Follow up question for Raw images using DatasetFolder
If I use transforms.ToTensor(), I get the following error:
img should be PIL Image. Got <class ‘torch.Tensor’>
If I used transforms.ToPILImage() I get the following error:
TypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class ‘PIL.Image.Image’>
If I change the return type of __raw_loader to PIL image:
TypeError: pic should be Tensor or ndarray. Got <class ‘PIL.Image.Image’>.
Kind of going in circles here. What is the proper way to load a raw image using DatasetFolder?
Code:
import torch
import os
import pandas as pd

import numpy as np
import matplotlib.pyplot as plt
import torchvision
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
from torchvision.datasets import DatasetFolder

class RawFolder(DatasetFolder):

    EXTENSIONS = ('.raw')

    def __init__(self, root, transform=None, target_transform=None, loader=None):
        

        super(RawFolder, self).__init__(root, self.__raw_loader, 
                                         self.EXTENSIONS,
                                         transform=transform,
                                         target_transform=target_transform)

    @staticmethod
    def __raw_loader(filename):
        data = np.fromfile(filename, dtype='float32').reshape(80,180)
        return Image.fromarray(data)

    def __check_file(filename):
        return os.path.isfile(filename) == True


data_transform = transforms.Compose([
        transforms.ToPILImage(),
        transforms.Grayscale(num_output_channels=1)
    ])


the_dataset = RawFolder(root=rootdir, transform=data_transform)

dataset_loader = torch.utils.data.DataLoader(the_dataset,
                                             batch_size=64, 
                                             shuffle=False,
                                             num_workers=0)

classes = ['1']

import numpy as np
# files already created, training and testing can start here.
# re-run first cell if necessary
def goshow(img):
    img = img / 2 + 0.5  # unnormalize
    plt.imshow(np.transpose(img, (1, 2, 0)))  # convert from Tensor image
    
# Visualize a few images
dataiter = iter(dataset_loader)
images, labels = dataiter.next()
images = images.numpy() # convert images to numpy for display

# plot the images in the batch, along with the corresponding labels
fig = plt.figure(figsize=(25, 12))

# display some images
for idx in np.arange(25):
    ax = fig.add_subplot(5, 5, idx+1, xticks=[], yticks=[])
    goshow(images[idx])
    ax.set_title(classes[labels[idx]])

Error for the last case mentioned above:
Errors always occur here: images, labels = dataiter.next()
TypeError                                 Traceback (most recent call last)
<ipython-input-148-ffafba196b64> in <module>
     55 # Visualize a few images
     56 dataiter = iter(dataset_loader)
---> 57 images, labels = dataiter.next()
     58 images = images.numpy() # convert images to numpy for display
     59 

~\anaconda3\lib\site-packages\torch\utils\data\dataloader.py in __next__(self)
    344     def __next__(self):
    345         index = self._next_index()  # may raise StopIteration
--> 346         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
    347         if self._pin_memory:
    348             data = _utils.pin_memory.pin_memory(data)

~\anaconda3\lib\site-packages\torch\utils\data\_utils\fetch.py in fetch(self, possibly_batched_index)
     42     def fetch(self, possibly_batched_index):
     43         if self.auto_collation:
---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]
     45         else:
     46             data = self.dataset[possibly_batched_index]

~\anaconda3\lib\site-packages\torch\utils\data\_utils\fetch.py in <listcomp>(.0)
     42     def fetch(self, possibly_batched_index):
     43         if self.auto_collation:
---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]
     45         else:
     46             data = self.dataset[possibly_batched_index]

~\anaconda3\lib\site-packages\torchvision\datasets\folder.py in __getitem__(self, index)
    138         sample = self.loader(path)
    139         if self.transform is not None:
--> 140             sample = self.transform(sample)
    141         if self.target_transform is not None:
    142             target = self.target_transform(target)

~\anaconda3\lib\site-packages\torchvision\transforms\transforms.py in __call__(self, img)
     68     def __call__(self, img):
     69         for t in self.transforms:
---> 70             img = t(img)
     71         return img
     72 

~\anaconda3\lib\site-packages\torchvision\transforms\transforms.py in __call__(self, pic)
    134 
    135         """
--> 136         return F.to_pil_image(pic, self.mode)
    137 
    138     def __repr__(self):

~\anaconda3\lib\site-packages\torchvision\transforms\functional.py in to_pil_image(pic, mode)
    117     """
    118     if not(isinstance(pic, torch.Tensor) or isinstance(pic, np.ndarray)):
--> 119         raise TypeError('pic should be Tensor or ndarray. Got {}.'.format(type(pic)))
    120 
    121     elif isinstance(pic, torch.Tensor):

TypeError: pic should be Tensor or ndarray. Got <class 'PIL.Image.Image'>.