Self-contained checkpoint `--resume` (#8839)

train.py

@@ -43,7 +43,7 @@ from utils.autoanchor import check_anchors
 from utils.autobatch import check_train_batch_size
 from utils.callbacks import Callbacks
 from utils.dataloaders import create_dataloader
-from utils.downloads import attempt_download
+from utils.downloads import attempt_download, is_url
 from utils.general import (LOGGER, check_amp, check_dataset, check_file, check_git_status, check_img_size,
                            check_requirements, check_suffix, check_yaml, colorstr, get_latest_run, increment_path,
                            init_seeds, intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods,
@@ -77,6 +77,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
         with open(hyp, errors='ignore') as f:
             hyp = yaml.safe_load(f)  # load hyps dict
     LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
+    opt.hyp = hyp.copy()  # for saving hyps to checkpoints
 
     # Save run settings
     if not evolve:
@@ -377,6 +378,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
                     'updates': ema.updates,
                     'optimizer': optimizer.state_dict(),
                     'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None,
+                    'opt': vars(opt),
                     'date': datetime.now().isoformat()}
 
                 # Save last, best and delete
@@ -472,8 +474,7 @@ def parse_opt(known=False):
     parser.add_argument('--bbox_interval', type=int, default=-1, help='W&B: Set bounding-box image logging interval')
     parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use')
 
-    opt = parser.parse_known_args()[0] if known else parser.parse_args()
-    return opt
+    return parser.parse_known_args()[0] if known else parser.parse_args()
 
 
 def main(opt, callbacks=Callbacks()):
@@ -484,12 +485,20 @@ def main(opt, callbacks=Callbacks()):
         check_requirements(exclude=['thop'])
 
     # Resume
-    if opt.resume and not check_wandb_resume(opt) and not opt.evolve:  # resume an interrupted run
-        ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run()  # specified or most recent path
-        assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist'
-        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
+    if opt.resume and not (check_wandb_resume(opt) or opt.evolve):  # resume an interrupted run
+        last = Path(opt.resume if isinstance(opt.resume, str) else get_latest_run())  # specified or most recent last.pt
+        assert last.is_file(), f'ERROR: --resume checkpoint {last} does not exist'
+        opt_yaml = last.parent.parent / 'opt.yaml'  # train options yaml
+        opt_data = opt.data  # original dataset
+        if opt_yaml.is_file():
+            with open(opt_yaml, errors='ignore') as f:
+                d = yaml.safe_load(f)
+        else:
+            d = torch.load(last, map_location='cpu')['opt']
+        opt = argparse.Namespace(**d)  # replace
+        opt.cfg, opt.weights, opt.resume = '', str(last), True  # reinstate
+        if is_url(opt.data):
+            opt.data = str(opt_data)  # avoid HUB resume auth timeout
     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/downloads.py

@@ -16,12 +16,14 @@ import requests
 import torch
 
 
-def is_url(url):
+def is_url(url, check_online=True):
     # Check if online file exists
     try:
-        r = urllib.request.urlopen(url)  # response
-        return r.getcode() == 200
-    except urllib.request.HTTPError:
+        url = str(url)
+        result = urllib.parse.urlparse(url)
+        assert all([result.scheme, result.netloc, result.path])  # check if is url
+        return (urllib.request.urlopen(url).getcode() == 200) if check_online else True  # check if exists online
+    except (AssertionError, urllib.request.HTTPError):
         return False
 
 

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