Unified '/project/name' results saving (#1377)

* Project/name update

* Update ci-testing.yml

* address project with path separator failure mode

* Project/name update

* address project with path separator failure mode

* Update ci-testing.yml

* detect.py default --name bug fix

* missing rstrip PR

* train/exp0 to train/exp

.github/workflows/ci-testing.yml

@@ -66,10 +66,10 @@ jobs:
           python train.py --img 256 --batch 8 --weights weights/${{ matrix.model }}.pt --cfg models/${{ matrix.model }}.yaml --epochs 1 --device $di
           # detect
           python detect.py --weights weights/${{ matrix.model }}.pt --device $di
-          python detect.py --weights runs/train/exp0/weights/last.pt --device $di
+          python detect.py --weights runs/train/exp/weights/last.pt --device $di
           # test
           python test.py --img 256 --batch 8 --weights weights/${{ matrix.model }}.pt --device $di
-          python test.py --img 256 --batch 8 --weights runs/train/exp0/weights/last.pt --device $di
+          python test.py --img 256 --batch 8 --weights runs/train/exp/weights/last.pt --device $di
 
           python models/yolo.py --cfg models/${{ matrix.model }}.yaml  # inspect
           python models/export.py --img 256 --batch 1 --weights weights/${{ matrix.model }}.pt  # export

README.md

@@ -96,7 +96,7 @@ Fusing layers...
 Model Summary: 140 layers, 7.45958e+06 parameters, 0 gradients
 image 1/2 data/images/bus.jpg: 640x480 4 persons, 1 buss, 1 skateboards, Done. (0.013s)
 image 2/2 data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.013s)
-Results saved to runs/detect/exp0
+Results saved to runs/detect/exp
 Done. (0.124s)
 ```
 <img src="https://user-images.githubusercontent.com/26833433/97107365-685a8d80-16c7-11eb-8c2e-83aac701d8b9.jpeg" width="500">  

detect.py

@@ -10,21 +10,18 @@ from numpy import random
 from models.experimental import attempt_load
 from utils.datasets import LoadStreams, LoadImages
 from utils.general import check_img_size, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, \
-    plot_one_box, strip_optimizer, set_logging, increment_dir
+    plot_one_box, strip_optimizer, set_logging, increment_path
 from utils.torch_utils import select_device, load_classifier, time_synchronized
 
 
 def detect(save_img=False):
-    save_dir, source, weights, view_img, save_txt, imgsz = \
+    source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
-        Path(opt.save_dir), opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
     webcam = source.isnumeric() or source.endswith('.txt') or \
              source.lower().startswith(('rtsp://', 'rtmp://', 'http://'))
 
     # Directories
-    if save_dir == Path('runs/detect'):  # if default
+    save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
-        save_dir.mkdir(parents=True, exist_ok=True)  # make base
+    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
-        save_dir = Path(increment_dir(save_dir / 'exp', opt.name))  # increment run
-    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make new dir
 
     # Initialize
     set_logging()
@@ -156,12 +153,13 @@ if __name__ == '__main__':
     parser.add_argument('--view-img', action='store_true', help='display results')
     parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
     parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
-    parser.add_argument('--save-dir', type=str, default='runs/detect', help='directory to save results')
-    parser.add_argument('--name', default='', help='name to append to --save-dir: i.e. runs/{N} -> runs/{N}_{name}')
     parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
     parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
     parser.add_argument('--augment', action='store_true', help='augmented inference')
     parser.add_argument('--update', action='store_true', help='update all models')
+    parser.add_argument('--project', default='runs/detect', help='save results to project/name')
+    parser.add_argument('--name', default='exp', help='save results to project/name')
+    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
     opt = parser.parse_args()
     print(opt)
 

test.py

@@ -13,7 +13,7 @@ from models.experimental import attempt_load
 from utils.datasets import create_dataloader
 from utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, compute_loss, \
     non_max_suppression, scale_coords, xyxy2xywh, clip_coords, plot_images, xywh2xyxy, box_iou, output_to_target, \
-    ap_per_class, set_logging, increment_dir
+    ap_per_class, set_logging, increment_path
 from utils.torch_utils import select_device, time_synchronized
 
 
@@ -46,10 +46,8 @@ def test(data,
         save_txt = opt.save_txt  # save *.txt labels
 
         # Directories
-        if save_dir == Path('runs/test'):  # if default
+        save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
-            save_dir.mkdir(parents=True, exist_ok=True)  # make base
+        (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
-            save_dir = Path(increment_dir(save_dir / 'exp', opt.name))  # increment run
-        (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make new dir
 
         # Load model
         model = attempt_load(weights, map_location=device)  # load FP32 model
@@ -279,7 +277,6 @@ if __name__ == '__main__':
     parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
     parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')
     parser.add_argument('--iou-thres', type=float, default=0.65, help='IOU threshold for NMS')
-    parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
     parser.add_argument('--task', default='val', help="'val', 'test', 'study'")
     parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
     parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
@@ -287,8 +284,10 @@ if __name__ == '__main__':
     parser.add_argument('--verbose', action='store_true', help='report mAP by class')
     parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
     parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
-    parser.add_argument('--save-dir', type=str, default='runs/test', help='directory to save results')
+    parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
-    parser.add_argument('--name', default='', help='name to append to --save-dir: i.e. runs/{N} -> runs/{N}_{name}')
+    parser.add_argument('--project', default='runs/test', help='save to project/name')
+    parser.add_argument('--name', default='exp', help='save to project/name')
+    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
     opt = parser.parse_args()
     opt.save_json |= opt.data.endswith('coco.yaml')
     opt.data = check_file(opt.data)  # check file
@@ -305,7 +304,6 @@ if __name__ == '__main__':
              opt.single_cls,
              opt.augment,
              opt.verbose,
-             save_dir=Path(opt.save_dir),
              save_txt=opt.save_txt,
              save_conf=opt.save_conf,
              )

train.py

@@ -27,7 +27,7 @@ from utils.datasets import create_dataloader
 from utils.general import (
     torch_distributed_zero_first, labels_to_class_weights, plot_labels, check_anchors, labels_to_image_weights,
     compute_loss, plot_images, fitness, strip_optimizer, plot_results, get_latest_run, check_dataset, check_file,
-    check_git_status, check_img_size, increment_dir, print_mutation, plot_evolution, set_logging, init_seeds)
+    check_git_status, check_img_size, increment_path, print_mutation, plot_evolution, set_logging, init_seeds)
 from utils.google_utils import attempt_download
 from utils.torch_utils import ModelEMA, select_device, intersect_dicts
 
@@ -36,19 +36,20 @@ logger = logging.getLogger(__name__)
 
 def train(hyp, opt, device, tb_writer=None, wandb=None):
     logger.info(f'Hyperparameters {hyp}')
-    log_dir = Path(tb_writer.log_dir) if tb_writer else Path(opt.logdir) / 'evolve'  # logging directory
+    save_dir, epochs, batch_size, total_batch_size, weights, rank = \
-    wdir = log_dir / 'weights'  # weights directory
+        opt.save_dir, opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
-    wdir.mkdir(parents=True, exist_ok=True)
+
+    # Directories
+    wdir = save_dir / 'weights'
+    wdir.mkdir(parents=True, exist_ok=True)  # make dir
     last = wdir / 'last.pt'
     best = wdir / 'best.pt'
-    results_file = log_dir / 'results.txt'
+    results_file = save_dir / 'results.txt'
-    epochs, batch_size, total_batch_size, weights, rank = \
-        opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
 
     # Save run settings
-    with open(log_dir / 'hyp.yaml', 'w') as f:
+    with open(save_dir / 'hyp.yaml', 'w') as f:
         yaml.dump(hyp, f, sort_keys=False)
-    with open(log_dir / 'opt.yaml', 'w') as f:
+    with open(save_dir / 'opt.yaml', 'w') as f:
         yaml.dump(vars(opt), f, sort_keys=False)
 
     # Configure
@@ -120,8 +121,10 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
 
     # Logging
     if wandb and wandb.run is None:
-        id = ckpt.get('wandb_id') if 'ckpt' in locals() else None
+        wandb_run = wandb.init(config=opt, resume="allow",
-        wandb_run = wandb.init(config=opt, resume="allow", project="YOLOv5", name=log_dir.stem, id=id)
+                               project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,
+                               name=save_dir.stem,
+                               id=ckpt.get('wandb_id') if 'ckpt' in locals() else None)
 
     # Resume
     start_epoch, best_fitness = 0, 0.0
@@ -188,7 +191,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
             c = torch.tensor(labels[:, 0])  # classes
             # cf = torch.bincount(c.long(), minlength=nc) + 1.  # frequency
             # model._initialize_biases(cf.to(device))
-            plot_labels(labels, save_dir=log_dir)
+            plot_labels(labels, save_dir=save_dir)
             if tb_writer:
                 # tb_writer.add_hparams(hyp, {})  # causes duplicate https://github.com/ultralytics/yolov5/pull/384
                 tb_writer.add_histogram('classes', c, 0)
@@ -215,7 +218,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
     scaler = amp.GradScaler(enabled=cuda)
     logger.info('Image sizes %g train, %g test\n'
                 'Using %g dataloader workers\nLogging results to %s\n'
-                'Starting training for %g epochs...' % (imgsz, imgsz_test, dataloader.num_workers, log_dir, epochs))
+                'Starting training for %g epochs...' % (imgsz, imgsz_test, dataloader.num_workers, save_dir, epochs))
     for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------
         model.train()
 
@@ -296,7 +299,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
 
                 # Plot
                 if ni < 3:
-                    f = str(log_dir / f'train_batch{ni}.jpg')  # filename
+                    f = str(save_dir / f'train_batch{ni}.jpg')  # filename
                     result = plot_images(images=imgs, targets=targets, paths=paths, fname=f)
                     # if tb_writer and result is not None:
                     # tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
@@ -321,7 +324,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
                                                  model=ema.ema,
                                                  single_cls=opt.single_cls,
                                                  dataloader=testloader,
-                                                 save_dir=log_dir,
+                                                 save_dir=save_dir,
                                                  plots=epoch == 0 or final_epoch,  # plot first and last
                                                  log_imgs=opt.log_imgs if wandb else 0)
 
@@ -369,7 +372,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
     if rank in [-1, 0]:
         # Strip optimizers
         n = opt.name if opt.name.isnumeric() else ''
-        fresults, flast, fbest = log_dir / f'results{n}.txt', wdir / f'last{n}.pt', wdir / f'best{n}.pt'
+        fresults, flast, fbest = save_dir / f'results{n}.txt', wdir / f'last{n}.pt', wdir / f'best{n}.pt'
         for f1, f2 in zip([wdir / 'last.pt', wdir / 'best.pt', results_file], [flast, fbest, fresults]):
             if f1.exists():
                 os.rename(f1, f2)  # rename
@@ -378,7 +381,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
                     os.system('gsutil cp %s gs://%s/weights' % (f2, opt.bucket)) if opt.bucket else None  # upload
         # Finish
         if not opt.evolve:
-            plot_results(save_dir=log_dir)  # save as results.png
+            plot_results(save_dir=save_dir)  # save as results.png
         logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
 
     dist.destroy_process_group() if rank not in [-1, 0] else None
@@ -410,11 +413,11 @@ if __name__ == '__main__':
     parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')
     parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
     parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
-    parser.add_argument('--logdir', type=str, default='runs/train', help='logging directory')
-    parser.add_argument('--name', default='', help='name to append to --save-dir: i.e. runs/{N} -> runs/{N}_{name}')
     parser.add_argument('--log-imgs', type=int, default=10, help='number of images for W&B logging, max 100')
     parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')
-
+    parser.add_argument('--project', default='runs/train', help='save to project/name')
+    parser.add_argument('--name', default='exp', help='save to project/name')
+    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
     opt = parser.parse_args()
 
     # Set DDP variables
@@ -428,19 +431,19 @@ if __name__ == '__main__':
     # Resume
     if opt.resume:  # resume an interrupted run
         ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run()  # specified or most recent path
-        log_dir = Path(ckpt).parent.parent  # runs/train/exp0
+        opt.save_dir = Path(ckpt).parent.parent  # runs/train/exp
         assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist'
-        with open(log_dir / 'opt.yaml') as f:
+        with open(opt.save_dir / 'opt.yaml') as f:
             opt = argparse.Namespace(**yaml.load(f, Loader=yaml.FullLoader))  # replace
         opt.cfg, opt.weights, opt.resume = '', ckpt, True
         logger.info('Resuming training from %s' % ckpt)
-
     else:
         # opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml')
         opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp)  # check files
         assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
         opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size)))  # extend to 2 sizes (train, test)
-        log_dir = increment_dir(Path(opt.logdir) / 'exp', opt.name)  # runs/exp1
+        opt.name = 'evolve' if opt.evolve else opt.name
+        opt.save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
 
     # DDP mode
     device = select_device(opt.device, batch_size=opt.batch_size)
@@ -466,8 +469,8 @@ if __name__ == '__main__':
         tb_writer, wandb = None, None  # init loggers
         if opt.global_rank in [-1, 0]:
             # Tensorboard
-            logger.info(f'Start Tensorboard with "tensorboard --logdir {opt.logdir}", view at http://localhost:6006/')
+            logger.info(f'Start Tensorboard with "tensorboard --logdir {opt.project}", view at http://localhost:6006/')
-            tb_writer = SummaryWriter(log_dir=log_dir)  # runs/train/exp0
+            tb_writer = SummaryWriter(opt.save_dir)  # runs/train/exp
 
             # W&B
             try:
@@ -514,7 +517,7 @@ if __name__ == '__main__':
         assert opt.local_rank == -1, 'DDP mode not implemented for --evolve'
         opt.notest, opt.nosave = True, True  # only test/save final epoch
         # ei = [isinstance(x, (int, float)) for x in hyp.values()]  # evolvable indices
-        yaml_file = Path(opt.logdir) / 'evolve' / 'hyp_evolved.yaml'  # save best result here
+        yaml_file = opt.save_dir / 'hyp_evolved.yaml'  # save best result here
         if opt.bucket:
             os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket)  # download evolve.txt if exists
 

tutorial.ipynb

@@ -597,7 +597,7 @@
       },
       "source": [
         "!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images/\n",
-        "Image(filename='runs/detect/exp0/zidane.jpg', width=600)"
+        "Image(filename='runs/detect/exp/zidane.jpg', width=600)"
       ],
       "execution_count": null,
       "outputs": [
@@ -611,7 +611,7 @@
             "Model Summary: 140 layers, 7.45958e+06 parameters, 0 gradients\n",
             "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 buss, 1 skateboards, Done. (0.012s)\n",
             "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.012s)\n",
-            "Results saved to runs/detect/exp0\n",
+            "Results saved to runs/detect/exp\n",
             "Done. (0.113s)\n"
           ],
           "name": "stdout"
@@ -887,7 +887,7 @@
       "source": [
         "Train a YOLOv5s model on [COCO128](https://www.kaggle.com/ultralytics/coco128) with `--data coco128.yaml`, starting from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights '' --cfg yolov5s.yaml`. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and **COCO, COCO128, and VOC datasets are downloaded automatically** on first use.\n",
         "\n",
-        "All training results are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp0`, `runs/train/exp1` etc.\n"
+        "All training results are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc.\n"
       ]
     },
     {
@@ -969,7 +969,7 @@
             "Analyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946\n",
             "Image sizes 640 train, 640 test\n",
             "Using 2 dataloader workers\n",
-            "Logging results to runs/train/exp0\n",
+            "Logging results to runs/train/exp\n",
             "Starting training for 3 epochs...\n",
             "\n",
             "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
@@ -986,8 +986,8 @@
             "       2/2     3.17G   0.04445   0.06545   0.01666    0.1266       149       640: 100% 8/8 [00:01<00:00,  4.33it/s]\n",
             "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100% 8/8 [00:02<00:00,  2.78it/s]\n",
             "                 all         128         929       0.395       0.766       0.701       0.455\n",
-            "Optimizer stripped from runs/train/exp0/weights/last.pt, 15.2MB\n",
+            "Optimizer stripped from runs/train/exp/weights/last.pt, 15.2MB\n",
-            "Optimizer stripped from runs/train/exp0/weights/best.pt, 15.2MB\n",
+            "Optimizer stripped from runs/train/exp/weights/best.pt, 15.2MB\n",
             "3 epochs completed in 0.005 hours.\n",
             "\n"
           ],
@@ -1030,7 +1030,7 @@
       "source": [
         "## Local Logging\n",
         "\n",
-        "All results are logged by default to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp0`, `runs/train/exp1`, etc. View train and test jpgs to see mosaics, labels, predictions and augmentation effects. Note a **Mosaic Dataloader** is used for training (shown below), a new concept developed by Ultralytics and first featured in [YOLOv4](https://arxiv.org/abs/2004.10934)."
+        "All results are logged by default to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc. View train and test jpgs to see mosaics, labels, predictions and augmentation effects. Note a **Mosaic Dataloader** is used for training (shown below), a new concept developed by Ultralytics and first featured in [YOLOv4](https://arxiv.org/abs/2004.10934)."
       ]
     },
     {
@@ -1039,9 +1039,9 @@
         "id": "riPdhraOTCO0"
       },
       "source": [
-        "Image(filename='runs/train/exp0/train_batch0.jpg', width=800)  # train batch 0 mosaics and labels\n",
+        "Image(filename='runs/train/exp/train_batch0.jpg', width=800)  # train batch 0 mosaics and labels\n",
-        "Image(filename='runs/train/exp0/test_batch0_labels.jpg', width=800)  # test batch 0 labels\n",
+        "Image(filename='runs/train/exp/test_batch0_labels.jpg', width=800)  # test batch 0 labels\n",
-        "Image(filename='runs/train/exp0/test_batch0_pred.jpg', width=800)  # test batch 0 predictions"
+        "Image(filename='runs/train/exp/test_batch0_pred.jpg', width=800)  # test batch 0 predictions"
       ],
       "execution_count": null,
       "outputs": []
@@ -1078,7 +1078,7 @@
       },
       "source": [
         "from utils.utils import plot_results \n",
-        "plot_results(save_dir='runs/train/exp0')  # plot results.txt as results.png\n",
+        "plot_results(save_dir='runs/train/exp')  # plot results.txt as results.png\n",
         "Image(filename='results.png', width=800) "
       ],
       "execution_count": null,
@@ -1170,9 +1170,9 @@
         "  for di in 0 cpu # inference devices\n",
         "  do\n",
         "    python detect.py --weights $x.pt --device $di  # detect official\n",
-        "    python detect.py --weights runs/train/exp0/weights/last.pt --device $di  # detect custom\n",
+        "    python detect.py --weights runs/train/exp/weights/last.pt --device $di  # detect custom\n",
         "    python test.py --weights $x.pt --device $di # test official\n",
-        "    python test.py --weights runs/train/exp0/weights/last.pt --device $di # test custom\n",
+        "    python test.py --weights runs/train/exp/weights/last.pt --device $di # test custom\n",
         "  done\n",
         "  python models/yolo.py --cfg $x.yaml # inspect\n",
         "  python models/export.py --weights $x.pt --img 640 --batch 1 # export\n",

utils/general.py

@@ -60,7 +60,7 @@ def init_seeds(seed=0):
     init_torch_seeds(seed)
 
 
-def get_latest_run(search_dir='./runs'):
+def get_latest_run(search_dir='.'):
     # Return path to most recent 'last.pt' in /runs (i.e. to --resume from)
     last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True)
     return max(last_list, key=os.path.getctime) if last_list else ''
@@ -951,23 +951,17 @@ def output_to_target(output, width, height):
     return np.array(targets)
 
 
-def increment_dir(dir, comment=''):
+def increment_path(path, exist_ok=True, sep=''):
-    # Increments a directory runs/exp1 --> runs/exp2_comment
+    # Increment path, i.e. runs/exp --> runs/exp{sep}0, runs/exp{sep}1 etc.
-    n = 0  # number
+    path = Path(path)  # os-agnostic
-    dir = str(Path(dir))  # os-agnostic
+    if (path.exists() and exist_ok) or (not path.exists()):
-    if os.path.isdir(dir):
+        return str(path)
-        stem = ''
-        dir += os.sep  # removed by Path
     else:
-        stem = Path(dir).stem
+        dirs = glob.glob(f"{path}{sep}*")  # similar paths
-
+        matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs]
-    dirs = sorted(glob.glob(dir + '*'))  # directories
+        i = [int(m.groups()[0]) for m in matches if m]  # indices
-    if dirs:
+        n = max(i) + 1 if i else 2  # increment number
-        matches = [re.search(r"%s(\d+)" % stem, d) for d in dirs]
+        return f"{path}{sep}{n}"  # update path
-        idxs = [int(m.groups()[0]) for m in matches if m]
-        if idxs:
-            n = max(idxs) + 1  # increment
-    return dir + str(n) + ('_' + comment if comment else '')
 
 
 # Plotting functions ---------------------------------------------------------------------------------------------------