feat: random choose the final stop node
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125
nav_src/agent.py
125
nav_src/agent.py
@ -1,4 +1,5 @@
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"""Agent that interacts with Matterport3D simulator via a hierarchical planning approach."""
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import random
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import json
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import yaml
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import re
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@ -711,123 +712,29 @@ class NavGPTAgent(BaseAgent):
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obs = self.env._get_obs()
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global FINAL_STOP_POINT
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global TEMP_STEPS_COUNTER
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global NOW_LOCATION
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global SUCCESS
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FINAL_STOP_POINT = obs[0]['stop']
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if TEMP_STEPS_COUNTER != 0:
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TEMP_STEPS_COUNTER = 0
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print(f"HAVE SET FINAL_STOP_POINT = {FINAL_STOP_POINT}")
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print(len(obs))
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print(obs[0].keys())
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print(obs[0]['obs'])
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print(obs[0]['obs_summary'])
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print(obs[0]['objects'])
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print(obs[0]['instr_id'])
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print(obs[0]['scan'])
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print(obs[0]['viewpoint'])
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print(obs[0]['heading'])
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print(obs[0]['elevation'])
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print(obs[0]['candidate'])
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print(obs[0]['instruction'])
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print(obs[0]['gt_path'])
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print(obs[0]['path_id'])
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print(obs[0]['stop'])
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print(obs[0]['start'])
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print(obs[0]['target'])
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NOW_LOCATION = obs[0]['start']
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print("==")
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# Initialize the trajectory
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self.init_trajecotry(obs)
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# Load the instruction
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# instructions = [ob['instruction'] for ob in obs]
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targets = [ob['target'] for ob in obs]
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print(obs[0].keys())
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print(obs[0]['start'])
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print(obs[0]['stop'])
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print(obs[0]['target'])
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candidates = self.env.env.sims[0].getNodesInTheRoom()
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candidates.remove(obs[0]['start'])
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next_point = random.choice(candidates)
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print(next_point)
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print(self.config.load_instruction)
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print(self.config.load_action_plan)
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if next_point == FINAL_STOP_POINT:
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print(" SUCCESS")
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SUCCESS += 1
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if self.config.load_instruction:
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# action_plans = instructions
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action_plans = targets
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elif self.config.load_action_plan:
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action_plans = [ob['action_plan'] for ob in obs]
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else:
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action_plans = []
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for instruction in instructions:
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action_plan = self.plan_chain.run(instruction = instruction)
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action_plans.append(action_plan)
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print(action_plans)
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for i, init_ob in enumerate(obs):
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# for our work
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# cur_action_plan is "target object with its location"
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self.cur_action_plan = action_plans[i]
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print("use_tool_chain:", self.config.use_tool_chain)
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# Take the first action
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if self.config.use_tool_chain: # we will not HERE
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first_obs = self.action_maker('')
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input = {
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'action_plan': self.cur_action_plan,
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'init_observation': init_ob['obs_summary'],
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'observation': first_obs,
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}
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else:
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# Get current feature
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# we are HERE
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feature = init_ob['obs']
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navigable = init_ob['candidate']
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objects = init_ob['objects']
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heading = np.rad2deg(init_ob['heading'])
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elevation = np.rad2deg(init_ob['elevation'])
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orientation = f'\nheading: {heading:.2f}, elevation: {elevation:.2f}'
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print("use_relative_angle:", self.config.use_relative_angle)
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print("use_relative_angle:", self.config.use_navigable)
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if self.config.use_relative_angle: # True
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feature = self.modify_heading_angles(heading, feature, navigable, objects)
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if self.config.use_navigable: # False
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navigable = self.get_navigable_str(heading, elevation, navigable)
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if self.config.use_relative_angle:
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if self.config.use_navigable:
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init_observation = f"\n\tCurrent Viewpoint:\n{feature}\n\tNavigable Viewpoints:\n{navigable}"
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else:
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init_observation = f"\n\tCurrent Viewpoint:\n{feature}"
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else:
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if self.config.use_navigable:
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init_observation = f"\n\tCurrent Orientation:\n{orientation}\n\tCurrent Viewpoint:\n{feature}\n\tNavigable Viewpoints:\n{navigable}"
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else:
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init_observation = f"\n\tCurrent Orientation:\n{orientation}\n\tCurrent Viewpoint:\n{feature}"
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input = {
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'action_plan': self.cur_action_plan, # here will be "object & its location" in our work
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'init_observation': init_observation, # 8 direction's observation caption & navigable point & objects
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}
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output = self.agent_executor(input)
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self.traj[i]['llm_output'] = output['output']
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self.traj[i]['action_plan'] = output['action_plan']
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# extract agent's thought from llm output
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intermediate_steps = output['intermediate_steps']
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self.traj[i]['llm_thought'] = []
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self.traj[i]['llm_observation'] = []
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for action, observation in intermediate_steps:
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thought = action.log
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self.traj[i]['llm_thought'].append(thought)
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self.traj[i]['llm_observation'].append(observation)
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print(f"SUCCESS={SUCCESS}")
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return self.traj
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@ -38,15 +38,18 @@ class BaseAgent(object):
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if iters is not None:
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# For each time, it will run the first 'iters' iterations. (It was shuffled before)
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for i in range(iters):
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print(i)
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for traj in self.rollout(**kwargs):
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self.loss = 0
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self.results[traj['instr_id']] = traj
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'''
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preds_detail = self.get_results(detailed_output=True)
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json.dump(
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preds_detail,
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open(os.path.join(self.config.log_dir, 'runtime.json'), 'w'),
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sort_keys=True, indent=4, separators=(',', ': ')
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)
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'''
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else: # Do a full round
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while True:
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for traj in self.rollout(**kwargs):
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@ -174,6 +174,15 @@ class Simulator(object):
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# Get candidate
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self.getCandidate()
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def getNodesInTheRoom(self):
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ans = []
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start_region = self.node_region[self.scan_ID][self.viewpoint_ID]
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for node, region_id in self.node_region[self.scan_ID].items():
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if region_id == start_region:
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ans.append(node)
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return ans
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def updateGraph(self):
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# build graph
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for candidate in self.candidate.keys():
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