Merge pull request #1 from 953250587/master

fix some rename bug

data/dataloader.py

@@ -35,7 +35,10 @@ def __init__(self, root, txt, transform=None):
         self.transform = transform
         with open(txt) as f:
             for line in f:
-                self.img_path.append(os.path.join(root, line.split()[0]))
+                if line.startswith('/'):
+                    self.img_path.append(root + line.split()[0])
+                else:
+                    self.img_path.append(os.path.join(root, line.split()[0]))
                 self.labels.append(int(line.split()[1]))
 
     def __len__(self):

main.py

@@ -27,7 +27,8 @@
 
 config = source_import(args.config).config
 training_opt = config['training_opt']
-relatin_opt = config['relations']
+# change
+relatin_opt = config['memory']
 dataset = training_opt['dataset']
 
 if not os.path.isdir(training_opt['log_dir']):
@@ -49,7 +50,7 @@
                                     batch_size=training_opt['batch_size'],
                                     sampler_dic=sampler_dic,
                                     num_workers=training_opt['num_workers'])
-            for x in (['train', 'val', 'train_plain'] if relatin_opt['init_centers'] else ['train', 'val'])}
+            for x in (['train', 'val', 'train_plain'] if relatin_opt['init_centroids'] else ['train', 'val'])}
 
     training_model = model(config, data, test=False)
 

models/MetaEmbeddingClassifier.py

@@ -31,7 +31,7 @@ def forward(self, x, centroids, *args):
         dist_cur = torch.norm(x_expand - centroids_expand, 2, 2)
         values_nn, labels_nn = torch.sort(dist_cur, 1)
         scale = 10.0
-        confidence = (scale / values_nn[:, 0]).unsqueeze(1).expand(-1, feat_size)
+        reachability = (scale / values_nn[:, 0]).unsqueeze(1).expand(-1, feat_size)
 
         # computing memory feature by querying and associating visual memory
         values_memory = self.fc_hallucinator(x)

models/ResNet10Feature.py

@@ -4,7 +4,7 @@
 def create_model(use_selfatt=False, use_fc=False, dropout=None, stage1_weights=False, dataset=None, test=False, *args):
 
     print('Loading Scratch ResNet 10 Feature Model.')
-    resnet10 = ResNet(BasicBlock, [1, 1, 1, 1], use_selfatt=use_selfatt, use_fc=use_fc, dropout=None)
+    resnet10 = ResNet(BasicBlock, [1, 1, 1, 1], use_modulatedatt=use_selfatt, use_fc=use_fc, dropout=None)
 
     if not test:
         if stage1_weights:

models/ResNet152Feature.py

@@ -4,7 +4,7 @@
 def create_model(use_selfatt=False, use_fc=False, dropout=None, stage1_weights=False, dataset=None, caffe=False, test=False):
 
     print('Loading Scratch ResNet 152 Feature Model.')
-    resnet152 = ResNet(Bottleneck, [3, 8, 36, 3], use_selfatt=use_selfatt, use_fc=use_fc, dropout=None)
+    resnet152 = ResNet(Bottleneck, [3, 8, 36, 3], use_modulatedatt=use_selfatt, use_fc=use_fc, dropout=None)
 
     if not test:
 
@@ -13,7 +13,7 @@ def create_model(use_selfatt=False, use_fc=False, dropout=None, stage1_weights=F
         if caffe:
             print('Loading Caffe Pretrained ResNet 152 Weights.')
             resnet152 = init_weights(model=resnet152,
-                                     weights_path='./logs/caffe_resnet152.pth',
+                                     weights_path='./logs/resnet152.pth',
                                      caffe=True)
         elif stage1_weights:
             assert(dataset)