Why am I getting these weird errors when using MKLDNN? (While copying the parameter named "xxx", whose dimensions in the model are torch.Size([yyy]) and whose dimensions in the checkpoint are torch.Size([yyy]).)

Shisho_Sama: <p>Trying to run some modules in my RetinaFace architecture using MKLDNN results in these errors :<br>
Any help regarding this is greatly appreciated :</p>
<pre><code class="lang-auto">align_MKLDNN.py 
Loading pretrained model from ./weights/mobilenet0.25_Final.pth
remove prefix 'module.'
Traceback (most recent call last):
  File "/home/user/.vscode-server/extensions/ms-python.python-2020.1.58038/pythonFiles/ptvsd_launcher.py", line 43, in &lt;module&gt;
    main(ptvsdArgs)
  File "/home/user/.vscode-server/extensions/ms-python.python-2020.1.58038/pythonFiles/lib/python/old_ptvsd/ptvsd/__main__.py", line 432, in main
    run()
  File "/home/user/.vscode-server/extensions/ms-python.python-2020.1.58038/pythonFiles/lib/python/old_ptvsd/ptvsd/__main__.py", line 316, in run_file
    runpy.run_path(target, run_name='__main__')
  File "/home/user/anaconda3/lib/python3.7/runpy.py", line 263, in run_path
    pkg_name=pkg_name, script_name=fname)
  File "/home/user/anaconda3/lib/python3.7/runpy.py", line 96, in _run_module_code
    mod_name, mod_spec, pkg_name, script_name)
  File "/home/user/anaconda3/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/mnt/d/Codes/Pytorch_Retinaface_MKLDN/test_detect_align_MKLDNN.py", line 318, in &lt;module&gt;
    model_weights=model.weight )
  File "/mnt/d/Codes/Pytorch_Retinaface_MKLDN/test_detect_align_MKLDNN.py", line 87, in detect
    net = load_model(net, trained_model, cpu)
  File "/mnt/d/Codes/Pytorch_Retinaface_MKLDN/test_detect_align_MKLDNN.py", line 60, in load_model
    model.load_state_dict(pretrained_dict, strict=False)
  File "/home/user/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 830, in load_state_dict
    self.__class__.__name__, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for RetinaFace:
        While copying the parameter named "body.stage1.0.0.weight", whose dimensions in the model are torch.Size([8, 3, 3, 3]) and whose dimensions in the checkpoint are torch.Size([8, 3, 3, 3]).
        While copying the parameter named "body.stage1.0.1.running_var", whose dimensions in the model are torch.Size([8]) and whose dimensions in the checkpoint are torch.Size([8]).
        While copying the parameter named "body.stage1.0.1.running_mean", whose dimensions in the model are torch.Size([8]) and whose dimensions in the checkpoint are torch.Size([8]).
        While copying the parameter named "body.stage1.0.1.weight", whose dimensions in the model are torch.Size([8]) and whose dimensions in the checkpoint are torch.Size([8]).
        While copying the parameter named "body.stage1.0.1.bias", whose dimensions in the model are torch.Size([8]) and whose dimensions in the checkpoint are torch.Size([8]).
        While copying the parameter named "body.stage1.1.0.weight", whose dimensions in the model are torch.Size([8, 1, 3, 3]) and whose dimensions in the checkpoint are torch.Size([8, 1, 3, 3]).
        While copying the parameter named "body.stage1.1.1.running_var", whose dimensions in the model are torch.Size([8]) and whose dimensions in the checkpoint are torch.Size([8]).
        While copying the parameter named "body.stage1.1.1.running_mean", whose dimensions in the model are torch.Size([8]) and whose dimensions in the checkpoint are torch.Size([8]).
        While copying the parameter named "body.stage1.1.1.weight", whose dimensions in the model are torch.Size([8]) and whose dimensions in the checkpoint are torch.Size([8]).
        While copying the parameter named "body.stage1.1.1.bias", whose dimensions in the model are torch.Size([8]) and whose dimensions in the checkpoint are torch.Size([8]).
        While copying the parameter named "body.stage1.1.3.weight", whose dimensions in the model are torch.Size([16, 8, 1, 1]) and whose dimensions in the checkpoint are torch.Size([16, 8, 1, 1]).
        While copying the parameter named "body.stage1.1.4.running_var", whose dimensions in the model are torch.Size([16]) and whose dimensions in the checkpoint are torch.Size([16]).
        While copying the parameter named "body.stage1.1.4.running_mean", whose dimensions in the model are torch.Size([16]) and whose dimensions in the checkpoint are torch.Size([16]).
        While copying the parameter named "body.stage1.1.4.weight", whose dimensions in the model are torch.Size([16]) and whose dimensions in the checkpoint are torch.Size([16]).
        While copying the parameter named "body.stage1.1.4.bias", whose dimensions in the model are torch.Size([16]) and whose dimensions in the checkpoint are torch.Size([16]).
        While copying the parameter named "body.stage1.2.0.weight", whose dimensions in the model are torch.Size([16, 1, 3, 3]) and whose dimensions in the checkpoint are torch.Size([16, 1, 3, 3]).
        While copying the parameter named "body.stage1.2.1.running_var", whose dimensions in the model are torch.Size([16]) and whose dimensions in the checkpoint are torch.Size([16]).
        While copying the parameter named "body.stage1.2.1.running_mean", whose dimensions in the model are torch.Size([16]) and whose dimensions in the checkpoint are torch.Size([16]).
        While copying the parameter named "body.stage1.2.1.weight", whose dimensions in the model are torch.Size([16]) and whose dimensions in the checkpoint are torch.Size([16]).
        While copying the parameter named "body.stage1.2.1.bias", whose dimensions in the model are torch.Size([16]) and whose dimensions in the checkpoint are torch.Size([16]).
        While copying the parameter named "body.stage1.2.3.weight", whose dimensions in the model are torch.Size([32, 16, 1, 1]) and whose dimensions in the checkpoint are torch.Size([32, 16, 1, 1]).
        While copying the parameter named "body.stage1.2.4.running_var", whose dimensions in the model are torch.Size([32]) and whose dimensions in the checkpoint are torch.Size([32]).
        While copying the parameter named "body.stage1.2.4.running_mean", whose dimensions in the model are torch.Size([32]) and whose dimensions in the checkpoint are torch.Size([32]).
        While copying the parameter named "body.stage1.2.4.weight", whose dimensions in the model are torch.Size([32]) and whose dimensions in the checkpoint are torch.Size([32]).
        While copying the parameter named "body.stage1.2.4.bias", whose dimensions in the model are torch.Size([32]) and whose dimensions in the checkpoint are torch.Size([32]).
        While copying the parameter named "body.stage1.3.0.weight", whose dimensions in the model are torch.Size([32, 1, 3, 3]) and whose dimensions in the checkpoint are torch.Size([32, 1, 3, 3]).
        While copying the parameter named "body.stage1.3.1.running_var", whose dimensions in the model are torch.Size([32]) and whose dimensions in the checkpoint are torch.Size([32]).
        While copying the parameter named "body.stage1.3.1.running_mean", whose dimensions in the model are torch.Size([32]) and whose dimensions in the checkpoint are torch.Size([32]).
        While copying the parameter named "body.stage1.3.1.weight", whose dimensions in the model are torch.Size([32]) and whose dimensions in the checkpoint are torch.Size([32]).
        While copying the parameter named "body.stage1.3.1.bias", whose dimensions in the model are torch.Size([32]) and whose dimensions in the checkpoint are torch.Size([32]).
        While copying the parameter named "body.stage1.3.3.weight", whose dimensions in the model are torch.Size([32, 32, 1, 1]) and whose dimensions in the checkpoint are torch.Size([32, 32, 1, 1]).
        While copying the parameter named "body.stage1.3.4.running_var", whose dimensions in the model are torch.Size([32]) and whose dimensions in the checkpoint are torch.Size([32]).
        While copying the parameter named "body.stage1.3.4.running_mean", whose dimensions in the model are torch.Size([32]) and whose dimensions in the checkpoint are torch.Size([32]).
        While copying the parameter named "body.stage1.3.4.weight", whose dimensions in the model are torch.Size([32]) and whose dimensions in the checkpoint are torch.Size([32]).
        While copying the parameter named "body.stage1.3.4.bias", whose dimensions in the model are torch.Size([32]) and whose dimensions in the checkpoint are torch.Size([32]).
        While copying the parameter named "body.stage1.4.0.weight", whose dimensions in the model are torch.Size([32, 1, 3, 3]) and whose dimensions in the checkpoint are torch.Size([32, 1, 3, 3]).
        While copying the parameter named "body.stage1.4.1.running_var", whose dimensions in the model are torch.Size([32]) and whose dimensions in the checkpoint are torch.Size([32]).
        While copying the parameter named "body.stage1.4.1.running_mean", whose dimensions in the model are torch.Size([32]) and whose dimensions in the checkpoint are torch.Size([32]).
        While copying the parameter named "body.stage1.4.1.weight", whose dimensions in the model are torch.Size([32]) and whose dimensions in the checkpoint are torch.Size([32]).
        While copying the parameter named "body.stage1.4.1.bias", whose dimensions in the model are torch.Size([32]) and whose dimensions in the checkpoint are torch.Size([32]).
        While copying the parameter named "body.stage1.4.3.weight", whose dimensions in the model are torch.Size([64, 32, 1, 1]) and whose dimensions in the checkpoint are torch.Size([64, 32, 1, 1]).
        While copying the parameter named "body.stage1.4.4.running_var", whose dimensions in the model are torch.Size([64]) and whose dimensions in the checkpoint are torch.Size([64]).
        While copying the parameter named "body.stage1.4.4.running_mean", whose dimensions in the model are torch.Size([64]) and whose dimensions in the checkpoint are torch.Size([64]).
        While copying the parameter named "body.stage1.4.4.weight", whose dimensions in the model are torch.Size([64]) and whose dimensions in the checkpoint are torch.Size([64]).
        While copying the parameter named "body.stage1.4.4.bias", whose dimensions in the model are torch.Size([64]) and whose dimensions in the checkpoint are torch.Size([64]).
        While copying the parameter named "body.stage1.5.0.weight", whose dimensions in the model are torch.Size([64, 1, 3, 3]) and whose dimensions in the checkpoint are torch.Size([64, 1, 3, 3]).
        While copying the parameter named "body.stage1.5.1.running_var", whose dimensions in the model are torch.Size([64]) and whose dimensions in the checkpoint are torch.Size([64]).
        While copying the parameter named "body.stage1.5.1.running_mean", whose dimensions in the model are torch.Size([64]) and whose dimensions in the checkpoint are torch.Size([64]).
        While copying the parameter named "body.stage1.5.1.weight", whose dimensions in the model are torch.Size([64]) and whose dimensions in the checkpoint are torch.Size([64]).
        While copying the parameter named "body.stage1.5.1.bias", whose dimensions in the model are torch.Size([64]) and whose dimensions in the checkpoint are torch.Size([64]).
        While copying the parameter named "body.stage1.5.3.weight", whose dimensions in the model are torch.Size([64, 64, 1, 1]) and whose dimensions in the checkpoint are torch.Size([64, 64, 1, 1]).
        While copying the parameter named "body.stage1.5.4.running_var", whose dimensions in the model are torch.Size([64]) and whose dimensions in the checkpoint are torch.Size([64]).
        While copying the parameter named "body.stage1.5.4.running_mean", whose dimensions in the model are torch.Size([64]) and whose dimensions in the checkpoint are torch.Size([64]).
        While copying the parameter named "body.stage1.5.4.weight", whose dimensions in the model are torch.Size([64]) and whose dimensions in the checkpoint are torch.Size([64]).
        While copying the parameter named "body.stage1.5.4.bias", whose dimensions in the model are torch.Size([64]) and whose dimensions in the checkpoint are torch.Size([64]).
        While copying the parameter named "body.stage2.0.0.weight", whose dimensions in the model are torch.Size([64, 1, 3, 3]) and whose dimensions in the checkpoint are torch.Size([64, 1, 3, 3]).
        While copying the parameter named "body.stage2.0.1.running_var", whose dimensions in the model are torch.Size([64]) and whose dimensions in the checkpoint are torch.Size([64]).
        While copying the parameter named "body.stage2.0.1.running_mean", whose dimensions in the model are torch.Size([64]) and whose dimensions in the checkpoint are torch.Size([64]).
        While copying the parameter named "body.stage2.0.1.weight", whose dimensions in the model are torch.Size([64]) and whose dimensions in the checkpoint are torch.Size([64]).
        While copying the parameter named "body.stage2.0.1.bias", whose dimensions in the model are torch.Size([64]) and whose dimensions in the checkpoint are torch.Size([64]).
        While copying the parameter named "body.stage2.0.3.weight", whose dimensions in the model are torch.Size([128, 64, 1, 1]) and whose dimensions in the checkpoint are torch.Size([128, 64, 1, 1]).
        While copying the parameter named "body.stage2.0.4.running_var", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.0.4.running_mean", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.0.4.weight", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.0.4.bias", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.1.0.weight", whose dimensions in the model are torch.Size([128, 1, 3, 3]) and whose dimensions in the checkpoint are torch.Size([128, 1, 3, 3]).
        While copying the parameter named "body.stage2.1.1.running_var", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.1.1.running_mean", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.1.1.weight", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.1.1.bias", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.1.3.weight", whose dimensions in the model are torch.Size([128, 128, 1, 1]) and whose dimensions in the checkpoint are torch.Size([128, 128, 1, 1]).
        While copying the parameter named "body.stage2.1.4.running_var", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.1.4.running_mean", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.1.4.weight", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.1.4.bias", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.2.0.weight", whose dimensions in the model are torch.Size([128, 1, 3, 3]) and whose dimensions in the checkpoint are torch.Size([128, 1, 3, 3]).
        While copying the parameter named "body.stage2.2.1.running_var", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.2.1.running_mean", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.2.1.weight", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.2.1.bias", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.2.3.weight", whose dimensions in the model are torch.Size([128, 128, 1, 1]) and whose dimensions in the checkpoint are torch.Size([128, 128, 1, 1]).
        While copying the parameter named "body.stage2.2.4.running_var", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.2.4.running_mean", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.2.4.weight", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.2.4.bias", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.3.0.weight", whose dimensions in the model are torch.Size([128, 1, 3, 3]) and whose dimensions in the checkpoint are torch.Size([128, 1, 3, 3]).
        While copying the parameter named "body.stage2.3.1.running_var", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.3.1.running_mean", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.3.1.weight", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.3.1.bias", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.3.3.weight", whose dimensions in the model are torch.Size([128, 128, 1, 1]) and whose dimensions in the checkpoint are torch.Size([128, 128, 1, 1]).
        While copying the parameter named "body.stage2.3.4.running_var", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.3.4.running_mean", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.3.4.weight", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.3.4.bias", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.4.0.weight", whose dimensions in the model are torch.Size([128, 1, 3, 3]) and whose dimensions in the checkpoint are torch.Size([128, 1, 3, 3]).
        While copying the parameter named "body.stage2.4.1.running_var", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.4.1.running_mean", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.4.1.weight", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.4.1.bias", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.4.3.weight", whose dimensions in the model are torch.Size([128, 128, 1, 1]) and whose dimensions in the checkpoint are torch.Size([128, 128, 1, 1]).
        While copying the parameter named "body.stage2.4.4.running_var", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.4.4.running_mean", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.4.4.weight", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.4.4.bias", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.5.0.weight", whose dimensions in the model are torch.Size([128, 1, 3, 3]) and whose dimensions in the checkpoint are torch.Size([128, 1, 3, 3]).
        While copying the parameter named "body.stage2.5.1.running_var", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.5.1.running_mean", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.5.1.weight", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.5.1.bias", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.5.3.weight", whose dimensions in the model are torch.Size([128, 128, 1, 1]) and whose dimensions in the checkpoint are torch.Size([128, 128, 1, 1]).
        While copying the parameter named "body.stage2.5.4.running_var", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.5.4.running_mean", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.5.4.weight", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage2.5.4.bias", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage3.0.0.weight", whose dimensions in the model are torch.Size([128, 1, 3, 3]) and whose dimensions in the checkpoint are torch.Size([128, 1, 3, 3]).
        While copying the parameter named "body.stage3.0.1.running_var", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage3.0.1.running_mean", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage3.0.1.weight", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage3.0.1.bias", whose dimensions in the model are torch.Size([128]) and whose dimensions in the checkpoint are torch.Size([128]).
        While copying the parameter named "body.stage3.0.3.weight", whose dimensions in the model are torch.Size([256, 128, 1, 1]) and whose dimensions in the checkpoint are torch.Size([256, 128, 1, 1]).
        While copying the parameter named "body.stage3.0.4.running_var", whose dimensions in the model are torch.Size([256]) and whose dimensions in the checkpoint are torch.Size([256]).
        While copying the parameter named "body.stage3.0.4.running_mean", whose dimensions in the model are torch.Size([256]) and whose dimensions in the checkpoint are torch.Size([256]).
        While copying the parameter named "body.stage3.0.4.weight", whose dimensions in the model are torch.Size([256]) and whose dimensions in the checkpoint are torch.Size([256]).
        While copying the parameter named "body.stage3.0.4.bias", whose dimensions in the model are torch.Size([256]) and whose dimensions in the checkpoint are torch.Size([256]).
        While copying the parameter named "body.stage3.1.0.weight", whose dimensions in the model are torch.Size([256, 1, 3, 3]) and whose dimensions in the checkpoint are torch.Size([256, 1, 3, 3]).
        While copying the parameter named "body.stage3.1.1.running_var", whose dimensions in the model are torch.Size([256]) and whose dimensions in the checkpoint are torch.Size([256]).
        While copying the parameter named "body.stage3.1.1.running_mean", whose dimensions in the model are torch.Size([256]) and whose dimensions in the checkpoint are torch.Size([256]).
        While copying the parameter named "body.stage3.1.1.weight", whose dimensions in the model are torch.Size([256]) and whose dimensions in the checkpoint are torch.Size([256]).
        While copying the parameter named "body.stage3.1.1.bias", whose dimensions in the model are torch.Size([256]) and whose dimensions in the checkpoint are torch.Size([256]).
        While copying the parameter named "body.stage3.1.3.weight", whose dimensions in the model are torch.Size([256, 256, 1, 1]) and whose dimensions in the checkpoint are torch.Size([256, 256, 1, 1]).
        While copying the parameter named "body.stage3.1.4.running_var", whose dimensions in the model are torch.Size([256]) and whose dimensions in the checkpoint are torch.Size([256]).
        While copying the parameter named "body.stage3.1.4.running_mean", whose dimensions in the model are torch.Size([256]) and whose dimensions in the checkpoint are torch.Size([256]).
        While copying the parameter named "body.stage3.1.4.weight", whose dimensions in the model are torch.Size([256]) and whose dimensions in the checkpoint are torch.Size([256]).
        While copying the parameter named "body.stage3.1.4.bias", whose dimensions in the model are torch.Size([256]) and whose dimensions in the checkpoint are torch.Size([256]).
        While copying the parameter named "ssh1.conv3X3.0.weight", whose dimensions in the model are torch.Size([32, 64, 3, 3]) and whose dimensions in the checkpoint are torch.Size([32, 64, 3, 3]).
        While copying the parameter named "ssh1.conv3X3.1.running_var", whose dimensions in the model are torch.Size([32]) and whose dimensions in the checkpoint are torch.Size([32]).
        While copying the parameter named "ssh1.conv3X3.1.running_mean", whose dimensions in the model are torch.Size([32]) and whose dimensions in the checkpoint are torch.Size([32]).
        While copying the parameter named "ssh1.conv3X3.1.weight", whose dimensions in the model are torch.Size([32]) and whose dimensions in the checkpoint are torch.Size([32]).
        While copying the parameter named "ssh1.conv3X3.1.bias", whose dimensions in the model are torch.Size([32]) and whose dimensions in the checkpoint are torch.Size([32]).


....

</code></pre>
<p>The full list can be viewed here : <a href="https://paste.ee/p/Vx4Wi" rel="nofollow noopener">https://paste.ee/p/Vx4Wi</a></p>
<p>The changes that I have made looks like this :</p>
<pre><code class="lang-auto">class RetinaFace(nn.Module):
    def __init__(self, cfg = None, phase = 'train'):
        """
        :param cfg:  Network related settings.
        :param phase: train or test.
        """
        super(RetinaFace,self).__init__()
        self.phase = phase
        backbone = None
        if cfg['name'] == 'mobilenet0.25':
            backbone = MobileNetV1()
            if cfg['pretrain']:
                checkpoint = torch.load("./weights/mobilenetV1X0.25_pretrain.tar", map_location=torch.device('cpu'))
                from collections import OrderedDict
                new_state_dict = OrderedDict()
                for k, v in checkpoint['state_dict'].items():
                    name = k[7:]  # remove module.
                    new_state_dict[name] = v
                # load params
                backbone.load_state_dict(new_state_dict)
        elif cfg['name'] == 'Resnet50':
            import torchvision.models as models
            backbone = models.resnet50(pretrained=cfg['pretrain'])

        # self.body = _utils.IntermediateLayerGetter(backbone, cfg['return_layers'])
        self.body = _utils.IntermediateLayerGetter(backbone, cfg['return_layers'])

        # convert to mkldnn 
        # self.body = mkldnn_utils.to_mkldnn(self.body)

        in_channels_stage2 = cfg['in_channel']
        in_channels_list = [
            in_channels_stage2 * 2,
            in_channels_stage2 * 4,
            in_channels_stage2 * 8,
        ]
        out_channels = cfg['out_channel']

        self.fpn = FPN(in_channels_list,out_channels)
        
        self.ssh1 = SSH(out_channels, out_channels)
        self.ssh2 = SSH(out_channels, out_channels)
        self.ssh3 = SSH(out_channels, out_channels)

        self.ClassHead = self._make_class_head(fpn_num=3, inchannels=cfg['out_channel'])
        self.BboxHead = self._make_bbox_head(fpn_num=3, inchannels=cfg['out_channel'])
        self.LandmarkHead = self._make_landmark_head(fpn_num=3, inchannels=cfg['out_channel'])

        # convert to mkldnn 
        self.body.eval()
        self.ssh1.eval()
        self.ssh2.eval()
        self.ssh3.eval()
        self.ClassHead.eval()
        self.BboxHead.eval()
        self.LandmarkHead.eval()

        self.body = mkldnn_utils.to_mkldnn(self.body)
        self.ssh1 = mkldnn_utils.to_mkldnn(self.ssh1)
        self.ssh2 = mkldnn_utils.to_mkldnn(self.ssh2)
        self.ssh3 = mkldnn_utils.to_mkldnn(self.ssh3)
        self.ClassHead = mkldnn_utils.to_mkldnn(self.ClassHead)
        self.BboxHead = mkldnn_utils.to_mkldnn(self.BboxHead)
        self.LandmarkHead = mkldnn_utils.to_mkldnn(self.LandmarkHead)



    def forward(self,inputs):
        

        inputs = inputs.to_mkldnn()
        out = self.body(inputs)
        
        # FPN
        fpn = self.fpn(out)

        # move to mkldnn
        fpn = fpn.to_mkldnn()

        # SSH
        feature1 = self.ssh1(fpn[0])
        feature2 = self.ssh2(fpn[1])
        feature3 = self.ssh3(fpn[2])
        features = [feature1, feature2, feature3]

        # move to mkldnn
        features = features.to_mkldnn()

        bbox_regressions = torch.cat([self.BboxHead[i](feature) for i, feature in enumerate(features)], dim=1)
        classifications = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)],dim=1)
        ldm_regressions = torch.cat([self.LandmarkHead[i](feature) for i, feature in enumerate(features)], dim=1)

        if self.phase == 'train':
            output = (bbox_regressions, classifications, ldm_regressions)
        else:
            output = (bbox_regressions, F.softmax(classifications, dim=-1), ldm_regressions)
        return output

</code></pre>
<p>Any help is greatly appreciated.</p>

Shisho_Sama: <p>From This, it is known that this is a generic message, that gets returned when an exception has occured.<br>
In order to see the actual exception, one needs to navigate to <code>torch/nn/modules/module.py</code> and edit :<br>
<code>_load_from_state_dict</code> .<br>
This repo has added the exception args to the mentioned method. (<a href="https://github.com/pytorch/pytorch/blob/ddc71d02374011d4c0999ff3f84233a977e140a8/torch/nn/modules/module.py#L711" rel="nofollow noopener">link</a>)</p>

una_li: <p>Hi, I’ve met exactly the same issue as you described.<br>
In my model, there are some layers which MKLDNN does not support and also some supported layers. So I convert to mkldnn in the forward function just as you did. Same runtime error occurred.<br>
Could u pls give me some advise on how to solve it?<br>
Any help would be greatly appreciated.</p>

Shisho_Sama: <p>This is fixed in the latest nightly build.<br>
basically you are getting this generic error when an exception occurs.<br>
Updating to the latest nightly build should show you exactly what the underlying exception is.</p>