Polish the error message of paddle.slice. (#35179)

* polish the error message of paddle.slice.

* polish code.

paddle/fluid/operators/slice_op.cc

@@ -113,7 +113,7 @@ class SliceOp : public framework::OperatorWithKernel {
     }
 
     ctx->SetOutputDim("Out", out_dims);
-    if (axes[0] != 0) {
+    if (axes.size() > 0 && axes[0] != 0) {
       ctx->ShareLoD("Input", /*->*/ "Out");
     }
   }

python/paddle/fluid/layers/nn.py

@@ -10975,6 +10975,22 @@ def slice(input, axes, starts, ends):
         attrs = ()
         starts_tensor = None
         ends_tensor = None
+
+        if isinstance(axes, (list, tuple)):
+            if len(axes) == 0:
+                raise ValueError(
+                    "Input axes should not be an empty list/tuple.")
+            for i in range(len(axes)):
+                if axes[i] < 0:
+                    axes[i] = max(0, axes[i] + len(input.shape))
+                else:
+                    axes[i] = min(len(input.shape) - 1, axes[i])
+
+        else:
+            raise ValueError(
+                "Input axes must be a python list or tuple, but reveived {}".
+                format(type(axes)))
+
         infer_flags = list(1 for i in range(len(axes)))
 
         if isinstance(starts, (list, tuple)):

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

@@ -694,6 +694,45 @@ class TestInferShape(unittest.TestCase):
         out0 = paddle.slice(x, axes=[1], starts=[0], ends=[3])
         self.assertEqual(out0.shape, (3, 3, 5))
 
+    def test_axis_less_than_zero(self):
+
+        # Using paddle.disable_static will make other unittests fail.
+        with fluid.dygraph.guard():
+            x_arr = np.arange(0, 24, dtype=np.float32).reshape([2, 3, 4])
+            x = paddle.to_tensor(x_arr)
+
+            pp_slice = paddle.slice(x, [100, ], [0], [1])
+            np_slice = x_arr[:, :, 0:1]
+            self.assertTrue(np.array_equal(pp_slice, np_slice))
+
+            pp_slice = paddle.slice(x, [-100, ], [0], [1])
+            np_slice = x_arr[0:1]
+            self.assertTrue(np.array_equal(pp_slice, np_slice))
+
+            x_arr = np.array([], dtype=np.float32)
+            x = paddle.to_tensor(np.reshape(x_arr, (0, 0, 0)))
+
+            starts = paddle.to_tensor(
+                np.reshape(
+                    np.array(
+                        [], dtype=np.int32), (0, )))
+            ends = paddle.to_tensor(
+                np.reshape(
+                    np.array(
+                        [], dtype=np.int32), (0, )))
+
+            with self.assertRaises(ValueError):
+                paddle.slice(x, [-1000000], starts, ends)
+
+            with self.assertRaises(ValueError):
+                paddle.slice(x, [1000000], starts, ends)
+
+            with self.assertRaises(ValueError):
+                paddle.slice(x, [], starts, ends)
+
+            with self.assertRaises(ValueError):
+                paddle.slice(x, 0, starts, ends)
+
 
 @unittest.skipIf(not core.is_compiled_with_cuda(),
                  "core is not compiled with CUDA")