RuntimeError: Error(s) in loading state_dict for DataParallel: Missing key(s) in state_dict:

Mona_Jalal · December 10, 2018, 7:05am

I want to load a pkl but I get the following error:

import collections

model_dic = torch.load('resnet50_finetuning_affectnet.pkl', map_location='cpu')
new_state_dict = collections.OrderedDict()
for k, v in model_dic.items():
    name = k.replace('module.', '')# remove `module.`
    new_state_dict[name] = v

model_ft.load_state_dict(new_state_dict)
model_ft = model_ft.to(device)
result = m.evaluate_classifier(model_ft, val_dataLoader, class_num)
confusion_matrix = result['Confusion-Matrix']
m.plot_confusion_matrix(confusion_matrix, enum_expression)
plt.savefig(output_path + output_id + '/confusion_matix.png', dpi=200)
logger.info("==>ACC={:.4f}, F1 score={:.4f}, Cohens Kappa={:.4f}, AUC={:.4f}, AUC-PR={:.4f}".format(
        result['ACC'], result['F1s'], result['Kappa'], result['AUC'], result['AUC-PR'] ))
logger.info("==>F1 scores of each class: {}".format(result['F1s-Class']))

here

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-19-67bf9c080b2e> in <module>()
      7     new_state_dict[name] = v
      8 
----> 9 model_ft.load_state_dict(new_state_dict)
     10 model_ft = model_ft.to(device)
     11 result = m.evaluate_classifier(model_ft, val_dataLoader, class_num)

/scratch/sjn-p3/anaconda/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py in load_state_dict(self, state_dict, strict)
    717         if len(error_msgs) > 0:
    718             raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
--> 719                                self.__class__.__name__, "\n\t".join(error_msgs)))
    720 
    721     def parameters(self):

RuntimeError: Error(s) in loading state_dict for DataParallel:
	Missing key(s) in state_dict: "module.conv1.weight", "module.bn1.weight", "module.bn1.bias", "module.bn1.running_mean", "module.bn1.running_var", "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.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.conv3.weight", "module.layer1.0.bn3.weight", "module.layer1.0.bn3.bias", "module.layer1.0.bn3.running_mean", "module.layer1.0.bn3.running_var", "module.layer1.0.downsample.0.weight", "module.layer1.0.downsample.1.weight", "module.layer1.0.downsample.1.bias", "module.layer1.0.downsample.1.running_mean", "module.layer1.0.downsample.1.running_var", "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.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.conv3.weight", "module.layer1.1.bn3.weight", "module.layer1.1.bn3.bias", "module.layer1.1.bn3.running_mean", "module.layer1.1.bn3.running_var", "module.layer1.2.conv1.weight", "module.layer1.2.bn1.weight", "module.layer1.2.bn1.bias", "module.layer1.2.bn1.running_mean", "module.layer1.2.bn1.running_var", "module.layer1.2.conv2.weight", "module.layer1.2.bn2.weight", "module.layer1.2.bn2.bias", "module.layer1.2.bn2.running_mean", "module.layer1.2.bn2.running_var", "module.layer1.2.conv3.weight", "module.layer1.2.bn3.weight", "module.layer1.2.bn3.bias", "module.layer1.2.bn3.running_mean", "module.layer1.2.bn3.running_var", "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.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.conv3.weight", "module.layer2.0.bn3.weight", "module.layer2.0.bn3.bias", "module.layer2.0.bn3.running_mean", "module.layer2.0.bn3.running_var", "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.layer2.1.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.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.conv3.weight", "module.layer2.1.bn3.weight", "module.layer2.1.bn3.bias", "module.layer2.1.bn3.running_mean", "module.layer2.1.bn3.running_var", "module.layer2.2.conv1.weight", "module.layer2.2.bn1.weight", "module.layer2.2.bn1.bias", "module.layer2.2.bn1.running_mean", "module.layer2.2.bn1.running_var", "module.layer2.2.conv2.weight", "module.layer2.2.bn2.weight", "module.layer2.2.bn2.bias", "module.layer2.2.bn2.running_mean", "module.layer2.2.bn2.running_var", "module.layer2.2.conv3.weight", "module.layer2.2.bn3.weight", "module.layer2.2.bn3.bias", "module.layer2.2.bn3.running_mean", "module.layer2.2.bn3.running_var", "module.layer2.3.conv1.weight", "module.layer2.3.bn1.weight", "module.layer2.3.bn1.bias", "module.layer2.3.bn1.running_mean", "module.layer2.3.bn1.running_var", "module.layer2.3.conv2.weight", "module.layer2.3.bn2.weight", "module.layer2.3.bn2.bias", "module.layer2.3.bn2.running_mean", "module.layer2.3.bn2.running_var", "module.layer2.3.conv3.weight", "module.layer2.3.bn3.weight", "module.layer2.3.bn3.bias", "module.layer2.3.bn3.running_mean", "module.layer2.3.bn3.running_var", "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.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.conv3.weight", "module.layer3.0.bn3.weight", "module.layer3.0.bn3.bias", "module.layer3.0.bn3.running_mean", "module.layer3.0.bn3.running_var", "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.layer3.1.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.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.conv3.weight", "module.layer3.1.bn3.weight", "module.layer3.1.bn3.bias", "module.layer3.1.bn3.running_mean", "module.layer3.1.bn3.running_var", "module.layer3.2.conv1.weight", "module.layer3.2.bn1.weight", "module.layer3.2.bn1.bias", "module.layer3.2.bn1.running_mean", "module.layer3.2.bn1.running_var", "module.layer3.2.conv2.weight", "module.layer3.2.bn2.weight", "module.layer3.2.bn2.bias", "module.layer3.2.bn2.running_mean", "module.layer3.2.bn2.running_var", "module.layer3.2.conv3.weight", "module.layer3.2.bn3.weight", "module.layer3.2.bn3.bias", "module.layer3.2.bn3.running_mean", "module.layer3.2.bn3.running_var", "module.layer3.3.conv1.weight", "module.layer3.3.bn1.weight", "module.layer3.3.bn1.bias", "module.layer3.3.bn1.running_mean", "module.layer3.3.bn1.running_var", "module.layer3.3.conv2.weight", "module.layer3.3.bn2.weight", "module.layer3.3.bn2.bias", "module.layer3.3.bn2.running_mean", "module.layer3.3.bn2.running_var", "module.layer3.3.conv3.weight", "module.layer3.3.bn3.weight", "module.layer3.3.bn3.bias", "module.layer3.3.bn3.running_mean", "module.layer3.3.bn3.running_var", "module.layer3.4.conv1.weight", "module.layer3.4.bn1.weight", "module.layer3.4.bn1.bias", "module.layer3.4.bn1.running_mean", "module.layer3.4.bn1.running_var", "module.layer3.4.conv2.weight", "module.layer3.4.bn2.weight", "module.layer3.4.bn2.bias", "module.layer3.4.bn2.running_mean", "module.layer3.4.bn2.running_var", "module.layer3.4.conv3.weight", "module.layer3.4.bn3.weight", "module.layer3.4.bn3.bias", "module.layer3.4.bn3.running_mean", "module.layer3.4.bn3.running_var", "module.layer3.5.conv1.weight", "module.layer3.5.bn1.weight", "module.layer3.5.bn1.bias", "module.layer3.5.bn1.running_mean", "module.layer3.5.bn1.running_var", "module.layer3.5.conv2.weight", "module.layer3.5.bn2.weight", "module.layer3.5.bn2.bias", "module.layer3.5.bn2.running_mean", "module.layer3.5.bn2.running_var", "module.layer3.5.conv3.weight", "module.layer3.5.bn3.weight", "module.layer3.5.bn3.bias", "module.layer3.5.bn3.running_mean", "module.layer3.5.bn3.running_var", "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.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.conv3.weight", "module.layer4.0.bn3.weight", "module.layer4.0.bn3.bias", "module.layer4.0.bn3.running_mean", "module.layer4.0.bn3.running_var", "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.layer4.1.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.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.conv3.weight", "module.layer4.1.bn3.weight", "module.layer4.1.bn3.bias", "module.layer4.1.bn3.running_mean", "module.layer4.1.bn3.running_var", "module.layer4.2.conv1.weight", "module.layer4.2.bn1.weight", "module.layer4.2.bn1.bias", "module.layer4.2.bn1.running_mean", "module.layer4.2.bn1.running_var", "module.layer4.2.conv2.weight", "module.layer4.2.bn2.weight", "module.layer4.2.bn2.bias", "module.layer4.2.bn2.running_mean", "module.layer4.2.bn2.running_var", "module.layer4.2.conv3.weight", "module.layer4.2.bn3.weight", "module.layer4.2.bn3.bias", "module.layer4.2.bn3.running_mean", "module.layer4.2.bn3.running_var", "module.fc.weight", "module.fc.bias". 
	Unexpected key(s) in state_dict: "conv1.weight", "bn1.weight", "bn1.bias", "bn1.running_mean", "bn1.running_var", "bn1.num_batches_tracked", "layer1.0.conv1.weight", "layer1.0.bn1.weight", "layer1.0.bn1.bias", "layer1.0.bn1.running_mean", "layer1.0.bn1.running_var", "layer1.0.bn1.num_batches_tracked", "layer1.0.conv2.weight", "layer1.0.bn2.weight", "layer1.0.bn2.bias", "layer1.0.bn2.running_mean", "layer1.0.bn2.running_var", "layer1.0.bn2.num_batches_tracked", "layer1.0.conv3.weight", "layer1.0.bn3.weight", "layer1.0.bn3.bias", "layer1.0.bn3.running_mean", "layer1.0.bn3.running_var", "layer1.0.bn3.num_batches_tracked", "layer1.0.downsample.0.weight", "layer1.0.downsample.1.weight", "layer1.0.downsample.1.bias", "layer1.0.downsample.1.running_mean", "layer1.0.downsample.1.running_var", "layer1.0.downsample.1.num_batches_tracked", "layer1.1.conv1.weight", "layer1.1.bn1.weight", "layer1.1.bn1.bias", "layer1.1.bn1.running_mean", "layer1.1.bn1.running_var", "layer1.1.bn1.num_batches_tracked", "layer1.1.conv2.weight", "layer1.1.bn2.weight", "layer1.1.bn2.bias", "layer1.1.bn2.running_mean", "layer1.1.bn2.running_var", "layer1.1.bn2.num_batches_tracked", "layer1.1.conv3.weight", "layer1.1.bn3.weight", "layer1.1.bn3.bias", "layer1.1.bn3.running_mean", "layer1.1.bn3.running_var", "layer1.1.bn3.num_batches_tracked", "layer1.2.conv1.weight", "layer1.2.bn1.weight", "layer1.2.bn1.bias", "layer1.2.bn1.running_mean", "layer1.2.bn1.running_var", "layer1.2.bn1.num_batches_tracked", "layer1.2.conv2.weight", "layer1.2.bn2.weight", "layer1.2.bn2.bias", "layer1.2.bn2.running_mean", "layer1.2.bn2.running_var", "layer1.2.bn2.num_batches_tracked", "layer1.2.conv3.weight", "layer1.2.bn3.weight", "layer1.2.bn3.bias", "layer1.2.bn3.running_mean", "layer1.2.bn3.running_var", "layer1.2.bn3.num_batches_tracked", "layer2.0.conv1.weight", "layer2.0.bn1.weight", "layer2.0.bn1.bias", "layer2.0.bn1.running_mean", "layer2.0.bn1.running_var", "layer2.0.bn1.num_batches_tracked", "layer2.0.conv2.weight", "layer2.0.bn2.weight", "layer2.0.bn2.bias", "layer2.0.bn2.running_mean", "layer2.0.bn2.running_var", "layer2.0.bn2.num_batches_tracked", "layer2.0.conv3.weight", "layer2.0.bn3.weight", "layer2.0.bn3.bias", "layer2.0.bn3.running_mean", "layer2.0.bn3.running_var", "layer2.0.bn3.num_batches_tracked", "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", "layer2.0.downsample.1.num_batches_tracked", "layer2.1.conv1.weight", "layer2.1.bn1.weight", "layer2.1.bn1.bias", "layer2.1.bn1.running_mean", "layer2.1.bn1.running_var", "layer2.1.bn1.num_batches_tracked", "layer2.1.conv2.weight", "layer2.1.bn2.weight", "layer2.1.bn2.bias", "layer2.1.bn2.running_mean", "layer2.1.bn2.running_var", "layer2.1.bn2.num_batches_tracked", "layer2.1.conv3.weight", "layer2.1.bn3.weight", "layer2.1.bn3.bias", "layer2.1.bn3.running_mean", "layer2.1.bn3.running_var", "layer2.1.bn3.num_batches_tracked", "layer2.2.conv1.weight", "layer2.2.bn1.weight", "layer2.2.bn1.bias", "layer2.2.bn1.running_mean", "layer2.2.bn1.running_var", "layer2.2.bn1.num_batches_tracked", "layer2.2.conv2.weight", "layer2.2.bn2.weight", "layer2.2.bn2.bias", "layer2.2.bn2.running_mean", "layer2.2.bn2.running_var", "layer2.2.bn2.num_batches_tracked", "layer2.2.conv3.weight", "layer2.2.bn3.weight", "layer2.2.bn3.bias", "layer2.2.bn3.running_mean", "layer2.2.bn3.running_var", "layer2.2.bn3.num_batches_tracked", "layer2.3.conv1.weight", "layer2.3.bn1.weight", "layer2.3.bn1.bias", "layer2.3.bn1.running_mean", "layer2.3.bn1.running_var", "layer2.3.bn1.num_batches_tracked", "layer2.3.conv2.weight", "layer2.3.bn2.weight", "layer2.3.bn2.bias", "layer2.3.bn2.running_mean", "layer2.3.bn2.running_var", "layer2.3.bn2.num_batches_tracked", "layer2.3.conv3.weight", "layer2.3.bn3.weight", "layer2.3.bn3.bias", "layer2.3.bn3.running_mean", "layer2.3.bn3.running_var", "layer2.3.bn3.num_batches_tracked", "layer3.0.conv1.weight", "layer3.0.bn1.weight", "layer3.0.bn1.bias", "layer3.0.bn1.running_mean", "layer3.0.bn1.running_var", "layer3.0.bn1.num_batches_tracked", "layer3.0.conv2.weight", "layer3.0.bn2.weight", "layer3.0.bn2.bias", "layer3.0.bn2.running_mean", "layer3.0.bn2.running_var", "layer3.0.bn2.num_batches_tracked", "layer3.0.conv3.weight", "layer3.0.bn3.weight", "layer3.0.bn3.bias", "layer3.0.bn3.running_mean", "layer3.0.bn3.running_var", "layer3.0.bn3.num_batches_tracked", "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", "layer3.0.downsample.1.num_batches_tracked", "layer3.1.conv1.weight", "layer3.1.bn1.weight", "layer3.1.bn1.bias", "layer3.1.bn1.running_mean", "layer3.1.bn1.running_var", "layer3.1.bn1.num_batches_tracked", "layer3.1.conv2.weight", "layer3.1.bn2.weight", "layer3.1.bn2.bias", "layer3.1.bn2.running_mean", "layer3.1.bn2.running_var", "layer3.1.bn2.num_batches_tracked", "layer3.1.conv3.weight", "layer3.1.bn3.weight", "layer3.1.bn3.bias", "layer3.1.bn3.running_mean", "layer3.1.bn3.running_var", "layer3.1.bn3.num_batches_tracked", "layer3.2.conv1.weight", "layer3.2.bn1.weight", "layer3.2.bn1.bias", "layer3.2.bn1.running_mean", "layer3.2.bn1.running_var", "layer3.2.bn1.num_batches_tracked", "layer3.2.conv2.weight", "layer3.2.bn2.weight", "layer3.2.bn2.bias", "layer3.2.bn2.running_mean", "layer3.2.bn2.running_var", "layer3.2.bn2.num_batches_tracked", "layer3.2.conv3.weight", "layer3.2.bn3.weight", "layer3.2.bn3.bias", "layer3.2.bn3.running_mean", "layer3.2.bn3.running_var", "layer3.2.bn3.num_batches_tracked", "layer3.3.conv1.weight", "layer3.3.bn1.weight", "layer3.3.bn1.bias", "layer3.3.bn1.running_mean", "layer3.3.bn1.running_var", "layer3.3.bn1.num_batches_tracked", "layer3.3.conv2.weight", "layer3.3.bn2.weight", "layer3.3.bn2.bias", "layer3.3.bn2.running_mean", "layer3.3.bn2.running_var", "layer3.3.bn2.num_batches_tracked", "layer3.3.conv3.weight", "layer3.3.bn3.weight", "layer3.3.bn3.bias", "layer3.3.bn3.running_mean", "layer3.3.bn3.running_var", "layer3.3.bn3.num_batches_tracked", "layer3.4.conv1.weight", "layer3.4.bn1.weight", "layer3.4.bn1.bias", "layer3.4.bn1.running_mean", "layer3.4.bn1.running_var", "layer3.4.bn1.num_batches_tracked", "layer3.4.conv2.weight", "layer3.4.bn2.weight", "layer3.4.bn2.bias", "layer3.4.bn2.running_mean", "layer3.4.bn2.running_var", "layer3.4.bn2.num_batches_tracked", "layer3.4.conv3.weight", "layer3.4.bn3.weight", "layer3.4.bn3.bias", "layer3.4.bn3.running_mean", "layer3.4.bn3.running_var", "layer3.4.bn3.num_batches_tracked", "layer3.5.conv1.weight", "layer3.5.bn1.weight", "layer3.5.bn1.bias", "layer3.5.bn1.running_mean", "layer3.5.bn1.running_var", "layer3.5.bn1.num_batches_tracked", "layer3.5.conv2.weight", "layer3.5.bn2.weight", "layer3.5.bn2.bias", "layer3.5.bn2.running_mean", "layer3.5.bn2.running_var", "layer3.5.bn2.num_batches_tracked", "layer3.5.conv3.weight", "layer3.5.bn3.weight", "layer3.5.bn3.bias", "layer3.5.bn3.running_mean", "layer3.5.bn3.running_var", "layer3.5.bn3.num_batches_tracked", "layer4.0.conv1.weight", "layer4.0.bn1.weight", "layer4.0.bn1.bias", "layer4.0.bn1.running_mean", "layer4.0.bn1.running_var", "layer4.0.bn1.num_batches_tracked", "layer4.0.conv2.weight", "layer4.0.bn2.weight", "layer4.0.bn2.bias", "layer4.0.bn2.running_mean", "layer4.0.bn2.running_var", "layer4.0.bn2.num_batches_tracked", "layer4.0.conv3.weight", "layer4.0.bn3.weight", "layer4.0.bn3.bias", "layer4.0.bn3.running_mean", "layer4.0.bn3.running_var", "layer4.0.bn3.num_batches_tracked", "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", "layer4.0.downsample.1.num_batches_tracked", "layer4.1.conv1.weight", "layer4.1.bn1.weight", "layer4.1.bn1.bias", "layer4.1.bn1.running_mean", "layer4.1.bn1.running_var", "layer4.1.bn1.num_batches_tracked", "layer4.1.conv2.weight", "layer4.1.bn2.weight", "layer4.1.bn2.bias", "layer4.1.bn2.running_mean", "layer4.1.bn2.running_var", "layer4.1.bn2.num_batches_tracked", "layer4.1.conv3.weight", "layer4.1.bn3.weight", "layer4.1.bn3.bias", "layer4.1.bn3.running_mean", "layer4.1.bn3.running_var", "layer4.1.bn3.num_batches_tracked", "layer4.2.conv1.weight", "layer4.2.bn1.weight", "layer4.2.bn1.bias", "layer4.2.bn1.running_mean", "layer4.2.bn1.running_var", "layer4.2.bn1.num_batches_tracked", "layer4.2.conv2.weight", "layer4.2.bn2.weight", "layer4.2.bn2.bias", "layer4.2.bn2.running_mean", "layer4.2.bn2.running_var", "layer4.2.bn2.num_batches_tracked", "layer4.2.conv3.weight", "layer4.2.bn3.weight", "layer4.2.bn3.bias", "layer4.2.bn3.running_mean", "layer4.2.bn3.running_var", "layer4.2.bn3.num_batches_tracked", "fc.weight", "fc.bias".

how do I know what’s exactly missing and how to fix it?

Here’s what I get as the output of model_ft.eval()

DataParallel(
  (module): ResNet(
    (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (relu): ReLU(inplace)
    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
    (layer1): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
    )
    (layer2): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (3): Bottleneck(
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
    )
    (layer3): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (3): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (4): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (5): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
    )
    (layer4): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
    )
    (avgpool): AvgPool2d(kernel_size=7, stride=1, padding=0)
    (fc): Linear(in_features=2048, out_features=11, bias=True)
  )
)

InnovArul · December 10, 2018, 7:07am

you have saved the model wrapped using DataParallel. When you want to load the model again, wrap the model with Dataparallel before loading.

Mona_Jalal · December 10, 2018, 7:10am

Could you please tell the code that I should use for wrapping the model again?

I added this line but still getting error model_ft = nn.DataParallel(model_ft)

import collections

model_dic = torch.load('resnet50_finetuning_affectnet.pkl', map_location='cpu')
new_state_dict = collections.OrderedDict()
for k, v in model_dic.items():
    name = k.replace('module.', '')# remove `module.`
    new_state_dict[name] = v

    
model_ft = nn.DataParallel(model_ft)
model_ft.load_state_dict(new_state_dict)
model_ft = model_ft.to(device)
result = m.evaluate_classifier(model_ft, val_dataLoader, class_num)
confusion_matrix = result['Confusion-Matrix']
m.plot_confusion_matrix(confusion_matrix, enum_expression)
plt.savefig(output_path + output_id + '/confusion_matix.png', dpi=200)
logger.info("==>ACC={:.4f}, F1 score={:.4f}, Cohens Kappa={:.4f}, AUC={:.4f}, AUC-PR={:.4f}".format(
        result['ACC'], result['F1s'], result['Kappa'], result['AUC'], result['AUC-PR'] ))
logger.info("==>F1 scores of each class: {}".format(result['F1s-Class']))

InnovArul · December 10, 2018, 7:14am

I see. you are manually replacing the network keys.

when you use DataParallel, it needs keys prepended with ‘module.’.

Mona_Jalal · December 10, 2018, 7:24am

I am not following you. Can you please tell me how I can exactly fix this by showing the correct version of code?

InnovArul · December 10, 2018, 1:09pm

If you have saved with the pretrained model that is wrapped with nn.DataParallel(), it will have all the state_dict() keys prepended with module.. In this case, while loading the saved state_dict() to a new model, you have to make sure that the new model is wrapped with nn.DataParallel() before calling model.load_state_dict().

I assume, your pretrained model was wrapped using nn.DataParallel(). You have to know how your pretrained model was saved and load it accordingly.

With the above code, you are already deleting module.. Check if it has really replaced the keys and whether new_state_dict and model_ft.state_dict() have same keys.