11. 网络模型保存与读取

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, -5.2376e-02]],

         ...,

         [[-2.9649e-03, -4.9510e-02,  3.9255e-02],
          [ 1.2340e-02,  6.5017e-02, -9.2098e-02],
          [ 3.9627e-02, -7.2954e-02, -9.8100e-02]],

         [[-2.0009e-02,  8.6935e-02,  1.1596e-02],
          [-3.6258e-02, -9.6375e-02,  5.8322e-02],
          [-5.5336e-02, -2.0238e-02, -1.9975e-02]],

         [[-9.0217e-02,  6.3687e-03,  3.2319e-02],
          [ 3.1559e-02,  3.4944e-02,  3.8751e-02],
          [-4.8232e-02,  2.0484e-02,  7.2250e-02]]]])), ('features.2.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.5.weight', tensor([[[[-1.6845e-02, -1.0685e-01, -1.9016e-02],
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          [-1.7033e-02, -3.1599e-02, -2.5797e-02],
          [ 4.3104e-02, -1.8700e-02, -3.2766e-02]],

         [[ 1.7642e-02,  2.5379e-02,  7.4383e-03],
          [-3.3848e-02,  3.2638e-02, -2.1453e-03],
          [-3.2359e-02, -4.9989e-03,  2.7601e-02]],

         [[ 8.8112e-03,  1.2808e-03,  2.5326e-02],
          [-3.3879e-02,  1.4739e-02,  1.8364e-02],
          [ 1.0528e-02,  5.5194e-03,  2.2541e-02]]],


        [[[ 5.3834e-03,  3.2623e-02, -2.0569e-02],
          [ 1.3641e-03,  4.1946e-03,  1.5896e-03],
          [ 9.1531e-03, -9.6968e-03,  4.7454e-02]],

         [[-1.7372e-02,  4.1250e-02,  6.1356e-03],
          [-7.2324e-03,  3.7743e-03,  1.4078e-05],
          [-1.5896e-02, -1.4262e-03, -2.8257e-03]],

         [[-3.1464e-02,  1.7238e-02, -9.8122e-03],
          [ 5.5918e-03, -1.7760e-02,  6.1406e-03],
          [ 1.2267e-02, -3.4215e-03, -3.2450e-03]],

         ...,

         [[-1.5220e-02,  5.2193e-03,  9.4695e-03],
          [-3.8029e-02,  3.8607e-03,  1.5828e-02],
          [-1.4532e-03,  7.5437e-03,  1.7361e-02]],

         [[ 5.6551e-02, -3.2545e-03, -9.7179e-03],
          [ 1.2210e-02,  9.7758e-04,  3.0518e-02],
          [-7.9859e-03,  1.3412e-03, -1.0720e-02]],

         [[ 1.3238e-04,  8.1517e-03, -7.6651e-03],
          [ 1.1113e-02,  1.4464e-02,  2.3805e-03],
          [-6.0287e-03, -4.6823e-02, -1.0711e-02]]],


        [[[ 9.1537e-03, -1.4295e-02, -4.7943e-03],
          [-3.5159e-02, -1.2149e-02, -1.2989e-03],
          [-2.4851e-03,  1.4979e-03,  1.3798e-02]],

         [[-1.5891e-02, -1.6270e-02,  1.6452e-02],
          [ 2.3183e-02,  5.1737e-03, -2.7102e-02],
          [-3.8776e-04, -1.5855e-02,  3.3479e-02]],

         [[ 2.0105e-02, -1.4821e-02,  1.0468e-02],
          [ 4.2938e-03, -2.6999e-02,  2.4144e-02],
          [ 3.9401e-02,  3.4655e-03,  1.5198e-02]],

         ...,

         [[-1.6571e-02, -4.7278e-02,  2.8212e-02],
          [ 2.4015e-02,  2.5208e-02,  3.4281e-02],
          [-7.8053e-03,  4.4283e-02,  3.8593e-03]],

         [[ 1.5538e-02,  3.0176e-02,  1.2698e-04],
          [-1.8918e-02,  1.6627e-03, -7.0983e-03],
          [ 3.8603e-03, -2.5210e-02, -3.1500e-02]],

         [[-5.0957e-03, -2.3828e-02,  3.5821e-03],
          [-8.6653e-03,  5.6471e-03, -5.9752e-04],
          [ 1.8408e-02,  1.7115e-02,  3.8465e-02]]]])), ('features.28.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.,
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        0., 0., 0., 0., 0., 0., 0., 0.])), ('classifier.0.weight', tensor([[ 0.0167, -0.0248,  0.0173,  ...,  0.0120,  0.0066, -0.0073],
        [-0.0257, -0.0113,  0.0073,  ..., -0.0020, -0.0136,  0.0054],
        [-0.0021, -0.0079, -0.0034,  ..., -0.0072, -0.0102, -0.0124],
        ...,
        [-0.0115, -0.0099,  0.0136,  ...,  0.0199, -0.0015,  0.0241],
        [-0.0021, -0.0264, -0.0215,  ..., -0.0039,  0.0132, -0.0150],
        [ 0.0124, -0.0091,  0.0064,  ..., -0.0131, -0.0041,  0.0048]])), ('classifier.0.bias', tensor([0., 0., 0.,  ..., 0., 0., 0.])), ('classifier.3.weight', tensor([[ 5.8521e-03, -1.4289e-02,  7.9223e-04,  ..., -1.1087e-03,
          5.1041e-03,  8.8665e-03],
        [ 5.7204e-03, -1.6027e-02, -8.0187e-03,  ...,  4.5884e-03,
         -1.1648e-02,  3.5097e-03],
        [-4.0447e-03,  6.9430e-03,  1.1891e-03,  ...,  6.1115e-03,
         -1.7927e-03,  1.6072e-02],
        ...,
        [ 1.3442e-02, -6.0078e-03, -1.4576e-02,  ...,  1.4749e-02,
          4.0596e-03,  9.6249e-05],
        [-6.9479e-03,  2.3929e-03, -1.9451e-03,  ...,  1.3393e-03,
          1.5306e-03,  1.1478e-02],
        [ 2.3176e-02, -1.6846e-02,  3.4696e-03,  ..., -8.4160e-03,
         -1.7889e-03, -5.7367e-03]])), ('classifier.3.bias', tensor([0., 0., 0.,  ..., 0., 0., 0.])), ('classifier.6.weight', tensor([[-0.0022,  0.0045,  0.0122,  ..., -0.0116,  0.0019, -0.0030],
        [-0.0186,  0.0166, -0.0101,  ...,  0.0038,  0.0142, -0.0040],
        [ 0.0081,  0.0070,  0.0035,  ...,  0.0024,  0.0212, -0.0017],
        ...,
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        [-0.0035, -0.0032, -0.0105,  ..., -0.0115,  0.0029, -0.0105],
        [ 0.0130, -0.0064,  0.0002,  ...,  0.0035,  0.0020, -0.0004]])), ('classifier.6.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.,
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        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]))])

 

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 nn

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()
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。

11. 网络模型保存与读取_第1张图片

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)
 in 
      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 

 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)

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