[PT FE] Fix issue with http error when using torch.hub (openvinotoolk…

…it#19901)

* [PT FE] Fix issue with http error when using torch.hub

* Mark failing models as xfail

* Remove incorrect model names

tests/model_hub_tests/models_hub_common/test_convert_model.py

@@ -99,7 +99,7 @@ def _run(self, model_name, model_link, ie_device):
         fw_outputs = self.infer_fw_model(fw_model, inputs)
         print("Infer ov::Model")
         ov_outputs = self.infer_ov_model(ov_model, inputs, ie_device)
-        print("Compare TensorFlow and OpenVINO results")
+        print("Compare framework and OpenVINO results")
         self.compare_results(fw_outputs, ov_outputs)
 
     def run(self, model_name, model_link, ie_device):

tests/model_hub_tests/models_hub_common/utils.py

@@ -22,7 +22,7 @@ def get_models_list(file_name: str):
                 model_name, model_link = model_info.split(',')
             elif len(model_info.split(',')) == 4:
                 model_name, model_link, mark, reason = model_info.split(',')
-                assert mark == "skip", "Incorrect failure mark for model info {}".format(model_info)
+                assert mark in ["skip", "xfail"], "Incorrect failure mark for model info {}".format(model_info)
             models.append((model_name, model_link, mark, reason))
 
     return models

tests/model_hub_tests/torch_tests/test_torchvision_models.py

@@ -1,16 +1,18 @@
 # Copyright (C) 2018-2023 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
+import os
 import pytest
 import torch
 import tempfile
 import torchvision.transforms.functional as F
-from models_hub_common.test_convert_model import TestConvertModel
 from openvino import convert_model
+from models_hub_common.test_convert_model import TestConvertModel
+from models_hub_common.utils import get_models_list
 
 
 def get_all_models() -> list:
-    m_list = torch.hub.list("pytorch/vision")
+    m_list = torch.hub.list("pytorch/vision", skip_validation=True)
     m_list.remove("get_model_weights")
     m_list.remove("get_weight")
     return m_list
@@ -36,7 +38,8 @@ def get_video():
 
 
 def prepare_frames_for_raft(name, frames1, frames2):
-    w = torch.hub.load("pytorch/vision", "get_model_weights", name=name).DEFAULT
+    w = torch.hub.load("pytorch/vision", "get_model_weights",
+                       name=name, skip_validation=True).DEFAULT
     img1_batch = torch.stack(frames1)
     img2_batch = torch.stack(frames2)
     img1_batch = F.resize(img1_batch, size=[520, 960], antialias=False)
@@ -50,13 +53,14 @@ def prepare_frames_for_raft(name, frames1, frames2):
 
 
 class TestTorchHubConvertModel(TestConvertModel):
-    def setup_method(self):
+    def setup_class(self):
         self.cache_dir = tempfile.TemporaryDirectory()
         # set temp dir for torch cache
         torch.hub.set_dir(str(self.cache_dir.name))
 
     def load_model(self, model_name, model_link):
-        m = torch.hub.load("pytorch/vision", model_name, weights='DEFAULT')
+        m = torch.hub.load("pytorch/vision", model_name,
+                           weights='DEFAULT', skip_validation=True)
         m.eval()
         if model_name == "s3d" or any([m in model_name for m in ["swin3d", "r3d_18", "mc3_18", "r2plus1d_18"]]):
             self.example = (torch.randn([1, 3, 224, 224, 224]),)
@@ -109,7 +113,8 @@ def teardown_method(self):
     def test_convert_model_precommit(self, model_name, ie_device):
         self.run(model_name, None, ie_device)
 
-    @pytest.mark.parametrize("model_name", get_all_models())
+    @pytest.mark.parametrize("name",
+                             [pytest.param(n, marks=pytest.mark.xfail) if m == "xfail" else n for n, _, m, r in get_models_list(os.path.join(os.path.dirname(__file__), "torchvision_models"))])
     @pytest.mark.nightly
-    def test_convert_model_all_models(self, model_name, ie_device):
-        self.run(model_name, None, ie_device)
+    def test_convert_model_all_models(self, name, ie_device):
+        self.run(name, None, ie_device)

tests/model_hub_tests/torch_tests/torchvision_models

@@ -0,0 +1,97 @@
+alexnet,none
+convnext_base,none
+convnext_large,none
+convnext_small,none
+convnext_tiny,none
+deeplabv3_mobilenet_v3_large,none
+deeplabv3_resnet101,none
+deeplabv3_resnet50,none
+densenet121,none
+densenet161,none
+densenet169,none
+densenet201,none
+efficientnet_b0,none
+efficientnet_b1,none
+efficientnet_b2,none
+efficientnet_b3,none
+efficientnet_b4,none
+efficientnet_b5,none
+efficientnet_b6,none
+efficientnet_b7,none
+efficientnet_v2_l,none
+efficientnet_v2_m,none
+efficientnet_v2_s,none
+fcn_resnet101,none
+fcn_resnet50,none
+googlenet,none
+inception_v3,none
+lraspp_mobilenet_v3_large,none
+maxvit_t,none
+mc3_18,none
+mnasnet0_5,none
+mnasnet0_75,none
+mnasnet1_0,none
+mnasnet1_3,none
+mobilenet_v2,none
+mobilenet_v3_large,none
+mobilenet_v3_small,none
+mvit_v1_b,none
+mvit_v2_s,none
+r2plus1d_18,none
+r3d_18,none
+raft_large,none
+raft_small,none
+regnet_x_16gf,none
+regnet_x_1_6gf,none
+regnet_x_32gf,none
+regnet_x_3_2gf,none
+regnet_x_400mf,none
+regnet_x_800mf,none
+regnet_x_8gf,none
+regnet_y_128gf,none
+regnet_y_16gf,none
+regnet_y_1_6gf,none
+regnet_y_32gf,none
+regnet_y_3_2gf,none
+regnet_y_400mf,none
+regnet_y_800mf,none
+regnet_y_8gf,none
+resnet101,none
+resnet152,none
+resnet18,none
+resnet34,none
+resnet50,none
+resnext101_32x8d,none
+resnext101_64x4d,none
+resnext50_32x4d,none
+s3d,none
+shufflenet_v2_x0_5,none
+shufflenet_v2_x1_0,none
+shufflenet_v2_x1_5,none
+shufflenet_v2_x2_0,none
+squeezenet1_0,none
+squeezenet1_1,none
+swin3d_b,none
+swin3d_s,none
+swin3d_t,none
+swin_b,none
+swin_s,none
+swin_t,none
+swin_v2_b,none
+swin_v2_s,none
+swin_v2_t,none
+vgg11,none
+vgg11_bn,none
+vgg13,none
+vgg13_bn,none
+vgg16,none
+vgg16_bn,none
+vgg19,none
+vgg19_bn,none
+vit_b_16,none,xfail,Tracing fails
+vit_b_32,none,xfail,Tracing fails
+vit_h_14,none,xfail,Tracing fails
+vit_l_16,none,xfail,Tracing fails
+vit_l_32,none,xfail,Tracing fails
+wide_resnet101_2,none
+wide_resnet50_2,none