ToPILImage : TypeError: Input type int64 is not supported

Question

我正在尝试使用 FastAi 开发 GAN。将张量转换为图像时出现此错误。

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

这是完整的代码

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')

代码的重点是生成口袋妖怪图像。但是每当我预测并将其转换为 PIL 图像时，代码都会因上述错误而失败。

Answer 1

我建议您使用此代码将模型的输出从张量转换为 PIL 图像：

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

ToPILImage : TypeError: Input type int64 is not supported

ToPILImage : TypeError: Input type int64 is not supported

python

pytorch

tensor

torchvision

fast-ai