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],
[-4.0771e-02, -5.7696e-02, 2.1114e-02],
[ 2.0548e-02, -5.5974e-03, 5.6080e-03]],
[[ 6.8871e-03, -4.3420e-02, -1.5478e-02],
[-2.4453e-03, 6.5895e-02, 4.2433e-02],
[-5.6135e-02, 4.3798e-02, -7.2255e-02]],
[[-2.1576e-02, -7.3287e-02, 2.0999e-02],
[-2.4178e-02, 4.6556e-02, 3.9748e-02],
[-2.3140e-02, 4.0290e-02, -1.4403e-02]],
...,
[[-3.3937e-02, -2.3407e-02, -4.4383e-02],
[-1.8374e-03, -1.4892e-02, -1.7407e-02],
[-8.9755e-02, 4.6855e-02, 6.1854e-02]],
[[ 5.0991e-03, 5.8257e-03, 8.1091e-03],
[ 5.6942e-03, 1.0450e-02, -1.9471e-02],
[-8.8708e-03, -1.4913e-03, 2.1217e-02]],
[[-8.5076e-03, -6.4368e-02, 5.8725e-02],
[-1.2360e-02, -3.6534e-02, -7.6520e-02],
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-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],
...,
[-0.0047, -0.0027, 0.0029, ..., -0.0075, -0.0200, 0.0156],
[-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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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。
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)