diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gather_nd.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gather_nd.py
index 0c7eae5f85f9557f6db58af8c4e6a677894ede05..6b6a9536d81bef87a44e7996ea234f4833e058df 100644
--- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gather_nd.py
+++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gather_nd.py
@@ -346,7 +346,7 @@ class TrtConvertGatherNdTest_dim_2_2(TrtLayerAutoScanTest):
             return np.random.random([2, 32]).astype(np.float32)
 
         def generate_input2():
-            return np.ones([2, 2]).astype(np.int32)
+            return np.array([[0, 3], [1, 9]]).astype(np.int32)
 
         ops_config = [{
             "op_type": "gather_nd",
@@ -408,23 +408,11 @@ class TrtConvertGatherNdTest_dim_2_2(TrtLayerAutoScanTest):
         # for dynamic_shape
         generate_dynamic_shape(attrs)
         self.trt_param.precision = paddle_infer.PrecisionType.Float32
-        yield self.create_inference_config(), (1, 3), 1e-5
+        yield self.create_inference_config(), (0, 4), 1e-5
         self.trt_param.precision = paddle_infer.PrecisionType.Half
-        yield self.create_inference_config(), (1, 3), 1e-5
-
-    def add_skip_trt_case(self):
-        def teller(program_config, predictor_config):
-            if len(self.dynamic_shape.min_input_shape) != 0:
-                return True
-            return False
-
-        self.add_skip_case(
-            teller, SkipReasons.TRT_NOT_SUPPORT,
-            "Need to repair the case: the output of trt and GPU has diff when inputs' dim is 1 and 2."
-        )
+        yield self.create_inference_config(), (0, 4), 1e-5
 
     def test(self):
-        self.add_skip_trt_case()
         self.run_test()
 
 
@@ -434,10 +422,11 @@ class TrtConvertGatherNdTest_dim_3_3(TrtLayerAutoScanTest):
 
     def sample_program_configs(self):
         def generate_input1():
-            return np.random.random([2, 32, 256]).astype(np.float32)
+            return np.random.random([16, 32, 256]).astype(np.float32)
 
         def generate_input2():
-            return np.ones([2, 2, 2]).astype(np.int32)
+            return np.array(
+                [[[2, 5], [3, 8]], [[0, 2], [0, 3]]]).astype(np.int32)
 
         ops_config = [{
             "op_type": "gather_nd",
@@ -471,7 +460,7 @@ class TrtConvertGatherNdTest_dim_3_3(TrtLayerAutoScanTest):
                 "index_data": [1, 1, 1]
             }
             self.dynamic_shape.max_input_shape = {
-                "input_data": [4, 64, 512],
+                "input_data": [16, 64, 512],
                 "index_data": [4, 2, 4]
             }
             self.dynamic_shape.opt_input_shape = {