add more tests

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

@@ -188,14 +188,27 @@ def set_output_grad(scope, outputs, place, feed_dict=None):
 
 class TestBatchNormOpInference(OpTest):
     def setUp(self):
+        self.op_type = "conv2d"
+        self.is_test = True
         self.dtype = np.float32
+        self.data_layout = "NCHW"
+        init_dtype()
+        init_data_layout()
+        init_test_case()
 
-    def test_python(self):
-        data_format = "NHWC"
         epsilon = 0.00001
-
-        n, h, w, c = 2, 3, 4, 5
+        shape = self.shape
+        if len(shape) == 2:
+            x_shape = shape
+            c = x_shape[1]
+        else:
+            n, h, w, c = shape[0], shape[1], shape[2], shape[3]
+            if self.data_layout == "NHWC":
                 x_shape = [n, h, w, c]
+            elif self.data_layout == "NCHW":
+                x_shape = [n, c, h, w]
+            else:
+                raise ValueError("Unknown data layout.")
         scale_shape = [c]
 
         x_val = np.random.random_sample(x_shape).astype(self.dtype)
@@ -205,7 +218,64 @@ class TestBatchNormOpInference(OpTest):
         mean = np.zeros(scale_shape).astype(self.dtype)
         variance = np.ones(scale_shape).astype(self.dtype)
 
-        # run forward
+        saved_mean = np.zeros(scale_shape).astype(self.dtype)
+        saved_variance = np.ones(scale_shape).astype(self.dtype)
+
+        y_out = _reference_testing(x_val, scale_val, bias_val, mean, variance,
+                                   epsilon, self.data_layout).astype(self.dtype)
+
+        self.inputs = {
+            'X': OpTest.np_dtype_to_fluid_dtype(x_val),
+            'Scale': OpTest.np_dtype_to_fluid_dtype(scale_val),
+            'Bias': OpTest.np_dtype_to_fluid_dtype(bias_val),
+            'Mean': OpTest.np_dtype_to_fluid_dtype(mean),
+            'Variance': OpTest.np_dtype_to_fluid_dtype(variance)
+        }
+        self.attrs = {
+            'is_test': self.is_test,
+            'epsilon': epsilon,
+            'data_layout': self.data_layout
+        }
+        self.outputs = {
+            'Y': y_out,
+            'MeanOut': mean,
+            'VarianceOut': variance,
+            'SavedMean': saved_mean,
+            'SavedVariance': saved_variance
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+    def init_dtype(self):
+        pass
+
+    def init_data_layout(self):
+        pass
+
+    def init_test_case(self):
+        self.shape = [2, 3, 4, 5]
+
+
+class TestBatchNormOpTraining(OpTest):
+    def __assert_close(self, tensor, np_array, msg, atol=1e-4):
+        self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol), msg)
+
+    def test_python_testing(self):
+        data_format = "NHWC"
+        epsilon = 0.00001
+
+        n, h, w, c = 2, 3, 4, 5
+        x_shape = [n, h, w, c]
+        scale_shape = [c]
+
+        x_val = np.random.random_sample(x_shape).astype(np.float32)
+        scale_val = np.random.random_sample(scale_shape).astype(np.float32)
+        bias_val = np.random.random_sample(scale_shape).astype(np.float32)
+
+        mean = np.zeros(scale_shape).astype(np.float32)
+        variance = np.ones(scale_shape).astype(np.float32)
+
         y_out = _reference_testing(x_val, scale_val, bias_val, mean, variance,
                                    epsilon, "NHWC")
 
@@ -218,15 +288,11 @@ class TestBatchNormOpInference(OpTest):
 
         # transfer (N, C, H, W) back to (N, H, W, C)
         y_out2_trans = np.transpose(y_out2, (0, 2, 3, 1))
-        self.__assert_close(y_out, y_out2_trans, "inference output")
+        self.__assert_close(y_out, y_out2_trans,
+                            "inference outputs of two formats have differences")
         print 'python: NHWC, NCHW, inference checking passed'
 
-
-class TestBatchNormOpTraining(OpTest):
-    def __assert_close(self, tensor, np_array, msg, atol=1e-4):
-        self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol), msg)
-
-    def test_python(self):
+    def test_python_training(self):
         data_format = "NHWC"
         epsilon = 0.00001
         momentum = 0.9
@@ -264,7 +330,7 @@ class TestBatchNormOpTraining(OpTest):
 
         # transfer (N, C, H, W) back to (N, H, W, C)
         y_out2_trans = np.transpose(y_out2, (0, 2, 3, 1))
-        self.__assert_close(y_out, y_out2_trans, "batch variance")
+        self.__assert_close(y_out, y_out2_trans, "batch output")
         print 'python: NHWC, NCHW, forward checking passed'
 
         # test backward now