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()

```

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 += [

{"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"}]}