-        mean_std["next", "observation", "image"] = mean_std["observation", "image"]
+        stats["next", "observation", "image"] = stats["observation", "image"]
-        mean_std["next", "observation", "state"] = mean_std["observation", "state"]
+        stats["next", "observation", "state"] = stats["observation", "state"]
         transform = NormalizeTransform(
-            mean_std,
+            stats,
             in_keys=[
-                ("observation", "image"),
+                # ("observation", "image"),
                 ("observation", "state"),
-                ("next", "observation", "image"),
+                # ("next", "observation", "image"),
                 ("next", "observation", "state"),
                 ("action"),
             ],
+            mode="min_max",
         )
 
         if writer is None:
@@ -282,7 +284,7 @@ class PushtExperienceReplay(TensorDictReplayBuffer):
 
         return TensorStorage(td_data.lock_())
 
-    def _compute_mean_std(self, storage, num_batch=10, batch_size=32):
+    def _compute_stats(self, storage, num_batch=100, batch_size=32):
         rb = TensorDictReplayBuffer(
             storage=storage,
             batch_size=batch_size,
@@ -291,15 +293,27 @@ class PushtExperienceReplay(TensorDictReplayBuffer):
         batch = rb.sample()
         image_mean = torch.zeros(batch["observation", "image"].shape[1])
         image_std = torch.zeros(batch["observation", "image"].shape[1])
+        image_max = -math.inf
+        image_min = math.inf
         state_mean = torch.zeros(batch["observation", "state"].shape[1])
         state_std = torch.zeros(batch["observation", "state"].shape[1])
+        state_max = -math.inf
+        state_min = math.inf
         action_mean = torch.zeros(batch["action"].shape[1])
         action_std = torch.zeros(batch["action"].shape[1])
+        action_max = -math.inf
+        action_min = math.inf
 
         for _ in tqdm.tqdm(range(num_batch)):
             image_mean += einops.reduce(batch["observation", "image"], "b c h w -> c", reduction="mean")
             state_mean += batch["observation", "state"].mean(dim=0)
             action_mean += batch["action"].mean(dim=0)
+            image_max = max(image_max, batch["observation", "image"].max().item())
+            image_min = min(image_min, batch["observation", "image"].min().item())
+            state_max = max(state_max, batch["observation", "state"].max().item())
+            state_min = min(state_min, batch["observation", "state"].min().item())
+            action_max = max(action_max, batch["action"].max().item())
+            action_min = min(action_min, batch["action"].min().item())
             batch = rb.sample()
 
         image_mean /= num_batch
@@ -311,6 +325,12 @@ class PushtExperienceReplay(TensorDictReplayBuffer):
             image_std += (image_mean_batch - image_mean) ** 2
             state_std += (batch["observation", "state"].mean(dim=0) - state_mean) ** 2
             action_std += (batch["action"].mean(dim=0) - action_mean) ** 2
+            image_max = max(image_max, batch["observation", "image"].max().item())
+            image_min = min(image_min, batch["observation", "image"].min().item())
+            state_max = max(state_max, batch["observation", "state"].max().item())
+            state_min = min(state_min, batch["observation", "state"].min().item())
+            action_max = max(action_max, batch["action"].max().item())
+            action_min = min(action_min, batch["action"].min().item())
             if i < num_batch - 1:
                 batch = rb.sample()
 
@@ -318,25 +338,31 @@ class PushtExperienceReplay(TensorDictReplayBuffer):
         state_std = torch.sqrt(state_std / num_batch)
         action_std = torch.sqrt(action_std / num_batch)
 
-        mean_std = TensorDict(
+        stats = TensorDict(
             {
                 ("observation", "image", "mean"): image_mean[None, :, None, None],
                 ("observation", "image", "std"): image_std[None, :, None, None],
+                ("observation", "image", "max"): torch.tensor(image_max),
+                ("observation", "image", "min"): torch.tensor(image_min),
                 ("observation", "state", "mean"): state_mean[None, :],
                 ("observation", "state", "std"): state_std[None, :],
+                ("observation", "state", "max"): torch.tensor(state_max),
+                ("observation", "state", "min"): torch.tensor(state_min),
                 ("action", "mean"): action_mean[None, :],
                 ("action", "std"): action_std[None, :],
+                ("action", "max"): torch.tensor(action_max),
+                ("action", "min"): torch.tensor(action_min),
             },
             batch_size=[],
         )
-        return mean_std
+        return stats
 
-    def _compute_or_load_mean_std(self, storage) -> TensorDict:
+    def _compute_or_load_stats(self, storage) -> TensorDict:
-        mean_std_path = self.root / self.dataset_id / "mean_std.pth"
+        stats_path = self.root / self.dataset_id / "stats.pth"
-        if mean_std_path.exists():
+        if stats_path.exists():
-            mean_std = torch.load(mean_std_path)
+            stats = torch.load(stats_path)
         else:
-            logging.info(f"compute_mean_std and save to {mean_std_path}")
+            logging.info(f"compute_stats and save to {stats_path}")
-            mean_std = self._compute_mean_std(storage)
+            stats = self._compute_stats(storage)
-            torch.save(mean_std, mean_std_path)
+            torch.save(stats, stats_path)
-        return mean_std
+        return stats

lerobot/common/envs/factory.py

@@ -1,7 +1,5 @@
 from torchrl.envs.transforms import StepCounter, TransformedEnv
 
-from lerobot.common.envs.transforms import Prod
-
 
 def make_env(cfg, transform=None):
     kwargs = {
@@ -28,12 +26,8 @@ def make_env(cfg, transform=None):
     # limit rollout to max_steps
     env = TransformedEnv(env, StepCounter(max_steps=cfg.env.episode_length))
 
-    if cfg.env.name == "pusht":
-        # to ensure pusht is in [0,255] like simxarm
-        env.append_transform(Prod(in_keys=[("observation", "image")], prod=255.0))
-
     if transform is not None:
-        # useful to add mean and std normalization
+        # useful to add normalization
         env.append_transform(transform)
 
     return env

lerobot/common/envs/transforms.py

@@ -28,11 +28,12 @@ class NormalizeTransform(Transform):
 
     def __init__(
         self,
-        mean_std: TensorDictBase,
+        stats: TensorDictBase,
         in_keys: Sequence[NestedKey] = None,
         out_keys: Sequence[NestedKey] | None = None,
         in_keys_inv: Sequence[NestedKey] | None = None,
         out_keys_inv: Sequence[NestedKey] | None = None,
+        mode="mean_std",
     ):
         if out_keys is None:
             out_keys = in_keys
@@ -43,7 +44,14 @@ class NormalizeTransform(Transform):
         super().__init__(
             in_keys=in_keys, out_keys=out_keys, in_keys_inv=in_keys_inv, out_keys_inv=out_keys_inv
         )
-        self.mean_std = mean_std
+        self.stats = stats
+        assert mode in ["mean_std", "min_max"]
+        self.mode = mode
+
+    def _reset(self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase) -> TensorDictBase:
+        # _reset is called once when the environment reset to normalize the first observation
+        tensordict_reset = self._call(tensordict_reset)
+        return tensordict_reset
 
     @dispatch(source="in_keys", dest="out_keys")
     def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
@@ -54,9 +62,17 @@ class NormalizeTransform(Transform):
             # TODO(rcadene): don't know how to do `inkey not in td`
             if td.get(inkey, None) is None:
                 continue
-            mean = self.mean_std[inkey]["mean"]
+            if self.mode == "mean_std":
-            std = self.mean_std[inkey]["std"]
+                mean = self.stats[inkey]["mean"]
-            td[outkey] = (td[inkey] - mean) / (std + 1e-8)
+                std = self.stats[inkey]["std"]
+                td[outkey] = (td[inkey] - mean) / (std + 1e-8)
+            else:
+                min = self.stats[inkey]["min"]
+                max = self.stats[inkey]["max"]
+                # normalize to [0,1]
+                td[outkey] = (td[inkey] - min) / (max - min)
+                # normalize to [-1, 1]
+                td[outkey] = td[outkey] * 2 - 1
         return td
 
     def _inv_call(self, td: TensorDictBase) -> TensorDictBase:
@@ -64,7 +80,13 @@ class NormalizeTransform(Transform):
             # TODO(rcadene): don't know how to do `inkey not in td`
             if td.get(inkey, None) is None:
                 continue
-            mean = self.mean_std[inkey]["mean"]
+            if self.mode == "mean_std":
-            std = self.mean_std[inkey]["std"]
+                mean = self.stats[inkey]["mean"]
-            td[outkey] = td[inkey] * std + mean
+                std = self.stats[inkey]["std"]
+                td[outkey] = td[inkey] * std + mean
+            else:
+                min = self.stats[inkey]["min"]
+                max = self.stats[inkey]["max"]
+                td[outkey] = (td[inkey] + 1) / 2
+                td[outkey] = td[outkey] * (max - min) + min
         return td

lerobot/scripts/eval.py

@@ -118,7 +118,7 @@ def eval(cfg: dict, out_dir=None):
     offline_buffer = make_offline_buffer(cfg)
 
     logging.info("make_env")
-    env = make_env(cfg, transform=offline_buffer.transform)
+    env = make_env(cfg, transform=offline_buffer._transform)
 
     if cfg.policy.pretrained_model_path:
         policy = make_policy(cfg)