change loss pattern

train.py

@@ -132,8 +132,8 @@ def trainer(ops,f_log):
                 # 优化器梯度清零
                 optimizer.zero_grad()
                 step += 1
-
-            torch.save(model_.state_dict(), ops.model_exp + '{}-epoch-{}.pth'.format(ops.model,epoch))
+            if epoch % 5 == 0 and epoch >0:
+                torch.save(model_.state_dict(), ops.model_exp + '{}-epoch-{}.pth'.format(ops.model,epoch))
 
     except Exception as e:
         print('Exception : ',e) # 打印异常
@@ -163,11 +163,11 @@ if __name__ == "__main__":
 
     parser.add_argument('--pretrained', type=bool, default = True,
         help = 'imageNet_Pretrain') # 初始化学习率
-    parser.add_argument('--fintune_model', type=str, default = './model_exp/2021-02-21_17-51-10/resnet_50-epoch-103.pth',
+    parser.add_argument('--fintune_model', type=str, default = './model_exp/2021-02-21_17-51-30/resnet_50-epoch-724.pth',
         help = 'fintune_model') # fintune model
     parser.add_argument('--loss_define', type=str, default = 'wing_loss',
         help = 'define_loss') # 损失函数定义
-    parser.add_argument('--init_lr', type=float, default = 1e-5,
+    parser.add_argument('--init_lr', type=float, default = 1e-3,
         help = 'init_learningRate') # 初始化学习率
     parser.add_argument('--lr_decay', type=float, default = 0.1,
         help = 'learningRate_decay') # 学习率权重衰减率