[Paddle Inference]Add yolo_box op TRT converter unittest (#35533)

* [Paddle Inference]Add yolo_box op TRT converter unittest

* add_yolo_box_teller

* add_yolo_box_teller

* add_yolo_box_teller

* add_yolo_box_teller

* add_yolo_box_teller

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_yolo_box.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 TrtConvertYoloBoxTest(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, channel):
+            if attrs[0]['iou_aware'] == True:
+                return np.ones(
+                    [batch, 3 * (channel + 6), 13, 13]).astype(np.float32)
+            else:
+                return np.ones(
+                    [batch, 3 * (channel + 5), 13, 13]).astype(np.float32)
+
+        def generate_input2(attrs: List[Dict[str, Any]], batch):
+            return np.random.random([batch, 2]).astype(np.int32)
+
+        for batch in [1, 2, 4]:
+            for class_num in [80, 30]:
+                for anchors in [[10, 13, 16, 30, 33, 23]]:
+                    for downsample_ratio in [32, 16]:
+                        for conf_thresh in [0.01, 0.02]:
+                            for clip_bbox in [True, False]:
+                                for scale_x_y in [1.0, 0.9]:
+                                    for iou_aware in [False, True]:
+                                        for iou_aware_factor in [0.5]:
+                                            dics = [{
+                                                "class_num": class_num,
+                                                "anchors": anchors,
+                                                "downsample_ratio":
+                                                downsample_ratio,
+                                                "conf_thresh": conf_thresh,
+                                                "clip_bbox": clip_bbox,
+                                                "scale_x_y": scale_x_y,
+                                                "iou_aware": iou_aware,
+                                                "iou_aware_factor":
+                                                iou_aware_factor
+                                            }, {}]
+                                            ops_config = [{
+                                                "op_type": "yolo_box",
+                                                "op_inputs": {
+                                                    "X": ["yolo_box_input"],
+                                                    "ImgSize": ["imgsize"]
+                                                },
+                                                "op_outputs": {
+                                                    "Boxes": ["boxes"],
+                                                    "Scores": ["scores"]
+                                                },
+                                                "op_attrs": dics[0]
+                                            }]
+                                            ops = self.generate_op_config(
+                                                ops_config)
+                                            program_config = ProgramConfig(
+                                                ops=ops,
+                                                weights={},
+                                                inputs={
+                                                    "yolo_box_input":
+                                                    TensorConfig(
+                                                        data_gen=partial(
+                                                            generate_input1,
+                                                            dics, batch,
+                                                            class_num)),
+                                                    "imgsize": TensorConfig(
+                                                        data_gen=partial(
+                                                            generate_input2,
+                                                            dics, batch))
+                                                },
+                                                outputs=["boxes", "scores"])
+
+                                            yield program_config
+
+    def sample_predictor_configs(
+            self, program_config) -> (paddle_infer.Config, List[int], float):
+        def generate_dynamic_shape(attrs):
+            if attrs[0]['iou_aware'] == True:
+                channel = 3 * (attrs[0]['class_num'] + 6)
+                self.dynamic_shape.min_input_shape = {
+                    "scale_input": [1, channel, 24, 24]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "scale_input": [4, channel, 48, 48]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "scale_input": [1, channel, 24, 48]
+                }
+            else:
+                channel = 3 * (attrs[0]['class_num'] + 5)
+                self.dynamic_shape.min_input_shape = {
+                    "scale_input": [1, channel, 24, 24]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "scale_input": [4, channel, 48, 48]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "scale_input": [1, channel, 24, 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):
+            if dynamic_shape == True:
+                return 0, 5
+            else:
+                return 1, 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-3
+
+        # 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-3
+
+    def add_skip_trt_case(self):
+        pass
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()