馃摎 The doc issue
And, setting a single value and a couple of values to alpha and sigma argument of ElasticTransform() gets the same kind of results as shown below so it seems like a single value is [alpha, alpha] and [sigma, sigma]:
from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import ElasticTransform
my_data = OxfordIIITPet(
root="data"
)
import matplotlib.pyplot as plt
def show_images(data, main_title=None, a=50, s=5, f=0):
plt.figure(figsize=(10, 5))
plt.suptitle(t=main_title, y=0.8, fontsize=14)
for i, (im, _) in zip(range(1, 6), data):
plt.subplot(1, 5, i)
et = ElasticTransform(alpha=a, sigma=s, fill=f) # Here
plt.imshow(X=et(im)) # Here
plt.xticks(ticks=[])
plt.yticks(ticks=[])
plt.tight_layout()
plt.show()
show_images2(data=my_data, main_title="alpha1000sigma10_data", a=1000, s=10) # Here
show_images2(data=my_data, main_title="alpha1000_1000sigma10_10_data", a=[1000, 1000], s=[10, 10]) # Here
Suggest a potential alternative/fix
So for alpha and sigma argument, the doc should have the explanation something like as shown below:
A single value is [alpha, alpha]`.
A single value is [sigma, sigma]`.
馃摎 The doc issue
And, setting a single value and a couple of values to
alphaandsigmaargument of ElasticTransform() gets the same kind of results as shown below so it seems like a single value is[alpha, alpha]and[sigma, sigma]:Suggest a potential alternative/fix
So for
alphaandsigmaargument, the doc should have the explanation something like as shown below: