prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  utils.make_grid should not return tensors of dimension 2 or 3 without normalizing them
  When passing in a single image of dimensionality 2 or 3 to utils.make_grid, the function currently returns this image, without normalizing it (but it should, according to the function's documentation).
  This is also problematic as utils.save_image calls utils.make_grid to normalize its images. 
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `torchvision/utils.py`
  Content:
  ```
  1 import torch
  2 import math
  3 irange = range
  4 
  5 
  6 def make_grid(tensor, nrow=8, padding=2,
  7               normalize=False, range=None, scale_each=False, pad_value=0):
  8     """Make a grid of images.
  9 
  10     Args:
  11         tensor (Tensor or list): 4D mini-batch Tensor of shape (B x C x H x W)
  12             or a list of images all of the same size.
  13         nrow (int, optional): Number of images displayed in each row of the grid.
  14             The Final grid size is (B / nrow, nrow). Default is 8.
  15         padding (int, optional): amount of padding. Default is 2.
  16         normalize (bool, optional): If True, shift the image to the range (0, 1),
  17             by subtracting the minimum and dividing by the maximum pixel value.
  18         range (tuple, optional): tuple (min, max) where min and max are numbers,
  19             then these numbers are used to normalize the image. By default, min and max
  20             are computed from the tensor.
  21         scale_each (bool, optional): If True, scale each image in the batch of
  22             images separately rather than the (min, max) over all images.
  23         pad_value (float, optional): Value for the padded pixels.
  24 
  25     Example:
  26         See this notebook `here <https://gist.github.com/anonymous/bf16430f7750c023141c562f3e9f2a91>`_
  27 
  28     """
  29     if not (torch.is_tensor(tensor) or
  30             (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):
  31         raise TypeError('tensor or list of tensors expected, got {}'.format(type(tensor)))
  32 
  33     # if list of tensors, convert to a 4D mini-batch Tensor
  34     if isinstance(tensor, list):
  35         tensor = torch.stack(tensor, dim=0)
  36 
  37     if tensor.dim() == 2:  # single image H x W
  38         tensor = tensor.view(1, tensor.size(0), tensor.size(1))
  39     if tensor.dim() == 3:  # single image
  40         if tensor.size(0) == 1:  # if single-channel, convert to 3-channel
  41             tensor = torch.cat((tensor, tensor, tensor), 0)
  42         return tensor
  43     if tensor.dim() == 4 and tensor.size(1) == 1:  # single-channel images
  44         tensor = torch.cat((tensor, tensor, tensor), 1)
  45 
  46     if normalize is True:
  47         tensor = tensor.clone()  # avoid modifying tensor in-place
  48         if range is not None:
  49             assert isinstance(range, tuple), \
  50                 "range has to be a tuple (min, max) if specified. min and max are numbers"
  51 
  52         def norm_ip(img, min, max):
  53             img.clamp_(min=min, max=max)
  54             img.add_(-min).div_(max - min)
  55 
  56         def norm_range(t, range):
  57             if range is not None:
  58                 norm_ip(t, range[0], range[1])
  59             else:
  60                 norm_ip(t, t.min(), t.max())
  61 
  62         if scale_each is True:
  63             for t in tensor:  # loop over mini-batch dimension
  64                 norm_range(t, range)
  65         else:
  66             norm_range(tensor, range)
  67 
  68     # make the mini-batch of images into a grid
  69     nmaps = tensor.size(0)
  70     xmaps = min(nrow, nmaps)
  71     ymaps = int(math.ceil(float(nmaps) / xmaps))
  72     height, width = int(tensor.size(2) + padding), int(tensor.size(3) + padding)
  73     grid = tensor.new(3, height * ymaps + padding, width * xmaps + padding).fill_(pad_value)
  74     k = 0
  75     for y in irange(ymaps):
  76         for x in irange(xmaps):
  77             if k >= nmaps:
  78                 break
  79             grid.narrow(1, y * height + padding, height - padding)\
  80                 .narrow(2, x * width + padding, width - padding)\
  81                 .copy_(tensor[k])
  82             k = k + 1
  83     return grid
  84 
  85 
  86 def save_image(tensor, filename, nrow=8, padding=2,
  87                normalize=False, range=None, scale_each=False, pad_value=0):
  88     """Save a given Tensor into an image file.
  89 
  90     Args:
  91         tensor (Tensor or list): Image to be saved. If given a mini-batch tensor,
  92             saves the tensor as a grid of images by calling ``make_grid``.
  93         **kwargs: Other arguments are documented in ``make_grid``.
  94     """
  95     from PIL import Image
  96     grid = make_grid(tensor, nrow=nrow, padding=padding, pad_value=pad_value,
  97                      normalize=normalize, range=range, scale_each=scale_each)
  98     ndarr = grid.mul(255).clamp(0, 255).byte().permute(1, 2, 0).cpu().numpy()
  99     im = Image.fromarray(ndarr)
  100     im.save(filename)
  101
  ```
  --- 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/torchvision/utils.py b/torchvision/utils.py
  --- a/torchvision/utils.py
  +++ b/torchvision/utils.py
  @@ -39,7 +39,8 @@
       if tensor.dim() == 3:  # single image
           if tensor.size(0) == 1:  # if single-channel, convert to 3-channel
               tensor = torch.cat((tensor, tensor, tensor), 0)
  -        return tensor
  +        tensor = tensor.view(1, tensor.size(0), tensor.size(1), tensor.size(2))
  +
       if tensor.dim() == 4 and tensor.size(1) == 1:  # single-channel images
           tensor = torch.cat((tensor, tensor, tensor), 1)
   
  @@ -65,6 +66,9 @@
           else:
               norm_range(tensor, range)
   
  +    if tensor.size(0) == 1:
  +        return tensor.squeeze()
  +
       # make the mini-batch of images into a grid
       nmaps = tensor.size(0)
       xmaps = min(nrow, nmaps)
  
verification_info: {"golden_diff": "diff --git a/torchvision/utils.py b/torchvision/utils.py\n--- a/torchvision/utils.py\n+++ b/torchvision/utils.py\n@@ -39,7 +39,8 @@\n     if tensor.dim() == 3:  # single image\n         if tensor.size(0) == 1:  # if single-channel, convert to 3-channel\n             tensor = torch.cat((tensor, tensor, tensor), 0)\n-        return tensor\n+        tensor = tensor.view(1, tensor.size(0), tensor.size(1), tensor.size(2))\n+\n     if tensor.dim() == 4 and tensor.size(1) == 1:  # single-channel images\n         tensor = torch.cat((tensor, tensor, tensor), 1)\n \n@@ -65,6 +66,9 @@\n         else:\n             norm_range(tensor, range)\n \n+    if tensor.size(0) == 1:\n+        return tensor.squeeze()\n+\n     # make the mini-batch of images into a grid\n     nmaps = tensor.size(0)\n     xmaps = min(nrow, nmaps)\n", "issue": "utils.make_grid should not return tensors of dimension 2 or 3 without normalizing them\nWhen passing in a single image of dimensionality 2 or 3 to utils.make_grid, the function currently returns this image, without normalizing it (but it should, according to the function's documentation).\r\nThis is also problematic as utils.save_image calls utils.make_grid to normalize its images. \n", "before_files": [{"content": "import torch\nimport math\nirange = range\n\n\ndef make_grid(tensor, nrow=8, padding=2,\n              normalize=False, range=None, scale_each=False, pad_value=0):\n    \"\"\"Make a grid of images.\n\n    Args:\n        tensor (Tensor or list): 4D mini-batch Tensor of shape (B x C x H x W)\n            or a list of images all of the same size.\n        nrow (int, optional): Number of images displayed in each row of the grid.\n            The Final grid size is (B / nrow, nrow). Default is 8.\n        padding (int, optional): amount of padding. Default is 2.\n        normalize (bool, optional): If True, shift the image to the range (0, 1),\n            by subtracting the minimum and dividing by the maximum pixel value.\n        range (tuple, optional): tuple (min, max) where min and max are numbers,\n            then these numbers are used to normalize the image. By default, min and max\n            are computed from the tensor.\n        scale_each (bool, optional): If True, scale each image in the batch of\n            images separately rather than the (min, max) over all images.\n        pad_value (float, optional): Value for the padded pixels.\n\n    Example:\n        See this notebook `here <https://gist.github.com/anonymous/bf16430f7750c023141c562f3e9f2a91>`_\n\n    \"\"\"\n    if not (torch.is_tensor(tensor) or\n            (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):\n        raise TypeError('tensor or list of tensors expected, got {}'.format(type(tensor)))\n\n    # if list of tensors, convert to a 4D mini-batch Tensor\n    if isinstance(tensor, list):\n        tensor = torch.stack(tensor, dim=0)\n\n    if tensor.dim() == 2:  # single image H x W\n        tensor = tensor.view(1, tensor.size(0), tensor.size(1))\n    if tensor.dim() == 3:  # single image\n        if tensor.size(0) == 1:  # if single-channel, convert to 3-channel\n            tensor = torch.cat((tensor, tensor, tensor), 0)\n        return tensor\n    if tensor.dim() == 4 and tensor.size(1) == 1:  # single-channel images\n        tensor = torch.cat((tensor, tensor, tensor), 1)\n\n    if normalize is True:\n        tensor = tensor.clone()  # avoid modifying tensor in-place\n        if range is not None:\n            assert isinstance(range, tuple), \\\n                \"range has to be a tuple (min, max) if specified. min and max are numbers\"\n\n        def norm_ip(img, min, max):\n            img.clamp_(min=min, max=max)\n            img.add_(-min).div_(max - min)\n\n        def norm_range(t, range):\n            if range is not None:\n                norm_ip(t, range[0], range[1])\n            else:\n                norm_ip(t, t.min(), t.max())\n\n        if scale_each is True:\n            for t in tensor:  # loop over mini-batch dimension\n                norm_range(t, range)\n        else:\n            norm_range(tensor, range)\n\n    # make the mini-batch of images into a grid\n    nmaps = tensor.size(0)\n    xmaps = min(nrow, nmaps)\n    ymaps = int(math.ceil(float(nmaps) / xmaps))\n    height, width = int(tensor.size(2) + padding), int(tensor.size(3) + padding)\n    grid = tensor.new(3, height * ymaps + padding, width * xmaps + padding).fill_(pad_value)\n    k = 0\n    for y in irange(ymaps):\n        for x in irange(xmaps):\n            if k >= nmaps:\n                break\n            grid.narrow(1, y * height + padding, height - padding)\\\n                .narrow(2, x * width + padding, width - padding)\\\n                .copy_(tensor[k])\n            k = k + 1\n    return grid\n\n\ndef save_image(tensor, filename, nrow=8, padding=2,\n               normalize=False, range=None, scale_each=False, pad_value=0):\n    \"\"\"Save a given Tensor into an image file.\n\n    Args:\n        tensor (Tensor or list): Image to be saved. If given a mini-batch tensor,\n            saves the tensor as a grid of images by calling ``make_grid``.\n        **kwargs: Other arguments are documented in ``make_grid``.\n    \"\"\"\n    from PIL import Image\n    grid = make_grid(tensor, nrow=nrow, padding=padding, pad_value=pad_value,\n                     normalize=normalize, range=range, scale_each=scale_each)\n    ndarr = grid.mul(255).clamp(0, 255).byte().permute(1, 2, 0).cpu().numpy()\n    im = Image.fromarray(ndarr)\n    im.save(filename)\n", "path": "torchvision/utils.py"}], "after_files": [{"content": "import torch\nimport math\nirange = range\n\n\ndef make_grid(tensor, nrow=8, padding=2,\n              normalize=False, range=None, scale_each=False, pad_value=0):\n    \"\"\"Make a grid of images.\n\n    Args:\n        tensor (Tensor or list): 4D mini-batch Tensor of shape (B x C x H x W)\n            or a list of images all of the same size.\n        nrow (int, optional): Number of images displayed in each row of the grid.\n            The Final grid size is (B / nrow, nrow). Default is 8.\n        padding (int, optional): amount of padding. Default is 2.\n        normalize (bool, optional): If True, shift the image to the range (0, 1),\n            by subtracting the minimum and dividing by the maximum pixel value.\n        range (tuple, optional): tuple (min, max) where min and max are numbers,\n            then these numbers are used to normalize the image. By default, min and max\n            are computed from the tensor.\n        scale_each (bool, optional): If True, scale each image in the batch of\n            images separately rather than the (min, max) over all images.\n        pad_value (float, optional): Value for the padded pixels.\n\n    Example:\n        See this notebook `here <https://gist.github.com/anonymous/bf16430f7750c023141c562f3e9f2a91>`_\n\n    \"\"\"\n    if not (torch.is_tensor(tensor) or\n            (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):\n        raise TypeError('tensor or list of tensors expected, got {}'.format(type(tensor)))\n\n    # if list of tensors, convert to a 4D mini-batch Tensor\n    if isinstance(tensor, list):\n        tensor = torch.stack(tensor, dim=0)\n\n    if tensor.dim() == 2:  # single image H x W\n        tensor = tensor.view(1, tensor.size(0), tensor.size(1))\n    if tensor.dim() == 3:  # single image\n        if tensor.size(0) == 1:  # if single-channel, convert to 3-channel\n            tensor = torch.cat((tensor, tensor, tensor), 0)\n        tensor = tensor.view(1, tensor.size(0), tensor.size(1), tensor.size(2))\n\n    if tensor.dim() == 4 and tensor.size(1) == 1:  # single-channel images\n        tensor = torch.cat((tensor, tensor, tensor), 1)\n\n    if normalize is True:\n        tensor = tensor.clone()  # avoid modifying tensor in-place\n        if range is not None:\n            assert isinstance(range, tuple), \\\n                \"range has to be a tuple (min, max) if specified. min and max are numbers\"\n\n        def norm_ip(img, min, max):\n            img.clamp_(min=min, max=max)\n            img.add_(-min).div_(max - min)\n\n        def norm_range(t, range):\n            if range is not None:\n                norm_ip(t, range[0], range[1])\n            else:\n                norm_ip(t, t.min(), t.max())\n\n        if scale_each is True:\n            for t in tensor:  # loop over mini-batch dimension\n                norm_range(t, range)\n        else:\n            norm_range(tensor, range)\n\n    if tensor.size(0) == 1:\n        return tensor.squeeze()\n\n    # make the mini-batch of images into a grid\n    nmaps = tensor.size(0)\n    xmaps = min(nrow, nmaps)\n    ymaps = int(math.ceil(float(nmaps) / xmaps))\n    height, width = int(tensor.size(2) + padding), int(tensor.size(3) + padding)\n    grid = tensor.new(3, height * ymaps + padding, width * xmaps + padding).fill_(pad_value)\n    k = 0\n    for y in irange(ymaps):\n        for x in irange(xmaps):\n            if k >= nmaps:\n                break\n            grid.narrow(1, y * height + padding, height - padding)\\\n                .narrow(2, x * width + padding, width - padding)\\\n                .copy_(tensor[k])\n            k = k + 1\n    return grid\n\n\ndef save_image(tensor, filename, nrow=8, padding=2,\n               normalize=False, range=None, scale_each=False, pad_value=0):\n    \"\"\"Save a given Tensor into an image file.\n\n    Args:\n        tensor (Tensor or list): Image to be saved. If given a mini-batch tensor,\n            saves the tensor as a grid of images by calling ``make_grid``.\n        **kwargs: Other arguments are documented in ``make_grid``.\n    \"\"\"\n    from PIL import Image\n    grid = make_grid(tensor, nrow=nrow, padding=padding, pad_value=pad_value,\n                     normalize=normalize, range=range, scale_each=scale_each)\n    ndarr = grid.mul(255).clamp(0, 255).byte().permute(1, 2, 0).cpu().numpy()\n    im = Image.fromarray(ndarr)\n    im.save(filename)\n", "path": "torchvision/utils.py"}]}