Resuming a crashed ConvLearner


I am running a multi-label classification image problem on a Paperspace instance.

Due to some problems I am facing with the quality of data not all images are fully loaded onto the computer (separate issue that I am working on).

I am running this snippet:

arch = resnet34
sz = 224
label_csv = '/home/paperspace/datasets/filtered_train.csv'

def get_data(sz):
    tfms = tfms_from_model(arch, sz, aug_tfms=transforms_side_on, max_zoom=1.05)
    return ImageClassifierData.from_csv('/home/paperspace/images/', 'train_images', label_csv, tfms=tfms,
                        val_idxs=get_cv_idxs(473249), suffix='.jpeg', test_name='test_images')

data = get_data(sz)
data = data.resize(int(sz*1.3), 'tmp')
learn = ConvLearner.pretrained(arch, data, precompute=True, metrics=[f1])

Whenever I encounter an image that is not fully loaded it crashes with these notices.

I run the “%debug” to identify the image and reprocess it but the bigger question is what I should do then? How do I recover safely from this?

  1. Run the above snippet again?
  2. Delete the “tmp” folder or not?
  3. Restart the kernel or not?


@jkdk When you have a crash the best thing I’ve found is to restart your kernel and run your steps again. Make sure you take note of the problem first and then work to resolve that as well as the error will simply show up again if the underlying root cause is not resolved.


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