6-3

net/utils/graph.py

@@ -129,14 +129,18 @@ def get_hop_distance(num_node, edge, max_hop=1):
         A[i, j] = 1
 
     # compute hop steps
-    hop_dis = np.zeros((num_node, num_node)) + np.inf
+    hop_dis = np.zeros((num_node, num_node)) + np.inf  # np.inf 表示一个无穷大的正数
+    # np.linalg.matrix_power(A, d)求矩阵A的d幂次方,transfer_mat矩阵(I,A)是一个将A矩阵拼接max_hop+1次的矩阵
     transfer_mat = [np.linalg.matrix_power(A, d) for d in range(max_hop + 1)]
+    # (np.stack(transfer_mat) > 0)矩阵中大于0的返回Ture,小于0的返回False,最终arrive_mat是一个布尔矩阵,大小与transfer_mat一样
     arrive_mat = (np.stack(transfer_mat) > 0)
+    # range(start,stop,step) step=-1表示倒着取
     for d in range(max_hop, -1, -1):
+        # 将arrive_mat[d]矩阵中为True的对应于hop_dis[]位置的数设置为d
         hop_dis[arrive_mat[d]] = d
     return hop_dis
 
-
+# 将矩阵A中的每一列的各个元素分别除以此列元素的形成新的矩阵
 def normalize_digraph(A):
     Dl = np.sum(A, 0)
     num_node = A.shape[0]

processor/io.py

@@ -36,6 +36,7 @@ def load_arg(self, argv=None):
         if p.config is not None:
             # load config file
             with open(p.config, 'r') as f:
+                # default_arg是字典的形式
                 default_arg = yaml.load(f)
 
             # update parser from config file

processor/recognition.py

@@ -19,9 +19,9 @@
 from .processor import Processor
 
 def weights_init(m):
-    classname = m.__class__.__name__   # __class__查看对象所在的类
-    if classname.find('Conv1d') != -1:
-        m.weight.data.normal_(0.0, 0.02)
+    classname = m.__class__.__name__     # __class__查看对象所在的类
+    if classname.find('Conv1d') != -1:   # 如果发现这个Conv1d字符串
+        m.weight.data.normal_(0.0, 0.02) #平均值为0.0,方差为0.02的数据正则化
         if m.bias is not None:
             m.bias.data.fill_(0)
     elif classname.find('Conv2d') != -1:
@@ -40,8 +40,8 @@ class REC_Processor(Processor):
     def load_model(self):
         self.model = self.io.load_model(self.arg.model,
                                         **(self.arg.model_args))
-        self.model.apply(weights_init)
-        self.loss = nn.CrossEntropyLoss()
+        self.model.apply(weights_init)  #apply()函数复制函数weights_init()的功能给self.modle
+        self.loss = nn.CrossEntropyLoss() #定义交叉商和损失函数
 
     def load_optimizer(self):
         if self.arg.optimizer == 'SGD':