Collect pretrained weight binary files in one place (#3656)

* Update cache dir

* Update pytorchcv cache root

Author: harimkang

src/otx/algo/classification/backbones/efficientnet.py

@@ -5,6 +5,7 @@
 
 import math
 import os
+from pathlib import Path
 
 import torch
 import torch.nn.functional as F
@@ -661,5 +662,6 @@ def init_weights(self, pretrained: bool | str | None = None):
             load_checkpoint_to_model(self, checkpoint)
             print(f"init weight - {pretrained}")
         elif pretrained is not None:
-            download_model(net=self, model_name=self.model_name)
+            cache_dir = Path.home() / ".cache" / "torch" / "hub" / "checkpoints"
+            download_model(net=self, model_name=self.model_name, local_model_store_dir_path=str(cache_dir))
             print(f"init weight - {pretrained_urls[self.model_name]}")

src/otx/algo/detection/backbones/pytorchcv_backbones.py

@@ -128,7 +128,7 @@ def _build_pytorchcv_model(
     **kwargs,
 ) -> nn.Module:
     """Build pytorchcv model."""
-    models_cache_root = kwargs.get("root", Path.home() / ".torch" / "models")
+    models_cache_root = kwargs.get("root", Path.home() / ".cache" / "torch" / "hub" / "checkpoints")
     is_pretrained = kwargs.get("pretrained", False)
     print(
         f"Init model {type}, pretrained={is_pretrained}, models cache {models_cache_root}",