ultralytics 8.0.34 security updates and fixes (#924)

Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Fabian Greavu <fabiangreavu@gmail.com>
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/yolo/engine/exporter.py

@@ -64,14 +64,13 @@ import numpy as np
 import pandas as pd
 import torch
 
-import ultralytics
 from ultralytics.nn.autobackend import check_class_names
 from ultralytics.nn.modules import Detect, Segment
 from ultralytics.nn.tasks import DetectionModel, SegmentationModel
 from ultralytics.yolo.cfg import get_cfg
 from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages
-from ultralytics.yolo.data.utils import check_det_dataset, IMAGENET_MEAN, IMAGENET_STD
-from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, callbacks, colorstr, get_default_args, yaml_save
+from ultralytics.yolo.data.utils import IMAGENET_MEAN, IMAGENET_STD, check_det_dataset
+from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, __version__, callbacks, colorstr, get_default_args, yaml_save
 from ultralytics.yolo.utils.checks import check_imgsz, check_requirements, check_version, check_yaml
 from ultralytics.yolo.utils.files import file_size
 from ultralytics.yolo.utils.ops import Profile
@@ -209,7 +208,7 @@ class Exporter:
             'description': f"Ultralytics {self.pretty_name} model trained on {self.model.args['data']}",
             'author': 'Ultralytics',
             'license': 'GPL-3.0 https://ultralytics.com/license',
-            'version': ultralytics.__version__,
+            'version': __version__,
             'stride': int(max(model.stride)),
             'names': model.names}  # model metadata
 

ultralytics/yolo/engine/model.py

@@ -1,16 +1,17 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
+import sys
 from pathlib import Path
 from typing import List
 
-import sys
 from ultralytics import yolo  # noqa
 from ultralytics.nn.tasks import (ClassificationModel, DetectionModel, SegmentationModel, attempt_load_one_weight,
                                   guess_model_task)
 from ultralytics.yolo.cfg import get_cfg
 from ultralytics.yolo.engine.exporter import Exporter
 from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK, callbacks, yaml_load
-from ultralytics.yolo.utils.checks import check_yaml, check_imgsz
+from ultralytics.yolo.utils.checks import check_imgsz, check_yaml
+from ultralytics.yolo.utils.downloads import GITHUB_ASSET_STEMS
 from ultralytics.yolo.utils.torch_utils import smart_inference_mode
 
 # Map head to model, trainer, validator, and predictor classes
@@ -58,10 +59,13 @@ class YOLO:
         # Load or create new YOLO model
         load_methods = {'.pt': self._load, '.yaml': self._new}
         suffix = Path(model).suffix
+        if not suffix and Path(model).stem in GITHUB_ASSET_STEMS:
+            model, suffix = Path(model).with_suffix('.pt'), '.pt'  # add suffix, i.e. yolov8n -> yolov8n.pt
         if suffix in load_methods:
             {'.pt': self._load, '.yaml': self._new}[suffix](model)
         else:
-            raise NotImplementedError(f"'{suffix}' model loading not implemented")
+            raise NotImplementedError(f"'{suffix}' models not supported. Try a *.pt and *.yaml model, "
+                                      "i.e. model='yolov8n.pt' or model='yolov8n.yaml'")
 
     def __call__(self, source=None, stream=False, **kwargs):
         return self.predict(source, stream, **kwargs)

ultralytics/yolo/engine/trainer.py

@@ -20,12 +20,11 @@ from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.optim import lr_scheduler
 from tqdm import tqdm
 
-from ultralytics import __version__
 from ultralytics.nn.tasks import attempt_load_one_weight, attempt_load_weights
 from ultralytics.yolo.cfg import get_cfg
 from ultralytics.yolo.data.utils import check_cls_dataset, check_det_dataset
-from ultralytics.yolo.utils import (DEFAULT_CFG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, colorstr, emojis,
-                                    yaml_save)
+from ultralytics.yolo.utils import (DEFAULT_CFG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, __version__, callbacks,
+                                    colorstr, emojis, yaml_save)
 from ultralytics.yolo.utils.autobatch import check_train_batch_size
 from ultralytics.yolo.utils.checks import check_file, check_imgsz, print_args
 from ultralytics.yolo.utils.dist import ddp_cleanup, generate_ddp_command
@@ -51,6 +50,7 @@ class BaseTrainer:
         wdir (Path): Directory to save weights.
         last (Path): Path to last checkpoint.
         best (Path): Path to best checkpoint.
+        save_period (int): Save checkpoint every x epochs (disabled if < 1).
         batch_size (int): Batch size for training.
         epochs (int): Number of epochs to train for.
         start_epoch (int): Starting epoch for training.
@@ -101,6 +101,7 @@ class BaseTrainer:
             self.args.save_dir = str(self.save_dir)
             yaml_save(self.save_dir / 'args.yaml', vars(self.args))  # save run args
         self.last, self.best = self.wdir / 'last.pt', self.wdir / 'best.pt'  # checkpoint paths
+        self.save_period = self.args.save_period
 
         self.batch_size = self.args.batch
         self.epochs = self.args.epochs
@@ -392,6 +393,8 @@ class BaseTrainer:
         torch.save(ckpt, self.last)
         if self.best_fitness == self.fitness:
             torch.save(ckpt, self.best)
+        if (self.epoch > 0) and (self.save_period > 0) and (self.epoch % self.save_period == 0):
+            torch.save(ckpt, self.wdir / f'epoch{self.epoch}.pt')
         del ckpt
 
     def get_dataset(self, data):