Prevent filtering of empty images (#1424)

* Update data.py

* Update data.py

* Update config.py

* Update data.py

* Update data.py

examples/FasterRCNN/config.py

@@ -106,6 +106,8 @@ _C.DATA.CLASS_NAMES = []  # NUM_CLASS (NUM_CATEGORY+1) strings, the first is "BG
 # whether the coordinates in your registered dataset are
 # absolute pixel values in range [0, W or H] or relative values in [0, 1]
 _C.DATA.ABSOLUTE_COORD = True
+# Filter Negative Samples from dataset
+_C.DATA.FILTER_EMPTY_ANNOTATIONS = True
 # Number of data loading workers.
 # In case of horovod training, this is the number of workers per-GPU (so you may want to use a smaller number).
 # Set to 0 to disable parallel data loading

examples/FasterRCNN/data.py

@@ -345,7 +345,8 @@ def get_train_dataflow():
     # Filter out images that have no gt boxes, but this filter shall not be applied for testing.
     # The model does support training with empty images, but it is not useful for COCO.
     num = len(roidbs)
-    roidbs = list(filter(lambda img: len(img["boxes"][img["is_crowd"] == 0]) > 0, roidbs))
+    if cfg.DATA.FILTER_EMPTY_ANNOTATIONS:
+        roidbs = list(filter(lambda img: len(img["boxes"][img["is_crowd"] == 0]) > 0, roidbs))       
     logger.info(
         "Filtered {} images which contain no non-crowd groudtruth boxes. Total #images for training: {}".format(
             num - len(roidbs), len(roidbs)