Getting the error while creating a ImageDataBunch for inference

Part 1 (2019)
21

Hello,
While I’m trying to load the model for inference:

data = ImageDataBunch.single_from_classes(path, classes, tfms=get_transforms(), size=224).normalize(imagenet_stats)
learner = create_cnn(data, models.resnet50, pretrained=False)
learner.load("model-weights");

I’m getting the following error:

  File "peruvian-food-detector.py", line 56, in <module>
    learner.load("model-weights");
  File "/usr/local/lib/python3.6/site-packages/fastai/basic_train.py", line 200, in load
    self.model.load_state_dict(torch.load(self.path/self.model_dir/f'{name}.pth', map_location=device))
  File "/usr/local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 723, in load_state_dict
    self.__class__.__name__, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for Sequential:
        Missing key(s) in state_dict: "0.0.weight", "0.1.weight", "0.1.bias", "0.1.running_mean", "0.1.running_var", "0.4.0.conv1.weight", "0.4.0.bn1.weight", "0.4.0.bn1.bias", "0.4.0.bn1.running_mean", "0.4.0.bn1.running_var", "0.4.0.conv2.weight", "0.4.0.bn2.weight", "0.4.0.bn2.bias", "0.4.0.bn2.running_mean", "0.4.0.bn2.running_var", "0.4.0.conv3.weight", "0.4.0.bn3.weight", "0.4.0.bn3.bias", "0.4.0.bn3.running_mean", "0.4.0.bn3.running_var", "0.4.0.downsample.0.weight", "0.4.0.downsample.1.weight", "0.4.0.downsample.1.bias", "0.4.0.downsample.1.running_mean", "0.4.0.downsample.1.running_var", "0.4.1.conv1.weight", "0.4.1.bn1.weight", "0.4.1.bn1.bias", "0.4.1.bn1.running_mean", "0.4.1.bn1.running_var", "0.4.1.conv2.weight", "0.4.1.bn2.weight", "0.4.1.bn2.bias", "0.4.1.bn2.running_mean", "0.4.1.bn2.running_var", "0.4.1.conv3.weight", "0.4.1.bn3.weight", "0.4.1.bn3.bias", "0.4.1.bn3.running_mean", "0.4.1.bn3.running_var", "0.4.2.conv1.weight", "0.4.2.bn1.weight", "0.4.2.bn1.bias", "0.4.2.bn1.running_mean", "0.4.2.bn1.running_var", "0.4.2.conv2.weight", "0.4.2.bn2.weight", "0.4.2.bn2.bias", "0.4.2.bn2.running_mean", "0.4.2.bn2.running_var", "0.4.2.conv3.weight", "0.4.2.bn3.weight", "0.4.2.bn3.bias", "0.4.2.bn3.running_mean", "0.4.2.bn3.running_var", "0.5.0.conv1.weight", "0.5.0.bn1.weight", "0.5.0.bn1.bias", "0.5.0.bn1.running_mean", "0.5.0.bn1.running_var", "0.5.0.conv2.weight", "0.5.0.bn2.weight", "0.5.0.bn2.bias", "0.5.0.bn2.running_mean", "0.5.0.bn2.running_var", "0.5.0.conv3.weight", "0.5.0.bn3.weight", "0.5.0.bn3.bias", "0.5.0.bn3.running_mean", "0.5.0.bn3.running_var", "0.5.0.downsample.0.weight", "0.5.0.downsample.1.weight", "0.5.0.downsample.1.bias", "0.5.0.downsample.1.running_mean", "0.5.0.downsample.1.running_var", "0.5.1.conv1.weight", "0.5.1.bn1.weight", "0.5.1.bn1.bias", "0.5.1.bn1.running_mean", "0.5.1.bn1.running_var", "0.5.1.conv2.weight", "0.5.1.bn2.weight", "0.5.1.bn2.bias", "0.5.1.bn2.running_mean", "0.5.1.bn2.running_var", "0.5.1.conv3.weight", "0.5.1.bn3.weight", "0.5.1.bn3.bias", "0.5.1.bn3.running_mean", "0.5.1.bn3.running_var", "0.5.2.conv1.weight", "0.5.2.bn1.weight", "0.5.2.bn1.bias", "0.5.2.bn1.running_mean", "0.5.2.bn1.running_var", "0.5.2.conv2.weight", "0.5.2.bn2.weight", "0.5.2.bn2.bias", "0.5.2.bn2.running_mean", "0.5.2.bn2.running_var", "0.5.2.conv3.weight", "0.5.2.bn3.weight", "0.5.2.bn3.bias", "0.5.2.bn3.running_mean", "0.5.2.bn3.running_var", "0.5.3.conv1.weight", "0.5.3.bn1.weight", "0.5.3.bn1.bias", "0.5.3.bn1.running_mean", "0.5.3.bn1.running_var", "0.5.3.conv2.weight", "0.5.3.bn2.weight", "0.5.3.bn2.bias", "0.5.3.bn2.running_mean", "0.5.3.bn2.running_var", "0.5.3.conv3.weight", "0.5.3.bn3.weight", "0.5.3.bn3.bias", "0.5.3.bn3.running_mean", "0.5.3.bn3.running_var", "0.6.0.conv1.weight", "0.6.0.bn1.weight", "0.6.0.bn1.bias", "0.6.0.bn1.running_mean", "0.6.0.bn1.running_var", "0.6.0.conv2.weight", "0.6.0.bn2.weight", "0.6.0.bn2.bias", "0.6.0.bn2.running_mean", "0.6.0.bn2.running_var", "0.6.0.conv3.weight", "0.6.0.bn3.weight", "0.6.0.bn3.bias", "0.6.0.bn3.running_mean", "0.6.0.bn3.running_var", "0.6.0.downsample.0.weight", "0.6.0.downsample.1.weight", "0.6.0.downsample.1.bias", "0.6.0.downsample.1.running_mean", "0.6.0.downsample.1.running_var", "0.6.1.conv1.weight", "0.6.1.bn1.weight", "0.6.1.bn1.bias", "0.6.1.bn1.running_mean", "0.6.1.bn1.running_var", "0.6.1.conv2.weight", "0.6.1.bn2.weight", "0.6.1.bn2.bias", "0.6.1.bn2.running_mean", "0.6.1.bn2.running_var", "0.6.1.conv3.weight", "0.6.1.bn3.weight", "0.6.1.bn3.bias", "0.6.1.bn3.running_mean", "0.6.1.bn3.running_var", "0.6.2.conv1.weight", "0.6.2.bn1.weight", "0.6.2.bn1.bias", "0.6.2.bn1.running_mean", "0.6.2.bn1.running_var", "0.6.2.conv2.weight", "0.6.2.bn2.weight", "0.6.2.bn2.bias", "0.6.2.bn2.running_mean", "0.6.2.bn2.running_var", "0.6.2.conv3.weight", "0.6.2.bn3.weight", "0.6.2.bn3.bias", "0.6.2.bn3.running_mean", "0.6.2.bn3.running_var", "0.6.3.conv1.weight", "0.6.3.bn1.weight", "0.6.3.bn1.bias", "0.6.3.bn1.running_mean", "0.6.3.bn1.running_var", "0.6.3.conv2.weight", "0.6.3.bn2.weight", "0.6.3.bn2.bias", "0.6.3.bn2.running_mean", "0.6.3.bn2.running_var", "0.6.3.conv3.weight", "0.6.3.bn3.weight", "0.6.3.bn3.bias", "0.6.3.bn3.running_mean", "0.6.3.bn3.running_var", "0.6.4.conv1.weight", "0.6.4.bn1.weight", "0.6.4.bn1.bias", "0.6.4.bn1.running_mean", "0.6.4.bn1.running_var", "0.6.4.conv2.weight", "0.6.4.bn2.weight", "0.6.4.bn2.bias", "0.6.4.bn2.running_mean", "0.6.4.bn2.running_var", "0.6.4.conv3.weight", "0.6.4.bn3.weight", "0.6.4.bn3.bias", "0.6.4.bn3.running_mean", "0.6.4.bn3.running_var", "0.6.5.conv1.weight", "0.6.5.bn1.weight", "0.6.5.bn1.bias", "0.6.5.bn1.running_mean", "0.6.5.bn1.running_var", "0.6.5.conv2.weight", "0.6.5.bn2.weight", "0.6.5.bn2.bias", "0.6.5.bn2.running_mean", "0.6.5.bn2.running_var", "0.6.5.conv3.weight", "0.6.5.bn3.weight", "0.6.5.bn3.bias", "0.6.5.bn3.running_mean", "0.6.5.bn3.running_var", "0.7.0.conv1.weight", "0.7.0.bn1.weight", "0.7.0.bn1.bias", "0.7.0.bn1.running_mean", "0.7.0.bn1.running_var", "0.7.0.conv2.weight", "0.7.0.bn2.weight", "0.7.0.bn2.bias", "0.7.0.bn2.running_mean", "0.7.0.bn2.running_var", "0.7.0.conv3.weight", "0.7.0.bn3.weight", "0.7.0.bn3.bias", "0.7.0.bn3.running_mean", "0.7.0.bn3.running_var", "0.7.0.downsample.0.weight", "0.7.0.downsample.1.weight", "0.7.0.downsample.1.bias", "0.7.0.downsample.1.running_mean", "0.7.0.downsample.1.running_var", "0.7.1.conv1.weight", "0.7.1.bn1.weight", "0.7.1.bn1.bias", "0.7.1.bn1.running_mean", "0.7.1.bn1.running_var", "0.7.1.conv2.weight", "0.7.1.bn2.weight", "0.7.1.bn2.bias", "0.7.1.bn2.running_mean", "0.7.1.bn2.running_var", "0.7.1.conv3.weight", "0.7.1.bn3.weight", "0.7.1.bn3.bias", "0.7.1.bn3.running_mean", "0.7.1.bn3.running_var", "0.7.2.conv1.weight", "0.7.2.bn1.weight", "0.7.2.bn1.bias", "0.7.2.bn1.running_mean", "0.7.2.bn1.running_var", "0.7.2.conv2.weight", "0.7.2.bn2.weight", "0.7.2.bn2.bias", "0.7.2.bn2.running_mean", "0.7.2.bn2.running_var", "0.7.2.conv3.weight", "0.7.2.bn3.weight", "0.7.2.bn3.bias", "0.7.2.bn3.running_mean", "0.7.2.bn3.running_var", "1.2.weight", "1.2.bias", "1.2.running_mean", "1.2.running_var", "1.4.weight", "1.4.bias", "1.6.weight", "1.6.bias", "1.6.running_mean", "1.6.running_var", "1.8.weight", "1.8.bias".
        Unexpected key(s) in state_dict: "model", "opt".

someone has any idea why I’m getting the error? I guess is due to this part of the model:

opt_func=functools.partial......

1 Like
22

Sorry for the very late response @Mauro . But the answer is exactly as @adrian mentioned.

My example of how it works with mnist_tiny .

Screenshot%20from%202018-12-08%2011-30-45
Screenshot from 2018-12-08 11-30-45.png965×386 29.8 KB

@jm0077 . Which version of fastai you use ? In the new version of fast.ai, there is no more single_from_classes . You can find it in the change log in github and the new API for inference in the doc is here; https://docs.fast.ai/tutorial.inference.html

2 Likes
23

Thanks @dhoa I was trying to install the fastai version in a Docker container then I realized that as I installed fastai there previously (1 month ago), it is not updated so the single_from_classes method still works in that version.

Also, if someone has the same problem, use the parameter with_opt=False when saving the model, so in that way the optimizer state is not saved and it will be loaded without issues later.

1 Like