prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  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 
  ```python
  KeyError: 'image_id'
  ```
  ```python
  AttributeError: 'NoneType' object has no attribute 'shape'
  ```
  
  it fails when trying to read the batch size automatically: in `accumulate, find_bs`
  ```python
  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
  
  
  
  </issue>
  <code>
  [start of icevision/models/torchvision/fastai/learner.py]
  1 __all__ = ["RCNNCallback", "rcnn_learner"]
  2 
  3 from icevision.imports import *
  4 from icevision.engines.fastai import *
  5 from icevision.models.torchvision.loss_fn import loss_fn
  6 from icevision.models.torchvision.fastai.callbacks import *
  7 
  8 
  9 def noop_watch(models, criterion=None, log="gradients", log_freq=1000, idx=None):
  10     return []
  11 
  12 
  13 def rcnn_learner(
  14     dls: List[Union[DataLoader, fastai.DataLoader]],
  15     model: nn.Module,
  16     cbs=None,
  17     **kwargs,
  18 ):
  19     learn = adapted_fastai_learner(
  20         dls=dls,
  21         model=model,
  22         cbs=cbs,
  23         loss_func=loss_fn,
  24         **kwargs,
  25     )
  26 
  27     # HACK: patch AvgLoss (in original, find_bs gives errors)
  28     class RCNNAvgLoss(fastai.AvgLoss):
  29         def accumulate(self, learn):
  30             bs = len(learn.yb)
  31             self.total += fastai.to_detach(learn.loss.mean()) * bs
  32             self.count += bs
  33 
  34     recorder = [cb for cb in learn.cbs if isinstance(cb, fastai.Recorder)][0]
  35     recorder.loss = RCNNAvgLoss()
  36 
  37     is_wandb = [cb for cb in learn.cbs if "WandbCallback" in str(type(cb))]
  38     if len(is_wandb) == 1:
  39         logger.warning("Wandb quickfix implemented, for more info check issue #527")
  40         wandb.watch = noop_watch
  41     if len(is_wandb) > 1:
  42         raise ValueError(
  43             f"It seems you are passing {len(is_wandb)} `WandbCallback` instances to the `learner`. Only 1 is allowed."
  44         )
  45 
  46     return learn
  47
  [end of icevision/models/torchvision/fastai/learner.py]
  [start of icevision/models/ross/efficientdet/fastai/learner.py]
  1 __all__ = ["learner"]
  2 
  3 from icevision.imports import *
  4 from icevision.engines.fastai import *
  5 from icevision.models.ross.efficientdet.loss_fn import loss_fn
  6 from icevision.models.ross.efficientdet.fastai.callbacks import EfficientDetCallback
  7 
  8 
  9 def learner(
  10     dls: List[Union[DataLoader, fastai.DataLoader]],
  11     model: nn.Module,
  12     cbs=None,
  13     **learner_kwargs,
  14 ):
  15     """Fastai `Learner` adapted for EfficientDet.
  16 
  17     # Arguments
  18         dls: `Sequence` of `DataLoaders` passed to the `Learner`.
  19         The first one will be used for training and the second for validation.
  20         model: The model to train.
  21         cbs: Optional `Sequence` of callbacks.
  22         **learner_kwargs: Keyword arguments that will be internally passed to `Learner`.
  23 
  24     # Returns
  25         A fastai `Learner`.
  26     """
  27     cbs = [EfficientDetCallback()] + L(cbs)
  28 
  29     learn = adapted_fastai_learner(
  30         dls=dls,
  31         model=model,
  32         cbs=cbs,
  33         loss_func=loss_fn,
  34         **learner_kwargs,
  35     )
  36 
  37     # HACK: patch AvgLoss (in original, find_bs gives errors)
  38     class PatchedAvgLoss(fastai.AvgLoss):
  39         def accumulate(self, learn):
  40             bs = len(learn.yb)
  41             self.total += fastai.to_detach(learn.loss.mean()) * bs
  42             self.count += bs
  43 
  44     recorder = [cb for cb in learn.cbs if isinstance(cb, fastai.Recorder)][0]
  45     recorder.loss = PatchedAvgLoss()
  46 
  47     return learn
  48
  [end of icevision/models/ross/efficientdet/fastai/learner.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/icevision/models/ross/efficientdet/fastai/learner.py b/icevision/models/ross/efficientdet/fastai/learner.py
  --- a/icevision/models/ross/efficientdet/fastai/learner.py
  +++ b/icevision/models/ross/efficientdet/fastai/learner.py
  @@ -34,14 +34,14 @@
           **learner_kwargs,
       )
   
  -    # HACK: patch AvgLoss (in original, find_bs gives errors)
  -    class PatchedAvgLoss(fastai.AvgLoss):
  +    # HACK: patch AvgLoss (in original, find_bs looks at the first element in dictionary and gives errors)
  +    class EffDetAvgLoss(fastai.AvgLoss):
           def accumulate(self, learn):
  -            bs = len(learn.yb)
  -            self.total += fastai.to_detach(learn.loss.mean()) * bs
  +            bs = len(first(learn.yb)["cls"])
  +            self.total += learn.to_detach(learn.loss.mean()) * bs
               self.count += bs
   
       recorder = [cb for cb in learn.cbs if isinstance(cb, fastai.Recorder)][0]
  -    recorder.loss = PatchedAvgLoss()
  +    recorder.loss = EffDetAvgLoss()
   
       return learn
  diff --git a/icevision/models/torchvision/fastai/learner.py b/icevision/models/torchvision/fastai/learner.py
  --- a/icevision/models/torchvision/fastai/learner.py
  +++ b/icevision/models/torchvision/fastai/learner.py
  @@ -27,7 +27,7 @@
       # HACK: patch AvgLoss (in original, find_bs gives errors)
       class RCNNAvgLoss(fastai.AvgLoss):
           def accumulate(self, learn):
  -            bs = len(learn.yb)
  +            bs = len(first(learn.yb))
               self.total += fastai.to_detach(learn.loss.mean()) * bs
               self.count += bs
  
verification_info: {"golden_diff": "diff --git a/icevision/models/ross/efficientdet/fastai/learner.py b/icevision/models/ross/efficientdet/fastai/learner.py\n--- a/icevision/models/ross/efficientdet/fastai/learner.py\n+++ b/icevision/models/ross/efficientdet/fastai/learner.py\n@@ -34,14 +34,14 @@\n         **learner_kwargs,\n     )\n \n-    # HACK: patch AvgLoss (in original, find_bs gives errors)\n-    class PatchedAvgLoss(fastai.AvgLoss):\n+    # HACK: patch AvgLoss (in original, find_bs looks at the first element in dictionary and gives errors)\n+    class EffDetAvgLoss(fastai.AvgLoss):\n         def accumulate(self, learn):\n-            bs = len(learn.yb)\n-            self.total += fastai.to_detach(learn.loss.mean()) * bs\n+            bs = len(first(learn.yb)[\"cls\"])\n+            self.total += learn.to_detach(learn.loss.mean()) * bs\n             self.count += bs\n \n     recorder = [cb for cb in learn.cbs if isinstance(cb, fastai.Recorder)][0]\n-    recorder.loss = PatchedAvgLoss()\n+    recorder.loss = EffDetAvgLoss()\n \n     return learn\ndiff --git a/icevision/models/torchvision/fastai/learner.py b/icevision/models/torchvision/fastai/learner.py\n--- a/icevision/models/torchvision/fastai/learner.py\n+++ b/icevision/models/torchvision/fastai/learner.py\n@@ -27,7 +27,7 @@\n     # HACK: patch AvgLoss (in original, find_bs gives errors)\n     class RCNNAvgLoss(fastai.AvgLoss):\n         def accumulate(self, learn):\n-            bs = len(learn.yb)\n+            bs = len(first(learn.yb))\n             self.total += fastai.to_detach(learn.loss.mean()) * bs\n             self.count += bs\n", "issue": "fastai efficientdet fails on learn.validate() with AttributeError: 'NoneType' object has no attribute 'shape'\n## \ud83d\udc1b Bug\r\nwhen trying to simply validate metrics for an efficientdet model with fastai \r\n```python\r\nKeyError: 'image_id'\r\n```\r\n```python\r\nAttributeError: 'NoneType' object has no attribute 'shape'\r\n```\r\n\r\nit fails when trying to read the batch size automatically: in `accumulate, find_bs`\r\n```python\r\nclass AvgLoss(Metric):\r\n    \"Average the losses taking into account potential different batch sizes\"\r\n    def reset(self):           self.total,self.count = 0.,0\r\n    def accumulate(self, learn):\r\n        bs = find_bs(learn.yb)\r\n        self.total += learn.to_detach(learn.loss.mean())*bs\r\n        self.count += bs\r\n    @property\r\n    def value(self): return self.total/self.count if self.count != 0 else None\r\n    @property\r\n    def name(self):  return \"loss\"\r\n```\r\n**To Reproduce**\r\nSteps to reproduce the behavior:\r\n1. Go to https://colab.research.google.com/drive/1i4aXYu4wIKA7eLUK86GwTm7lq7zku_oF?usp=sharing\r\n\r\n\n", "before_files": [{"content": "__all__ = [\"RCNNCallback\", \"rcnn_learner\"]\n\nfrom icevision.imports import *\nfrom icevision.engines.fastai import *\nfrom icevision.models.torchvision.loss_fn import loss_fn\nfrom icevision.models.torchvision.fastai.callbacks import *\n\n\ndef noop_watch(models, criterion=None, log=\"gradients\", log_freq=1000, idx=None):\n    return []\n\n\ndef rcnn_learner(\n    dls: List[Union[DataLoader, fastai.DataLoader]],\n    model: nn.Module,\n    cbs=None,\n    **kwargs,\n):\n    learn = adapted_fastai_learner(\n        dls=dls,\n        model=model,\n        cbs=cbs,\n        loss_func=loss_fn,\n        **kwargs,\n    )\n\n    # HACK: patch AvgLoss (in original, find_bs gives errors)\n    class RCNNAvgLoss(fastai.AvgLoss):\n        def accumulate(self, learn):\n            bs = len(learn.yb)\n            self.total += fastai.to_detach(learn.loss.mean()) * bs\n            self.count += bs\n\n    recorder = [cb for cb in learn.cbs if isinstance(cb, fastai.Recorder)][0]\n    recorder.loss = RCNNAvgLoss()\n\n    is_wandb = [cb for cb in learn.cbs if \"WandbCallback\" in str(type(cb))]\n    if len(is_wandb) == 1:\n        logger.warning(\"Wandb quickfix implemented, for more info check issue #527\")\n        wandb.watch = noop_watch\n    if len(is_wandb) > 1:\n        raise ValueError(\n            f\"It seems you are passing {len(is_wandb)} `WandbCallback` instances to the `learner`. Only 1 is allowed.\"\n        )\n\n    return learn\n", "path": "icevision/models/torchvision/fastai/learner.py"}, {"content": "__all__ = [\"learner\"]\n\nfrom icevision.imports import *\nfrom icevision.engines.fastai import *\nfrom icevision.models.ross.efficientdet.loss_fn import loss_fn\nfrom icevision.models.ross.efficientdet.fastai.callbacks import EfficientDetCallback\n\n\ndef learner(\n    dls: List[Union[DataLoader, fastai.DataLoader]],\n    model: nn.Module,\n    cbs=None,\n    **learner_kwargs,\n):\n    \"\"\"Fastai `Learner` adapted for EfficientDet.\n\n    # Arguments\n        dls: `Sequence` of `DataLoaders` passed to the `Learner`.\n        The first one will be used for training and the second for validation.\n        model: The model to train.\n        cbs: Optional `Sequence` of callbacks.\n        **learner_kwargs: Keyword arguments that will be internally passed to `Learner`.\n\n    # Returns\n        A fastai `Learner`.\n    \"\"\"\n    cbs = [EfficientDetCallback()] + L(cbs)\n\n    learn = adapted_fastai_learner(\n        dls=dls,\n        model=model,\n        cbs=cbs,\n        loss_func=loss_fn,\n        **learner_kwargs,\n    )\n\n    # HACK: patch AvgLoss (in original, find_bs gives errors)\n    class PatchedAvgLoss(fastai.AvgLoss):\n        def accumulate(self, learn):\n            bs = len(learn.yb)\n            self.total += fastai.to_detach(learn.loss.mean()) * bs\n            self.count += bs\n\n    recorder = [cb for cb in learn.cbs if isinstance(cb, fastai.Recorder)][0]\n    recorder.loss = PatchedAvgLoss()\n\n    return learn\n", "path": "icevision/models/ross/efficientdet/fastai/learner.py"}]}