SAN/config.yaml

@@ -67,7 +67,7 @@ hybrid_tree:
 
 # 训练权重存放路径
 optimizer_save: False
-checkpoint_dir: 'checkpoints'
+checkpoint_dir: '/model/checkpoints'
 finetune: False
 checkpoint: ""
 

SAN/inference.py

@@ -10,9 +10,9 @@ from infer.Backbone import Backbone
 from dataset import Words
 
 parser = argparse.ArgumentParser(description='Spatial channel attention')
-parser.add_argument('--config', default='config.yaml', type=str, help='配置文件路径')
-parser.add_argument('--image_path', default='/home/yuanye/work/data/CROHME2014/14_off_image_test', type=str, help='测试image路径')
-parser.add_argument('--label_path', default='/home/yuanye/work/data/CROHME2014/test_caption.txt', type=str, help='测试label路径')
+parser.add_argument('--config', default='14.yaml', type=str, help='配置文件路径')
+parser.add_argument('--image_path', default='data/off_image_test', type=str, help='测试image路径')
+parser.add_argument('--label_path', default='data/test_caption.txt', type=str, help='测试label路径')
 args = parser.parse_args()
 
 if not args.config:

SAN/models/CNN/densenet.py

@@ -85,8 +85,6 @@ class DenseNet(nn.Module):
 
         nChannels = nOutChannels
         self.dense3 = self._make_dense(nChannels, growthRate, nDenseBlocks, bottleneck, use_dropout)
-        self.feature_proj = nn.Conv2d(684, 512, kernel_size=1)
-
     def _make_dense(self, nChannels, growthRate, nDenseBlocks, bottleneck, use_dropout):
         layers = []
         for i in range(int(nDenseBlocks)):