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问如何在保存为.pth文件的AI模型上获得层执行时间？
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Stack Overflow用户

提问于 2021-04-01 19:28:28

回答 1查看 266关注 0票数 0

我试图在CPU上运行一个类似Resnet的图像分类模型，并想知道运行模型的每一层所需的时间。

我面临的问题是github链接https://github.com/facebookresearch/semi-supervised-ImageNet1K-models将模型保存为.pth文件。它非常大(100 MB)，我不知道它和py手电有什么不同，只是它是二进制的。我使用下面的脚本从这个文件加载模型。但我看不出有什么方法可以修改模型或在模型层之间插入t = time.time()变量/语句来分解每个层的时间。

问题：

将在下面的脚本中运行模型，给出在CPU上运行模型所需的端到端时间(t2-t1)的正确估计值，还是也包括pytorch编译时间？

。

如何在连续层之间插入时间语句以获得细分？

在github链接上没有推理/训练脚本，只有.pth文件。那么，一个人究竟应该如何进行推理或训练呢？如何在.pth模型的连续层之间插入附加层并保存它们？

#!/usr/bin/env python
import torch torchvision time

model=torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnext50_32x4d_swsl', force_reload=False)
in = torch.randn(1, 3, 224, 224)
t1 = time.time()
out = model.forward(in)
t2 = time.time()
```**strong text**

checkpointing

machine-learning

pytorch

torch

resnet

回答 1

Stack Overflow用户

回答已采纳

发布于 2021-04-01 19:50:05

实现这种需求的一个简单方法是在模型的每个模块上注册前向挂钩，更新用于存储时间的全局变量，并计算上一次计算和当前计算之间的时间差。

例如：

import torch
import torchvision
import time

global_time = None
exec_times = []


def store_time(self, input, output):
    global global_time, exec_times
    exec_times.append(time.time() - global_time)
    global_time = time.time()


model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnext50_32x4d_swsl', force_reload=False)
x = torch.randn(1, 3, 224, 224)

# Register a hook for each module for computing the time difference
for module in model.modules():
    module.register_forward_hook(store_time)

global_time = time.time()
out = model(x)
t2 = time.time()

for module, t in zip(model.modules(), exec_times):
    print(f"{module.__class__}: {t}")

我得到的输出是：

<class 'torchvision.models.resnet.ResNet'>: 0.004999876022338867
<class 'torch.nn.modules.conv.Conv2d'>: 0.002006053924560547
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0009946823120117188
<class 'torch.nn.modules.activation.ReLU'>: 0.007998466491699219
<class 'torch.nn.modules.pooling.MaxPool2d'>: 0.0010004043579101562
<class 'torch.nn.modules.container.Sequential'>: 0.0020003318786621094
<class 'torchvision.models.resnet.Bottleneck'>: 0.0010023117065429688
<class 'torch.nn.modules.conv.Conv2d'>: 0.017997026443481445
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0010018348693847656
<class 'torch.nn.modules.conv.Conv2d'>: 0.0009999275207519531
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.003000497817993164
<class 'torch.nn.modules.conv.Conv2d'>: 0.003999948501586914
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.001997232437133789
<class 'torch.nn.modules.activation.ReLU'>: 0.004001140594482422
<class 'torch.nn.modules.container.Sequential'>: 0.0
<class 'torch.nn.modules.conv.Conv2d'>: 0.001999378204345703
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0
<class 'torchvision.models.resnet.Bottleneck'>: 0.003001689910888672
<class 'torch.nn.modules.conv.Conv2d'>: 0.0020008087158203125
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0009992122650146484
<class 'torch.nn.modules.conv.Conv2d'>: 0.0019991397857666016
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0010001659393310547
<class 'torch.nn.modules.conv.Conv2d'>: 0.0009999275207519531
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.002998828887939453
<class 'torch.nn.modules.activation.ReLU'>: 0.0010013580322265625
<class 'torchvision.models.resnet.Bottleneck'>: 0.0029997825622558594
<class 'torch.nn.modules.conv.Conv2d'>: 0.0
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.002999544143676758
<class 'torch.nn.modules.conv.Conv2d'>: 0.0010006427764892578
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.001001119613647461
<class 'torch.nn.modules.conv.Conv2d'>: 0.0019979476928710938
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0010018348693847656
<class 'torch.nn.modules.activation.ReLU'>: 0.0010001659393310547
<class 'torch.nn.modules.container.Sequential'>: 0.00299835205078125
<class 'torchvision.models.resnet.Bottleneck'>: 0.002004384994506836
<class 'torch.nn.modules.conv.Conv2d'>: 0.0009975433349609375
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0
<class 'torch.nn.modules.conv.Conv2d'>: 0.0
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.005999088287353516
<class 'torch.nn.modules.conv.Conv2d'>: 0.0020003318786621094
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0010001659393310547
<class 'torch.nn.modules.activation.ReLU'>: 0.0020017623901367188
<class 'torch.nn.modules.container.Sequential'>: 0.0009970664978027344
<class 'torch.nn.modules.conv.Conv2d'>: 0.0
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0029997825622558594
<class 'torchvision.models.resnet.Bottleneck'>: 0.0010008811950683594
<class 'torch.nn.modules.conv.Conv2d'>: 0.00500035285949707
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0009984970092773438
<class 'torch.nn.modules.conv.Conv2d'>: 0.0
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0020020008087158203
<class 'torch.nn.modules.conv.Conv2d'>: 0.0
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0019979476928710938
<class 'torch.nn.modules.activation.ReLU'>: 0.0010018348693847656
<class 'torchvision.models.resnet.Bottleneck'>: 0.0
<class 'torch.nn.modules.conv.Conv2d'>: 0.00099945068359375
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.001001119613647461
<class 'torch.nn.modules.conv.Conv2d'>: 0.0
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.002997875213623047
<class 'torch.nn.modules.conv.Conv2d'>: 0.0010013580322265625
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.002000570297241211
<class 'torch.nn.modules.activation.ReLU'>: 0.0
<class 'torchvision.models.resnet.Bottleneck'>: 0.001997232437133789
<class 'torch.nn.modules.conv.Conv2d'>: 0.0010008811950683594
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0
<class 'torch.nn.modules.conv.Conv2d'>: 0.001001596450805664
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.00099945068359375
<class 'torch.nn.modules.conv.Conv2d'>: 0.0
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.002998828887939453
<class 'torch.nn.modules.activation.ReLU'>: 0.0010020732879638672
<class 'torch.nn.modules.container.Sequential'>: 0.0010020732879638672
<class 'torchvision.models.resnet.Bottleneck'>: 0.0
<class 'torch.nn.modules.conv.Conv2d'>: 0.001995563507080078
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.002001523971557617
<class 'torch.nn.modules.conv.Conv2d'>: 0.0
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0010001659393310547
<class 'torch.nn.modules.conv.Conv2d'>: 0.0010008811950683594
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0
<class 'torch.nn.modules.activation.ReLU'>: 0.0029985904693603516
<class 'torch.nn.modules.container.Sequential'>: 0.0009989738464355469
<class 'torch.nn.modules.conv.Conv2d'>: 0.0010068416595458984
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0
<class 'torchvision.models.resnet.Bottleneck'>: 0.0
<class 'torch.nn.modules.conv.Conv2d'>: 0.004993438720703125
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0010013580322265625
<class 'torch.nn.modules.conv.Conv2d'>: 0.0010001659393310547
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0010018348693847656
<class 'torch.nn.modules.conv.Conv2d'>: 0.001997709274291992
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0
<class 'torch.nn.modules.activation.ReLU'>: 0.0019991397857666016
<class 'torchvision.models.resnet.Bottleneck'>: 0.0029990673065185547
<class 'torch.nn.modules.conv.Conv2d'>: 0.0030128955841064453
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0019872188568115234
<class 'torch.nn.modules.conv.Conv2d'>: 0.0
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0
<class 'torch.nn.modules.conv.Conv2d'>: 0.0
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0029993057250976562
<class 'torch.nn.modules.activation.ReLU'>: 0.0010008811950683594
<class 'torchvision.models.resnet.Bottleneck'>: 0.0
<class 'torch.nn.modules.conv.Conv2d'>: 0.0010006427764892578
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0009992122650146484
<class 'torch.nn.modules.conv.Conv2d'>: 0.0
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.003001689910888672
<class 'torch.nn.modules.conv.Conv2d'>: 0.0019986629486083984
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0010008811950683594
<class 'torch.nn.modules.activation.ReLU'>: 0.0
<class 'torchvision.models.resnet.Bottleneck'>: 0.002000093460083008
<class 'torch.nn.modules.conv.Conv2d'>: 0.0019986629486083984
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0
<class 'torch.nn.modules.conv.Conv2d'>: 0.0
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0020012855529785156
<class 'torch.nn.modules.conv.Conv2d'>: 0.0
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0019981861114501953
<class 'torch.nn.modules.activation.ReLU'>: 0.0030014514923095703
<class 'torchvision.models.resnet.Bottleneck'>: 0.0
<class 'torch.nn.modules.conv.Conv2d'>: 0.0
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0029985904693603516
<class 'torch.nn.modules.conv.Conv2d'>: 0.0010008811950683594
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0
<class 'torch.nn.modules.conv.Conv2d'>: 0.0010013580322265625
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0009989738464355469
<class 'torch.nn.modules.activation.ReLU'>: 0.0
<class 'torch.nn.modules.container.Sequential'>: 0.002998828887939453
<class 'torchvision.models.resnet.Bottleneck'>: 0.002000570297241211
<class 'torch.nn.modules.conv.Conv2d'>: 0.0
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0
<class 'torch.nn.modules.conv.Conv2d'>: 0.003000497817993164
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0020020008087158203
<class 'torch.nn.modules.conv.Conv2d'>: 0.0
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0009982585906982422
<class 'torch.nn.modules.activation.ReLU'>: 0.0009996891021728516
<class 'torch.nn.modules.container.Sequential'>: 0.0
<class 'torch.nn.modules.conv.Conv2d'>: 0.0029990673065185547
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0020003318786621094
<class 'torchvision.models.resnet.Bottleneck'>: 0.0010025501251220703
<class 'torch.nn.modules.conv.Conv2d'>: 0.0
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0019981861114501953
<class 'torch.nn.modules.conv.Conv2d'>: 0.0019996166229248047
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0
<class 'torch.nn.modules.conv.Conv2d'>: 0.0
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0019996166229248047
<class 'torch.nn.modules.activation.ReLU'>: 0.0
<class 'torchvision.models.resnet.Bottleneck'>: 0.0030002593994140625
<class 'torch.nn.modules.conv.Conv2d'>: 0.0020012855529785156
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0
<class 'torch.nn.modules.conv.Conv2d'>: 0.0
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0
<class 'torch.nn.modules.conv.Conv2d'>: 0.006000518798828125
<class 'torch.nn.modules.batchnorm.BatchNorm2d'>: 0.0019979476928710938
<class 'torch.nn.modules.activation.ReLU'>: 0.0
<class 'torch.nn.modules.pooling.AdaptiveAvgPool2d'>: 0.002003192901611328
<class 'torch.nn.modules.linear.Linear'>: 0.0019965171813964844

Process finished with exit code 0

票数 2

页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持

原文链接：

https://stackoverflow.com/questions/66910479

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