11.1 网络模型保存(方式一)

import torchvision
import torch
vgg16 = torchvision.models.vgg16(pretrained=False)
torch.save(vgg16,"./model/vgg16_method1.pth") # 保存方式一：模型结构 + 模型参数      
print(vgg16)

结果：

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))
)

11.2 网络模型导入(方式一) 

import torch
model = torch.load("./model/vgg16_method1.pth") # 保存方式一对应的加载模型    
print(model)

结果：

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))
)

11.3 网络模型保存(方式二) 

import torchvision
import torch
vgg16 = torchvision.models.vgg16(pretrained=False)
torch.save(vgg16.state_dict(),"./model/vgg16_method2.pth") # 保存方式二：模型参数（官方推荐）,不再保存网络模型结构  
print(vgg16)

结果：

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))
)

11.4 网络模型导入(方式二) 

import torch
import torchvision
model = torch.load("./model/vgg16_method2.pth") # 导入模型参数   
print(model)

结果：

OrderedDict([('features.0.weight', tensor([[[[-0.0040,  0.0626,  0.0621],[-0.0136,  0.0981,  0.0697],[ 0.0022, -0.0291, -0.0770]],[[-0.0834,  0.0266,  0.0966],[-0.0460, -0.0137, -0.0662],[-0.0210,  0.0950,  0.0561]],[[-0.0502,  0.0219,  0.0184],[-0.0760,  0.0086,  0.0012],[-0.1154,  0.0661, -0.0271]]],[[[ 0.0185,  0.1026, -0.0609],[-0.1181, -0.0330, -0.0959],[-0.0051, -0.0306, -0.0252]],[[-0.0387,  0.0845, -0.0161],[-0.0070,  0.0384,  0.0372],[-0.0292,  0.0017, -0.0180]],[[ 0.0043, -0.0387,  0.0904],[ 0.0292,  0.0310,  0.0618],[-0.0687, -0.0400, -0.0319]]],[[[-0.0853, -0.1003, -0.0753],[ 0.0956, -0.0230, -0.0512],[-0.0790,  0.0973, -0.0948]],[[-0.0627,  0.0834,  0.0308],[-0.0471, -0.0289,  0.0510],[ 0.0272,  0.0454,  0.0243]],[[ 0.0203,  0.0219,  0.1468],[ 0.1805, -0.0544, -0.0677],[-0.0661,  0.0018, -0.0775]]],...,[[[ 0.0975,  0.0102, -0.0031],[-0.0713, -0.0369,  0.0412],[ 0.0418,  0.1035, -0.0707]],[[ 0.0715,  0.0932,  0.0417],[ 0.0253,  0.0198,  0.0291],[-0.0582,  0.0339,  0.0083]],[[ 0.0047, -0.0141,  0.0356],[-0.0075,  0.0874, -0.0623],[-0.0803,  0.0384, -0.0279]]],[[[ 0.0279,  0.1049,  0.0093],[ 0.0487,  0.0960,  0.0020],[-0.0282,  0.0206,  0.0837]],[[-0.0426,  0.0447, -0.0618],[ 0.0219,  0.0134,  0.0645],[ 0.0879, -0.0265, -0.0373]],[[ 0.1272,  0.0632,  0.0462],[-0.0101,  0.0410, -0.0651],[-0.0053, -0.0628,  0.0121]]],[[[ 0.0049, -0.0038,  0.0085],[ 0.0792, -0.0189,  0.0337],[ 0.0839,  0.0261,  0.0669]],[[-0.0059,  0.0361, -0.0233],[ 0.1031,  0.0462, -0.0449],[-0.0398,  0.0584,  0.0880]],[[ 0.0970, -0.0274,  0.0102],[ 0.0522,  0.0888, -0.0318],[ 0.0214, -0.0370, -0.0698]]]])), ('features.0.bias', tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])), ('features.2.weight', tensor([[[[-5.0967e-03,  3.4367e-02, -3.3054e-03],[ 1.4598e-02,  1.3033e-01,  8.1374e-03],[-8.0162e-02,  7.7383e-02,  2.8270e-02]],[[-1.4885e-02,  4.6058e-02,  1.3956e-02],[-3.9590e-02,  2.1446e-02, -7.1749e-02],[ 4.3048e-03, -5.1860e-03, -3.2426e-03]],[[-4.7485e-03, -5.8750e-02, -3.9225e-02],[ 5.3058e-02,  4.3474e-02,  3.7377e-02],[-5.4272e-02,  5.0986e-02, -6.5362e-03]],...,[[ 3.4790e-02,  3.4280e-02,  3.7325e-02],[ 8.3817e-04,  2.3898e-04,  6.0374e-02],[-7.9998e-02,  4.2538e-02,  3.9728e-02]],[[ 4.9162e-02,  3.5074e-02, -5.9139e-02],[-6.9303e-03,  1.3166e-02, -1.8707e-02],[ 6.8836e-02, -8.7236e-02, -3.9377e-02]],[[ 1.0358e-02,  3.4845e-02,  2.4139e-02],[ 3.8719e-02,  2.2152e-02, -4.6146e-02],[ 2.4336e-02,  7.0200e-02,  3.9884e-02]]],[[[-2.3998e-02, -4.6025e-02, -1.1408e-02],[ 1.7735e-02, -2.5891e-03,  4.0926e-02],[ 2.2270e-02,  2.7152e-02,  1.2580e-02]],[[-9.9553e-03, -3.8664e-02,  5.8608e-02],[-6.3725e-02,  6.8370e-02, -1.3848e-02],[ 1.4720e-02,  6.9760e-02,  3.6311e-03]],[[ 8.0472e-03,  5.7496e-02, -3.2233e-02],[-1.8367e-02, -6.5699e-02, -2.5250e-02],[ 6.3503e-02,  1.6145e-02,  1.0705e-01]],...,[[-5.8280e-02, -1.1586e-02,  4.5907e-02],[-1.7476e-02, -2.7693e-02, -3.6684e-02],[ 6.6822e-03,  4.8410e-02,  3.5693e-02]],[[ 1.0239e-01,  1.5463e-01,  3.3202e-02],[-8.7305e-03,  6.3578e-02,  5.1896e-02],[ 9.8891e-02,  3.4662e-02,  1.3262e-01]],[[ 1.9356e-02,  2.3273e-02, -3.9040e-02],[-2.0945e-02,  7.2473e-02, -8.2880e-02],[-7.2948e-02, -3.8305e-02, -7.2308e-02]]],[[[ 8.6159e-02,  3.3536e-02, -5.1061e-02],[-2.1509e-02, -8.0831e-03,  1.2278e-02],[ 5.7887e-02,  3.7741e-02, -4.3882e-02]],[[ 8.6380e-02, -1.6426e-02,  1.9811e-02],[ 7.2714e-02,  4.8379e-02,  3.8398e-02],[-9.0779e-02, -1.3111e-01, -2.3699e-02]],[[ 8.5638e-02,  5.8435e-03, -5.3302e-03],[ 6.6348e-02, -3.7983e-02, -7.9441e-02],[ 2.7901e-02, -3.4243e-02,  8.3240e-03]],...,[[ 3.8307e-02,  1.5580e-02, -8.1724e-02],[ 1.0553e-01, -6.3641e-02,  9.2080e-03],[-3.2122e-03,  9.3782e-02,  4.8964e-02]],[[ 1.3627e-02, -1.0449e-01,  8.6183e-03],[ 7.7844e-02,  5.5644e-02,  1.4909e-03],[ 3.2584e-02, -2.1830e-02, -3.0474e-02]],[[ 6.7886e-02, -2.0512e-02, -1.1325e-02],[-4.1406e-02,  8.7536e-02, -4.6433e-02],[ 3.8628e-03, -7.9638e-02, -8.5177e-03]]],...,[[[-1.3447e-01,  7.6999e-02,  1.3819e-01],[-3.4482e-03,  3.6168e-02,  8.6888e-02],[-6.1376e-02, -4.7030e-02, -2.1683e-02]],[[ 6.1050e-02, -2.0326e-02,  1.7210e-04],[ 1.1920e-01, -1.3982e-01,  2.5464e-02],[-2.1845e-03, -3.6796e-02, -4.0025e-02]],[[-5.2059e-02,  2.8119e-02, -6.0796e-02],[ 7.8354e-02,  3.0191e-02,  1.0595e-01],[ 2.8620e-02,  6.9772e-03,  6.8883e-02]],...,[[-1.7497e-02,  7.1148e-02, -4.0866e-02],[-8.2038e-02,  8.6979e-03, -9.1651e-03],[ 2.5035e-02, -8.9589e-02, -4.5515e-03]],[[ 3.6921e-02,  3.9946e-02,  1.0042e-01],[ 1.5761e-02, -2.9576e-02,  8.9088e-03],[ 7.1609e-02, -4.0912e-02, -3.9656e-02]],[[-6.6821e-02,  6.9773e-02,  3.2577e-02],[ 1.8143e-01, -3.6483e-02, -7.0825e-02],[-1.4579e-01,  1.4954e-01,  9.6300e-03]]],[[[-7.1204e-02, -2.4612e-02,  1.1590e-02],[-3.6893e-03,  2.3576e-02, -3.6828e-02],[-1.2422e-02,  1.7466e-02, -1.7121e-02]],[[ 5.3783e-02, -3.9715e-02,  3.1925e-02],[-5.4467e-02,  5.2707e-02, -4.3558e-02],[-5.7051e-02,  1.0501e-01, -1.4250e-02]],[[ 4.3103e-02,  8.2510e-03,  1.5530e-02],[-5.1402e-02,  2.3176e-02, -5.8602e-02],[ 9.6317e-02,  3.6468e-02,  5.0107e-02]],...,[[-1.6779e-03, -1.5342e-02,  1.6849e-01],[-5.4935e-02, -7.3766e-02,  9.4189e-02],[ 7.6479e-02, -2.8278e-02,  1.7094e-02]],[[ 3.2554e-03,  6.2916e-03, -4.5004e-02],[-8.4192e-02,  7.4603e-02,  5.2246e-03],[ 4.0496e-02, -7.2485e-03, -7.6363e-02]],[[ 1.0459e-02,  1.0689e-01,  5.2779e-02],[-2.2706e-02, -1.3479e-02,  2.9088e-02],[-5.6618e-03,  3.6200e-02, -6.4712e-02]]],[[[-3.5881e-02, -4.5090e-02,  3.5317e-02],[ 1.2177e-01, -6.4123e-02, -5.4346e-02],[ 1.2016e-01, -1.3192e-01,  6.4105e-03]],[[ 6.8677e-02,  9.9664e-03,  2.7289e-02],[-8.2896e-02,  6.3473e-02,  3.6986e-02],[-1.0164e-02,  1.4043e-02,  1.0922e-02]],[[ 1.2712e-01,  2.3604e-03,  6.9012e-02],[ 3.9896e-02, -5.4565e-03, -2.4938e-02],[ 7.4982e-03, -2.2892e-03, 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import torch
import torchvision
vgg16 = torchvision.models.vgg16(pretrained=False)
print(vgg16)
vgg16.load_state_dict(torch.load("./model/vgg16_method2.pth")) # 将模型参数导入到模型结构中   
print(vgg16)

 结果：

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))
)
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))
)

11.5 网络陷阱-创建模型 

import torch
from torch import nnclass Tudui(nn.Module):def __init__(self):super(Tudui,self).__init__()self.conv1 = nn.Conv2d(3,64,kernel_size=3)def forward(self,x):x = self.conv1(x)return xtudui = Tudui()
torch.save(tudui, "./model/tudui_method1.pth")

11.6 网络陷阱-失败加载模型

① 点击 Kernel，再点击 Restart。

② 再运行下面的代码，即下面为第1个代码块运行，无法直接导入网络模型。 

import torch
model = torch.load("./model/tudui_method1.pth")  # 无法直接加载方式一保存的网络结构    
print(model)

结果：

AttributeError                            Traceback (most recent call last)
<ipython-input-1-8827af8ec374> in <module>1 import torch
----> 2 model = torch.load("./model/tudui_method1.pth")  # 无法直接加载方式一保存的网络结构3 print(model)D:\11_Anaconda\envs\py3.6.3\lib\site-packages\torch\serialization.py in load(f, map_location, pickle_module, **pickle_load_args)605                     opened_file.seek(orig_position)606                     return torch.jit.load(opened_file)
--> 607                 return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)608         return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)609 D:\11_Anaconda\envs\py3.6.3\lib\site-packages\torch\serialization.py in _load(zip_file, map_location, pickle_module, pickle_file, **pickle_load_args)880     unpickler = UnpicklerWrapper(data_file, **pickle_load_args)881     unpickler.persistent_load = persistent_load
--> 882     result = unpickler.load()883 884     torch._utils._validate_loaded_sparse_tensors()D:\11_Anaconda\envs\py3.6.3\lib\site-packages\torch\serialization.py in find_class(self, mod_name, name)873         def find_class(self, mod_name, name):874             mod_name = load_module_mapping.get(mod_name, mod_name)
--> 875             return super().find_class(mod_name, name)876 877     # Load the data (which may in turn use `persistent_load` to load tensors)AttributeError: Can't get attribute 'Tudui' on <module '__main__'>

 11.7 网络陷阱-成功加载模型(方式一)

import torch
from torch import nn# 确保网络模型是我们想要的网络模型,要在加载前还写明网络模型
class Tudui(nn.Module):def __init__(self):super(Tudui,self).__init__()self.conv1 = nn.Conv2d(3,64,kernel_size=3)def forward(self,x):x = self.conv1(x)return x#tudui = Tudui # 不需要写这一步，不需要创建网络模型    
model = torch.load("./model/tudui_method1.pth")  # 无法直接加载方式一保存的网络结构    
print(model)

结果：

Tudui((conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1))
)

11.8  网络陷阱-成功加载模型(方式二）

import torch
import model_save import * # 它就相当于把 model_save.py 里的网络模型定义写到这里了#tudui = Tudui # 不需要写这一步，不需要创建网络模型    model = torch.load("tudui_method1.pth")
print(model)