I want to get my model inference time by this code:

```
with torch.no_grad() :
input = np.random.rand(1, 3, 368, 368).astype(np.float32)
input = torch.from_numpy(input)
start = time.time()
for i in range(100):
t1 = time.clock()
_, _ = net(input)
t2 = time.clock()
print('every_time: %04d: '%i, t2 - t1)
end = time.time()
print('total time: ', end - start)
```

the print result is:

```
every_time: 0000: 0.37265799999999993
every_time: 0001: 0.32706800000000014
.
.
.
every_time: 0098: 0.32011200000000173
every_time: 0099: 0.3260919999999956
total time: 8.159255981445312
```

for `every_time`

, it about 0.3~0.4, so the total time should be (0.3~0.4)*100=(30~40), but according to `total time`

, itâ€™s about 8.16. Actually, in my opinion, 8.16 is the correct time. So, why `every_time`

plus 100 not equals `total time`

?