pytorch中内置网络模型
Docs -> torchvision -> Models and pre-trained weights
MODELS AND PRE-TRAINED WEIGHTS
The torchvision.models subpackage contains definitions of models for addressing different tasks, including: image classification, pixelwise semantic segmentation, object detection, instance segmentation, person keypoint detection, video classification, and optical flow.
如：VGG(一种分类模型)
最常用的有vgg16和vgg19
VGG16
torchvision.models.vgg16( **, weights: Optional[VGG16_Weights] = None, progress: bool = True, *** kwargs: Any) → VGG[SOURCE]
VGG-16 from Very Deep Convolutional Networks for Large-Scale Image Recognition.
Parameters:
- weights (
VGG16_Weights, optional) – The pretrained weights to use. SeeVGG16_Weightsbelow for more details, and possible values. By default, no pre-trained weights are used. - progress (bool , optional) – If True, displays a progress bar of the download to stderr. Default is True.
-
- *kwargs – parameters passed to the
torchvision.models.vgg.VGGbase class. Please refer to the source code for more details about this class.
- *kwargs – parameters passed to the
注意
pretrained参数被弃用（deprecated）了，需要更新为weights参数
UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
在旧版本的写法 pretrained = True 中，对于预训练权重参数我们没有太多选择的余地，一执行起来就要使用默认的预训练权重文件版本。但问题是，现在深度学习的发展日新月异，很快就有性能更强的模型横空出世。
而使用新版本写法 weights=预训练模型参数版本 ，相当于我们掌握了预训练权重参数文件的选择权。我们就可以尽情地使用更准更快更强更新的预训练权重参数文件，帮助我们的研究更上一层楼。
示例
from torchvision import models
# 加载精度为76.130%的旧权重参数文件V1
model_v1 = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V1)
# 等价写法
model_v1 = models.resnet50(weights="IMAGENET1K_V1")
# 加载精度为80.858%的新权重参数文件V2
model_v2 = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V2)
# 等价写法
model_v1 = models.resnet50(weights="IMAGENET1K_V2")
# 如果你不知道哪个版本是最新, 直接选择默认DEFAULT即可
model_new = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
示例
import torchvision
from torch import nn
# # 注意，该模型现无法公开访问，download参数已弃用
# train_data = torchvision.datasets.ImageNet(root='./dataset', split='train', download=True,
# transform=torchvision.transforms.ToTensor())
vgg16_true = torchvision.models.vgg16(weights='DEFAULT')
vgg16_false = torchvision.models.vgg16(weights=None)
print(vgg16_true)
# 该模型输出的分类数是1000，如何修改使之应用到别的数据集上呢？
train_data = torchvision.datasets.CIFAR10(root='./dataset', train=True, download=True,
transform=torchvision.transforms.ToTensor())
# vgg16_true.add_module('add_linear', nn.Linear(in_features=1000, out_features=10))
vgg16_true.classifier.add_module('add_linear', nn.Linear(in_features=1000, out_features=10))
print(vgg16_true)
vgg16_false.classifier[6] = nn.Linear(in_features=4096, out_features=10)
print(vgg16_false)
预训练后的结果
未预训练的结果
模型的保存和加载
保存
import torch
import torchvision
from torch import nn
vgg16 = torchvision.models.vgg16(weights=None)
# 保存方式1
# 保存网络模型结构+网络内部参数
torch.save(vgg16, f=r"./models/vgg16_method1.pth")
# 保存方式2（官方推荐，占用空间小）
# 只保存网络模型的参数，不保存结构
torch.save(vgg16.state_dict(), f=r"./models/vgg16_method2.pth")
# 方式1的陷阱
class XiaoMo(nn.Module):
def __init__(self):
super(XiaoMo, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3)
def forward(self, x):
return self.conv1(x)
xiaomo = XiaoMo()
torch.save(xiaomo, f=r"./models/xiaomo.pth")
加载
import torch
import torchvision
# 保存方式1->加载模型
model = torch.load("./models/vgg16_method1.pth")
print(model)
# 保存方式2->加载模型模型
# vgg16 = torchvision.models.vgg16(weights=torchvision.models.VGG16_Weights.DEFAULT) # 先新建网络模型结构
vgg16 = torchvision.models.vgg16(weights=None) # 先新建网络模型结构
vgg16.load_state_dict(torch.load("./models/vgg16_method2.pth")) # 加载模型参数
# model = torch.load("./models/vgg16_method2.pth")
print(vgg16)
# 方式1保存的陷阱
model = torch.load("./models/xiaomo.pth")
print(model)
# AttributeError: Can't get attribute 'XiaoMo' on <module '__main__' from 'C:\Users\31058\Desktop\MyProject\ML\test\20_model_load.py'>
# 原因是类未声明，需要重新声明或者导入（一般采用导入的方法）
