Lack of seed during random labeled image selection may be leading to better performance on training resumption

In the function x_u_split(…) in dataset/cifar.py the labeled images are generated without a seed. If training runs consist of multiple start and stops then it is possible that total number of labeled images that the model sees exceeds the set value. For instance, training on 40 labels with 2 stops will lead to 120 unique labeled images over the entire course of training even though the model only sees 40 labeled images at a time. I think this can explain the much higher accuracy obtained by this implementation, especially for the low label tasks.

A quick fix would be adding below snippet before random label generation in the x_u_split(…) function.

np.random.seed(args.seed)

[zou-yiqi]

What I mean is that the the seed has been declared and called in main() function, and x_u_split() is used in DATASET_GETTERS() function, which is included in main(), so the seed declared in main() can take effect in the x_u_split(). Thus, we don’t need to add np.random.seed(args.seed) in the x_u_split(…) function. ^ ^

[dsouzinator]

@zou-yiqi Yes you’re right. The seed does indeed work in that function. Thanks!