Merge pull request #3238 from qingqing01/unit_test

Refine unit test in op_test_util

python/paddle/v2/framework/tests/op_test_util.py

@@ -33,23 +33,28 @@ class OpTestMeta(type):
 
             for place in places:
                 for in_name in func.all_input_args:
-                    if hasattr(self, in_name):
+                    if hasattr(self, "inputs") and in_name in self.inputs:
                         kwargs[in_name] = in_name
                         var = scope.new_var(in_name).get_tensor()
-                        arr = getattr(self, in_name)
+                        arr = self.inputs[in_name]
                         var.set_dims(arr.shape)
                         var.set(arr, place)
                     else:
                         kwargs[in_name] = "@EMPTY@"
 
                 for out_name in func.all_output_args:
-                    if hasattr(self, out_name):
+                    if not hasattr(self, "outputs"):
+                        raise ValueError(
+                            "The test op must set self.outputs dict.")
+                    if out_name not in self.outputs:
+                        raise ValueError("The %s is not in self.outputs dict." %
+                                         (out_name))
                     kwargs[out_name] = out_name
                     scope.new_var(out_name).get_tensor()
 
                 for attr_name in func.all_attr_args:
-                    if hasattr(self, attr_name):
-                        kwargs[attr_name] = getattr(self, attr_name)
+                    if hasattr(self, "attrs") and attr_name in self.attrs:
+                        kwargs[attr_name] = self.attrs[attr_name]
 
                 op = func(**kwargs)
 
@@ -60,7 +65,7 @@ class OpTestMeta(type):
 
                 for out_name in func.all_output_args:
                     actual = numpy.array(scope.find_var(out_name).get_tensor())
-                    expect = getattr(self, out_name)
+                    expect = self.outputs[out_name]
                     numpy.isclose(actual, expect)
 
         obj.test_all = test_all

python/paddle/v2/framework/tests/test_add_two_op.py

@@ -12,9 +12,11 @@ class TestAddOp(unittest.TestCase):
 
     def setUp(self):
         self.type = "add_two"
-        self.X = numpy.random.random((102, 105)).astype("float32")
-        self.Y = numpy.random.random((102, 105)).astype("float32")
-        self.Out = self.X + self.Y
+        self.inputs = {
+            'X': numpy.random.random((102, 105)).astype("float32"),
+            'Y': numpy.random.random((102, 105)).astype("float32")
+        }
+        self.outputs = {'Out': self.inputs['X'] + self.inputs['Y']}
 
 
 class TestAddGradOp(unittest.TestCase):

python/paddle/v2/framework/tests/test_cross_entropy_op.py

@@ -7,15 +7,17 @@ class TestSGD(unittest.TestCase):
     __metaclass__ = OpTestMeta
 
     def setUp(self):
+        # TODO this unit test is not passed
         self.type = "onehot_cross_entropy"
         batch_size = 100
         class_num = 10
-        self.X = numpy.random.random((batch_size, class_num)).astype("float32")
-        self.label = 5 * numpy.ones(batch_size).astype("int32")
+        X = numpy.random.random((batch_size, class_num)).astype("float32")
+        label = 5 * numpy.ones(batch_size).astype("int32")
+        self.inputs = {'X': X, 'label': label}
         Y = []
         for i in range(0, batch_size):
-            Y.append(-numpy.log(self.X[i][self.label[i]]))
-        self.Y = numpy.array(Y).astype("float32")
+            Y.append(-numpy.log(X[i][label[i]]))
+        self.outputs = {'Y': numpy.array(Y).astype("float32")}
 
 
 # TODO(superjom) add gradient check

python/paddle/v2/framework/tests/test_mean_op.py

@@ -8,8 +8,8 @@ class TestMeanOp(unittest.TestCase):
 
     def setUp(self):
         self.type = "mean"
-        self.X = np.random.random((32, 784)).astype("float32")
-        self.Out = np.mean(self.X)
+        self.inputs = {'X': np.random.random((32, 784)).astype("float32")}
+        self.outputs = {'Out': np.mean(self.inputs['X'])}
 
 
 if __name__ == '__main__':

python/paddle/v2/framework/tests/test_mul_op.py

@@ -8,9 +8,11 @@ class TestMulOp(unittest.TestCase):
 
     def setUp(self):
         self.type = "mul"
-        self.X = np.random.random((32, 84)).astype("float32")
-        self.Y = np.random.random((84, 100)).astype("float32")
-        self.Out = np.dot(self.X, self.Y)
+        self.inputs = {
+            'X': np.random.random((32, 84)).astype("float32"),
+            'Y': np.random.random((84, 100)).astype("float32")
+        }
+        self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
 
 
 if __name__ == '__main__':

python/paddle/v2/framework/tests/test_rowwise_add_op.py

@@ -8,9 +8,11 @@ class TestRowwiseAddOp(unittest.TestCase):
 
     def setUp(self):
         self.type = "rowwise_add"
-        self.X = np.random.random((32, 84)).astype("float32")
-        self.b = np.random.random(84).astype("float32")
-        self.Out = np.add(self.X, self.b)
+        self.inputs = {
+            'X': np.random.random((32, 84)).astype("float32"),
+            'b': np.random.random(84).astype("float32")
+        }
+        self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])}
 
 
 if __name__ == '__main__':

python/paddle/v2/framework/tests/test_sgd_op.py

@@ -8,10 +8,13 @@ class TestSGD(unittest.TestCase):
 
     def setUp(self):
         self.type = "sgd"
-        self.param = numpy.random.random((102, 105)).astype("float32")
-        self.grad = numpy.random.random((102, 105)).astype("float32")
-        self.learning_rate = 0.1
-        self.param_out = self.param - self.learning_rate * self.grad
+        w = numpy.random.random((102, 105)).astype("float32")
+        g = numpy.random.random((102, 105)).astype("float32")
+        lr = 0.1
+
+        self.inputs = {'param': w, 'grad': g}
+        self.attrs = {'learning_rate': lr}
+        self.outputs = {'param_out': w - lr * g}
 
 
 if __name__ == "__main__":

python/paddle/v2/framework/tests/test_sigmoid_op.py

@@ -8,8 +8,8 @@ class TestSigmoidOp(unittest.TestCase):
 
     def setUp(self):
         self.type = "sigmoid"
-        self.X = np.random.random((32, 100)).astype("float32")
-        self.Y = 1 / (1 + np.exp(-self.X))
+        self.inputs = {'X': np.random.random((32, 100)).astype("float32")}
+        self.outputs = {'Y': 1 / (1 + np.exp(-self.inputs['X']))}
 
 
 if __name__ == '__main__':

python/paddle/v2/framework/tests/test_softmax_op.py

@@ -19,8 +19,10 @@ class TestSoftmaxOp(unittest.TestCase):
 
     def setUp(self):
         self.type = "softmax"
-        self.X = np.random.random((32, 100)).astype("float32")
-        self.Y = np.apply_along_axis(stable_softmax, 1, self.X)
+        self.inputs = {'X': np.random.random((32, 100)).astype("float32")}
+        self.outputs = {
+            'Y': np.apply_along_axis(stable_softmax, 1, self.inputs['X'])
+        }
 
 
 class TestSoftmaxGradOp(unittest.TestCase):