updates

utils/utils.py

@@ -236,17 +236,17 @@ def build_targets(target, anchor_wh, nA, nC, nG):
     returns nT, nCorrect, tx, ty, tw, th, tconf, tcls
     """
     nB = len(target)  # number of images in batch
-    nT = [len(x) for x in target]
+
     txy = torch.zeros(nB, nA, nG, nG, 2)  # batch size, anchors, grid size
     twh = torch.zeros(nB, nA, nG, nG, 2)
     tconf = torch.ByteTensor(nB, nA, nG, nG).fill_(0)
     tcls = torch.ByteTensor(nB, nA, nG, nG, nC).fill_(0)  # nC = number of classes
 
     for b in range(nB):
-        nTb = nT[b]  # number of targets
+        t = target[b]
+        nTb = len(t)  # number of targets
         if nTb == 0:
             continue
-        t = target[b]
 
         gxy, gwh = t[:, 1:3] * nG, t[:, 3:5] * nG
 
@@ -267,14 +267,13 @@ def build_targets(target, anchor_wh, nA, nC, nG):
             iou_order = torch.argsort(-iou_best)  # best to worst
 
             # Unique anchor selection
-            u = torch.cat((gi, gj, a), 0).view((3, -1))
-            # u = torch.stack((gi, gj, a),0)
-            _, first_unique = np.unique(u[:, iou_order], axis=1, return_index=True)  # first unique indices
-            # _, first_unique = torch.unique(u[:, iou_order], dim=1, return_inverse=True)  # different than numpy?
+            u = torch.stack((gi, gj, a), 0)[:, iou_order]
+            # _, first_unique = np.unique(u, axis=1, return_index=True)  # first unique indices
+            first_unique = return_torch_unique_index(u, torch.unique(u, dim=1))  # torch alternative
 
             i = iou_order[first_unique]
             # best anchor must share significant commonality (iou) with target
-            i = i[iou_best[i] > 0.10]  # TODO: arbitrary threshold is problematic
+            i = i[iou_best[i] > 0.10]  # TODO: examine arbitrary threshold
             if len(i) == 0:
                 continue
 
@@ -428,6 +427,15 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
     return output
 
 
+def return_torch_unique_index(u, uv):
+    n = uv.shape[1]  # number of columns
+    first_unique = torch.zeros(n, device=u.device).long()
+    for j in range(n):
+        first_unique[j] = (uv[:, j:j + 1] == u).all(0).nonzero()[0]
+
+    return first_unique
+
+
 def strip_optimizer_from_checkpoint(filename='weights/best.pt'):
     # Strip optimizer from *.pt files for lighter files (reduced by 2/3 size)