Hi @ptrblck,
I saved my model as follow  :
        save_checkpoint({
            'epoch': epoch + 1,
            'arch': args.arch,
            'state_dict': model.state_dict(),
            'best_prec1': best_prec1,
            'optimizer': optimizer.state_dict(),
        }, is_best)

From
resnet152=torch.load('model_best.pth.tar')
l have the following options :
resnet152.clear
resnet152.copy
resnet152.fromkeys
resnet152.get
resnet152.has_key
resnent152.items
resnet152.iteritems
resnet152.iterkeys
resnet152.intervalues
resnet152.keys
resnet152.pop
resnet152.popitem
resnet152.setdefault
resnet152.update
resnet152.values
resnet152viewtimes
resnet152.viewkeys
resnet152.viewvalues
For instance
resnet152.keys() returns
[u’state_dict’, u’epoch’, u’arch’, u’optimizer’, u’best_prec1’]
and
my_dict=resnet152[u’state_dict’]
my_dict.keys() returns
[u’module.conv1.weight’, u’module.bn1.weight’, u’module.bn1.bias’, u’module.bn1.running_mean’, u’module.bn1.running_var’, u’module.layer1.0.conv1.weight’, u’module.layer1.0.bn1.weight’, u’module.layer1.0.bn1.bias’, u’module.layer1.0.bn1.running_mean’, u’module.layer1.0.bn1.running_var’, u’module.layer1.0.conv2.weight’, u’module.layer1.0.bn2.weight’, u’module.layer1.0.bn2.bias’, u’module.layer1.0.bn2.running_mean’, u’module.layer1.0.bn2.running_var’, u’module.layer1.0.conv3.weight’, u’module.layer1.0.bn3.weight’, u’module.layer1.0.bn3.bias’, u’module.layer1.0.bn3.running_mean’, u’module.layer1.0.bn3.running_var’, u’module.layer1.0.downsample.0.weight’, u’module.layer1.0.downsample.1.weight’, u’module.layer1.0.downsample.1.bias’, u’module.layer1.0.downsample.1.running_mean’, u’module.layer1.0.downsample.1.running_var’, u’module.layer1.1.conv1.weight’, u’module.layer1.1.bn1.weight’, u’module.layer1.1.bn1.bias’, u’module.layer1.1.bn1.running_mean’, u’module.layer1.1.bn1.running_var’, u’module.layer1.1.conv2.weight’, u’module.layer1.1.bn2.weight’, u’module.layer1.1.bn2.bias’, u’module.layer1.1.bn2.running_mean’, u’module.layer1.1.bn2.running_var’, u’module.layer1.1.conv3.weight’, u’module.layer1.1.bn3.weight’, u’module.layer1.1.bn3.bias’, u’module.layer1.1.bn3.running_mean’, u’module.layer1.1.bn3.running_var’, u’module.layer1.2.conv1.weight’, u’module.layer1.2.bn1.weight’, u’module.layer1.2.bn1.bias’, u’module.layer1.2.bn1.running_mean’, u’module.layer1.2.bn1.running_var’, u’module.layer1.2.conv2.weight’, u’module.layer1.2.bn2.weight’, u’module.layer1.2.bn2.bias’, u’module.layer1.2.bn2.running_mean’, u’module.layer1.2.bn2.running_var’, u’module.layer1.2.conv3.weight’, u’module.layer1.2.bn3.weight’, u’module.layer1.2.bn3.bias’, u’module.layer1.2.bn3.running_mean’, u’module.layer1.2.bn3.running_var’, u’module.layer2.0.conv1.weight’, u’module.layer2.0.bn1.weight’, u’module.layer2.0.bn1.bias’, u’module.layer2.0.bn1.running_mean’, u’module.layer2.0.bn1.running_var’, u’module.layer2.0.conv2.weight’, u’module.layer2.0.bn2.weight’, u’module.layer2.0.bn2.bias’, u’module.layer2.0.bn2.running_mean’, u’module.layer2.0.bn2.running_var’, u’module.layer2.0.conv3.weight’, u’module.layer2.0.bn3.weight’, u’module.layer2.0.bn3.bias’, u’module.layer2.0.bn3.running_mean’, u’module.layer2.0.bn3.running_var’, u’module.layer2.0.downsample.0.weight’, u’module.layer2.0.downsample.1.weight’, u’module.layer2.0.downsample.1.bias’, u’module.layer2.0.downsample.1.running_mean’, u’module.layer2.0.downsample.1.running_var’, u’module.layer2.1.conv1.weight’, u’module.layer2.1.bn1.weight’, u’module.layer2.1.bn1.bias’, u’module.layer2.1.bn1.running_mean’, u’module.layer2.1.bn1.running_var’, u’module.layer2.1.conv2.weight’, u’module.layer2.1.bn2.weight’, u’module.layer2.1.bn2.bias’, u’module.layer2.1.bn2.running_mean’, u’module.layer2.1.bn2.running_var’, u’module.layer2.1.conv3.weight’, u’module.layer2.1.bn3.weight’, u’module.layer2.1.bn3.bias’, u’module.layer2.1.bn3.running_mean’, u’module.layer2.1.bn3.running_var’, u’module.layer2.2.conv1.weight’, u’module.layer2.2.bn1.weight’, u’module.layer2.2.bn1.bias’, u’module.layer2.2.bn1.running_mean’, u’module.layer2.2.bn1.running_var’, u’module.layer2.2.conv2.weight’, u’module.layer2.2.bn2.weight’, u’module.layer2.2.bn2.bias’, u’module.layer2.2.bn2.running_mean’, u’module.layer2.2.bn2.running_var’, u’module.layer2.2.conv3.weight’, u’module.layer2.2.bn3.weight’, u’module.layer2.2.bn3.bias’, u’module.layer2.2.bn3.running_mean’, u’module.layer2.2.bn3.running_var’, u’module.layer2.3.conv1.weight’, u’module.layer2.3.bn1.weight’, u’module.layer2.3.bn1.bias’, u’module.layer2.3.bn1.running_mean’, u’module.layer2.3.bn1.running_var’, u’module.layer2.3.conv2.weight’, u’module.layer2.3.bn2.weight’, u’module.layer2.3.bn2.bias’, u’module.layer2.3.bn2.running_mean’, u’module.layer2.3.bn2.running_var’, u’module.layer2.3.conv3.weight’, u’module.layer2.3.bn3.weight’, u’module.layer2.3.bn3.bias’, u’module.layer2.3.bn3.running_mean’, u’module.layer2.3.bn3.running_var’, u’module.layer2.4.conv1.weight’, u’module.layer2.4.bn1.weight’, u’module.layer2.4.bn1.bias’, u’module.layer2.4.bn1.running_mean’, u’module.layer2.4.bn1.running_var’, u’module.layer2.4.conv2.weight’, u’module.layer2.4.bn2.weight’, u’module.layer2.4.bn2.bias’, u’module.layer2.4.bn2.running_mean’, u’module.layer2.4.bn2.running_var’, u’module.layer2.4.conv3.weight’, u’module.layer2.4.bn3.weight’, u’module.layer2.4.bn3.bias’, u’module.layer2.4.bn3.running_mean’, u’module.layer2.4.bn3.running_var’, u’module.layer2.5.conv1.weight’, u’module.layer2.5.bn1.weight’, u’module.layer2.5.bn1.bias’, u’module.layer2.5.bn1.running_mean’, u’module.layer2.5.bn1.running_var’, u’module.layer2.5.conv2.weight’, u’module.layer2.5.bn2.weight’, u’module.layer2.5.bn2.bias’, u’module.layer2.5.bn2.running_mean’, u’module.layer2.5.bn2.running_var’, u’module.layer2.5.conv3.weight’, u’module.layer2.5.bn3.weight’, u’module.layer2.5.bn3.bias’, u’module.layer2.5.bn3.running_mean’, u’module.layer2.5.bn3.running_var’, u’module.layer2.6.conv1.weight’, u’module.layer2.6.bn1.weight’, u’module.layer2.6.bn1.bias’, u’module.layer2.6.bn1.running_mean’, u’module.layer2.6.bn1.running_var’, u’module.layer2.6.conv2.weight’, u’module.layer2.6.bn2.weight’, u’module.layer2.6.bn2.bias’, u’module.layer2.6.bn2.running_mean’, u’module.layer2.6.bn2.running_var’, u’module.layer2.6.conv3.weight’, u’module.layer2.6.bn3.weight’, u’module.layer2.6.bn3.bias’, u’module.layer2.6.bn3.running_mean’, u’module.layer2.6.bn3.running_var’, u’module.layer2.7.conv1.weight’, u’module.layer2.7.bn1.weight’, u’module.layer2.7.bn1.bias’, u’module.layer2.7.bn1.running_mean’, u’module.layer2.7.bn1.running_var’, u’module.layer2.7.conv2.weight’, u’module.layer2.7.bn2.weight’, u’module.layer2.7.bn2.bias’, u’module.layer2.7.bn2.running_mean’, u’module.layer2.7.bn2.running_var’, u’module.layer2.7.conv3.weight’, u’module.layer2.7.bn3.weight’, u’module.layer2.7.bn3.bias’, u’module.layer2.7.bn3.running_mean’, u’module.layer2.7.bn3.running_var’, u’module.layer3.0.conv1.weight’, u’module.layer3.0.bn1.weight’, u’module.layer3.0.bn1.bias’, u’module.layer3.0.bn1.running_mean’, u’module.layer3.0.bn1.running_var’, u’module.layer3.0.conv2.weight’, u’module.layer3.0.bn2.weight’, u’module.layer3.0.bn2.bias’, u’module.layer3.0.bn2.running_mean’, u’module.layer3.0.bn2.running_var’, u’module.layer3.0.conv3.weight’, u’module.layer3.0.bn3.weight’, u’module.layer3.0.bn3.bias’, u’module.layer3.0.bn3.running_mean’, u’module.layer3.0.bn3.running_var’, u’module.layer3.0.downsample.0.weight’, u’module.layer3.0.downsample.1.weight’, u’module.layer3.0.downsample.1.bias’, u’module.layer3.0.downsample.1.running_mean’, u’module.layer3.0.downsample.1.running_var’, u’module.layer3.1.conv1.weight’, u’module.layer3.1.bn1.weight’, u’module.layer3.1.bn1.bias’, u’module.layer3.1.bn1.running_mean’, u’module.layer3.1.bn1.running_var’, u’module.layer3.1.conv2.weight’, u’module.layer3.1.bn2.weight’, u’module.layer3.1.bn2.bias’, u’module.layer3.1.bn2.running_mean’, u’module.layer3.1.bn2.running_var’, u’module.layer3.1.conv3.weight’, u’module.layer3.1.bn3.weight’, u’module.layer3.1.bn3.bias’, u’module.layer3.1.bn3.running_mean’, u’module.layer3.1.bn3.running_var’, u’module.layer3.2.conv1.weight’, u’module.layer3.2.bn1.weight’, u’module.layer3.2.bn1.bias’, u’module.layer3.2.bn1.running_mean’, u’module.layer3.2.bn1.running_var’, u’module.layer3.2.conv2.weight’, u’module.layer3.2.bn2.weight’, u’module.layer3.2.bn2.bias’, u’module.layer3.2.bn2.running_mean’, u’module.layer3.2.bn2.running_var’, u’module.layer3.2.conv3.weight’, u’module.layer3.2.bn3.weight’, u’module.layer3.2.bn3.bias’, u’module.layer3.2.bn3.running_mean’, u’module.layer3.2.bn3.running_var’, u’module.layer3.3.conv1.weight’, u’module.layer3.3.bn1.weight’, u’module.layer3.3.bn1.bias’, u’module.layer3.3.bn1.running_mean’, u’module.layer3.3.bn1.running_var’, u’module.layer3.3.conv2.weight’, u’module.layer3.3.bn2.weight’, u’module.layer3.3.bn2.bias’, u’module.layer3.3.bn2.running_mean’, u’module.layer3.3.bn2.running_var’, u’module.layer3.3.conv3.weight’, u’module.layer3.3.bn3.weight’, u’module.layer3.3.bn3.bias’, u’module.layer3.3.bn3.running_mean’, u’module.layer3.3.bn3.running_var’, u’module.layer3.4.conv1.weight’, u’module.layer3.4.bn1.weight’, u’module.layer3.4.bn1.bias’, u’module.layer3.4.bn1.running_mean’, u’module.layer3.4.bn1.running_var’, u’module.layer3.4.conv2.weight’, u’module.layer3.4.bn2.weight’, u’module.layer3.4.bn2.bias’, u’module.layer3.4.bn2.running_mean’, u’module.layer3.4.bn2.running_var’, u’module.layer3.4.conv3.weight’, u’module.layer3.4.bn3.weight’, u’module.layer3.4.bn3.bias’, u’module.layer3.4.bn3.running_mean’, u’module.layer3.4.bn3.running_var’, u’module.layer3.5.conv1.weight’, u’module.layer3.5.bn1.weight’, u’module.layer3.5.bn1.bias’, u’module.layer3.5.bn1.running_mean’, u’module.layer3.5.bn1.running_var’, u’module.layer3.5.conv2.weight’, u’module.layer3.5.bn2.weight’, u’module.layer3.5.bn2.bias’, u’module.layer3.5.bn2.running_mean’, u’module.layer3.5.bn2.running_var’, u’module.layer3.5.conv3.weight’, u’module.layer3.5.bn3.weight’, u’module.layer3.5.bn3.bias’, u’module.layer3.5.bn3.running_mean’, u’module.layer3.5.bn3.running_var’, u’module.layer3.6.conv1.weight’, u’module.layer3.6.bn1.weight’, u’module.layer3.6.bn1.bias’, u’module.layer3.6.bn1.running_mean’, u’module.layer3.6.bn1.running_var’, u’module.layer3.6.conv2.weight’, u’module.layer3.6.bn2.weight’, u’module.layer3.6.bn2.bias’, u’module.layer3.6.bn2.running_mean’, u’module.layer3.6.bn2.running_var’, u’module.layer3.6.conv3.weight’, u’module.layer3.6.bn3.weight’, u’module.layer3.6.bn3.bias’, u’module.layer3.6.bn3.running_mean’, u’module.layer3.6.bn3.running_var’, u’module.layer3.7.conv1.weight’, u’module.layer3.7.bn1.weight’, u’module.layer3.7.bn1.bias’, u’module.layer3.7.bn1.running_mean’, u’module.layer3.7.bn1.running_var’, u’module.layer3.7.conv2.weight’, u’module.layer3.7.bn2.weight’, u’module.layer3.7.bn2.bias’, u’module.layer3.7.bn2.running_mean’, u’module.layer3.7.bn2.running_var’, u’module.layer3.7.conv3.weight’, u’module.layer3.7.bn3.weight’, u’module.layer3.7.bn3.bias’, u’module.layer3.7.bn3.running_mean’, u’module.layer3.7.bn3.running_var’, u’module.layer3.8.conv1.weight’, u’module.layer3.8.bn1.weight’, u’module.layer3.8.bn1.bias’, u’module.layer3.8.bn1.running_mean’, u’module.layer3.8.bn1.running_var’, u’module.layer3.8.conv2.weight’, u’module.layer3.8.bn2.weight’, u’module.layer3.8.bn2.bias’, u’module.layer3.8.bn2.running_mean’, u’module.layer3.8.bn2.running_var’, u’module.layer3.8.conv3.weight’, u’module.layer3.8.bn3.weight’, u’module.layer3.8.bn3.bias’, u’module.layer3.8.bn3.running_mean’, u’module.layer3.8.bn3.running_var’, u’module.layer3.9.conv1.weight’, u’module.layer3.9.bn1.weight’, u’module.layer3.9.bn1.bias’, u’module.layer3.9.bn1.running_mean’, u’module.layer3.9.bn1.running_var’, u’module.layer3.9.conv2.weight’, u’module.layer3.9.bn2.weight’, u’module.layer3.9.bn2.bias’, u’module.layer3.9.bn2.running_mean’, u’module.layer3.9.bn2.running_var’, u’module.layer3.9.conv3.weight’, u’module.layer3.9.bn3.weight’, u’module.layer3.9.bn3.bias’, u’module.layer3.9.bn3.running_mean’, u’module.layer3.9.bn3.running_var’, u’module.layer3.10.conv1.weight’, u’module.layer3.10.bn1.weight’, u’module.layer3.10.bn1.bias’, u’module.layer3.10.bn1.running_mean’, u’module.layer3.10.bn1.running_var’, u’module.layer3.10.conv2.weight’, u’module.layer3.10.bn2.weight’, u’module.layer3.10.bn2.bias’, u’module.layer3.10.bn2.running_mean’, u’module.layer3.10.bn2.running_var’, u’module.layer3.10.conv3.weight’, u’module.layer3.10.bn3.weight’, u’module.layer3.10.bn3.bias’, u’module.layer3.10.bn3.running_mean’, u’module.layer3.10.bn3.running_var’, u’module.layer3.11.conv1.weight’, u’module.layer3.11.bn1.weight’, u’module.layer3.11.bn1.bias’, u’module.layer3.11.bn1.running_mean’, u’module.layer3.11.bn1.running_var’, u’module.layer3.11.conv2.weight’, u’module.layer3.11.bn2.weight’, u’module.layer3.11.bn2.bias’, u’module.layer3.11.bn2.running_mean’, u’module.layer3.11.bn2.running_var’, u’module.layer3.11.conv3.weight’, u’module.layer3.11.bn3.weight’, u’module.layer3.11.bn3.bias’, u’module.layer3.11.bn3.running_mean’, u’module.layer3.11.bn3.running_var’, u’module.layer3.12.conv1.weight’, u’module.layer3.12.bn1.weight’, u’module.layer3.12.bn1.bias’, u’module.layer3.12.bn1.running_mean’, u’module.layer3.12.bn1.running_var’, u’module.layer3.12.conv2.weight’, u’module.layer3.12.bn2.weight’, u’module.layer3.12.bn2.bias’, u’module.layer3.12.bn2.running_mean’, u’module.layer3.12.bn2.running_var’, u’module.layer3.12.conv3.weight’, u’module.layer3.12.bn3.weight’, u’module.layer3.12.bn3.bias’, u’module.layer3.12.bn3.running_mean’, u’module.layer3.12.bn3.running_var’, u’module.layer3.13.conv1.weight’, u’module.layer3.13.bn1.weight’, u’module.layer3.13.bn1.bias’, u’module.layer3.13.bn1.running_mean’, u’module.layer3.13.bn1.running_var’, u’module.layer3.13.conv2.weight’, u’module.layer3.13.bn2.weight’, u’module.layer3.13.bn2.bias’, u’module.layer3.13.bn2.running_mean’, u’module.layer3.13.bn2.running_var’, u’module.layer3.13.conv3.weight’, u’module.layer3.13.bn3.weight’, u’module.layer3.13.bn3.bias’, u’module.layer3.13.bn3.running_mean’, u’module.layer3.13.bn3.running_var’, u’module.layer3.14.conv1.weight’, u’module.layer3.14.bn1.weight’, u’module.layer3.14.bn1.bias’, u’module.layer3.14.bn1.running_mean’, u’module.layer3.14.bn1.running_var’, u’module.layer3.14.conv2.weight’, u’module.layer3.14.bn2.weight’, u’module.layer3.14.bn2.bias’, u’module.layer3.14.bn2.running_mean’, u’module.layer3.14.bn2.running_var’, u’module.layer3.14.conv3.weight’, u’module.layer3.14.bn3.weight’, u’module.layer3.14.bn3.bias’, u’module.layer3.14.bn3.running_mean’, u’module.layer3.14.bn3.running_var’, u’module.layer3.15.conv1.weight’, u’module.layer3.15.bn1.weight’, u’module.layer3.15.bn1.bias’, u’module.layer3.15.bn1.running_mean’, u’module.layer3.15.bn1.running_var’, u’module.layer3.15.conv2.weight’, u’module.layer3.15.bn2.weight’, u’module.layer3.15.bn2.bias’, u’module.layer3.15.bn2.running_mean’, u’module.layer3.15.bn2.running_var’, u’module.layer3.15.conv3.weight’, u’module.layer3.15.bn3.weight’, u’module.layer3.15.bn3.bias’, u’module.layer3.15.bn3.running_mean’, u’module.layer3.15.bn3.running_var’, u’module.layer3.16.conv1.weight’, u’module.layer3.16.bn1.weight’, u’module.layer3.16.bn1.bias’, u’module.layer3.16.bn1.running_mean’, u’module.layer3.16.bn1.running_var’, u’module.layer3.16.conv2.weight’, u’module.layer3.16.bn2.weight’, u’module.layer3.16.bn2.bias’, u’module.layer3.16.bn2.running_mean’, u’module.layer3.16.bn2.running_var’, u’module.layer3.16.conv3.weight’, u’module.layer3.16.bn3.weight’, u’module.layer3.16.bn3.bias’, u’module.layer3.16.bn3.running_mean’, u’module.layer3.16.bn3.running_var’, u’module.layer3.17.conv1.weight’, u’module.layer3.17.bn1.weight’, 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Thank you for your help