Add box consistency loss in DeepMAC.

PiperOrigin-RevId: 385646058

research/object_detection/meta_architectures/deepmac_meta_arch.py

@@ -26,6 +26,7 @@ from object_detection.utils import spatial_transform_ops
 INSTANCE_EMBEDDING = 'INSTANCE_EMBEDDING'
 PIXEL_EMBEDDING = 'PIXEL_EMBEDDING'
 DEEP_MASK_ESTIMATION = 'deep_mask_estimation'
+DEEP_MASK_BOX_CONSISTENCY = 'deep_mask_box_consistency'
 LOSS_KEY_PREFIX = center_net_meta_arch.LOSS_KEY_PREFIX
 
 
@@ -35,7 +36,7 @@ class DeepMACParams(
         'allowed_masked_classes_ids', 'mask_size', 'mask_num_subsamples',
         'use_xy', 'network_type', 'use_instance_embedding', 'num_init_channels',
         'predict_full_resolution_masks', 'postprocess_crop_size',
-        'max_roi_jitter_ratio', 'roi_jitter_mode'
+        'max_roi_jitter_ratio', 'roi_jitter_mode', 'box_consistency_loss_weight'
     ])):
   """Class holding the DeepMAC network configutration."""
 
@@ -46,7 +47,7 @@ class DeepMACParams(
               mask_num_subsamples, use_xy, network_type, use_instance_embedding,
               num_init_channels, predict_full_resolution_masks,
               postprocess_crop_size, max_roi_jitter_ratio,
-              roi_jitter_mode):
+              roi_jitter_mode, box_consistency_loss_weight):
     return super(DeepMACParams,
                  cls).__new__(cls, classification_loss, dim,
                               task_loss_weight, pixel_embedding_dim,
@@ -55,7 +56,7 @@ class DeepMACParams(
                               use_instance_embedding, num_init_channels,
                               predict_full_resolution_masks,
                               postprocess_crop_size, max_roi_jitter_ratio,
-                              roi_jitter_mode)
+                              roi_jitter_mode, box_consistency_loss_weight)
 
 
 def subsample_instances(classes, weights, boxes, masks, num_subsamples):
@@ -206,6 +207,61 @@ def filter_masked_classes(masked_class_ids, classes, weights, masks):
   )
 
 
+def crop_and_resize_feature_map(features, boxes, size):
+  """Crop and resize regions from a single feature map given a set of boxes.
+
+  Args:
+    features: A [H, W, C] float tensor.
+    boxes: A [N, 4] tensor of norrmalized boxes.
+    size: int, the size of the output features.
+
+  Returns:
+    per_box_features: A [N, size, size, C] tensor of cropped and resized
+      features.
+  """
+  return spatial_transform_ops.matmul_crop_and_resize(
+      features[tf.newaxis], boxes[tf.newaxis], [size, size])[0]
+
+
+def crop_and_resize_instance_masks(masks, boxes, mask_size):
+  """Crop and resize each mask according to the given boxes.
+
+  Args:
+    masks: A [N, H, W] float tensor.
+    boxes: A [N, 4] float tensor of normalized boxes.
+    mask_size: int, the size of the output masks.
+
+  Returns:
+    masks: A [N, mask_size, mask_size] float tensor of cropped and resized
+      instance masks.
+  """
+  cropped_masks = spatial_transform_ops.matmul_crop_and_resize(
+      masks[:, :, :, tf.newaxis], boxes[:, tf.newaxis, :],
+      [mask_size, mask_size])
+  cropped_masks = tf.squeeze(cropped_masks, axis=[1, 4])
+
+  return cropped_masks
+
+
+def fill_boxes(boxes, height, width):
+  """Fills the area included in the box."""
+  blist = box_list.BoxList(boxes)
+  blist = box_list_ops.to_absolute_coordinates(blist, height, width)
+  boxes = blist.get()
+  ymin, xmin, ymax, xmax = tf.unstack(
+      boxes[:, tf.newaxis, tf.newaxis, :], 4, axis=3)
+
+  ygrid, xgrid = tf.meshgrid(tf.range(height), tf.range(width), indexing='ij')
+  ygrid, xgrid = tf.cast(ygrid, tf.float32), tf.cast(xgrid, tf.float32)
+  ygrid, xgrid = ygrid[tf.newaxis, :, :], xgrid[tf.newaxis, :, :]
+
+  filled_boxes = tf.logical_and(
+      tf.logical_and(ygrid >= ymin, ygrid <= ymax),
+      tf.logical_and(xgrid >= xmin, xgrid <= xmax))
+
+  return tf.cast(filled_boxes, tf.float32)
+
+
 class ResNetMaskNetwork(tf.keras.layers.Layer):
   """A small wrapper around ResNet blocks to predict masks."""
 
@@ -379,7 +435,8 @@ def deepmac_proto_to_params(deepmac_config):
       deepmac_config.predict_full_resolution_masks,
       postprocess_crop_size=deepmac_config.postprocess_crop_size,
       max_roi_jitter_ratio=deepmac_config.max_roi_jitter_ratio,
-      roi_jitter_mode=jitter_mode
+      roi_jitter_mode=jitter_mode,
+      box_consistency_loss_weight=deepmac_config.box_consistency_loss_weight
   )
 
 
@@ -402,6 +459,13 @@ class DeepMACMetaArch(center_net_meta_arch.CenterNetMetaArch):
     """Constructs the super class with object center & detection params only."""
 
     self._deepmac_params = deepmac_params
+    if (self._deepmac_params.predict_full_resolution_masks and
+        self._deepmac_params.max_roi_jitter_ratio > 0.0):
+      raise ValueError('Jittering is not supported for full res masks.')
+
+    if self._deepmac_params.mask_num_subsamples > 0:
+      raise ValueError('Subsampling masks is currently not supported.')
+
     super(DeepMACMetaArch, self).__init__(
         is_training=is_training, add_summaries=add_summaries,
         num_classes=num_classes, feature_extractor=feature_extractor,
@@ -462,18 +526,31 @@ class DeepMACMetaArch(center_net_meta_arch.CenterNetMetaArch):
       pixel_embedding = pixel_embedding[tf.newaxis, :, :, :]
       pixel_embeddings_processed = tf.tile(pixel_embedding,
                                            [num_instances, 1, 1, 1])
+      image_shape = tf.shape(pixel_embeddings_processed)
+      image_height, image_width = image_shape[1], image_shape[2]
+      y_grid, x_grid = tf.meshgrid(tf.linspace(0.0, 1.0, image_height),
+                                   tf.linspace(0.0, 1.0, image_width),
+                                   indexing='ij')
+
+      blist = box_list.BoxList(boxes)
+      ycenter, xcenter, _, _ = blist.get_center_coordinates_and_sizes()
+      y_grid = y_grid[tf.newaxis, :, :]
+      x_grid = x_grid[tf.newaxis, :, :]
+
+      y_grid -= ycenter[:, tf.newaxis, tf.newaxis]
+      x_grid -= xcenter[:, tf.newaxis, tf.newaxis]
+      coords = tf.stack([y_grid, x_grid], axis=3)
+
     else:
       # TODO(vighneshb) Explore multilevel_roi_align and align_corners=False.
-      pixel_embeddings_cropped = spatial_transform_ops.matmul_crop_and_resize(
-          pixel_embedding[tf.newaxis], boxes[tf.newaxis],
-          [mask_size, mask_size])
-      pixel_embeddings_processed = pixel_embeddings_cropped[0]
-
+      pixel_embeddings_processed = crop_and_resize_feature_map(
+          pixel_embedding, boxes, mask_size)
       mask_shape = tf.shape(pixel_embeddings_processed)
       mask_height, mask_width = mask_shape[1], mask_shape[2]
       y_grid, x_grid = tf.meshgrid(tf.linspace(-1.0, 1.0, mask_height),
                                    tf.linspace(-1.0, 1.0, mask_width),
                                    indexing='ij')
+
       coords = tf.stack([y_grid, x_grid], axis=2)
       coords = coords[tf.newaxis, :, :, :]
       coords = tf.tile(coords, [num_instances, 1, 1, 1])
@@ -528,11 +605,9 @@ class DeepMACMetaArch(center_net_meta_arch.CenterNetMetaArch):
     if self._deepmac_params.predict_full_resolution_masks:
       return masks
     else:
-      cropped_masks = spatial_transform_ops.matmul_crop_and_resize(
-          masks[:, :, :, tf.newaxis], boxes[:, tf.newaxis, :],
-          [mask_size, mask_size])
+      cropped_masks = crop_and_resize_instance_masks(
+          masks, boxes, mask_size)
       cropped_masks = tf.stop_gradient(cropped_masks)
-      cropped_masks = tf.squeeze(cropped_masks, axis=[1, 4])
 
       # TODO(vighneshb) should we discretize masks?
       return cropped_masks
@@ -543,7 +618,64 @@ class DeepMACMetaArch(center_net_meta_arch.CenterNetMetaArch):
 
     return resize_instance_masks(logits, (height, width))
 
-  def _compute_per_instance_mask_loss(
+  def _compute_per_instance_mask_prediction_loss(
+      self, boxes, mask_logits, mask_gt):
+    num_instances = tf.shape(boxes)[0]
+    mask_logits = self._resize_logits_like_gt(mask_logits, mask_gt)
+
+    mask_logits = tf.reshape(mask_logits, [num_instances, -1, 1])
+    mask_gt = tf.reshape(mask_gt, [num_instances, -1, 1])
+    loss = self._deepmac_params.classification_loss(
+        prediction_tensor=mask_logits,
+        target_tensor=mask_gt,
+        weights=tf.ones_like(mask_logits))
+
+    # TODO(vighneshb) Make this configurable via config.
+    # Skip normalization for dice loss because the denominator term already
+    # does normalization.
+    if isinstance(self._deepmac_params.classification_loss,
+                  losses.WeightedDiceClassificationLoss):
+      return tf.reduce_sum(loss, axis=1)
+    else:
+      return tf.reduce_mean(loss, axis=[1, 2])
+
+  def _compute_per_instance_box_consistency_loss(
+      self, boxes_gt, boxes_for_crop, mask_logits):
+
+    height, width = tf.shape(mask_logits)[1], tf.shape(mask_logits)[2]
+    filled_boxes = fill_boxes(boxes_gt, height, width)[:, :, :, tf.newaxis]
+    mask_logits = mask_logits[:, :, :, tf.newaxis]
+
+    if self._deepmac_params.predict_full_resolution_masks:
+      gt_crop = filled_boxes[:, :, :, 0]
+      pred_crop = mask_logits[:, :, :, 0]
+    else:
+      gt_crop = crop_and_resize_instance_masks(
+          filled_boxes, boxes_for_crop, self._deepmac_params.mask_size)
+      pred_crop = crop_and_resize_instance_masks(
+          mask_logits, boxes_for_crop, self._deepmac_params.mask_size)
+
+    loss = 0.0
+    for axis in [1, 2]:
+      pred_max = tf.reduce_max(pred_crop, axis=axis)[:, :, tf.newaxis]
+      gt_max = tf.reduce_max(gt_crop, axis=axis)[:, :, tf.newaxis]
+
+      axis_loss = self._deepmac_params.classification_loss(
+          prediction_tensor=pred_max,
+          target_tensor=gt_max,
+          weights=tf.ones_like(pred_max))
+      loss += axis_loss
+
+    # Skip normalization for dice loss because the denominator term already
+    # does normalization.
+    # TODO(vighneshb) Make this configurable via config.
+    if isinstance(self._deepmac_params.classification_loss,
+                  losses.WeightedDiceClassificationLoss):
+      return tf.reduce_sum(loss, axis=1)
+    else:
+      return tf.reduce_mean(loss, axis=[1, 2])
+
+  def _compute_per_instance_deepmac_losses(
       self, boxes, masks, instance_embedding, pixel_embedding):
     """Returns the mask loss per instance.
 
@@ -558,40 +690,36 @@ class DeepMACMetaArch(center_net_meta_arch.CenterNetMetaArch):
         pixel_embedding_size] float tensor containing the per-pixel embeddings.
 
     Returns:
-      mask_loss: A [num_instances] shaped float tensor containing the
+      mask_prediction_loss: A [num_instances] shaped float tensor containing the
         mask loss for each instance.
-    """
+      box_consistency_loss: A [num_instances] shaped float tensor containing
+        the box consistency loss for each instance.
 
-    num_instances = tf.shape(boxes)[0]
+    """
 
     if tf.keras.backend.learning_phase():
-      boxes = preprocessor.random_jitter_boxes(
+      boxes_for_crop = preprocessor.random_jitter_boxes(
           boxes, self._deepmac_params.max_roi_jitter_ratio,
           jitter_mode=self._deepmac_params.roi_jitter_mode)
+    else:
+      boxes_for_crop = boxes
+
     mask_input = self._get_mask_head_input(
-        boxes, pixel_embedding)
+        boxes_for_crop, pixel_embedding)
     instance_embeddings = self._get_instance_embeddings(
-        boxes, instance_embedding)
-
+        boxes_for_crop, instance_embedding)
     mask_logits = self._mask_net(
         instance_embeddings, mask_input,
         training=tf.keras.backend.learning_phase())
-    mask_gt = self._get_groundtruth_mask_output(boxes, masks)
-    mask_logits = self._resize_logits_like_gt(mask_logits, mask_gt)
+    mask_gt = self._get_groundtruth_mask_output(boxes_for_crop, masks)
 
-    mask_logits = tf.reshape(mask_logits, [num_instances, -1, 1])
-    mask_gt = tf.reshape(mask_gt, [num_instances, -1, 1])
-    loss = self._deepmac_params.classification_loss(
-        prediction_tensor=mask_logits,
-        target_tensor=mask_gt,
-        weights=tf.ones_like(mask_logits))
+    mask_prediction_loss = self._compute_per_instance_mask_prediction_loss(
+        boxes_for_crop, mask_logits, mask_gt)
 
-    # TODO(vighneshb) Make this configurable via config.
-    if isinstance(self._deepmac_params.classification_loss,
-                  losses.WeightedDiceClassificationLoss):
-      return tf.reduce_sum(loss, axis=1)
-    else:
-      return tf.reduce_mean(loss, axis=[1, 2])
+    box_consistency_loss = self._compute_per_instance_box_consistency_loss(
+        boxes, boxes_for_crop, mask_logits)
+
+    return mask_prediction_loss, box_consistency_loss
 
   def _compute_instance_masks_loss(self, prediction_dict):
     """Computes the mask loss.
@@ -603,7 +731,7 @@ class DeepMACMetaArch(center_net_meta_arch.CenterNetMetaArch):
         [batch_size, height, width, embedding_size].
 
     Returns:
-      loss: float, the mask loss as a scalar.
+      loss_dict: A dict mapping string (loss names) to scalar floats.
     """
     gt_boxes_list = self.groundtruth_lists(fields.BoxListFields.boxes)
     gt_weights_list = self.groundtruth_lists(fields.BoxListFields.weights)
@@ -613,7 +741,10 @@ class DeepMACMetaArch(center_net_meta_arch.CenterNetMetaArch):
     allowed_masked_classes_ids = (
         self._deepmac_params.allowed_masked_classes_ids)
 
-    total_loss = 0.0
+    loss_dict = {
+        DEEP_MASK_ESTIMATION: 0.0,
+        DEEP_MASK_BOX_CONSISTENCY: 0.0
+    }
 
     # Iterate over multiple preidctions by backbone (for hourglass length=2)
     for instance_pred, pixel_pred in zip(
@@ -625,24 +756,31 @@ class DeepMACMetaArch(center_net_meta_arch.CenterNetMetaArch):
       for i, (boxes, weights, classes, masks) in enumerate(
           zip(gt_boxes_list, gt_weights_list, gt_classes_list, gt_masks_list)):
 
-        _, weights, masks = filter_masked_classes(allowed_masked_classes_ids,
-                                                  classes, weights, masks)
-        num_subsample = self._deepmac_params.mask_num_subsamples
-        _, weights, boxes, masks = subsample_instances(
-            classes, weights, boxes, masks, num_subsample)
+        # TODO(vighneshb) Add sub-sampling back if required.
+        classes, valid_mask_weights, masks = filter_masked_classes(
+            allowed_masked_classes_ids, classes, weights, masks)
 
-        per_instance_loss = self._compute_per_instance_mask_loss(
-            boxes, masks, instance_pred[i], pixel_pred[i])
-        per_instance_loss *= weights
+        per_instance_mask_loss, per_instance_consistency_loss = (
+            self._compute_per_instance_deepmac_losses(
+                boxes, masks, instance_pred[i], pixel_pred[i]))
+        per_instance_mask_loss *= valid_mask_weights
+        per_instance_consistency_loss *= weights
 
         num_instances = tf.maximum(tf.reduce_sum(weights), 1.0)
+        num_instances_allowed = tf.maximum(
+            tf.reduce_sum(valid_mask_weights), 1.0)
+
+        loss_dict[DEEP_MASK_ESTIMATION] += (
+            tf.reduce_sum(per_instance_mask_loss) / num_instances_allowed)
 
-        total_loss += tf.reduce_sum(per_instance_loss) / num_instances
+        loss_dict[DEEP_MASK_BOX_CONSISTENCY] += (
+            tf.reduce_sum(per_instance_consistency_loss) / num_instances)
 
     batch_size = len(gt_boxes_list)
     num_predictions = len(prediction_dict[INSTANCE_EMBEDDING])
 
-    return total_loss / float(batch_size * num_predictions)
+    return dict((key, loss / float(batch_size * num_predictions))
+                for key, loss in loss_dict.items())
 
   def loss(self, prediction_dict, true_image_shapes, scope=None):
 
@@ -650,13 +788,19 @@ class DeepMACMetaArch(center_net_meta_arch.CenterNetMetaArch):
         prediction_dict, true_image_shapes, scope)
 
     if self._deepmac_params is not None:
-      mask_loss = self._compute_instance_masks_loss(
+      mask_loss_dict = self._compute_instance_masks_loss(
           prediction_dict=prediction_dict)
-      key = LOSS_KEY_PREFIX + '/' + DEEP_MASK_ESTIMATION
-      losses_dict[key] = (
-          self._deepmac_params.task_loss_weight * mask_loss
+
+      losses_dict[LOSS_KEY_PREFIX + '/' + DEEP_MASK_ESTIMATION] = (
+          self._deepmac_params.task_loss_weight * mask_loss_dict[
+              DEEP_MASK_ESTIMATION]
       )
 
+      if self._deepmac_params.box_consistency_loss_weight > 0.0:
+        losses_dict[LOSS_KEY_PREFIX + '/' + DEEP_MASK_BOX_CONSISTENCY] = (
+            self._deepmac_params.box_consistency_loss_weight * mask_loss_dict[
+                DEEP_MASK_BOX_CONSISTENCY]
+        )
     return losses_dict
 
   def postprocess(self, prediction_dict, true_image_shapes, **params):

research/object_detection/meta_architectures/deepmac_meta_arch_test.py

@@ -60,7 +60,8 @@ class MockMaskNet(tf.keras.layers.Layer):
     return tf.zeros_like(pixel_embedding[:, :, :, 0]) + 0.9
 
 
-def build_meta_arch(predict_full_resolution_masks=False, use_dice_loss=False):
+def build_meta_arch(predict_full_resolution_masks=False, use_dice_loss=False,
+                    mask_num_subsamples=-1):
   """Builds the DeepMAC meta architecture."""
 
   feature_extractor = DummyFeatureExtractor(
@@ -94,7 +95,7 @@ def build_meta_arch(predict_full_resolution_masks=False, use_dice_loss=False):
       pixel_embedding_dim=2,
       allowed_masked_classes_ids=[],
       mask_size=16,
-      mask_num_subsamples=-1,
+      mask_num_subsamples=mask_num_subsamples,
       use_xy=True,
       network_type='hourglass10',
       use_instance_embedding=True,
@@ -102,7 +103,8 @@ def build_meta_arch(predict_full_resolution_masks=False, use_dice_loss=False):
       predict_full_resolution_masks=predict_full_resolution_masks,
       postprocess_crop_size=128,
       max_roi_jitter_ratio=0.0,
-      roi_jitter_mode='random'
+      roi_jitter_mode='random',
+      box_consistency_loss_weight=1.0,
   )
 
   object_detection_params = center_net_meta_arch.ObjectDetectionParams(
@@ -140,6 +142,33 @@ class DeepMACUtilsTest(tf.test.TestCase):
     self.assertAllClose(result[2], boxes)
     self.assertAllClose(result[3], masks)
 
+  def test_fill_boxes(self):
+
+    boxes = tf.constant([[0., 0., 0.5, 0.5], [0.5, 0.5, 1.0, 1.0]])
+
+    filled_boxes = deepmac_meta_arch.fill_boxes(boxes, 32, 32)
+    expected = np.zeros((2, 32, 32))
+    expected[0, :17, :17] = 1.0
+    expected[1, 16:, 16:] = 1.0
+
+    self.assertAllClose(expected, filled_boxes.numpy(), rtol=1e-3)
+
+  def test_crop_and_resize_instance_masks(self):
+
+    boxes = tf.zeros((5, 4))
+    masks = tf.zeros((5, 128, 128))
+    output = deepmac_meta_arch.crop_and_resize_instance_masks(
+        masks, boxes, 32)
+    self.assertEqual(output.shape, (5, 32, 32))
+
+  def test_crop_and_resize_feature_map(self):
+
+    boxes = tf.zeros((5, 4))
+    features = tf.zeros((128, 128, 7))
+    output = deepmac_meta_arch.crop_and_resize_feature_map(
+        features, boxes, 32)
+    self.assertEqual(output.shape, (5, 32, 32, 7))
+
 
 @unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
 class DeepMACMetaArchTest(tf.test.TestCase):
@@ -199,7 +228,7 @@ class DeepMACMetaArchTest(tf.test.TestCase):
   def test_get_mask_head_input_no_crop_resize(self):
 
     model = build_meta_arch(predict_full_resolution_masks=True)
-    boxes = tf.constant([[0., 0., 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]],
+    boxes = tf.constant([[0., 0., 1.0, 1.0], [0.0, 0.0, 0.5, 1.0]],
                         dtype=tf.float32)
 
     pixel_embedding_np = np.random.randn(32, 32, 4).astype(np.float32)
@@ -208,12 +237,15 @@ class DeepMACMetaArchTest(tf.test.TestCase):
     mask_inputs = model._get_mask_head_input(boxes, pixel_embedding)
     self.assertEqual(mask_inputs.shape, (2, 32, 32, 6))
 
-    y_grid, x_grid = tf.meshgrid(np.linspace(-1.0, 1.0, 32),
-                                 np.linspace(-1.0, 1.0, 32), indexing='ij')
+    y_grid, x_grid = tf.meshgrid(np.linspace(.0, 1.0, 32),
+                                 np.linspace(.0, 1.0, 32), indexing='ij')
+
+    ys = [0.5, 0.25]
+    xs = [0.5, 0.5]
     for i in range(2):
       mask_input = mask_inputs[i]
-      self.assertAllClose(y_grid, mask_input[:, :, 0])
-      self.assertAllClose(x_grid, mask_input[:, :, 1])
+      self.assertAllClose(y_grid - ys[i], mask_input[:, :, 0])
+      self.assertAllClose(x_grid - xs[i], mask_input[:, :, 1])
       pixel_embedding = mask_input[:, :, 2:]
       self.assertAllClose(pixel_embedding_np, pixel_embedding)
 
@@ -262,7 +294,7 @@ class DeepMACMetaArchTest(tf.test.TestCase):
     masks[1, 16:, 16:] = 1.0
     masks = tf.constant(masks)
 
-    loss = model._compute_per_instance_mask_loss(
+    loss, _ = model._compute_per_instance_deepmac_losses(
         boxes, masks, tf.zeros((32, 32, 2)), tf.zeros((32, 32, 2)))
     self.assertAllClose(
         loss, np.zeros(2) - tf.math.log(tf.nn.sigmoid(0.9)))
@@ -275,7 +307,7 @@ class DeepMACMetaArchTest(tf.test.TestCase):
     masks = np.ones((2, 128, 128), dtype=np.float32)
     masks = tf.constant(masks)
 
-    loss = model._compute_per_instance_mask_loss(
+    loss, _ = model._compute_per_instance_deepmac_losses(
         boxes, masks, tf.zeros((32, 32, 2)), tf.zeros((32, 32, 2)))
     self.assertAllClose(
         loss, np.zeros(2) - tf.math.log(tf.nn.sigmoid(0.9)))
@@ -289,7 +321,7 @@ class DeepMACMetaArchTest(tf.test.TestCase):
     masks = np.ones((2, 128, 128), dtype=np.float32)
     masks = tf.constant(masks)
 
-    loss = model._compute_per_instance_mask_loss(
+    loss, _ = model._compute_per_instance_deepmac_losses(
         boxes, masks, tf.zeros((32, 32, 2)), tf.zeros((32, 32, 2)))
     pred = tf.nn.sigmoid(0.9)
     expected = (1.0 - ((2.0 * pred) / (1.0 + pred)))
@@ -299,7 +331,7 @@ class DeepMACMetaArchTest(tf.test.TestCase):
     boxes = tf.zeros([0, 4])
     masks = tf.zeros([0, 128, 128])
 
-    loss = self.model._compute_per_instance_mask_loss(
+    loss, _ = self.model._compute_per_instance_deepmac_losses(
         boxes, masks, tf.zeros((32, 32, 2)), tf.zeros((32, 32, 2)))
     self.assertEqual(loss.shape, (0,))
 
@@ -394,6 +426,59 @@ class DeepMACMetaArchTest(tf.test.TestCase):
     out = call_func(tf.zeros((2, 4)), tf.zeros((2, 32, 32, 8)), training=True)
     self.assertEqual(out.shape, (2, 32, 32))
 
+  def test_box_consistency_loss(self):
+
+    boxes_gt = tf.constant([[0., 0., 0.49, 1.0]])
+    boxes_jittered = tf.constant([[0.0, 0.0, 1.0, 1.0]])
+
+    mask_prediction = np.zeros((1, 32, 32)).astype(np.float32)
+    mask_prediction[0, :24, :24] = 1.0
+
+    loss = self.model._compute_per_instance_box_consistency_loss(
+        boxes_gt, boxes_jittered, tf.constant(mask_prediction))
+
+    yloss = tf.nn.sigmoid_cross_entropy_with_logits(
+        labels=tf.constant([1.0] * 8 + [0.0] * 8),
+        logits=[1.0] * 12 + [0.0] * 4)
+    xloss = tf.nn.sigmoid_cross_entropy_with_logits(
+        labels=tf.constant([1.0] * 16),
+        logits=[1.0] * 12 + [0.0] * 4)
+
+    self.assertAllClose(loss, [tf.reduce_mean(yloss + xloss).numpy()])
+
+  def test_box_consistency_dice_loss(self):
+
+    model = build_meta_arch(use_dice_loss=True)
+    boxes_gt = tf.constant([[0., 0., 0.49, 1.0]])
+    boxes_jittered = tf.constant([[0.0, 0.0, 1.0, 1.0]])
+
+    almost_inf = 1e10
+    mask_prediction = np.full((1, 32, 32), -almost_inf, dtype=np.float32)
+    mask_prediction[0, :24, :24] = almost_inf
+
+    loss = model._compute_per_instance_box_consistency_loss(
+        boxes_gt, boxes_jittered, tf.constant(mask_prediction))
+
+    yloss = 1 - 6.0 / 7
+    xloss = 0.2
+
+    self.assertAllClose(loss, [yloss + xloss])
+
+  def test_box_consistency_dice_loss_full_res(self):
+
+    model = build_meta_arch(use_dice_loss=True,
+                            predict_full_resolution_masks=True)
+    boxes_gt = tf.constant([[0., 0., 1.0, 1.0]])
+    boxes_jittered = None
+
+    almost_inf = 1e10
+    mask_prediction = np.full((1, 32, 32), -almost_inf, dtype=np.float32)
+    mask_prediction[0, :16, :32] = almost_inf
+
+    loss = model._compute_per_instance_box_consistency_loss(
+        boxes_gt, boxes_jittered, tf.constant(mask_prediction))
+    self.assertAlmostEqual(loss[0].numpy(), 1 / 3)
+
 
 @unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
 class FullyConnectedMaskHeadTest(tf.test.TestCase):

research/object_detection/protos/center_net.proto

@@ -446,6 +446,10 @@ message CenterNet {
     // The mode for jitterting box ROIs. See RandomJitterBoxes in
     // preprocessor.proto for more details
     optional RandomJitterBoxes.JitterMode jitter_mode = 15 [default=DEFAULT];
+
+    // Weight for the box consistency loss as described in the BoxInst paper
+    // https://arxiv.org/abs/2012.02310
+    optional float box_consistency_loss_weight = 16 [default=0.0];
   }
 
   optional DeepMACMaskEstimation deepmac_mask_estimation = 14;