prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  bug when using PIL backend in classification references
  ### 🐛 Describe the bug
  
  When I try to train a model using the train.py script under references/classification with the PIL backend, I encounter an error:
  ```bash
  ValueError: backend can be 'tensor' or 'pil', but got pil
  ```
  
  To reproduce this issue, you can write:
  ```bash
  git clone https://github.com/pytorch/vision && cd vision
  conda create -n vision_env python=3.9
  conda activate vision_env
  pip install torch==1.13.1 torchvision
  cd references/classification/
  python train.py --data-path "path-to-dataset" --test-only --backend pil
  ```
  
  ### Versions
  
  [pip3] mypy-extensions==1.0.0
  [pip3] numpy==1.24.3
  [pip3] torch==1.13.1
  [pip3] torchvision==0.14.1
  [conda] numpy                     1.24.3                   pypi_0    pypi
  [conda] torch                     1.13.1                   pypi_0    pypi
  [conda] torchvision               0.14.1                   pypi_0    pypi
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `references/classification/presets.py`
  Content:
  ```
  1 import torch
  2 from torchvision.transforms import autoaugment, transforms
  3 from torchvision.transforms.functional import InterpolationMode
  4 
  5 
  6 class ClassificationPresetTrain:
  7     def __init__(
  8         self,
  9         *,
  10         crop_size,
  11         mean=(0.485, 0.456, 0.406),
  12         std=(0.229, 0.224, 0.225),
  13         interpolation=InterpolationMode.BILINEAR,
  14         hflip_prob=0.5,
  15         auto_augment_policy=None,
  16         ra_magnitude=9,
  17         augmix_severity=3,
  18         random_erase_prob=0.0,
  19         backend="pil",
  20     ):
  21         trans = []
  22         backend = backend.lower()
  23         if backend == "tensor":
  24             trans.append(transforms.PILToTensor())
  25         elif backend != "pil":
  26             raise ValueError(f"backend can be 'tensor' or 'pil', but got {backend}")
  27 
  28         trans.append(transforms.RandomResizedCrop(crop_size, interpolation=interpolation, antialias=True))
  29         if hflip_prob > 0:
  30             trans.append(transforms.RandomHorizontalFlip(hflip_prob))
  31         if auto_augment_policy is not None:
  32             if auto_augment_policy == "ra":
  33                 trans.append(autoaugment.RandAugment(interpolation=interpolation, magnitude=ra_magnitude))
  34             elif auto_augment_policy == "ta_wide":
  35                 trans.append(autoaugment.TrivialAugmentWide(interpolation=interpolation))
  36             elif auto_augment_policy == "augmix":
  37                 trans.append(autoaugment.AugMix(interpolation=interpolation, severity=augmix_severity))
  38             else:
  39                 aa_policy = autoaugment.AutoAugmentPolicy(auto_augment_policy)
  40                 trans.append(autoaugment.AutoAugment(policy=aa_policy, interpolation=interpolation))
  41 
  42         if backend == "pil":
  43             trans.append(transforms.PILToTensor())
  44 
  45         trans.extend(
  46             [
  47                 transforms.ConvertImageDtype(torch.float),
  48                 transforms.Normalize(mean=mean, std=std),
  49             ]
  50         )
  51         if random_erase_prob > 0:
  52             trans.append(transforms.RandomErasing(p=random_erase_prob))
  53 
  54         self.transforms = transforms.Compose(trans)
  55 
  56     def __call__(self, img):
  57         return self.transforms(img)
  58 
  59 
  60 class ClassificationPresetEval:
  61     def __init__(
  62         self,
  63         *,
  64         crop_size,
  65         resize_size=256,
  66         mean=(0.485, 0.456, 0.406),
  67         std=(0.229, 0.224, 0.225),
  68         interpolation=InterpolationMode.BILINEAR,
  69         backend="pil",
  70     ):
  71         trans = []
  72 
  73         backend = backend.lower()
  74         if backend == "tensor":
  75             trans.append(transforms.PILToTensor())
  76         else:
  77             raise ValueError(f"backend can be 'tensor' or 'pil', but got {backend}")
  78 
  79         trans += [
  80             transforms.Resize(resize_size, interpolation=interpolation, antialias=True),
  81             transforms.CenterCrop(crop_size),
  82         ]
  83 
  84         if backend == "pil":
  85             trans.append(transforms.PILToTensor())
  86 
  87         trans += [
  88             transforms.ConvertImageDtype(torch.float),
  89             transforms.Normalize(mean=mean, std=std),
  90         ]
  91 
  92         self.transforms = transforms.Compose(trans)
  93 
  94     def __call__(self, img):
  95         return self.transforms(img)
  96
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/references/classification/presets.py b/references/classification/presets.py
  --- a/references/classification/presets.py
  +++ b/references/classification/presets.py
  @@ -69,11 +69,10 @@
           backend="pil",
       ):
           trans = []
  -
           backend = backend.lower()
           if backend == "tensor":
               trans.append(transforms.PILToTensor())
  -        else:
  +        elif backend != "pil":
               raise ValueError(f"backend can be 'tensor' or 'pil', but got {backend}")
   
           trans += [
  
verification_info: {"golden_diff": "diff --git a/references/classification/presets.py b/references/classification/presets.py\n--- a/references/classification/presets.py\n+++ b/references/classification/presets.py\n@@ -69,11 +69,10 @@\n         backend=\"pil\",\n     ):\n         trans = []\n-\n         backend = backend.lower()\n         if backend == \"tensor\":\n             trans.append(transforms.PILToTensor())\n-        else:\n+        elif backend != \"pil\":\n             raise ValueError(f\"backend can be 'tensor' or 'pil', but got {backend}\")\n \n         trans += [\n", "issue": "bug when using PIL backend in classification references\n### \ud83d\udc1b Describe the bug\n\nWhen I try to train a model using the train.py script under references/classification with the PIL backend, I encounter an error:\r\n```bash\r\nValueError: backend can be 'tensor' or 'pil', but got pil\r\n```\r\n\r\nTo reproduce this issue, you can write:\r\n```bash\r\ngit clone https://github.com/pytorch/vision && cd vision\r\nconda create -n vision_env python=3.9\r\nconda activate vision_env\r\npip install torch==1.13.1 torchvision\r\ncd references/classification/\r\npython train.py --data-path \"path-to-dataset\" --test-only --backend pil\r\n```\n\n### Versions\n\n[pip3] mypy-extensions==1.0.0\r\n[pip3] numpy==1.24.3\r\n[pip3] torch==1.13.1\r\n[pip3] torchvision==0.14.1\r\n[conda] numpy                     1.24.3                   pypi_0    pypi\r\n[conda] torch                     1.13.1                   pypi_0    pypi\r\n[conda] torchvision               0.14.1                   pypi_0    pypi\n", "before_files": [{"content": "import torch\nfrom torchvision.transforms import autoaugment, transforms\nfrom torchvision.transforms.functional import InterpolationMode\n\n\nclass ClassificationPresetTrain:\n    def __init__(\n        self,\n        *,\n        crop_size,\n        mean=(0.485, 0.456, 0.406),\n        std=(0.229, 0.224, 0.225),\n        interpolation=InterpolationMode.BILINEAR,\n        hflip_prob=0.5,\n        auto_augment_policy=None,\n        ra_magnitude=9,\n        augmix_severity=3,\n        random_erase_prob=0.0,\n        backend=\"pil\",\n    ):\n        trans = []\n        backend = backend.lower()\n        if backend == \"tensor\":\n            trans.append(transforms.PILToTensor())\n        elif backend != \"pil\":\n            raise ValueError(f\"backend can be 'tensor' or 'pil', but got {backend}\")\n\n        trans.append(transforms.RandomResizedCrop(crop_size, interpolation=interpolation, antialias=True))\n        if hflip_prob > 0:\n            trans.append(transforms.RandomHorizontalFlip(hflip_prob))\n        if auto_augment_policy is not None:\n            if auto_augment_policy == \"ra\":\n                trans.append(autoaugment.RandAugment(interpolation=interpolation, magnitude=ra_magnitude))\n            elif auto_augment_policy == \"ta_wide\":\n                trans.append(autoaugment.TrivialAugmentWide(interpolation=interpolation))\n            elif auto_augment_policy == \"augmix\":\n                trans.append(autoaugment.AugMix(interpolation=interpolation, severity=augmix_severity))\n            else:\n                aa_policy = autoaugment.AutoAugmentPolicy(auto_augment_policy)\n                trans.append(autoaugment.AutoAugment(policy=aa_policy, interpolation=interpolation))\n\n        if backend == \"pil\":\n            trans.append(transforms.PILToTensor())\n\n        trans.extend(\n            [\n                transforms.ConvertImageDtype(torch.float),\n                transforms.Normalize(mean=mean, std=std),\n            ]\n        )\n        if random_erase_prob > 0:\n            trans.append(transforms.RandomErasing(p=random_erase_prob))\n\n        self.transforms = transforms.Compose(trans)\n\n    def __call__(self, img):\n        return self.transforms(img)\n\n\nclass ClassificationPresetEval:\n    def __init__(\n        self,\n        *,\n        crop_size,\n        resize_size=256,\n        mean=(0.485, 0.456, 0.406),\n        std=(0.229, 0.224, 0.225),\n        interpolation=InterpolationMode.BILINEAR,\n        backend=\"pil\",\n    ):\n        trans = []\n\n        backend = backend.lower()\n        if backend == \"tensor\":\n            trans.append(transforms.PILToTensor())\n        else:\n            raise ValueError(f\"backend can be 'tensor' or 'pil', but got {backend}\")\n\n        trans += [\n            transforms.Resize(resize_size, interpolation=interpolation, antialias=True),\n            transforms.CenterCrop(crop_size),\n        ]\n\n        if backend == \"pil\":\n            trans.append(transforms.PILToTensor())\n\n        trans += [\n            transforms.ConvertImageDtype(torch.float),\n            transforms.Normalize(mean=mean, std=std),\n        ]\n\n        self.transforms = transforms.Compose(trans)\n\n    def __call__(self, img):\n        return self.transforms(img)\n", "path": "references/classification/presets.py"}], "after_files": [{"content": "import torch\nfrom torchvision.transforms import autoaugment, transforms\nfrom torchvision.transforms.functional import InterpolationMode\n\n\nclass ClassificationPresetTrain:\n    def __init__(\n        self,\n        *,\n        crop_size,\n        mean=(0.485, 0.456, 0.406),\n        std=(0.229, 0.224, 0.225),\n        interpolation=InterpolationMode.BILINEAR,\n        hflip_prob=0.5,\n        auto_augment_policy=None,\n        ra_magnitude=9,\n        augmix_severity=3,\n        random_erase_prob=0.0,\n        backend=\"pil\",\n    ):\n        trans = []\n        backend = backend.lower()\n        if backend == \"tensor\":\n            trans.append(transforms.PILToTensor())\n        elif backend != \"pil\":\n            raise ValueError(f\"backend can be 'tensor' or 'pil', but got {backend}\")\n\n        trans.append(transforms.RandomResizedCrop(crop_size, interpolation=interpolation, antialias=True))\n        if hflip_prob > 0:\n            trans.append(transforms.RandomHorizontalFlip(hflip_prob))\n        if auto_augment_policy is not None:\n            if auto_augment_policy == \"ra\":\n                trans.append(autoaugment.RandAugment(interpolation=interpolation, magnitude=ra_magnitude))\n            elif auto_augment_policy == \"ta_wide\":\n                trans.append(autoaugment.TrivialAugmentWide(interpolation=interpolation))\n            elif auto_augment_policy == \"augmix\":\n                trans.append(autoaugment.AugMix(interpolation=interpolation, severity=augmix_severity))\n            else:\n                aa_policy = autoaugment.AutoAugmentPolicy(auto_augment_policy)\n                trans.append(autoaugment.AutoAugment(policy=aa_policy, interpolation=interpolation))\n\n        if backend == \"pil\":\n            trans.append(transforms.PILToTensor())\n\n        trans.extend(\n            [\n                transforms.ConvertImageDtype(torch.float),\n                transforms.Normalize(mean=mean, std=std),\n            ]\n        )\n        if random_erase_prob > 0:\n            trans.append(transforms.RandomErasing(p=random_erase_prob))\n\n        self.transforms = transforms.Compose(trans)\n\n    def __call__(self, img):\n        return self.transforms(img)\n\n\nclass ClassificationPresetEval:\n    def __init__(\n        self,\n        *,\n        crop_size,\n        resize_size=256,\n        mean=(0.485, 0.456, 0.406),\n        std=(0.229, 0.224, 0.225),\n        interpolation=InterpolationMode.BILINEAR,\n        backend=\"pil\",\n    ):\n        trans = []\n        backend = backend.lower()\n        if backend == \"tensor\":\n            trans.append(transforms.PILToTensor())\n        elif backend != \"pil\":\n            raise ValueError(f\"backend can be 'tensor' or 'pil', but got {backend}\")\n\n        trans += [\n            transforms.Resize(resize_size, interpolation=interpolation, antialias=True),\n            transforms.CenterCrop(crop_size),\n        ]\n\n        if backend == \"pil\":\n            trans.append(transforms.PILToTensor())\n\n        trans += [\n            transforms.ConvertImageDtype(torch.float),\n            transforms.Normalize(mean=mean, std=std),\n        ]\n\n        self.transforms = transforms.Compose(trans)\n\n    def __call__(self, img):\n        return self.transforms(img)\n", "path": "references/classification/presets.py"}]}