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https://github.com/hzwer/ECCV2022-RIFE.git
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30 lines
812 B
Python
30 lines
812 B
Python
import cv2
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import sys
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sys.path.append('.')
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import time
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import torch
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import torch.nn as nn
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from model.RIFE import Model
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model = Model()
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model.eval()
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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torch.set_grad_enabled(False)
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if torch.cuda.is_available():
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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.benchmark = True
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I0 = torch.rand(1, 3, 480, 640).to(device)
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I1 = torch.rand(1, 3, 480, 640).to(device)
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with torch.no_grad():
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for i in range(100):
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pred = model.inference(I0, I1)
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if torch.cuda.is_available():
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torch.cuda.synchronize()
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time_stamp = time.time()
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for i in range(100):
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pred = model.inference(I0, I1)
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if torch.cuda.is_available():
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torch.cuda.synchronize()
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print((time.time() - time_stamp) / 100)
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