From 9e5f22ae1eeed57244093b1410e187537fa122f3 Mon Sep 17 00:00:00 2001
From: zhiboniu <31800336+zhiboniu@users.noreply.github.com>
Date: Fri, 8 Jul 2022 10:37:54 +0800
Subject: [PATCH] attribute docs update (#6381)
* attribute docs update
* fix mtmct
* update docs
---
deploy/pipeline/config/infer_cfg_pphuman.yml | 2 +-
deploy/pipeline/docs/tutorials/attribute.md | 36 +++++++--
.../pipeline/docs/tutorials/attribute_en.md | 27 ++++++-
deploy/pipeline/pipeline.py | 20 ++---
deploy/pipeline/pphuman/mtmct.py | 3 +
.../customization/attribute.md | 74 +++++++++++++-----
docs/images/add_attribute.png | Bin 0 -> 168627 bytes
7 files changed, 121 insertions(+), 41 deletions(-)
create mode 100644 docs/images/add_attribute.png
diff --git a/deploy/pipeline/config/infer_cfg_pphuman.yml b/deploy/pipeline/config/infer_cfg_pphuman.yml
index 6317c2672..2e91a4bff 100644
--- a/deploy/pipeline/config/infer_cfg_pphuman.yml
+++ b/deploy/pipeline/config/infer_cfg_pphuman.yml
@@ -20,7 +20,7 @@ KPT:
batch_size: 8
ATTR:
- model_dir: output_inference/strongbaseline_r50_30e_pa100k/
+ model_dir: output_inference/PPLCNet_x1_0_person_attribute_945_infer/
batch_size: 8
basemode: "idbased"
enable: False
diff --git a/deploy/pipeline/docs/tutorials/attribute.md b/deploy/pipeline/docs/tutorials/attribute.md
index 067a017e7..299cdf42f 100644
--- a/deploy/pipeline/docs/tutorials/attribute.md
+++ b/deploy/pipeline/docs/tutorials/attribute.md
@@ -6,21 +6,40 @@
| 任务 | 算法 | 精度 | 预测速度(ms) |下载链接 |
|:---------------------|:---------:|:------:|:------:| :---------------------------------------------------------------------------------: |
-| 行人属性高精度模型 | PP-HGNet_small | mA: 95.4 | 单人 1.54ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPLCNet_x1_0_person_attribute_945_infer.tar) |
-| 行人属性快速版模型 | PP-LCNet_x1_0 | mA: 94.5 | 单人 0.54ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPLCNet_x1_0_person_attribute_945_infer.tar) |
-| 行人属性平衡模型 | PP-HGNet_tiny | mA: 95.2 | 单人 1.14ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPHGNet_tiny_person_attribute_952_infer.tar) |
+| 行人属性高精度模型 | PP-HGNet_small | mA: 95.4 | 单人 1.54ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPHGNet_small_person_attribute_954_infer.tar) |
+| 行人属性轻量级模型 | PP-LCNet_x1_0 | mA: 94.5 | 单人 0.54ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPLCNet_x1_0_person_attribute_945_infer.tar) |
+| 行人属性精度与速度均衡模型 | PP-HGNet_tiny | mA: 95.2 | 单人 1.14ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPHGNet_tiny_person_attribute_952_infer.tar) |
1. 行人属性分析精度为[PA100k](https://github.com/xh-liu/HydraPlus-Net#pa-100k-dataset)，[RAPv2](http://www.rapdataset.com/rapv2.html)，[PETA](http://mmlab.ie.cuhk.edu.hk/projects/PETA.html)和部分业务数据融合训练测试得到
-2. 预测速度为V100 机器上使用TensorRT FP16时的速度, 速度包含数据预处理、模型预测、后处理全流程
+2. 预测速度为V100 机器上使用TensorRT FP16时的速度, 该处测速速度为模型预测速度
+3. 属性模型应用依赖跟踪模型结果，请在[跟踪模型页面](./mot.md)下载跟踪模型，依自身需求选择高精或轻量级下载。
+4. 模型下载后解压放置在PaddleDetection/output_inference/目录下。
## 使用方法
-1. 从上表链接中下载模型并解压到```./output_inference```路径下，并且设置infer_cfg_pphuman.yml中`ATTR`的enable: True
+1. 从上表链接中下载模型并解压到```PaddleDetection/output_inference```路径下，并且设置```deploy/pipeline/config/infer_cfg_pphuman.yml```中`ATTR`的enable: True
+
+`infer_cfg_pphuman.yml`中配置项说明：
+```
+ATTR: #模块名称
+ model_dir: output_inference/PPLCNet_x1_0_person_attribute_945_infer/ #模型路径
+ batch_size: 8 #推理最大batchsize
+ basemode: "idbased" #流程类型，'idbased'表示基于跟踪模型
+ enable: False #功能是否开启
+```
+
2. 图片输入时，启动命令如下
```python
+#单张图片
python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \
--image_file=test_image.jpg \
--device=gpu \
+
+#图片文件夹
+python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \
+ --image_dir=images/ \
+ --device=gpu \
+
```
3. 视频输入时，启动命令如下
```python
@@ -28,15 +47,16 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph
--video_file=test_video.mp4 \
--device=gpu \
```
+
4. 若修改模型路径，有以下两种方式：
- - ```./deploy/pipeline/config/infer_cfg_pphuman.yml```下可以配置不同模型路径，属性识别模型修改ATTR字段下配置
- - **(推荐)**命令行中增加`--model_dir`修改模型路径：
+ - 方法一：```./deploy/pipeline/config/infer_cfg_pphuman.yml```下可以配置不同模型路径，属性识别模型修改ATTR字段下配置
+ - 方法二：命令行中增加`--model_dir`修改模型路径：
```python
python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \
--video_file=test_video.mp4 \
--device=gpu \
- --model_dir det=ppyoloe/
+ --model_dir attr=output_inference/PPLCNet_x1_0_person_attribute_945_infer/
```
测试效果如下：
diff --git a/deploy/pipeline/docs/tutorials/attribute_en.md b/deploy/pipeline/docs/tutorials/attribute_en.md
index 3d8fab989..d77bd0e3e 100644
--- a/deploy/pipeline/docs/tutorials/attribute_en.md
+++ b/deploy/pipeline/docs/tutorials/attribute_en.md
@@ -6,21 +6,40 @@ Pedestrian attribute recognition has been widely used in the intelligent communi
| Task | Algorithm | Precision | Inference Speed(ms) | Download Link |
|:---------------------|:---------:|:------:|:------:| :---------------------------------------------------------------------------------: |
-| High-Precision Model | PP-HGNet_small | mA: 95.4 | per person 1.54ms | [Download](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPLCNet_x1_0_person_attribute_945_infer.tar) |
+| High-Precision Model | PP-HGNet_small | mA: 95.4 | per person 1.54ms | [Download](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPHGNet_small_person_attribute_954_infer.tar) |
| Fast Model | PP-LCNet_x1_0 | mA: 94.5 | per person 0.54ms | [Download](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPLCNet_x1_0_person_attribute_945_infer.tar) |
| Balanced Model | PP-HGNet_tiny | mA: 95.2 | per person 1.14ms | [Download](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPHGNet_tiny_person_attribute_952_infer.tar) |
1. The precision of pedestiran attribute analysis is obtained by training and testing on the dataset consist of [PA100k](https://github.com/xh-liu/HydraPlus-Net#pa-100k-dataset)，[RAPv2](http://www.rapdataset.com/rapv2.html)，[PETA](http://mmlab.ie.cuhk.edu.hk/projects/PETA.html) and some business data.
2. The inference speed is V100, the speed of using TensorRT FP16.
+3. This model of Attribute is based on the result of tracking, please download tracking model in the [Page of Mot](./mot_en.md). The High precision and Faster model are both available.
+4. You should place the model unziped in the directory of `PaddleDetection/output_inference/`.
## Instruction
1. Download the model from the link in the above table, and unzip it to```./output_inference```, and set the "enable: True" in ATTR of infer_cfg_pphuman.yml
+
+The meaning of configs of `infer_cfg_pphuman.yml`：
+```
+ATTR: #module name
+ model_dir: output_inference/PPLCNet_x1_0_person_attribute_945_infer/ #model path
+ batch_size: 8 #maxmum batchsize when inference
+ basemode: "idbased" #the routing type of pipeline，'idbased' means this model is based on tracking.
+ enable: False #whether to enable this model
+```
+
2. When inputting the image, run the command as follows:
```python
+#single image
python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \
--image_file=test_image.jpg \
--device=gpu \
+
+#image directory
+python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \
+ --image_dir=images/ \
+ --device=gpu \
+
```
3. When inputting the video, run the command as follows:
```python
@@ -30,13 +49,13 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph
```
4. If you want to change the model path, there are two methods：
- - In ```./deploy/pipeline/config/infer_cfg_pphuman.yml``` you can configurate different model paths. In attribute recognition models, you can modify the configuration in the field of ATTR.
- - Add `--model_dir` in the command line to change the model path：
+ - The first: In ```./deploy/pipeline/config/infer_cfg_pphuman.yml``` you can configurate different model paths. In attribute recognition models, you can modify the configuration in the field of ATTR.
+ - The second: Add `--model_dir` in the command line to change the model path：
```python
python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \
--video_file=test_video.mp4 \
--device=gpu \
- --model_dir det=ppyoloe/
+ --model_dir attr=output_inference/PPLCNet_x1_0_person_attribute_945_infer/
```
The test result is：
diff --git a/deploy/pipeline/pipeline.py b/deploy/pipeline/pipeline.py
index 703221949..ef56e622d 100644
--- a/deploy/pipeline/pipeline.py
+++ b/deploy/pipeline/pipeline.py
@@ -444,6 +444,17 @@ class PipePredictor(object):
trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads,
enable_mkldnn, color_threshold, type_threshold)
+ if self.with_mtmct:
+ reid_cfg = self.cfg['REID']
+ model_dir = reid_cfg['model_dir']
+ batch_size = reid_cfg['batch_size']
+ basemode = reid_cfg['basemode']
+ self.modebase[basemode] = True
+ self.reid_predictor = ReID(
+ model_dir, device, run_mode, batch_size, trt_min_shape,
+ trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads,
+ enable_mkldnn)
+
if self.with_mot or self.modebase["idbased"] or self.modebase[
"skeletonbased"]:
mot_cfg = self.cfg['MOT']
@@ -493,15 +504,6 @@ class PipePredictor(object):
cpu_threads=cpu_threads,
enable_mkldnn=enable_mkldnn)
- if self.with_mtmct:
- reid_cfg = self.cfg['REID']
- model_dir = reid_cfg['model_dir']
- batch_size = reid_cfg['batch_size']
- self.reid_predictor = ReID(
- model_dir, device, run_mode, batch_size, trt_min_shape,
- trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads,
- enable_mkldnn)
-
def set_file_name(self, path):
if path is not None:
self.file_name = os.path.split(path)[-1]
diff --git a/deploy/pipeline/pphuman/mtmct.py b/deploy/pipeline/pphuman/mtmct.py
index 721008f63..d873794a7 100644
--- a/deploy/pipeline/pphuman/mtmct.py
+++ b/deploy/pipeline/pphuman/mtmct.py
@@ -329,6 +329,9 @@ def res2dict(multi_res):
def mtmct_process(multi_res, captures, mtmct_vis=True, output_dir="output"):
cid_tid_dict = res2dict(multi_res)
+ if len(cid_tid_dict) == 0:
+ print("no tracking result found, mtmct will be skiped.")
+ return
map_tid = sub_cluster(cid_tid_dict)
if not os.path.exists(output_dir):
diff --git a/docs/advanced_tutorials/customization/attribute.md b/docs/advanced_tutorials/customization/attribute.md
index 763ee1359..15dcefb8e 100644
--- a/docs/advanced_tutorials/customization/attribute.md
+++ b/docs/advanced_tutorials/customization/attribute.md
@@ -70,36 +70,61 @@ train.txt文件内为所有训练图片名称（相对于根路径的文件路
00001.jpg 0,0,1,0,....
```
-注意：图片与标注值之间是以Tab[\t]符号隔开, 标注值之间是以逗号[,]隔开。该格式不能错，否则解析失败。
+注意：1)图片与标注值之间是以Tab[\t]符号隔开, 2)标注值之间是以逗号[,]隔开。该格式不能错，否则解析失败。
### 修改配置开始训练
-该任务的训练功能集成在[PaddleClas](https://github.com/PaddlePaddle/PaddleClas)套件中。
+首先执行以下命令下载训练代码：
-需要在配置文件[PPLCNet_x1_0.yaml](https://github.com/PaddlePaddle/PaddleClas/blob/develop/ppcls/configs/PULC/person_attribute/PPLCNet_x1_0.yaml)中，修改的配置项如下：
+```shell
+git clone https://github.com/PaddlePaddle/PaddleClas
+```
+
+需要在配置文件`PaddleClas/blob/develop/ppcls/configs/PULC/person_attribute/PPLCNet_x1_0.yaml`中，修改的配置项如下：
```
- image_root: "dataset/attribute/data/" 指定训练图片所在根路径
- cls_label_path: "dataset/attribute/trainval.txt" 指定训练列表文件位置
+DataLoader:
+ Train:
+ dataset:
+ name: MultiLabelDataset
+ image_root: "dataset/pa100k/" #指定训练图片所在根路径
+ cls_label_path: "dataset/pa100k/train_list.txt" #指定训练列表文件位置
+ label_ratio: True
+ transform_ops:
+
+ Eval:
+ dataset:
+ name: MultiLabelDataset
+ image_root: "dataset/pa100k/" #指定评估图片所在根路径
+ cls_label_path: "dataset/pa100k/val_list.txt" #指定评估列表文件位置
+ label_ratio: True
+ transform_ops:
```
注意：
-
-1. 这里image_root路径+train.txt中图片相对路径，对应图片存放的完整路径。
-
-如果有修改属性数量，则还需修改内容配置项：
-
+1. 这里image_root路径+train.txt中图片相对路径，对应图片的完整路径位置。
+2. 如果有修改属性数量，则还需修改内容配置项中属性种类数量：
```
- class_num: 26 #属性种类数量
+# model architecture
+Arch:
+ name: "PPLCNet_x1_0"
+ pretrained: True
+ use_ssld: True
+ class_num: 26 #属性种类数量
```
然后运行以下命令开始训练。
```
+#多卡训练
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/person_attribute/PPLCNet_x1_0.yaml
+
+#单卡训练
+python3 tools/train.py \
+ -c ./ppcls/configs/PULC/person_attribute/PPLCNet_x1_0.yaml
```
### 模型导出
@@ -113,23 +138,34 @@ python3 tools/export_model.py \
-o Global.save_inference_dir=deploy/models/PPLCNet_x1_0_person_attribute_infer
```
-导出模型后，然后将PP-Human中提供的部署模型[PPLCNet_x1_0](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPLCNet_x1_0_person_attribute_945_infer.tar)中的infer_cfg.yml文件拷贝到导出的模型文件夹'PPLCNet_x1_0_person_attribute_infer'中。
+导出模型后，将PP-Human中提供的部署模型[PPLCNet_x1_0](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPLCNet_x1_0_person_attribute_945_infer.tar)中的`infer_cfg.yml`文件拷贝到导出的模型文件夹`PPLCNet_x1_0_person_attribute_infer`中。
-使用时在PP-Human中的配置文件infer_cfg_pphuman.yml中修改
+使用时在PP-Human中的配置文件`./deploy/pipeline/config/infer_cfg_pphuman.yml`中修改新的模型路径
```
ATTR:
- model_dir: [YOUR_DEPLOY_MODEL_DIR]/PPLCNet_x1_0_person_attribute_infer/
- enable: True
+ model_dir: [YOUR_DEPLOY_MODEL_DIR]/PPLCNet_x1_0_person_attribute_infer/ #新导出的模型路径位置
+ enable: True #开启功能
```
-然后可以使用。
+然后可以使用-->至此即完成新增属性类别识别任务。
## 属性增减
上述是以26个属性为例的标注、训练过程。
-如果需要增加、减少属性数量，则只需修改1)标注、2)训练中train.txt所使用的属性数量和名称。
+如果需要增加、减少属性数量，则需要：
+1)标注时需增加新属性类别信息或删减属性类别信息；
+2)对应修改训练中train.txt所使用的属性数量和名称；
+3)修改训练配置，例如``PaddleClas/blob/develop/ppcls/configs/PULC/person_attribute/PPLCNet_x1_0.yaml``文件中的属性数量,详细见上述`修改配置开始训练`部分。
+
+增加属性示例：
-删减属性，例如，如果不需要年龄属性，则位置[1,2,3]的数值可以去掉。只需在train.txt中标注的26个数字中全部删除第1-3位数值即可，同时标注数据时也不再需要标注这3位属性值。
+1. 在标注数据时在26位后继续增加新的属性标注数值；
+2. 在train.txt文件的标注数值中也增加新的属性数值。
+3. 注意属性类型在train.txt中属性数值列表中的位置的对应关系需要时固定的，例如第1-3位表示年龄，所有图片都要使用1-3位置表示年龄，不再赘述。
-同理进行增加属性，在标注数据时在26位后继续增加新的属性标注数值，在train.txt文件的标注数值中也增加新的属性数值。注意属性类型在train.txt中属性数值列表中的位置的对应关系需要时固定的，例如第1-3位表示年龄，所有图片都要使用1-3位置表示年龄，不再赘述。
+
+
+