# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause import pathlib import sys # Allow for import of items from the ray workflow. UTIL_DIR = pathlib.Path(__file__).parent.parent.parent sys.path.append(str(UTIL_DIR)) import tuner import util from ray import tune class CameraJobCfg(tuner.JobCfg): """In order to be compatible with :meth: invoke_tuning_run, and :class:IsaacLabTuneTrainable , configurations should be in a similar format to this class. This class can vary env count/horizon length, CNN structure, and MLP structure. Broad possible ranges are set, the specific values that work can be found via tuning. Tuning results can inform better ranges for a second tuning run. These ranges were selected for demonstration purposes. Best ranges are run/task specific.""" @staticmethod def _get_batch_size_divisors(batch_size: int, min_size: int = 128) -> list[int]: """Get valid batch divisors to combine with num_envs and horizon length""" divisors = [i for i in range(min_size, batch_size + 1) if batch_size % i == 0] return divisors if divisors else [min_size] def __init__(self, cfg={}, vary_env_count: bool = False, vary_cnn: bool = False, vary_mlp: bool = False): cfg = util.populate_isaac_ray_cfg_args(cfg) # Basic configuration cfg["runner_args"]["headless_singleton"] = "--headless" cfg["runner_args"]["enable_cameras_singleton"] = "--enable_cameras" cfg["hydra_args"]["agent.params.config.max_epochs"] = 200 if vary_env_count: # Vary the env count, and horizon length, and select a compatible mini-batch size # Check from 512 to 8196 envs in powers of 2 # check horizon lengths of 8 to 256 # More envs should be better, but different batch sizes can improve gradient estimation env_counts = [2**x for x in range(9, 13)] horizon_lengths = [2**x for x in range(3, 8)] selected_env_count = tune.choice(env_counts) selected_horizon = tune.choice(horizon_lengths) cfg["runner_args"]["--num_envs"] = selected_env_count cfg["hydra_args"]["agent.params.config.horizon_length"] = selected_horizon def get_valid_batch_size(config): num_envs = config["runner_args"]["--num_envs"] horizon_length = config["hydra_args"]["agent.params.config.horizon_length"] total_batch = horizon_length * num_envs divisors = self._get_batch_size_divisors(total_batch) return divisors[0] cfg["hydra_args"]["agent.params.config.minibatch_size"] = tune.sample_from(get_valid_batch_size) if vary_cnn: # Vary the depth, and size of the layers in the CNN part of the agent # Also varies kernel size, and stride. num_layers = tune.randint(2, 3) cfg["hydra_args"]["agent.params.network.cnn.type"] = "conv2d" cfg["hydra_args"]["agent.params.network.cnn.activation"] = tune.choice(["relu", "elu"]) cfg["hydra_args"]["agent.params.network.cnn.initializer"] = "{name:default}" cfg["hydra_args"]["agent.params.network.cnn.regularizer"] = "{name:None}" def get_cnn_layers(_): layers = [] size = 64 # Initial input size for _ in range(num_layers.sample()): # Get valid kernel sizes for current size valid_kernels = [k for k in [3, 4, 6, 8, 10, 12] if k <= size] if not valid_kernels: break kernel = int(tune.choice([str(k) for k in valid_kernels]).sample()) stride = int(tune.choice(["1", "2", "3", "4"]).sample()) padding = int(tune.choice(["0", "1"]).sample()) # Calculate next size next_size = ((size + 2 * padding - kernel) // stride) + 1 if next_size <= 0: break layers.append( { "filters": tune.randint(16, 32).sample(), "kernel_size": str(kernel), "strides": str(stride), "padding": str(padding), } ) size = next_size return layers cfg["hydra_args"]["agent.params.network.cnn.convs"] = tune.sample_from(get_cnn_layers) if vary_mlp: # Vary the MLP structure; neurons (units) per layer, number of layers, max_num_layers = 6 max_neurons_per_layer = 128 if "env.observations.policy.image.params.model_name" in cfg["hydra_args"]: # By decreasing MLP size when using pretrained helps prevent out of memory on L4 max_num_layers = 3 max_neurons_per_layer = 32 if "agent.params.network.cnn.convs" in cfg["hydra_args"]: # decrease MLP size to prevent running out of memory on L4 max_num_layers = 2 max_neurons_per_layer = 32 num_layers = tune.randint(1, max_num_layers) def get_mlp_layers(_): return [tune.randint(4, max_neurons_per_layer).sample() for _ in range(num_layers.sample())] cfg["hydra_args"]["agent.params.network.mlp.units"] = tune.sample_from(get_mlp_layers) cfg["hydra_args"]["agent.params.network.mlp.initializer.name"] = tune.choice(["default"]).sample() cfg["hydra_args"]["agent.params.network.mlp.activation"] = tune.choice( ["relu", "tanh", "sigmoid", "elu"] ).sample() super().__init__(cfg) class ResNetCameraJob(CameraJobCfg): """Try different ResNet sizes.""" def __init__(self, cfg: dict = {}): cfg = util.populate_isaac_ray_cfg_args(cfg) cfg["hydra_args"]["env.observations.policy.image.params.model_name"] = tune.choice( ["resnet18", "resnet34", "resnet50", "resnet101"] ) super().__init__(cfg, vary_env_count=True, vary_cnn=False, vary_mlp=True) class TheiaCameraJob(CameraJobCfg): """Try different Theia sizes.""" def __init__(self, cfg: dict = {}): cfg = util.populate_isaac_ray_cfg_args(cfg) cfg["hydra_args"]["env.observations.policy.image.params.model_name"] = tune.choice( [ "theia-tiny-patch16-224-cddsv", "theia-tiny-patch16-224-cdiv", "theia-small-patch16-224-cdiv", "theia-base-patch16-224-cdiv", "theia-small-patch16-224-cddsv", "theia-base-patch16-224-cddsv", ] ) super().__init__(cfg, vary_env_count=True, vary_cnn=False, vary_mlp=True)