model.modules(),model.parameters(),model.state_dict()_model.module.state_dict()

作者：小小林熬夜学编程 | 2024-02-18 01:50:42

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model.module.state_dict()

import torch
import torch.nn as nn
class ConvModel(nn.Module):
    def __init__(self):
        super(ConvModel, self).__init__()
        
        # 定义卷积层
        self.conv = nn.Conv1d(in_channels=1, out_channels=32, kernel_size=3, stride=1)
        self.pool = nn.MaxPool1d(kernel_size=2, stride=2)
        
        # 定义全连接层
        self.fc1 = nn.Linear(in_features=3168, out_features=64)
        self.fc2 = nn.Linear(in_features=64, out_features=2)
        
    def forward(self, x):
        # 卷积操作
        x = x.unsqueeze(1)
        x = self.conv(x)
        x = nn.functional.relu(x)
        x = self.pool(x)
        
        # 展平
        x = torch.flatten(x, start_dim=1)
        
        # 全连接层
        x = self.fc1(x)
        x = nn.functional.relu(x)
        x = self.fc2(x)
        return x

input = torch.randn(64,200)
model = ConvModel()
output = model(input)
for param in model.parameters():
    print(param)

for mods in model.modules():
    print(mods)

print(model.state_dict())
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model.modules()
是一个PyTorch中的方法，它会返回一个包含模型所有组成层(Module)的迭代器，包括模型中嵌套的子层级。通过调用model.modules()方法可以遍历整个模型，并访问到每一层的名称、参数等信息。
具体来说，model.modules()返回的是一个生成器(generator)，其中包括了整个模型的所有层级，包括父层级和子层级，但是不包括网络中的连接层和节点。这个生成器按照树形结构遍历整个模型，并按照层级有序输出每个层级。

ConvModel(
  (conv): Conv1d(1, 32, kernel_size=(3,), stride=(1,))
  (pool): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (fc1): Linear(in_features=3168, out_features=64, bias=True)
  (fc2): Linear(in_features=64, out_features=2, bias=True)
)
Conv1d(1, 32, kernel_size=(3,), stride=(1,))
MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
Linear(in_features=3168, out_features=64, bias=True)
Linear(in_features=64, out_features=2, bias=True)
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model.parameters()
是一个PyTorch中的方法，它会返回一个包含模型所有可训练参数的迭代器，这些参数可以被用于优化器的更新或者保存与加载模型。具体来说，model.parameters()返回的是一个生成器(generator)，里面只有模型的参数，没有对应的网络层名称。

Parameter containing:
tensor([[[-0.5126,  0.5331,  0.2893]],

        [[ 0.4555, -0.1294,  0.0125]],

        [[ 0.1551,  0.4624,  0.2719]],

        [[-0.4037, -0.5695,  0.2529]],

        [[ 0.0677, -0.5520, -0.4953]],

        [[ 0.0964, -0.5395,  0.0329]],

        [[-0.3732,  0.2867,  0.3513]],

        [[-0.2955, -0.0611, -0.5704]],

        [[-0.5759,  0.5438,  0.2083]],

        [[ 0.1635, -0.1977,  0.1897]],

        [[-0.1315,  0.0727,  0.4758]],

        [[ 0.5563, -0.1573, -0.0435]],

        [[ 0.4147,  0.3957, -0.0221]],

        [[-0.4292,  0.5694,  0.3400]],

        [[ 0.2282, -0.2961, -0.0415]],

        [[ 0.2947,  0.1851,  0.4878]],

        [[ 0.3008,  0.2580,  0.3571]],

        [[ 0.0821,  0.2338, -0.3678]],

        [[-0.1976,  0.1668, -0.1952]],

        [[ 0.4718,  0.2912,  0.0277]],

        [[ 0.0925, -0.5663,  0.4554]],

        [[-0.3790,  0.5210,  0.4064]],

        [[ 0.0432,  0.2155, -0.3406]],

        [[-0.2695,  0.4273, -0.2091]],

        [[ 0.5320, -0.2330,  0.3117]],

        [[-0.2894,  0.1364,  0.5271]],

        [[ 0.5254,  0.2866,  0.0733]],

        [[ 0.4851, -0.1229, -0.4983]],

        [[ 0.1804,  0.4486, -0.0619]],

        [[-0.5312,  0.5690, -0.3322]],

        [[ 0.0271,  0.2029,  0.0384]],

        [[-0.0130, -0.2701,  0.3163]]], requires_grad=True)
Parameter containing:
tensor([ 0.0342,  0.3380,  0.0396,  0.0872,  0.5354,  0.4468, -0.4234, -0.4960,
         0.4873, -0.4876,  0.5049, -0.4638, -0.0434, -0.3543, -0.0406, -0.4049,
         0.4760, -0.3240, -0.2755, -0.0046,  0.1204, -0.1654,  0.2997,  0.0625,
        -0.1786, -0.0292,  0.3281, -0.4937, -0.3622, -0.3028,  0.2650,  0.0713],
       requires_grad=True)
Parameter containing:
tensor([[ 0.0160, -0.0104, -0.0111,  ...,  0.0129,  0.0091,  0.0010],
        [ 0.0101,  0.0083,  0.0145,  ...,  0.0079,  0.0081,  0.0054],
        [-0.0059, -0.0004,  0.0028,  ..., -0.0139, -0.0017, -0.0005],
        ...,
        [-0.0160, -0.0081, -0.0147,  ...,  0.0150, -0.0108,  0.0030],
        [-0.0066, -0.0151,  0.0050,  ..., -0.0126, -0.0041,  0.0100],
        [ 0.0141, -0.0058, -0.0081,  ...,  0.0026,  0.0083,  0.0022]],
       requires_grad=True)
Parameter containing:
tensor([-1.4286e-02, -4.8758e-03, -9.6254e-03,  3.8352e-03,  1.7488e-02,
         9.5369e-03, -1.2196e-02, -4.0154e-03, -1.5040e-02, -1.7715e-02,
         1.4567e-02, -1.7208e-02, -1.7108e-02, -1.4707e-02, -1.2141e-03,
         1.0333e-02,  1.3588e-02,  1.4712e-02, -1.3024e-02, -1.6053e-02,
        -1.0188e-02, -1.5649e-02, -7.5805e-03, -1.3627e-02,  1.4144e-02,
         5.0014e-03, -1.0649e-02, -1.3394e-02,  4.9695e-03,  4.9918e-03,
         1.1173e-02, -1.5761e-02,  1.4373e-02,  8.3734e-04,  2.2160e-05,
        -1.5278e-02, -1.6162e-02,  3.3545e-03, -7.0366e-03,  1.2571e-02,
         1.0598e-02,  8.7718e-03, -5.5483e-03,  1.2729e-02, -1.5751e-02,
         9.5723e-03, -1.7305e-02, -1.3221e-02, -6.7073e-03, -1.6645e-03,
        -1.2975e-02,  1.1121e-02,  1.4769e-02,  1.3430e-03,  6.8799e-03,
         1.5452e-02,  1.4529e-02,  1.0451e-02, -7.1500e-03, -1.2924e-02,
        -7.7933e-03, -2.8951e-03,  8.1708e-03, -8.0176e-03],
       requires_grad=True)
Parameter containing:
tensor([[-0.0286, -0.1145, -0.0556, -0.0326, -0.1048, -0.1136, -0.1209,  0.0143,
         -0.0974, -0.1066, -0.0110, -0.1067,  0.0064,  0.0431,  0.0437, -0.0485,
          0.0760,  0.0398, -0.0526,  0.0138, -0.0278,  0.0861, -0.0675,  0.1166,
          0.0165,  0.0697, -0.0966, -0.0170, -0.1015, -0.1014, -0.0581,  0.1092,
          0.1147,  0.0013,  0.0246, -0.0419,  0.1228,  0.0919, -0.0277, -0.0781,
         -0.0209,  0.0984, -0.0507, -0.0988, -0.0766, -0.1127, -0.0171, -0.0945,
          0.0552, -0.1151,  0.1186,  0.0960,  0.0265,  0.1094,  0.1215, -0.0884,
          0.0510,  0.0182,  0.0710, -0.0617, -0.0658,  0.0838, -0.0267,  0.0384],
        [ 0.0812, -0.0191, -0.0470,  0.0184, -0.0612,  0.0393,  0.1248,  0.0249,
         -0.1226,  0.0047,  0.0882,  0.0487,  0.1045,  0.0559,  0.0201,  0.0271,
         -0.0657,  0.0610,  0.0036,  0.1152,  0.0320,  0.0100,  0.0171, -0.0890,
          0.0910, -0.0298, -0.0979,  0.0838,  0.0191, -0.0210,  0.0581,  0.0309,
          0.1167, -0.0727,  0.0471, -0.0216,  0.0969,  0.0827, -0.0934,  0.0181,
         -0.0922, -0.0406,  0.0906, -0.0490, -0.0792, -0.0736, -0.0735,  0.1206,
         -0.0137, -0.0138, -0.1037, -0.0516, -0.1138,  0.0146, -0.0104,  0.0561,
          0.0404,  0.0993,  0.0587,  0.0061, -0.0620, -0.0294,  0.0185, -0.0011]],
       requires_grad=True)
Parameter containing:
tensor([-0.0527,  0.1059], requires_grad=True)
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model.state_dict()
是一个 PyTorch 中的方法，用于返回模型中可训练参数以及其对应值的字典（dictionary）。即将每个层的权重和偏差等参数保存到一个 Python 字典中，字典的 key 为参数的名称，value 为该参数的值，可以将这个字典用于后面的优化器、断点续训或者模型的保存和加载。state_dict() 方法的返回结果是一个 OrderedDict 类型的对象，其中包括了模型中所有可学习参数的名称和对应的张量值。由于 state_dict() 的输出仅仅是模型中可学习参数的值，因此在加载模型状态时，必须已经实例化相应的网络结构，并且网络结构必须与之前保存的模型相同才能进行恢复。

OrderedDict([('conv.weight', tensor([[[-0.5126,  0.5331,  0.2893]],

        [[ 0.4555, -0.1294,  0.0125]],

        [[ 0.1551,  0.4624,  0.2719]],

        [[-0.4037, -0.5695,  0.2529]],

        [[ 0.0677, -0.5520, -0.4953]],

        [[ 0.0964, -0.5395,  0.0329]],

        [[-0.3732,  0.2867,  0.3513]],

        [[-0.2955, -0.0611, -0.5704]],

        [[-0.5759,  0.5438,  0.2083]],

        [[ 0.1635, -0.1977,  0.1897]],

        [[-0.1315,  0.0727,  0.4758]],

        [[ 0.5563, -0.1573, -0.0435]],

        [[ 0.4147,  0.3957, -0.0221]],

        [[-0.4292,  0.5694,  0.3400]],

        [[ 0.2282, -0.2961, -0.0415]],

        [[ 0.2947,  0.1851,  0.4878]],

        [[ 0.3008,  0.2580,  0.3571]],

        [[ 0.0821,  0.2338, -0.3678]],

        [[-0.1976,  0.1668, -0.1952]],

        [[ 0.4718,  0.2912,  0.0277]],

        [[ 0.0925, -0.5663,  0.4554]],

        [[-0.3790,  0.5210,  0.4064]],

        [[ 0.0432,  0.2155, -0.3406]],

        [[-0.2695,  0.4273, -0.2091]],

        [[ 0.5320, -0.2330,  0.3117]],

        [[-0.2894,  0.1364,  0.5271]],

        [[ 0.5254,  0.2866,  0.0733]],

        [[ 0.4851, -0.1229, -0.4983]],

        [[ 0.1804,  0.4486, -0.0619]],

        [[-0.5312,  0.5690, -0.3322]],

        [[ 0.0271,  0.2029,  0.0384]],

        [[-0.0130, -0.2701,  0.3163]]])), ('conv.bias', tensor([ 0.0342,  0.3380,  0.0396,  0.0872,  0.5354,  0.4468, -0.4234, -0.4960,
         0.4873, -0.4876,  0.5049, -0.4638, -0.0434, -0.3543, -0.0406, -0.4049,
         0.4760, -0.3240, -0.2755, -0.0046,  0.1204, -0.1654,  0.2997,  0.0625,
        -0.1786, -0.0292,  0.3281, -0.4937, -0.3622, -0.3028,  0.2650,  0.0713])), ('fc1.weight', tensor([[ 0.0160, -0.0104, -0.0111,  ...,  0.0129,  0.0091,  0.0010],
        [ 0.0101,  0.0083,  0.0145,  ...,  0.0079,  0.0081,  0.0054],
        [-0.0059, -0.0004,  0.0028,  ..., -0.0139, -0.0017, -0.0005],
        ...,
        [-0.0160, -0.0081, -0.0147,  ...,  0.0150, -0.0108,  0.0030],
        [-0.0066, -0.0151,  0.0050,  ..., -0.0126, -0.0041,  0.0100],
        [ 0.0141, -0.0058, -0.0081,  ...,  0.0026,  0.0083,  0.0022]])), ('fc1.bias', tensor([-1.4286e-02, -4.8758e-03, -9.6254e-03,  3.8352e-03,  1.7488e-02,
         9.5369e-03, -1.2196e-02, -4.0154e-03, -1.5040e-02, -1.7715e-02,
         1.4567e-02, -1.7208e-02, -1.7108e-02, -1.4707e-02, -1.2141e-03,
         1.0333e-02,  1.3588e-02,  1.4712e-02, -1.3024e-02, -1.6053e-02,
        -1.0188e-02, -1.5649e-02, -7.5805e-03, -1.3627e-02,  1.4144e-02,
         5.0014e-03, -1.0649e-02, -1.3394e-02,  4.9695e-03,  4.9918e-03,
         1.1173e-02, -1.5761e-02,  1.4373e-02,  8.3734e-04,  2.2160e-05,
        -1.5278e-02, -1.6162e-02,  3.3545e-03, -7.0366e-03,  1.2571e-02,
         1.0598e-02,  8.7718e-03, -5.5483e-03,  1.2729e-02, -1.5751e-02,
         9.5723e-03, -1.7305e-02, -1.3221e-02, -6.7073e-03, -1.6645e-03,
        -1.2975e-02,  1.1121e-02,  1.4769e-02,  1.3430e-03,  6.8799e-03,
         1.5452e-02,  1.4529e-02,  1.0451e-02, -7.1500e-03, -1.2924e-02,
        -7.7933e-03, -2.8951e-03,  8.1708e-03, -8.0176e-03])), ('fc2.weight', tensor([[-0.0286, -0.1145, -0.0556, -0.0326, -0.1048, -0.1136, -0.1209,  0.0143,
         -0.0974, -0.1066, -0.0110, -0.1067,  0.0064,  0.0431,  0.0437, -0.0485,
          0.0760,  0.0398, -0.0526,  0.0138, -0.0278,  0.0861, -0.0675,  0.1166,
          0.0165,  0.0697, -0.0966, -0.0170, -0.1015, -0.1014, -0.0581,  0.1092,
          0.1147,  0.0013,  0.0246, -0.0419,  0.1228,  0.0919, -0.0277, -0.0781,
         -0.0209,  0.0984, -0.0507, -0.0988, -0.0766, -0.1127, -0.0171, -0.0945,
          0.0552, -0.1151,  0.1186,  0.0960,  0.0265,  0.1094,  0.1215, -0.0884,
          0.0510,  0.0182,  0.0710, -0.0617, -0.0658,  0.0838, -0.0267,  0.0384],
        [ 0.0812, -0.0191, -0.0470,  0.0184, -0.0612,  0.0393,  0.1248,  0.0249,
         -0.1226,  0.0047,  0.0882,  0.0487,  0.1045,  0.0559,  0.0201,  0.0271,
         -0.0657,  0.0610,  0.0036,  0.1152,  0.0320,  0.0100,  0.0171, -0.0890,
          0.0910, -0.0298, -0.0979,  0.0838,  0.0191, -0.0210,  0.0581,  0.0309,
          0.1167, -0.0727,  0.0471, -0.0216,  0.0969,  0.0827, -0.0934,  0.0181,
         -0.0922, -0.0406,  0.0906, -0.0490, -0.0792, -0.0736, -0.0735,  0.1206,
         -0.0137, -0.0138, -0.1037, -0.0516, -0.1138,  0.0146, -0.0104,  0.0561,
          0.0404,  0.0993,  0.0587,  0.0061, -0.0620, -0.0294,  0.0185, -0.0011]])), ('fc2.bias', tensor([-0.0527,  0.1059]))])
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model.state_dict()的迭代输出:
conv.weight tensor([[[-0.5126,  0.5331,  0.2893]],

        [[ 0.4555, -0.1294,  0.0125]],

        [[ 0.1551,  0.4624,  0.2719]],

        [[-0.4037, -0.5695,  0.2529]],

        [[ 0.0677, -0.5520, -0.4953]],

        [[ 0.0964, -0.5395,  0.0329]],

        [[-0.3732,  0.2867,  0.3513]],

        [[-0.2955, -0.0611, -0.5704]],

        [[-0.5759,  0.5438,  0.2083]],

        [[ 0.1635, -0.1977,  0.1897]],

        [[-0.1315,  0.0727,  0.4758]],

        [[ 0.5563, -0.1573, -0.0435]],

        [[ 0.4147,  0.3957, -0.0221]],

        [[-0.4292,  0.5694,  0.3400]],

        [[ 0.2282, -0.2961, -0.0415]],

        [[ 0.2947,  0.1851,  0.4878]],

        [[ 0.3008,  0.2580,  0.3571]],

        [[ 0.0821,  0.2338, -0.3678]],

        [[-0.1976,  0.1668, -0.1952]],

        [[ 0.4718,  0.2912,  0.0277]],

        [[ 0.0925, -0.5663,  0.4554]],

        [[-0.3790,  0.5210,  0.4064]],

        [[ 0.0432,  0.2155, -0.3406]],

        [[-0.2695,  0.4273, -0.2091]],

        [[ 0.5320, -0.2330,  0.3117]],

        [[-0.2894,  0.1364,  0.5271]],

        [[ 0.5254,  0.2866,  0.0733]],

        [[ 0.4851, -0.1229, -0.4983]],

        [[ 0.1804,  0.4486, -0.0619]],

        [[-0.5312,  0.5690, -0.3322]],

        [[ 0.0271,  0.2029,  0.0384]],

        [[-0.0130, -0.2701,  0.3163]]])
conv.bias tensor([ 0.0342,  0.3380,  0.0396,  0.0872,  0.5354,  0.4468, -0.4234, -0.4960,
         0.4873, -0.4876,  0.5049, -0.4638, -0.0434, -0.3543, -0.0406, -0.4049,
         0.4760, -0.3240, -0.2755, -0.0046,  0.1204, -0.1654,  0.2997,  0.0625,
        -0.1786, -0.0292,  0.3281, -0.4937, -0.3622, -0.3028,  0.2650,  0.0713])
fc1.weight tensor([[ 0.0160, -0.0104, -0.0111,  ...,  0.0129,  0.0091,  0.0010],
        [ 0.0101,  0.0083,  0.0145,  ...,  0.0079,  0.0081,  0.0054],
        [-0.0059, -0.0004,  0.0028,  ..., -0.0139, -0.0017, -0.0005],
        ...,
        [-0.0160, -0.0081, -0.0147,  ...,  0.0150, -0.0108,  0.0030],
        [-0.0066, -0.0151,  0.0050,  ..., -0.0126, -0.0041,  0.0100],
        [ 0.0141, -0.0058, -0.0081,  ...,  0.0026,  0.0083,  0.0022]])
fc1.bias tensor([-1.4286e-02, -4.8758e-03, -9.6254e-03,  3.8352e-03,  1.7488e-02,
         9.5369e-03, -1.2196e-02, -4.0154e-03, -1.5040e-02, -1.7715e-02,
         1.4567e-02, -1.7208e-02, -1.7108e-02, -1.4707e-02, -1.2141e-03,
         1.0333e-02,  1.3588e-02,  1.4712e-02, -1.3024e-02, -1.6053e-02,
        -1.0188e-02, -1.5649e-02, -7.5805e-03, -1.3627e-02,  1.4144e-02,
         5.0014e-03, -1.0649e-02, -1.3394e-02,  4.9695e-03,  4.9918e-03,
         1.1173e-02, -1.5761e-02,  1.4373e-02,  8.3734e-04,  2.2160e-05,
        -1.5278e-02, -1.6162e-02,  3.3545e-03, -7.0366e-03,  1.2571e-02,
         1.0598e-02,  8.7718e-03, -5.5483e-03,  1.2729e-02, -1.5751e-02,
         9.5723e-03, -1.7305e-02, -1.3221e-02, -6.7073e-03, -1.6645e-03,
        -1.2975e-02,  1.1121e-02,  1.4769e-02,  1.3430e-03,  6.8799e-03,
         1.5452e-02,  1.4529e-02,  1.0451e-02, -7.1500e-03, -1.2924e-02,
        -7.7933e-03, -2.8951e-03,  8.1708e-03, -8.0176e-03])
fc2.weight tensor([[-0.0286, -0.1145, -0.0556, -0.0326, -0.1048, -0.1136, -0.1209,  0.0143,
         -0.0974, -0.1066, -0.0110, -0.1067,  0.0064,  0.0431,  0.0437, -0.0485,
          0.0760,  0.0398, -0.0526,  0.0138, -0.0278,  0.0861, -0.0675,  0.1166,
          0.0165,  0.0697, -0.0966, -0.0170, -0.1015, -0.1014, -0.0581,  0.1092,
          0.1147,  0.0013,  0.0246, -0.0419,  0.1228,  0.0919, -0.0277, -0.0781,
         -0.0209,  0.0984, -0.0507, -0.0988, -0.0766, -0.1127, -0.0171, -0.0945,
          0.0552, -0.1151,  0.1186,  0.0960,  0.0265,  0.1094,  0.1215, -0.0884,
          0.0510,  0.0182,  0.0710, -0.0617, -0.0658,  0.0838, -0.0267,  0.0384],
        [ 0.0812, -0.0191, -0.0470,  0.0184, -0.0612,  0.0393,  0.1248,  0.0249,
         -0.1226,  0.0047,  0.0882,  0.0487,  0.1045,  0.0559,  0.0201,  0.0271,
         -0.0657,  0.0610,  0.0036,  0.1152,  0.0320,  0.0100,  0.0171, -0.0890,
          0.0910, -0.0298, -0.0979,  0.0838,  0.0191, -0.0210,  0.0581,  0.0309,
          0.1167, -0.0727,  0.0471, -0.0216,  0.0969,  0.0827, -0.0934,  0.0181,
         -0.0922, -0.0406,  0.0906, -0.0490, -0.0792, -0.0736, -0.0735,  0.1206,
         -0.0137, -0.0138, -0.1037, -0.0516, -0.1138,  0.0146, -0.0104,  0.0561,
          0.0404,  0.0993,  0.0587,  0.0061, -0.0620, -0.0294,  0.0185, -0.0011]])
fc2.bias tensor([-0.0527,  0.1059])
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