修改打印信息及初始训练参数

train_embed.py

@@ -336,7 +336,7 @@ def train(hyp, opt, device, tb_writer=None):
             if rank in [-1, 0]:
                 mloss = (mloss * i + loss_items) / (i + 1)  # update mean losses
                 mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0)  # (GB)
-                s = ('%10s' * 2 + '%10.4g' * 7) % (
+                s = ('%12s' * 2 + '%12.4g' * 7) % (
                     '%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1], embed_loss)
                 pbar.set_description(s)
 
@@ -474,7 +474,7 @@ if __name__ == '__main__':
     parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path')
     parser.add_argument('--hyp', type=str, default='data/hyp.scratch.yaml', help='hyperparameters path')
     parser.add_argument('--epochs', type=int, default=300)
-    parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs')
+    parser.add_argument('--batch-size', type=int, default=12, help='total batch size for all GPUs')
     parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes')
     parser.add_argument('--rect', action='store_true', help='rectangular training')
     parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')