diff --git a/develop/doc_cn/_sources/howto/dev/new_op_cn.md.txt b/develop/doc_cn/_sources/howto/dev/new_op_cn.md.txt index 3e71a0a592861b32861c0e402911e274208fe714..4e37c652b2b57091546f9bf0f1459596b389f380 100644 --- a/develop/doc_cn/_sources/howto/dev/new_op_cn.md.txt +++ b/develop/doc_cn/_sources/howto/dev/new_op_cn.md.txt @@ -286,28 +286,50 @@ class TestMulOp(unittest.TestCase): 反向Op单测继承自`GradientChecker`，而`GradientChecker`集成自`unittest.TestCase`，所以反向单测函数需要`test_`开头。 - ``` - class MulGradOpTest(GradientChecker): - def test_mul(self): - op = create_op("mul") - inputs = { +``` +class TestMulGradOp(GradientChecker): + def setUp(self): + self.op = create_op("mul") + self.inputs = { 'X': np.random.random((32, 84)).astype("float32"), 'Y': np.random.random((84, 100)).astype("float32") } - self.compare_grad(op, inputs) + + def test_cpu_gpu_compare(self): + self.compare_grad(self.op, self.inputs) + + def test_normal(self): # mul op will enlarge the relative error self.check_grad( - op, inputs, set(["X", "Y"]), "Out", max_relative_error=0.5) - ``` + self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5) + + def test_ignore_x(self): + self.check_grad( + self.op, + self.inputs, ["Y"], + "Out", + max_relative_error=0.5, + no_grad_set={"X"}) + + def test_ignore_y(self): + self.check_grad( + self.op, + self.inputs, ["X"], + "Out", + max_relative_error=0.5, + no_grad_set={"Y"}) +``` + +下面解释一些关键的地方: - 调用`create_op("mul")`创建反向Op对应的前向Op。 - - 定义输入`inputs`。 - 调用`compare_grad`函数对比CPU、GPU计算结果。 - - 调用`check_grad`检查梯度稳定性，这里采用数值法检测梯度正确性。 - - 第一个参数`op` : 前向op。 - - 第二个参数`inputs` : 输入词典，词典的Key和`ProtoMaker`定义保持一致。 - - 第三个参数`set(["X", "Y"])` : 指定对输入变量`X`、`Y`做梯度检测。 + - `test_normal`中调用`check_grad`检查梯度稳定性，这里采用数值法检测梯度正确性。 + - 第一个参数`self.op` : 前向Op。 + - 第二个参数`self.inputs` : 输入词典，词典的Key和`ProtoMaker`定义保持一致。 + - 第三个参数`["X", "Y"]` : 指定对输入变量`X`、`Y`做梯度检测。 - 第四个参数`"Out"` : 指定前向网络最终的输出目标变量`Out` + - `test_ignore_x`和`test_ignore_y`分支测试只需要计算一个输入梯度的情况。 ### 编译和执行 diff --git a/develop/doc_cn/howto/dev/new_op_cn.html b/develop/doc_cn/howto/dev/new_op_cn.html index 41375354cf94efd10791dbb76e56a6558feaaf08..33ef2fee4e4078118a38771df854d7a75e2ae227 100644 --- a/develop/doc_cn/howto/dev/new_op_cn.html +++ b/develop/doc_cn/howto/dev/new_op_cn.html @@ -439,30 +439,51 @@ Kernel实现 | CPU、GPU共享Kernel在

反向Operator单测

反向Op单测继承自GradientChecker，而GradientChecker集成自unittest.TestCase，所以反向单测函数需要test_开头。

-
class MulGradOpTest(GradientChecker):
-   def test_mul(self):
-       op = create_op("mul")
-       inputs = {
-           'X': np.random.random((32, 84)).astype("float32"),
-           'Y': np.random.random((84, 100)).astype("float32")
-       }
-       self.compare_grad(op, inputs)      
-       # mul op will enlarge the relative error
-       self.check_grad(
-           op, inputs, set(["X", "Y"]), "Out", max_relative_error=0.5)
+
class TestMulGradOp(GradientChecker):
+    def setUp(self):
+        self.op = create_op("mul")
+        self.inputs = {
+            'X': np.random.random((32, 84)).astype("float32"),
+            'Y': np.random.random((84, 100)).astype("float32")
+        }
+
+    def test_cpu_gpu_compare(self):
+        self.compare_grad(self.op, self.inputs)
+
+    def test_normal(self):
+        # mul op will enlarge the relative error
+        self.check_grad(
+            self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5)
+
+    def test_ignore_x(self):
+        self.check_grad(
+            self.op,
+            self.inputs, ["Y"],
+            "Out",
+            max_relative_error=0.5,
+            no_grad_set={"X"})
+
+    def test_ignore_y(self):
+        self.check_grad(
+            self.op,
+            self.inputs, ["X"],
+            "Out",
+            max_relative_error=0.5,
+            no_grad_set={"Y"})
 

 

+
下面解释一些关键的地方:

 
    
 
  • 调用create_op("mul")创建反向Op对应的前向Op。
    • 
-
  • 定义输入inputs。
    • 
 
  • 调用compare_grad函数对比CPU、GPU计算结果。
    • 
-
  • 调用check_grad检查梯度稳定性，这里采用数值法检测梯度正确性。
      
-
    • 第一个参数op : 前向op。
      • 
-
    • 第二个参数inputs : 输入词典，词典的Key和ProtoMaker定义保持一致。
      • 
-
    • 第三个参数set(["X", "Y"]) : 指定对输入变量X、Y做梯度检测。
      • 
+
    • test_normal中调用check_grad检查梯度稳定性，这里采用数值法检测梯度正确性。
        
+
      • 第一个参数self.op : 前向Op。
        • 
+
      • 第二个参数self.inputs : 输入词典，词典的Key和ProtoMaker定义保持一致。
        • 
+
      • 第三个参数["X", "Y"] : 指定对输入变量X、Y做梯度检测。
        • 
 
      • 第四个参数"Out" : 指定前向网络最终的输出目标变量Out
        • 
 
      
 
      • 
+
    • test_ignore_x和test_ignore_y分支测试只需要计算一个输入梯度的情况。
      •
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