Check for model initialization in eval (#9)

* Check for model initialization during eval

* Change from warning to assert

* change assertion to ValueError

* fix isort and rename are_weights_initialized

* cleanup set_initialized()

* fix variable name bug

* Make load_state_dict forward compatible

Co-authored-by: Paul-Edouard Sarlin <15985472+sarlinpe@users.noreply.github.com>

* Remove unused imports

---------

Co-authored-by: Paul-Edouard Sarlin <15985472+sarlinpe@users.noreply.github.com>

gluefactory/eval/io.py

@@ -89,6 +89,11 @@ def load_model(model_conf, checkpoint):
         model = load_experiment(checkpoint, conf=model_conf).eval()
     else:
         model = get_model("two_view_pipeline")(model_conf).eval()
+    if not model.is_initialized():
+        raise ValueError(
+            "The provided model has non-initialized parameters. "
+            + "Try to load a checkpoint instead."
+        )
     return model
 
 

gluefactory/models/backbones/dinov2.py

@@ -10,6 +10,7 @@ class DinoV2(BaseModel):
 
     def _init(self, conf):
         self.net = torch.hub.load("facebookresearch/dinov2", conf.weights)
+        self.set_initialized()
 
     def _forward(self, data):
         img = data["image"]

gluefactory/models/base_model.py

@@ -60,6 +60,8 @@ class BaseModel(nn.Module, metaclass=MetaModel):
     required_data_keys = []
     strict_conf = False
 
+    are_weights_initialized = False
+
     def __init__(self, conf):
         """Perform some logic and call the _init method of the child model."""
         super().__init__()
@@ -125,3 +127,31 @@ class BaseModel(nn.Module, metaclass=MetaModel):
     def loss(self, pred, data):
         """To be implemented by the child class."""
         raise NotImplementedError
+
+    def load_state_dict(self, *args, **kwargs):
+        """Load the state dict of the model, and set the model to initialized."""
+        ret = super().load_state_dict(*args, **kwargs)
+        self.set_initialized()
+        return ret
+
+    def is_initialized(self):
+        """Recursively check if the model is initialized, i.e. weights are loaded"""
+        is_initialized = True  # initialize to true and perform recursive and
+        for _, w in self.named_children():
+            if isinstance(w, BaseModel):
+                # if children is BaseModel, we perform recursive check
+                is_initialized = is_initialized and w.is_initialized()
+            else:
+                # else, we check if self is initialized or the children has no params
+                n_params = len(list(w.parameters()))
+                is_initialized = is_initialized and (
+                    n_params == 0 or self.are_weights_initialized
+                )
+        return is_initialized
+
+    def set_initialized(self, to: bool = True):
+        """Recursively set the initialization state."""
+        self.are_weights_initialized = to
+        for _, w in self.named_parameters():
+            if isinstance(w, BaseModel):
+                w.set_initialized(to)

gluefactory/models/extractors/disk_kornia.py

@@ -21,6 +21,7 @@ class DISK(BaseModel):
 
     def _init(self, conf):
         self.model = kornia.feature.DISK.from_pretrained(conf.weights)
+        self.set_initialized()
 
     def _get_dense_outputs(self, images):
         B = images.shape[0]

gluefactory/models/extractors/keynet_affnet_hardnet.py

@@ -21,6 +21,7 @@ class KeyNetAffNetHardNet(BaseModel):
             upright=conf.upright,
             scale_laf=conf.scale_laf,
         )
+        self.set_initialized()
 
     def _forward(self, data):
         image = data["image"]

gluefactory/models/extractors/sift_kornia.py

@@ -19,6 +19,7 @@ class KorniaSIFT(BaseModel):
         self.sift = kornia.feature.SIFTFeature(
             num_features=self.conf.max_num_keypoints, rootsift=self.conf.rootsift
         )
+        self.set_initialized()
 
     def _forward(self, data):
         lafs, scores, descriptors = self.sift(data["image"])

gluefactory/models/lines/deeplsd.py

@@ -34,6 +34,7 @@ class DeepLSD(BaseModel):
         ckpt = torch.load(ckpt, map_location="cpu")
         self.net = deeplsd_inference.DeepLSD(conf.model_conf).eval()
         self.net.load_state_dict(ckpt["model"])
+        self.set_initialized()
 
     def download_model(self, path):
         import subprocess

gluefactory/models/matchers/kornia_loftr.py

@@ -13,6 +13,7 @@ class LoFTRModule(BaseModel):
 
     def _init(self, conf):
         self.net = kornia.feature.LoFTR(pretrained="outdoor")
+        self.set_initialized()
 
     def _forward(self, data):
         image0 = data["view0"]["image"]

gluefactory/models/matchers/lightglue_pretrained.py

@@ -18,7 +18,7 @@ class LightGlue(BaseModel):
     def _init(self, conf):
         dconf = OmegaConf.to_container(conf)
         self.net = LightGlue_(dconf.pop("features"), **dconf).cuda()
-        # self.net.compile()
+        self.set_initialized()
 
     def _forward(self, data):
         view0 = {