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[tool.poetry.dependencies] python = "^3.8" pytorch-lightning = ">1.8.0.post1" -rul-datasets = ">=0.14.1,<0.15.0" +rul-datasets = ">=0.15.0" tqdm = "^4.62.2" hydra-core = "^1.3.1" pywavelets = "^1.4.1" diff --git a/rul_adapt/approach/pseudo_labels.py b/rul_adapt/approach/pseudo_labels.py index 2dbcc2a9..f9cca561 100644 --- a/rul_adapt/approach/pseudo_labels.py +++ b/rul_adapt/approach/pseudo_labels.py @@ -51,6 +51,7 @@ import numpy as np import rul_datasets import torch +from rul_datasets.utils import feature_to_tensor def generate_pseudo_labels( @@ -74,7 +75,7 @@ def generate_pseudo_labels( A list of pseudo labels for the dev set of the data module. """ features, _ = dm.load_split("test" if inductive else "dev", alias="dev") - last_timestep = torch.stack([f[-1] for f in features]) + last_timestep = feature_to_tensor(np.stack([f[-1] for f in features])) pseudo_labels = model(last_timestep).squeeze(axis=1) max_rul = get_max_rul(dm.reader) diff --git a/tests/test_approach/test_pseudo_labels.py b/tests/test_approach/test_pseudo_labels.py index 6ab16aec..bc3660f2 100644 --- a/tests/test_approach/test_pseudo_labels.py +++ b/tests/test_approach/test_pseudo_labels.py @@ -2,8 +2,10 @@ import numpy as np import pytest +import pytorch_lightning as pl import rul_datasets import torch +from torch.utils.data import ConcatDataset, DataLoader from rul_adapt import model from rul_adapt.approach import SupervisedApproach @@ -11,6 +13,7 @@ generate_pseudo_labels, patch_pseudo_labels, _PseudoLabelReader, + get_max_rul, ) @@ -22,7 +25,10 @@ def test_generate_pseudo_labels(mocker, inductive, exp_split): approach.set_model(fe, reg) mock_dm = mocker.MagicMock(rul_datasets.RulDataModule) mock_dm.reader.max_rul = 50 - mock_dm.load_split.return_value = ([torch.randn(15, 1, 10)], [torch.randn(15)]) + mock_dm.load_split.return_value = ( + [np.random.randn(15, 10, 1)], + [np.random.randn(15)], + ) pseudo_labels = generate_pseudo_labels(mock_dm, approach, inductive=inductive) @@ -159,3 +165,49 @@ def test_pseudo_label_generation_fttp(self, norm_rul, inductive, exp_split): if fttp < 10: # if FTTP is too long, all RULs are 1.0 assert np.all(t[fttp:] < max_rul) assert t[-1] == pl + + +@pytest.mark.integration +def test_on_dummy(): + pl.seed_everything(42) + + fd1 = rul_datasets.RulDataModule(rul_datasets.reader.DummyReader(fd=1), 16) + + feature_extractor = model.CnnExtractor(1, [10], seq_len=10, fc_units=16) + regressor = model.FullyConnectedHead(16, [1], act_func_on_last_layer=False) + + approach = SupervisedApproach("mse", 130) + approach.set_model(feature_extractor, regressor) + + trainer = pl.Trainer( + logger=False, + enable_progress_bar=False, + enable_model_summary=False, + enable_checkpointing=False, + max_epochs=10, + ) + trainer.fit(approach, fd1) + + fd2 = rul_datasets.RulDataModule( + fd1.reader.get_compatible(fd=2, percent_broken=0.8), 16 + ) + pseudo_rul = generate_pseudo_labels(fd2, approach) + max_rul = get_max_rul(fd2.reader) + pseudo_rul = [min(max_rul, max(0.0, pr)) for pr in pseudo_rul] + patch_pseudo_labels(fd2, pseudo_rul) + + source_data = fd1.to_dataset("dev") + target_data = fd2.to_dataset("dev") + combined_data = ConcatDataset([source_data, target_data]) + combined_dl = DataLoader(combined_data, fd2.batch_size, shuffle=True) + + trainer = pl.Trainer( + logger=False, + enable_progress_bar=False, + enable_model_summary=False, + enable_checkpointing=False, + max_epochs=10, + ) + trainer.fit( + approach, train_dataloaders=combined_dl, val_dataloaders=fd2.val_dataloader() + )