disable proposal subsampling due to focal loss

jhultman · Feb 22, 2020 · 08ea6d4 · 08ea6d4
1 parent 4bddc94
commit 08ea6d4
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Showing 3 changed files with 10 additions and 6 deletions.
diff --git a/pvrcnn/core/config.py b/pvrcnn/core/config.py
@@ -42,7 +42,7 @@
         'center_z': -0.6,
     },
 ]
-_C.NUM_PROPOSAL_SAMPLE = 256
+_C.NUM_PROPOSAL_SAMPLE = -1
 _C.ALLOW_LOW_QUALITY_MATCHES = True
 _C.NUM_CLASSES = len(_C.ANCHORS)
 _C.NUM_YAW = 2
@@ -89,11 +89,11 @@
 
 # Train
 _C.TRAIN = CN()
-_C.TRAIN.LR = 1e-4
+_C.TRAIN.LR = 2.5e-3
 _C.TRAIN.LAMBDA = 1.0
-_C.TRAIN.EPOCHS = 100
+_C.TRAIN.EPOCHS = 80
 _C.TRAIN.BATCH_SIZE = 6
-_C.TRAIN.PROPOSAL_NUM_NEGATIVES = 128
+_C.TRAIN.REFINEMENT_NUM_NEGATIVES = 128
 
 # Data augmentation
 _C.AUG = CN()

diff --git a/pvrcnn/core/proposal_targets.py b/pvrcnn/core/proposal_targets.py
@@ -32,6 +32,8 @@ def compute_iou_matrix(self, boxes, anchors):
 
     def resample_pos_neg(self, match_labels):
         """Tries to sample positives and negatives 50/50."""
+        if self.cfg.NUM_PROPOSAL_SAMPLE < 0:
+            return
         match_labels = match_labels.view(-1)
         pos_idx, neg_idx = subsample_labels(
             match_labels, self.cfg.NUM_PROPOSAL_SAMPLE, 0.5, 0
@@ -64,7 +66,7 @@ def get_cls_targets(self, match_labels):
     def get_reg_targets(self, boxes, matches, match_labels):
         """
         Standard VoxelNet-style box encoding.
-        TODO: Angle binning.
+        TODO: Angle binning, angle periodicity.
         """
         A = self.anchors[match_labels == 1]
         G = boxes[matches[match_labels == 1]].cuda()

diff --git a/pvrcnn/core/refinement_targets.py b/pvrcnn/core/refinement_targets.py
@@ -30,7 +30,9 @@ def batch_correspondence_mask(self, box_counts, device):
         return mask
 
     def fill_negatives(self, targets_cls):
-        (B, N, _), M = targets_cls.shape, self.cfg.TRAIN.PROPOSAL_NUM_NEGATIVES
+        """TODO: REFINEMENT_NUM_NEGATIVES needs rethinking."""
+        M = self.cfg.TRAIN.REFINEMENT_NUM_NEGATIVES
+        (B, N, _) = targets_cls.shape
         inds = torch.randint(N, (B, M), dtype=torch.long)
         targets_cls[:, inds, -2] = 1
         targets_cls[:, inds, -1] = 0