feat: random explore

nav_src/NavGPT.py

@@ -8,7 +8,7 @@ from utils.logger import write_to_record_file
 from utils.data import ImageObservationsDB
 from parser import parse_args
 from env import REVERIENavBatch
-from agent import NavGPTAgent
+from agent import NavGPTAgent, RandomAgent
 
 def build_dataset(args, data_limit=100):
 
@@ -35,7 +35,7 @@ def build_dataset(args, data_limit=100):
 
 def valid(args, val_envs):
 
-    agent = NavGPTAgent(next(iter(val_envs.values())), args)
+    agent = RandomAgent(next(iter(val_envs.values())), args)
 
     with open(os.path.join(args.log_dir, 'validation_args.json'), 'w') as outf:
         json.dump(vars(args), outf, indent=4)

nav_src/agent.py

@@ -5,6 +5,7 @@ import re
 import warnings
 import numpy as np
 from typing import Any, Callable, List, NamedTuple, Optional, Sequence, Tuple, Dict, Union
+import random
 
 from env import REVERIENavBatch
 from argparse import Namespace
@@ -884,42 +885,46 @@ class RandomAgent(BaseAgent):
 
         global FINAL_STOP_POINT
         global TEMP_STEPS_COUNTER
+        global STEPS_COUNTER
+        global SUCCESS
 
         FINAL_STOP_POINT = obs[0]['stop']
 
         if TEMP_STEPS_COUNTER != 0:
             TEMP_STEPS_COUNTER = 0
 
-        print(obs[0].keys())
+        print("=="*20)
-        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'])
-
-
-        print("==")
 
         # Initialize the trajectory
         self.init_trajecotry(obs)
 
+        for iteration in range(self.config.max_iterations):
+            next_point = None
+            print(obs[0].keys())
+            print(obs[0]['viewpoint'])
             for i, init_ob in enumerate(obs):
+                navigable = [ k for k, v in init_ob['candidate'].items() ]
+                next_point = random.choice(navigable)
+                print(next_point)
+                turned_angle, obs = self.make_equiv_action([next_point])
+                obs = [obs]
+
+                print(f"TEMP_STEPS_COUNTER={TEMP_STEPS_COUNTER}")
+                print(f"STEPS_COUNTER={STEPS_COUNTER}")
+                TEMP_STEPS_COUNTER += 1
+
+            if next_point == FINAL_STOP_POINT:
+                print("    SUCCESS")
+                STEPS_COUNTER += TEMP_STEPS_COUNTER
+                SUCCESS += 1
+                TEMP_STEPS_COUNTER = 0
+                break
+        
+        print(f"FINAL_STOP_POINT={FINAL_STOP_POINT}")
+        print(f"SUCCESS={SUCCESS}")
+        print(f"TEMP_STEPS_COUNTER={TEMP_STEPS_COUNTER}")
+        print(f"STEPS_COUNTER={STEPS_COUNTER}")
 
-                navigable = init_ob['candidate']
-                heading = np.rad2deg(init_ob['heading'])
-                elevation = np.rad2deg(init_ob['elevation'])
-                orientation = f'\nheading: {heading:.2f}, elevation: {elevation:.2f}'
 
-            output = self.agent_executor(input)
 
         return self.traj

nav_src/agent_base.py

@@ -14,10 +14,6 @@ class BaseAgent(object):
             output.append({'instr_id': k, 'trajectory': v['path']})
             if detailed_output:
                 output[-1]['details'] = v['details']
-                output[-1]['action_plan'] = v['action_plan']
-                output[-1]['llm_output'] = v['llm_output']
-                output[-1]['llm_thought'] = v['llm_thought']
-                output[-1]['llm_observation'] = v['llm_observation']
         return output
 
     def rollout(self, **args):