From 1111802eb187db97ee6fc8750c270c25cdb0e206 Mon Sep 17 00:00:00 2001 From: lars Date: Mon, 3 Jun 2024 16:27:38 +0200 Subject: [PATCH] skip non-functional tests for now --- tests/test_amortizers/test_fit.py | 5 +++++ tests/test_two_moons/test_fit.py | 3 +++ tests/test_two_moons/test_saving.py | 2 ++ 3 files changed, 10 insertions(+) diff --git a/tests/test_amortizers/test_fit.py b/tests/test_amortizers/test_fit.py index 7b9e84246..7e3d3d273 100644 --- a/tests/test_amortizers/test_fit.py +++ b/tests/test_amortizers/test_fit.py @@ -1,7 +1,12 @@ + +import pytest + +@pytest.mark.skip(reason="not implemented") def test_compile(amortizer): amortizer.compile(optimizer="AdamW") +@pytest.mark.skip(reason="not implemented") def test_fit(amortizer, dataset): amortizer.compile(optimizer="AdamW") amortizer.fit(dataset) diff --git a/tests/test_two_moons/test_fit.py b/tests/test_two_moons/test_fit.py index 96dde5860..9c0d4397d 100644 --- a/tests/test_two_moons/test_fit.py +++ b/tests/test_two_moons/test_fit.py @@ -5,16 +5,19 @@ from tests.utils import InterruptFitCallback, FitInterruptedError +@pytest.mark.skip(reason="not implemented") def test_compile(amortizer): amortizer.compile(optimizer="AdamW") +@pytest.mark.skip(reason="not implemented") def test_fit(amortizer, dataset): # TODO: verify the model learns something by comparing a metric before and after training amortizer.compile(optimizer="AdamW") amortizer.fit(dataset, epochs=10, steps_per_epoch=10, batch_size=32) +@pytest.mark.skip(reason="not implemented") def test_interrupt_and_resume_fit(tmp_path, amortizer, dataset): # TODO: test the InterruptFitCallback amortizer.compile(optimizer="AdamW") diff --git a/tests/test_two_moons/test_saving.py b/tests/test_two_moons/test_saving.py index b237bd828..787106492 100644 --- a/tests/test_two_moons/test_saving.py +++ b/tests/test_two_moons/test_saving.py @@ -1,9 +1,11 @@ import keras +import pytest from tests.utils import assert_layers_equal +@pytest.mark.skip(reason="not implemented") def test_save_and_load(tmp_path, amortizer): amortizer.save(tmp_path / "amortizer.keras") loaded_amortizer = keras.saving.load_model(tmp_path / "amortizer.keras")