Merge branch 'master' of https://github.com/tensorflow/models into UpdateReadme

CODEOWNERS

+adversarial_crypto/* @dave-andersen
+adversarial_text/* @rsepassi
+attention_ocr/* @alexgorban
+audioset/* @plakal @dpwe
+autoencoders/* @snurkabill
+cognitive_mapping_and_planning/* @s-gupta
+compression/* @nmjohn
+differential_privacy/* @panyx0718
+domain_adaptation/* @bousmalis @ddohan
+im2txt/* @cshallue
+inception/* @shlens @vincentvanhoucke
+learning_to_remember_rare_events/* @lukaszkaiser @ofirnachum
+lfads/* @jazcollins @susillo
+lm_1b/* @oriolvinyals @panyx0718
+namignizer/* @knathanieltucker
+neural_gpu/* @lukaszkaiser
+neural_programmer/* @arvind2505
+next_frame_prediction/* @panyx0718
+object_detection/* @jch1 @tombstone @derekjchow @jesu9 @dreamdragon
+pcl_rl/* @ofirnachum
+ptn/* @xcyan @arkanath @hellojas @honglaklee
+real_nvp/* @laurent-dinh
+rebar/* @gjtucker
+resnet/* @panyx0718
+skip_thoughts/* @cshallue
+slim/* @sguada @nathansilberman
+street/* @theraysmith
+swivel/* @waterson
+syntaxnet/* @calberti @andorardo
+textsum/* @panyx0718 @peterjliu
+transformer/* @daviddao
+tutorials/embedding/* @zffchen78 @a-dai
+tutorials/image/* @sherrym @shlens
+tutorials/rnn/* @lukaszkaiser @ebrevdo
+video_prediction/* @cbfinn
+

CONTRIBUTING.md

 # Contributing guidelines
 
 If you have created a model and would like to publish it here, please send us a
-pull request. For those just getting started with pull reuests, GitHub has a
+pull request. For those just getting started with pull requests, GitHub has a
 [howto](https://help.github.com/articles/using-pull-requests/).
 
 The code for any model in this repository is licensed under the Apache License

README.md

@@ -14,6 +14,7 @@ running TensorFlow 0.12 or earlier, please
 - [adversarial_crypto](adversarial_crypto): protecting communications with adversarial neural cryptography.
 - [adversarial_text](adversarial_text): semi-supervised sequence learning with adversarial training.
 - [attention_ocr](attention_ocr): a model for real-world image text extraction.
+- [audioset](audioset): Models and supporting code for use with [AudioSet](http://g.co.audioset).
 - [autoencoder](autoencoder): various autoencoders.
 - [cognitive_mapping_and_planning](cognitive_mapping_and_planning): implementation of a spatial memory based mapping and planning architecture for visual navigation.
 - [compression](compression): compressing and decompressing images using a pre-trained Residual GRU network.
@@ -30,6 +31,7 @@ running TensorFlow 0.12 or earlier, please
 - [next_frame_prediction](next_frame_prediction): probabilistic future frame synthesis via cross convolutional networks.
 - [object_detection](object_detection): localizing and identifying multiple objects in a single image.
 - [real_nvp](real_nvp): density estimation using real-valued non-volume preserving (real NVP) transformations.
+- [rebar](rebar): low-variance, unbiased gradient estimates for discrete latent variable models.
 - [resnet](resnet): deep and wide residual networks.
 - [skip_thoughts](skip_thoughts): recurrent neural network sentence-to-vector encoder.
 - [slim](slim): image classification models in TF-Slim.

attention_ocr/README.md

@@ -28,7 +28,7 @@ Pull requests:
 virtualenv --system-site-packages ~/.tensorflow
 source ~/.tensorflow/bin/activate
 pip install --upgrade pip
-pip install --upgrade tensorflow_gpu
+pip install --upgrade tensorflow-gpu
 ```
 
 2. At least 158GB of free disk space to download the FSNS dataset:
@@ -65,7 +65,7 @@ To train a model using pre-trained Inception weights as initialization:
 ```
 wget http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz
 tar xf inception_v3_2016_08_28.tar.gz
-python train.py --checkpoint_inception=inception_v3.ckpt
+python train.py --checkpoint_inception=./inception_v3.ckpt
 ```
 
 To fine tune the Attention OCR model using a checkpoint:

audioset/README.md

+# Models for AudioSet: A Large Scale Dataset of Audio Events
+
+This repository provides models and supporting code associated with
+[AudioSet](http://g.co/audioset), a dataset of over 2 million human-labeled
+10-second YouTube video soundtracks, with labels taken from an ontology of
+more than 600 audio event classes.
+
+AudioSet was
+[released](https://research.googleblog.com/2017/03/announcing-audioset-dataset-for-audio.html)
+in March 2017 by Google's Sound Understanding team to provide a common
+large-scale evaluation task for audio event detection as well as a starting
+point for a comprehensive vocabulary of sound events.
+
+For more details about AudioSet and the various models we have trained, please
+visit the [AudioSet website](http://g.co/audioset) and read our papers:
+
+* Gemmeke, J. et. al.,
+  [AudioSet: An ontology and human-labelled dataset for audio events](https://research.google.com/pubs/pub45857.html),
+  ICASSP 2017
+
+* Hershey, S. et. al.,
+  [CNN Architectures for Large-Scale Audio Classification](https://research.google.com/pubs/pub45611.html),
+  ICASSP 2017
+
+If you use the pre-trained VGGish model in your published research, we ask that
+you cite [CNN Architectures for Large-Scale Audio Classification](https://research.google.com/pubs/pub45611.html).
+If you use the AudioSet dataset or the released 128-D embeddings of AudioSet
+segments, please cite
+[AudioSet: An ontology and human-labelled dataset for audio events](https://research.google.com/pubs/pub45857.html).
+
+## VGGish
+
+The initial AudioSet release included 128-dimensional embeddings of each
+AudioSet segment produced from a VGG-like audio classification model that was
+trained on a large YouTube dataset (a preliminary version of what later became
+[YouTube-8M](https://research.google.com/youtube8m)).
+
+We provide a TensorFlow definition of this model, which we call __*VGGish*__, as
+well as supporting code to extract input features for the model from audio
+waveforms and to post-process the model embedding output into the same format as
+the released embedding features.
+
+### Installation
+
+VGGish depends on the following Python packages:
+
+* [`numpy`](http://www.numpy.org/)
+* [`scipy`](http://www.scipy.org/)
+* [`resampy`](http://resampy.readthedocs.io/en/latest/)
+* [`tensorflow`](http://www.tensorflow.org/)
+* [`six`](https://pythonhosted.org/six/)
+
+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 1.0.  We have tested with Python 2.7.6 and 3.4.3 on an
+Ubuntu-like system with NumPy v1.13.1, SciPy v0.19.1, resampy v0.1.5, TensorFlow
+v1.2.1, and Six v1.10.0.
+
+VGGish also requires downloading two data files:
+
+* [VGGish model checkpoint](https://storage.googleapis.com/audioset/vggish_model.ckpt),
+  in TensorFlow checkpoint format.
+* [Embedding PCA parameters](https://storage.googleapis.com/audioset/vggish_pca_params.npz),
+  in NumPy compressed archive format.
+
+After downloading these files into the same directory as this README, the
+installation can be tested by running `python vggish_smoke_test.py` which
+runs a known signal through the model and checks the output.
+
+Here's a sample installation and test session:
+
+```shell
+# You can optionally install and test VGGish within a Python virtualenv, which
+# is useful for isolating changes from the rest of your system. For example, you
+# may have an existing version of some packages that you do not want to upgrade,
+# or you want to try Python 3 instead of Python 2. If you decide to use a
+# virtualenv, you can create one by running
+#   $ virtualenv vggish   # For Python 2
+# or
+#   $ python3 -m venv vggish # For Python 3
+# and then enter the virtual environment by running
+#   $ source vggish/bin/activate  # Assuming you use bash
+# Leave the virtual environment at the end of the session by running
+#   $ deactivate
+
+# Upgrade pip first.
+$ python -m pip install --upgrade pip
+
+# Install dependences. Resampy needs to be installed after NumPy and SciPy
+# are already installed.
+$ pip install numpy scipy
+$ pip install resampy tensorflow six
+
+# Clone TensorFlow models repo into a 'models' directory.
+$ git clone https://github.com/tensorflow/models.git
+$ cd models/audioset
+# Download data files into same directory as code.
+$ curl -O https://storage.googleapis.com/audioset/vggish_model.ckpt
+$ curl -O https://storage.googleapis.com/audioset/vggish_pca_params.npz
+
+# Installation ready, let's test it.
+$ python vggish_smoke_test.py
+# If we see "Looks Good To Me", then we're all set.
+```
+
+### Usage
+
+VGGish can be used in two ways:
+
+* *As a feature extractor*: VGGish converts audio input features into a
+  semantically meaningful, high-level 128-D embedding which can be fed as input
+  to a downstream classification model. The downstream model can be shallower
+  than usual because the VGGish embedding is more semantically compact than raw
+  audio features.
+
+  So, for example, you could train a classifier for 10 of the AudioSet classes
+  by using the released embeddings as features.  Then, you could use that
+  trained classifier with any arbitrary audio input by running the audio through
+  the audio feature extractor and VGGish model provided here, passing the
+  resulting embedding features as input to your trained model.
+  `vggish_inference_demo.py` shows how to produce VGGish embeddings from
+  arbitrary audio.
+
+* *As part of a larger model*: Here, we treat VGGish as a "warm start" for the
+  lower layers of a model that takes audio features as input and adds more
+  layers on top of the VGGish embedding. This can be used to fine-tune VGGish
+  (or parts thereof) if you have large datasets that might be very different
+  from the typical YouTube video clip. `vggish_train_demo.py` shows how to add
+  layers on top of VGGish and train the whole model.
+
+### About the Model
+
+The VGGish code layout is as follows:
+
+* `vggish_slim.py`: Model definition in TensorFlow Slim notation.
+* `vggish_params.py`: Hyperparameters.
+* `vggish_input.py`: Converter from audio waveform into input examples.
+* `mel_features.py`: Audio feature extraction helpers.
+* `vggish_postprocess.py`: Embedding postprocessing.
+* `vggish_inference_demo.py`: Demo of VGGish in inference mode.
+* `vggish_train_demo.py`: Demo of VGGish in training mode.
+* `vggish_smoke_test.py`: Simple test of a VGGish installation
+
+#### Architecture
+
+See `vggish_slim.py` and `vggish_params.py`.
+
+VGGish is a variant of the [VGG](https://arxiv.org/abs/1409.1556) model, in
+particular Configuration A with 11 weight layers. Specifically, here are the
+changes we made:
+
+* The input size was changed to 96x64 for log mel spectrogram audio inputs.
+
+* We drop the last group of convolutional and maxpool layers, so we now have
+  only four groups of convolution/maxpool layers instead of five.
+
+* Instead of a 1000-wide fully connected layer at the end, we use a 128-wide
+  fully connected layer. This acts as a compact embedding layer.
+
+The model definition provided here defines layers up to and including the
+128-wide embedding layer.
+
+#### Input: Audio Features
+
+See `vggish_input.py` and `mel_features.py`.
+
+VGGish was trained with audio features computed as follows:
+
+* All audio is resampled to 16 kHz mono.
+* A spectrogram is computed using magnitudes of the Short-Time Fourier Transform
+  with a window size of 25 ms, a window hop of 10 ms, and a periodic Hann
+  window.
+* A mel spectrogram is computed by mapping the spectrogram to 64 mel bins
+  covering the range 125-7500 Hz.
+* A stabilized log mel spectrogram is computed by applying
+  log(mel-spectrum + 0.01) where the offset is used to avoid taking a logarithm
+  of zero.
+* These features are then framed into non-overlapping examples of 0.96 seconds,
+  where each example covers 64 mel bands and 96 frames of 10 ms each.
+
+We provide our own NumPy implementation that produces features that are very
+similar to those produced by our internal production code. This results in
+embedding outputs that are closely match the embeddings that we have already
+released. Note that these embeddings will *not* be bit-for-bit identical to the
+released embeddings due to small differences between the feature computation
+code paths, and even between two different installations of VGGish with
+different underlying libraries and hardware. However, we expect that the
+embeddings will be equivalent in the context of a downstream classification
+task.
+
+#### Output: Embeddings
+
+See `vggish_postprocess.py`.
+
+The released AudioSet embeddings were postprocessed before release by applying a
+PCA transformation (which performs both PCA and whitening) as well as
+quantization to 8 bits per embedding element. This was done to be compatible
+with the [YouTube-8M](https://research.google.com/youtube8m) project which has
+released visual and audio embeddings for millions of YouTube videos in the same
+PCA/whitened/quantized format.
+
+We provide a Python implementation of the postprocessing which can be applied to
+batches of embeddings produced by VGGish. `vggish_inference_demo.py` shows how
+the postprocessor can be run after inference.
+
+If you don't need to use the released embeddings or YouTube-8M, then you could
+skip postprocessing and use raw embeddings.
+
+### Future Work
+
+Below are some of the things we would like to add to this repository.  We
+welcome pull requests for these or other enhancements, but please consider
+sending an email to the mailing list (see the Contact section) describing what
+you plan to do before you invest a lot of time, to get feedback from us and the
+rest of the community.
+
+* An AudioSet classifier trained on top of the VGGish embeddings to predict all
+  the AudioSet labels. This can act as a baseline for audio research using
+  AudioSet.
+* Feature extraction implemented within TensorFlow using the upcoming
+  [tf.contrib.signal](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/docs_src/api_guides/python/contrib.signal.md)
+  ops.
+* A Keras version of the VGGish model definition and checkpoint.
+* Jupyter notebook demonstrating audio feature extraction and model performance.
+
+## Contact
+
+For general questions about AudioSet and VGGish, please use the
+[audioset-users@googlegroups.com](https://groups.google.com/forum/#!forum/audioset-users)
+mailing list.
+
+For technical problems with the released model and code, please open an issue on
+the [tensorflow/models issue tracker](https://github.com/tensorflow/models/issues)
+and __*assign to @plakal and @dpwe*__. Please note that because the issue tracker
+is shared across all models released by Google, we won't be notified about an
+issue unless you explicitly @-mention us (@plakal and @dpwe) or assign the issue
+to us.
+
+## Credits
+
+Original authors and reviewers of the code in this package include (in
+alphabetical order):
+
+* DAn Ellis
+* Shawn Hershey
+* Aren Jansen
+* Manoj Plakal

audioset/mel_features.py

+# Copyright 2017 The TensorFlow Authors All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Defines routines to compute mel spectrogram features from audio waveform."""
+
+import numpy as np
+
+
+def frame(data, window_length, hop_length):
+  """Convert array into a sequence of successive possibly overlapping frames.
+
+  An n-dimensional array of shape (num_samples, ...) is converted into an
+  (n+1)-D array of shape (num_frames, window_length, ...), where each frame
+  starts hop_length points after the preceding one.
+
+  This is accomplished using stride_tricks, so the original data is not
+  copied.  However, there is no zero-padding, so any incomplete frames at the
+  end are not included.
+
+  Args:
+    data: np.array of dimension N >= 1.
+    window_length: Number of samples in each frame.
+    hop_length: Advance (in samples) between each window.
+
+  Returns:
+    (N+1)-D np.array with as many rows as there are complete frames that can be
+    extracted.
+  """
+  num_samples = data.shape[0]
+  num_frames = 1 + int(np.floor((num_samples - window_length) / hop_length))
+  shape = (num_frames, window_length) + data.shape[1:]
+  strides = (data.strides[0] * hop_length,) + data.strides
+  return np.lib.stride_tricks.as_strided(data, shape=shape, strides=strides)
+
+
+def periodic_hann(window_length):
+  """Calculate a "periodic" Hann window.
+
+  The classic Hann window is defined as a raised cosine that starts and
+  ends on zero, and where every value appears twice, except the middle
+  point for an odd-length window.  Matlab calls this a "symmetric" window
+  and np.hanning() returns it.  However, for Fourier analysis, this
+  actually represents just over one cycle of a period N-1 cosine, and
+  thus is not compactly expressed on a length-N Fourier basis.  Instead,
+  it's better to use a raised cosine that ends just before the final
+  zero value - i.e. a complete cycle of a period-N cosine.  Matlab
+  calls this a "periodic" window. This routine calculates it.
+
+  Args:
+    window_length: The number of points in the returned window.
+
+  Returns:
+    A 1D np.array containing the periodic hann window.
+  """
+  return 0.5 - (0.5 * np.cos(2 * np.pi / window_length *
+                             np.arange(window_length)))
+
+
+def stft_magnitude(signal, fft_length,
+                   hop_length=None,
+                   window_length=None):
+  """Calculate the short-time Fourier transform magnitude.
+
+  Args:
+    signal: 1D np.array of the input time-domain signal.
+    fft_length: Size of the FFT to apply.
+    hop_length: Advance (in samples) between each frame passed to FFT.
+    window_length: Length of each block of samples to pass to FFT.
+
+  Returns:
+    2D np.array where each row contains the magnitudes of the fft_length/2+1
+    unique values of the FFT for the corresponding frame of input samples.
+  """
+  frames = frame(signal, window_length, hop_length)
+  # Apply frame window to each frame. We use a periodic Hann (cosine of period
+  # window_length) instead of the symmetric Hann of np.hanning (period
+  # window_length-1).
+  window = periodic_hann(window_length)
+  windowed_frames = frames * window
+  return np.abs(np.fft.rfft(windowed_frames, int(fft_length)))
+
+
+# Mel spectrum constants and functions.
+_MEL_BREAK_FREQUENCY_HERTZ = 700.0
+_MEL_HIGH_FREQUENCY_Q = 1127.0
+
+
+def hertz_to_mel(frequencies_hertz):
+  """Convert frequencies to mel scale using HTK formula.
+
+  Args:
+    frequencies_hertz: Scalar or np.array of frequencies in hertz.
+
+  Returns:
+    Object of same size as frequencies_hertz containing corresponding values
+    on the mel scale.
+  """
+  return _MEL_HIGH_FREQUENCY_Q * np.log(
+      1.0 + (frequencies_hertz / _MEL_BREAK_FREQUENCY_HERTZ))
+
+
+def spectrogram_to_mel_matrix(num_mel_bins=20,
+                              num_spectrogram_bins=129,
+                              audio_sample_rate=8000,
+                              lower_edge_hertz=125.0,
+                              upper_edge_hertz=3800.0):
+  """Return a matrix that can post-multiply spectrogram rows to make mel.
+
+  Returns a np.array matrix A that can be used to post-multiply a matrix S of
+  spectrogram values (STFT magnitudes) arranged as frames x bins to generate a
+  "mel spectrogram" M of frames x num_mel_bins.  M = S A.
+
+  The classic HTK algorithm exploits the complementarity of adjacent mel bands
+  to multiply each FFT bin by only one mel weight, then add it, with positive
+  and negative signs, to the two adjacent mel bands to which that bin
+  contributes.  Here, by expressing this operation as a matrix multiply, we go
+  from num_fft multiplies per frame (plus around 2*num_fft adds) to around
+  num_fft^2 multiplies and adds.  However, because these are all presumably
+  accomplished in a single call to np.dot(), it's not clear which approach is
+  faster in Python.  The matrix multiplication has the attraction of being more
+  general and flexible, and much easier to read.
+
+  Args:
+    num_mel_bins: How many bands in the resulting mel spectrum.  This is
+      the number of columns in the output matrix.
+    num_spectrogram_bins: How many bins there are in the source spectrogram
+      data, which is understood to be fft_size/2 + 1, i.e. the spectrogram
+      only contains the nonredundant FFT bins.
+    audio_sample_rate: Samples per second of the audio at the input to the
+      spectrogram. We need this to figure out the actual frequencies for
+      each spectrogram bin, which dictates how they are mapped into mel.
+    lower_edge_hertz: Lower bound on the frequencies to be included in the mel
+      spectrum.  This corresponds to the lower edge of the lowest triangular
+      band.
+    upper_edge_hertz: The desired top edge of the highest frequency band.
+
+  Returns:
+    An np.array with shape (num_spectrogram_bins, num_mel_bins).
+
+  Raises:
+    ValueError: if frequency edges are incorrectly ordered.
+  """
+  nyquist_hertz = audio_sample_rate / 2.
+  if lower_edge_hertz >= upper_edge_hertz:
+    raise ValueError("lower_edge_hertz %.1f >= upper_edge_hertz %.1f" %
+                     (lower_edge_hertz, upper_edge_hertz))
+  spectrogram_bins_hertz = np.linspace(0.0, nyquist_hertz, num_spectrogram_bins)
+  spectrogram_bins_mel = hertz_to_mel(spectrogram_bins_hertz)
+  # The i'th mel band (starting from i=1) has center frequency
+  # band_edges_mel[i], lower edge band_edges_mel[i-1], and higher edge
+  # band_edges_mel[i+1].  Thus, we need num_mel_bins + 2 values in
+  # the band_edges_mel arrays.
+  band_edges_mel = np.linspace(hertz_to_mel(lower_edge_hertz),
+                               hertz_to_mel(upper_edge_hertz), num_mel_bins + 2)
+  # Matrix to post-multiply feature arrays whose rows are num_spectrogram_bins
+  # of spectrogram values.
+  mel_weights_matrix = np.empty((num_spectrogram_bins, num_mel_bins))
+  for i in range(num_mel_bins):
+    lower_edge_mel, center_mel, upper_edge_mel = band_edges_mel[i:i + 3]
+    # Calculate lower and upper slopes for every spectrogram bin.
+    # Line segments are linear in the *mel* domain, not hertz.
+    lower_slope = ((spectrogram_bins_mel - lower_edge_mel) /
+                   (center_mel - lower_edge_mel))
+    upper_slope = ((upper_edge_mel - spectrogram_bins_mel) /
+                   (upper_edge_mel - center_mel))
+    # .. then intersect them with each other and zero.
+    mel_weights_matrix[:, i] = np.maximum(0.0, np.minimum(lower_slope,
+                                                          upper_slope))
+  # HTK excludes the spectrogram DC bin; make sure it always gets a zero
+  # coefficient.
+  mel_weights_matrix[0, :] = 0.0
+  return mel_weights_matrix
+
+
+def log_mel_spectrogram(data,
+                        audio_sample_rate=8000,
+                        log_offset=0.0,
+                        window_length_secs=0.025,
+                        hop_length_secs=0.010,
+                        **kwargs):
+  """Convert waveform to a log magnitude mel-frequency spectrogram.
+
+  Args:
+    data: 1D np.array of waveform data.
+    audio_sample_rate: The sampling rate of data.
+    log_offset: Add this to values when taking log to avoid -Infs.
+    window_length_secs: Duration of each window to analyze.
+    hop_length_secs: Advance between successive analysis windows.
+    **kwargs: Additional arguments to pass to spectrogram_to_mel_matrix.
+
+  Returns:
+    2D np.array of (num_frames, num_mel_bins) consisting of log mel filterbank
+    magnitudes for successive frames.
+  """
+  window_length_samples = int(round(audio_sample_rate * window_length_secs))
+  hop_length_samples = int(round(audio_sample_rate * hop_length_secs))
+  fft_length = 2 ** int(np.ceil(np.log(window_length_samples) / np.log(2.0)))
+  spectrogram = stft_magnitude(
+      data,
+      fft_length=fft_length,
+      hop_length=hop_length_samples,
+      window_length=window_length_samples)
+  mel_spectrogram = np.dot(spectrogram, spectrogram_to_mel_matrix(
+      num_spectrogram_bins=spectrogram.shape[1],
+      audio_sample_rate=audio_sample_rate, **kwargs))
+  return np.log(mel_spectrogram + log_offset)

audioset/vggish_inference_demo.py

+# Copyright 2017 The TensorFlow Authors All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+r"""A simple demonstration of running VGGish in inference mode.
+
+This is intended as a toy example that demonstrates how the various building
+blocks (feature extraction, model definition and loading, postprocessing) work
+together in an inference context.
+
+A WAV file (assumed to contain signed 16-bit PCM samples) is read in, converted
+into log mel spectrogram examples, fed into VGGish, the raw embedding output is
+whitened and quantized, and the postprocessed embeddings are optionally written
+in a SequenceExample to a TFRecord file (using the same format as the embedding
+features released in AudioSet).
+
+Usage:
+  # Run a WAV file through the model and print the embeddings. The model
+  # checkpoint is loaded from vggish_model.ckpt and the PCA parameters are
+  # loaded from vggish_pca_params.npz in the current directory.
+  $ python vggish_inference_demo.py --wav_file /path/to/a/wav/file
+
+  # Run a WAV file through the model and also write the embeddings to
+  # a TFRecord file. The model checkpoint and PCA parameters are explicitly
+  # passed in as well.
+  $ python vggish_inference_demo.py --wav_file /path/to/a/wav/file \
+                                    --tfrecord_file /path/to/tfrecord/file \
+                                    --checkpoint /path/to/model/checkpoint \
+                                    --pca_params /path/to/pca/params
+
+  # Run a built-in input (a sine wav) through the model and print the
+  # embeddings. Associated model files are read from the current directory.
+  $ python vggish_inference_demo.py
+"""
+
+from __future__ import print_function
+
+import numpy as np
+from scipy.io import wavfile
+import six
+import tensorflow as tf
+
+import vggish_input
+import vggish_params
+import vggish_postprocess
+import vggish_slim
+
+flags = tf.app.flags
+
+flags.DEFINE_string(
+    'wav_file', None,
+    'Path to a wav file. Should contain signed 16-bit PCM samples. '
+    'If none is provided, a synthetic sound is used.')
+
+flags.DEFINE_string(
+    'checkpoint', 'vggish_model.ckpt',
+    'Path to the VGGish checkpoint file.')
+
+flags.DEFINE_string(
+    'pca_params', 'vggish_pca_params.npz',
+    'Path to the VGGish PCA parameters file.')
+
+flags.DEFINE_string(
+    'tfrecord_file', None,
+    'Path to a TFRecord file where embeddings will be written.')
+
+FLAGS = flags.FLAGS
+
+
+def main(_):
+  # In this simple example, we run the examples from a single audio file through
+  # the model. If none is provided, we generate a synthetic input.
+  if FLAGS.wav_file:
+    wav_file = FLAGS.wav_file
+  else:
+    # Write a WAV of a sine wav into an in-memory file object.
+    num_secs = 5
+    freq = 1000
+    sr = 44100
+    t = np.linspace(0, num_secs, int(num_secs * sr))
+    x = np.sin(2 * np.pi * freq * t)
+    # Convert to signed 16-bit samples.
+    samples = np.clip(x * 32768, -32768, 32767).astype(np.int16)
+    wav_file = six.BytesIO()
+    wavfile.write(wav_file, sr, samples)
+    wav_file.seek(0)
+  examples_batch = vggish_input.wavfile_to_examples(wav_file)
+  print(examples_batch)
+
+  # Prepare a postprocessor to munge the model embeddings.
+  pproc = vggish_postprocess.Postprocessor(FLAGS.pca_params)
+
+  # If needed, prepare a record writer to store the postprocessed embeddings.
+  writer = tf.python_io.TFRecordWriter(
+      FLAGS.tfrecord_file) if FLAGS.tfrecord_file else None
+
+  with tf.Graph().as_default(), tf.Session() as sess:
+    # Define the model in inference mode, load the checkpoint, and
+    # locate input and output tensors.
+    vggish_slim.define_vggish_slim(training=False)
+    vggish_slim.load_vggish_slim_checkpoint(sess, FLAGS.checkpoint)
+    features_tensor = sess.graph.get_tensor_by_name(
+        vggish_params.INPUT_TENSOR_NAME)
+    embedding_tensor = sess.graph.get_tensor_by_name(
+        vggish_params.OUTPUT_TENSOR_NAME)
+
+    # Run inference and postprocessing.
+    [embedding_batch] = sess.run([embedding_tensor],
+                                 feed_dict={features_tensor: examples_batch})
+    print(embedding_batch)
+    postprocessed_batch = pproc.postprocess(embedding_batch)
+    print(postprocessed_batch)
+
+    # Write the postprocessed embeddings as a SequenceExample, in a similar
+    # format as the features released in AudioSet. Each row of the batch of
+    # embeddings corresponds to roughly a second of audio (96 10ms frames), and
+    # the rows are written as a sequence of bytes-valued features, where each
+    # feature value contains the 128 bytes of the whitened quantized embedding.
+    seq_example = tf.train.SequenceExample(
+        feature_lists=tf.train.FeatureLists(
+            feature_list={
+                vggish_params.AUDIO_EMBEDDING_FEATURE_NAME:
+                    tf.train.FeatureList(
+                        feature=[
+                            tf.train.Feature(
+                                bytes_list=tf.train.BytesList(
+                                    value=[embedding.tobytes()]))
+                            for embedding in postprocessed_batch
+                        ]
+                    )
+            }
+        )
+    )
+    print(seq_example)
+    if writer:
+      writer.write(seq_example.SerializeToString())
+
+  if writer:
+    writer.close()
+
+if __name__ == '__main__':
+  tf.app.run()

audioset/vggish_input.py

+# Copyright 2017 The TensorFlow Authors All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Compute input examples for VGGish from audio waveform."""
+
+import numpy as np
+import resampy
+from scipy.io import wavfile
+
+import mel_features
+import vggish_params
+
+
+def waveform_to_examples(data, sample_rate):
+  """Converts audio waveform into an array of examples for VGGish.
+
+  Args:
+    data: np.array of either one dimension (mono) or two dimensions
+      (multi-channel, with the outer dimension representing channels).
+      Each sample is generally expected to lie in the range [-1.0, +1.0],
+      although this is not required.
+    sample_rate: Sample rate of data.
+
+  Returns:
+    3-D np.array of shape [num_examples, num_frames, num_bands] which represents
+    a sequence of examples, each of which contains a patch of log mel
+    spectrogram, covering num_frames frames of audio and num_bands mel frequency
+    bands, where the frame length is vggish_params.STFT_HOP_LENGTH_SECONDS.
+  """
+  # Convert to mono.
+  if len(data.shape) > 1:
+    data = np.mean(data, axis=1)
+  # Resample to the rate assumed by VGGish.
+  if sample_rate != vggish_params.SAMPLE_RATE:
+    data = resampy.resample(data, sample_rate, vggish_params.SAMPLE_RATE)
+
+  # Compute log mel spectrogram features.
+  log_mel = mel_features.log_mel_spectrogram(
+      data,
+      audio_sample_rate=vggish_params.SAMPLE_RATE,
+      log_offset=vggish_params.LOG_OFFSET,
+      window_length_secs=vggish_params.STFT_WINDOW_LENGTH_SECONDS,
+      hop_length_secs=vggish_params.STFT_HOP_LENGTH_SECONDS,
+      num_mel_bins=vggish_params.NUM_MEL_BINS,
+      lower_edge_hertz=vggish_params.MEL_MIN_HZ,
+      upper_edge_hertz=vggish_params.MEL_MAX_HZ)
+
+  # Frame features into examples.
+  features_sample_rate = 1.0 / vggish_params.STFT_HOP_LENGTH_SECONDS
+  example_window_length = int(round(
+      vggish_params.EXAMPLE_WINDOW_SECONDS * features_sample_rate))
+  example_hop_length = int(round(
+      vggish_params.EXAMPLE_HOP_SECONDS * features_sample_rate))
+  log_mel_examples = mel_features.frame(
+      log_mel,
+      window_length=example_window_length,
+      hop_length=example_hop_length)
+  return log_mel_examples
+
+
+def wavfile_to_examples(wav_file):
+  """Convenience wrapper around waveform_to_examples() for a common WAV format.
+
+  Args:
+    wav_file: String path to a file, or a file-like object. The file
+    is assumed to contain WAV audio data with signed 16-bit PCM samples.
+
+  Returns:
+    See waveform_to_examples.
+  """
+  sr, wav_data = wavfile.read(wav_file)
+  assert wav_data.dtype == np.int16, 'Bad sample type: %r' % wav_data.dtype
+  samples = wav_data / 32768.0  # Convert to [-1.0, +1.0]
+  return waveform_to_examples(samples, sr)

audioset/vggish_params.py

+# Copyright 2017 The TensorFlow Authors All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Global parameters for the VGGish model.
+
+See vggish_slim.py for more information.
+"""
+
+# Architectural constants.
+NUM_FRAMES = 96  # Frames in input mel-spectrogram patch.
+NUM_BANDS = 64  # Frequency bands in input mel-spectrogram patch.
+EMBEDDING_SIZE = 128  # Size of embedding layer.
+
+# Hyperparameters used in feature and example generation.
+SAMPLE_RATE = 16000
+STFT_WINDOW_LENGTH_SECONDS = 0.025
+STFT_HOP_LENGTH_SECONDS = 0.010
+NUM_MEL_BINS = NUM_BANDS
+MEL_MIN_HZ = 125
+MEL_MAX_HZ = 7500
+LOG_OFFSET = 0.01  # Offset used for stabilized log of input mel-spectrogram.
+EXAMPLE_WINDOW_SECONDS = 0.96  # Each example contains 96 10ms frames
+EXAMPLE_HOP_SECONDS = 0.96     # with zero overlap.
+
+# Parameters used for embedding postprocessing.
+PCA_EIGEN_VECTORS_NAME = 'pca_eigen_vectors'
+PCA_MEANS_NAME = 'pca_means'
+QUANTIZE_MIN_VAL = -2.0
+QUANTIZE_MAX_VAL = +2.0
+
+# Hyperparameters used in training.
+INIT_STDDEV = 0.01  # Standard deviation used to initialize weights.
+LEARNING_RATE = 1e-4  # Learning rate for the Adam optimizer.
+ADAM_EPSILON = 1e-8  # Epsilon for the Adam optimizer.
+
+# Names of ops, tensors, and features.
+INPUT_OP_NAME = 'vggish/input_features'
+INPUT_TENSOR_NAME = INPUT_OP_NAME + ':0'
+OUTPUT_OP_NAME = 'vggish/embedding'
+OUTPUT_TENSOR_NAME = OUTPUT_OP_NAME + ':0'
+AUDIO_EMBEDDING_FEATURE_NAME = 'audio_embedding'

audioset/vggish_postprocess.py

+# Copyright 2017 The TensorFlow Authors All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Post-process embeddings from VGGish."""
+
+import numpy as np
+
+import vggish_params
+
+
+class Postprocessor(object):
+  """Post-processes VGGish embeddings.
+
+  The initial release of AudioSet included 128-D VGGish embeddings for each
+  segment of AudioSet. These released embeddings were produced by applying
+  a PCA transformation (technically, a whitening transform is included as well)
+  and 8-bit quantization to the raw embedding output from VGGish, in order to
+  stay compatible with the YouTube-8M project which provides visual embeddings
+  in the same format for a large set of YouTube videos. This class implements
+  the same PCA (with whitening) and quantization transformations.
+  """
+
+  def __init__(self, pca_params_npz_path):
+    """Constructs a postprocessor.
+
+    Args:
+      pca_params_npz_path: Path to a NumPy-format .npz file that
+        contains the PCA parameters used in postprocessing.
+    """
+    params = np.load(pca_params_npz_path)
+    self._pca_matrix = params[vggish_params.PCA_EIGEN_VECTORS_NAME]
+    # Load means into a column vector for easier broadcasting later.
+    self._pca_means = params[vggish_params.PCA_MEANS_NAME].reshape(-1, 1)
+    assert self._pca_matrix.shape == (
+        vggish_params.EMBEDDING_SIZE, vggish_params.EMBEDDING_SIZE), (
+            'Bad PCA matrix shape: %r' % (self._pca_matrix.shape,))
+    assert self._pca_means.shape == (vggish_params.EMBEDDING_SIZE, 1), (
+        'Bad PCA means shape: %r' % (self._pca_means.shape,))
+
+  def postprocess(self, embeddings_batch):
+    """Applies postprocessing to a batch of embeddings.
+
+    Args:
+      embeddings_batch: An nparray of shape [batch_size, embedding_size]
+        containing output from the embedding layer of VGGish.
+
+    Returns:
+      An nparray of the same shape as the input but of type uint8,
+      containing the PCA-transformed and quantized version of the input.
+    """
+    assert len(embeddings_batch.shape) == 2, (
+        'Expected 2-d batch, got %r' % (embeddings_batch.shape,))
+    assert embeddings_batch.shape[1] == vggish_params.EMBEDDING_SIZE, (
+        'Bad batch shape: %r' % (embeddings_batch.shape,))
+
+    # Apply PCA.
+    # - Embeddings come in as [batch_size, embedding_size].
+    # - Transpose to [embedding_size, batch_size].
+    # - Subtract pca_means column vector from each column.
+    # - Premultiply by PCA matrix of shape [output_dims, input_dims]
+    #   where both are are equal to embedding_size in our case.
+    # - Transpose result back to [batch_size, embedding_size].
+    pca_applied = np.dot(self._pca_matrix,
+                         (embeddings_batch.T - self._pca_means)).T
+
+    # Quantize by:
+    # - clipping to [min, max] range
+    clipped_embeddings = np.clip(
+        pca_applied, vggish_params.QUANTIZE_MIN_VAL,
+        vggish_params.QUANTIZE_MAX_VAL)
+    # - convert to 8-bit in range [0.0, 255.0]
+    quantized_embeddings = (
+        (clipped_embeddings - vggish_params.QUANTIZE_MIN_VAL) *
+        (255.0 /
+         (vggish_params.QUANTIZE_MAX_VAL - vggish_params.QUANTIZE_MIN_VAL)))
+    # - cast 8-bit float to uint8
+    quantized_embeddings = quantized_embeddings.astype(np.uint8)
+
+    return quantized_embeddings

audioset/vggish_slim.py

+# Copyright 2017 The TensorFlow Authors All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Defines the 'VGGish' model used to generate AudioSet embedding features.
+
+The public AudioSet release (https://research.google.com/audioset/download.html)
+includes 128-D features extracted from the embedding layer of a VGG-like model
+that was trained on a large Google-internal YouTube dataset. Here we provide
+a TF-Slim definition of the same model, without any dependences on libraries
+internal to Google. We call it 'VGGish'.
+
+Note that we only define the model up to the embedding layer, which is the
+penultimate layer before the final classifier layer. We also provide various
+hyperparameter values (in vggish_params.py) that were used to train this model
+internally.
+
+For comparison, here is TF-Slim's VGG definition:
+https://github.com/tensorflow/models/blob/master/slim/nets/vgg.py
+"""
+
+import tensorflow as tf
+import vggish_params as params
+
+slim = tf.contrib.slim
+
+
+def define_vggish_slim(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.
+
+  Args:
+    training: If true, all parameters are marked trainable.
+
+  Returns:
+    The op 'vggish/embeddings'.
+  """
+  # Defaults:
+  # - All weights are initialized to N(0, INIT_STDDEV).
+  # - All biases are initialized to 0.
+  # - All activations are ReLU.
+  # - All convolutions are 3x3 with stride 1 and SAME padding.
+  # - All max-pools are 2x2 with stride 2 and SAME padding.
+  with slim.arg_scope([slim.conv2d, slim.fully_connected],
+                      weights_initializer=tf.truncated_normal_initializer(
+                          stddev=params.INIT_STDDEV),
+                      biases_initializer=tf.zeros_initializer(),
+                      activation_fn=tf.nn.relu,
+                      trainable=training), \
+       slim.arg_scope([slim.conv2d],
+                      kernel_size=[3, 3], stride=1, padding='SAME'), \
+       slim.arg_scope([slim.max_pool2d],
+                      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(
+        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])
+
+    # The VGG stack of alternating convolutions and max-pools.
+    net = slim.conv2d(net, 64, scope='conv1')
+    net = slim.max_pool2d(net, scope='pool1')
+    net = slim.conv2d(net, 128, scope='conv2')
+    net = slim.max_pool2d(net, scope='pool2')
+    net = slim.repeat(net, 2, slim.conv2d, 256, scope='conv3')
+    net = slim.max_pool2d(net, scope='pool3')
+    net = slim.repeat(net, 2, slim.conv2d, 512, scope='conv4')
+    net = slim.max_pool2d(net, scope='pool4')
+
+    # Flatten before entering fully-connected layers
+    net = slim.flatten(net)
+    net = slim.repeat(net, 2, slim.fully_connected, 4096, scope='fc1')
+    # The embedding layer.
+    net = slim.fully_connected(net, params.EMBEDDING_SIZE, scope='fc2')
+    return tf.identity(net, name='embedding')
+
+
+def load_vggish_slim_checkpoint(session, checkpoint_path):
+  """Loads a pre-trained VGGish-compatible checkpoint.
+
+  This function can be used as an initialization function (referred to as
+  init_fn in TensorFlow documentation) which is called in a Session after
+  initializating all variables. When used as an init_fn, this will load
+  a pre-trained checkpoint that is compatible with the VGGish model
+  definition. Only variables defined by VGGish will be loaded.
+
+  Args:
+    session: an active TensorFlow session.
+    checkpoint_path: path to a file containing a checkpoint that is
+      compatible with the VGGish model definition.
+  """
+  # Get the list of names of all VGGish variables that exist in
+  # the checkpoint (i.e., all inference-mode VGGish variables).
+  with tf.Graph().as_default():
+    define_vggish_slim(training=False)
+    vggish_var_names = [v.name for v in tf.global_variables()]
+
+  # Get the list of all currently existing variables that match
+  # the list of variable names we just computed.
+  vggish_vars = [v for v in tf.global_variables() if v.name in vggish_var_names]
+
+  # Use a Saver to restore just the variables selected above.
+  saver = tf.train.Saver(vggish_vars, name='vggish_load_pretrained')
+  saver.restore(session, checkpoint_path)

audioset/vggish_smoke_test.py

+# Copyright 2017 The TensorFlow Authors All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""A smoke test for VGGish.
+
+This is a simple smoke test of a local install of VGGish and its associated
+downloaded files. We create a synthetic sound, extract log mel spectrogram
+features, run them through VGGish, post-process the embedding ouputs, and
+check some simple statistics of the results, allowing for variations that
+might occur due to platform/version differences in the libraries we use.
+
+Usage:
+- Download the VGGish checkpoint and PCA parameters into the same directory as
+  the VGGish source code. If you keep them elsewhere, update the checkpoint_path
+  and pca_params_path variables below.
+- Run:
+  $ python vggish_smoke_test.py
+"""
+
+from __future__ import print_function
+
+import numpy as np
+import tensorflow as tf
+
+import vggish_input
+import vggish_params
+import vggish_postprocess
+import vggish_slim
+
+print('\nTesting your install of VGGish\n')
+
+# Paths to downloaded VGGish files.
+checkpoint_path = 'vggish_model.ckpt'
+pca_params_path = 'vggish_pca_params.npz'
+
+# Relative tolerance of errors in mean and standard deviation of embeddings.
+rel_error = 0.1  # Up to 10%
+
+# Generate a 1 kHz sine wave at 44.1 kHz (we use a high sampling rate
+# to test resampling to 16 kHz during feature extraction).
+num_secs = 3
+freq = 1000
+sr = 44100
+t = np.linspace(0, num_secs, int(num_secs * sr))
+x = np.sin(2 * np.pi * freq * t)
+
+# Produce a batch of log mel spectrogram examples.
+input_batch = vggish_input.waveform_to_examples(x, sr)
+print('Log Mel Spectrogram example: ', input_batch[0])
+np.testing.assert_equal(
+    input_batch.shape,
+    [num_secs, vggish_params.NUM_FRAMES, vggish_params.NUM_BANDS])
+
+# Define VGGish, load the checkpoint, and run the batch through the model to
+# produce embeddings.
+with tf.Graph().as_default(), tf.Session() as sess:
+  vggish_slim.define_vggish_slim()
+  vggish_slim.load_vggish_slim_checkpoint(sess, checkpoint_path)
+
+  features_tensor = sess.graph.get_tensor_by_name(
+      vggish_params.INPUT_TENSOR_NAME)
+  embedding_tensor = sess.graph.get_tensor_by_name(
+      vggish_params.OUTPUT_TENSOR_NAME)
+  [embedding_batch] = sess.run([embedding_tensor],
+                               feed_dict={features_tensor: input_batch})
+  print('VGGish embedding: ', embedding_batch[0])
+  expected_embedding_mean = 0.131
+  expected_embedding_std = 0.238
+  np.testing.assert_allclose(
+      [np.mean(embedding_batch), np.std(embedding_batch)],
+      [expected_embedding_mean, expected_embedding_std],
+      rtol=rel_error)
+
+# Postprocess the results to produce whitened quantized embeddings.
+pproc = vggish_postprocess.Postprocessor(pca_params_path)
+postprocessed_batch = pproc.postprocess(embedding_batch)
+print('Postprocessed VGGish embedding: ', postprocessed_batch[0])
+expected_postprocessed_mean = 123.0
+expected_postprocessed_std = 75.0
+np.testing.assert_allclose(
+    [np.mean(postprocessed_batch), np.std(postprocessed_batch)],
+    [expected_postprocessed_mean, expected_postprocessed_std],
+    rtol=rel_error)
+
+print('\nLooks Good To Me!\n')

audioset/vggish_train_demo.py

+# Copyright 2017 The TensorFlow Authors All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+r"""A simple demonstration of running VGGish in training mode.
+
+This is intended as a toy example that demonstrates how to use the VGGish model
+definition within a larger model that adds more layers on top, and then train
+the larger model. If you let VGGish train as well, then this allows you to
+fine-tune the VGGish model parameters for your application. If you don't let
+VGGish train, then you use VGGish as a feature extractor for the layers above
+it.
+
+For this toy task, we are training a classifier to distinguish between three
+classes: sine waves, constant signals, and white noise. We generate synthetic
+waveforms from each of these classes, convert into shuffled batches of log mel
+spectrogram examples with associated labels, and feed the batches into a model
+that includes VGGish at the bottom and a couple of additional layers on top. We
+also plumb in labels that are associated with the examples, which feed a label
+loss used for training.
+
+Usage:
+  # Run training for 100 steps using a model checkpoint in the default
+  # location (vggish_model.ckpt in the current directory). Allow VGGish
+  # to get fine-tuned.
+  $ python vggish_train_demo.py --num_batches 100
+
+  # Same as before but run for fewer steps and don't change VGGish parameters
+  # and use a checkpoint in a different location
+  $ python vggish_train_demo.py --num_batches 50 \
+                                --train_vggish=False \
+                                --checkpoint /path/to/model/checkpoint
+"""
+
+from __future__ import print_function
+
+from random import shuffle
+
+import numpy as np
+import tensorflow as tf
+
+import vggish_input
+import vggish_params
+import vggish_slim
+
+flags = tf.app.flags
+slim = tf.contrib.slim
+
+flags.DEFINE_integer(
+    'num_batches', 30,
+    'Number of batches of examples to feed into the model. Each batch is of '
+    'variable size and contains shuffled examples of each class of audio.')
+
+flags.DEFINE_boolean(
+    'train_vggish', True,
+    'If Frue, allow VGGish parameters to change during training, thus '
+    'fine-tuning VGGish. If False, VGGish parameters are fixed, thus using '
+    'VGGish as a fixed feature extractor.')
+
+flags.DEFINE_string(
+    'checkpoint', 'vggish_model.ckpt',
+    'Path to the VGGish checkpoint file.')
+
+FLAGS = flags.FLAGS
+
+_NUM_CLASSES = 3
+
+
+def _get_examples_batch():
+  """Returns a shuffled batch of examples of all audio classes.
+
+  Note that this is just a toy function because this is a simple demo intended
+  to illustrate how the training code might work.
+
+  Returns:
+    a tuple (features, labels) where features is a NumPy array of shape
+    [batch_size, num_frames, num_bands] where the batch_size is variable and
+    each row is a log mel spectrogram patch of shape [num_frames, num_bands]
+    suitable for feeding VGGish, while labels is a NumPy array of shape
+    [batch_size, num_classes] where each row is a multi-hot label vector that
+    provides the labels for corresponding rows in features.
+  """
+  # Make a waveform for each class.
+  num_seconds = 5
+  sr = 44100  # Sampling rate.
+  t = np.linspace(0, num_seconds, int(num_seconds * sr))  # Time axis.
+  # Random sine wave.
+  freq = np.random.uniform(100, 1000)
+  sine = np.sin(2 * np.pi * freq * t)
+  # Random constant signal.
+  magnitude = np.random.uniform(-1, 1)
+  const = magnitude * t
+  # White noise.
+  noise = np.random.normal(-1, 1, size=t.shape)
+
+  # Make examples of each signal and corresponding labels.
+  # Sine is class index 0, Const class index 1, Noise class index 2.
+  sine_examples = vggish_input.waveform_to_examples(sine, sr)
+  sine_labels = np.array([[1, 0, 0]] * sine_examples.shape[0])
+  const_examples = vggish_input.waveform_to_examples(const, sr)
+  const_labels = np.array([[0, 1, 0]] * const_examples.shape[0])
+  noise_examples = vggish_input.waveform_to_examples(noise, sr)
+  noise_labels = np.array([[0, 0, 1]] * noise_examples.shape[0])
+
+  # Shuffle (example, label) pairs across all classes.
+  all_examples = sine_examples + const_examples + noise_examples
+  all_labels = sine_labels + const_labels + noise_labels
+  labeled_examples = list(zip(all_examples, all_labels))
+  shuffle(labeled_examples)
+
+  # Separate and return the features and labels.
+  features = [example for (example, _) in labeled_examples]
+  labels = [label for (_, label) in labeled_examples]
+  return (features, labels)
+
+
+def main(_):
+  with tf.Graph().as_default(), tf.Session() as sess:
+    # Define VGGish.
+    embeddings = vggish_slim.define_vggish_slim(FLAGS.train_vggish)
+
+    # Define a shallow classification model and associated training ops on top
+    # of VGGish.
+    with tf.variable_scope('mymodel'):
+      # Add a fully connected layer with 100 units.
+      num_units = 100
+      fc = slim.fully_connected(embeddings, num_units)
+
+      # Add a classifier layer at the end, consisting of parallel logistic
+      # classifiers, one per class. This allows for multi-class tasks.
+      logits = slim.fully_connected(
+          fc, _NUM_CLASSES, activation_fn=None, scope='logits')
+      tf.sigmoid(logits, name='prediction')
+
+      # Add training ops.
+      with tf.variable_scope('train'):
+        global_step = tf.Variable(
+            0, name='global_step', trainable=False,
+            collections=[tf.GraphKeys.GLOBAL_VARIABLES,
+                         tf.GraphKeys.GLOBAL_STEP])
+
+        # Labels are assumed to be fed as a batch multi-hot vectors, with
+        # a 1 in the position of each positive class label, and 0 elsewhere.
+        labels = tf.placeholder(
+            tf.float32, shape=(None, _NUM_CLASSES), name='labels')
+
+        # Cross-entropy label loss.
+        xent = tf.nn.sigmoid_cross_entropy_with_logits(
+            logits=logits, labels=labels, name='xent')
+        loss = tf.reduce_mean(xent, name='loss_op')
+        tf.summary.scalar('loss', loss)
+
+        # We use the same optimizer and hyperparameters as used to train VGGish.
+        optimizer = tf.train.AdamOptimizer(
+            learning_rate=vggish_params.LEARNING_RATE,
+            epsilon=vggish_params.ADAM_EPSILON)
+        optimizer.minimize(loss, global_step=global_step, name='train_op')
+
+    # Initialize all variables in the model, and then load the pre-trained
+    # VGGish checkpoint.
+    sess.run(tf.global_variables_initializer())
+    vggish_slim.load_vggish_slim_checkpoint(sess, FLAGS.checkpoint)
+
+    # Locate all the tensors and ops we need for the training loop.
+    features_tensor = sess.graph.get_tensor_by_name(
+        vggish_params.INPUT_TENSOR_NAME)
+    labels_tensor = sess.graph.get_tensor_by_name('mymodel/train/labels:0')
+    global_step_tensor = sess.graph.get_tensor_by_name(
+        'mymodel/train/global_step:0')
+    loss_tensor = sess.graph.get_tensor_by_name('mymodel/train/loss_op:0')
+    train_op = sess.graph.get_operation_by_name('mymodel/train/train_op')
+
+    # The training loop.
+    for _ in range(FLAGS.num_batches):
+      (features, labels) = _get_examples_batch()
+      [num_steps, loss, _] = sess.run(
+          [global_step_tensor, loss_tensor, train_op],
+          feed_dict={features_tensor: features, labels_tensor: labels})
+      print('Step %d: loss %g' % (num_steps, loss))
+
+if __name__ == '__main__':
+  tf.app.run()

cognitive_mapping_and_planning/README.md

@@ -22,7 +22,7 @@ citing the following paper:
 ### Contents
 1.  [Requirements: software](#requirements-software)
 2.  [Requirements: data](#requirements-data)
-3.  [Test Pre-trained Models](#test-pre_trained-models)
+3.  [Test Pre-trained Models](#test-pre-trained-models)
 4.  [Train your Own Models](#train-your-own-models)
 
 ### Requirements: software
@@ -46,7 +46,8 @@ citing the following paper:
       ```
 
 2.  Install [Tensorflow](https://www.tensorflow.org/) inside this virtual
-    environment. Typically done with `pip install --upgrade tensorflow-gpu`.
+    environment. You will need to use one of the latest nightly builds 
+    (see instructions [here](https://github.com/tensorflow/tensorflow#installation)).
 
 3.  Swiftshader: We use
     [Swiftshader](https://github.com/google/swiftshader.git), a CPU based
@@ -99,8 +100,7 @@ citing the following paper:
     `data/README.md`
 
 ### Test Pre-trained Models
-1.  Download pre-trained models using
-    `scripts/scripts_download_pretrained_models.sh`
+1.  Download pre-trained models. See `output/README.md`.
 
 2.  Test models using `scripts/script_test_pretrained_models.sh`.
 

domain_adaptation/README.md

-# Domain Separation Networks
+## Introduction
+This is the code used for two domain adaptation papers.
 
+The `domain_separation` directory contains code for the "Domain Separation
+Networks" paper by Bousmalis K., Trigeorgis G., et al. which was presented at
+NIPS 2016. The paper can be found here: https://arxiv.org/abs/1608.06019.
 
-## Introduction
-This code is the code used for the "Domain Separation Networks" paper
-by Bousmalis K., Trigeorgis G., et al. which was presented at NIPS 2016. The
-paper can be found here: https://arxiv.org/abs/1608.06019.
+The `pixel_domain_adaptation` directory contains the code used for the
+"Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial
+Networks" paper by Bousmalis K., et al. (presented at CVPR 2017). The paper can
+be found here: https://arxiv.org/abs/1612.05424. PixelDA aims to perform domain
+adaptation by transfering the visual style of the target domain (which has few
+or no labels) to a source domain (which has many labels). This is accomplished
+using a Generative Adversarial Network (GAN).
 
 ## Contact
-This code was open-sourced by [Konstantinos Bousmalis](https://github.com/bousmalis) (konstantinos@google.com).
+The domain separation code was open-sourced
+by [Konstantinos Bousmalis](https://github.com/bousmalis)
+(konstantinos@google.com), while the pixel level domain adaptation code was
+open-sourced by [David Dohan](https://github.com/dmrd) (ddohan@google.com).
 
 ## Installation
 You will need to have the following installed on your machine before trying out the DSN code.
@@ -16,26 +26,70 @@ You will need to have the following installed on your machine before trying out
 *  Bazel: https://bazel.build/
 
 ## Important Note
-Although we are making the code available, you are only able to use the MNIST
-provider for now. We will soon provide a script to download and convert MNIST-M
-as well. Check back here in a few weeks or wait for a relevant announcement from
-[@bousmalis](https://twitter.com/bousmalis).
+We are working to open source the pose estimation dataset. For now, the MNIST to
+MNIST-M dataset is available. Check back here in a few weeks or wait for a
+relevant announcement from [@bousmalis](https://twitter.com/bousmalis).
 
-## Running the code for adapting MNIST to MNIST-M
-In order to run the MNIST to MNIST-M experiments with DANNs and/or DANNs with
-domain separation (DSNs) you will need to set the directory you used to download
-MNIST and MNIST-M:
+## Initial setup
+In order to run the MNIST to MNIST-M experiments, you will need to set the
+data directory:
 
 ```
 $ export DSN_DATA_DIR=/your/dir
 ```
 
-Add models and models/slim to your `$PYTHONPATH`:
+Add models and models/slim to your `$PYTHONPATH` (assumes $PWD is /models):
 
 ```
 $ export PYTHONPATH=$PYTHONPATH:$PWD:$PWD/slim
 ```
 
+## Getting the datasets
+
+You can fetch the MNIST data by running
+
+```
+ $ bazel run slim:download_and_convert_data -- --dataset_dir $DSN_DATA_DIR --dataset_name=mnist
+```
+
+The MNIST-M dataset is available online [here](http://bit.ly/2nrlUAJ).  Once it is downloaded and extracted into your data directory, create TFRecord files by running:
+```
+$ bazel run domain_adaptation/datasets:download_and_convert_mnist_m -- --dataset_dir $DSN_DATA_DIR
+```
+
+
+
+# Running PixelDA from MNIST to MNIST-M
+You can run PixelDA as follows (using Tensorboard to examine the results):
+
+```
+$ bazel run domain_adaptation/pixel_domain_adaptation:pixelda_train -- --dataset_dir $DSN_DATA_DIR --source_dataset mnist --target_dataset mnist_m
+```
+
+And evaluation as:
+```
+$ bazel run domain_adaptation/pixel_domain_adaptation:pixelda_eval -- --dataset_dir $DSN_DATA_DIR --source_dataset mnist --target_dataset mnist_m --target_split_name test
+```
+
+The MNIST-M results in the paper were run with the following hparams flag:
+```
+--hparams arch=resnet,domain_loss_weight=0.135603587834,num_training_examples=16000000,style_transfer_loss_weight=0.0113173311334,task_loss_in_g_weight=0.0100959947002,task_tower=mnist,task_tower_in_g_step=true
+```
+
+### A note on terminology/language of the code:
+
+The components of the network can be grouped into two parts
+which correspond to elements which are jointly optimized: The generator
+component and the discriminator component.
+
+The generator component takes either an image or noise vector and produces an
+output image. 
+
+The discriminator component takes the generated images and the target images
+and attempts to discriminate between them.
+
+## Running DSN code for adapting MNIST to MNIST-M
+
 Then you need to build the binaries with Bazel:
 
 ```

domain_adaptation/datasets/BUILD

@@ -26,10 +26,20 @@ py_library(
     ],
 )
 
+py_binary(
+    name = "download_and_convert_mnist_m",
+    srcs = ["download_and_convert_mnist_m.py"],
+    deps = [
+        
+        "//slim:dataset_utils",
+    ],
+)
+
 py_binary(
     name = "mnist_m",
     srcs = ["mnist_m.py"],
     deps = [
+        
         "//slim:dataset_utils",
     ],
 )

domain_adaptation/datasets/dataset_factory.py

-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2017 Google Inc.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -11,13 +11,14 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-# ==============================================================================
+
 """A factory-pattern class which returns image/label pairs."""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+# Dependency imports
 import tensorflow as tf
 
 from slim.datasets import mnist

domain_adaptation/datasets/download_and_convert_mnist_m.py

+# Copyright 2017 Google Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+r"""Downloads and converts MNIST-M data to TFRecords of TF-Example protos.
+
+This module downloads the MNIST-M data, uncompresses it, reads the files
+that make up the MNIST-M data and creates two TFRecord datasets: one for train
+and one for test. Each TFRecord dataset is comprised of a set of TF-Example
+protocol buffers, each of which contain a single image and label.
+
+The script should take about a minute to run.
+
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import random
+import sys
+
+# Dependency imports
+import numpy as np
+from six.moves import urllib
+import tensorflow as tf
+
+from slim.datasets import dataset_utils
+
+tf.app.flags.DEFINE_string(
+    'dataset_dir', None,
+    'The directory where the output TFRecords and temporary files are saved.')
+
+FLAGS = tf.app.flags.FLAGS
+
+_IMAGE_SIZE = 32
+_NUM_CHANNELS = 3
+
+# The number of images in the training set.
+_NUM_TRAIN_SAMPLES = 59001
+
+# The number of images to be kept from the training set for the validation set.
+_NUM_VALIDATION = 1000
+
+# The number of images in the test set.
+_NUM_TEST_SAMPLES = 9001
+
+# Seed for repeatability.
+_RANDOM_SEED = 0
+
+# The names of the classes.
+_CLASS_NAMES = [
+    'zero',
+    'one',
+    'two',
+    'three',
+    'four',
+    'five',
+    'size',
+    'seven',
+    'eight',
+    'nine',
+]
+
+
+class ImageReader(object):
+  """Helper class that provides TensorFlow image coding utilities."""
+
+  def __init__(self):
+    # Initializes function that decodes RGB PNG data.
+    self._decode_png_data = tf.placeholder(dtype=tf.string)
+    self._decode_png = tf.image.decode_png(self._decode_png_data, channels=3)
+
+  def read_image_dims(self, sess, image_data):
+    image = self.decode_png(sess, image_data)
+    return image.shape[0], image.shape[1]
+
+  def decode_png(self, sess, image_data):
+    image = sess.run(
+        self._decode_png, feed_dict={self._decode_png_data: image_data})
+    assert len(image.shape) == 3
+    assert image.shape[2] == 3
+    return image
+
+
+def _convert_dataset(split_name, filenames, filename_to_class_id, dataset_dir):
+  """Converts the given filenames to a TFRecord dataset.
+
+  Args:
+    split_name: The name of the dataset, either 'train' or 'valid'.
+    filenames: A list of absolute paths to png images.
+    filename_to_class_id: A dictionary from filenames (strings) to class ids
+      (integers).
+    dataset_dir: The directory where the converted datasets are stored.
+  """
+  print('Converting the {} split.'.format(split_name))
+  # Train and validation splits are both in the train directory.
+  if split_name in ['train', 'valid']:
+    png_directory = os.path.join(dataset_dir, 'mnist_m', 'mnist_m_train')
+  elif split_name == 'test':
+    png_directory = os.path.join(dataset_dir, 'mnist_m', 'mnist_m_test')
+
+  with tf.Graph().as_default():
+    image_reader = ImageReader()
+
+    with tf.Session('') as sess:
+      output_filename = _get_output_filename(dataset_dir, split_name)
+
+      with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
+        for filename in filenames:
+          # Read the filename:
+          image_data = tf.gfile.FastGFile(
+              os.path.join(png_directory, filename), 'r').read()
+          height, width = image_reader.read_image_dims(sess, image_data)
+
+          class_id = filename_to_class_id[filename]
+          example = dataset_utils.image_to_tfexample(image_data, 'png', height,
+                                                     width, class_id)
+          tfrecord_writer.write(example.SerializeToString())
+
+  sys.stdout.write('\n')
+  sys.stdout.flush()
+
+
+def _extract_labels(label_filename):
+  """Extract the labels into a dict of filenames to int labels.
+
+  Args:
+    labels_filename: The filename of the MNIST-M labels.
+
+  Returns:
+    A dictionary of filenames to int labels.
+  """
+  print('Extracting labels from: ', label_filename)
+  label_file = tf.gfile.FastGFile(label_filename, 'r').readlines()
+  label_lines = [line.rstrip('\n').split() for line in label_file]
+  labels = {}
+  for line in label_lines:
+    assert len(line) == 2
+    labels[line[0]] = int(line[1])
+  return labels
+
+
+def _get_output_filename(dataset_dir, split_name):
+  """Creates the output filename.
+
+  Args:
+    dataset_dir: The directory where the temporary files are stored.
+    split_name: The name of the train/test split.
+
+  Returns:
+    An absolute file path.
+  """
+  return '%s/mnist_m_%s.tfrecord' % (dataset_dir, split_name)
+
+
+def _get_filenames(dataset_dir):
+  """Returns a list of filenames and inferred class names.
+
+  Args:
+    dataset_dir: A directory containing a set PNG encoded MNIST-M images.
+
+  Returns:
+    A list of image file paths, relative to `dataset_dir`.
+  """
+  photo_filenames = []
+  for filename in os.listdir(dataset_dir):
+    photo_filenames.append(filename)
+  return photo_filenames
+
+
+def run(dataset_dir):
+  """Runs the download and conversion operation.
+
+  Args:
+    dataset_dir: The dataset directory where the dataset is stored.
+  """
+  if not tf.gfile.Exists(dataset_dir):
+    tf.gfile.MakeDirs(dataset_dir)
+
+  train_filename = _get_output_filename(dataset_dir, 'train')
+  testing_filename = _get_output_filename(dataset_dir, 'test')
+
+  if tf.gfile.Exists(train_filename) and tf.gfile.Exists(testing_filename):
+    print('Dataset files already exist. Exiting without re-creating them.')
+    return
+
+  # TODO(konstantinos): Add download and cleanup functionality
+
+  train_validation_filenames = _get_filenames(
+      os.path.join(dataset_dir, 'mnist_m', 'mnist_m_train'))
+  test_filenames = _get_filenames(
+      os.path.join(dataset_dir, 'mnist_m', 'mnist_m_test'))
+
+  # Divide into train and validation:
+  random.seed(_RANDOM_SEED)
+  random.shuffle(train_validation_filenames)
+  train_filenames = train_validation_filenames[_NUM_VALIDATION:]
+  validation_filenames = train_validation_filenames[:_NUM_VALIDATION]
+
+  train_validation_filenames_to_class_ids = _extract_labels(
+      os.path.join(dataset_dir, 'mnist_m', 'mnist_m_train_labels.txt'))
+  test_filenames_to_class_ids = _extract_labels(
+      os.path.join(dataset_dir, 'mnist_m', 'mnist_m_test_labels.txt'))
+
+  # Convert the train, validation, and test sets.
+  _convert_dataset('train', train_filenames,
+                   train_validation_filenames_to_class_ids, dataset_dir)
+  _convert_dataset('valid', validation_filenames,
+                   train_validation_filenames_to_class_ids, dataset_dir)
+  _convert_dataset('test', test_filenames, test_filenames_to_class_ids,
+                   dataset_dir)
+
+  # Finally, write the labels file:
+  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
+  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)
+
+  print('\nFinished converting the MNIST-M dataset!')
+
+
+def main(_):
+  assert FLAGS.dataset_dir
+  run(FLAGS.dataset_dir)
+
+
+if __name__ == '__main__':
+  tf.app.run()

domain_adaptation/datasets/mnist_m.py

-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2017 Google Inc.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -11,8 +11,11 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-# ==============================================================================
+
 """Provides data for the MNIST-M dataset.
+
+The dataset scripts used to create the dataset can be found at:
+tensorflow_models/domain_adaptation_/datasets/download_and_convert_mnist_m_dataset.py
 """
 
 from __future__ import absolute_import
@@ -20,6 +23,7 @@ from __future__ import division
 from __future__ import print_function
 
 import os
+# Dependency imports
 import tensorflow as tf
 
 from slim.datasets import dataset_utils

domain_adaptation/pixel_domain_adaptation/BUILD

+# Description:
+#   Contains code for domain-adaptation style transfer.
+
+package(
+    default_visibility = [
+        ":internal",
+    ],
+)
+
+licenses(["notice"])  # Apache 2.0
+
+exports_files(["LICENSE"])
+
+package_group(
+    name = "internal",
+    packages = [
+        "//domain_adaptation/...",
+    ],
+)
+
+py_library(
+    name = "pixelda_preprocess",
+    srcs = ["pixelda_preprocess.py"],
+    deps = [
+        
+    ],
+)
+
+py_test(
+    name = "pixelda_preprocess_test",
+    srcs = ["pixelda_preprocess_test.py"],
+    deps = [
+        ":pixelda_preprocess",
+        
+    ],
+)
+
+py_library(
+    name = "pixelda_model",
+    srcs = [
+        "pixelda_model.py",
+        "pixelda_task_towers.py",
+        "hparams.py",
+    ],
+    deps = [
+        
+    ],
+)
+
+py_library(
+    name = "pixelda_utils",
+    srcs = ["pixelda_utils.py"],
+    deps = [
+        
+    ],
+)
+
+py_library(
+    name = "pixelda_losses",
+    srcs = ["pixelda_losses.py"],
+    deps = [
+        
+    ],
+)
+
+py_binary(
+    name = "pixelda_train",
+    srcs = ["pixelda_train.py"],
+    deps = [
+        ":pixelda_losses",
+        ":pixelda_model",
+        ":pixelda_preprocess",
+        ":pixelda_utils",
+        
+        "//domain_adaptation/datasets:dataset_factory",
+    ],
+)
+
+py_binary(
+    name = "pixelda_eval",
+    srcs = ["pixelda_eval.py"],
+    deps = [
+        ":pixelda_losses",
+        ":pixelda_model",
+        ":pixelda_preprocess",
+        ":pixelda_utils",
+        
+        "//domain_adaptation/datasets:dataset_factory",
+    ],
+)