fix_uniform

ppcls/utils/config.py

@@ -236,6 +236,8 @@ def dump_infer_config(inference_config, path):
     setup_orderdict()
     infer_cfg = OrderedDict()
     config = copy.deepcopy(inference_config)
+    if config["Global"].get("pdx_model_name", None):
+        infer_cfg["Global"] = {"model_name": config["Global"]["pdx_model_name"]}
     if config.get("Infer"):
         transforms = config["Infer"]["transforms"]
     elif config["DataLoader"]["Eval"].get("Query"):
@@ -251,9 +253,15 @@ def dump_infer_config(inference_config, path):
         transform = next((item for item in transforms
                           if 'ResizeImage' in item), None)
         if transform:
+            if isinstance(transform["ResizeImage"]["size"], list):
                 dynamic_shapes = transform["ResizeImage"]["size"][0]
+            elif isinstance(transform["ResizeImage"]["size"], int):
+                dynamic_shapes = transform["ResizeImage"]["size"]
             else:
-            dynamic_shapes = 224
+                raise ValueError(
+                    "ResizeImage size must be either a list or an int.")
+        else:
+            raise ValueError("No valid transform found.")
     # Configuration required config for high-performance inference.
     if config["Global"].get("hpi_config_path", None):
         hpi_config = convert_to_dict(
@@ -272,9 +280,6 @@ def dump_infer_config(inference_config, path):
             hpi_config["Hpi"]["backend_config"]["tensorrt"][
                 "max_batch_size"] = 1
         infer_cfg["Hpi"] = hpi_config["Hpi"]
-    if config["Global"].get("pdx_model_name", None):
-        infer_cfg["Global"] = {}
-        infer_cfg["Global"]["model_name"] = config["Global"]["pdx_model_name"]
     for transform in transforms:
         if "NormalizeImage" in transform:
             transform["NormalizeImage"]["channel_num"] = 3

ppcls/utils/save_load.py

@@ -215,6 +215,8 @@ def save_model_info(model_info, save_path, prefix):
     """
     save model info to the target path
     """
+    if paddle.distributed.get_rank() != 0:
+        return
     save_path = os.path.join(save_path, prefix)
     if not os.path.exists(save_path):
         os.makedirs(save_path)