Support dot-product in consistency metric.

PiperOrigin-RevId: 472760525

research/object_detection/meta_architectures/deepmac_meta_arch.py

@@ -449,15 +449,24 @@ def gaussian_pixel_similarity(a, b, theta):
   return similarity
 
 
-def dilated_cross_pixel_similarity(feature_map, dilation=2, theta=2.0):
-  """Dilated cross pixel similarity as defined in [1].
+def dotprod_pixel_similarity(a, b):
+  return tf.reduce_sum(a * b, axis=-1)
 
-  [1]: https://arxiv.org/abs/2012.02310
+
+def dilated_cross_pixel_similarity(feature_map, dilation=2, theta=2.0,
+                                   method='gaussian'):
+  """Dilated cross pixel similarity.
+
+  method supports 2 values
+  - 'gaussian' from https://arxiv.org/abs/2012.02310
+  - 'dotprod' computes the dot product between feature vector for similarity.
+     This assumes that the features are normalized.
 
   Args:
     feature_map: A float tensor of shape [batch_size, height, width, channels]
     dilation: int, the dilation factor.
     theta: The denominator while taking difference inside the gaussian.
+    method: str, either 'gaussian' or 'dotprod'.
 
   Returns:
     dilated_similarity: A tensor of shape [8, batch_size, height, width]
@@ -465,7 +474,12 @@ def dilated_cross_pixel_similarity(feature_map, dilation=2, theta=2.0):
   neighbors = generate_2d_neighbors(feature_map, dilation)
   feature_map = feature_map[tf.newaxis]
 
-  return gaussian_pixel_similarity(feature_map, neighbors, theta=theta)
+  if method == 'gaussian':
+    return gaussian_pixel_similarity(feature_map, neighbors, theta=theta)
+  elif method == 'dotprod':
+    return dotprod_pixel_similarity(feature_map, neighbors)
+  else:
+    raise ValueError('Unknown method for pixel sim %s' % method)
 
 
 def dilated_cross_same_mask_label(instance_masks, dilation=2):