*Memos:
- My post explains ToPILImage() about
modeargument (1). - My post explains ToPILImage() about
modeargument (2). - My post explains ToPILImage() about
modeargument (3). - My post explains ToPILImage() about
modeargument (4). - My post explains how to convert and scale a PIL image to an Image in PyTorch.
- My post explains Compose().
- My post explains ToImage().
- My post explains ToDtype() about
scale=True. - My post explains ToTensor().
- My post explains PILToTensor().
- My post explains OxfordIIITPet().
ToPILImage() can convert an Image([..., C, H, W]), tensor or ndarray to a PIL(Pillow library) image([H, W, C]) and doesn't scale its values to [0.0, 1.0] as shown below. *It's about no arguments:
*Memos:
- The 1st argument for initialization is
mode(Optional-Default:None-Type:PIL.Image mode): *Memos:- If it's
None, a mode is automatically and basically set depending onimg. - For 2D or the 1 channel(
int) of 3D, e.g.[[0, 1, 2, 3]],[[[0]]], etc,'I'is used. - For the 1 channel(
float) of 2D or 3D, e.g.[[0], [1], [2], [3]],[[[0.]]], etc,'L'is used. - For the 2 channels(
int/float/complex/bool) of 3D, e.g.[[[0], [[1]]],[[[0.], [[1.]]], etc,'LA'is used. - For the 3 channels(
int/float/complex/bool) of 3D, e.g.[[[0], [[1]], [[2]]],[[[0.], [[1.]], [[2.]]], etc,'RGB','YCbCr'or'HSV'is used. - For the 4 channels(
int/float/complex/bool) of 3D, e.g.[[[0], [[1]], [[2]], [[3]]],[[[0.], [[1.]], [[2.]], [[3.]]], etc,'RGBA','CMYK'or'RGBX'is used.
- If it's
- The 1st argument is
img(Required-Type:PIL image, Image ortensor/ndarray(int/float/complex/bool)): *Memos:- A tensor must be 2D or 3D.
- A ndarray must be 2D or 3D.
- Don't use
img=.
-
v2is recommended to use according to V1 or V2? Which one should I use?.
from torchvision.datasets import OxfordIIITPet from torchvision.transforms.v2 import ToPILImage, ToImage, PILToTensor import torch import numpy as np tp = ToPILImage() tp = ToPILImage(mode=None) tp # ToPILImage() print(tp.mode) # None Image_data = OxfordIIITPet( root="data", transform=ToImage() ) Tensor_data = OxfordIIITPet( root="data", transform=PILToTensor() ) tp = ToPILImage() tp(Image_data) # It's still `Image`. # Dataset OxfordIIITPet # Number of datapoints: 3680 # Root location: data # StandardTransform # Transform: ToImage() tp(Tensor_data) # It's still `tensor`. # Dataset OxfordIIITPet # Number of datapoints: 3680 # Root location: data # StandardTransform # Transform: ToTensor() tp(Image_data[0]) tp(Tensor_data[0]) # (<PIL.Image.Image image mode=RGB size=394x500>, 0) print(tp(Image_data[0][0])) print(tp(Tensor_data[0][0])) # <PIL.Image.Image image mode=RGB size=394x500 at 0x1A80D3608F0> tp((torch.tensor([[0, 1, 2, 3]]), 0)) # int64 tp((torch.tensor([[0, 1, 2, 3]], dtype=torch.int64), 0)) tp(torch.tensor([[0, 1, 2, 3]])) # TypeError: Input type int64 is not supported tp((torch.tensor([[0, 1, 2, 3]], dtype=torch.int32), 0)) tp((torch.tensor([[[0, 1, 2, 3]]], dtype=torch.int32), 0)) # (<PIL.Image.Image image mode=I size=4x1>, 0) tp((torch.tensor([[0., 1., 2., 3.]]), 0)) # float32 tp((torch.tensor([[0., 1., 2., 3.]], dtype=torch.float32), 0)) tp((torch.tensor([[0., 1., 2., 3.]], dtype=torch.float64), 0)) # (<PIL.Image.Image image mode=L size=4x1>, 0) tp((torch.tensor([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]), 0)) # complex64 tp((torch.tensor([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]], dtype=torch.complex64), 0)) # TypeError: Input type complex64 is not supported tp((torch.tensor([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]], dtype=torch.complex128), 0)) # TypeError: Input type complex128 is not supported tp((torch.tensor([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]], dtype=torch.complex32), 0)) # TypeError: Got unsupported ScalarType ComplexHalf tp((torch.tensor([[True, False, True, False]]), 0)) # bool tp((torch.tensor([[True, False, True, False]], dtype=torch.bool), 0)) # TypeError: Input type bool is not supported tp((np.array([[0, 1, 2, 3]]), 0)) # int32 tp((np.array([[0, 1, 2, 3]], dtype=np.int32), 0)) # (<PIL.Image.Image image mode=I size=4x1>, 0) tp((np.array([[0, 1, 2, 3]], dtype=np.int64), 0)) # TypeError: Input type int64 is not supported tp((np.array([[0., 1., 2., 3.]]), 0)) # float64 tp((np.array([[0., 1., 2., 3.]], dtype=np.float64), 0)) tp((np.array([[0., 1., 2., 3.]], dtype=np.float32), 0)) # (<PIL.Image.Image image mode=L size=4x1>, 0) tp((np.array([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]), 0)) # complex128 tp((np.array([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]], dtype=np.complex128), 0)) # TypeError: Input type complex128 is not supported tp((np.array([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]], dtype=np.complex64), 0)) # TypeError: Input type complex64 is not supported tp((np.array([[True, False, True, False]]), 0)) # bool tp((np.array([[True, False, True, False]], dtype=bool), 0)) # TypeError: Input type bool is not supported 
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