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https://github.com/THU-MIG/yolov10.git
synced 2025-05-23 21:44:22 +08:00
Command injection and Path traversal security fixes (#888)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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@ -54,7 +54,7 @@ for orientation in ExifTags.TAGS.keys():
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def get_hash(paths):
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# Returns a single hash value of a list of paths (files or dirs)
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size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes
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h = hashlib.md5(str(size).encode()) # hash sizes
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h = hashlib.sha256(str(size).encode()) # hash sizes
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h.update(''.join(paths).encode()) # hash paths
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return h.hexdigest() # return hash
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@ -44,7 +44,7 @@ def img2label_paths(img_paths):
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def get_hash(paths):
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# Returns a single hash value of a list of paths (files or dirs)
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size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes
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h = hashlib.md5(str(size).encode()) # hash sizes
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h = hashlib.sha256(str(size).encode()) # hash sizes
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h.update("".join(paths).encode()) # hash paths
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return h.hexdigest() # return hash
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@ -5,6 +5,7 @@ Simple training loop; Boilerplate that could apply to any arbitrary neural netwo
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import os
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import subprocess
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import sys
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import time
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from collections import defaultdict
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from copy import deepcopy
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@ -28,10 +29,10 @@ from ultralytics.yolo.utils import (DEFAULT_CFG, LOGGER, RANK, SETTINGS, TQDM_BA
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yaml_save)
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from ultralytics.yolo.utils.autobatch import check_train_batch_size
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from ultralytics.yolo.utils.checks import check_file, check_imgsz, print_args
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from ultralytics.yolo.utils.dist import ddp_cleanup, generate_ddp_command
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from ultralytics.yolo.utils.dist import ddp_cleanup, generate_ddp_file, find_free_network_port
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from ultralytics.yolo.utils.files import get_latest_run, increment_path
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from ultralytics.yolo.utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, init_seeds, one_cycle,
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select_device, strip_optimizer)
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select_device, strip_optimizer, TORCH_1_9)
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class BaseTrainer:
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@ -174,13 +175,18 @@ class BaseTrainer:
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# Run subprocess if DDP training, else train normally
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if world_size > 1 and "LOCAL_RANK" not in os.environ:
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command = generate_ddp_command(world_size, self)
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# cmd, file = generate_ddp_command(world_size, self) # security vulnerability in Snyk scans
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file = generate_ddp_file(self) if sys.argv[0].endswith('yolo') else os.path.abspath(sys.argv[0])
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torch_distributed_cmd = "torch.distributed.run" if TORCH_1_9 else "torch.distributed.launch"
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cmd = [
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sys.executable, "-m", torch_distributed_cmd, "--nproc_per_node", f"{world_size}", "--master_port",
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f"{find_free_network_port()}", file] + sys.argv[1:]
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try:
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subprocess.run(command)
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subprocess.run(cmd, check=True)
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except Exception as e:
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self.console(e)
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self.console.warning(e)
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finally:
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ddp_cleanup(command, self)
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ddp_cleanup(self, file)
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else:
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self._do_train(int(os.getenv("RANK", -1)), world_size)
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@ -44,21 +44,15 @@ def generate_ddp_file(trainer):
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def generate_ddp_command(world_size, trainer):
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import __main__ # noqa local import to avoid https://github.com/Lightning-AI/lightning/issues/15218
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file_name = os.path.abspath(sys.argv[0])
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using_cli = not file_name.endswith(".py")
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if using_cli:
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file_name = generate_ddp_file(trainer)
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file = generate_ddp_file(trainer) if sys.argv[0].endswith('yolo') else os.path.abspath(sys.argv[0])
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torch_distributed_cmd = "torch.distributed.run" if TORCH_1_9 else "torch.distributed.launch"
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return [
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cmd = [
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sys.executable, "-m", torch_distributed_cmd, "--nproc_per_node", f"{world_size}", "--master_port",
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f"{find_free_network_port()}", file_name] + sys.argv[1:]
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f"{find_free_network_port()}", file] + sys.argv[1:]
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return cmd, file
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def ddp_cleanup(command, trainer):
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def ddp_cleanup(trainer, file):
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# delete temp file if created
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tempfile_suffix = f"{id(trainer)}.py"
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if tempfile_suffix in "".join(command):
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for chunk in command:
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if tempfile_suffix in chunk:
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os.remove(chunk)
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break
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if f"{id(trainer)}.py" in file: # if temp_file suffix in file
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os.remove(file)
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