ToPILImage in PyTorch (4)

Buy Me a Coffee☕ Memos: My post explains ToPILImage() about no arguments. My post explains ToPILImage() about mode argument (1). My post explains ToPILImage() about mode argument (2). 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 mode argument (3): from torchvision.datasets import OxfordIIITPet from torchvision.transforms.v2 import ToPILImage, ToImage, PILToTensor import torch Image_data = OxfordIIITPet( root="data", transform=ToImage() ) Tensor_data = OxfordIIITPet( root="data", transform=PILToTensor() ) def show_images(im, m=None): if m == None: m = [None for _ in range(len(im))] if len(im) > len(m): for _ in range(len(im)-len(m)): m.append(None) plt.figure(figsize=[13, 6]) for i in range(len(im)): image = im[i] if torch.is_tensor(image): dpart = str(image.dtype).split(".")[1] elif isinstance(image, np.ndarray): dpart = str(image.dtype) title = "m" + str(m[i]) + "_PILImage from " \ + type(image).name+ "(" + dpart + ")" plt.subplot(1, 3, (i+1)) tp = ToPILImage(mode=m[i]) plt.title(label=title, y=1, fontsize=14) plt.imshow(X=tp(image)) plt.xticks(ticks=[]) plt.yticks(ticks=[]) plt.tight_layout() plt.show() show_images(im=[Image_data[0][0], Tensor_data[0][0]], m=["RGB", "RGB"]) show_images(im=[Image_data[0][0], Tensor_data[0][0]], m=["YCbCr", "YCbCr"]) show_images(im=[Image_data[0][0], Tensor_data[0][0]], m=["HSV", "HSV"]) print() show_images(im=[torch.tensor([[[0]], [[1]]])], m=["LA"]) # int64 show_images(im=[torch.tensor([[[0]], [[1]], [[2]]]), torch.tensor([[[0]], [[1]], [[2]]]), torch.tensor([[[0]], [[1]], [[2]]])], m=["RGB", "YCbCr", "HSV"]) show_images(im=[torch.tensor([[[0]], [[1]], [[2]], [[3]]]), torch.tensor([[[0]], [[1]], [[2]], [[3]]]), torch.tensor([[[0]], [[1]], [[2]], [[3]]])], m=["RGBA", "CMYK", "RGBX"]) print() show_images(im=[torch.tensor([[[0]]], dtype=torch.int32), torch.tensor([[[0]], [[1]]], dtype=torch.int32)], m=["I", "LA"]) show_images(im=[torch.tensor([[[0]], [[1]], [[2]]], dtype=torch.int32), torch.tensor([[[0]], [[1]], [[2]]], dtype=torch.int32), torch.tensor([[[0]], [[1]], [[2]]], dtype=torch.int32)], m=["RGB", "YCbCr", "HSV"]) show_images(im=[torch.tensor([[[0]], [[1]], [[2]], [[3]]], dtype=torch.int32), torch.tensor([[[0]], [[1]], [[2]], [[3]]], dtype=torch.int32), torch.tensor([[[0]], [[1]], [[2]], [[3]]], dtype=torch.int32)], m=["RGBA", "CMYK", "RGBX"]) print() show_images(im=[torch.tensor([[[0.]]]), # float32 torch.tensor([[[0.]], [[1.]]])], m=["L", "LA"]) show_images(im=[torch.tensor([[[0.]], [[1.]], [[2.]]]), torch.tensor([[[0.]], [[1.]], [[2.]]]), torch.tensor([[[0.]], [[1.]], [[2.]]])], m=["RGB", "YCbCr", "HSV"]) show_images(im=[torch.tensor([[[0.]], [[1.]], [[2.]], [[3.]]]), torch.tensor([[[0.]], [[1.]], [[2.]], [[3.]]]), torch.tensor([[[0.]], [[1.]], [[2.]], [[3.]]])], m=["RGBA", "CMYK", "RGBX"]) print() show_images(im=[torch.tensor([[[0.]]], dtype=torch.float64), torch.tensor([[[0.]], [[1.]]], dtype=torch.float64)], m=["L", "LA"]) show_images(im=[torch.tensor([[[0.]], [[1.]], [[2.]]], dtype=torch.float64), torch.tensor([[[0.]], [[1.]], [[2.]]], dtype=torch.float64), torch.tensor([[[0.]], [[1.]], [[2.]]], dtype=torch.float64)], m=["RGB", "YCbCr", "HSV"]) show_images(im=[torch.tensor([[[0.]], [[1.]], [[2.]], [[3.]]], dtype=torch.float64), torch.tensor([[[0.]], [[1.]], [[2.]], [[3.]]], dtype=torch.float64), torch.tensor([[[0.]], [[1.]], [[2.]], [[3.]]], dtype=torch.float64)], m=["RGBA", "CMYK", "RGBX"]) print() show_images(im=[torch.tensor([[[0.+0.j]], [[1.+0.j]]])], # complex64 m=["LA"]) show_images(im=[torch.tensor([[[0.+0.j]], [[1.+0.j]], [[2.+0.j]]]), torch.tensor([[[0.+0.j]], [[1.+0.j]], [[2.+0.j]]]), torch.tensor([[[0.+0.j]], [[1.+0.j]], [[2.+0.j]]])], m=["RGB", "YCbCr", "HSV"]) show_images(im=[torch.tensor([[[0.+0.j]], [[1.+0.j]], [[2.+0.j]], [[3.+0.j]]]), torch.tensor([[[0.+0.j]], [[1.+0.j]], [[2.+0.j]], [[3.+0.j]]]), torch.tensor([[[0.+0.j]], [[1.+0.j]], [[2.+0.j]], [[3.+0.j]]])], m=["RGBA", "CMYK", "RGBX"]) print() show_images(im=[torch.tensor([[[True]], [[False]]])], m=["LA"]) show_ima

Apr 29, 2025 - 14:14

*Memos:

My post explains ToPILImage() about no arguments.
My post explains ToPILImage() about mode argument (1).
My post explains ToPILImage() about mode argument (2).

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 mode argument (3):

from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import ToPILImage, ToImage, PILToTensor
import torch

Image_data = OxfordIIITPet(
    root="data",
    transform=ToImage()
)

Tensor_data = OxfordIIITPet(
    root="data",
    transform=PILToTensor()
)

def show_images(im, m=None):
    if m == None:
        m = [None for _ in range(len(im))]
    if len(im) > len(m):
        for _ in range(len(im)-len(m)):
            m.append(None)
    plt.figure(figsize=[13, 6])
    for i in range(len(im)):
        image = im[i]
        if torch.is_tensor(image):
            dpart = str(image.dtype).split(".")[1]
        elif isinstance(image, np.ndarray):
            dpart = str(image.dtype)
        title = "m" + str(m[i]) + "_PILImage from " \
                + type(image).__name__+ "(" + dpart + ")"
        plt.subplot(1, 3, (i+1))
        tp = ToPILImage(mode=m[i])
        plt.title(label=title, y=1, fontsize=14)
        plt.imshow(X=tp(image))
        plt.xticks(ticks=[])
        plt.yticks(ticks=[])
    plt.tight_layout()
    plt.show()

show_images(im=[Image_data[0][0], Tensor_data[0][0]], m=["RGB", "RGB"])
show_images(im=[Image_data[0][0], Tensor_data[0][0]], m=["YCbCr", "YCbCr"])
show_images(im=[Image_data[0][0], Tensor_data[0][0]], m=["HSV", "HSV"])
print()
show_images(im=[torch.tensor([[[0]], [[1]]])], m=["LA"]) # int64
show_images(im=[torch.tensor([[[0]], [[1]], [[2]]]),
                torch.tensor([[[0]], [[1]], [[2]]]),
                torch.tensor([[[0]], [[1]], [[2]]])],
            m=["RGB", "YCbCr", "HSV"])
show_images(im=[torch.tensor([[[0]], [[1]], [[2]], [[3]]]),
                torch.tensor([[[0]], [[1]], [[2]], [[3]]]),
                torch.tensor([[[0]], [[1]], [[2]], [[3]]])],
            m=["RGBA", "CMYK", "RGBX"])
print()
show_images(im=[torch.tensor([[[0]]], dtype=torch.int32),
                torch.tensor([[[0]], [[1]]], dtype=torch.int32)],
            m=["I", "LA"])
show_images(im=[torch.tensor([[[0]], [[1]], [[2]]], dtype=torch.int32),
                torch.tensor([[[0]], [[1]], [[2]]], dtype=torch.int32),
                torch.tensor([[[0]], [[1]], [[2]]], dtype=torch.int32)],
            m=["RGB", "YCbCr", "HSV"])
show_images(im=[torch.tensor([[[0]], [[1]], [[2]], [[3]]], dtype=torch.int32),
                torch.tensor([[[0]], [[1]], [[2]], [[3]]], dtype=torch.int32),
                torch.tensor([[[0]], [[1]], [[2]], [[3]]], dtype=torch.int32)],
            m=["RGBA", "CMYK", "RGBX"])
print()
show_images(im=[torch.tensor([[[0.]]]), # float32
                torch.tensor([[[0.]], [[1.]]])], m=["L", "LA"])
show_images(im=[torch.tensor([[[0.]], [[1.]], [[2.]]]),
                torch.tensor([[[0.]], [[1.]], [[2.]]]),
                torch.tensor([[[0.]], [[1.]], [[2.]]])],
            m=["RGB", "YCbCr", "HSV"])
show_images(im=[torch.tensor([[[0.]], [[1.]], [[2.]], [[3.]]]),
                torch.tensor([[[0.]], [[1.]], [[2.]], [[3.]]]),
                torch.tensor([[[0.]], [[1.]], [[2.]], [[3.]]])],
            m=["RGBA", "CMYK", "RGBX"])
print()
show_images(im=[torch.tensor([[[0.]]], dtype=torch.float64),
                torch.tensor([[[0.]], [[1.]]], dtype=torch.float64)],
            m=["L", "LA"])
show_images(im=[torch.tensor([[[0.]], [[1.]], [[2.]]], dtype=torch.float64),
                torch.tensor([[[0.]], [[1.]], [[2.]]], dtype=torch.float64),
                torch.tensor([[[0.]], [[1.]], [[2.]]], dtype=torch.float64)],
            m=["RGB", "YCbCr", "HSV"])
show_images(im=[torch.tensor([[[0.]], [[1.]], [[2.]], [[3.]]],
                dtype=torch.float64),
                torch.tensor([[[0.]], [[1.]], [[2.]], [[3.]]],
                dtype=torch.float64),
                torch.tensor([[[0.]], [[1.]], [[2.]], [[3.]]],
                dtype=torch.float64)],
            m=["RGBA", "CMYK", "RGBX"])
print()
show_images(im=[torch.tensor([[[0.+0.j]], [[1.+0.j]]])], # complex64
            m=["LA"])
show_images(im=[torch.tensor([[[0.+0.j]], [[1.+0.j]], [[2.+0.j]]]),
                torch.tensor([[[0.+0.j]], [[1.+0.j]], [[2.+0.j]]]),
                torch.tensor([[[0.+0.j]], [[1.+0.j]], [[2.+0.j]]])],
            m=["RGB", "YCbCr", "HSV"])
show_images(im=[torch.tensor([[[0.+0.j]], [[1.+0.j]], [[2.+0.j]],
                              [[3.+0.j]]]),
                torch.tensor([[[0.+0.j]], [[1.+0.j]], [[2.+0.j]],
                              [[3.+0.j]]]),
                torch.tensor([[[0.+0.j]], [[1.+0.j]], [[2.+0.j]],
                              [[3.+0.j]]])], m=["RGBA", "CMYK", "RGBX"])
print()
show_images(im=[torch.tensor([[[True]], [[False]]])], m=["LA"])
show_images(im=[torch.tensor([[[True]], [[False]], [[True]]]), # bool
                torch.tensor([[[True]], [[False]], [[True]]]),
                torch.tensor([[[True]], [[False]], [[True]]])],
            m=["RGB", "YCbCr", "HSV"])
show_images(im=[torch.tensor([[[True]], [[False]], [[True]], [[False]]]),
                torch.tensor([[[True]], [[False]], [[True]], [[False]]]),
                torch.tensor([[[True]], [[False]], [[True]], [[False]]])],
            m=["RGBA", "CMYK", "RGBX"])