backup wip

lerobot/common/datasets/factory.py

@@ -4,7 +4,7 @@ from pathlib import Path
 import torch
 from torchvision.transforms import v2
 
-from lerobot.common.transforms import NormalizeTransform
+from lerobot.common.transforms import IdentityTransform
 
 DATA_DIR = Path(os.environ["DATA_DIR"]) if "DATA_DIR" in os.environ else None
 
@@ -38,7 +38,7 @@ def make_dataset(
         # TODO(rcadene): make normalization strategy configurable between mean_std, min_max, manual_min_max,
         # min_max_from_spec
         # TODO(rcadene): remove this and put it in config. Ideally we want to reproduce SOTA results just with mean_std
-        normalization_mode = "mean_std" if cfg.env.name == "aloha" else "min_max"
+        # normalization_mode = "mean_std" if cfg.env.name == "aloha" else "min_max"
 
         if cfg.policy.name == "diffusion" and cfg.env.name == "pusht":
             stats = {}
@@ -62,14 +62,15 @@ def make_dataset(
 
         transforms = v2.Compose(
             [
-                NormalizeTransform(
+                # TODO(now): Use the transform
-                    stats,
+                # NormalizeTransform(
-                    in_keys=[
+                #     in_keys=[
-                        "observation.state",
+                #         "observation.state",
-                        "action",
+                #         "action",
-                    ],
+                #     ],
-                    mode=normalization_mode,
+                #     mode=normalization_mode,
-                ),
+                # ),
+                IdentityTransform()
             ]
         )
 

lerobot/common/transforms.py

@@ -63,3 +63,13 @@ class NormalizeTransform(Transform):
                 item[outkey] = (item[inkey] + 1) / 2
                 item[outkey] = item[outkey] * (max - min) + min
         return item
+
+
+class IdentityTransform(Transform):
+    invertible = True
+
+    def forward(self, item):
+        return item
+
+    def inverse_transform(self, item):
+        return item

lerobot/configs/policy/tdmpc.yaml

@@ -46,7 +46,6 @@ policy:
   kappa: 0.1
   lr: 3e-4
   std_schedule: ${policy.min_std}
-  horizon_schedule: ${policy.horizon}
   per: true
   per_alpha: 0.6
   per_beta: 0.4
@@ -79,7 +78,7 @@ policy:
   latent_dim: 50
 
   delta_timestamps:
-    observation.image: "[i / ${fps} for i in range(6)]"
+    observation.image: "[i / ${fps} for i in range(${policy.horizon} + 1)]"
-    observation.state: "[i / ${fps} for i in range(6)]"
+    observation.state: "[i / ${fps} for i in range(${policy.horizon} + 1)]"
-    action: "[i / ${fps} for i in range(5)]"
+    action: "[i / ${fps} for i in range(${policy.horizon})]"
-    next.reward: "[i / ${fps} for i in range(5)]"
+    next.reward: "[i / ${fps} for i in range(${policy.horizon})]"

lerobot/scripts/eval.py

@@ -157,7 +157,11 @@ def eval_policy(
 
         # get the next action for the environment
         with torch.inference_mode():
-            action = policy.select_action(observation, step=step)
+            # TODO(now): restore
+            observation["observation.image"] *= 255
+            # TODO(now): train_step
+            action = policy.select_action(observation)
+            observation["observation.image"] /= 255
 
         # Send action:
         while True:

lerobot/scripts/train.py

@@ -270,7 +270,7 @@ def train(cfg: dict, out_dir=None, job_name=None):
     # create dataloader for offline training
     dataloader = torch.utils.data.DataLoader(
         offline_dataset,
-        num_workers=4,
+        num_workers=32,
         batch_size=cfg.policy.batch_size,
         shuffle=True,
         pin_memory=cfg.device != "cpu",
@@ -316,7 +316,7 @@ def train(cfg: dict, out_dir=None, job_name=None):
     )
     dataloader = torch.utils.data.DataLoader(
         concat_dataset,
-        num_workers=4,
+        num_workers=32,
         batch_size=cfg.policy.batch_size,
         sampler=sampler,
         pin_memory=cfg.device != "cpu",