cl

models.py

@@ -185,7 +185,7 @@ class YOLOLayer(nn.Module):
             #loss = torch.exp(-self.s_r)*lbox + torch.exp(-self.s_c)*lconf + torch.exp(-self.s_id)*lid + \
             #       (self.s_r + self.s_c + self.s_id)
             #loss *= 0.5
-            loss = 1*lbox + 1*lconf + 1*lid
+            loss = 0*lbox + 0*lconf + 1*lid
 
             return loss, loss.item(), lbox.item(), lconf.item(), lid.item(), nT
 

test.py

@@ -55,7 +55,6 @@ def test(
     outputs, mAPs, mR, mP, TP, confidence, pred_class, target_class, jdict = \
         [], [], [], [], [], [], [], [], []
     AP_accum, AP_accum_count = np.zeros(nC), np.zeros(nC)
-    coco91class = coco80_to_coco91_class()
     for batch_i, (imgs, targets, paths, shapes, targets_len) in enumerate(dataloader):
         t = time.time()
         output = model(imgs.cuda())