When I was checking the feature maps of my model, I found something bizarre:
Although each channel of the input tensor X (the size of x is 256 * 9 * 9, C * H * W) is a constant value. The result of X.mean(0) is not in accord with expectations. The following is the result of plt.imshow(X.mean(0).numpy()):

What this visualized result tells us is that the value in X.mean(0) are different according to the position although their inputs are the same. Actually the value of X.mean(0)[0, 0] is 0.07183486223220825, while the value of X.mean(0)[-1, -1] is 0.07183484733104706 (their difference is 1.4901161e-08).
I know this difference is very very tiny, but I am curious why there is a difference.
The torch version is 1.8.2+cu102 and the numpy version is 1.20.3.