fix(zjow): fix complex obs demo for ppo pipeline

.github/workflows/unit_test.yml

@@ -26,6 +26,11 @@ jobs:
           python -m pip install .
           python -m pip install ".[test,k8s]"
           python -m pip install transformers
+          if python --version | grep -q "Python 3.7"; then
+              python -m pip install wandb==0.16.4
+          else
+              echo "Python version is not 3.7, skipping wandb installation"
+          fi
           ./ding/scripts/install-k8s-tools.sh
           make unittest
       - name: Upload coverage to Codecov
@@ -55,5 +60,10 @@ jobs:
           python -m pip install .
           python -m pip install ".[test,k8s]"
           python -m pip install transformers
+          if python --version | grep -q "Python 3.7"; then
+              python -m pip install wandb==0.16.4
+          else
+              echo "Python version is not 3.7, skipping wandb installation"
+          fi
           ./ding/scripts/install-k8s-tools.sh
           make benchmark

ding/example/ppo_with_complex_obs.py

@@ -30,7 +30,12 @@
         cuda=True,
         action_space='discrete',
         model=dict(
-            obs_shape=None,
+            obs_shape=dict(
+                key_0=dict(k1=(), k2=()),
+                key_1=(5, 10),
+                key_2=(10, 10, 3),
+                key_3=(2, ),
+            ),
             action_shape=2,
             action_space='discrete',
             critic_head_hidden_size=138,

ding/framework/middleware/functional/advantage_estimator.py

@@ -8,6 +8,7 @@
 from ding.rl_utils import gae, gae_data, get_train_sample
 from ding.framework import task
 from ding.utils.data import ttorch_collate
+from ding.utils.dict_helper import convert_easy_dict_to_dict
 from ding.torch_utils import to_device
 
 if TYPE_CHECKING:
@@ -33,10 +34,8 @@ def gae_estimator(cfg: EasyDict, policy: Policy, buffer_: Optional[Buffer] = Non
     # Unify the shape of obs and action
     obs_shape = cfg['policy']['model']['obs_shape']
     obs_shape = torch.Size(torch.tensor(obs_shape)) if isinstance(obs_shape, list) \
+        else ttorch.size.Size(convert_easy_dict_to_dict(obs_shape)) if isinstance(obs_shape, dict) \
         else torch.Size(torch.tensor(obs_shape).unsqueeze(0))
-    action_shape = cfg['policy']['model']['action_shape']
-    action_shape = torch.Size(torch.tensor(action_shape)) if isinstance(action_shape, list) \
-        else torch.Size(torch.tensor(action_shape).unsqueeze(0))
 
     def _gae(ctx: "OnlineRLContext"):
         """

ding/utils/__init__.py

@@ -5,6 +5,7 @@
     LimitedSpaceContainer, deep_merge_dicts, set_pkg_seed, flatten_dict, one_time_warning, split_data_generator, \
     RunningMeanStd, make_key_as_identifier, remove_illegal_item
 from .design_helper import SingletonMetaclass
+from .dict_helper import convert_easy_dict_to_dict
 from .file_helper import read_file, save_file, remove_file
 from .import_helper import try_import_ceph, try_import_mc, try_import_link, import_module, try_import_redis, \
     try_import_rediscluster

ding/utils/dict_helper.py

@@ -0,0 +1,13 @@
+from easydict import EasyDict
+
+
+def convert_easy_dict_to_dict(easy_dict: EasyDict) -> dict:
+    """
+    Overview:
+        Convert an EasyDict object to a dict object recursively.
+    Arguments:
+        - easy_dict (:obj:`EasyDict`): The EasyDict object to be converted.
+    Returns:
+        - dict: The converted dict object.
+    """
+    return {k: convert_easy_dict_to_dict(v) if isinstance(v, EasyDict) else v for k, v in easy_dict.items()}