backup wip

Makefile

@@ -39,7 +39,7 @@ test-act-ete-train:
 		eval.n_episodes=1 \
 		eval.batch_size=1 \
 		device=cpu \
-		training.save_model=true \
+		training.save_checkpoint=true \
 		training.save_freq=2 \
 		policy.n_action_steps=20 \
 		policy.chunk_size=20 \
@@ -65,7 +65,7 @@ test-act-ete-train-amp:
 		eval.n_episodes=1 \
 		eval.batch_size=1 \
 		device=cpu \
-		training.save_model=true \
+		training.save_checkpoint=true \
 		training.save_freq=2 \
 		policy.n_action_steps=20 \
 		policy.chunk_size=20 \
@@ -95,7 +95,7 @@ test-diffusion-ete-train:
 		eval.n_episodes=1 \
 		eval.batch_size=1 \
 		device=cpu \
-		training.save_model=true \
+		training.save_checkpoint=true \
 		training.save_freq=2 \
 		training.batch_size=2 \
 		hydra.run.dir=tests/outputs/diffusion/
@@ -122,7 +122,7 @@ test-tdmpc-ete-train:
 		eval.batch_size=1 \
 		env.episode_length=2 \
 		device=cpu \
-		training.save_model=true \
+		training.save_checkpoint=true \
 		training.save_freq=2 \
 		training.batch_size=2 \
 		hydra.run.dir=tests/outputs/tdmpc/

lerobot/common/logger.py

@@ -13,8 +13,10 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+"""Borrowed from https://github.com/fyhMer/fowm/blob/main/src/logger.py
+
 # TODO(rcadene, alexander-soare): clean this file
-"""Borrowed from https://github.com/fyhMer/fowm/blob/main/src/logger.py"""
+"""
 
 import logging
 import os
@@ -33,10 +35,6 @@ from lerobot.common.policies.policy_protocol import Policy
 from lerobot.common.utils.utils import get_global_random_state, set_global_random_state
 
 
-def log_output_dir(out_dir):
-    logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {out_dir}")
-
-
 def cfg_to_group(cfg, return_list=False):
     """Return a group name for logging. Optionally returns group name as list."""
     lst = [
@@ -57,13 +55,12 @@ class Logger:
         self._job_name = job_name
         self._checkpoint_dir = self._log_dir / "checkpoints"
         self._last_checkpoint_path = self._checkpoint_dir / "last"
-        self._buffer_dir = self._log_dir / "buffers"
-        self._save_model = cfg.training.save_model
         self._disable_wandb_artifact = cfg.wandb.disable_artifact
         self._group = cfg_to_group(cfg)
         self._seed = cfg.seed
         self._cfg = cfg
-        self._eval = []
+
+        # Set up WandB.
         project = cfg.get("wandb", {}).get("project")
         entity = cfg.get("wandb", {}).get("entity")
         enable_wandb = cfg.get("wandb", {}).get("enable", False)
@@ -112,20 +109,19 @@ class Logger:
         return self._last_checkpoint_path
 
     def save_model(self, policy: Policy, identifier: str):
-        if self._save_model:
-            self._checkpoint_dir.mkdir(parents=True, exist_ok=True)
-            save_dir = self._checkpoint_dir / str(identifier)
-            policy.save_pretrained(save_dir)
-            # Also save the full Hydra config for the env configuration.
-            OmegaConf.save(self._cfg, save_dir / "config.yaml")
-            if self._wandb and not self._disable_wandb_artifact:
-                # note wandb artifact does not accept ":" or "/" in its name
-                artifact = self._wandb.Artifact(
-                    f"{self._group.replace(':', '_').replace('/', '_')}-{self._seed}-{identifier}",
-                    type="model",
-                )
-                artifact.add_file(save_dir / SAFETENSORS_SINGLE_FILE)
-                self._wandb.log_artifact(artifact)
+        self._checkpoint_dir.mkdir(parents=True, exist_ok=True)
+        save_dir = self._checkpoint_dir / str(identifier)
+        policy.save_pretrained(save_dir)
+        # Also save the full Hydra config for the env configuration.
+        OmegaConf.save(self._cfg, save_dir / "config.yaml")
+        if self._wandb and not self._disable_wandb_artifact:
+            # note wandb artifact does not accept ":" or "/" in its name
+            artifact = self._wandb.Artifact(
+                f"{self._group.replace(':', '_').replace('/', '_')}-{self._seed}-{identifier}",
+                type="model",
+            )
+            artifact.add_file(save_dir / SAFETENSORS_SINGLE_FILE)
+            self._wandb.log_artifact(artifact)
         if self._last_checkpoint_path.exists():
             os.remove(self._last_checkpoint_path)
         os.symlink(save_dir.absolute(), self._last_checkpoint_path)  # TODO(now): Check this works

lerobot/configs/default.yaml

@@ -38,7 +38,7 @@ training:
   eval_freq: ???
   save_freq: ???
   log_freq: 250
-  save_model: true
+  save_checkpoint: true
 
 eval:
   n_episodes: 1
@@ -49,7 +49,7 @@ eval:
 
 wandb:
   enable: false
-  # Set to true to disable saving an artifact despite save_model == True
+  # Set to true to disable saving an artifact despite save_checkpoint == True
   disable_artifact: false
   project: lerobot
   notes: ""

lerobot/configs/policy/act.yaml

@@ -15,7 +15,7 @@ training:
   eval_freq: 10000
   save_freq: 100000
   log_freq: 250
-  save_model: true
+  save_checkpoint: true
 
   batch_size: 8
   lr: 1e-5

lerobot/configs/policy/diffusion.yaml

@@ -27,7 +27,7 @@ training:
   eval_freq: 5000
   save_freq: 5000
   log_freq: 250
-  save_model: true
+  save_checkpoint: true
 
   batch_size: 64
   grad_clip_norm: 10

lerobot/scripts/train.py

@@ -262,7 +262,7 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
                 f"You have set resume=True, but {str(logger.last_checkpoint_path)} does not exist."
             )
         # Get the configuration file from the last checkpoint.
-        checkpoint_cfg = init_hydra_config(str(logger.last_checkpoint_path))
+        checkpoint_cfg = init_hydra_config(str(logger.last_checkpoint_path / "config.yaml"))
         # TODO(now): Do a diff check.
         cfg = checkpoint_cfg
         step = logger.load_last_training_state(optimizer, lr_scheduler)
@@ -297,7 +297,7 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
                 logger.log_video(eval_info["video_paths"][0], step, mode="eval")
             logging.info("Resume training")
 
-        if cfg.training.save_model and step % cfg.training.save_freq == 0:
+        if cfg.training.save_checkpoint and step % cfg.training.save_freq == 0:
             logging.info(f"Checkpoint policy after step {step}")
             # Note: Save with step as the identifier, and format it to have at least 6 digits but more if
             # needed (choose 6 as a minimum for consistency without being overkill).