Error with __len__(self) function while creating Custom Dataset Object in Pytorch-CodePudding

I am following a video course for image classification and therein I have created a custom Dataset Class as follows:

from torch.utils.data import Dataset

class ChestXRayDataSet(Dataset):
    def __init__(self, image_dirs, transform):
        # Initialize the Object
        def get_image(class_name):
            # define a function to get images from the provided image directories
            images = [x for x in os.listdir(image_dirs[class_name]) if x.lower().endswith('png')]
            print(f'Found {len(images)} Images of Class {class_name}')
            return images
        
        # create a directory to store the images
        self.images = {}
        self.class_names = ['normal', 'viral', 'covid']
        
        for c in self.class_names:
            # store the images in directory with class names
            self.images[c] = get_image(c)
            
        self.image_dirs = image_dirs
        self.transform = transform
                    
        def __len__(self):
            # return the number of images in the dataset
            num_images = sum([len(self.images[class_name]) for class_name in self.class_names])
            return num_images
            
        def __getitem__(self, index):
            class_name = random.choice(self.class_names)
            index = index % len(self.images[class_name]) # to avoid index out of bound error
            image_name = self.images[class_name][index] # this is the selected images
            image_path = os.path.join(self.image_dirs[class_name], image_name)
            image = Image.open(image_path).convert('RGB')
            # Finally we return the example, and its index as required by
            # Dataset class
            return self.transform(image), self.class_names.index(class_name)

I create a Dataloader using this dataset as follows:

batch_size = 6
dl_train = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size)

as a result, I get the following Error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/tmp/ipykernel_28/1549945295.py in <module>
      3 dl_test = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size)
      4 
----> 5 print('Num of Training Batches : ', len(dl_train))
      6 #print('Num of Test Batches : ', len(dl_test))

/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py in __len__(self)
    411         self._timeout = loader.timeout
    412         self._collate_fn = loader.collate_fn
--> 413         self._sampler_iter = iter(self._index_sampler)
    414         self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item()
    415         self._persistent_workers = loader.persistent_workers

/opt/conda/lib/python3.7/site-packages/torch/utils/data/sampler.py in __len__(self)
    240         if self.drop_last:
    241             return len(self.sampler) // self.batch_size  # type: ignore
--> 242         else:
    243             return (len(self.sampler)   self.batch_size - 1) // self.batch_size  # type: ignore

/opt/conda/lib/python3.7/site-packages/torch/utils/data/sampler.py in __len__(self)
     67         return iter(range(len(self.data_source)))
     68 
---> 69     def __len__(self) -> int:
     70         return len(self.data_source)
     71 

TypeError: object of type 'ChestXRayDataSet' has no len()

I am using Kaggle's notebook with Pytorch version 1.11.0 cpu to perform this task.

Help to resolve this error will be much appreciated.

Regards.

CodePudding user response：

The __len__ and __getitem__ method are indented too deeply, they should be at the same level as __init__.