add person_demo docs
cuicheng01
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Global:
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infer_imgs: "./images/cls_demo/person/objects365_02035329.jpg"
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inference_model_dir: "./models/PPLCNet_x1_0_person/"
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batch_size: 1
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use_gpu: True
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enable_mkldnn: True
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cpu_num_threads: 10
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enable_benchmark: True
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use_fp16: False
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ir_optim: True
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use_tensorrt: False
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gpu_mem: 8000
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enable_profile: False
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PreProcess:
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transform_ops:
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- ResizeImage:
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resize_short: 256
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- CropImage:
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size: 224
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- NormalizeImage:
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scale: 0.00392157
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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order: ''
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channel_num: 3
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- ToCHWImage:
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PostProcess:
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main_indicator: Topk
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Topk:
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topk: 5
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class_id_map_file: "../ppcls/utils/cls_demo//person_label_list.txt"
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SavePreLabel:
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save_dir: ./pre_label/
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# PaddleClas构建有人/无人分类案例
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此处提供了用户使用 PaddleClas 快速构建轻量级、高精度、可落地的有人/无人的分类模型教程，主要基于有人/无人场景的数据，融合了轻量级骨干网络PPLCNet、SSLD预训练权重、EDA数据增强策略、KL-JS-UGI知识蒸馏策略、SHAS超参数搜索策略，得到精度高、速度快、易于部署的二分类模型。
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请事先参考[安装指南](../installation/install_paddleclas.md)配置运行环境和克隆 PaddleClas 代码。
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------
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## 目录
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- [1. 数据准备](#1)
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- [2. 模型训练](#2)
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- [2.1 基于搜索好的超参数训练](#2.1)
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- [2.1.1 基于搜索好的超参数训练轻量级模型](#2.1.1)
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- [2.1.2 基于搜索好的超参数训练教师模型](#2.1.2)
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- [2.1.3 基于搜索好的超参数进行蒸馏训练](#2.1.3)
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- [2.2 超参数搜索训练](2.2)
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- [3. 模型评估与推理](#3)
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- [3.1 模型评估](#3.1)
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- [3.2 模型预测](#3.2)
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- [3.3 使用 inference 模型进行模型推理](#3.3)
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- [3.3.1 导出 inference 模型](#3.3.1)
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- [3.3.2 模型推理预测](#3.3.2)
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<a name="1"></a>
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## 1. 数据准备
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进入 PaddleClas 目录。
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```
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cd path_to_PaddleClas
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```
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进入 `dataset/` 目录，下载并解压有人/无人场景的数据。
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```shell
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cd dataset
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wget https://paddleclas.bj.bcebos.com/data/cls_demo/person.tar
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tar -xf person.tar
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cd ../
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```
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执行上述命令后，`dataset/`下存在`person`目录，该目录中具有以下数据：
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```
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├── train
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│ ├── 000000000009.jpg
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│ ├── 000000000025.jpg
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...
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├── val
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│ ├── objects365_01780637.jpg
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│ ├── objects365_01780640.jpg
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...
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├── ImageNet_val
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│ ├── ILSVRC2012_val_00000001.JPEG
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│ ├── ILSVRC2012_val_00000002.JPEG
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...
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├── train_list.txt
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├── train_list.txt.debug
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├── train_list_for_distill.txt
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├── val_list.txt
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└── val_list.txt.debug
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```
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其中`train/`和`val/`分别为训练集和验证集。`train_list.txt`和`val_list.txt`分别为训练集和验证集的标签文件，`train_list.txt.debug`和`val_list.txt.debug`分别为训练集和验证集的`debug`标签文件，其分别是`train_list.txt`和`val_list.txt`的子集，用该文件可以快速体验本案例的流程。`ImageNet_val/`是ImageNet的验证集，该集合和`train`集合的混合数据用于本案例的`KL-JS-UGI知识蒸馏策略`，对应的训练标签文件为`train_list_for_distill.txt`。
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* **注意**:
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* 本案例中所使用的所有数据集均为开源数据，`train`集合为[MS-COCO数据的](https://cocodataset.org/#overview)训练集的子集，`val`集合为[Object365数据](https://www.objects365.org/overview.html)的训练集的子集，`ImageNet_val`为[ImageNet数据](https://www.image-net.org/)的验证集。
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<a name="2"></a>
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## 2. 模型训练
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<a name="2.1"></a>
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### 2.1 基于搜索好的超参数训练
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<a name="2.1.1"></a>
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#### 2.1.1 基于搜索好的超参数训练轻量级模型
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在`ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0.yaml`中提供了基于该场景中已经搜索好的超参数，可以通过如下脚本启动训练：
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```shell
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export CUDA_VISIBLE_DEVICES=0,1,2,3
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python3 -m paddle.distributed.launch \
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--gpus="0,1,2,3" \
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tools/train.py \
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-c ./ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0.yaml
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```
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验证集的最佳 metric 在0.94-0.95之间（数据集较小，容易造成波动）。
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<a name="2.1.2"></a>
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#### 2.1.2 基于搜索好的超参数训练教师模型
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复用`ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0.yaml`中的超参数，训练教师模型，训练脚本如下：
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```shell
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export CUDA_VISIBLE_DEVICES=0,1,2,3
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python3 -m paddle.distributed.launch \
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--gpus="0,1,2,3" \
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tools/train.py \
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-c ./ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0.yaml \
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-o Arch.name=ResNet101_vd
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```
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验证集的最佳 metric 为0.97-0.98之间，当前教师模型最好的权重保存在`output/ResNet101_vd/best_model.pdparams`。
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<a name="2.1.3"></a>
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#### 2.1.3 基于搜索好的超参数进行蒸馏训练
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配置文件`ppcls/configs/cls_demo/person/Distillation/PPLCNet_x1_0_distillation.yaml`提供了`KL-JS-UGI知识蒸馏策略`的配置。该配置将`ResNet101_vd`当作教师模型，`PPLCNet_x1_0`当作学生模型，使用ImageNet数据集的验证集作为新增的无标签数据。训练脚本如下：
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```shell
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export CUDA_VISIBLE_DEVICES=0,1,2,3
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python3 -m paddle.distributed.launch \
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--gpus="0,1,2,3" \
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tools/train.py \
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-c .ppcls/configs/cls_demo/person/Distillation/PPLCNet_x1_0_distillation.yaml \
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-o Arch.models.0.Teacher.pretrained=output/ResNet101_vd/best_model
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```
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<a name="2.2"></a>
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### 2.2 超参数搜索训练
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2.1 小节提供了在已经搜索并得到的超参数上进行了训练，此部分内容提供了搜索的过程，此过程是为了得到更好的训练超参数。
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* 搜索运行脚本如下：
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```shell
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python tools/search_strategy.py -c ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0_search.yaml
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```
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* **注意**:
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* 此过程基于当前数据集在 V100 4 卡上大概需要耗时 6 小时，如果缺少机器资源，希望体验搜索过程，可以将`ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0_search.yaml`中的`train_list.txt`和`val_list.txt`分别替换为`train_list.txt.debug`和`val_list.txt.debug`。替换list只是为了加速跑通整个搜索过程，由于数据量较小，其搜素的结果没有参考性。
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<a name="3"></a>
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## 3. 模型评估与推理
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<a name="3.1"></a>
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### 3.1 模型评估
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训练好模型之后，可以通过以下命令实现对模型精度的评估。
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```bash
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python3 tools/eval.py \
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-c ./ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0.yaml \
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-o Global.pretrained_model="output/PPLCNet_x1_0/best_model"
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```
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<a name="3.2"></a>
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### 3.2 模型预测
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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：
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```python
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python3 tools/infer.py \
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-c ./ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0.yaml \
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-o Infer.infer_imgs=./dataset/person/val/objects365_01780637.jpg \
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-o Global.pretrained_model=output/PPLCNet_x1_0/best_model
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```
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<a name="3.3"></a>
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### 3.3 使用 inference 模型进行模型推理
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<a name="3.3.1"></a>
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### 3.3.1 导出 inference 模型
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通过导出 inference 模型，PaddlePaddle 支持使用预测引擎进行预测推理。接下来介绍如何用预测引擎进行推理：
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首先，对训练好的模型进行转换：
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```bash
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python3 tools/export_model.py \
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-c ./ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0.yaml \
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-o Global.pretrained_model=output/PPLCNet_x1_0/best_model \
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-o Global.save_inference_dir=deploy/models/PPLCNet_x1_0_person
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```
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* 默认会在 `deploy/models/PPLCNet_x1_0_person` 文件夹下生成 `inference.pdiparams`、`inference.pdmodel` 和 `inference.pdiparams.info` 文件。其中`inference.pdiparams`、`inference.pdmodel` 分别存储了模型的权重和结构，用于推理预测。
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<a name="3.3.2"></a>
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### 3.3.2 模型推理预测
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进入 deploy 目录下：
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```bash
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cd deploy
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```
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执行下面的命令进行预测：
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```bash
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python python/predict_cls.py -c configs/cls_demo/person/inference_person_cls.yaml
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```
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输出结果为：
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```
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objects365_02035329.jpg: class id(s): [1, 0], score(s): [1.00, 0.00], label_name(s): ['someone', 'nobody']
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```
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如果希望预测整个文件夹的图片，可以通过`-o `来重写配置文件中的`Global.infer_imgs`字段，如预测`./images/cls_demo/person/`下所有的图片的命令为：
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```bash
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python python/predict_cls.py -c configs/cls_demo/person/inference_person_cls.yaml -o Global.infer_imgs=./images/cls_demo/person/
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```
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输出结果为：
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```
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objects365_01780782.jpg: class id(s): [0, 1], score(s): [1.00, 0.00], label_name(s): ['nobody', 'someone']
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objects365_02035329.jpg: class id(s): [1, 0], score(s): [1.00, 0.00], label_name(s): ['someone', 'nobody']
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```
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- Teacher:
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name: ResNet101_vd
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class_num: *class_num
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use_sync_bn: True
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- Student:
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name: PPLCNet_x1_0
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class_num: *class_num
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pretrained: True
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use_ssld: True
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use_sync_bn: True
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lr_mult_list: [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
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infer_model_name: "Student"
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@ -77,21 +80,21 @@ DataLoader:
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to_rgb: True
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channel_first: False
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- RandCropImage:
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size: 224
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size: 192
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- RandFlipImage:
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flip_code: 1
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- TimmAutoAugment:
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prob: 0.0
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config_str: rand-m9-mstd0.5-inc1
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interpolation: bicubic
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img_size: 224
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img_size: 192
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- NormalizeImage:
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scale: 1.0/255.0
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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order: ''
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- RandomErasing:
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EPSILON: 0.0
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EPSILON: 0.1
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sl: 0.02
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sh: 1.0/3.0
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r1: 0.3
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# global configs
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Global:
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checkpoints: null
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pretrained_model: null
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output_dir: ./output
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device: gpu
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save_interval: 1
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eval_during_train: True
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start_eval_epoch: 10
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eval_interval: 1
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epochs: 20
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print_batch_step: 10
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use_visualdl: False
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# used for static mode and model export
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image_shape: [3, 224, 224]
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save_inference_dir: ./inference
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# training model under @to_static
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to_static: False
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use_dali: False
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# model architecture
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Arch:
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name: "DistillationModel"
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class_num: &class_num 2
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# if not null, its lengths should be same as models
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pretrained_list:
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# if not null, its lengths should be same as models
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freeze_params_list:
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- True
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- False
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use_sync_bn: True
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models:
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- Teacher:
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name: ResNet101_vd
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class_num: *class_num
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- Student:
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name: PPLCNet_x1_0
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class_num: *class_num
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pretrained: True
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use_ssld: True
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infer_model_name: "Student"
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# loss function config for traing/eval process
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Loss:
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Train:
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- DistillationDMLLoss:
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weight: 1.0
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model_name_pairs:
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- ["Student", "Teacher"]
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Eval:
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- CELoss:
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weight: 1.0
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Optimizer:
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name: Momentum
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momentum: 0.9
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lr:
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name: Cosine
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learning_rate: 0.01
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warmup_epoch: 5
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regularizer:
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name: 'L2'
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coeff: 0.00004
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# data loader for train and eval
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DataLoader:
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Train:
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dataset:
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name: ImageNetDataset
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image_root: ./dataset/person/
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cls_label_path: ./dataset/person/train_list_for_distill.txt
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transform_ops:
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- DecodeImage:
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to_rgb: True
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channel_first: False
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- RandCropImage:
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size: 224
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- RandFlipImage:
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flip_code: 1
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- TimmAutoAugment:
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prob: 0.0
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config_str: rand-m9-mstd0.5-inc1
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interpolation: bicubic
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img_size: 224
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- NormalizeImage:
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scale: 1.0/255.0
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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order: ''
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- RandomErasing:
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EPSILON: 0.0
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sl: 0.02
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sh: 1.0/3.0
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r1: 0.3
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attempt: 10
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use_log_aspect: True
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mode: pixel
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sampler:
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name: DistributedBatchSampler
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batch_size: 64
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drop_last: False
|
||||
shuffle: True
|
||||
loader:
|
||||
num_workers: 16
|
||||
use_shared_memory: True
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: ImageNetDataset
|
||||
image_root: ./dataset/person/
|
||||
cls_label_path: ./dataset/person/val_list.txt
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 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: docs/images/inference_deployment/whl_demo.jpg
|
||||
batch_size: 10
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 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: Topk
|
||||
topk: 5
|
||||
class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
|
||||
|
||||
Metric:
|
||||
Train:
|
||||
- DistillationTopkAcc:
|
||||
model_key: "Student"
|
||||
topk: [1, 2]
|
||||
Eval:
|
||||
- TprAtFpr:
|
||||
- TopkAcc:
|
||||
topk: [1, 2]
|
||||
|
|
@ -26,6 +26,7 @@ Arch:
|
|||
pretrained: True
|
||||
use_ssld: True
|
||||
use_sync_bn: True
|
||||
lr_mult_list: [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
|
||||
|
||||
# loss function config for traing/eval process
|
||||
Loss:
|
||||
|
|
@ -61,21 +62,21 @@ DataLoader:
|
|||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
size: 192
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- TimmAutoAugment:
|
||||
prob: 0.0
|
||||
config_str: rand-m9-mstd0.5-inc1
|
||||
interpolation: bicubic
|
||||
img_size: 224
|
||||
img_size: 192
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- RandomErasing:
|
||||
EPSILON: 0.0
|
||||
EPSILON: 0.1
|
||||
sl: 0.02
|
||||
sh: 1.0/3.0
|
||||
r1: 0.3
|
||||
|
|
|
|||
|
|
@ -0,0 +1,150 @@
|
|||
# global configs
|
||||
Global:
|
||||
checkpoints: null
|
||||
pretrained_model: null
|
||||
output_dir: ./output/
|
||||
device: gpu
|
||||
save_interval: 1
|
||||
eval_during_train: True
|
||||
eval_interval: 1
|
||||
start_eval_epoch: 10
|
||||
epochs: 20
|
||||
print_batch_step: 10
|
||||
use_visualdl: False
|
||||
# used for static mode and model export
|
||||
image_shape: [3, 224, 224]
|
||||
save_inference_dir: ./inference
|
||||
# training model under @to_static
|
||||
to_static: False
|
||||
use_dali: False
|
||||
|
||||
|
||||
# model architecture
|
||||
Arch:
|
||||
name: PPLCNet_x1_0
|
||||
class_num: 2
|
||||
pretrained: True
|
||||
use_ssld: True
|
||||
use_sync_bn: True
|
||||
|
||||
# loss function config for traing/eval process
|
||||
Loss:
|
||||
Train:
|
||||
- CELoss:
|
||||
weight: 1.0
|
||||
Eval:
|
||||
- CELoss:
|
||||
weight: 1.0
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Momentum
|
||||
momentum: 0.9
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.01
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
coeff: 0.00004
|
||||
|
||||
|
||||
# data loader for train and eval
|
||||
DataLoader:
|
||||
Train:
|
||||
dataset:
|
||||
name: ImageNetDataset
|
||||
image_root: ./dataset/person/
|
||||
cls_label_path: ./dataset/person/train_list.txt
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- TimmAutoAugment:
|
||||
prob: 0.0
|
||||
config_str: rand-m9-mstd0.5-inc1
|
||||
interpolation: bicubic
|
||||
img_size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- RandomErasing:
|
||||
EPSILON: 0.0
|
||||
sl: 0.02
|
||||
sh: 1.0/3.0
|
||||
r1: 0.3
|
||||
attempt: 10
|
||||
use_log_aspect: True
|
||||
mode: pixel
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 64
|
||||
drop_last: False
|
||||
shuffle: True
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: ImageNetDataset
|
||||
image_root: ./dataset/person/
|
||||
cls_label_path: ./dataset/person/val_list.txt
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 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: docs/images/inference_deployment/whl_demo.jpg
|
||||
batch_size: 10
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 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: Topk
|
||||
topk: 5
|
||||
class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
|
||||
|
||||
Metric:
|
||||
Train:
|
||||
- TopkAcc:
|
||||
topk: [1, 2]
|
||||
Eval:
|
||||
- TprAtFpr:
|
||||
- TopkAcc:
|
||||
topk: [1, 2]
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
0 nobody
|
||||
1 someone
|
||||
Loading…
Reference in New Issue