Error(s) in loading state_dict for ResNet_AT

[minnieteng]

Hello! When I try to instantiate the trained model this way:

atType = ['self-attention', 'self_relation-attention'][0]    
model = networks.resnet18_at(at_type=atType)        
model.load_state_dict(torch.load('trained_model_debian.pth', map_location=get_default_device()))

I keep getting this error: what do I do??

`---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
in
23 model = networks.resnet18_at(at_type=atType)
24 softmax = torch.nn.Softmax(dim=1)
---> 25 model.load_state_dict(torch.load('trained_model_debian.pt', map_location=get_default_device()))
26
27 def predict(x):

~/anaconda3/envs/emotion_fan/lib/python3.9/site-packages/torch/nn/modules/module.py in load_state_dict(self, state_dict, strict)
1221
1222 if len(error_msgs) > 0:
-> 1223 raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
1224 self.class.name, "\n\t".join(error_msgs)))
1225 return _IncompatibleKeys(missing_keys, unexpected_keys)

RuntimeError: Error(s) in loading state_dict for ResNet_AT:
Missing key(s) in state_dict: "conv1.weight", "bn1.weight", "bn1.bias", "bn1.running_mean", "bn1.running_var", "layer1.0.conv1.weight", "layer1.0.bn1.weight", "layer1.0.bn1.bias", "layer1.0.bn1.running_mean", "layer1.0.bn1.running_var", "layer1.0.conv2.weight", "layer1.0.bn2.weight", "layer1.0.bn2.bias", "layer1.0.bn2.running_mean", "layer1.0.bn2.running_var", "layer1.1.conv1.weight", "layer1.1.bn1.weight", "layer1.1.bn1.bias", "layer1.1.bn1.running_mean", "layer1.1.bn1.running_var", "layer1.1.conv2.weight", "layer1.1.bn2.weight", "layer1.1.bn2.bias", "layer1.1.bn2.running_mean", "layer1.1.bn2.running_var", "layer2.0.conv1.weight", "layer2.0.bn1.weight", "layer2.0.bn1.bias", "layer2.0.bn1.running_mean", "layer2.0.bn1.running_var", "layer2.0.conv2.weight", "layer2.0.bn2.weight", "layer2.0.bn2.bias", "layer2.0.bn2.running_mean", "layer2.0.bn2.running_var", "layer2.0.downsample.0.weight", "layer2.0.downsample.1.weight", "layer2.0.downsample.1.bias", "layer2.0.downsample.1.running_mean", "layer2.0.downsample.1.running_var", "layer2.1.conv1.weight", "layer2.1.bn1.weight", "layer2.1.bn1.bias", "layer2.1.bn1.running_mean", "layer2.1.bn1.running_var", "layer2.1.conv2.weight", "layer2.1.bn2.weight", "layer2.1.bn2.bias", "layer2.1.bn2.running_mean", "layer2.1.bn2.running_var", "layer3.0.conv1.weight", "layer3.0.bn1.weight", "layer3.0.bn1.bias", "layer3.0.bn1.running_mean", "layer3.0.bn1.running_var", "layer3.0.conv2.weight", "layer3.0.bn2.weight", "layer3.0.bn2.bias", "layer3.0.bn2.running_mean", "layer3.0.bn2.running_var", "layer3.0.downsample.0.weight", "layer3.0.downsample.1.weight", "layer3.0.downsample.1.bias", "layer3.0.downsample.1.running_mean", "layer3.0.downsample.1.running_var", "layer3.1.conv1.weight", "layer3.1.bn1.weight", "layer3.1.bn1.bias", "layer3.1.bn1.running_mean", "layer3.1.bn1.running_var", "layer3.1.conv2.weight", "layer3.1.bn2.weight", "layer3.1.bn2.bias", "layer3.1.bn2.running_mean", "layer3.1.bn2.running_var", "layer4.0.conv1.weight", "layer4.0.bn1.weight", "layer4.0.bn1.bias", "layer4.0.bn1.running_mean", "layer4.0.bn1.running_var", "layer4.0.conv2.weight", "layer4.0.bn2.weight", "layer4.0.bn2.bias", "layer4.0.bn2.running_mean", "layer4.0.bn2.running_var", "layer4.0.downsample.0.weight", "layer4.0.downsample.1.weight", "layer4.0.downsample.1.bias", "layer4.0.downsample.1.running_mean", "layer4.0.downsample.1.running_var", "layer4.1.conv1.weight", "layer4.1.bn1.weight", "layer4.1.bn1.bias", "layer4.1.bn1.running_mean", "layer4.1.bn1.running_var", "layer4.1.conv2.weight", "layer4.1.bn2.weight", "layer4.1.bn2.bias", "layer4.1.bn2.running_mean", "layer4.1.bn2.running_var", "alpha.0.weight", "alpha.0.bias", "beta.0.weight", "beta.0.bias", "pred_fc1.weight", "pred_fc1.bias", "pred_fc2.weight", "pred_fc2.bias".
Unexpected key(s) in state_dict: "module.conv1.weight", "module.bn1.weight", "module.bn1.bias", "module.bn1.running_mean", "module.bn1.running_var", "module.bn1.num_batches_tracked", "module.layer1.0.conv1.weight", "module.layer1.0.bn1.weight", "module.layer1.0.bn1.bias", "module.layer1.0.bn1.running_mean", "module.layer1.0.bn1.running_var", "module.layer1.0.bn1.num_batches_tracked", "module.layer1.0.conv2.weight", "module.layer1.0.bn2.weight", "module.layer1.0.bn2.bias", "module.layer1.0.bn2.running_mean", "module.layer1.0.bn2.running_var", "module.layer1.0.bn2.num_batches_tracked", "module.layer1.1.conv1.weight", "module.layer1.1.bn1.weight", "module.layer1.1.bn1.bias", "module.layer1.1.bn1.running_mean", "module.layer1.1.bn1.running_var", "module.layer1.1.bn1.num_batches_tracked", "module.layer1.1.conv2.weight", "module.layer1.1.bn2.weight", "module.layer1.1.bn2.bias", "module.layer1.1.bn2.running_mean", "module.layer1.1.bn2.running_var", "module.layer1.1.bn2.num_batches_tracked", "module.layer2.0.conv1.weight", "module.layer2.0.bn1.weight", "module.layer2.0.bn1.bias", "module.layer2.0.bn1.running_mean", "module.layer2.0.bn1.running_var", "module.layer2.0.bn1.num_batches_tracked", "module.layer2.0.conv2.weight", "module.layer2.0.bn2.weight", "module.layer2.0.bn2.bias", "module.layer2.0.bn2.running_mean", "module.layer2.0.bn2.running_var", "module.layer2.0.bn2.num_batches_tracked", "module.layer2.0.downsample.0.weight", "module.layer2.0.downsample.1.weight", "module.layer2.0.downsample.1.bias", "module.layer2.0.downsample.1.running_mean", "module.layer2.0.downsample.1.running_var", "module.layer2.0.downsample.1.num_batches_tracked", "module.layer2.1.conv1.weight", "module.layer2.1.bn1.weight", "module.layer2.1.bn1.bias", "module.layer2.1.bn1.running_mean", "module.layer2.1.bn1.running_var", "module.layer2.1.bn1.num_batches_tracked", "module.layer2.1.conv2.weight", "module.layer2.1.bn2.weight", "module.layer2.1.bn2.bias", "module.layer2.1.bn2.running_mean", "module.layer2.1.bn2.running_var", "module.layer2.1.bn2.num_batches_tracked", "module.layer3.0.conv1.weight", "module.layer3.0.bn1.weight", "module.layer3.0.bn1.bias", "module.layer3.0.bn1.running_mean", "module.layer3.0.bn1.running_var", "module.layer3.0.bn1.num_batches_tracked", "module.layer3.0.conv2.weight", "module.layer3.0.bn2.weight", "module.layer3.0.bn2.bias", "module.layer3.0.bn2.running_mean", "module.layer3.0.bn2.running_var", "module.layer3.0.bn2.num_batches_tracked", "module.layer3.0.downsample.0.weight", "module.layer3.0.downsample.1.weight", "module.layer3.0.downsample.1.bias", "module.layer3.0.downsample.1.running_mean", "module.layer3.0.downsample.1.running_var", "module.layer3.0.downsample.1.num_batches_tracked", "module.layer3.1.conv1.weight", "module.layer3.1.bn1.weight", "module.layer3.1.bn1.bias", "module.layer3.1.bn1.running_mean", "module.layer3.1.bn1.running_var", "module.layer3.1.bn1.num_batches_tracked", "module.layer3.1.conv2.weight", "module.layer3.1.bn2.weight", "module.layer3.1.bn2.bias", "module.layer3.1.bn2.running_mean", "module.layer3.1.bn2.running_var", "module.layer3.1.bn2.num_batches_tracked", "module.layer4.0.conv1.weight", "module.layer4.0.bn1.weight", "module.layer4.0.bn1.bias", "module.layer4.0.bn1.running_mean", "module.layer4.0.bn1.running_var", "module.layer4.0.bn1.num_batches_tracked", "module.layer4.0.conv2.weight", "module.layer4.0.bn2.weight", "module.layer4.0.bn2.bias", "module.layer4.0.bn2.running_mean", "module.layer4.0.bn2.running_var", "module.layer4.0.bn2.num_batches_tracked", "module.layer4.0.downsample.0.weight", "module.layer4.0.downsample.1.weight", "module.layer4.0.downsample.1.bias", "module.layer4.0.downsample.1.running_mean", "module.layer4.0.downsample.1.running_var", "module.layer4.0.downsample.1.num_batches_tracked", "module.layer4.1.conv1.weight", "module.layer4.1.bn1.weight", "module.layer4.1.bn1.bias", "module.layer4.1.bn1.running_mean", "module.layer4.1.bn1.running_var", "module.layer4.1.bn1.num_batches_tracked", "module.layer4.1.conv2.weight", "module.layer4.1.bn2.weight", "module.layer4.1.bn2.bias", "module.layer4.1.bn2.running_mean", "module.layer4.1.bn2.running_var", "module.layer4.1.bn2.num_batches_tracked", "module.fc.weight", "module.fc.bias". `

[Open-Debin]

It loads based on the Diction of Python. I feel that you can check whether the key value is consistent.