add yolo v10 and modify pipeline

ultralytics/utils/dist.py

@@ -1,47 +1,53 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
 import os
-import re
 import shutil
 import socket
 import sys
 import tempfile
-from pathlib import Path
 
 from . import USER_CONFIG_DIR
 from .torch_utils import TORCH_1_9
 
 
 def find_free_network_port() -> int:
-    """Finds a free port on localhost.
+    """
+    Finds a free port on localhost.
 
     It is useful in single-node training when we don't want to connect to a real main node but have to set the
     `MASTER_PORT` environment variable.
     """
     with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
-        s.bind(('127.0.0.1', 0))
+        s.bind(("127.0.0.1", 0))
         return s.getsockname()[1]  # port
 
 
 def generate_ddp_file(trainer):
     """Generates a DDP file and returns its file name."""
-    module, name = f'{trainer.__class__.__module__}.{trainer.__class__.__name__}'.rsplit('.', 1)
+    module, name = f"{trainer.__class__.__module__}.{trainer.__class__.__name__}".rsplit(".", 1)
 
-    content = f'''overrides = {vars(trainer.args)} \nif __name__ == "__main__":
+    content = f"""
+# Ultralytics Multi-GPU training temp file (should be automatically deleted after use)
+overrides = {vars(trainer.args)}
+
+if __name__ == "__main__":
     from {module} import {name}
     from ultralytics.utils import DEFAULT_CFG_DICT
 
     cfg = DEFAULT_CFG_DICT.copy()
     cfg.update(save_dir='')   # handle the extra key 'save_dir'
     trainer = {name}(cfg=cfg, overrides=overrides)
-    trainer.train()'''
-    (USER_CONFIG_DIR / 'DDP').mkdir(exist_ok=True)
-    with tempfile.NamedTemporaryFile(prefix='_temp_',
-                                     suffix=f'{id(trainer)}.py',
-                                     mode='w+',
-                                     encoding='utf-8',
-                                     dir=USER_CONFIG_DIR / 'DDP',
-                                     delete=False) as file:
+    results = trainer.train()
+"""
+    (USER_CONFIG_DIR / "DDP").mkdir(exist_ok=True)
+    with tempfile.NamedTemporaryFile(
+        prefix="_temp_",
+        suffix=f"{id(trainer)}.py",
+        mode="w+",
+        encoding="utf-8",
+        dir=USER_CONFIG_DIR / "DDP",
+        delete=False,
+    ) as file:
         file.write(content)
     return file.name
 
@@ -49,19 +55,17 @@ def generate_ddp_file(trainer):
 def generate_ddp_command(world_size, trainer):
     """Generates and returns command for distributed training."""
     import __main__  # noqa local import to avoid https://github.com/Lightning-AI/lightning/issues/15218
+
     if not trainer.resume:
         shutil.rmtree(trainer.save_dir)  # remove the save_dir
-    file = str(Path(sys.argv[0]).resolve())
-    safe_pattern = re.compile(r'^[a-zA-Z0-9_. /\\-]{1,128}$')  # allowed characters and maximum of 100 characters
-    if not (safe_pattern.match(file) and Path(file).exists() and file.endswith('.py')):  # using CLI
-        file = generate_ddp_file(trainer)
-    dist_cmd = 'torch.distributed.run' if TORCH_1_9 else 'torch.distributed.launch'
+    file = generate_ddp_file(trainer)
+    dist_cmd = "torch.distributed.run" if TORCH_1_9 else "torch.distributed.launch"
     port = find_free_network_port()
-    cmd = [sys.executable, '-m', dist_cmd, '--nproc_per_node', f'{world_size}', '--master_port', f'{port}', file]
+    cmd = [sys.executable, "-m", dist_cmd, "--nproc_per_node", f"{world_size}", "--master_port", f"{port}", file]
     return cmd, file
 
 
 def ddp_cleanup(trainer, file):
     """Delete temp file if created."""
-    if f'{id(trainer)}.py' in file:  # if temp_file suffix in file
+    if f"{id(trainer)}.py" in file:  # if temp_file suffix in file
         os.remove(file)