[Paddle Inference]Add Roi_align op TRT converter unittest (#35549)

* add_roi_align

* add_roi_align

* add_roi_align_teller

* add_roi_align

* add-roi_align

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -793,6 +793,36 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
                    "the roi_align will change the batch size.";
         return false;
       }
+      std::vector<std::string> attrs{"pooled_height", "pooled_width",
+                                     "spatial_scale", "sampling_ratio"};
+      for (auto const attr : attrs) {
+        if (!desc.HasAttr(attr)) return false;
+      }
+
+      const auto pooled_height =
+          BOOST_GET_CONST(int, desc.GetAttr("pooled_height"));
+      if (pooled_height <= 0) return false;
+
+      const auto pooled_width =
+          BOOST_GET_CONST(int, desc.GetAttr("pooled_width"));
+      if (pooled_width <= 0) return false;
+
+      const auto spatial_scale =
+          BOOST_GET_CONST(float, desc.GetAttr("spatial_scale"));
+      if (spatial_scale <= 0.f) return false;
+
+      const auto sampling_ratio =
+          BOOST_GET_CONST(int, desc.GetAttr("sampling_ratio"));
+      const auto aligned = BOOST_GET_CONST(bool, desc.GetAttr("aligned"));
+
+      if (sampling_ratio == -1 && aligned == true) return false;
+
+      auto roi_align_inputs = desc.Inputs();
+      if (roi_align_inputs.find("RoisNum") != roi_align_inputs.end()) {
+        if (desc.Input("RoisNum").size() >= 1) {
+          return false;
+        }
+      }
     }
 
     if (op_type == "shuffle_channel") {

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_roi_align.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 TrtConvertRoiAlignTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+        def generate_input1(attrs: List[Dict[str, Any]], batch):
+            return np.ones([batch, 256, 32, 32]).astype(np.float32)
+
+        def generate_input2(attrs: List[Dict[str, Any]], batch):
+            return np.random.random([3, 4]).astype(np.float32)
+
+        def generate_input3(attrs: List[Dict[str, Any]], batch):
+            return np.random.random([batch]).astype(np.int32)
+
+        for num_input in [0, 1]:
+            for batch in [1, 2, 4]:
+                for spatial_scale in [0.5, 0.6]:
+                    for pooled_height in [7, 1]:
+                        for pooled_width in [7, 1]:
+                            for sampling_ratio in [-1, 4, 8]:
+                                for aligned in [True, False]:
+                                    self.num_input = num_input
+                                    if num_input == 1:
+                                        batch = 1
+                                    dics = [{
+                                        "spatial_scale": spatial_scale,
+                                        "pooled_height": pooled_height,
+                                        "pooled_width": pooled_width,
+                                        "sampling_ratio": sampling_ratio,
+                                        "aligned": aligned
+                                    }, {}]
+                                    dics_input = [{
+                                        "X": ["roi_align_input"],
+                                        "ROIs": ["ROIs"],
+                                        "RoisNum": ["RoisNum"]
+                                    }, {
+                                        "X": ["roi_align_input"],
+                                        "ROIs": ["ROIs"]
+                                    }]
+                                    program_input = [{
+                                        "roi_align_input": TensorConfig(
+                                            data_gen=partial(generate_input1,
+                                                             dics, batch)),
+                                        "ROIs": TensorConfig(data_gen=partial(
+                                            generate_input2, dics, batch)),
+                                        "RoisNum": TensorConfig(
+                                            data_gen=partial(generate_input3,
+                                                             dics, batch))
+                                    }, {
+                                        "roi_align_input": TensorConfig(
+                                            data_gen=partial(generate_input1,
+                                                             dics, batch)),
+                                        "ROIs": TensorConfig(
+                                            data_gen=partial(generate_input2,
+                                                             dics, batch),
+                                            lod=[[32, 3]])
+                                    }]
+                                    ops_config = [{
+                                        "op_type": "roi_align",
+                                        "op_inputs": dics_input[num_input],
+                                        "op_outputs": {
+                                            "Out": ["roi_align_out"]
+                                        },
+                                        "op_attrs": dics[0]
+                                    }]
+                                    ops = self.generate_op_config(ops_config)
+                                    program_config = ProgramConfig(
+                                        ops=ops,
+                                        weights={},
+                                        inputs=program_input[num_input],
+                                        outputs=["roi_align_out"])
+
+                                    yield program_config
+
+    def sample_predictor_configs(
+            self, program_config) -> (paddle_infer.Config, List[int], float):
+        def generate_dynamic_shape(attrs):
+            if self.num_input == 0:
+                self.dynamic_shape.min_input_shape = {
+                    "roi_align_input": [1, 256, 32, 32],
+                    "ROIs": [3, 4],
+                    "RoisNum": [1]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "roi_align_input": [1, 256, 64, 64],
+                    "ROIs": [3, 4],
+                    "RoisNum": [1]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "roi_align_input": [1, 256, 64, 64],
+                    "ROIs": [3, 4],
+                    "RoisNum": [1]
+                }
+            elif self.num_input == 1:
+                self.dynamic_shape.min_input_shape = {
+                    "roi_align_input": [1, 256, 32, 32],
+                    "ROIs": [3, 4]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "roi_align_input": [1, 256, 64, 64],
+                    "ROIs": [3, 4]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "roi_align_input": [1, 256, 64, 64],
+                    "ROIs": [3, 4]
+                }
+
+        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):
+            if self.num_input == 0:
+                if dynamic_shape == True:
+                    return 0, 5
+            elif self.num_input == 1:
+                if dynamic_shape == True:
+                    return 1, 3
+                else:
+                    return 0, 4
+
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+
+        # 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.inputs) == 3:
+                return True
+            return False
+
+        self.add_skip_case(teller1, SkipReasons.TRT_NOT_SUPPORT,
+                           "INPUT RoisNum NOT SUPPORT")
+
+        def teller2(program_config, predictor_config):
+            if (program_config.ops[0].attrs['sampling_ratio'] == -1 and
+                    program_config.ops[0].attrs['aligned'] == True):
+                return True
+            return False
+
+        self.add_skip_case(
+            teller2, SkipReasons.TRT_NOT_SUPPORT,
+            "SAMPLING_RATIO EQUAL TO - 1 WHEN ALIGNED IS TRUE IS NOT SUPPORT")
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()