fastai efficientdet fails on learn.validate() with AttributeError: 'NoneType' object has no attribute 'shape'

🐛 Bug

when trying to simply validate metrics for an efficientdet model with fastai

KeyError: 'image_id'

AttributeError: 'NoneType' object has no attribute 'shape'

it fails when trying to read the batch size automatically: in accumulate, find_bs

class AvgLoss(Metric):
    "Average the losses taking into account potential different batch sizes"
    def reset(self):           self.total,self.count = 0.,0
    def accumulate(self, learn):
        bs = find_bs(learn.yb)
        self.total += learn.to_detach(learn.loss.mean())*bs
        self.count += bs
    @property
    def value(self): return self.total/self.count if self.count != 0 else None
    @property
    def name(self):  return "loss"

To Reproduce Steps to reproduce the behavior:

1. Go to https://colab.research.google.com/drive/1i4aXYu4wIKA7eLUK86GwTm7lq7zku_oF?usp=sharing

[potipot]

I found that applying a following patch:

from fastai.learner import AvgLoss
from fastai.torch_core import find_bs

@patch
def accumulate(self:AvgLoss, learn):
    #bs = find_bs(learn.yb)
    bs = find_bs(learn.xb)
    self.total += learn.to_detach(learn.loss.mean())*bs
    self.count += bs

fixes the issue. Im not sure though how should we store that patch in icevision. Anny suggestions?

[potipot]

The problem is that the test dataset (fridge_ds) has only 1 element in the validation set. Therefore I cannot test which list corresponds to the actual batch size, as it is always 1. 😄 I will increase the size of validation set to say 3 and then test again.