RuntimeError: Error(s) in loading state_dict

I have the following PyTorch model:

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),
                             )

I am initializing the model like this:

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

After training, I save the model like this:

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

I then attempt to load the saved model:

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'))

But I get the following error:

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". 

What is going wrong here?