polish(nyz): add torch1.1.0 compatibility for torch.utils.data

ding/torch_utils/checkpoint_helper.py

@@ -4,6 +4,7 @@ import sys
 import traceback
 from typing import Callable
 import torch
+import torch.utils.data  # torch1.1.0 compatibility
 from ding.utils import read_file, save_file
 
 logger = logging.getLogger('default_logger')

ding/utils/data/collate_fn.py

@@ -15,6 +15,10 @@ default_collate_err_msg_format = (
 )
 
 
+def torch_gt_131():
+    return int("".join(list(filter(str.isdigit, torch.__version__)))) >= 131
+
+
 def default_collate(batch: Sequence,
                     cat_1dim: bool = True,
                     ignore_prefix: list = ['collate_ignore']) -> Union[torch.Tensor, Mapping, Sequence]:
@@ -50,7 +54,7 @@ def default_collate(batch: Sequence,
     elem_type = type(elem)
     if isinstance(elem, torch.Tensor):
         out = None
-        if torch.utils.data.get_worker_info() is not None:
+        if torch_gt_131() and torch.utils.data.get_worker_info() is not None:
             # If we're in a background process, directly concatenate into a
             # shared memory tensor to avoid an extra copy
             numel = sum([x.numel() for x in batch])

dizoo/classic_control/cartpole/entry/cartpole_c51_main.py

@@ -74,13 +74,6 @@ def main(cfg, seed=0):
             if train_data is None:
                 break
             learner.train(train_data, collector.envstep)
-    # evaluate
-    evaluator_env = BaseEnvManager(env_fn=[wrapped_cartpole_env for _ in range(evaluator_env_num)], cfg=cfg.env.manager)
-    evaluator_env.enable_save_replay(cfg.env.replay_path)  # switch save replay interface
-    evaluator = InteractionSerialEvaluator(
-        cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name
-    )
-    evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep)
 
 
 if __name__ == "__main__":