Removed most of the new checks, leaving only the one after calling 'o…

…n_train_batch_end'

train.py

@@ -383,8 +383,6 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
                 best_fitness = fi
             log_vals = list(mloss) + list(results) + lr
             callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi)
-            if callbacks.stop_training:
-                return
 
             # Save model
             if (not nosave) or (final_epoch and not evolve):  # if save
@@ -405,8 +403,6 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
                     torch.save(ckpt, w / f'epoch{epoch}.pt')
                 del ckpt
                 callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi)
-                if callbacks.stop_training:
-                    return
 
             # Stop Single-GPU
             if RANK == -1 and stopper(epoch=epoch, fitness=fi):
@@ -446,8 +442,6 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
                                             compute_loss=compute_loss)  # val best model with plots
                     if is_coco:
                         callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi)
-                        if callbacks.stop_training:
-                            return
 
         callbacks.run('on_train_end', last, best, plots, epoch, results)
         LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")