It is well known that IN is simply 1-batch of BN. It is true in training phase, but not in inference.

In inference phase, BN uses popular mini-batch statistic for mean and standard variation, while IN uses them unchanged from training time, as noted in AdaIN paper below.

Reference: arxiv.org/abs/1703.06868

Rank is the number of linearly independent rows or columns in a matrix. Following matrix is a 2-rank matrix(not 3) as 3rd row can be made by composition of 1st and 2nd row.

Reference: https://blog.naver.com/dhkdwnddml/220313237499

매 번 다시 검색해서 알아내는 것이라 여기에 정리해둔다.

Tensorflow (버전 2.2와 1.14에서 확인)

import tensorflow as tf
tf.test.is_gpu_available(
    cuda_only=False, min_cuda_compute_capability=None
)

PyTorch (버전 1.6에서 확인)

import torch
torch.cuda.is_available()

참고:

• tf.test.is_gpu_available
• PyTorch GPU check