yifan123
commented
1 year ago Please add line breaks to the error message above or upload a screenshot. The current format is too messy and it's hard to see where the error occurred.
Closed Cyydz closed 1 year ago
yifan123
commented
1 year ago Please add line breaks to the error message above or upload a screenshot. The current format is too messy and it's hard to see where the error occurred.
yifan123
commented
1 year ago "D:\Program Files\Anaconda3\envs\ACE\lib\site-packages\gfootball\env\football_env_core.py", line 117, in reset\n self._reset(self._env.game_config.render, inc=inc)\nAttributeError("'NoneType' object has no attribute 'game_config'")')It seems that the gfootball environment was not installed successfully. You can test it separately.
Cyydz
commented
1 year ago "D:\Program Files\Anaconda3\envs\ACE\lib\site-packages\gfootball\env\football_env_core.py", line 117, in reset\n self._reset(self._env.game_config.render, inc=inc)\nAttributeError("'NoneType' object has no attribute 'game_config'")')It seems that the gfootball environment was not installed successfully. You can test it separately.
is the gfootball's version is the lastest?
"D:\Program Files\Anaconda3\envs\ACE\lib\site-packages\gfootball\env\football_env_core.py", line 117, in reset\n self._reset(self._env.game_config.render, inc=inc)\nAttributeError("'NoneType' object has no attribute 'game_config'")')It seems that the gfootball environment was not installed successfully. You can test it separately.
ok, i will try it
yifan123
commented
1 year ago Of course it is, because the grf was last updated a year ago
"D:\Program Files\Anaconda3\envs\ACE\Scripts\python.exe" F:/Documents/ACE/exp/grf/academy_counterattack_hard/config.py WARNING:root:If you want to use numba to speed up segment tree, please install numba first D:\Program Files\Anaconda3\envs\ACE\lib\site-packages\torch\_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ..\aten\src\ATen\native\BinaryOps.cpp:467.) return torch.floor_divide(self, other) INFO:learner_logger:[RANK0]: DI-engine DRL Policy GRFACE( (_action_encoder): Sequential( (0): Linear(in_features=128, out_features=128, bias=True) (1): ReLU(inplace=True) (2): Linear(in_features=128, out_features=2432, bias=True) (3): ReLU(inplace=True) ) (_state_encoder): Sequential( (0): Linear(in_features=21, out_features=128, bias=True) (1): ReLU(inplace=True) (2): Linear(in_features=128, out_features=128, bias=True) (3): ReLU(inplace=True) ) (_relation_encoder): Sequential( (0): Linear(in_features=132, out_features=128, bias=True) (1): ReLU(inplace=True) (2): Linear(in_features=128, out_features=256, bias=True) (3): ReLU(inplace=True) ) (_relation_aggregator): RelationAggregator( (_state_encoder): Sequential( (0): Linear(in_features=384, out_features=128, bias=True) (1): ReLU(inplace=True) ) ) (_decision_encoder): DecisionEncoder( (_decision_encoder): Sequential( (0): Linear(in_features=128, out_features=128, bias=True) (1): ReLU(inplace=True) (2): Linear(in_features=128, out_features=256, bias=True) (3): ReLU(inplace=True) ) (_logit_encoder): Sequential( (0): Linear(in_features=256, out_features=128, bias=True) (1): ReLU(inplace=True) (2): Linear(in_features=128, out_features=1, bias=True) ) ) (_local_predictor): Sequential( (0): Linear(in_features=128, out_features=64, bias=True) (1): ReLU(inplace=True) (2): Linear(in_features=64, out_features=7, bias=True) ) (_global_predictor): Sequential( (0): Linear(in_features=256, out_features=128, bias=True) (1): ReLU(inplace=True) (2): Linear(in_features=128, out_features=14, bias=True) ) ) WARNING:root:If you want to use numba to speed up segment tree, please install numba first WARNING:root:If you want to use numba to speed up segment tree, please install numba first WARNING:root:If you want to use numba to speed up segment tree, please install numba first D:\Program Files\Anaconda3\envs\ACE\lib\site-packages\torch\_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ..\aten\src\ATen\native\BinaryOps.cpp:467.) return torch.floor_divide(self, other) D:\Program Files\Anaconda3\envs\ACE\lib\site-packages\torch\_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ..\aten\src\ATen\native\BinaryOps.cpp:467.) return torch.floor_divide(self, other) D:\Program Files\Anaconda3\envs\ACE\lib\site-packages\torch\_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ..\aten\src\ATen\native\BinaryOps.cpp:467.) return torch.floor_divide(self, other) WARNING:root:If you want to use numba to speed up segment tree, please install numba first WARNING:root:If you want to use numba to speed up segment tree, please install numba first WARNING:root:If you want to use numba to speed up segment tree, please install numba first WARNING:root:If you want to use numba to speed up segment tree, please install numba first D:\Program Files\Anaconda3\envs\ACE\lib\site-packages\torch\_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ..\aten\src\ATen\native\BinaryOps.cpp:467.) return torch.floor_divide(self, other) WARNING:root:If you want to use numba to speed up segment tree, please install numba first D:\Program Files\Anaconda3\envs\ACE\lib\site-packages\torch\_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ..\aten\src\ATen\native\BinaryOps.cpp:467.) return torch.floor_divide(self, other) D:\Program Files\Anaconda3\envs\ACE\lib\site-packages\torch\_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ..\aten\src\ATen\native\BinaryOps.cpp:467.) return torch.floor_divide(self, other) D:\Program Files\Anaconda3\envs\ACE\lib\site-packages\torch\_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ..\aten\src\ATen\native\BinaryOps.cpp:467.) return torch.floor_divide(self, other) D:\Program Files\Anaconda3\envs\ACE\lib\site-packages\torch\_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ..\aten\src\ATen\native\BinaryOps.cpp:467.) return torch.floor_divide(self, other) WARNING:root:Timeout wrapper is not implemented in windows platform, so ignore it default WARNING:root:Timeout wrapper is not implemented in windows platform, so ignore it default WARNING:root:Timeout wrapper is not implemented in windows platform, so ignore it default WARNING:root:Timeout wrapper is not implemented in windows platform, so ignore it default WARNING:root:Timeout wrapper is not implemented in windows platform, so ignore it default WARNING:root:Timeout wrapper is not implemented in windows platform, so ignore it default WARNING:root:Timeout wrapper is not implemented in windows platform, so ignore it default WARNING:root:Timeout wrapper is not implemented in windows platform, so ignore it default F:\Documents\ACE\dizoo\gfootball\envs\gfootball_academy_env_ace.py:147: FutureWarning: In the futurenp.boolwill be defined as the corresponding NumPy scalar. self.action_mask = np.ones((self.left_player_num, self.action_len), dtype=np.bool) # all actions are valid F:\Documents\ACE\dizoo\gfootball\envs\gfootball_academy_env_ace.py:147: FutureWarning: In the futurenp.boolwill be defined as the corresponding NumPy scalar. self.action_mask = np.ones((self.left_player_num, self.action_len), dtype=np.bool) # all actions are valid F:\Documents\ACE\dizoo\gfootball\envs\gfootball_academy_env_ace.py:147: FutureWarning: In the futurenp.boolwill be defined as the corresponding NumPy scalar. self.action_mask = np.ones((self.left_player_num, self.action_len), dtype=np.bool) # all actions are valid F:\Documents\ACE\dizoo\gfootball\envs\gfootball_academy_env_ace.py:147: FutureWarning: In the futurenp.boolwill be defined as the corresponding NumPy scalar. self.action_mask = np.ones((self.left_player_num, self.action_len), dtype=np.bool) # all actions are valid F:\Documents\ACE\dizoo\gfootball\envs\gfootball_academy_env_ace.py:147: FutureWarning: In the futurenp.boolwill be defined as the corresponding NumPy scalar. self.action_mask = np.ones((self.left_player_num, self.action_len), dtype=np.bool) # all actions are valid F:\Documents\ACE\dizoo\gfootball\envs\gfootball_academy_env_ace.py:147: FutureWarning: In the futurenp.boolwill be defined as the corresponding NumPy scalar. self.action_mask = np.ones((self.left_player_num, self.action_len), dtype=np.bool) # all actions are valid F:\Documents\ACE\dizoo\gfootball\envs\gfootball_academy_env_ace.py:147: FutureWarning: In the futurenp.boolwill be defined as the corresponding NumPy scalar. self.action_mask = np.ones((self.left_player_num, self.action_len), dtype=np.bool) # all actions are valid F:\Documents\ACE\dizoo\gfootball\envs\gfootball_academy_env_ace.py:147: FutureWarning: In the futurenp.bool` will be defined as the corresponding NumPy scalar. self.action_mask = np.ones((self.left_player_num, self.action_len), dtype=np.bool) # all actions are valid ERROR:root:Function <function AsyncSubprocessEnvManager._reset.The above exception was the direct cause of the following exception:
Traceback (most recent call last): File "F:\Documents\ACE\exp\grf\academy_counterattack_hard\config.py", line 102, in
train(args)
File "F:\Documents\ACE\exp\grf\academy_counterattack_hard\config.py", line 93, in train
serial_pipeline(config, seed=args.seed)
File "F:\Documents\ACE\ding\entry\serial_entry.py", line 61, in serial_pipeline
collector = create_serial_collector(
File "F:\Documents\ACE\ding\worker\collector\base_serial_collector.py", line 87, in create_serial_collector
return SERIAL_COLLECTOR_REGISTRY.build(cfg.type, cfg=cfg, *kwargs)
File "F:\Documents\ACE\ding\utils\registry.py", line 90, in build
raise e
File "F:\Documents\ACE\ding\utils\registry.py", line 76, in build
return build_fn(obj_args, **obj_kwargs)
File "F:\Documents\ACE\ding\worker\collector\episode_serial_collector.py", line 62, in init
self.reset(policy, env)
File "F:\Documents\ACE\ding\worker\collector\episode_serial_collector.py", line 120, in reset
self.reset_env(_env)
File "F:\Documents\ACE\ding\worker\collector\episode_serial_collector.py", line 77, in reset_env
self._env.launch()
File "F:\Documents\ACE\ding\envs\env_manager\subprocess_env_manager.py", line 302, in launch
self.reset(reset_param)
File "F:\Documents\ACE\ding\envs\env_manager\subprocess_env_manager.py", line 357, in reset
t.join()
File "F:\Documents\ACE\ding\utils\system_helper.py", line 64, in join
raise RuntimeError('Exception in thread({})'.format(id(self))) from self.exc
RuntimeError: Exception in thread(2190794540800)
进程已结束,退出代码1 `