update test case for reset_all_parameters

tests/recon/torch/modules/test_generator.py

@@ -6,16 +6,56 @@
 
 import torch
 import torch.nn as nn
+from torchvision.models import get_model
 
 from bdpy.recon.torch.modules import generator as generator_module
 
 
 class TestResetAllParameters(unittest.TestCase):
     """Tests for bdpy.recon.torch.modules.generator.reset_all_parameters."""
+    def setUp(self):
+        self.model_ids = [
+            "alexnet",
+            "efficientnet_b0",
+            "fasterrcnn_resnet50_fpn",
+            "inception_v3",
+            "resnet18",
+            "vgg11",
+            "vit_b_16",
+        ]
+        # NOTE: The following modules are excluded from validation because they
+        #       initialize their parameters as constants every time.
+        self.excluded_modules = [
+            nn.modules.batchnorm._BatchNorm,
+            nn.LayerNorm,
+        ]
+
+    def _validate_module(self, module: nn.Module, module_copy: nn.Module, parent_name: str = ""):
+        if isinstance(module, tuple(self.excluded_modules)):
+            return
+        for (name_p1, p1), (_, p2) in zip(module.named_parameters(recurse=False), module_copy.named_parameters(recurse=False)):
+            # NOTE: skip parameters that are prbably not randomly initialized
+            if "weight" not in name_p1:
+                continue
+            self.assertFalse(
+                torch.equal(p1, p2),
+                msg=f"Parameter {parent_name}.{name_p1} does not change after reset_all_parameters."
+            )
+        for (name_m1, m1), (_, m2) in zip(module.named_children(), module_copy.named_children()):
+            self._validate_module(m1, m2, f"{parent_name}.{name_m1}")
 
     def test_reset_all_parameters(self):
         """Test reset_all_parameters."""
-        pass
+        for model_id in self.model_ids:
+            model = get_model(model_id)
+            model_copy = copy.deepcopy(model)
+            for (name_p1, p1), (_, p2) in zip(model.named_parameters(), model_copy.named_parameters()):
+                self.assertTrue(
+                    torch.equal(p1, p2),
+                    msg=f"Parameter {name_p1} of {model_id} has been changed by deepcopy."
+                )
+            model.apply(generator_module.reset_all_parameters)
+            self._validate_module(model, model_copy, model_id)
 
 
 class TestBaseGenerator(unittest.TestCase):