GradCAM Load_learner

Hi all

Here’s my situation:

  1. Test image (no label) that I want to classify based on a fine-tuned model trained on a similar dataset as the test image.
    test_img = test_img.resize(torch.Size([test_img.shape[0],224, 224]))
    torch.Size([3, 224, 224])
  2. Load an inference learner
    learned_model = load_learner(path, ‘export_models/new_model_weights.pkl’)
  3. Predict classification (CORRECT!)
    (Category class 3,
    tensor([0.0157, 0.0804, 0.0055, 0.8985]))
  4. To see the corresponding heat map:
    interp = ClassificationInterpretation.GradCAM(learned_model, test_img)

`AttributeError Traceback (most recent call last)
----> 1 interp = ClassificationInterpretation.GradCAM(learn_se_rn50, test[0])

/exp/home/rdass/Research/tensorflowEnv/lib64/python3.6/site-packages/fastai/vision/ in _cl_int_gradcam(self, idx, ds_type, heatmap_thresh, image)
138 def _cl_int_gradcam(self, idx, ds_type:DatasetType=DatasetType.Valid, heatmap_thresh:int=16, image:bool=True):
–> 139 m = self.learn.model.eval()
140 im,cl =[idx]
141 cl = int(cl)

AttributeError: ‘Learner’ object has no attribute ‘learn’`

This error seems to be raised by a FasiAI wrapper, so maybe @jphoward knows what might be wrong.

@alejo30 could you please create a github issue (at with this information?