51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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@project:
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@File : testfps
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@Author : qiqq
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@create_time : 2023/11/6 16:40
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"""
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import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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import torch
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import numpy as np
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from taihuyuan_pv.compared_experiment.unet.model.unet_model import UNet
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model = UNet(n_channels=3, n_classes=2)
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model_path = "/home/qiqq/q3dl/code/applicationproject/taihuyuan_pv/compared_experiment/unet/train_val_test/checkpoints/2023-08-08-15.15/best.pth"
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model_dict = torch.load(model_path)
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model.load_state_dict(model_dict['net'])
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print("权重加载")
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model.eval()
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model.cuda()
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device = torch.device("cuda")
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model.to(device)
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dummy_input = torch.randn(1, 3, 512, 512,dtype=torch.float).to(device)
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starter, ender = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)
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repetitions = 300
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timings=np.zeros((repetitions,1))
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#GPU-WARM-UP
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for _ in range(10):
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_ = model(dummy_input)
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# MEASURE PERFORMANCE
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with torch.no_grad():
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for rep in range(repetitions):
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starter.record()
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_ = model(dummy_input)
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ender.record()
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# WAIT FOR GPU SYNC
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torch.cuda.synchronize()
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curr_time = starter.elapsed_time(ender)
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timings[rep] = curr_time
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mean_syn = np.sum(timings) / repetitions
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std_syn = np.std(timings)
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mean_fps = 1000. / mean_syn
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print(' * Mean@1 {mean_syn:.3f}ms Std@5 {std_syn:.3f}ms FPS@1 {mean_fps:.2f}'.format(mean_syn=mean_syn, std_syn=std_syn, mean_fps=mean_fps))
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print(mean_syn)
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