Examples for HWF

examples/datasets/hwf/get_hwf.py

@@ -7,7 +7,7 @@ img_transform = transforms.Compose([
                         transforms.Normalize((0.5,), (1,))
                     ])
 
-def get_data(file, get_pseudo_label, precision_num = 2):
+def get_data(file, get_pseudo_label):
     X = []
     if get_pseudo_label:
         Z = []
@@ -27,20 +27,20 @@ def get_data(file, get_pseudo_label, precision_num = 2):
             X.append(imgs)
             if get_pseudo_label:
                 Z.append(imgs_pseudo_label)
-            Y.append(round(data[idx]['res'], precision_num))
+            Y.append(data[idx]['res'])
     
     if get_pseudo_label:
         return X, Z, Y
     else:
         return X, None, Y
 
-def get_hwf(train = True, get_pseudo_label = False, precision_num = 2):
+def get_hwf(train = True, get_pseudo_label = False):
     if(train):
         file = './datasets/hwf/data/expr_train.json'
     else:
         file = './datasets/hwf/data/expr_test.json'
     
-    return get_data(file, get_pseudo_label, precision_num)
+    return get_data(file, get_pseudo_label)
 
 if __name__ == "__main__":
     train_X, train_Y = get_hwf(train = True)

examples/example.py

@@ -33,48 +33,41 @@ from abl import framework_hed
 def run_test():
 
     # kb = add_KB()
-    # kb = HWF_KB()
-    # abducer = AbducerBase(kb, 'confidence')
+    kb = HWF_KB(GKB_flag=True)
+    abducer = AbducerBase(kb, 'confidence')
 
-    kb = HED_prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='../examples/datasets/hed/learn_add.pl')
-    abducer = HED_Abducer(kb)
+    # kb = HED_prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='../examples/datasets/hed/learn_add.pl')
+    # abducer = HED_Abducer(kb)
 
     recorder = logger()
 
-    total_train_data = get_hed(train=True)
-    train_data, val_data = split_equation(total_train_data, 3, 1)
-    test_data = get_hed(train=False)
+    # total_train_data = get_hed(train=True)
+    # train_data, val_data = split_equation(total_train_data, 3, 1)
+    # test_data = get_hed(train=False)
     
-    # train_data = get_mnist_add(train = True, get_pseudo_label = True)
-    # test_data = get_mnist_add(train = False, get_pseudo_label = True)
+    # train_data = get_mnist_add(train=True, get_pseudo_label=True)
+    # test_data = get_mnist_add(train=False, get_pseudo_label=True)
 
-    # train_data = get_hwf(train = True, get_pseudo_label = True)
-    # test_data = get_hwf(train = False, get_pseudo_label = True)
+    train_data = get_hwf(train=True, get_pseudo_label=True)
+    test_data = get_hwf(train=False, get_pseudo_label=True)
     
     # cls = LeNet5(num_classes=len(kb.pseudo_label_list), image_size=(train_data[0][0][0].shape[1:]))
-    cls = SymbolNet(num_classes=len(kb.pseudo_label_list))
+    cls = SymbolNet(num_classes=len(kb.pseudo_label_list), image_size=(train_data[0][0][0].shape[1:]))
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     
-    framework_hed.hed_pretrain(kb, cls, recorder)
+    # framework_hed.hed_pretrain(kb, cls, recorder)
     
     criterion = nn.CrossEntropyLoss()
     optimizer = torch.optim.RMSprop(cls.parameters(), lr=0.001, weight_decay=1e-6)
     # optimizer = torch.optim.Adam(cls.parameters(), lr=0.001, betas=(0.9, 0.99))
     
-
-    base_model = BasicModel(cls, criterion, optimizer, device, save_interval=1, save_dir=recorder.save_dir, batch_size=32, num_epochs=10, recorder=recorder)
+    base_model = BasicModel(cls, criterion, optimizer, device, save_interval=1, save_dir=recorder.save_dir, batch_size=32, num_epochs=1, recorder=recorder)
     model = WABLBasicModel(base_model, kb.pseudo_label_list)
-    
-    # train_X, train_Z, train_Y = get_mnist_add(train = True, get_pseudo_label = True)
-    # test_X, test_Z, test_Y = get_mnist_add(train = False, get_pseudo_label = True)
-
-    # train_data = get_hwf(train = True, get_pseudo_label = True)
-    # test_data = get_hwf(train = False, get_pseudo_label = True)
 
-    model, mapping = framework_hed.train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len=5, max_len=8)
-    framework_hed.hed_test(model, abducer, mapping, train_data, test_data, min_len=5, max_len=8)
+    # model, mapping = framework_hed.train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len=5, max_len=8)
+    # framework_hed.hed_test(model, abducer, mapping, train_data, test_data, min_len=5, max_len=8)
     
-    # framework_hed.train(model, abducer, train_data, test_data, sample_num=10000, verbose=1)
+    framework_hed.train(model, abducer, train_data, test_data, sample_num=-1, verbose=1)
 
     recorder.dump()
     return True