updates

utils/utils.py

@@ -251,26 +251,26 @@ def compute_loss(p, targets):  # predictions, targets
     BCE = nn.BCEWithLogitsLoss()
 
     # Compute losses
-    # bs = p[0].shape[0]  # batch size
+    bs = p[0].shape[0]  # batch size
     # gp = [x.numel() for x in tconf]  # grid points
     for i, pi0 in enumerate(p):  # layer i predictions, i
         b, a, gj, gi = indices[i]  # image, anchor, gridx, gridy
         tconf = torch.zeros_like(pi0[..., 0])  # conf
 
         # Compute losses
-        k = 135.8
+        k = 8.4875 * bs
         if len(b):  # number of targets
             pi = pi0[b, a, gj, gi]  # predictions closest to anchors
             tconf[b, a, gj, gi] = 1  # conf
 
-            lxy += (k * 0.07997) * MSE(torch.sigmoid(pi[..., 0:2]), txy[i])  # xy loss
+            lxy += (k * 0.079756) * MSE(torch.sigmoid(pi[..., 0:2]), txy[i])  # xy loss
-            lwh += (k * 0.007867) * MSE(pi[..., 2:4], twh[i])  # wh yolo loss
+            lwh += (k * 0.010461) * MSE(pi[..., 2:4], twh[i])  # wh yolo loss
-            # lwh += (k * 0.007867) * MSE(torch.sigmoid(pi[..., 2:4]), twh[i])  # wh power loss
+            # lwh += (k * 0.010461) * MSE(torch.sigmoid(pi[..., 2:4]), twh[i])  # wh power loss
-            lcls += (k * 0.02111) * CE(pi[..., 5:], tcls[i])  # class_conf loss
+            lcls += (k * 0.02105) * CE(pi[..., 5:], tcls[i])  # class_conf loss
 
         # pos_weight = ft([gp[i] / min(gp) * 4.])
         # BCE = nn.BCEWithLogitsLoss(pos_weight=pos_weight)
-        lconf += (k * 0.8911) * BCE(pi0[..., 4], tconf)  # obj_conf loss
+        lconf += (k * 0.88873) * BCE(pi0[..., 4], tconf)  # obj_conf loss
     loss = lxy + lwh + lconf + lcls
 
     return loss, torch.cat((lxy, lwh, lconf, lcls, loss)).detach()