From 5d03c3eb3bfb2aaebe23bbe945f657051f4e4256 Mon Sep 17 00:00:00 2001 From: xiaoxiaohehe001 <49090790+xiaoxiaohehe001@users.noreply.github.com> Date: Tue, 14 Sep 2021 16:50:31 +0800 Subject: [PATCH] [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 --- .../ir/inference/test_trt_convert_yolo_box.py | 161 ++++++++++++++++++ 1 file changed, 161 insertions(+) create mode 100644 python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_yolo_box.py diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_yolo_box.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_yolo_box.py new file mode 100644 index 0000000000..d6a0aac75c --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_yolo_box.py @@ -0,0 +1,161 @@ +# 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() -- GitLab