prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  The ASPP layer has a hard-coded value that only works if the atrous_rates param is of length 3
  ## 🐛 Bug
  
  The ASPP layer has a hard-coded value that only works if the list of atrous rates provided is of length 3. The hard-coded value in question is the `5` on line 83 here:
  https://github.com/pytorch/vision/blob/master/torchvision/models/segmentation/deeplabv3.py#L82-L86
  
  ## To Reproduce
  
  ```python3
  aspp = tv.models.segmentation.deeplabv3.ASPP(1, [12, 24]) # DeepLab uses [12, 24, 36], which works
  
  aspp.eval()
  with torch.no_grad():
      out = aspp(torch.empty(1, 1, 256, 256))
  ```
  Results in
  ```
  RuntimeError: Given groups=1, weight of size [256, 1280, 1, 1], expected input[1, 1024, 256, 256] to have 1280 channels, but got 1024 channels instead
  ```
  
  ## Expected behavior
  
  It should work with different number of atrous rates. This can be fixed simply by replacing
  ```python3
  nn.Conv2d(5 * out_channels, out_channels, 1, bias=False)
  ```
  with
  
  ```python3
  nn.Conv2d(len(self.convs) * out_channels, out_channels, 1, bias=False)
  ```
  
  ## Environment
  
  ```
  PyTorch version: 1.6.0
  Is debug build: False
  CUDA used to build PyTorch: 10.2
  ROCM used to build PyTorch: N/A
  
  OS: Ubuntu 20.04.1 LTS (x86_64)
  GCC version: (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
  Clang version: Could not collect
  CMake version: version 3.18.2
  
  Python version: 3.7 (64-bit runtime)
  Is CUDA available: True
  CUDA runtime version: Could not collect
  GPU models and configuration: GPU 0: GeForce GTX 1060 with Max-Q Design
  Nvidia driver version: 450.66
  cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.1
  HIP runtime version: N/A
  MIOpen runtime version: N/A
  
  Versions of relevant libraries:
  [pip3] numpy==1.19.1
  [pip3] torch==1.6.0
  [pip3] torchvision==0.7.0
  [conda] blas                      1.0                         mkl  
  [conda] cudatoolkit               10.2.89              hfd86e86_1  
  [conda] mkl                       2020.2                      256  
  [conda] mkl-service               2.3.0            py37he904b0f_0  
  [conda] mkl_fft                   1.1.0            py37h23d657b_0  
  [conda] mkl_random                1.1.1            py37h0573a6f_0  
  [conda] numpy                     1.19.1           py37hbc911f0_0  
  [conda] numpy-base                1.19.1           py37hfa32c7d_0  
  [conda] torch                     1.6.0                    pypi_0    pypi
  [conda] torchvision               0.7.0                    pypi_0    pypi
  ```
  
  
  cc @vfdev-5
  
  --- 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/models/segmentation/deeplabv3.py`
  Content:
  ```
  1 import torch
  2 from torch import nn
  3 from torch.nn import functional as F
  4 
  5 from ._utils import _SimpleSegmentationModel
  6 
  7 
  8 __all__ = ["DeepLabV3"]
  9 
  10 
  11 class DeepLabV3(_SimpleSegmentationModel):
  12     """
  13     Implements DeepLabV3 model from
  14     `"Rethinking Atrous Convolution for Semantic Image Segmentation"
  15     <https://arxiv.org/abs/1706.05587>`_.
  16 
  17     Arguments:
  18         backbone (nn.Module): the network used to compute the features for the model.
  19             The backbone should return an OrderedDict[Tensor], with the key being
  20             "out" for the last feature map used, and "aux" if an auxiliary classifier
  21             is used.
  22         classifier (nn.Module): module that takes the "out" element returned from
  23             the backbone and returns a dense prediction.
  24         aux_classifier (nn.Module, optional): auxiliary classifier used during training
  25     """
  26     pass
  27 
  28 
  29 class DeepLabHead(nn.Sequential):
  30     def __init__(self, in_channels, num_classes):
  31         super(DeepLabHead, self).__init__(
  32             ASPP(in_channels, [12, 24, 36]),
  33             nn.Conv2d(256, 256, 3, padding=1, bias=False),
  34             nn.BatchNorm2d(256),
  35             nn.ReLU(),
  36             nn.Conv2d(256, num_classes, 1)
  37         )
  38 
  39 
  40 class ASPPConv(nn.Sequential):
  41     def __init__(self, in_channels, out_channels, dilation):
  42         modules = [
  43             nn.Conv2d(in_channels, out_channels, 3, padding=dilation, dilation=dilation, bias=False),
  44             nn.BatchNorm2d(out_channels),
  45             nn.ReLU()
  46         ]
  47         super(ASPPConv, self).__init__(*modules)
  48 
  49 
  50 class ASPPPooling(nn.Sequential):
  51     def __init__(self, in_channels, out_channels):
  52         super(ASPPPooling, self).__init__(
  53             nn.AdaptiveAvgPool2d(1),
  54             nn.Conv2d(in_channels, out_channels, 1, bias=False),
  55             nn.BatchNorm2d(out_channels),
  56             nn.ReLU())
  57 
  58     def forward(self, x):
  59         size = x.shape[-2:]
  60         for mod in self:
  61             x = mod(x)
  62         return F.interpolate(x, size=size, mode='bilinear', align_corners=False)
  63 
  64 
  65 class ASPP(nn.Module):
  66     def __init__(self, in_channels, atrous_rates, out_channels=256):
  67         super(ASPP, self).__init__()
  68         modules = []
  69         modules.append(nn.Sequential(
  70             nn.Conv2d(in_channels, out_channels, 1, bias=False),
  71             nn.BatchNorm2d(out_channels),
  72             nn.ReLU()))
  73 
  74         rates = tuple(atrous_rates)
  75         for rate in rates:
  76             modules.append(ASPPConv(in_channels, out_channels, rate))
  77 
  78         modules.append(ASPPPooling(in_channels, out_channels))
  79 
  80         self.convs = nn.ModuleList(modules)
  81 
  82         self.project = nn.Sequential(
  83             nn.Conv2d(5 * out_channels, out_channels, 1, bias=False),
  84             nn.BatchNorm2d(out_channels),
  85             nn.ReLU(),
  86             nn.Dropout(0.5))
  87 
  88     def forward(self, x):
  89         res = []
  90         for conv in self.convs:
  91             res.append(conv(x))
  92         res = torch.cat(res, dim=1)
  93         return self.project(res)
  94
  ```
  --- 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/models/segmentation/deeplabv3.py b/torchvision/models/segmentation/deeplabv3.py
  --- a/torchvision/models/segmentation/deeplabv3.py
  +++ b/torchvision/models/segmentation/deeplabv3.py
  @@ -80,7 +80,7 @@
           self.convs = nn.ModuleList(modules)
   
           self.project = nn.Sequential(
  -            nn.Conv2d(5 * out_channels, out_channels, 1, bias=False),
  +            nn.Conv2d(len(self.convs) * out_channels, out_channels, 1, bias=False),
               nn.BatchNorm2d(out_channels),
               nn.ReLU(),
               nn.Dropout(0.5))
  
verification_info: {"golden_diff": "diff --git a/torchvision/models/segmentation/deeplabv3.py b/torchvision/models/segmentation/deeplabv3.py\n--- a/torchvision/models/segmentation/deeplabv3.py\n+++ b/torchvision/models/segmentation/deeplabv3.py\n@@ -80,7 +80,7 @@\n         self.convs = nn.ModuleList(modules)\n \n         self.project = nn.Sequential(\n-            nn.Conv2d(5 * out_channels, out_channels, 1, bias=False),\n+            nn.Conv2d(len(self.convs) * out_channels, out_channels, 1, bias=False),\n             nn.BatchNorm2d(out_channels),\n             nn.ReLU(),\n             nn.Dropout(0.5))\n", "issue": "The ASPP layer has a hard-coded value that only works if the atrous_rates param is of length 3\n## \ud83d\udc1b Bug\r\n\r\nThe ASPP layer has a hard-coded value that only works if the list of atrous rates provided is of length 3. The hard-coded value in question is the `5` on line 83 here:\r\nhttps://github.com/pytorch/vision/blob/master/torchvision/models/segmentation/deeplabv3.py#L82-L86\r\n\r\n## To Reproduce\r\n\r\n```python3\r\naspp = tv.models.segmentation.deeplabv3.ASPP(1, [12, 24]) # DeepLab uses [12, 24, 36], which works\r\n\r\naspp.eval()\r\nwith torch.no_grad():\r\n    out = aspp(torch.empty(1, 1, 256, 256))\r\n```\r\nResults in\r\n```\r\nRuntimeError: Given groups=1, weight of size [256, 1280, 1, 1], expected input[1, 1024, 256, 256] to have 1280 channels, but got 1024 channels instead\r\n```\r\n\r\n## Expected behavior\r\n\r\nIt should work with different number of atrous rates. This can be fixed simply by replacing\r\n```python3\r\nnn.Conv2d(5 * out_channels, out_channels, 1, bias=False)\r\n```\r\nwith\r\n\r\n```python3\r\nnn.Conv2d(len(self.convs) * out_channels, out_channels, 1, bias=False)\r\n```\r\n\r\n## Environment\r\n\r\n```\r\nPyTorch version: 1.6.0\r\nIs debug build: False\r\nCUDA used to build PyTorch: 10.2\r\nROCM used to build PyTorch: N/A\r\n\r\nOS: Ubuntu 20.04.1 LTS (x86_64)\r\nGCC version: (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0\r\nClang version: Could not collect\r\nCMake version: version 3.18.2\r\n\r\nPython version: 3.7 (64-bit runtime)\r\nIs CUDA available: True\r\nCUDA runtime version: Could not collect\r\nGPU models and configuration: GPU 0: GeForce GTX 1060 with Max-Q Design\r\nNvidia driver version: 450.66\r\ncuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.1\r\nHIP runtime version: N/A\r\nMIOpen runtime version: N/A\r\n\r\nVersions of relevant libraries:\r\n[pip3] numpy==1.19.1\r\n[pip3] torch==1.6.0\r\n[pip3] torchvision==0.7.0\r\n[conda] blas                      1.0                         mkl  \r\n[conda] cudatoolkit               10.2.89              hfd86e86_1  \r\n[conda] mkl                       2020.2                      256  \r\n[conda] mkl-service               2.3.0            py37he904b0f_0  \r\n[conda] mkl_fft                   1.1.0            py37h23d657b_0  \r\n[conda] mkl_random                1.1.1            py37h0573a6f_0  \r\n[conda] numpy                     1.19.1           py37hbc911f0_0  \r\n[conda] numpy-base                1.19.1           py37hfa32c7d_0  \r\n[conda] torch                     1.6.0                    pypi_0    pypi\r\n[conda] torchvision               0.7.0                    pypi_0    pypi\r\n```\r\n\n\ncc @vfdev-5\n", "before_files": [{"content": "import torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom ._utils import _SimpleSegmentationModel\n\n\n__all__ = [\"DeepLabV3\"]\n\n\nclass DeepLabV3(_SimpleSegmentationModel):\n    \"\"\"\n    Implements DeepLabV3 model from\n    `\"Rethinking Atrous Convolution for Semantic Image Segmentation\"\n    <https://arxiv.org/abs/1706.05587>`_.\n\n    Arguments:\n        backbone (nn.Module): the network used to compute the features for the model.\n            The backbone should return an OrderedDict[Tensor], with the key being\n            \"out\" for the last feature map used, and \"aux\" if an auxiliary classifier\n            is used.\n        classifier (nn.Module): module that takes the \"out\" element returned from\n            the backbone and returns a dense prediction.\n        aux_classifier (nn.Module, optional): auxiliary classifier used during training\n    \"\"\"\n    pass\n\n\nclass DeepLabHead(nn.Sequential):\n    def __init__(self, in_channels, num_classes):\n        super(DeepLabHead, self).__init__(\n            ASPP(in_channels, [12, 24, 36]),\n            nn.Conv2d(256, 256, 3, padding=1, bias=False),\n            nn.BatchNorm2d(256),\n            nn.ReLU(),\n            nn.Conv2d(256, num_classes, 1)\n        )\n\n\nclass ASPPConv(nn.Sequential):\n    def __init__(self, in_channels, out_channels, dilation):\n        modules = [\n            nn.Conv2d(in_channels, out_channels, 3, padding=dilation, dilation=dilation, bias=False),\n            nn.BatchNorm2d(out_channels),\n            nn.ReLU()\n        ]\n        super(ASPPConv, self).__init__(*modules)\n\n\nclass ASPPPooling(nn.Sequential):\n    def __init__(self, in_channels, out_channels):\n        super(ASPPPooling, self).__init__(\n            nn.AdaptiveAvgPool2d(1),\n            nn.Conv2d(in_channels, out_channels, 1, bias=False),\n            nn.BatchNorm2d(out_channels),\n            nn.ReLU())\n\n    def forward(self, x):\n        size = x.shape[-2:]\n        for mod in self:\n            x = mod(x)\n        return F.interpolate(x, size=size, mode='bilinear', align_corners=False)\n\n\nclass ASPP(nn.Module):\n    def __init__(self, in_channels, atrous_rates, out_channels=256):\n        super(ASPP, self).__init__()\n        modules = []\n        modules.append(nn.Sequential(\n            nn.Conv2d(in_channels, out_channels, 1, bias=False),\n            nn.BatchNorm2d(out_channels),\n            nn.ReLU()))\n\n        rates = tuple(atrous_rates)\n        for rate in rates:\n            modules.append(ASPPConv(in_channels, out_channels, rate))\n\n        modules.append(ASPPPooling(in_channels, out_channels))\n\n        self.convs = nn.ModuleList(modules)\n\n        self.project = nn.Sequential(\n            nn.Conv2d(5 * out_channels, out_channels, 1, bias=False),\n            nn.BatchNorm2d(out_channels),\n            nn.ReLU(),\n            nn.Dropout(0.5))\n\n    def forward(self, x):\n        res = []\n        for conv in self.convs:\n            res.append(conv(x))\n        res = torch.cat(res, dim=1)\n        return self.project(res)\n", "path": "torchvision/models/segmentation/deeplabv3.py"}], "after_files": [{"content": "import torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom ._utils import _SimpleSegmentationModel\n\n\n__all__ = [\"DeepLabV3\"]\n\n\nclass DeepLabV3(_SimpleSegmentationModel):\n    \"\"\"\n    Implements DeepLabV3 model from\n    `\"Rethinking Atrous Convolution for Semantic Image Segmentation\"\n    <https://arxiv.org/abs/1706.05587>`_.\n\n    Arguments:\n        backbone (nn.Module): the network used to compute the features for the model.\n            The backbone should return an OrderedDict[Tensor], with the key being\n            \"out\" for the last feature map used, and \"aux\" if an auxiliary classifier\n            is used.\n        classifier (nn.Module): module that takes the \"out\" element returned from\n            the backbone and returns a dense prediction.\n        aux_classifier (nn.Module, optional): auxiliary classifier used during training\n    \"\"\"\n    pass\n\n\nclass DeepLabHead(nn.Sequential):\n    def __init__(self, in_channels, num_classes):\n        super(DeepLabHead, self).__init__(\n            ASPP(in_channels, [12, 24, 36]),\n            nn.Conv2d(256, 256, 3, padding=1, bias=False),\n            nn.BatchNorm2d(256),\n            nn.ReLU(),\n            nn.Conv2d(256, num_classes, 1)\n        )\n\n\nclass ASPPConv(nn.Sequential):\n    def __init__(self, in_channels, out_channels, dilation):\n        modules = [\n            nn.Conv2d(in_channels, out_channels, 3, padding=dilation, dilation=dilation, bias=False),\n            nn.BatchNorm2d(out_channels),\n            nn.ReLU()\n        ]\n        super(ASPPConv, self).__init__(*modules)\n\n\nclass ASPPPooling(nn.Sequential):\n    def __init__(self, in_channels, out_channels):\n        super(ASPPPooling, self).__init__(\n            nn.AdaptiveAvgPool2d(1),\n            nn.Conv2d(in_channels, out_channels, 1, bias=False),\n            nn.BatchNorm2d(out_channels),\n            nn.ReLU())\n\n    def forward(self, x):\n        size = x.shape[-2:]\n        for mod in self:\n            x = mod(x)\n        return F.interpolate(x, size=size, mode='bilinear', align_corners=False)\n\n\nclass ASPP(nn.Module):\n    def __init__(self, in_channels, atrous_rates, out_channels=256):\n        super(ASPP, self).__init__()\n        modules = []\n        modules.append(nn.Sequential(\n            nn.Conv2d(in_channels, out_channels, 1, bias=False),\n            nn.BatchNorm2d(out_channels),\n            nn.ReLU()))\n\n        rates = tuple(atrous_rates)\n        for rate in rates:\n            modules.append(ASPPConv(in_channels, out_channels, rate))\n\n        modules.append(ASPPPooling(in_channels, out_channels))\n\n        self.convs = nn.ModuleList(modules)\n\n        self.project = nn.Sequential(\n            nn.Conv2d(len(self.convs) * out_channels, out_channels, 1, bias=False),\n            nn.BatchNorm2d(out_channels),\n            nn.ReLU(),\n            nn.Dropout(0.5))\n\n    def forward(self, x):\n        res = []\n        for conv in self.convs:\n            res.append(conv(x))\n        res = torch.cat(res, dim=1)\n        return self.project(res)\n", "path": "torchvision/models/segmentation/deeplabv3.py"}]}