Why are state_dict keys getting prepended with the string "module."

username: Megh_Bhalerao
cooked: <p>Hello everyone,<br>
I am saving model using:</p>
<pre><code class="lang-auto">torch.save({
                    'step': step,
                    'arch': args.net,
                    'G_state_dict': G.state_dict(),
                    'F1_state_dict': F1.state_dict(),
                    'best_acc_test': best_acc_test,
                    'optimizer_g' : optimizer_g.state_dict(),
                    'optimizer_f' : optimizer_f.state_dict(),
                    'feat_dict_target': feat_dict_target
                    },os.path.join(args.checkpath,"%s_%s_%s_%d.ckpt.pth.tar"%(args.net,args.source,args.target,step)))
</code></pre>
<p>But when I try <code>model.load_state_dict(the_state_dict)</code>, it seems like every key of the dictionary is getting <strong>prepended</strong> by this string: <code>module.</code> When I iterate through this state_dict and remove this string from each of the keys, the <strong>state_dict loads just fine</strong>. I am using <strong>resnet34</strong>.<br>
Please help.<br>
Thanks</p>

username: albanD
cooked: <p>Hi,</p>
<p>This is most likely because your actual model is saved in <code>G.model</code>? If this is the only part you’re interested in you can save it directly by doing <code>G.model.state_dict()</code>.</p>

username: Megh_Bhalerao
cooked: <p>Thanks for your reply. I am using <code>resnet34</code>. My architecture code is:</p>
<pre><code class="lang-auto">from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from torch.autograd import Function
import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo

__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
           'resnet152']

model_urls = {
    'resnet18': 'https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth',
    'resnet34': 'https://s3.amazonaws.com/pytorch/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://s3.amazonaws.com/pytorch/models/resnet50-19c8e357.pth',
    'resnet101':
        'https://s3.amazonaws.com/pytorch/models/resnet101-5d3b4d8f.pth',
    'resnet152':
        'https://s3.amazonaws.com/pytorch/models/resnet152-b121ed2d.pth',
}


def init_weights(m):
    classname = m.__class__.__name__
    if classname.find('Conv2d') != -1 or \
       classname.find('ConvTranspose2d') != -1:
        nn.init.kaiming_uniform_(m.weight)
        nn.init.zeros_(m.bias)
    elif classname.find('BatchNorm') != -1:
        nn.init.normal_(m.weight, 1.0, 0.02)
        nn.init.zeros_(m.bias)
    elif classname.find('Linear') != -1:
        nn.init.xavier_normal_(m.weight)


def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class GradReverse(Function):
    def __init__(self, lambd):
        self.lambd = lambd

    def forward(self, x):
        return x.view_as(x)

    def backward(self, grad_output):
        return (grad_output * -self.lambd)


def grad_reverse(x, lambd=1.0):
    return GradReverse(lambd)(x)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, nobn=False):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride
        self.nobn = nobn

    def forward(self, x, source=True):

        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)
        out += residual
        out = self.relu(out)

        return out


class ScaleLayer(nn.Module):
    def __init__(self, init_value=1e-3):
        super(ScaleLayer, self).__init__()
        self.scale = nn.Parameter(torch.FloatTensor([init_value]))

    def forward(self, input):
        print(self.scale)
        return input * self.scale


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, nobn=False):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1,
                               stride=stride, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)

        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
                               stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)

        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample

        self.stride = stride
        self.nobn = nobn

    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)
        out = self.conv3(out)
        out = self.bn3(out)
        if self.downsample is not None:
            residual = self.downsample(x)
        out += residual
        out = self.relu(out)

        return out


class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.in1 = nn.InstanceNorm2d(64)
        self.in2 = nn.InstanceNorm2d(128)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2,
                                    padding=0, ceil_mode=True)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AvgPool2d(7)
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        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.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1, nobn=False):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion

        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, nobn=nobn))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        return x

def resnet34(pretrained=True):
    """Constructs a ResNet-34 model.
    Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3])
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
    return model
</code></pre>
<p>Is there some issue with this code?<br>
Thanks,<br>
Megh</p>

username: albanD
cooked: <p>I don’t think there is any issue. it is just the way your Model is structured.<br>
As I mentioned, if you don’t want to see the first level you can just get the state_dict for <code>G.model</code> directly.</p>

username: Megh_Bhalerao
cooked: <p>Thanks, I will try doing that.</p>

username: ptrblck
cooked: <p>Side info: Besides the mentions possibility that you are adding the <code>.module</code> attribute manually, it would also be added automatically by e.g. <code>nn.DataParallel</code> as described <a href="https://pytorch.org/tutorials/beginner/saving_loading_models.html#saving-torch-nn-dataparallel-models">here</a>.</p>

username: Megh_Bhalerao
cooked: <p>Yes, <a class="mention" href="/u/ptrblck">@ptrblck</a>! I am doing parallel training. That is why I guess it is being added.<br>
Thank you so much!</p>