Trouble with Inference in Tensorflow Lite model

I've trained a Tensorflow Lite (TFLite) model saved as a *.tflite file.

I'm writing code that lets me pick a tflite file, and a folder containing images, and then runs inference on this images using that model.

Here is what I have written:

def testModel(self, testData):

        #Test any model on any dataset
        model = "**path to model file**"

        #Loading TFLite model and allocating tensors.
        interpreter = tf.lite.Interpreter(model_path=model)
        interpreter.allocate_tensors()

        # Get input and output tensors.
        input_details = interpreter.get_input_details()
        output_details = interpreter.get_output_details()

        rawImg = "**path to test images folder**"

        imgNameList = glob.glob(os.path.join(os.getcwd(), rawImg) + os.sep + '*') # gets list of image names in dir

        #creates dataset and dataloader from images

        testDataset = SalObjDataset(img_name_list = imgNameList,lbl_name_list = [], transform=transforms.Compose([RescaleT(224),ToTensorLab(flag=0)]))
        testDataloader = DataLoader(testDataset,batch_size=1,shuffle=False)
        
        #loops through dataloader (goes through each image file)
        for _, data in enumerate(testDataloader):
            inputImg = data['image']

            if torch.cuda.is_available():
                inputImg = Variable(inputImg.cuda())
            else:
                inputImg = Variable(inputImg)
            
            #rearranges dimensions in image file to match the expected input dimensions
            #also changes the type to uint8 as expected
            inputImg = tf.transpose(inputImg.cpu(), perm = [0,2,3,1])
            inputImg = tf.cast(inputImg, tf.uint8)

            interpreter.set_tensor(input_details[0]['index'], inputImg)

            interpreter.invoke()

            output_data = interpreter.get_tensor_details()
            print(output_data)

if __name__ == '__main__':

    #initialise object with the modelID of the model you want to test
    #pass the testing data folder name to testModel()
    #this is the folder where the model is
    modelID = "model_1"
    tester = ModelTrainer(modelID)
    #this is the folder where the testing images are
    tester.testModel("model_1/model_1/plant")

The way our it's setup, the images for each label are stored in their own subdirectory, so all images of a 'plant' would be in folder/plant/image-1.jpg.

I'm not sure if I'm using 'interpreter.set_tensor' correctly, I've gone through the documentation quite intensively and I'm still a bit confused.

I'm also not sure how to make sense of the output, I would like to somehow get a loss/accuracy value, how do I go about doing this?

My output is currently just [[255]] for each image.

Thanks!