model output size

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

@@ -286,8 +286,7 @@ 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,
-        output_size = [self._image_h, self._image_w])
+        boxes, infos, shuffle_boxes=False, area_thresh=0.0, augment=True)
     classes = tf.gather(classes, inds)
     info = infos[-1]
 

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

@@ -115,5 +115,5 @@ class YoloDetectionInputTest(tf.test.TestCase, parameterized.TestCase):
 
 
 if __name__ == '__main__':
-  tf.test.main()
-  test_yolo_pipeline_visually(is_training=False, num=20)
+  # tf.test.main()
+  test_yolo_pipeline_visually(is_training=True, num=20)
\ No newline at end of file

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

@@ -180,8 +180,7 @@ class Mosaic:
         area_thresh=self._area_thresh,
         shuffle_boxes=False,
         augment=True,
-        seed=self._seed,
-        output_size=[self._output_size[0], self._output_size[1]])
+        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
 
@@ -230,8 +229,7 @@ class Mosaic:
         None,
         affine=affine,
         area_thresh=self._area_thresh,
-        seed=self._seed,
-        output_size=[self._output_size[0], self._output_size[1]])
+        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, area
 

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

@@ -780,8 +780,8 @@ def boxes_candidates(clipped_boxes,
                   clipped_height / (clipped_width + 1e-16))
 
   # Ensure the clipped width adn height are larger than a preset threshold.
-  conda = clipped_width > wh_thr
-  condb = clipped_height > wh_thr
+  conda = clipped_width >= wh_thr
+  condb = clipped_height >= wh_thr
 
   # Ensure the area of the clipped box is larger than the area threshold.
   area = (clipped_height * clipped_width) / (og_width * og_height + 1e-16)
@@ -839,8 +839,7 @@ def transform_and_clip_boxes(boxes,
                              shuffle_boxes=False,
                              area_thresh=0.1,
                              seed=None,
-                             augment=True,
-                             output_size = None):
+                             augment=True):
   """Clips and cleans the boxes.
 
   Args:
@@ -871,10 +870,11 @@ 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)
+  # 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:
     boxes = tf.math.maximum(tf.math.minimum(boxes, 1.0), 0.0)
   cond = get_valid_boxes(boxes)
@@ -925,15 +925,16 @@ def transform_and_clip_boxes(boxes,
 
   # Threshold the existing boxes.
   if augment:
-    boxes_ = bbox_ops.denormalize_boxes(boxes, output_size)
-    box_history_ = bbox_ops.denormalize_boxes(box_history, output_size)
-    inds = boxes_candidates(boxes_, box_history_, area_thr=area_thresh)
+    if output_size is not None:
+      boxes_ = bbox_ops.denormalize_boxes(boxes, output_size)
+      box_history_ = bbox_ops.denormalize_boxes(box_history, output_size)
+      inds = boxes_candidates(boxes_, box_history_, area_thr=area_thresh)
+    else:
+      inds = boxes_candidates(boxes, box_history, wh_thr = 0.0, area_thr=area_thresh)
     # Select and gather the good boxes.
     if shuffle_boxes:
       inds = tf.random.shuffle(inds, seed=seed)
   else:
-    boxes = box_history
-    boxes_ = bbox_ops.denormalize_boxes(boxes, output_size)
-    inds = bbox_ops.get_non_empty_box_indices(boxes_)
+    inds = bbox_ops.get_non_empty_box_indices(boxes)
   boxes = tf.gather(boxes, inds)
   return boxes, inds