Chengfang Lu
7c43b44c57
Huaxu Sentinel Active Safety Platform with embedded algorithm code, Docker Compose setup, and vendored dataset scaffolds for clone-and-run. Co-authored-by: Cursor <cursoragent@cursor.com>
32 lines
802 B
Python
Executable File
32 lines
802 B
Python
Executable File
import torch
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import time
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import numpy as np
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from model.model import parsingNet
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# torch.backends.cudnn.deterministic = False
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torch.backends.cudnn.benchmark = True
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net = parsingNet(pretrained = False, backbone='18',cls_dim = (100+1,56,4),use_aux=False).cuda()
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# net = parsingNet(pretrained = False, backbone='18',cls_dim = (200+1,18,4),use_aux=False).cuda()
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net.eval()
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x = torch.zeros((1,3,288,800)).cuda() + 1
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for i in range(10):
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y = net(x)
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t_all = []
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for i in range(100):
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t1 = time.time()
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y = net(x)
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t2 = time.time()
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t_all.append(t2 - t1)
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print('average time:', np.mean(t_all) / 1)
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print('average fps:',1 / np.mean(t_all))
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print('fastest time:', min(t_all) / 1)
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print('fastest fps:',1 / min(t_all))
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print('slowest time:', max(t_all) / 1)
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print('slowest fps:',1 / max(t_all)) |