model

official/vision/beta/projects/yolo/dataloaders/yolo_input.py

@@ -237,7 +237,7 @@ class Parser(parser.Parser):
           affine=affine,
           shuffle_boxes=False,
           area_thresh=self._area_thresh,
-          augment=True,
+          filter_and_clip_boxes=True,
           seed=self._seed)
       classes = tf.gather(classes, inds)
       info = infos[-1]
@@ -286,7 +286,8 @@ class Parser(parser.Parser):
     # Clip and clean boxes.
     image = image / 255.0
     boxes, inds = preprocessing_ops.transform_and_clip_boxes(
-        boxes, infos, shuffle_boxes=False, area_thresh=0.0, augment=True)
+        boxes, infos, shuffle_boxes=False, area_thresh=0.0, 
+        filter_and_clip_boxes=False)
     classes = tf.gather(classes, inds)
     info = infos[-1]
 

official/vision/beta/projects/yolo/ops/mosaic.py

@@ -179,7 +179,7 @@ class Mosaic:
         infos,
         area_thresh=self._area_thresh,
         shuffle_boxes=False,
-        augment=True,
+        filter_and_clip_boxes=True,
         seed=self._seed)
     classes, is_crowd, area = self._select_ind(inds, classes, is_crowd, area)  # pylint:disable=unbalanced-tuple-unpacking
     return image, boxes, classes, is_crowd, area, crop_points

official/vision/beta/projects/yolo/ops/preprocessing_ops.py

@@ -839,7 +839,7 @@ def transform_and_clip_boxes(boxes,
                              shuffle_boxes=False,
                              area_thresh=0.1,
                              seed=None,
-                             augment=True):
+                             filter_and_clip_boxes=True):
   """Clips and cleans the boxes.
 
   Args:
@@ -870,12 +870,8 @@ def transform_and_clip_boxes(boxes,
 
   # Make sure all boxes are valid to start, clip to [0, 1] and get only the
   # valid boxes.
-  # if output_size is None:
-  #   output_size = tf.cast([640, 640], tf.float32)
-  # else:
-  #   output_size = tf.cast(output_size, tf.float32)
   output_size = None
-  if augment:
+  if filter_and_clip_boxes:
     boxes = tf.math.maximum(tf.math.minimum(boxes, 1.0), 0.0)
   cond = get_valid_boxes(boxes)
 
@@ -924,7 +920,7 @@ def transform_and_clip_boxes(boxes,
   boxes *= tf.cast(tf.expand_dims(cond, axis=-1), boxes.dtype)
 
   # Threshold the existing boxes.
-  if augment:
+  if filter_and_clip_boxes:
     if output_size is not None:
       boxes_ = bbox_ops.denormalize_boxes(boxes, output_size)
       box_history_ = bbox_ops.denormalize_boxes(box_history, output_size)