add python batch norm inference test

paddle/fluid/operators/batch_norm_op.cu.cc

@@ -125,8 +125,8 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
 
     auto &dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
     math::SetConstant<platform::CUDADeviceContext, T> functor;
-    functor(dev_ctx, saved_mean, 0);
-    functor(dev_ctx, saved_variance, 0);
+    functor(dev_ctx, saved_mean, static_cast<T>(0));
+    functor(dev_ctx, saved_variance, static_cast<T>(0));
 
     auto handle = dev_ctx.cudnn_handle();
 

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

@@ -31,6 +31,37 @@ def get_backward_op(scope, op, no_grad_set):
     return backward_op
 
 
+def _reference_testing(x, scale, offset, mean, var, epsilon, data_format):
+    x_shape = x.shape
+    if len(x_shape) == 2:
+        if data_format == "NCHW":
+            x = np.reshape(x, (x.shape[0], x.shape[1], 1, 1))
+        else:
+            x = np.reshape(x, (x.shape[0], 1, 1, x.shape[1]))
+
+    if data_format == "NCHW":
+        n, c, h, w = x.shape
+        mean_tile = np.reshape(mean, (1, c, 1, 1))
+        mean_tile = np.tile(mean_tile, (n, 1, h, w))
+        var_tile = np.reshape(var, (1, c, 1, 1))
+        var_tile = np.tile(var_tile, (n, 1, h, w))
+        normalized = (x - mean_tile) / np.sqrt(var_tile + epsilon)
+        scale_tile = np.reshape(scale, (1, c, 1, 1))
+        scale_tile = np.tile(scale_tile, (n, 1, h, w))
+        offset_tile = np.reshape(offset, (1, c, 1, 1))
+        offset_tile = np.reshape(offset_tile, (1, c, 1, 1))
+        y = normalized * scale_tile + offset_tile
+    elif data_format == "NHWC":
+        normalized = (x - mean) / np.sqrt(var + epsilon)
+        y = normalized * scale + offset
+    else:
+        raise ValueError("Unknown data order.")
+
+    if len(x_shape) == 2:
+        y = np.reshape(y, x_shape)
+    return y
+
+
 def _reference_training(x, scale, offset, epsilon, data_format):
     x_shape = x.shape
     if len(x_shape) == 2:
@@ -155,7 +186,43 @@ def set_output_grad(scope, outputs, place, feed_dict=None):
         __set_tensor__(output, data)
 
 
-class TestBatchNormOp(OpTest):
+class TestBatchNormOpInference(OpTest):
+    def setUp(self):
+        self.dtype = np.float32
+
+    def test_python(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(self.dtype)
+        scale_val = np.random.random_sample(scale_shape).astype(self.dtype)
+        bias_val = np.random.random_sample(scale_shape).astype(self.dtype)
+
+        mean = np.zeros(scale_shape).astype(self.dtype)
+        variance = np.ones(scale_shape).astype(self.dtype)
+
+        # run forward
+        y_out = _reference_testing(x_val, scale_val, bias_val, mean, variance,
+                                   epsilon, "NHWC")
+
+        # running N, C, H, W case
+        # should produce the same results
+        x_shape2 = [n, c, h, w]
+        x_val2 = np.transpose(x_val, (0, 3, 1, 2))
+        y_out2 = _reference_testing(x_val2, scale_val, bias_val, mean, variance,
+                                    epsilon, "NCHW")
+
+        # 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")
+        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)