Module `saev.test_training`

Functions

def test_one_training_step(monkeypatch)
def test_one_training_step_matryoshka(monkeypatch): A minimal end-to-end training-loop smoke test for the Matryoshka objective.
def test_split_cfgs_no_bad_keys()
def test_split_cfgs_on_multiple_keys_with_multiple_per_key()
def test_split_cfgs_on_single_key()
def test_split_cfgs_on_single_key_with_multiple_per_key()

Classes

class DummyDS (n, d)

An abstract class representing a :class:Dataset.

All datasets that represent a map from keys to data samples should subclass it. All subclasses should overwrite :meth:__getitem__, supporting fetching a data sample for a given key. Subclasses could also optionally overwrite :meth:__len__, which is expected to return the size of the dataset by many :class:~torch.utils.data.Sampler implementations and the default options of :class:~torch.utils.data.DataLoader. Subclasses could also optionally implement :meth:__getitems__, for speedup batched samples loading. This method accepts list of indices of samples of batch and returns list of samples.

Note

:class:~torch.utils.data.DataLoader by default constructs an index sampler that yields integral indices. To make it work with a map-style dataset with non-integral indices/keys, a custom sampler must be provided.

Expand source code

class DummyDS(torch.utils.data.Dataset):
    def __init__(self, n, d):
        self.x = torch.randn(n, d)

    def __getitem__(self, i):
        return dict(act=self.x[i])

    def __len__(self):
        return len(self.x)

Ancestors

torch.utils.data.dataset.Dataset
typing.Generic