Add a streaming_op() to the pr_curves plugin.

tensorboard/plugins/pr_curve/summary.py

@@ -164,6 +164,104 @@ def op(
         description,
         collections)
 
+
+def streaming_op(tag,
+                 labels,
+                 predictions,
+                 num_thresholds=200,
+                 weights=None,
+                 metrics_collections=None,
+                 updates_collections=None,
+                 display_name=None,
+                 description=None):
+  """Computes a precision-recall curve summary across batches of data.
+
+  This function is similar to op() above, but can be used to compute the PR
+  curve across multiple batches of labels and predictions, in the same style
+  as the metrics found in tf.metrics.
+
+  This function creates multiple local variables for storing true positives,
+  true negative, etc. accumulated over each batch of data, and uses these local
+  variables for computing the final PR curve summary. These variables can be
+  updated with the returned update_op.
+
+  Args:
+    tag: A tag attached to the summary. Used by TensorBoard for organization.
+    labels: The ground truth values, a `Tensor` whose dimensions must match
+      `predictions`. Will be cast to `bool`.
+    predictions: A floating point `Tensor` of arbitrary shape and whose values
+      are in the range `[0, 1]`.
+    num_thresholds: The number of evenly spaced thresholds to generate for
+      computing the PR curve.
+    weights: Optional `Tensor` whose rank is either 0, or the same rank as
+      `labels`, and must be broadcastable to `labels` (i.e., all dimensions must
+      be either `1`, or the same as the corresponding `labels` dimension).
+    metrics_collections: An optional list of collections that `auc` should be
+      added to.
+    updates_collections: An optional list of collections that `update_op` should
+      be added to.
+    display_name: Optional name for this summary in TensorBoard, as a
+        constant `str`. Defaults to `name`.
+    description: Optional long-form description for this summary, as a
+        constant `str`. Markdown is supported. Defaults to empty.
+
+  Returns:
+    pr_curve: A string `Tensor` containing a single value: the
+      serialized PR curve Tensor summary. The summary contains a
+      float32 `Tensor` of dimension (6, num_thresholds). The first
+      dimension (of length 6) is of the order: true positives, false
+      positives, true negatives, false negatives, precision, recall.
+    update_op: An operation that updates the summary with the latest data.
+  """
+  thresholds = [i / float(num_thresholds - 1)
+                for i in range(num_thresholds)]
+
+  with tf.name_scope(tag, values=[labels, predictions, weights]):
+    tp, update_tp = tf.metrics.true_positives_at_thresholds(
+        labels=labels,
+        predictions=predictions,
+        thresholds=thresholds,
+        weights=weights)
+    fp, update_fp = tf.metrics.false_positives_at_thresholds(
+        labels=labels,
+        predictions=predictions,
+        thresholds=thresholds,
+        weights=weights)
+    tn, update_tn = tf.metrics.true_negatives_at_thresholds(
+        labels=labels,
+        predictions=predictions,
+        thresholds=thresholds,
+        weights=weights)
+    fn, update_fn = tf.metrics.false_negatives_at_thresholds(
+        labels=labels,
+        predictions=predictions,
+        thresholds=thresholds,
+        weights=weights)
+
+    def compute_summary(tp, fp, tn, fn, collections):
+      precision = tp / tf.maximum(_MINIMUM_COUNT, tp + fp)
+      recall = tp / tf.maximum(_MINIMUM_COUNT, tp + fn)
+
+      return _create_tensor_summary(
+          tag,
+          tp,
+          fp,
+          tn,
+          fn,
+          precision,
+          recall,
+          num_thresholds,
+          display_name,
+          description,
+          collections)
+
+    pr_curve = compute_summary(tp, fp, tn, fn, metrics_collections)
+    update_op = compute_summary(update_tp, update_fp, update_tn, update_fn,
+                                updates_collections)
+
+    return pr_curve, update_op
+
+
 def raw_data_op(
     tag,
     true_positive_counts,

tensorboard/plugins/pr_curve/summary_test.py

@@ -320,5 +320,81 @@ def testRawDataOp(self):
     ], tensor_events[0])
 
 
+class StreamingOpTest(tf.test.TestCase):
+
+  def setUp(self):
+    super(StreamingOpTest, self).setUp()
+    tf.reset_default_graph()
+    np.random.seed(1)
+
+  def pb_via_op(self, summary_op):
+    actual_pbtxt = summary_op.eval()
+    actual_proto = tf.Summary()
+    actual_proto.ParseFromString(actual_pbtxt)
+    return actual_proto
+
+  def tensor_via_op(self, summary_op):
+    actual_pbtxt = summary_op.eval()
+    actual_proto = tf.Summary()
+    actual_proto.ParseFromString(actual_pbtxt)
+    return actual_proto
+
+  def testMatchesOp(self):
+    predictions = tf.constant([0.2, 0.4, 0.5, 0.6, 0.8], dtype=tf.float32)
+    labels = tf.constant([False, True, True, False, True], dtype=tf.bool)
+
+    pr_curve, update_op = summary.streaming_op(tag='pr_curve',
+                                               predictions=predictions,
+                                               labels=labels,
+                                               num_thresholds=10)
+    expected_pr_curve = summary.op(tag='pr_curve',
+                                   predictions=predictions,
+                                   labels=labels,
+                                   num_thresholds=10)
+    with self.test_session() as sess:
+      sess.run(tf.local_variables_initializer())
+      sess.run([update_op])
+
+      proto = self.pb_via_op(pr_curve)
+      expected_proto = self.pb_via_op(expected_pr_curve)
+
+      # Need to detect and fix the automatic _1 appended to second namespace.
+      self.assertEqual(proto.value[0].tag, 'pr_curve/pr_curves')
+      self.assertEqual(expected_proto.value[0].tag, 'pr_curve_1/pr_curves')
+      expected_proto.value[0].tag = 'pr_curve/pr_curves'
+
+      self.assertProtoEquals(expected_proto, proto)
+
+  def testMatchesOpWithUpdates(self):
+    predictions = tf.constant([0.2, 0.4, 0.5, 0.6, 0.8], dtype=tf.float32)
+    labels = tf.constant([False, True, True, False, True], dtype=tf.bool)
+    pr_curve, update_op = summary.streaming_op(tag='pr_curve',
+                                               predictions=predictions,
+                                               labels=labels,
+                                               num_thresholds=10)
+
+    complete_predictions = tf.tile(predictions, [3])
+    complete_labels = tf.tile(labels, [3])
+    expected_pr_curve = summary.op(tag='pr_curve',
+                                   predictions=complete_predictions,
+                                   labels=complete_labels,
+                                   num_thresholds=10)
+    with self.test_session() as sess:
+      sess.run(tf.local_variables_initializer())
+      sess.run([update_op])
+      sess.run([update_op])
+      sess.run([update_op])
+
+      proto = self.pb_via_op(pr_curve)
+      expected_proto = self.pb_via_op(expected_pr_curve)
+
+      # Need to detect and fix the automatic _1 appended to second namespace.
+      self.assertEqual(proto.value[0].tag, 'pr_curve/pr_curves')
+      self.assertEqual(expected_proto.value[0].tag, 'pr_curve_1/pr_curves')
+      expected_proto.value[0].tag = 'pr_curve/pr_curves'
+
+      self.assertProtoEquals(expected_proto, proto)
+
+
 if __name__ == "__main__":
   tf.test.main()

tensorboard/summary.py

@@ -39,6 +39,7 @@
 image_pb = _image_summary.pb
 
 pr_curve = _pr_curve_summary.op
+pr_curve_streaming_op = _pr_curve_summary.streaming_op
 pr_curve_raw_data = _pr_curve_summary.raw_data_op
 
 scalar = _scalar_summary.op