() if self.use_progress_bar: pass else: for _ in range(self.num_batches_per_epoch): # 小循环 l = self.run_iteration(self.tr_gen, True) train_losses_epoch.append(l) self.all_tr_losses.append(np.mean(train_losses_epoch)) with torch.no_grad(): # validation with train=False self...
CONFIGURATION can be 2d, 3d_lowres or 3d_fullres. TASK_NAME_OR_ID refers to the task you would like to train and FOLD is the fold of the cross-validation. GPUS (integer value) specifies the number of GPUs you wish to train on. To specify which GPUs you want to use, please make ...
Will disable progrewss bar! ' help='Set this to print a lot of stuff. Useful for debugging. Will disable progress bar! ' 'Recommended for cluster environments') args, unrecognized_args = parser.parse_known_args() if args.np is None: @@ -173,7 +173,7 @@ def plan_and_preprocess_...
Will disable progrewss bar! ' help='Set this to print a lot of stuff. Useful for debugging. Will disable progress bar! ' 'Recommended for cluster environments') args, unrecognized_args = parser.parse_known_args() if args.np is None: @@ -173,7 +173,7 @@ def plan_and_preprocess_...
Will disable progrewss bar! ' help='Set this to print a lot of stuff. Useful for debugging. Will disable progress bar! ' 'Recommended for cluster environments') args, unrecognized_args = parser.parse_known_args() if args.np is None: @@ -173,7 +173,7 @@ def plan_and_preprocess_...