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import cv2 | |
import sys | |
sys.path.append('.') | |
import time | |
import torch | |
import torch.nn as nn | |
from model.RIFE import Model | |
model = Model() | |
model.eval() | |
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
torch.set_grad_enabled(False) | |
if torch.cuda.is_available(): | |
torch.backends.cudnn.enabled = True | |
torch.backends.cudnn.benchmark = True | |
I0 = torch.rand(1, 3, 480, 640).to(device) | |
I1 = torch.rand(1, 3, 480, 640).to(device) | |
with torch.no_grad(): | |
for i in range(100): | |
pred = model.inference(I0, I1) | |
if torch.cuda.is_available(): | |
torch.cuda.synchronize() | |
time_stamp = time.time() | |
for i in range(100): | |
pred = model.inference(I0, I1) | |
if torch.cuda.is_available(): | |
torch.cuda.synchronize() | |
print((time.time() - time_stamp) / 100) | |