shape '[1, 5, 20]' is invalid for input of size 25 when running run_trainer.py about libfewshot HOT 5 CLOSED

Hi, could you try to run the model with following config?

way_num: 5
shot_num: 4
query_num: 1

from libfewshot.

It now works, I guess it was really just a configuration problem. Can you provide me an insight on why 15 doesn't work but 1 does ?
Many thanks.

EDIT :
or so I thought. I does indeed go through the training, validation and test steps, but those finished I have a similar error with
shape '[1, 5, 5]' is invalid for input of size 5

I have reduced my config file to

includes:
  - headers/dataZSL.yaml
  - headers/device.yaml
  - headers/misc.yaml
  - headers/model.yaml
  - headers/optimizer.yaml
  - classifiers/Proto.yaml
  - backbones/Conv64F.yaml

way_num: 5
shot_num: 4
query_num: 1

backbone:
  name: Conv64F
  kwargs:
    is_flatten: True
    is_feature: False
    leaky_relu: False
    negative_slope: 0.2
    last_pool: True

from libfewshot.

The train.csv is used to sample training support sets and query sets. So the shot_num+query_num must be larger than the number of images in each class. When you set shot_num+query=20, only 5 images of each class are sampled in sampler.py, we do not check out-of-bound settings for it.

Additionally, you may misunderstand the dataset structure. The test.csv is only used to sample support sets and query sets in the few-shot meta-testing stage. In common, the set of classes in train.csv is disjointed with the classes in test.csv.

from libfewshot.

In the testing stage, your test.csv contains only one sample in each class, so the test_shot+test_query > 1. (In default, when test_* is not specified, the *_num will replace it).

from libfewshot.

Oh, I see.
I have changed the number of examples per classes in the test set and it does work.
I won't deny that I may have misunderstood the dataset structure.
Thank you for your time

from libfewshot.