Code cleanup.

PiperOrigin-RevId: 283837279

official/vision/detection/evaluation/coco_evaluator.py

@@ -42,6 +42,35 @@ from official.vision.detection.evaluation import coco_utils
 from official.vision.detection.utils import class_utils
 
 
+class MetricWrapper(object):
+  # This is only a wrapper for COCO metric and works on for numpy array. So it
+  # doesn't inherit from tf.keras.layers.Layer or tf.keras.metrics.Metric.
+
+  def __init__(self, evaluator):
+    self._evaluator = evaluator
+
+  def update_state(self, y_true, y_pred):
+    labels = tf.nest.map_structure(lambda x: x.numpy(), y_true)
+    outputs = tf.nest.map_structure(lambda x: x.numpy(), y_pred)
+    groundtruths = {}
+    predictions = {}
+    for key, val in outputs.items():
+      if isinstance(val, tuple):
+        val = np.concatenate(val)
+      predictions[key] = val
+    for key, val in labels.items():
+      if isinstance(val, tuple):
+        val = np.concatenate(val)
+      groundtruths[key] = val
+    self._evaluator.update(predictions, groundtruths)
+
+  def result(self):
+    return self._evaluator.evaluate()
+
+  def reset_states(self):
+    return self._evaluator.reset()
+
+
 class COCOEvaluator(object):
   """COCO evaluation metric class."""
 

official/vision/detection/evaluation/factory.py

@@ -32,4 +32,4 @@ def evaluator_generator(params):
   else:
     raise ValueError('Evaluator %s is not supported.' % params.type)
 
-  return evaluator
+  return coco_evaluator.MetricWrapper(evaluator)

official/vision/detection/modeling/retinanet_model.py

@@ -32,35 +32,6 @@ from official.vision.detection.modeling.architecture import factory
 from official.vision.detection.ops import postprocess_ops
 
 
-class COCOMetrics(object):
-  # This is only a wrapper for COCO metric and works on for numpy array. So it
-  # doesn't inherit from tf.keras.layers.Layer or tf.keras.metrics.Metric.
-
-  def __init__(self, params):
-    self._evaluator = eval_factory.evaluator_generator(params.eval)
-
-  def update_state(self, y_true, y_pred):
-    labels = tf.nest.map_structure(lambda x: x.numpy(), y_true)
-    outputs = tf.nest.map_structure(lambda x: x.numpy(), y_pred)
-    groundtruths = {}
-    predictions = {}
-    for key, val in outputs.items():
-      if isinstance(val, tuple):
-        val = np.concatenate(val)
-      predictions[key] = val
-    for key, val in labels.items():
-      if isinstance(val, tuple):
-        val = np.concatenate(val)
-      groundtruths[key] = val
-    self._evaluator.update(predictions, groundtruths)
-
-  def result(self):
-    return self._evaluator.evaluate()
-
-  def reset_states(self):
-    return self._evaluator.reset()
-
-
 class RetinanetModel(base_model.Model):
   """RetinaNet model function."""
 
@@ -97,6 +68,11 @@ class RetinanetModel(base_model.Model):
         dtype=tf.bfloat16 if self._use_bfloat16 else tf.float32)
 
   def build_outputs(self, inputs, mode):
+    # If the input image is transposed (from NHWC to HWCN), we need to revert it
+    # back to the original shape before it's used in the computation.
+    if self._transpose_input:
+      inputs = tf.transpose(inputs, [3, 0, 1, 2])
+
     backbone_features = self._backbone_fn(
         inputs, is_training=(mode == mode_keys.TRAIN))
     fpn_features = self._fpn_fn(
@@ -192,4 +168,4 @@ class RetinanetModel(base_model.Model):
     return labels, outputs
 
   def eval_metrics(self):
-    return COCOMetrics(self._params)
+    return eval_factory.evaluator_generator(self._params.eval)