[Newton] Increase usage of Logging over Prints.

scripts/benchmarks/eval_all_for_newton_alpha_for_scaling.sh

@@ -2,7 +2,7 @@
 
 ISAAC_EXEC_SCRIPT=/home/antoiner/Documents/IsaacLab-Internal/isaaclab.sh
 RSL_SCRIPT=scripts/benchmarks/benchmark_non_rl.py
-TASKS="Isaac-Repose-Cube-Allegro-Direct-v0 Isaac-Ant-Direct-v0 Isaac-Cartpole-Direct-v0 Isaac-Cartpole-RGB-Camera-Direct-v0 Isaac-Cartpole-Depth-Camera-Direct-v0 Isaac-Humanoid-Direct-v0 Isaac-Ant-v0 Isaac-Cartpole-v0 Isaac-Humanoid-v0 Isaac-Velocity-Flat-Unitree-A1-v0 Isaac-Velocity-Flat-Anymal-B-v0 Isaac-Velocity-Flat-Anymal-C-v0 Isaac-Velocity-Flat-Anymal-D-v0 Isaac-Velocity-Flat-Cassie-v0 Isaac-Velocity-Flat-G1-v0 Isaac-Velocity-Flat-G1-v1 Isaac-Velocity-Flat-Unitree-Go1-v0 Isaac-Velocity-Flat-Unitree-Go2-v0 Isaac-Velocity-Flat-H1-v0 Isaac-Velocity-Flat-Spot-v0 Isaac-Reach-Franka-v0 Isaac-Reach-UR10-v0"
+TASKS="Isaac-Repose-Cube-Allegro-Direct-v0 Isaac-Ant-Direct-v0 Isaac-Cartpole-Direct-v0 Isaac-Humanoid-Direct-v0 Isaac-Ant-v0 Isaac-Cartpole-v0 Isaac-Humanoid-v0 Isaac-Velocity-Flat-Unitree-A1-v0 Isaac-Velocity-Flat-Anymal-B-v0 Isaac-Velocity-Flat-Anymal-C-v0 Isaac-Velocity-Flat-Anymal-D-v0 Isaac-Velocity-Flat-Cassie-v0 Isaac-Velocity-Flat-G1-v0 Isaac-Velocity-Flat-G1-v1 Isaac-Velocity-Flat-Unitree-Go1-v0 Isaac-Velocity-Flat-Unitree-Go2-v0 Isaac-Velocity-Flat-H1-v0 Isaac-Reach-Franka-v0 Isaac-Reach-UR10-v0"
 NUM_FRAMES=100
 NUM_ENVS="1024 2048 4096 8192 16384"
 TARGET_FOLDER=$1

source/isaaclab/isaaclab/cloner/utils.py

@@ -3,6 +3,7 @@
 #
 # SPDX-License-Identifier: BSD-3-Clause
 
+import logging
 import numpy as np
 from typing import Any
 
@@ -12,6 +13,9 @@
 
 from isaaclab.utils.timer import Timer
 
+# Import logging
+logger = logging.getLogger(__name__)
+
 
 @Timer(name="replicate_environment", msg="Replicate environment took:", enable=True, format="ms")
 def replicate_environment(
@@ -56,7 +60,7 @@ def replicate_environment(
         # up_axis sanity check
         stage_up_axis = stage_info.get("up_axis")
         if isinstance(stage_up_axis, str) and stage_up_axis.upper() != up_axis.upper():
-            print(f"WARNING: up_axis '{up_axis}' does not match USD stage up_axis '{stage_up_axis}'")
+            logger.warning(f"WARNING: up_axis '{up_axis}' does not match USD stage up_axis '{stage_up_axis}'")
 
     with Timer(name="newton_prototype_builder", msg="Prototype Builder took:", enable=True, format="ms"):
         # Get child xforms from the prototype path

source/isaaclab/isaaclab/envs/direct_rl_env.py

@@ -111,12 +111,12 @@ def __init__(self, cfg: DirectRLEnvCfg, render_mode: str | None = None, **kwargs
             torch.cuda.set_device(self.device)
 
         # print useful information
-        print("[INFO]: Base environment:")
-        print(f"\tEnvironment device    : {self.device}")
-        print(f"\tEnvironment seed      : {self.cfg.seed}")
-        print(f"\tPhysics step-size     : {self.physics_dt}")
-        print(f"\tRendering step-size   : {self.physics_dt * self.cfg.sim.render_interval}")
-        print(f"\tEnvironment step-size : {self.step_dt}")
+        logger.info("Base environment:")
+        logger.info(f"\tEnvironment device    : {self.device}")
+        logger.info(f"\tEnvironment seed      : {self.cfg.seed}")
+        logger.info(f"\tPhysics step-size     : {self.physics_dt}")
+        logger.info(f"\tRendering step-size   : {self.physics_dt * self.cfg.sim.render_interval}")
+        logger.info(f"\tEnvironment step-size : {self.step_dt}")
 
         if self.cfg.sim.render_interval < self.cfg.decimation:
             msg = (
@@ -133,7 +133,7 @@ def __init__(self, cfg: DirectRLEnvCfg, render_mode: str | None = None, **kwargs
                 self.scene = InteractiveScene(self.cfg.scene)
                 self._setup_scene()
                 # attach_stage_to_usd_context()
-        print("[INFO]: Scene manager: ", self.scene)
+        logger.info(f"Scene manager: {self.scene}")
 
         # set up camera viewport controller
         # viewport is not available in other rendering modes so the function will throw a warning
@@ -158,7 +158,7 @@ def __init__(self, cfg: DirectRLEnvCfg, render_mode: str | None = None, **kwargs
         # note: this activates the physics simulation view that exposes TensorAPIs
         # note: when started in extension mode, first call sim.reset_async() and then initialize the managers
         # if builtins.ISAAC_LAUNCHED_FROM_TERMINAL is False:
-        print("[INFO]: Starting the simulation. This may take a few seconds. Please wait...")
+        logger.info("Starting the simulation. This may take a few seconds. Please wait...")
         with Timer("[INFO]: Time taken for simulation start", "simulation_start"):
             # since the reset can trigger callbacks which use the stage,
             # we need to set the stage context here
@@ -213,7 +213,7 @@ def __init__(self, cfg: DirectRLEnvCfg, render_mode: str | None = None, **kwargs
         # perform events at the start of the simulation
         if self.cfg.events:
             # we print it here to make the logging consistent
-            print("[INFO] Event Manager: ", self.event_manager)
+            logger.info(f"Event Manager: {self.event_manager}")
 
             if "startup" in self.event_manager.available_modes:
                 self.event_manager.apply(mode="startup")
@@ -223,7 +223,7 @@ def __init__(self, cfg: DirectRLEnvCfg, render_mode: str | None = None, **kwargs
         self.metadata["render_fps"] = 1 / self.step_dt
 
         # print the environment information
-        print("[INFO]: Completed setting up the environment...")
+        logger.info("Completed setting up the environment...")
 
     def __del__(self):
         """Cleanup for the environment."""

source/isaaclab/isaaclab/envs/manager_based_env.py

@@ -122,12 +122,12 @@ def __init__(self, cfg: ManagerBasedEnvCfg):
             torch.cuda.set_device(self.device)
 
         # print useful information
-        print("[INFO]: Base environment:")
-        print(f"\tEnvironment device    : {self.device}")
-        print(f"\tEnvironment seed      : {self.cfg.seed}")
-        print(f"\tPhysics step-size     : {self.physics_dt}")
-        print(f"\tRendering step-size   : {self.physics_dt * self.cfg.sim.render_interval}")
-        print(f"\tEnvironment step-size : {self.step_dt}")
+        logger.info("Base environment:")
+        logger.info(f"\tEnvironment device    : {self.device}")
+        logger.info(f"\tEnvironment seed      : {self.cfg.seed}")
+        logger.info(f"\tPhysics step-size     : {self.physics_dt}")
+        logger.info(f"\tRendering step-size   : {self.physics_dt * self.cfg.sim.render_interval}")
+        logger.info(f"\tEnvironment step-size : {self.step_dt}")
 
         if self.cfg.sim.render_interval < self.cfg.decimation:
             msg = (
@@ -149,7 +149,7 @@ def __init__(self, cfg: ManagerBasedEnvCfg):
             with use_stage(self.sim.get_initial_stage()):
                 self.scene = InteractiveScene(self.cfg.scene)
                 # attach_stage_to_usd_context()
-        print("[INFO]: Scene manager: ", self.scene)
+        logger.info(f"Scene manager: {self.scene}")
 
         # set up camera viewport controller
         # viewport is not available in other rendering modes so the function will throw a warning
@@ -173,7 +173,7 @@ def __init__(self, cfg: ManagerBasedEnvCfg):
         # note: this activates the physics simulation view that exposes TensorAPIs
         # note: when started in extension mode, first call sim.reset_async() and then initialize the managers
         # if builtins.ISAAC_LAUNCHED_FROM_TERMINAL is False:
-        print("[INFO]: Starting the simulation. This may take a few seconds. Please wait...")
+        logger.info("Starting the simulation. This may take a few seconds. Please wait...")
         with Timer("[INFO]: Time taken for simulation start", "simulation_start"):
             # since the reset can trigger callbacks which use the stage,
             # we need to set the stage context here
@@ -292,7 +292,7 @@ def export_IO_descriptors(self, output_dir: str | None = None):
             os.makedirs(output_dir, exist_ok=True)
 
         with open(os.path.join(output_dir, "IO_descriptors.yaml"), "w") as f:
-            print(f"[INFO]: Exporting IO descriptors to {os.path.join(output_dir, 'IO_descriptors.yaml')}")
+            logger.info(f"Exporting IO descriptors to {os.path.join(output_dir, 'IO_descriptors.yaml')}")
             yaml.safe_dump(IO_descriptors, f)
 
     """
@@ -317,16 +317,16 @@ def load_managers(self):
         """
         # prepare the managers
         # -- event manager (we print it here to make the logging consistent)
-        print("[INFO] Event Manager: ", self.event_manager)
+        logger.info(f"Event Manager: {self.event_manager}")
         # -- recorder manager
         self.recorder_manager = RecorderManager(self.cfg.recorders, self)
-        print("[INFO] Recorder Manager: ", self.recorder_manager)
+        logger.info(f"Recorder Manager: {self.recorder_manager}")
         # -- action manager
         self.action_manager = ActionManager(self.cfg.actions, self)
-        print("[INFO] Action Manager: ", self.action_manager)
+        logger.info(f"Action Manager: {self.action_manager}")
         # -- observation manager
         self.observation_manager = ObservationManager(self.cfg.observations, self)
-        print("[INFO] Observation Manager:", self.observation_manager)
+        logger.info(f"Observation Manager: {self.observation_manager}")
 
         # perform events at the start of the simulation
         # in-case a child implementation creates other managers, the randomization should happen

source/isaaclab/isaaclab/envs/manager_based_rl_env.py

@@ -7,6 +7,7 @@
 from __future__ import annotations
 
 import gymnasium as gym
+import logging
 import math
 import numpy as np
 import torch
@@ -20,6 +21,9 @@
 from .manager_based_env import ManagerBasedEnv
 from .manager_based_rl_env_cfg import ManagerBasedRLEnvCfg
 
+# import logger
+logger = logging.getLogger(__name__)
+
 
 class ManagerBasedRLEnv(ManagerBasedEnv, gym.Env):
     """The superclass for the manager-based workflow reinforcement learning-based environments.
@@ -85,7 +89,7 @@ def __init__(self, cfg: ManagerBasedRLEnvCfg, render_mode: str | None = None, **
         # -- set the framerate of the gym video recorder wrapper so that the playback speed of the produced video matches the simulation
         self.metadata["render_fps"] = 1 / self.step_dt
 
-        print("[INFO]: Completed setting up the environment...")
+        logger.info("Completed setting up the environment...")
 
     """
     Properties.
@@ -110,21 +114,21 @@ def load_managers(self):
         # and the reward manager needs to know the termination manager
         # -- command manager
         self.command_manager: CommandManager = CommandManager(self.cfg.commands, self)
-        print("[INFO] Command Manager: ", self.command_manager)
+        logger.info(f"Command Manager: {self.command_manager}")
 
         # call the parent class to load the managers for observations and actions.
         super().load_managers()
 
         # prepare the managers
         # -- termination manager
         self.termination_manager = TerminationManager(self.cfg.terminations, self)
-        print("[INFO] Termination Manager: ", self.termination_manager)
+        logger.info(f"Termination Manager: {self.termination_manager}")
         # -- reward manager
         self.reward_manager = RewardManager(self.cfg.rewards, self)
-        print("[INFO] Reward Manager: ", self.reward_manager)
+        logger.info(f"Reward Manager: {self.reward_manager}")
         # -- curriculum manager
         self.curriculum_manager = CurriculumManager(self.cfg.curriculum, self)
-        print("[INFO] Curriculum Manager: ", self.curriculum_manager)
+        logger.info(f"Curriculum Manager: {self.curriculum_manager}")
 
         # setup the action and observation spaces for Gym
         self._configure_gym_env_spaces()

source/isaaclab/isaaclab/managers/action_manager.py

@@ -8,6 +8,7 @@
 from __future__ import annotations
 
 import inspect
+import logging
 import re
 import torch
 import weakref
@@ -25,6 +26,9 @@
 if TYPE_CHECKING:
     from isaaclab.envs import ManagerBasedEnv
 
+# import logger
+logger = logging.getLogger(__name__)
+
 
 class ActionTerm(ManagerTermBase):
     """Base class for action terms.
@@ -291,13 +295,13 @@ def get_IO_descriptors(self) -> list[dict[str, Any]]:
             try:
                 data.append(term.IO_descriptor.__dict__.copy())
             except Exception as e:
-                print(f"Error getting IO descriptor for term '{term_name}': {e}")
+                logger.warning(f"Failed to get IO descriptor for term '{term_name}': {e}")
 
         formatted_data = []
         for item in data:
             name = item.pop("name")
             formatted_item = {"name": name, "extras": item.pop("extras")}
-            print(item["export"])
+            logger.info(item["export"])
             if not item.pop("export"):
                 continue
             for k, v in item.items():

source/isaaclab/isaaclab/managers/observation_manager.py

@@ -8,6 +8,7 @@
 from __future__ import annotations
 
 import inspect
+import logging
 import numpy as np
 import torch
 from collections.abc import Sequence
@@ -23,6 +24,9 @@
 if TYPE_CHECKING:
     from isaaclab.envs import ManagerBasedEnv
 
+# import logger
+logger = logging.getLogger(__name__)
+
 
 class ObservationManager(ManagerBase):
     """Manager for computing observation signals for a given world.
@@ -264,7 +268,7 @@ def get_IO_descriptors(self, group_names_to_export: list[str] = ["policy"]):
                     desc.update(overloads)
                     group_data[group_name].append(desc)
                 except Exception as e:
-                    print(f"Error getting IO descriptor for term '{term_name}' in group '{group_name}': {e}")
+                    logger.warning(f"Error getting IO descriptor for term '{term_name}' in group '{group_name}': {e}")
         # Format the data for YAML export
         formatted_data = {}
         for group_name, data in group_data.items():

source/isaaclab/isaaclab/sensors/contact_sensor/contact_sensor.py

@@ -8,6 +8,7 @@
 
 from __future__ import annotations
 
+import logging
 import torch
 from collections.abc import Sequence
 from typing import TYPE_CHECKING
@@ -26,6 +27,9 @@
 if TYPE_CHECKING:
     from .contact_sensor_cfg import ContactSensorCfg
 
+# import logger
+logger = logging.getLogger(__name__)
+
 
 class ContactSensor(SensorBase):
     """A contact reporting sensor.
@@ -274,7 +278,7 @@ def _create_buffers(self):
                 "Number of bodies is not an integer multiple of the number of environments. Received:"
                 f" {self._num_bodies} bodies and {self._num_envs} environments."
             )
-        print(f"[INFO] Contact sensor initialized with {self._num_bodies} bodies.")
+        logger.info(f"Contact sensor initialized with {self._num_bodies} bodies.")
 
         # Assume homogeneous envs, i.e. all envs have the same number of bodies / shapes
         # Only get the names for the first env. Expected structure: /World/envs/env_.*/...

source/isaaclab/isaaclab/sim/_impl/newton_manager.py

@@ -3,6 +3,7 @@
 #
 # SPDX-License-Identifier: BSD-3-Clause
 
+import logging
 import numpy as np
 import re
 
@@ -17,6 +18,9 @@
 from isaaclab.sim.utils.stage import get_current_stage
 from isaaclab.utils.timer import Timer
 
+# import logger
+logger = logging.getLogger(__name__)
+
 
 def flipped_match(x: str, y: str) -> re.Match | None:
     """Flipped match function.
@@ -117,13 +121,13 @@ def start_simulation(cls) -> None:
         This function finalizes the model and initializes the simulation state.
         """
 
-        print(f"[INFO] Builder: {NewtonManager._builder}")
+        logger.info(f"Builder: {NewtonManager._builder}")
         if NewtonManager._builder is None:
             NewtonManager.instantiate_builder_from_stage()
-        print("[INFO] Running on init callbacks")
+        logger.info("Running on init callbacks")
         for callback in NewtonManager._on_init_callbacks:
             callback()
-        print(f"[INFO] Finalizing model on device: {NewtonManager._device}")
+        logger.info(f"Finalizing model on device: {NewtonManager._device}")
         NewtonManager._builder.gravity = np.array(NewtonManager._gravity_vector)[-1]
         NewtonManager._builder.up_axis = Axis.from_string(NewtonManager._up_axis)
         with Timer(name="newton_finalize_builder", msg="Finalize builder took:", enable=True, format="ms"):
@@ -141,7 +145,7 @@ def start_simulation(cls) -> None:
             )
         else:
             NewtonManager._contacts = Contacts(0, 0)
-        print("[INFO] Running on start callbacks")
+        logger.info("Running on start callbacks")
         for callback in NewtonManager._on_start_callbacks:
             callback()
         if not NewtonManager._clone_physics_only:
@@ -186,7 +190,6 @@ def initialize_solver(cls):
         with Timer(name="newton_initialize_solver", msg="Initialize solver took:", enable=True, format="ms"):
             NewtonManager._num_substeps = NewtonManager._cfg.num_substeps
             NewtonManager._solver_dt = NewtonManager._dt / NewtonManager._num_substeps
-            print(NewtonManager._model.gravity)
             NewtonManager._solver = NewtonManager._get_solver(NewtonManager._model, NewtonManager._cfg.solver_cfg)
             if isinstance(NewtonManager._solver, SolverMuJoCo):
                 NewtonManager._needs_collision_pipeline = not NewtonManager._cfg.solver_cfg.get(
@@ -291,9 +294,9 @@ def step(cls) -> None:
 
         if NewtonManager._cfg.debug_mode:
             convergence_data = NewtonManager.get_solver_convergence_steps()
-            # print(f"solver niter: {convergence_data}")
+            logger.info(f"solver niter: {convergence_data}")
             if convergence_data["max"] == NewtonManager._solver.mjw_model.opt.iterations:
-                print("solver didn't converge!", convergence_data["max"])
+                logger.warning("solver didn't converge!", convergence_data["max"])
 
         NewtonManager._sim_time += NewtonManager._solver_dt * NewtonManager._num_substeps
 
@@ -402,21 +405,19 @@ def add_contact_sensor(
         if contact_partners_body_expr is not None and contact_partners_shape_expr is not None:
             raise ValueError("Only one of contact_partners_body_expr or contact_partners_shape_expr must be provided")
         if contact_partners_body_expr is None and contact_partners_shape_expr is None:
-            print(f"[INFO] Adding contact view for {body_names_expr}. It will report contacts with all bodies/shapes.")
+            logger.info(f"Adding contact view for {body_names_expr}. It will report contacts with all bodies/shapes.")
         else:
             if body_names_expr is not None:
                 if contact_partners_body_expr is not None:
-                    print(f"[INFO] Adding contact view for {body_names_expr} with filter {contact_partners_body_expr}.")
+                    logger.info(f"Adding contact view for {body_names_expr} with filter {contact_partners_body_expr}.")
                 else:
-                    print(f"[INFO] Adding contact view for {body_names_expr} with filter {shape_names_expr}.")
+                    logger.info(f"Adding contact view for {body_names_expr} with filter {shape_names_expr}.")
             else:
                 if contact_partners_body_expr is not None:
-                    print(
-                        f"[INFO] Adding contact view for {shape_names_expr} with filter {contact_partners_body_expr}."
-                    )
+                    logger.info(f"Adding contact view for {shape_names_expr} with filter {contact_partners_body_expr}.")
                 else:
-                    print(
-                        f"[INFO] Adding contact view for {shape_names_expr} with filter {contact_partners_shape_expr}."
+                    logger.info(
+                        f"Adding contact view for {shape_names_expr} with filter {contact_partners_shape_expr}."
                     )
 
         NewtonManager._newton_contact_sensor = NewtonContactSensor(