removed strict parameter for checkpointing

src/ptbench/scripts/predict.py

@@ -131,7 +131,7 @@ def predict(
         weight_fullpath = os.path.abspath(weight)
 
     checkpointer = Checkpointer(model)
-    checkpointer.load(weight_fullpath, strict=False)
+    checkpointer.load(weight_fullpath)
 
     # Logistic regressor weights
     if model.name == "logistic_regression":

src/ptbench/utils/checkpointer.py

@@ -51,7 +51,7 @@ class Checkpointer:
         with open(self._last_checkpoint_filename, "w") as f:
             f.write(name)
 
-    def load(self, f=None, strict=True):
+    def load(self, f=None):
         """Loads model, optimizer and scheduler from file.
 
         Parameters
@@ -62,9 +62,6 @@ class Checkpointer:
             contains the checkpoint data to load into the model, and optionally
             into the optimizer and the scheduler.  If not specified, loads data
             from current path.
-
-        partial : :py:class:`bool`, Optional
-            If True, loading is not strict and only the model is loaded
         """
         if f is None:
             f = self.last_checkpoint()
@@ -79,13 +76,12 @@ class Checkpointer:
         checkpoint = torch.load(f, map_location=torch.device("cpu"))
 
         # converts model entry to model parameters
-        self.model.load_state_dict(checkpoint.pop("model"), strict=strict)
+        self.model.load_state_dict(checkpoint.pop("model"))
 
-        if strict:
-            if self.optimizer is not None:
-                self.optimizer.load_state_dict(checkpoint.pop("optimizer"))
-            if self.scheduler is not None:
-                self.scheduler.load_state_dict(checkpoint.pop("scheduler"))
+        if self.optimizer is not None:
+            self.optimizer.load_state_dict(checkpoint.pop("optimizer"))
+        if self.scheduler is not None:
+            self.scheduler.load_state_dict(checkpoint.pop("scheduler"))
 
         return checkpoint