Fix mobilenet input download in ci (pytorch#19001)

Downloading the image is flakey and Im not sure its actually used for
anything so removing.

Author: JacobSzwejbka

examples/models/mobilenet_v2/model.py

@@ -15,8 +15,7 @@
 
 
 class MV2Model(EagerModelBase):
-    def __init__(self, use_real_input=True):
-        self.use_real_input = use_real_input
+    def __init__(self):
         pass
 
     def get_eager_model(self) -> torch.nn.Module:
@@ -26,38 +25,7 @@ def get_eager_model(self) -> torch.nn.Module:
         return mv2
 
     def get_example_inputs(self):
-        tensor_size = (1, 3, 224, 224)
-        input_batch = (torch.randn(tensor_size),)
-        if self.use_real_input:
-            logging.info("Loaded real input image dog.jpg")
-            import urllib
-
-            url, filename = (
-                "https://github.com/pytorch/hub/raw/master/images/dog.jpg",
-                "dog.jpg",
-            )
-            try:
-                urllib.URLopener().retrieve(url, filename)
-            except:
-                urllib.request.urlretrieve(url, filename)
-            from PIL import Image
-            from torchvision import transforms
-
-            input_image = Image.open(filename)
-            preprocess = transforms.Compose(
-                [
-                    transforms.Resize(256),
-                    transforms.CenterCrop(224),
-                    transforms.ToTensor(),
-                    transforms.Normalize(
-                        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-                    ),
-                ]
-            )
-            input_tensor = preprocess(input_image)
-            input_batch = input_tensor.unsqueeze(0)
-            input_batch = (input_batch,)
-        return input_batch
+        return (torch.randn(1, 3, 224, 224),)
 
 
 class MV2UntrainedModel(EagerModelBase):

examples/samsung/scripts/mobilenet_v2.py

@@ -128,7 +128,7 @@ def get_data_loader():
 
     # build pte
     pte_filename = "mobilenetV2_enn"
-    instance = MV2Model(False)
+    instance = MV2Model()
     model = MV2Model().get_eager_model().eval()
     assert args.calibration_number
     if args.dataset: