load from pretrained

enhancer/models/model.py

@@ -1,12 +1,21 @@
-from typing import Optional, Union, List
+from importlib import import_module
+from huggingface_hub import cached_download, hf_hub_url
+import os
+from typing import Optional, Union, List, Path, Text
 from torch.optim import Adam
-import pytorch_lightning as pl
 import torch
+import pytorch_lightning as pl
+from pytorch_lightning.utilities.cloud_io import load as pl_load
+from urllib.parse import urlparse
+
 
 from enhancer import __version__
 from enhancer.data.dataset import Dataset
 from enhancer.utils.loss import Avergeloss
 
+CACHE_DIR = ""
+HF_TORCH_WEIGHTS = ""
+DEFAULT_DEVICE = "cpu"
 
 class Model(pl.LightningModule):
 
@@ -91,5 +100,66 @@ class Model(pl.LightningModule):
         }
 
     @classmethod
-    def from_pretrained(cls,):
-        pass
+    def from_pretrained(
+        cls,
+        checkpoint: Union[Path, Text],
+        map_location = None,
+        hparams_file: Union[Path, Text] = None,
+        strict: bool = True,
+        use_auth_token: Union[Text, None] = None,
+        cached_dir: Union[Path, Text]=CACHE_DIR,
+        **kwargs
+    ):
+
+        checkpoint = str(checkpoint)
+        if hparams_file is not None:
+            hparams_file = str(hparams_file)
+
+        if os.path.isfile(checkpoint):
+            model_path_pl = checkpoint
+        elif urlparse(checkpoint).scheme in ("http","https"):
+            model_path_pl = checkpoint
+        else:
+            
+            if "@" in checkpoint:
+                model_id = checkpoint.split("@")[0]
+                revision_id = checkpoint.split("@")[1]
+            else:
+                model_id = checkpoint
+                revision_id = None
+            
+            url = hf_hub_url(
+                model_id,filename=HF_TORCH_WEIGHTS,revision=revision_id
+            )
+            model_path_pl = cached_download(
+                url=url,library_name="enhancer",library_version=__version__,
+                cache_dir=cached_dir,use_auth_token=use_auth_token
+            )
+
+        if map_location is None:
+            map_location = torch.device(DEFAULT_DEVICE)
+
+        loaded_checkpoint = pl_load(model_path_pl,map_location)
+        module_name = loaded_checkpoint["architecture"]["module"]
+        class_name =  loaded_checkpoint["architecture"]["class"]
+        module = import_module(module_name)
+        Klass = getattr(module, class_name)
+
+        try:
+            model = Klass.load_from_checkpoint(
+                checkpoint_path = model_path_pl,
+                map_location = map_location,
+                hparams_file = hparams_file,
+                strict = strict,
+                **kwargs
+            )
+        except Exception as e:
+            print(e)
+
+
+        
+
+            
+
+
+