Made VGGish and YAMNet work in TF2 without disabling TF2 behavior. (#9077)

* Made VGGish and YAMNet work in TF2 without disabling TF2 behavior.

Allowed TF2 behavior and allowed passing in a features tensor into the
VGGish model definition. Both of these changes are needed for making
TF-Hub exports of these models. Lifted constraints on TF versions since
tf_slim has been updated to work with TF 2.

* Responded to DAn's comments in https://github.com/tensorflow/models/pull/9077

* Fixed typo in comment.

research/audioset/vggish/README.md

@@ -16,17 +16,14 @@ VGGish depends on the following Python packages:
 
 * [`numpy`](http://www.numpy.org/)
 * [`resampy`](http://resampy.readthedocs.io/en/latest/)
-* [`tensorflow`](http://www.tensorflow.org/) (currently, only TF v1.x)
+* [`tensorflow`](http://www.tensorflow.org/)
 * [`tf_slim`](https://github.com/google-research/tf-slim)
 * [`six`](https://pythonhosted.org/six/)
 * [`soundfile`](https://pysoundfile.readthedocs.io/)
 
 These are all easily installable via, e.g., `pip install numpy` (as in the
-sample installation session below).
-
-Any reasonably recent version of these packages shold work. Note that we currently only support
-TensorFlow v1.x due to a [`tf_slim` limitation](https://github.com/google-research/tf-slim/pull/1).
-TensorFlow v1.15 (the latest version as of Jan 2020) has been tested to work.
+sample installation session below). Any reasonably recent version of these
+packages shold work.
 
 VGGish also requires downloading two data files:
 
@@ -60,7 +57,7 @@ Here's a sample installation and test session:
 $ sudo python -m pip install --upgrade pip wheel
 
 # Install all dependences.
-$ sudo pip install numpy resampy tensorflow==1.15 tf_slim six soundfile
+$ sudo pip install numpy resampy tensorflow tf_slim six soundfile
 
 # Clone TensorFlow models repo into a 'models' directory.
 $ git clone https://github.com/tensorflow/models.git

research/audioset/vggish/vggish_inference_demo.py

@@ -50,7 +50,6 @@ import numpy as np
 import six
 import soundfile
 import tensorflow.compat.v1 as tf
-tf.disable_v2_behavior()
 
 import vggish_input
 import vggish_params

research/audioset/vggish/vggish_slim.py

@@ -31,28 +31,31 @@ https://github.com/tensorflow/models/blob/master/research/slim/nets/vgg.py
 """
 
 import tensorflow.compat.v1 as tf
-tf.disable_v2_behavior()
 import tf_slim as slim
 
 import vggish_params as params
 
 
-def define_vggish_slim(training=False):
+def define_vggish_slim(features_tensor=None, training=False):
   """Defines the VGGish TensorFlow model.
 
   All ops are created in the current default graph, under the scope 'vggish/'.
 
-  The input is a placeholder named 'vggish/input_features' of type float32 and
-  shape [batch_size, num_frames, num_bands] where batch_size is variable and
-  num_frames and num_bands are constants, and [num_frames, num_bands] represents
-  a log-mel-scale spectrogram patch covering num_bands frequency bands and
-  num_frames time frames (where each frame step is usually 10ms). This is
-  produced by computing the stabilized log(mel-spectrogram + params.LOG_OFFSET).
-  The output is an op named 'vggish/embedding' which produces the activations of
-  a 128-D embedding layer, which is usually the penultimate layer when used as
-  part of a full model with a final classifier layer.
+  The input is either a tensor passed in via the optional 'features_tensor'
+  argument or a placeholder created below named 'vggish/input_features'. The
+  input is expected to have dtype float32 and shape [batch_size, num_frames,
+  num_bands] where batch_size is variable and num_frames and num_bands are
+  constants, and [num_frames, num_bands] represents a log-mel-scale spectrogram
+  patch covering num_bands frequency bands and num_frames time frames (where
+  each frame step is usually 10ms). This is produced by computing the stabilized
+  log(mel-spectrogram + params.LOG_OFFSET).  The output is a tensor named
+  'vggish/embedding' which produces the activations of a 128-D embedding layer,
+  which is usually the penultimate layer when used as part of a full model with
+  a final classifier layer.
 
   Args:
+    features_tensor: If not None, the tensor containing the input features.
+      If None, a placeholder input is created.
     training: If true, all parameters are marked trainable.
 
   Returns:
@@ -76,11 +79,13 @@ def define_vggish_slim(training=False):
                       kernel_size=[2, 2], stride=2, padding='SAME'), \
        tf.variable_scope('vggish'):
     # Input: a batch of 2-D log-mel-spectrogram patches.
-    features = tf.placeholder(
+    if features_tensor is None:
+      features_tensor = tf.placeholder(
           tf.float32, shape=(None, params.NUM_FRAMES, params.NUM_BANDS),
           name='input_features')
     # Reshape to 4-D so that we can convolve a batch with conv2d().
-    net = tf.reshape(features, [-1, params.NUM_FRAMES, params.NUM_BANDS, 1])
+    net = tf.reshape(features_tensor,
+                     [-1, params.NUM_FRAMES, params.NUM_BANDS, 1])
 
     # The VGG stack of alternating convolutions and max-pools.
     net = slim.conv2d(net, 64, scope='conv1')

research/audioset/vggish/vggish_smoke_test.py

@@ -33,7 +33,6 @@ from __future__ import print_function
 
 import numpy as np
 import tensorflow.compat.v1 as tf
-tf.disable_v2_behavior()
 
 import vggish_input
 import vggish_params

research/audioset/vggish/vggish_train_demo.py

@@ -49,7 +49,6 @@ from random import shuffle
 
 import numpy as np
 import tensorflow.compat.v1 as tf
-tf.disable_v2_behavior()
 import tf_slim as slim
 
 import vggish_input
@@ -129,7 +128,7 @@ def _get_examples_batch():
 def main(_):
   with tf.Graph().as_default(), tf.Session() as sess:
     # Define VGGish.
-    embeddings = vggish_slim.define_vggish_slim(FLAGS.train_vggish)
+    embeddings = vggish_slim.define_vggish_slim(training=FLAGS.train_vggish)
 
     # Define a shallow classification model and associated training ops on top
     # of VGGish.

research/audioset/yamnet/README.md

@@ -18,12 +18,8 @@ YAMNet depends on the following Python packages:
 * [`pysoundfile`](https://pysoundfile.readthedocs.io/)
 
 These are all easily installable via, e.g., `pip install numpy` (as in the
-example command sequence below).
-
-Any reasonably recent version of these packages should work. TensorFlow should
-be at least version 1.8 to ensure Keras support is included. Note that while
-the code works fine with TensorFlow v1.x or v2.x, we explicitly enable v1.x
-behavior.
+example command sequence below). Any reasonably recent version of these
+packages should work.
 
 YAMNet also requires downloading the following data file: