[MaskRCNN] bugfix in RPN.CROWD_OVERLAP_THRESH

examples/FasterRCNN/data.py

@@ -160,7 +160,7 @@ def get_anchor_labels(anchors, gt_boxes, crowd_boxes):
         cand_inds = np.where(anchor_labels >= 0)[0]
         cand_anchors = anchors[cand_inds]
         ioas = np_ioa(crowd_boxes, cand_anchors)
-        overlap_with_crowd = cand_inds[ioas.max(axis=0) > cfg.RPN.CROWD_OVERLAP_THRES]
+        overlap_with_crowd = cand_inds[ioas.max(axis=0) > cfg.RPN.CROWD_OVERLAP_THRESH]
         anchor_labels[overlap_with_crowd] = -1
 
     # Subsample fg labels: ignore some fg if fg is too many

examples/FasterRCNN/model_fpn.py

@@ -165,22 +165,19 @@ def multilevel_rpn_losses(
 
 @under_name_scope()
 def generate_fpn_proposals(
-    multilevel_anchors, multilevel_label_logits,
-        multilevel_box_logits, image_shape2d):
+        multilevel_pred_boxes, multilevel_label_logits, image_shape2d):
     """
     Args:
-        multilevel_anchors: #lvl RPNAnchors
+        multilevel_pred_boxes: #lvl HxWxAx4 boxes
         multilevel_label_logits: #lvl tensors of shape HxWxA
-        multilevel_box_logits: #lvl tensors of shape HxWxAx4
 
     Returns:
         boxes: kx4 float
         scores: k logits
     """
     num_lvl = len(cfg.FPN.ANCHOR_STRIDES)
-    assert len(multilevel_anchors) == num_lvl
+    assert len(multilevel_pred_boxes) == num_lvl
     assert len(multilevel_label_logits) == num_lvl
-    assert len(multilevel_box_logits) == num_lvl
 
     ctx = get_current_tower_context()
     all_boxes = []
@@ -189,8 +186,7 @@ def generate_fpn_proposals(
         fpn_nms_topk = cfg.RPN.TRAIN_PER_LEVEL_NMS_TOPK if ctx.is_training else cfg.RPN.TEST_PER_LEVEL_NMS_TOPK
         for lvl in range(num_lvl):
             with tf.name_scope('Lvl{}'.format(lvl + 2)):
-                anchors = multilevel_anchors[lvl]
-                pred_boxes_decoded = anchors.decode_logits(multilevel_box_logits[lvl])
+                pred_boxes_decoded = multilevel_pred_boxes[lvl]
 
                 proposal_boxes, proposal_scores = generate_rpn_proposals(
                     tf.reshape(pred_boxes_decoded, [-1, 4]),
@@ -207,8 +203,7 @@ def generate_fpn_proposals(
     else:
         for lvl in range(num_lvl):
             with tf.name_scope('Lvl{}'.format(lvl + 2)):
-                anchors = multilevel_anchors[lvl]
-                pred_boxes_decoded = anchors.decode_logits(multilevel_box_logits[lvl])
+                pred_boxes_decoded = multilevel_pred_boxes[lvl]
                 all_boxes.append(tf.reshape(pred_boxes_decoded, [-1, 4]))
                 all_scores.append(tf.reshape(multilevel_label_logits[lvl], [-1]))
         all_boxes = tf.concat(all_boxes, axis=0)

examples/FasterRCNN/train.py

@@ -249,10 +249,11 @@ class ResNetFPNModel(DetectionModel):
                        for pi in p23456]
         multilevel_label_logits = [k[0] for k in rpn_outputs]
         multilevel_box_logits = [k[1] for k in rpn_outputs]
+        multilevel_pred_boxes = [anchor.decode_logits(logits)
+                                 for anchor, logits in zip(multilevel_anchors, multilevel_box_logits)]
 
         proposal_boxes, proposal_scores = generate_fpn_proposals(
-            multilevel_anchors, multilevel_label_logits,
-            multilevel_box_logits, image_shape2d)
+            multilevel_pred_boxes, multilevel_label_logits, image_shape2d)
 
         gt_boxes, gt_labels = inputs['gt_boxes'], inputs['gt_labels']
         if is_training: