update expt name comment and folder parsing for training

train.py

@@ -6,6 +6,7 @@
 import shutil
 import time
 from pathlib import Path
+from pprint import pprint
 
 import numpy as np
 import torch.distributed as dist
@@ -207,7 +208,8 @@ def train(hyp, opt, device, tb_writer=None):
     results = (0, 0, 0, 0, 0, 0, 0)  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
     scheduler.last_epoch = start_epoch - 1  # do not move
     scaler = amp.GradScaler(enabled=cuda)
-    logger.info('Image sizes %g train, %g test\nUsing %g dataloader workers\nLogging results to %s\n'
+    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))
     for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------
         model.train()
@@ -393,7 +395,7 @@ def train(hyp, opt, device, tb_writer=None):
     parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
     parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
     parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
-    parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied')
+    parser.add_argument('--name', default='', help='renames experiment folder exp{N} to exp{N}_{name} if supplied')
     parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
     parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
     parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
@@ -440,15 +442,15 @@ def train(hyp, opt, device, tb_writer=None):
         assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count'
         opt.batch_size = opt.total_batch_size // opt.world_size
 
-    logger.info(opt)
+    pprint(vars(opt))
     with open(opt.hyp) as f:
         hyp = yaml.load(f, Loader=yaml.FullLoader)  # load hyps
 
     # Train
     if not opt.evolve:
         tb_writer = None
         if opt.global_rank in [-1, 0]:
-            logger.info('Start Tensorboard with "tensorboard --logdir %s", view at http://localhost:6006/' % opt.logdir)
+            logger.info(f'Start Tensorboard with "tensorboard --logdir {opt.logdir}", view at http://localhost:6006/')
             tb_writer = SummaryWriter(log_dir=log_dir)  # runs/exp0
 
         train(hyp, opt, device, tb_writer)
@@ -470,7 +472,8 @@ def train(hyp, opt, device, tb_writer=None):
                 'obj_pw': (1, 0.5, 2.0),  # obj BCELoss positive_weight
                 'iou_t': (0, 0.1, 0.7),  # IoU training threshold
                 'anchor_t': (1, 2.0, 8.0),  # anchor-multiple threshold
-                'anchors': (2, 2.0, 10.0),  # anchors per output grid (0 to ignore)
+                # temp fix for https://github.com/ultralytics/yolov5/issues/607#issuecomment-692589883
+                # 'anchors': (2, 2.0, 10.0),  # anchors per output grid (0 to ignore)
                 'fl_gamma': (0, 0.0, 2.0),  # focal loss gamma (efficientDet default gamma=1.5)
                 'hsv_h': (1, 0.0, 0.1),  # image HSV-Hue augmentation (fraction)
                 'hsv_s': (1, 0.0, 0.9),  # image HSV-Saturation augmentation (fraction)
@@ -488,11 +491,11 @@ def train(hyp, opt, device, tb_writer=None):
         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('runs/evolve/hyp_evolved.yaml')  # save best result here
+        yaml_file = Path(opt.logdir) / 'evolve' / '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
 
-        for _ in range(300):  # generations to evolve
+        for _ in tqdm(range(300), desc='perform evolve >>'):  # generations to evolve
             if os.path.exists('evolve.txt'):  # if evolve.txt exists: select best hyps and mutate
                 # Select parent(s)
                 parent = 'single'  # parent selection method: 'single' or 'weighted'
@@ -532,5 +535,5 @@ def train(hyp, opt, device, tb_writer=None):
 
         # Plot results
         plot_evolution(yaml_file)
-        print('Hyperparameter evolution complete. Best results saved as: %s\nCommand to train a new model with these '
-              'hyperparameters: $ python train.py --hyp %s' % (yaml_file, yaml_file))
+        print(f'Hyperparameter evolution complete. Best results saved as: {yaml_file}\n'
+              f'Command to train a new model with these hyperparameters: $ python train.py --hyp {yaml_file}')

utils/general.py

@@ -7,6 +7,7 @@
 import shutil
 import subprocess
 import time
+import re
 from contextlib import contextmanager
 from copy import copy
 from pathlib import Path
@@ -952,9 +953,12 @@ def increment_dir(dir, comment=''):
     # Increments a directory runs/exp1 --> runs/exp2_comment
     n = 0  # number
     dir = str(Path(dir))  # os-agnostic
-    d = sorted(glob.glob(dir + '*'))  # directories
-    if len(d):
-        n = max([int(x[len(dir):x.rfind('_') if '_' in Path(x).name else None]) for x in d]) + 1  # increment
+    dirs = sorted(glob.glob(dir + '*'))  # directories
+    if dirs:
+        matches = [re.search(r"exp(\d+)", d) for d in dirs]
+        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 '')