update train.py and experimental.py

models/experimental.py

@@ -119,7 +119,10 @@ class Ensemble(nn.ModuleList):
         y = []
         for module in self:
             y.append(module(x, augment)[0])
-        return torch.cat(y, 1), None  # ensembled inference output, train output
+        # y = torch.stack(y).max(0)[0]  # max ensemble
+        # y = torch.cat(y, 1)  # nms ensemble
+        y = torch.stack(y).mean(0)  # mean ensemble
+        return y, None  # inference, train output
 
 
 def attempt_load(weights, map_location=None):

train.py

@@ -101,11 +101,13 @@ def train(hyp):
         optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
     optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']})  # add pg1 with weight_decay
     optimizer.add_param_group({'params': pg2})  # add pg2 (biases)
+    print('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
+    del pg0, pg1, pg2
+
     # Scheduler https://arxiv.org/pdf/1812.01187.pdf
     lf = lambda x: (((1 + math.cos(x * math.pi / epochs)) / 2) ** 1.0) * 0.9 + 0.1  # cosine
     scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
-    print('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
-    del pg0, pg1, pg2
+    # plot_lr_scheduler(optimizer, scheduler, epochs)
 
     # Load Model
     google_utils.attempt_download(weights)
@@ -147,12 +149,7 @@ def train(hyp):
     if mixed_precision:
         model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0)
 
-
-    scheduler.last_epoch = start_epoch - 1  # do not move
-    # https://discuss.pytorch.org/t/a-problem-occured-when-resuming-an-optimizer/28822
-    # plot_lr_scheduler(optimizer, scheduler, epochs)
-
-    # Initialize distributed training
+    # Distributed training
     if device.type != 'cpu' and torch.cuda.device_count() > 1 and torch.distributed.is_available():
         dist.init_process_group(backend='nccl',  # distributed backend
                                 init_method='tcp://127.0.0.1:9999',  # init method
@@ -198,9 +195,10 @@ def train(hyp):
     # Start training
     t0 = time.time()
     nb = len(dataloader)  # number of batches
-    n_burn = max(3 * nb, 1e3)  # burn-in iterations, max(3 epochs, 1k iterations)
+    nw = max(3 * nb, 1e3)  # number of warmup iterations, max(3 epochs, 1k iterations)
     maps = np.zeros(nc)  # mAP per class
     results = (0, 0, 0, 0, 0, 0, 0)  # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
+    scheduler.last_epoch = start_epoch - 1  # do not move
     print('Image sizes %g train, %g test' % (imgsz, imgsz_test))
     print('Using %g dataloader workers' % dataloader.num_workers)
     print('Starting training for %g epochs...' % epochs)
@@ -225,9 +223,9 @@ def train(hyp):
             ni = i + nb * epoch  # number integrated batches (since train start)
             imgs = imgs.to(device).float() / 255.0  # uint8 to float32, 0 - 255 to 0.0 - 1.0
 
-            # Burn-in
-            if ni <= n_burn:
-                xi = [0, n_burn]  # x interp
+            # Warmup
+            if ni <= nw:
+                xi = [0, nw]  # x interp
                 # model.gr = np.interp(ni, xi, [0.0, 1.0])  # giou loss ratio (obj_loss = 1.0 or giou)
                 accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round())
                 for j, x in enumerate(optimizer.param_groups):