[人工智能]pytorc 加载本地模型

模型下载

从pytorch官网找到对应模型的下载地址， 并使用浏览器下载 https://pytorch.org/hub/

模型加载(VGG11为例)

1. vgg11下载地址
   https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py
   

2. 本地加载

   import torch
   import torchvision.models as models
   model = models.vgg11(pretrained=False)
   model.load_state_dict(torch.load(R'pytorch\vgg11-8a719046.pth'))
   print(model.eval())
   
   # 输出
   VGG(
     (features): Sequential(
       (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
       (1): ReLU(inplace=True)
       (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
       (3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
       (4): ReLU(inplace=True)
       (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
       (6): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
       (7): ReLU(inplace=True)
       (8): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
       (9): ReLU(inplace=True)
       (10): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
       (11): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
       (12): ReLU(inplace=True)
       (13): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
       (14): ReLU(inplace=True)
       (15): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
       (16): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
       (17): ReLU(inplace=True)
       (18): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
       (19): ReLU(inplace=True)
       (20): 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)
     )
   )
   

   参考：https://blog.csdn.net/TomorrowAndTuture/article/details/100219240