convert tf.debugging.assert_none_equal to standard assert for single …

…value checks, remove model train callback test bc basically duplicate with more work.

basic_pitch/data/tf_example_deserialization.py

@@ -73,10 +73,7 @@ def prepare_datasets(
 
     # check that the base dataset returned by ds_function is FINITE
     for ds in [ds_train, ds_validation]:
-        tf.debugging.assert_none_equal(
-            tf.cast(tf.data.experimental.cardinality(ds), tf.int32),
-            tf.data.experimental.INFINITE_CARDINALITY,
-        )
+        assert tf.cast(tf.data.experimental.cardinality(ds), tf.int32) != tf.data.experimental.INFINITE_CARDINALITY
 
     # training dataset
     if training_shuffle_buffer_size > 0:
@@ -137,10 +134,7 @@ def prepare_visualization_datasets(
 
     # check that the base dataset returned by ds_function is FINITE
     for ds in [ds_train, ds_validation]:
-        tf.debugging.assert_none_equal(
-            tf.cast(tf.data.experimental.cardinality(ds), tf.int32),
-            tf.data.experimental.INFINITE_CARDINALITY,
-        )
+        assert tf.cast(tf.data.experimental.cardinality(ds), tf.int32) != tf.data.experimental.INFINITE_CARDINALITY
 
     # training dataset
     ds_train = ds_train.repeat().batch(batch_size).prefetch(tf.data.AUTOTUNE)

tests/test_callbacks.py

@@ -24,8 +24,6 @@
 from basic_pitch.callbacks import VisualizeCallback
 from basic_pitch.constants import AUDIO_N_SAMPLES, ANNOTATIONS_N_SEMITONES, ANNOT_N_FRAMES
 
-os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
-
 
 class MockModel(tf.keras.Model):
     def __init__(self) -> None:
@@ -62,27 +60,3 @@ def test_visualize_callback_on_epoch_end(tmpdir: str) -> None:
     vc.model = MockModel()
 
     vc.on_epoch_end(1, {"loss": np.random.random(), "val_loss": np.random.random()})
-
-
-def test_visualize_callback_on_epoch_end_with_model(tmpdir: str) -> None:
-    model = MockModel()
-    model.compile(optimizer="adam", loss="mse")
-
-    batch_size = 2  # needs to be at least 2 bc validation_split required
-
-    x_train = np.random.random((batch_size, AUDIO_N_SAMPLES, 1))
-    y_train = {
-        key: np.random.random((batch_size, ANNOTATIONS_N_SEMITONES, ANNOT_N_FRAMES))
-        for key in ["onset", "contour", "note"]
-    }
-
-    vc = VisualizeCallback(
-        train_ds=create_mock_dataset(),
-        validation_ds=create_mock_dataset(),
-        tensorboard_dir=str(tmpdir),
-        original_validation_ds=create_mock_dataset(),
-        contours=True,
-    )
-
-    history = model.fit(x_train, y_train, epochs=1, validation_split=0.5, callbacks=[vc], verbose=0)
-    assert history