Add Automatic Mixed Precision option for training and evaluation. (#199)

lerobot/scripts/eval.py

@@ -46,6 +46,7 @@ import json
 import logging
 import threading
 import time
+from contextlib import nullcontext
 from copy import deepcopy
 from datetime import datetime as dt
 from pathlib import Path
@@ -520,7 +521,7 @@ def eval(
         raise NotImplementedError()
 
     # Check device is available
-    get_safe_torch_device(hydra_cfg.device, log=True)
+    device = get_safe_torch_device(hydra_cfg.device, log=True)
 
     torch.backends.cudnn.benchmark = True
     torch.backends.cuda.matmul.allow_tf32 = True
@@ -539,16 +540,17 @@ def eval(
         policy = make_policy(hydra_cfg=hydra_cfg, dataset_stats=make_dataset(hydra_cfg).stats)
     policy.eval()
 
-    info = eval_policy(
-        env,
-        policy,
-        hydra_cfg.eval.n_episodes,
-        max_episodes_rendered=10,
-        video_dir=Path(out_dir) / "eval",
-        start_seed=hydra_cfg.seed,
-        enable_progbar=True,
-        enable_inner_progbar=True,
-    )
+    with torch.no_grad(), torch.autocast(device_type=device.type) if hydra_cfg.use_amp else nullcontext():
+        info = eval_policy(
+            env,
+            policy,
+            hydra_cfg.eval.n_episodes,
+            max_episodes_rendered=10,
+            video_dir=Path(out_dir) / "eval",
+            start_seed=hydra_cfg.seed,
+            enable_progbar=True,
+            enable_inner_progbar=True,
+        )
     print(info["aggregated"])
 
     # Save info