[FasterRCNN] use DECODE_BBOX_CLIP constant instead of an argument

examples/FasterRCNN/config.py

@@ -25,6 +25,8 @@ ANCHOR_RATIOS = (0.5, 1., 2.)
 NUM_ANCHOR = len(ANCHOR_SIZES) * len(ANCHOR_RATIOS)
 POSITIVE_ANCHOR_THRES = 0.7
 NEGATIVE_ANCHOR_THRES = 0.3
+# just to avoid too large numbers.
+BBOX_DECODE_CLIP = np.log(MAX_SIZE / 16.0)
 
 # rpn training -------------------------
 # keep fg ratio in a batch in this range

examples/FasterRCNN/model.py

@@ -96,12 +96,11 @@ def rpn_losses(anchor_labels, anchor_boxes, label_logits, box_logits):
 
 
 @under_name_scope()
-def decode_bbox_target(box_predictions, anchors, stride):
+def decode_bbox_target(box_predictions, anchors):
     """
     Args:
         box_predictions: fHxfWxNAx4, logits
         anchors: fHxfWxNAx4, floatbox
-        stride (int): the stride of the anchors
 
     Returns:
         box_decoded: (fHxfWxNA)x4, float32
@@ -116,7 +115,7 @@ def decode_bbox_target(box_predictions, anchors, stride):
     xaya = tf.to_float(anchors_x2y2 + anchors_x1y1) * 0.5
 
     wbhb = tf.exp(tf.minimum(
-        box_pred_twth, np.log(config.MAX_SIZE * 1.0 / stride))) * waha
+        box_pred_twth, config.BBOX_DECODE_CLIP)) * waha
     xbyb = box_pred_txty * waha + xaya
     x1y1 = xbyb - wbhb * 0.5
     x2y2 = xbyb + wbhb * 0.5

examples/FasterRCNN/train.py

@@ -92,7 +92,7 @@ class Model(ModelDesc):
         rpn_label_loss, rpn_box_loss = rpn_losses(
             anchor_labels, anchor_boxes_encoded, rpn_label_logits, rpn_box_logits)
 
-        decoded_boxes = decode_bbox_target(rpn_box_logits, fm_anchors, config.ANCHOR_STRIDE)  # (fHxfWxNA)x4, floatbox
+        decoded_boxes = decode_bbox_target(rpn_box_logits, fm_anchors)  # (fHxfWxNA)x4, floatbox
         proposal_boxes, proposal_scores = generate_rpn_proposals(
             decoded_boxes,
             tf.reshape(rpn_label_logits, [-1]),
@@ -131,7 +131,7 @@ class Model(ModelDesc):
             fg_boxes = tf.gather(proposal_boxes, fg_ind)
 
             fg_box_logits = fg_box_logits / tf.constant(config.FASTRCNN_BBOX_REG_WEIGHTS)
-            decoded_boxes = decode_bbox_target(fg_box_logits, fg_boxes, config.ANCHOR_STRIDE)  # Nfx4, floatbox
+            decoded_boxes = decode_bbox_target(fg_box_logits, fg_boxes)  # Nfx4, floatbox
             decoded_boxes = tf.identity(decoded_boxes, name='fastrcnn_fg_boxes')
 
     def _get_optimizer(self):