add test from loading from pre-trained (#238)

* add test from loading from pre-trained

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* update

* remove config in model kwargs - optional

* use more recent models

* remove huggingface mixing

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* fix

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

pvnet/models/base_model.py

@@ -13,11 +13,10 @@
 import torch.nn.functional as F
 import wandb
 import yaml
-from huggingface_hub import ModelCard, ModelCardData, PyTorchModelHubMixin
+from huggingface_hub import ModelCard, ModelCardData
 from huggingface_hub.constants import CONFIG_NAME, PYTORCH_WEIGHTS_NAME
 from huggingface_hub.file_download import hf_hub_download
 from huggingface_hub.hf_api import HfApi
-from huggingface_hub.utils._deprecation import _deprecate_positional_args
 from ocf_datapipes.batch import BatchKey
 from ocf_ml_metrics.evaluation.evaluation import evaluation
 
@@ -141,33 +140,34 @@ def minimize_data_config(input_path, output_path, model):
         yaml.dump(config, outfile, default_flow_style=False)
 
 
-class PVNetModelHubMixin(PyTorchModelHubMixin):
+class PVNetModelHubMixin:
     """
     Implementation of [`PyTorchModelHubMixin`] to provide model Hub upload/download capabilities.
     """
 
     @classmethod
-    @_deprecate_positional_args(version="0.16")
-    def _from_pretrained(
+    def from_pretrained(
         cls,
         *,
         model_id: str,
         revision: str,
-        cache_dir: str,
-        force_download: bool,
-        proxies: Optional[Dict],
-        resume_download: bool,
-        local_files_only: bool,
-        token: Union[str, bool, None],
+        cache_dir: Optional[Union[str, Path]] = None,
+        force_download: bool = False,
+        proxies: Optional[Dict] = None,
+        resume_download: Optional[bool] = None,
+        local_files_only: bool = False,
+        token: Union[str, bool, None] = None,
         map_location: str = "cpu",
         strict: bool = False,
-        **model_kwargs,
     ):
         """Load Pytorch pretrained weights and return the loaded model."""
+
         if os.path.isdir(model_id):
             print("Loading weights from local directory")
             model_file = os.path.join(model_id, PYTORCH_WEIGHTS_NAME)
+            config_file = os.path.join(model_id, CONFIG_NAME)
         else:
+            # load model file
             model_file = hf_hub_download(
                 repo_id=model_id,
                 filename=PYTORCH_WEIGHTS_NAME,
@@ -180,11 +180,23 @@ def _from_pretrained(
                 local_files_only=local_files_only,
             )
 
-        if "config" not in model_kwargs:
-            raise ValueError("Config must be supplied to instantiate model")
+            # load config file
+            config_file = hf_hub_download(
+                repo_id=model_id,
+                filename=CONFIG_NAME,
+                revision=revision,
+                cache_dir=cache_dir,
+                force_download=force_download,
+                proxies=proxies,
+                resume_download=resume_download,
+                token=token,
+                local_files_only=local_files_only,
+            )
+
+        with open(config_file, "r", encoding="utf-8") as f:
+            config = json.load(f)
 
-        model_kwargs.update(model_kwargs.pop("config"))
-        model = hydra.utils.instantiate(model_kwargs)
+        model = hydra.utils.instantiate(config)
 
         state_dict = torch.load(model_file, map_location=torch.device(map_location))
         model.load_state_dict(state_dict, strict=strict)  # type: ignore

pvnet/models/utils.py

@@ -149,7 +149,8 @@ def __init__(self, decay_rate: Optional[int] = None, forecast_length: int = 6):
             self.decay_rate = math.log(2)
 
         # make weights from decay rate
-        weights = torch.from_numpy(np.exp(-self.decay_rate * np.arange(self.forecast_length)))
+        weights = np.exp(-self.decay_rate * np.arange(self.forecast_length))
+        weights = torch.tensor(weights)
 
         # normalized the weights, so there mean is 1.
         # To calculate the loss, we times the weights by the differences between truth

tests/models/multimodal/test_from_pretrained.py

@@ -0,0 +1,11 @@
+from pvnet.models.base_model import BaseModel
+
+
+def test_from_pretrained():
+    model_name = "openclimatefix/pvnet_uk_region"
+    model_version = "92266cd9040c590a9e90ee33eafd0e7b92548be8"
+
+    _ = BaseModel.from_pretrained(
+        model_id=model_name,
+        revision=model_version,
+    )