Adding compatibility with tensorflow2

.github/workflows/main.yml

@@ -3,7 +3,7 @@
 
 name: Python package
 
-on: [push, pull_request]
+on: [push]
 
 jobs:
   build:
@@ -22,11 +22,11 @@ jobs:
     - name: Install dependencies
       run: |
         sudo apt-get install -y wget libsndfile-dev sox
-        python -m pip install "tensorflow<1.14"
         python -m pip install flake8
         python -m pip install 'h5py==2.10.0' --force-reinstall
     - name: Install library
       run: |
+        python -m pip install .[keras_tf]  
         python -m pip install .[tests]        
     - name: Lint with flake8
       run: |
@@ -48,4 +48,28 @@ jobs:
         name: codecov-umbrella
         fail_ci_if_error: false
         path_to_write_report: ./coverage/codecov_report.txt
-        verbose: true
+        verbose: true
+
+  build_tf2:
+
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.6, 3.7]
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        sudo apt-get install -y wget libsndfile-dev sox
+    - name: Install library
+      run: |
+        python -m pip install .[tf2]
+        python -m pip install .[tests]        
+    - name: Test with pytest
+      run: |
+        pytest --cov-report term-missing --cov-report=xml --cov dcase_models ./tests

README.md

@@ -7,6 +7,8 @@
 
 </pre>
 
+![example workflow](https://github.com/MTG/DCASE-models/actions/workflows/main.yml/badge.svg)
+[![codecov](https://codecov.io/gh/MTG/DCASE-models/branch/master/graph/badge.svg?token=xOOVldiH0J)](https://codecov.io/gh/MTG/DCASE-models)
 [![PyPI](https://img.shields.io/pypi/v/DCASE-models)](https://pypi.org/project/DCASE-models/)
 [![GitHub license](https://img.shields.io/github/license/pzinemanas/DCASE-models)](https://github.com/pzinemanas/DCASE-models/blob/master/LICENSE)
 
@@ -31,16 +33,13 @@ DCASE-models uses [SoX](http://sox.sourceforge.net/) for functions related to th
 ```
 conda install -c conda-forge sox
 ```
-Before installing the library, you must install only one of the Tensorflow variants: CPU-only or GPU.
+When installing the library, you must select the tensorflow variant: version 1 (CPU-only or GPU) or version 2.
 ``` 
-pip install "tensorflow<1.14" # for CPU-only version
-pip install "tensorflow-gpu<1.14" # for GPU version
+pip install DCASE-models[keras_tf] # for tensorflow 1 CPU-only version
+pip install DCASE-models[keras_tf_gpu] # for tensorflow 1 GPU version
+pip install DCASE-models[tf2] # for tensorflow 2
 ```
 
-Then to install the package:
-```
-pip install DCASE-models
-```
 To include visualization related dependencies, run the following instead:
 ```
 pip install DCASE-models[visualization]

dcase_models/data/data_generator.py

@@ -3,7 +3,13 @@
 import inspect
 import random
 
-from keras.utils import Sequence
+import tensorflow as tf
+tensorflow2 = tf.__version__.split('.')[0] == '2'
+
+if tensorflow2:
+    from tensorflow.keras.utils import Sequence
+else:
+    from keras.utils import Sequence
 
 from dcase_models.data.feature_extractor import FeatureExtractor
 from dcase_models.data.dataset_base import Dataset

dcase_models/data/features.py

@@ -1,6 +1,5 @@
 import numpy as np
 import librosa
-import openl3
 import inspect
 import sys
 
@@ -415,14 +414,19 @@ def __init__(self, sequence_time=1.0, sequence_hop_time=0.5,
                          sequence_hop_time=sequence_hop_time,
                          audio_win=audio_win, audio_hop=audio_hop,
                          sr=sr)
-
+        import tensorflow as tf
+        tensorflow2 = tf.__version__.split('.')[0] == '2'
+        if tensorflow2:
+            raise ImportError("Openl3 requires tensorflow1")
+        import openl3
         self.content_type = content_type
         self.input_repr = input_repr
         self.embedding_size = embedding_size
         self.openl3 = openl3.models.load_audio_embedding_model(
             input_repr, content_type, embedding_size)
 
     def calculate(self, file_name):
+        import openl3
         audio = self.load_audio(file_name, change_sampling_rate=False)
         emb, ts = openl3.get_audio_embedding(
             audio, self.sr,

dcase_models/model/container.py

@@ -2,10 +2,19 @@
 import os
 import json
 
-import keras.backend as K
-from keras.callbacks import CSVLogger, ModelCheckpoint
-from keras.models import model_from_json, Model
-from keras.layers import Dense, Input
+import tensorflow as tf
+tensorflow2 = tf.__version__.split('.')[0] == '2'
+
+if tensorflow2:
+    import tensorflow.keras.backend as K
+    from tensorflow.keras.callbacks import CSVLogger, ModelCheckpoint
+    from tensorflow.keras.models import model_from_json, Model
+    from tensorflow.keras.layers import Dense, Input
+else:
+    import keras.backend as K
+    from keras.callbacks import CSVLogger, ModelCheckpoint
+    from keras.models import model_from_json, Model
+    from keras.layers import Dense, Input 
 
 from dcase_models.util.files import save_json
 from dcase_models.util.metrics import evaluate_metrics
@@ -197,7 +206,10 @@ def train(self, data_train, data_val, weights_path='./',
             Verbose mode for fit method of Keras model
 
         """
-        import keras.optimizers as optimizers
+        if tensorflow2:
+            import tensorflow.keras.optimizers as optimizers
+        else:
+            import keras.optimizers as optimizers
         optimizer_function = getattr(optimizers, optimizer)
         opt = optimizer_function(lr=learning_rate)
 

dcase_models/model/models.py

@@ -3,19 +3,37 @@
 import sys
 import os
 
-from keras.layers import GRU, Bidirectional
-from keras.layers import TimeDistributed, Activation, Reshape
-from keras.layers import GlobalAveragePooling2D
-from keras.layers import GlobalMaxPooling2D
-from keras.layers import Input, Lambda, Conv2D, MaxPooling2D
-from keras.layers import Conv1D
-from keras.layers import Dropout, Dense, Flatten
-from keras.layers import BatchNormalization
-from keras.layers import Layer
-from keras.models import Model
-from keras.regularizers import l2
-import keras.backend as K
-from autopool import AutoPool1D
+import tensorflow as tf
+tensorflow2 = tf.__version__.split('.')[0] == '2'
+
+if tensorflow2:
+    from tensorflow.keras.layers import GRU, Bidirectional
+    from tensorflow.keras.layers import TimeDistributed, Activation, Reshape
+    from tensorflow.keras.layers import GlobalAveragePooling2D
+    from tensorflow.keras.layers import GlobalMaxPooling2D
+    from tensorflow.keras.layers import Input, Lambda, Conv2D, MaxPooling2D
+    from tensorflow.keras.layers import Conv1D
+    from tensorflow.keras.layers import Dropout, Dense, Flatten
+    from tensorflow.keras.layers import BatchNormalization
+    from tensorflow.keras.layers import Layer
+    from tensorflow.keras.models import Model
+    from tensorflow.keras.regularizers import l2
+    import tensorflow.keras.backend as K
+else:
+    from keras.layers import GRU, Bidirectional
+    from keras.layers import TimeDistributed, Activation, Reshape
+    from keras.layers import GlobalAveragePooling2D
+    from keras.layers import GlobalMaxPooling2D
+    from keras.layers import Input, Lambda, Conv2D, MaxPooling2D
+    from keras.layers import Conv1D
+    from keras.layers import Dropout, Dense, Flatten
+    from keras.layers import BatchNormalization
+    from keras.layers import Layer
+    from keras.models import Model
+    from keras.regularizers import l2
+    import keras.backend as K
+
+
 from tensorflow import clip_by_value
 
 from dcase_models.model.container import KerasModelContainer
@@ -167,6 +185,10 @@ def build(self):
             elif self.temporal_integration == 'sum':
                 y = Lambda(lambda x: K.sum(x, 1), name='temporal_integration')(y)
             elif self.temporal_integration == 'autopool':
+                try:
+                    from autopool import AutoPool1D
+                except:
+                    raise ImportError("Autopool is not installed")
                 y = AutoPool1D(axis=1, name='output')(y)
 
         # Create model

dcase_models/util/callbacks.py

@@ -2,7 +2,14 @@
 """Callback functions"""
 
 from dcase_models.util.metrics import evaluate_metrics
-from keras.callbacks import Callback
+
+import tensorflow as tf
+tensorflow2 = tf.__version__.split('.')[0] == '2'
+
+if tensorflow2:
+    from tensorflow.keras.callbacks import Callback
+else:
+    from keras.callbacks import Callback
 
 eps = 1e-6
 

setup.py

@@ -19,22 +19,22 @@
     download_url='http://github.com/pzinemanas/DCASE-models/releases',
     packages=setuptools.find_packages(),
     install_requires=[
-        'numpy==1.17.4',
-        'pandas==0.25.3',
-        'SoundFile==0.10.3.post1',
-        'PyYAML==5.2',
-        'numba==0.48.0',
-        'librosa==0.7.1',
-        'openl3==0.3.1',
-        'scikit-learn==0.22.1',
-        'keras==2.2.4',
-       # 'tensorflow-gpu==1.12.0',
-        'autopool==0.1.0',
-        'wget==3.2',
-        'sox==1.3.7',
-        'sed_eval==0.2.1',
+        'numpy>=1.1',
+        'pandas>=0.25',
+        'SoundFile>=0.1',
+        'PyYAML>=5.0',
+        'librosa>=0.7',
+        'scikit-learn>=0.20',
+        'wget>=3.0',
+        'sox<1.4',
+        'sed_eval>=0.2',
     ],
     extras_require={
+        'keras_tf': ['tensorflow<1.14', 'keras==2.2.4'],
+        'keras_tf_gpu': ['tensorflow-gpu<1.14', 'keras==2.2.4'],
+        'tf2': ['tensorflow>2.0'],
+        'openl3': ['openl3==0.3.1'],
+        'autopool': ['autopool==0.1.0'],
         'docs': ['numpydoc', 'sphinx!=1.3.1', 'sphinx_rtd_theme'],
         'tests': ['pytest >= 5.4.3', 'pytest-cov >= 2.11.1'],
         'visualization': [

tests/test_container.py

@@ -1,8 +1,16 @@
 from dcase_models.model.container import ModelContainer, KerasModelContainer
 from dcase_models.data.data_generator import KerasDataGenerator
 
-from keras.layers import Input, Dense
-from keras.models import Model
+import tensorflow as tf
+
+tensorflow2 = tf.__version__.split(".")[0] == "2"
+
+if tensorflow2:
+    from tensorflow.keras.layers import Input, Dense
+    from tensorflow.keras.models import Model
+else:
+    from keras.layers import Input, Dense
+    from keras.models import Model
 
 import os
 import numpy as np

tests/test_features.py

@@ -5,12 +5,22 @@
 from dcase_models.data.data_generator import DataGenerator
 from dcase_models.util.files import load_json, mkdir_if_not_exists
 
+
+try:
+    import openl3
+except:
+    Openl3 = None
+
 import os
 import numpy as np
 import pytest
 import shutil
 import librosa
 
+import tensorflow as tf
+
+tensorflow2 = tf.__version__.split(".")[0] == "2"
+
 
 def test_spectrogram():
     feature_extractor = Spectrogram(pad_mode="constant")
@@ -60,6 +70,8 @@ def test_mfcc():
     assert shape == (1, 32, 20)
 
 
+@pytest.mark.skipif(tensorflow2, reason="Openl3 requires tensorflow1")
+@pytest.mark.skipif(Openl3 is None, reason="Openl3 is not installed")
 def test_openl3():
     feature_extractor = Openl3()
     shape = feature_extractor.get_shape(2.0)

tests/test_models.py

@@ -16,6 +16,14 @@
 import os
 import numpy as np
 import pytest
+import tensorflow as tf
+
+tensorflow2 = tf.__version__.split(".")[0] == "2"
+
+try:
+    import autopool
+except:
+    autopool = None
 
 
 def test_mlp():
@@ -37,11 +45,12 @@ def test_mlp():
     outputs = model_container.model.predict(inputs)
     assert outputs.shape == (3, 10)
 
-    model_container = MLP(temporal_integration="autopool")
-    assert len(model_container.model.layers) == 7
-    inputs = np.zeros((3, 64, 12))
-    outputs = model_container.model.predict(inputs)
-    assert outputs.shape == (3, 10)
+    if (not tensorflow2) & (autopool is not None):
+        model_container = MLP(temporal_integration="autopool")
+        assert len(model_container.model.layers) == 7
+        inputs = np.zeros((3, 64, 12))
+        outputs = model_container.model.predict(inputs)
+        assert outputs.shape == (3, 10)
 
 
 def test_sb_cnn():