Fix unit tests, Refactor, Add pusht env, (TODO pusht replay buffer, image preprocessing)

lerobot/scripts/eval.py

@@ -71,24 +71,35 @@ def eval(cfg: dict):
     print(colored("Log dir:", "yellow", attrs=["bold"]), cfg.log_dir)
 
     env = make_env(cfg)
-    policy = TDMPC(cfg)
-    # ckpt_path = "/home/rcadene/code/fowm/logs/xarm_lift/all/default/2/models/offline.pt"
-    ckpt_path = "/home/rcadene/code/fowm/logs/xarm_lift/all/default/2/models/final.pt"
-    policy.load(ckpt_path)
 
-    policy = TensorDictModule(
-        policy,
-        in_keys=["observation", "step_count"],
-        out_keys=["action"],
-    )
+    if cfg.pretrained_model_path:
+        policy = TDMPC(cfg)
+        ckpt_path = (
+            "/home/rcadene/code/fowm/logs/xarm_lift/all/default/2/models/offline.pt"
+        )
+        if "offline" in cfg.pretrained_model_path:
+            policy.step = 25000
+        elif "final" in cfg.pretrained_model_path:
+            policy.step = 100000
+        else:
+            raise NotImplementedError()
+        policy.load(ckpt_path)
+
+        policy = TensorDictModule(
+            policy,
+            in_keys=["observation", "step_count"],
+            out_keys=["action"],
+        )
+    else:
+        # when policy is None, rollout a random policy
+        policy = None
 
-    # policy can be None to rollout a random policy
     metrics = eval_policy(
         env,
         policy=policy,
         num_episodes=20,
-        save_video=False,
-        video_dir=Path("tmp/2023_01_29_xarm_lift_final"),
+        save_video=True,
+        video_dir=Path("tmp/2023_02_19_pusht"),
     )
     print(metrics)
 

lerobot/scripts/train.py

@@ -10,6 +10,7 @@ from torchrl.data.datasets.d4rl import D4RLExperienceReplay
 from torchrl.data.datasets.openx import OpenXExperienceReplay
 from torchrl.data.replay_buffers import PrioritizedSliceSampler
 
+from lerobot.common.datasets.factory import make_offline_buffer
 from lerobot.common.datasets.simxarm import SimxarmExperienceReplay
 from lerobot.common.envs.factory import make_env
 from lerobot.common.logger import Logger
@@ -26,11 +27,17 @@ def train(cfg: dict):
 
     env = make_env(cfg)
     policy = TDMPC(cfg)
-    # ckpt_path = "/home/rcadene/code/fowm/logs/xarm_lift/all/default/2/models/offline.pt"
-    # policy.step = 25000
-    # # ckpt_path = "/home/rcadene/code/fowm/logs/xarm_lift/all/default/2/models/final.pt"
-    # # policy.step = 100000
-    # policy.load(ckpt_path)
+    if cfg.pretrained_model_path:
+        ckpt_path = (
+            "/home/rcadene/code/fowm/logs/xarm_lift/all/default/2/models/offline.pt"
+        )
+        if "offline" in cfg.pretrained_model_path:
+            policy.step = 25000
+        elif "final" in cfg.pretrained_model_path:
+            policy.step = 100000
+        else:
+            raise NotImplementedError()
+        policy.load(ckpt_path)
 
     td_policy = TensorDictModule(
         policy,
@@ -40,32 +47,7 @@ def train(cfg: dict):
 
     # initialize offline dataset
 
-    dataset_id = f"xarm_{cfg.task}_medium"
-
-    num_traj_per_batch = cfg.batch_size  # // cfg.horizon
-    # TODO(rcadene): Sampler outputs a batch_size <= cfg.batch_size.
-    # We would need to add a transform to pad the tensordict to ensure batch_size == cfg.batch_size.
-    sampler = PrioritizedSliceSampler(
-        max_capacity=100_000,
-        alpha=cfg.per_alpha,
-        beta=cfg.per_beta,
-        num_slices=num_traj_per_batch,
-        strict_length=False,
-    )
-
-    # TODO(rcadene): add PrioritizedSliceSampler inside Simxarm to not have to `sampler.extend(index)` here
-    offline_buffer = SimxarmExperienceReplay(
-        dataset_id,
-        # download="force",
-        download=True,
-        streaming=False,
-        root="data",
-        sampler=sampler,
-    )
-
-    num_steps = len(offline_buffer)
-    index = torch.arange(0, num_steps, 1)
-    sampler.extend(index)
+    offline_buffer = make_offline_buffer(cfg)
 
     if cfg.balanced_sampling:
         online_sampler = PrioritizedSliceSampler(