Merge branch 'master' into cifar10_experiment

domain_adaptation/pixel_domain_adaptation/pixelda_losses.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.
+
+"""Defines the various loss functions in use by the PIXELDA model."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# Dependency imports
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+
+def add_domain_classifier_losses(end_points, hparams):
+  """Adds losses related to the domain-classifier.
+
+  Args:
+    end_points: A map of network end point names to `Tensors`.
+    hparams: The hyperparameters struct.
+
+  Returns:
+    loss: A `Tensor` representing the total task-classifier loss.
+  """
+  if hparams.domain_loss_weight == 0:
+    tf.logging.info(
+        'Domain classifier loss weight is 0, so not creating losses.')
+    return 0
+
+  # The domain prediction loss is minimized with respect to the domain
+  # classifier features only. Its aim is to predict the domain of the images.
+  # Note: 1 = 'real image' label, 0 = 'fake image' label
+  transferred_domain_loss = tf.losses.sigmoid_cross_entropy(
+      multi_class_labels=tf.zeros_like(end_points['transferred_domain_logits']),
+      logits=end_points['transferred_domain_logits'])
+  tf.summary.scalar('Domain_loss_transferred', transferred_domain_loss)
+
+  target_domain_loss = tf.losses.sigmoid_cross_entropy(
+      multi_class_labels=tf.ones_like(end_points['target_domain_logits']),
+      logits=end_points['target_domain_logits'])
+  tf.summary.scalar('Domain_loss_target', target_domain_loss)
+
+  # Compute the total domain loss:
+  total_domain_loss = transferred_domain_loss + target_domain_loss
+  total_domain_loss *= hparams.domain_loss_weight
+  tf.summary.scalar('Domain_loss_total', total_domain_loss)
+
+  return total_domain_loss
+
+def log_quaternion_loss_batch(predictions, labels, params):
+  """A helper function to compute the error between quaternions.
+
+  Args:
+    predictions: A Tensor of size [batch_size, 4].
+    labels: A Tensor of size [batch_size, 4].
+    params: A dictionary of parameters. Expecting 'use_logging', 'batch_size'.
+
+  Returns:
+    A Tensor of size [batch_size], denoting the error between the quaternions.
+  """
+  use_logging = params['use_logging']
+  assertions = []
+  if use_logging:
+    assertions.append(
+        tf.Assert(
+            tf.reduce_all(
+                tf.less(
+                    tf.abs(tf.reduce_sum(tf.square(predictions), [1]) - 1),
+                    1e-4)),
+            ['The l2 norm of each prediction quaternion vector should be 1.']))
+    assertions.append(
+        tf.Assert(
+            tf.reduce_all(
+                tf.less(
+                    tf.abs(tf.reduce_sum(tf.square(labels), [1]) - 1), 1e-4)),
+            ['The l2 norm of each label quaternion vector should be 1.']))
+
+  with tf.control_dependencies(assertions):
+    product = tf.multiply(predictions, labels)
+  internal_dot_products = tf.reduce_sum(product, [1])
+
+  if use_logging:
+    internal_dot_products = tf.Print(internal_dot_products, [
+        internal_dot_products,
+        tf.shape(internal_dot_products)
+    ], 'internal_dot_products:')
+
+  logcost = tf.log(1e-4 + 1 - tf.abs(internal_dot_products))
+  return logcost
+
+
+def log_quaternion_loss(predictions, labels, params):
+  """A helper function to compute the mean error between batches of quaternions.
+
+  The caller is expected to add the loss to the graph.
+
+  Args:
+    predictions: A Tensor of size [batch_size, 4].
+    labels: A Tensor of size [batch_size, 4].
+    params: A dictionary of parameters. Expecting 'use_logging', 'batch_size'.
+
+  Returns:
+    A Tensor of size 1, denoting the mean error between batches of quaternions.
+  """
+  use_logging = params['use_logging']
+  logcost = log_quaternion_loss_batch(predictions, labels, params)
+  logcost = tf.reduce_sum(logcost, [0])
+  batch_size = params['batch_size']
+  logcost = tf.multiply(logcost, 1.0 / batch_size, name='log_quaternion_loss')
+  if use_logging:
+    logcost = tf.Print(
+        logcost, [logcost], '[logcost]', name='log_quaternion_loss_print')
+  return logcost
+
+def _quaternion_loss(labels, predictions, weight, batch_size, domain,
+                     add_summaries):
+  """Creates a Quaternion Loss.
+
+  Args:
+    labels: The true quaternions.
+    predictions: The predicted quaternions.
+    weight: A scalar weight.
+    batch_size: The size of the batches.
+    domain: The name of the domain from which the labels were taken.
+    add_summaries: Whether or not to add summaries for the losses.
+
+  Returns:
+    A `Tensor` representing the loss.
+  """
+  assert domain in ['Source', 'Transferred']
+
+  params = {'use_logging': False, 'batch_size': batch_size}
+  loss = weight * log_quaternion_loss(labels, predictions, params)
+
+  if add_summaries:
+    assert_op = tf.Assert(tf.is_finite(loss), [loss])
+    with tf.control_dependencies([assert_op]):
+      tf.summary.histogram(
+          'Log_Quaternion_Loss_%s' % domain, loss, collections='losses')
+      tf.summary.scalar(
+          'Task_Quaternion_Loss_%s' % domain, loss, collections='losses')
+
+  return loss
+
+
+def _add_task_specific_losses(end_points, source_labels, num_classes, hparams,
+                              add_summaries=False):
+  """Adds losses related to the task-classifier.
+
+  Args:
+    end_points: A map of network end point names to `Tensors`.
+    source_labels: A dictionary of output labels to `Tensors`.
+    num_classes: The number of classes used by the classifier.
+    hparams: The hyperparameters struct.
+    add_summaries: Whether or not to add the summaries.
+
+  Returns:
+    loss: A `Tensor` representing the total task-classifier loss.
+  """
+  # TODO(ddohan): Make sure the l2 regularization is added to the loss
+
+  one_hot_labels = slim.one_hot_encoding(source_labels['class'], num_classes)
+  total_loss = 0
+
+  if 'source_task_logits' in end_points:
+    loss = tf.losses.softmax_cross_entropy(
+        onehot_labels=one_hot_labels,
+        logits=end_points['source_task_logits'],
+        weights=hparams.source_task_loss_weight)
+    if add_summaries:
+      tf.summary.scalar('Task_Classifier_Loss_Source', loss)
+    total_loss += loss
+
+  if 'transferred_task_logits' in end_points:
+    loss = tf.losses.softmax_cross_entropy(
+        onehot_labels=one_hot_labels,
+        logits=end_points['transferred_task_logits'],
+        weights=hparams.transferred_task_loss_weight)
+    if add_summaries:
+      tf.summary.scalar('Task_Classifier_Loss_Transferred', loss)
+    total_loss += loss
+
+  #########################
+  # Pose specific losses. #
+  #########################
+  if 'quaternion' in source_labels:
+    total_loss += _quaternion_loss(
+        source_labels['quaternion'],
+        end_points['source_quaternion'],
+        hparams.source_pose_weight,
+        hparams.batch_size,
+        'Source',
+        add_summaries)
+
+    total_loss += _quaternion_loss(
+        source_labels['quaternion'],
+        end_points['transferred_quaternion'],
+        hparams.transferred_pose_weight,
+        hparams.batch_size,
+        'Transferred',
+        add_summaries)
+
+  if add_summaries:
+    tf.summary.scalar('Task_Loss_Total', total_loss)
+
+  return total_loss
+
+
+def _transferred_similarity_loss(reconstructions,
+                                 source_images,
+                                 weight=1.0,
+                                 method='mse',
+                                 max_diff=0.4,
+                                 name='similarity'):
+  """Computes a loss encouraging similarity between source and transferred.
+
+  Args:
+    reconstructions: A `Tensor` of shape [batch_size, height, width, channels]
+    source_images: A `Tensor` of shape [batch_size, height, width, channels].
+    weight: Multiple similarity loss by this weight before returning
+    method: One of:
+      mpse = Mean Pairwise Squared Error
+      mse = Mean Squared Error
+      hinged_mse = Computes the mean squared error using squared differences
+        greater than hparams.transferred_similarity_max_diff
+      hinged_mae = Computes the mean absolute error using absolute
+        differences greater than hparams.transferred_similarity_max_diff.
+    max_diff: Maximum unpenalized difference for hinged losses
+    name: Identifying name to use for creating summaries
+
+
+  Returns:
+    A `Tensor` representing the transferred similarity loss.
+
+  Raises:
+    ValueError: if `method` is not recognized.
+  """
+  if weight == 0:
+    return 0
+
+  source_channels = source_images.shape.as_list()[-1]
+  reconstruction_channels = reconstructions.shape.as_list()[-1]
+
+  # Convert grayscale source to RGB if target is RGB
+  if source_channels == 1 and reconstruction_channels != 1:
+    source_images = tf.tile(source_images, [1, 1, 1, reconstruction_channels])
+  if reconstruction_channels == 1 and source_channels != 1:
+    reconstructions = tf.tile(reconstructions, [1, 1, 1, source_channels])
+
+  if method == 'mpse':
+    reconstruction_similarity_loss_fn = (
+        tf.contrib.losses.mean_pairwise_squared_error)
+  elif method == 'masked_mpse':
+
+    def masked_mpse(predictions, labels, weight):
+      """Masked mpse assuming we have a depth to create a mask from."""
+      assert labels.shape.as_list()[-1] == 4
+      mask = tf.to_float(tf.less(labels[:, :, :, 3:4], 0.99))
+      mask = tf.tile(mask, [1, 1, 1, 4])
+      predictions *= mask
+      labels *= mask
+      tf.image_summary('masked_pred', predictions)
+      tf.image_summary('masked_label', labels)
+      return tf.contrib.losses.mean_pairwise_squared_error(
+          predictions, labels, weight)
+
+    reconstruction_similarity_loss_fn = masked_mpse
+  elif method == 'mse':
+    reconstruction_similarity_loss_fn = tf.contrib.losses.mean_squared_error
+  elif method == 'hinged_mse':
+
+    def hinged_mse(predictions, labels, weight):
+      diffs = tf.square(predictions - labels)
+      diffs = tf.maximum(0.0, diffs - max_diff)
+      return tf.reduce_mean(diffs) * weight
+
+    reconstruction_similarity_loss_fn = hinged_mse
+  elif method == 'hinged_mae':
+
+    def hinged_mae(predictions, labels, weight):
+      diffs = tf.abs(predictions - labels)
+      diffs = tf.maximum(0.0, diffs - max_diff)
+      return tf.reduce_mean(diffs) * weight
+
+    reconstruction_similarity_loss_fn = hinged_mae
+  else:
+    raise ValueError('Unknown reconstruction loss %s' % method)
+
+  reconstruction_similarity_loss = reconstruction_similarity_loss_fn(
+      reconstructions, source_images, weight)
+
+  name = '%s_Similarity_(%s)' % (name, method)
+  tf.summary.scalar(name, reconstruction_similarity_loss)
+  return reconstruction_similarity_loss
+
+
+def g_step_loss(source_images, source_labels, end_points, hparams, num_classes):
+  """Configures the loss function which runs during the g-step.
+
+  Args:
+    source_images: A `Tensor` of shape [batch_size, height, width, channels].
+    source_labels: A dictionary of `Tensors` of shape [batch_size]. Valid keys
+      are 'class' and 'quaternion'.
+    end_points: A map of the network end points.
+    hparams: The hyperparameters struct.
+    num_classes: Number of classes for classifier loss
+
+  Returns:
+    A `Tensor` representing a loss function.
+
+  Raises:
+    ValueError: if hparams.transferred_similarity_loss_weight is non-zero but
+      hparams.transferred_similarity_loss is invalid.
+  """
+  generator_loss = 0
+
+  ################################################################
+  # Adds a loss which encourages the discriminator probabilities #
+  # to be high (near one).
+  ################################################################
+
+  # As per the GAN paper, maximize the log probs, instead of minimizing
+  # log(1-probs). Since we're minimizing, we'll minimize -log(probs) which is
+  # the same thing.
+  style_transfer_loss = tf.losses.sigmoid_cross_entropy(
+      logits=end_points['transferred_domain_logits'],
+      multi_class_labels=tf.ones_like(end_points['transferred_domain_logits']),
+      weights=hparams.style_transfer_loss_weight)
+  tf.summary.scalar('Style_transfer_loss', style_transfer_loss)
+  generator_loss += style_transfer_loss
+
+  # Optimizes the style transfer network to produce transferred images similar
+  # to the source images.
+  generator_loss += _transferred_similarity_loss(
+      end_points['transferred_images'],
+      source_images,
+      weight=hparams.transferred_similarity_loss_weight,
+      method=hparams.transferred_similarity_loss,
+      name='transferred_similarity')
+
+  # Optimizes the style transfer network to maximize classification accuracy.
+  if source_labels is not None and hparams.task_tower_in_g_step:
+    generator_loss += _add_task_specific_losses(
+        end_points, source_labels, num_classes,
+        hparams) * hparams.task_loss_in_g_weight
+
+  return generator_loss
+
+
+def d_step_loss(end_points, source_labels, num_classes, hparams):
+  """Configures the losses during the D-Step.
+
+  Note that during the D-step, the model optimizes both the domain (binary)
+  classifier and the task classifier.
+
+  Args:
+    end_points: A map of the network end points.
+    source_labels: A dictionary of output labels to `Tensors`.
+    num_classes: The number of classes used by the classifier.
+    hparams: The hyperparameters struct.
+
+  Returns:
+    A `Tensor` representing the value of the D-step loss.
+  """
+  domain_classifier_loss = add_domain_classifier_losses(end_points, hparams)
+
+  task_classifier_loss = 0
+  if source_labels is not None:
+    task_classifier_loss = _add_task_specific_losses(
+        end_points, source_labels, num_classes, hparams, add_summaries=True)
+
+  return domain_classifier_loss + task_classifier_loss

domain_adaptation/pixel_domain_adaptation/pixelda_preprocess.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.
+
+"""Contains functions for preprocessing the inputs."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# Dependency imports
+
+import tensorflow as tf
+
+
+def preprocess_classification(image, labels, is_training=False):
+  """Preprocesses the image and labels for classification purposes.
+
+  Preprocessing includes shifting the images to be 0-centered between -1 and 1.
+  This is not only a popular method of preprocessing (inception) but is also
+  the mechanism used by DSNs.
+
+  Args:
+    image: A `Tensor` of size [height, width, 3].
+    labels: A dictionary of labels.
+    is_training: Whether or not we're training the model.
+
+  Returns:
+    The preprocessed image and labels.
+  """
+  # If the image is uint8, this will scale it to 0-1.
+  image = tf.image.convert_image_dtype(image, tf.float32)
+  image -= 0.5
+  image *= 2
+
+  return image, labels
+
+
+def preprocess_style_transfer(image,
+                              labels,
+                              augment=False,
+                              size=None,
+                              is_training=False):
+  """Preprocesses the image and labels for style transfer purposes.
+
+  Args:
+    image: A `Tensor` of size [height, width, 3].
+    labels: A dictionary of labels.
+    augment: Whether to apply data augmentation to inputs
+    size: The height and width to which images should be resized. If left as
+      `None`, then no resizing is performed
+    is_training: Whether or not we're training the model
+
+  Returns:
+    The preprocessed image and labels. Scaled to [-1, 1]
+  """
+  # If the image is uint8, this will scale it to 0-1.
+  image = tf.image.convert_image_dtype(image, tf.float32)
+  if augment and is_training:
+    image = image_augmentation(image)
+
+  if size:
+    image = resize_image(image, size)
+
+  image -= 0.5
+  image *= 2
+
+  return image, labels
+
+
+def image_augmentation(image):
+  """Performs data augmentation by randomly permuting the inputs.
+
+  Args:
+    image: A float `Tensor` of size [height, width, channels] with values
+      in range[0,1].
+
+  Returns:
+    The mutated batch of images
+  """
+  # Apply photometric data augmentation (contrast etc.)
+  num_channels = image.shape_as_list()[-1]
+  if num_channels == 4:
+    # Only augment image part
+    image, depth = image[:, :, 0:3], image[:, :, 3:4]
+  elif num_channels == 1:
+    image = tf.image.grayscale_to_rgb(image)
+  image = tf.image.random_brightness(image, max_delta=0.1)
+  image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
+  image = tf.image.random_hue(image, max_delta=0.032)
+  image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
+  image = tf.clip_by_value(image, 0, 1.0)
+  if num_channels == 4:
+    image = tf.concat(2, [image, depth])
+  elif num_channels == 1:
+    image = tf.image.rgb_to_grayscale(image)
+  return image
+
+
+def resize_image(image, size=None):
+  """Resize image to target size.
+
+  Args:
+    image: A `Tensor` of size [height, width, 3].
+    size: (height, width) to resize image to.
+
+  Returns:
+    resized image
+  """
+  if size is None:
+    raise ValueError('Must specify size')
+
+  if image.shape_as_list()[:2] == size:
+    # Don't resize if not necessary
+    return image
+  image = tf.expand_dims(image, 0)
+  image = tf.image.resize_images(image, size)
+  image = tf.squeeze(image, 0)
+  return image

domain_adaptation/pixel_domain_adaptation/pixelda_preprocess_test.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.
+
+"""Tests for domain_adaptation.pixel_domain_adaptation.pixelda_preprocess."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# Dependency imports
+
+import tensorflow as tf
+
+from domain_adaptation.pixel_domain_adaptation import pixelda_preprocess
+
+
+class PixelDAPreprocessTest(tf.test.TestCase):
+
+  def assert_preprocess_classification_is_centered(self, dtype, is_training):
+    tf.set_random_seed(0)
+
+    if dtype == tf.uint8:
+      image = tf.random_uniform((100, 200, 3), maxval=255, dtype=tf.int64)
+      image = tf.cast(image, tf.uint8)
+    else:
+      image = tf.random_uniform((100, 200, 3), maxval=1.0, dtype=dtype)
+
+    labels = {}
+    image, labels = pixelda_preprocess.preprocess_classification(
+        image, labels, is_training=is_training)
+
+    with self.test_session() as sess:
+      np_image = sess.run(image)
+
+      self.assertTrue(np_image.min() <= -0.95)
+      self.assertTrue(np_image.min() >= -1.0)
+      self.assertTrue(np_image.max() >= 0.95)
+      self.assertTrue(np_image.max() <= 1.0)
+
+  def testPreprocessClassificationZeroCentersUint8DuringTrain(self):
+    self.assert_preprocess_classification_is_centered(
+        tf.uint8, is_training=True)
+
+  def testPreprocessClassificationZeroCentersUint8DuringTest(self):
+    self.assert_preprocess_classification_is_centered(
+        tf.uint8, is_training=False)
+
+  def testPreprocessClassificationZeroCentersFloatDuringTrain(self):
+    self.assert_preprocess_classification_is_centered(
+        tf.float32, is_training=True)
+
+  def testPreprocessClassificationZeroCentersFloatDuringTest(self):
+    self.assert_preprocess_classification_is_centered(
+        tf.float32, is_training=False)
+
+
+if __name__ == '__main__':
+  tf.test.main()

domain_adaptation/pixel_domain_adaptation/pixelda_task_towers.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.
+
+"""Task towers for PixelDA model."""
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+
+def add_task_specific_model(images,
+                            hparams,
+                            num_classes=10,
+                            is_training=False,
+                            reuse_private=False,
+                            private_scope=None,
+                            reuse_shared=False,
+                            shared_scope=None):
+  """Create a classifier for the given images.
+
+  The classifier is composed of a few 'private' layers followed by a few
+  'shared' layers. This lets us account for different image 'style', while
+  sharing the last few layers as 'content' layers.
+
+  Args:
+    images: A `Tensor` of size [batch_size, height, width, 3].
+    hparams: model hparams
+    num_classes: The number of output classes.
+    is_training: whether model is training
+    reuse_private: Whether or not to reuse the private weights, which are the
+      first few layers in the classifier
+    private_scope: The name of the variable_scope for the private (unshared)
+      components of the classifier.
+    reuse_shared: Whether or not to reuse the shared weights, which are the last
+      few layers in the classifier
+    shared_scope: The name of the variable_scope for the shared components of
+      the classifier.
+
+  Returns:
+    The logits, a `Tensor` of shape [batch_size, num_classes].
+
+  Raises:
+    ValueError: If hparams.task_classifier is an unknown value
+  """
+
+  model = hparams.task_tower
+  # Make sure the classifier name shows up in graph
+  shared_scope = shared_scope or (model + '_shared')
+  kwargs = {
+      'num_classes': num_classes,
+      'is_training': is_training,
+      'reuse_private': reuse_private,
+      'reuse_shared': reuse_shared,
+  }
+
+  if private_scope:
+    kwargs['private_scope'] = private_scope
+  if shared_scope:
+    kwargs['shared_scope'] = shared_scope
+
+  quaternion_pred = None
+  with slim.arg_scope(
+      [slim.conv2d, slim.fully_connected],
+      activation_fn=tf.nn.relu,
+      weights_regularizer=tf.contrib.layers.l2_regularizer(
+          hparams.weight_decay_task_classifier)):
+    with slim.arg_scope([slim.conv2d], padding='SAME'):
+      if model == 'doubling_pose_estimator':
+        logits, quaternion_pred = doubling_cnn_class_and_quaternion(
+            images, num_private_layers=hparams.num_private_layers, **kwargs)
+      elif model == 'mnist':
+        logits, _ = mnist_classifier(images, **kwargs)
+      elif model == 'svhn':
+        logits, _ = svhn_classifier(images, **kwargs)
+      elif model == 'gtsrb':
+        logits, _ = gtsrb_classifier(images, **kwargs)
+      elif model == 'pose_mini':
+        logits, quaternion_pred = pose_mini_tower(images, **kwargs)
+      else:
+        raise ValueError('Unknown task classifier %s' % model)
+
+  return logits, quaternion_pred
+
+
+#####################################
+# Classifiers used in the DSN paper #
+#####################################
+
+
+def mnist_classifier(images,
+                     is_training=False,
+                     num_classes=10,
+                     reuse_private=False,
+                     private_scope='mnist',
+                     reuse_shared=False,
+                     shared_scope='task_model'):
+  """Creates the convolutional MNIST model from the gradient reversal paper.
+
+  Note that since the output is a set of 'logits', the values fall in the
+  interval of (-infinity, infinity). Consequently, to convert the outputs to a
+  probability distribution over the characters, one will need to convert them
+  using the softmax function:
+        logits, endpoints = conv_mnist(images, is_training=False)
+        predictions = tf.nn.softmax(logits)
+
+  Args:
+    images: the MNIST digits, a tensor of size [batch_size, 28, 28, 1].
+    is_training: specifies whether or not we're currently training the model.
+      This variable will determine the behaviour of the dropout layer.
+    num_classes: the number of output classes to use.
+
+  Returns:
+    the output logits, a tensor of size [batch_size, num_classes].
+    a dictionary with key/values the layer names and tensors.
+  """
+
+  net = {}
+
+  with tf.variable_scope(private_scope, reuse=reuse_private):
+    net['conv1'] = slim.conv2d(images, 32, [5, 5], scope='conv1')
+    net['pool1'] = slim.max_pool2d(net['conv1'], [2, 2], 2, scope='pool1')
+
+  with tf.variable_scope(shared_scope, reuse=reuse_shared):
+    net['conv2'] = slim.conv2d(net['pool1'], 48, [5, 5], scope='conv2')
+    net['pool2'] = slim.max_pool2d(net['conv2'], [2, 2], 2, scope='pool2')
+    net['fc3'] = slim.fully_connected(
+        slim.flatten(net['pool2']), 100, scope='fc3')
+    net['fc4'] = slim.fully_connected(
+        slim.flatten(net['fc3']), 100, scope='fc4')
+    logits = slim.fully_connected(
+        net['fc4'], num_classes, activation_fn=None, scope='fc5')
+  return logits, net
+
+
+def svhn_classifier(images,
+                    is_training=False,
+                    num_classes=10,
+                    reuse_private=False,
+                    private_scope=None,
+                    reuse_shared=False,
+                    shared_scope='task_model'):
+  """Creates the convolutional SVHN model from the gradient reversal paper.
+
+  Note that since the output is a set of 'logits', the values fall in the
+  interval of (-infinity, infinity). Consequently, to convert the outputs to a
+  probability distribution over the characters, one will need to convert them
+  using the softmax function:
+        logits = mnist.Mnist(images, is_training=False)
+        predictions = tf.nn.softmax(logits)
+
+  Args:
+    images: the SVHN digits, a tensor of size [batch_size, 40, 40, 3].
+    is_training: specifies whether or not we're currently training the model.
+      This variable will determine the behaviour of the dropout layer.
+    num_classes: the number of output classes to use.
+
+  Returns:
+    the output logits, a tensor of size [batch_size, num_classes].
+    a dictionary with key/values the layer names and tensors.
+  """
+
+  net = {}
+
+  with tf.variable_scope(private_scope, reuse=reuse_private):
+    net['conv1'] = slim.conv2d(images, 64, [5, 5], scope='conv1')
+    net['pool1'] = slim.max_pool2d(net['conv1'], [3, 3], 2, scope='pool1')
+
+  with tf.variable_scope(shared_scope, reuse=reuse_shared):
+    net['conv2'] = slim.conv2d(net['pool1'], 64, [5, 5], scope='conv2')
+    net['pool2'] = slim.max_pool2d(net['conv2'], [3, 3], 2, scope='pool2')
+    net['conv3'] = slim.conv2d(net['pool2'], 128, [5, 5], scope='conv3')
+
+    net['fc3'] = slim.fully_connected(
+        slim.flatten(net['conv3']), 3072, scope='fc3')
+    net['fc4'] = slim.fully_connected(
+        slim.flatten(net['fc3']), 2048, scope='fc4')
+
+    logits = slim.fully_connected(
+        net['fc4'], num_classes, activation_fn=None, scope='fc5')
+
+  return logits, net
+
+
+def gtsrb_classifier(images,
+                     is_training=False,
+                     num_classes=43,
+                     reuse_private=False,
+                     private_scope='gtsrb',
+                     reuse_shared=False,
+                     shared_scope='task_model'):
+  """Creates the convolutional GTSRB model from the gradient reversal paper.
+
+  Note that since the output is a set of 'logits', the values fall in the
+  interval of (-infinity, infinity). Consequently, to convert the outputs to a
+  probability distribution over the characters, one will need to convert them
+  using the softmax function:
+        logits = mnist.Mnist(images, is_training=False)
+        predictions = tf.nn.softmax(logits)
+
+  Args:
+    images: the SVHN digits, a tensor of size [batch_size, 40, 40, 3].
+    is_training: specifies whether or not we're currently training the model.
+      This variable will determine the behaviour of the dropout layer.
+    num_classes: the number of output classes to use.
+    reuse_private: Whether or not to reuse the private components of the model.
+    private_scope: The name of the private scope.
+    reuse_shared: Whether or not to reuse the shared components of the model.
+    shared_scope: The name of the shared scope.
+
+  Returns:
+    the output logits, a tensor of size [batch_size, num_classes].
+    a dictionary with key/values the layer names and tensors.
+  """
+
+  net = {}
+
+  with tf.variable_scope(private_scope, reuse=reuse_private):
+    net['conv1'] = slim.conv2d(images, 96, [5, 5], scope='conv1')
+    net['pool1'] = slim.max_pool2d(net['conv1'], [2, 2], 2, scope='pool1')
+  with tf.variable_scope(shared_scope, reuse=reuse_shared):
+    net['conv2'] = slim.conv2d(net['pool1'], 144, [3, 3], scope='conv2')
+    net['pool2'] = slim.max_pool2d(net['conv2'], [2, 2], 2, scope='pool2')
+    net['conv3'] = slim.conv2d(net['pool2'], 256, [5, 5], scope='conv3')
+    net['pool3'] = slim.max_pool2d(net['conv3'], [2, 2], 2, scope='pool3')
+
+    net['fc3'] = slim.fully_connected(
+        slim.flatten(net['pool3']), 512, scope='fc3')
+    logits = slim.fully_connected(
+        net['fc3'], num_classes, activation_fn=None, scope='fc4')
+
+    return logits, net
+
+
+#########################
+# pose_mini task towers #
+#########################
+
+
+def pose_mini_tower(images,
+                    num_classes=11,
+                    is_training=False,
+                    reuse_private=False,
+                    private_scope='pose_mini',
+                    reuse_shared=False,
+                    shared_scope='task_model'):
+  """Task tower for the pose_mini dataset."""
+
+  with tf.variable_scope(private_scope, reuse=reuse_private):
+    net = slim.conv2d(images, 32, [5, 5], scope='conv1')
+    net = slim.max_pool2d(net, [2, 2], stride=2, scope='pool1')
+  with tf.variable_scope(shared_scope, reuse=reuse_shared):
+    net = slim.conv2d(net, 64, [5, 5], scope='conv2')
+    net = slim.max_pool2d(net, [2, 2], stride=2, scope='pool2')
+    net = slim.flatten(net)
+
+    net = slim.fully_connected(net, 128, scope='fc3')
+    net = slim.dropout(net, 0.5, is_training=is_training, scope='dropout')
+    with tf.variable_scope('quaternion_prediction'):
+      quaternion_pred = slim.fully_connected(
+          net, 4, activation_fn=tf.tanh, scope='fc_q')
+      quaternion_pred = tf.nn.l2_normalize(quaternion_pred, 1)
+
+    logits = slim.fully_connected(
+        net, num_classes, activation_fn=None, scope='fc4')
+
+    return logits, quaternion_pred
+
+
+def doubling_cnn_class_and_quaternion(images,
+                                      num_private_layers=1,
+                                      num_classes=10,
+                                      is_training=False,
+                                      reuse_private=False,
+                                      private_scope='doubling_cnn',
+                                      reuse_shared=False,
+                                      shared_scope='task_model'):
+  """Alternate conv, pool while doubling filter count."""
+  net = images
+  depth = 32
+  layer_id = 1
+
+  with tf.variable_scope(private_scope, reuse=reuse_private):
+    while num_private_layers > 0 and net.shape.as_list()[1] > 5:
+      net = slim.conv2d(net, depth, [3, 3], scope='conv%s' % layer_id)
+      net = slim.max_pool2d(net, [2, 2], stride=2, scope='pool%s' % layer_id)
+      depth *= 2
+      layer_id += 1
+      num_private_layers -= 1
+
+  with tf.variable_scope(shared_scope, reuse=reuse_shared):
+    while net.shape.as_list()[1] > 5:
+      net = slim.conv2d(net, depth, [3, 3], scope='conv%s' % layer_id)
+      net = slim.max_pool2d(net, [2, 2], stride=2, scope='pool%s' % layer_id)
+      depth *= 2
+      layer_id += 1
+
+    net = slim.flatten(net)
+    net = slim.fully_connected(net, 100, scope='fc1')
+    net = slim.dropout(net, 0.5, is_training=is_training, scope='dropout')
+    quaternion_pred = slim.fully_connected(
+        net, 4, activation_fn=tf.tanh, scope='fc_q')
+    quaternion_pred = tf.nn.l2_normalize(quaternion_pred, 1)
+
+    logits = slim.fully_connected(
+        net, num_classes, activation_fn=None, scope='fc_logits')
+
+    return logits, quaternion_pred

domain_adaptation/pixel_domain_adaptation/pixelda_train.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"""Trains the PixelDA model."""
+
+from functools import partial
+import os
+
+# Dependency imports
+
+import tensorflow as tf
+
+from domain_adaptation.datasets import dataset_factory
+from domain_adaptation.pixel_domain_adaptation import pixelda_losses
+from domain_adaptation.pixel_domain_adaptation import pixelda_model
+from domain_adaptation.pixel_domain_adaptation import pixelda_preprocess
+from domain_adaptation.pixel_domain_adaptation import pixelda_utils
+from domain_adaptation.pixel_domain_adaptation.hparams import create_hparams
+
+slim = tf.contrib.slim
+
+flags = tf.app.flags
+FLAGS = flags.FLAGS
+
+flags.DEFINE_string('master', '', 'BNS name of the TensorFlow master to use.')
+
+flags.DEFINE_integer(
+    'ps_tasks', 0,
+    'The number of parameter servers. If the value is 0, then the parameters '
+    'are handled locally by the worker.')
+
+flags.DEFINE_integer(
+    'task', 0,
+    'The Task ID. This value is used when training with multiple workers to '
+    'identify each worker.')
+
+flags.DEFINE_string('train_log_dir', '/tmp/pixelda/',
+                    'Directory where to write event logs.')
+
+flags.DEFINE_integer(
+    'save_summaries_steps', 500,
+    'The frequency with which summaries are saved, in seconds.')
+
+flags.DEFINE_integer('save_interval_secs', 300,
+                     'The frequency with which the model is saved, in seconds.')
+
+flags.DEFINE_boolean('summarize_gradients', False,
+                     'Whether to summarize model gradients')
+
+flags.DEFINE_integer(
+    'print_loss_steps', 100,
+    'The frequency with which the losses are printed, in steps.')
+
+flags.DEFINE_string('source_dataset', 'mnist', 'The name of the source dataset.'
+                    ' If hparams="arch=dcgan", this flag is ignored.')
+
+flags.DEFINE_string('target_dataset', 'mnist_m',
+                    'The name of the target dataset.')
+
+flags.DEFINE_string('source_split_name', 'train',
+                    'Name of the train split for the source.')
+
+flags.DEFINE_string('target_split_name', 'train',
+                    'Name of the train split for the target.')
+
+flags.DEFINE_string('dataset_dir', '',
+                    'The directory where the datasets can be found.')
+
+flags.DEFINE_integer(
+    'num_readers', 4,
+    'The number of parallel readers that read data from the dataset.')
+
+flags.DEFINE_integer('num_preprocessing_threads', 4,
+                     'The number of threads used to create the batches.')
+
+# HParams
+
+flags.DEFINE_string('hparams', '', 'Comma separated hyperparameter values')
+
+
+def _get_vars_and_update_ops(hparams, scope):
+  """Returns the variables and update ops for a particular variable scope.
+
+  Args:
+    hparams: The hyperparameters struct.
+    scope: The variable scope.
+
+  Returns:
+    A tuple consisting of trainable variables and update ops.
+  """
+  is_trainable = lambda x: x in tf.trainable_variables()
+  var_list = filter(is_trainable, slim.get_model_variables(scope))
+  global_step = slim.get_or_create_global_step()
+
+  update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope)
+
+  tf.logging.info('All variables for scope: %s',
+                  slim.get_model_variables(scope))
+  tf.logging.info('Trainable variables for scope: %s', var_list)
+
+  return var_list, update_ops
+
+
+def _train(discriminator_train_op,
+           generator_train_op,
+           logdir,
+           master='',
+           is_chief=True,
+           scaffold=None,
+           hooks=None,
+           chief_only_hooks=None,
+           save_checkpoint_secs=600,
+           save_summaries_steps=100,
+           hparams=None):
+  """Runs the training loop.
+
+  Args:
+    discriminator_train_op: A `Tensor` that, when executed, will apply the
+      gradients and return the loss value for the discriminator.
+    generator_train_op: A `Tensor` that, when executed, will apply the
+      gradients and return the loss value for the generator.
+    logdir: The directory where the graph and checkpoints are saved.
+    master: The URL of the master.
+    is_chief: Specifies whether or not the training is being run by the primary
+      replica during replica training.
+    scaffold: An tf.train.Scaffold instance.
+    hooks: List of `tf.train.SessionRunHook` callbacks which are run inside the
+      training loop.
+    chief_only_hooks: List of `tf.train.SessionRunHook` instances which are run
+      inside the training loop for the chief trainer only.
+    save_checkpoint_secs: The frequency, in seconds, that a checkpoint is saved
+      using a default checkpoint saver. If `save_checkpoint_secs` is set to
+      `None`, then the default checkpoint saver isn't used.
+    save_summaries_steps: The frequency, in number of global steps, that the
+      summaries are written to disk using a default summary saver. If
+      `save_summaries_steps` is set to `None`, then the default summary saver
+      isn't used.
+    hparams: The hparams struct.
+
+  Returns:
+    the value of the loss function after training.
+
+  Raises:
+    ValueError: if `logdir` is `None` and either `save_checkpoint_secs` or
+    `save_summaries_steps` are `None.
+  """
+  global_step = slim.get_or_create_global_step()
+
+  scaffold = scaffold or tf.train.Scaffold()
+
+  hooks = hooks or []
+
+  if is_chief:
+    session_creator = tf.train.ChiefSessionCreator(
+        scaffold=scaffold, checkpoint_dir=logdir, master=master)
+
+    if chief_only_hooks:
+      hooks.extend(chief_only_hooks)
+    hooks.append(tf.train.StepCounterHook(output_dir=logdir))
+
+    if save_summaries_steps:
+      if logdir is None:
+        raise ValueError(
+            'logdir cannot be None when save_summaries_steps is None')
+      hooks.append(
+          tf.train.SummarySaverHook(
+              scaffold=scaffold,
+              save_steps=save_summaries_steps,
+              output_dir=logdir))
+
+    if save_checkpoint_secs:
+      if logdir is None:
+        raise ValueError(
+            'logdir cannot be None when save_checkpoint_secs is None')
+      hooks.append(
+          tf.train.CheckpointSaverHook(
+              logdir, save_secs=save_checkpoint_secs, scaffold=scaffold))
+  else:
+    session_creator = tf.train.WorkerSessionCreator(
+        scaffold=scaffold, master=master)
+
+  with tf.train.MonitoredSession(
+      session_creator=session_creator, hooks=hooks) as session:
+    loss = None
+    while not session.should_stop():
+      # Run the domain classifier op X times.
+      for _ in range(hparams.discriminator_steps):
+        if session.should_stop():
+          return loss
+        loss, np_global_step = session.run(
+            [discriminator_train_op, global_step])
+        if np_global_step % FLAGS.print_loss_steps == 0:
+          tf.logging.info('Step %d: Discriminator Loss = %.2f', np_global_step,
+                          loss)
+
+      # Run the generator op X times.
+      for _ in range(hparams.generator_steps):
+        if session.should_stop():
+          return loss
+        loss, np_global_step = session.run([generator_train_op, global_step])
+        if np_global_step % FLAGS.print_loss_steps == 0:
+          tf.logging.info('Step %d: Generator Loss = %.2f', np_global_step,
+                          loss)
+  return loss
+
+
+def run_training(run_dir, checkpoint_dir, hparams):
+  """Runs the training loop.
+
+  Args:
+    run_dir: The directory where training specific logs are placed
+    checkpoint_dir: The directory where the checkpoints and log files are
+      stored.
+    hparams: The hyperparameters struct.
+
+  Raises:
+    ValueError: if hparams.arch is not recognized.
+  """
+  for path in [run_dir, checkpoint_dir]:
+    if not tf.gfile.Exists(path):
+      tf.gfile.MakeDirs(path)
+
+  # Serialize hparams to log dir
+  hparams_filename = os.path.join(checkpoint_dir, 'hparams.json')
+  with tf.gfile.FastGFile(hparams_filename, 'w') as f:
+    f.write(hparams.to_json())
+
+  with tf.Graph().as_default():
+    with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)):
+      global_step = slim.get_or_create_global_step()
+
+      #########################
+      # Preprocess the inputs #
+      #########################
+      target_dataset = dataset_factory.get_dataset(
+          FLAGS.target_dataset,
+          split_name='train',
+          dataset_dir=FLAGS.dataset_dir)
+      target_images, _ = dataset_factory.provide_batch(
+          FLAGS.target_dataset, 'train', FLAGS.dataset_dir, FLAGS.num_readers,
+          hparams.batch_size, FLAGS.num_preprocessing_threads)
+      num_target_classes = target_dataset.num_classes
+
+      if hparams.arch not in ['dcgan']:
+        source_dataset = dataset_factory.get_dataset(
+            FLAGS.source_dataset,
+            split_name='train',
+            dataset_dir=FLAGS.dataset_dir)
+        num_source_classes = source_dataset.num_classes
+        source_images, source_labels = dataset_factory.provide_batch(
+            FLAGS.source_dataset, 'train', FLAGS.dataset_dir, FLAGS.num_readers,
+            hparams.batch_size, FLAGS.num_preprocessing_threads)
+        # Data provider provides 1 hot labels, but we expect categorical.
+        source_labels['class'] = tf.argmax(source_labels['classes'], 1)
+        del source_labels['classes']
+        if num_source_classes != num_target_classes:
+          raise ValueError(
+              'Source and Target datasets must have same number of classes. '
+              'Are %d and %d' % (num_source_classes, num_target_classes))
+      else:
+        source_images = None
+        source_labels = None
+
+      ####################
+      # Define the model #
+      ####################
+      end_points = pixelda_model.create_model(
+          hparams,
+          target_images,
+          source_images=source_images,
+          source_labels=source_labels,
+          is_training=True,
+          num_classes=num_target_classes)
+
+      #################################
+      # Get the variables to optimize #
+      #################################
+      generator_vars, generator_update_ops = _get_vars_and_update_ops(
+          hparams, 'generator')
+      discriminator_vars, discriminator_update_ops = _get_vars_and_update_ops(
+          hparams, 'discriminator')
+
+      ########################
+      # Configure the losses #
+      ########################
+      generator_loss = pixelda_losses.g_step_loss(
+          source_images,
+          source_labels,
+          end_points,
+          hparams,
+          num_classes=num_target_classes)
+      discriminator_loss = pixelda_losses.d_step_loss(
+          end_points, source_labels, num_target_classes, hparams)
+
+      ###########################
+      # Create the training ops #
+      ###########################
+      learning_rate = hparams.learning_rate
+      if hparams.lr_decay_steps:
+        learning_rate = tf.train.exponential_decay(
+            learning_rate,
+            slim.get_or_create_global_step(),
+            decay_steps=hparams.lr_decay_steps,
+            decay_rate=hparams.lr_decay_rate,
+            staircase=True)
+      tf.summary.scalar('Learning_rate', learning_rate)
+
+
+      if hparams.discriminator_steps == 0:
+        discriminator_train_op = tf.no_op()
+      else:
+        discriminator_optimizer = tf.train.AdamOptimizer(
+            learning_rate, beta1=hparams.adam_beta1)
+
+        discriminator_train_op = slim.learning.create_train_op(
+            discriminator_loss,
+            discriminator_optimizer,
+            update_ops=discriminator_update_ops,
+            variables_to_train=discriminator_vars,
+            clip_gradient_norm=hparams.clip_gradient_norm,
+            summarize_gradients=FLAGS.summarize_gradients)
+
+      if hparams.generator_steps == 0:
+        generator_train_op = tf.no_op()
+      else:
+        generator_optimizer = tf.train.AdamOptimizer(
+            learning_rate, beta1=hparams.adam_beta1)
+        generator_train_op = slim.learning.create_train_op(
+            generator_loss,
+            generator_optimizer,
+            update_ops=generator_update_ops,
+            variables_to_train=generator_vars,
+            clip_gradient_norm=hparams.clip_gradient_norm,
+            summarize_gradients=FLAGS.summarize_gradients)
+
+      #############
+      # Summaries #
+      #############
+      pixelda_utils.summarize_model(end_points)
+      pixelda_utils.summarize_transferred_grid(
+          end_points['transferred_images'], source_images, name='Transferred')
+      if 'source_images_recon' in end_points:
+        pixelda_utils.summarize_transferred_grid(
+            end_points['source_images_recon'],
+            source_images,
+            name='Source Reconstruction')
+      pixelda_utils.summaries_color_distributions(end_points['transferred_images'],
+                                               'Transferred')
+      pixelda_utils.summaries_color_distributions(target_images, 'Target')
+
+      if source_images is not None:
+        pixelda_utils.summarize_transferred(source_images,
+                                         end_points['transferred_images'])
+        pixelda_utils.summaries_color_distributions(source_images, 'Source')
+        pixelda_utils.summaries_color_distributions(
+            tf.abs(source_images - end_points['transferred_images']),
+            'Abs(Source_minus_Transferred)')
+
+      number_of_steps = None
+      if hparams.num_training_examples:
+        # Want to control by amount of data seen, not # steps
+        number_of_steps = hparams.num_training_examples / hparams.batch_size
+
+      hooks = [tf.train.StepCounterHook(),]
+
+      chief_only_hooks = [
+          tf.train.CheckpointSaverHook(
+              saver=tf.train.Saver(),
+              checkpoint_dir=run_dir,
+              save_secs=FLAGS.save_interval_secs)
+      ]
+
+      if number_of_steps:
+        hooks.append(tf.train.StopAtStepHook(last_step=number_of_steps))
+
+      _train(
+          discriminator_train_op,
+          generator_train_op,
+          logdir=run_dir,
+          master=FLAGS.master,
+          is_chief=FLAGS.task == 0,
+          hooks=hooks,
+          chief_only_hooks=chief_only_hooks,
+          save_checkpoint_secs=None,
+          save_summaries_steps=FLAGS.save_summaries_steps,
+          hparams=hparams)
+
+def main(_):
+  tf.logging.set_verbosity(tf.logging.INFO)
+  hparams = create_hparams(FLAGS.hparams)
+  run_training(
+      run_dir=FLAGS.train_log_dir,
+      checkpoint_dir=FLAGS.train_log_dir,
+      hparams=hparams)
+
+
+if __name__ == '__main__':
+  tf.app.run()

domain_adaptation/pixel_domain_adaptation/pixelda_utils.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.
+
+"""Utilities for PixelDA model."""
+import math
+
+# Dependency imports
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+flags = tf.app.flags
+FLAGS = flags.FLAGS
+
+
+def remove_depth(images):
+  """Takes a batch of images and remove depth channel if present."""
+  if images.shape.as_list()[-1] == 4:
+    return images[:, :, :, 0:3]
+  return images
+
+
+def image_grid(images, max_grid_size=4):
+  """Given images and N, return first N^2 images as an NxN image grid.
+
+  Args:
+    images: a `Tensor` of size [batch_size, height, width, channels]
+    max_grid_size: Maximum image grid height/width
+
+  Returns:
+    Single image batch, of dim [1, h*n, w*n, c]
+  """
+  images = remove_depth(images)
+  batch_size = images.shape.as_list()[0]
+  grid_size = min(int(math.sqrt(batch_size)), max_grid_size)
+  assert images.shape.as_list()[0] >= grid_size * grid_size
+
+  # If we have a depth channel
+  if images.shape.as_list()[-1] == 4:
+    images = images[:grid_size * grid_size, :, :, 0:3]
+    depth = tf.image.grayscale_to_rgb(images[:grid_size * grid_size, :, :, 3:4])
+
+    images = tf.reshape(images, [-1, images.shape.as_list()[2], 3])
+    split = tf.split(0, grid_size, images)
+    depth = tf.reshape(depth, [-1, images.shape.as_list()[2], 3])
+    depth_split = tf.split(0, grid_size, depth)
+    grid = tf.concat(split + depth_split, 1)
+    return tf.expand_dims(grid, 0)
+  else:
+    images = images[:grid_size * grid_size, :, :, :]
+    images = tf.reshape(
+        images, [-1, images.shape.as_list()[2],
+                 images.shape.as_list()[3]])
+    split = tf.split(images, grid_size, 0)
+    grid = tf.concat(split, 1)
+    return tf.expand_dims(grid, 0)
+
+
+def source_and_output_image_grid(output_images,
+                                 source_images=None,
+                                 max_grid_size=4):
+  """Create NxN image grid for output, concatenate source grid if given.
+
+  Makes grid out of output_images and, if provided, source_images, and
+  concatenates them.
+
+  Args:
+    output_images: [batch_size, h, w, c] tensor of images
+    source_images: optional[batch_size, h, w, c] tensor of images
+    max_grid_size: Image grid height/width
+
+  Returns:
+    Single image batch, of dim [1, h*n, w*n, c]
+
+
+  """
+  output_grid = image_grid(output_images, max_grid_size=max_grid_size)
+  if source_images is not None:
+    source_grid = image_grid(source_images, max_grid_size=max_grid_size)
+    # Make sure they have the same # of channels before concat
+    # Assumes either 1 or 3 channels
+    if output_grid.shape.as_list()[-1] != source_grid.shape.as_list()[-1]:
+      if output_grid.shape.as_list()[-1] == 1:
+        output_grid = tf.tile(output_grid, [1, 1, 1, 3])
+      if source_grid.shape.as_list()[-1] == 1:
+        source_grid = tf.tile(source_grid, [1, 1, 1, 3])
+    output_grid = tf.concat([output_grid, source_grid], 1)
+  return output_grid
+
+
+def summarize_model(end_points):
+  """Summarizes the given model via its end_points.
+
+  Args:
+    end_points: A dictionary of end_point names to `Tensor`.
+  """
+  tf.summary.histogram('domain_logits_transferred',
+                       tf.sigmoid(end_points['transferred_domain_logits']))
+
+  tf.summary.histogram('domain_logits_target',
+                       tf.sigmoid(end_points['target_domain_logits']))
+
+
+def summarize_transferred_grid(transferred_images,
+                               source_images=None,
+                               name='Transferred'):
+  """Produces a visual grid summarization of the image transferrence.
+
+  Args:
+    transferred_images: A `Tensor` of size [batch_size, height, width, c].
+    source_images: A `Tensor` of size [batch_size, height, width, c].
+    name: Name to use in summary name
+  """
+  if source_images is not None:
+    grid = source_and_output_image_grid(transferred_images, source_images)
+  else:
+    grid = image_grid(transferred_images)
+  tf.summary.image('%s_Images_Grid' % name, grid, max_outputs=1)
+
+
+def summarize_transferred(source_images,
+                          transferred_images,
+                          max_images=20,
+                          name='Transferred'):
+  """Produces a visual summary of the image transferrence.
+
+  This summary displays the source image, transferred image, and a grayscale
+  difference image which highlights the differences between input and output.
+
+  Args:
+    source_images: A `Tensor` of size [batch_size, height, width, channels].
+    transferred_images: A `Tensor` of size [batch_size, height, width, channels]
+    max_images: The number of images to show.
+    name: Name to use in summary name
+
+  Raises:
+    ValueError: If number of channels in source and target are incompatible
+  """
+  source_channels = source_images.shape.as_list()[-1]
+  transferred_channels = transferred_images.shape.as_list()[-1]
+  if source_channels < transferred_channels:
+    if source_channels != 1:
+      raise ValueError(
+          'Source must be 1 channel or same # of channels as target')
+    source_images = tf.tile(source_images, [1, 1, 1, transferred_channels])
+  if transferred_channels < source_channels:
+    if transferred_channels != 1:
+      raise ValueError(
+          'Target must be 1 channel or same # of channels as source')
+    transferred_images = tf.tile(transferred_images, [1, 1, 1, source_channels])
+  diffs = tf.abs(source_images - transferred_images)
+  diffs = tf.reduce_max(diffs, reduction_indices=[3], keep_dims=True)
+  diffs = tf.tile(diffs, [1, 1, 1, max(source_channels, transferred_channels)])
+
+  transition_images = tf.concat([
+      source_images,
+      transferred_images,
+      diffs,
+  ], 2)
+
+  tf.summary.image(
+      '%s_difference' % name, transition_images, max_outputs=max_images)
+
+
+def summaries_color_distributions(images, name):
+  """Produces a histogram of the color distributions of the images.
+
+  Args:
+    images: A `Tensor` of size [batch_size, height, width, 3].
+    name: The name of the images being summarized.
+  """
+  tf.summary.histogram('color_values/%s' % name, images)
+
+
+def summarize_images(images, name):
+  """Produces a visual summary of the given images.
+
+  Args:
+    images: A `Tensor` of size [batch_size, height, width, 3].
+    name: The name of the images being summarized.
+  """
+  grid = image_grid(images)
+  tf.summary.image('%s_Images' % name, grid, max_outputs=1)

im2txt/README.md

@@ -322,7 +322,7 @@ bazel-bin/im2txt/run_inference \
 
 Example output:
 
-```shell
+```
 Captions for image COCO_val2014_000000224477.jpg:
   0) a man riding a wave on top of a surfboard . (p=0.040413)
   1) a person riding a surf board on a wave (p=0.017452)

lfads/README.md

@@ -7,9 +7,7 @@ This code implements the model from the paper "[LFADS - Latent Factor Analysis v
 
 The code is written in Python 2.7.6. You will also need:
 
-* **TensorFlow** version 1.1 ([install](http://tflearn.org/installation/)) -
-  there is an incompatibility with LFADS and TF v1.2, which we are in the
-  process of resolving
+* **TensorFlow** version 1.2.1 ([install](https://www.tensorflow.org/install/)) -
 * **NumPy, SciPy, Matplotlib** ([install SciPy stack](https://www.scipy.org/install.html), contains all of them)
 * **h5py** ([install](https://pypi.python.org/pypi/h5py))
 
@@ -38,10 +36,10 @@ These synthetic datasets are provided 1. to gain insight into how the LFADS algo
 
 ## Train an LFADS model
 
-Now that we have our example datasets, we can train some models! To spin up an LFADS model on the synthetic data, run any of the following commands. For the examples that are in the paper, the important hyperparameters are roughly replicated. Most hyperparameters are insensitive to small changes or won't ever be changed unless you want a very fine level of control. In the first example, all hyperparameter flags are enumerated for easy copy-pasting, but for the rest of the examples only the most important flags (~the first 8) are specified for brevity. For a full list of flags, their descriptions, and their default values, refer to the top of `run_lfads.py`.  Please see Table 1 in the Online Methods of the associated paper for definitions of the most important hyperparameters.
+Now that we have our example datasets, we can train some models! To spin up an LFADS model on the synthetic data, run any of the following commands. For the examples that are in the paper, the important hyperparameters are roughly replicated. Most hyperparameters are insensitive to small changes or won't ever be changed unless you want a very fine level of control. In the first example, all hyperparameter flags are enumerated for easy copy-pasting, but for the rest of the examples only the most important flags (~the first 9) are specified for brevity. For a full list of flags, their descriptions, and their default values, refer to the top of `run_lfads.py`.  Please see Table 1 in the Online Methods of the associated paper for definitions of the most important hyperparameters.
 
 ```sh
-# Run LFADS on chaotic rnn data with no input pulses (g = 1.5)
+# Run LFADS on chaotic rnn data with no input pulses (g = 1.5) with spiking noise
 $ python run_lfads.py --kind=train \
 --data_dir=/tmp/rnn_synth_data_v1.0/ \
 --data_filename_stem=chaotic_rnn_no_inputs \
@@ -108,14 +106,16 @@ $ python run_lfads.py --kind=train \
 --data_filename_stem=chaotic_rnn_inputs_g2p5 \
 --lfads_save_dir=/tmp/lfads_chaotic_rnn_inputs_g2p5 \
 --co_dim=1 \
---factors_dim=20
+--factors_dim=20 \
+--output_dist=poisson
 
 # Run LFADS on multi-session RNN data
 $ python run_lfads.py --kind=train \
 --data_dir=/tmp/rnn_synth_data_v1.0/ \
 --data_filename_stem=chaotic_rnn_multisession \
 --lfads_save_dir=/tmp/lfads_chaotic_rnn_multisession \
---factors_dim=10
+--factors_dim=10 \
+--output_dist=poisson
 
 # Run LFADS on integration to bound model data
 $ python run_lfads.py --kind=train \
@@ -124,7 +124,8 @@ $ python run_lfads.py --kind=train \
 --lfads_save_dir=/tmp/lfads_itb_rnn \
 --co_dim=1 \
 --factors_dim=20 \
---controller_input_lag=0
+--controller_input_lag=0 \
+--output_dist=poisson
 
 # Run LFADS on chaotic RNN data with labels
 $ python run_lfads.py --kind=train \
@@ -134,7 +135,20 @@ $ python run_lfads.py --kind=train \
 --co_dim=0 \
 --factors_dim=20 \
 --controller_input_lag=0 \
---ext_input_dim=1
+--ext_input_dim=1 \
+--output_dist=poisson
+
+# Run LFADS on chaotic rnn data with no input pulses (g = 1.5) with Gaussian noise
+$ python run_lfads.py --kind=train \
+--data_dir=/tmp/rnn_synth_data_v1.0/ \
+--data_filename_stem=chaotic_rnn_no_inputs \
+--lfads_save_dir=/tmp/lfads_chaotic_rnn_no_inputs \
+--co_dim=0 \
+--factors_dim=20 \
+--ext_input_dim=0 \
+--controller_input_lag=1 \
+--output_dist=gaussian \
+
 
 ```
 

lfads/lfads.py

@@ -43,6 +43,10 @@ The nested dictionary is the DATA DICTIONARY, which has the following keys:
      output adapter for each dataset. These matrices, if provided, must be of
      size [data_dim x factors] where data_dim is the number of neurons recorded
      on that day, and factors is chosen and set through the '--factors' flag.
+   'alignment_bias_c' - See alignment_matrix_cxf.  This bias will used to
+     the offset for the alignment transformation.  It will *subtract* off the
+     bias from the data, so pca style inits can align factors across sessions.
+
 
   If one runs LFADS on data where the true rates are known for some trials,
   (say simulated, testing data, as in the example shipped with the paper), then
@@ -277,7 +281,7 @@ class LFADS(object):
     """Create an LFADS model.
 
        train - a model for training, sampling of posteriors is used
-       posterior_sample_and_average - sample from the posterior, this is used 
+       posterior_sample_and_average - sample from the posterior, this is used
          for evaluating the expected value of the outputs of LFADS, given a
          specific input, by averaging over multiple samples from the approx
          posterior.  Also used for the lower bound on the negative
@@ -356,18 +360,36 @@ class LFADS(object):
     for d, name in enumerate(dataset_names):
       data_dim = hps.dataset_dims[name]
       in_mat_cxf = None
+      in_bias_1xf = None
+      align_bias_1xc = None
+
       if datasets and 'alignment_matrix_cxf' in datasets[name].keys():
         dataset = datasets[name]
         print("Using alignment matrix provided for dataset:", name)
         in_mat_cxf = dataset['alignment_matrix_cxf'].astype(np.float32)
         if in_mat_cxf.shape != (data_dim, factors_dim):
-          raise ValueError("""Alignment matrix must have dimensions %d x %d 
+          raise ValueError("""Alignment matrix must have dimensions %d x %d
           (data_dim x factors_dim), but currently has %d x %d."""%
                            (data_dim, factors_dim, in_mat_cxf.shape[0],
                             in_mat_cxf.shape[1]))
+      if datasets and 'alignment_bias_c' in datasets[name].keys():
+        dataset = datasets[name]
+        print("Using alignment bias provided for dataset:", name)
+        align_bias_c = dataset['alignment_bias_c'].astype(np.float32)
+        align_bias_1xc = np.expand_dims(align_bias_c, axis=0)
+        if align_bias_1xc.shape[1] != data_dim:
+          raise ValueError("""Alignment bias must have dimensions %d
+          (data_dim), but currently has %d."""%
+                           (data_dim, in_mat_cxf.shape[0]))
+        if in_mat_cxf is not None and align_bias_1xc is not None:
+          # (data - alignment_bias) * W_in
+          # data * W_in - alignment_bias * W_in
+          # So b = -alignment_bias * W_in to accommodate PCA style offset.
+          in_bias_1xf = -np.dot(align_bias_1xc, in_mat_cxf)
 
       in_fac_lin = init_linear(data_dim, used_in_factors_dim, do_bias=True,
                                mat_init_value=in_mat_cxf,
+                               bias_init_value=in_bias_1xf,
                                identity_if_possible=in_identity_if_poss,
                                normalized=False, name="x_2_infac_"+name,
                                collections=['IO_transformations'])
@@ -387,13 +409,22 @@ class LFADS(object):
           dataset = datasets[name]
           in_mat_cxf = dataset['alignment_matrix_cxf'].astype(np.float32)
 
-        out_mat_cxf = None
+        if datasets and 'alignment_bias_c' in datasets[name].keys():
+          dataset = datasets[name]
+          align_bias_c = dataset['alignment_bias_c'].astype(np.float32)
+          align_bias_1xc = np.expand_dims(align_bias_c, axis=0)
+
+        out_mat_fxc = None
+        out_bias_1xc = None
         if in_mat_cxf is not None:
-          out_mat_cxf = in_mat_cxf.T
+          out_mat_fxc = np.linalg.pinv(in_mat_cxf)
+        if align_bias_1xc is not None:
+          out_bias_1xc = align_bias_1xc
 
         if hps.output_dist == 'poisson':
           out_fac_lin = init_linear(factors_dim, data_dim, do_bias=True,
-                                    mat_init_value=out_mat_cxf,
+                                    mat_init_value=out_mat_fxc,
+                                    bias_init_value=out_bias_1xc,
                                     identity_if_possible=out_identity_if_poss,
                                     normalized=False,
                                     name="fac_2_logrates_"+name,
@@ -403,13 +434,19 @@ class LFADS(object):
         elif hps.output_dist == 'gaussian':
           out_fac_lin_mean = \
               init_linear(factors_dim, data_dim, do_bias=True,
-                          mat_init_value=out_mat_cxf,
+                          mat_init_value=out_mat_fxc,
+                          bias_init_value=out_bias_1xc,
                           normalized=False,
                           name="fac_2_means_"+name,
                           collections=['IO_transformations'])
+          out_fac_W_mean, out_fac_b_mean = out_fac_lin_mean
+
+          mat_init_value = np.zeros([factors_dim, data_dim]).astype(np.float32)
+          bias_init_value = np.ones([1, data_dim]).astype(np.float32)
           out_fac_lin_logvar = \
               init_linear(factors_dim, data_dim, do_bias=True,
-                          mat_init_value=out_mat_cxf,
+                          mat_init_value=mat_init_value,
+                          bias_init_value=bias_init_value,
                           normalized=False,
                           name="fac_2_logvars_"+name,
                           collections=['IO_transformations'])
@@ -432,11 +469,15 @@ class LFADS(object):
     pf_pairs_out_fac_Ws = zip(preds, fns_out_fac_Ws)
     pf_pairs_out_fac_bs = zip(preds, fns_out_fac_bs)
 
-    case_default = lambda: tf.constant([-8675309.0])
-    this_in_fac_W = tf.case(pf_pairs_in_fac_Ws, case_default, exclusive=True)
-    this_in_fac_b = tf.case(pf_pairs_in_fac_bs, case_default, exclusive=True)
-    this_out_fac_W = tf.case(pf_pairs_out_fac_Ws, case_default, exclusive=True)
-    this_out_fac_b = tf.case(pf_pairs_out_fac_bs, case_default, exclusive=True)
+    def _case_with_no_default(pairs):
+      def _default_value_fn():
+        with tf.control_dependencies([tf.Assert(False, ["Reached default"])]):
+          return tf.identity(pairs[0][1]())
+      return tf.case(pairs, _default_value_fn, exclusive=True)
+    this_in_fac_W = _case_with_no_default(pf_pairs_in_fac_Ws)
+    this_in_fac_b = _case_with_no_default(pf_pairs_in_fac_bs)
+    this_out_fac_W = _case_with_no_default(pf_pairs_out_fac_Ws)
+    this_out_fac_b = _case_with_no_default(pf_pairs_out_fac_bs)
 
     # External inputs (not changing by dataset, by definition).
     if hps.ext_input_dim > 0:
@@ -952,7 +993,7 @@ class LFADS(object):
 
     session = tf.get_default_session()
     self.logfile = os.path.join(hps.lfads_save_dir, "lfads_log")
-    self.writer = tf.summary.FileWriter(self.logfile, session.graph)
+    self.writer = tf.summary.FileWriter(self.logfile)
 
   def build_feed_dict(self, train_name, data_bxtxd, ext_input_bxtxi=None,
                       keep_prob=None):
@@ -1678,7 +1719,7 @@ class LFADS(object):
       out_dist_params = np.zeros([E_to_process, T, D+D])
     else:
       assert False, "NIY"
-    
+
     costs = np.zeros(E_to_process)
     nll_bound_vaes = np.zeros(E_to_process)
     nll_bound_iwaes = np.zeros(E_to_process)
@@ -1878,7 +1919,7 @@ class LFADS(object):
     for i, (var, var_eval) in enumerate(zip(all_tf_vars, all_tf_vars_eval)):
       if any(s in include_strs for s in var.name):
         if not isinstance(var_eval, np.ndarray): # for H5PY
-          print(var.name, """ is not numpy array, saving as numpy array 
+          print(var.name, """ is not numpy array, saving as numpy array
                 with value: """, var_eval, type(var_eval))
           e = np.array(var_eval)
           print(e, type(e))

lfads/synth_data/generate_chaotic_rnn_data.py

@@ -24,7 +24,7 @@ from utils import write_datasets
 from synthetic_data_utils import add_alignment_projections, generate_data
 from synthetic_data_utils import generate_rnn, get_train_n_valid_inds
 from synthetic_data_utils import nparray_and_transpose
-from synthetic_data_utils import spikify_data, split_list_by_inds
+from synthetic_data_utils import spikify_data, gaussify_data, split_list_by_inds
 import matplotlib
 import matplotlib.pyplot as plt
 import scipy.signal
@@ -37,6 +37,7 @@ flags.DEFINE_string("save_dir", "/tmp/" + DATA_DIR + "/",
                     "Directory for saving data.")
 flags.DEFINE_string("datafile_name", "thits_data",
                     "Name of data file for input case.")
+flags.DEFINE_string("noise_type", "poisson", "Noise type for data.")
 flags.DEFINE_integer("synth_data_seed", 5, "Random seed for RNN generation.")
 flags.DEFINE_float("T", 1.0, "Time in seconds to generate.")
 flags.DEFINE_integer("C", 100, "Number of conditions")
@@ -45,8 +46,8 @@ flags.DEFINE_integer("S", 50, "Number of sampled units from RNN")
 flags.DEFINE_integer("npcs", 10, "Number of PCS for multi-session case.")
 flags.DEFINE_float("train_percentage", 4.0/5.0,
                    "Percentage of train vs validation trials")
-flags.DEFINE_integer("nspikifications", 40,
-                     "Number of spikifications of the same underlying rates.")
+flags.DEFINE_integer("nreplications", 40,
+                     "Number of noise replications of the same underlying rates.")
 flags.DEFINE_float("g", 1.5, "Complexity of dynamics")
 flags.DEFINE_float("x0_std", 1.0,
                    "Volume from which to pull initial conditions (affects diversity of dynamics.")
@@ -73,8 +74,8 @@ C = FLAGS.C
 N = FLAGS.N
 S = FLAGS.S
 input_magnitude = FLAGS.input_magnitude
-nspikifications = FLAGS.nspikifications
-E = nspikifications * C         # total number of trials
+nreplications = FLAGS.nreplications
+E = nreplications * C         # total number of trials
 # S is the number of measurements in each datasets, w/ each
 # dataset having a different set of observations.
 ndatasets = N/S                 # ok if rounded down
@@ -87,9 +88,9 @@ rnn = generate_rnn(rng, N, FLAGS.g, FLAGS.tau, FLAGS.dt, FLAGS.max_firing_rate)
 # Check to make sure the RNN is the one we used in the paper.
 if N == 50:
   assert abs(rnn['W'][0,0] - 0.06239899) < 1e-8, 'Error in random seed?'
-  rem_check = nspikifications * train_percentage
+  rem_check = nreplications * train_percentage
   assert  abs(rem_check - int(rem_check)) < 1e-8, \
-    'Train percentage  * nspikifications should be integral number.'
+    'Train percentage  * nreplications should be integral number.'
 
 
 # Initial condition generation, and condition label generation.  This
@@ -100,9 +101,9 @@ x0s = []
 condition_labels = []
 for c in range(C):
   x0 = FLAGS.x0_std * rng.randn(N, 1)
-  x0s.append(np.tile(x0, nspikifications)) # replicate x0 nspikifications times
-  # replicate the condition label nspikifications times
-  for ns in range(nspikifications):
+  x0s.append(np.tile(x0, nreplications)) # replicate x0 nreplications times
+  # replicate the condition label nreplications times
+  for ns in range(nreplications):
     condition_labels.append(condition_number)
   condition_number += 1
 x0s = np.concatenate(x0s, axis=1)
@@ -113,8 +114,8 @@ for n in range(ndatasets):
   print(n+1, " of ", ndatasets)
 
   # First generate all firing rates. in the next loop, generate all
-  # spikifications this allows the random state for rate generation to be
-  # independent of n_spikifications.
+  # replications this allows the random state for rate generation to be
+  # independent of n_replications.
   dataset_name = 'dataset_N' + str(N) + '_S' + str(S)
   if S < N:
     dataset_name += '_n' + str(n+1)
@@ -136,17 +137,23 @@ for n in range(ndatasets):
       generate_data(rnn, T=T, E=E, x0s=x0s, P_sxn=P_sxn,
                     input_magnitude=input_magnitude,
                     input_times=input_times)
-  spikes = spikify_data(rates, rng, rnn['dt'], rnn['max_firing_rate'])
 
-  # split into train and validation sets
+  if FLAGS.noise_type == "poisson":
+    noisy_data = spikify_data(rates, rng, rnn['dt'], rnn['max_firing_rate'])
+  elif FLAGS.noise_type == "gaussian":
+    noisy_data = gaussify_data(rates, rng, rnn['dt'], rnn['max_firing_rate'])
+  else:
+    raise ValueError("Only noise types supported are poisson or gaussian")
+
+    # split into train and validation sets
   train_inds, valid_inds = get_train_n_valid_inds(E, train_percentage,
-                                                  nspikifications)
+                                                  nreplications)
 
   # Split the data, inputs, labels and times into train vs. validation.
   rates_train, rates_valid = \
       split_list_by_inds(rates, train_inds, valid_inds)
-  spikes_train, spikes_valid = \
-      split_list_by_inds(spikes, train_inds, valid_inds)
+  noisy_data_train, noisy_data_valid = \
+      split_list_by_inds(noisy_data, train_inds, valid_inds)
   input_train, inputs_valid = \
       split_list_by_inds(inputs, train_inds, valid_inds)
   condition_labels_train, condition_labels_valid = \
@@ -154,25 +161,25 @@ for n in range(ndatasets):
   input_times_train, input_times_valid = \
       split_list_by_inds(input_times, train_inds, valid_inds)
 
-  # Turn rates, spikes, and input into numpy arrays.
+  # Turn rates, noisy_data, and input into numpy arrays.
   rates_train = nparray_and_transpose(rates_train)
   rates_valid = nparray_and_transpose(rates_valid)
-  spikes_train = nparray_and_transpose(spikes_train)
-  spikes_valid = nparray_and_transpose(spikes_valid)
+  noisy_data_train = nparray_and_transpose(noisy_data_train)
+  noisy_data_valid = nparray_and_transpose(noisy_data_valid)
   input_train = nparray_and_transpose(input_train)
   inputs_valid = nparray_and_transpose(inputs_valid)
 
   # Note that we put these 'truth' rates and input into this
-  # structure, the only data that is used in LFADS are the spike
-  # trains.  The rest is either for printing or posterity.
+  # structure, the only data that is used in LFADS are the noisy
+  # data e.g. spike trains.  The rest is either for printing or posterity.
   data = {'train_truth': rates_train,
           'valid_truth': rates_valid,
           'input_train_truth' : input_train,
           'input_valid_truth' : inputs_valid,
-          'train_data' : spikes_train,
-          'valid_data' : spikes_valid,
+          'train_data' : noisy_data_train,
+          'valid_data' : noisy_data_valid,
           'train_percentage' : train_percentage,
-          'nspikifications' : nspikifications,
+          'nreplications' : nreplications,
           'dt' : rnn['dt'],
           'input_magnitude' : input_magnitude,
           'input_times_train' : input_times_train,

lfads/synth_data/run_generate_synth_data.sh

@@ -18,20 +18,23 @@
 
 SYNTH_PATH=/tmp/rnn_synth_data_v1.0/
 
-echo "Generating chaotic rnn data with no input pulses (g=1.5)"
-python generate_chaotic_rnn_data.py --save_dir=$SYNTH_PATH --datafile_name=chaotic_rnn_no_inputs --synth_data_seed=5 --T=1.0 --C=400 --N=50 --S=50 --train_percentage=0.8 --nspikifications=10 --g=1.5 --x0_std=1.0 --tau=0.025 --dt=0.01 --input_magnitude=0.0 --max_firing_rate=30.0
+ echo "Generating chaotic rnn data with no input pulses (g=1.5) with spiking noise"
+ python generate_chaotic_rnn_data.py --save_dir=$SYNTH_PATH --datafile_name=chaotic_rnn_no_inputs --synth_data_seed=5 --T=1.0 --C=400 --N=50 --S=50 --train_percentage=0.8 --nspikifications=10 --g=1.5 --x0_std=1.0 --tau=0.025 --dt=0.01 --input_magnitude=0.0 --max_firing_rate=30.0 --noise_type='poisson'
 
-echo "Generating chaotic rnn data with input pulses (g=1.5)"
-python generate_chaotic_rnn_data.py --save_dir=$SYNTH_PATH --datafile_name=chaotic_rnn_inputs_g1p5 --synth_data_seed=5 --T=1.0 --C=400 --N=50 --S=50 --train_percentage=0.8 --nspikifications=10 --g=1.5 --x0_std=1.0 --tau=0.025 --dt=0.01 --input_magnitude=20.0 --max_firing_rate=30.0
+echo "Generating chaotic rnn data with no input pulses (g=1.5) with Gaussian noise"
+python generate_chaotic_rnn_data.py --save_dir=$SYNTH_PATH --datafile_name=chaotic_rnn_no_inputs_gaussian --synth_data_seed=5 --T=1.0 --C=400 --N=50 --S=50 --train_percentage=0.8 --nspikifications=10 --g=1.5 --x0_std=1.0 --tau=0.025 --dt=0.01 --input_magnitude=0.0 --max_firing_rate=30.0 --noise_type='gaussian'
 
-echo "Generating chaotic rnn data with input pulses (g=2.5)"
-python generate_chaotic_rnn_data.py --save_dir=$SYNTH_PATH --datafile_name=chaotic_rnn_inputs_g2p5 --synth_data_seed=5 --T=1.0 --C=400 --N=50 --S=50 --train_percentage=0.8 --nspikifications=10 --g=2.5 --x0_std=1.0 --tau=0.025 --dt=0.01 --input_magnitude=20.0 --max_firing_rate=30.0
+ echo "Generating chaotic rnn data with input pulses (g=1.5)"
+ python generate_chaotic_rnn_data.py --save_dir=$SYNTH_PATH --datafile_name=chaotic_rnn_inputs_g1p5 --synth_data_seed=5 --T=1.0 --C=400 --N=50 --S=50 --train_percentage=0.8 --nspikifications=10 --g=1.5 --x0_std=1.0 --tau=0.025 --dt=0.01 --input_magnitude=20.0 --max_firing_rate=30.0 --noise_type='poisson'
 
-echo "Generate the multi-session RNN data (no multi-session synth example in paper)"
-python generate_chaotic_rnn_data.py --save_dir=$SYNTH_PATH --datafile_name=chaotic_rnn_multisession --synth_data_seed=5 --T=1.0 --C=150 --N=100 --S=20 --npcs=10 --train_percentage=0.8 --nspikifications=40 --g=1.5 --x0_std=1.0 --tau=0.025 --dt=0.01 --input_magnitude=0.0 --max_firing_rate=30.0
+ echo "Generating chaotic rnn data with input pulses (g=2.5)"
+ python generate_chaotic_rnn_data.py --save_dir=$SYNTH_PATH --datafile_name=chaotic_rnn_inputs_g2p5 --synth_data_seed=5 --T=1.0 --C=400 --N=50 --S=50 --train_percentage=0.8 --nspikifications=10 --g=2.5 --x0_std=1.0 --tau=0.025 --dt=0.01 --input_magnitude=20.0 --max_firing_rate=30.0 --noise_type='poisson'
 
-echo "Generating Integration-to-bound RNN data"
-python generate_itb_data.py --save_dir=$SYNTH_PATH --datafile_name=itb_rnn --u_std=0.25 --checkpoint_path=SAMPLE_CHECKPOINT --synth_data_seed=5 --T=1.0 --C=800 --N=50 --train_percentage=0.8 --nspikifications=5 --tau=0.025 --dt=0.01 --max_firing_rate=30.0
+ echo "Generate the multi-session RNN data (no multi-session synth example in paper)"
+ python generate_chaotic_rnn_data.py --save_dir=$SYNTH_PATH --datafile_name=chaotic_rnn_multisession --synth_data_seed=5 --T=1.0 --C=150 --N=100 --S=20 --npcs=10 --train_percentage=0.8 --nspikifications=40 --g=1.5 --x0_std=1.0 --tau=0.025 --dt=0.01 --input_magnitude=0.0 --max_firing_rate=30.0 --noise_type='poisson'
 
-echo "Generating chaotic rnn data with external input labels (no external input labels example in paper)"
-python generate_labeled_rnn_data.py --save_dir=$SYNTH_PATH --datafile_name=chaotic_rnns_labeled --synth_data_seed=5 --T=1.0 --C=400 --N=50  --train_percentage=0.8 --nspikifications=10 --g=1.5 --x0_std=1.0 --tau=0.025 --dt=0.01 --max_firing_rate=30.0
+ echo "Generating Integration-to-bound RNN data"
+ python generate_itb_data.py --save_dir=$SYNTH_PATH --datafile_name=itb_rnn --u_std=0.25 --checkpoint_path=SAMPLE_CHECKPOINT --synth_data_seed=5 --T=1.0 --C=800 --N=50 --train_percentage=0.8 --nspikifications=5 --tau=0.025 --dt=0.01 --max_firing_rate=30.0
+
+ echo "Generating chaotic rnn data with external input labels (no external input labels example in paper)"
+ python generate_labeled_rnn_data.py --save_dir=$SYNTH_PATH --datafile_name=chaotic_rnns_labeled --synth_data_seed=5 --T=1.0 --C=400 --N=50  --train_percentage=0.8 --nspikifications=10 --g=1.5 --x0_std=1.0 --tau=0.025 --dt=0.01 --max_firing_rate=30.0

lfads/synth_data/synthetic_data_utils.py

@@ -132,11 +132,10 @@ def spikify_data(data_e, rng, dt=1.0, max_firing_rate=100):
     dt: how often the data are sampled
     max_firing_rate: the firing rate that is associated with a value of 1.0
   Returns:
-    spikified_data_e: a list of length b of the data represented as spikes,
+    spikified_e: a list of length b of the data represented as spikes,
     sampled from the underlying poisson process.
     """
 
-  spikifies_data_e = []
   E = len(data_e)
   spikes_e = []
   for e in range(E):
@@ -152,6 +151,31 @@ def spikify_data(data_e, rng, dt=1.0, max_firing_rate=100):
   return spikes_e
 
 
+def gaussify_data(data_e, rng, dt=1.0, max_firing_rate=100):
+  """ Apply gaussian noise to a continuous dataset whose values are between
+  0.0 and 1.0
+
+  Args:
+    data_e: nexamples length list of NxT trials
+    dt: how often the data are sampled
+    max_firing_rate: the firing rate that is associated with a value of 1.0
+  Returns:
+    gauss_e: a list of length b of the data with noise.
+    """
+
+  E = len(data_e)
+  mfr = max_firing_rate
+  gauss_e = []
+  for e in range(E):
+    data = data_e[e]
+    N,T = data.shape
+    noisy_data = data * mfr + np.random.randn(N,T) * (5.0*mfr) * np.sqrt(dt)
+    gauss_e.append(noisy_data)
+
+  return gauss_e
+
+
+
 def get_train_n_valid_inds(num_trials, train_fraction, nspikifications):
   """Split the numbers between 0 and num_trials-1 into two portions for
   training and validation, based on the train fraction.
@@ -295,6 +319,8 @@ def add_alignment_projections(datasets, npcs, ntime=None, nsamples=None):
     W_chxp, _, _, _ = \
         np.linalg.lstsq(all_data_zm_chxtc.T, all_data_pca_pxtc.T)
     dataset['alignment_matrix_cxf'] = W_chxp
+    alignment_bias_cx1 = all_data_mean_nx1[cidx_s:cidx_f]
+    dataset['alignment_bias_c'] = np.squeeze(alignment_bias_cx1, axis=1)
 
   do_debug_plot = False
   if do_debug_plot:

lfads/utils.py

@@ -82,9 +82,9 @@ def linear(x, out_size, do_bias=True, alpha=1.0, identity_if_possible=False,
     return tf.matmul(x, W)
 
 
-def init_linear(in_size, out_size, do_bias=True, mat_init_value=None, alpha=1.0,
-                identity_if_possible=False, normalized=False,
-                name=None, collections=None):
+def init_linear(in_size, out_size, do_bias=True, mat_init_value=None,
+                bias_init_value=None, alpha=1.0, identity_if_possible=False,
+                normalized=False, name=None, collections=None):
   """Linear (affine) transformation, y = x W + b, for a variety of
   configurations.
 
@@ -110,6 +110,9 @@ def init_linear(in_size, out_size, do_bias=True, mat_init_value=None, alpha=1.0,
   if mat_init_value is not None and mat_init_value.shape != (in_size, out_size):
     raise ValueError(
         'Provided mat_init_value must have shape [%d, %d].'%(in_size, out_size))
+  if bias_init_value is not None and bias_init_value.shape != (1,out_size):
+    raise ValueError(
+        'Provided bias_init_value must have shape [1,%d].'%(out_size,))
 
   if mat_init_value is None:
     stddev = alpha/np.sqrt(float(in_size))
@@ -143,16 +146,20 @@ def init_linear(in_size, out_size, do_bias=True, mat_init_value=None, alpha=1.0,
       w = tf.get_variable(wname, [in_size, out_size], initializer=mat_init,
                           collections=w_collections)
 
+  b = None
   if do_bias:
     b_collections = [tf.GraphKeys.GLOBAL_VARIABLES]
     if collections:
       b_collections += collections
     bname = (name + "/b") if name else "/b"
-    b = tf.get_variable(bname, [1, out_size],
-                        initializer=tf.zeros_initializer(),
-                        collections=b_collections)
-  else:
-    b = None
+    if bias_init_value is None:
+      b = tf.get_variable(bname, [1, out_size],
+                          initializer=tf.zeros_initializer(),
+                          collections=b_collections)
+    else:
+      b = tf.Variable(bias_init_value, name=bname,
+                      collections=b_collections)
+
 
   return (w, b)
 

object_detection/README.md

@@ -54,6 +54,17 @@ Extras:
       Exporting a trained model for inference</a><br>
 * <a href='g3doc/defining_your_own_model.md'>
       Defining your own model architecture</a><br>
+* <a href='g3doc/using_your_own_dataset.md'>
+      Bringing in your own dataset</a><br>
+
+## Getting Help
+
+Please report bugs to the tensorflow/models/ Github
+[issue tracker](https://github.com/tensorflow/models/issues), prefixing the
+issue name with "object_detection". To get help with issues you may encounter
+using the Tensorflow Object Detection API, create a new question on
+[StackOverflow](https://stackoverflow.com/) with the tags "tensorflow" and
+"object-detection".
 
 ## Release information
 

object_detection/core/BUILD

@@ -270,6 +270,7 @@ py_library(
     deps = [
         "//tensorflow",
         "//tensorflow_models/object_detection/utils:ops",
+        "//tensorflow_models/object_detection/utils:shape_utils",
         "//tensorflow_models/object_detection/utils:static_shape",
     ],
 )

object_detection/core/box_predictor.py

@@ -29,6 +29,7 @@ few box predictor architectures are shared across many models.
 from abc import abstractmethod
 import tensorflow as tf
 from object_detection.utils import ops
+from object_detection.utils import shape_utils
 from object_detection.utils import static_shape
 
 slim = tf.contrib.slim
@@ -316,6 +317,8 @@ class MaskRCNNBoxPredictor(BoxPredictor):
     self._predict_instance_masks = predict_instance_masks
     self._mask_prediction_conv_depth = mask_prediction_conv_depth
     self._predict_keypoints = predict_keypoints
+    if self._predict_instance_masks:
+      raise ValueError('Mask prediction is unimplemented.')
     if self._predict_keypoints:
       raise ValueError('Keypoint prediction is unimplemented.')
     if ((self._predict_instance_masks or self._predict_keypoints) and
@@ -524,23 +527,21 @@ class ConvolutionalBoxPredictor(BoxPredictor):
           class_predictions_with_background = tf.sigmoid(
               class_predictions_with_background)
 
-    batch_size = static_shape.get_batch_size(image_features.get_shape())
-    if batch_size is None:
-      features_height = static_shape.get_height(image_features.get_shape())
-      features_width = static_shape.get_width(image_features.get_shape())
-      flattened_predictions_size = (features_height * features_width *
-                                    num_predictions_per_location)
-      box_encodings = tf.reshape(
-          box_encodings,
-          [-1, flattened_predictions_size, 1, self._box_code_size])
-      class_predictions_with_background = tf.reshape(
-          class_predictions_with_background,
-          [-1, flattened_predictions_size, num_class_slots])
-    else:
-      box_encodings = tf.reshape(
-          box_encodings, [batch_size, -1, 1, self._box_code_size])
-      class_predictions_with_background = tf.reshape(
-          class_predictions_with_background, [batch_size, -1, num_class_slots])
+    combined_feature_map_shape = shape_utils.combined_static_and_dynamic_shape(
+        image_features)
+    box_encodings = tf.reshape(
+        box_encodings, tf.stack([combined_feature_map_shape[0],
+                                 combined_feature_map_shape[1] *
+                                 combined_feature_map_shape[2] *
+                                 num_predictions_per_location,
+                                 1, self._box_code_size]))
+    class_predictions_with_background = tf.reshape(
+        class_predictions_with_background,
+        tf.stack([combined_feature_map_shape[0],
+                  combined_feature_map_shape[1] *
+                  combined_feature_map_shape[2] *
+                  num_predictions_per_location,
+                  num_class_slots]))
     return {BOX_ENCODINGS: box_encodings,
             CLASS_PREDICTIONS_WITH_BACKGROUND:
             class_predictions_with_background}

object_detection/core/model.py

@@ -228,25 +228,24 @@ class DetectionModel(object):
           fields.BoxListFields.keypoints] = groundtruth_keypoints_list
 
   @abstractmethod
-  def restore_fn(self, checkpoint_path, from_detection_checkpoint=True):
-    """Return callable for loading a foreign checkpoint into tensorflow graph.
+  def restore_map(self, from_detection_checkpoint=True):
+    """Returns a map of variables to load from a foreign checkpoint.
 
-    Loads variables from a different tensorflow graph (typically feature
-    extractor variables). This enables the model to initialize based on weights
-    from another task. For example, the feature extractor variables from a
+    Returns a map of variable names to load from a checkpoint to variables in
+    the model graph. This enables the model to initialize based on weights from
+    another task. For example, the feature extractor variables from a
     classification model can be used to bootstrap training of an object
     detector. When loading from an object detection model, the checkpoint model
     should have the same parameters as this detection model with exception of
     the num_classes parameter.
 
     Args:
-      checkpoint_path: path to checkpoint to restore.
       from_detection_checkpoint: whether to restore from a full detection
         checkpoint (with compatible variable names) or to restore from a
         classification checkpoint for initialization prior to training.
 
     Returns:
-      a callable which takes a tf.Session as input and loads a checkpoint when
-        run.
+      A dict mapping variable names (to load from a checkpoint) to variables in
+      the model graph.
     """
     pass

object_detection/core/post_processing.py

@@ -174,7 +174,8 @@ def batch_multiclass_non_max_suppression(boxes,
                                          change_coordinate_frame=False,
                                          num_valid_boxes=None,
                                          masks=None,
-                                         scope=None):
+                                         scope=None,
+                                         parallel_iterations=32):
   """Multi-class version of non maximum suppression that operates on a batch.
 
   This op is similar to `multiclass_non_max_suppression` but operates on a batch
@@ -208,26 +209,28 @@ def batch_multiclass_non_max_suppression(boxes,
       float32 tensor containing box masks. `q` can be either number of classes
       or 1 depending on whether a separate mask is predicted per class.
     scope: tf scope name.
+    parallel_iterations: (optional) number of batch items to process in
+      parallel.
 
   Returns:
-    A dictionary containing the following entries:
-    'detection_boxes': A [batch_size, max_detections, 4] float32 tensor
+    'nmsed_boxes': A [batch_size, max_detections, 4] float32 tensor
       containing the non-max suppressed boxes.
-    'detection_scores': A [bath_size, max_detections] float32 tensor containing
+    'nmsed_scores': A [batch_size, max_detections] float32 tensor containing
       the scores for the boxes.
-    'detection_classes': A [batch_size, max_detections] float32 tensor
+    'nmsed_classes': A [batch_size, max_detections] float32 tensor
       containing the class for boxes.
-    'num_detections': A [batchsize] float32 tensor indicating the number of
+    'nmsed_masks': (optional) a
+      [batch_size, max_detections, mask_height, mask_width] float32 tensor
+      containing masks for each selected box. This is set to None if input
+      `masks` is None.
+    'num_detections': A [batch_size] int32 tensor indicating the number of
       valid detections per batch item. Only the top num_detections[i] entries in
       nms_boxes[i], nms_scores[i] and nms_class[i] are valid. the rest of the
       entries are zero paddings.
-    'detection_masks': (optional) a
-      [batch_size, max_detections, mask_height, mask_width] float32 tensor
-      containing masks for each selected box.
 
   Raises:
-    ValueError: if iou_thresh is not in [0, 1] or if input boxlist does not have
-      a valid scores field.
+    ValueError: if `q` in boxes.shape is not 1 or not equal to number of
+      classes as inferred from scores.shape.
   """
   q = boxes.shape[2].value
   num_classes = scores.shape[2].value
@@ -235,36 +238,45 @@ def batch_multiclass_non_max_suppression(boxes,
     raise ValueError('third dimension of boxes must be either 1 or equal '
                      'to the third dimension of scores')
 
+  original_masks = masks
   with tf.name_scope(scope, 'BatchMultiClassNonMaxSuppression'):
-    per_image_boxes_list = tf.unstack(boxes)
-    per_image_scores_list = tf.unstack(scores)
-    num_valid_boxes_list = len(per_image_boxes_list) * [None]
-    per_image_masks_list = len(per_image_boxes_list) * [None]
-    if num_valid_boxes is not None:
-      num_valid_boxes_list = tf.unstack(num_valid_boxes)
-    if masks is not None:
-      per_image_masks_list = tf.unstack(masks)
+    boxes_shape = boxes.shape
+    batch_size = boxes_shape[0].value
+    num_anchors = boxes_shape[1].value
+
+    if batch_size is None:
+      batch_size = tf.shape(boxes)[0]
+    if num_anchors is None:
+      num_anchors = tf.shape(boxes)[1]
+
+    # If num valid boxes aren't provided, create one and mark all boxes as
+    # valid.
+    if num_valid_boxes is None:
+      num_valid_boxes = tf.ones([batch_size], dtype=tf.int32) * num_anchors
 
-    detection_boxes_list = []
-    detection_scores_list = []
-    detection_classes_list = []
-    num_detections_list = []
-    detection_masks_list = []
-    for (per_image_boxes, per_image_scores, per_image_masks, num_valid_boxes
-        ) in zip(per_image_boxes_list, per_image_scores_list,
-                 per_image_masks_list, num_valid_boxes_list):
-      if num_valid_boxes is not None:
-        per_image_boxes = tf.reshape(
-            tf.slice(per_image_boxes, 3*[0],
-                     tf.stack([num_valid_boxes, -1, -1])), [-1, q, 4])
-        per_image_scores = tf.reshape(
-            tf.slice(per_image_scores, [0, 0],
-                     tf.stack([num_valid_boxes, -1])), [-1, num_classes])
-        if masks is not None:
-          per_image_masks = tf.reshape(
-              tf.slice(per_image_masks, 4*[0],
-                       tf.stack([num_valid_boxes, -1, -1, -1])),
-              [-1, q, masks.shape[3].value, masks.shape[4].value])
+    # If masks aren't provided, create dummy masks so we can only have one copy
+    # of single_image_nms_fn and discard the dummy masks after map_fn.
+    if masks is None:
+      masks_shape = tf.stack([batch_size, num_anchors, 1, 0, 0])
+      masks = tf.zeros(masks_shape)
+
+    def single_image_nms_fn(args):
+      """Runs NMS on a single image and returns padded output."""
+      (per_image_boxes, per_image_scores, per_image_masks,
+       per_image_num_valid_boxes) = args
+      per_image_boxes = tf.reshape(
+          tf.slice(per_image_boxes, 3 * [0],
+                   tf.stack([per_image_num_valid_boxes, -1, -1])), [-1, q, 4])
+      per_image_scores = tf.reshape(
+          tf.slice(per_image_scores, [0, 0],
+                   tf.stack([per_image_num_valid_boxes, -1])),
+          [-1, num_classes])
+
+      per_image_masks = tf.reshape(
+          tf.slice(per_image_masks, 4 * [0],
+                   tf.stack([per_image_num_valid_boxes, -1, -1, -1])),
+          [-1, q, per_image_masks.shape[2].value,
+           per_image_masks.shape[3].value])
       nmsed_boxlist = multiclass_non_max_suppression(
           per_image_boxes,
           per_image_scores,
@@ -275,24 +287,26 @@ def batch_multiclass_non_max_suppression(boxes,
           masks=per_image_masks,
           clip_window=clip_window,
           change_coordinate_frame=change_coordinate_frame)
-      num_detections_list.append(tf.to_float(nmsed_boxlist.num_boxes()))
       padded_boxlist = box_list_ops.pad_or_clip_box_list(nmsed_boxlist,
                                                          max_total_size)
-      detection_boxes_list.append(padded_boxlist.get())
-      detection_scores_list.append(
-          padded_boxlist.get_field(fields.BoxListFields.scores))
-      detection_classes_list.append(
-          padded_boxlist.get_field(fields.BoxListFields.classes))
-      if masks is not None:
-        detection_masks_list.append(
-            padded_boxlist.get_field(fields.BoxListFields.masks))
+      num_detections = nmsed_boxlist.num_boxes()
+      nmsed_boxes = padded_boxlist.get()
+      nmsed_scores = padded_boxlist.get_field(fields.BoxListFields.scores)
+      nmsed_classes = padded_boxlist.get_field(fields.BoxListFields.classes)
+      nmsed_masks = padded_boxlist.get_field(fields.BoxListFields.masks)
+      return [nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
+              num_detections]
 
-    nms_dict = {
-        'detection_boxes': tf.stack(detection_boxes_list),
-        'detection_scores': tf.stack(detection_scores_list),
-        'detection_classes': tf.stack(detection_classes_list),
-        'num_detections': tf.stack(num_detections_list)
-    }
-    if masks is not None:
-      nms_dict['detection_masks'] = tf.stack(detection_masks_list)
-    return nms_dict
+    (batch_nmsed_boxes, batch_nmsed_scores,
+     batch_nmsed_classes, batch_nmsed_masks,
+     batch_num_detections) = tf.map_fn(
+         single_image_nms_fn,
+         elems=[boxes, scores, masks, num_valid_boxes],
+         dtype=[tf.float32, tf.float32, tf.float32, tf.float32, tf.int32],
+         parallel_iterations=parallel_iterations)
+
+    if original_masks is None:
+      batch_nmsed_masks = None
+
+    return (batch_nmsed_boxes, batch_nmsed_scores, batch_nmsed_classes,
+            batch_nmsed_masks, batch_num_detections)

object_detection/core/post_processing_test.py

@@ -496,15 +496,21 @@ class MulticlassNonMaxSuppressionTest(tf.test.TestCase):
     exp_nms_scores = [[.95, .9, .85, .3]]
     exp_nms_classes = [[0, 0, 1, 0]]
 
-    nms_dict = post_processing.batch_multiclass_non_max_suppression(
-        boxes, scores, score_thresh, iou_thresh,
-        max_size_per_class=max_output_size, max_total_size=max_output_size)
+    (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
+     num_detections) = post_processing.batch_multiclass_non_max_suppression(
+         boxes, scores, score_thresh, iou_thresh,
+         max_size_per_class=max_output_size, max_total_size=max_output_size)
+
+    self.assertIsNone(nmsed_masks)
+
     with self.test_session() as sess:
-      nms_output = sess.run(nms_dict)
-      self.assertAllClose(nms_output['detection_boxes'], exp_nms_corners)
-      self.assertAllClose(nms_output['detection_scores'], exp_nms_scores)
-      self.assertAllClose(nms_output['detection_classes'], exp_nms_classes)
-      self.assertEqual(nms_output['num_detections'], [4])
+      (nmsed_boxes, nmsed_scores, nmsed_classes,
+       num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
+                                   num_detections])
+      self.assertAllClose(nmsed_boxes, exp_nms_corners)
+      self.assertAllClose(nmsed_scores, exp_nms_scores)
+      self.assertAllClose(nmsed_classes, exp_nms_classes)
+      self.assertEqual(num_detections, [4])
 
   def test_batch_multiclass_nms_with_batch_size_2(self):
     boxes = tf.constant([[[[0, 0, 1, 1], [0, 0, 4, 5]],
@@ -524,28 +530,42 @@ class MulticlassNonMaxSuppressionTest(tf.test.TestCase):
     iou_thresh = .5
     max_output_size = 4
 
-    exp_nms_corners = [[[0, 10, 1, 11],
-                        [0, 0, 1, 1],
-                        [0, 0, 0, 0],
-                        [0, 0, 0, 0]],
-                       [[0, 999, 2, 1004],
-                        [0, 10.1, 1, 11.1],
-                        [0, 100, 1, 101],
-                        [0, 0, 0, 0]]]
-    exp_nms_scores = [[.95, .9, 0, 0],
-                      [.85, .5, .3, 0]]
-    exp_nms_classes = [[0, 0, 0, 0],
-                       [1, 0, 0, 0]]
+    exp_nms_corners = np.array([[[0, 10, 1, 11],
+                                 [0, 0, 1, 1],
+                                 [0, 0, 0, 0],
+                                 [0, 0, 0, 0]],
+                                [[0, 999, 2, 1004],
+                                 [0, 10.1, 1, 11.1],
+                                 [0, 100, 1, 101],
+                                 [0, 0, 0, 0]]])
+    exp_nms_scores = np.array([[.95, .9, 0, 0],
+                               [.85, .5, .3, 0]])
+    exp_nms_classes = np.array([[0, 0, 0, 0],
+                                [1, 0, 0, 0]])
+
+    (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
+     num_detections) = post_processing.batch_multiclass_non_max_suppression(
+         boxes, scores, score_thresh, iou_thresh,
+         max_size_per_class=max_output_size, max_total_size=max_output_size)
+
+    self.assertIsNone(nmsed_masks)
+    # Check static shapes
+    self.assertAllEqual(nmsed_boxes.shape.as_list(),
+                        exp_nms_corners.shape)
+    self.assertAllEqual(nmsed_scores.shape.as_list(),
+                        exp_nms_scores.shape)
+    self.assertAllEqual(nmsed_classes.shape.as_list(),
+                        exp_nms_classes.shape)
+    self.assertEqual(num_detections.shape.as_list(), [2])
 
-    nms_dict = post_processing.batch_multiclass_non_max_suppression(
-        boxes, scores, score_thresh, iou_thresh,
-        max_size_per_class=max_output_size, max_total_size=max_output_size)
     with self.test_session() as sess:
-      nms_output = sess.run(nms_dict)
-      self.assertAllClose(nms_output['detection_boxes'], exp_nms_corners)
-      self.assertAllClose(nms_output['detection_scores'], exp_nms_scores)
-      self.assertAllClose(nms_output['detection_classes'], exp_nms_classes)
-      self.assertAllClose(nms_output['num_detections'], [2, 3])
+      (nmsed_boxes, nmsed_scores, nmsed_classes,
+       num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
+                                   num_detections])
+      self.assertAllClose(nmsed_boxes, exp_nms_corners)
+      self.assertAllClose(nmsed_scores, exp_nms_scores)
+      self.assertAllClose(nmsed_classes, exp_nms_classes)
+      self.assertAllClose(num_detections, [2, 3])
 
   def test_batch_multiclass_nms_with_masks(self):
     boxes = tf.constant([[[[0, 0, 1, 1], [0, 0, 4, 5]],
@@ -574,38 +594,126 @@ class MulticlassNonMaxSuppressionTest(tf.test.TestCase):
     iou_thresh = .5
     max_output_size = 4
 
-    exp_nms_corners = [[[0, 10, 1, 11],
-                        [0, 0, 1, 1],
-                        [0, 0, 0, 0],
-                        [0, 0, 0, 0]],
-                       [[0, 999, 2, 1004],
-                        [0, 10.1, 1, 11.1],
-                        [0, 100, 1, 101],
-                        [0, 0, 0, 0]]]
-    exp_nms_scores = [[.95, .9, 0, 0],
-                      [.85, .5, .3, 0]]
-    exp_nms_classes = [[0, 0, 0, 0],
-                       [1, 0, 0, 0]]
-    exp_nms_masks = [[[[6, 7], [8, 9]],
-                      [[0, 1], [2, 3]],
-                      [[0, 0], [0, 0]],
-                      [[0, 0], [0, 0]]],
-                     [[[13, 14], [15, 16]],
-                      [[8, 9], [10, 11]],
-                      [[10, 11], [12, 13]],
-                      [[0, 0], [0, 0]]]]
+    exp_nms_corners = np.array([[[0, 10, 1, 11],
+                                 [0, 0, 1, 1],
+                                 [0, 0, 0, 0],
+                                 [0, 0, 0, 0]],
+                                [[0, 999, 2, 1004],
+                                 [0, 10.1, 1, 11.1],
+                                 [0, 100, 1, 101],
+                                 [0, 0, 0, 0]]])
+    exp_nms_scores = np.array([[.95, .9, 0, 0],
+                               [.85, .5, .3, 0]])
+    exp_nms_classes = np.array([[0, 0, 0, 0],
+                                [1, 0, 0, 0]])
+    exp_nms_masks = np.array([[[[6, 7], [8, 9]],
+                               [[0, 1], [2, 3]],
+                               [[0, 0], [0, 0]],
+                               [[0, 0], [0, 0]]],
+                              [[[13, 14], [15, 16]],
+                               [[8, 9], [10, 11]],
+                               [[10, 11], [12, 13]],
+                               [[0, 0], [0, 0]]]])
+
+    (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
+     num_detections) = post_processing.batch_multiclass_non_max_suppression(
+         boxes, scores, score_thresh, iou_thresh,
+         max_size_per_class=max_output_size, max_total_size=max_output_size,
+         masks=masks)
+
+    # Check static shapes
+    self.assertAllEqual(nmsed_boxes.shape.as_list(), exp_nms_corners.shape)
+    self.assertAllEqual(nmsed_scores.shape.as_list(), exp_nms_scores.shape)
+    self.assertAllEqual(nmsed_classes.shape.as_list(), exp_nms_classes.shape)
+    self.assertAllEqual(nmsed_masks.shape.as_list(), exp_nms_masks.shape)
+    self.assertEqual(num_detections.shape.as_list(), [2])
+
+    with self.test_session() as sess:
+      (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
+       num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
+                                   nmsed_masks, num_detections])
+
+      self.assertAllClose(nmsed_boxes, exp_nms_corners)
+      self.assertAllClose(nmsed_scores, exp_nms_scores)
+      self.assertAllClose(nmsed_classes, exp_nms_classes)
+      self.assertAllClose(num_detections, [2, 3])
+      self.assertAllClose(nmsed_masks, exp_nms_masks)
+
+  def test_batch_multiclass_nms_with_dynamic_batch_size(self):
+    boxes_placeholder = tf.placeholder(tf.float32, shape=(None, None, 2, 4))
+    scores_placeholder = tf.placeholder(tf.float32, shape=(None, None, 2))
+    masks_placeholder = tf.placeholder(tf.float32, shape=(None, None, 2, 2, 2))
+
+    boxes = np.array([[[[0, 0, 1, 1], [0, 0, 4, 5]],
+                       [[0, 0.1, 1, 1.1], [0, 0.1, 2, 1.1]],
+                       [[0, -0.1, 1, 0.9], [0, -0.1, 1, 0.9]],
+                       [[0, 10, 1, 11], [0, 10, 1, 11]]],
+                      [[[0, 10.1, 1, 11.1], [0, 10.1, 1, 11.1]],
+                       [[0, 100, 1, 101], [0, 100, 1, 101]],
+                       [[0, 1000, 1, 1002], [0, 999, 2, 1004]],
+                       [[0, 1000, 1, 1002.1], [0, 999, 2, 1002.7]]]])
+    scores = np.array([[[.9, 0.01], [.75, 0.05],
+                        [.6, 0.01], [.95, 0]],
+                       [[.5, 0.01], [.3, 0.01],
+                        [.01, .85], [.01, .5]]])
+    masks = np.array([[[[[0, 1], [2, 3]], [[1, 2], [3, 4]]],
+                       [[[2, 3], [4, 5]], [[3, 4], [5, 6]]],
+                       [[[4, 5], [6, 7]], [[5, 6], [7, 8]]],
+                       [[[6, 7], [8, 9]], [[7, 8], [9, 10]]]],
+                      [[[[8, 9], [10, 11]], [[9, 10], [11, 12]]],
+                       [[[10, 11], [12, 13]], [[11, 12], [13, 14]]],
+                       [[[12, 13], [14, 15]], [[13, 14], [15, 16]]],
+                       [[[14, 15], [16, 17]], [[15, 16], [17, 18]]]]])
+    score_thresh = 0.1
+    iou_thresh = .5
+    max_output_size = 4
+
+    exp_nms_corners = np.array([[[0, 10, 1, 11],
+                                 [0, 0, 1, 1],
+                                 [0, 0, 0, 0],
+                                 [0, 0, 0, 0]],
+                                [[0, 999, 2, 1004],
+                                 [0, 10.1, 1, 11.1],
+                                 [0, 100, 1, 101],
+                                 [0, 0, 0, 0]]])
+    exp_nms_scores = np.array([[.95, .9, 0, 0],
+                               [.85, .5, .3, 0]])
+    exp_nms_classes = np.array([[0, 0, 0, 0],
+                                [1, 0, 0, 0]])
+    exp_nms_masks = np.array([[[[6, 7], [8, 9]],
+                               [[0, 1], [2, 3]],
+                               [[0, 0], [0, 0]],
+                               [[0, 0], [0, 0]]],
+                              [[[13, 14], [15, 16]],
+                               [[8, 9], [10, 11]],
+                               [[10, 11], [12, 13]],
+                               [[0, 0], [0, 0]]]])
+
+    (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
+     num_detections) = post_processing.batch_multiclass_non_max_suppression(
+         boxes_placeholder, scores_placeholder, score_thresh, iou_thresh,
+         max_size_per_class=max_output_size, max_total_size=max_output_size,
+         masks=masks_placeholder)
+
+    # Check static shapes
+    self.assertAllEqual(nmsed_boxes.shape.as_list(), [None, 4, 4])
+    self.assertAllEqual(nmsed_scores.shape.as_list(), [None, 4])
+    self.assertAllEqual(nmsed_classes.shape.as_list(), [None, 4])
+    self.assertAllEqual(nmsed_masks.shape.as_list(), [None, 4, 2, 2])
+    self.assertEqual(num_detections.shape.as_list(), [None])
 
-    nms_dict = post_processing.batch_multiclass_non_max_suppression(
-        boxes, scores, score_thresh, iou_thresh,
-        max_size_per_class=max_output_size, max_total_size=max_output_size,
-        masks=masks)
     with self.test_session() as sess:
-      nms_output = sess.run(nms_dict)
-      self.assertAllClose(nms_output['detection_boxes'], exp_nms_corners)
-      self.assertAllClose(nms_output['detection_scores'], exp_nms_scores)
-      self.assertAllClose(nms_output['detection_classes'], exp_nms_classes)
-      self.assertAllClose(nms_output['num_detections'], [2, 3])
-      self.assertAllClose(nms_output['detection_masks'], exp_nms_masks)
+      (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
+       num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
+                                   nmsed_masks, num_detections],
+                                  feed_dict={boxes_placeholder: boxes,
+                                             scores_placeholder: scores,
+                                             masks_placeholder: masks})
+      self.assertAllClose(nmsed_boxes, exp_nms_corners)
+      self.assertAllClose(nmsed_scores, exp_nms_scores)
+      self.assertAllClose(nmsed_classes, exp_nms_classes)
+      self.assertAllClose(num_detections, [2, 3])
+      self.assertAllClose(nmsed_masks, exp_nms_masks)
 
   def test_batch_multiclass_nms_with_masks_and_num_valid_boxes(self):
     boxes = tf.constant([[[[0, 0, 1, 1], [0, 0, 4, 5]],
@@ -656,17 +764,21 @@ class MulticlassNonMaxSuppressionTest(tf.test.TestCase):
                       [[0, 0], [0, 0]],
                       [[0, 0], [0, 0]]]]
 
-    nms_dict = post_processing.batch_multiclass_non_max_suppression(
-        boxes, scores, score_thresh, iou_thresh,
-        max_size_per_class=max_output_size, max_total_size=max_output_size,
-        num_valid_boxes=num_valid_boxes, masks=masks)
+    (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
+     num_detections) = post_processing.batch_multiclass_non_max_suppression(
+         boxes, scores, score_thresh, iou_thresh,
+         max_size_per_class=max_output_size, max_total_size=max_output_size,
+         num_valid_boxes=num_valid_boxes, masks=masks)
+
     with self.test_session() as sess:
-      nms_output = sess.run(nms_dict)
-      self.assertAllClose(nms_output['detection_boxes'], exp_nms_corners)
-      self.assertAllClose(nms_output['detection_scores'], exp_nms_scores)
-      self.assertAllClose(nms_output['detection_classes'], exp_nms_classes)
-      self.assertAllClose(nms_output['num_detections'], [1, 1])
-      self.assertAllClose(nms_output['detection_masks'], exp_nms_masks)
+      (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
+       num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
+                                   nmsed_masks, num_detections])
+      self.assertAllClose(nmsed_boxes, exp_nms_corners)
+      self.assertAllClose(nmsed_scores, exp_nms_scores)
+      self.assertAllClose(nmsed_classes, exp_nms_classes)
+      self.assertAllClose(num_detections, [1, 1])
+      self.assertAllClose(nmsed_masks, exp_nms_masks)
 
 
 if __name__ == '__main__':