[FIXED] ToPILImage : TypeError: Input type int64 is not supported

Issue

I'm trying to develop a GAN using FastAi. When converting the Tensor to an Image I get this error.

Traceback (most recent call last):
  File "/Users/DevDog/Documents/vsc/pokemon/dementad.py", line 44, in <module>
    im =transforms.ToPILImage()(img[0]).convert('RGBA')
  File "/Users/DevDog/miniforge3/envs/python386/lib/python3.8/site-packages/torchvision/transforms/transforms.py", line 179, in __call__
    return F.to_pil_image(pic, self.mode)
  File "/Users/DevDog/miniforge3/envs/python386/lib/python3.8/site-packages/torchvision/transforms/functional.py", line 290, in to_pil_image
    raise TypeError('Input type {} is not supported'.format(npimg.dtype))
TypeError: Input type int64 is not supported

Here's the full code

import fastai
from fastai.data import transforms
from fastai.data.block import DataBlock, TransformBlock
from fastai.data.transforms import get_image_files
from fastai.optimizer import RMSProp
from fastai.vision.data import ImageBlock, ImageDataLoaders
from fastcore.imports import noop
from numpy import negative
import torch
import cv2
import PIL
from torchvision import transforms
from PIL import Image
from torch import nn
from fastai.vision import *
from fastai.vision.augment import *
from fastai.imports import *
from fastai.vision.gan import *
from fastai.data.block import *
from fastai.data.transforms import *
from fastai.callback.all import *
path = Path('pokeman')

bs=100
size=64
dblock = DataBlock(blocks = (TransformBlock, ImageBlock),
                   get_x = generate_noise,
                   get_items = get_image_files,
                   splitter = IndexSplitter([]),
                   item_tfms=Resize(size, method=ResizeMethod.Crop), 
                   batch_tfms = Normalize.from_stats(torch.tensor([0.5,0.5,0.5]), torch.tensor([0.5,0.5,0.5])))
dls = dblock.dataloaders(path,path=path,bs=bs)
generator = basic_generator(64,3,n_extra_layers=1)
critic = basic_critic(64, 3, n_extra_layers=1,act_cls=partial(nn.LeakyReLU))
student = GANLearner.wgan(dls,generator,critic,opt_func = RMSProp)
student.recorder.train_metrics=True
student.recorder.valid_metrics=False
student.fit(1,2e-4,wd=0.)
#cv2.waitKey(0)
student.show_results(max_n=9,ds_idx=0)
student.gan_trainer.switch(gen_mode=True)
img = student.predict(generate_noise('pocheman',size=100))
print(img[0].size())
im =transforms.ToPILImage()(img[0]).convert('RGB')

The point of the Code is to generate pokemon images. But whenever I predict and convert it to a PIL Image the code fails with the aforementioned error.

Solution

I suggest for you to use this code to convert the output of your model from a tensor to a PIL image:

img = Image.fromarray((255*imgs[0]).numpy().astype(np.uint8).transpose(1, 2, 0))

Answered By - A.Mounir