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+2025-01-06 20:00:56,738 INFO    MainThread:2103006 [wandb_setup.py:_flush():76] Loading settings from /home/leonmkim/.config/wandb/settings
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'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': None, 'depth_crop_type': None, 'segmask_crop_type': None, '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': 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': 15000, 'validate_every_epochs_bc': 5, 'validate_every_epochs_diffusion': 250, 'validate_diffusion_on_action_loss_every_epochs': 250, 'train_eval_diffusion_on_action_loss_every_epochs': 250, 'check_topk_every_epochs': 5, 'check_topk_every_epochs_diffusion': 250, 'save_snapshot_every_epochs_diffusion': 1500, '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', '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-v1/1017_0', 'residual_weight': '/home/leonmkim/fish_leon/FISH/weights/frankagym_pixels/FrankaInsertion-v1/weight.pt', 'final_experiment_dir': './exp_local/frankagym_pixels/FrankaInsertion-v1/1017_0/up4gjipz'}
+2025-01-06 20:00:56,739 INFO    MainThread:2103006 [wandb_init.py:init():619] starting backend
+2025-01-06 20:00:56,740 INFO    MainThread:2103006 [wandb_init.py:init():623] setting up manager
+2025-01-06 20:00:56,744 INFO    MainThread:2103006 [backend.py:_multiprocessing_setup():105] multiprocessing start_methods=fork,spawn,forkserver, using: spawn
+2025-01-06 20:00:56,745 INFO    MainThread:2103006 [wandb_init.py:init():631] backend started and connected
+2025-01-06 20:00:56,757 INFO    MainThread:2103006 [wandb_init.py:init():720] updated telemetry
+2025-01-06 20:00:56,769 INFO    MainThread:2103006 [wandb_init.py:init():753] communicating run to backend with 90.0 second timeout
+2025-01-06 20:00:57,041 INFO    MainThread:2103006 [wandb_run.py:_on_init():2435] communicating current version
+2025-01-06 20:00:57,083 INFO    MainThread:2103006 [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 20:00:57,084 INFO    MainThread:2103006 [wandb_init.py:init():804] starting run threads in backend
+2025-01-06 20:00:57,447 INFO    MainThread:2103006 [wandb_run.py:_console_start():2413] atexit reg
+2025-01-06 20:00:57,447 INFO    MainThread:2103006 [wandb_run.py:_redirect():2255] redirect: wrap_raw
+2025-01-06 20:00:57,447 INFO    MainThread:2103006 [wandb_run.py:_redirect():2320] Wrapping output streams.
+2025-01-06 20:00:57,447 INFO    MainThread:2103006 [wandb_run.py:_redirect():2345] Redirects installed.
+2025-01-06 20:00:57,448 INFO    MainThread:2103006 [wandb_init.py:init():847] run started, returning control to user process
+2025-01-06 20:00:57,448 INFO    MainThread:2103006 [wandb_run.py:_tensorboard_callback():1544] tensorboard callback: /home/leonmkim/fish_leon/FISH/exp_local/frankagym_pixels/FrankaInsertion-v1/1017_0/up4gjipz/tb, True
+2025-01-06 20:01:00,348 INFO    MainThread:2103006 [wandb_run.py:_config_callback():1382] config_cb None None {'grasped_obj_name': 'fowlers', 'left_book_slot': 'twodim'}
+2025-01-06 20:05:40,743 WARNING MsgRouterThr:2103006 [router.py:message_loop():77] message_loop has been closed

up4gjipz/wandb/run-20250106_200056-up4gjipz/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()

up4gjipz/wandb/run-20250106_200056-up4gjipz/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: 0
+dataset_shuffle_seed:
+  desc: null
+  value: 1
+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: '1017_0'
+wandb_notes:
+  desc: null
+  value: 1017_0_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-v1/1017_0
+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: 0
+      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-v1/1017_0
+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-v1/1017_0/up4gjipz
+_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: 1736211656
+    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

up4gjipz/wandb/run-20250106_200056-up4gjipz/files/diff.patch

@@ -0,0 +1,63 @@
+diff --git a/FISH/cfgs/config_eval.yaml b/FISH/cfgs/config_eval.yaml
+index d2afe29..54e6cd0 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

up4gjipz/wandb/run-20250106_200056-up4gjipz/files/media/table/eval/success_rate_ci_5_1b420c32327aa0ba7f75.table.json

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+{"columns": ["success_rate", "success_rate_lb_0.01", "success_rate_ub_0.01", "success_rate_lb_0.025", "success_rate_ub_0.025", "success_rate_lb_0.05", "success_rate_ub_0.05", "success_rate_lb_0.1", "success_rate_ub_0.1"], "data": [[1.0, 0.449680686007073, 1, 0.47999376058978616, 1, 0.6638181209236573, 1, 1, 1]]}

up4gjipz/wandb/run-20250106_200056-up4gjipz/files/media/table/eval/total_success_rate_ci_6_9da6f5d41b864e33184b.table.json

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+{"columns": ["total_num_successes", "total_num_episodes", "total_success_rate", "total_success_rate_lb_0.01", "total_success_rate_ub_0.01", "total_success_rate_lb_0.025", "total_success_rate_ub_0.025", "total_success_rate_lb_0.05", "total_success_rate_ub_0.05", "total_success_rate_lb_0.1", "total_success_rate_ub_0.1"], "data": [[15, 15, 1.0, 0.7578194736918749, 1, 0.8468897341988106, 1, 0.881214111967138, 1, 0.8713090381773767, 1]]}

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+oid sha256:10ebed5c632009219ad7d9302f7f5a63e08a512951d50a98abbf25a2373cb531
+size 1449386

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up4gjipz/wandb/run-20250106_200056-up4gjipz/files/output.log

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+
+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] [1736211660.349319]: resetting environment
+[INFO] [1736211660.353106]: cleared current plan
+[INFO] [1736211660.353932]: moving to home
+[INFO] [1736211662.356923]: reached home
+[INFO] [1736211662.358652]: reset action history
+[INFO] [1736211665.067761]: environment reset
+Starting episode 0
+[INFO] [1736211665.072186]: resetting environment
+[INFO] [1736211665.076947]: cleared current plan
+[INFO] [1736211665.077774]: moving to home
+[INFO] [1736211666.080106]: reached home
+[INFO] [1736211666.081390]: reset action history
+[INFO] [1736211668.792786]: 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] [1736211673.372360]: resetting environment
+[INFO] [1736211673.377371]: cleared current plan
+[INFO] [1736211673.378291]: moving to home
+[INFO] [1736211674.380775]: reached home
+[INFO] [1736211674.381930]: reset action history
+[INFO] [1736211677.090562]: environment reset
+bag_path_name:=/home/leonmkim/fish_leon/FISH/exp_local/frankagym_pixels/FrankaInsertion-v1/1017_0/up4gjipz/episode_rosbags/episode_0_2025-01-06-20-01-17.bag
+... logging to /home/leonmkim/.ros/log/a8a09c64-c64e-11ef-96d2-5defea869005/roslaunch-leonmkim-ROG-Strix-G15CS-G15CS-2103006.log
+started roslaunch server http://158.130.50.37:45743/
+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 [2103297]
+process[print_text-2]: started with pid [2103321]
+process[rosbag_record-3]: started with pid [2103322]
+started bagging!
+For topic gripper_width: timestamp difference is 105638663 for nearest: 1736211679499960105 - target: 1736211679394321442 at idx 28
+For topic gripper_width: timestamp difference is 113856143 for nearest: 1736211689899920529 - target: 1736211689786064386 at idx 171
+For topic gripper_width: timestamp difference is -102568187 for nearest: 1736211710733212910 - target: 1736211710835781097 at idx 249
+[INFO] [1736211711.926512]: Storing episode...
+[rosbag_record-3] killing on exit
+[print_text-2] killing on exit
+[pub_text-1] killing on exit
+[INFO] [1736211712.652085]: Stored episode 1.
+[INFO] [1736211712.652358]: Saving video...
+[INFO] [1736211712.843338]: Video saved!
+Starting episode 1
+[INFO] [1736211712.863166]: resetting environment
+[INFO] [1736211712.879828]: cleared current plan
+[INFO] [1736211712.880116]: moving to home
+[INFO] [1736211716.586677]: reached home
+[INFO] [1736211716.587105]: reset action history
+[INFO] [1736211719.348493]: 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] [1736211721.057543]: resetting environment
+[INFO] [1736211721.061523]: cleared current plan
+[INFO] [1736211721.061725]: moving to home
+[INFO] [1736211722.063168]: reached home
+[INFO] [1736211722.063590]: reset action history
+[INFO] [1736211724.773265]: environment reset
+bag_path_name:=/home/leonmkim/fish_leon/FISH/exp_local/frankagym_pixels/FrankaInsertion-v1/1017_0/up4gjipz/episode_rosbags/episode_1_2025-01-06-20-02-04.bag
+... logging to /home/leonmkim/.ros/log/a8a09c64-c64e-11ef-96d2-5defea869005/roslaunch-leonmkim-ROG-Strix-G15CS-G15CS-2103006.log
+started roslaunch server http://158.130.50.37:43525/
+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 [2103446]
+process[print_text-5]: started with pid [2103447]
+process[rosbag_record-6]: started with pid [2103448]
+started bagging!
+For topic gripper_width: timestamp difference is 116652413 for nearest: 1736211727299890919 - target: 1736211727183238506 at idx 37
+For topic gripper_width: timestamp difference is 121131386 for nearest: 1736211740299919810 - target: 1736211740178788424 at idx 224
+For topic gripper_width: timestamp difference is 126882683 for nearest: 1736211747299919735 - target: 1736211747173037052 at idx 249
+[INFO] [1736211759.492835]: Storing episode...
+[pub_text-4] killing on exit[print_text-5] killing on exit
+[rosbag_record-6] killing on exit
+[INFO] [1736211760.225026]: Stored episode 2.
+[INFO] [1736211760.225942]: Saving video...
+[INFO] [1736211760.600671]: Video saved!
+Starting episode 2
+[INFO] [1736211760.633743]: resetting environment
+[INFO] [1736211760.652103]: cleared current plan
+[INFO] [1736211760.652822]: moving to home
+[INFO] [1736211764.355531]: reached home
+[INFO] [1736211764.357739]: reset action history
+[INFO] [1736211767.079796]: 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] [1736211780.375838]: resetting environment
+[INFO] [1736211780.386518]: cleared current plan
+[INFO] [1736211780.386790]: moving to home
+[INFO] [1736211781.388307]: reached home
+[INFO] [1736211781.389590]: reset action history
+[INFO] [1736211784.102747]: environment reset
+bag_path_name:=/home/leonmkim/fish_leon/FISH/exp_local/frankagym_pixels/FrankaInsertion-v1/1017_0/up4gjipz/episode_rosbags/episode_2_2025-01-06-20-03-04.bag
+... logging to /home/leonmkim/.ros/log/a8a09c64-c64e-11ef-96d2-5defea869005/roslaunch-leonmkim-ROG-Strix-G15CS-G15CS-2103006.log
+started roslaunch server http://158.130.50.37:33607/
+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 [2103590]
+process[print_text-8]: started with pid [2103591]
+process[rosbag_record-9]: started with pid [2103592]
+started bagging!
+For topic gripper_width: timestamp difference is 105822448 for nearest: 1736211803833250884 - target: 1736211803727428436 at idx 249
+For topic gripper_width: timestamp difference is 108203384 for nearest: 1736211806699958774 - target: 1736211806591755390 at idx 249
+[INFO] [1736211818.940250]: Storing episode...
+[rosbag_record-9] killing on exit
+[print_text-8] killing on exit
+[pub_text-7] killing on exit
+[INFO] [1736211819.673929]: Stored episode 3.
+[INFO] [1736211819.677274]: Saving video...
+[INFO] [1736211820.350251]: Video saved!
+Starting episode 3
+[INFO] [1736211820.403688]: resetting environment
+[INFO] [1736211820.434097]: cleared current plan
+[INFO] [1736211820.438171]: moving to home
+[WARN] [1736211820.315088]: Plan exhausted
+[WARN] [1736211820.363302]: Plan exhausted
+[WARN] [1736211820.395109]: Plan exhausted
+[INFO] [1736211824.040593]: reached home
+[INFO] [1736211824.043114]: reset action history
+[INFO] [1736211826.773317]: 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] [1736211829.791981]: resetting environment
+[INFO] [1736211829.800776]: cleared current plan
+[INFO] [1736211829.801183]: moving to home
+[INFO] [1736211830.802690]: reached home
+[INFO] [1736211830.809264]: reset action history
+[INFO] [1736211833.535059]: environment reset
+bag_path_name:=/home/leonmkim/fish_leon/FISH/exp_local/frankagym_pixels/FrankaInsertion-v1/1017_0/up4gjipz/episode_rosbags/episode_3_2025-01-06-20-03-53.bag
+... logging to /home/leonmkim/.ros/log/a8a09c64-c64e-11ef-96d2-5defea869005/roslaunch-leonmkim-ROG-Strix-G15CS-G15CS-2103006.log
+started roslaunch server http://158.130.50.37:42249/
+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 [2103731]
+process[print_text-11]: started with pid [2103732]
+process[rosbag_record-12]: started with pid [2103756]
+started bagging!
+For topic gripper_width: timestamp difference is -107224260 for nearest: 1736211836433251347 - target: 1736211836540475607 at idx 43
+[INFO] [1736211868.365720]: Storing episode...
+[rosbag_record-12] killing on exit
+[pub_text-10] killing on exit
+[print_text-11] killing on exit
+[INFO] [1736211869.108990]: Stored episode 4.
+[INFO] [1736211869.111494]: Saving video...
+[INFO] [1736211869.549411]: Video saved!
+Starting episode 4
+[INFO] [1736211869.601057]: resetting environment
+[INFO] [1736211869.627710]: cleared current plan
+[INFO] [1736211869.629492]: moving to home
+[INFO] [1736211873.237368]: reached home
+[INFO] [1736211873.246453]: reset action history
+[INFO] [1736211876.031523]: 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] [1736211878.738951]: resetting environment
+[INFO] [1736211878.766559]: cleared current plan
+[INFO] [1736211878.768920]: moving to home
+[INFO] [1736211879.773239]: reached home
+[INFO] [1736211879.775372]: reset action history
+[INFO] [1736211882.502597]: environment reset
+bag_path_name:=/home/leonmkim/fish_leon/FISH/exp_local/frankagym_pixels/FrankaInsertion-v1/1017_0/up4gjipz/episode_rosbags/episode_4_2025-01-06-20-04-42.bag
+... logging to /home/leonmkim/.ros/log/a8a09c64-c64e-11ef-96d2-5defea869005/roslaunch-leonmkim-ROG-Strix-G15CS-G15CS-2103006.log
+started roslaunch server http://158.130.50.37:38215/
+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 [2103873]
+process[print_text-14]: started with pid [2103874]
+process[rosbag_record-15]: started with pid [2103898]
+started bagging!
+For topic gripper_width: timestamp difference is 106997889 for nearest: 1736211887899872464 - target: 1736211887792874575 at idx 80
+[INFO] [1736211917.238783]: Storing episode...
+[rosbag_record-15] killing on exit
+[print_text-14] killing on exit
+[pub_text-13] killing on exit
+[INFO] [1736211917.988698]: Stored episode 5.
+[INFO] [1736211917.993749]: Saving video...
+[INFO] [1736211918.322463]: Video saved!
+[INFO] [1736211918.366302]: resetting environment
+[INFO] [1736211918.383186]: cleared current plan
+[INFO] [1736211918.384957]: moving to home
+[INFO] [1736211922.090842]: reached home
+[INFO] [1736211922.094313]: reset action history
+[INFO] [1736211924.873733]: 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!

up4gjipz/wandb/run-20250106_200056-up4gjipz/files/requirements.txt

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up4gjipz/wandb/run-20250106_200056-up4gjipz/logs/debug.log

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