深度学习速成：模型的使用与修改，保存与读取

1.使用与修改

VGG16为例

import torchvision import torch #trian_data=torchvision.datasets.imagenet("../data_imgnet",train=True,transform=torchvision.transforms.ToTensor(),download=True) vgg16_false=torchvision.models.vgg16(pretrained=False) vgg16_true=torchvision.models.vgg16(weights=torchvision.models.VGG16_Weights.IMAGENET1K_V1) print(vgg16_true) #vgg16_true.add_module("new_fc",torch.nn.Linear(1000,10))#追加全結合層 vgg16_true.classifier.add_module("new_fc",torch.nn.Linear(1000,10))#追加全結合層 print(vgg16_true) print(vgg16_false) vgg16_false.classifier[6]=torch.nn.Linear(4096,10)#在（6）那里修改全连接层 print(vgg16_false)

修改完的输出

2.保存与读取

2.1保存

import torch import torchvision import torch.nn as nn vgg16=torchvision.models.vgg16(weights=None) #保存1 保存整个模型（结构+参数） torch.save(vgg16,"vgg16.pth") #保存2 只保存模型参数（官方推荐，内存小） torch.save(vgg16.state_dict(),"vgg16_params.pth") class tudui(nn.Module): def __init__(self): super().__init__() self.conv1=nn.Conv2d(3,32,5,padding=2)# self.maxpool1=nn.MaxPool2d(2)# self.conv2=nn.Conv2d(32,32,5,padding=2) self.maxpool2=nn.MaxPool2d(2) self.conv3=nn.Conv2d(32,64,5,padding=2) self.maxpool3=nn.MaxPool2d(2) self.flatten=nn.Flatten()# self.linear1=nn.Linear(1024,64)# self.linear2=nn.Linear(64,10)# def forward(self,x): x=self.conv1(x) x=self.maxpool1(x) x=self.conv2(x) x=self.maxpool2(x) x=self.conv3(x) x=self.maxpool3(x) x=self.flatten(x) x=self.linear1(x) x=self.linear2(x) return x tudui_model=tudui() torch.save(tudui_model,"tudui_params.pth")#方法一保存

2.2 读取

import torch import torchvision from model_save import * #方式一 加载模型 """ vgg16=torch.load("vgg16.pth") print(vgg16) """ #方式二 加载模型参数 vgg16=torchvision.models.vgg16(weights=None) vgg16.load_state_dict(torch.load("vgg16_params.pth")) print(vgg16) """ vgg16_params=torch.load("vgg16_params.pth")#字典形式 print(vgg16_params) """ #方式一有陷阱 需要能访问到save时的类定义 im model=torch.load("tudui_params.pth") print(model)

2.3输出结果

(base) PS E:\desktop\deeplearning> & D:\miniconda3\envs\pytorch_py312\python.exe e:/desktop/deeplearning/src/model_load.py e:\desktop\deeplearning\src\model_load.py:10: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. vgg16.load_state_dict(torch.load("vgg16_params.pth")) VGG( (features): Sequential( (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): ReLU(inplace=True) (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): ReLU(inplace=True) (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (6): ReLU(inplace=True) (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (8): ReLU(inplace=True) (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (11): ReLU(inplace=True) (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (13): ReLU(inplace=True) (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (15): ReLU(inplace=True) (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (18): ReLU(inplace=True) (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (20): ReLU(inplace=True) (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (22): ReLU(inplace=True) (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (25): ReLU(inplace=True) (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (27): ReLU(inplace=True) (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (29): ReLU(inplace=True) (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ) (avgpool): AdaptiveAvgPool2d(output_size=(7, 7)) (classifier): Sequential( (0): Linear(in_features=25088, out_features=4096, bias=True) (1): ReLU(inplace=True) (2): Dropout(p=0.5, inplace=False) (3): Linear(in_features=4096, out_features=4096, bias=True) (4): ReLU(inplace=True) (5): Dropout(p=0.5, inplace=False) (6): Linear(in_features=4096, out_features=1000, bias=True) ) ) e:\desktop\deeplearning\src\model_load.py:19: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. model=torch.load("tudui_params.pth") tudui( (conv1): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)) (maxpool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (conv2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)) (maxpool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (conv3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)) (maxpool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (flatten): Flatten(start_dim=1, end_dim=-1) (linear1): Linear(in_features=1024, out_features=64, bias=True) (linear2): Linear(in_features=64, out_features=10, bias=True) )