Widen TensorFlow dependency to add support for Py3.12.

.github/workflows/tox.yml

@@ -13,32 +13,32 @@ jobs:
         os:
           - Ubuntu
           - Windows
-          - MacOs
+          - macOS
         py:
+          - "3.12"
           - "3.11"
           - "3.10"
           - "3.9"
-          - "3.8"
     steps:
       - name: Setup python for test ${{ matrix.py }}
         uses: actions/setup-python@v3
         with:
           python-version: ${{ matrix.py }}
       - uses: actions/checkout@v3
-      - name: Install soundlibs Ubuntu
+      - name: Install soundlibs on Ubuntu
         run: sudo apt-get update && sudo apt-get install --no-install-recommends -y --fix-missing pkg-config libsndfile1
         if: matrix.os == 'Ubuntu'
-      - name: Install soundlibs MacOs
+      - name: Install soundlibs on macOS
         run: brew install libsndfile
-        if: matrix.os == 'MacOs'
-      - name: Install soundlibs Windows
+        if: matrix.os == 'macOS'
+      - name: Install soundlibs on Windows
         run: choco install libsndfile
         if: matrix.os == 'Windows'
       - name: Install tox
         run: python -m pip install tox
       # We will only check this on the minimum python version
       - name: Check formatting, lint and mypy
         run: tox -c tox.ini -e check-formatting,lint,mypy
-        if: matrix.py == '3.8'
+        if: matrix.py == '3.9'
       - name: Run test suite
         run: tox -c tox.ini -e py,manifest

realbook/layers/compatibility.py

@@ -16,18 +16,21 @@
 # limitations under the License.
 
 import os
+from typing import Any, Dict, List, Optional, Tuple, Union, cast
+
 import tensorflow as tf
 from google.protobuf.json_format import (
     MessageToDict as SerializeProtobufToDict,
+)
+from google.protobuf.json_format import (
     ParseDict as ParseDictToProtobuf,
 )
-from typing import Any, cast, Dict, List, Optional, Tuple, Union
+from tensorflow.python.eager.wrap_function import WrappedFunction
 from tensorflow.python.framework.convert_to_constants import (
     convert_variables_to_constants_v2_as_graph,
 )
 from tensorflow.python.framework.importer import _IsControlInput as is_control_input
 from tensorflow.python.ops.op_selector import UnliftableError
-from tensorflow.python.eager.wrap_function import WrappedFunction
 
 
 def _load_concrete_function_from_graph_def(
@@ -132,7 +135,9 @@ def call(self, _input: Union[tf.Tensor, Dict[str, tf.Tensor]]) -> Union[tf.Tenso
         return self.model(_input)
 
 
-def get_saved_model_input_tensors(saved_model_or_path: Union[tf.keras.Model, str]) -> List[tf.Tensor]:
+def get_saved_model_input_tensors(
+    saved_model_or_path: Union[tf.keras.Model, str],
+) -> List[tf.Tensor]:
     """
     Given a path to a SavedModel or an already loaded SavedModel,
     return a list of its input tensors. Useful for figuring out the
@@ -150,7 +155,9 @@ def get_saved_model_input_tensors(saved_model_or_path: Union[tf.keras.Model, str
     return [tensor for tensor in model.inputs if tensor.dtype != "resource"]
 
 
-def get_all_tensors_from_saved_model(saved_model_or_path: Union[tf.keras.Model, str]) -> List[tf.Tensor]:
+def get_all_tensors_from_saved_model(
+    saved_model_or_path: Union[tf.keras.Model, str],
+) -> List[tf.Tensor]:
     """
     Given a path to a SavedModel or an already loaded SavedModel,
     return a list of all of its tensors. Useful for figuring out the
@@ -208,7 +215,9 @@ def get_all_tensors_from_saved_model(saved_model_or_path: Union[tf.keras.Model,
     return res
 
 
-def get_saved_model_output_tensors(saved_model_or_path: Union[tf.keras.Model, str]) -> List[tf.Tensor]:
+def get_saved_model_output_tensors(
+    saved_model_or_path: Union[tf.keras.Model, str],
+) -> List[tf.Tensor]:
     """
     Given a path to a SavedModel or an already loaded SavedModel,
     return a list of its output tensors. Useful for figuring out the

realbook/layers/math.py

@@ -42,7 +42,7 @@ class NormalizedLog(tf.keras.layers.Layer):
 
     def build(self, input_shape: tf.Tensor) -> 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)

realbook/layers/signal.py

@@ -18,8 +18,8 @@
 import warnings
 from typing import Any, Callable, Dict, Optional, Union
 
-import tensorflow as tf
 import numpy as np
+import tensorflow as tf
 
 from realbook.layers.math import log_base_b
 from realbook.vendor import librosa_filters
@@ -86,7 +86,7 @@ def __init__(
         self.pad_mode = pad_mode
 
     def build(self, input_shape: tf.TensorShape) -> None:
-        if input_shape.rank > 2:
+        if len(input_shape) > 2:
             raise ValueError(
                 "realbook.layers.signal.Stft received an input shape of "
                 f"{input_shape}, but only supports inputs shaped "
@@ -104,7 +104,7 @@ def build(self, input_shape: tf.TensorShape) -> None:
             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(len(input_shape) - 1)] + [[self.fft_length // 2, self.fft_length // 2]],
                     mode=self.pad_mode,
                 )
             )
@@ -198,7 +198,7 @@ def __init__(
             )
 
     def build(self, input_shape: tf.TensorShape) -> None:
-        if input_shape.rank > 3 or input_shape.rank <= 1:
+        if len(input_shape) > 3 or len(input_shape) <= 1:
             raise ValueError(
                 "realbook.layers.signal.Istft received an input shape of "
                 f"{input_shape}, but only supports inputs shaped "
@@ -211,7 +211,7 @@ def build(self, input_shape: tf.TensorShape) -> None:
 
         self.window_sum = librosa_filters.window_sumsquare(  # type: ignore
             window=self.window.numpy(),
-            n_frames=input_shape[0] if input_shape.rank == 2 else input_shape[1],
+            n_frames=input_shape[0] if len(input_shape) == 2 else input_shape[1],
             win_length=self.window_length,
             n_fft=self.fft_length,
             hop_length=self.hop_length,
@@ -229,7 +229,7 @@ def build(self, input_shape: tf.TensorShape) -> None:
 
         self.slice_op = tf.keras.layers.Lambda(lambda x: x)
         if self.center:
-            if input_shape.rank == 2:  # unbatched
+            if len(input_shape) == 2:  # unbatched
                 self.slice_op = tf.keras.layers.Lambda(
                     lambda x: x[int(self.fft_length // 2) : -int(self.fft_length // 2)]
                 )

setup.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 1.0.3
+current_version = 1.0.4
 commit = True
 tag = True
 
@@ -22,10 +22,10 @@ classifiers =
 	Operating System :: MacOS :: MacOS X
 	Operating System :: Microsoft :: Windows
 	Programming Language :: Python
-	Programming Language :: Python :: 3.8
 	Programming Language :: Python :: 3.9
 	Programming Language :: Python :: 3.10
 	Programming Language :: Python :: 3.11
+	Programming Language :: Python :: 3.12
 	Programming Language :: Python :: Implementation :: CPython
 
 [options]
@@ -35,9 +35,9 @@ include_package_data = True
 # Note we don't yet support TensorFlow 2.16 or later due to Keras 3,
 # which does not support complex datatypes.
 install_requires = 
-	tensorflow>=2.4,<2.16; sys_platform != 'darwin' or platform.machine != 'arm64'
+	tensorflow>=2.4,<=2.18; sys_platform != 'darwin' or platform.machine != 'arm64'
 	# TF 2.13 finally publishes ARM wheels:
-	tensorflow>=2.13,<2.16; sys_platform == 'darwin' and platform.machine == 'arm64'
+	tensorflow>=2.13,<=2.18; sys_platform == 'darwin' and platform.machine == 'arm64'
 	types-protobuf
 	numpy
 	typing_extensions
@@ -61,9 +61,9 @@ test =
 	nnaudio
 	numpy>=1.22
 	librosa>=0.9
-	tensorflow>=2.4,<2.16; sys_platform != 'darwin' or platform.machine != 'arm64'
+	tensorflow>=2.4,<=2.18; sys_platform != 'darwin' or platform.machine != 'arm64'
 	# TF 2.13 finally publishes ARM wheels:
-	tensorflow>=2.13,<2.16; sys_platform == 'darwin' and platform.machine == 'arm64'
+	tensorflow>=2.13,<=2.18; sys_platform == 'darwin' and platform.machine == 'arm64'
 
 [bumpversion:file:realbook/__init__.py]
 

tests/layers/test_compatibility.py

@@ -17,24 +17,25 @@
 
 import os
 import tempfile
+from contextlib import contextmanager
 from typing import Iterator, List, Tuple
-import tensorflow as tf
-from tensorflow.keras.layers import Dense, Concatenate
+
 import numpy as np
 import pytest
-from contextlib import contextmanager
-from tensorflow.python.framework.importer import _IsControlInput as is_control_input
+import tensorflow as tf
+from tensorflow.keras.layers import Concatenate, Dense
+from tensorflow.lite.python.util import get_grappler_config, run_graph_optimizations
 from tensorflow.python.framework.convert_to_constants import (
     convert_variables_to_constants_v2_as_graph,
 )
-from tensorflow.lite.python.util import run_graph_optimizations, get_grappler_config
+from tensorflow.python.framework.importer import _IsControlInput as is_control_input
 
 from realbook.layers.compatibility import (
     FrozenGraphLayer,
     SavedModelLayer,
-    get_all_tensors_from_saved_model,
-    create_function_from_tensors,
     TensorWrapperLayer,
+    create_function_from_tensors,
+    get_all_tensors_from_saved_model,
 )
 
 NUM_INPUT_VALUES = 10
@@ -70,7 +71,7 @@ def train_addition_model() -> tf.keras.models.Model:
     # Learn a model that sums numbers:
     model = tf.keras.Sequential(
         [
-            tf.keras.layers.InputLayer(NUM_INPUT_VALUES),
+            tf.keras.layers.InputLayer((NUM_INPUT_VALUES,)),
             tf.keras.layers.Dense(1, name="my_layer"),
         ]
     )
@@ -83,7 +84,7 @@ def train_addition_model() -> tf.keras.models.Model:
 
 def train_named_input_and_output() -> tf.keras.models.Model:
     # Learn a model that sums numbers:
-    inputs = {"named_input": tf.keras.layers.Input(NUM_INPUT_VALUES)}
+    inputs = {"named_input": tf.keras.layers.Input((NUM_INPUT_VALUES,))}
     outputs = {"named_output": tf.keras.layers.Dense(1)(inputs["named_input"])}
     model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
     model.compile(loss="MSE")
@@ -97,7 +98,7 @@ def train_multi_input_multi_output() -> tf.keras.models.Model:
     """Train a dummy model to add numbers, but with multiple inputs and outputs: outAB = inA + inB, etc."""
 
     input_names = ["inA", "inB", "inC"]
-    inputs = {input_name: tf.keras.layers.Input(1) for input_name in input_names}
+    inputs = {input_name: tf.keras.layers.Input((1,)) for input_name in input_names}
 
     output_names = ["outAB", "outBC", "outAC"]
     concatenated = Concatenate()([v for _, v in sorted(inputs.items(), key=lambda t: t[0])])
@@ -177,7 +178,7 @@ def test_reserialize_model() -> None:
 
         from_frozen = tf.keras.Sequential(
             [
-                tf.keras.layers.InputLayer(NUM_INPUT_VALUES),
+                tf.keras.layers.InputLayer((NUM_INPUT_VALUES,)),
                 FrozenGraphLayer(
                     f.name,
                     input_tensor_names=[t.name for t in input_tensors],

tests/layers/test_nnaudio.py

@@ -15,16 +15,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import tensorflow as tf
-import torch
+import platform
+
 import numpy as np
 import pytest
-import platform
+import tensorflow as tf
+import torch
 
 try:
     import librosa
-    from realbook.layers import nnaudio as our_nnaudio
     from nnAudio.Spectrogram import CQT2010v2
+
+    from realbook.layers import nnaudio as our_nnaudio
 except ImportError as e:
     if "numpy.core.multiarray failed to import" in str(e) and platform.system() == "Windows":
         librosa = None
@@ -35,7 +37,6 @@
 
 from typing import Tuple, Union
 
-
 TEST_SAMPLE_RATE = 22050
 
 

tests/layers/test_signal.py

@@ -15,9 +15,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Optional, Union, List
-
 import platform
+from typing import List, Optional, Union
+
 import numpy as np
 import pytest
 import tensorflow as tf

tox.ini

@@ -1,5 +1,5 @@
 [tox]
-envlist = py37,py38,py39,py310,manifest,check-formatting,lint,mypy
+envlist = py39,py310,py311,py312,manifest,check-formatting,lint,mypy
 skipsdist = True
 usedevelop = True
 
@@ -18,27 +18,27 @@ skip_install = true
 commands = check-manifest --ignore 'tests/*'
 
 [testenv:check-formatting]
-basepython = python3.8
+basepython = python3.9
 deps = black
 skip_install = true
 commands =
     black {env:SOURCE} tests --line-length {env:LINE_LENGTH} --diff --check
 
 [testenv:format]
-basepython = python3.8
+basepython = python3.9
 deps = black
 skip_install = true
 commands =
     black {env:SOURCE} tests --line-length {env:LINE_LENGTH}
 
 [testenv:lint]
-basepython = python3.8
+basepython = python3.9
 deps = flake8
 skip_install = true
 commands = flake8
 
 [testenv:mypy]
-basepython = python3.8
+basepython = python3.9
 deps =
     mypy
     types-protobuf