ultralytics 8.0.33 security updates and fixes (#896)

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

ultralytics/yolo/engine/exporter.py

@@ -67,7 +67,7 @@ import torch
 import ultralytics
 from ultralytics.nn.autobackend import check_class_names
 from ultralytics.nn.modules import Detect, Segment
-from ultralytics.nn.tasks import ClassificationModel, DetectionModel, SegmentationModel, guess_model_task
+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

ultralytics/yolo/engine/model.py

@@ -33,7 +33,7 @@ class YOLO:
     A python interface which emulates a model-like behaviour by wrapping trainers.
     """
 
-    def __init__(self, model='yolov8n.yaml', type="v8") -> None:
+    def __init__(self, model='yolov8n.pt', type="v8") -> None:
         """
         Initializes the YOLO object.
 

ultralytics/yolo/engine/trainer.py

@@ -5,7 +5,6 @@ Simple training loop; Boilerplate that could apply to any arbitrary neural netwo
 
 import os
 import subprocess
-import sys
 import time
 from collections import defaultdict
 from copy import deepcopy
@@ -29,10 +28,10 @@ from ultralytics.yolo.utils import (DEFAULT_CFG, LOGGER, RANK, SETTINGS, TQDM_BA
                                     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_file, find_free_network_port
+from ultralytics.yolo.utils.dist import ddp_cleanup, generate_ddp_command
 from ultralytics.yolo.utils.files import get_latest_run, increment_path
 from ultralytics.yolo.utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, init_seeds, one_cycle,
-                                                select_device, strip_optimizer, TORCH_1_9)
+                                                select_device, strip_optimizer)
 
 
 class BaseTrainer:
@@ -175,12 +174,7 @@ class BaseTrainer:
 
         # Run subprocess if DDP training, else train normally
         if world_size > 1 and "LOCAL_RANK" not in os.environ:
-            # cmd, file = generate_ddp_command(world_size, self)  # security vulnerability in Snyk scans
-            file = generate_ddp_file(self) if sys.argv[0].endswith('yolo') else os.path.abspath(sys.argv[0])
-            torch_distributed_cmd = "torch.distributed.run" if TORCH_1_9 else "torch.distributed.launch"
-            cmd = [
-                sys.executable, "-m", torch_distributed_cmd, "--nproc_per_node", f"{world_size}", "--master_port",
-                f"{find_free_network_port()}", file] + sys.argv[1:]
+            cmd, file = generate_ddp_command(world_size, self)  # security vulnerability in Snyk scans
             try:
                 subprocess.run(cmd, check=True)
             except Exception as e:

ultralytics/yolo/engine/validator.py

@@ -119,8 +119,7 @@ class BaseValidator:
                 self.args.workers = 0  # faster CPU val as time dominated by inference, not dataloading
             if not pt:
                 self.args.rect = False
-            self.dataloader = self.dataloader or \
-                              self.get_dataloader(self.data.get("val") or self.data.get("test"), self.args.batch)
+            self.dataloader = self.dataloader or self.get_dataloader(self.data.get(self.args.split), self.args.batch)
 
             model.eval()
             model.warmup(imgsz=(1 if pt else self.args.batch, 3, imgsz, imgsz))  # warmup