From 52382e460db5accd0e0978af5696e147170e1671 Mon Sep 17 00:00:00 2001 From: Yunhao Meng Date: Thu, 23 Oct 2025 13:06:13 +0800 Subject: [PATCH] =?UTF-8?q?=E7=A7=BB=E9=99=A4=E8=AE=AD=E7=BB=83=E8=BF=87?= =?UTF-8?q?=E7=A8=8B=E4=B8=AD=E7=9A=84=E8=B0=83=E8=AF=95=E6=89=93=E5=8D=B0?= =?UTF-8?q?=E8=AF=AD=E5=8F=A5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- fed_algo_cs/client_base.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/fed_algo_cs/client_base.py b/fed_algo_cs/client_base.py index 7d86735..85ea227 100644 --- a/fed_algo_cs/client_base.py +++ b/fed_algo_cs/client_base.py @@ -120,7 +120,7 @@ class FedYoloClient(object): # track_model = self.model.module if is_ddp else self.model ema = util.EMA(self.model) if args.local_rank == 0 else None - print(type(self.train_dataset)) + # print(type(self.train_dataset)) # ---- Data ---- dataset = Dataset( @@ -188,7 +188,7 @@ class FedYoloClient(object): loss_dfl_meter = util.AverageMeter() for i, (images, targets) in enumerate(loader): - print(f"Client {self.name} - Epoch {epoch + 1}/{args.epochs} - Step {i + 1}/{num_steps}") + # print(f"Client {self.name} - Epoch {epoch + 1}/{args.epochs} - Step {i + 1}/{num_steps}") step = i + epoch * num_steps # scheduler per-step (your util.LinearLR expects step) @@ -257,9 +257,9 @@ class FedYoloClient(object): else self.model ) # print loss to test - print( - f"loss: {total_loss.item() * accumulate:.4f}, box: {box_loss.item():.4f}, cls: {cls_loss.item():.4f}, dfl: {dfl_loss.item():.4f}" - ) + # print( + # f"loss: {total_loss.item() * accumulate:.4f}, box: {box_loss.item():.4f}, cls: {cls_loss.item():.4f}, dfl: {dfl_loss.item():.4f}" + # ) torch.cuda.synchronize() # ---- Final average loss (per image) over the whole epoch span ----