| import numpy as np |
| import torch |
|
|
| from fall_detection.data import group_train_val_test_split |
| from fall_detection.models import PoseGRU, PoseTCN |
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|
| def test_group_split_has_no_leakage() -> None: |
| groups = np.repeat(np.asarray([f"g{i}" for i in range(20)]), 4) |
| labels = np.tile(np.asarray([0, 1, 0, 1]), 20) |
| splits = group_train_val_test_split(labels, groups, seed=7) |
| group_sets = [set(groups[index]) for index in (splits.train, splits.val, splits.test)] |
| assert group_sets[0].isdisjoint(group_sets[1]) |
| assert group_sets[0].isdisjoint(group_sets[2]) |
| assert group_sets[1].isdisjoint(group_sets[2]) |
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|
|
| def test_gru_returns_one_logit_per_sequence() -> None: |
| model = PoseGRU(input_size=140, hidden_size=16) |
| result = model(torch.randn(5, 40, 140)) |
| assert result.shape == (5,) |
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|
|
| def test_tcn_returns_one_logit_per_sequence() -> None: |
| model = PoseTCN(input_size=60, channels=16) |
| result = model(torch.randn(5, 40, 60)) |
| assert result.shape == (5,) |
|
|
