Fix concatenation in , docstring of truths' shape in , infinity in en…

…coded target boxes, zero object detection in , credit to amdegroot#116, amdegroot#144, amdegroot#194

layers/box_utils.py

@@ -22,8 +22,8 @@ def center_size(boxes):
     Return:
         boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
     """
-    return torch.cat((boxes[:, 2:] + boxes[:, :2])/2,  # cx, cy
-                     boxes[:, 2:] - boxes[:, :2], 1)  # w, h
+    return torch.cat(((boxes[:, 2:] + boxes[:, :2])/2,  # cx, cy
+                     boxes[:, 2:] - boxes[:, :2]), 1)  # w, h
 
 
 def intersect(box_a, box_b):
@@ -74,7 +74,7 @@ def match(threshold, truths, priors, variances, labels, loc_t, conf_t, idx):
     corresponding to both confidence and location preds.
     Args:
         threshold: (float) The overlap threshold used when mathing boxes.
-        truths: (tensor) Ground truth boxes, Shape: [num_obj, num_priors].
+        truths: (tensor) Ground truth boxes, Shape: [num_obj, 4].
         priors: (tensor) Prior boxes from priorbox layers, Shape: [n_priors,4].
         variances: (tensor) Variances corresponding to each prior coord,
             Shape: [num_priors, 4].
@@ -131,7 +131,7 @@ def encode(matched, priors, variances):
     g_cxcy /= (variances[0] * priors[:, 2:])
     # match wh / prior wh
     g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:]
-    g_wh = torch.log(g_wh) / variances[1]
+    g_wh = torch.log(g_wh + 1e-10) / variances[1]
     # return target for smooth_l1_loss
     return torch.cat([g_cxcy, g_wh], 1)  # [num_priors,4]
 
@@ -186,7 +186,7 @@ def nms(boxes, scores, overlap=0.5, top_k=200):
 
     keep = scores.new(scores.size(0)).zero_().long()
     if boxes.numel() == 0:
-        return keep
+        return keep, 0
     x1 = boxes[:, 0]
     y1 = boxes[:, 1]
     x2 = boxes[:, 2]