1

attention.py

@@ -6,8 +6,8 @@
 
 
 class Patten(nn.Module):
-    def __int__(self, in_dim, n):
-        super(Patten, self).__int__()
+    def __init__(self, in_dim, n):
+        super(Patten, self).__init__()
         self.in_channel = in_dim
 
         self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // n, kernel_size=1)
@@ -33,8 +33,8 @@ def forward(self, x):
 
 
 class Catten(nn.Module):
-    def __int__(self, in_dim):
-        super(Catten, self).__int__()
+    def __init__(self, in_dim):
+        super(Catten, self).__init__()
         self.channel_in = in_dim
 
         self.gamma = nn.Parameter(torch.zeros(1))

config.py

@@ -57,8 +57,10 @@ def init_extra(self):
             raise Exception("数据集不存在，名字打错了")
 
     def merge_from_args(self, args):
-        self.gpu = args.gpu
-        self.epochs = args.epochs
+        if args.gpu:
+            self.gpu = args.gpu
+        if args.epochs:
+            self.epochs = args.epochs
         if args.train_seg:
             self.train_mode = "seg"
         elif args.train_dec:
@@ -106,3 +108,4 @@ def get_as_dict(self):
             'input_h': self.input_h,
             'input_c': self.input_c,
         }
+        return params

run.py

@@ -15,8 +15,8 @@ def parse_args():
     parser.add_argument('--train_seg', type=str, required=False, help="train part")
     parser.add_argument('--train_dec', type=str, required=False, help="train part")
     parser.add_argument('--train_total', type=str, required=False, help="train part")
-
-    return parser
+    args = parser.parse_args()
+    return args
 
 if __name__ == '__main__':
     args = parse_args()

train.py

@@ -36,6 +36,7 @@ def train(self):
 
         self._set_rets_path()
         self._create_results_dirs()
+        self.print_run_params()
 
         device = self._get_device()
         model = self._get_model().to(device)
@@ -333,3 +334,9 @@ def training_iteration(self, data, device, model, criterion_seg, criterion_dec,
         optimizer.zero_grad()
 
         return total_loss_seg, total_loss_dec, total_loss, total_correct
+
+
+    def print_run_params(self):
+        for l in sorted(map(lambda e: e[0] + ":" + str(e[1]) + "\n", self.cfg.get_as_dict().items())):
+            k, v = l.split(":")
+            self._log(f"{k:25s} : {str(v.strip())}")