Added fixed random seed to not depend of randomness of initialized weights

tests/unit_tests/repvgg_unit_test.py

@@ -47,20 +47,25 @@ def test_deployment_architecture(self):
         """
         image_size = 224
         in_channels = 3
+
         for arch_name in ARCHITECTURES:
             # skip custom constructors to keep all_arch_params as general as a possible
             if "repvgg" not in arch_name or "custom" in arch_name:
                 continue
 
             with self.subTest(arch_name=arch_name):
+                # Set the seed to 0 to ensure that the model is initialized with the same weights
+                torch.manual_seed(0)
                 model = ARCHITECTURES[arch_name](arch_params=self.all_arch_params)
                 self.assertTrue(hasattr(model.stem, "branch_3x3"))  # check single layer for training mode
                 self.assertTrue(model.build_residual_branches)
 
                 training_mode_sd = model.state_dict()
                 for module in training_mode_sd:
                     self.assertFalse("reparam" in module)  # deployment block included in training mode
-                test_input = torch.ones((1, in_channels, image_size, image_size))
+
+                # Initializing input with 0.1 instead of 1.0 to move mean of input closer to 0
+                test_input = torch.ones((1, in_channels, image_size, image_size)) * 0.1
                 model.eval()
                 training_mode_output = model(test_input)
 
@@ -83,6 +88,8 @@ def test_backbone_mode(self):
         """
         image_size = 224
         in_channels = 3
+        # Set the seed to 0 to ensure that the model is initialized with the same weights
+        torch.manual_seed(0)
         test_input = torch.rand((1, in_channels, image_size, image_size))
         backbone_model = RepVggA1(self.backbone_arch_params)
         model = BackboneBasedModel(backbone_model, backbone_output_channel=1280, num_classes=self.backbone_arch_params.num_classes)