Built my own data loader to isolate files via a wildcard pattern in glob and then loop through those to create a tensor for each image, passing that to my model which required it to be converted to a float. Extract the base name (image name) from the path (ex. img_frame1.jpg). Save result to my style folder. This method gives me total control over the files via the wildcard. I have included the other functions used in the solution. Note: I am not using a gpu to process these so I can run it on a standard python web server. Hopefully this helps someone in the future. Simple is sometimes better 
# Load image file
# def load_image(path):
#     # Images loaded as BGR
#     img = cv2.imread(path)
#     return img

# def itot(img, max_size=None):
#     # Rescale the image
#     if (max_size == None):
#         itot_t = transforms.Compose([
#             # transforms.ToPILImage(),
#             transforms.ToTensor(),
#             transforms.Lambda(lambda x: x.mul(255))
#         ])
#     else:
#         H, W, C = img.shape
#         image_size = tuple([int((float(max_size) / max([H, W])) * x) for x in [H, W]])
#         itot_t = transforms.Compose([
#             transforms.ToPILImage(),
#             transforms.Resize(image_size),
#             transforms.ToTensor(),
#             transforms.Lambda(lambda x: x.mul(255))
#         ])
# 
#     # Convert image to tensor
#     tensor = itot_t(img)
# 
#     # Add the batch_size dimension
#     tensor = tensor.unsqueeze(dim=0)
#     return tensor

folder_data = glob.glob(folder_containing_the_content_folder + "content_folder/" +  video_token + "_frame*.jpg")

# image_dataset = utils.ImageFolderWithPaths(folder_containing_the_content_folder, transform)
# image_loader = torch.utils.data.DataLoader(image_dataset, batch_size=batch_size)

# Load Transformer Network
net = transformer.TransformerNetwork()
net.load_state_dict(torch.load(style_path))
net = net.to(device)

torch.cuda.empty_cache()

with torch.no_grad():
    for image_name in folder_data:
        img = utils.load_image(image_name)
        img = img / 255.0  # standardize the data/transform
        img_tensor = utils.itot(img)

        # style image tensor
        generated_tensor = net(img_tensor.float())
        
        # convert image the model modified tensor back to an image
        generated_image = utils.ttoi(generated_tensor)
        image_name = os.path.basename(image_name)
        
        # save generated image to folder
        utils.saveimg(generated_image, save_folder + image_name)