Skip to content

GitLab

Commit fccfdfa5 authored by dlyrm's avatar dlyrm
Browse files

update code

parent dcc7bf4f
Pipeline #681 canceled with stages
Hide whitespace changes
Inline Side-by-side
Showing with 840 additions and 0 deletions
deploy/TENSOR_RT.md 0 → 100644
View file @ fccfdfa5
# TensorRT预测部署教程
TensorRT是NVIDIA提出的用于统一模型部署的加速库，可以应用于V100、JETSON Xavier等硬件，它可以极大提高预测速度。Paddle TensorRT教程请参考文档[使用Paddle-TensorRT库预测](https://www.paddlepaddle.org.cn/inference/optimize/paddle_trt.html)
## 1. 安装PaddleInference预测库
- Python安装包，请从[这里](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html#python) 下载带有tensorrt的安装包进行安装
- CPP预测库，请从[这里](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/05_inference_deployment/inference/build_and_install_lib_cn.html) 下载带有TensorRT编译的预测库
- 如果Python和CPP官网没有提供已编译好的安装包或预测库，请参考[源码安装](https://www.paddlepaddle.org.cn/documentation/docs/zh/install/compile/linux-compile.html) 自行编译
**注意：**
- 您的机器上TensorRT的版本需要跟您使用的预测库中TensorRT版本保持一致。
- PaddleDetection中部署预测要求TensorRT版本 > 6.0。
## 2. 导出模型
模型导出具体请参考文档[PaddleDetection模型导出教程](./EXPORT_MODEL.md)。
## 3. 开启TensorRT加速
### 3.1 配置TensorRT
在使用Paddle预测库构建预测器配置config时，打开TensorRT引擎就可以了：
```
config->EnableUseGpu(100, 0); // 初始化100M显存，使用GPU ID为0
config->GpuDeviceId(); // 返回正在使用的GPU ID
// 开启TensorRT预测，可提升GPU预测性能，需要使用带TensorRT的预测库
config->EnableTensorRtEngine(1 << 20 /*workspace_size*/,
batch_size /*max_batch_size*/,
3 /*min_subgraph_size*/,
AnalysisConfig::Precision::kFloat32 /*precision*/,
false /*use_static*/,
false /*use_calib_mode*/);
```
**注意:**
--run_benchmark如果设置为True，则需要安装依赖`pip install pynvml psutil GPUtil`。
### 3.2 TensorRT固定尺寸预测
例如在模型Reader配置文件中设置：
```yaml
TestReader:
inputs_def:
image_shape: [3,608,608]
...
```
或者在导出模型时设置`-o TestReader.inputs_def.image_shape=[3,608,608]`，模型将会进行固定尺寸预测，具体请参考[PaddleDetection模型导出教程](./EXPORT_MODEL.md) 。
可以通过[visualdl](https://www.paddlepaddle.org.cn/paddle/visualdl/demo/graph) 打开`model.pdmodel`文件，查看输入的第一个Tensor尺寸是否是固定的，如果不指定，尺寸会用`？`表示，如下图所示：
![img](../docs/images/input_shape.png)
注意：由于TesnorRT不支持在batch维度进行slice操作，Faster RCNN 和 Mask RCNN不能使用固定尺寸输入预测，所以不能设置`TestReader.inputs_def.image_shape`字段。
以`YOLOv3`为例，使用固定尺寸输入预测：
```
python python/infer.py --model_dir=./output_inference/yolov3_darknet53_270e_coco/ --image_file=./demo/000000014439.jpg --device=GPU --run_mode=trt_fp32 --run_benchmark=True
```
### 3.3 TensorRT动态尺寸预测
TensorRT版本>=6时，使用TensorRT预测时，可以支持动态尺寸输入。如果模型Reader配置文件中没有设置例如`TestReader.inputs_def.image_shape=[3,608,608]`的字段，或者`image_shape=[3.-1,-1]`，导出模型将以动态尺寸进行预测。一般RCNN系列模型使用动态图尺寸预测。
Paddle预测库关于动态尺寸输入请查看[Paddle CPP预测](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/05_inference_deployment/inference/native_infer.html) 的`SetTRTDynamicShapeInfo`函数说明。
`python/infer.py`设置动态尺寸输入参数说明：
- trt_min_shape 用于设定TensorRT的输入图像height、width中的最小尺寸，默认值：1
- trt_max_shape 用于设定TensorRT的输入图像height、width中的最大尺寸，默认值：1280
- trt_opt_shape 用于设定TensorRT的输入图像height、width中的最优尺寸，默认值：640
**注意：`TensorRT`中动态尺寸设置是4维的，这里只设置输入图像的尺寸。**
以`Faster RCNN`为例，使用动态尺寸输入预测：
```
python python/infer.py --model_dir=./output_inference/faster_rcnn_r50_fpn_1x_coco/ --image_file=./demo/000000014439.jpg --device=GPU --run_mode=trt_fp16 --run_benchmark=True --trt_max_shape=1280 --trt_min_shape=800 --trt_opt_shape=960
```
## 4、常见问题QA
**Q:** 提示没有`tensorrt_op`</br>
**A:** 请检查是否使用带有TensorRT的Paddle Python包或预测库。
**Q:** 提示`op out of memory`</br>
**A:** 检查GPU是否是别人也在使用，请尝试使用空闲GPU
**Q:** 提示`some trt inputs dynamic shape info not set`</br>
**A:** 这是由于`TensorRT`会把网络结果划分成多个子图，我们只设置了输入数据的动态尺寸，划分的其他子图的输入并未设置动态尺寸。有两个解决方法：
- 方法一：通过增大`min_subgraph_size`，跳过对这些子图的优化。根据提示，设置min_subgraph_size大于并未设置动态尺寸输入的子图中OP个数即可。
`min_subgraph_size`的意思是，在加载TensorRT引擎的时候，大于`min_subgraph_size`的OP才会被优化，并且这些OP是连续的且是TensorRT可以优化的。
- 方法二：找到子图的这些输入，按照上面方式也设置子图的输入动态尺寸。
**Q:** 如何打开日志</br>
**A:** 预测库默认是打开日志的，只要注释掉`config.disable_glog_info()`就可以打开日志
**Q:** 开启TensorRT，预测时提示Slice on batch axis is not supported in TensorRT</br>
**A:** 请尝试使用动态尺寸输入
deploy/auto_compression/README.md 0 → 100644
View file @ fccfdfa5
# 自动化压缩
目录：
- [1.简介](#1简介)
- [2.Benchmark](#2Benchmark)
- [3.开始自动压缩](#自动压缩流程)
- [3.1 环境准备](#31-准备环境)
- [3.2 准备数据集](#32-准备数据集)
- [3.3 准备预测模型](#33-准备预测模型)
- [3.4 测试模型精度](#34-测试模型精度)
- [3.5 自动压缩并产出模型](#35-自动压缩并产出模型)
- [4.预测部署](#4预测部署)
## 1. 简介
本示例使用PaddleDetection中Inference部署模型进行自动化压缩，使用的自动化压缩策略为量化蒸馏。
## 2.Benchmark
### PP-YOLOE+
| 模型 | Base mAP | 离线量化mAP | ACT量化mAP | TRT-FP32 | TRT-FP16 | TRT-INT8 | 配置文件 | 量化模型 |
| :-------- |:-------- |:--------: | :---------------------: | :----------------: | :----------------: | :---------------: | :----------------------: | :---------------------: |
| PP-YOLOE+_s | 43.7 | - | 42.9 | - | - | - | [config](./configs/ppyoloe_plus_s_qat_dis.yaml) | [Quant Model](https://bj.bcebos.com/v1/paddledet/deploy/Inference/ppyoloe_plus_s_qat_dis.tar) |
| PP-YOLOE+_m | 49.8 | - | 49.3 | - | - | - | [config](./configs/ppyoloe_plus_m_qat_dis.yaml) | [Quant Model](https://bj.bcebos.com/v1/paddledet/deploy/Inference/ppyoloe_plus_m_qat_dis.tar) |
| PP-YOLOE+_l | 52.9 | - | 52.6 | - | - | - | [config](./configs/ppyoloe_plus_l_qat_dis.yaml) | [Quant Model](https://bj.bcebos.com/v1/paddledet/deploy/Inference/ppyoloe_plus_l_qat_dis.tar) |
| PP-YOLOE+_x | 54.7 | - | 54.4 | - | - | - | [config](./configs/ppyoloe_plus_x_qat_dis.yaml) | [Quant Model](https://bj.bcebos.com/v1/paddledet/deploy/Inference/ppyoloe_plus_x_qat_dis.tar) |
- mAP的指标均在COCO val2017数据集中评测得到，IoU=0.5:0.95。
### YOLOv8
| 模型 | Base mAP | 离线量化mAP | ACT量化mAP | TRT-FP32 | TRT-FP16 | TRT-INT8 | 配置文件 | 量化模型 |
| :-------- |:-------- |:--------: | :---------------------: | :----------------: | :----------------: | :---------------: | :----------------------: | :---------------------: |
| YOLOv8-s | 44.9 | 43.9 | 44.3 | 9.27ms | 4.65ms | **3.78ms** | [config](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/detection/configs/yolov8_s_qat_dis.yaml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov8_s_500e_coco_trt_nms_quant.tar) |
**注意：**
- 表格中YOLOv8模型均为带NMS的模型，可直接在TRT中部署，如果需要对齐测试标准，需要测试不带NMS的模型。
- mAP的指标均在COCO val2017数据集中评测得到，IoU=0.5:0.95。
- 表格中的性能在Tesla T4的GPU环境下测试，并且开启TensorRT，batch_size=1。
### PP-YOLOE
| 模型 | Base mAP | 离线量化mAP | ACT量化mAP | TRT-FP32 | TRT-FP16 | TRT-INT8 | 配置文件 | 量化模型 |
| :-------- |:-------- |:--------: | :---------------------: | :----------------: | :----------------: | :---------------: | :----------------------: | :---------------------: |
| PP-YOLOE-l | 50.9 | - | 50.6 | 11.2ms | 7.7ms | **6.7ms** | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/deploy/auto_compression/configs/ppyoloe_l_qat_dis.yaml) | [Quant Model](https://bj.bcebos.com/v1/paddle-slim-models/act/ppyoloe_crn_l_300e_coco_quant.tar) |
| PP-YOLOE-SOD | 38.5 | - | 37.6 | - | - | - | [config](./configs/ppyoloe_crn_l_80e_sliced_visdrone_640_025_qat.yml) | [Quant Model](https://bj.bcebos.com/v1/paddle-slim-models/act/ppyoloe_sod_visdrone.tar) |
git
- PP-YOLOE-l mAP的指标在COCO val2017数据集中评测得到，IoU=0.5:0.95。
- PP-YOLOE-l模型在Tesla V100的GPU环境下测试，并且开启TensorRT，batch_size=1，包含NMS，测试脚本是[benchmark demo](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/deploy/python)。
- PP-YOLOE-SOD 的指标在VisDrone-DET数据集切图后的COCO格式[数据集](https://bj.bcebos.com/v1/paddledet/data/smalldet/visdrone_sliced.zip)中评测得到，IoU=0.5:0.95。定义文件[ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml](../../configs/smalldet/ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml)
### PP-PicoDet
| 模型 | 策略 | mAP | FP32 | FP16 | INT8 | 配置文件 | 模型 |
| :-------- |:-------- |:--------: | :----------------: | :----------------: | :---------------: | :----------------------: | :---------------------: |
| PicoDet-S-NPU | Baseline | 30.1 | - | - | - | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_s_416_coco_npu.yml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/picodet_s_416_coco_npu.tar) |
| PicoDet-S-NPU | 量化训练 | 29.7 | - | - | - | [config](https://github.com/PaddlePaddle/PaddleSlim/tree/develop/demo/full_quantization/detection/configs/picodet_s_qat_dis.yaml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/picodet_s_npu_quant.tar) |
- mAP的指标均在COCO val2017数据集中评测得到，IoU=0.5:0.95。
### RT-DETR
| 模型 | Base mAP | ACT量化mAP | TRT-FP32 | TRT-FP16 | TRT-INT8 | 配置文件 | 量化模型 |
| :---------------- | :------- | :--------: | :------: | :------: | :--------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
| RT-DETR-R50 | 53.1 | 53.0 | 32.05ms | 9.12ms | **6.96ms** | [config](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/detection/configs/rtdetr_r50vd_qat_dis.yaml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/rtdetr_r50vd_6x_coco_quant.tar) |
| RT-DETR-R101 | 54.3 | 54.1 | 54.13ms | 12.68ms | **9.20ms** | [config](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/detection/configs/rtdetr_r101vd_qat_dis.yaml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/rtdetr_r101vd_6x_coco_quant.tar) |
| RT-DETR-HGNetv2-L | 53.0 | 52.9 | 26.16ms | 8.54ms | **6.65ms** | [config](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/detection/configs/rtdetr_hgnetv2_l_qat_dis.yaml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/rtdetr_hgnetv2_l_6x_coco_quant.tar) |
| RT-DETR-HGNetv2-X | 54.8 | 54.6 | 49.22ms | 12.50ms | **9.24ms** | [config](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/detection/configs/rtdetr_hgnetv2_x_qat_dis.yaml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/rtdetr_hgnetv2_x_6x_coco_quant.tar) |
- 上表测试环境：Tesla T4，TensorRT 8.6.0，CUDA 11.7，batch_size=1。
| 模型 | Base mAP | ACT量化mAP | TRT-FP32 | TRT-FP16 | TRT-INT8 | 配置文件 | 量化模型 |
| :---------------- | :------- | :--------: | :------: | :------: | :--------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
| RT-DETR-R50 | 53.1 | 53.0 | 9.64ms | 5.00ms | **3.99ms** | [config](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/detection/configs/rtdetr_r50vd_qat_dis.yaml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/rtdetr_r50vd_6x_coco_quant.tar) |
| RT-DETR-R101 | 54.3 | 54.1 | 14.93ms | 7.15ms | **5.12ms** | [config](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/detection/configs/rtdetr_r101vd_qat_dis.yaml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/rtdetr_r101vd_6x_coco_quant.tar) |
| RT-DETR-HGNetv2-L | 53.0 | 52.9 | 8.17ms | 4.77ms | **4.00ms** | [config](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/detection/configs/rtdetr_hgnetv2_l_qat_dis.yaml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/rtdetr_hgnetv2_l_6x_coco_quant.tar) |
| RT-DETR-HGNetv2-X | 54.8 | 54.6 | 12.81ms | 6.97ms | **5.32ms** | [config](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/detection/configs/rtdetr_hgnetv2_x_qat_dis.yaml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/rtdetr_hgnetv2_x_6x_coco_quant.tar) |
- 上表测试环境：A10，TensorRT 8.6.0，CUDA 11.6，batch_size=1。
- mAP的指标均在COCO val2017数据集中评测得到，IoU=0.5:0.95。
## 3. 自动压缩流程
#### 3.1 准备环境
- PaddlePaddle >= 2.4 （可从[Paddle官网](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html)下载安装）
- PaddleSlim >= 2.4.1
- PaddleDet >= 2.5
- opencv-python
安装paddlepaddle：
```shell
# CPU
pip install paddlepaddle
# GPU
pip install paddlepaddle-gpu
```
安装paddleslim：
```shell
pip install paddleslim
```
安装paddledet：
```shell
pip install paddledet
```
**注意：** YOLOv8模型的自动化压缩需要依赖安装最新[Develop Paddle](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/develop/install/pip/linux-pip.html)和[Develop PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim#%E5%AE%89%E8%A3%85)版本。
#### 3.2 准备数据集
本案例默认以COCO数据进行自动压缩实验，如果自定义COCO数据，或者其他格式数据，请参考[数据准备文档](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/docs/tutorials/data/PrepareDataSet.md) 来准备数据。
如果数据集为非COCO格式数据，请修改[configs](./configs)中reader配置文件中的Dataset字段。
以PP-YOLOE模型为例，如果已经准备好数据集，请直接修改[./configs/yolo_reader.yml]中`EvalDataset`的`dataset_dir`字段为自己数据集路径即可。
#### 3.3 准备预测模型
预测模型的格式为：`model.pdmodel` 和 `model.pdiparams`两个，带`pdmodel`的是模型文件，带`pdiparams`后缀的是权重文件。
根据[PaddleDetection文档](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/GETTING_STARTED_cn.md#8-%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA) 导出Inference模型，具体可参考下方PP-YOLOE模型的导出示例：
- 下载代码
```
git clone https://github.com/PaddlePaddle/PaddleDetection.git
```
- 导出预测模型
PPYOLOE-l模型，包含NMS：如快速体验，可直接下载[PP-YOLOE-l导出模型](https://bj.bcebos.com/v1/paddle-slim-models/act/ppyoloe_crn_l_300e_coco.tar)
```shell
python tools/export_model.py \
-c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml \
-o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams \
trt=True \
```
YOLOv8-s模型，包含NMS，具体可参考[YOLOv8模型文档](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8), 然后执行：
```shell
python tools/export_model.py \
-c configs/yolov8/yolov8_s_500e_coco.yml \
-o weights=https://paddledet.bj.bcebos.com/models/yolov8_s_500e_coco.pdparams \
trt=True
```
如快速体验，可直接下载[YOLOv8-s导出模型](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov8_s_500e_coco_trt_nms.tar)
#### 3.4 自动压缩并产出模型
蒸馏量化自动压缩示例通过run.py脚本启动，会使用接口```paddleslim.auto_compression.AutoCompression```对模型进行自动压缩。配置config文件中模型路径、蒸馏、量化、和训练等部分的参数，配置完成后便可对模型进行量化和蒸馏。具体运行命令为：
- 单卡训练：
```
export CUDA_VISIBLE_DEVICES=0
python run.py --config_path=./configs/ppyoloe_l_qat_dis.yaml --save_dir='./output/'
```
- 多卡训练：
```
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m paddle.distributed.launch --log_dir=log --gpus 0,1,2,3 run.py \
--config_path=./configs/ppyoloe_l_qat_dis.yaml --save_dir='./output/'
```
#### 3.5 测试模型精度
使用eval.py脚本得到模型的mAP：
```
export CUDA_VISIBLE_DEVICES=0
python eval.py --config_path=./configs/ppyoloe_l_qat_dis.yaml
```
使用paddle inference并使用trt int8得到模型的mAP:
```
export CUDA_VISIBLE_DEVICES=0
python paddle_inference_eval.py --model_path ./output/ --reader_config configs/ppyoloe_reader.yml --precision int8 --use_trt=True
```
**注意**：
- 要测试的模型路径可以在配置文件中`model_dir`字段下进行修改。
- --precision 默认为paddle，如果使用trt，需要设置--use_trt=True，同时--precision 可设置为fp32/fp16/int8
## 4.预测部署
- 可以参考[PaddleDetection部署教程](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/deploy)，GPU上量化模型开启TensorRT并设置trt_int8模式进行部署。
deploy/auto_compression/configs/picodet_reader.yml 0 → 100644
View file @ fccfdfa5
metric: COCO
num_classes: 80
# Datset configuration
TrainDataset:
!COCODataSet
image_dir: train2017
anno_path: annotations/instances_train2017.json
dataset_dir: dataset/coco/
EvalDataset:
!COCODataSet
image_dir: val2017
anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco/
worker_num: 6
eval_height: &eval_height 416
eval_width: &eval_width 416
eval_size: &eval_size [*eval_height, *eval_width]
EvalReader:
sample_transforms:
- Decode: {}
- Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
- NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1], is_scale: True}
- Permute: {}
batch_transforms:
- PadBatch: {pad_to_stride: 32}
batch_size: 8
shuffle: false
deploy/auto_compression/configs/picodet_s_qat_dis.yaml 0 → 100644
View file @ fccfdfa5
Global:
reader_config: ./configs/picodet_reader.yml
include_nms: True
Evaluation: True
model_dir: ./picodet_s_416_coco_npu/
model_filename: model.pdmodel
params_filename: model.pdiparams
Distillation:
alpha: 1.0
loss: l2
QuantAware:
use_pact: true
activation_quantize_type: 'moving_average_abs_max'
weight_bits: 8
activation_bits: 8
quantize_op_types:
- conv2d
- depthwise_conv2d
TrainConfig:
train_iter: 8000
eval_iter: 1000
learning_rate:
type: CosineAnnealingDecay
learning_rate: 0.00001
T_max: 8000
optimizer_builder:
optimizer:
type: SGD
weight_decay: 4.0e-05
deploy/auto_compression/configs/ppyoloe_crn_l_80e_sliced_visdrone_640_025_qat.yml 0 → 100644
View file @ fccfdfa5
Global:
reader_config: configs/ppyoloe_crn_l_80e_sliced_visdrone_640_025_reader.yml
input_list: ['image', 'scale_factor']
arch: YOLO
include_nms: True
Evaluation: True
model_dir: ../../output_inference/ppyoloe_crn_l_80e_sliced_visdrone_640_025
model_filename: model.pdmodel
params_filename: model.pdiparams
Distillation:
alpha: 1.0
loss: soft_label
QuantAware:
onnx_format: True
use_pact: False
activation_quantize_type: 'moving_average_abs_max'
quantize_op_types:
- conv2d
- depthwise_conv2d
TrainConfig:
train_iter: 8000
eval_iter: 500
learning_rate:
type: CosineAnnealingDecay
learning_rate: 0.00003
T_max: 6000
optimizer_builder:
optimizer:
type: SGD
weight_decay: 4.0e-05
deploy/auto_compression/configs/ppyoloe_crn_l_80e_sliced_visdrone_640_025_reader.yml 0 → 100644
View file @ fccfdfa5
metric: COCO
num_classes: 10
# Datset configuration
TrainDataset:
!COCODataSet
image_dir: train_images_640_025
anno_path: train_640_025.json
dataset_dir: dataset/visdrone_sliced
EvalDataset:
!COCODataSet
image_dir: val_images_640_025
anno_path: val_640_025.json
dataset_dir: dataset/visdrone_sliced
worker_num: 0
# preprocess reader in test
EvalReader:
sample_transforms:
- Decode: {}
- Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
#- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
- Permute: {}
batch_size: 16
deploy/auto_compression/configs/ppyoloe_l_qat_dis.yaml 0 → 100644
View file @ fccfdfa5
Global:
reader_config: configs/ppyoloe_reader.yml
include_nms: True
Evaluation: True
model_dir: ./ppyoloe_crn_l_300e_coco
model_filename: model.pdmodel
params_filename: model.pdiparams
Distillation:
alpha: 1.0
loss: soft_label
QuantAware:
use_pact: true
activation_quantize_type: 'moving_average_abs_max'
quantize_op_types:
- conv2d
- depthwise_conv2d
TrainConfig:
train_iter: 5000
eval_iter: 1000
learning_rate:
type: CosineAnnealingDecay
learning_rate: 0.00003
T_max: 6000
optimizer_builder:
optimizer:
type: SGD
weight_decay: 4.0e-05
deploy/auto_compression/configs/ppyoloe_plus_crn_t_auxhead_300e_coco_qat.yml 0 → 100644
View file @ fccfdfa5
Global:
reader_config: configs/ppyoloe_plus_reader.yml
include_nms: True
Evaluation: True
model_dir: ../../output_inference/ppyoloe_plus_crn_t_auxhead_300e_coco/
model_filename: model.pdmodel
params_filename: model.pdiparams
Distillation:
alpha: 1.0
loss: soft_label
QuantAware:
onnx_format: True
use_pact: False
activation_quantize_type: 'moving_average_abs_max'
quantize_op_types:
- conv2d
- depthwise_conv2d
TrainConfig:
train_iter: 8000
eval_iter: 1000
learning_rate:
type: CosineAnnealingDecay
learning_rate: 0.00003
T_max: 6000
optimizer_builder:
optimizer:
type: SGD
weight_decay: 4.0e-05
deploy/auto_compression/configs/ppyoloe_plus_l_qat_dis.yaml 0 → 100644
View file @ fccfdfa5
Global:
reader_config: configs/ppyoloe_plus_reader.yml
include_nms: True
Evaluation: True
model_dir: ./ppyoloe_plus_crn_l_80e_coco
model_filename: model.pdmodel
params_filename: model.pdiparams
Distillation:
alpha: 1.0
loss: soft_label
QuantAware:
use_pact: true
activation_quantize_type: 'moving_average_abs_max'
quantize_op_types:
- conv2d
- depthwise_conv2d
TrainConfig:
train_iter: 5000
eval_iter: 1000
learning_rate:
type: CosineAnnealingDecay
learning_rate: 0.00003
T_max: 6000
optimizer_builder:
optimizer:
type: SGD
weight_decay: 4.0e-05
deploy/auto_compression/configs/ppyoloe_plus_m_qat_dis.yaml 0 → 100644
View file @ fccfdfa5
Global:
reader_config: configs/ppyoloe_plus_reader.yml
include_nms: True
Evaluation: True
model_dir: ./ppyoloe_plus_crn_m_80e_coco
model_filename: model.pdmodel
params_filename: model.pdiparams
Distillation:
alpha: 1.0
loss: soft_label
QuantAware:
use_pact: true
activation_quantize_type: 'moving_average_abs_max'
quantize_op_types:
- conv2d
- depthwise_conv2d
TrainConfig:
train_iter: 5000
eval_iter: 1000
learning_rate:
type: CosineAnnealingDecay
learning_rate: 0.00003
T_max: 6000
optimizer_builder:
optimizer:
type: SGD
weight_decay: 4.0e-05
deploy/auto_compression/configs/ppyoloe_plus_reader.yml 0 → 100644
View file @ fccfdfa5
metric: COCO
num_classes: 80
# Datset configuration
TrainDataset:
!COCODataSet
image_dir: train2017
anno_path: annotations/instances_train2017.json
dataset_dir: dataset/coco/
EvalDataset:
!COCODataSet
image_dir: val2017
anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco/
worker_num: 0
# preprocess reader in test
EvalReader:
sample_transforms:
- Decode: {}
- Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
- Permute: {}
batch_size: 4
deploy/auto_compression/configs/ppyoloe_plus_s_qat_dis.yaml 0 → 100644
View file @ fccfdfa5
Global:
reader_config: configs/ppyoloe_plus_reader.yml
include_nms: True
Evaluation: True
model_dir: ./ppyoloe_plus_crn_s_80e_coco
model_filename: model.pdmodel
params_filename: model.pdiparams
Distillation:
alpha: 1.0
loss: soft_label
QuantAware:
use_pact: true
activation_quantize_type: 'moving_average_abs_max'
quantize_op_types:
- conv2d
- depthwise_conv2d
TrainConfig:
train_iter: 5000
eval_iter: 1000
learning_rate:
type: CosineAnnealingDecay
learning_rate: 0.00003
T_max: 6000
optimizer_builder:
optimizer:
type: SGD
weight_decay: 4.0e-05
deploy/auto_compression/configs/ppyoloe_plus_sod_crn_l_qat_dis.yaml 0 → 100644
View file @ fccfdfa5
Global:
reader_config: configs/ppyoloe_plus_reader.yml
include_nms: True
Evaluation: True
model_dir: ../../output_inference/ppyoloe_plus_sod_crn_l_80e_coco
model_filename: model.pdmodel
params_filename: model.pdiparams
Distillation:
alpha: 1.0
loss: soft_label
QuantAware:
onnx_format: True
use_pact: true
activation_quantize_type: 'moving_average_abs_max'
quantize_op_types:
- conv2d
- depthwise_conv2d
TrainConfig:
train_iter: 1
eval_iter: 1
learning_rate:
type: CosineAnnealingDecay
learning_rate: 0.00003
T_max: 6000
optimizer_builder:
optimizer:
type: SGD
weight_decay: 4.0e-05
deploy/auto_compression/configs/ppyoloe_plus_x_qat_dis.yaml 0 → 100644
View file @ fccfdfa5
Global:
reader_config: configs/ppyoloe_plus_reader.yml
include_nms: True
Evaluation: True
model_dir: ./ppyoloe_plus_crn_x_80e_coco
model_filename: model.pdmodel
params_filename: model.pdiparams
Distillation:
alpha: 1.0
loss: soft_label
QuantAware:
use_pact: true
activation_quantize_type: 'moving_average_abs_max'
quantize_op_types:
- conv2d
- depthwise_conv2d
TrainConfig:
train_iter: 5000
eval_iter: 1000
learning_rate:
type: CosineAnnealingDecay
learning_rate: 0.00003
T_max: 6000
optimizer_builder:
optimizer:
type: SGD
weight_decay: 4.0e-05
deploy/auto_compression/configs/ppyoloe_reader.yml 0 → 100644
View file @ fccfdfa5
metric: COCO
num_classes: 80
# Datset configuration
TrainDataset:
!COCODataSet
image_dir: train2017
anno_path: annotations/instances_train2017.json
dataset_dir: dataset/coco/
EvalDataset:
!COCODataSet
image_dir: val2017
anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco/
worker_num: 0
# preprocess reader in test
EvalReader:
sample_transforms:
- Decode: {}
- Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- Permute: {}
batch_size: 4
deploy/auto_compression/configs/rtdetr_hgnetv2_l_qat_dis.yaml 0 → 100644
View file @ fccfdfa5
Global:
reader_config: configs/rtdetr_reader.yml
include_nms: True
Evaluation: True
model_dir: ./rtdetr_hgnetv2_l_6x_coco/
model_filename: model.pdmodel
params_filename: model.pdiparams
Distillation:
alpha: 1.0
loss: soft_label
QuantAware:
onnx_format: true
activation_quantize_type: 'moving_average_abs_max'
quantize_op_types:
- conv2d
- depthwise_conv2d
- matmul_v2
TrainConfig:
train_iter: 200
eval_iter: 50
learning_rate:
type: CosineAnnealingDecay
learning_rate: 0.00003
T_max: 10000
optimizer_builder:
optimizer:
type: SGD
weight_decay: 4.0e-05
deploy/auto_compression/configs/rtdetr_hgnetv2_x_qat_dis.yaml 0 → 100644
View file @ fccfdfa5
Global:
reader_config: configs/rtdetr_reader.yml
include_nms: True
Evaluation: True
model_dir: ./rtdetr_r50vd_6x_coco/
model_filename: model.pdmodel
params_filename: model.pdiparams
Distillation:
alpha: 1.0
loss: soft_label
QuantAware:
onnx_format: true
activation_quantize_type: 'moving_average_abs_max'
quantize_op_types:
- conv2d
- depthwise_conv2d
- matmul_v2
TrainConfig:
train_iter: 500
eval_iter: 100
learning_rate:
type: CosineAnnealingDecay
learning_rate: 0.00003
T_max: 10000
optimizer_builder:
optimizer:
type: SGD
weight_decay: 4.0e-05
deploy/auto_compression/configs/rtdetr_r101vd_qat_dis.yaml 0 → 100644
View file @ fccfdfa5
Global:
reader_config: configs/rtdetr_reader.yml
include_nms: True
Evaluation: True
model_dir: ./rtdetr_hgnetv2_x_6x_coco/
model_filename: model.pdmodel
params_filename: model.pdiparams
Distillation:
alpha: 1.0
loss: soft_label
QuantAware:
onnx_format: true
activation_quantize_type: 'moving_average_abs_max'
quantize_op_types:
- conv2d
- depthwise_conv2d
- matmul_v2
TrainConfig:
train_iter: 200
eval_iter: 50
learning_rate:
type: CosineAnnealingDecay
learning_rate: 0.00003
T_max: 10000
optimizer_builder:
optimizer:
type: SGD
weight_decay: 4.0e-05
deploy/auto_compression/configs/rtdetr_r50vd_qat_dis.yaml 0 → 100644
View file @ fccfdfa5
Global:
reader_config: configs/rtdetr_reader.yml
include_nms: True
Evaluation: True
model_dir: ./rtdetr_r50vd_6x_coco/
model_filename: model.pdmodel
params_filename: model.pdiparams
Distillation:
alpha: 1.0
loss: soft_label
QuantAware:
onnx_format: true
activation_quantize_type: 'moving_average_abs_max'
quantize_op_types:
- conv2d
- depthwise_conv2d
- matmul_v2
TrainConfig:
train_iter: 500
eval_iter: 100
learning_rate:
type: CosineAnnealingDecay
learning_rate: 0.00003
T_max: 10000
optimizer_builder:
optimizer:
type: SGD
weight_decay: 4.0e-05
deploy/auto_compression/configs/rtdetr_reader.yml 0 → 100644
View file @ fccfdfa5
metric: COCO
num_classes: 80
# Datset configuration
TrainDataset:
!COCODataSet
image_dir: train2017
anno_path: annotations/instances_train2017.json
dataset_dir: dataset/coco/
!COCODataSet
image_dir: val2017
anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco/
worker_num: 0
# preprocess reader in test
EvalReader:
sample_transforms:
- Decode: {}
- Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
- Permute: {}
batch_size: 1
shuffle: false
drop_last: false
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment