Self-contained checkpoint --resume (#8839)

* Single checkpoint resume

* Update train.py

* Add hyp

* Add hyp

* Add hyp

* FIX

* avoid resume on url data

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* avoid resume on url data

* avoid resume on url data

* Update

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

utils/torch_utils.py

@@ -317,8 +317,9 @@ def smart_resume(ckpt, optimizer, ema=None, weights='yolov5s.pt', epochs=300, re
         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')
+        assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.\n' \
+                                f"Start a new training without --resume, i.e. 'python train.py --weights {weights}'"
+        LOGGER.info(f'Resuming training from {weights} from epoch {start_epoch} 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