pretrained=True、False的区别？

pretrained=True、False的区别？

pretrained 设置为 True，会自动下载模型所对应权重，并加载到模型中

以下内容用于加深理解，你懂的话就别花时间看！！！

代码是 pretrained 分别为 True 和 False 的模型 load 及 print

import torch
import torchvision
v = torchvision.models.vgg16(pretrained=True)
print('_____v pretrained_____')
# print(v)
torch.save(v.state_dict(), "vgg16_method.pth")
model = torch.load("vgg16_method.pth")
print(model)

v1 = torchvision.models.vgg16(pretrained=False)
print('_____v not pretrained_____')
# print(v1)
torch.save(v1.state_dict(), "vgg16_method1.pth")
model1 = torch.load("vgg16_method1.pth")
print(model1)

pretrained = True

(‘classifier.6.weight’, tensor([[ 1.3450e-02, 4.2154e-02, -2.3040e-03, …, -4.9025e-04,
1.8880e-02, -1.4209e-02],
[ 7.6020e-03, 4.7305e-02, -5.0164e-03, …, -4.9127e-03,
-5.6295e-03, -1.4197e-02],
[ 2.1238e-03, -1.2520e-02, -1.8903e-02, …, -1.0263e-02, 3.0020e-02, -2.8852e-02],
…,
[-9.8278e-03, 3.2054e-02, 3.5979e-02, …, -5.6409e-03,
8.3202e-03, -7.5155e-03],
[ 1.6646e-02, -1.1247e-03, 1.5044e-03, …, -9.1578e-03,
-8.6418e-03, -2.0923e-02],
[-6.4900e-06, -2.2274e-02, 5.2750e-04, …, 4.4403e-02,
-9.4047e-03, -1.2332e-02]])),

(‘classifier.6.bias’, tensor([ 2.0239e-02, -2.9844e-02, -6.0355e-03, 5.3569e-03, 2.4703e-02,
9.4541e-04, 7.6767e-03, -1.2997e-02, -1.7257e-02, 2.4917e-03,
-2.9578e-03, -1.2410e-02, 1.9179e-02, 2.2878e-02, 1.6132e-02,
-1.5368e-02, 1.3967e-03, 4.3597e-04, 1.4483e-02, 1.6664e-02,
1.0173e-02, 2.3670e-02, -1.2096e-03, 1.6110e-02, -5.3769e-03,
-1.1735e-02, -1.0727e-02, -2.9511e-02, -7.2404e-03, 3.9187e-02,
1.1590e-02, 2.8063e-03, -5.4244e-03, -4.0255e-03, 1.3839e-04,
-1.1612e-02, -1.8595e-03, 1.5770e-02, -1.7732e-02, 1.0556e-02,
1.2518e-02, 6.8095e-03, -1.2896e-02, -4.4475e-03, 3.5410e-03,
-2.7491e-03, 2.2288e-02, 2.3229e-02, 1.0078e-02, 1.2641e-02,
7.9056e-03, 2.1109e-02, -1.6828e-02, 6.2936e-03, -2.8307e-02,
7.4882e-03, -2.9438e-02, 4.9023e-02, 4.9702e-02, 3.8240e-02,
-6.2107e-02, 1.7567e-02, 1.3668e-02, 5.5128e-02, 6.3641e-02,
-1.5907e-02, -2.1543e-02, 2.4153e-02, 2.9221e-03, 3.6656e-02,
-2.5589e-04, 1.0257e-02, 6.0446e-02, -2.0747e-02, -2.8418e-02,
3.8297e-02, 2.2449e-02, 1.2476e-02, -1.6173e-02, -6.9245e-03,
-1.2505e-02, -5.7967e-03, -1.4107e-02, -3.1422e-02, -1.4501e-02,
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7.0744e-02, 3.5352e-02, 4.5257e-03, -6.7377e-03, 4.8965e-02,
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1.7470e-02, 2.7788e-02, -3.4156e-02, -5.0233e-03, -2.0081e-02,
-5.3831e-02, -7.0464e-03, -4.0125e-02, 5.2068e-02, 5.3731e-03,
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2.2551e-02, 9.0139e-03, -2.1590e-02, -4.8878e-02, 2.6130e-02,
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