[MaskRCNN] allow an empty id mapping (#1189)

examples/FasterRCNN/dataset/coco.py

@@ -21,13 +21,16 @@ class COCODetection(DatasetSplit):
         'minival2014': 'val2014',
     }
 
-    COCO_id_to_category_id = {1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9, 10: 10, 11: 11, 13: 12, 14: 13, 15: 14, 16: 15, 17: 16, 18: 17, 19: 18, 20: 19, 21: 20, 22: 21, 23: 22, 24: 23, 25: 24, 27: 25, 28: 26, 31: 27, 32: 28, 33: 29, 34: 30, 35: 31, 36: 32, 37: 33, 38: 34, 39: 35, 40: 36, 41: 37, 42: 38, 43: 39, 44: 40, 46: 41, 47: 42, 48: 43, 49: 44, 50: 45, 51: 46, 52: 47, 53: 48, 54: 49, 55: 50, 56: 51, 57: 52, 58: 53, 59: 54, 60: 55, 61: 56, 62: 57, 63: 58, 64: 59, 65: 60, 67: 61, 70: 62, 72: 63, 73: 64, 74: 65, 75: 66, 76: 67, 77: 68, 78: 69, 79: 70, 80: 71, 81: 72, 82: 73, 84: 74, 85: 75, 86: 76, 87: 77, 88: 78, 89: 79, 90: 80}  # noqa
     """
     Mapping from the incontinuous COCO category id to an id in [1, #category]
-    For your own dataset, this should usually be an identity mapping.
+    For your own coco-format, dataset, change this to an **empty dict**.
     """
+    COCO_id_to_category_id = {13: 12, 14: 13, 15: 14, 16: 15, 17: 16, 18: 17, 19: 18, 20: 19, 21: 20, 22: 21, 23: 22, 24: 23, 25: 24, 27: 25, 28: 26, 31: 27, 32: 28, 33: 29, 34: 30, 35: 31, 36: 32, 37: 33, 38: 34, 39: 35, 40: 36, 41: 37, 42: 38, 43: 39, 44: 40, 46: 41, 47: 42, 48: 43, 49: 44, 50: 45, 51: 46, 52: 47, 53: 48, 54: 49, 55: 50, 56: 51, 57: 52, 58: 53, 59: 54, 60: 55, 61: 56, 62: 57, 63: 58, 64: 59, 65: 60, 67: 61, 70: 62, 72: 63, 73: 64, 74: 65, 75: 66, 76: 67, 77: 68, 78: 69, 79: 70, 80: 71, 81: 72, 82: 73, 84: 74, 85: 75, 86: 76, 87: 77, 88: 78, 89: 79, 90: 80}  # noqa
 
-    # 80 names for COCO
+    """
+    80 names for COCO
+    For your own coco-format dataset, change this.
+    """
     class_names = [
         "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"]  # noqa
     cfg.DATA.CLASS_NAMES = ["BG"] + class_names
@@ -162,7 +165,7 @@ class COCODetection(DatasetSplit):
         # all geometrically-valid boxes are returned
         boxes = np.asarray([obj['bbox'] for obj in valid_objs], dtype='float32')  # (n, 4)
         cls = np.asarray([
-            self.COCO_id_to_category_id[obj['category_id']]
+            self.COCO_id_to_category_id.get(obj['category_id'], obj['category_id'])
             for obj in valid_objs], dtype='int32')  # (n,)
         is_crowd = np.asarray([obj['iscrowd'] for obj in valid_objs], dtype='int8')
 
@@ -185,6 +188,7 @@ class COCODetection(DatasetSplit):
         continuous_id_to_COCO_id = {v: k for k, v in self.COCO_id_to_category_id.items()}
         for res in results:
             # convert to COCO's incontinuous category id
+            if res['category_id'] in continuous_id_to_COCO_id:
                 res['category_id'] = continuous_id_to_COCO_id[res['category_id']]
             # COCO expects results in xywh format
             box = res['bbox']