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+0a7h4b5r/eval_video/4_eval.mp4 filter=lfs diff=lfs merge=lfs -text +0a7h4b5r/eval_video/1_eval.mp4 filter=lfs diff=lfs merge=lfs -text +0a7h4b5r/eval_video/2_eval.mp4 filter=lfs diff=lfs merge=lfs -text +0a7h4b5r/eval_video/0_eval.mp4 filter=lfs diff=lfs merge=lfs -text diff --git a/0a7h4b5r/.hydra/config.yaml b/0a7h4b5r/.hydra/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5e7a10df419e9a14bfc8d2bca5db66c7f85b18e3 --- /dev/null +++ b/0a7h4b5r/.hydra/config.yaml @@ -0,0 +1,350 @@ +root_dir: /home/${oc.env:USER}/fish_leon +nstep: 3 +seed: 41 +dataset_shuffle_seed: ${seed} +device: cuda +save_video: true +save_buffer: true +use_tb: true +baseline: false +use_wandb: true +eval: true +process_contact_features: ${eval} +obs_type: pixels +use_color: true +use_depth: true +use_masks: false +mask_list: +- EE_obj_mask +mask_representation: channels +crop_hw: +- 144 +- 144 +crop_down_offset: 48 +color_crop_type: null +depth_crop_type: null +segmask_crop_type: null +add_crop_binary_mask: false +add_coord_conv_map: false +use_context_color: false +use_context_depth: false +use_context_segmask: false +context_color_crop_type: null +context_depth_crop_type: null +context_segmask_crop_type: null +context_add_crop_binary_mask: false +context_add_coord_conv_map: false +use_contact_map: false +use_sdf_maps: false +use_normals_maps: false +which_objects: both +max_contact_prob: 0.1 +max_depth: 2.0 +grasped_dtc_max_value: 0.105 +env_dtc_max_value: 0.425 +grasped_normals_mask_max_dtc_value: 0.105 +env_normals_mask_max_dtc_value: 0.425 +clamp_dtc: true +dtc_adaptive_normalization: false +mask_normals_within_sdf: true +adaptive_normals_mask: true +learnable_contact_preprocess_params: false +contact_model_name: local_multitask_outhd64all_home_crop_h144w144d48_ctxt_seed_183386_epoch_9 +contact_estimation_model_ckpt_path: ~/fish_leon/contact_estimation/artifacts/175604_2/checkpoints/epoch=09-val_loss=0.00.ckpt +num_eval: 5 +debug_timestamps: false +open_loop: false +action_trajectories: true +stop_after_action: false +interpolation_frequency: 25 +policy_frequency: 5 +wait_for_new_camera_frames: true +random_start: false +eval_starts: ${root_dir}/FISH/eval_starts/${suite.name}_${obs_type}/${task_name} +train_demo_idxs_list_or_num: null +num_valid_demos: null +val_num_groups: 3 +name_of_expert_demo: 112_240x320_all_twodim_left_to_right_annotated_start_idx_5hz_zstd7_EE_pxl_coords_expert_demos_imp_act +expert_dataset_dirpath: ${root_dir}/FISH/expert_demos/${suite.name}/${task_name}/${name_of_expert_demo} +expert_dataset: ${expert_dataset_dirpath}/demos.zarr +action_key: ${oc.if_else:${action_trajectories}, 'action_trajectory_${interpolation_frequency}hz', + 'action'} +semantic_demo_grouping_name: semantic_demo_grouping.yaml +semantic_demo_grouping: ${expert_dataset_dirpath}/${semantic_demo_grouping_name} +expert_dataset_config: ${expert_dataset_dirpath}/demo_config.yaml +bc_regularize: false +bc_weight_type: qfilter +load_checkpoint: ${agent.load_checkpoint} +wandb_run_id: '1013_1' +true_action_history: false +wandb_notes: null +checkpoint_epoch: 12000 +load_residual_weight: false +checkpoint_root_dir: /home/${oc.env:USER}/fish_leon/FISH +checkpoint_weight_dir: ${checkpoint_root_dir}/exp_local/${suite.name}_${obs_type}/${task_name}/${wandb_run_id} +residual_weight: ${root_dir}/FISH/weights/${suite.name}_${obs_type}/${task_name}/weight.pt +experiment_dir: ./exp_local/${suite.name}_${obs_type}/${task_name}/${wandb_run_id} +final_experiment_dir: ${experiment_dir}/${oc.generate_run_id:} +agent: + _target_: agent.diffusion_policy.DiffusionPolicyAgent + name: diffusion_policy + load_checkpoint: ${eval} + device: ${device} + n_obs_steps: ${.config.policy_cfg.n_obs_steps} + suite_name: ${suite.name} + obs_type: ${obs_type} + enable_arm: ${eval} + enable_camera: ${eval} + use_tb: ${use_tb} + desired_image_shape: + - 13 + - 180 + - 240 + orig_cam_shape: + - 3 + - 240 + - 320 + config: + _target_: agent.diffusion_policy.DiffusionPolicyAgentConfig + compile: false + device: ${device} + cam_resize_shape: ${agent.desired_image_shape} + orig_cam_shape: ${agent.orig_cam_shape} + policy_frequency: ${policy_frequency} + interpolation_frequency: ${interpolation_frequency} + policy_cfg: + _target_: lerobot.common.policies.diffusion.configuration_diffusion.DiffusionConfig + n_obs_steps: 1 + horizon: 36 + n_action_steps: ${agent.config.policy_cfg.horizon} + input_shapes: + observation.image: ${agent.config.cam_resize_shape} + context_observation.image: ${agent.config.cam_resize_shape} + observation.state: + - 8 + observation.action_history: + - 7 + output_shapes: + action: + - 7 + input_normalization_modes: + observation.image: mean_std + observation.state: min_max + observation.action_history: min_max + output_normalization_modes: + action: min_max + vision_backbone: resnet18 + pretrained_backbone_weights: null + transforms: + - _target_: torchaug.transforms.RandomAffine + degrees: + - -5 + - 5 + translate: + - 0.05 + - 0.05 + batch_transform: true + num_chunks: -1 + batch_inplace: true + - _target_: torchaug.transforms.RandomColorJitter + brightness: 0.3 + contrast: 0.4 + saturation: 0.5 + hue: 0.08 + batch_transform: true + num_chunks: -1 + batch_inplace: true + use_group_norm: true + spatial_softmax_num_keypoints: 32 + action_history_encoder_config: + _target_: lerobot.common.policies.diffusion.configuration_diffusion.Unet1dEncoderConfig + in_channels: 7 + out_channels: 32 + history_length: ${agent.config.policy_cfg.n_action_steps} + kernel_size: ${agent.config.policy_cfg.kernel_size} + downsample_kernel_size: 3 + downsample_stride: 2 + downsample_padding: 1 + down_dims: + - 256 + - 512 + - 1024 + kernel_size: 5 + n_groups: 8 + diffusion_step_embed_dim: 128 + use_film_scale_modulation: true + noise_scheduler_type: DDIM + beta_schedule: squaredcos_cap_v2 + beta_start: 0.0001 + beta_end: 0.02 + prediction_type: epsilon + clip_sample: true + clip_sample_range: 1.0 + num_train_timesteps: 50 + num_inference_steps: 10 + do_mask_loss_for_padding: false + train_cfg: + _target_: utils.TrainConfig + lr: 0.0001 + lr_scheduler: cosine + lr_warmup_steps: 500 + adam_betas: + - 0.95 + - 0.999 + adam_eps: 1.0e-08 + adam_weight_decay: 1.0e-06 + grad_clip_norm: 10 + offline_steps: ${num_train_frames_diffusion} + use_amp: true + observation_cfg: + _target_: agent.encoder.VisualFeatureSet + use_depth: ${use_depth} + use_color: ${use_color} + mask_input_dict: + _target_: agent.encoder.MaskInputDict + enable: ${use_masks} + representation: ${mask_representation} + mask_list: ${mask_list} + crop_input_config: + _target_: agent.encoder.CropInputConfig + color_crop_type: ${color_crop_type} + depth_crop_type: ${depth_crop_type} + segmask_crop_type: ${segmask_crop_type} + crop_hw: ${crop_hw} + crop_down_offset: ${crop_down_offset} + add_crop_binary_mask: ${add_crop_binary_mask} + add_coord_conv_map: ${add_coord_conv_map} + context_input_config: + _target_: agent.encoder.ContextInputConfig + use_color: ${use_context_color} + use_depth: ${use_context_depth} + mask_input_dict: + _target_: agent.encoder.MaskInputDict + enable: ${use_context_segmask} + representation: ${mask_representation} + mask_list: ${mask_list} + crop_input_config: + _target_: agent.encoder.CropInputConfig + color_crop_type: ${context_color_crop_type} + depth_crop_type: ${context_depth_crop_type} + segmask_crop_type: ${context_segmask_crop_type} + crop_hw: ${crop_hw} + crop_down_offset: ${crop_down_offset} + add_crop_binary_mask: ${context_add_crop_binary_mask} + add_coord_conv_map: ${context_add_coord_conv_map} + mask_soft_approx_scheduler_config: + _target_: agent.encoder.MaskSoftApproxSchedulerConfig + num_steps: 40000 + initial_value: 10.0 + final_value: 1000.0 + interpolation_scheme: constant + use_contact_map: ${use_contact_map} + use_sdf_maps: ${use_sdf_maps} + use_normals_maps: ${use_normals_maps} + which_objects: ${which_objects} + grasped_dtc_max_value: ${grasped_dtc_max_value} + env_dtc_max_value: ${env_dtc_max_value} + grasped_normals_mask_max_dtc_value: ${grasped_normals_mask_max_dtc_value} + env_normals_mask_max_dtc_value: ${env_normals_mask_max_dtc_value} + clamp_dtc: ${clamp_dtc} + max_contact_prob: ${max_contact_prob} + mask_normals_within_sdf: ${mask_normals_within_sdf} + dtc_adaptive_normalization: ${dtc_adaptive_normalization} + adaptive_normals_mask: ${adaptive_normals_mask} + max_depth: ${max_depth} + image_shape: ${agent.desired_image_shape} + learnable_contact_preprocess_params: ${learnable_contact_preprocess_params} + learning_rate: ${agent.config.train_cfg.lr} + weight_decay: 0.0 + contact_model_name: ${contact_model_name} + zero_centered: false +suite: + suite: frankagym + name: frankagym + frame_stack: ${agent.n_obs_steps} + action_repeat: 1 + discount: 0.99 + hidden_dim: 1024 + num_train_frames: 2010 + num_seed_frames: 260 + num_train_epochs: 5000 + validate_every_epochs: 100 + validate_diffusion_on_action_loss_every_epochs: 500 + train_eval_diffusion_on_action_loss_every_epochs: 500 + check_topk_every_epochs: 10 + save_snapshot_every_epochs: 5000 + eval_every_frames: 2000 + num_eval_episodes: 5 + save_snapshot: true + wait_for_user_to_start_episode: true + task_make_fn: + _target_: suite.frankagym.make + name: ${task_name} + height: 240 + width: 320 + frame_stack: ${suite.frame_stack} + action_repeat: ${suite.action_repeat} + seed: ${seed} + enable_arm: ${agent.enable_arm} + enable_gripper: ${enable_gripper} + start_with_gripper_open: ${start_with_gripper_open} + enable_camera: ${agent.enable_camera} + path_to_depth_extrinsics: ${path_to_depth_extrinsics} + contact_estimation_model_ckpt_path: ${contact_estimation_model_ckpt_path} + x_limit: ${x_limit} + y_limit: ${y_limit} + z_limit: ${z_limit} + device: ${device} + interpolation_frequency: ${interpolation_frequency} + policy_frequency: ${policy_frequency} + debug_timestamps: ${debug_timestamps} + stop_after_action: ${stop_after_action} + open_loop: ${open_loop} + wait_for_new_camera_frames: ${wait_for_new_camera_frames} + action_key: ${action_key} + action_trajectory_horizon: ${agent.config.policy_cfg.horizon} + action_trajectories: ${action_trajectories} + path_to_zarr_dataset: ${expert_dataset} + agent_policy_cfg: ??? + true_action_history: ${true_action_history} +num_train_frames_bc: 50000 +num_train_frames_drq: 1100000 +stddev_schedule_drq: linear(1.0,0.1,100000) +task_name: FrankaInsertion-v1 +num_train_frames_vinn: 25000 +num_train_frames_diffusion: 1000000 +num_train_epochs_bc: 5000 +num_train_epochs_diffusion: 5000 +validate_every_epochs_bc: 5 +validate_every_epochs_diffusion: 25 +validate_diffusion_on_action_loss_every_epochs: 50 +train_eval_diffusion_on_action_loss_every_epochs: 500 +check_topk_every_epochs: 5 +check_topk_every_epochs_diffusion: ${validate_diffusion_on_action_loss_every_epochs} +save_snapshot_every_epochs_diffusion: 5000 +x_limit: +- 0.2 +- 0.7 +y_limit: +- -0.4 +- 0.4 +z_limit: +- -0.05 +- 0.55 +home_displacement: +- 0.55 +- 0.0 +- 0.55 +- 180.0 +- 0.0 +- 0.0 +enable_gripper: true +start_with_gripper_open: true +offset_mask: +- 1 +- 1 +- 1 +- 1 +- 1 +- 1 +path_to_depth_extrinsics: ~/fish_leon/FISH/cfgs/camera_poses/camera_poses_L515/20240904-122305/color_tf_world.npy diff --git a/0a7h4b5r/.hydra/hydra.yaml b/0a7h4b5r/.hydra/hydra.yaml new file mode 100644 index 0000000000000000000000000000000000000000..801b598b66bd1f2a72128d16f106045165f06111 --- /dev/null +++ b/0a7h4b5r/.hydra/hydra.yaml @@ -0,0 +1,169 @@ +hydra: + run: + dir: ${final_experiment_dir} + sweep: + dir: ${final_experiment_dir} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${final_experiment_dir}/.slurm + timeout_min: 60 + cpus_per_task: null + gpus_per_node: null + tasks_per_node: 1 + mem_gb: null + nodes: 1 + name: ${hydra.job.name} + stderr_to_stdout: false + _target_: hydra_plugins.hydra_submitit_launcher.submitit_launcher.LocalLauncher + sweeper: + _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper + max_batch_size: null + params: null + help: + app_name: ${hydra.job.name} + header: '${hydra.help.app_name} is powered by Hydra. + + ' + footer: 'Powered by Hydra (https://hydra.cc) + + Use --hydra-help to view Hydra specific help + + ' + template: '${hydra.help.header} + + == Configuration groups == + + Compose your configuration from those groups (group=option) + + + $APP_CONFIG_GROUPS + + + == Config == + + Override anything in the config (foo.bar=value) + + + $CONFIG + + + ${hydra.help.footer} + + ' + hydra_help: + template: 'Hydra (${hydra.runtime.version}) + + See https://hydra.cc for more info. + + + == Flags == + + $FLAGS_HELP + + + == Configuration groups == + + Compose your configuration from those groups (For example, append hydra/job_logging=disabled + to command line) + + + $HYDRA_CONFIG_GROUPS + + + Use ''--cfg hydra'' to Show the Hydra config. + + ' + hydra_help: ??? + hydra_logging: + version: 1 + formatters: + simple: + format: '[%(asctime)s][HYDRA] %(message)s' + handlers: + console: + class: logging.StreamHandler + formatter: simple + stream: ext://sys.stdout + root: + level: INFO + handlers: + - console + loggers: + logging_example: + level: DEBUG + disable_existing_loggers: false + job_logging: + version: 1 + formatters: + simple: + format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s' + handlers: + console: + class: logging.StreamHandler + formatter: simple + stream: ext://sys.stdout + file: + class: logging.FileHandler + formatter: simple + filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log + root: + level: INFO + handlers: + - console + - file + disable_existing_loggers: false + env: {} + mode: RUN + searchpath: [] + callbacks: {} + output_subdir: .hydra + overrides: + hydra: + - hydra.mode=RUN + task: + - agent=diffusion + - suite=frankagym + - suite/frankagym_task@_global_=insertion + job: + name: eval_robot + chdir: true + override_dirname: agent=diffusion,suite/frankagym_task@_global_=insertion,suite=frankagym + id: ??? + num: ??? + config_name: config_eval + env_set: {} + env_copy: [] + config: + override_dirname: + kv_sep: '=' + item_sep: ',' + exclude_keys: [] + runtime: + version: 1.3.2 + version_base: '1.1' + cwd: /home/leonmkim/fish_leon/FISH + config_sources: + - path: hydra.conf + schema: pkg + provider: hydra + - path: /home/leonmkim/fish_leon/FISH/cfgs + schema: file + provider: main + - path: '' + schema: structured + provider: schema + output_dir: /home/leonmkim/fish_leon/FISH/exp_local/frankagym_pixels/FrankaInsertion-v1/1013_1/0a7h4b5r + choices: + suite: frankagym + suite/frankagym_task@_global_: insertion + agent: diffusion + hydra/env: default + hydra/callbacks: null + hydra/job_logging: default + hydra/hydra_logging: default + hydra/hydra_help: default + hydra/help: default + hydra/sweeper: basic + hydra/launcher: submitit_local + hydra/output: default + verbose: false diff --git 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'~/fish_leon/FISH/cfgs/camera_poses/camera_poses_L515/20240904-122305/color_tf_world.npy', 'feature_type': '180x240_1_RGB_D_2.0_msk_channels_EE_obj_mask_cntct_0.1_DTC_clmpd_lrnbl_nrmls_DTCmask_adpt_lrnbl_both_lr_0.0001_wd_0.0_local_multitask_outhd64all_home_crop_h144w144d48_mask_ctxtmask_seed_220979_epoch_9_acthst_hst6_out32_dwnkrnl3_dwnstrd2_dwnpd1', 'save_buffer': True, 'num_eval': 5, 'random_start': False, 'eval_starts': '/home/leonmkim/fish_leon/FISH/eval_starts/frankagym_pixels/FrankaInsertion-v1', 'num_valid_demos': None, 'load_checkpoint': True, 'checkpoint_epoch': 12000, 'load_residual_weight': False, 'checkpoint_root_dir': '/home/leonmkim/fish_leon/FISH', 'checkpoint_weight_dir': '/home/leonmkim/fish_leon/FISH/exp_local/frankagym_pixels/FrankaInsertion-v1/1013_1', 'residual_weight': '/home/leonmkim/fish_leon/FISH/weights/frankagym_pixels/FrankaInsertion-v1/weight.pt', 'final_experiment_dir': './exp_local/frankagym_pixels/FrankaInsertion-v1/1013_1/0a7h4b5r'} +2025-01-06 20:10:58,923 INFO MainThread:2105545 [wandb_init.py:init():619] starting backend +2025-01-06 20:10:58,923 INFO MainThread:2105545 [wandb_init.py:init():623] setting up manager +2025-01-06 20:10:58,926 INFO MainThread:2105545 [backend.py:_multiprocessing_setup():105] multiprocessing start_methods=fork,spawn,forkserver, using: spawn +2025-01-06 20:10:58,927 INFO MainThread:2105545 [wandb_init.py:init():631] backend started and connected +2025-01-06 20:10:58,940 INFO MainThread:2105545 [wandb_init.py:init():720] updated telemetry +2025-01-06 20:10:58,948 INFO MainThread:2105545 [wandb_init.py:init():753] communicating run to backend with 90.0 second timeout +2025-01-06 20:10:59,216 INFO MainThread:2105545 [wandb_run.py:_on_init():2435] communicating current version +2025-01-06 20:10:59,308 INFO MainThread:2105545 [wandb_run.py:_on_init():2444] got version response upgrade_message: "wandb version 0.19.1 is available! To upgrade, please run:\n $ pip install wandb --upgrade" + +2025-01-06 20:10:59,308 INFO MainThread:2105545 [wandb_init.py:init():804] starting run threads in backend +2025-01-06 20:10:59,652 INFO MainThread:2105545 [wandb_run.py:_console_start():2413] atexit reg +2025-01-06 20:10:59,652 INFO MainThread:2105545 [wandb_run.py:_redirect():2255] redirect: wrap_raw +2025-01-06 20:10:59,652 INFO MainThread:2105545 [wandb_run.py:_redirect():2320] Wrapping output streams. +2025-01-06 20:10:59,652 INFO MainThread:2105545 [wandb_run.py:_redirect():2345] Redirects installed. +2025-01-06 20:10:59,654 INFO MainThread:2105545 [wandb_init.py:init():847] run started, returning control to user process +2025-01-06 20:10:59,654 INFO MainThread:2105545 [wandb_run.py:_tensorboard_callback():1544] tensorboard callback: /home/leonmkim/fish_leon/FISH/exp_local/frankagym_pixels/FrankaInsertion-v1/1013_1/0a7h4b5r/tb, True +2025-01-06 20:11:03,748 INFO MainThread:2105545 [wandb_run.py:_config_callback():1382] config_cb None None {'grasped_obj_name': 'fowlers', 'left_book_slot': 'twodim'} +2025-01-06 20:15:49,103 WARNING MsgRouterThr:2105545 [router.py:message_loop():77] message_loop has been closed diff --git a/0a7h4b5r/wandb/run-20250106_201058-0a7h4b5r/files/code/FISH/eval_robot.py b/0a7h4b5r/wandb/run-20250106_201058-0a7h4b5r/files/code/FISH/eval_robot.py new file mode 100644 index 0000000000000000000000000000000000000000..50493fea183960d2ea4fa0b904b2e6077c21a710 --- /dev/null +++ b/0a7h4b5r/wandb/run-20250106_201058-0a7h4b5r/files/code/FISH/eval_robot.py @@ -0,0 +1,605 @@ +#%% +import warnings +import os + +os.environ['MKL_SERVICE_FORCE_INTEL'] = '1' +os.environ['MUJOCO_GL'] = 'egl' +from pathlib import Path +#%% +import hydra +import numpy as np +import torch + +import utils +from utils import get_feature_dirname_from_configs + +from video import VideoRecorder +import pickle +import time +import threading +import shutil +from logger import Logger + +import wandb +from omegaconf import OmegaConf, open_dict + +from replay_buffer_robot import RosbagEvalReplayBufferStorage +from lerobot.common.utils.utils import _relative_path_between + +torch.backends.cudnn.benchmark = True +warnings.filterwarnings('ignore', category=DeprecationWarning) + +# import specs for replay buffer +from dm_env import specs + +import sys, signal +import yaml + +import binomial_cis as bc + +# get path of current file +current_path = os.path.dirname(os.path.realpath(__file__)) +sys.path.append(os.path.join(current_path, os.pardir)) +# from contact_estimation.src.utils.viz_utils import normalized_surface_normal_to_rgb, depth_map_to_im, grasped_env_dtc_map_to_im, contact_prob_map_to_im, desaturate_color_image, masked_overlay_im_list + +def make_agent(obs_spec, action_spec, cfg): + cfg.obs_shape = obs_spec['pixels'].shape + dataset_statistics = None # this will be loaded from the checkpoint + try: + cfg.action_shape = action_spec.shape + except: + pass + return hydra.utils.instantiate(cfg, dataset_statistics) + +class Workspace: + def __init__(self, cfg): + self.work_dir = Path.cwd() + print(f'workspace: {self.work_dir}') + + signal.signal(signal.SIGINT, self.signal_handler) + + self.cfg = cfg + self.loading_uncompiled_checkpoint_with_compile = False + self.loading_compiled_checkpoint_with_no_compile = False + + snapshot_path = Path(self.cfg.checkpoint_weight_dir) / f'snapshot_{self.cfg.checkpoint_epoch}.pt' + self.load_checkpoint_conf(snapshot_path=snapshot_path) + + # load config for action trajectories + utils.set_seed_everywhere(self.cfg.seed) + self.device = torch.device(self.cfg.device) + self.setup() + + # self.agent = make_agent(self.eval_env.observation_spec(), + # self.eval_env.action_spec(), self.cfg.agent) + self.timer = utils.Timer() + # self._global_step = 0 + self._global_episode = 0 + self._global_epoch = 0 + self.num_episode_successes = 0 + + self.alpha_range = [.01, .025, .05, .1] + + # Need to convert hydra config to primitive container for wandb https://docs.wandb.ai/guides/integrations/hydra + with open_dict(self.cfg): + self.cfg.feature_type = get_feature_dirname_from_configs( + hydra.utils.instantiate(self.cfg.agent.config.observation_cfg), + self.cfg.agent.config.policy_cfg.input_shapes, + hydra.utils.instantiate(self.cfg.agent.config.policy_cfg.action_history_encoder_config) if 'observation.action_history' in self.cfg.agent.config.policy_cfg.input_shapes else None, + ) + + wandb_config = OmegaConf.to_container( + self.cfg, resolve=True, throw_on_missing=True + ) + # must be called before any tf summary writer is created + if self.cfg.use_wandb: + # get the run id from the final_experiment_dir directory + run_id = os.path.basename(os.path.normpath(self.cfg.final_experiment_dir)) + wandb.init(project='extrinsic_contact_downstream', entity='serialexperimentsleon', job_type='eval', sync_tensorboard=self.cfg.use_tb, config=wandb_config, id=run_id) + + self.logger = Logger(self.work_dir, use_tb=self.cfg.use_tb, use_wandb=self.cfg.use_wandb) + + # if not self.loading_uncompiled_checkpoint_with_compile and self.cfg.agent.config.compile: + # self.agent.compile_modules() + + # self.load_checkpoint(snapshot_path=snapshot_path) + + # if self.loading_uncompiled_checkpoint_with_compile: # need to call compile after loading the checkpoint + # self.agent.compile_modules() + + print(f"loaded agent with feature_type: {self.cfg.feature_type}") + + def check_for_key_press(self): + while self.continue_keypress_thread: + inp = input("Press 'r' to restart current episode, 'n' to stop current episode and skip to next, 'q' to break entire eval\n") + if inp == 'n': + self.preempt_episode = True + print("preempting episode") + elif inp in ['', '0', '1']: # enter key + if inp in ['0', '1']: + self.num_episode_successes += int(inp) + self.proceed_after_env_reset_event.set() + print("proceeding to start episode!") + elif inp == 'q': + self.proceed_after_env_reset_event.set() + self.preempt_episode = True + self.exit_eval = True + self.continue_keypress_thread = False # will stop the keypress thread + print("quitting eval") + break + elif inp == 'r': + print('restarting episode') + self.preempt_episode = True + self.restart_episode = True + else: + print("Invalid key press, try again") + + # self.keypress_input_thread.join() # wait for the keypress thread to finish + + def signal_handler(self, signal, frame): + print("\nprogram exiting gracefully") + self.proceed_after_env_reset_event.set() + self.preempt_episode = True + self.exit_eval = True + self.continue_keypress_thread = False # will stop the keypress thread + self.keypress_input_thread.join() # wait for the keypress thread to finish + video_filepath = self.video_recorder.save() + # get the video file and convert to video tensor to log + self.logger.log_video('eval/video', video_filepath, self.global_step) + wandb.finish() + sys.exit(0) + + def setup(self): + # create envs + self.eval_env = hydra.utils.call(self.cfg.suite.task_make_fn) + # expert_demo_config_path = os.path.join(os.path.dirname(self.cfg.expert_dataset), 'demo_config.yaml') + # self.expert_demo_config = yaml.load(open(expert_demo_config_path, 'r'), Loader=yaml.FullLoader) + # self.eval_env._env.action_trans_norm = expert_demo_config['max_translation_action_norm'] + # self.eval_env._env.action_rot_norm = expert_demo_config['max_rotation_action_norm'] + # self.eval_env._env.action_period = expert_demo_config['sample_period'] + # print(f"setting max_translation_action_norm to {expert_demo_config['max_translation_action_norm']} and sample_period to {expert_demo_config['sample_period']}") + # print(f"setting max_rotation_action_norm to {expert_demo_config['max_rotation_action_norm']}") + + # self.eval_env.set_demo_params(self.cfg.expert_dataset) + + # Turn off random start + self.eval_env.random_start = False + + # create replay buffer + # data_specs = [ + # { + # 'observation': self.eval_env.observation_spec(), + # }, + # # self.eval_env.observation_spec()['features'], + # self.eval_env.action_spec(), + # specs.Array(self.eval_env.action_spec().shape, self.eval_env.action_spec().dtype, 'vinn_action'), + # specs.Array((1, ), np.float32, 'reward'), + # specs.Array((1, ), np.float32, 'discount'), + # ] + + # self.eval_replay_storage = ZarrEvalReplayBufferStorage(data_specs, self.work_dir / 'eval_buffer', debug_timestamps=self.cfg.debug_timestamps, save_buffer=self.cfg.save_buffer, debug_info_data_specs=self.eval_env.debug_info_data_specs, camera_info_dict=self.eval_env.get_camera_info_dict()) + self.eval_replay_storage = RosbagEvalReplayBufferStorage(self.work_dir) + + self.video_recorder = VideoRecorder( + self.work_dir if self.cfg.save_video else None, + ros_enabled=True, + fps=self.cfg.agent.config.policy_frequency, + ) + + print('workspace setup complete') + + @property + def global_step(self): + # return self._global_step + return self.eval_env.get_global_step() + + @property + def global_episode(self): + return self._global_episode + + @property + def global_frame(self): + return self.global_step * self.cfg.action_repeat + + @property + def global_epoch(self): + return self._global_epoch + + def reset(self, eval_idx): + if not self.eval_env.enable_arm: + return np.array([0,0,0], dtype=np.float32) + self.eval_env.arm_refresh(reset=False) + # Set start position + try: + self.eval_env.set_position(self.start_pos[eval_idx]) + except: + self.eval_env.arm.set_position(self.start_pos[eval_idx]) + if self.eval_env.arm.keep_gripper_closed: + self.eval_env.arm.close_gripper_fully() + else: + self.eval_env.arm.open_gripper_fully() + time.sleep(0.1) + time_step = self.eval_env.step(np.zeros(self.eval_env.action_spec().shape[0], dtype=np.float32), + np.zeros(self.eval_env.action_spec().shape[0], dtype=np.float32)) + return time_step + + def eval(self): + # before evals start, prompt user for name of grasped object and the left book of the slot location + grasped_obj_name = input("Enter the name of the grasped object: ") + left_book_slot = input("Enter the left book slot location: ") + # update wandb config + if self.cfg.use_wandb: + wandb.config.update({'grasped_obj_name': grasped_obj_name, 'left_book_slot': left_book_slot}) + + self.preempt_episode = False + self.exit_eval = False + self.restart_episode = False + + self.continue_keypress_thread = True + self.proceed_after_env_reset_event = threading.Event() + self.keypress_input_thread = threading.Thread(target=self.check_for_key_press) + self.keypress_input_thread.start() + + # # Set model to eval mode + # self.agent.train(False) + + eval_until_episode = utils.Until(self.cfg.num_eval) + + self.use_action_history = False + # if "dp" in repr(self.agent) and "observation.action_history" in self.cfg.agent.config.policy_cfg.input_shapes: + if "observation.action_history" in self.cfg.agent.config.policy_cfg.input_shapes: + self.use_action_history = True + + # self.eval_replay_storage._new_eval_step(0) + + # if 'vinn' in repr(self.agent) or 'openloop' in repr(self.agent): + # with open(self.cfg.expert_dataset, 'rb') as f: + # if self.cfg.obs_type == 'pixels': + # self.expert_demo, _, self.expert_action, self.expert_reward = pickle.load(f) + # elif self.cfg.obs_type == 'features': + # _, self.expert_demo, self.expert_action, self.expert_reward = pickle.load(f) + + # if self.cfg.action_trajectories: + # with open(self.cfg.expert_action_trajectories, 'rb') as f: + # self.expert_action = pickle.load(f) + + # if isinstance(self.cfg.train_demo_idxs_list_or_num, int): + # if self.cfg.train_demo_idxs_list_or_num == -1: + # self.cfg.train_demo_idxs_list_or_num = len(self.expert_demo) + # train_demo_idxs_list_or_num = list(range(self.cfg.train_demo_idxs_list_or_num)) + + # self.expert_demo = self.expert_demo[train_demo_idxs_list_or_num] + # self.expert_action = self.expert_action[train_demo_idxs_list_or_num] + # self.expert_reward = self.expert_reward[train_demo_idxs_list_or_num] + # # if self.cfg.action_plans: + # # self.expert_action_plans = self.expert_action_plans[self.cfg.train_demo_idxs_list_or_num] + # # self.expert_demo = self.expert_demo[:self.cfg.num_demos] + # # self.expert_action = self.expert_action[:self.cfg.num_demos] + # # self.expert_reward = self.expert_reward[:self.cfg.num_demos] + + # self.expert_demo = np.concatenate(self.expert_demo, axis=0) + # self.expert_rgb_obs = np.ascontiguousarray(np.transpose(self.expert_demo, (0,2,3,1))[:, :,:,:3].astype(np.uint8)) + # self.expert_action = np.concatenate(self.expert_action, axis=0) + + # self.agent.save_representations(self.expert_demo, self.expert_action, 128, config=self.expert_demo_config) + + # Get start points + if self.cfg.random_start: + eval_starts = Path(self.cfg.eval_starts) / 'starts.pkl' + if eval_starts.exists(): + with eval_starts.open('rb') as f: + self.start_pos = pickle.load(f) + else: + eval_starts = Path(self.cfg.eval_starts) + eval_starts.mkdir(parents=True, exist_ok=True) + + # Generate start points + self.start_pos = [] + try: + for _ in range(self.cfg.num_eval): + self.start_pos.append(self.eval_env.get_random_pos()) + except: + for _ in range(self.cfg.num_eval): + self.start_pos.append(self.eval_env.arm.get_random_pos()) + + # Save start points for the task + eval_starts = eval_starts / 'starts.pkl' + with eval_starts.open('wb') as f: + pickle.dump(self.start_pos, f) + + time_step = self.eval_env.reset() + # replay_thread = None + while eval_until_episode(self.global_episode) and not self.exit_eval: + # self.video_recorder.init(self.eval_env, video_filename=f'{self.global_episode}_eval.mp4') + print(f"Starting episode {self.global_episode}") + time_step = self.eval_env.reset() #Leon: need to call reset twice in case objects are trapped + self.video_recorder.init(self.eval_env, video_filename=f'{self.global_episode}_eval.mp4') + # x = input("Press Enter to continue... after reseting env") + print("Press Enter to continue... after reseting env. To rate prev episode, press 0 for failure and 1 for success") + self.proceed_after_env_reset_event.clear() # clear the event flag + self.proceed_after_env_reset_event.wait() # blocking wait for the event flag to be set + if self.global_episode > 0: + self.logger.log_metrics({'num_success': self.num_episode_successes}, self.global_step, 'eval', episode=self.global_episode) + self.logger.log_metrics({'success_rate': self.num_episode_successes/self.global_episode}, self.global_step, 'eval', episode=self.global_episode) + + # log confidence intervals for success rate + k = self.num_episode_successes # number of successes + n = self.global_episode # number of trials + + table_columns = [] + table_data = [] + for alpha in self.alpha_range: + lb = bc.binom_ci(k, n, alpha, 'lb') + ub = bc.binom_ci(k, n, alpha, 'ub') + + self.logger.log_metrics({f'success_rate_lb_{alpha}': lb}, self.global_step, 'eval', episode=self.global_episode) + self.logger.log_metrics({f'success_rate_ub_{alpha}': ub}, self.global_step, 'eval', episode=self.global_episode) + + time_step = self.eval_env.reset() + # debug_info_dict = self.eval_env.debug_info_dict + # if replay_thread is not None: + # # wait for the last replay thread to finish + # replay_thread.join() + + # self.eval_replay_storage.add(time_step._replace(observation=time_step.observation[self.cfg.obs_type]), debug_info_dict) + # replay_thread = threading.Thread(target=self.eval_replay_storage.add, args=(time_step._replace(observation=time_step.observation[self.cfg.obs_type]), debug_info_dict)) + # replay_thread = threading.Thread(target=self.eval_replay_storage.add, args=(time_step, debug_info_dict)) + + # replay_thread.start() + if self.cfg.random_start: + time_step = self.reset(self.global_episode) + time.sleep(2) #5) + # if 'vinn' in repr(self.agent): + # self.agent.reset() + # # self.agent.buffer.reset() + # # if self.cfg.open_loop: + # # self.agent.current_step = 0 + # if 'openloop' in repr(self.agent): + # self.agent.curr_step = 0 + # at start of each episode, provide zero action for policies that use action history + # shape should be (T_o, T_a, action_dim) + + # while not time_step.last() and not self.preempt_episode: + self.video_recorder.ros_start_recording() + self.eval_replay_storage.start_episode() + self.eval_env.start_policy_timer() + while not self.eval_env.episode_done() and not self.preempt_episode: + # with torch.no_grad(), utils.eval_mode(self.agent): + # # if self.cfg.agent.provide_topk: + # # action, vinn_action, topk = self.agent.act( + # # time_step.observation['pixels'], + # # self.global_step, + # # eval_mode=True) + # # elif self.cfg.agent.provide_obs: + # # action, vinn_action, obs = self.agent.act( + # # time_step.observation['pixels'], + # # self.global_step, + # # eval_mode=True) + # # else: + # action, vinn_action = self.agent.act( + # time_step.observation, + # self.global_step, + # eval_mode=True, + # obs_timestamp=time_step.observation['timestamp'], + # obs_seq=time_step.observation['seq'], + # action_history=action_history, + # action_history_start_timestamp=action_history_start_timestamp, + # ) + # DONT WAIT FOR POLICY TO GET AN ACTION + # we dont want to slow down grabbing obs and passing to sam/contact features + + self.eval_env.run_policy_threads() # this just does a rospy sleep + + # if self.use_action_history: + # action_history_start_timestamp = time_step.observation['timestamp'] + # # action_history = action[:self.cfg.agent.config.policy_cfg.action_history_encoder_config.history_length, ...] + # # add n_obs_steps dimension to action_history, for now we assume n_obs_steps = 1 + # # TODO: handle n_obs_steps > 1 + # action_history = action[np.newaxis, ...] + + # time_step = self.eval_env.step(action, vinn_action) # obs, reward after action has been taken + # debug_info_dict = self.eval_env.debug_info_dict + + # time_step = self.eval_env.ros_step() + + # replay_thread.join() + + # time how long it takes to execute the step + # time_before_add = time.perf_counter() + # self.eval_replay_storage.add(time_step._replace(observation=time_step.observation[self.cfg.obs_type]), debug_info_dict) + # use thread to call the add function in a separate thread + # replay_thread = threading.Thread(target=self.eval_replay_storage.add, args=(time_step._replace(observation=time_step.observation[self.cfg.obs_type]), debug_info_dict)) + + # replay_thread = threading.Thread(target=self.eval_replay_storage.add, args=(time_step, debug_info_dict)) + # replay_thread.start() + + # print(f"Time to add to replay buffer: {time.perf_counter() - time_before_add}") + + # self.video_recorder.record(self.eval_env) + # self._global_step += 1 + + self.eval_env.stop_policy_timer() + + if self.restart_episode: + # means we should delete the current episode and start again + self.restart_episode = False + self.eval_replay_storage.reset_current_episode() + self.video_recorder.reset_current_episode() + + else: + self.eval_replay_storage.store_current_episode() + video_filepath = self.video_recorder.save() + self.logger.log_video(f"eval/{video_filepath.name.rstrip('.mp4')}", video_filepath, self.global_step) + self._global_episode += 1 + + self.preempt_episode = False # reset preempt_episode flag + + # self.video_recorder.save(f'{episode}_eval.mp4') + # get the video file and convert to video tensor to log + + self.eval_env.reset() + + print("Evaluation finished. To wrap up, rate prev episode, press 0 for failure and 1 for success") + self.proceed_after_env_reset_event.clear() # clear the event flag + self.proceed_after_env_reset_event.wait() # blocking wait for the event flag to be set + if self.global_episode > 0: + # self.logger.log_metrics({'num_success': self.num_episode_successes}, self.global_step, 'eval', episode=self.global_episode) + self.logger.log_metrics({'num_success': self.num_episode_successes}, self.global_step, 'eval', episode=self.global_episode) + self.logger.log_metrics({'success_rate': self.num_episode_successes/self.global_episode}, self.global_step, 'eval', episode=self.global_episode) + + # log confidence intervals for success rate + k = self.num_episode_successes # number of successes + n = self.global_episode # number of trials + + table_columns = ['success_rate'] + table_data = [self.num_episode_successes/self.global_episode] + for alpha in self.alpha_range: + lb = bc.binom_ci(k, n, alpha, 'lb') + ub = bc.binom_ci(k, n, alpha, 'ub') + + self.logger.log_metrics({f'success_rate_lb_{alpha}': lb}, self.global_step, 'eval', episode=self.global_episode) + self.logger.log_metrics({f'success_rate_ub_{alpha}': ub}, self.global_step, 'eval', episode=self.global_episode) + + table_columns.extend([f'success_rate_lb_{alpha}', f'success_rate_ub_{alpha}']) + table_data.extend([lb, ub]) + + table_data = [table_data] + + # seperately log as a table + wandb.log({ + "eval/success_rate_ci": wandb.Table(data=table_data, columns=table_columns) + }) + + # also accumulate eval metrics across previous eval runs + # TODO: change wandb init to resume from an existing run!!! + run_filter={ + "jobType": "eval", + "config.wandb_run_id": self.cfg.wandb_run_id, + "summary_metrics.episode": {"$gte": 5}, + "config.checkpoint_epoch": self.cfg.checkpoint_epoch, + "state": "finished", + # "config.grasped_obj_name": grasped_obj_name, + # "config.left_book_slot": left_book_slot, + } + + api = wandb.Api() + filtered_runs = api.runs("serialexperimentsleon/extrinsic_contact_downstream", filters=run_filter) + total_num_successes = self.num_episode_successes + total_num_episodes = self.global_episode + list_of_historical_run_ids = [] + if len(filtered_runs) > 0: + for filtered_run in filtered_runs: + total_num_successes += filtered_run.summary_metrics['eval/num_success'] + # total_num_episodes += filtered_run.summary_metrics['episode'] + total_num_episodes += filtered_run.config['num_eval'] + list_of_historical_run_ids.append(filtered_run.id) + + wandb.summary['total_num_successes'] = total_num_successes + wandb.summary['total_num_episodes'] = total_num_episodes + wandb.summary['total_success_rate'] = total_num_successes/total_num_episodes + + # log the accumulated metrics as a table + total_table_columns = ['total_num_successes', 'total_num_episodes', 'total_success_rate'] + total_table_data = [total_num_successes, total_num_episodes, total_num_successes/total_num_episodes] + self.logger.log_metrics({'total_success_rate': total_num_successes/total_num_episodes}, self.global_step, 'eval', episode=total_num_episodes) + + for alpha in self.alpha_range: + lb = bc.binom_ci(total_num_successes, total_num_episodes, alpha, 'lb') + ub = bc.binom_ci(total_num_successes, total_num_episodes, alpha, 'ub') + total_table_columns.extend([f'total_success_rate_lb_{alpha}', f'total_success_rate_ub_{alpha}']) + total_table_data.extend([lb, ub]) + wandb.summary[f'total_success_rate_lb_{alpha}'] = lb + wandb.summary[f'total_success_rate_ub_{alpha}'] = ub + + self.logger.log_metrics({f'total_success_rate_lb_{alpha}': lb}, self.global_step, 'eval', episode=total_num_episodes) + self.logger.log_metrics({f'total_success_rate_ub_{alpha}': ub}, self.global_step, 'eval', episode=total_num_episodes) + + total_table_data = [total_table_data] + wandb.log({ + 'eval/total_success_rate_ci': wandb.Table(data=total_table_data, columns=total_table_columns) + }) + + self.continue_keypress_thread = False # will stop the keypress thread + self.keypress_input_thread.join() # wait for the keypress thread to finish + + def load_checkpoint_conf(self, snapshot_path): + config_path = snapshot_path.parent / 'config.yaml' + if not config_path.exists(): + raise FileNotFoundError(f'No snapshot conf found at {config_path}') + else: + # load the omegaconf config + hydra.core.global_hydra.GlobalHydra.instance().clear() + hydra.initialize( + str(_relative_path_between(Path(config_path).absolute().parent, Path(__file__).absolute().parent)), + ) + cfg = hydra.compose(Path(config_path).stem) + from deepdiff import DeepDiff + from omegaconf import open_dict + diff = DeepDiff(OmegaConf.to_container(cfg), OmegaConf.to_container(self.cfg)) # old, new + # import re + overwriteable_keys = [f"root{overwritable_key}" for overwritable_key in ["['use_wandb']", "['path_to_depth_extrinsics']", "['eval']", "['root_dir']", "['wandb_notes']", "['agent']['config']['train_cfg']['use_amp']", "['agent']['config']['compile']", "['agent']['config']['policy_cfg']['num_inference_steps']"]] + if "values_changed" in diff: + # top_k_checkpoints, wandb_notes, agent.config.train_cfg.use_amp, save_snapshot_every_epochs_diffusion, check_topk_every_epochs_diffusion, validate_diffusion_on_action_loss_every_epochs, train_eval_diffusion_on_action_loss_every_epochs, validate_every_epochs_diffusion + # for keys above, overwrite the old config with the new config + for k, v in diff['values_changed'].items(): + # replace any keys that are under "root['suite']" + if k in overwriteable_keys or k.startswith("root['suite']"): + print(f"Found changed key {k} with value {v}. Overwriting old checkpoint config") + if k == "root['agent']['config']['compile']": + if diff['values_changed'][k]['new_value']: + self.loading_uncompiled_checkpoint_with_compile = True + elif not diff['values_changed'][k]['new_value']: + # raise ValueError("Cannot load a compiled checkpoint without compile") + self.loading_compiled_checkpoint_with_no_compile = True + exec(f"{k.replace('root[', 'cfg[')} = {k.replace('root[', 'self.cfg[')}") + # for any new values, update the old checkpoint config + if "dictionary_item_added" in diff: + for new_key in diff['dictionary_item_added']: # this is a list + # if new_key == "root['suite']['task_make_fn']['observation_cfg']": + if new_key == "root['suite']['task_make_fn']['agent_policy_cfg']": + # pass the agents observation_cfg to the suite task_make_fn + with open_dict(cfg): # to allow addition of non-existing keys + # cfg.suite.task_make_fn.observation_cfg = cfg.agent.config.observation_cfg + cfg.suite.task_make_fn.agent_policy_cfg = cfg.agent.config + continue + elif "['agent']['config']['policy_cfg']['input_shapes']" in new_key: + # skip adding the new key if it is the input_shapes of the policy_cfg + continue + else: + print(f"Found new key {new_key} with value {eval(new_key.replace('root[', 'self.cfg['))}. Adding to checkpoint config") + # eval(new_key.replace('root', 'cfg')) = eval(new_key.replace('root', 'self.cfg')) + if new_key == "root['agent']['config']['compile']": + if self.cfg.agent.config.compile: + self.loading_uncompiled_checkpoint_with_compile = True + + with open_dict(cfg): + exec(f"{new_key.replace('root[', 'cfg[')}={new_key.replace('root[', 'self.cfg[')}") + self.cfg = cfg + + def load_checkpoint(self, snapshot_path, bc=False): + print(f'resuming {repr(self.agent)}: {snapshot_path}') + with snapshot_path.open('rb') as f: + payload = torch.load(f) + agent_payload = {} + for k, v in payload.items(): + if k not in self.__dict__: + agent_payload[k] = v + elif k == '_global_epoch': + self._global_epoch = v + print(f'loaded epoch: {v}') + if self.cfg.use_wandb: + # add to config of wandb + wandb.config.update({'epoch': v}) + + # self.agent.load_snapshot_eval(agent_payload, bc) + +@hydra.main(config_path='cfgs', config_name='config_eval') +def main(cfg): + from eval_robot import Workspace as W + root_dir = Path.cwd() + workspace = W(cfg) + + workspace.eval() + +if __name__ == '__main__': + main() diff --git a/0a7h4b5r/wandb/run-20250106_201058-0a7h4b5r/files/config.yaml b/0a7h4b5r/wandb/run-20250106_201058-0a7h4b5r/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fd6fd641bc01787437a65fa061d0fb8de9961ffb --- /dev/null +++ b/0a7h4b5r/wandb/run-20250106_201058-0a7h4b5r/files/config.yaml @@ -0,0 +1,895 @@ +wandb_version: 1 + +root_dir: + desc: null + value: /home/leonmkim/fish_leon +replay_buffer_size: + desc: null + value: 150000 +replay_buffer_num_workers: + desc: null + value: 2 +nstep: + desc: null + value: 3 +batch_size: + desc: null + value: 128 +seed: + desc: null + value: 1 +dataset_shuffle_seed: + desc: null + value: 0 +device: + desc: null + value: cuda +save_video: + desc: null + value: true +save_train_video: + desc: null + value: true +use_tb: + desc: null + value: true +use_wandb: + desc: null + value: true +wandb_run_id: + desc: null + value: '1013_1' +wandb_notes: + desc: null + value: 1013_1_restarted_11 +eval: + desc: null + value: true +true_action_history: + desc: null + value: false +train_pad_after: + desc: null + value: 4 +process_contact_features: + desc: null + value: true +obs_type: + desc: null + value: pixels +use_color: + desc: null + value: true +use_depth: + desc: null + value: true +use_masks: + desc: null + value: true +mask_list: + desc: null + value: + - EE_obj_mask +mask_representation: + desc: null + value: channels +crop_hw: + desc: null + value: + - 144 + - 144 +crop_down_offset: + desc: null + value: 48 +color_crop_type: + desc: null + value: null +depth_crop_type: + desc: null + value: null +segmask_crop_type: + desc: null + value: null +add_crop_binary_mask: + desc: null + value: false +add_coord_conv_map: + desc: null + value: false +use_context_color: + desc: null + value: false +use_context_depth: + desc: null + value: false +use_context_segmask: + desc: null + value: false +context_color_crop_type: + desc: null + value: null +context_depth_crop_type: + desc: null + value: null +context_segmask_crop_type: + desc: null + value: null +context_add_crop_binary_mask: + desc: null + value: false +context_add_coord_conv_map: + desc: null + value: false +use_contact_map: + desc: null + value: true +use_sdf_maps: + desc: null + value: true +use_normals_maps: + desc: null + value: true +which_objects: + desc: null + value: both +max_contact_prob: + desc: null + value: 0.1 +max_depth: + desc: null + value: 2.0 +grasped_dtc_max_value: + desc: null + value: 0.2 +env_dtc_max_value: + desc: null + value: 0.4 +grasped_normals_mask_max_dtc_value: + desc: null + value: 0.2 +env_normals_mask_max_dtc_value: + desc: null + value: 0.4 +clamp_dtc: + desc: null + value: true +dtc_adaptive_normalization: + desc: null + value: false +mask_normals_within_sdf: + desc: null + value: true +adaptive_normals_mask: + desc: null + value: true +learnable_contact_preprocess_params: + desc: null + value: true +contact_model_name: + desc: null + value: local_multitask_outhd64all_home_crop_h144w144d48_mask_ctxtmask_seed_220979_epoch_9 +contact_estimation_model_ckpt_path: + desc: null + value: ~/fish_leon/contact_estimation/artifacts/175604_2/checkpoints/epoch=09-val_loss=0.00.ckpt +encoder_type: + desc: null + value: small +debug_timestamps: + desc: null + value: false +open_loop: + desc: null + value: false +action_trajectories: + desc: null + value: true +stop_after_action: + desc: null + value: false +interpolation_frequency: + desc: null + value: 25 +policy_frequency: + desc: null + value: 5 +wait_for_new_camera_frames: + desc: null + value: true +baseline: + desc: null + value: false +train_demo_idxs_list_or_num: + desc: null + value: -1 +log_train_every_steps: + desc: null + value: 25 +name_of_expert_demo: + desc: null + value: 120_240x320_all_twodim_left_to_right_annotated_start_idx_5hz_zstd7_EE_pxl_coords_expert_demos_imp_act +expert_dataset_dirpath: + desc: null + value: /home/leonmkim/fish_leon/FISH/expert_demos/frankagym/FrankaInsertion-v1/120_240x320_all_twodim_left_to_right_annotated_start_idx_5hz_zstd7_EE_pxl_coords_expert_demos_imp_act +store_dataset_in_memory: + desc: null + value: false +expert_dataset: + desc: null + value: /home/leonmkim/fish_leon/FISH/expert_demos/frankagym/FrankaInsertion-v1/120_240x320_all_twodim_left_to_right_annotated_start_idx_5hz_zstd7_EE_pxl_coords_expert_demos_imp_act/demos.zarr +action_key: + desc: null + value: action_trajectory_25hz +semantic_demo_grouping_name: + desc: null + value: semantic_demo_grouping.yaml +semantic_demo_grouping: + desc: null + value: /home/leonmkim/fish_leon/FISH/expert_demos/frankagym/FrankaInsertion-v1/120_240x320_all_twodim_left_to_right_annotated_start_idx_5hz_zstd7_EE_pxl_coords_expert_demos_imp_act/semantic_demo_grouping.yaml +include_groups_list: + desc: null + value: all +expert_dataset_config: + desc: null + value: /home/leonmkim/fish_leon/FISH/expert_demos/frankagym/FrankaInsertion-v1/120_240x320_all_twodim_left_to_right_annotated_start_idx_5hz_zstd7_EE_pxl_coords_expert_demos_imp_act/demo_config.yaml +name_of_valid_demo: + desc: null + value: 120_240x320_all_twodim_left_to_right_annotated_start_idx_5hz_zstd7_EE_pxl_coords_expert_demos_imp_act +valid_dataset_dir: + desc: null + value: /home/leonmkim/fish_leon/FISH/expert_demos/frankagym/FrankaInsertion-v1/120_240x320_all_twodim_left_to_right_annotated_start_idx_5hz_zstd7_EE_pxl_coords_expert_demos_imp_act/demos.zarr +valid_demo_idxs_list_or_num: + desc: null + value: null +val_num_groups: + desc: null + value: 5 +load_bc: + desc: null + value: true +checkpoint_epoch_list: + desc: null + value: + - 99 + - 199 + - 299 + - 399 + - 499 + - 599 + - 699 + - 799 + - 899 + - 999 + - 1249 + - 1499 + - 1749 + - 1999 + - 2999 + - 3999 + - 4999 + - 5999 + - 6999 + - 7999 + - 8999 + - 9999 +snapshot_root_dir: + desc: null + value: /mnt/grasp_high_usage/leonmkim/contact_estimation/FISH +save_snapshot: + desc: null + value: true +save_last_snapshot: + desc: null + value: true +save_snapshot_when_done: + desc: null + value: true +top_k_checkpoints: + desc: null + value: 5 +save_snapshot_link_to_weights_dir: + desc: null + value: deprecated +bc_regularize: + desc: null + value: false +bc_weight_type: + desc: null + value: qfilter +experiment_dir: + desc: null + value: ./exp_local/frankagym_pixels/FrankaInsertion-v1/1013_1 +agent: + desc: null + value: + _target_: agent.diffusion_policy.DiffusionPolicyAgent + name: diffusion_policy + load_checkpoint: true + device: cuda + n_obs_steps: 1 + suite_name: frankagym + obs_type: pixels + enable_arm: true + enable_camera: true + use_tb: true + desired_image_shape: + - 13 + - 180 + - 240 + orig_cam_shape: + - 3 + - 240 + - 320 + config: + _target_: agent.diffusion_policy.DiffusionPolicyAgentConfig + compile: false + device: cuda + cam_resize_shape: + - 13 + - 180 + - 240 + orig_cam_shape: + - 3 + - 240 + - 320 + policy_frequency: 5 + interpolation_frequency: 25 + policy_cfg: + _target_: lerobot.common.policies.diffusion.configuration_diffusion.DiffusionConfig + n_obs_steps: 1 + horizon: 36 + n_action_steps: 36 + output_shapes: + action: + - 7 + input_normalization_modes: + observation.image: mean_std + observation.state: min_max + observation.action_history: min_max + output_normalization_modes: + action: min_max + vision_backbone: resnet18 + pretrained_backbone_weights: null + transforms: + - _target_: torchaug.transforms.RandomAffine + degrees: + - -5 + - 5 + translate: + - 0.05 + - 0.05 + batch_transform: true + num_chunks: -1 + batch_inplace: true + - _target_: torchaug.transforms.RandomColorJitter + brightness: 0.3 + contrast: 0.4 + saturation: 0.5 + hue: 0.08 + batch_transform: true + num_chunks: -1 + batch_inplace: true + use_group_norm: true + spatial_softmax_num_keypoints: 32 + action_history_encoder_config: + _target_: lerobot.common.policies.diffusion.configuration_diffusion.Unet1dEncoderConfig + in_channels: 7 + out_channels: 32 + history_length: 6 + kernel_size: 5 + downsample_kernel_size: 3 + downsample_stride: 2 + downsample_padding: 1 + down_dims: + - 256 + - 512 + - 1024 + kernel_size: 5 + n_groups: 8 + diffusion_step_embed_dim: 128 + use_film_scale_modulation: true + noise_scheduler_type: DDIM + beta_schedule: squaredcos_cap_v2 + beta_start: 0.0001 + beta_end: 0.02 + prediction_type: epsilon + clip_sample: true + clip_sample_range: 1.0 + num_train_timesteps: 50 + num_inference_steps: 10 + do_mask_loss_for_padding: false + input_shapes: + observation.image: + - 13 + - 180 + - 240 + context_observation.image: + - 13 + - 180 + - 240 + observation.state: + - 8 + observation.action_history: + - 7 + train_cfg: + _target_: utils.TrainConfig + lr: 0.0001 + lr_scheduler: cosine + lr_warmup_steps: 500 + adam_betas: + - 0.95 + - 0.999 + adam_eps: 1.0e-08 + adam_weight_decay: 1.0e-06 + grad_clip_norm: 10 + offline_steps: 1000000 + use_amp: true + observation_cfg: + _target_: agent.encoder.VisualFeatureSet + use_depth: true + use_color: true + mask_input_dict: + _target_: agent.encoder.MaskInputDict + enable: true + representation: channels + mask_list: + - EE_obj_mask + crop_input_config: + _target_: agent.encoder.CropInputConfig + color_crop_type: null + depth_crop_type: null + segmask_crop_type: null + crop_hw: + - 144 + - 144 + crop_down_offset: 48 + add_crop_binary_mask: false + add_coord_conv_map: false + context_input_config: + _target_: agent.encoder.ContextInputConfig + use_color: false + use_depth: false + mask_input_dict: + _target_: agent.encoder.MaskInputDict + enable: false + representation: channels + mask_list: + - EE_obj_mask + crop_input_config: + _target_: agent.encoder.CropInputConfig + color_crop_type: null + depth_crop_type: null + segmask_crop_type: null + crop_hw: + - 144 + - 144 + crop_down_offset: 48 + add_crop_binary_mask: false + add_coord_conv_map: false + mask_soft_approx_scheduler_config: + _target_: agent.encoder.MaskSoftApproxSchedulerConfig + num_steps: 40000 + initial_value: 10.0 + final_value: 1000.0 + interpolation_scheme: cosine + use_contact_map: true + use_sdf_maps: true + use_normals_maps: true + which_objects: both + grasped_dtc_max_value: 0.2 + env_dtc_max_value: 0.4 + grasped_normals_mask_max_dtc_value: 0.2 + env_normals_mask_max_dtc_value: 0.4 + clamp_dtc: true + max_contact_prob: 0.1 + mask_normals_within_sdf: true + dtc_adaptive_normalization: false + adaptive_normals_mask: true + max_depth: 2.0 + image_shape: + - 13 + - 180 + - 240 + learnable_contact_preprocess_params: true + learning_rate: 0.0001 + weight_decay: 0.0 + contact_model_name: local_multitask_outhd64all_home_crop_h144w144d48_mask_ctxtmask_seed_220979_epoch_9 + zero_centered: false +suite: + desc: null + value: + suite: frankagym + name: frankagym + frame_stack: 1 + action_repeat: 1 + discount: 0.99 + hidden_dim: 1024 + num_train_frames: 2010 + num_seed_frames: 260 + num_train_epochs: 5000 + validate_every_epochs: 100 + validate_diffusion_on_action_loss_every_epochs: 500 + train_eval_diffusion_on_action_loss_every_epochs: 500 + check_topk_every_epochs: 10 + save_snapshot_every_epochs: 5000 + eval_every_frames: 2000 + num_eval_episodes: 5 + save_snapshot: true + wait_for_user_to_start_episode: true + task_make_fn: + _target_: suite.frankagym.make + name: FrankaInsertion-v1 + height: 240 + width: 320 + frame_stack: 1 + action_repeat: 1 + seed: 1 + enable_arm: true + enable_gripper: true + start_with_gripper_open: true + enable_camera: true + path_to_depth_extrinsics: ~/fish_leon/FISH/cfgs/camera_poses/camera_poses_L515/20240904-122305/color_tf_world.npy + contact_estimation_model_ckpt_path: ~/fish_leon/contact_estimation/artifacts/175604_2/checkpoints/epoch=09-val_loss=0.00.ckpt + x_limit: + - 0.2 + - 0.7 + y_limit: + - -0.4 + - 0.4 + z_limit: + - -0.05 + - 0.55 + device: cuda + interpolation_frequency: 25 + policy_frequency: 5 + debug_timestamps: false + stop_after_action: false + open_loop: false + wait_for_new_camera_frames: true + action_key: action_trajectory_25hz + action_trajectory_horizon: 36 + action_trajectories: true + path_to_zarr_dataset: /home/leonmkim/fish_leon/FISH/expert_demos/frankagym/FrankaInsertion-v1/120_240x320_all_twodim_left_to_right_annotated_start_idx_5hz_zstd7_EE_pxl_coords_expert_demos_imp_act/demos.zarr + agent_policy_cfg: + _target_: agent.diffusion_policy.DiffusionPolicyAgentConfig + compile: false + device: cuda + cam_resize_shape: + - 13 + - 180 + - 240 + orig_cam_shape: + - 3 + - 240 + - 320 + policy_frequency: 5 + interpolation_frequency: 25 + policy_cfg: + _target_: lerobot.common.policies.diffusion.configuration_diffusion.DiffusionConfig + n_obs_steps: 1 + horizon: 36 + n_action_steps: 36 + output_shapes: + action: + - 7 + input_normalization_modes: + observation.image: mean_std + observation.state: min_max + observation.action_history: min_max + output_normalization_modes: + action: min_max + vision_backbone: resnet18 + pretrained_backbone_weights: null + transforms: + - _target_: torchaug.transforms.RandomAffine + degrees: + - -5 + - 5 + translate: + - 0.05 + - 0.05 + batch_transform: true + num_chunks: -1 + batch_inplace: true + - _target_: torchaug.transforms.RandomColorJitter + brightness: 0.3 + contrast: 0.4 + saturation: 0.5 + hue: 0.08 + batch_transform: true + num_chunks: -1 + batch_inplace: true + use_group_norm: true + spatial_softmax_num_keypoints: 32 + action_history_encoder_config: + _target_: lerobot.common.policies.diffusion.configuration_diffusion.Unet1dEncoderConfig + in_channels: 7 + out_channels: 32 + history_length: 6 + kernel_size: 5 + downsample_kernel_size: 3 + downsample_stride: 2 + downsample_padding: 1 + down_dims: + - 256 + - 512 + - 1024 + kernel_size: 5 + n_groups: 8 + diffusion_step_embed_dim: 128 + use_film_scale_modulation: true + noise_scheduler_type: DDIM + beta_schedule: squaredcos_cap_v2 + beta_start: 0.0001 + beta_end: 0.02 + prediction_type: epsilon + clip_sample: true + clip_sample_range: 1.0 + num_train_timesteps: 50 + num_inference_steps: 10 + do_mask_loss_for_padding: false + input_shapes: + observation.image: + - 13 + - 180 + - 240 + context_observation.image: + - 13 + - 180 + - 240 + observation.state: + - 8 + observation.action_history: + - 7 + train_cfg: + _target_: utils.TrainConfig + lr: 0.0001 + lr_scheduler: cosine + lr_warmup_steps: 500 + adam_betas: + - 0.95 + - 0.999 + adam_eps: 1.0e-08 + adam_weight_decay: 1.0e-06 + grad_clip_norm: 10 + offline_steps: 1000000 + use_amp: true + observation_cfg: + _target_: agent.encoder.VisualFeatureSet + use_depth: true + use_color: true + mask_input_dict: + _target_: agent.encoder.MaskInputDict + enable: true + representation: channels + mask_list: + - EE_obj_mask + crop_input_config: + _target_: agent.encoder.CropInputConfig + color_crop_type: null + depth_crop_type: null + segmask_crop_type: null + crop_hw: + - 144 + - 144 + crop_down_offset: 48 + add_crop_binary_mask: false + add_coord_conv_map: false + context_input_config: + _target_: agent.encoder.ContextInputConfig + use_color: false + use_depth: false + mask_input_dict: + _target_: agent.encoder.MaskInputDict + enable: false + representation: channels + mask_list: + - EE_obj_mask + crop_input_config: + _target_: agent.encoder.CropInputConfig + color_crop_type: null + depth_crop_type: null + segmask_crop_type: null + crop_hw: + - 144 + - 144 + crop_down_offset: 48 + add_crop_binary_mask: false + add_coord_conv_map: false + mask_soft_approx_scheduler_config: + _target_: agent.encoder.MaskSoftApproxSchedulerConfig + num_steps: 40000 + initial_value: 10.0 + final_value: 1000.0 + interpolation_scheme: cosine + use_contact_map: true + use_sdf_maps: true + use_normals_maps: true + which_objects: both + grasped_dtc_max_value: 0.2 + env_dtc_max_value: 0.4 + grasped_normals_mask_max_dtc_value: 0.2 + env_normals_mask_max_dtc_value: 0.4 + clamp_dtc: true + max_contact_prob: 0.1 + mask_normals_within_sdf: true + dtc_adaptive_normalization: false + adaptive_normals_mask: true + max_depth: 2.0 + image_shape: + - 13 + - 180 + - 240 + learnable_contact_preprocess_params: true + learning_rate: 0.0001 + weight_decay: 0.0 + contact_model_name: local_multitask_outhd64all_home_crop_h144w144d48_mask_ctxtmask_seed_220979_epoch_9 + zero_centered: false + true_action_history: false +num_train_frames_bc: + desc: null + value: 50000 +num_train_frames_drq: + desc: null + value: 1100000 +stddev_schedule_drq: + desc: null + value: linear(1.0,0.1,100000) +task_name: + desc: null + value: FrankaInsertion-v1 +num_train_frames_vinn: + desc: null + value: 25000 +num_train_frames_diffusion: + desc: null + value: 1000000 +num_train_epochs_bc: + desc: null + value: 5000 +num_train_epochs_diffusion: + desc: null + value: 15000 +validate_every_epochs_bc: + desc: null + value: 5 +validate_every_epochs_diffusion: + desc: null + value: 250 +validate_diffusion_on_action_loss_every_epochs: + desc: null + value: 250 +train_eval_diffusion_on_action_loss_every_epochs: + desc: null + value: 250 +check_topk_every_epochs: + desc: null + value: 5 +check_topk_every_epochs_diffusion: + desc: null + value: 250 +save_snapshot_every_epochs_diffusion: + desc: null + value: 1500 +x_limit: + desc: null + value: + - 0.2 + - 0.7 +y_limit: + desc: null + value: + - -0.4 + - 0.4 +z_limit: + desc: null + value: + - -0.05 + - 0.55 +home_displacement: + desc: null + value: + - 0.55 + - 0.0 + - 0.55 + - 180.0 + - 0.0 + - 0.0 +enable_gripper: + desc: null + value: true +start_with_gripper_open: + desc: null + value: true +offset_mask: + desc: null + value: + - 1 + - 1 + - 1 + - 1 + - 1 + - 1 +path_to_depth_extrinsics: + desc: null + value: ~/fish_leon/FISH/cfgs/camera_poses/camera_poses_L515/20240904-122305/color_tf_world.npy +feature_type: + desc: null + value: 180x240_1_RGB_D_2.0_msk_channels_EE_obj_mask_cntct_0.1_DTC_clmpd_lrnbl_nrmls_DTCmask_adpt_lrnbl_both_lr_0.0001_wd_0.0_local_multitask_outhd64all_home_crop_h144w144d48_mask_ctxtmask_seed_220979_epoch_9_acthst_hst6_out32_dwnkrnl3_dwnstrd2_dwnpd1 +save_buffer: + desc: null + value: true +num_eval: + desc: null + value: 5 +random_start: + desc: null + value: false +eval_starts: + desc: null + value: /home/leonmkim/fish_leon/FISH/eval_starts/frankagym_pixels/FrankaInsertion-v1 +num_valid_demos: + desc: null + value: null +load_checkpoint: + desc: null + value: true +checkpoint_epoch: + desc: null + value: 12000 +load_residual_weight: + desc: null + value: false +checkpoint_root_dir: + desc: null + value: /home/leonmkim/fish_leon/FISH +checkpoint_weight_dir: + desc: null + value: /home/leonmkim/fish_leon/FISH/exp_local/frankagym_pixels/FrankaInsertion-v1/1013_1 +residual_weight: + desc: null + value: /home/leonmkim/fish_leon/FISH/weights/frankagym_pixels/FrankaInsertion-v1/weight.pt +final_experiment_dir: + desc: null + value: ./exp_local/frankagym_pixels/FrankaInsertion-v1/1013_1/0a7h4b5r +_wandb: + desc: null + value: + code_path: code/FISH/eval_robot.py + python_version: 3.10.14 + cli_version: 0.17.5 + framework: torch + is_jupyter_run: false + is_kaggle_kernel: false + start_time: 1736212258 + t: + 1: + - 1 + - 41 + - 49 + - 50 + - 55 + - 83 + 2: + - 1 + - 41 + - 49 + - 50 + - 55 + - 83 + 3: + - 14 + - 16 + - 23 + - 35 + - 62 + 4: 3.10.14 + 5: 0.17.5 + 8: + - 5 + 13: linux-x86_64 +grasped_obj_name: + desc: null + value: fowlers +left_book_slot: + desc: null + value: twodim diff --git a/0a7h4b5r/wandb/run-20250106_201058-0a7h4b5r/files/diff.patch b/0a7h4b5r/wandb/run-20250106_201058-0a7h4b5r/files/diff.patch new file mode 100644 index 0000000000000000000000000000000000000000..eaab2121dca194bbf5d21d04e789d724aa2ffaa3 --- /dev/null +++ b/0a7h4b5r/wandb/run-20250106_201058-0a7h4b5r/files/diff.patch @@ -0,0 +1,63 @@ +diff --git a/FISH/cfgs/config_eval.yaml b/FISH/cfgs/config_eval.yaml +index d2afe29..36d45ad 100644 +--- a/FISH/cfgs/config_eval.yaml ++++ b/FISH/cfgs/config_eval.yaml +@@ -134,11 +134,18 @@ load_checkpoint: ${agent.load_checkpoint} + # all books, 10/20 demos per book + # RGBD+mask+act history + # wandb_run_id: '1002_0' ++# wandb_run_id: '1009_1' # seed 1 ++# wandb_run_id: '1022_0' # dataset shuffle seed 1 ++ ++# RGBD+mask+contact(all ftrs)+act history ++# wandb_run_id: '1003_0' ++wandb_run_id: '1013_1' # seed 1 ++# wandb_run_id: '1017_0' # dataset shuffle seed 1 + + # all books, 6/20 demos per book + # RGBD+mask+act history + # wandb_run_id: '1007_0' +-wandb_run_id: '1922_1' # seed 1 ++# wandb_run_id: '1922_1' # seed 1 + # wandb_run_id: '1938_0' # dataset shuffle seed 1 + + # RGBD+mask+contact(all ftrs)+act history +@@ -146,11 +153,11 @@ wandb_run_id: '1922_1' # seed 1 + # wandb_run_id: '1926_1' # seed 1 + # wandb_run_id: '1930_0' # dataset shuffle seed 1 + +- +- + # RGBD+mask+contact(contact+DTC)+act history + # wandb_run_id: '1745_0' + ++# all books, 4/20 demos per book ++ + # 112_240x320_all_twodim_left_to_right_annotated_start_idx_5hz_zstd7_EE_pxl_coords_expert_demos_imp_act + # 14 demos + +diff --git a/FISH/download_model_checkpoints.py b/FISH/download_model_checkpoints.py +index c7eadce..e3c7dc1 100644 +--- a/FISH/download_model_checkpoints.py ++++ b/FISH/download_model_checkpoints.py +@@ -161,8 +161,18 @@ run_id_list = [ + run_id_list = [ + # '1922_1', + # '1922_2', +- '1926_1', +- '1930_0', ++ # '1944_0', ++ # '1972_0', ++ # '1976_1', ++ # '1948_0', ++ # '1950_0', ++ # '1954_1', ++ '1002_0', ++ '1009_1', ++ '1022_0', ++ '1003_0', ++ '1013_1', ++ '1017_0', + ] + + checkpoint_epoch = 12000 diff --git a/0a7h4b5r/wandb/run-20250106_201058-0a7h4b5r/files/media/table/eval/success_rate_ci_5_d8821c50291f0fbf979f.table.json b/0a7h4b5r/wandb/run-20250106_201058-0a7h4b5r/files/media/table/eval/success_rate_ci_5_d8821c50291f0fbf979f.table.json new file mode 100644 index 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b/0a7h4b5r/wandb/run-20250106_201058-0a7h4b5r/files/output.log @@ -0,0 +1,270 @@ + +loaded agent with feature_type: 180x240_1_RGB_D_2.0_msk_channels_EE_obj_mask_cntct_0.1_DTC_clmpd_lrnbl_nrmls_DTCmask_adpt_lrnbl_both_lr_0.0001_wd_0.0_local_multitask_outhd64all_home_crop_h144w144d48_mask_ctxtmask_seed_220979_epoch_9_acthst_hst6_out32_dwnkrnl3_dwnstrd2_dwnpd1 +[INFO] [1736212263.749770]: resetting environment +[INFO] [1736212263.754437]: cleared current plan +[INFO] [1736212263.755410]: moving to home +[INFO] [1736212264.757260]: reached home +[INFO] [1736212264.758521]: reset action history +[INFO] [1736212267.472449]: environment reset +Starting episode 0 +[INFO] [1736212267.478269]: resetting environment +[INFO] [1736212267.482555]: cleared current plan +[INFO] [1736212267.483399]: moving to home +[INFO] [1736212268.485278]: reached home +[INFO] [1736212268.486665]: reset action history +[INFO] [1736212271.195668]: environment reset +Press Enter to continue... after reseting env. To rate prev episode, press 0 for failure and 1 for success +proceeding to start episode![INFO] [1736212273.764618]: resetting environment +[INFO] [1736212273.772876]: cleared current plan +[INFO] [1736212273.774282]: moving to home +[INFO] [1736212274.776742]: reached home +[INFO] [1736212274.778037]: reset action history +[INFO] [1736212277.486093]: environment reset +bag_path_name:=/home/leonmkim/fish_leon/FISH/exp_local/frankagym_pixels/FrankaInsertion-v1/1013_1/0a7h4b5r/episode_rosbags/episode_0_2025-01-06-20-11-17.bag +... logging to /home/leonmkim/.ros/log/a8a09c64-c64e-11ef-96d2-5defea869005/roslaunch-leonmkim-ROG-Strix-G15CS-G15CS-2105545.log +started roslaunch server http://158.130.50.37:42171/ +SUMMARY +======== +PARAMETERS + * /rosdistro: noetic + * /rosversion: 1.16.0 +NODES + / + print_text (rostopic/rostopic) + pub_text (rostopic/rostopic) + rosbag_record (rosbag/record) +ROS_MASTER_URI=http://localhost:11311 +process[pub_text-1]: started with pid [2105884] +process[print_text-2]: started with pid [2105885] +process[rosbag_record-3]: started with pid [2105886] +started bagging! +For topic gripper_width: timestamp difference is 101205899 for nearest: 1736212293299913956 - target: 1736212293198708057 at idx 227 +For topic gripper_width: timestamp difference is 105829570 for nearest: 1736212298999938819 - target: 1736212298894109249 at idx 249 +[INFO] [1736212312.519242]: Storing episode... +[rosbag_record-3] killing on exit[print_text-2] killing on exit +[pub_text-1] killing on exit +[INFO] [1736212313.242766]: Stored episode 1. +[INFO] [1736212313.243533]: Saving video... +[INFO] [1736212313.444716]: Video saved! +Starting episode 1 +[INFO] [1736212313.456418]: resetting environment +[INFO] [1736212313.464829]: cleared current plan +[INFO] [1736212313.465086]: moving to home +[INFO] [1736212317.067974]: reached home +[INFO] [1736212317.068832]: reset action history +[INFO] [1736212319.777428]: environment reset +Press Enter to continue... after reseting env. To rate prev episode, press 0 for failure and 1 for success +proceeding to start episode!Comuting lower confidence bound +Comuting upper confidence bound +Comuting lower confidence bound +Comuting upper confidence bound +Comuting lower confidence bound +Comuting upper confidence bound +Comuting lower confidence bound +Comuting upper confidence bound +[INFO] [1736212322.060395]: resetting environment +[INFO] [1736212322.067713]: cleared current plan +[INFO] [1736212322.067898]: moving to home +[INFO] [1736212323.069261]: reached home +[INFO] [1736212323.070659]: reset action history +[INFO] [1736212325.779198]: environment reset +bag_path_name:=/home/leonmkim/fish_leon/FISH/exp_local/frankagym_pixels/FrankaInsertion-v1/1013_1/0a7h4b5r/episode_rosbags/episode_1_2025-01-06-20-12-05.bag +... logging to /home/leonmkim/.ros/log/a8a09c64-c64e-11ef-96d2-5defea869005/roslaunch-leonmkim-ROG-Strix-G15CS-G15CS-2105545.log +started roslaunch server http://158.130.50.37:33517/ +SUMMARY +======== +PARAMETERS + * /rosdistro: noetic + * /rosversion: 1.16.0 +NODES + / + print_text (rostopic/rostopic) + pub_text (rostopic/rostopic) + rosbag_record (rosbag/record) +ROS_MASTER_URI=http://localhost:11311 +process[pub_text-4]: started with pid [2106032] +process[print_text-5]: started with pid [2106033] +process[rosbag_record-6]: started with pid [2106034] +started bagging! +For topic gripper_width: timestamp difference is -104509531 for nearest: 1736212336333288015 - target: 1736212336437797546 at idx 150 +For topic gripper_width: timestamp difference is -100248804 for nearest: 1736212338433246384 - target: 1736212338533495188 at idx 172 +For topic gripper_width: timestamp difference is 105981400 for nearest: 1736212341699914982 - target: 1736212341593933582 at idx 214 +For topic gripper_width: timestamp difference is 110349211 for nearest: 1736212343799969229 - target: 1736212343689620018 at idx 246 +For topic gripper_width: timestamp difference is 112340797 for nearest: 1736212344799913276 - target: 1736212344687572479 at idx 249 +For topic gripper_width: timestamp difference is 119232364 for nearest: 1736212356799869008 - target: 1736212356680636644 at idx 249 +[INFO] [1736212361.696527]: Storing episode... +[rosbag_record-6] killing on exit +[print_text-5] killing on exit +[pub_text-4] killing on exit +[INFO] [1736212362.567245]: Stored episode 2. +[INFO] [1736212362.569718]: Saving video... +[INFO] [1736212362.775844]: Video saved! +Starting episode 2 +[INFO] [1736212362.854163]: resetting environment +[INFO] [1736212362.879364]: cleared current plan +[INFO] [1736212362.879966]: moving to home +[INFO] [1736212366.692691]: reached home +[INFO] [1736212366.696672]: reset action history +[INFO] [1736212369.470148]: environment reset +Press Enter to continue... after reseting env. To rate prev episode, press 0 for failure and 1 for success +proceeding to start episode!Comuting lower confidence bound +Comuting upper confidence bound +Comuting lower confidence bound +Comuting upper confidence bound +Comuting lower confidence bound +Comuting upper confidence bound +Comuting lower confidence bound +Comuting upper confidence bound +[INFO] [1736212373.474377]: resetting environment +[INFO] [1736212373.490193]: cleared current plan +[INFO] [1736212373.490473]: moving to home +[INFO] [1736212374.493899]: reached home +[INFO] [1736212374.497572]: reset action history +[INFO] [1736212377.280358]: environment reset +bag_path_name:=/home/leonmkim/fish_leon/FISH/exp_local/frankagym_pixels/FrankaInsertion-v1/1013_1/0a7h4b5r/episode_rosbags/episode_2_2025-01-06-20-12-57.bag +... logging to /home/leonmkim/.ros/log/a8a09c64-c64e-11ef-96d2-5defea869005/roslaunch-leonmkim-ROG-Strix-G15CS-G15CS-2105545.log +started roslaunch server http://158.130.50.37:45919/ +SUMMARY +======== +PARAMETERS + * /rosdistro: noetic + * /rosversion: 1.16.0 +NODES + / + print_text (rostopic/rostopic) + pub_text (rostopic/rostopic) + rosbag_record (rosbag/record) +ROS_MASTER_URI=http://localhost:11311 +process[pub_text-7]: started with pid [2106175] +process[print_text-8]: started with pid [2106176] +process[rosbag_record-9]: started with pid [2106177] +started bagging! +For topic gripper_width: timestamp difference is -114233856 for nearest: 1736212397733210201 - target: 1736212397847444057 at idx 249 +[INFO] [1736212414.015372]: Storing episode... +[rosbag_record-9] killing on exit +[print_text-8] killing on exit +[pub_text-7] killing on exit +[INFO] [1736212414.807417]: Stored episode 3. +[INFO] [1736212414.816902]: Saving video... +[INFO] [1736212415.052689]: Video saved! +Starting episode 3 +[INFO] [1736212415.094047]: resetting environment +[INFO] [1736212415.115891]: cleared current plan +[INFO] [1736212415.116939]: moving to home +[INFO] [1736212418.822154]: reached home +[INFO] [1736212418.826801]: reset action history +[INFO] [1736212421.746144]: environment reset +Press Enter to continue... after reseting env. To rate prev episode, press 0 for failure and 1 for success +proceeding to start episode!Comuting lower confidence bound +Comuting upper confidence bound +Comuting lower confidence bound +Comuting upper confidence bound +Comuting lower confidence bound +Comuting upper confidence bound +Comuting lower confidence bound +Comuting upper confidence bound +[INFO] [1736212424.864105]: resetting environment +[INFO] [1736212424.882628]: cleared current plan +[INFO] [1736212424.887287]: moving to home +[INFO] [1736212425.892785]: reached home +[INFO] [1736212425.897940]: reset action history +[INFO] [1736212428.693007]: environment reset +bag_path_name:=/home/leonmkim/fish_leon/FISH/exp_local/frankagym_pixels/FrankaInsertion-v1/1013_1/0a7h4b5r/episode_rosbags/episode_3_2025-01-06-20-13-48.bag +... logging to /home/leonmkim/.ros/log/a8a09c64-c64e-11ef-96d2-5defea869005/roslaunch-leonmkim-ROG-Strix-G15CS-G15CS-2105545.log +started roslaunch server http://158.130.50.37:37685/ +SUMMARY +======== +PARAMETERS + * /rosdistro: noetic + * /rosversion: 1.16.0 +NODES + / + print_text (rostopic/rostopic) + pub_text (rostopic/rostopic) + rosbag_record (rosbag/record) +ROS_MASTER_URI=http://localhost:11311 +process[pub_text-10]: started with pid [2106316] +process[print_text-11]: started with pid [2106317] +process[rosbag_record-12]: started with pid [2106318] +started bagging! +For topic gripper_width: timestamp difference is 119500051 for nearest: 1736212436699945102 - target: 1736212436580445051 at idx 104 +For topic gripper_width: timestamp difference is 121048797 for nearest: 1736212437499862064 - target: 1736212437378813267 at idx 114 +[INFO] [1736212465.032912]: Storing episode... +[rosbag_record-12] killing on exit +[print_text-11] killing on exit +[pub_text-10] killing on exit +[INFO] [1736212465.847686]: Stored episode 4. +[INFO] [1736212465.849906]: Saving video... +[INFO] [1736212466.069664]: Video saved! +Starting episode 4 +[INFO] [1736212466.125650]: resetting environment +[INFO] [1736212466.147738]: cleared current plan +[INFO] [1736212466.148762]: moving to home +[INFO] [1736212469.858123]: reached home +[INFO] [1736212469.872574]: reset action history +[INFO] [1736212472.668222]: environment reset +Press Enter to continue... after reseting env. To rate prev episode, press 0 for failure and 1 for success +proceeding to start episode!Comuting lower confidence bound +Comuting upper confidence bound +Comuting lower confidence bound +Comuting upper confidence bound +Comuting lower confidence bound +Comuting upper confidence bound +Comuting lower confidence bound +Comuting upper confidence bound +[INFO] [1736212480.095166]: resetting environment +[INFO] [1736212480.130832]: cleared current plan +[INFO] [1736212480.132802]: moving to home +[INFO] [1736212481.136706]: reached home +[INFO] [1736212481.139007]: reset action history +[INFO] [1736212483.934565]: environment reset +bag_path_name:=/home/leonmkim/fish_leon/FISH/exp_local/frankagym_pixels/FrankaInsertion-v1/1013_1/0a7h4b5r/episode_rosbags/episode_4_2025-01-06-20-14-43.bag +... logging to /home/leonmkim/.ros/log/a8a09c64-c64e-11ef-96d2-5defea869005/roslaunch-leonmkim-ROG-Strix-G15CS-G15CS-2105545.log +started roslaunch server http://158.130.50.37:36915/ +SUMMARY +======== +PARAMETERS + * /rosdistro: noetic + * /rosversion: 1.16.0 +NODES + / + print_text (rostopic/rostopic) + pub_text (rostopic/rostopic) + rosbag_record (rosbag/record) +ROS_MASTER_URI=http://localhost:11311 +process[pub_text-13]: started with pid [2106461] +process[print_text-14]: started with pid [2106462] +process[rosbag_record-15]: started with pid [2106463] +started bagging! +For topic gripper_width: timestamp difference is 111773574 for nearest: 1736212507933270299 - target: 1736212507821496725 at idx 249 +[INFO] [1736212521.029276]: Storing episode... +[pub_text-13] killing on exit +[rosbag_record-15] killing on exit +[print_text-14] killing on exit +[INFO] [1736212521.827081]: Stored episode 5. +[INFO] [1736212521.832175]: Saving video... +[INFO] [1736212522.077129]: Video saved! +[INFO] [1736212522.144270]: resetting environment +[INFO] [1736212522.165007]: cleared current plan +[INFO] [1736212522.165359]: moving to home +[INFO] [1736212525.970696]: reached home +[INFO] [1736212525.975686]: reset action history +[INFO] [1736212528.785131]: environment reset +Evaluation finished. To wrap up, rate prev episode, press 0 for failure and 1 for success +proceeding to start episode!Comuting lower confidence bound +Comuting upper confidence bound +Comuting lower confidence bound +Comuting upper confidence bound +Comuting lower confidence bound +Comuting upper confidence bound +Comuting lower confidence bound +Comuting upper confidence bound +Comuting lower confidence bound +Comuting upper confidence bound +Comuting lower confidence bound +Comuting upper confidence bound +Comuting lower confidence bound +Comuting upper confidence bound +Comuting lower confidence bound +Comuting upper confidence bound +proceeding to start episode! \ No newline at end of file diff --git a/0a7h4b5r/wandb/run-20250106_201058-0a7h4b5r/files/requirements.txt b/0a7h4b5r/wandb/run-20250106_201058-0a7h4b5r/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..0012abb707c844944e9c10167241911549abc034 --- /dev/null +++ b/0a7h4b5r/wandb/run-20250106_201058-0a7h4b5r/files/requirements.txt @@ -0,0 +1,340 @@ +Cython==3.0.10 +Farama-Notifications==0.0.4 +GitPython==3.1.43 +Jinja2==3.1.4 +Markdown==3.6 +MarkupSafe==2.1.5 +POT==0.7.0 +PyOpenGL==3.1.7 +PySocks==1.7.1 +PyYAML==6.0.1 +Pygments==2.18.0 +Rtree==1.3.0 +Werkzeug==3.0.3 +absl-py==2.1.0 +accelerate==0.33.0 +actionlib-msgs==1.13.0.post3 +actionlib==1.12.0 +actionlib==1.14.0 +aiohttp==3.9.5 +aiosignal==1.3.1 +angles==1.9.13 +antlr4-python3-runtime==4.9.3 +anyio==4.4.0 +asciitree==0.3.3 +async-timeout==4.0.3 +attrs==23.2.0 +autoprop==4.1.0 +beartype==0.18.5 +beautifulsoup4==4.12.3 +binomial_cis==0.0.11 +bondpy==1.8.6 +byol-pytorch==0.8.0 +cachetools==5.4.0 +camera-calibration-parsers==1.12.0 +camera-calibration==1.17.0 +cascadio==0.0.13 +catkin-pkg==1.0.0 +catkin==0.7.18 +catkin==0.8.10 +certifi==2024.7.4 +cffi==1.16.0 +chardet==5.2.0 +charset-normalizer==3.3.2 +click==8.1.7 +cloudpickle==3.0.0 +cmake==3.30.1 +colorlog==6.8.2 +contourpy==1.2.1 +controller-manager-msgs==0.20.0 +controller-manager==0.20.0 +coverage==7.6.0 +coveralls==4.0.1 +cv-bridge==1.16.2 +cycler==0.12.1 +datasets==2.20.0 +decorator==4.4.2 +deepdiff==7.0.1 +defusedxml==0.7.1 +diagnostic-analysis==1.11.0 +diagnostic-common-diagnostics==1.11.0 +diagnostic-updater==1.11.0 +diffusers==0.27.2 +dill==0.3.8 +distro==1.9.0 +dm-control==1.0.8 +dm-env==1.6 +dm-tree==0.1.8 +docker-pycreds==0.4.0 +docopt==0.6.2 +docutils==0.21.2 +dynamic-reconfigure==1.7.3 +einops==0.8.0 +embreex==2.17.7.post5 +empy==3.3.4 +etils==1.7.0 +exceptiongroup==1.2.2 +ezdxf==1.3.2 +fasteners==0.19 +filelock==3.15.4 +fonttools==4.53.1 +freetype-py==2.4.0 +frozenlist==1.4.1 +fsspec==2024.5.0 +gazebo_plugins==2.9.2 +gazebo_ros==2.9.2 +gdown==5.2.0 +gencpp==0.7.0 +geneus==3.0.0 +genlisp==0.4.18 +genmsg==0.5.12 +genmsg==0.6.0 +gennodejs==2.0.2 +genpy==0.6.14 +genpy==0.6.15 +geometry-msgs==1.13.0.post2 +gitdb==4.0.11 +glfw==2.7.0 +glooey==0.3.6 +gmsh==4.12.2 +gnupg==2.3.1 +google-auth-oauthlib==1.0.0 +google-auth==2.32.0 +grpcio==1.65.1 +gym-envs==0.0.1 +gym-notices==0.0.8 +gym==0.22.0 +gymnasium==0.29.1 +h11==0.14.0 +h5py==3.11.0 +hf_transfer==0.1.8 +httpcore==1.0.5 +httpx==0.27.0 +huggingface-hub==0.23.5 +hydra-core==1.3.2 +hydra-submitit-launcher==1.2.0 +idna==3.7 +image-geometry==1.16.2 +imageio-ffmpeg==0.5.1 +imageio==2.34.2 +importlib_metadata==8.2.0 +importlib_resources==6.4.0 +iniconfig==2.0.0 +interactive-markers==1.12.0 +joint-state-publisher-gui==1.15.1 +joint-state-publisher==1.15.1 +jsonschema-specifications==2023.12.1 +jsonschema==4.23.0 +kiwisolver==1.4.5 +kornia==0.7.3 +kornia_rs==0.1.5 +labmaze==1.0.6 +laser_geometry==1.6.7 +lazy_loader==0.4 +lerobot==0.1.0 +lightning-utilities==0.11.6 +llvmlite==0.43.0 +lxml==5.2.2 +manifold3d==2.5.1 +mapbox-earcut==1.0.1 +markdown-it-py==3.0.0 +matplotlib==3.9.1 +mdurl==0.1.2 +meshio==5.3.5 +message-filters==1.16.0 +more-itertools==10.3.0 +moviepy==1.0.3 +mpmath==1.3.0 +mujoco==3.2.0 +multidict==6.0.5 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MainThread:2105545 [wandb_init.py:init():623] setting up manager +2025-01-06 20:10:58,926 INFO MainThread:2105545 [backend.py:_multiprocessing_setup():105] multiprocessing start_methods=fork,spawn,forkserver, using: spawn +2025-01-06 20:10:58,927 INFO MainThread:2105545 [wandb_init.py:init():631] backend started and connected +2025-01-06 20:10:58,940 INFO MainThread:2105545 [wandb_init.py:init():720] updated telemetry +2025-01-06 20:10:58,948 INFO MainThread:2105545 [wandb_init.py:init():753] communicating run to backend with 90.0 second timeout +2025-01-06 20:10:59,216 INFO MainThread:2105545 [wandb_run.py:_on_init():2435] communicating current version +2025-01-06 20:10:59,308 INFO MainThread:2105545 [wandb_run.py:_on_init():2444] got version response upgrade_message: "wandb version 0.19.1 is available! To upgrade, please run:\n $ pip install wandb --upgrade" + +2025-01-06 20:10:59,308 INFO MainThread:2105545 [wandb_init.py:init():804] starting run threads in backend +2025-01-06 20:10:59,652 INFO MainThread:2105545 [wandb_run.py:_console_start():2413] atexit reg +2025-01-06 20:10:59,652 INFO MainThread:2105545 [wandb_run.py:_redirect():2255] redirect: wrap_raw +2025-01-06 20:10:59,652 INFO MainThread:2105545 [wandb_run.py:_redirect():2320] Wrapping output streams. +2025-01-06 20:10:59,652 INFO MainThread:2105545 [wandb_run.py:_redirect():2345] Redirects installed. +2025-01-06 20:10:59,654 INFO MainThread:2105545 [wandb_init.py:init():847] run started, returning control to user process +2025-01-06 20:10:59,654 INFO MainThread:2105545 [wandb_run.py:_tensorboard_callback():1544] tensorboard callback: /home/leonmkim/fish_leon/FISH/exp_local/frankagym_pixels/FrankaInsertion-v1/1013_1/0a7h4b5r/tb, True +2025-01-06 20:11:03,748 INFO MainThread:2105545 [wandb_run.py:_config_callback():1382] config_cb None None {'grasped_obj_name': 'fowlers', 'left_book_slot': 'twodim'} +2025-01-06 20:15:49,103 WARNING MsgRouterThr:2105545 [router.py:message_loop():77] message_loop has been closed diff --git a/0a7h4b5r/wandb/run-20250106_201058-0a7h4b5r/run-0a7h4b5r.wandb b/0a7h4b5r/wandb/run-20250106_201058-0a7h4b5r/run-0a7h4b5r.wandb new file mode 100644 index 0000000000000000000000000000000000000000..d15ea3b43361626b3bc3273be53791182ceba225 Binary files /dev/null and b/0a7h4b5r/wandb/run-20250106_201058-0a7h4b5r/run-0a7h4b5r.wandb differ diff --git a/73h7fwcu/.hydra/config.yaml b/73h7fwcu/.hydra/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5e7a10df419e9a14bfc8d2bca5db66c7f85b18e3 --- /dev/null +++ b/73h7fwcu/.hydra/config.yaml @@ -0,0 +1,350 @@ +root_dir: /home/${oc.env:USER}/fish_leon +nstep: 3 +seed: 41 +dataset_shuffle_seed: ${seed} +device: cuda +save_video: true +save_buffer: true +use_tb: true +baseline: false +use_wandb: true +eval: true +process_contact_features: ${eval} +obs_type: pixels +use_color: true +use_depth: true +use_masks: false +mask_list: +- EE_obj_mask +mask_representation: channels +crop_hw: +- 144 +- 144 +crop_down_offset: 48 +color_crop_type: null +depth_crop_type: null +segmask_crop_type: null +add_crop_binary_mask: false +add_coord_conv_map: false +use_context_color: false +use_context_depth: false +use_context_segmask: false +context_color_crop_type: null +context_depth_crop_type: null +context_segmask_crop_type: null +context_add_crop_binary_mask: false +context_add_coord_conv_map: false +use_contact_map: false +use_sdf_maps: false +use_normals_maps: false +which_objects: both +max_contact_prob: 0.1 +max_depth: 2.0 +grasped_dtc_max_value: 0.105 +env_dtc_max_value: 0.425 +grasped_normals_mask_max_dtc_value: 0.105 +env_normals_mask_max_dtc_value: 0.425 +clamp_dtc: true +dtc_adaptive_normalization: false +mask_normals_within_sdf: true +adaptive_normals_mask: true +learnable_contact_preprocess_params: false +contact_model_name: local_multitask_outhd64all_home_crop_h144w144d48_ctxt_seed_183386_epoch_9 +contact_estimation_model_ckpt_path: ~/fish_leon/contact_estimation/artifacts/175604_2/checkpoints/epoch=09-val_loss=0.00.ckpt +num_eval: 5 +debug_timestamps: false +open_loop: false +action_trajectories: true +stop_after_action: false +interpolation_frequency: 25 +policy_frequency: 5 +wait_for_new_camera_frames: true +random_start: false +eval_starts: ${root_dir}/FISH/eval_starts/${suite.name}_${obs_type}/${task_name} +train_demo_idxs_list_or_num: null +num_valid_demos: null +val_num_groups: 3 +name_of_expert_demo: 112_240x320_all_twodim_left_to_right_annotated_start_idx_5hz_zstd7_EE_pxl_coords_expert_demos_imp_act +expert_dataset_dirpath: ${root_dir}/FISH/expert_demos/${suite.name}/${task_name}/${name_of_expert_demo} +expert_dataset: ${expert_dataset_dirpath}/demos.zarr +action_key: ${oc.if_else:${action_trajectories}, 'action_trajectory_${interpolation_frequency}hz', + 'action'} +semantic_demo_grouping_name: semantic_demo_grouping.yaml +semantic_demo_grouping: ${expert_dataset_dirpath}/${semantic_demo_grouping_name} +expert_dataset_config: ${expert_dataset_dirpath}/demo_config.yaml +bc_regularize: false +bc_weight_type: qfilter +load_checkpoint: ${agent.load_checkpoint} +wandb_run_id: '1013_1' +true_action_history: false +wandb_notes: null +checkpoint_epoch: 12000 +load_residual_weight: false +checkpoint_root_dir: /home/${oc.env:USER}/fish_leon/FISH +checkpoint_weight_dir: ${checkpoint_root_dir}/exp_local/${suite.name}_${obs_type}/${task_name}/${wandb_run_id} +residual_weight: ${root_dir}/FISH/weights/${suite.name}_${obs_type}/${task_name}/weight.pt +experiment_dir: ./exp_local/${suite.name}_${obs_type}/${task_name}/${wandb_run_id} +final_experiment_dir: ${experiment_dir}/${oc.generate_run_id:} +agent: + _target_: agent.diffusion_policy.DiffusionPolicyAgent + name: diffusion_policy + load_checkpoint: ${eval} + device: ${device} + n_obs_steps: ${.config.policy_cfg.n_obs_steps} + suite_name: ${suite.name} + obs_type: ${obs_type} + enable_arm: ${eval} + enable_camera: ${eval} + use_tb: ${use_tb} + desired_image_shape: + - 13 + - 180 + - 240 + orig_cam_shape: + - 3 + - 240 + - 320 + config: + _target_: agent.diffusion_policy.DiffusionPolicyAgentConfig + compile: false + device: ${device} + cam_resize_shape: ${agent.desired_image_shape} + orig_cam_shape: ${agent.orig_cam_shape} + policy_frequency: ${policy_frequency} + interpolation_frequency: ${interpolation_frequency} + policy_cfg: + _target_: lerobot.common.policies.diffusion.configuration_diffusion.DiffusionConfig + n_obs_steps: 1 + horizon: 36 + n_action_steps: ${agent.config.policy_cfg.horizon} + input_shapes: + observation.image: ${agent.config.cam_resize_shape} + context_observation.image: ${agent.config.cam_resize_shape} + observation.state: + - 8 + observation.action_history: + - 7 + output_shapes: + action: + - 7 + input_normalization_modes: + observation.image: mean_std + observation.state: min_max + observation.action_history: min_max + output_normalization_modes: + action: min_max + vision_backbone: resnet18 + pretrained_backbone_weights: null + transforms: + - _target_: torchaug.transforms.RandomAffine + degrees: + - -5 + - 5 + translate: + - 0.05 + - 0.05 + batch_transform: true + num_chunks: -1 + batch_inplace: true + - _target_: torchaug.transforms.RandomColorJitter + brightness: 0.3 + contrast: 0.4 + saturation: 0.5 + hue: 0.08 + batch_transform: true + num_chunks: -1 + batch_inplace: true + use_group_norm: true + spatial_softmax_num_keypoints: 32 + action_history_encoder_config: + _target_: lerobot.common.policies.diffusion.configuration_diffusion.Unet1dEncoderConfig + in_channels: 7 + out_channels: 32 + history_length: ${agent.config.policy_cfg.n_action_steps} + kernel_size: ${agent.config.policy_cfg.kernel_size} + downsample_kernel_size: 3 + downsample_stride: 2 + downsample_padding: 1 + down_dims: + - 256 + - 512 + - 1024 + kernel_size: 5 + n_groups: 8 + diffusion_step_embed_dim: 128 + use_film_scale_modulation: true + noise_scheduler_type: DDIM + beta_schedule: squaredcos_cap_v2 + beta_start: 0.0001 + beta_end: 0.02 + prediction_type: epsilon + clip_sample: true + clip_sample_range: 1.0 + num_train_timesteps: 50 + num_inference_steps: 10 + do_mask_loss_for_padding: false + train_cfg: + _target_: utils.TrainConfig + lr: 0.0001 + lr_scheduler: cosine + lr_warmup_steps: 500 + adam_betas: + - 0.95 + - 0.999 + adam_eps: 1.0e-08 + adam_weight_decay: 1.0e-06 + grad_clip_norm: 10 + offline_steps: ${num_train_frames_diffusion} + use_amp: true + observation_cfg: + _target_: agent.encoder.VisualFeatureSet + use_depth: ${use_depth} + use_color: ${use_color} + mask_input_dict: + _target_: agent.encoder.MaskInputDict + enable: ${use_masks} + representation: ${mask_representation} + mask_list: ${mask_list} + crop_input_config: + _target_: agent.encoder.CropInputConfig + color_crop_type: ${color_crop_type} + depth_crop_type: ${depth_crop_type} + segmask_crop_type: ${segmask_crop_type} + crop_hw: ${crop_hw} + crop_down_offset: ${crop_down_offset} + add_crop_binary_mask: ${add_crop_binary_mask} + add_coord_conv_map: ${add_coord_conv_map} + context_input_config: + _target_: agent.encoder.ContextInputConfig + use_color: ${use_context_color} + use_depth: ${use_context_depth} + mask_input_dict: + _target_: agent.encoder.MaskInputDict + enable: ${use_context_segmask} + representation: ${mask_representation} + mask_list: ${mask_list} + crop_input_config: + _target_: agent.encoder.CropInputConfig + color_crop_type: ${context_color_crop_type} + depth_crop_type: ${context_depth_crop_type} + segmask_crop_type: ${context_segmask_crop_type} + crop_hw: ${crop_hw} + crop_down_offset: ${crop_down_offset} + add_crop_binary_mask: ${context_add_crop_binary_mask} + add_coord_conv_map: ${context_add_coord_conv_map} + mask_soft_approx_scheduler_config: + _target_: agent.encoder.MaskSoftApproxSchedulerConfig + num_steps: 40000 + initial_value: 10.0 + final_value: 1000.0 + interpolation_scheme: constant + use_contact_map: ${use_contact_map} + use_sdf_maps: ${use_sdf_maps} + use_normals_maps: ${use_normals_maps} + which_objects: ${which_objects} + grasped_dtc_max_value: ${grasped_dtc_max_value} + env_dtc_max_value: ${env_dtc_max_value} + grasped_normals_mask_max_dtc_value: ${grasped_normals_mask_max_dtc_value} + env_normals_mask_max_dtc_value: ${env_normals_mask_max_dtc_value} + clamp_dtc: ${clamp_dtc} + max_contact_prob: ${max_contact_prob} + mask_normals_within_sdf: ${mask_normals_within_sdf} + dtc_adaptive_normalization: ${dtc_adaptive_normalization} + adaptive_normals_mask: ${adaptive_normals_mask} + max_depth: ${max_depth} + image_shape: ${agent.desired_image_shape} + learnable_contact_preprocess_params: ${learnable_contact_preprocess_params} + learning_rate: ${agent.config.train_cfg.lr} + weight_decay: 0.0 + contact_model_name: ${contact_model_name} + zero_centered: false +suite: + suite: frankagym + name: frankagym + frame_stack: ${agent.n_obs_steps} + action_repeat: 1 + discount: 0.99 + hidden_dim: 1024 + num_train_frames: 2010 + num_seed_frames: 260 + num_train_epochs: 5000 + validate_every_epochs: 100 + validate_diffusion_on_action_loss_every_epochs: 500 + train_eval_diffusion_on_action_loss_every_epochs: 500 + check_topk_every_epochs: 10 + save_snapshot_every_epochs: 5000 + eval_every_frames: 2000 + num_eval_episodes: 5 + save_snapshot: true + wait_for_user_to_start_episode: true + task_make_fn: + _target_: suite.frankagym.make + name: ${task_name} + height: 240 + width: 320 + frame_stack: ${suite.frame_stack} + action_repeat: ${suite.action_repeat} + seed: ${seed} + enable_arm: ${agent.enable_arm} + enable_gripper: ${enable_gripper} + start_with_gripper_open: ${start_with_gripper_open} + enable_camera: ${agent.enable_camera} + path_to_depth_extrinsics: ${path_to_depth_extrinsics} + contact_estimation_model_ckpt_path: ${contact_estimation_model_ckpt_path} + x_limit: ${x_limit} + y_limit: ${y_limit} + z_limit: ${z_limit} + device: ${device} + interpolation_frequency: ${interpolation_frequency} + policy_frequency: ${policy_frequency} + debug_timestamps: ${debug_timestamps} + stop_after_action: ${stop_after_action} + open_loop: ${open_loop} + wait_for_new_camera_frames: ${wait_for_new_camera_frames} + action_key: ${action_key} + action_trajectory_horizon: ${agent.config.policy_cfg.horizon} + action_trajectories: ${action_trajectories} + path_to_zarr_dataset: ${expert_dataset} + agent_policy_cfg: ??? + true_action_history: ${true_action_history} +num_train_frames_bc: 50000 +num_train_frames_drq: 1100000 +stddev_schedule_drq: linear(1.0,0.1,100000) +task_name: FrankaInsertion-v1 +num_train_frames_vinn: 25000 +num_train_frames_diffusion: 1000000 +num_train_epochs_bc: 5000 +num_train_epochs_diffusion: 5000 +validate_every_epochs_bc: 5 +validate_every_epochs_diffusion: 25 +validate_diffusion_on_action_loss_every_epochs: 50 +train_eval_diffusion_on_action_loss_every_epochs: 500 +check_topk_every_epochs: 5 +check_topk_every_epochs_diffusion: ${validate_diffusion_on_action_loss_every_epochs} +save_snapshot_every_epochs_diffusion: 5000 +x_limit: +- 0.2 +- 0.7 +y_limit: +- -0.4 +- 0.4 +z_limit: +- -0.05 +- 0.55 +home_displacement: +- 0.55 +- 0.0 +- 0.55 +- 180.0 +- 0.0 +- 0.0 +enable_gripper: true +start_with_gripper_open: true +offset_mask: +- 1 +- 1 +- 1 +- 1 +- 1 +- 1 +path_to_depth_extrinsics: ~/fish_leon/FISH/cfgs/camera_poses/camera_poses_L515/20240904-122305/color_tf_world.npy diff --git a/73h7fwcu/.hydra/hydra.yaml b/73h7fwcu/.hydra/hydra.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d7bf0a25ce3998aa89ec864ae87c680beecab6a3 --- /dev/null +++ b/73h7fwcu/.hydra/hydra.yaml @@ -0,0 +1,169 @@ +hydra: + run: + dir: ${final_experiment_dir} + sweep: + dir: ${final_experiment_dir} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${final_experiment_dir}/.slurm + timeout_min: 60 + cpus_per_task: null + gpus_per_node: null + tasks_per_node: 1 + mem_gb: null + nodes: 1 + name: ${hydra.job.name} + stderr_to_stdout: false + _target_: hydra_plugins.hydra_submitit_launcher.submitit_launcher.LocalLauncher + sweeper: + _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper + max_batch_size: null + params: null + help: + app_name: ${hydra.job.name} + header: '${hydra.help.app_name} is powered by Hydra. + + ' + footer: 'Powered by Hydra (https://hydra.cc) + + Use --hydra-help to view Hydra specific help + + ' + template: '${hydra.help.header} + + == Configuration groups == + + Compose your configuration from those groups (group=option) + + + $APP_CONFIG_GROUPS + + + == Config == + + Override anything in the config (foo.bar=value) + + + $CONFIG + + + ${hydra.help.footer} + + ' + hydra_help: + template: 'Hydra (${hydra.runtime.version}) + + See https://hydra.cc for more info. + + + == Flags == + + $FLAGS_HELP + + + == Configuration groups == + + Compose your configuration from those groups (For example, append hydra/job_logging=disabled + to command line) + + + $HYDRA_CONFIG_GROUPS + + + Use ''--cfg hydra'' to Show the Hydra config. + + ' + hydra_help: ??? + hydra_logging: + version: 1 + formatters: + simple: + format: '[%(asctime)s][HYDRA] %(message)s' + handlers: + console: + class: logging.StreamHandler + formatter: simple + stream: ext://sys.stdout + root: + level: INFO + handlers: + - console + loggers: + logging_example: + level: DEBUG + disable_existing_loggers: false + job_logging: + version: 1 + formatters: + simple: + format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s' + handlers: + console: + class: logging.StreamHandler + formatter: simple + stream: ext://sys.stdout + file: + class: logging.FileHandler + formatter: simple + filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log + root: + level: INFO + handlers: + - console + - file + disable_existing_loggers: false + env: {} + mode: RUN + searchpath: [] + callbacks: {} + output_subdir: .hydra + overrides: + hydra: + - hydra.mode=RUN + task: + - agent=diffusion + - suite=frankagym + - suite/frankagym_task@_global_=insertion + job: + name: eval_robot + chdir: true + override_dirname: agent=diffusion,suite/frankagym_task@_global_=insertion,suite=frankagym + id: ??? + num: ??? + config_name: config_eval + env_set: {} + env_copy: [] + config: + override_dirname: + kv_sep: '=' + item_sep: ',' + exclude_keys: [] + runtime: + version: 1.3.2 + version_base: '1.1' + cwd: /home/leonmkim/fish_leon/FISH + config_sources: + - path: hydra.conf + schema: pkg + provider: hydra + - path: /home/leonmkim/fish_leon/FISH/cfgs + schema: file + provider: main + - path: '' + schema: structured + provider: schema + output_dir: /home/leonmkim/fish_leon/FISH/exp_local/frankagym_pixels/FrankaInsertion-v1/1013_1/73h7fwcu + choices: + suite: frankagym + suite/frankagym_task@_global_: insertion + agent: diffusion + hydra/env: default + hydra/callbacks: null + hydra/job_logging: default + hydra/hydra_logging: default + hydra/hydra_help: default + hydra/help: default + hydra/sweeper: basic + hydra/launcher: submitit_local + hydra/output: default + verbose: false diff --git a/73h7fwcu/.hydra/overrides.yaml b/73h7fwcu/.hydra/overrides.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5cb78bcdb3a5a10e7701d3b2e8898a2e783d11cc --- /dev/null +++ b/73h7fwcu/.hydra/overrides.yaml @@ -0,0 +1,3 @@ +- agent=diffusion +- suite=frankagym +- suite/frankagym_task@_global_=insertion diff --git a/73h7fwcu/eval_video/0_eval.mp4 b/73h7fwcu/eval_video/0_eval.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..04c91440811ec3b6b6ab8b086e2e80600e53484a --- /dev/null +++ b/73h7fwcu/eval_video/0_eval.mp4 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:67a649dac0864a0b9776f767bc7576b313825aedd1fb1e5bd8232dcfb9a9c3be +size 1465999 diff --git a/73h7fwcu/eval_video/1_eval.mp4 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sha256:620cab8a63739959dfb3a3aff59867f43709679f50c8a3d336a98e9544702caa +size 6539 diff --git a/73h7fwcu/wandb/run-20250107_163950-73h7fwcu/files/code/FISH/eval_robot.py b/73h7fwcu/wandb/run-20250107_163950-73h7fwcu/files/code/FISH/eval_robot.py new file mode 100644 index 0000000000000000000000000000000000000000..50493fea183960d2ea4fa0b904b2e6077c21a710 --- /dev/null +++ b/73h7fwcu/wandb/run-20250107_163950-73h7fwcu/files/code/FISH/eval_robot.py @@ -0,0 +1,605 @@ +#%% +import warnings +import os + +os.environ['MKL_SERVICE_FORCE_INTEL'] = '1' +os.environ['MUJOCO_GL'] = 'egl' +from pathlib import Path +#%% +import hydra +import numpy as np +import torch + +import utils +from utils import get_feature_dirname_from_configs + +from video import VideoRecorder +import pickle +import time +import threading +import shutil +from logger import Logger + +import wandb +from omegaconf import OmegaConf, open_dict + +from replay_buffer_robot import RosbagEvalReplayBufferStorage +from lerobot.common.utils.utils import _relative_path_between + +torch.backends.cudnn.benchmark = True +warnings.filterwarnings('ignore', category=DeprecationWarning) + +# import specs for replay buffer +from dm_env import specs + +import sys, signal +import yaml + +import binomial_cis as bc + +# get path of current file +current_path = os.path.dirname(os.path.realpath(__file__)) +sys.path.append(os.path.join(current_path, os.pardir)) +# from contact_estimation.src.utils.viz_utils import normalized_surface_normal_to_rgb, depth_map_to_im, grasped_env_dtc_map_to_im, contact_prob_map_to_im, desaturate_color_image, masked_overlay_im_list + +def make_agent(obs_spec, action_spec, cfg): + cfg.obs_shape = obs_spec['pixels'].shape + dataset_statistics = None # this will be loaded from the checkpoint + try: + cfg.action_shape = action_spec.shape + except: + pass + return hydra.utils.instantiate(cfg, dataset_statistics) + +class Workspace: + def __init__(self, cfg): + self.work_dir = Path.cwd() + print(f'workspace: {self.work_dir}') + + signal.signal(signal.SIGINT, self.signal_handler) + + self.cfg = cfg + self.loading_uncompiled_checkpoint_with_compile = False + self.loading_compiled_checkpoint_with_no_compile = False + + snapshot_path = Path(self.cfg.checkpoint_weight_dir) / f'snapshot_{self.cfg.checkpoint_epoch}.pt' + self.load_checkpoint_conf(snapshot_path=snapshot_path) + + # load config for action trajectories + utils.set_seed_everywhere(self.cfg.seed) + self.device = torch.device(self.cfg.device) + self.setup() + + # self.agent = make_agent(self.eval_env.observation_spec(), + # self.eval_env.action_spec(), self.cfg.agent) + self.timer = utils.Timer() + # self._global_step = 0 + self._global_episode = 0 + self._global_epoch = 0 + self.num_episode_successes = 0 + + self.alpha_range = [.01, .025, .05, .1] + + # Need to convert hydra config to primitive container for wandb https://docs.wandb.ai/guides/integrations/hydra + with open_dict(self.cfg): + self.cfg.feature_type = get_feature_dirname_from_configs( + hydra.utils.instantiate(self.cfg.agent.config.observation_cfg), + self.cfg.agent.config.policy_cfg.input_shapes, + hydra.utils.instantiate(self.cfg.agent.config.policy_cfg.action_history_encoder_config) if 'observation.action_history' in self.cfg.agent.config.policy_cfg.input_shapes else None, + ) + + wandb_config = OmegaConf.to_container( + self.cfg, resolve=True, throw_on_missing=True + ) + # must be called before any tf summary writer is created + if self.cfg.use_wandb: + # get the run id from the final_experiment_dir directory + run_id = os.path.basename(os.path.normpath(self.cfg.final_experiment_dir)) + wandb.init(project='extrinsic_contact_downstream', entity='serialexperimentsleon', job_type='eval', sync_tensorboard=self.cfg.use_tb, config=wandb_config, id=run_id) + + self.logger = Logger(self.work_dir, use_tb=self.cfg.use_tb, use_wandb=self.cfg.use_wandb) + + # if not self.loading_uncompiled_checkpoint_with_compile and self.cfg.agent.config.compile: + # self.agent.compile_modules() + + # self.load_checkpoint(snapshot_path=snapshot_path) + + # if self.loading_uncompiled_checkpoint_with_compile: # need to call compile after loading the checkpoint + # self.agent.compile_modules() + + print(f"loaded agent with feature_type: {self.cfg.feature_type}") + + def check_for_key_press(self): + while self.continue_keypress_thread: + inp = input("Press 'r' to restart current episode, 'n' to stop current episode and skip to next, 'q' to break entire eval\n") + if inp == 'n': + self.preempt_episode = True + print("preempting episode") + elif inp in ['', '0', '1']: # enter key + if inp in ['0', '1']: + self.num_episode_successes += int(inp) + self.proceed_after_env_reset_event.set() + print("proceeding to start episode!") + elif inp == 'q': + self.proceed_after_env_reset_event.set() + self.preempt_episode = True + self.exit_eval = True + self.continue_keypress_thread = False # will stop the keypress thread + print("quitting eval") + break + elif inp == 'r': + print('restarting episode') + self.preempt_episode = True + self.restart_episode = True + else: + print("Invalid key press, try again") + + # self.keypress_input_thread.join() # wait for the keypress thread to finish + + def signal_handler(self, signal, frame): + print("\nprogram exiting gracefully") + self.proceed_after_env_reset_event.set() + self.preempt_episode = True + self.exit_eval = True + self.continue_keypress_thread = False # will stop the keypress thread + self.keypress_input_thread.join() # wait for the keypress thread to finish + video_filepath = self.video_recorder.save() + # get the video file and convert to video tensor to log + self.logger.log_video('eval/video', video_filepath, self.global_step) + wandb.finish() + sys.exit(0) + + def setup(self): + # create envs + self.eval_env = hydra.utils.call(self.cfg.suite.task_make_fn) + # expert_demo_config_path = os.path.join(os.path.dirname(self.cfg.expert_dataset), 'demo_config.yaml') + # self.expert_demo_config = yaml.load(open(expert_demo_config_path, 'r'), Loader=yaml.FullLoader) + # self.eval_env._env.action_trans_norm = expert_demo_config['max_translation_action_norm'] + # self.eval_env._env.action_rot_norm = expert_demo_config['max_rotation_action_norm'] + # self.eval_env._env.action_period = expert_demo_config['sample_period'] + # print(f"setting max_translation_action_norm to {expert_demo_config['max_translation_action_norm']} and sample_period to {expert_demo_config['sample_period']}") + # print(f"setting max_rotation_action_norm to {expert_demo_config['max_rotation_action_norm']}") + + # self.eval_env.set_demo_params(self.cfg.expert_dataset) + + # Turn off random start + self.eval_env.random_start = False + + # create replay buffer + # data_specs = [ + # { + # 'observation': self.eval_env.observation_spec(), + # }, + # # self.eval_env.observation_spec()['features'], + # self.eval_env.action_spec(), + # specs.Array(self.eval_env.action_spec().shape, self.eval_env.action_spec().dtype, 'vinn_action'), + # specs.Array((1, ), np.float32, 'reward'), + # specs.Array((1, ), np.float32, 'discount'), + # ] + + # self.eval_replay_storage = ZarrEvalReplayBufferStorage(data_specs, self.work_dir / 'eval_buffer', debug_timestamps=self.cfg.debug_timestamps, save_buffer=self.cfg.save_buffer, debug_info_data_specs=self.eval_env.debug_info_data_specs, camera_info_dict=self.eval_env.get_camera_info_dict()) + self.eval_replay_storage = RosbagEvalReplayBufferStorage(self.work_dir) + + self.video_recorder = VideoRecorder( + self.work_dir if self.cfg.save_video else None, + ros_enabled=True, + fps=self.cfg.agent.config.policy_frequency, + ) + + print('workspace setup complete') + + @property + def global_step(self): + # return self._global_step + return self.eval_env.get_global_step() + + @property + def global_episode(self): + return self._global_episode + + @property + def global_frame(self): + return self.global_step * self.cfg.action_repeat + + @property + def global_epoch(self): + return self._global_epoch + + def reset(self, eval_idx): + if not self.eval_env.enable_arm: + return np.array([0,0,0], dtype=np.float32) + self.eval_env.arm_refresh(reset=False) + # Set start position + try: + self.eval_env.set_position(self.start_pos[eval_idx]) + except: + self.eval_env.arm.set_position(self.start_pos[eval_idx]) + if self.eval_env.arm.keep_gripper_closed: + self.eval_env.arm.close_gripper_fully() + else: + self.eval_env.arm.open_gripper_fully() + time.sleep(0.1) + time_step = self.eval_env.step(np.zeros(self.eval_env.action_spec().shape[0], dtype=np.float32), + np.zeros(self.eval_env.action_spec().shape[0], dtype=np.float32)) + return time_step + + def eval(self): + # before evals start, prompt user for name of grasped object and the left book of the slot location + grasped_obj_name = input("Enter the name of the grasped object: ") + left_book_slot = input("Enter the left book slot location: ") + # update wandb config + if self.cfg.use_wandb: + wandb.config.update({'grasped_obj_name': grasped_obj_name, 'left_book_slot': left_book_slot}) + + self.preempt_episode = False + self.exit_eval = False + self.restart_episode = False + + self.continue_keypress_thread = True + self.proceed_after_env_reset_event = threading.Event() + self.keypress_input_thread = threading.Thread(target=self.check_for_key_press) + self.keypress_input_thread.start() + + # # Set model to eval mode + # self.agent.train(False) + + eval_until_episode = utils.Until(self.cfg.num_eval) + + self.use_action_history = False + # if "dp" in repr(self.agent) and "observation.action_history" in self.cfg.agent.config.policy_cfg.input_shapes: + if "observation.action_history" in self.cfg.agent.config.policy_cfg.input_shapes: + self.use_action_history = True + + # self.eval_replay_storage._new_eval_step(0) + + # if 'vinn' in repr(self.agent) or 'openloop' in repr(self.agent): + # with open(self.cfg.expert_dataset, 'rb') as f: + # if self.cfg.obs_type == 'pixels': + # self.expert_demo, _, self.expert_action, self.expert_reward = pickle.load(f) + # elif self.cfg.obs_type == 'features': + # _, self.expert_demo, self.expert_action, self.expert_reward = pickle.load(f) + + # if self.cfg.action_trajectories: + # with open(self.cfg.expert_action_trajectories, 'rb') as f: + # self.expert_action = pickle.load(f) + + # if isinstance(self.cfg.train_demo_idxs_list_or_num, int): + # if self.cfg.train_demo_idxs_list_or_num == -1: + # self.cfg.train_demo_idxs_list_or_num = len(self.expert_demo) + # train_demo_idxs_list_or_num = list(range(self.cfg.train_demo_idxs_list_or_num)) + + # self.expert_demo = self.expert_demo[train_demo_idxs_list_or_num] + # self.expert_action = self.expert_action[train_demo_idxs_list_or_num] + # self.expert_reward = self.expert_reward[train_demo_idxs_list_or_num] + # # if self.cfg.action_plans: + # # self.expert_action_plans = self.expert_action_plans[self.cfg.train_demo_idxs_list_or_num] + # # self.expert_demo = self.expert_demo[:self.cfg.num_demos] + # # self.expert_action = self.expert_action[:self.cfg.num_demos] + # # self.expert_reward = self.expert_reward[:self.cfg.num_demos] + + # self.expert_demo = np.concatenate(self.expert_demo, axis=0) + # self.expert_rgb_obs = np.ascontiguousarray(np.transpose(self.expert_demo, (0,2,3,1))[:, :,:,:3].astype(np.uint8)) + # self.expert_action = np.concatenate(self.expert_action, axis=0) + + # self.agent.save_representations(self.expert_demo, self.expert_action, 128, config=self.expert_demo_config) + + # Get start points + if self.cfg.random_start: + eval_starts = Path(self.cfg.eval_starts) / 'starts.pkl' + if eval_starts.exists(): + with eval_starts.open('rb') as f: + self.start_pos = pickle.load(f) + else: + eval_starts = Path(self.cfg.eval_starts) + eval_starts.mkdir(parents=True, exist_ok=True) + + # Generate start points + self.start_pos = [] + try: + for _ in range(self.cfg.num_eval): + self.start_pos.append(self.eval_env.get_random_pos()) + except: + for _ in range(self.cfg.num_eval): + self.start_pos.append(self.eval_env.arm.get_random_pos()) + + # Save start points for the task + eval_starts = eval_starts / 'starts.pkl' + with eval_starts.open('wb') as f: + pickle.dump(self.start_pos, f) + + time_step = self.eval_env.reset() + # replay_thread = None + while eval_until_episode(self.global_episode) and not self.exit_eval: + # self.video_recorder.init(self.eval_env, video_filename=f'{self.global_episode}_eval.mp4') + print(f"Starting episode {self.global_episode}") + time_step = self.eval_env.reset() #Leon: need to call reset twice in case objects are trapped + self.video_recorder.init(self.eval_env, video_filename=f'{self.global_episode}_eval.mp4') + # x = input("Press Enter to continue... after reseting env") + print("Press Enter to continue... after reseting env. To rate prev episode, press 0 for failure and 1 for success") + self.proceed_after_env_reset_event.clear() # clear the event flag + self.proceed_after_env_reset_event.wait() # blocking wait for the event flag to be set + if self.global_episode > 0: + self.logger.log_metrics({'num_success': self.num_episode_successes}, self.global_step, 'eval', episode=self.global_episode) + self.logger.log_metrics({'success_rate': self.num_episode_successes/self.global_episode}, self.global_step, 'eval', episode=self.global_episode) + + # log confidence intervals for success rate + k = self.num_episode_successes # number of successes + n = self.global_episode # number of trials + + table_columns = [] + table_data = [] + for alpha in self.alpha_range: + lb = bc.binom_ci(k, n, alpha, 'lb') + ub = bc.binom_ci(k, n, alpha, 'ub') + + self.logger.log_metrics({f'success_rate_lb_{alpha}': lb}, self.global_step, 'eval', episode=self.global_episode) + self.logger.log_metrics({f'success_rate_ub_{alpha}': ub}, self.global_step, 'eval', episode=self.global_episode) + + time_step = self.eval_env.reset() + # debug_info_dict = self.eval_env.debug_info_dict + # if replay_thread is not None: + # # wait for the last replay thread to finish + # replay_thread.join() + + # self.eval_replay_storage.add(time_step._replace(observation=time_step.observation[self.cfg.obs_type]), debug_info_dict) + # replay_thread = threading.Thread(target=self.eval_replay_storage.add, args=(time_step._replace(observation=time_step.observation[self.cfg.obs_type]), debug_info_dict)) + # replay_thread = threading.Thread(target=self.eval_replay_storage.add, args=(time_step, debug_info_dict)) + + # replay_thread.start() + if self.cfg.random_start: + time_step = self.reset(self.global_episode) + time.sleep(2) #5) + # if 'vinn' in repr(self.agent): + # self.agent.reset() + # # self.agent.buffer.reset() + # # if self.cfg.open_loop: + # # self.agent.current_step = 0 + # if 'openloop' in repr(self.agent): + # self.agent.curr_step = 0 + # at start of each episode, provide zero action for policies that use action history + # shape should be (T_o, T_a, action_dim) + + # while not time_step.last() and not self.preempt_episode: + self.video_recorder.ros_start_recording() + self.eval_replay_storage.start_episode() + self.eval_env.start_policy_timer() + while not self.eval_env.episode_done() and not self.preempt_episode: + # with torch.no_grad(), utils.eval_mode(self.agent): + # # if self.cfg.agent.provide_topk: + # # action, vinn_action, topk = self.agent.act( + # # time_step.observation['pixels'], + # # self.global_step, + # # eval_mode=True) + # # elif self.cfg.agent.provide_obs: + # # action, vinn_action, obs = self.agent.act( + # # time_step.observation['pixels'], + # # self.global_step, + # # eval_mode=True) + # # else: + # action, vinn_action = self.agent.act( + # time_step.observation, + # self.global_step, + # eval_mode=True, + # obs_timestamp=time_step.observation['timestamp'], + # obs_seq=time_step.observation['seq'], + # action_history=action_history, + # action_history_start_timestamp=action_history_start_timestamp, + # ) + # DONT WAIT FOR POLICY TO GET AN ACTION + # we dont want to slow down grabbing obs and passing to sam/contact features + + self.eval_env.run_policy_threads() # this just does a rospy sleep + + # if self.use_action_history: + # action_history_start_timestamp = time_step.observation['timestamp'] + # # action_history = action[:self.cfg.agent.config.policy_cfg.action_history_encoder_config.history_length, ...] + # # add n_obs_steps dimension to action_history, for now we assume n_obs_steps = 1 + # # TODO: handle n_obs_steps > 1 + # action_history = action[np.newaxis, ...] + + # time_step = self.eval_env.step(action, vinn_action) # obs, reward after action has been taken + # debug_info_dict = self.eval_env.debug_info_dict + + # time_step = self.eval_env.ros_step() + + # replay_thread.join() + + # time how long it takes to execute the step + # time_before_add = time.perf_counter() + # self.eval_replay_storage.add(time_step._replace(observation=time_step.observation[self.cfg.obs_type]), debug_info_dict) + # use thread to call the add function in a separate thread + # replay_thread = threading.Thread(target=self.eval_replay_storage.add, args=(time_step._replace(observation=time_step.observation[self.cfg.obs_type]), debug_info_dict)) + + # replay_thread = threading.Thread(target=self.eval_replay_storage.add, args=(time_step, debug_info_dict)) + # replay_thread.start() + + # print(f"Time to add to replay buffer: {time.perf_counter() - time_before_add}") + + # self.video_recorder.record(self.eval_env) + # self._global_step += 1 + + self.eval_env.stop_policy_timer() + + if self.restart_episode: + # means we should delete the current episode and start again + self.restart_episode = False + self.eval_replay_storage.reset_current_episode() + self.video_recorder.reset_current_episode() + + else: + self.eval_replay_storage.store_current_episode() + video_filepath = self.video_recorder.save() + self.logger.log_video(f"eval/{video_filepath.name.rstrip('.mp4')}", video_filepath, self.global_step) + self._global_episode += 1 + + self.preempt_episode = False # reset preempt_episode flag + + # self.video_recorder.save(f'{episode}_eval.mp4') + # get the video file and convert to video tensor to log + + self.eval_env.reset() + + print("Evaluation finished. To wrap up, rate prev episode, press 0 for failure and 1 for success") + self.proceed_after_env_reset_event.clear() # clear the event flag + self.proceed_after_env_reset_event.wait() # blocking wait for the event flag to be set + if self.global_episode > 0: + # self.logger.log_metrics({'num_success': self.num_episode_successes}, self.global_step, 'eval', episode=self.global_episode) + self.logger.log_metrics({'num_success': self.num_episode_successes}, self.global_step, 'eval', episode=self.global_episode) + self.logger.log_metrics({'success_rate': self.num_episode_successes/self.global_episode}, self.global_step, 'eval', episode=self.global_episode) + + # log confidence intervals for success rate + k = self.num_episode_successes # number of successes + n = self.global_episode # number of trials + + table_columns = ['success_rate'] + table_data = [self.num_episode_successes/self.global_episode] + for alpha in self.alpha_range: + lb = bc.binom_ci(k, n, alpha, 'lb') + ub = bc.binom_ci(k, n, alpha, 'ub') + + self.logger.log_metrics({f'success_rate_lb_{alpha}': lb}, self.global_step, 'eval', episode=self.global_episode) + self.logger.log_metrics({f'success_rate_ub_{alpha}': ub}, self.global_step, 'eval', episode=self.global_episode) + + table_columns.extend([f'success_rate_lb_{alpha}', f'success_rate_ub_{alpha}']) + table_data.extend([lb, ub]) + + table_data = [table_data] + + # seperately log as a table + wandb.log({ + "eval/success_rate_ci": wandb.Table(data=table_data, columns=table_columns) + }) + + # also accumulate eval metrics across previous eval runs + # TODO: change wandb init to resume from an existing run!!! + run_filter={ + "jobType": "eval", + "config.wandb_run_id": self.cfg.wandb_run_id, + "summary_metrics.episode": {"$gte": 5}, + "config.checkpoint_epoch": self.cfg.checkpoint_epoch, + "state": "finished", + # "config.grasped_obj_name": grasped_obj_name, + # "config.left_book_slot": left_book_slot, + } + + api = wandb.Api() + filtered_runs = api.runs("serialexperimentsleon/extrinsic_contact_downstream", filters=run_filter) + total_num_successes = self.num_episode_successes + total_num_episodes = self.global_episode + list_of_historical_run_ids = [] + if len(filtered_runs) > 0: + for filtered_run in filtered_runs: + total_num_successes += filtered_run.summary_metrics['eval/num_success'] + # total_num_episodes += filtered_run.summary_metrics['episode'] + total_num_episodes += filtered_run.config['num_eval'] + list_of_historical_run_ids.append(filtered_run.id) + + wandb.summary['total_num_successes'] = total_num_successes + wandb.summary['total_num_episodes'] = total_num_episodes + wandb.summary['total_success_rate'] = total_num_successes/total_num_episodes + + # log the accumulated metrics as a table + total_table_columns = ['total_num_successes', 'total_num_episodes', 'total_success_rate'] + total_table_data = [total_num_successes, total_num_episodes, total_num_successes/total_num_episodes] + self.logger.log_metrics({'total_success_rate': total_num_successes/total_num_episodes}, self.global_step, 'eval', episode=total_num_episodes) + + for alpha in self.alpha_range: + lb = bc.binom_ci(total_num_successes, total_num_episodes, alpha, 'lb') + ub = bc.binom_ci(total_num_successes, total_num_episodes, alpha, 'ub') + total_table_columns.extend([f'total_success_rate_lb_{alpha}', f'total_success_rate_ub_{alpha}']) + total_table_data.extend([lb, ub]) + wandb.summary[f'total_success_rate_lb_{alpha}'] = lb + wandb.summary[f'total_success_rate_ub_{alpha}'] = ub + + self.logger.log_metrics({f'total_success_rate_lb_{alpha}': lb}, self.global_step, 'eval', episode=total_num_episodes) + self.logger.log_metrics({f'total_success_rate_ub_{alpha}': ub}, self.global_step, 'eval', episode=total_num_episodes) + + total_table_data = [total_table_data] + wandb.log({ + 'eval/total_success_rate_ci': wandb.Table(data=total_table_data, columns=total_table_columns) + }) + + self.continue_keypress_thread = False # will stop the keypress thread + self.keypress_input_thread.join() # wait for the keypress thread to finish + + def load_checkpoint_conf(self, snapshot_path): + config_path = snapshot_path.parent / 'config.yaml' + if not config_path.exists(): + raise FileNotFoundError(f'No snapshot conf found at {config_path}') + else: + # load the omegaconf config + hydra.core.global_hydra.GlobalHydra.instance().clear() + hydra.initialize( + str(_relative_path_between(Path(config_path).absolute().parent, Path(__file__).absolute().parent)), + ) + cfg = hydra.compose(Path(config_path).stem) + from deepdiff import DeepDiff + from omegaconf import open_dict + diff = DeepDiff(OmegaConf.to_container(cfg), OmegaConf.to_container(self.cfg)) # old, new + # import re + overwriteable_keys = [f"root{overwritable_key}" for overwritable_key in ["['use_wandb']", "['path_to_depth_extrinsics']", "['eval']", "['root_dir']", "['wandb_notes']", "['agent']['config']['train_cfg']['use_amp']", "['agent']['config']['compile']", "['agent']['config']['policy_cfg']['num_inference_steps']"]] + if "values_changed" in diff: + # top_k_checkpoints, wandb_notes, agent.config.train_cfg.use_amp, save_snapshot_every_epochs_diffusion, check_topk_every_epochs_diffusion, validate_diffusion_on_action_loss_every_epochs, train_eval_diffusion_on_action_loss_every_epochs, validate_every_epochs_diffusion + # for keys above, overwrite the old config with the new config + for k, v in diff['values_changed'].items(): + # replace any keys that are under "root['suite']" + if k in overwriteable_keys or k.startswith("root['suite']"): + print(f"Found changed key {k} with value {v}. Overwriting old checkpoint config") + if k == "root['agent']['config']['compile']": + if diff['values_changed'][k]['new_value']: + self.loading_uncompiled_checkpoint_with_compile = True + elif not diff['values_changed'][k]['new_value']: + # raise ValueError("Cannot load a compiled checkpoint without compile") + self.loading_compiled_checkpoint_with_no_compile = True + exec(f"{k.replace('root[', 'cfg[')} = {k.replace('root[', 'self.cfg[')}") + # for any new values, update the old checkpoint config + if "dictionary_item_added" in diff: + for new_key in diff['dictionary_item_added']: # this is a list + # if new_key == "root['suite']['task_make_fn']['observation_cfg']": + if new_key == "root['suite']['task_make_fn']['agent_policy_cfg']": + # pass the agents observation_cfg to the suite task_make_fn + with open_dict(cfg): # to allow addition of non-existing keys + # cfg.suite.task_make_fn.observation_cfg = cfg.agent.config.observation_cfg + cfg.suite.task_make_fn.agent_policy_cfg = cfg.agent.config + continue + elif "['agent']['config']['policy_cfg']['input_shapes']" in new_key: + # skip adding the new key if it is the input_shapes of the policy_cfg + continue + else: + print(f"Found new key {new_key} with value {eval(new_key.replace('root[', 'self.cfg['))}. Adding to checkpoint config") + # eval(new_key.replace('root', 'cfg')) = eval(new_key.replace('root', 'self.cfg')) + if new_key == "root['agent']['config']['compile']": + if self.cfg.agent.config.compile: + self.loading_uncompiled_checkpoint_with_compile = True + + with open_dict(cfg): + exec(f"{new_key.replace('root[', 'cfg[')}={new_key.replace('root[', 'self.cfg[')}") + self.cfg = cfg + + def load_checkpoint(self, snapshot_path, bc=False): + print(f'resuming {repr(self.agent)}: {snapshot_path}') + with snapshot_path.open('rb') as f: + payload = torch.load(f) + agent_payload = {} + for k, v in payload.items(): + if k not in self.__dict__: + agent_payload[k] = v + elif k == '_global_epoch': + self._global_epoch = v + print(f'loaded epoch: {v}') + if self.cfg.use_wandb: + # add to config of wandb + wandb.config.update({'epoch': v}) + + # self.agent.load_snapshot_eval(agent_payload, bc) + +@hydra.main(config_path='cfgs', config_name='config_eval') +def main(cfg): + from eval_robot import Workspace as W + root_dir = Path.cwd() + workspace = W(cfg) + + workspace.eval() + +if __name__ == '__main__': + main() diff --git a/73h7fwcu/wandb/run-20250107_163950-73h7fwcu/logs/debug-internal.log b/73h7fwcu/wandb/run-20250107_163950-73h7fwcu/logs/debug-internal.log new file mode 100644 index 0000000000000000000000000000000000000000..29dd169160c2eeba1f59fac8aeb9d29d9373c19f --- /dev/null +++ b/73h7fwcu/wandb/run-20250107_163950-73h7fwcu/logs/debug-internal.log @@ -0,0 +1,851 @@ +2025-01-07 16:39:50,213 INFO StreamThr :2187469 [internal.py:wandb_internal():85] W&B internal server running at pid: 2187469, started at: 2025-01-07 16:39:50.213449 +2025-01-07 16:39:50,214 DEBUG HandlerThread:2187469 [handler.py:handle_request():158] handle_request: status +2025-01-07 16:39:50,226 INFO WriterThread:2187469 [datastore.py:open_for_write():87] open: 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