diff --git a/mindspore/nn/layer/image.py b/mindspore/nn/layer/image.py
index f06c5fd30ae0c81327153d58161d4062daf59ab2..7448d5fd4925a31b4a29f869973244431e8a87d5 100644
--- a/mindspore/nn/layer/image.py
+++ b/mindspore/nn/layer/image.py
@@ -58,7 +58,8 @@ class ImageGradients(Cell):
         super(ImageGradients, self).__init__()
 
     def construct(self, images):
-        _check_input_4d(F.shape(images), "images", self.cls_name)
+        check = _check_input_4d(F.shape(images), "images", self.cls_name)
+        images = F.depend(images, check)
         batch_size, depth, height, width = P.Shape()(images)
         dy = images[:, :, 1:, :] - images[:, :, :height - 1, :]
         dy_last = P.Fill()(P.DType()(images), (batch_size, depth, 1, width), 0)
diff --git a/mindspore/ops/_grad/grad_nn_ops.py b/mindspore/ops/_grad/grad_nn_ops.py
index 362bda73686c4ceb2bbd5009467dcf412361cb44..9bf0c208981fcaf63e61d1e7d2a7209c84ae11cc 100755
--- a/mindspore/ops/_grad/grad_nn_ops.py
+++ b/mindspore/ops/_grad/grad_nn_ops.py
@@ -627,7 +627,7 @@ def get_bprop_conv2d_backprop_input(self):
     def bprop(x, w, f_sizes, out, dout):
         dx = input_grad(dout, w)
         dw = filter_grad(x, dout, F.shape(w))
-        return dx, dw
+        return dx, dw, zeros_like(f_sizes)
 
     return bprop