Save option for mnist_add

examples/example.py

@@ -32,31 +32,37 @@ from abl import framework_hed
 
 def run_test():
 
-    # kb = add_KB(True)
+    kb = add_KB()
     # kb = HWF_KB(True)
-    # abducer = AbducerBase(kb)
+    abducer = AbducerBase(kb, 'confidence')
 
-    kb = prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='../examples/datasets/hed/learn_add.pl')
-    abducer = AbducerBase(kb, zoopt=True, multiple_predictions=True)
+    # kb = prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='../examples/datasets/hed/learn_add.pl')
+    # abducer = AbducerBase(kb, zoopt=True, multiple_predictions=True)
 
     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)
     
-    # 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))
+    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)
+    
+    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))
     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=0.00001, betas=(0.9, 0.99))
+    # 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)
@@ -65,8 +71,10 @@ def run_test():
     # 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)
 
     recorder.dump()
     return True