[paddle-trt] fix_teller_reshape (#34583)

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -703,8 +703,9 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
         return false;
       }
       // Paddle-TRT does not support the input tensors: Shape and ShapeTensor
-      if (desc.Input("Shape").size() >= 1 ||
-          desc.Input("ShapeTensor").size() >= 1) {
+      auto reshape_inputs = desc.Inputs();
+      if (reshape_inputs.find("Shape") != reshape_inputs.end() ||
+          reshape_inputs.find("ShapeTensor") != reshape_inputs.end()) {
         return false;
       }
       std::vector<int> shape =

python/paddle/fluid/tests/unittests/ir/inference/test_trt_reshape_op.py

@@ -80,6 +80,33 @@ class TRTReshapeTest1(TRTReshapeTest):
 
 
 class TRTReshapeTest2(TRTReshapeTest):
+    def setUp(self):
+        self.bs = 2
+        self.input_shape = [23, 13, 24]
+        self.reshape = [2, 0, -1, 12]
+        self.data_shape = [
+            self.bs, self.input_shape[0], self.input_shape[1],
+            self.input_shape[2]
+        ]
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name='data', shape=self.data_shape, dtype='float32')
+            actual_reshape = fluid.data(
+                name='actual_reshape', shape=[4], dtype='int32')
+            reshape_out = fluid.layers.reshape(
+                x=data, shape=self.reshape, actual_shape=actual_reshape)
+            out = fluid.layers.batch_norm(reshape_out, is_test=True)
+        self.feeds = {
+            'data': np.random.random(self.data_shape).astype('float32'),
+            'actual_reshape': np.array([2, 0, -1, 12]).astype('int32')
+        }
+        self.enable_trt = True
+        self.trt_parameters = TRTReshapeTest.TensorRTParam(
+            1 << 30, self.bs, 1, AnalysisConfig.Precision.Float32, False, False)
+        self.fetch_list = [out]
+
+
+class TRTReshapeTest3(TRTReshapeTest):
     def setUp(self):
         self.bs = 1
         self.input_shape = [14, 48, 27]