Merge branch 'Dev' of https://github.com/AbductiveLearning/ABL-Package into Dev

abducer/abducer_base.py

@@ -11,6 +11,7 @@
 #================================================================#
 
 import abc
+# from kb import add_KB, hwf_KB
 from abducer.kb import add_KB, hwf_KB
 import numpy as np
 

abducer/kb.py

@@ -34,10 +34,6 @@ class KBBase(ABC):
     def logic_forward(self):
         pass
     
-    @abstractmethod
-    def valid_candidate(self):
-        pass
-    
     def _length(self, length):
         if length is None:
             length = list(self.base.keys())
@@ -57,6 +53,7 @@ class ClsKB(KBBase):
         self.len_list = len_list
         
         if GKB_flag:
+            # self.base = np.load('abducer/hwf.npy', allow_pickle=True).item()
             self.base = {}
             X, Y = self.get_GKB(self.pseudo_label_list, self.len_list)
             for x, y in zip(X, Y):
@@ -70,14 +67,12 @@ class ClsKB(KBBase):
         X = []
         Y = []
         for x in all_X:
-            if self.valid_candidate(x):
+            y = self.logic_forward(x)
+            if y != np.inf:
                 X.append(x)
-                Y.append(self.logic_forward(x))
+                Y.append(y)
         return X, Y
     
-    def valid_candidate(self):
-        pass
-    
     def logic_forward(self):
         pass
 
@@ -88,7 +83,7 @@ class ClsKB(KBBase):
         if key is None:
             return self.get_all_candidates()
         
-        if (type(length) is int and length not in self.len_list):
+        if type(length) is int and length not in self.len_list:
             return []
         length = self._length(length)
         return sum([self.base[l][key] for l in length], [])
@@ -117,9 +112,6 @@ class add_KB(ClsKB):
                     pseudo_label_list = list(range(10)), \
                     len_list = [2]):
         super().__init__(GKB_flag, pseudo_label_list, len_list)
-        
-    def valid_candidate(self, x):
-        return True
     
     def logic_forward(self, nums):
         return sum(nums)

datasets/hwf/get_hwf.py

@@ -9,7 +9,7 @@ img_transform = transforms.Compose([
 
 def get_data(file, get_pseudo_label, precision_num = 2):
     X = []
-    if(get_pseudo_label):
+    if get_pseudo_label:
         Z = []
     Y = []
     img_dir = './datasets/hwf/data/Handwritten_Math_Symbols/'
@@ -22,15 +22,14 @@ def get_data(file, get_pseudo_label, precision_num = 2):
                 img = Image.open(img_dir + img_path).convert('L')
                 img = img_transform(img)
                 imgs.append(img)
-                if(get_pseudo_label):
+                if get_pseudo_label:
                     imgs_pseudo_label.append(img_path.split('/')[0])
-            if(len(imgs) == 3):
-                X.append(imgs)
-                if(get_pseudo_label):
-                    Z.append(imgs_pseudo_label)
-                Y.append(round(data[idx]['res'], precision_num))
+            X.append(imgs)
+            if get_pseudo_label:
+                Z.append(imgs_pseudo_label)
+            Y.append(round(data[idx]['res'], precision_num))
     
-    if(get_pseudo_label):
+    if get_pseudo_label:
         return X, Z, Y
     else:
         return X, None, Y

datasets/mnist_add/get_mnist_add.py

@@ -5,27 +5,27 @@ from torchvision.transforms import transforms
 
 def get_data(file, img_dataset, get_pseudo_label):
     X = []
-    if(get_pseudo_label):
+    if get_pseudo_label:
         Z = []
     Y = []
     with open(file) as f:
         for line in f:
             line = line.strip().split(' ')
             X.append([img_dataset[int(line[0])][0], img_dataset[int(line[1])][0]])
-            if(get_pseudo_label):
+            if get_pseudo_label:
                 Z.append([img_dataset[int(line[0])][1], img_dataset[int(line[1])][1]])
             Y.append(int(line[2]))
             
-    if(get_pseudo_label):
+    if get_pseudo_label:
         return X, Z, Y
     else:
         return X, None, Y
 
 def get_mnist_add(train = True, get_pseudo_label = False):
     transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081, ))])
-    img_dataset = torchvision.datasets.MNIST(root='./datasets/mnist_add/', train=True, download=True, transform=transform)
+    img_dataset = torchvision.datasets.MNIST(root='./datasets/mnist_add/', train=train, download=True, transform=transform)
     
-    if(train):
+    if train:
         file = './datasets/mnist_add/train_data.txt'
     else:
         file = './datasets/mnist_add/test_data.txt'

models/lenet5.py

@@ -52,48 +52,32 @@ class LeNet5(nn.Module):
         return x
 
     def num_flat_features(self, x):
-        #x.size()返回值为(256, 16, 5, 5)，size的值为(16, 5, 5)，256是batch_size
-        size = x.size()[1:]        #x.size返回的是一个元组，size表示截取元组中第二个开始的数字
+        size = x.size()[1:] 
         num_features = 1
         for s in size:
             num_features *= s
         return num_features 
 
-class Params:
-    imgH = 28
-    imgW = 28
-    keep_ratio = True
-    saveInterval = 10
-    batchSize = 16
-    num_workers = 16
 
-def get_data():  #数据预处理
-    transform = transforms.Compose([transforms.ToTensor(),
-                                    transforms.Normalize((0.5), (0.5))])
-                                    #transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
-    #训练集
-    train_set = torchvision.datasets.MNIST(root='data/', train=True, transform=transform, download=True)
-    train_loader = torch.utils.data.DataLoader(train_set, batch_size=1024, shuffle=True, num_workers = 16)
-    #测试集
-    test_set = torchvision.datasets.MNIST(root='data/', train=False, transform=transform, download=True)
-    test_loader = torch.utils.data.DataLoader(test_set, batch_size = 1024, shuffle = False, num_workers = 16)
-    classes = ('plane','car','bird','cat','deer','dog','frog','horse','ship','truck')
-
-    return train_loader, test_loader, classes
-
-if __name__ == "__main__":
-    recorder = plog.ResultRecorder()
-    cls = LeNet5()
-    criterion = nn.CrossEntropyLoss(size_average=True)
-    optimizer = torch.optim.Adam(cls.parameters(), lr=0.001, betas=(0.9, 0.99))
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    model = BasicModel(cls, criterion, optimizer, None, device, Params(), recorder)
-    
-    train_loader, test_loader, classes = get_data()
-
-    #model.val(test_loader, print_prefix = "before training")
-    model.fit(train_loader, n_epoch = 100)
-    model.val(test_loader, print_prefix = "after trained")
-    res = model.predict(test_loader, print_prefix = "predict")
-    print(res.argmax(axis=1)[:10])
+class SymbolNet(nn.Module):
+    def __init__(self, num_classes=14):
+        super(SymbolNet, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, 3, stride = 1, padding = 1)
+        self.conv2 = nn.Conv2d(32, 64, 3, stride = 1, padding = 1)
+        self.dropout1 = nn.Dropout2d(0.25)
+        self.dropout2 = nn.Dropout2d(0.5)
+        self.fc1 = nn.Linear(30976, 128)
+        self.fc2 = nn.Linear(128, num_classes)
 
+    def forward(self, x):
+        x = self.conv1(x)
+        x = F.relu(x)
+        x = self.conv2(x)
+        x = F.max_pool2d(x, 2)
+        x = self.dropout1(x)
+        x = torch.flatten(x, 1)
+        x = self.fc1(x)
+        x = F.relu(x)
+        x = self.dropout2(x)
+        x = self.fc2(x)
+        return x