Internal change

PiperOrigin-RevId: 295849975

official/modeling/training/distributed_executor.py

@@ -64,7 +64,7 @@ class SummaryWriter(object):
   """Simple SummaryWriter for writing dictionary of metrics.
 
   Attributes:
-    _writer: The tf.SummaryWriter.
+    writer: The tf.SummaryWriter.
   """
 
   def __init__(self, model_dir: Text, name: Text):
@@ -74,7 +74,7 @@ def __init__(self, model_dir: Text, name: Text):
       model_dir: the model folder path.
       name: the summary subfolder name.
     """
-    self._writer = tf.summary.create_file_writer(os.path.join(model_dir, name))
+    self.writer = tf.summary.create_file_writer(os.path.join(model_dir, name))
 
   def __call__(self, metrics: Union[Dict[Text, float], float], step: int):
     """Write metrics to summary with the given writer.
@@ -88,10 +88,10 @@ def __call__(self, metrics: Union[Dict[Text, float], float], step: int):
       logging.warning('Warning: summary writer prefer metrics as dictionary.')
       metrics = {'metric': metrics}
 
-    with self._writer.as_default():
+    with self.writer.as_default():
       for k, v in metrics.items():
         tf.summary.scalar(k, v, step=step)
-      self._writer.flush()
+      self.writer.flush()
 
 
 class DistributedExecutor(object):
@@ -122,6 +122,9 @@ def __init__(self,
     self._strategy = strategy
     self._checkpoint_name = 'ctl_step_{step}.ckpt'
     self._is_multi_host = is_multi_host
+    self.train_summary_writer = None
+    self.eval_summary_writer = None
+    self.global_train_step = None
 
   @property
   def checkpoint_name(self):
@@ -395,7 +398,10 @@ def _run_callbacks_on_batch_end(batch):
       eval_metric = eval_metric_fn()
       train_metric = train_metric_fn()
       train_summary_writer = summary_writer_fn(model_dir, 'eval_train')
+      self.train_summary_writer = train_summary_writer.writer
+
       test_summary_writer = summary_writer_fn(model_dir, 'eval_test')
+      self.eval_summary_writer = test_summary_writer.writer
 
     # Continue training loop.
     train_step = self._create_train_step(
@@ -406,6 +412,7 @@ def _run_callbacks_on_batch_end(batch):
         metric=train_metric)
     test_step = None
     if eval_input_fn and eval_metric:
+      self.global_train_step = model.optimizer.iterations
       test_step = self._create_test_step(strategy, model, metric=eval_metric)
 
     logging.info('Training started')
@@ -549,6 +556,7 @@ def terminate_eval():
       return True
 
     summary_writer = summary_writer_fn(model_dir, 'eval')
+    self.eval_summary_writer = summary_writer.writer
 
     # Read checkpoints from the given model directory
     # until `eval_timeout` seconds elapses.
@@ -615,6 +623,7 @@ def evaluate_checkpoint(self,
           'checkpoint', checkpoint_path)
       checkpoint.restore(checkpoint_path)
 
+      self.global_train_step = model.optimizer.iterations
       eval_iterator = self._get_input_iterator(eval_input_fn, strategy)
       eval_metric_result = self._run_evaluation(test_step, current_step,
                                                 eval_metric, eval_iterator)

official/vision/detection/configs/retinanet_config.py

@@ -70,6 +70,9 @@
         'val_json_file': '',
         'eval_file_pattern': '',
         'input_sharding': True,
+        # When visualizing images, set evaluation batch size to 40 to avoid
+        # potential OOM.
+        'num_images_to_visualize': 0,
     },
     'predict': {
         'predict_batch_size': 8,

official/vision/detection/executor/detection_executor.py

@@ -25,6 +25,7 @@
 import json
 import tensorflow.compat.v2 as tf
 from official.modeling.training import distributed_executor as executor
+from official.vision.detection.utils import box_utils
 
 
 class DetectionDistributedExecutor(executor.DistributedExecutor):
@@ -38,13 +39,19 @@ def __init__(self,
                trainable_variables_filter=None,
                **kwargs):
     super(DetectionDistributedExecutor, self).__init__(**kwargs)
-    params = kwargs['params']
     if predict_post_process_fn:
       assert callable(predict_post_process_fn)
     if trainable_variables_filter:
       assert callable(trainable_variables_filter)
     self._predict_post_process_fn = predict_post_process_fn
     self._trainable_variables_filter = trainable_variables_filter
+    self.eval_steps = tf.Variable(
+        0,
+        trainable=False,
+        dtype=tf.int32,
+        synchronization=tf.VariableSynchronization.ON_READ,
+        aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,
+        shape=[])
 
   def _create_replicated_step(self,
                               strategy,
@@ -90,31 +97,49 @@ def _create_test_step(self, strategy, model, metric):
     """Creates a distributed test step."""
 
     @tf.function
-    def test_step(iterator):
+    def test_step(iterator, eval_steps):
       """Calculates evaluation metrics on distributed devices."""
 
-      def _test_step_fn(inputs):
+      def _test_step_fn(inputs, eval_steps):
         """Replicated accuracy calculation."""
         inputs, labels = inputs
         model_outputs = model(inputs, training=False)
         if self._predict_post_process_fn:
           labels, prediction_outputs = self._predict_post_process_fn(
               labels, model_outputs)
+          num_remaining_visualizations = (
+              self._params.eval.num_images_to_visualize - eval_steps)
+          # If there are remaining number of visualizations that needs to be
+          # done, add next batch outputs for visualization.
+          #
+          # TODO(hongjunchoi): Once dynamic slicing is supported on TPU, only
+          # write correct slice of outputs to summary file.
+          if num_remaining_visualizations > 0:
+            box_utils.visualize_bounding_boxes(
+                inputs, prediction_outputs['detection_boxes'],
+                self.global_train_step, self.eval_summary_writer)
+
         return labels, prediction_outputs
 
       labels, outputs = strategy.experimental_run_v2(
-          _test_step_fn, args=(next(iterator),))
+          _test_step_fn, args=(
+              next(iterator),
+              eval_steps,
+          ))
       outputs = tf.nest.map_structure(strategy.experimental_local_results,
                                       outputs)
       labels = tf.nest.map_structure(strategy.experimental_local_results,
                                      labels)
+
+      eval_steps.assign_add(self._params.eval.batch_size)
       return labels, outputs
 
     return test_step
 
   def _run_evaluation(self, test_step, current_training_step, metric,
                       test_iterator):
     """Runs validation steps and aggregate metrics."""
+    self.eval_steps.assign(0)
     if not test_iterator or not metric:
       logging.warning(
           'Both test_iterator (%s) and metrics (%s) must not be None.',
@@ -123,7 +148,7 @@ def _run_evaluation(self, test_step, current_training_step, metric,
     logging.info('Running evaluation after step: %s.', current_training_step)
     while True:
       try:
-        labels, outputs = test_step(test_iterator)
+        labels, outputs = test_step(test_iterator, self.eval_steps)
         if metric:
           metric.update_state(labels, outputs)
       except (StopIteration, tf.errors.OutOfRangeError):

official/vision/detection/main.py

@@ -239,4 +239,5 @@ def main(argv):
 
 if __name__ == '__main__':
   assert tf.version.VERSION.startswith('2.')
+  tf.config.set_soft_device_placement(True)
   app.run(main)

official/vision/detection/utils/box_utils.py

@@ -26,6 +26,22 @@
 BBOX_XFORM_CLIP = np.log(1000. / 16.)
 
 
+def visualize_images_with_bounding_boxes(images, box_outputs, step,
+                                         summary_writer):
+  """Records subset of evaluation images with bounding boxes."""
+  image_shape = tf.shape(images[0])
+  image_height = tf.cast(image_shape[0], tf.float32)
+  image_width = tf.cast(image_shape[1], tf.float32)
+  normalized_boxes = normalize_boxes(box_outputs, [image_height, image_width])
+
+  bounding_box_color = tf.constant([[1.0, 1.0, 0.0, 1.0]])
+  image_summary = tf.image.draw_bounding_boxes(images, normalized_boxes,
+                                               bounding_box_color)
+  with summary_writer.as_default():
+    tf.summary.image('bounding_box_summary', image_summary, step=step)
+    summary_writer.flush()
+
+
 def yxyx_to_xywh(boxes):
   """Converts boxes from ymin, xmin, ymax, xmax to xmin, ymin, width, height.