`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>

docker/Dockerfile-arm64

@@ -3,7 +3,7 @@
 # Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi
 
 # Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
-FROM arm64v8/ubuntu:20.04
+FROM arm64v8/ubuntu:rolling
 
 # Downloads to user config dir
 ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/

docker/Dockerfile-cpu

@@ -3,7 +3,7 @@
 # Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv8 deployments
 
 # Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
-FROM ubuntu:20.04
+FROM ubuntu:rolling
 
 # Downloads to user config dir
 ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/

docs/app.md

@@ -1,10 +1,38 @@
 # Ultralytics HUB App for YOLOv8
 
-<div align="center">
-  <a href="https://ultralytics.com/app_install" target="_blank">
-    <img width="1024" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-app.png"></a>
+<a href="https://bit.ly/ultralytics_hub" target="_blank">
+<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
+<br>
 <br>
+<div align="center">
+  <a href="https://github.com/ultralytics" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
+  <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
+  <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
+  <a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-producthunt.png" width="2%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
+  <a href="https://youtube.com/ultralytics" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
+  <a href="https://www.facebook.com/ultralytics" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-facebook.png" width="2%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
+  <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="" /></a>
+  <br>
+  <br>
+  <a href="https://github.com/ultralytics/hub/actions/workflows/ci.yaml">
+    <img src="https://github.com/ultralytics/hub/actions/workflows/ci.yaml/badge.svg" alt="CI CPU"></a>
+  <a href="https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb">
+    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> 
 </div>
+<br>
 
 Welcome to the Ultralytics HUB app for demonstrating YOLOv5 and YOLOv8 models! In this app, available on the [Apple App
 Store](https://apps.apple.com/xk/app/ultralytics/id1583935240) and the 

docs/cfg.md

@@ -154,18 +154,19 @@ process include the size and composition of the validation dataset and the speci
 is important to carefully tune and experiment with these settings to ensure that the model is performing well on the
 validation dataset and to detect and prevent overfitting.
 
-| Key         | Value | Description                                                     |
-|-------------|-------|-----------------------------------------------------------------|
-| save_json   | False | save results to JSON file                                       |
-| save_hybrid | False | save hybrid version of labels (labels + additional predictions) |
-| conf        | 0.001 | object confidence threshold for detection                       |
-| iou         | 0.6   | intersection over union (IoU) threshold for NMS                 |
-| max_det     | 300   | maximum number of detections per image                          |
-| half        | True  | use half precision (FP16)                                       |
-| device      | null  | device to run on, i.e. cuda device=0/1/2/3 or device=cpu        |
-| dnn         | False | use OpenCV DNN for ONNX inference                               |
-| plots       | False | show plots during training                                      |
-| rect        | False | support rectangular evaluation                                  |
+| Key         | Value | Description                                                        |
+|-------------|-------|--------------------------------------------------------------------|
+| save_json   | False | save results to JSON file                                          |
+| save_hybrid | False | save hybrid version of labels (labels + additional predictions)    |
+| conf        | 0.001 | object confidence threshold for detection                          |
+| iou         | 0.6   | intersection over union (IoU) threshold for NMS                    |
+| max_det     | 300   | maximum number of detections per image                             |
+| half        | True  | use half precision (FP16)                                          |
+| device      | null  | device to run on, i.e. cuda device=0/1/2/3 or device=cpu           |
+| dnn         | False | use OpenCV DNN for ONNX inference                                  |
+| plots       | False | show plots during training                                         |
+| rect        | False | support rectangular evaluation                                     |
+| split       | val   | dataset split to use for validation, i.e. 'val', 'test' or 'train' |
 
 ### Export
 

ultralytics/__init__.py

@@ -1,9 +1,8 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-__version__ = "8.0.32"
+__version__ = "8.0.33"
 
 from ultralytics.yolo.engine.model import YOLO
-from ultralytics.yolo.utils import ops
 from ultralytics.yolo.utils.checks import check_yolo as checks
 
 __all__ = ["__version__", "YOLO", "hub", "checks"]  # allow simpler import

ultralytics/yolo/cfg/__init__.py

@@ -202,6 +202,7 @@ def entrypoint(debug=''):
         LOGGER.info(CLI_HELP_MSG)
         return
 
+    # Add tasks, modes, special, and special with dash keys, i.e. -help, --help
     tasks = 'detect', 'segment', 'classify'
     modes = 'train', 'val', 'predict', 'export'
     special = {
@@ -211,6 +212,7 @@ def entrypoint(debug=''):
         'settings': lambda: yaml_print(USER_CONFIG_DIR / 'settings.yaml'),
         'cfg': lambda: yaml_print(DEFAULT_CFG_PATH),
         'copy-cfg': copy_default_cfg}
+    special = {**special, **{f'-{k}': v for k, v in special.items()}, **{f'--{k}': v for k, v in special.items()}}
 
     overrides = {}  # basic overrides, i.e. imgsz=320
     for a in merge_equals_args(args):  # merge spaces around '=' sign

ultralytics/yolo/cfg/default.yaml

@@ -38,6 +38,7 @@ dropout: 0.0  # use dropout regularization (classify train only)
 
 # Val/Test settings ----------------------------------------------------------------------------------------------------
 val: True  # validate/test during training
+split: val  # dataset split to use for validation, i.e. 'val', 'test' or 'train'
 save_json: False  # save results to JSON file
 save_hybrid: False  # save hybrid version of labels (labels + additional predictions)
 conf:  # object confidence threshold for detection (default 0.25 predict, 0.001 val)

ultralytics/yolo/data/dataloaders/v5loader.py

@@ -27,13 +27,13 @@ from PIL import ExifTags, Image, ImageOps
 from torch.utils.data import DataLoader, Dataset, dataloader, distributed
 from tqdm import tqdm
 
-from ultralytics.yolo.data.utils import check_det_dataset, unzip_file
+from ultralytics.yolo.data.utils import check_det_dataset
 from ultralytics.yolo.utils import (DATASETS_DIR, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, is_colab, is_dir_writeable,
                                     is_kaggle, yaml_load)
 from ultralytics.yolo.utils.checks import check_requirements, check_yaml
+from ultralytics.yolo.utils.downloads import unzip_file
 from ultralytics.yolo.utils.ops import clean_str, segments2boxes, xyn2xy, xywh2xyxy, xywhn2xyxy, xyxy2xywhn
 from ultralytics.yolo.utils.torch_utils import torch_distributed_zero_first
-
 from .v5augmentations import (Albumentations, augment_hsv, classify_albumentations, classify_transforms, copy_paste,
                               letterbox, mixup, random_perspective)
 

ultralytics/yolo/data/utils.py

@@ -11,13 +11,11 @@ from zipfile import is_zipfile
 
 import cv2
 import numpy as np
-import torch
 from PIL import ExifTags, Image, ImageOps
 
 from ultralytics.yolo.utils import DATASETS_DIR, LOGGER, ROOT, colorstr, emojis, yaml_load
 from ultralytics.yolo.utils.checks import check_file, check_font, is_ascii
 from ultralytics.yolo.utils.downloads import download, safe_download
-from ultralytics.yolo.utils.files import unzip_file
 from ultralytics.yolo.utils.ops import segments2boxes
 
 HELP_URL = "See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data"

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

ultralytics/yolo/utils/__init__.py

@@ -110,6 +110,15 @@ class IterableSimpleNamespace(SimpleNamespace):
     def __str__(self):
         return '\n'.join(f"{k}={v}" for k, v in vars(self).items())
 
+    def __getattr__(self, attr):
+        name = self.__class__.__name__
+        raise AttributeError(f"""
+            '{name}' object has no attribute '{attr}'. This may be caused by a modified or out of date ultralytics
+            'default.yaml' file.\nPlease update your code with 'pip install -U ultralytics' and if necessary replace
+            {DEFAULT_CFG_PATH} with the latest version from
+            https://github.com/ultralytics/ultralytics/blob/main/ultralytics/yolo/cfg/default.yaml
+            """)
+
     def get(self, key, default=None):
         return getattr(self, key, default)
 
@@ -442,6 +451,19 @@ def colorstr(*input):
     return "".join(colors[x] for x in args) + f"{string}" + colors["end"]
 
 
+def remove_ansi_codes(string):
+    """
+    Remove ANSI escape sequences from a string.
+
+    Args:
+        string (str): The input string that may contain ANSI escape sequences.
+
+    Returns:
+        str: The input string with ANSI escape sequences removed.
+    """
+    return re.sub(r'\x1B\[([0-9]{1,2}(;[0-9]{1,2})?)?[m|K]', '', string)
+
+
 def set_logging(name=LOGGING_NAME, verbose=True):
     # sets up logging for the given name
     rank = int(os.getenv('RANK', -1))  # rank in world for Multi-GPU trainings

ultralytics/yolo/utils/downloads.py

@@ -6,7 +6,7 @@ from itertools import repeat
 from multiprocessing.pool import ThreadPool
 from pathlib import Path
 from urllib import parse, request
-from zipfile import ZipFile
+from zipfile import ZipFile, is_zipfile, BadZipFile
 
 import requests
 import torch
@@ -33,6 +33,8 @@ def unzip_file(file, path=None, exclude=('.DS_Store', '__MACOSX')):
     Unzip a *.zip file to path/, excluding files containing strings in exclude list
     Replaces: ZipFile(file).extractall(path=path)
     """
+    if not (Path(file).exists() and is_zipfile(file)):
+        raise BadZipFile(f"File '{file}' does not exist or is a bad zip file.")
     if path is None:
         path = Path(file).parent  # default path
     with ZipFile(file) as zipObj:

ultralytics/yolo/utils/files.py

@@ -6,7 +6,6 @@ import os
 import urllib
 from datetime import datetime
 from pathlib import Path
-from zipfile import ZipFile
 
 
 class WorkingDirectory(contextlib.ContextDecorator):
@@ -57,16 +56,6 @@ def increment_path(path, exist_ok=False, sep='', mkdir=False):
     return path
 
 
-def unzip_file(file, path=None, exclude=('.DS_Store', '__MACOSX')):
-    # Unzip a *.zip file to path/, excluding files containing strings in exclude list
-    if path is None:
-        path = Path(file).parent  # default path
-    with ZipFile(file) as zipObj:
-        for f in zipObj.namelist():  # list all archived filenames in the zip
-            if all(x not in f for x in exclude):
-                zipObj.extract(f, path=path)
-
-
 def file_age(path=__file__):
     # Return days since last file update
     dt = (datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime))  # delta

ultralytics/yolo/v8/classify/predict.py

@@ -1,5 +1,4 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
-import sys
 
 import torch
 

ultralytics/yolo/v8/classify/val.py

@@ -1,5 +1,4 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
-import sys
 
 from ultralytics.yolo.data import build_classification_dataloader
 from ultralytics.yolo.engine.validator import BaseValidator

ultralytics/yolo/v8/detect/predict.py

@@ -1,5 +1,4 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
-import sys
 
 import torch
 

ultralytics/yolo/v8/detect/train.py

@@ -1,5 +1,4 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
-import sys
 from copy import copy
 
 import torch

ultralytics/yolo/v8/detect/val.py

@@ -1,7 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
 import os
-import sys
 from pathlib import Path
 
 import numpy as np
@@ -10,8 +9,8 @@ import torch
 from ultralytics.yolo.data import build_dataloader
 from ultralytics.yolo.data.dataloaders.v5loader import create_dataloader
 from ultralytics.yolo.engine.validator import BaseValidator
-from ultralytics.yolo.utils import DEFAULT_CFG, colorstr, ops, yaml_load
-from ultralytics.yolo.utils.checks import check_file, check_requirements
+from ultralytics.yolo.utils import DEFAULT_CFG, colorstr, ops
+from ultralytics.yolo.utils.checks import check_requirements
 from ultralytics.yolo.utils.metrics import ConfusionMatrix, DetMetrics, box_iou
 from ultralytics.yolo.utils.plotting import output_to_target, plot_images
 from ultralytics.yolo.utils.torch_utils import de_parallel
@@ -42,7 +41,7 @@ class DetectionValidator(BaseValidator):
 
     def init_metrics(self, model):
         head = model.model[-1] if self.training else model.model.model[-1]
-        val = self.data.get('val', '')  # validation path
+        val = self.data.get(self.args.split, '')  # validation path
         self.is_coco = isinstance(val, str) and val.endswith(f'coco{os.sep}val2017.txt')  # is COCO dataset
         self.class_map = ops.coco80_to_coco91_class() if self.is_coco else list(range(1000))
         self.args.save_json |= self.is_coco and not self.training  # run on final val if training COCO

ultralytics/yolo/v8/segment/predict.py

@@ -1,7 +1,5 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-import sys
-
 import torch
 
 from ultralytics.yolo.engine.results import Results

ultralytics/yolo/v8/segment/train.py

@@ -1,5 +1,4 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
-import sys
 from copy import copy
 
 import torch

ultralytics/yolo/v8/segment/val.py

@@ -1,7 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
 import os
-import sys
 from multiprocessing.pool import ThreadPool
 from pathlib import Path
 
@@ -30,7 +29,7 @@ class SegmentationValidator(DetectionValidator):
 
     def init_metrics(self, model):
         head = model.model[-1] if self.training else model.model.model[-1]
-        val = self.data.get('val', '')  # validation path
+        val = self.data.get(self.args.split, '')  # validation path
         self.is_coco = isinstance(val, str) and val.endswith(f'coco{os.sep}val2017.txt')  # is COCO dataset
         self.class_map = ops.coco80_to_coco91_class() if self.is_coco else list(range(1000))
         self.args.save_json |= self.is_coco and not self.training  # run on final val if training COCO