updates

train.py

@@ -123,7 +123,7 @@ def train(
                 if int(name.split('.')[1]) < cutoff:  # if layer < 75
                     p.requires_grad = False if epoch == 0 else True
 
-        mloss = defaultdict(float)  # mean loss
+        mloss = torch.zeros(5).to(device) # mean losses
         for i, (imgs, targets, _, _) in enumerate(dataloader):
             imgs = imgs.to(device)
             targets = targets.to(device)
@@ -148,7 +148,7 @@ def train(
             target_list = build_targets(model, targets)
 
             # Compute loss
-            loss, loss_dict = compute_loss(pred, target_list)
+            loss, loss_items = compute_loss(pred, target_list)
 
             # Compute gradient
             if mixed_precision:
@@ -162,14 +162,13 @@ def train(
                 optimizer.step()
                 optimizer.zero_grad()
 
-            # Running epoch-means of tracked metrics
-            for key, val in loss_dict.items():
-                mloss[key] = (mloss[key] * i + val) / (i + 1)
+            # Update running mean of tracked metrics
+            mloss = (mloss * i + loss_items) / (i + 1)
 
+            # Print batch results
             s = ('%8s%12s' + '%10.3g' * 7) % (
-                '%g/%g' % (epoch, epochs - 1), '%g/%g' % (i, nB - 1),
-                mloss['xy'], mloss['wh'], mloss['conf'], mloss['cls'],
-                mloss['total'], nt, time.time() - t)
+                '%g/%g' % (epoch, epochs - 1),
+                '%g/%g' % (i, nB - 1), *mloss, nt, time.time() - t)
             t = time.time()
             print(s)
 

utils/utils.py

@@ -1,6 +1,5 @@
 import glob
 import random
-from collections import defaultdict
 
 import cv2
 import matplotlib
@@ -244,7 +243,7 @@ def wh_iou(box1, box2):
 
 
 def compute_loss(p, targets):  # predictions, targets
-    FT = torch.cuda.FloatTensor if p[0].is_cuda else torch.FloatTensor
+    FT = torch.cuda.Tensor if p[0].is_cuda else torch.Tensor
     lxy, lwh, lcls, lconf = FT([0]), FT([0]), FT([0]), FT([0])
     txy, twh, tcls, indices = targets
     MSE = nn.MSELoss()
@@ -274,13 +273,7 @@ def compute_loss(p, targets):  # predictions, targets
         lconf += (k * 64) * BCE(pi0[..., 4], tconf)  # obj_conf loss
     loss = lxy + lwh + lconf + lcls
 
-    # Add to dictionary
-    d = defaultdict(float)
-    losses = [loss.item(), lxy.item(), lwh.item(), lconf.item(), lcls.item()]
-    for k, v in zip(['total', 'xy', 'wh', 'conf', 'cls'], losses):
-        d[k] = v
-
-    return loss, d
+    return loss, torch.cat((lxy, lwh, lconf, lcls, loss)).detach()
 
 
 def build_targets(model, targets):