from efficientnet_pytorch import EfficientNet
class modelController:
    def __init__(self):
        self.model = self.get_model()
        self.data_transforms = transforms.Compose([
            transforms.Resize((224,112)),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
            ])
    def get_model(self):
        model = EfficientNet.from_pretrained('efficientnet-b0', num_classes = 2)
        model.cuda()
        model.eval()
        model.half()
        return model
   
    def get_model_inference(self, img):
        with torch.no_grad():       
            result = self.model(img)
            result = self.get_inference(result)
            return result

import cv2
import numpy as np
from classifier import modelController
import torch
import time
import copy
mc = modelController()

tensor = torch.rand((32,3,224,112))
tensor = tensor.cuda()
tensor = tensor.half()
print(tensor.size())
# print(final_img.size())
total_time = 0
for i in range(50):
  t1 = time.time()
  get_person_cat(tensor)
  t2 = time.time() - t1
  print("time taken: ", t2)
  total_time += t2
print("avg totatl time: ", total_time/50)

I have an efficientnet-b0 model. I am getting 0.0173 ms average inference time with model.half() and tensor.half() but when I comment out model.half() and tensor.half(), I get 0.0172 ms average inference time. Why is the fp16 model not taking less time to infer?