ci: Fix L0_model_control_stress (#8627)

Author: yinggeh

qa/python_models/torchvision/resnet50/model.py

@@ -1,4 +1,4 @@
-# Copyright 2023-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Copyright 2023-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -25,6 +25,7 @@
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 import torch
+import torchvision
 import triton_python_backend_utils as pb_utils
 from torch.utils.dlpack import to_dlpack
 
@@ -35,14 +36,9 @@ def initialize(self, args):
         This function initializes pre-trained ResNet50 model.
         """
         self.device = "cuda" if args["model_instance_kind"] == "GPU" else "cpu"
-        # Avoid the "HTTP Error 403: rate limit exceeded" error
-        torch.hub._validate_not_a_forked_repo = lambda a, b, c: True
-        # Our tests currently depend on torchvision=0.14,
-        # to make sure `torch.hub` loads Resnet50 implementation
-        # compatible with torchvision=0.14, we need to provide tag
         self.model = (
-            torch.hub.load(
-                "pytorch/vision:v0.14.1", "resnet50", weights="IMAGENET1K_V2"
+            torchvision.models.resnet50(
+                weights=torchvision.models.ResNet50_Weights.DEFAULT
             )
             .to(self.device)
             .eval()