--resume EMA fix #292

train.py

@@ -163,6 +163,7 @@ def train(hyp):
     dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
                                             hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect)
     mlc = np.concatenate(dataset.labels, 0)[:, 0].max()  # max label class
+    nb = len(dataloader)  # number of batches
     assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Correct your labels or your model.' % (mlc, nc, opt.cfg)
 
     # Testloader
@@ -191,11 +192,10 @@ def train(hyp):
         check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
 
     # Exponential moving average
-    ema = torch_utils.ModelEMA(model)
+    ema = torch_utils.ModelEMA(model, updates=start_epoch * nb / accumulate)
 
     # Start training
     t0 = time.time()
-    nb = len(dataloader)  # number of batches
     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'