WIP add test_backward_compatibility

tests/test_regression.py

@@ -3,7 +3,7 @@ import torch
 
 from lerobot.common.datasets.factory import make_dataset
 from lerobot.common.policies.factory import make_policy
-from lerobot.common.utils.utils import init_hydra_config
+from lerobot.common.utils.utils import init_hydra_config, set_global_seed
 from lerobot.scripts.train import make_optimizer
 from tests.utils import DEFAULT_CONFIG_PATH, DEVICE, require_env
 
@@ -14,68 +14,66 @@ from tests.utils import DEFAULT_CONFIG_PATH, DEVICE, require_env
         # ("xarm", "tdmpc", ["policy.mpc=true"]),
         # ("pusht", "tdmpc", ["policy.mpc=false"]),
         ("pusht", "diffusion", []),
-        ("aloha", "act", ["env.task=AlohaInsertion-v0", "dataset.repo_id=lerobot/aloha_sim_insertion_human"]),
+        ("aloha", "act"),
-        (
-            "aloha",
-            "act",
-            ["env.task=AlohaInsertion-v0", "dataset.repo_id=lerobot/aloha_sim_insertion_scripted"],
-        ),
-        (
-            "aloha",
-            "act",
-            ["env.task=AlohaTransferCube-v0", "dataset.repo_id=lerobot/aloha_sim_transfer_cube_human"],
-        ),
-        (
-            "aloha",
-            "act",
-            ["env.task=AlohaTransferCube-v0", "dataset.repo_id=lerobot/aloha_sim_transfer_cube_scripted"],
-        ),
     ],
 )
 @require_env
-def test_backward(env_name, policy_name, extra_overrides):
+def test_backward_compatibility(env_name, policy_name):
     cfg = init_hydra_config(
         DEFAULT_CONFIG_PATH,
         overrides=[
             f"env={env_name}",
             f"policy={policy_name}",
             f"device={DEVICE}",
-        ]
+        ],
-        + extra_overrides,
     )
+    set_global_seed(1337)
     dataset = make_dataset(cfg)
     policy = make_policy(cfg, dataset_stats=dataset.stats)
     policy.train()
     policy.to(DEVICE)
-    optimizer, lr_scheduler = make_optimizer(cfg, policy)
+    optimizer, _ = make_optimizer(cfg, policy)
 
     dataloader = torch.utils.data.DataLoader(
         dataset,
-        num_workers=4,
+        num_workers=0,
-        batch_size=cfg.policy.batch_size,
+        batch_size=cfg.training.batch_size,
-        shuffle=True,
+        shuffle=False,
-        pin_memory=torch.device("cpu") != DEVICE,
-        drop_last=True,
     )
 
-    step = 0
+    batch = next(iter(dataloader))
-    done = False
-    training_steps = 1
-    while not done:
-        for batch in dataloader:
-            batch = {k: v.to(DEVICE, non_blocking=True) for k, v in batch.items()}
     output_dict = policy.forward(batch)
     loss = output_dict["loss"]
+    # TODO Check output dict values
+
     loss.backward()
+    grad_stats = {}
+    for key, param in policy.named_parameters():
+        if param.requires_grad:
+            grad_stats[f"{key}_mean"] = param.grad.mean()
+            grad_stats[f"{key}_std"] = param.grad.std()
+
     optimizer.step()
+    param_stats = {}
+    for key, param in policy.named_parameters():
+        param_stats[f"{key}_mean"] = param.mean()
+        param_stats[f"{key}_std"] = param.std()
+
     optimizer.zero_grad()
-            step += 1
+    policy.reset()
-            if step >= training_steps:
+
-                done = True
+    dataset.delta_timestamps = None
-                break
+    batch = next(iter(dataloader))
+
+    # TODO(aliberts): refacor `select_action` methods so that it expects `obs` instead of `batch`
+    if policy_name == "diffusion":
+        batch = {k: batch[k] for k in ["observation.image", "observation.state"]}
+
+    actions = {i: policy.select_action(batch) for i in range(cfg.policy.n_action_steps)}
+
+    print(len(actions))
 
 
 if __name__ == "__main__":
-    test_backward(
+    # test_backward_compatibility("aloha", "act")
-        "aloha", "act", ["env.task=AlohaInsertion-v0", "dataset.repo_id=lerobot/aloha_sim_insertion_scripted"]
+    test_backward_compatibility("pusht", "diffusion")
-    )