96 lines
4.1 KiB
Python
96 lines
4.1 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 argparse
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import torch
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import numpy as np
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from taihuyuan_pv.compared_experiment.mySwinUnet.model.vision_transformer import SwinUnet
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from taihuyuan_pv.compared_experiment.mySwinUnet.configs.config import get_config
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def get_args():
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parser = argparse.ArgumentParser(description='Train the UNet on images and target masks')
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parser.add_argument('--epochs', '-e', metavar='E', type=int, default=200, help='Number of epochs')
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parser.add_argument('--train_batch-size', '-tb', dest='train_batch_size', metavar='TB', type=int, default=8,
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help='Train_Batch size')
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parser.add_argument('--val_batch-size', '-vb', dest='val_batch_size', metavar='VB', type=int, default=1,
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help='Val_Batch size')
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parser.add_argument('--learning-rate', '-l', metavar='LR', type=float, default=1e-3,
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help='Learning rate', dest='lr')
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parser.add_argument('--load', '-f', type=str,
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default="/home/qiqq/q3dl/code/pretrain_weight/pretrained/resnet/resnet50-0676ba61.pth",
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help='Load model from a .pth file') # 有没有预训练。。
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parser.add_argument('--ignore_index', '-i', type=int, dest='ignore_index', default=255,
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help='ignore index defult 100')
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parser.add_argument('--origin_shape', action='store_true', default=(512, 512), help='原始输入尺寸')
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parser.add_argument('--resume', '-r', type=str, default="", help='is use Resume')
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parser.add_argument('--useDice', '-ud', type=str, default=False, help='训练的时候是否使用dice')
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parser.add_argument('--valIndex', '-vI', type=str, default=["Valloss", "IouMiouP"],
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help='评价指标要使用哪些,注意IouMiouP= acc_global, acc, iu,precion,recall,f1,miou,并且建议旨在2分类的时候用dice否则会出错')
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parser.add_argument('--classes', '-c', type=int, default=2, help='Number of classes')
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parser.add_argument('--num_classes', type=int,
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default=2, help='output channel of network')
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parser.add_argument('--img_size', type=int,
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default=512, help='input patch size of network input')
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parser.add_argument('--cfg', type=str,
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default="/home/qiqq/q3dl/code/rooftoprecognition/pv_recognition/compared_experiment/mySwinUnet/configs/swin_tiny_patch4_window7_224_lite.yaml",
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metavar="FILE", help='path to config file', )
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parser.add_argument(
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"--opts",
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help="Modify config options by adding 'KEY VALUE' pairs. ",
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default=None,
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nargs='+',
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)
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return parser.parse_args()
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args = get_args()
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config = get_config(args)
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model = SwinUnet(config, img_size=args.img_size, num_classes=args.num_classes)
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model_path = "/home/qiqq/q3dl/code/applicationproject/taihuyuan_pv/compared_experiment/mySwinUnet/train_val_test/checkpoints/2023-08-09-00.08/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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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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# * Mean@1 13.584ms Std@5 1.811ms FPS@1 73.61
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