[MaskRCNN] remove cfg.DATA.NUM_CLASS

examples/FasterRCNN/config.py

@@ -216,7 +216,6 @@ def finalize_configs(is_training):
     Run some sanity checks, and populate some configs from others
     """
     _C.freeze(False)  # populate new keys now
-    _C.DATA.NUM_CLASS = _C.DATA.NUM_CATEGORY + 1  # +1 background
     _C.DATA.BASEDIR = os.path.expanduser(_C.DATA.BASEDIR)
     if isinstance(_C.DATA.VAL, six.string_types):  # support single string (the typical case) as well
         _C.DATA.VAL = (_C.DATA.VAL, )

examples/FasterRCNN/generalized_rcnn.py

@@ -137,7 +137,7 @@ class ResNetC4Model(GeneralizedRCNN):
         feature_fastrcnn = resnet_conv5(roi_resized, cfg.BACKBONE.RESNET_NUM_BLOCKS[-1])    # nxcx7x7
         # Keep C5 feature to be shared with mask branch
         feature_gap = GlobalAvgPooling('gap', feature_fastrcnn, data_format='channels_first')
-        fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_outputs('fastrcnn', feature_gap, cfg.DATA.NUM_CLASS)
+        fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_outputs('fastrcnn', feature_gap, cfg.DATA.NUM_CATEGORY)
 
         fastrcnn_head = FastRCNNHead(proposals, fastrcnn_box_logits, fastrcnn_label_logits, gt_boxes,
                                      tf.constant(cfg.FRCNN.BBOX_REG_WEIGHTS, dtype=tf.float32))
@@ -254,7 +254,7 @@ class ResNetFPNModel(GeneralizedRCNN):
 
             head_feature = fastrcnn_head_func('fastrcnn', roi_feature_fastrcnn)
             fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_outputs(
-                'fastrcnn/outputs', head_feature, cfg.DATA.NUM_CLASS)
+                'fastrcnn/outputs', head_feature, cfg.DATA.NUM_CATEGORY)
             fastrcnn_head = FastRCNNHead(proposals, fastrcnn_box_logits, fastrcnn_label_logits,
                                          gt_boxes, tf.constant(cfg.FRCNN.BBOX_REG_WEIGHTS, dtype=tf.float32))
         else:
@@ -263,7 +263,7 @@ class ResNetFPNModel(GeneralizedRCNN):
 
             fastrcnn_head = CascadeRCNNHead(
                 proposals, roi_func, fastrcnn_head_func,
-                (gt_boxes, gt_labels), image_shape2d, cfg.DATA.NUM_CLASS)
+                (gt_boxes, gt_labels), image_shape2d, cfg.DATA.NUM_CATEGORY)
 
         if self.training:
             all_losses = fastrcnn_head.losses()

examples/FasterRCNN/model_cascade.py

@@ -10,7 +10,8 @@ from utils.box_ops import pairwise_iou
 
 class CascadeRCNNHead(object):
     def __init__(self, proposals,
-                 roi_func, fastrcnn_head_func, gt_targets, image_shape2d, num_classes):
+                 roi_func, fastrcnn_head_func, gt_targets, image_shape2d,
+                 num_categories):
         """
         Args:
             proposals: BoxProposals
@@ -66,7 +67,7 @@ class CascadeRCNNHead(object):
         pooled_feature = self.scale_gradient(pooled_feature)
         head_feature = self.fastrcnn_head_func('head', pooled_feature)
         label_logits, box_logits = fastrcnn_outputs(
-            'outputs', head_feature, self.num_classes, class_agnostic_regression=True)
+            'outputs', head_feature, self.num_categories, class_agnostic_regression=True)
         head = FastRCNNHead(proposals, box_logits, label_logits, self.gt_boxes, reg_weights)
 
         refined_boxes = head.decoded_output_boxes_class_agnostic()
@@ -107,7 +108,7 @@ class CascadeRCNNHead(object):
         """
         ret = self._cascade_boxes[-1]
         ret = tf.expand_dims(ret, 1)     # class-agnostic
-        return tf.tile(ret, [1, self.num_classes, 1])
+        return tf.tile(ret, [1, self.num_categories + 1, 1])
 
     def output_scores(self, name=None):
         """

examples/FasterRCNN/model_frcnn.py

@@ -102,17 +102,18 @@ def sample_fast_rcnn_targets(boxes, gt_boxes, gt_labels):
 
 
 @layer_register(log_shape=True)
-def fastrcnn_outputs(feature, num_classes, class_agnostic_regression=False):
+def fastrcnn_outputs(feature, num_categories, class_agnostic_regression=False):
     """
     Args:
         feature (any shape):
-        num_classes(int): num_category + 1
+        num_categories (int):
         class_agnostic_regression (bool): if True, regression to N x 1 x 4
 
     Returns:
         cls_logits: N x num_class classification logits
         reg_logits: N x num_classx4 or Nx2x4 if class agnostic
     """
+    num_classes = num_categories + 1
     classification = FullyConnected(
         'class', feature, num_classes,
         kernel_initializer=tf.random_normal_initializer(stddev=0.01))
@@ -186,8 +187,7 @@ def fastrcnn_predictions(boxes, scores):
         scores: K
         labels: K
     """
-    assert boxes.shape[1] == cfg.DATA.NUM_CLASS
-    assert scores.shape[1] == cfg.DATA.NUM_CLASS
+    assert boxes.shape[1] == scores.shape[1]
     boxes = tf.transpose(boxes, [1, 0, 2])[1:, :, :]  # #catxnx4
     scores = tf.transpose(scores[:, 1:], [1, 0])  # #catxn
 
@@ -353,6 +353,7 @@ class FastRCNNHead(object):
             if k != 'self' and v is not None:
                 setattr(self, k, v)
         self._bbox_class_agnostic = int(box_logits.shape[1]) == 1
+        self._num_classes = box_logits.shape[1]
 
     @memoized_method
     def fg_box_logits(self):
@@ -373,7 +374,7 @@ class FastRCNNHead(object):
     def decoded_output_boxes(self):
         """ Returns: N x #class x 4 """
         anchors = tf.tile(tf.expand_dims(self.proposals.boxes, 1),
-                          [1, cfg.DATA.NUM_CLASS, 1])   # N x #class x 4
+                          [1, self._num_classes, 1])   # N x #class x 4
         decoded_boxes = decode_bbox_target(
             self.box_logits / self.bbox_regression_weights,
             anchors