Merge branch 'add_person_demo' of github.com:cuicheng01/PaddleClas into add_person_demo

deploy/configs/cls_demo/person/inference_person_cls.yaml

+Global:
+  infer_imgs: "./images/cls_demo/person/objects365_02035329.jpg"
+  inference_model_dir: "./models/person_cls_infer"
+  batch_size: 1
+  use_gpu: True
+  enable_mkldnn: True
+  cpu_num_threads: 10
+  enable_benchmark: True
+  use_fp16: False
+  ir_optim: True
+  use_tensorrt: False
+  gpu_mem: 8000
+  enable_profile: False
+
+PreProcess:
+  transform_ops:
+    - ResizeImage:
+        resize_short: 256
+    - CropImage:
+        size: 224
+    - NormalizeImage:
+        scale: 0.00392157
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+        order: ''
+        channel_num: 3
+    - ToCHWImage:
+
+PostProcess:
+  main_indicator: Topk
+  Topk:
+    topk: 5
+    class_id_map_file: "../ppcls/utils/cls_demo/person_label_list.txt"
+  SavePreLabel:
+    save_dir: ./pre_label/

docs/zh_CN/cls_demo/quick_start_cls_demo.md

+# PaddleClas构建有人/无人分类案例
+
+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。
+
+------
+
+
+## 目录
+
+- [1. 环境配置](#1)
+- [2. 有人/无人场景推理预测](#2)
+  - [2.1 下载模型](#2.1)  
+  - [2.2 模型推理预测](#2.2)
+      - [2.2.1 预测单张图像](#2.2.1)
+      - [2.2.2 基于文件夹的批量预测](#2.2.2)
+- [3.有人/无人场景训练](#3)
+    - [3.1 数据准备](#3.1)
+    - [3.2 模型训练](#3.2)
+      - [3.2.1 基于默认超参数训练](#3.2.1)
+        - [3.2.1.1 基于默认超参数训练轻量级模型](#3.2.1.1)
+        - [3.2.1.2 基于默认超参数训练教师模型](#3.2.1.2)
+        - [3.2.1.3 基于默认超参数进行蒸馏训练](#3.2.1.3)
+      - [3.2.2 超参数搜索训练](#3.2)  
+- [4. 模型评估与推理](#4)
+  - [4.1 模型评估](#3.1)
+  - [4.2 模型预测](#3.2)
+  - [4.3 使用 inference 模型进行推理](#4.3)
+    - [4.3.1 导出 inference 模型](#4.3.1)
+    - [4.3.2 模型推理预测](#4.3.2)
+    
+    
+<a name="1"></a>
+
+## 1. 环境配置
+
+* 安装：请先参考 [Paddle 安装教程](../installation/install_paddle.md) 以及 [PaddleClas 安装教程](../installation/install_paddleclas.md) 配置 PaddleClas 运行环境。
+ 
+<a name="2"></a> 
+
+## 2. 有人/无人场景推理预测
+
+<a name="2.1"></a> 
+
+### 2.1 下载模型
+
+* 进入 `deploy` 运行目录。
+
+```
+cd deploy
+```
+
+下载有人/无人分类的模型。
+
+```
+mkdir models
+cd models
+# 下载inference 模型并解压
+wget https://paddleclas.bj.bcebos.com/models/cls_demo/person_cls_infer.tar && tar -xf person_cls_infer.tar
+```
+
+解压完毕后，`models` 文件夹下应有如下文件结构：
+
+```
+├── person_cls_infer
+│   ├── inference.pdiparams
+│   ├── inference.pdiparams.info
+│   └── inference.pdmodel
+```
+
+<a name="2.2"></a> 
+
+### 2.2 模型推理预测
+
+<a name="2.2.1"></a> 
+
+#### 2.2.1 预测单张图像
+
+运行下面的命令，对图像 `./images/cls_demo/person/objects365_02035329.jpg` 进行有人/无人分类。
+
+```shell
+# 使用下面的命令使用 GPU 进行预测
+python3.7 python/predict_cls.py -c configs/cls_demo/person/inference_person_cls.yaml
+# 使用下面的命令使用 CPU 进行预测
+python3.7 python/predict_system.py -c configs/inference_general.yaml -o Global.use_gpu=False
+```
+
+输出结果如下。
+
+```
+objects365_02035329.jpg:	class id(s): [1, 0], score(s): [1.00, 0.00], label_name(s): ['someone', 'nobody']
+```
+
+其中，`someone` 表示该图里存在人，`nobody` 表示该图里不存在人。
+
+
+<a name="2.2.2"></a> 
+
+#### 2.2.2 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_system.py -c configs/inference_general.yaml -o Global.infer_imgs="./images/cls_demo/person/"
+```
+
+终端中会输出该文件夹内所有图像的分类结果，如下所示。
+
+```
+objects365_01780782.jpg:	class id(s): [0, 1], score(s): [1.00, 0.00], label_name(s): ['nobody', 'someone']
+objects365_02035329.jpg:	class id(s): [1, 0], score(s): [1.00, 0.00], label_name(s): ['someone', 'nobody']
+```
+
+<a name="3"></a> 
+
+## 3.有人/无人场景训练
+
+<a name="3.1"></a> 
+
+### 3.1 数据准备
+
+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/cls_demo/person.tar
+tar -xf person.tar
+cd ../
+```
+
+执行上述命令后，`dataset/`下存在`person`目录，该目录中具有以下数据：
+
+```
+
+├── train
+│   ├── 000000000009.jpg
+│   ├── 000000000025.jpg
+...
+├── val
+│   ├── objects365_01780637.jpg
+│   ├── objects365_01780640.jpg
+...
+├── ImageNet_val
+│   ├── ILSVRC2012_val_00000001.JPEG
+│   ├── ILSVRC2012_val_00000002.JPEG
+...
+├── train_list.txt
+├── train_list.txt.debug
+├── train_list_for_distill.txt
+├── val_list.txt
+└── val_list.txt.debug
+```
+
+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`。
+
+* **注意**: 
+
+* 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](https://cocodataset.org/#overview)的训练集的子集，`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。
+
+<a name="3.2"></a> 
+
+### 3.2 模型训练
+
+<a name="3.2.1"></a> 
+
+#### 3.2.1 基于默认超参数训练
+
+<a name="3.2.1.1"></a> 
+
+##### 3.2.1.1 基于默认超参数训练轻量级模型
+
+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：
+
+```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/cls_demo/person/PPLCNet/PPLCNet_x1_0.yaml 
+```
+
+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）。
+
+<a name="3.2.1.2"></a> 
+
+##### 3.2.1.2 基于默认超参数训练教师模型
+
+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：
+
+```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/cls_demo/person/PPLCNet/PPLCNet_x1_0.yaml \
+        -o Arch.name=ResNet101_vd
+```
+
+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`。
+
+<a name="3.2.1.3"></a> 
+
+##### 3.2.1.3 基于默认超参数进行蒸馏训练
+
+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：
+
+```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/cls_demo/person/Distillation/PPLCNet_x1_0_distillation.yaml \
+        -o Arch.models.0.Teacher.pretrained=output/ResNet101_vd/best_model
+```
+
+<a name="3.2.2"></a>
+
+#### 3.2.2 超参数搜索训练
+
+[3.2 小节](#3.2) 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。
+
+* 搜索运行脚本如下：
+
+```shell
+python tools/search_strategy.py -c ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0_search.yaml
+```
+
+* **注意**: 
+
+* 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。
+
+* 如果此过程搜索的得到的超参数与3.2.1小节提供的超参数不一致，主要是由于训练数据较小造成的波动导致，可以忽略。
+
+<a name="4"></a>
+
+## 4. 模型评估与推理
+
+
+<a name="4.1"></a> 
+
+### 4.1 模型评估
+
+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。
+
+```bash
+python3 tools/eval.py \
+    -c ./ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0.yaml \
+    -o Global.pretrained_model="output/PPLCNet_x1_0/best_model"
+```
+
+<a name="4.2"></a> 
+
+### 4.2 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：
+
+```python
+python3 tools/infer.py \
+    -c ./ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0.yaml \
+    -o Infer.infer_imgs=./dataset/person/val/objects365_01780637.jpg  \
+    -o Global.pretrained_model=output/PPLCNet_x1_0/best_model
+```
+
+<a name="4.3"></a> 
+
+### 4.3 使用 inference 模型进行推理
+
+<a name="4.3.1"></a> 
+
+### 4.3.1 导出 inference 模型
+
+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：
+首先，对训练好的模型进行转换：
+
+```bash
+python3 tools/export_model.py \
+    -c ./ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0.yaml \
+    -o Global.pretrained_model=output/PPLCNet_x1_0/best_model \
+    -o Global.save_inference_dir=deploy/models/PPLCNet_x1_0_person
+```
+执行完该脚本后会在`deploy/models/`下生成`PPLCNet_x1_0_person`文件夹，该文件夹中的模型与 2.2 节下载的推理预测模型格式一致。
+
+<a name="4.3.2"></a> 
+
+### 4.3.2 基于 inference 模型推理预测
+推理预测的脚本为：
+
+```
+python3.7 python/predict_cls.py -c configs/cls_demo/person/inference_person_cls.yaml -o Global.inference_model_dir="models/PPLCNet_x1_0_person"
+```
+
+更多关于推理的细节，可以参考[2.2节](#2.2)。
+

ppcls/configs/cls_demo/person/Distillation/PPLCNet_x1_0_distillation.yaml

@@ -33,11 +33,14 @@ Arch:
     - Teacher:
         name: ResNet101_vd
         class_num: *class_num
+        use_sync_bn: True
     - Student:
         name: PPLCNet_x1_0
         class_num: *class_num
         pretrained: True
         use_ssld: True
+        use_sync_bn: True
+        lr_mult_list: [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
 
   infer_model_name: "Student"
  
@@ -77,21 +80,21 @@ 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

ppcls/configs/cls_demo/person/Distillation/PPLCNet_x1_0_distillation_search.yaml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: ./output
+  device: gpu
+  save_interval: 1
+  eval_during_train: True
+  start_eval_epoch: 10
+  eval_interval: 1
+  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: "DistillationModel"
+  class_num: &class_num 2
+  # if not null, its lengths should be same as models
+  pretrained_list:
+  # if not null, its lengths should be same as models
+  freeze_params_list:
+  - True
+  - False
+  use_sync_bn: True
+  models:
+    - Teacher:
+        name: ResNet101_vd
+        class_num: *class_num
+        use_sync_bn: True
+    - Student:
+        name: PPLCNet_x1_0
+        class_num: *class_num
+        pretrained: True
+        use_ssld: True
+        use_sync_bn: True
+
+  infer_model_name: "Student"
+ 
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - DistillationDMLLoss:
+        weight: 1.0
+        model_name_pairs:
+        - ["Student", "Teacher"]
+  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_for_distill.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: 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]

ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0.yaml

@@ -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

ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0_search.yaml

+# 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]

ppcls/engine/engine.py

@@ -344,15 +344,15 @@ class Engine(object):
 
             if self.use_dali:
                 self.train_dataloader.reset()
-            metric_msg = ", ".join([
-                self.output_info[key].avg_info for key in self.output_info
-            ])
+            metric_msg = ", ".join(
+                [self.output_info[key].avg_info for key in self.output_info])
             logger.info("[Train][Epoch {}/{}][Avg]{}".format(
                 epoch_id, self.config["Global"]["epochs"], metric_msg))
             self.output_info.clear()
 
             # eval model and save model if possible
-            start_eval_epoch = self.config["Global"].get("start_eval_epoch", 0) - 1
+            start_eval_epoch = self.config["Global"].get("start_eval_epoch",
+                                                         0) - 1
             if self.config["Global"][
                     "eval_during_train"] and epoch_id % self.config["Global"][
                         "eval_interval"] == 0 and epoch_id > start_eval_epoch:
@@ -367,7 +367,8 @@ class Engine(object):
                         self.output_dir,
                         model_name=self.config["Arch"]["name"],
                         prefix="best_model",
-                        loss=self.train_loss_func)
+                        loss=self.train_loss_func,
+                        save_student_model=True)
                 logger.info("[Eval][Epoch {}][best metric: {}]".format(
                     epoch_id, best_metric["metric"]))
                 logger.scaler(

ppcls/utils/cls_demo/person_label_list.txt

+0 nobody
+1 someone

ppcls/utils/save_load.py

@@ -42,6 +42,14 @@ def _mkdir_if_not_exist(path):
                 raise OSError('Failed to mkdir {}'.format(path))
 
 
+def _extract_student_weights(all_params, student_prefix="Student."):
+    s_params = {
+        key[len(student_prefix):]: all_params[key]
+        for key in all_params if student_prefix in key
+    }
+    return s_params
+
+
 def load_dygraph_pretrain(model, path=None):
     if not (os.path.isdir(path) or os.path.exists(path + '.pdparams')):
         raise ValueError("Model pretrain path {}.pdparams does not "
@@ -126,7 +134,8 @@ def save_model(net,
                model_path,
                model_name="",
                prefix='ppcls',
-               loss: paddle.nn.Layer=None):
+               loss: paddle.nn.Layer=None,
+               save_student_model=False):
     """
     save model to the target path
     """
@@ -137,12 +146,19 @@ def save_model(net,
     model_path = os.path.join(model_path, prefix)
 
     params_state_dict = net.state_dict()
+    if loss is not None:
         loss_state_dict = loss.state_dict()
-    keys_inter = set(params_state_dict.keys()) & set(loss_state_dict.keys())
+        keys_inter = set(params_state_dict.keys()) & set(loss_state_dict.keys(
+        ))
         assert len(keys_inter) == 0, \
             f"keys in model and loss state_dict must be unique, but got intersection {keys_inter}"
         params_state_dict.update(loss_state_dict)
 
+    if save_student_model:
+        s_params = _extract_student_weights(params_state_dict)
+        if len(s_params) > 0:
+            paddle.save(s_params, model_path + "_student.pdparams")
+
     paddle.save(params_state_dict, model_path + ".pdparams")
     paddle.save([opt.state_dict() for opt in optimizer], model_path + ".pdopt")
     paddle.save(metric_info, model_path + ".pdstates")