*Memos:
- My post explains InterpolationMode about image tensor.
- My post explains InterpolationMode about
NEARESTandNEAREST_EXACT. - My post explains Resize().
- My post explains RandomRotation().
- My post explains OxfordIIITPet().
You can set InterpolationMode as shown below. It's about image:
*Memos:
- Image interpolation is to estimate and create unknown pixels using known pixels when resampling(resizing) an image.
- Anti-aliasing is to smooth the jaggies in an image.
- In PyTorch, there are 9 interpolation modes NEAREST which matches the OpenCV's INTER_NEAREST which is buggy, NEAREST_EXACT which matches the Scikit-image's Nearest-neighbor and PIL's(Pillow's) NEAREST which aren't buggy, LINEAR, BILINEAR, TRILINEAR, BICUBIC, BOX, HAMMING, AREA and LANCZOS according to Source code for torchvision.transforms.functional and torch.nn.functional.interpolate. *I will say again that NEAREST is buggy while NEAREST_EXACT isn't buggy.
-
interpolationargument withantialiasargument is for Resize(), RandomResizedCrop(), etc. -
interpolationargument withoutantialiasargument is for RandomRotation(), RandomAffine(), RandomPerspective(), ElasticTransform(), RandAugment(), TrivialAugmentWide(), AutoAugment(), AugMix(), etc.
from torchvision.datasets import OxfordIIITPet from torchvision.transforms.v2 import Resize, RandomRotation from torchvision.transforms.functional import InterpolationMode origin_data = OxfordIIITPet( root="data", transform=None ) import matplotlib.pyplot as plt def show_rimages(im, s=None, ip=None): title = "s" + str(s) + "ip" + str(ip).split(".")[1] + "a" title1 = title + "True_data" title2 = title + "False_data" plt.figure(figsize=[10, 8]) for i in range(1, 3): plt.subplot(1, 2, i) r = Resize(size=s, interpolation=ip, antialias=True if i == 1 else False) plt.title(label=title1 if i == 1 else title2, y=1, fontsize=14) plt.imshow(X=r(im)) plt.tight_layout() plt.show() def show_rrimages(im, d=None, ip=None): plt.figure(figsize=[10, 8]) for i in range(2): if isinstance(d, collections.abc.Sequence): d1 = str(d[0]) if d[0] >= 0 else "n" + str(-1*d[0]) d2 = str(d[1]) if d[1] >= 0 else "n" + str(-1*d[1]) dpart = d1 + d2 if "n" in d2 else d1 + "_" + d2 else: dpart = str(d) title = "d" + dpart + "ip" + str(ip[i]).split(".")[1] + "_data" plt.subplot(1, 2, (i+1)) rr = RandomRotation(degrees=d, interpolation=ip[i]) plt.title(label=title, y=1, fontsize=14) plt.imshow(X=rr(im)) plt.tight_layout() plt.show() plt.figure(figsize=[7, 9]) plt.title(label="s500_394origin_data", fontsize=14) plt.imshow(X=origin_data[0][0]) plt.show() print() show_rimages(im=origin_data[0][0], s=50, ip=InterpolationMode.NEAREST) show_rimages(im=origin_data[0][0], s=50, ip=InterpolationMode.NEAREST_EXACT) show_rimages(im=origin_data[0][0], s=50, ip=InterpolationMode.BILINEAR) show_rimages(im=origin_data[0][0], s=50, ip=InterpolationMode.BICUBIC) show_rimages(im=origin_data[0][0], s=50, ip=InterpolationMode.BOX) show_rimages(im=origin_data[0][0], s=50, ip=InterpolationMode.HAMMING) show_rimages(im=origin_data[0][0], s=50, ip=InterpolationMode.LANCZOS) print() show_rrimages(im=origin_data[0][0], d=[45, 45], ip=[InterpolationMode.NEAREST, InterpolationMode.NEAREST_EXACT]) show_rrimages(im=origin_data[0][0], d=[45, 45], ip=[InterpolationMode.BILINEAR, InterpolationMode.BICUBIC]) 
Resize():
RandomRotation():
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