prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Return type ambiguous when extracting image features
  The return type of the extracted image features (if the features file is present or not) is different. If the file is present then it returns a numpy.ndarray type object otherwise it returns a torch.autograd.variable.Variable object.
  ( https://github.com/facebookresearch/ParlAI/blob/3d86ccdbb4d87002cc6c4782afd0ee5277e742f1/parlai/core/image_featurizers.py#L149 )
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `parlai/core/image_featurizers.py`
  Content:
  ```
  1 # All rights reserved.
  2 # This source code is licensed under the BSD-style license found in the
  3 # LICENSE file in the root directory of this source tree. An additional grant
  4 # of patent rights can be found in the PATENTS file in the same directory.
  5 
  6 import parlai.core.build_data as build_data
  7 
  8 import os
  9 import copy
  10 import numpy as np
  11 from PIL import Image
  12 
  13 _greyscale = '  .,:;crsA23hHG#98&@'
  14 
  15 class ImageLoader():
  16     """Extract image feature using pretrained CNN network.
  17     """
  18     def __init__(self, opt):
  19         self.opt = copy.deepcopy(opt)
  20         self.netCNN = None
  21 
  22     def init_cnn(self):
  23         """Lazy initialization of preprocessor model in case we don't need any image preprocessing."""
  24         try:
  25             import torch
  26         except ModuleNotFoundError:
  27             raise ModuleNotFoundError('Need to install Pytorch: go to pytorch.org')
  28         from torch.autograd import Variable
  29         import torchvision
  30         import torchvision.transforms as transforms
  31         import torch.nn as nn
  32 
  33         opt = self.opt
  34         self.image_size = opt['image_size']
  35         self.crop_size = opt['image_cropsize']
  36         self.datatype = opt['datatype']
  37         self.image_mode = opt['image_mode']
  38 
  39         opt['cuda'] = not opt['no_cuda'] and torch.cuda.is_available()
  40         self.use_cuda = opt['cuda']
  41 
  42         if self.use_cuda:
  43             print('[ Using CUDA ]')
  44             torch.cuda.set_device(opt['gpu'])
  45 
  46         cnn_type, layer_num = self.image_mode_switcher()
  47 
  48         # initialize the pretrained CNN using pytorch.
  49         CNN = getattr(torchvision.models, cnn_type)
  50 
  51         # cut off the additional layer.
  52         self.netCNN = nn.Sequential(*list(CNN(pretrained=True).children())[:layer_num])
  53 
  54         # initialize the transform function using torch vision.
  55         self.transform = transforms.Compose([
  56                             transforms.Scale(self.image_size),
  57                             transforms.CenterCrop(self.crop_size),
  58                             transforms.ToTensor(),
  59                             transforms.Normalize(mean=[0.485, 0.456, 0.406],
  60                                     std=[0.229, 0.224, 0.225])
  61                             ])
  62 
  63         # container for single image
  64         self.xs = torch.FloatTensor(1, 3, self.crop_size, self.crop_size).fill_(0)
  65 
  66         if self.use_cuda:
  67             self.cuda()
  68             self.xs = self.xs.cuda()
  69 
  70         # make self.xs variable.
  71         self.xs = Variable(self.xs)
  72 
  73     def cuda(self):
  74         self.netCNN.cuda()
  75 
  76     def save(self, feature, path):
  77         feature = feature.cpu().data.numpy()
  78         np.save(path, feature)
  79 
  80     def image_mode_switcher(self):
  81         switcher = {
  82             'resnet152': ['resnet152', -1],
  83             'resnet101': ['resnet101', -1],
  84             'resnet50': ['resnet50', -1],
  85             'resnet34': ['resnet34', -1],
  86             'resnet18': ['resnet18', -1],
  87             'resnet152_spatial': ['resnet152', -2],
  88             'resnet101_spatial': ['resnet101', -2],
  89             'resnet50_spatial': ['resnet50', -2],
  90             'resnet34_spatial': ['resnet34', -2],
  91             'resnet18_spatial': ['resnet18', -2],
  92         }
  93 
  94         if self.image_mode not in switcher:
  95             raise NotImplementedError('image preprocessing mode' +
  96                                       '{} not supported yet'.format(self.image_mode))
  97 
  98         return switcher.get(self.image_mode)
  99 
  100     def extract(self, image, path):
  101         # check whether initlize CNN network.
  102         if not self.netCNN:
  103             self.init_cnn()
  104 
  105         self.xs.data.copy_(self.transform(image))
  106         # extract the image feature
  107         feature = self.netCNN(self.xs)
  108         # save the feature
  109         self.save(feature, path)
  110         return feature
  111 
  112     def img_to_ascii(self, path):
  113         im = Image.open(path)
  114         im.thumbnail((60, 40), Image.BICUBIC)
  115         im = im.convert('L')
  116         asc = []
  117         for y in range(0, im.size[1]):
  118             for x in range(0, im.size[0]):
  119                 lum = 255 - im.getpixel((x, y))
  120                 asc.append(_greyscale[lum * len(_greyscale) // 256])
  121             asc.append('\n')
  122         return ''.join(asc)
  123 
  124     def load(self, path):
  125         opt = self.opt
  126         mode = opt.get('image_mode', 'raw')
  127         if mode is None or mode == 'none':
  128             # don't need to load images
  129             return None
  130         elif mode == 'raw':
  131             # raw just returns RGB values
  132             return Image.open(path).convert('RGB')
  133         elif mode == 'ascii':
  134             # convert images to ascii ¯\_(ツ)_/¯
  135             return self.img_to_ascii(path)
  136         else:
  137             # otherwise, looks for preprocessed version under 'mode' directory
  138             prepath, imagefn = os.path.split(path)
  139 
  140             dpath = os.path.join(prepath, mode)
  141 
  142             if not os.path.exists(dpath):
  143                 build_data.make_dir(dpath)
  144 
  145             imagefn = imagefn + '.npy'
  146             new_path = os.path.join(prepath, mode, imagefn)
  147 
  148             if not os.path.isfile(new_path):
  149                 return self.extract(Image.open(path).convert('RGB'), new_path)
  150             else:
  151                 return np.load(new_path)
  152
  ```
  --- 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/parlai/core/image_featurizers.py b/parlai/core/image_featurizers.py
  --- a/parlai/core/image_featurizers.py
  +++ b/parlai/core/image_featurizers.py
  @@ -74,7 +74,6 @@
           self.netCNN.cuda()
   
       def save(self, feature, path):
  -        feature = feature.cpu().data.numpy()
           np.save(path, feature)
   
       def image_mode_switcher(self):
  @@ -105,6 +104,7 @@
           self.xs.data.copy_(self.transform(image))
           # extract the image feature
           feature = self.netCNN(self.xs)
  +        feature = feature.cpu().data.numpy()
           # save the feature
           self.save(feature, path)
           return feature
  
verification_info: {"golden_diff": "diff --git a/parlai/core/image_featurizers.py b/parlai/core/image_featurizers.py\n--- a/parlai/core/image_featurizers.py\n+++ b/parlai/core/image_featurizers.py\n@@ -74,7 +74,6 @@\n         self.netCNN.cuda()\n \n     def save(self, feature, path):\n-        feature = feature.cpu().data.numpy()\n         np.save(path, feature)\n \n     def image_mode_switcher(self):\n@@ -105,6 +104,7 @@\n         self.xs.data.copy_(self.transform(image))\n         # extract the image feature\n         feature = self.netCNN(self.xs)\n+        feature = feature.cpu().data.numpy()\n         # save the feature\n         self.save(feature, path)\n         return feature\n", "issue": "Return type ambiguous when extracting image features\nThe return type of the extracted image features (if the features file is present or not) is different. If the file is present then it returns a numpy.ndarray type object otherwise it returns a torch.autograd.variable.Variable object.\r\n( https://github.com/facebookresearch/ParlAI/blob/3d86ccdbb4d87002cc6c4782afd0ee5277e742f1/parlai/core/image_featurizers.py#L149 )\n", "before_files": [{"content": "# All rights reserved.\n# This source code is licensed under the BSD-style license found in the\n# LICENSE file in the root directory of this source tree. An additional grant\n# of patent rights can be found in the PATENTS file in the same directory.\n\nimport parlai.core.build_data as build_data\n\nimport os\nimport copy\nimport numpy as np\nfrom PIL import Image\n\n_greyscale = '  .,:;crsA23hHG#98&@'\n\nclass ImageLoader():\n    \"\"\"Extract image feature using pretrained CNN network.\n    \"\"\"\n    def __init__(self, opt):\n        self.opt = copy.deepcopy(opt)\n        self.netCNN = None\n\n    def init_cnn(self):\n        \"\"\"Lazy initialization of preprocessor model in case we don't need any image preprocessing.\"\"\"\n        try:\n            import torch\n        except ModuleNotFoundError:\n            raise ModuleNotFoundError('Need to install Pytorch: go to pytorch.org')\n        from torch.autograd import Variable\n        import torchvision\n        import torchvision.transforms as transforms\n        import torch.nn as nn\n\n        opt = self.opt\n        self.image_size = opt['image_size']\n        self.crop_size = opt['image_cropsize']\n        self.datatype = opt['datatype']\n        self.image_mode = opt['image_mode']\n\n        opt['cuda'] = not opt['no_cuda'] and torch.cuda.is_available()\n        self.use_cuda = opt['cuda']\n\n        if self.use_cuda:\n            print('[ Using CUDA ]')\n            torch.cuda.set_device(opt['gpu'])\n\n        cnn_type, layer_num = self.image_mode_switcher()\n\n        # initialize the pretrained CNN using pytorch.\n        CNN = getattr(torchvision.models, cnn_type)\n\n        # cut off the additional layer.\n        self.netCNN = nn.Sequential(*list(CNN(pretrained=True).children())[:layer_num])\n\n        # initialize the transform function using torch vision.\n        self.transform = transforms.Compose([\n                            transforms.Scale(self.image_size),\n                            transforms.CenterCrop(self.crop_size),\n                            transforms.ToTensor(),\n                            transforms.Normalize(mean=[0.485, 0.456, 0.406],\n                                    std=[0.229, 0.224, 0.225])\n                            ])\n\n        # container for single image\n        self.xs = torch.FloatTensor(1, 3, self.crop_size, self.crop_size).fill_(0)\n\n        if self.use_cuda:\n            self.cuda()\n            self.xs = self.xs.cuda()\n\n        # make self.xs variable.\n        self.xs = Variable(self.xs)\n\n    def cuda(self):\n        self.netCNN.cuda()\n\n    def save(self, feature, path):\n        feature = feature.cpu().data.numpy()\n        np.save(path, feature)\n\n    def image_mode_switcher(self):\n        switcher = {\n            'resnet152': ['resnet152', -1],\n            'resnet101': ['resnet101', -1],\n            'resnet50': ['resnet50', -1],\n            'resnet34': ['resnet34', -1],\n            'resnet18': ['resnet18', -1],\n            'resnet152_spatial': ['resnet152', -2],\n            'resnet101_spatial': ['resnet101', -2],\n            'resnet50_spatial': ['resnet50', -2],\n            'resnet34_spatial': ['resnet34', -2],\n            'resnet18_spatial': ['resnet18', -2],\n        }\n\n        if self.image_mode not in switcher:\n            raise NotImplementedError('image preprocessing mode' +\n                                      '{} not supported yet'.format(self.image_mode))\n\n        return switcher.get(self.image_mode)\n\n    def extract(self, image, path):\n        # check whether initlize CNN network.\n        if not self.netCNN:\n            self.init_cnn()\n\n        self.xs.data.copy_(self.transform(image))\n        # extract the image feature\n        feature = self.netCNN(self.xs)\n        # save the feature\n        self.save(feature, path)\n        return feature\n\n    def img_to_ascii(self, path):\n        im = Image.open(path)\n        im.thumbnail((60, 40), Image.BICUBIC)\n        im = im.convert('L')\n        asc = []\n        for y in range(0, im.size[1]):\n            for x in range(0, im.size[0]):\n                lum = 255 - im.getpixel((x, y))\n                asc.append(_greyscale[lum * len(_greyscale) // 256])\n            asc.append('\\n')\n        return ''.join(asc)\n\n    def load(self, path):\n        opt = self.opt\n        mode = opt.get('image_mode', 'raw')\n        if mode is None or mode == 'none':\n            # don't need to load images\n            return None\n        elif mode == 'raw':\n            # raw just returns RGB values\n            return Image.open(path).convert('RGB')\n        elif mode == 'ascii':\n            # convert images to ascii \u00af\\_(\u30c4)_/\u00af\n            return self.img_to_ascii(path)\n        else:\n            # otherwise, looks for preprocessed version under 'mode' directory\n            prepath, imagefn = os.path.split(path)\n\n            dpath = os.path.join(prepath, mode)\n\n            if not os.path.exists(dpath):\n                build_data.make_dir(dpath)\n\n            imagefn = imagefn + '.npy'\n            new_path = os.path.join(prepath, mode, imagefn)\n\n            if not os.path.isfile(new_path):\n                return self.extract(Image.open(path).convert('RGB'), new_path)\n            else:\n                return np.load(new_path)\n", "path": "parlai/core/image_featurizers.py"}], "after_files": [{"content": "# All rights reserved.\n# This source code is licensed under the BSD-style license found in the\n# LICENSE file in the root directory of this source tree. An additional grant\n# of patent rights can be found in the PATENTS file in the same directory.\n\nimport parlai.core.build_data as build_data\n\nimport os\nimport copy\nimport numpy as np\nfrom PIL import Image\n\n_greyscale = '  .,:;crsA23hHG#98&@'\n\nclass ImageLoader():\n    \"\"\"Extract image feature using pretrained CNN network.\n    \"\"\"\n    def __init__(self, opt):\n        self.opt = copy.deepcopy(opt)\n        self.netCNN = None\n\n    def init_cnn(self):\n        \"\"\"Lazy initialization of preprocessor model in case we don't need any image preprocessing.\"\"\"\n        try:\n            import torch\n        except ModuleNotFoundError:\n            raise ModuleNotFoundError('Need to install Pytorch: go to pytorch.org')\n        from torch.autograd import Variable\n        import torchvision\n        import torchvision.transforms as transforms\n        import torch.nn as nn\n\n        opt = self.opt\n        self.image_size = opt['image_size']\n        self.crop_size = opt['image_cropsize']\n        self.datatype = opt['datatype']\n        self.image_mode = opt['image_mode']\n\n        opt['cuda'] = not opt['no_cuda'] and torch.cuda.is_available()\n        self.use_cuda = opt['cuda']\n\n        if self.use_cuda:\n            print('[ Using CUDA ]')\n            torch.cuda.set_device(opt['gpu'])\n\n        cnn_type, layer_num = self.image_mode_switcher()\n\n        # initialize the pretrained CNN using pytorch.\n        CNN = getattr(torchvision.models, cnn_type)\n\n        # cut off the additional layer.\n        self.netCNN = nn.Sequential(*list(CNN(pretrained=True).children())[:layer_num])\n\n        # initialize the transform function using torch vision.\n        self.transform = transforms.Compose([\n                            transforms.Scale(self.image_size),\n                            transforms.CenterCrop(self.crop_size),\n                            transforms.ToTensor(),\n                            transforms.Normalize(mean=[0.485, 0.456, 0.406],\n                                    std=[0.229, 0.224, 0.225])\n                            ])\n\n        # container for single image\n        self.xs = torch.FloatTensor(1, 3, self.crop_size, self.crop_size).fill_(0)\n\n        if self.use_cuda:\n            self.cuda()\n            self.xs = self.xs.cuda()\n\n        # make self.xs variable.\n        self.xs = Variable(self.xs)\n\n    def cuda(self):\n        self.netCNN.cuda()\n\n    def save(self, feature, path):\n        np.save(path, feature)\n\n    def image_mode_switcher(self):\n        switcher = {\n            'resnet152': ['resnet152', -1],\n            'resnet101': ['resnet101', -1],\n            'resnet50': ['resnet50', -1],\n            'resnet34': ['resnet34', -1],\n            'resnet18': ['resnet18', -1],\n            'resnet152_spatial': ['resnet152', -2],\n            'resnet101_spatial': ['resnet101', -2],\n            'resnet50_spatial': ['resnet50', -2],\n            'resnet34_spatial': ['resnet34', -2],\n            'resnet18_spatial': ['resnet18', -2],\n        }\n\n        if self.image_mode not in switcher:\n            raise NotImplementedError('image preprocessing mode' +\n                                      '{} not supported yet'.format(self.image_mode))\n\n        return switcher.get(self.image_mode)\n\n    def extract(self, image, path):\n        # check whether initlize CNN network.\n        if not self.netCNN:\n            self.init_cnn()\n\n        self.xs.data.copy_(self.transform(image))\n        # extract the image feature\n        feature = self.netCNN(self.xs)\n        feature = feature.cpu().data.numpy()\n        # save the feature\n        self.save(feature, path)\n        return feature\n\n    def img_to_ascii(self, path):\n        im = Image.open(path)\n        im.thumbnail((60, 40), Image.BICUBIC)\n        im = im.convert('L')\n        asc = []\n        for y in range(0, im.size[1]):\n            for x in range(0, im.size[0]):\n                lum = 255 - im.getpixel((x, y))\n                asc.append(_greyscale[lum * len(_greyscale) // 256])\n            asc.append('\\n')\n        return ''.join(asc)\n\n    def load(self, path):\n        opt = self.opt\n        mode = opt.get('image_mode', 'raw')\n        if mode is None or mode == 'none':\n            # don't need to load images\n            return None\n        elif mode == 'raw':\n            # raw just returns RGB values\n            return Image.open(path).convert('RGB')\n        elif mode == 'ascii':\n            # convert images to ascii \u00af\\_(\u30c4)_/\u00af\n            return self.img_to_ascii(path)\n        else:\n            # otherwise, looks for preprocessed version under 'mode' directory\n            prepath, imagefn = os.path.split(path)\n\n            dpath = os.path.join(prepath, mode)\n\n            if not os.path.exists(dpath):\n                build_data.make_dir(dpath)\n\n            imagefn = imagefn + '.npy'\n            new_path = os.path.join(prepath, mode, imagefn)\n\n            if not os.path.isfile(new_path):\n                return self.extract(Image.open(path).convert('RGB'), new_path)\n            else:\n                return np.load(new_path)\n", "path": "parlai/core/image_featurizers.py"}]}