Update TensorBoard (#3669)

train.py

@@ -42,7 +42,6 @@
 def train(hyp,  # path/to/hyp.yaml or hyp dictionary
           opt,
           device,
-          tb_writer=None
           ):
     save_dir, epochs, batch_size, total_batch_size, weights, rank, single_cls = \
         Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank, \
@@ -74,9 +73,16 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
     with open(opt.data) as f:
         data_dict = yaml.safe_load(f)  # data dict
 
-    # Logging- Doing this before checking the dataset. Might update data_dict
-    loggers = {'wandb': None}  # loggers dict
+    # Loggers
+    loggers = {'wandb': None, 'tb': None}  # loggers dict
     if rank in [-1, 0]:
+        # TensorBoard
+        if not opt.evolve:
+            prefix = colorstr('tensorboard: ')
+            logger.info(f"{prefix}Start with 'tensorboard --logdir {opt.project}', view at http://localhost:6006/")
+            loggers['tb'] = SummaryWriter(opt.save_dir)
+
+        # W&B
         opt.hyp = hyp  # add hyperparameters
         run_id = torch.load(weights).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None
         wandb_logger = WandbLogger(opt, save_dir.stem, run_id, data_dict)
@@ -219,8 +225,8 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
             # model._initialize_biases(cf.to(device))
             if plots:
                 plot_labels(labels, names, save_dir, loggers)
-                if tb_writer:
-                    tb_writer.add_histogram('classes', c, 0)
+                if loggers['tb']:
+                    loggers['tb'].add_histogram('classes', c, 0)  # TensorBoard
 
             # Anchors
             if not opt.noautoanchor:
@@ -341,18 +347,18 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
                 if plots and ni < 3:
                     f = save_dir / f'train_batch{ni}.jpg'  # filename
                     Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start()
-                    if tb_writer and ni == 0:
+                    if loggers['tb'] and ni == 0:  # TensorBoard
                         with warnings.catch_warnings():
                             warnings.simplefilter('ignore')  # suppress jit trace warning
-                            tb_writer.add_graph(torch.jit.trace(de_parallel(model), imgs[0:1], strict=False), [])
+                            loggers['tb'].add_graph(torch.jit.trace(de_parallel(model), imgs[0:1], strict=False), [])
                 elif plots and ni == 10 and wandb_logger.wandb:
                     wandb_logger.log({'Mosaics': [wandb_logger.wandb.Image(str(x), caption=x.name) for x in
                                                   save_dir.glob('train*.jpg') if x.exists()]})
 
             # end batch ------------------------------------------------------------------------------------------------
 
         # Scheduler
-        lr = [x['lr'] for x in optimizer.param_groups]  # for tensorboard
+        lr = [x['lr'] for x in optimizer.param_groups]  # for loggers
         scheduler.step()
 
         # DDP process 0 or single-GPU
@@ -385,8 +391,8 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
                     'val/box_loss', 'val/obj_loss', 'val/cls_loss',  # val loss
                     'x/lr0', 'x/lr1', 'x/lr2']  # params
             for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
-                if tb_writer:
-                    tb_writer.add_scalar(tag, x, epoch)  # tensorboard
+                if loggers['tb']:
+                    loggers['tb'].add_scalar(tag, x, epoch)  # TensorBoard
                 if wandb_logger.wandb:
                     wandb_logger.log({tag: x})  # W&B
 
@@ -537,12 +543,7 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
     # Train
     logger.info(opt)
     if not opt.evolve:
-        tb_writer = None  # init loggers
-        if opt.global_rank in [-1, 0]:
-            prefix = colorstr('tensorboard: ')
-            logger.info(f"{prefix}Start with 'tensorboard --logdir {opt.project}', view at http://localhost:6006/")
-            tb_writer = SummaryWriter(opt.save_dir)  # Tensorboard
-        train(opt.hyp, opt, device, tb_writer)
+        train(opt.hyp, opt, device)
 
     # Evolve hyperparameters (optional)
     else: