获取预训练模型的网络输入尺寸

初学神经网络之时，常常会用到预训练的网络包。
例如

from torchvision import models
resnet = models.res18(pretrained=True)

但是初学者在使用的时候会犯难：我需要输入多大尺寸的图片呢？
解决方案：

方法一：读torchvision.models的说明文档

打开torchvision.models的网站：
https://pytorch.org/hub/research-models

搜索你需要的模型名称，得到resnet的网站：
https://pytorch.org/hub/pytorch_vision_resnet/
在里面看到resnet的指导文档：

import torch
model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18', pretrained=True)
# or any of these variants
# model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet34', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet50', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet101', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet152', pretrained=True)
model.eval()

All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].
Here’s a sample execution.

# sample execution (requires torchvision)
from PIL import Image
from torchvision import transforms
input_image = Image.open(filename)
prepro