[Paddle Inference]Add reshape op TRT converter unittest. (#35166)

* add_reshape_teller

* add_reshape_teller

* add_reshape_teller

* add_reshape_teller

* add_reshape_teller

* add_reshape_teller

* add_reshape_teller

* add_reshape_teller

* add_reshape_teller

* add_reshape_teller

* add_reshape_teller

* add_reshape_teller

* add_reshape_teller

* add_reshape_teller

* add_reshape_teller

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -839,7 +839,8 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
       std::vector<int> shape =
           BOOST_GET_CONST(std::vector<int>, desc.GetAttr("shape"));
       if (shape.size() >= nvinfer1::Dims::MAX_DIMS) return false;
-      if (!with_dynamic_shape && shape[0] == -1) return false;
+      if (!with_dynamic_shape && (shape[0] == -1 || shape.size() == 1))
+        return false;
     }
 
     if (op_type == "reduce_sum" || op_type == "reduce_mean") {

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

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons
+from program_config import TensorConfig, ProgramConfig
+import numpy as np
+import paddle.inference as paddle_infer
+from functools import partial
+from typing import Optional, List, Callable, Dict, Any, Set
+
+
+class TrtConvertReshapeTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+        if self.dims == 1:
+            if len(attrs[0]['shape']) != 1:
+                return False
+
+        #To test if the shape contains 0
+        if len(attrs[0]['shape']) == 3:
+            if attrs[0]['shape'][1] == 0:
+                if self.dims != 3:
+                    return False
+
+        if len(attrs[0]['shape']) == 4:
+            if attrs[0]['shape'][2] == 0:
+                if self.dims != 4:
+                    return False
+
+        return True
+
+    def sample_program_configs(self):
+        def generate_input1(attrs: List[Dict[str, Any]]):
+            if self.dims == 4:
+                return np.ones([1, 2, 4, 6]).astype(np.float32)
+            elif self.dims == 3:
+                return np.ones([1, 8, 6]).astype(np.float32)
+            elif self.dims == 2:
+                return np.ones([1, 48]).astype(np.float32)
+            elif self.dims == 1:
+                return np.ones([48]).astype(np.float32)
+
+        def generate_weight1(attrs: List[Dict[str, Any]]):
+            return np.array([1, 48]).astype(np.int32)
+
+        def generate_shapeT1_data(attrs: List[Dict[str, Any]]):
+            return np.array([2]).astype(np.int32)
+
+        def generate_shapeT2_data(attrs: List[Dict[str, Any]]):
+            return np.array([24]).astype(np.int32)
+
+        for dims in [4, 3, 2, 1]:
+            for num_input in [0, 1, 2, 3]:
+                for shape in [[1, 6, 8], [1, 2, 4, 6], [1, 1, 0, 12],
+                              [1, 0, 6], [1, -1, 12], [2, -1], [3, 16],
+                              [3, 4, 4], [48]]:
+                    dics = [{"shape": shape, }, {}]
+                    self.num_input = num_input
+                    self.dims = dims
+                    dics_intput = [{
+                        "X": ["reshape_input"],
+                        "Shape": ["shape_data"],
+                        "ShapeTensor": ["shapeT1_data", "shapeT2_data"],
+                    }, {
+                        "X": ["reshape_input"],
+                        "Shape": ["shape_data"],
+                    }, {
+                        "X": ["reshape_input"],
+                        "ShapeTensor": ["shapeT1_data", "shapeT2_data"],
+                    }, {
+                        "X": ["reshape_input"]
+                    }]
+
+                    dics_weight = [{
+                        "shape_data":
+                        TensorConfig(data_gen=partial(generate_weight1, dics)),
+                        "shapeT1_data": TensorConfig(data_gen=partial(
+                            generate_shapeT1_data, dics)),
+                        "shapeT2_data": TensorConfig(data_gen=partial(
+                            generate_shapeT2_data, dics))
+                    }, {
+                        "shape_data":
+                        TensorConfig(data_gen=partial(generate_weight1, dics))
+                    }, {
+                        "shapeT1_data": TensorConfig(data_gen=partial(
+                            generate_shapeT1_data, dics)),
+                        "shapeT2_data": TensorConfig(data_gen=partial(
+                            generate_shapeT2_data, dics))
+                    }, {}]
+
+                    ops_config = [{
+                        "op_type": "reshape",
+                        "op_inputs": dics_intput[num_input],
+                        "op_outputs": {
+                            "Out": ["reshape_out"]
+                        },
+                        "op_attrs": dics[0]
+                    }]
+                    ops = self.generate_op_config(ops_config)
+                    program_config = ProgramConfig(
+                        ops=ops,
+                        weights=dics_weight[num_input],
+                        inputs={
+                            "reshape_input": TensorConfig(data_gen=partial(
+                                generate_input1, dics))
+                        },
+                        outputs=["reshape_out"])
+
+                    yield program_config
+
+    def sample_predictor_configs(
+            self, program_config) -> (paddle_infer.Config, List[int], float):
+        def generate_dynamic_shape(attrs):
+            if self.dims == 4:
+                self.dynamic_shape.min_input_shape = {
+                    "reshape_input": [1, 2, 4, 6]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "reshape_input": [4, 2, 4, 6]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "reshape_input": [1, 2, 4, 6]
+                }
+            elif self.dims == 3:
+                self.dynamic_shape.min_input_shape = {
+                    "reshape_input": [1, 8, 6]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "reshape_input": [4, 8, 6]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "reshape_input": [1, 8, 6]
+                }
+            elif self.dims == 2:
+                self.dynamic_shape.min_input_shape = {"reshape_input": [1, 48]}
+                self.dynamic_shape.max_input_shape = {"reshape_input": [4, 48]}
+                self.dynamic_shape.opt_input_shape = {"reshape_input": [1, 48]}
+            elif self.dims == 1:
+                self.dynamic_shape.min_input_shape = {"reshape_input": [48]}
+                self.dynamic_shape.max_input_shape = {"reshape_input": [48]}
+                self.dynamic_shape.opt_input_shape = {"reshape_input": [48]}
+
+        def clear_dynamic_shape():
+            self.dynamic_shape.min_input_shape = {}
+            self.dynamic_shape.max_input_shape = {}
+            self.dynamic_shape.opt_input_shape = {}
+
+        def generate_trt_nodes_num(attrs, dynamic_shape):
+            return 1, 2
+
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+        if attrs[0]['shape'][0] > 1 and len(attrs[0]['shape']) > 1:
+            pass
+        else:
+            # for static_shape
+            clear_dynamic_shape()
+            self.trt_param.precision = paddle_infer.PrecisionType.Float32
+            yield self.create_inference_config(), generate_trt_nodes_num(
+                attrs, False), 1e-5
+            self.trt_param.precision = paddle_infer.PrecisionType.Half
+            yield self.create_inference_config(), generate_trt_nodes_num(
+                attrs, False), 1e-5
+
+        # for dynamic_shape
+        generate_dynamic_shape(attrs)
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
+        yield self.create_inference_config(), generate_trt_nodes_num(attrs,
+                                                                     True), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), generate_trt_nodes_num(attrs,
+                                                                     True), 1e-5
+
+    def add_skip_trt_case(self):
+        def teller1(program_config, predictor_config):
+            if len(program_config.weights) >= 1:
+                return True
+            return False
+
+        self.add_skip_case(teller1, SkipReasons.TRT_NOT_SUPPORT,
+                           "INPUT ShapeTensor and Shape NOT SUPPORT")
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()