RuntimeError：加载 state_dict 时出错

问题描述 投票：0回答：3

我有以下 PyTorch 模型：

import math
from abc import abstractmethod

import torch.nn as nn


class AlexNet3D(nn.Module):
    @abstractmethod
    def get_head(self):
        pass

    def __init__(self, input_size):
        super().__init__()
        self.input_size = input_size
        self.features = nn.Sequential(
            nn.Conv3d(1, 64, kernel_size=(5, 5, 5), stride=(2, 2, 2), padding=0),
            nn.BatchNorm3d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool3d(kernel_size=3, stride=3),

            nn.Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=0),
            nn.BatchNorm3d(128),
            nn.ReLU(inplace=True),
            nn.MaxPool3d(kernel_size=3, stride=3),

            nn.Conv3d(128, 192, kernel_size=(3, 3, 3), padding=1),
            nn.BatchNorm3d(192),
            nn.ReLU(inplace=True),

            nn.Conv3d(192, 192, kernel_size=(3, 3, 3), padding=1),
            nn.BatchNorm3d(192),
            nn.ReLU(inplace=True),

            nn.Conv3d(192, 128, kernel_size=(3, 3, 3), padding=1),
            nn.BatchNorm3d(128),
            nn.ReLU(inplace=True),
            nn.MaxPool3d(kernel_size=3, stride=3),
        )

        self.classifier = self.get_head()

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm3d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def forward(self, x):
        xp = self.features(x)
        x = xp.view(xp.size(0), -1)
        x = self.classifier(x)
        return [x, xp]


class AlexNet3DDropoutRegression(AlexNet3D):
    def get_head(self):
        return nn.Sequential(nn.Dropout(),
                             nn.Linear(self.input_size, 64),
                             nn.ReLU(inplace=True),
                             nn.Dropout(),
                             nn.Linear(64, 1),
                             )

我正在这样初始化模型：

def init_model(self):
    model = AlexNet3DDropoutRegression(4608)
    if self.use_cuda:
        log.info("Using CUDA; {} devices.".format(torch.cuda.device_count()))
        if torch.cuda.device_count() > 1:
            model = nn.DataParallel(model)
        model = model.to(self.device)
    return model

训练结束后，我这样保存模型：

    torch.save(self.model.state_dict(), self.cli_args.model_save_location)

然后我尝试加载保存的模型：

import torch
from reprex.models import AlexNet3DDropoutRegression


model_save_location = "/home/feczk001/shared/data/AlexNet/LoesScoring/loes_scoring_01.pt"

model = AlexNet3DDropoutRegression(4608)
model.load_state_dict(torch.load(model_save_location,
                                 map_location='cpu'))

但是我得到以下错误：

RuntimeError: Error(s) in loading state_dict for AlexNet3DDropoutRegression:
    Missing key(s) in state_dict: "features.0.weight", "features.0.bias", "features.1.weight", "features.1.bias", "features.1.running_mean", "features.1.running_var", "features.4.weight", "features.4.bias", "features.5.weight", "features.5.bias", "features.5.running_mean", "features.5.running_var", "features.8.weight", "features.8.bias", "features.9.weight", "features.9.bias", "features.9.running_mean", "features.9.running_var", "features.11.weight", "features.11.bias", "features.12.weight", "features.12.bias", "features.12.running_mean", "features.12.running_var", "features.14.weight", "features.14.bias", "features.15.weight", "features.15.bias", "features.15.running_mean", "features.15.running_var", "classifier.1.weight", "classifier.1.bias", "classifier.4.weight", "classifier.4.bias". 
    Unexpected key(s) in state_dict: "module.features.0.weight", "module.features.0.bias", "module.features.1.weight", "module.features.1.bias", "module.features.1.running_mean", "module.features.1.running_var", "module.features.1.num_batches_tracked", "module.features.4.weight", "module.features.4.bias", "module.features.5.weight", "module.features.5.bias", "module.features.5.running_mean", "module.features.5.running_var", "module.features.5.num_batches_tracked", "module.features.8.weight", "module.features.8.bias", "module.features.9.weight", "module.features.9.bias", "module.features.9.running_mean", "module.features.9.running_var", "module.features.9.num_batches_tracked", "module.features.11.weight", "module.features.11.bias", "module.features.12.weight", "module.features.12.bias", "module.features.12.running_mean", "module.features.12.running_var", "module.features.12.num_batches_tracked", "module.features.14.weight", "module.features.14.bias", "module.features.15.weight", "module.features.15.bias", "module.features.15.running_mean", "module.features.15.running_var", "module.features.15.num_batches_tracked", "module.classifier.1.weight", "module.classifier.1.bias", "module.classifier.4.weight", "module.classifier.4.bias".

这里出了什么问题？

python deep-learning pytorch
3个回答
3
投票

问题是您使用 

DataParallel
训练模型，然后尝试在非并行网络中重新加载模型。 
DataParallel
是一个包装类，它使原始模型（一个 
torch.nn.module
对象）成为名为 
DataParallel
的 
module
对象的类属性。此问题已解决 在 pytorch discuss, stack overflow 和 github 所以我也不会在这里重复细节，但你可以通过以下任一方式解决这个问题：

  1. 将模型专门保存和加载为 

    DataParallel
    对象，当您想使用模型进行推理时，这可能会失效，或者

  2. 保存

    DataParallel
    对象的
    module
    state_dict
    改为这样：

    # save state dict of DataParallel object
torch.save(model.module.state_dict(), path)


 .... Later
# reload weights on non-parallel model
model.load_state_dict(torch.load(path)

这是一个简单的例子：

model = AlexNet3DDropoutRegression(4608) # on cpu
model = nn.DataParallel(model)
model = model.to("cuda") # DataParallel object on GPU(s)


torch.save(model.module.state_dict(),"example_path.pt")

del model
model = AlexNet3DDropoutRegression(4608)

ret = model.load_state_dict(torch.load("example_path.pt")) 
print(ret)

输出：

>>> <All keys successfully matched>
  1. 或者，如果您已经保存了
    state_dict
    ，则可能更有用您需要重新加载，您还可以为
    state_dict
    模型加载
    DataParallel
    ，重新映射键名称以排除“模块”，然后使用重新-键控
    state_dict
    。像这样的东西：
incompatible_state_dict = torch.load("DataParallel_save_file.pt")
state_dict = {}
for key in incompatible_state_dict():
    state_dict[key.split("module.")[-1]] = incompatible_state_dict[key]

 ret = model.load_state_dict(state_dict)
 print(ret)

输出：

>>> <All keys successfully matched>
2
投票

nn.DataParallel
是一个包装类，它增加了一个“模块”。状态字典中所有键的前缀。因此，您会在意想不到的键中看到 
module.features
和 
module.classifier
。要解决这个问题，你需要做的就是在加载模型时去掉
module.
前缀
state_dict
.

model = AlexNet3DDropoutRegression(4608)
model_save_location = "/home/feczk001/shared/data/AlexNet/LoesScoring/loes_scoring_01.pt"

state_dict = torch.load(model_save_location, map_location='cpu')
model.load_state_dict({k.replace("module.", ""): v for k, v in state_dict.items()})
0
投票

你的问题是你正在从一个已经训练好的 DataParallel 模型中加载一个状态字典，然后你创建一个不使用 DataParallel 的新模型。 

module
在使用 DataParallel 和 PyTorch 时已经有前缀。因此，如果您删除
module
前缀，就可以了。除非您想将 DataParallel 用于新模型初始化，否则最好只删除 
module
前缀。

这个片段应该做到：

model = AlexNet3DDropoutRegression(4608)
state_dict = torch.load(model_save_location, map_location='cpu')
new_state_dict = {}
for key in state_dict.keys():
    new_key = key.replace("module.", "")
    new_state_dict[new_key] = state_dict[key]
model.load_state_dict(new_state_dict)
最新问题
© www.soinside.com 2019 - 2024. All rights reserved.