我正在尝试在CPU上运行一个类似Resnet的图像分类模型，并想知道运行模型中每一层的具体时间消耗情况。

我面临的问题是GitHub链接 https://github.com/facebookresearch/semi-supervised-ImageNet1K-models 上的模型是以.pth文件形式保存的。这个文件非常大（数百MB），除了它是二进制文件外，我不清楚它与PyTorch的具体区别。我使用以下脚本加载模型，但我不清楚如何修改模型或在模型层之间插入t = time.time()变量/语句来分解每一层的执行时间。

问题：

1. 运行以下脚本是否能正确估计在CPU上运行模型的端到端时间（t2-t1），还是会包括PyTorch的编译时间？

2. 如何在连续的层之间插入时间语句以获得时间分解？

3. GitHub链接上没有推理/训练脚本，只有.pth文件。那么应该如何进行推理或训练？如何在.pth模型的连续层之间插入额外的层并保存它们？

#!/usr/bin/env pythonimport torch torchvision timemodel=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**

回答：

实现这种需求的一个简单方法是为模型的每个模块注册前向钩子，这些钩子会更新一个全局变量来存储时间，并计算最后一次和当前计算之间的时间差。

例如：

import torchimport torchvisionimport timeglobal_time = Noneexec_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)# 为每个模块注册钩子以计算时间差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'>: 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'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'>: 0.0019965171813964844Process finished with exit code 0