From 74eb371dd68c5cbda31a60e84bd67203213402c4 Mon Sep 17 00:00:00 2001 From: cuicheng01 Date: Tue, 20 Sep 2022 04:37:52 +0000 Subject: [PATCH] add table_attribute docs --- .../inference_table_attribute.yaml | 2 +- .../images/PULC/table_attribute/val_3253.jpg | Bin 0 -> 217399 bytes .../images/PULC/table_attribute/val_3610.jpg | Bin 86685 -> 0 bytes .../images/PULC/table_attribute/val_851.jpg | Bin 55573 -> 0 bytes deploy/images/PULC/table_attribute/val_9.jpg | Bin 0 -> 57284 bytes .../zh_CN/models/PULC/PULC_table_attribute.md | 434 ++++++++++++++++++ docs/zh_CN/models/PULC/model_list.md | 3 +- .../PPLCNet_x1_0_distillation.yaml | 155 +++++++ ppcls/data/postprocess/__init__.py | 2 +- 9 files changed, 593 insertions(+), 3 deletions(-) create mode 100644 deploy/images/PULC/table_attribute/val_3253.jpg delete mode 100644 deploy/images/PULC/table_attribute/val_3610.jpg delete mode 100644 deploy/images/PULC/table_attribute/val_851.jpg create mode 100644 deploy/images/PULC/table_attribute/val_9.jpg create mode 100644 docs/zh_CN/models/PULC/PULC_table_attribute.md create mode 100644 ppcls/configs/PULC/table_attribute/PPLCNet_x1_0_distillation.yaml diff --git a/deploy/configs/PULC/table_attribute/inference_table_attribute.yaml b/deploy/configs/PULC/table_attribute/inference_table_attribute.yaml index 580ca18f..366854ec 100644 --- a/deploy/configs/PULC/table_attribute/inference_table_attribute.yaml +++ b/deploy/configs/PULC/table_attribute/inference_table_attribute.yaml @@ -1,5 +1,5 @@ Global: - infer_imgs: "images/PULC/table_attribute/val_3610.jpg" + infer_imgs: "images/PULC/table_attribute/val_3253.jpg" inference_model_dir: "./models/table_attribute_infer" batch_size: 1 use_gpu: True diff --git a/deploy/images/PULC/table_attribute/val_3253.jpg b/deploy/images/PULC/table_attribute/val_3253.jpg new file mode 100644 index 0000000000000000000000000000000000000000..878122eb1b4858941b6dc2d6491a3b5f31286195 GIT binary patch literal 217399 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zDSW850tdETupSFp?#u#S)iv^FO{2Uj=}G(Ook5Enj)EDL~#y{!U1G}u=IT}&&vE(5e0*QZvU(&vOenrrZaF>d4l__gK?krY{2w- zK>7Rou79_{f7Svr$SRP(rN}a<-G?9s83F`xs>9GeQXfbYbr5WBhRVJsuKuQ#yjIEc zj7%cWXHQQz^(>V+aEb2-oJ2`*yuk@LFBJMYu}4_8h3H(2-taycdw>$BTqhn z=baWC22?7~S9dFP4Yt#%B=z!{&Tl5HK(re}#)FOPC*0_Q04HM8n9N2dxj2d_&#)fzQn@OneJ;(={`*9_Rh2MYq5bAD`MjHq4l~Jaq8`o$hxl??C+SFC|3w z%Y#}F%Ixprn(uWvsZU=@a(KILI~||r9&fc}%?QhH_W8Gk$=Q{A#qX{wZc=WmJ>+4; zYDpF?3y$iG@JdnfVA1r<9ZAsR4A)A% zxXl_i_bPm-%Q9a)MN8u4O&rY#+bs;#n$r5|nTwU!fx9v9-}kouL1@FDgEN55ope2o z2NVR!OP_{VynEv#l1r|VnVHFmL6}z}N39dRudk+0^i{ttD~ti*)SZ0|m3<(*vz58w zCmJtMbF0Scj*W`KO>z&(cg+IACB zzavxs>ze;xs)YFd!BRHS$j5IBj;Ty(DKS&JQF;e@}XPpOS}~5PMSr; zLt8gzoNGC2*btroI23|be2A)wS5Toz!--jAU + +## 1. 模型和应用场景介绍 + +该案例提供了用户使用 PaddleClas 的超轻量图像分类方案(PULC,Practical Ultra Lightweight image Classification)快速构建轻量级、高精度、可落地的表格属性识别模型。该模型可以广泛应用于表格识别、表格后处理等任务中。下表为表格属性识别的指标情况。 + +| 模型 | mA(%) | 延时(ms) | 存储(M) | +|-------|-----------|----------|---------------| +| PPLCNet_x1_0 | 88.1 | 2.36 | 7.2 | + + +**备注:** + +* 延时是基于 Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz 测试得到,开启 MKLDNN 加速策略,线程数为10。 +* 关于PP-LCNet的介绍可以参考[PP-LCNet介绍](../ImageNet1k/PP-LCNet.md),相关论文可以查阅[PP-LCNet paper](https://arxiv.org/abs/2109.15099)。 + + + + +## 2. 模型快速体验 + + + +### 2.1 安装 paddlepaddle + +- 您的机器安装的是 CUDA9 或 CUDA10,请运行以下命令安装 + +```bash +python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple +``` + +- 您的机器是CPU,请运行以下命令安装 + +```bash +python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple +``` + +更多的版本需求,请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。 + + + +### 2.2 安装 paddleclas + +使用如下命令快速安装 paddleclas + +``` +wget https://paddleclas.bj.bcebos.com/whl/paddleclas-0.0.0-py3-none-any.whl +pip install paddleclas-0.0.0-py3-none-any.whl +``` + +**备注:** 表格识别的功能还未集成到发布的whl包中,此处下载的为develop的whl包,未来支持后此处可以直接`pip install paddleclas`进行安装。 + + + +### 2.3 预测 + +点击[这里](https://paddleclas.bj.bcebos.com/data/PULC/pulc_demo_imgs.zip)下载 demo 数据并解压,然后在终端中切换到相应目录。 + +* 使用命令行快速预测 + +```bash +paddleclas --model_name=table_attribute --infer_imgs=pulc_demo_imgs/table_attribute/val_3253.jpg +``` + +结果如下: +``` +>>> result +attributes: ['Photo', 'Little', 'Black-and-White', 'Blurry', 'Without-Obstacles', 'Tilted'], output: [0, 1, 1, 0, 1, 0], filename: pulc_demo_imgs/table_attribute/val_3253.jpg +Predict complete! +``` + +**备注**: 更换其他预测的数据时,只需要改变 `--infer_imgs=xx` 中的字段即可,支持传入整个文件夹。 + + +* 在 Python 代码中预测 +```python +import paddleclas +model = paddleclas.PaddleClas(model_name="table_attribute") +result = model.predict(input_data="pulc_demo_imgs/table_attribute/val_3253.jpg") +print(next(result)) +``` + +**备注**:`model.predict()` 为可迭代对象(`generator`),因此需要使用 `next()` 函数或 `for` 循环对其迭代调用。每次调用将以 `batch_size` 为单位进行一次预测,并返回预测结果, 默认 `batch_size` 为 1,如果需要更改 `batch_size`,实例化模型时,需要指定 `batch_size`,如 `model = paddleclas.PaddleClas(model_name="table_attribute", batch_size=2)`, 使用默认的代码返回结果示例如下: + +``` +>>> result +[{'attributes': ['Photo', 'Little', 'Black-and-White', 'Blurry', 'Without-Obstacles', 'Tilted'], 'output': [0, 1, 1, 0, 1, 0], 'filename': 'pulc_demo_imgs/table_attribute/val_3253.jpg'}] +``` + +**备注**:其中 `output` 的值索引值 0 表示图片的来源,有 Photo (拍照)和 Scanned (扫描)两种方式,1 表示表格数量,有 Little (少量)和 Numerous (大量)两种方式,2 表示表格颜色,有 Black-and-White (黑白)和 Multicolor (多种颜色)两种方式,3 表示表格清晰度,有 Clear (清晰)和 Blurry (模糊)两种方式,4 表示表格有无干扰,有 Without-Obstacles (无干扰)和 With-Obstacles (有干扰)两种方式,5 表示表格的角度,有 Horizontal (竖直)和 Tilted (倾斜)两种方式。`output` 中含有1的个数越多,表示表格的质量越好。 + + + +## 3. 模型训练、评估和预测 + + + +### 3.1 环境配置 + +* 安装:请先参考文档 [环境准备](../../installation.md) 配置 PaddleClas 运行环境。 + + + +### 3.2 数据准备 + + + +#### 3.2.1 数据集来源 + +本案例中所使用的数据为[WTW 数据集](https://github.com/wangwen-whu/WTW-Dataset),属性值通过对原始数据的标注得到。 + + + +#### 3.2.2 数据集获取 + +部分数据可视化如下所示。 + +

+ +
+ + +我们提供了 WTW 属性识别数据的子集,可以直接下载。 + +进入 PaddleClas 目录。 + +``` +cd path_to_PaddleClas +``` + +进入 `dataset/` 目录,下载并解压有人/无人场景的数据。 + +```shell +cd dataset +wget https://paddleclas.bj.bcebos.com/data/PULC/table_attribute.tar +tar -xf table_attribute.tar +cd ../ +``` + +执行上述命令后,`dataset/` 下存在 `table_attribute` 目录,该目录中具有以下数据: + +``` +table_attribute +├── Table_train +│   ├── train_10516.jpg +│   ├── train_10524.jpg +... +├── Table_val +│   ├── val_1004.jpg +│   ├── val_1011.jpg +... +├── train_list.txt +├── val_list.txt +``` + + + +### 3.3 模型训练 + + +在 `ppcls/configs/PULC/table_attribute/PPLCNet_x1_0.yaml` 中提供了基于该场景的训练配置,可以通过如下脚本启动训练: + +```shell +export CUDA_VISIBLE_DEVICES=0,1,2,3 +python3 -m paddle.distributed.launch \ + --gpus="0,1,2,3" \ + tools/train.py \ + -c ./ppcls/configs/PULC/table_attribute/PPLCNet_x1_0.yaml +``` + +验证集的最佳指标在 `83.7%` 左右(数据集较小,一般有1%左右的波动)。 + + + + +### 3.4 模型评估 + +训练好模型之后,可以通过以下命令实现对模型指标的评估。 + +```bash +python3 tools/eval.py \ + -c ./ppcls/configs/PULC/table_attribute/PPLCNet_x1_0.yaml \ + -o Global.pretrained_model="output/PPLCNet_x1_0/best_model" +``` + +其中 `-o Global.pretrained_model="output/PPLCNet_x1_0/best_model"` 指定了当前最佳权重所在的路径,如果指定其他权重,只需替换对应的路径即可。 + + + +### 3.5 模型预测 + +模型训练完成之后,可以加载训练得到的预训练模型,进行模型预测。在模型库的 `tools/infer.py` 中提供了完整的示例,只需执行下述命令即可完成模型预测: + +```bash +python3 tools/infer.py \ + -c ./ppcls/configs/PULC/table_attribute/PPLCNet_x1_0.yaml \ + -o Global.pretrained_model=output/PPLCNet_x1_0/best_model +``` + +输出结果如下: + +``` +[{'attributes': ['Photo', 'Little', 'Black-and-White', 'Blurry', 'Without-Obstacles', 'Tilted'], 'output': [0, 1, 1, 0, 1, 0], 'file_name': 'deploy/images/PULC/table_attribute/val_3253.jpg'}] +``` + +**备注:** + +* 这里`-o Global.pretrained_model="output/PPLCNet_x1_0/best_model"` 指定了当前最佳权重所在的路径,如果指定其他权重,只需替换对应的路径即可。 + +* 默认是对 `./deploy/images/PULC/table_attribute/val_3253.jpg` 进行预测,此处也可以通过增加字段 `-o Infer.infer_imgs=xxx` 对其他图片预测。 + + + +## 4. 模型压缩 + + + +### 4.1 SKL-UGI 知识蒸馏 + +SKL-UGI 知识蒸馏是 PaddleClas 提出的一种简单有效的知识蒸馏方法,关于该方法的介绍,可以参考[SKL-UGI 知识蒸馏](../../training/advanced/ssld.md)。 + + + +#### 4.1.1 教师模型训练 + +复用 `ppcls/configs/PULC/table_attribute/PPLCNet_x1_0.yaml` 中的超参数,训练教师模型,训练脚本如下: + +```shell +export CUDA_VISIBLE_DEVICES=0,1,2,3 +python3 -m paddle.distributed.launch \ + --gpus="0,1,2,3" \ + tools/train.py \ + -c ./ppcls/configs/PULC/table_attribute/PPLCNet_x1_0.yaml \ + -o Arch.name=ResNet50_vd +``` + +验证集的最佳指标为 `86.10%` 左右,当前教师模型最好的权重保存在 `output/ResNet50_vd/best_model.pdparams`。 + + + +#### 4.1.2 蒸馏训练 + +配置文件`ppcls/configs/PULC/table_attribute/PPLCNet_x1_0_distillation.yaml`提供了`SKL-UGI知识蒸馏策略`的配置。该配置将`ResNet50_vd`当作教师模型,`PPLCNet_x1_0`当作学生模型。训练脚本如下: + +```shell +export CUDA_VISIBLE_DEVICES=0,1,2,3 +python3 -m paddle.distributed.launch \ + --gpus="0,1,2,3" \ + tools/train.py \ + -c ./ppcls/configs/PULC/table_attribute/PPLCNet_x1_0_distillation.yaml \ + -o Arch.models.0.Teacher.pretrained=output/ResNet50_vd/best_model +``` + +验证集的最佳指标为 `84.4%` 左右,当前模型最好的权重保存在 `output/DistillationModel/best_model_student.pdparams`。 + + + +## 5. 超参搜索 + +在 [3.3 节](#3.3)和 [4.1 节](#4.1)所使用的超参数是根据 PaddleClas 提供的 `超参数搜索策略` 搜索得到的,如果希望在自己的数据集上得到更好的结果,可以参考[超参数搜索策略](../../training/PULC.md#4-超参搜索)来获得更好的训练超参数。 + +**备注:** 此部分内容是可选内容,搜索过程需要较长的时间,您可以根据自己的硬件情况来选择执行。如果没有更换数据集,可以忽略此节内容。 + + + +## 6. 模型推理部署 + + + +### 6.1 推理模型准备 + +Paddle Inference 是飞桨的原生推理库, 作用于服务器端和云端,提供高性能的推理能力。相比于直接基于预训练模型进行预测,Paddle Inference可使用MKLDNN、CUDNN、TensorRT 进行预测加速,从而实现更优的推理性能。更多关于Paddle Inference推理引擎的介绍,可以参考[Paddle Inference官网教程](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/infer/inference/inference_cn.html)。 + +当使用 Paddle Inference 推理时,加载的模型类型为 inference 模型。本案例提供了两种获得 inference 模型的方法,如果希望得到和文档相同的结果,请选择[直接下载 inference 模型](#6.1.2)的方式。 + + + +### 6.1.1 基于训练得到的权重导出 inference 模型 + +此处,我们提供了将权重和模型转换的脚本,执行该脚本可以得到对应的 inference 模型: + +```bash +python3 tools/export_model.py \ + -c ./ppcls/configs/PULC/table_attribute/PPLCNet_x1_0.yaml \ + -o Global.pretrained_model=output/DistillationModel/best_model_student \ + -o Global.save_inference_dir=deploy/models/PPLCNet_x1_0_table_attribute_infer +``` +执行完该脚本后会在 `deploy/models/` 下生成 `PPLCNet_x1_0_table_attribute_infer` 文件夹,`models` 文件夹下应有如下文件结构: + +``` +└── PPLCNet_x1_0_table_attribute_infer + ├── inference.pdiparams + ├── inference.pdiparams.info + └── inference.pdmodel +``` + +**备注:** 此处的最佳权重是经过知识蒸馏后的权重路径,如果没有执行知识蒸馏的步骤,最佳模型保存在`output/PPLCNet_x1_0/best_model.pdparams`中。 + + + +### 6.1.2 直接下载 inference 模型 + +[6.1.1 小节](#6.1.1)提供了导出 inference 模型的方法,此处也提供了该场景可以下载的 inference 模型,可以直接下载体验。 + +``` +cd deploy/models +# 下载 inference 模型并解压 +wget https://paddleclas.bj.bcebos.com/models/PULC/inference/table_attribute_infer.tar && tar -xf table_attribute_infer.tar +``` + +解压完毕后,`models` 文件夹下应有如下文件结构: + +``` +├── table_attribute_infer +│ ├── inference.pdiparams +│ ├── inference.pdiparams.info +│ └── inference.pdmodel +``` + + + +### 6.2 基于 Python 预测引擎推理 + + + + +#### 6.2.1 预测单张图像 + +返回 `deploy` 目录: + +``` +cd ../ +``` + +运行下面的命令,对图像 `./images/PULC/table_attribute/val_3253.jpg` 进行表格属性识别。 + +```shell +# 使用下面的命令使用 GPU 进行预测 +python3.7 python/predict_cls.py -c configs/PULC/table_attribute/inference_table_attribute.yaml -o Global.use_gpu=True +# 使用下面的命令使用 CPU 进行预测 +python3.7 python/predict_cls.py -c configs/PULC/table_attribute/inference_table_attribute.yaml -o Global.use_gpu=False +``` + +输出结果如下。 + +``` +val_3253.jpg: {'attributes': ['Photo', 'Little', 'Black-and-White', 'Blurry', 'Without-Obstacles', 'Tilted'], 'output': [0, 1, 1, 0, 1, 0]} +``` + + + +#### 6.2.2 基于文件夹的批量预测 + +如果希望预测文件夹内的图像,可以直接修改配置文件中的 `Global.infer_imgs` 字段,也可以通过下面的 `-o` 参数修改对应的配置。 + +```shell +# 使用下面的命令使用 GPU 进行预测,如果希望使用 CPU 预测,可以在命令后面添加 -o Global.use_gpu=False +python3.7 python/predict_cls.py -c configs/PULC/table_attribute/inference_table_attribute.yaml -o Global.infer_imgs="./images/PULC/table_attribute/" +``` + +终端中会输出该文件夹内所有图像的属性识别结果,如下所示。 + +``` +val_3253.jpg: {'attributes': ['Photo', 'Little', 'Black-and-White', 'Blurry', 'Without-Obstacles', 'Tilted'], 'output': [0, 1, 1, 0, 1, 0]} +val_9.jpg: {'attributes': ['Scanned', 'Little', 'Black-and-White', 'Clear', 'Without-Obstacles', 'Horizontal'], 'output': [1, 1, 1, 1, 1, 1]} +``` + + + +### 6.3 基于 C++ 预测引擎推理 + +PaddleClas 提供了基于 C++ 预测引擎推理的示例,您可以参考[服务器端 C++ 预测](../../deployment/image_classification/cpp/linux.md)来完成相应的推理部署。如果您使用的是 Windows 平台,可以参考[基于 Visual Studio 2019 Community CMake 编译指南](../../deployment/image_classification/cpp/windows.md)完成相应的预测库编译和模型预测工作。 + + + +### 6.4 服务化部署 + +Paddle Serving 提供高性能、灵活易用的工业级在线推理服务。Paddle Serving 支持 RESTful、gRPC、bRPC 等多种协议,提供多种异构硬件和多种操作系统环境下推理解决方案。更多关于Paddle Serving 的介绍,可以参考[Paddle Serving 代码仓库](https://github.com/PaddlePaddle/Serving)。 + +PaddleClas 提供了基于 Paddle Serving 来完成模型服务化部署的示例,您可以参考[模型服务化部署](../../deployment/image_classification/paddle_serving.md)来完成相应的部署工作。 + + + +### 6.5 端侧部署 + +Paddle Lite 是一个高性能、轻量级、灵活性强且易于扩展的深度学习推理框架,定位于支持包括移动端、嵌入式以及服务器端在内的多硬件平台。更多关于 Paddle Lite 的介绍,可以参考[Paddle Lite 代码仓库](https://github.com/PaddlePaddle/Paddle-Lite)。 + +PaddleClas 提供了基于 Paddle Lite 来完成模型端侧部署的示例,您可以参考[端侧部署](../../deployment/image_classification/paddle_lite.md)来完成相应的部署工作。 + + + +### 6.6 Paddle2ONNX 模型转换与预测 + +Paddle2ONNX 支持将 PaddlePaddle 模型格式转化到 ONNX 模型格式。通过 ONNX 可以完成将 Paddle 模型到多种推理引擎的部署,包括TensorRT/OpenVINO/MNN/TNN/NCNN,以及其它对 ONNX 开源格式进行支持的推理引擎或硬件。更多关于 Paddle2ONNX 的介绍,可以参考[Paddle2ONNX 代码仓库](https://github.com/PaddlePaddle/Paddle2ONNX)。 + +PaddleClas 提供了基于 Paddle2ONNX 来完成 inference 模型转换 ONNX 模型并作推理预测的示例,您可以参考[Paddle2ONNX 模型转换与预测](../../deployment/image_classification/paddle2onnx.md)来完成相应的部署工作。 diff --git a/docs/zh_CN/models/PULC/model_list.md b/docs/zh_CN/models/PULC/model_list.md index 4b2d7a87..d85810cc 100644 --- a/docs/zh_CN/models/PULC/model_list.md +++ b/docs/zh_CN/models/PULC/model_list.md @@ -16,10 +16,11 @@ | text_image_orientation |[PULC含文字图像方向分类模型](PULC_text_image_orientation.md)| 99.06 | 7.1M | 2.16ms |[推理模型](https://paddleclas.bj.bcebos.com/models/PULC/inference/text_image_orientation_infer.tar) / [预训练模型](https://paddleclas.bj.bcebos.com/models/PULC/pretrained/text_image_orientation_pretrained.pdparams)| | textline_orientation |[PULC文本行方向分类模型](PULC_textline_orientation.md)| 96.01 |7.0M|2.72ms|[推理模型](https://paddleclas.bj.bcebos.com/models/PULC/inference/textline_orientation_infer.tar) / [预训练模型](https://paddleclas.bj.bcebos.com/models/PULC/pretrained/textline_orientation_pretrained.pdparams)| | language_classification |[PULC语种分类模型](PULC_language_classification.md)| 99.26 |7.1M|2.58ms|[推理模型](https://paddleclas.bj.bcebos.com/models/PULC/inference/language_classification_infer.tar) / [预训练模型](https://paddleclas.bj.bcebos.com/models/PULC/pretrained/language_classification_pretrained.pdparams)| +| table_attribute |[PULC表格属性识别模型](PULC_table_attribute.md)| 88.1 |7.1M|2.58ms|[推理模型](https://paddleclas.bj.bcebos.com/models/PULC/inference/table_attribute_infer.tar) / [预训练模型](https://paddleclas.bj.bcebos.com/models/PULC/pretrained/table_attribute_pretrained.pdparams)| **备注:** * 以上所有的模型的 backbone 均为 PPLCNet_x1_0,部分模型大小不同是由于分类的输出大小不同导致的,推理耗时是基于Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz 测试得到,其中测试过程开启 MKLDNN 加速策略,线程数为10。速度测试过程会有轻微波动。 -* person_exists、safety_helmet、car_exists 的评测指标为 TprAtFpr,person_attribute、vehicle_attribute的评测指标为ma、traffic_sign、text_image_orientation、textline_orientation、language_classification的评测指标为Top-1 Acc。 +* person_exists、safety_helmet、car_exists 的评测指标为 TprAtFpr,person_attribute、vehicle_attribute、table_attribute 的评测指标为mA、traffic_sign、text_image_orientation、textline_orientation、language_classification的评测指标为Top-1 Acc。 diff --git a/ppcls/configs/PULC/table_attribute/PPLCNet_x1_0_distillation.yaml b/ppcls/configs/PULC/table_attribute/PPLCNet_x1_0_distillation.yaml new file mode 100644 index 00000000..5b48f4a1 --- /dev/null +++ b/ppcls/configs/PULC/table_attribute/PPLCNet_x1_0_distillation.yaml @@ -0,0 +1,155 @@ +# global configs +Global: + checkpoints: null + pretrained_model: null + output_dir: "./output/" + device: "gpu" + save_interval: 1 + eval_during_train: True + eval_interval: 1 + epochs: 20 + print_batch_step: 20 + use_visualdl: False + # used for static mode and model export + image_shape: [3, 224, 224] + save_inference_dir: "./inference" + use_multilabel: True + +# model architecture +Arch: + name: "DistillationModel" + class_num: &class_num 6 + # if not null, its lengths should be same as models + pretrained_list: + # if not null, its lengths should be same as models + infer_model_name: "Student" + freeze_params_list: + - True + - False + use_ssld: True + models: + - Teacher: + name: ResNet50_vd + class_num: *class_num + - Student: + name: PPLCNet_x1_0 + class_num: *class_num + pretrained: True + use_ssld: True + +# loss function config for traing/eval process +Loss: + Train: + - DistillationMultiLabelLoss: + weight: 1.0 + model_names: ["Student"] + weight_ratio: True + size_sum: True + - DistillationDMLLoss: + weight: 1.0 + weight_ratio: True + sum_across_class_dim: False + model_name_pairs: + - ["Student", "Teacher"] + + Eval: + - MultiLabelLoss: + weight: 1.0 + weight_ratio: True + size_sum: True + +Optimizer: + name: Momentum + momentum: 0.9 + lr: + name: Cosine + learning_rate: 0.02 + warmup_epoch: 5 + regularizer: + name: 'L2' + coeff: 0.0005 + +# data loader for train and eval +DataLoader: + Train: + dataset: + name: MultiLabelDataset + image_root: "dataset/table_attribute/" + cls_label_path: "dataset/table_attribute/train_list.txt" + label_ratio: True + transform_ops: + - DecodeImage: + to_rgb: True + channel_first: False + - ResizeImage: + size: [224, 224] + - RandFlipImage: + flip_code: 1 + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + sampler: + name: DistributedBatchSampler + batch_size: 64 + drop_last: True + shuffle: True + loader: + num_workers: 4 + use_shared_memory: True + Eval: + dataset: + name: MultiLabelDataset + image_root: "dataset/table_attribute/" + cls_label_path: "dataset/table_attribute/val_list.txt" + label_ratio: True + transform_ops: + - DecodeImage: + to_rgb: True + channel_first: False + - ResizeImage: + size: [224, 224] + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + sampler: + name: DistributedBatchSampler + batch_size: 64 + drop_last: False + shuffle: False + loader: + num_workers: 4 + use_shared_memory: True + +Infer: + infer_imgs: deploy/images/PULC/table_attribute/val_3253.jpg + batch_size: 10 + transforms: + - DecodeImage: + to_rgb: True + channel_first: False + - ResizeImage: + size: [224, 224] + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: + PostProcess: + name: TableAttribute + source_threshold: 0.5 + number_threshold: 0.5 + color_threshold: 0.5 + clarity_threshold : 0.5 + obstruction_threshold: 0.5 + angle_threshold: 0.5 + +Metric: + Eval: + - ATTRMetric: + + diff --git a/ppcls/data/postprocess/__init__.py b/ppcls/data/postprocess/__init__.py index 130ff225..12b1f212 100644 --- a/ppcls/data/postprocess/__init__.py +++ b/ppcls/data/postprocess/__init__.py @@ -18,7 +18,7 @@ from . import topk, threshoutput from .topk import Topk from .threshoutput import ThreshOutput, MultiLabelThreshOutput -from .attr_rec import VehicleAttribute, PersonAttribute +from .attr_rec import VehicleAttribute, PersonAttribute, TableAttribute -- GitLab