train.py --logdir argparser addition (#660)

* train.py --logdir argparser addition

* train.py --logdir argparser addition

train.py

@@ -55,20 +55,20 @@ hyp = {'lr0': 0.01,  # initial learning rate (SGD=1E-2, Adam=1E-3)
 
 def train(hyp, opt, device, tb_writer=None):
     print(f'Hyperparameters {hyp}')
-    log_dir = tb_writer.log_dir if tb_writer else 'runs/evolve'  # run directory
-    wdir = str(Path(log_dir) / 'weights') + os.sep  # weights directory
+    log_dir = Path(tb_writer.log_dir) if tb_writer else Path(opt.logdir) / 'evolve'  # logging directory
+    wdir = str(log_dir / 'weights') + os.sep  # weights directory
     os.makedirs(wdir, exist_ok=True)
     last = wdir + 'last.pt'
     best = wdir + 'best.pt'
-    results_file = log_dir + os.sep + 'results.txt'
+    results_file = str(log_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
     
     # TODO: Use DDP logging. Only the first process is allowed to log.
     # Save run settings
-    with open(Path(log_dir) / 'hyp.yaml', 'w') as f:
+    with open(log_dir / 'hyp.yaml', 'w') as f:
         yaml.dump(hyp, f, sort_keys=False)
-    with open(Path(log_dir) / 'opt.yaml', 'w') as f:
+    with open(log_dir / 'opt.yaml', 'w') as f:
         yaml.dump(vars(opt), f, sort_keys=False)
 
     # Configure
@@ -325,7 +325,7 @@ def train(hyp, opt, device, tb_writer=None):
 
                 # Plot
                 if ni < 3:
-                    f = str(Path(log_dir) / ('train_batch%g.jpg' % ni))  # filename
+                    f = str(log_dir / ('train_batch%g.jpg' % ni))  # 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)
@@ -433,7 +433,8 @@ if __name__ == '__main__':
     parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
     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('--local-rank', type=int, default=-1, help='DDP parameter, do not modify')
+    parser.add_argument('--logdir', type=str, default='runs/', help='logging directory')
     opt = parser.parse_args()
 
     # Resume
@@ -472,8 +473,8 @@ if __name__ == '__main__':
     if not opt.evolve:
         tb_writer = None
         if opt.global_rank in [-1, 0]:
-            print('Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/')
-            tb_writer = SummaryWriter(log_dir=increment_dir('runs/exp', opt.name))
+            print('Start Tensorboard with "tensorboard --logdir %s", view at http://localhost:6006/' % opt.logdir)
+            tb_writer = SummaryWriter(log_dir=increment_dir(Path(opt.logdir) / 'exp', opt.name))  # runs/exp
 
         train(hyp, opt, device, tb_writer)