From 2f20cee41114d286548d687bd7fccab1ca432179 Mon Sep 17 00:00:00 2001 From: c00818886 Date: Thu, 19 Oct 2023 17:41:01 +0800 Subject: [PATCH] YoloX torch_aie adaption & README --- .../built-in/cv/detection/YoloX/README.md | 211 ++++++++++++++++++ .../cv/detection/YoloX/requirements.txt | 12 + .../built-in/cv/detection/YoloX/test.sh | 62 +++++ .../YoloX/yolox_coco_evaluator.patch | 67 ++++++ .../built-in/cv/detection/YoloX/yolox_eval.py | 96 ++++++++ .../YoloX/yolox_export_torch_aie_ts.py | 57 +++++ 6 files changed, 505 insertions(+) create mode 100644 AscendIE/TorchAIE/built-in/cv/detection/YoloX/README.md create mode 100644 AscendIE/TorchAIE/built-in/cv/detection/YoloX/requirements.txt create mode 100644 AscendIE/TorchAIE/built-in/cv/detection/YoloX/test.sh create mode 100755 AscendIE/TorchAIE/built-in/cv/detection/YoloX/yolox_coco_evaluator.patch create mode 100644 AscendIE/TorchAIE/built-in/cv/detection/YoloX/yolox_eval.py create mode 100644 AscendIE/TorchAIE/built-in/cv/detection/YoloX/yolox_export_torch_aie_ts.py diff --git a/AscendIE/TorchAIE/built-in/cv/detection/YoloX/README.md b/AscendIE/TorchAIE/built-in/cv/detection/YoloX/README.md new file mode 100644 index 0000000000..28badefec4 --- /dev/null +++ b/AscendIE/TorchAIE/built-in/cv/detection/YoloX/README.md @@ -0,0 +1,211 @@ +# YOLOX模型-AIE推理引擎部署指导 + +- [概述](#ZH-CN_TOPIC_0000001172161501) + + - [输入输出数据](#section540883920406) + +- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) + +- [模型部署](#ZH-CN_TOPIC_0000001126281700) + + - [安装依赖](#section4622531142816) + - [准备数据集](#section183221994411) + - [模型推理](#section741711594517) + +- [模型推理性能&精度](#ZH-CN_TOPIC_0000001172201573) + +****** + +# 概述 + +YOLOX是基于往年对YOLO系列众多改进而产生的目标检测模型，其采用无锚方式，并应用了解耦头和领先的标签分配策略 SimOTA.其在众多数据集中均获得了最佳结果。 + +- 参考实现： + + ``` + url=https://github.com/Megvii-BaseDetection/YOLOX + commit_id=6880e3999eb5cf83037e1818ee63d589384587bd + code_path=ACL_PyTorch/contrib/cv/detection/YOLOX + model_name=YOLOX + ``` + +## 输入输出数据 + +- 输入数据 + + | 输入数据 | 数据类型 | 大小 | 数据排布格式 | + | -------- | -------- | ------------------------- | ------------ | + | input | RGB_FP32 | batchsize x 3 x 640 x 640 | NCHW | + +- 输出数据 + + | 输出数据 | 数据类型 | 大小 | + | -------- | -------- | ------- | + | output | FLOAT32 | 1 x 8400 x 85 | + +# 推理环境准备 + +- 该模型需要以下插件与驱动 + + **表 1** 版本配套表 + + | 配套 | 版本 | 环境准备指导 | + | --------------------------------------------------------------- | ------- | ----------------------------------------------------------------------------------------------------- | + | 固件与驱动 | 23.0.rc1 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | + | CANN | 7.0.RC1.alpha003 | - | + | Python | 3.9.11 | - | + | PyTorch | 2.0.1 | - | + | torch_aie | 6.3rc2 | + | 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | + +# 模型部署 + +## 安装依赖 + +1. 获取源码 + + ```shell + git clone https://github.com/Megvii-BaseDetection/YOLOX + cd YOLOX + git reset 6880e3999eb5cf83037e1818ee63d589384587bd --hard + patch -p1 < ../yolox_coco_evaluator.patch + pip install -v -e . # or python3 setup.py develop + cd .. + ``` + +2. 安装需要的Python Library + + ```shell + apt-get install libprotobuf-dev protobuf-compiler + apt-get install libgl1-mesa-glx + pip install -r requirements.txt + ``` + +3. 设置环境变量 + + 执行环境中推理引擎安装路径下的环境变量设置脚本 + + ```shell + source {aie}/set_env.sh + ``` + +## 准备数据集 + +1. 获取原始数据集。（解压命令参考tar –xvf \*.tar与 unzip \*.zip） + + 请参考开源代码仓方式获得[COCO2017数据集](https://cocodataset.org/)，并根据需要置于服务器上（如 `datasets_path=/data/dataset/coco`），val2017目录存放coco数据集的验证集图片，annotations目录存放coco数据集的instances_val2017.json，文件目录结构如下： + + ``` + data + ├── dataset + │ ├── coco + │ │ ├── annotations + │ │ ├── val2017 + ``` + +## 模型推理 + +### 1. 模型转换 + + 使用PyTorch将模型权重文件.pth转换为torchscript文件 + + 1. 获取权重文件 + + 我们利用官方的PTH文件进行验证，官方PTH文件可从原始开源库中获取，我们需要[yolox_x.pth](https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_x.pth)文件,请将其放在与README.md文件同一目录内。 + + 2. 导出torchscript文件 + + ```shell + cd YOLOX + python tools/export_torchscript.py \ + --output-name ../yolox.torchscript.pt \ + -n yolox-x \ + -c ../yolox_x.pth + cd .. + ``` + + 获得yolox.torchscript.pt文件。 + + + 参数说明 + + `--output-name`：输出文件名称 + + `-n`：模型名称 + + `-c`：权重文件路径 + +### 2. 开始推理验证 + + 1. 执行命令查看芯片名称（$\{chip\_name\}）。 + + ```shell + npu-smi info + #该设备芯片名为Ascend310P3 （在下一步中赋值给soc_version环境变量） + 回显如下： + +-------------------+-----------------+------------------------------------------------------+ + | NPU Name | Health | Power(W) Temp(C) Hugepages-Usage(page) | + | Chip Device | Bus-Id | AICore(%) Memory-Usage(MB) | + +===================+=================+======================================================+ + | 0 310P3 | OK | 15.8 42 0 / 0 | + | 0 0 | 0000:82:00.0 | 0 1074 / 21534 | + +===================+=================+======================================================+ + | 1 310P3 | OK | 15.4 43 0 / 0 | + | 0 1 | 0000:89:00.0 | 0 1070 / 21534 | + +===================+=================+======================================================+ + ``` + + 2. 对原生ts文件执行torch_aie编译，导出NPU支持的ts文件 + + ```shell + soc_version="Ascend310P3" # User-defined + python yolox_export_torch_aie_ts.py \ + --torch-script-path ./yolox.torchscript.pt \ + --batch-size 1 \ + --save-path ./yoloxb1_torch_aie.pt \ + --soc-version ${soc_version} + ``` + + 参数说明 + + `--torch-script-path`：原生ts文件路径 + + `--batch-size`：用户自定义的batch size + + `--save-path`：AIE编译后的ts文件保存路径 + + `--soc-version`：NPU型号 + + 3. 执行推理并验证精度与性能 + + COCO2017数据集需要约5分钟完成所有推理任务，请耐心等待。 + + ```shell + python yolox_eval.py \ + --dataroot /data/dataset/coco \ + --batch 1 \ + --ts ./yoloxb1_torch_aie.pt + ``` + + - 参数说明： + - --dataroot：COCO数据集的路径，同上 + - --batch：用户自定义的batch size + - --ts：AIE编译后的ts文件路径 + + 运行成功后将打印该模型在NPU推理结果的精度信息与性能信息。 + + +# 模型推理性能&精度 + +基于推理引擎完成推理计算，精度与性能可参考下列数据： + +| Soc version | Batch Size | Dataset | Accuracy || +| ---------- | ---------- | ---------- | ---------- | ---------- | +| 310P3精度 | 1 | coco2017 | Average Precision(IoU=0.50:0.95): 0.498 | Average Precision(IoU=0.50): 0.678 | +| 310P3精度 | 4 | coco2017 | Average Precision(IoU=0.50:0.95): 0.498 | Average Precision(IoU=0.50): 0.678 | +| 310P3精度 | 8 | coco2017 | Average Precision(IoU=0.50:0.95): 0.498 | Average Precision(IoU=0.50): 0.678 | +| 310P3精度 | 20 | coco2017 | Average Precision(IoU=0.50:0.95): 0.498 | Average Precision(IoU=0.50): 0.678 | + +| Soc version | Batch Size | Dataset | Performance | +| -------- | ---------- | ---------- | ---------- | +| 310P3 | 1 | coco2017 | 35.65 ms/pic | +| 310P3 | 4 | coco2017 | 33.09 ms/pic | +| 310P3 | 8 | coco2017 | 33.63 ms/pic | +| 310P3 | 20 | coco2017 | 32.67 ms/pic | + +# FAQ +1. 若遇到类似报错：ImportError: /lib/aarch64-linux-gnu/libGLdispatch.so.0: cannot allocate memory in static TLS block + + 解决方法： + export LD_PRELOAD=$LD_PRELOAD:{报错信息中的路径} \ No newline at end of file diff --git a/AscendIE/TorchAIE/built-in/cv/detection/YoloX/requirements.txt b/AscendIE/TorchAIE/built-in/cv/detection/YoloX/requirements.txt new file mode 100644 index 0000000000..92675b2315 --- /dev/null +++ b/AscendIE/TorchAIE/built-in/cv/detection/YoloX/requirements.txt @@ -0,0 +1,12 @@ +numpy +torch==2.0.1 +opencv_python +loguru +scikit-image +tqdm +torchvision +Pillow +thop +ninja +tabulate +tensorboard diff --git a/AscendIE/TorchAIE/built-in/cv/detection/YoloX/test.sh b/AscendIE/TorchAIE/built-in/cv/detection/YoloX/test.sh new file mode 100644 index 0000000000..7f767ca7cd --- /dev/null +++ b/AscendIE/TorchAIE/built-in/cv/detection/YoloX/test.sh @@ -0,0 +1,62 @@ +# Copyright(C) 2023. Huawei Technologies Co.,Ltd. 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. + +# Description: Test script for running YoloX model with Ascend Inference Engine +# Author: chenchuwei c00818886 +# Create: 2023/10/20 + +soc_version="Ascend310P3" +data_root="/data/dataset/coco" +batch=1 + +if [ ! -f "yolox_x.pth" ]; then + echo "[ERROR] yolox_x.pth not found in current dir, please make sure it exists." + exit 1 +fi + +if [ ! -d "YOLOX" ]; then + echo "[INFO] Preparing YoloX's dependencies" + git clone https://github.com/Megvii-BaseDetection/YOLOX + cd YOLOX + git reset 6880e3999eb5cf83037e1818ee63d589384587bd --hard + patch -p1 < ../yolox_coco_evaluator.patch + pip install -v -e . + cd .. +fi + +echo "[INFO] Installing Python dependencies" +apt-get install libprotobuf-dev protobuf-compiler -y +apt-get install libgl1-mesa-glx -y +pip install -r requirements.txt + +if [ ! -f "yolox.torchscript.pt" ]; then + echo "[INFO] Exporting torchscript module" + cd YOLOX + python tools/export_torchscript.py --output-name ../yolox.torchscript.pt -n yolox-x -c ../yolox_x.pth + cd .. +fi + +if [ ! -f "yoloxb${batch}_torch_aie.pt" ]; then + echo "[INFO] AIE Compiling" + python yolox_export_torch_aie_ts.py \ + --torch-script-path ./yolox.torchscript.pt \ + --batch-size ${batch} \ + --soc-version ${soc_version} +fi + +echo "[INFO] Start AIE evaluation" +python yolox_eval.py \ + --dataroot ${data_root} \ + --batch ${batch} \ + --ts ./yoloxb${batch}_torch_aie.pt \ No newline at end of file diff --git a/AscendIE/TorchAIE/built-in/cv/detection/YoloX/yolox_coco_evaluator.patch b/AscendIE/TorchAIE/built-in/cv/detection/YoloX/yolox_coco_evaluator.patch new file mode 100755 index 0000000000..a81cb0971e --- /dev/null +++ b/AscendIE/TorchAIE/built-in/cv/detection/YoloX/yolox_coco_evaluator.patch @@ -0,0 +1,67 @@ +diff --git a/yolox/evaluators/coco_evaluator.py b/yolox/evaluators/coco_evaluator.py +index 96eb56a..64827fc 100644 +--- a/yolox/evaluators/coco_evaluator.py ++++ b/yolox/evaluators/coco_evaluator.py +@@ -164,33 +164,34 @@ class COCOEvaluator: + data_list.append(pred_data) + return data_list + +- def evaluate_prediction(self, data_dict, statistics): ++ def evaluate_prediction(self, data_dict, statistics=None): + if not is_main_process(): + return 0, 0, None + + logger.info("Evaluate in main process...") + + annType = ["segm", "bbox", "keypoints"] ++ ++ if statistics is not None: ++ inference_time = statistics[0].item() ++ nms_time = statistics[1].item() ++ n_samples = statistics[2].item() ++ ++ a_infer_time = 1000 * inference_time / (n_samples * self.dataloader.batch_size) ++ a_nms_time = 1000 * nms_time / (n_samples * self.dataloader.batch_size) ++ ++ time_info = ", ".join( ++ [ ++ "Average {} time: {:.2f} ms".format(k, v) ++ for k, v in zip( ++ ["forward", "NMS", "inference"], ++ [a_infer_time, a_nms_time, (a_infer_time + a_nms_time)], ++ ) ++ ] ++ ) + +- inference_time = statistics[0].item() +- nms_time = statistics[1].item() +- n_samples = statistics[2].item() +- +- a_infer_time = 1000 * inference_time / (n_samples * self.dataloader.batch_size) +- a_nms_time = 1000 * nms_time / (n_samples * self.dataloader.batch_size) +- +- time_info = ", ".join( +- [ +- "Average {} time: {:.2f} ms".format(k, v) +- for k, v in zip( +- ["forward", "NMS", "inference"], +- [a_infer_time, a_nms_time, (a_infer_time + a_nms_time)], +- ) +- ] +- ) +- +- info = time_info + "\n" +- ++ info = time_info + "\n" ++ info = "\n" + # Evaluate the Dt (detection) json comparing with the ground truth + if len(data_dict) > 0: + cocoGt = self.dataloader.dataset.coco +@@ -216,6 +217,7 @@ class COCOEvaluator: + with contextlib.redirect_stdout(redirect_string): + cocoEval.summarize() + info += redirect_string.getvalue() +- return cocoEval.stats[0], cocoEval.stats[1], info ++ return info + else: + return 0, 0, info diff --git a/AscendIE/TorchAIE/built-in/cv/detection/YoloX/yolox_eval.py b/AscendIE/TorchAIE/built-in/cv/detection/YoloX/yolox_eval.py new file mode 100644 index 0000000000..3dd182eca6 --- /dev/null +++ b/AscendIE/TorchAIE/built-in/cv/detection/YoloX/yolox_eval.py @@ -0,0 +1,96 @@ +# Copyright(C) 2023. Huawei Technologies Co.,Ltd. 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. + +import os +import sys +import argparse +import time +import torch +import torch_aie +import numpy as np + +from tqdm import tqdm +from ais_bench.infer.interface import InferSession +from yolox.data import COCODataset, ValTransform +from yolox.evaluators import COCOEvaluator +from yolox.utils.boxes import postprocess +from yolox.utils.demo_utils import demo_postprocess + + +def main(): + print("[INFO] YoloX AIE evaluation process start") + + parser = argparse.ArgumentParser(description="YOLOX Preprocess") + parser.add_argument('--dataroot', dest='dataroot', + help='data root dirname', default='/data/datasets/coco', + type=str) + parser.add_argument('--batch', + help='validation batch size', default=1, + type=int) + parser.add_argument('--ts', + help='root of ts module', default="./yoloxb1_torch_aie.pt", + type=str) + opt = parser.parse_args() + + valdataset = COCODataset( + data_dir=opt.dataroot, + json_file='instances_val2017.json', + name="val2017", + img_size=(640, 640), + preproc=ValTransform(legacy=False), + ) + sampler = torch.utils.data.SequentialSampler(valdataset) + + dataloader_kwargs = { + "num_workers": 8, "pin_memory": True, "sampler": sampler, "batch_size": opt.batch + } + + val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs) + + torch_aie.set_device(0) + + print("[INFO] Loading TS module") + aie_module = torch.jit.load(opt.ts) + aie_module.eval() + + aie_cost = 0 + infer_times = 0 + data_list = [] + coco_evaluator = COCOEvaluator(val_loader, img_size=(640, 640), confthre=0.001, nmsthre=0.65, num_classes=80) + + print(f"[INFO] Start AIE inference, please be patient (batch={opt.batch})") + for _, datas in enumerate(tqdm(val_loader)): + data = datas[0] + + # Inference with AIE-compiled TS module + start = time.time() + result = aie_module(data) + cost = time.time() - start + + outputs = demo_postprocess(result, [640, 640]) + outputs = postprocess(outputs, num_classes=80, conf_thre=0.001, nms_thre=0.65) + + data_list.extend(coco_evaluator.convert_to_coco_format(outputs, datas[2], datas[3])) + aie_cost += cost + infer_times += 1 + + # Use COCO_Evaluator to evaluate the accuracy + coco_result = coco_evaluator.evaluate_prediction(data_list) + print(coco_result) + print(f'\n[INFO] PT-AIE inference avg cost: {aie_cost / infer_times * 1000 / opt.batch} ms/pic') + print(f'[INFO] Total sample count = {infer_times * opt.batch} pics') + print('[INFO] YoloX AIE evaluation process finished') + +if __name__ == "__main__": + main() diff --git a/AscendIE/TorchAIE/built-in/cv/detection/YoloX/yolox_export_torch_aie_ts.py b/AscendIE/TorchAIE/built-in/cv/detection/YoloX/yolox_export_torch_aie_ts.py new file mode 100644 index 0000000000..8c1ca305ce --- /dev/null +++ b/AscendIE/TorchAIE/built-in/cv/detection/YoloX/yolox_export_torch_aie_ts.py @@ -0,0 +1,57 @@ +# Copyright(C) 2023. Huawei Technologies Co.,Ltd. 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. + +import sys +import os +import argparse +import torch +import torch_aie +from torch_aie import _enums + + +def export_torch_aie(model_path, batch_size, soc_version, save_path="./"): + trace_model = torch.jit.load(model_path) + trace_model.eval() + input_info = [torch_aie.Input((batch_size, 3, 640, 640))] + torch_aie.set_device(0) + torchaie_model = torch_aie.compile( + trace_model, + inputs=input_info, + torch_executed_ops=[], + precision_policy=_enums.PrecisionPolicy.FP32, + soc_version=soc_version, + ) + suffix = os.path.splitext(model_path)[-1] + saved_name = os.path.basename(model_path).split('.')[0] + f"b{batch_size}_torch_aie" + suffix + torchaie_model.save(os.path.join(save_path, saved_name)) + print("[INFO] torch_aie compile for YoloX finished, model saved in: ", os.path.join(save_path, saved_name)) + + +def parse_opt(): + parser = argparse.ArgumentParser() + parser.add_argument('--torch-script-path', type=str, default='./yolox.torchscript.pt', help='trace model path') + parser.add_argument('--batch-size', type=int, default=1, help='batch size') + parser.add_argument('--save-path', type=str, default='./', help='compiled model path') + parser.add_argument('--soc-version', type=str, default='Ascend310P3', help='soc version') + opt_args = parser.parse_args() + return opt_args + + +def main(opt_args): + print("[INFO] torch_aie compile for YoloX start") + export_torch_aie(opt_args.torch_script_path, opt_args.batch_size, opt_args.soc_version, opt_args.save_path) + +if __name__ == '__main__': + opt = parse_opt() + main(opt) \ No newline at end of file -- Gitee