Why does this not work?
import torchvision.transforms.functional as tf
from torchvision import transforms
pic = np.random.randint(0, 255+1, size=28*28).reshape(28, 28)
pic = pic.astype(int)
plt.imshow(pic)
t = transforms.ToPILImage()
t(pic.reshape(28, 28, 1))
# tf.to_pil_image(pic.reshape(28, 28, 1))
A beautiful random picture is plotted by matplotlib, but no matter what datatype I chose for my NumPy ndarray, neither to_pil_image or ToPILImage work as expected.
The docs have this to say:
Converts a tensor … or a numpy ndarray of shape H x W x C to a PIL Image while preserving the value range.
…
If the input has 1 channel, themodeis determined by the data type (i.eint,float,short).
None of these datatypes work except for “short”.
Everything else results in:
TypeError: Input type int64/float64 is not supported
thrown from torchvision/transforms/functional.py in to_pil_image().
Further, even though the short datatype will work for the stand alone code snippet I provided first, it breaks down when used inside a transform.Compose() called from a Dataset object’s __getitem__:
choices = transforms.RandomChoice([transforms.RandomAffine(30),
transforms.RandomPerspective()])
transform = transforms.Compose([transforms.ToPILImage(),
transforms.RandomApply([choices], 0.5),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))])
trainset = MNIST('data/train.csv', transform=transform)
trainloader = DataLoader(trainset, batch_size=32, shuffle=True, num_workers=4)
RuntimeError: DataLoader worker (pid 12917) is killed by signal: Floating point exception.
RuntimeError: DataLoader worker (pid(s) 12917) exited unexpectedly