feat: random choose the final stop node

nav_src/agent.py

@@ -1,4 +1,5 @@
 """Agent that interacts with Matterport3D simulator via a hierarchical planning approach."""
+import random
 import json
 import yaml
 import re
@@ -711,123 +712,29 @@ class NavGPTAgent(BaseAgent):
             obs = self.env._get_obs()
 
         global FINAL_STOP_POINT
-        global TEMP_STEPS_COUNTER
+        global SUCCESS
-        global NOW_LOCATION
 
         FINAL_STOP_POINT = obs[0]['stop']
 
-        if TEMP_STEPS_COUNTER != 0:
-            TEMP_STEPS_COUNTER = 0
-
-        print(f"HAVE SET FINAL_STOP_POINT = {FINAL_STOP_POINT}")
-        
-        print(len(obs))
-
-        print(obs[0].keys())
-        print(obs[0]['obs'])
-        print(obs[0]['obs_summary'])
-        print(obs[0]['objects'])
-        print(obs[0]['instr_id'])
-        print(obs[0]['scan'])
-        print(obs[0]['viewpoint'])
-        print(obs[0]['heading'])
-        print(obs[0]['elevation'])
-        print(obs[0]['candidate'])
-        print(obs[0]['instruction'])
-        print(obs[0]['gt_path'])
-        print(obs[0]['path_id'])
-        print(obs[0]['stop'])
-        print(obs[0]['start'])
-        print(obs[0]['target'])
-        NOW_LOCATION = obs[0]['start']
-
-
-        print("==")
-
         # Initialize the trajectory
         self.init_trajecotry(obs)
 
-        # Load the instruction
+        print(obs[0].keys())
-        # instructions = [ob['instruction'] for ob in obs]
+        print(obs[0]['start'])
-        targets = [ob['target'] for ob in obs]
+        print(obs[0]['stop'])
+        print(obs[0]['target'])
+        candidates = self.env.env.sims[0].getNodesInTheRoom()
+        candidates.remove(obs[0]['start'])
+
+        next_point = random.choice(candidates)
+        print(next_point)
 
 
-        print(self.config.load_instruction)
+        if next_point == FINAL_STOP_POINT:
-        print(self.config.load_action_plan)
+            print("    SUCCESS")
+            SUCCESS += 1
 
-        if self.config.load_instruction:
+        print(f"SUCCESS={SUCCESS}")
-            # action_plans = instructions
-            action_plans = targets
-        elif self.config.load_action_plan:
-            action_plans = [ob['action_plan'] for ob in obs]
-        else:
-            action_plans = []
-            for instruction in instructions:
-                action_plan = self.plan_chain.run(instruction = instruction)
-                action_plans.append(action_plan)
-        print(action_plans)
-
-        for i, init_ob in enumerate(obs):
-            
-            # for our work
-            # cur_action_plan is "target object with its location"
-            self.cur_action_plan = action_plans[i]
-
-            print("use_tool_chain:", self.config.use_tool_chain)
-
-            # Take the first action
-            if self.config.use_tool_chain:                  # we will not HERE
-                first_obs = self.action_maker('')
-                input = {
-                    'action_plan': self.cur_action_plan,
-                    'init_observation': init_ob['obs_summary'],
-                    'observation': first_obs,
-                }
-            else:
-                # Get current feature
-
-                # we are HERE
-                feature = init_ob['obs']
-                navigable = init_ob['candidate']
-                objects = init_ob['objects']
-                heading = np.rad2deg(init_ob['heading'])
-                elevation = np.rad2deg(init_ob['elevation'])
-                orientation = f'\nheading: {heading:.2f}, elevation: {elevation:.2f}'
-
-                print("use_relative_angle:", self.config.use_relative_angle)
-                print("use_relative_angle:", self.config.use_navigable)
-                if self.config.use_relative_angle:      # True
-                    feature = self.modify_heading_angles(heading, feature, navigable, objects)
-                if self.config.use_navigable:           # False
-                    navigable = self.get_navigable_str(heading, elevation, navigable)
-
-                if self.config.use_relative_angle:
-                    if self.config.use_navigable:
-                        init_observation = f"\n\tCurrent Viewpoint:\n{feature}\n\tNavigable Viewpoints:\n{navigable}"
-                    else:
-                        init_observation = f"\n\tCurrent Viewpoint:\n{feature}"
-                else:
-                    if self.config.use_navigable:
-                        init_observation = f"\n\tCurrent Orientation:\n{orientation}\n\tCurrent Viewpoint:\n{feature}\n\tNavigable Viewpoints:\n{navigable}"
-                    else:
-                        init_observation = f"\n\tCurrent Orientation:\n{orientation}\n\tCurrent Viewpoint:\n{feature}"
-
-
-                input = {
-                    'action_plan': self.cur_action_plan,            # here will be "object & its location" in our work
-                    'init_observation': init_observation,           # 8 direction's observation caption & navigable point & objects
-                }
-            output = self.agent_executor(input)
-
-            self.traj[i]['llm_output'] = output['output']
-            self.traj[i]['action_plan'] = output['action_plan']
-            # extract agent's thought from llm output
-            intermediate_steps = output['intermediate_steps']
-            self.traj[i]['llm_thought'] = []
-            self.traj[i]['llm_observation'] = []
-            for action, observation in intermediate_steps:
-                thought = action.log
-                self.traj[i]['llm_thought'].append(thought)
-                self.traj[i]['llm_observation'].append(observation)
 
         return self.traj
+

nav_src/agent_base.py

@@ -38,15 +38,18 @@ class BaseAgent(object):
         if iters is not None:
             # For each time, it will run the first 'iters' iterations. (It was shuffled before)
             for i in range(iters):
+                print(i)
                 for traj in self.rollout(**kwargs):
                     self.loss = 0
                     self.results[traj['instr_id']] = traj
+                    '''
                     preds_detail = self.get_results(detailed_output=True)
                     json.dump(
                         preds_detail,
                         open(os.path.join(self.config.log_dir, 'runtime.json'), 'w'),
                         sort_keys=True, indent=4, separators=(',', ': ')
                     )
+                    '''
         else:   # Do a full round
             while True:
                 for traj in self.rollout(**kwargs):

nav_src/env.py

@@ -173,6 +173,15 @@ class Simulator(object):
 
         # Get candidate
         self.getCandidate()
+
+    def getNodesInTheRoom(self):
+        ans = []
+        start_region = self.node_region[self.scan_ID][self.viewpoint_ID]
+        for node, region_id in self.node_region[self.scan_ID].items():
+            if region_id == start_region:
+                ans.append(node)
+        return ans
+        
     
     def updateGraph(self):
         # build graph