modify as review

Author: huangxu96

python/paddle/fluid/tests/unittests/test_determinant_op.py

@@ -40,14 +40,16 @@ def test_check_grad(self):
         pass
 
     def init_data(self):
-        self.case = np.random.randn(3, 3, 3, 3, 3).astype('float64')
+        np.random.seed(0)
+        self.case = np.random.rand(3, 3, 3, 3, 3).astype('float64')
         self.inputs = {'Input': self.case}
         self.target = np.linalg.det(self.case)
 
 
 class TestDeterminantOpCase1(TestDeterminantOp):
     def init_data(self):
-        self.case = np.random.randn(3, 3, 3, 3).astype('float32')
+        np.random.seed(0)
+        self.case = np.random.rand(3, 3, 3, 3).astype(np.float32)
         self.inputs = {'Input': self.case}
         self.target = np.linalg.det(self.case)
 
@@ -57,7 +59,8 @@ def test_check_grad(self):
 
 class TestDeterminantOpCase2(TestDeterminantOp):
     def init_data(self):
-        self.case = np.random.randint(0, 2, (4, 2, 4, 4)).astype('float64')
+        np.random.seed(0)
+        self.case = np.random.rand(4, 2, 4, 4).astype('float64')
         self.inputs = {'Input': self.case}
         self.target = np.linalg.det(self.case)
 
@@ -68,7 +71,8 @@ def test_check_grad(self):
 class TestDeterminantAPI(unittest.TestCase):
     def setUp(self):
         self.shape = [3, 3, 3, 3]
-        self.x = np.random.random((3, 3, 3, 3)).astype(np.float32)
+        np.random.seed(0)
+        self.x = np.random.rand(3, 3, 3, 3).astype(np.float32)
         self.place = paddle.CPUPlace()
 
     def test_api_static(self):
@@ -106,22 +110,25 @@ def test_check_grad(self):
         pass
 
     def init_data(self):
-        self.case = np.random.randn(3, 3, 3, 3).astype('float64')
+        np.random.seed(0)
+        self.case = np.random.rand(3, 3, 3, 3).astype('float64')
         self.inputs = {'Input': self.case}
         self.target = np.array(np.linalg.slogdet(self.case))
 
 
 class TestSlogDeterminantOpCase1(TestSlogDeterminantOp):
     def init_data(self):
-        self.case = np.random.randn(3, 3, 3, 3).astype('float32')
+        np.random.seed(0)
+        self.case = np.random.rand(2, 2, 5, 5).astype(np.float32)
         self.inputs = {'Input': self.case}
         self.target = np.array(np.linalg.slogdet(self.case))
 
 
 class TestSlogDeterminantAPI(unittest.TestCase):
     def setUp(self):
         self.shape = [3, 3, 3, 3]
-        self.x = np.random.random((3, 3, 3, 3)).astype(np.float32)
+        np.random.seed(0)
+        self.x = np.random.rand(3, 3, 3, 3).astype(np.float32)
         self.place = paddle.CPUPlace()
 
     def test_api_static(self):

python/paddle/tensor/linalg.py

@@ -1378,21 +1378,19 @@ def det(x):
     if in_dygraph_mode():
         return core.ops.determinant(x)
 
-    def _check_input(input):
-        check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'det')
+    check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'det')
 
-        input_shape = list(x.shape)
-        assert len(input_shape) >= 2,                     \
-                "The x must be at least 2-dimensional, "   \
-                "but received Input x's dimensional: %s.\n" %  \
-                len(input_shape)
+    input_shape = list(x.shape)
+    assert len(input_shape) >= 2,                     \
+            "The x must be at least 2-dimensional, "   \
+            "but received Input x's dimensional: %s.\n" %  \
+            len(input_shape)
 
-        assert (input_shape[-1] == input_shape[-2]),    \
-                "Expect squared input," \
-                "but received %s by %s matrix.\n" \
-                %(input_shape[-2], input_shape[-1]) \
+    assert (input_shape[-1] == input_shape[-2]),    \
+            "Expect squared input," \
+            "but received %s by %s matrix.\n" \
+            %(input_shape[-2], input_shape[-1]) \
 
-    _check_input(input)
     helper = LayerHelper('determinant', **locals())
     out = helper.create_variable_for_type_inference(dtype=x.dtype)
 
@@ -1435,21 +1433,19 @@ def slogdet(x):
     if in_dygraph_mode():
         return core.ops.slogdeterminant(x)
 
-    def _check_input(x):
-        check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'slogdet')
+    check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'slogdet')
 
-        input_shape = list(x.shape)
-        assert len(input_shape) >= 2,                     \
-                "The x must be at least 2-dimensional, "   \
-                "but received Input x's dimensional: %s.\n" %  \
-                len(input_shape)
+    input_shape = list(x.shape)
+    assert len(input_shape) >= 2,                     \
+            "The x must be at least 2-dimensional, "   \
+            "but received Input x's dimensional: %s.\n" %  \
+            len(input_shape)
 
-        assert (input_shape[-1] == input_shape[-2]),    \
-                "Expect squared input," \
-                "but received %s by %s matrix.\n" \
-                %(input_shape[-2], input_shape[-1]) \
+    assert (input_shape[-1] == input_shape[-2]),    \
+            "Expect squared input," \
+            "but received %s by %s matrix.\n" \
+            %(input_shape[-2], input_shape[-1]) \
 
-    _check_input(x)
     helper = LayerHelper('slogdeterminant', **locals())
     out = helper.create_variable_for_type_inference(dtype=x.dtype)