Hi,
Im trying to make my first CNN using pyTorch and am following online help and code already people wrote. i am trying to reproduce their results. I’m using the Kaggle Dogs Breed Dataset for this and below is the error I get. The trainloader does not return my images and labels and any attempt to get them leads in an error:
Traceback (most recent call last):
  File "E:\Program Files\JetBrains\PyCharm Community Edition 2018.2.4\helpers\pydev\pydevd.py", line 1664, in <module>
    main()
  File "E:\Program Files\JetBrains\PyCharm Community Edition 2018.2.4\helpers\pydev\pydevd.py", line 1658, in main
    globals = debugger.run(setup['file'], None, None, is_module)
  File "E:\Program Files\JetBrains\PyCharm Community Edition 2018.2.4\helpers\pydev\pydevd.py", line 1068, in run
    pydev_imports.execfile(file, globals, locals)  # execute the script
  File "E:\Program Files\JetBrains\PyCharm Community Edition 2018.2.4\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
  File "C:/Users/sbzfk/PycharmProjects/my_FCN_attempt/Kaggle_Dogs_Competition.py", line 85, in <module>
    img, label = next(iter(train_loader))
  File "C:\Users\sbzfk\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\utils\data\dataloader.py", line 314, in __next__
    batch = self.collate_fn([self.dataset[i] for i in indices])
  File "C:\Users\sbzfk\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\utils\data\dataloader.py", line 314, in <listcomp>
    batch = self.collate_fn([self.dataset[i] for i in indices])
  File "C:/Users/sbzfk/PycharmProjects/my_FCN_attempt/Kaggle_Dogs_Competition.py", line 42, in __getitem__
    img = self.transform(img)
  File "C:\Users\sbzfk\AppData\Local\Programs\Python\Python37\lib\site-packages\torchvision\transforms.py", line 34, in __call__
    img = t(img)
  File "C:\Users\sbzfk\AppData\Local\Programs\Python\Python37\lib\site-packages\torchvision\transforms.py", line 187, in __call__
    w, h = img.size
TypeError: cannot unpack non-iterable int object


Below is my code:
class DogsDataset(Dataset):

    def __init__(self, filenames, labels, root_dir, transform=None):
        assert len(filenames) == len(labels)        # if the two are not of equal length throw an error
        self.filenames = filenames
        self.labels = labels
        self.root_dir = root_dir
        self.transform = transform

    def __len__(self):
        return len(self.filenames)

    def __getitem__(self, idx):
        this_img = join(self.root_dir, 'train', self.filenames[idx]+'.jpg')
        print(this_img)
        img = io.imread(this_img)
        label = self.labels[idx]
        print(label)

        if self.transform:
            img = self.transform(img)

        return [img, label]



batch_size = 64
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

dataset_root = expanduser(join('~', 'Documents', 'kaggle_dogs_dataset'))
# join will intelligently join directories irrespective of OS, and expanduser will
# replace with /home/ in linux or the username in Windows

csv_file = pd.read_csv(join(dataset_root, 'labels.csv'))        # csv file has two columns, id which are filenames and breed which are labels
filenames = csv_file.id.values              # convert that column to an array, id is the column name and values converty to numpy array
# le = LabelEncoder()
# labels = le.fit_transform(csv_file.breed)           # this will just encode the names between 0 to models-1 , basically changing strings to integers
labels = csv_file.breed.values

filenames_train, filenames_eval, labels_train, labels_eval = train_test_split(filenames, labels,
                                                                              test_size=0.1, stratify=labels)       # this is an import from sklearn as the name implies, it randomly splits data into train and eval, 10% of it to test and rest train

data_transform = transforms.Compose([transforms.Scale(224),
                                     transforms.CenterCrop(224),
                                     transforms.ToTensor(),
                                     transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))])

dog_train = DogsDataset(filenames_train, labels_train, dataset_root, transform=data_transform)
train_loader = DataLoader(dog_train, batch_size, shuffle=True)

dog_eval = DogsDataset(filenames_eval, labels_eval, dataset_root, transform=data_transform)
eval_loader = DataLoader(dog_eval, batch_size, shuffle=True)


def im_show(axis, inp):
    """Denormalize and show"""
    inp = inp.numpy().transpose((1, 2, 0))
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    inp = std * inp + mean
    axis.imshow(inp)


img, label = next(iter(train_loader))
print(img.size(), label.size())
fig = plt.figure(1, figsize=(16, 4))
grid = ImageGrid(fig, 111, nrows_ncols=(1, 4), axes_pad=0.05)
for i in range(img.size()[0]):
    ax = grid[i]
    im_show(ax, img[i])