Add new get_save_dir() function (#4602)

ultralytics/cfg/__init__.py

@@ -8,9 +8,9 @@ from pathlib import Path
 from types import SimpleNamespace
 from typing import Dict, List, Union
 
-from ultralytics.utils import (ASSETS, DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_PATH, LOGGER, SETTINGS, SETTINGS_YAML,
+from ultralytics.utils import (ASSETS, DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_PATH, LOGGER, RANK, SETTINGS,
-                               IterableSimpleNamespace, __version__, checks, colorstr, deprecation_warn, yaml_load,
+                               SETTINGS_YAML, IterableSimpleNamespace, __version__, checks, colorstr, deprecation_warn,
-                               yaml_print)
+                               yaml_load, yaml_print)
 
 # Define valid tasks and modes
 MODES = 'train', 'val', 'predict', 'export', 'track', 'benchmark'
@@ -146,8 +146,23 @@ def get_cfg(cfg: Union[str, Path, Dict, SimpleNamespace] = DEFAULT_CFG_DICT, ove
     return IterableSimpleNamespace(**cfg)
 
 
+def get_save_dir(args):
+    """Return save_dir as created from train/val/predict arguments."""
+
+    if getattr(args, 'save_dir', None):
+        save_dir = args.save_dir
+    else:
+        from ultralytics.utils.files import increment_path
+
+        project = args.project or Path(SETTINGS['runs_dir']) / args.task
+        name = args.name or f'{args.mode}'
+        save_dir = increment_path(Path(project) / name, exist_ok=args.exist_ok if RANK in (-1, 0) else True)
+
+    return Path(save_dir)
+
+
 def _handle_deprecation(custom):
-    """Hardcoded function to handle deprecated config keys"""
+    """Hardcoded function to handle deprecated config keys."""
 
     for key in custom.copy().keys():
         if key == 'hide_labels':
@@ -171,6 +186,7 @@ def check_dict_alignment(base: Dict, custom: Dict, e=None):
     Args:
         custom (dict): a dictionary of custom configuration options
         base (dict): a dictionary of base configuration options
+        e (Error, optional): An optional error that is passed by the calling function.
     """
     custom = _handle_deprecation(custom)
     base_keys, custom_keys = (set(x.keys()) for x in (base, custom))

ultralytics/engine/model.py

@@ -5,7 +5,7 @@ import sys
 from pathlib import Path
 from typing import Union
 
-from ultralytics.cfg import get_cfg
+from ultralytics.cfg import get_cfg, get_save_dir
 from ultralytics.engine.exporter import Exporter
 from ultralytics.hub.utils import HUB_WEB_ROOT
 from ultralytics.nn.tasks import attempt_load_one_weight, guess_model_task, nn, yaml_model_load
@@ -239,7 +239,7 @@ class Model:
         else:  # only update args if predictor is already setup
             self.predictor.args = get_cfg(self.predictor.args, overrides)
             if 'project' in overrides or 'name' in overrides:
-                self.predictor.save_dir = self.predictor.get_save_dir()
+                self.predictor.save_dir = get_save_dir(self.predictor.args)
         # Set prompts for SAM/FastSAM
         if len and hasattr(self.predictor, 'set_prompts'):
             self.predictor.set_prompts(prompts)

ultralytics/engine/predictor.py

@@ -34,11 +34,11 @@ import cv2
 import numpy as np
 import torch
 
-from ultralytics.cfg import get_cfg
+from ultralytics.cfg import get_cfg, get_save_dir
 from ultralytics.data import load_inference_source
 from ultralytics.data.augment import LetterBox, classify_transforms
 from ultralytics.nn.autobackend import AutoBackend
-from ultralytics.utils import DEFAULT_CFG, LOGGER, MACOS, SETTINGS, WINDOWS, callbacks, colorstr, ops
+from ultralytics.utils import DEFAULT_CFG, LOGGER, MACOS, WINDOWS, callbacks, colorstr, ops
 from ultralytics.utils.checks import check_imgsz, check_imshow
 from ultralytics.utils.files import increment_path
 from ultralytics.utils.torch_utils import select_device, smart_inference_mode
@@ -84,7 +84,7 @@ class BasePredictor:
             overrides (dict, optional): Configuration overrides. Defaults to None.
         """
         self.args = get_cfg(cfg, overrides)
-        self.save_dir = self.get_save_dir()
+        self.save_dir = get_save_dir(self.args)
         if self.args.conf is None:
             self.args.conf = 0.25  # default conf=0.25
         self.done_warmup = False
@@ -108,11 +108,6 @@ class BasePredictor:
         self.txt_path = None
         callbacks.add_integration_callbacks(self)
 
-    def get_save_dir(self):
-        project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task
-        name = self.args.name or f'{self.args.mode}'
-        return increment_path(Path(project) / name, exist_ok=self.args.exist_ok)
-
     def preprocess(self, im):
         """Prepares input image before inference.
 

ultralytics/engine/results.py

@@ -323,14 +323,10 @@ class Results(SimpleClass):
         if self.probs is not None:
             LOGGER.warning('WARNING ⚠️ Classify task do not support `save_crop`.')
             return
-        if isinstance(save_dir, str):
-            save_dir = Path(save_dir)
-        if isinstance(file_name, str):
-            file_name = Path(file_name)
         for d in self.boxes:
             save_one_box(d.xyxy,
                          self.orig_img.copy(),
-                         file=save_dir / self.names[int(d.cls)] / f'{file_name.stem}.jpg',
+                         file=Path(save_dir) / self.names[int(d.cls)] / f'{Path(file_name).stem}.jpg',
                          BGR=True)
 
     def tojson(self, normalize=False):

ultralytics/engine/trainer.py

@@ -23,15 +23,15 @@ from torch.cuda import amp
 from torch.nn.parallel import DistributedDataParallel as DDP
 from tqdm import tqdm
 
-from ultralytics.cfg import get_cfg
+from ultralytics.cfg import get_cfg, get_save_dir
 from ultralytics.data.utils import check_cls_dataset, check_det_dataset
 from ultralytics.nn.tasks import attempt_load_one_weight, attempt_load_weights
-from ultralytics.utils import (DEFAULT_CFG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, __version__, callbacks, clean_url,
+from ultralytics.utils import (DEFAULT_CFG, LOGGER, RANK, TQDM_BAR_FORMAT, __version__, callbacks, clean_url, colorstr,
-                               colorstr, emojis, yaml_save)
+                               emojis, yaml_save)
 from ultralytics.utils.autobatch import check_train_batch_size
 from ultralytics.utils.checks import check_amp, check_file, check_imgsz, print_args
 from ultralytics.utils.dist import ddp_cleanup, generate_ddp_command
-from ultralytics.utils.files import get_latest_run, increment_path
+from ultralytics.utils.files import get_latest_run
 from ultralytics.utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, init_seeds, one_cycle, select_device,
                                            strip_optimizer)
 
@@ -91,13 +91,7 @@ class BaseTrainer:
         init_seeds(self.args.seed + 1 + RANK, deterministic=self.args.deterministic)
 
         # Dirs
-        project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task
+        self.save_dir = get_save_dir(self.args)
-        name = self.args.name or f'{self.args.mode}'
-        if hasattr(self.args, 'save_dir'):
-            self.save_dir = Path(self.args.save_dir)
-        else:
-            self.save_dir = Path(
-                increment_path(Path(project) / name, exist_ok=self.args.exist_ok if RANK in (-1, 0) else True))
         self.wdir = self.save_dir / 'weights'  # weights dir
         if RANK in (-1, 0):
             self.wdir.mkdir(parents=True, exist_ok=True)  # make dir

ultralytics/engine/validator.py

@@ -26,12 +26,11 @@ import numpy as np
 import torch
 from tqdm import tqdm
 
-from ultralytics.cfg import get_cfg
+from ultralytics.cfg import get_cfg, get_save_dir
 from ultralytics.data.utils import check_cls_dataset, check_det_dataset
 from ultralytics.nn.autobackend import AutoBackend
-from ultralytics.utils import LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, colorstr, emojis
+from ultralytics.utils import LOGGER, TQDM_BAR_FORMAT, callbacks, colorstr, emojis
 from ultralytics.utils.checks import check_imgsz
-from ultralytics.utils.files import increment_path
 from ultralytics.utils.ops import Profile
 from ultralytics.utils.torch_utils import de_parallel, select_device, smart_inference_mode
 
@@ -71,7 +70,7 @@ class BaseValidator:
 
         Args:
             dataloader (torch.utils.data.DataLoader): Dataloader to be used for validation.
-            save_dir (Path): Directory to save results.
+            save_dir (Path, optional): Directory to save results.
             pbar (tqdm.tqdm): Progress bar for displaying progress.
             args (SimpleNamespace): Configuration for the validator.
             _callbacks (dict): Dictionary to store various callback functions.
@@ -93,12 +92,8 @@ class BaseValidator:
         self.jdict = None
         self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
 
-        project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task
+        self.save_dir = save_dir or get_save_dir(self.args)
-        name = self.args.name or f'{self.args.mode}'
-        self.save_dir = save_dir or increment_path(Path(project) / name,
-                                                   exist_ok=self.args.exist_ok if RANK in (-1, 0) else True)
         (self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
-
         if self.args.conf is None:
             self.args.conf = 0.001  # default conf=0.001