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deploy/configs/cls_demo/person/inference_person_cls.yaml 0 → 100644
浏览文件 @ 6e8f395c
Global:
infer_imgs: "./images/cls_demo/person/objects365_02035329.jpg"
inference_model_dir: "./models/PPLCNet_x1_0_person/"
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/quick_start/cls_demo/quick_start_cls_demo.md 0 → 100644
浏览文件 @ 6e8f395c
# 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。
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。
------
## 目录
- [1. 数据准备](#1)
- [2. 模型训练](#2)
- [2.1 基于搜索好的超参数训练](#2.1)
- [2.1.1 基于搜索好的超参数训练轻量级模型](#2.1.1)
- [2.1.2 基于搜索好的超参数训练教师模型](#2.1.2)
- [2.1.3 基于搜索好的超参数进行蒸馏训练](#2.1.3)
- [2.2 超参数搜索训练](2.2)
- [3. 模型评估与推理](#3)
- [3.1 模型评估](#3.1)
- [3.2 模型预测](#3.2)
- [3.3 使用 inference 模型进行模型推理](#3.3)
- [3.3.1 导出 inference 模型](#3.3.1)
- [3.3.2 模型推理预测](#3.3.2)
<a name="1"></a>
## 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。
<a name="2"></a>
## 2. 模型训练
<a name="2.1"></a>
### 2.1 基于搜索好的超参数训练
<a name="2.1.1"></a>
#### 2.1.1 基于搜索好的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="2.1.2"></a>
#### 2.1.2 基于搜索好的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="2.1.3"></a>
#### 2.1.3 基于搜索好的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="2.2"></a>
### 2.2 超参数搜索训练
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。
* 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。
<a name="3"></a>
## 3. 模型评估与推理
<a name="3.1"></a>
### 3.1 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="3.2"></a>
### 3.2 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="3.3"></a>
### 3.3 使用 inference 模型进行模型推理
<a name="3.3.1"></a>
### 3.3.1 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
```
* 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。
<a name="3.3.2"></a>
### 3.3.2 模型推理预测
进入 deploy 目录下：
```bash
cd deploy
```
执行下面的命令进行预测：
```bash
python python/predict_cls.py -c configs/cls_demo/person/inference_person_cls.yaml
```
输出结果为：
```
objects365_02035329.jpg: class id(s): [1, 0], score(s): [1.00, 0.00], label_name(s): ['someone', 'nobody']
```
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：
```bash
python python/predict_cls.py -c configs/cls_demo/person/inference_person_cls.yaml -o Global.infer_imgs=./images/cls_demo/person/
```
输出结果为：
```
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']
```
ppcls/configs/cls_demo/person/Distillation/PPLCNet_x1_0_distillation.yaml
浏览文件 @ 6e8f395c
......@@ -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 0 → 100644
浏览文件 @ 6e8f395c
# 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
- Student:
name: PPLCNet_x1_0
class_num: *class_num
pretrained: True
use_ssld: 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
浏览文件 @ 6e8f395c
......@@ -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 0 → 100644
浏览文件 @ 6e8f395c
# 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]
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