do not use tf2.20, adapt to keras 3 api

Author: hyperc54

.github/workflows/tox.yml

@@ -14,7 +14,6 @@ jobs:
           - ubuntu-latest
           - windows-latest
         py:
-          - "3.13"
           - "3.12"
           - "3.11"
           - "3.10"
@@ -23,6 +22,8 @@ jobs:
             py: "3.10.11"
           - os: macos-latest-xlarge
             py: "3.11.8"
+          - os: macos-latest-xlarge
+            py: "3.12.12"
     steps:
       - name: Setup python for test ${{ matrix.py }}
         uses: actions/setup-python@v4

basic_pitch/layers/signal.py

@@ -15,7 +15,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Callable, Optional
+from typing import Any, Callable, Optional, Tuple
 import tensorflow as tf
 from basic_pitch.layers.math import log_base_b
 
@@ -62,7 +62,7 @@ def __init__(
         self.center = center
         self.pad_mode = pad_mode
 
-    def build(self, input_shape: tf.TensorShape) -> None:
+    def build(self, input_shape: Tuple[Optional[int], ...]) -> None:
         if self.window_length < self.fft_length:
             lpad = (self.fft_length - self.window_length) // 2
             rpad = self.fft_length - self.window_length - lpad
@@ -77,10 +77,11 @@ def padded_window(window_length: int, dtype: tf.dtypes.DType = tf.float32) -> tf
             self.final_window_fn = padded_window
 
         if self.center:
+            rank = len(input_shape)
             self.spec = tf.keras.layers.Lambda(
                 lambda x: tf.pad(
                     x,
-                    [[0, 0] for _ in range(input_shape.rank - 1)] + [[self.fft_length // 2, self.fft_length // 2]],
+                    [[0, 0] for _ in range(rank - 1)] + [[self.fft_length // 2, self.fft_length // 2]],
                     mode=self.pad_mode,
                 )
             )
@@ -159,9 +160,9 @@ class NormalizedLog(tf.keras.layers.Layer):
     This layer adds 1e-10 to all values as a way to avoid NaN math.
     """
 
-    def build(self, input_shape: tf.Tensor) -> None:
+    def build(self, input_shape: Tuple[Optional[int], ...]) -> None:
         self.squeeze_batch = lambda batch: batch
-        rank = input_shape.rank
+        rank = len(input_shape)
         if rank == 4:
             assert input_shape[1] == 1, "If the rank is 4, the second dimension must be length 1"
             self.squeeze_batch = lambda batch: tf.squeeze(batch, axis=1)

basic_pitch/models.py

@@ -184,7 +184,7 @@ def get_cqt(inputs: tf.Tensor, n_harmonics: int, use_batchnorm: bool) -> tf.Tens
         bins_per_octave=12 * CONTOURS_BINS_PER_SEMITONE,
     )(x)
     x = signal.NormalizedLog()(x)
-    x = tf.expand_dims(x, -1)
+    x = tfkl.Lambda(lambda t: tf.expand_dims(t, -1))(x)
     if use_batchnorm:
         x = tfkl.BatchNormalization()(x)
     return x
@@ -263,7 +263,7 @@ def model(
         x_contours = nn.FlattenFreqCh(name=contour_name)(x_contours)  # contour output
 
         # reduced contour output as input to notes
-        x_contours_reduced = tf.expand_dims(x_contours, -1)
+        x_contours_reduced = tfkl.Lambda(lambda t: tf.expand_dims(t, -1))(x_contours)
     else:
         x_contours_reduced = x_contours
 

basic_pitch/train.py

@@ -150,7 +150,6 @@ def main(
     model.compile(
         loss=loss,
         optimizer=tf.keras.optimizers.Adam(learning_rate),
-        sample_weight_mode={"contour": None, "note": None, "onset": None},
     )
 
     logging.info("--- Model Training specs ---")

pyproject.toml

@@ -15,20 +15,18 @@ classifiers = [
 	"Programming Language :: Python :: 3.10",
 	"Programming Language :: Python :: 3.11",
 	"Programming Language :: Python :: 3.12",
-	"Programming Language :: Python :: 3.13",
 	"Programming Language :: Python :: Implementation :: CPython",
 ]
 dependencies = [
 	"coremltools; platform_system == 'Darwin'",
 	"librosa>=0.8.0",
 	"mir_eval>=0.6.0",
-	"numpy>=1.18",
 	"onnxruntime; platform_system == 'Windows' and python_version < '3.11'",
 	"pretty_midi>=0.2.9",
 	"resampy>=0.2.2,<0.4.3",
 	"scikit-learn",
 	"scipy>=1.4.1",
-	"tensorflow>=2.16.0;python_version >= '3.11'",
+	"tensorflow>=2.16.0,<2.20.0;python_version >= '3.11'",
 	"tflite-runtime; platform_system == 'Linux' and python_version < '3.11'",
 	"typing_extensions",
 ]
@@ -68,7 +66,7 @@ test = [
 	"mido"
 ]
 tf = [
-	"tensorflow>=2.16.1"
+	"tensorflow>=2.16.0,<2.20.0"
 ]
 coreml = ["coremltools"] 
 onnx = ["onnxruntime"]

tests/data/conftest.py

@@ -12,21 +12,21 @@
 SLAKH_TEST_INDEX = json.load(open(RESOURCES_PATH / "data" / "slakh" / "dummy_index.json"))
 
 
-@pytest.fixture  # type: ignore[misc]
+@pytest.fixture  # type: ignore[untyped-decorator]
 def mock_slakh_index() -> None:  # type: ignore[misc]
     with mock.patch("mirdata.datasets.slakh.Dataset.download"):
         with mock.patch("mirdata.datasets.slakh.Dataset._index", new=SLAKH_TEST_INDEX):
             yield
 
 
-@pytest.fixture  # type: ignore[misc]
+@pytest.fixture  # type: ignore[untyped-decorator]
 def mock_medleydb_pitch_index() -> None:  # type: ignore[misc]
     with mock.patch("mirdata.datasets.medleydb_pitch.Dataset.download"):
         with mock.patch("mirdata.datasets.medleydb_pitch.Dataset._index", new=MEDLEYDB_PITCH_TEST_INDEX):
             yield
 
 
-@pytest.fixture  # type: ignore[misc]
+@pytest.fixture  # type: ignore[untyped-decorator]
 def mock_maestro_index() -> None:  # type: ignore[misc]
     index_with_metadata = MAESTRO_TEST_INDEX
     metadata = {mdata["midi_filename"].split(".")[0]: mdata for mdata in METADATA_TEST_INDEX}
@@ -36,14 +36,14 @@ def mock_maestro_index() -> None:  # type: ignore[misc]
                 yield
 
 
-@pytest.fixture  # type: ignore[misc]
+@pytest.fixture  # type: ignore[untyped-decorator]
 def mock_guitarset_index() -> None:  # type: ignore[misc]
     with mock.patch("mirdata.datasets.guitarset.Dataset.download"):
         with mock.patch("mirdata.datasets.guitarset.Dataset._index", new=GUITAR_SET_TEST_INDEX):
             yield
 
 
-@pytest.fixture  # type: ignore[misc]
+@pytest.fixture  # type: ignore[untyped-decorator]
 def mock_ikala_index() -> None:  # type: ignore[misc]
     with mock.patch("mirdata.datasets.ikala.Dataset.download"):
         with mock.patch("mirdata.datasets.ikala.Dataset._index", new=IKALA_TEST_INDEX):

tests/test_callbacks.py

@@ -56,6 +56,6 @@ def test_visualize_callback_on_epoch_end(tmpdir: str) -> None:
         contours=True,
     )
 
-    vc.model = MockModel()
+    vc.set_model(MockModel())
 
     vc.on_epoch_end(1, {"loss": np.random.random(), "val_loss": np.random.random()})

tests/test_nn.py

@@ -67,7 +67,6 @@ def test_defaults(self) -> None:
         model.compile(
             loss="binary_crossentropy",
             optimizer=tf.keras.optimizers.Adam(0.1),
-            sample_weight_mode=None,
         )
 
         model.fit(
@@ -102,7 +101,6 @@ def test_fractions(self) -> None:
         model.compile(
             loss="binary_crossentropy",
             optimizer=tf.keras.optimizers.Adam(0.1),
-            sample_weight_mode=None,
         )
 
         model.fit(

tox.ini

@@ -1,5 +1,5 @@
 [tox]
-envlist = py313,py312,py311,py310,full,data,manifest,check-formatting,lint,mypy
+envlist = py312,py311,py310,full,data,manifest,check-formatting,lint,mypy
 skipsdist = True
 usedevelop = True
 requires =