Can't get torch model to run on GPU even though it's a recognized device

I'm using resnet18 for for grounded image semgmentation but when I pass the input to the model I get this torch error:

RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same or input should be a MKLDNN tensor and weight is a dense tensor

I got the weights from here:

'https://download.pytorch.org/models/resnet18-5c106cde.pth'

I assume the weights are trained on nvidia hardware because why wouldn't they be, so that leaves out my input tensor as being processed on my CPU (r7 1600x), but that's strange because I treid to make sure everything is being run on my nvidia GPU (rtx 3060) which is recognized (torch.cuda.is_available() == True).

Here's the code I think is relevant

class Masking:
    def __init__(
            self, 
            device_handle:Provider='cuda:0',  #<--------------------------
            classifier:Module=None,
            face_parser:Module=None,
            num_classes=19
    ) -> None:
        self.device = device(device_handle)
        self.classifier = classifier
        self.face_parser = face_parser
        self.num_classes = num_classes
        self.provided_image = None 

        print("is cuda available:  ", torch.cuda.is_available())  # True

    def startup_model(self):
        self.classifier.to(self.device)  #<-----------------------------
        self.classifier.load_state_dict(self.face_parser)
        self.classifier.eval()

    def preprocess_image(self, image_path):
        to_tensor = transforms.Compose([ 
            transforms.ToTensor(),
            transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
        ])

        self.provided_image = Image.open(image_path)
        w, h = self.provided_image.size
        interpolated_image = self.provided_image.resize((w, h), Image.BILINEAR)
        composed_image = to_tensor(interpolated_image)
        expanded_tensor = unsqueeze(composed_image, 0)
        expanded_tensor.to(self.device) #<--------------------------------
        #expanded_tensor.to("cuda:0") # doesn't work either
        #expanded_tensor.cuda() # no dice
        out = self.classifier(expanded_tensor)[0]  # <<<<< ERROR

        return out


    masker = Masking(
        'cuda:0',
        BiSeNet(n_classes=19),
        load('path/to/my/models/79999_iter.pth', device('cuda:0')) #<---------------------
    )

....Then I run the methods I defined above etc

Unlike nn.Module.to, torch.Tensor.to doesn't work in place.
So you must assign the result back to a variable:

expanded_tensor = expanded_tensor.to(self.device)