train_transform = transforms.Compose([    transforms.ToPILImage(),
transforms.Resize((205, 205)),
transforms.RandomVerticalFlip(p=0.5),
transforms.RandomCrop((200, 200)),
transforms.ToTensor()
])
I tried to make it PIL using transforms but it still doesnt work
I posted log

TypeError                                 Traceback (most recent call last)
 in 
6     ])
7 LSP_dataset = LSPLoader(train_transform)
----> 8 plotSamples(LSP_dataset)
9 plt.show()
 in plotSamples(dataset)
3     for i in range(25):
4         ax = axes[i // 5, i % 5]
----> 5         sample = dataset[i]
6         image = sample[‘image’]
7         joints = sample[‘joints’]
 in getitem(self, idx)
68         sample = {‘image’: image, ‘joints’: joints}
69         if self.transform:
—> 70             sample = self.transform(sample)
71             img = sample[‘image’]
72             label = sample[‘landmarks’]
~\anaconda3\lib\site-packages\torchvision\transforms\transforms.py in call(self, img)
58     def call(self, img):
59         for t in self.transforms:
—> 60             img = t(img)
61         return img
62
~\anaconda3\lib\site-packages\torchvision\transforms\transforms.py in call(self, pic)
177
178         “”"
 → 179         return F.to_pil_image(pic, self.mode)
180
181     def repr(self):
~\anaconda3\lib\site-packages\torchvision\transforms\functional.py in to_pil_image(pic, mode)
217     “”"
218     if not(isinstance(pic, torch.Tensor) or isinstance(pic, np.ndarray)):
 → 219         raise TypeError(‘pic should be Tensor or ndarray. Got {}.’.format(type(pic)))
220
221     elif isinstance(pic, torch.Tensor):
TypeError: pic should be Tensor or ndarray. Got <class ‘dict’>.