New smart_resume()

train.py

@@ -54,7 +54,7 @@
 from utils.metrics import fitness
 from utils.plots import plot_evolve, plot_labels
 from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer,
-                               torch_distributed_zero_first)
+                               smart_resume, torch_distributed_zero_first)
 
 LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
 RANK = int(os.getenv('RANK', -1))
@@ -163,26 +163,9 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
     ema = ModelEMA(model) if RANK in {-1, 0} else None
 
     # Resume
-    start_epoch, best_fitness = 0, 0.0
+    best_fitness, start_epoch = 0.0, 0
     if pretrained:
-        # Optimizer
-        if ckpt['optimizer'] is not None:
-            optimizer.load_state_dict(ckpt['optimizer'])
-            best_fitness = ckpt['best_fitness']
-
-        # EMA
-        if ema and ckpt.get('ema'):
-            ema.ema.load_state_dict(ckpt['ema'].float().state_dict())
-            ema.updates = ckpt['updates']
-
-        # Epochs
-        start_epoch = ckpt['epoch'] + 1
-        if resume:
-            assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.'
-        if epochs < start_epoch:
-            LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.")
-            epochs += ckpt['epoch']  # finetune additional epochs
-
+        best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume)
         del ckpt, csd
 
     # DP mode
@@ -212,8 +195,8 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
                                               quad=opt.quad,
                                               prefix=colorstr('train: '),
                                               shuffle=True)
-    mlc = int(np.concatenate(dataset.labels, 0)[:, 0].max())  # max label class
-    nb = len(train_loader)  # number of batches
+    labels = np.concatenate(dataset.labels, 0)
+    mlc = int(labels[:, 0].max())  # max label class
     assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}'
 
     # Process 0
@@ -232,10 +215,6 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
                                        prefix=colorstr('val: '))[0]
 
         if not resume:
-            labels = np.concatenate(dataset.labels, 0)
-            # c = torch.tensor(labels[:, 0])  # classes
-            # cf = torch.bincount(c.long(), minlength=nc) + 1.  # frequency
-            # model._initialize_biases(cf.to(device))
             if plots:
                 plot_labels(labels, names, save_dir)
 
@@ -263,6 +242,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
 
     # Start training
     t0 = time.time()
+    nb = len(train_loader)  # number of batches
     nw = max(round(hyp['warmup_epochs'] * nb), 100)  # number of warmup iterations, max(3 epochs, 100 iterations)
     # nw = min(nw, (epochs - start_epoch) / 2 * nb)  # limit warmup to < 1/2 of training
     last_opt_step = -1
@@ -510,7 +490,6 @@ def main(opt, callbacks=Callbacks()):
         with open(Path(ckpt).parent.parent / 'opt.yaml', errors='ignore') as f:
             opt = argparse.Namespace(**yaml.safe_load(f))  # replace
         opt.cfg, opt.weights, opt.resume = '', ckpt, True  # reinstate
-        LOGGER.info(f'Resuming training from {ckpt}')
     else:
         opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \
             check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project)  # checks

utils/torch_utils.py

@@ -306,6 +306,25 @@ def smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, weight_decay=1e-
     return optimizer
 
 
+def smart_resume(ckpt, optimizer, ema=None, weights='yolov5s.pt', epochs=300, resume=True):
+    # Resume training from a partially trained checkpoint
+    best_fitness = 0.0
+    start_epoch = ckpt['epoch'] + 1
+    if ckpt['optimizer'] is not None:
+        optimizer.load_state_dict(ckpt['optimizer'])  # optimizer
+        best_fitness = ckpt['best_fitness']
+    if ema and ckpt.get('ema'):
+        ema.ema.load_state_dict(ckpt['ema'].float().state_dict())  # EMA
+        ema.updates = ckpt['updates']
+    if resume:
+        assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.'
+        LOGGER.info(f'Resuming training from {weights} for {epochs - start_epoch} more epochs to {epochs} total epochs')
+    if epochs < start_epoch:
+        LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.")
+        epochs += ckpt['epoch']  # finetune additional epochs
+    return best_fitness, start_epoch, epochs
+
+
 class EarlyStopping:
     # YOLOv5 simple early stopper
     def __init__(self, patience=30):