diff --git a/deploy/configs/cls_demo/person/inference_person_cls.yaml b/deploy/configs/cls_demo/person/inference_person_cls.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cf7a75659dbfe5690fb903e91b6df775bc506814 --- /dev/null +++ b/deploy/configs/cls_demo/person/inference_person_cls.yaml @@ -0,0 +1,35 @@ +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/ diff --git a/deploy/images/cls_demo/person/objects365_01780782.jpg b/deploy/images/cls_demo/person/objects365_01780782.jpg new file mode 100755 index 0000000000000000000000000000000000000000..a0dd0df59ae5a6386a04a8e0cf9cdbc529139c16 Binary files /dev/null and b/deploy/images/cls_demo/person/objects365_01780782.jpg differ diff --git a/deploy/images/cls_demo/person/objects365_02035329.jpg b/deploy/images/cls_demo/person/objects365_02035329.jpg new file mode 100755 index 0000000000000000000000000000000000000000..16d7f2d08cd87bda1b67d21655f00f94a0c6e4e4 Binary files /dev/null and b/deploy/images/cls_demo/person/objects365_02035329.jpg differ diff --git a/docs/zh_CN/cls_demo/quick_start_cls_demo.md b/docs/zh_CN/cls_demo/quick_start_cls_demo.md new file mode 100644 index 0000000000000000000000000000000000000000..dc1f8cd34be2ce9808380e75b1f8652d73cb11a6 --- /dev/null +++ b/docs/zh_CN/cls_demo/quick_start_cls_demo.md @@ -0,0 +1,299 @@ +# 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) + + + + +## 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。 + + + +## 2. 有人/无人场景推理预测 + + + +### 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 +``` + + + +### 2.2 模型推理预测 + + + +#### 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。 + + + + +#### 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'] +``` + + + +## 3.有人/无人场景训练 + + + +### 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。 + + + +### 3.2 模型训练 + + + +#### 3.2.1 基于默认超参数训练 + + + +##### 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）。 + + + +##### 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`。 + + + +##### 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 +``` + + + +#### 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。 + + + +## 4. 模型评估与推理 + + + + +### 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" +``` + + + +### 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 +``` + + + +### 4.3 使用 inference 模型进行推理 + + + +### 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。 + + + +### 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)。 + diff --git a/ppcls/configs/cls_demo/person/Distillation/PPLCNet_x1_0_distillation.yaml b/ppcls/configs/cls_demo/person/Distillation/PPLCNet_x1_0_distillation.yaml index 54e1a313c69afeebb2ec69a8b0257f6554a4ea61..a9c3db29682933f19cf93ef000d5b6ec83007aa7 100644 --- a/ppcls/configs/cls_demo/person/Distillation/PPLCNet_x1_0_distillation.yaml +++ b/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 diff --git a/ppcls/configs/cls_demo/person/Distillation/PPLCNet_x1_0_distillation_search.yaml b/ppcls/configs/cls_demo/person/Distillation/PPLCNet_x1_0_distillation_search.yaml new file mode 100644 index 0000000000000000000000000000000000000000..231766d846459e5157a48fcde110b391f7f5cd5a --- /dev/null +++ b/ppcls/configs/cls_demo/person/Distillation/PPLCNet_x1_0_distillation_search.yaml @@ -0,0 +1,169 @@ +# 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] diff --git a/ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0.yaml b/ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0.yaml index 3c9bfb5dbb10fdea9c1209ecaace08c2fb59ac6a..97ae1ba73b0c499a5e2b80f5d32c62964b061a40 100644 --- a/ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0.yaml +++ b/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 diff --git a/ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0_search.yaml b/ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0_search.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3c9bfb5dbb10fdea9c1209ecaace08c2fb59ac6a --- /dev/null +++ b/ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0_search.yaml @@ -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] diff --git a/ppcls/engine/engine.py b/ppcls/engine/engine.py index 99dd5f3b83bc9e47608b934afb7ee0c9c167a535..9c2dc2e66fa55120eebf1271758a2d62d09c92f4 100644 --- a/ppcls/engine/engine.py +++ b/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( diff --git a/ppcls/utils/cls_demo/person_label_list.txt b/ppcls/utils/cls_demo/person_label_list.txt new file mode 100644 index 0000000000000000000000000000000000000000..8eea2b6dc2433abf303a0ea508021698559b749b --- /dev/null +++ b/ppcls/utils/cls_demo/person_label_list.txt @@ -0,0 +1,2 @@ +0 nobody +1 someone diff --git a/ppcls/utils/save_load.py b/ppcls/utils/save_load.py index 4e27f12c1d4830f2f16580bfa976cf3ace78d934..b7ce8684fadfd3fd75a33294f731fd64947d7b98 100644 --- a/ppcls/utils/save_load.py +++ b/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 " @@ -117,7 +125,7 @@ def init_model(config, net, optimizer=None, loss: paddle.nn.Layer=None): else: # common load load_dygraph_pretrain(net, path=pretrained_model) logger.info("Finish load pretrained model from {}".format( - pretrained_model)) + pretrained_model)) def save_model(net, @@ -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 """ @@ -137,11 +146,18 @@ def save_model(net, model_path = os.path.join(model_path, prefix) params_state_dict = net.state_dict() - loss_state_dict = loss.state_dict() - 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 loss is not None: + loss_state_dict = loss.state_dict() + 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")