[Fix] refactor evaluator, env, and extensions (#121)

* update evaluator and remove vlnpe part eval and env

* add env

* update env utils; resolve import evaluator issue in server

* update env utils; resolve import evaluator issue in server

* isolate dependency in env

* fix default; fix test

Author: kew6688

.gitignore

@@ -132,8 +132,8 @@ data
 kujiale_data
 interiornav_data
 images/
-internutopia
-internutopia_extension
+# internutopia
+# internutopia_extension
 *.pyc
 
 pre-commit*

internnav/agent/base.py

@@ -22,6 +22,8 @@ def register(cls, agent_type: str):
         """
 
         def decorator(agent_class):
+            if agent_type in cls.agents:
+                raise ValueError(f"Agent {agent_type} already registered.")
             cls.agents[agent_type] = agent_class
 
         return decorator

internnav/agent/cma_agent.py

@@ -5,10 +5,9 @@
 from gym import spaces
 
 from internnav.agent.base import Agent
+from internnav.agent.utils.common import batch_obs, set_seed_model
 from internnav.configs.agent import AgentCfg
 from internnav.configs.model.base_encoders import ModelCfg
-from internnav.evaluator.utils.common import set_seed_model
-from internnav.evaluator.utils.models import batch_obs
 from internnav.model import get_config, get_policy
 
 
@@ -22,6 +21,7 @@ class CmaAgent(Agent):
     )
 
     def __init__(self, agent_config: AgentCfg):
+
         super().__init__(agent_config)
         self._model_settings = ModelCfg(**agent_config.model_settings)
         model_settings = self._model_settings
@@ -119,13 +119,20 @@ def inference(self, obs):
             dtype=torch.bool,
         )
         end = time.time()
-        print(f'CmaAgent step time: {round(end-start,4)}s')
+        print(f'CmaAgent step time: {round(end-start, 4)}s')
         return actions.cpu().numpy().tolist()
 
     def step(self, obs):
         print('CmaPolicyAgent step')
         start = time.time()
         action = self.inference(obs)
         end = time.time()
-        print(f'Time: {round(end-start,4)}s')
-        return action
+        print(f'Time: {round(end-start, 4)}s')
+
+        # convert from [[x],[y]] to [{'action': [x],'ideal_flag':True}, {'action': [y],'ideal_flag':True}]
+        actions = []
+        for a in action:
+            if not isinstance(a, list):
+                a = [a]
+            actions.append({'action': a, 'ideal_flag': True})
+        return actions

internnav/agent/rdp_agent.py

@@ -1,14 +1,13 @@
-import random
 import time
 
 import numpy as np
 import torch
 from gym import spaces
 
 from internnav.agent.base import Agent
+from internnav.agent.utils.common import batch_obs, set_seed_model
 from internnav.configs.agent import AgentCfg
 from internnav.configs.model.base_encoders import ModelCfg
-from internnav.evaluator.utils.models import batch_obs
 from internnav.model import get_config, get_policy
 from internnav.model.basemodel.LongCLIP.model import longclip
 from internnav.model.basemodel.rdp.utils import (
@@ -25,17 +24,9 @@
     extract_image_features,
     extract_instruction_tokens,
 )
-from internnav.utils import common_log_util
 from internnav.utils.common_log_util import common_logger as log
 
 
-def set_random_seed(seed):
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-
-
 @Agent.register('rdp')
 class RdpAgent(Agent):
     observation_space = spaces.Box(
@@ -47,7 +38,7 @@ class RdpAgent(Agent):
 
     def __init__(self, config: AgentCfg):
         super().__init__(config)
-        set_random_seed(0)
+        set_seed_model(0)
         self._model_settings = self.config.model_settings
         self._model_settings = ModelCfg(**self._model_settings)
         env_num = getattr(self._model_settings, 'env_num', 1)
@@ -348,5 +339,12 @@ def step(self, obs):
         start = time.time()
         action = self.inference(obs)
         end = time.time()
-        print(f'总时间： {round(end-start,4)}s')
-        return action
+        print(f'总时间： {round(end-start, 4)}s')
+
+        # convert from [[a1],[a2]] to [{'action': [a1],'ideal_flag':True}, {'action': [a2],'ideal_flag':True}]
+        actions = []
+        for a in action:
+            if not isinstance(a, list):
+                a = [a]
+            actions.append({'action': a, 'ideal_flag': True})
+        return actions

internnav/evaluator/utils/models.py internnav/agent/utils/common.pyinternnav/evaluator/utils/models.py renamed to internnav/agent/utils/common.py

@@ -1,11 +1,25 @@
 from collections import defaultdict
 from typing import DefaultDict, List, Optional
 
+import numpy as np
 import torch
 
 from .tensor_dict import TensorDict
 
 
+def set_seed_model(seed):
+    import random
+
+    import torch
+
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.backends.cudnn.benchmark = False
+    torch.backends.cudnn.deterministic = False
+
+
 def batch_obs(
     observations,
     device: Optional[torch.device] = None,

internnav/evaluator/utils/tensor_dict.py internnav/agent/utils/tensor_dict.pyinternnav/evaluator/utils/tensor_dict.py renamed to internnav/agent/utils/tensor_dict.py

@@ -56,11 +56,12 @@ class EvalDatasetCfg(BaseModel):
 
 
 class EvalCfg(BaseModel):
+    eval_type: Optional[str] = None
+    eval_settings: Optional[Dict[str, Any]] = {}
     agent: Optional[AgentCfg] = None
     env: EnvCfg
     task: TaskCfg
     dataset: EvalDatasetCfg
-    eval_settings: Optional[Dict[str, Any]] = {}
 
 
 __all__ = [

internnav/configs/evaluator/__init__.py

@@ -1,5 +1,4 @@
 from internnav.env.base import Env
-from internnav.env.vln_pe_env import VlnPeEnv
-from internnav.env.vln_multi_env import VlnMultiEnv
+from internnav.env.internutopia_env import InternutopiaEnv
 
-__all__ = ['Env', 'VlnPeEnv', 'VlnMultiEnv']
+__all__ = ['Env', 'InternutopiaEnv']

internnav/dist.py

@@ -39,6 +39,8 @@ def register(cls, env_type: str):
         """
 
         def decorator(env_class):
+            if env_type in cls.envs:
+                raise ValueError(f"Env {env_type} already registered.")
             cls.envs[env_type] = env_class
 
         return decorator