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PaddleSeg
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Merge branch 'develop' of https://github.com/PaddlePaddle/PaddleSeg into develop

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README.md
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...@@ -6,10 +6,26 @@ ...@@ -6,10 +6,26 @@
## 简介 ## 简介
PaddleSeg是基于[PaddlePaddle](https://www.paddlepaddle.org.cn)开发的语义分割库，覆盖了DeepLabv3+, U-Net, ICNet三类主流的分割模型。通过统一的配置，帮助用户更便捷地完成从训练到部署的全流程图像分割应用。 PaddleSeg是基于[PaddlePaddle](https://www.paddlepaddle.org.cn)开发的语义分割库，覆盖了DeepLabv3+, U-Net, ICNet, PSPNet, HRNet等主流分割模型。通过统一的配置，帮助用户更便捷地完成从训练到部署的全流程图像分割应用。
PaddleSeg具备高性能、丰富的数据增强、工业级部署、全流程应用的特点: </br>
- [特点](#特点)
- [安装](#安装)
- [使用教程](#使用教程)
- [快速入门](#快速入门)
- [基础功能](#基础功能)
- [预测部署](#预测部署)
- [高级功能](#高级功能)
- [在线体验](#在线体验)
- [FAQ](#FAQ)
- [交流与反馈](#交流与反馈)
- [更新日志](#更新日志)
- [贡献代码](#贡献代码)
</br>
## 特点
- **丰富的数据增强** - **丰富的数据增强**
...@@ -17,29 +33,42 @@ PaddleSeg具备高性能、丰富的数据增强、工业级部署、全流程 ...@@ -17,29 +33,42 @@ PaddleSeg具备高性能、丰富的数据增强、工业级部署、全流程
- **模块化设计** - **模块化设计**
支持U-Net, DeepLabv3+, ICNet, PSPNet四种主流分割网络，结合预训练模型和可调节的骨干网络，满足不同性能和精度的要求；选择不同的损失函数如Dice Loss, BCE Loss等方式可以强化小目标和不均衡样本场景下的分割精度。 支持U-Net, DeepLabv3+, ICNet, PSPNet, HRNet五种主流分割网络，结合预训练模型和可调节的骨干网络，满足不同性能和精度的要求；选择不同的损失函数如Dice Loss, BCE Loss等方式可以强化小目标和不均衡样本场景下的分割精度。
- **高性能** - **高性能**
PaddleSeg支持多进程IO、多卡并行、跨卡Batch Norm同步等训练加速策略，结合飞桨核心框架的显存优化功能，可以大幅度减少分割模型的显存开销，更快完成分割模型训练。 PaddleSeg支持多进程I/O、多卡并行、跨卡Batch Norm同步等训练加速策略，结合飞桨核心框架的显存优化功能，可大幅度减少分割模型的显存开销，让开发者更低成本、更高效地完成图像分割训练。
- **工业级部署** - **工业级部署**
基于[Paddle Serving](https://github.com/PaddlePaddle/Serving)和PaddlePaddle高性能预测引擎，结合百度开放的AI能力，轻松搭建人像分割和车道线分割服务。 全面提供**服务端**和**移动端**的工业级部署能力，依托飞桨高性能推理引擎和高性能图像处理实现，开发者可以轻松完成高性能的分割模型部署和集成。通过[Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite)，可以在移动设备或者嵌入式设备上完成轻量级、高性能的人像分割模型部署。
</br> ## 安装
## 环境依赖 ### 1. 安装PaddlePaddle
版本要求
* PaddlePaddle >= 1.6.1 * PaddlePaddle >= 1.6.1
* Python 2.7 or 3.5+ * Python 2.7 or 3.5+
通过以下命令安装python包依赖，请确保在该分支上至少执行过一次以下命令 由于图像分割模型计算开销大，推荐在GPU版本的PaddlePaddle下使用PaddleSeg.
```shell ```
$ pip install -r requirements.txt pip install -U paddlepaddle-gpu
``` ```
同时请保证您参考NVIDIA官网，已经正确配置和安装了显卡驱动，CUDA 9，cuDNN 7.3，NCCL2等依赖，其他更加详细的安装信息请参考：[PaddlePaddle安装说明](https://www.paddlepaddle.org.cn/install/doc/index)。
其他如CUDA版本、cuDNN版本等兼容信息请查看[PaddlePaddle安装](https://www.paddlepaddle.org.cn/install/doc/index) ### 2. 下载PaddleSeg代码
```
git clone https://github.com/PaddlePaddle/PaddleSeg
```
### 3. 安装PaddleSeg依赖
通过以下命令安装python包依赖，请确保在该分支上至少执行过一次以下命令：
```
cd PaddleSeg
pip install -r requirements.txt
```
</br> </br>
...@@ -51,35 +80,49 @@ $ pip install -r requirements.txt ...@@ -51,35 +80,49 @@ $ pip install -r requirements.txt
### 快速入门 ### 快速入门
* [安装说明](./docs/installation.md) * [PaddleSeg快速入门](./docs/usage.md)
* [训练/评估/可视化](./docs/usage.md)
### 基础功能 ### 基础功能
* [分割模型介绍](./docs/models.md) * [自定义数据的标注与准备](./docs/data_prepare.md)
* [预训练模型列表](./docs/model_zoo.md) * [脚本使用和配置说明](./docs/config.md)
* [自定义数据的准备与标注](./docs/data_prepare.md)
* [数据和配置校验](./docs/check.md) * [数据和配置校验](./docs/check.md)
* [如何训练DeepLabv3+](./turtorial/finetune_deeplabv3plus.md) * [分割模型介绍](./docs/models.md)
* [如何训练U-Net](./turtorial/finetune_unet.md) * [预训练模型下载](./docs/model_zoo.md)
* [如何训练ICNet](./turtorial/finetune_icnet.md) * [DeepLabv3+模型使用教程](./turtorial/finetune_deeplabv3plus.md)
* [如何训练PSPNet](./turtorial/finetune_pspnet.md) * [U-Net模型使用教程](./turtorial/finetune_unet.md)
* [如何训练HRNet](./turtorial/finetune_hrnet.md) * [ICNet模型使用教程](./turtorial/finetune_icnet.md)
* [PSPNet模型使用教程](./turtorial/finetune_pspnet.md)
* [HRNet模型使用教程](./turtorial/finetune_hrnet.md)
* [Fast-SCNN模型使用教程](./turtorial/finetune_fast_scnn.md)
### 预测部署 ### 预测部署
* [模型导出](./docs/model_export.md) * [模型导出](./docs/model_export.md)
* [使用Python预测](./deploy/python/) * [Python预测](./deploy/python/)
* [使用C++预测](./deploy/cpp/) * [C++预测](./deploy/cpp/)
* [移动端预测部署](./deploy/lite/) * [Paddle-Lite移动端预测部署](./deploy/lite/)
### 高级功能 ### 高级功能
* [PaddleSeg的数据增强](./docs/data_aug.md) * [PaddleSeg的数据增强](./docs/data_aug.md)
* [PaddleSeg的loss选择](./docs/loss_select.md) * [如何解决二分类中类别不均衡问题](./docs/loss_select.md)
* [特色垂类模型使用](./contrib) * [特色垂类模型使用](./contrib)
* [多进程训练和混合精度训练](./docs/multiple_gpus_train_and_mixed_precision_train.md) * [多进程训练和混合精度训练](./docs/multiple_gpus_train_and_mixed_precision_train.md)
* 使用PaddleSlim进行分割模型压缩([量化](./slim/quantization/README.md), [蒸馏](./slim/distillation/README.md), [剪枝](./slim/prune/README.md), [搜索](./slim/nas/README.md))
## 在线体验
我们在AI Studio平台上提供了在线体验的教程，欢迎体验：
|在线教程|链接|
|-|-|
|快速开始|[点击体验](https://aistudio.baidu.com/aistudio/projectdetail/100798)|
|U-Net图像分割|[点击体验](https://aistudio.baidu.com/aistudio/projectDetail/102889)|
|DeepLabv3+图像分割|[点击体验](https://aistudio.baidu.com/aistudio/projectDetail/226703)|
|工业质检（零件瑕疵检测）|[点击体验](https://aistudio.baidu.com/aistudio/projectdetail/184392)|
|人像分割|[点击体验](https://aistudio.baidu.com/aistudio/projectdetail/188833)|
|PaddleSeg特色垂类模型|[点击体验](https://aistudio.baidu.com/aistudio/projectdetail/226710)|
</br> </br>
...@@ -104,25 +147,14 @@ python pdseg/train.py --cfg xxx.yaml TRAIN.RESUME_MODEL_DIR /PATH/TO/MODEL_CKPT/ ...@@ -104,25 +147,14 @@ python pdseg/train.py --cfg xxx.yaml TRAIN.RESUME_MODEL_DIR /PATH/TO/MODEL_CKPT/
A: 降低Batch size，使用Group Norm策略；请注意训练过程中当`DEFAULT_NORM_TYPE`选择`bn`时，为了Batch Norm计算稳定性，batch size需要满足>=2 A: 降低Batch size，使用Group Norm策略；请注意训练过程中当`DEFAULT_NORM_TYPE`选择`bn`时，为了Batch Norm计算稳定性，batch size需要满足>=2
</br>
#### Q: 出现错误 ModuleNotFoundError: No module named 'paddle.fluid.contrib.mixed_precision' #### Q: 出现错误 ModuleNotFoundError: No module named 'paddle.fluid.contrib.mixed_precision'
A: 请将PaddlePaddle升级至1.5.2版本或以上。 A: 请将PaddlePaddle升级至1.5.2版本或以上。
## 在线体验
PaddleSeg在AI Studio平台上提供了在线体验的教程，欢迎体验：
|教程|链接|
|-|-|
|U-Net宠物分割|[点击体验](https://aistudio.baidu.com/aistudio/projectDetail/102889)|
|DeepLabv3+图像分割|[点击体验](https://aistudio.baidu.com/aistudio/projectDetail/101696)|
|PaddleSeg特色垂类模型|[点击体验](https://aistudio.baidu.com/aistudio/projectdetail/115541)|
</br> </br>
## 交流与反馈 ## 交流与反馈
* 欢迎您通过[Github Issues](https://github.com/PaddlePaddle/PaddleSeg/issues)来提交问题、报告与建议 * 欢迎您通过[Github Issues](https://github.com/PaddlePaddle/PaddleSeg/issues)来提交问题、报告与建议
* 微信公众号：飞桨PaddlePaddle * 微信公众号：飞桨PaddlePaddle
* QQ群: 796771754 * QQ群: 796771754
...@@ -131,25 +163,36 @@ PaddleSeg在AI Studio平台上提供了在线体验的教程，欢迎体验： ...@@ -131,25 +163,36 @@ PaddleSeg在AI Studio平台上提供了在线体验的教程，欢迎体验：
<p align="center"> &#8194;&#8194;&#8194;微信公众号&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;官方技术交流QQ群</p> <p align="center"> &#8194;&#8194;&#8194;微信公众号&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;官方技术交流QQ群</p>
## 更新日志 ## 更新日志
* 2019.12.15
**`v0.3.0`**
* 新增HRNet分割网络，提供基于cityscapes和ImageNet的[预训练模型](./docs/model_zoo.md)8个
* 支持使用[伪彩色标签](./docs/data_prepare.md#%E7%81%B0%E5%BA%A6%E6%A0%87%E6%B3%A8vs%E4%BC%AA%E5%BD%A9%E8%89%B2%E6%A0%87%E6%B3%A8)进行训练/评估/预测，提升训练体验，并提供将灰度标注图转为伪彩色标注图的脚本
* 新增[学习率warmup](./docs/configs/solver_group.md#lr_warmup)功能，支持与不同的学习率Decay策略配合使用
* 新增图像归一化操作的GPU化实现，进一步提升预测速度。
* 新增Python部署方案，更低成本完成工业级部署。
* 新增Paddle-Lite移动端部署方案，支持人像分割模型的移动端部署。
* 新增不同分割模型的预测[性能数据Benchmark](./deploy/python/docs/PaddleSeg_Infer_Benchmark.md), 便于开发者提供模型选型性能参考。
* 2019.11.04 * 2019.11.04
**`v0.2.0`** **`v0.2.0`**
* 新增PSPNet分割网络，提供基于COCO和cityscapes数据集的[预训练模型](./docs/model_zoo.md)4个 * 新增PSPNet分割网络，提供基于COCO和cityscapes数据集的[预训练模型](./docs/model_zoo.md)4个。
* 新增Dice Loss、BCE Loss以及组合Loss配置，支持样本不均衡场景下的[模型优化](./docs/loss_select.md) * 新增Dice Loss、BCE Loss以及组合Loss配置，支持样本不均衡场景下的[模型优化](./docs/loss_select.md)。
* 支持[FP16混合精度训练](./docs/multiple_gpus_train_and_mixed_precision_train.md)以及动态Loss Scaling，在不损耗精度的情况下，训练速度提升30%+ * 支持[FP16混合精度训练](./docs/multiple_gpus_train_and_mixed_precision_train.md)以及动态Loss Scaling，在不损耗精度的情况下，训练速度提升30%+。
* 支持[PaddlePaddle多卡多进程训练](./docs/multiple_gpus_train_and_mixed_precision_train.md)，多卡训练时训练速度提升15%+ * 支持[PaddlePaddle多卡多进程训练](./docs/multiple_gpus_train_and_mixed_precision_train.md)，多卡训练时训练速度提升15%+。
* 发布基于UNet的[工业标记表盘分割模型](./contrib#%E5%B7%A5%E4%B8%9A%E7%94%A8%E8%A1%A8%E5%88%86%E5%89%B2) * 发布基于UNet的[工业标记表盘分割模型](./contrib#%E5%B7%A5%E4%B8%9A%E7%94%A8%E8%A1%A8%E5%88%86%E5%89%B2)。
* 2019.09.10 * 2019.09.10
**`v0.1.0`** **`v0.1.0`**
* PaddleSeg分割库初始版本发布，包含DeepLabv3+, U-Net, ICNet三类分割模型, 其中DeepLabv3+支持Xception, MobileNet v2两种可调节的骨干网络。 * PaddleSeg分割库初始版本发布，包含DeepLabv3+, U-Net, ICNet三类分割模型, 其中DeepLabv3+支持Xception, MobileNet v2两种可调节的骨干网络。
* CVPR19 LIP人体部件分割比赛冠军预测模型发布[ACE2P](./contrib/ACE2P) * CVPR19 LIP人体部件分割比赛冠军预测模型发布[ACE2P](./contrib/ACE2P)。
* 预置基于DeepLabv3+网络的[人像分割](./contrib/HumanSeg/)和[车道线分割](./contrib/RoadLine)预测模型发布 * 预置基于DeepLabv3+网络的[人像分割](./contrib/HumanSeg/)和[车道线分割](./contrib/RoadLine)预测模型发布。
</br> </br>
## 如何贡献代码 ## 贡献代码
我们非常欢迎您为PaddleSeg贡献代码或者提供使用建议。 我们非常欢迎您为PaddleSeg贡献代码或者提供使用建议。如果您可以修复某个issue或者增加一个新功能，欢迎给我们提交pull requests.
configs/cityscape_fast_scnn.yaml 0 → 100644
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EVAL_CROP_SIZE: (2048, 1024) # (width, height), for unpadding rangescaling and stepscaling
TRAIN_CROP_SIZE: (1024, 1024) # (width, height), for unpadding rangescaling and stepscaling
AUG:
AUG_METHOD: "stepscaling" # choice unpadding rangescaling and stepscaling
FIX_RESIZE_SIZE: (640, 640) # (width, height), for unpadding
INF_RESIZE_VALUE: 500 # for rangescaling
MAX_RESIZE_VALUE: 600 # for rangescaling
MIN_RESIZE_VALUE: 400 # for rangescaling
MAX_SCALE_FACTOR: 2.0 # for stepscaling
MIN_SCALE_FACTOR: 0.5 # for stepscaling
SCALE_STEP_SIZE: 0.25 # for stepscaling
MIRROR: True
FLIP: False
FLIP_RATIO: 0.2
RICH_CROP:
ENABLE: True
ASPECT_RATIO: 0.0
BLUR: False
BLUR_RATIO: 0.1
MAX_ROTATION: 0
MIN_AREA_RATIO: 0.0
BRIGHTNESS_JITTER_RATIO: 0.4
CONTRAST_JITTER_RATIO: 0.4
SATURATION_JITTER_RATIO: 0.4
BATCH_SIZE: 12
MEAN: [0.5, 0.5, 0.5]
STD: [0.5, 0.5, 0.5]
DATASET:
DATA_DIR: "./dataset/cityscapes/"
IMAGE_TYPE: "rgb" # choice rgb or rgba
NUM_CLASSES: 19
TEST_FILE_LIST: "dataset/cityscapes/val.list"
TRAIN_FILE_LIST: "dataset/cityscapes/train.list"
VAL_FILE_LIST: "dataset/cityscapes/val.list"
IGNORE_INDEX: 255
FREEZE:
MODEL_FILENAME: "model"
PARAMS_FILENAME: "params"
MODEL:
DEFAULT_NORM_TYPE: "bn"
MODEL_NAME: "fast_scnn"
TEST:
TEST_MODEL: "snapshots/cityscape_fast_scnn/final/"
TRAIN:
MODEL_SAVE_DIR: "snapshots/cityscape_fast_scnn/"
SNAPSHOT_EPOCH: 10
SOLVER:
LR: 0.001
LR_POLICY: "poly"
OPTIMIZER: "sgd"
NUM_EPOCHS: 100
configs/deeplabv3p_xception65_pet.yaml → configs/deeplabv3p_mobilenetv2_cityscapes.yaml
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TRAIN_CROP_SIZE: (512, 512) # (width, height), for unpadding rangescaling and stepscaling EVAL_CROP_SIZE: (2049, 1025) # (width, height), for unpadding rangescaling and stepscaling
EVAL_CROP_SIZE: (512, 512) # (width, height), for unpadding rangescaling and stepscaling TRAIN_CROP_SIZE: (769, 769) # (width, height), for unpadding rangescaling and stepscaling
AUG: AUG:
AUG_METHOD: "unpadding" # choice unpadding rangescaling and stepscaling AUG_METHOD: "stepscaling" # choice unpadding rangescaling and stepscaling
FIX_RESIZE_SIZE: (512, 512) # (width, height), for unpadding FIX_RESIZE_SIZE: (2048, 1024) # (width, height), for unpadding
INF_RESIZE_VALUE: 500 # for rangescaling INF_RESIZE_VALUE: 500 # for rangescaling
MAX_RESIZE_VALUE: 600 # for rangescaling MAX_RESIZE_VALUE: 600 # for rangescaling
MIN_RESIZE_VALUE: 400 # for rangescaling MIN_RESIZE_VALUE: 400 # for rangescaling
MAX_SCALE_FACTOR: 2.0 # for stepscaling
MAX_SCALE_FACTOR: 1.25 # for stepscaling MIN_SCALE_FACTOR: 0.5 # for stepscaling
MIN_SCALE_FACTOR: 0.75 # for stepscaling
SCALE_STEP_SIZE: 0.25 # for stepscaling SCALE_STEP_SIZE: 0.25 # for stepscaling
MIRROR: True MIRROR: True
BATCH_SIZE: 4 BATCH_SIZE: 4
DATASET: DATASET:
DATA_DIR: "./dataset/mini_pet/" DATA_DIR: "./dataset/cityscapes/"
IMAGE_TYPE: "rgb" # choice rgb or rgba IMAGE_TYPE: "rgb" # choice rgb or rgba
NUM_CLASSES: 3 NUM_CLASSES: 19
TEST_FILE_LIST: "./dataset/mini_pet/file_list/test_list.txt" TEST_FILE_LIST: "dataset/cityscapes/val.list"
TRAIN_FILE_LIST: "./dataset/mini_pet/file_list/train_list.txt" TRAIN_FILE_LIST: "dataset/cityscapes/train.list"
VAL_FILE_LIST: "./dataset/mini_pet/file_list/val_list.txt" VAL_FILE_LIST: "dataset/cityscapes/val.list"
VIS_FILE_LIST: "./dataset/mini_pet/file_list/test_list.txt"
IGNORE_INDEX: 255 IGNORE_INDEX: 255
SEPARATOR: " " SEPARATOR: " "
FREEZE: FREEZE:
MODEL_FILENAME: "__model__" MODEL_FILENAME: "model"
PARAMS_FILENAME: "__params__" PARAMS_FILENAME: "params"
MODEL: MODEL:
MODEL_NAME: "deeplabv3p"
DEFAULT_NORM_TYPE: "bn" DEFAULT_NORM_TYPE: "bn"
MODEL_NAME: "deeplabv3p"
DEEPLAB: DEEPLAB:
BACKBONE: "xception_65" BACKBONE: "mobilenetv2"
ASPP_WITH_SEP_CONV: True
DECODER_USE_SEP_CONV: True
ENCODER_WITH_ASPP: False
ENABLE_DECODER: False
TRAIN: TRAIN:
PRETRAINED_MODEL_DIR: "./pretrained_model/deeplabv3p_xception65_bn_coco/" PRETRAINED_MODEL_DIR: u"pretrained_model/deeplabv3p_mobilenetv2-1-0_bn_coco"
MODEL_SAVE_DIR: "./saved_model/deeplabv3p_xception65_bn_pet/" MODEL_SAVE_DIR: "saved_model/deeplabv3p_mobilenetv2_cityscapes"
SNAPSHOT_EPOCH: 10 SNAPSHOT_EPOCH: 10
SYNC_BATCH_NORM: True
TEST: TEST:
TEST_MODEL: "./saved_model/deeplabv3p_xception65_bn_pet/final" TEST_MODEL: "saved_model/deeplabv3p_mobilenetv2_cityscapes/final"
SOLVER: SOLVER:
NUM_EPOCHS: 100 LR: 0.01
LR: 0.005
LR_POLICY: "poly" LR_POLICY: "poly"
OPTIMIZER: "sgd" OPTIMIZER: "sgd"
NUM_EPOCHS: 100
configs/pspnet.yaml → configs/deeplabv3p_xception65_cityscapes.yaml
浏览文件 @ 1942c445
EVAL_CROP_SIZE: (2049, 1025) # (width, height), for unpadding rangescaling and stepscaling EVAL_CROP_SIZE: (2049, 1025) # (width, height), for unpadding rangescaling and stepscaling
TRAIN_CROP_SIZE: (713, 713) # (width, height), for unpadding rangescaling and stepscaling TRAIN_CROP_SIZE: (769, 769) # (width, height), for unpadding rangescaling and stepscaling
AUG: AUG:
AUG_METHOD: "stepscaling" # choice unpadding rangescaling and stepscaling AUG_METHOD: "stepscaling" # choice unpadding rangescaling and stepscaling
FIX_RESIZE_SIZE: (640, 640) # (width, height), for unpadding FIX_RESIZE_SIZE: (2048, 1024) # (width, height), for unpadding
INF_RESIZE_VALUE: 500 # for rangescaling INF_RESIZE_VALUE: 500 # for rangescaling
MAX_RESIZE_VALUE: 600 # for rangescaling MAX_RESIZE_VALUE: 600 # for rangescaling
MIN_RESIZE_VALUE: 400 # for rangescaling MIN_RESIZE_VALUE: 400 # for rangescaling
...@@ -19,23 +19,25 @@ DATASET: ...@@ -19,23 +19,25 @@ DATASET:
TRAIN_FILE_LIST: "dataset/cityscapes/train.list" TRAIN_FILE_LIST: "dataset/cityscapes/train.list"
VAL_FILE_LIST: "dataset/cityscapes/val.list" VAL_FILE_LIST: "dataset/cityscapes/val.list"
IGNORE_INDEX: 255 IGNORE_INDEX: 255
SEPARATOR: " "
FREEZE: FREEZE:
MODEL_FILENAME: "model" MODEL_FILENAME: "model"
PARAMS_FILENAME: "params" PARAMS_FILENAME: "params"
MODEL: MODEL:
MODEL_NAME: "pspnet"
DEFAULT_NORM_TYPE: "bn" DEFAULT_NORM_TYPE: "bn"
PSPNET: MODEL_NAME: "deeplabv3p"
DEPTH_MULTIPLIER: 1 DEEPLAB:
LAYERS: 50 ASPP_WITH_SEP_CONV: True
TEST: DECODER_USE_SEP_CONV: True
TEST_MODEL: "snapshots/cityscapes_pspnet50/final"
TRAIN: TRAIN:
MODEL_SAVE_DIR: "snapshots/cityscapes_pspnet50/" PRETRAINED_MODEL_DIR: u"pretrained_model/deeplabv3p_xception65_bn_coco"
PRETRAINED_MODEL_DIR: u"pretrained_model/pspnet50_bn_cityscapes/" MODEL_SAVE_DIR: "saved_model/deeplabv3p_xception65_bn_cityscapes"
SNAPSHOT_EPOCH: 10 SNAPSHOT_EPOCH: 10
SYNC_BATCH_NORM: True
TEST:
TEST_MODEL: "saved_model/deeplabv3p_xception65_bn_cityscapes/final"
SOLVER: SOLVER:
LR: 0.001 LR: 0.01
LR_POLICY: "poly" LR_POLICY: "poly"
OPTIMIZER: "sgd" OPTIMIZER: "sgd"
NUM_EPOCHS: 700 NUM_EPOCHS: 100
configs/deeplabv3p_xception65_optic.yaml 0 → 100644
浏览文件 @ 1942c445
# 数据集配置
DATASET:
DATA_DIR: "./dataset/optic_disc_seg/"
NUM_CLASSES: 2
TEST_FILE_LIST: "./dataset/optic_disc_seg/test_list.txt"
TRAIN_FILE_LIST: "./dataset/optic_disc_seg/train_list.txt"
VAL_FILE_LIST: "./dataset/optic_disc_seg/val_list.txt"
VIS_FILE_LIST: "./dataset/optic_disc_seg/test_list.txt"
# 预训练模型配置
MODEL:
MODEL_NAME: "deeplabv3p"
DEFAULT_NORM_TYPE: "bn"
DEEPLAB:
BACKBONE: "xception_65"
# 其他配置
TRAIN_CROP_SIZE: (512, 512)
EVAL_CROP_SIZE: (512, 512)
AUG:
AUG_METHOD: "unpadding"
FIX_RESIZE_SIZE: (512, 512)
BATCH_SIZE: 4
TRAIN:
PRETRAINED_MODEL_DIR: "./pretrained_model/deeplabv3p_xception65_bn_coco/"
MODEL_SAVE_DIR: "./saved_model/deeplabv3p_xception65_bn_optic/"
SNAPSHOT_EPOCH: 5
TEST:
TEST_MODEL: "./saved_model/deeplabv3p_xception65_bn_optic/final"
SOLVER:
NUM_EPOCHS: 10
LR: 0.001
LR_POLICY: "poly"
OPTIMIZER: "adam"
\ No newline at end of file
configs/unet_pet.yaml → configs/fast_scnn_pet.yaml
浏览文件 @ 1942c445
...@@ -27,16 +27,17 @@ FREEZE: ...@@ -27,16 +27,17 @@ FREEZE:
MODEL_FILENAME: "__model__" MODEL_FILENAME: "__model__"
PARAMS_FILENAME: "__params__" PARAMS_FILENAME: "__params__"
MODEL: MODEL:
MODEL_NAME: "unet" MODEL_NAME: "fast_scnn"
DEFAULT_NORM_TYPE: "bn" DEFAULT_NORM_TYPE: "bn"
TEST:
TEST_MODEL: "./saved_model/unet_pet/final/"
TRAIN: TRAIN:
MODEL_SAVE_DIR: "./saved_model/unet_pet/" PRETRAINED_MODEL_DIR: "./pretrained_model/fast_scnn_cityscapes/"
PRETRAINED_MODEL_DIR: "./pretrained_model/unet_bn_coco/" MODEL_SAVE_DIR: "./saved_model/fast_scnn_pet/"
SNAPSHOT_EPOCH: 10 SNAPSHOT_EPOCH: 10
TEST:
TEST_MODEL: "./saved_model/fast_scnn_pet/final"
SOLVER: SOLVER:
NUM_EPOCHS: 100 NUM_EPOCHS: 100
LR: 0.005 LR: 0.005
LR_POLICY: "poly" LR_POLICY: "poly"
OPTIMIZER: "adam" OPTIMIZER: "sgd"
configs/hrnet_optic.yaml 0 → 100644
浏览文件 @ 1942c445
# 数据集配置
DATASET:
DATA_DIR: "./dataset/optic_disc_seg/"
NUM_CLASSES: 2
TEST_FILE_LIST: "./dataset/optic_disc_seg/test_list.txt"
TRAIN_FILE_LIST: "./dataset/optic_disc_seg/train_list.txt"
VAL_FILE_LIST: "./dataset/optic_disc_seg/val_list.txt"
VIS_FILE_LIST: "./dataset/optic_disc_seg/test_list.txt"
# 预训练模型配置
MODEL:
MODEL_NAME: "hrnet"
DEFAULT_NORM_TYPE: "bn"
HRNET:
STAGE2:
NUM_CHANNELS: [18, 36]
STAGE3:
NUM_CHANNELS: [18, 36, 72]
STAGE4:
NUM_CHANNELS: [18, 36, 72, 144]
# 其他配置
TRAIN_CROP_SIZE: (512, 512)
EVAL_CROP_SIZE: (512, 512)
AUG:
AUG_METHOD: "unpadding"
FIX_RESIZE_SIZE: (512, 512)
BATCH_SIZE: 4
TRAIN:
PRETRAINED_MODEL_DIR: "./pretrained_model/hrnet_w18_bn_cityscapes/"
MODEL_SAVE_DIR: "./saved_model/hrnet_optic/"
SNAPSHOT_EPOCH: 5
TEST:
TEST_MODEL: "./saved_model/hrnet_optic/final"
SOLVER:
NUM_EPOCHS: 10
LR: 0.001
LR_POLICY: "poly"
OPTIMIZER: "adam"
configs/icnet_optic.yaml 0 → 100644
浏览文件 @ 1942c445
# 数据集配置
DATASET:
DATA_DIR: "./dataset/optic_disc_seg/"
NUM_CLASSES: 2
TEST_FILE_LIST: "./dataset/optic_disc_seg/test_list.txt"
TRAIN_FILE_LIST: "./dataset/optic_disc_seg/train_list.txt"
VAL_FILE_LIST: "./dataset/optic_disc_seg/val_list.txt"
VIS_FILE_LIST: "./dataset/optic_disc_seg/test_list.txt"
# 预训练模型配置
MODEL:
MODEL_NAME: "icnet"
DEFAULT_NORM_TYPE: "bn"
MULTI_LOSS_WEIGHT: "[1.0, 0.4, 0.16]"
ICNET:
DEPTH_MULTIPLIER: 0.5
# 其他配置
TRAIN_CROP_SIZE: (512, 512)
EVAL_CROP_SIZE: (512, 512)
AUG:
AUG_METHOD: "unpadding"
FIX_RESIZE_SIZE: (512, 512)
BATCH_SIZE: 4
TRAIN:
PRETRAINED_MODEL_DIR: "./pretrained_model/icnet_bn_cityscapes/"
MODEL_SAVE_DIR: "./saved_model/icnet_optic/"
SNAPSHOT_EPOCH: 5
TEST:
TEST_MODEL: "./saved_model/icnet_optic/final"
SOLVER:
NUM_EPOCHS: 10
LR: 0.001
LR_POLICY: "poly"
OPTIMIZER: "adam"
configs/icnet_pet.yaml 已删除 100644 → 0
浏览文件 @ b5967e0f
TRAIN_CROP_SIZE: (512, 512) # (width, height), for unpadding rangescaling and stepscaling
EVAL_CROP_SIZE: (512, 512) # (width, height), for unpadding rangescaling and stepscaling
AUG:
AUG_METHOD: "unpadding" # choice unpadding rangescaling and stepscaling
FIX_RESIZE_SIZE: (512, 512) # (width, height), for unpadding
INF_RESIZE_VALUE: 500 # for rangescaling
MAX_RESIZE_VALUE: 600 # for rangescaling
MIN_RESIZE_VALUE: 400 # for rangescaling
MAX_SCALE_FACTOR: 1.25 # for stepscaling
MIN_SCALE_FACTOR: 0.75 # for stepscaling
SCALE_STEP_SIZE: 0.25 # for stepscaling
MIRROR: True
BATCH_SIZE: 4
DATASET:
DATA_DIR: "./dataset/mini_pet/"
IMAGE_TYPE: "rgb" # choice rgb or rgba
NUM_CLASSES: 3
TEST_FILE_LIST: "./dataset/mini_pet/file_list/test_list.txt"
TRAIN_FILE_LIST: "./dataset/mini_pet/file_list/train_list.txt"
VAL_FILE_LIST: "./dataset/mini_pet/file_list/val_list.txt"
VIS_FILE_LIST: "./dataset/mini_pet/file_list/test_list.txt"
IGNORE_INDEX: 255
SEPARATOR: " "
FREEZE:
MODEL_FILENAME: "__model__"
PARAMS_FILENAME: "__params__"
MODEL:
MODEL_NAME: "icnet"
DEFAULT_NORM_TYPE: "bn"
MULTI_LOSS_WEIGHT: "[1.0, 0.4, 0.16]"
ICNET:
DEPTH_MULTIPLIER: 0.5
TRAIN:
PRETRAINED_MODEL_DIR: "./pretrained_model/icnet_bn_cityscapes/"
MODEL_SAVE_DIR: "./saved_model/icnet_pet/"
SNAPSHOT_EPOCH: 10
TEST:
TEST_MODEL: "./saved_model/icnet_pet/final"
SOLVER:
NUM_EPOCHS: 100
LR: 0.005
LR_POLICY: "poly"
OPTIMIZER: "sgd"
configs/pspnet_optic.yaml 0 → 100644
浏览文件 @ 1942c445
# 数据集配置
DATASET:
DATA_DIR: "./dataset/optic_disc_seg/"
NUM_CLASSES: 2
TEST_FILE_LIST: "./dataset/optic_disc_seg/test_list.txt"
TRAIN_FILE_LIST: "./dataset/optic_disc_seg/train_list.txt"
VAL_FILE_LIST: "./dataset/optic_disc_seg/val_list.txt"
VIS_FILE_LIST: "./dataset/optic_disc_seg/test_list.txt"
# 预训练模型配置
MODEL:
MODEL_NAME: "pspnet"
DEFAULT_NORM_TYPE: "bn"
PSPNET:
DEPTH_MULTIPLIER: 1
LAYERS: 50
# 其他配置
TRAIN_CROP_SIZE: (512, 512)
EVAL_CROP_SIZE: (512, 512)
AUG:
AUG_METHOD: "unpadding"
FIX_RESIZE_SIZE: (512, 512)
BATCH_SIZE: 4
TRAIN:
PRETRAINED_MODEL_DIR: "./pretrained_model/pspnet50_bn_cityscapes/"
MODEL_SAVE_DIR: "./saved_model/pspnet_optic/"
SNAPSHOT_EPOCH: 5
TEST:
TEST_MODEL: "./saved_model/pspnet_optic/final"
SOLVER:
NUM_EPOCHS: 10
LR: 0.001
LR_POLICY: "poly"
OPTIMIZER: "adam"
configs/unet_optic.yaml 0 → 100644
浏览文件 @ 1942c445
# 数据集配置
DATASET:
DATA_DIR: "./dataset/optic_disc_seg/"
NUM_CLASSES: 2
TEST_FILE_LIST: "./dataset/optic_disc_seg/test_list.txt"
TRAIN_FILE_LIST: "./dataset/optic_disc_seg/train_list.txt"
VAL_FILE_LIST: "./dataset/optic_disc_seg/val_list.txt"
VIS_FILE_LIST: "./dataset/optic_disc_seg/test_list.txt"
# 预训练模型配置
MODEL:
MODEL_NAME: "unet"
DEFAULT_NORM_TYPE: "bn"
# 其他配置
TRAIN_CROP_SIZE: (512, 512)
EVAL_CROP_SIZE: (512, 512)
AUG:
AUG_METHOD: "unpadding"
FIX_RESIZE_SIZE: (512, 512)
BATCH_SIZE: 4
TRAIN:
PRETRAINED_MODEL_DIR: "./pretrained_model/unet_bn_coco/"
MODEL_SAVE_DIR: "./saved_model/unet_optic/"
SNAPSHOT_EPOCH: 5
TEST:
TEST_MODEL: "./saved_model/unet_optic/final"
SOLVER:
NUM_EPOCHS: 10
LR: 0.001
LR_POLICY: "poly"
OPTIMIZER: "adam"
contrib/ACE2P/config.py
浏览文件 @ 1942c445
...@@ -6,13 +6,13 @@ args = get_arguments() ...@@ -6,13 +6,13 @@ args = get_arguments()
cfg = AttrDict() cfg = AttrDict()
# 待预测图像所在路径 # 待预测图像所在路径
cfg.data_dir = os.path.join(args.example , "data", "testing_images") cfg.data_dir = os.path.join("data", "testing_images")
# 待预测图像名称列表 # 待预测图像名称列表
cfg.data_list_file = os.path.join(args.example , "data", "test_id.txt") cfg.data_list_file = os.path.join("data", "test_id.txt")
# 模型加载路径 # 模型加载路径
cfg.model_path = os.path.join(args.example , "ACE2P") cfg.model_path = args.example
# 预测结果保存路径 # 预测结果保存路径
cfg.vis_dir = os.path.join(args.example , "result") cfg.vis_dir = "result"
# 预测类别数 # 预测类别数
cfg.class_num = 20 cfg.class_num = 20
......
contrib/ACE2P/download_ACE2P.py 0 → 100644
浏览文件 @ 1942c445
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import os
LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
TEST_PATH = os.path.join(LOCAL_PATH, "..", "..", "test")
sys.path.append(TEST_PATH)
from test_utils import download_file_and_uncompress
if __name__ == "__main__":
download_file_and_uncompress(
url='https://paddleseg.bj.bcebos.com/models/ACE2P.tgz',
savepath=LOCAL_PATH,
extrapath=LOCAL_PATH,
extraname='ACE2P')
print("Pretrained Model download success!")
contrib/ACE2P/infer.py 0 → 100644
浏览文件 @ 1942c445
# -*- coding: utf-8 -*-
import os
import cv2
import numpy as np
from utils.util import get_arguments
from utils.palette import get_palette
from PIL import Image as PILImage
import importlib
args = get_arguments()
config = importlib.import_module('config')
cfg = getattr(config, 'cfg')
# paddle垃圾回收策略FLAG，ACE2P模型较大，当显存不够时建议开启
os.environ['FLAGS_eager_delete_tensor_gb']='0.0'
import paddle.fluid as fluid
# 预测数据集类
class TestDataSet():
def __init__(self):
self.data_dir = cfg.data_dir
self.data_list_file = cfg.data_list_file
self.data_list = self.get_data_list()
self.data_num = len(self.data_list)
def get_data_list(self):
# 获取预测图像路径列表
data_list = []
data_file_handler = open(self.data_list_file, 'r')
for line in data_file_handler:
img_name = line.strip()
name_prefix = img_name.split('.')[0]
if len(img_name.split('.')) == 1:
img_name = img_name + '.jpg'
img_path = os.path.join(self.data_dir, img_name)
data_list.append(img_path)
return data_list
def preprocess(self, img):
# 图像预处理
if cfg.example == 'ACE2P':
reader = importlib.import_module('reader')
ACE2P_preprocess = getattr(reader, 'preprocess')
img = ACE2P_preprocess(img)
else:
img = cv2.resize(img, cfg.input_size).astype(np.float32)
img -= np.array(cfg.MEAN)
img /= np.array(cfg.STD)
img = img.transpose((2, 0, 1))
img = np.expand_dims(img, axis=0)
return img
def get_data(self, index):
# 获取图像信息
img_path = self.data_list[index]
img = cv2.imread(img_path, cv2.IMREAD_COLOR)
if img is None:
return img, img,img_path, None
img_name = img_path.split(os.sep)[-1]
name_prefix = img_name.replace('.'+img_name.split('.')[-1],'')
img_shape = img.shape[:2]
img_process = self.preprocess(img)
return img, img_process, name_prefix, img_shape
def infer():
if not os.path.exists(cfg.vis_dir):
os.makedirs(cfg.vis_dir)
palette = get_palette(cfg.class_num)
# 人像分割结果显示阈值
thresh = 120
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
# 加载预测模型
test_prog, feed_name, fetch_list = fluid.io.load_inference_model(
dirname=cfg.model_path, executor=exe, params_filename='__params__')
#加载预测数据集
test_dataset = TestDataSet()
data_num = test_dataset.data_num
for idx in range(data_num):
# 数据获取
ori_img, image, im_name, im_shape = test_dataset.get_data(idx)
if image is None:
print(im_name, 'is None')
continue
# 预测
if cfg.example == 'ACE2P':
# ACE2P模型使用多尺度预测
reader = importlib.import_module('reader')
multi_scale_test = getattr(reader, 'multi_scale_test')
parsing, logits = multi_scale_test(exe, test_prog, feed_name, fetch_list, image, im_shape)
else:
# HumanSeg,RoadLine模型单尺度预测
result = exe.run(program=test_prog, feed={feed_name[0]: image}, fetch_list=fetch_list)
parsing = np.argmax(result[0][0], axis=0)
parsing = cv2.resize(parsing.astype(np.uint8), im_shape[::-1])
# 预测结果保存
result_path = os.path.join(cfg.vis_dir, im_name + '.png')
if cfg.example == 'HumanSeg':
logits = result[0][0][1]*255
logits = cv2.resize(logits, im_shape[::-1])
ret, logits = cv2.threshold(logits, thresh, 0, cv2.THRESH_TOZERO)
logits = 255 *(logits - thresh)/(255 - thresh)
# 将分割结果添加到alpha通道
rgba = np.concatenate((ori_img, np.expand_dims(logits, axis=2)), axis=2)
cv2.imwrite(result_path, rgba)
else:
output_im = PILImage.fromarray(np.asarray(parsing, dtype=np.uint8))
output_im.putpalette(palette)
output_im.save(result_path)
if (idx + 1) % 100 == 0:
print('%d processd' % (idx + 1))
print('%d processd done' % (idx + 1))
return 0
if __name__ == "__main__":
infer()
contrib/ACE2P/reader.py
浏览文件 @ 1942c445
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
import numpy as np import numpy as np
import paddle.fluid as fluid import paddle.fluid as fluid
from ACE2P.config import cfg from config import cfg
import cv2 import cv2
def get_affine_points(src_shape, dst_shape, rot_grad=0): def get_affine_points(src_shape, dst_shape, rot_grad=0):
......
contrib/infer.py → contrib/HumanSeg/infer.py
浏览文件 @ 1942c445
...@@ -8,7 +8,7 @@ from PIL import Image as PILImage ...@@ -8,7 +8,7 @@ from PIL import Image as PILImage
import importlib import importlib
args = get_arguments() args = get_arguments()
config = importlib.import_module(args.example+'.config') config = importlib.import_module('config')
cfg = getattr(config, 'cfg') cfg = getattr(config, 'cfg')
# paddle垃圾回收策略FLAG，ACE2P模型较大，当显存不够时建议开启 # paddle垃圾回收策略FLAG，ACE2P模型较大，当显存不够时建议开启
......
contrib/HumanSeg/utils/palette.py 0 → 100644
浏览文件 @ 1942c445
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: RainbowSecret
## Microsoft Research
## yuyua@microsoft.com
## Copyright (c) 2018
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import cv2
def get_palette(num_cls):
""" Returns the color map for visualizing the segmentation mask.
Args:
num_cls: Number of classes
Returns:
The color map
"""
n = num_cls
palette = [0] * (n * 3)
for j in range(0, n):
lab = j
palette[j * 3 + 0] = 0
palette[j * 3 + 1] = 0
palette[j * 3 + 2] = 0
i = 0
while lab:
palette[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i))
palette[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i))
palette[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i))
i += 1
lab >>= 3
return palette
contrib/HumanSeg/utils/util.py 0 → 100644
浏览文件 @ 1942c445
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import os
def get_arguments():
parser = argparse.ArgumentParser()
parser.add_argument("--use_gpu",
action="store_true",
help="Use gpu or cpu to test.")
parser.add_argument('--example',
type=str,
help='RoadLine, HumanSeg or ACE2P')
return parser.parse_args()
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
def __getattr__(self, name):
if name in self.__dict__:
return self.__dict__[name]
elif name in self:
return self[name]
else:
raise AttributeError(name)
def __setattr__(self, name, value):
if name in self.__dict__:
self.__dict__[name] = value
else:
self[name] = value
def merge_cfg_from_args(args, cfg):
"""Merge config keys, values in args into the global config."""
for k, v in vars(args).items():
d = cfg
try:
value = eval(v)
except:
value = v
if value is not None:
cfg[k] = value
contrib/LaneNet/README.md 0 → 100644
浏览文件 @ 1942c445
# LaneNet 模型训练教程
* 本教程旨在介绍如何通过使用PaddleSeg进行车道线检测
* 在阅读本教程前，请确保您已经了解过PaddleSeg的[快速入门](../README.md#快速入门)和[基础功能](../README.md#基础功能)等章节，以便对PaddleSeg有一定的了解
## 环境依赖
* PaddlePaddle >= 1.7.0 或develop版本
* Python 3.5+
通过以下命令安装python包依赖，请确保在该分支上至少执行过一次以下命令
```shell
$ pip install -r requirements.txt
```
## 一. 准备待训练数据
我们提前准备好了一份处理好的数据集，通过以下代码进行下载，该数据集由图森车道线检测数据集转换而来，你也可以在这个[页面](https://github.com/TuSimple/tusimple-benchmark/issues/3)下载原始数据集。
```shell
python dataset/download_tusimple.py
```
数据目录结构
```
LaneNet
|-- dataset
|-- tusimple_lane_detection
|-- training
|-- gt_binary_image
|-- gt_image
|-- gt_instance_image
|-- train_part.txt
|-- val_part.txt
```
## 二. 下载预训练模型
下载[vgg预训练模型](https://paddle-imagenet-models-name.bj.bcebos.com/VGG16_pretrained.tar)，放在```pretrained_models```文件夹下。
## 三. 准备配置
接着我们需要确定相关配置，从本教程的角度，配置分为三部分：
* 数据集
* 训练集主目录
* 训练集文件列表
* 测试集文件列表
* 评估集文件列表
* 预训练模型
* 预训练模型名称
* 预训练模型的backbone网络
* 预训练模型路径
* 其他
* 学习率
* Batch大小
* ...
在三者中，预训练模型的配置尤为重要，如果模型或者BACKBONE配置错误，会导致预训练的参数没有加载，进而影响收敛速度。预训练模型相关的配置如第二步所展示。
数据集的配置和数据路径有关，在本教程中，数据存放在`dataset/tusimple_lane_detection`中
其他配置则根据数据集和机器环境的情况进行调节，最终我们保存一个如下内容的yaml配置文件，存放路径为**configs/lanenet.yaml**
```yaml
# 数据集配置
DATASET:
DATA_DIR: "./dataset/tusimple_lane_detection"
IMAGE_TYPE: "rgb" # choice rgb or rgba
NUM_CLASSES: 2
TEST_FILE_LIST: "./dataset/tusimple_lane_detection/training/val_part.txt"
TRAIN_FILE_LIST: "./dataset/tusimple_lane_detection/training/train_part.txt"
VAL_FILE_LIST: "./dataset/tusimple_lane_detection/training/val_part.txt"
SEPARATOR: " "
# 预训练模型配置
MODEL:
MODEL_NAME: "lanenet"
# 其他配置
EVAL_CROP_SIZE: (512, 256)
TRAIN_CROP_SIZE: (512, 256)
AUG:
AUG_METHOD: u"unpadding" # choice unpadding rangescaling and stepscaling
FIX_RESIZE_SIZE: (512, 256) # (width, height), for unpadding
MIRROR: False
RICH_CROP:
ENABLE: False
BATCH_SIZE: 4
TEST:
TEST_MODEL: "./saved_model/lanenet/final/"
TRAIN:
MODEL_SAVE_DIR: "./saved_model/lanenet/"
PRETRAINED_MODEL_DIR: "./pretrained_models/VGG16_pretrained"
SNAPSHOT_EPOCH: 5
SOLVER:
NUM_EPOCHS: 100
LR: 0.0005
LR_POLICY: "poly"
OPTIMIZER: "sgd"
WEIGHT_DECAY: 0.001
```
## 五. 开始训练
使用下述命令启动训练
```shell
CUDA_VISIBLE_DEVICES=0 python -u train.py --cfg configs/lanenet.yaml --use_gpu --use_mpio --do_eval
```
## 六. 进行评估
模型训练完成，使用下述命令启动评估
```shell
CUDA_VISIBLE_DEVICES=0 python -u eval.py --use_gpu --cfg configs/lanenet.yaml
```
## 七. 可视化
需要先下载一个车前视角和鸟瞰图视角转换所需文件，点击[链接](https://paddleseg.bj.bcebos.com/resources/tusimple_ipm_remap.tar)，下载后放在```./utils```下。同时我们提供了一个训练好的模型，点击[链接](https://paddleseg.bj.bcebos.com/models/lanenet_vgg_tusimple.tar)，下载后放在```./pretrained_models/```下，使用如下命令进行可视化
```shell
CUDA_VISIBLE_DEVICES=0 python -u ./vis.py --cfg configs/lanenet.yaml --use_gpu --vis_dir vis_result \
TEST.TEST_MODEL pretrained_models/LaneNet_vgg_tusimple/
```
可视化结果示例：
预测结果：<br/>
![](imgs/0005_pred_lane.png)
分割结果：<br/>
![](imgs/0005_pred_binary.png)<br/>
车道线实例预测结果：<br/>
![](imgs/0005_pred_instance.png)
configs/hrnet_w18_pet.yaml → contrib/LaneNet/configs/lanenet.yaml
浏览文件 @ 1942c445
TRAIN_CROP_SIZE: (512, 512) # (width, height), for unpadding rangescaling and stepscaling EVAL_CROP_SIZE: (512, 256) # (width, height), for unpadding rangescaling and stepscaling
EVAL_CROP_SIZE: (512, 512) # (width, height), for unpadding rangescaling and stepscaling TRAIN_CROP_SIZE: (512, 256) # (width, height), for unpadding rangescaling and stepscaling
AUG: AUG:
AUG_METHOD: "unpadding" # choice unpadding rangescaling and stepscaling AUG_METHOD: u"unpadding" # choice unpadding rangescaling and stepscaling
FIX_RESIZE_SIZE: (512, 512) # (width, height), for unpadding FIX_RESIZE_SIZE: (512, 256) # (width, height), for unpadding
INF_RESIZE_VALUE: 500 # for rangescaling INF_RESIZE_VALUE: 500 # for rangescaling
MAX_RESIZE_VALUE: 600 # for rangescaling MAX_RESIZE_VALUE: 600 # for rangescaling
MIN_RESIZE_VALUE: 400 # for rangescaling MIN_RESIZE_VALUE: 400 # for rangescaling
MAX_SCALE_FACTOR: 2.0 # for stepscaling
MAX_SCALE_FACTOR: 1.25 # for stepscaling MIN_SCALE_FACTOR: 0.5 # for stepscaling
MIN_SCALE_FACTOR: 0.75 # for stepscaling
SCALE_STEP_SIZE: 0.25 # for stepscaling SCALE_STEP_SIZE: 0.25 # for stepscaling
MIRROR: True MIRROR: False
RICH_CROP:
ENABLE: False
BATCH_SIZE: 4 BATCH_SIZE: 4
DATASET:
DATA_DIR: "./dataset/mini_pet/" DATALOADER:
BUF_SIZE: 256
NUM_WORKERS: 4
DATASET:
DATA_DIR: "./dataset/tusimple_lane_detection"
IMAGE_TYPE: "rgb" # choice rgb or rgba IMAGE_TYPE: "rgb" # choice rgb or rgba
NUM_CLASSES: 3 NUM_CLASSES: 2
TEST_FILE_LIST: "./dataset/mini_pet/file_list/test_list.txt" TEST_FILE_LIST: "./dataset/tusimple_lane_detection/training/val_part.txt"
TRAIN_FILE_LIST: "./dataset/mini_pet/file_list/train_list.txt" TEST_TOTAL_IMAGES: 362
VAL_FILE_LIST: "./dataset/mini_pet/file_list/val_list.txt" TRAIN_FILE_LIST: "./dataset/tusimple_lane_detection/training/train_part.txt"
VIS_FILE_LIST: "./dataset/mini_pet/file_list/test_list.txt" TRAIN_TOTAL_IMAGES: 3264
IGNORE_INDEX: 255 VAL_FILE_LIST: "./dataset/tusimple_lane_detection/training/val_part.txt"
VAL_TOTAL_IMAGES: 362
SEPARATOR: " " SEPARATOR: " "
IGNORE_INDEX: 255
FREEZE: FREEZE:
MODEL_FILENAME: "__model__" MODEL_FILENAME: "__model__"
PARAMS_FILENAME: "__params__" PARAMS_FILENAME: "__params__"
MODEL: MODEL:
MODEL_NAME: "hrnet" MODEL_NAME: "lanenet"
DEFAULT_NORM_TYPE: "bn" DEFAULT_NORM_TYPE: "bn"
HRNET:
STAGE2:
NUM_CHANNELS: [18, 36]
STAGE3:
NUM_CHANNELS: [18, 36, 72]
STAGE4:
NUM_CHANNELS: [18, 36, 72, 144]
TRAIN:
PRETRAINED_MODEL_DIR: "./pretrained_model/hrnet_w18_bn_cityscapes/"
MODEL_SAVE_DIR: "./saved_model/hrnet_w18_bn_pet/"
SNAPSHOT_EPOCH: 10
TEST: TEST:
TEST_MODEL: "./saved_model/hrnet_w18_bn_pet/final" TEST_MODEL: "./saved_model/lanenet/final/"
TRAIN:
MODEL_SAVE_DIR: "./saved_model/lanenet/"
PRETRAINED_MODEL_DIR: "./pretrained_models/VGG16_pretrained"
SNAPSHOT_EPOCH: 1
SOLVER: SOLVER:
NUM_EPOCHS: 100 NUM_EPOCHS: 100
LR: 0.005 LR: 0.0005
LR_POLICY: "poly" LR_POLICY: "poly"
OPTIMIZER: "sgd" OPTIMIZER: "sgd"
WEIGHT_DECAY: 0.001
contrib/LaneNet/data_aug.py 0 → 100644
浏览文件 @ 1942c445
# coding: utf8
# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import cv2
import numpy as np
from utils.config import cfg
from models.model_builder import ModelPhase
from pdseg.data_aug import get_random_scale, randomly_scale_image_and_label, random_rotation, \
rand_scale_aspect, hsv_color_jitter, rand_crop
def resize(img, grt=None, grt_instance=None, mode=ModelPhase.TRAIN):
"""
改变图像及标签图像尺寸
AUG.AUG_METHOD为unpadding，所有模式均直接resize到AUG.FIX_RESIZE_SIZE的尺寸
AUG.AUG_METHOD为stepscaling, 按比例resize，训练时比例范围AUG.MIN_SCALE_FACTOR到AUG.MAX_SCALE_FACTOR,间隔为AUG.SCALE_STEP_SIZE，其他模式返回原图
AUG.AUG_METHOD为rangescaling，长边对齐，短边按比例变化，训练时长边对齐范围AUG.MIN_RESIZE_VALUE到AUG.MAX_RESIZE_VALUE，其他模式长边对齐AUG.INF_RESIZE_VALUE
Args：
img(numpy.ndarray): 输入图像
grt(numpy.ndarray): 标签图像，默认为None
mode(string): 模式, 默认训练模式，即ModelPhase.TRAIN
Returns：
resize后的图像和标签图
"""
if cfg.AUG.AUG_METHOD == 'unpadding':
target_size = cfg.AUG.FIX_RESIZE_SIZE
img = cv2.resize(img, target_size, interpolation=cv2.INTER_LINEAR)
if grt is not None:
grt = cv2.resize(grt, target_size, interpolation=cv2.INTER_NEAREST)
if grt_instance is not None:
grt_instance = cv2.resize(grt_instance, target_size, interpolation=cv2.INTER_NEAREST)
elif cfg.AUG.AUG_METHOD == 'stepscaling':
if mode == ModelPhase.TRAIN:
min_scale_factor = cfg.AUG.MIN_SCALE_FACTOR
max_scale_factor = cfg.AUG.MAX_SCALE_FACTOR
step_size = cfg.AUG.SCALE_STEP_SIZE
scale_factor = get_random_scale(min_scale_factor, max_scale_factor,
step_size)
img, grt = randomly_scale_image_and_label(
img, grt, scale=scale_factor)
elif cfg.AUG.AUG_METHOD == 'rangescaling':
min_resize_value = cfg.AUG.MIN_RESIZE_VALUE
max_resize_value = cfg.AUG.MAX_RESIZE_VALUE
if mode == ModelPhase.TRAIN:
if min_resize_value == max_resize_value:
random_size = min_resize_value
else:
random_size = int(
np.random.uniform(min_resize_value, max_resize_value) + 0.5)
else:
random_size = cfg.AUG.INF_RESIZE_VALUE
value = max(img.shape[0], img.shape[1])
scale = float(random_size) / float(value)
img = cv2.resize(
img, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)
if grt is not None:
grt = cv2.resize(
grt, (0, 0),
fx=scale,
fy=scale,
interpolation=cv2.INTER_NEAREST)
else:
raise Exception("Unexpect data augmention method: {}".format(
cfg.AUG.AUG_METHOD))
return img, grt, grt_instance
contrib/LaneNet/dataset/download_tusimple.py 0 → 100644
浏览文件 @ 1942c445
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import os
LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
TEST_PATH = os.path.join(LOCAL_PATH, "../../../", "test")
sys.path.append(TEST_PATH)
from test_utils import download_file_and_uncompress
def download_tusimple_dataset(savepath, extrapath):
url = "https://paddleseg.bj.bcebos.com/dataset/tusimple_lane_detection.tar"
download_file_and_uncompress(
url=url, savepath=savepath, extrapath=extrapath)
if __name__ == "__main__":
download_tusimple_dataset(LOCAL_PATH, LOCAL_PATH)
print("Dataset download finish!")
contrib/LaneNet/eval.py 0 → 100644
浏览文件 @ 1942c445
# coding: utf8
# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
# GPU memory garbage collection optimization flags
os.environ['FLAGS_eager_delete_tensor_gb'] = "0.0"
import sys
cur_path = os.path.abspath(os.path.dirname(__file__))
root_path = os.path.split(os.path.split(cur_path)[0])[0]
LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
SEG_PATH = os.path.join(LOCAL_PATH, "../../../")
sys.path.append(SEG_PATH)
sys.path.append(root_path)
import time
import argparse
import functools
import pprint
import cv2
import numpy as np
import paddle
import paddle.fluid as fluid
from utils.config import cfg
from pdseg.utils.timer import Timer, calculate_eta
from models.model_builder import build_model
from models.model_builder import ModelPhase
from reader import LaneNetDataset
def parse_args():
parser = argparse.ArgumentParser(description='PaddleSeg model evalution')
parser.add_argument(
'--cfg',
dest='cfg_file',
help='Config file for training (and optionally testing)',
default=None,
type=str)
parser.add_argument(
'--use_gpu',
dest='use_gpu',
help='Use gpu or cpu',
action='store_true',
default=False)
parser.add_argument(
'--use_mpio',
dest='use_mpio',
help='Use multiprocess IO or not',
action='store_true',
default=False)
parser.add_argument(
'opts',
help='See utils/config.py for all options',
default=None,
nargs=argparse.REMAINDER)
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
return parser.parse_args()
def evaluate(cfg, ckpt_dir=None, use_gpu=False, use_mpio=False, **kwargs):
np.set_printoptions(precision=5, suppress=True)
startup_prog = fluid.Program()
test_prog = fluid.Program()
dataset = LaneNetDataset(
file_list=cfg.DATASET.VAL_FILE_LIST,
mode=ModelPhase.TRAIN,
shuffle=True,
data_dir=cfg.DATASET.DATA_DIR)
def data_generator():
#TODO: check is batch reader compatitable with Windows
if use_mpio:
data_gen = dataset.multiprocess_generator(
num_processes=cfg.DATALOADER.NUM_WORKERS,
max_queue_size=cfg.DATALOADER.BUF_SIZE)
else:
data_gen = dataset.generator()
for b in data_gen:
yield b
py_reader, pred, grts, masks, accuracy, fp, fn = build_model(
test_prog, startup_prog, phase=ModelPhase.EVAL)
py_reader.decorate_sample_generator(
data_generator, drop_last=False, batch_size=cfg.BATCH_SIZE)
# Get device environment
places = fluid.cuda_places() if use_gpu else fluid.cpu_places()
place = places[0]
dev_count = len(places)
print("#Device count: {}".format(dev_count))
exe = fluid.Executor(place)
exe.run(startup_prog)
test_prog = test_prog.clone(for_test=True)
ckpt_dir = cfg.TEST.TEST_MODEL if not ckpt_dir else ckpt_dir
if ckpt_dir is not None:
print('load test model:', ckpt_dir)
fluid.io.load_params(exe, ckpt_dir, main_program=test_prog)
# Use streaming confusion matrix to calculate mean_iou
np.set_printoptions(
precision=4, suppress=True, linewidth=160, floatmode="fixed")
fetch_list = [pred.name, grts.name, masks.name, accuracy.name, fp.name, fn.name]
num_images = 0
step = 0
avg_acc = 0.0
avg_fp = 0.0
avg_fn = 0.0
# cur_images = 0
all_step = cfg.DATASET.TEST_TOTAL_IMAGES // cfg.BATCH_SIZE + 1
timer = Timer()
timer.start()
py_reader.start()
while True:
try:
step += 1
pred, grts, masks, out_acc, out_fp, out_fn = exe.run(
test_prog, fetch_list=fetch_list, return_numpy=True)
avg_acc += np.mean(out_acc) * pred.shape[0]
avg_fp += np.mean(out_fp) * pred.shape[0]
avg_fn += np.mean(out_fn) * pred.shape[0]
num_images += pred.shape[0]
speed = 1.0 / timer.elapsed_time()
print(
"[EVAL]step={} accuracy={:.4f} fp={:.4f} fn={:.4f} step/sec={:.2f} | ETA {}"
.format(step, avg_acc / num_images, avg_fp / num_images, avg_fn / num_images, speed,
calculate_eta(all_step - step, speed)))
timer.restart()
sys.stdout.flush()
except fluid.core.EOFException:
break
print("[EVAL]#image={} accuracy={:.4f} fp={:.4f} fn={:.4f}".format(
num_images, avg_acc / num_images, avg_fp / num_images, avg_fn / num_images))
return avg_acc / num_images, avg_fp / num_images, avg_fn / num_images
def main():
args = parse_args()
if args.cfg_file is not None:
cfg.update_from_file(args.cfg_file)
if args.opts:
cfg.update_from_list(args.opts)
cfg.check_and_infer()
print(pprint.pformat(cfg))
evaluate(cfg, **args.__dict__)
if __name__ == '__main__':
main()
contrib/LaneNet/loss.py 0 → 100644
浏览文件 @ 1942c445
# coding: utf8
# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle.fluid as fluid
import numpy as np
from utils.config import cfg
def unsorted_segment_sum(data, segment_ids, unique_labels, feature_dims):
zeros = fluid.layers.fill_constant_batch_size_like(unique_labels, shape=[1, feature_dims],
dtype='float32', value=0)
segment_ids = fluid.layers.unsqueeze(segment_ids, axes=[1])
segment_ids.stop_gradient = True
segment_sum = fluid.layers.scatter_nd_add(zeros, segment_ids, data)
zeros.stop_gradient = True
return segment_sum
def norm(x, axis=-1):
distance = fluid.layers.reduce_sum(fluid.layers.abs(x), dim=axis, keep_dim=True)
return distance
def discriminative_loss_single(
prediction,
correct_label,
feature_dim,
label_shape,
delta_v,
delta_d,
param_var,
param_dist,
param_reg):
correct_label = fluid.layers.reshape(
correct_label, [
label_shape[1] * label_shape[0]])
prediction = fluid.layers.transpose(prediction, [1, 2, 0])
reshaped_pred = fluid.layers.reshape(
prediction, [
label_shape[1] * label_shape[0], feature_dim])
unique_labels, unique_id, counts = fluid.layers.unique_with_counts(correct_label)
correct_label.stop_gradient = True
counts = fluid.layers.cast(counts, 'float32')
num_instances = fluid.layers.shape(unique_labels)
segmented_sum = unsorted_segment_sum(
reshaped_pred, unique_id, unique_labels, feature_dims=feature_dim)
counts_rsp = fluid.layers.reshape(counts, (-1, 1))
mu = fluid.layers.elementwise_div(segmented_sum, counts_rsp)
counts_rsp.stop_gradient = True
mu_expand = fluid.layers.gather(mu, unique_id)
tmp = fluid.layers.elementwise_sub(mu_expand, reshaped_pred)
distance = norm(tmp)
distance = distance - delta_v
distance_pos = fluid.layers.greater_equal(distance, fluid.layers.zeros_like(distance))
distance_pos = fluid.layers.cast(distance_pos, 'float32')
distance = distance * distance_pos
distance = fluid.layers.square(distance)
l_var = unsorted_segment_sum(distance, unique_id, unique_labels, feature_dims=1)
l_var = fluid.layers.elementwise_div(l_var, counts_rsp)
l_var = fluid.layers.reduce_sum(l_var)
l_var = l_var / fluid.layers.cast(num_instances * (num_instances - 1), 'float32')
mu_interleaved_rep = fluid.layers.expand(mu, [num_instances, 1])
mu_band_rep = fluid.layers.expand(mu, [1, num_instances])
mu_band_rep = fluid.layers.reshape(mu_band_rep, (num_instances * num_instances, feature_dim))
mu_diff = fluid.layers.elementwise_sub(mu_band_rep, mu_interleaved_rep)
intermediate_tensor = fluid.layers.reduce_sum(fluid.layers.abs(mu_diff), dim=1)
intermediate_tensor.stop_gradient = True
zero_vector = fluid.layers.zeros([1], 'float32')
bool_mask = fluid.layers.not_equal(intermediate_tensor, zero_vector)
temp = fluid.layers.where(bool_mask)
mu_diff_bool = fluid.layers.gather(mu_diff, temp)
mu_norm = norm(mu_diff_bool)
mu_norm = 2. * delta_d - mu_norm
mu_norm_pos = fluid.layers.greater_equal(mu_norm, fluid.layers.zeros_like(mu_norm))
mu_norm_pos = fluid.layers.cast(mu_norm_pos, 'float32')
mu_norm = mu_norm * mu_norm_pos
mu_norm_pos.stop_gradient = True
mu_norm = fluid.layers.square(mu_norm)
l_dist = fluid.layers.reduce_mean(mu_norm)
l_reg = fluid.layers.reduce_mean(norm(mu, axis=1))
l_var = param_var * l_var
l_dist = param_dist * l_dist
l_reg = param_reg * l_reg
loss = l_var + l_dist + l_reg
return loss, l_var, l_dist, l_reg
def discriminative_loss(prediction, correct_label, feature_dim, image_shape,
delta_v, delta_d, param_var, param_dist, param_reg):
batch_size = int(cfg.BATCH_SIZE_PER_DEV)
output_ta_loss = 0.
output_ta_var = 0.
output_ta_dist = 0.
output_ta_reg = 0.
for i in range(batch_size):
disc_loss_single, l_var_single, l_dist_single, l_reg_single = discriminative_loss_single(
prediction[i], correct_label[i], feature_dim, image_shape, delta_v, delta_d, param_var, param_dist,
param_reg)
output_ta_loss += disc_loss_single
output_ta_var += l_var_single
output_ta_dist += l_dist_single
output_ta_reg += l_reg_single
disc_loss = output_ta_loss / batch_size
l_var = output_ta_var / batch_size
l_dist = output_ta_dist / batch_size
l_reg = output_ta_reg / batch_size
return disc_loss, l_var, l_dist, l_reg
contrib/LaneNet/models/__init__.py 0 → 100644
浏览文件 @ 1942c445
# coding: utf8
# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import models.modeling
#import models.backbone
contrib/LaneNet/models/model_builder.py 0 → 100644
浏览文件 @ 1942c445
# coding: utf8
# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
sys.path.append("..")
import struct
import paddle.fluid as fluid
from paddle.fluid.proto.framework_pb2 import VarType
from pdseg import solver
from utils.config import cfg
from pdseg.loss import multi_softmax_with_loss
from loss import discriminative_loss
from models.modeling import lanenet
class ModelPhase(object):
"""
Standard name for model phase in PaddleSeg
The following standard keys are defined:
* `TRAIN`: training mode.
* `EVAL`: testing/evaluation mode.
* `PREDICT`: prediction/inference mode.
* `VISUAL` : visualization mode
"""
TRAIN = 'train'
EVAL = 'eval'
PREDICT = 'predict'
VISUAL = 'visual'
@staticmethod
def is_train(phase):
return phase == ModelPhase.TRAIN
@staticmethod
def is_predict(phase):
return phase == ModelPhase.PREDICT
@staticmethod
def is_eval(phase):
return phase == ModelPhase.EVAL
@staticmethod
def is_visual(phase):
return phase == ModelPhase.VISUAL
@staticmethod
def is_valid_phase(phase):
""" Check valid phase """
if ModelPhase.is_train(phase) or ModelPhase.is_predict(phase) \
or ModelPhase.is_eval(phase) or ModelPhase.is_visual(phase):
return True
return False
def seg_model(image, class_num):
model_name = cfg.MODEL.MODEL_NAME
if model_name == 'lanenet':
logits = lanenet.lanenet(image, class_num)
else:
raise Exception(
"unknow model name, only support unet, deeplabv3p, icnet, pspnet, hrnet"
)
return logits
def softmax(logit):
logit = fluid.layers.transpose(logit, [0, 2, 3, 1])
logit = fluid.layers.softmax(logit)
logit = fluid.layers.transpose(logit, [0, 3, 1, 2])
return logit
def sigmoid_to_softmax(logit):
"""
one channel to two channel
"""
logit = fluid.layers.transpose(logit, [0, 2, 3, 1])
logit = fluid.layers.sigmoid(logit)
logit_back = 1 - logit
logit = fluid.layers.concat([logit_back, logit], axis=-1)
logit = fluid.layers.transpose(logit, [0, 3, 1, 2])
return logit
def build_model(main_prog, start_prog, phase=ModelPhase.TRAIN):
if not ModelPhase.is_valid_phase(phase):
raise ValueError("ModelPhase {} is not valid!".format(phase))
if ModelPhase.is_train(phase):
width = cfg.TRAIN_CROP_SIZE[0]
height = cfg.TRAIN_CROP_SIZE[1]
else:
width = cfg.EVAL_CROP_SIZE[0]
height = cfg.EVAL_CROP_SIZE[1]
image_shape = [cfg.DATASET.DATA_DIM, height, width]
grt_shape = [1, height, width]
class_num = cfg.DATASET.NUM_CLASSES
with fluid.program_guard(main_prog, start_prog):
with fluid.unique_name.guard():
image = fluid.layers.data(
name='image', shape=image_shape, dtype='float32')
label = fluid.layers.data(
name='label', shape=grt_shape, dtype='int32')
if cfg.MODEL.MODEL_NAME == 'lanenet':
label_instance = fluid.layers.data(
name='label_instance', shape=grt_shape, dtype='int32')
mask = fluid.layers.data(
name='mask', shape=grt_shape, dtype='int32')
# use PyReader when doing traning and evaluation
if ModelPhase.is_train(phase) or ModelPhase.is_eval(phase):
py_reader = fluid.io.PyReader(
feed_list=[image, label, label_instance, mask],
capacity=cfg.DATALOADER.BUF_SIZE,
iterable=False,
use_double_buffer=True)
loss_type = cfg.SOLVER.LOSS
if not isinstance(loss_type, list):
loss_type = list(loss_type)
logits = seg_model(image, class_num)
if ModelPhase.is_train(phase):
loss_valid = False
valid_loss = []
if cfg.MODEL.MODEL_NAME == 'lanenet':
embeding_logit = logits[1]
logits = logits[0]
disc_loss, _, _, l_reg = discriminative_loss(embeding_logit, label_instance, 4,
image_shape[1:], 0.5, 3.0, 1.0, 1.0, 0.001)
if "softmax_loss" in loss_type:
weight = None
if cfg.MODEL.MODEL_NAME == 'lanenet':
weight = get_dynamic_weight(label)
seg_loss = multi_softmax_with_loss(logits, label, mask, class_num, weight)
loss_valid = True
valid_loss.append("softmax_loss")
if not loss_valid:
raise Exception("SOLVER.LOSS: {} is set wrong. it should "
"include one of (softmax_loss, bce_loss, dice_loss) at least"
" example: ['softmax_loss']".format(cfg.SOLVER.LOSS))
invalid_loss = [x for x in loss_type if x not in valid_loss]
if len(invalid_loss) > 0:
print("Warning: the loss {} you set is invalid. it will not be included in loss computed.".format(invalid_loss))
avg_loss = disc_loss + 0.00001 * l_reg + seg_loss
#get pred result in original size
if isinstance(logits, tuple):
logit = logits[0]
else:
logit = logits
if logit.shape[2:] != label.shape[2:]:
logit = fluid.layers.resize_bilinear(logit, label.shape[2:])
# return image input and logit output for inference graph prune
if ModelPhase.is_predict(phase):
if class_num == 1:
logit = sigmoid_to_softmax(logit)
else:
logit = softmax(logit)
return image, logit
if class_num == 1:
out = sigmoid_to_softmax(logit)
out = fluid.layers.transpose(out, [0, 2, 3, 1])
else:
out = fluid.layers.transpose(logit, [0, 2, 3, 1])
pred = fluid.layers.argmax(out, axis=3)
pred = fluid.layers.unsqueeze(pred, axes=[3])
if ModelPhase.is_visual(phase):
if cfg.MODEL.MODEL_NAME == 'lanenet':
return pred, logits[1]
if class_num == 1:
logit = sigmoid_to_softmax(logit)
else:
logit = softmax(logit)
return pred, logit
accuracy, fp, fn = compute_metric(pred, label)
if ModelPhase.is_eval(phase):
return py_reader, pred, label, mask, accuracy, fp, fn
if ModelPhase.is_train(phase):
optimizer = solver.Solver(main_prog, start_prog)
decayed_lr = optimizer.optimise(avg_loss)
return py_reader, avg_loss, decayed_lr, pred, label, mask, disc_loss, seg_loss, accuracy, fp, fn
def compute_metric(pred, label):
label = fluid.layers.transpose(label, [0, 2, 3, 1])
idx = fluid.layers.where(pred == 1)
pix_cls_ret = fluid.layers.gather_nd(label, idx)
correct_num = fluid.layers.reduce_sum(fluid.layers.cast(pix_cls_ret, 'float32'))
gt_num = fluid.layers.cast(fluid.layers.shape(fluid.layers.gather_nd(label,
fluid.layers.where(label == 1)))[0], 'int64')
pred_num = fluid.layers.cast(fluid.layers.shape(fluid.layers.gather_nd(pred, idx))[0], 'int64')
accuracy = correct_num / gt_num
false_pred = pred_num - correct_num
fp = fluid.layers.cast(false_pred, 'float32') / fluid.layers.cast(fluid.layers.shape(pix_cls_ret)[0], 'int64')
label_cls_ret = fluid.layers.gather_nd(label, fluid.layers.where(label == 1))
mis_pred = fluid.layers.cast(fluid.layers.shape(label_cls_ret)[0], 'int64') - correct_num
fn = fluid.layers.cast(mis_pred, 'float32') / fluid.layers.cast(fluid.layers.shape(label_cls_ret)[0], 'int64')
accuracy.stop_gradient = True
fp.stop_gradient = True
fn.stop_gradient = True
return accuracy, fp, fn
def get_dynamic_weight(label):
label = fluid.layers.reshape(label, [-1])
unique_labels, unique_id, counts = fluid.layers.unique_with_counts(label)
counts = fluid.layers.cast(counts, 'float32')
weight = 1.0 / fluid.layers.log((counts / fluid.layers.reduce_sum(counts) + 1.02))
return weight
def to_int(string, dest="I"):
return struct.unpack(dest, string)[0]
def parse_shape_from_file(filename):
with open(filename, "rb") as file:
version = file.read(4)
lod_level = to_int(file.read(8), dest="Q")
for i in range(lod_level):
_size = to_int(file.read(8), dest="Q")
_ = file.read(_size)
version = file.read(4)
tensor_desc_size = to_int(file.read(4))
tensor_desc = VarType.TensorDesc()
tensor_desc.ParseFromString(file.read(tensor_desc_size))
return tuple(tensor_desc.dims)
contrib/LaneNet/models/modeling/lanenet.py 0 → 100644
浏览文件 @ 1942c445
# coding: utf8
# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import division
from __future__ import print_function
import paddle.fluid as fluid
from utils.config import cfg
from pdseg.models.libs.model_libs import scope, name_scope
from pdseg.models.libs.model_libs import bn, bn_relu, relu
from pdseg.models.libs.model_libs import conv, max_pool, deconv
from pdseg.models.backbone.vgg import VGGNet as vgg_backbone
#from models.backbone.vgg import VGGNet as vgg_backbone
# Bottleneck type
REGULAR = 1
DOWNSAMPLING = 2
UPSAMPLING = 3
DILATED = 4
ASYMMETRIC = 5
def prelu(x, decoder=False):
# If decoder, then perform relu else perform prelu
if decoder:
return fluid.layers.relu(x)
return fluid.layers.prelu(x, 'channel')
def iniatial_block(inputs, name_scope='iniatial_block'):
'''
The initial block for Enet has 2 branches: The convolution branch and Maxpool branch.
The conv branch has 13 filters, while the maxpool branch gives 3 channels corresponding to the RGB channels.
Both output layers are then concatenated to give an output of 16 channels.
:param inputs(Tensor): A 4D tensor of shape [batch_size, height, width, channels]
:return net_concatenated(Tensor): a 4D Tensor of new shape [batch_size, height, width, channels]
'''
# Convolutional branch
with scope(name_scope):
net_conv = conv(inputs, 13, 3, stride=2, padding=1)
net_conv = bn(net_conv)
net_conv = fluid.layers.prelu(net_conv, 'channel')
# Max pool branch
net_pool = max_pool(inputs, [2, 2], stride=2, padding='SAME')
# Concatenated output - does it matter max pool comes first or conv comes first? probably not.
net_concatenated = fluid.layers.concat([net_conv, net_pool], axis=1)
return net_concatenated
def bottleneck(inputs,
output_depth,
filter_size,
regularizer_prob,
projection_ratio=4,
type=REGULAR,
seed=0,
output_shape=None,
dilation_rate=None,
decoder=False,
name_scope='bottleneck'):
# Calculate the depth reduction based on the projection ratio used in 1x1 convolution.
reduced_depth = int(inputs.shape[1] / projection_ratio)
# DOWNSAMPLING BOTTLENECK
if type == DOWNSAMPLING:
#=============MAIN BRANCH=============
#Just perform a max pooling
with scope('down_sample'):
inputs_shape = inputs.shape
with scope('main_max_pool'):
net_main = fluid.layers.conv2d(inputs, inputs_shape[1], filter_size=3, stride=2, padding='SAME')
#First get the difference in depth to pad, then pad with zeros only on the last dimension.
depth_to_pad = abs(inputs_shape[1] - output_depth)
paddings = [0, 0, 0, depth_to_pad, 0, 0, 0, 0]
with scope('main_padding'):
net_main = fluid.layers.pad(net_main, paddings=paddings)
with scope('block1'):
net = conv(inputs, reduced_depth, [2, 2], stride=2, padding='same')
net = bn(net)
net = prelu(net, decoder=decoder)
with scope('block2'):
net = conv(net, reduced_depth, [filter_size, filter_size], padding='same')
net = bn(net)
net = prelu(net, decoder=decoder)
with scope('block3'):
net = conv(net, output_depth, [1, 1], padding='same')
net = bn(net)
net = prelu(net, decoder=decoder)
# Regularizer
net = fluid.layers.dropout(net, regularizer_prob, seed=seed)
# Finally, combine the two branches together via an element-wise addition
net = fluid.layers.elementwise_add(net, net_main)
net = prelu(net, decoder=decoder)
return net, inputs_shape
# DILATION CONVOLUTION BOTTLENECK
# Everything is the same as a regular bottleneck except for the dilation rate argument
elif type == DILATED:
#Check if dilation rate is given
if not dilation_rate:
raise ValueError('Dilation rate is not given.')
with scope('dilated'):
# Save the main branch for addition later
net_main = inputs
# First projection with 1x1 kernel (dimensionality reduction)
with scope('block1'):
net = conv(inputs, reduced_depth, [1, 1])
net = bn(net)
net = prelu(net, decoder=decoder)
# Second conv block --- apply dilated convolution here
with scope('block2'):
net = conv(net, reduced_depth, filter_size, padding='SAME', dilation=dilation_rate)
net = bn(net)
net = prelu(net, decoder=decoder)
# Final projection with 1x1 kernel (Expansion)
with scope('block3'):
net = conv(net, output_depth, [1,1])
net = bn(net)
net = prelu(net, decoder=decoder)
# Regularizer
net = fluid.layers.dropout(net, regularizer_prob, seed=seed)
net = prelu(net, decoder=decoder)
# Add the main branch
net = fluid.layers.elementwise_add(net_main, net)
net = prelu(net, decoder=decoder)
return net
# ASYMMETRIC CONVOLUTION BOTTLENECK
# Everything is the same as a regular bottleneck except for a [5,5] kernel decomposed into two [5,1] then [1,5]
elif type == ASYMMETRIC:
# Save the main branch for addition later
with scope('asymmetric'):
net_main = inputs
# First projection with 1x1 kernel (dimensionality reduction)
with scope('block1'):
net = conv(inputs, reduced_depth, [1, 1])
net = bn(net)
net = prelu(net, decoder=decoder)
# Second conv block --- apply asymmetric conv here
with scope('block2'):
with scope('asymmetric_conv2a'):
net = conv(net, reduced_depth, [filter_size, 1], padding='same')
with scope('asymmetric_conv2b'):
net = conv(net, reduced_depth, [1, filter_size], padding='same')
net = bn(net)
net = prelu(net, decoder=decoder)
# Final projection with 1x1 kernel
with scope('block3'):
net = conv(net, output_depth, [1, 1])
net = bn(net)
net = prelu(net, decoder=decoder)
# Regularizer
net = fluid.layers.dropout(net, regularizer_prob, seed=seed)
net = prelu(net, decoder=decoder)
# Add the main branch
net = fluid.layers.elementwise_add(net_main, net)
net = prelu(net, decoder=decoder)
return net
# UPSAMPLING BOTTLENECK
# Everything is the same as a regular one, except convolution becomes transposed.
elif type == UPSAMPLING:
#Check if pooling indices is given
#Check output_shape given or not
if output_shape is None:
raise ValueError('Output depth is not given')
#=======MAIN BRANCH=======
#Main branch to upsample. output shape must match with the shape of the layer that was pooled initially, in order
#for the pooling indices to work correctly. However, the initial pooled layer was padded, so need to reduce dimension
#before unpooling. In the paper, padding is replaced with convolution for this purpose of reducing the depth!
with scope('upsampling'):
with scope('unpool'):
net_unpool = conv(inputs, output_depth, [1, 1])
net_unpool = bn(net_unpool)
net_unpool = fluid.layers.resize_bilinear(net_unpool, out_shape=output_shape[2:])
# First 1x1 projection to reduce depth
with scope('block1'):
net = conv(inputs, reduced_depth, [1, 1])
net = bn(net)
net = prelu(net, decoder=decoder)
with scope('block2'):
net = deconv(net, reduced_depth, filter_size=filter_size, stride=2, padding='same')
net = bn(net)
net = prelu(net, decoder=decoder)
# Final projection with 1x1 kernel
with scope('block3'):
net = conv(net, output_depth, [1, 1])
net = bn(net)
net = prelu(net, decoder=decoder)
# Regularizer
net = fluid.layers.dropout(net, regularizer_prob, seed=seed)
net = prelu(net, decoder=decoder)
# Finally, add the unpooling layer and the sub branch together
net = fluid.layers.elementwise_add(net, net_unpool)
net = prelu(net, decoder=decoder)
return net
# REGULAR BOTTLENECK
else:
with scope('regular'):
net_main = inputs
# First projection with 1x1 kernel
with scope('block1'):
net = conv(inputs, reduced_depth, [1, 1])
net = bn(net)
net = prelu(net, decoder=decoder)
# Second conv block
with scope('block2'):
net = conv(net, reduced_depth, [filter_size, filter_size], padding='same')
net = bn(net)
net = prelu(net, decoder=decoder)
# Final projection with 1x1 kernel
with scope('block3'):
net = conv(net, output_depth, [1, 1])
net = bn(net)
net = prelu(net, decoder=decoder)
# Regularizer
net = fluid.layers.dropout(net, regularizer_prob, seed=seed)
net = prelu(net, decoder=decoder)
# Add the main branch
net = fluid.layers.elementwise_add(net_main, net)
net = prelu(net, decoder=decoder)
return net
def ENet_stage1(inputs, name_scope='stage1_block'):
with scope(name_scope):
with scope('bottleneck1_0'):
net, inputs_shape_1 \
= bottleneck(inputs, output_depth=64, filter_size=3, regularizer_prob=0.01, type=DOWNSAMPLING,
name_scope='bottleneck1_0')
with scope('bottleneck1_1'):
net = bottleneck(net, output_depth=64, filter_size=3, regularizer_prob=0.01,
name_scope='bottleneck1_1')
with scope('bottleneck1_2'):
net = bottleneck(net, output_depth=64, filter_size=3, regularizer_prob=0.01,
name_scope='bottleneck1_2')
with scope('bottleneck1_3'):
net = bottleneck(net, output_depth=64, filter_size=3, regularizer_prob=0.01,
name_scope='bottleneck1_3')
with scope('bottleneck1_4'):
net = bottleneck(net, output_depth=64, filter_size=3, regularizer_prob=0.01,
name_scope='bottleneck1_4')
return net, inputs_shape_1
def ENet_stage2(inputs, name_scope='stage2_block'):
with scope(name_scope):
net, inputs_shape_2 \
= bottleneck(inputs, output_depth=128, filter_size=3, regularizer_prob=0.1, type=DOWNSAMPLING,
name_scope='bottleneck2_0')
for i in range(2):
with scope('bottleneck2_{}'.format(str(4 * i + 1))):
net = bottleneck(net, output_depth=128, filter_size=3, regularizer_prob=0.1,
name_scope='bottleneck2_{}'.format(str(4 * i + 1)))
with scope('bottleneck2_{}'.format(str(4 * i + 2))):
net = bottleneck(net, output_depth=128, filter_size=3, regularizer_prob=0.1, type=DILATED, dilation_rate=(2 ** (2*i+1)),
name_scope='bottleneck2_{}'.format(str(4 * i + 2)))
with scope('bottleneck2_{}'.format(str(4 * i + 3))):
net = bottleneck(net, output_depth=128, filter_size=5, regularizer_prob=0.1, type=ASYMMETRIC,
name_scope='bottleneck2_{}'.format(str(4 * i + 3)))
with scope('bottleneck2_{}'.format(str(4 * i + 4))):
net = bottleneck(net, output_depth=128, filter_size=3, regularizer_prob=0.1, type=DILATED, dilation_rate=(2 ** (2*i+2)),
name_scope='bottleneck2_{}'.format(str(4 * i + 4)))
return net, inputs_shape_2
def ENet_stage3(inputs, name_scope='stage3_block'):
with scope(name_scope):
for i in range(2):
with scope('bottleneck3_{}'.format(str(4 * i + 0))):
net = bottleneck(inputs, output_depth=128, filter_size=3, regularizer_prob=0.1,
name_scope='bottleneck3_{}'.format(str(4 * i + 0)))
with scope('bottleneck3_{}'.format(str(4 * i + 1))):
net = bottleneck(net, output_depth=128, filter_size=3, regularizer_prob=0.1, type=DILATED, dilation_rate=(2 ** (2*i+1)),
name_scope='bottleneck3_{}'.format(str(4 * i + 1)))
with scope('bottleneck3_{}'.format(str(4 * i + 2))):
net = bottleneck(net, output_depth=128, filter_size=5, regularizer_prob=0.1, type=ASYMMETRIC,
name_scope='bottleneck3_{}'.format(str(4 * i + 2)))
with scope('bottleneck3_{}'.format(str(4 * i + 3))):
net = bottleneck(net, output_depth=128, filter_size=3, regularizer_prob=0.1, type=DILATED, dilation_rate=(2 ** (2*i+2)),
name_scope='bottleneck3_{}'.format(str(4 * i + 3)))
return net
def ENet_stage4(inputs, inputs_shape, connect_tensor,
skip_connections=True, name_scope='stage4_block'):
with scope(name_scope):
with scope('bottleneck4_0'):
net = bottleneck(inputs, output_depth=64, filter_size=3, regularizer_prob=0.1,
type=UPSAMPLING, decoder=True, output_shape=inputs_shape,
name_scope='bottleneck4_0')
if skip_connections:
net = fluid.layers.elementwise_add(net, connect_tensor)
with scope('bottleneck4_1'):
net = bottleneck(net, output_depth=64, filter_size=3, regularizer_prob=0.1, decoder=True,
name_scope='bottleneck4_1')
with scope('bottleneck4_2'):
net = bottleneck(net, output_depth=64, filter_size=3, regularizer_prob=0.1, decoder=True,
name_scope='bottleneck4_2')
return net
def ENet_stage5(inputs, inputs_shape, connect_tensor, skip_connections=True,
name_scope='stage5_block'):
with scope(name_scope):
net = bottleneck(inputs, output_depth=16, filter_size=3, regularizer_prob=0.1, type=UPSAMPLING,
decoder=True, output_shape=inputs_shape,
name_scope='bottleneck5_0')
if skip_connections:
net = fluid.layers.elementwise_add(net, connect_tensor)
with scope('bottleneck5_1'):
net = bottleneck(net, output_depth=16, filter_size=3, regularizer_prob=0.1, decoder=True,
name_scope='bottleneck5_1')
return net
def decoder(input, num_classes):
if 'enet' in cfg.MODEL.LANENET.BACKBONE:
# Segmentation branch
with scope('LaneNetSeg'):
initial, stage1, stage2, inputs_shape_1, inputs_shape_2 = input
segStage3 = ENet_stage3(stage2)
segStage4 = ENet_stage4(segStage3, inputs_shape_2, stage1)
segStage5 = ENet_stage5(segStage4, inputs_shape_1, initial)
segLogits = deconv(segStage5, num_classes, filter_size=2, stride=2, padding='SAME')
# Embedding branch
with scope('LaneNetEm'):
emStage3 = ENet_stage3(stage2)
emStage4 = ENet_stage4(emStage3, inputs_shape_2, stage1)
emStage5 = ENet_stage5(emStage4, inputs_shape_1, initial)
emLogits = deconv(emStage5, 4, filter_size=2, stride=2, padding='SAME')
elif 'vgg' in cfg.MODEL.LANENET.BACKBONE:
encoder_list = ['pool5', 'pool4', 'pool3']
# score stage
input_tensor = input[encoder_list[0]]
with scope('score_origin'):
score = conv(input_tensor, 64, 1)
encoder_list = encoder_list[1:]
for i in range(len(encoder_list)):
with scope('deconv_{:d}'.format(i + 1)):
deconv_out = deconv(score, 64, filter_size=4, stride=2, padding='SAME')
input_tensor = input[encoder_list[i]]
with scope('score_{:d}'.format(i + 1)):
score = conv(input_tensor, 64, 1)
score = fluid.layers.elementwise_add(deconv_out, score)
with scope('deconv_final'):
emLogits = deconv(score, 64, filter_size=16, stride=8, padding='SAME')
with scope('score_final'):
segLogits = conv(emLogits, num_classes, 1)
emLogits = relu(conv(emLogits, 4, 1))
return segLogits, emLogits
def encoder(input):
if 'vgg' in cfg.MODEL.LANENET.BACKBONE:
model = vgg_backbone(layers=16)
#output = model.net(input)
_, encode_feature_dict = model.net(input, end_points=13, decode_points=[7, 10, 13])
output = {}
output['pool3'] = encode_feature_dict[7]
output['pool4'] = encode_feature_dict[10]
output['pool5'] = encode_feature_dict[13]
elif 'enet' in cfg.MODEL.LANET.BACKBONE:
with scope('LaneNetBase'):
initial = iniatial_block(input)
stage1, inputs_shape_1 = ENet_stage1(initial)
stage2, inputs_shape_2 = ENet_stage2(stage1)
output = (initial, stage1, stage2, inputs_shape_1, inputs_shape_2)
else:
raise Exception("LaneNet expect enet and vgg backbone, but received {}".
format(cfg.MODEL.LANENET.BACKBONE))
return output
def lanenet(img, num_classes):
output = encoder(img)
segLogits, emLogits = decoder(output, num_classes)
return segLogits, emLogits
contrib/LaneNet/reader.py 0 → 100644
浏览文件 @ 1942c445
# coding: utf8
# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import sys
import os
import time
import codecs
import numpy as np
import cv2
from utils.config import cfg
import data_aug as aug
from pdseg.data_utils import GeneratorEnqueuer
from models.model_builder import ModelPhase
import copy
def cv2_imread(file_path, flag=cv2.IMREAD_COLOR):
# resolve cv2.imread open Chinese file path issues on Windows Platform.
return cv2.imdecode(np.fromfile(file_path, dtype=np.uint8), flag)
class LaneNetDataset():
def __init__(self,
file_list,
data_dir,
shuffle=False,
mode=ModelPhase.TRAIN):
self.mode = mode
self.shuffle = shuffle
self.data_dir = data_dir
self.shuffle_seed = 0
# NOTE: Please ensure file list was save in UTF-8 coding format
with codecs.open(file_list, 'r', 'utf-8') as flist:
self.lines = [line.strip() for line in flist]
self.all_lines = copy.deepcopy(self.lines)
if shuffle and cfg.NUM_TRAINERS > 1:
np.random.RandomState(self.shuffle_seed).shuffle(self.all_lines)
elif shuffle:
np.random.shuffle(self.lines)
def generator(self):
if self.shuffle and cfg.NUM_TRAINERS > 1:
np.random.RandomState(self.shuffle_seed).shuffle(self.all_lines)
num_lines = len(self.all_lines) // cfg.NUM_TRAINERS
self.lines = self.all_lines[num_lines * cfg.TRAINER_ID: num_lines * (cfg.TRAINER_ID + 1)]
self.shuffle_seed += 1
elif self.shuffle:
np.random.shuffle(self.lines)
for line in self.lines:
yield self.process_image(line, self.data_dir, self.mode)
def sharding_generator(self, pid=0, num_processes=1):
"""
Use line id as shard key for multiprocess io
It's a normal generator if pid=0, num_processes=1
"""
for index, line in enumerate(self.lines):
# Use index and pid to shard file list
if index % num_processes == pid:
yield self.process_image(line, self.data_dir, self.mode)
def batch_reader(self, batch_size):
br = self.batch(self.reader, batch_size)
for batch in br:
yield batch[0], batch[1], batch[2]
def multiprocess_generator(self, max_queue_size=32, num_processes=8):
# Re-shuffle file list
if self.shuffle and cfg.NUM_TRAINERS > 1:
np.random.RandomState(self.shuffle_seed).shuffle(self.all_lines)
num_lines = len(self.all_lines) // self.num_trainers
self.lines = self.all_lines[num_lines * self.trainer_id: num_lines * (self.trainer_id + 1)]
self.shuffle_seed += 1
elif self.shuffle:
np.random.shuffle(self.lines)
# Create multiple sharding generators according to num_processes for multiple processes
generators = []
for pid in range(num_processes):
generators.append(self.sharding_generator(pid, num_processes))
try:
enqueuer = GeneratorEnqueuer(generators)
enqueuer.start(max_queue_size=max_queue_size, workers=num_processes)
while True:
generator_out = None
while enqueuer.is_running():
if not enqueuer.queue.empty():
generator_out = enqueuer.queue.get(timeout=5)
break
else:
time.sleep(0.01)
if generator_out is None:
break
yield generator_out
finally:
if enqueuer is not None:
enqueuer.stop()
def batch(self, reader, batch_size, is_test=False, drop_last=False):
def batch_reader(is_test=False, drop_last=drop_last):
if is_test:
imgs, grts, grts_instance, img_names, valid_shapes, org_shapes = [], [], [], [], [], []
for img, grt, grt_instance, img_name, valid_shape, org_shape in reader():
imgs.append(img)
grts.append(grt)
grts_instance.append(grt_instance)
img_names.append(img_name)
valid_shapes.append(valid_shape)
org_shapes.append(org_shape)
if len(imgs) == batch_size:
yield np.array(imgs), np.array(
grts), np.array(grts_instance), img_names, np.array(valid_shapes), np.array(
org_shapes)
imgs, grts, grts_instance, img_names, valid_shapes, org_shapes = [], [], [], [], [], []
if not drop_last and len(imgs) > 0:
yield np.array(imgs), np.array(grts), np.array(grts_instance), img_names, np.array(
valid_shapes), np.array(org_shapes)
else:
imgs, labs, labs_instance, ignore = [], [], [], []
bs = 0
for img, lab, lab_instance, ig in reader():
imgs.append(img)
labs.append(lab)
labs_instance.append(lab_instance)
ignore.append(ig)
bs += 1
if bs == batch_size:
yield np.array(imgs), np.array(labs), np.array(labs_instance), np.array(ignore)
bs = 0
imgs, labs, labs_instance, ignore = [], [], [], []
if not drop_last and bs > 0:
yield np.array(imgs), np.array(labs), np.array(labs_instance), np.array(ignore)
return batch_reader(is_test, drop_last)
def load_image(self, line, src_dir, mode=ModelPhase.TRAIN):
# original image cv2.imread flag setting
cv2_imread_flag = cv2.IMREAD_COLOR
if cfg.DATASET.IMAGE_TYPE == "rgba":
# If use RBGA 4 channel ImageType, use IMREAD_UNCHANGED flags to
# reserver alpha channel
cv2_imread_flag = cv2.IMREAD_UNCHANGED
parts = line.strip().split(cfg.DATASET.SEPARATOR)
if len(parts) != 3:
if mode == ModelPhase.TRAIN or mode == ModelPhase.EVAL:
raise Exception("File list format incorrect! It should be"
" image_name{}label_name\\n".format(
cfg.DATASET.SEPARATOR))
img_name, grt_name, grt_instance_name = parts[0], None, None
else:
img_name, grt_name, grt_instance_name = parts[0], parts[1], parts[2]
img_path = os.path.join(src_dir, img_name)
img = cv2_imread(img_path, cv2_imread_flag)
if grt_name is not None:
grt_path = os.path.join(src_dir, grt_name)
grt_instance_path = os.path.join(src_dir, grt_instance_name)
grt = cv2_imread(grt_path, cv2.IMREAD_GRAYSCALE)
grt[grt == 255] = 1
grt[grt != 1] = 0
grt_instance = cv2_imread(grt_instance_path, cv2.IMREAD_GRAYSCALE)
else:
grt = None
grt_instance = None
if img is None:
raise Exception(
"Empty image, src_dir: {}, img: {} & lab: {}".format(
src_dir, img_path, grt_path))
img_height = img.shape[0]
img_width = img.shape[1]
if grt is not None:
grt_height = grt.shape[0]
grt_width = grt.shape[1]
if img_height != grt_height or img_width != grt_width:
raise Exception(
"source img and label img must has the same size")
else:
if mode == ModelPhase.TRAIN or mode == ModelPhase.EVAL:
raise Exception(
"Empty image, src_dir: {}, img: {} & lab: {}".format(
src_dir, img_path, grt_path))
if len(img.shape) < 3:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
img_channels = img.shape[2]
if img_channels < 3:
raise Exception("PaddleSeg only supports gray, rgb or rgba image")
if img_channels != cfg.DATASET.DATA_DIM:
raise Exception(
"Input image channel({}) is not match cfg.DATASET.DATA_DIM({}), img_name={}"
.format(img_channels, cfg.DATASET.DATADIM, img_name))
if img_channels != len(cfg.MEAN):
raise Exception(
"img name {}, img chns {} mean size {}, size unequal".format(
img_name, img_channels, len(cfg.MEAN)))
if img_channels != len(cfg.STD):
raise Exception(
"img name {}, img chns {} std size {}, size unequal".format(
img_name, img_channels, len(cfg.STD)))
return img, grt, grt_instance, img_name, grt_name
def normalize_image(self, img):
""" 像素归一化后减均值除方差 """
img = img.transpose((2, 0, 1)).astype('float32') / 255.0
img_mean = np.array(cfg.MEAN).reshape((len(cfg.MEAN), 1, 1))
img_std = np.array(cfg.STD).reshape((len(cfg.STD), 1, 1))
img -= img_mean
img /= img_std
return img
def process_image(self, line, data_dir, mode):
""" process_image """
img, grt, grt_instance, img_name, grt_name = self.load_image(
line, data_dir, mode=mode)
if mode == ModelPhase.TRAIN:
img, grt, grt_instance = aug.resize(img, grt, grt_instance, mode)
if cfg.AUG.RICH_CROP.ENABLE:
if cfg.AUG.RICH_CROP.BLUR:
if cfg.AUG.RICH_CROP.BLUR_RATIO <= 0:
n = 0
elif cfg.AUG.RICH_CROP.BLUR_RATIO >= 1:
n = 1
else:
n = int(1.0 / cfg.AUG.RICH_CROP.BLUR_RATIO)
if n > 0:
if np.random.randint(0, n) == 0:
radius = np.random.randint(3, 10)
if radius % 2 != 1:
radius = radius + 1
if radius > 9:
radius = 9
img = cv2.GaussianBlur(img, (radius, radius), 0, 0)
img, grt = aug.random_rotation(
img,
grt,
rich_crop_max_rotation=cfg.AUG.RICH_CROP.MAX_ROTATION,
mean_value=cfg.DATASET.PADDING_VALUE)
img, grt = aug.rand_scale_aspect(
img,
grt,
rich_crop_min_scale=cfg.AUG.RICH_CROP.MIN_AREA_RATIO,
rich_crop_aspect_ratio=cfg.AUG.RICH_CROP.ASPECT_RATIO)
img = aug.hsv_color_jitter(
img,
brightness_jitter_ratio=cfg.AUG.RICH_CROP.
BRIGHTNESS_JITTER_RATIO,
saturation_jitter_ratio=cfg.AUG.RICH_CROP.
SATURATION_JITTER_RATIO,
contrast_jitter_ratio=cfg.AUG.RICH_CROP.
CONTRAST_JITTER_RATIO)
if cfg.AUG.FLIP:
if cfg.AUG.FLIP_RATIO <= 0:
n = 0
elif cfg.AUG.FLIP_RATIO >= 1:
n = 1
else:
n = int(1.0 / cfg.AUG.FLIP_RATIO)
if n > 0:
if np.random.randint(0, n) == 0:
img = img[::-1, :, :]
grt = grt[::-1, :]
if cfg.AUG.MIRROR:
if np.random.randint(0, 2) == 1:
img = img[:, ::-1, :]
grt = grt[:, ::-1]
img, grt = aug.rand_crop(img, grt, mode=mode)
elif ModelPhase.is_eval(mode):
img, grt, grt_instance = aug.resize(img, grt, grt_instance, mode=mode)
elif ModelPhase.is_visual(mode):
ori_img = img.copy()
img, grt, grt_instance = aug.resize(img, grt, grt_instance, mode=mode)
valid_shape = [img.shape[0], img.shape[1]]
else:
raise ValueError("Dataset mode={} Error!".format(mode))
# Normalize image
img = self.normalize_image(img)
if ModelPhase.is_train(mode) or ModelPhase.is_eval(mode):
grt = np.expand_dims(np.array(grt).astype('int32'), axis=0)
ignore = (grt != cfg.DATASET.IGNORE_INDEX).astype('int32')
if ModelPhase.is_train(mode):
return (img, grt, grt_instance, ignore)
elif ModelPhase.is_eval(mode):
return (img, grt, grt_instance, ignore)
elif ModelPhase.is_visual(mode):
return (img, grt, grt_instance, img_name, valid_shape, ori_img)
contrib/LaneNet/requirements.txt 0 → 100644
浏览文件 @ 1942c445
pre-commit
yapf == 0.26.0
flake8
pyyaml >= 5.1
tb-paddle
tensorboard >= 1.15.0
Pillow
numpy
six
opencv-python
tqdm
requests
sklearn
contrib/LaneNet/train.py 0 → 100644
浏览文件 @ 1942c445
# coding: utf8
# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
# GPU memory garbage collection optimization flags
os.environ['FLAGS_eager_delete_tensor_gb'] = "0.0"
import sys
cur_path = os.path.abspath(os.path.dirname(__file__))
root_path = os.path.split(os.path.split(cur_path)[0])[0]
SEG_PATH = os.path.join(cur_path, "../../../")
sys.path.append(SEG_PATH)
sys.path.append(root_path)
import argparse
import pprint
import numpy as np
import paddle.fluid as fluid
from utils.config import cfg
from pdseg.utils.timer import Timer, calculate_eta
from reader import LaneNetDataset
from models.model_builder import build_model
from models.model_builder import ModelPhase
from models.model_builder import parse_shape_from_file
from eval import evaluate
from vis import visualize
from utils import dist_utils
def parse_args():
parser = argparse.ArgumentParser(description='PaddleSeg training')
parser.add_argument(
'--cfg',
dest='cfg_file',
help='Config file for training (and optionally testing)',
default=None,
type=str)
parser.add_argument(
'--use_gpu',
dest='use_gpu',
help='Use gpu or cpu',
action='store_true',
default=False)
parser.add_argument(
'--use_mpio',
dest='use_mpio',
help='Use multiprocess I/O or not',
action='store_true',
default=False)
parser.add_argument(
'--log_steps',
dest='log_steps',
help='Display logging information at every log_steps',
default=10,
type=int)
parser.add_argument(
'--debug',
dest='debug',
help='debug mode, display detail information of training',
action='store_true')
parser.add_argument(
'--use_tb',
dest='use_tb',
help='whether to record the data during training to Tensorboard',
action='store_true')
parser.add_argument(
'--tb_log_dir',
dest='tb_log_dir',
help='Tensorboard logging directory',
default=None,
type=str)
parser.add_argument(
'--do_eval',
dest='do_eval',
help='Evaluation models result on every new checkpoint',
action='store_true')
parser.add_argument(
'opts',
help='See utils/config.py for all options',
default=None,
nargs=argparse.REMAINDER)
return parser.parse_args()
def save_vars(executor, dirname, program=None, vars=None):
"""
Temporary resolution for Win save variables compatability.
Will fix in PaddlePaddle v1.5.2
"""
save_program = fluid.Program()
save_block = save_program.global_block()
for each_var in vars:
# NOTE: don't save the variable which type is RAW
if each_var.type == fluid.core.VarDesc.VarType.RAW:
continue
new_var = save_block.create_var(
name=each_var.name,
shape=each_var.shape,
dtype=each_var.dtype,
type=each_var.type,
lod_level=each_var.lod_level,
persistable=True)
file_path = os.path.join(dirname, new_var.name)
file_path = os.path.normpath(file_path)
save_block.append_op(
type='save',
inputs={'X': [new_var]},
outputs={},
attrs={'file_path': file_path})
executor.run(save_program)
def save_checkpoint(exe, program, ckpt_name):
"""
Save checkpoint for evaluation or resume training
"""
ckpt_dir = os.path.join(cfg.TRAIN.MODEL_SAVE_DIR, str(ckpt_name))
print("Save model checkpoint to {}".format(ckpt_dir))
if not os.path.isdir(ckpt_dir):
os.makedirs(ckpt_dir)
save_vars(
exe,
ckpt_dir,
program,
vars=list(filter(fluid.io.is_persistable, program.list_vars())))
return ckpt_dir
def load_checkpoint(exe, program):
"""
Load checkpoiont from pretrained model directory for resume training
"""
print('Resume model training from:', cfg.TRAIN.RESUME_MODEL_DIR)
if not os.path.exists(cfg.TRAIN.RESUME_MODEL_DIR):
raise ValueError("TRAIN.PRETRAIN_MODEL {} not exist!".format(
cfg.TRAIN.RESUME_MODEL_DIR))
fluid.io.load_persistables(
exe, cfg.TRAIN.RESUME_MODEL_DIR, main_program=program)
model_path = cfg.TRAIN.RESUME_MODEL_DIR
# Check is path ended by path spearator
if model_path[-1] == os.sep:
model_path = model_path[0:-1]
epoch_name = os.path.basename(model_path)
# If resume model is final model
if epoch_name == 'final':
begin_epoch = cfg.SOLVER.NUM_EPOCHS
# If resume model path is end of digit, restore epoch status
elif epoch_name.isdigit():
epoch = int(epoch_name)
begin_epoch = epoch + 1
else:
raise ValueError("Resume model path is not valid!")
print("Model checkpoint loaded successfully!")
return begin_epoch
def print_info(*msg):
if cfg.TRAINER_ID == 0:
print(*msg)
def train(cfg):
startup_prog = fluid.Program()
train_prog = fluid.Program()
drop_last = True
dataset = LaneNetDataset(
file_list=cfg.DATASET.TRAIN_FILE_LIST,
mode=ModelPhase.TRAIN,
shuffle=True,
data_dir=cfg.DATASET.DATA_DIR)
def data_generator():
if args.use_mpio:
data_gen = dataset.multiprocess_generator(
num_processes=cfg.DATALOADER.NUM_WORKERS,
max_queue_size=cfg.DATALOADER.BUF_SIZE)
else:
data_gen = dataset.generator()
batch_data = []
for b in data_gen:
batch_data.append(b)
if len(batch_data) == (cfg.BATCH_SIZE // cfg.NUM_TRAINERS):
for item in batch_data:
yield item
batch_data = []
# Get device environment
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
place = fluid.CUDAPlace(gpu_id) if args.use_gpu else fluid.CPUPlace()
places = fluid.cuda_places() if args.use_gpu else fluid.cpu_places()
# Get number of GPU
dev_count = cfg.NUM_TRAINERS if cfg.NUM_TRAINERS > 1 else len(places)
print_info("#Device count: {}".format(dev_count))
# Make sure BATCH_SIZE can divided by GPU cards
assert cfg.BATCH_SIZE % dev_count == 0, (
'BATCH_SIZE:{} not divisble by number of GPUs:{}'.format(
cfg.BATCH_SIZE, dev_count))
# If use multi-gpu training mode, batch data will allocated to each GPU evenly
batch_size_per_dev = cfg.BATCH_SIZE // dev_count
cfg.BATCH_SIZE_PER_DEV = batch_size_per_dev
print_info("batch_size_per_dev: {}".format(batch_size_per_dev))
py_reader, avg_loss, lr, pred, grts, masks, emb_loss, seg_loss, accuracy, fp, fn = build_model(
train_prog, startup_prog, phase=ModelPhase.TRAIN)
py_reader.decorate_sample_generator(
data_generator, batch_size=batch_size_per_dev, drop_last=drop_last)
exe = fluid.Executor(place)
exe.run(startup_prog)
exec_strategy = fluid.ExecutionStrategy()
# Clear temporary variables every 100 iteration
if args.use_gpu:
exec_strategy.num_threads = fluid.core.get_cuda_device_count()
exec_strategy.num_iteration_per_drop_scope = 100
build_strategy = fluid.BuildStrategy()
if cfg.NUM_TRAINERS > 1 and args.use_gpu:
dist_utils.prepare_for_multi_process(exe, build_strategy, train_prog)
exec_strategy.num_threads = 1
if cfg.TRAIN.SYNC_BATCH_NORM and args.use_gpu:
if dev_count > 1:
# Apply sync batch norm strategy
print_info("Sync BatchNorm strategy is effective.")
build_strategy.sync_batch_norm = True
else:
print_info(
"Sync BatchNorm strategy will not be effective if GPU device"
" count <= 1")
compiled_train_prog = fluid.CompiledProgram(train_prog).with_data_parallel(
loss_name=avg_loss.name,
exec_strategy=exec_strategy,
build_strategy=build_strategy)
# Resume training
begin_epoch = cfg.SOLVER.BEGIN_EPOCH
if cfg.TRAIN.RESUME_MODEL_DIR:
begin_epoch = load_checkpoint(exe, train_prog)
# Load pretrained model
elif os.path.exists(cfg.TRAIN.PRETRAINED_MODEL_DIR):
print_info('Pretrained model dir: ', cfg.TRAIN.PRETRAINED_MODEL_DIR)
load_vars = []
load_fail_vars = []
def var_shape_matched(var, shape):
"""
Check whehter persitable variable shape is match with current network
"""
var_exist = os.path.exists(
os.path.join(cfg.TRAIN.PRETRAINED_MODEL_DIR, var.name))
if var_exist:
var_shape = parse_shape_from_file(
os.path.join(cfg.TRAIN.PRETRAINED_MODEL_DIR, var.name))
if var_shape != shape:
print(var.name, var_shape, shape)
return var_shape == shape
return False
for x in train_prog.list_vars():
if isinstance(x, fluid.framework.Parameter):
shape = tuple(fluid.global_scope().find_var(
x.name).get_tensor().shape())
if var_shape_matched(x, shape):
load_vars.append(x)
else:
load_fail_vars.append(x)
fluid.io.load_vars(
exe, dirname=cfg.TRAIN.PRETRAINED_MODEL_DIR, vars=load_vars)
for var in load_vars:
print_info("Parameter[{}] loaded sucessfully!".format(var.name))
for var in load_fail_vars:
print_info(
"Parameter[{}] don't exist or shape does not match current network, skip"
" to load it.".format(var.name))
print_info("{}/{} pretrained parameters loaded successfully!".format(
len(load_vars),
len(load_vars) + len(load_fail_vars)))
else:
print_info(
'Pretrained model dir {} not exists, training from scratch...'.
format(cfg.TRAIN.PRETRAINED_MODEL_DIR))
# fetch_list = [avg_loss.name, lr.name, accuracy.name, precision.name, recall.name]
fetch_list = [avg_loss.name, lr.name, seg_loss.name, emb_loss.name, accuracy.name, fp.name, fn.name]
if args.debug:
# Fetch more variable info and use streaming confusion matrix to
# calculate IoU results if in debug mode
np.set_printoptions(
precision=4, suppress=True, linewidth=160, floatmode="fixed")
fetch_list.extend([pred.name, grts.name, masks.name])
# cm = ConfusionMatrix(cfg.DATASET.NUM_CLASSES, streaming=True)
if args.use_tb:
if not args.tb_log_dir:
print_info("Please specify the log directory by --tb_log_dir.")
exit(1)
from tb_paddle import SummaryWriter
log_writer = SummaryWriter(args.tb_log_dir)
# trainer_id = int(os.getenv("PADDLE_TRAINER_ID", 0))
# num_trainers = int(os.environ.get('PADDLE_TRAINERS_NUM', 1))
global_step = 0
all_step = cfg.DATASET.TRAIN_TOTAL_IMAGES // cfg.BATCH_SIZE
if cfg.DATASET.TRAIN_TOTAL_IMAGES % cfg.BATCH_SIZE and drop_last != True:
all_step += 1
all_step *= (cfg.SOLVER.NUM_EPOCHS - begin_epoch + 1)
avg_loss = 0.0
avg_seg_loss = 0.0
avg_emb_loss = 0.0
avg_acc = 0.0
avg_fp = 0.0
avg_fn = 0.0
timer = Timer()
timer.start()
if begin_epoch > cfg.SOLVER.NUM_EPOCHS:
raise ValueError(
("begin epoch[{}] is larger than cfg.SOLVER.NUM_EPOCHS[{}]").format(
begin_epoch, cfg.SOLVER.NUM_EPOCHS))
if args.use_mpio:
print_info("Use multiprocess reader")
else:
print_info("Use multi-thread reader")
for epoch in range(begin_epoch, cfg.SOLVER.NUM_EPOCHS + 1):
py_reader.start()
while True:
try:
# If not in debug mode, avoid unnessary log and calculate
loss, lr, out_seg_loss, out_emb_loss, out_acc, out_fp, out_fn = exe.run(
program=compiled_train_prog,
fetch_list=fetch_list,
return_numpy=True)
avg_loss += np.mean(np.array(loss))
avg_seg_loss += np.mean(np.array(out_seg_loss))
avg_emb_loss += np.mean(np.array(out_emb_loss))
avg_acc += np.mean(out_acc)
avg_fp += np.mean(out_fp)
avg_fn += np.mean(out_fn)
global_step += 1
if global_step % args.log_steps == 0 and cfg.TRAINER_ID == 0:
avg_loss /= args.log_steps
avg_seg_loss /= args.log_steps
avg_emb_loss /= args.log_steps
avg_acc /= args.log_steps
avg_fp /= args.log_steps
avg_fn /= args.log_steps
speed = args.log_steps / timer.elapsed_time()
print((
"epoch={} step={} lr={:.5f} loss={:.4f} seg_loss={:.4f} emb_loss={:.4f} accuracy={:.4} fp={:.4} fn={:.4} step/sec={:.3f} | ETA {}"
).format(epoch, global_step, lr[0], avg_loss, avg_seg_loss, avg_emb_loss, avg_acc, avg_fp, avg_fn, speed,
calculate_eta(all_step - global_step, speed)))
if args.use_tb:
log_writer.add_scalar('Train/loss', avg_loss,
global_step)
log_writer.add_scalar('Train/lr', lr[0],
global_step)
log_writer.add_scalar('Train/speed', speed,
global_step)
sys.stdout.flush()
avg_loss = 0.0
avg_seg_loss = 0.0
avg_emb_loss = 0.0
avg_acc = 0.0
avg_fp = 0.0
avg_fn = 0.0
timer.restart()
except fluid.core.EOFException:
py_reader.reset()
break
except Exception as e:
print(e)
if epoch % cfg.TRAIN.SNAPSHOT_EPOCH == 0 and cfg.TRAINER_ID == 0:
ckpt_dir = save_checkpoint(exe, train_prog, epoch)
if args.do_eval:
print("Evaluation start")
accuracy, fp, fn = evaluate(
cfg=cfg,
ckpt_dir=ckpt_dir,
use_gpu=args.use_gpu,
use_mpio=args.use_mpio)
if args.use_tb:
log_writer.add_scalar('Evaluate/accuracy', accuracy,
global_step)
log_writer.add_scalar('Evaluate/fp', fp,
global_step)
log_writer.add_scalar('Evaluate/fn', fn,
global_step)
# Use Tensorboard to visualize results
if args.use_tb and cfg.DATASET.VIS_FILE_LIST is not None:
visualize(
cfg=cfg,
use_gpu=args.use_gpu,
vis_file_list=cfg.DATASET.VIS_FILE_LIST,
vis_dir="visual",
ckpt_dir=ckpt_dir,
log_writer=log_writer)
# save final model
if cfg.TRAINER_ID == 0:
save_checkpoint(exe, train_prog, 'final')
def main(args):
if args.cfg_file is not None:
cfg.update_from_file(args.cfg_file)
if args.opts:
cfg.update_from_list(args.opts)
cfg.TRAINER_ID = int(os.getenv("PADDLE_TRAINER_ID", 0))
cfg.NUM_TRAINERS = int(os.environ.get('PADDLE_TRAINERS_NUM', 1))
cfg.check_and_infer()
print_info(pprint.pformat(cfg))
train(cfg)
if __name__ == '__main__':
args = parse_args()
if fluid.core.is_compiled_with_cuda() != True and args.use_gpu == True:
print(
"You can not set use_gpu = True in the model because you are using paddlepaddle-cpu."
)
print(
"Please: 1. Install paddlepaddle-gpu to run your models on GPU or 2. Set use_gpu=False to run models on CPU."
)
sys.exit(1)
main(args)
contrib/LaneNet/utils/config.py 0 → 100644
浏览文件 @ 1942c445
# -*- coding: utf-8 -*-
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
from __future__ import unicode_literals
import os
import sys
# LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
# PDSEG_PATH = os.path.join(LOCAL_PATH, "../../../", "pdseg")
# print(PDSEG_PATH)
# sys.path.insert(0, PDSEG_PATH)
# print(sys.path)
from pdseg.utils.collect import SegConfig
import numpy as np
cfg = SegConfig()
########################## 基本配置 ###########################################
# 均值，图像预处理减去的均值
cfg.MEAN = [0.5, 0.5, 0.5]
# 标准差，图像预处理除以标准差·
cfg.STD = [0.5, 0.5, 0.5]
# 批处理大小
cfg.BATCH_SIZE = 1
# 验证时图像裁剪尺寸（宽，高）
cfg.EVAL_CROP_SIZE = tuple()
# 训练时图像裁剪尺寸（宽，高）
cfg.TRAIN_CROP_SIZE = tuple()
# 多进程训练总进程数
cfg.NUM_TRAINERS = 1
# 多进程训练进程ID
cfg.TRAINER_ID = 0
# 每张gpu上的批大小，无需设置，程序会自动根据batch调整
cfg.BATCH_SIZE_PER_DEV = 1
########################## 数据载入配置 #######################################
# 数据载入时的并发数, 建议值8
cfg.DATALOADER.NUM_WORKERS = 8
# 数据载入时缓存队列大小, 建议值256
cfg.DATALOADER.BUF_SIZE = 256
########################## 数据集配置 #########################################
# 数据主目录目录
cfg.DATASET.DATA_DIR = './dataset/cityscapes/'
# 训练集列表
cfg.DATASET.TRAIN_FILE_LIST = './dataset/cityscapes/train.list'
# 训练集数量
cfg.DATASET.TRAIN_TOTAL_IMAGES = 2975
# 验证集列表
cfg.DATASET.VAL_FILE_LIST = './dataset/cityscapes/val.list'
# 验证数据数量
cfg.DATASET.VAL_TOTAL_IMAGES = 500
# 测试数据列表
cfg.DATASET.TEST_FILE_LIST = './dataset/cityscapes/test.list'
# 测试数据数量
cfg.DATASET.TEST_TOTAL_IMAGES = 500
# Tensorboard 可视化的数据集
cfg.DATASET.VIS_FILE_LIST = None
# 类别数(需包括背景类)
cfg.DATASET.NUM_CLASSES = 19
# 输入图像类型, 支持三通道'rgb',四通道'rgba',单通道灰度图'gray'
cfg.DATASET.IMAGE_TYPE = 'rgb'
# 输入图片的通道数
cfg.DATASET.DATA_DIM = 3
# 数据列表分割符, 默认为空格
cfg.DATASET.SEPARATOR = ' '
# 忽略的像素标签值, 默认为255，一般无需改动
cfg.DATASET.IGNORE_INDEX = 255
# 数据增强是图像的padding值
cfg.DATASET.PADDING_VALUE = [127.5,127.5,127.5]
########################### 数据增强配置 ######################################
# 图像镜像左右翻转
cfg.AUG.MIRROR = True
# 图像上下翻转开关，True/False
cfg.AUG.FLIP = False
# 图像启动上下翻转的概率，0-1
cfg.AUG.FLIP_RATIO = 0.5
# 图像resize的固定尺寸（宽，高），非负
cfg.AUG.FIX_RESIZE_SIZE = tuple()
# 图像resize的方式有三种：
# unpadding（固定尺寸），stepscaling（按比例resize），rangescaling（长边对齐）
cfg.AUG.AUG_METHOD = 'rangescaling'
# 图像resize方式为stepscaling，resize最小尺度，非负
cfg.AUG.MIN_SCALE_FACTOR = 0.5
# 图像resize方式为stepscaling，resize最大尺度，不小于MIN_SCALE_FACTOR
cfg.AUG.MAX_SCALE_FACTOR = 2.0
# 图像resize方式为stepscaling，resize尺度范围间隔，非负
cfg.AUG.SCALE_STEP_SIZE = 0.25
# 图像resize方式为rangescaling，训练时长边resize的范围最小值，非负
cfg.AUG.MIN_RESIZE_VALUE = 400
# 图像resize方式为rangescaling，训练时长边resize的范围最大值，
# 不小于MIN_RESIZE_VALUE
cfg.AUG.MAX_RESIZE_VALUE = 600
# 图像resize方式为rangescaling, 测试验证可视化模式下长边resize的长度，
# 在MIN_RESIZE_VALUE到MAX_RESIZE_VALUE范围内
cfg.AUG.INF_RESIZE_VALUE = 500
# RichCrop数据增广开关，用于提升模型鲁棒性
cfg.AUG.RICH_CROP.ENABLE = False
# 图像旋转最大角度，0-90
cfg.AUG.RICH_CROP.MAX_ROTATION = 15
# 裁取图像与原始图像面积比，0-1
cfg.AUG.RICH_CROP.MIN_AREA_RATIO = 0.5
# 裁取图像宽高比范围，非负
cfg.AUG.RICH_CROP.ASPECT_RATIO = 0.33
# 亮度调节范围，0-1
cfg.AUG.RICH_CROP.BRIGHTNESS_JITTER_RATIO = 0.5
# 饱和度调节范围，0-1
cfg.AUG.RICH_CROP.SATURATION_JITTER_RATIO = 0.5
# 对比度调节范围，0-1
cfg.AUG.RICH_CROP.CONTRAST_JITTER_RATIO = 0.5
# 图像模糊开关，True/False
cfg.AUG.RICH_CROP.BLUR = False
# 图像启动模糊百分比，0-1
cfg.AUG.RICH_CROP.BLUR_RATIO = 0.1
########################### 训练配置 ##########################################
# 模型保存路径
cfg.TRAIN.MODEL_SAVE_DIR = ''
# 预训练模型路径
cfg.TRAIN.PRETRAINED_MODEL_DIR = ''
# 是否resume，继续训练
cfg.TRAIN.RESUME_MODEL_DIR = ''
# 是否使用多卡间同步BatchNorm均值和方差
cfg.TRAIN.SYNC_BATCH_NORM = False
# 模型参数保存的epoch间隔数，可用来继续训练中断的模型
cfg.TRAIN.SNAPSHOT_EPOCH = 10
########################### 模型优化相关配置 ##################################
# 初始学习率
cfg.SOLVER.LR = 0.1
# 学习率下降方法, 支持poly piecewise cosine 三种
cfg.SOLVER.LR_POLICY = "poly"
# 优化算法, 支持SGD和Adam两种算法
cfg.SOLVER.OPTIMIZER = "sgd"
# 动量参数
cfg.SOLVER.MOMENTUM = 0.9
# 二阶矩估计的指数衰减率
cfg.SOLVER.MOMENTUM2 = 0.999
# 学习率Poly下降指数
cfg.SOLVER.POWER = 0.9
# step下降指数
cfg.SOLVER.GAMMA = 0.1
# step下降间隔
cfg.SOLVER.DECAY_EPOCH = [10, 20]
# 学习率权重衰减，0-1
cfg.SOLVER.WEIGHT_DECAY = 0.00004
# 训练开始epoch数，默认为1
cfg.SOLVER.BEGIN_EPOCH = 1
# 训练epoch数，正整数
cfg.SOLVER.NUM_EPOCHS = 30
# loss的选择，支持softmax_loss, bce_loss, dice_loss
cfg.SOLVER.LOSS = ["softmax_loss"]
# cross entropy weight, 默认为None，如果设置为'dynamic'，会根据每个batch中各个类别的数目，
# 动态调整类别权重。
# 也可以设置一个静态权重(list的方式)，比如有3类，每个类别权重可以设置为[0.1, 2.0, 0.9]
cfg.SOLVER.CROSS_ENTROPY_WEIGHT = None
########################## 测试配置 ###########################################
# 测试模型路径
cfg.TEST.TEST_MODEL = ''
########################## 模型通用配置 #######################################
# 模型名称, 支持deeplab, unet, icnet三种
cfg.MODEL.MODEL_NAME = ''
# BatchNorm类型: bn、gn(group_norm)
cfg.MODEL.DEFAULT_NORM_TYPE = 'bn'
# 多路损失加权值
cfg.MODEL.MULTI_LOSS_WEIGHT = [1.0]
# DEFAULT_NORM_TYPE为gn时group数
cfg.MODEL.DEFAULT_GROUP_NUMBER = 32
# 极小值, 防止分母除0溢出，一般无需改动
cfg.MODEL.DEFAULT_EPSILON = 1e-5
# BatchNorm动量, 一般无需改动
cfg.MODEL.BN_MOMENTUM = 0.99
# 是否使用FP16训练
cfg.MODEL.FP16 = False
# 混合精度训练需对LOSS进行scale, 默认为动态scale，静态scale可以设置为512.0
cfg.MODEL.SCALE_LOSS = "DYNAMIC"
########################## DeepLab模型配置 ####################################
# DeepLab backbone 配置, 可选项xception_65, mobilenetv2
cfg.MODEL.DEEPLAB.BACKBONE = "xception_65"
# DeepLab output stride
cfg.MODEL.DEEPLAB.OUTPUT_STRIDE = 16
# MobileNet backbone scale 设置
cfg.MODEL.DEEPLAB.DEPTH_MULTIPLIER = 1.0
# MobileNet backbone scale 设置
cfg.MODEL.DEEPLAB.ENCODER_WITH_ASPP = True
# MobileNet backbone scale 设置
cfg.MODEL.DEEPLAB.ENABLE_DECODER = True
# ASPP是否使用可分离卷积
cfg.MODEL.DEEPLAB.ASPP_WITH_SEP_CONV = True
# 解码器是否使用可分离卷积
cfg.MODEL.DEEPLAB.DECODER_USE_SEP_CONV = True
########################## UNET模型配置 #######################################
# 上采样方式, 默认为双线性插值
cfg.MODEL.UNET.UPSAMPLE_MODE = 'bilinear'
########################## ICNET模型配置 ######################################
# RESNET backbone scale 设置
cfg.MODEL.ICNET.DEPTH_MULTIPLIER = 0.5
# RESNET 层数 设置
cfg.MODEL.ICNET.LAYERS = 50
########################## PSPNET模型配置 ######################################
# Lannet backbone name
cfg.MODEL.LANENET.BACKBONE = "vgg"
########################## LaneNet模型配置 ######################################
########################## 预测部署模型配置 ###################################
# 预测保存的模型名称
cfg.FREEZE.MODEL_FILENAME = '__model__'
# 预测保存的参数名称
cfg.FREEZE.PARAMS_FILENAME = '__params__'
# 预测模型参数保存的路径
cfg.FREEZE.SAVE_DIR = 'freeze_model'
contrib/LaneNet/utils/dist_utils.py 0 → 100755
浏览文件 @ 1942c445
#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import paddle.fluid as fluid
def nccl2_prepare(args, startup_prog, main_prog):
config = fluid.DistributeTranspilerConfig()
config.mode = "nccl2"
t = fluid.DistributeTranspiler(config=config)
envs = args.dist_env
t.transpile(
envs["trainer_id"],
trainers=','.join(envs["trainer_endpoints"]),
current_endpoint=envs["current_endpoint"],
startup_program=startup_prog,
program=main_prog)
def pserver_prepare(args, train_prog, startup_prog):
config = fluid.DistributeTranspilerConfig()
config.slice_var_up = args.split_var
t = fluid.DistributeTranspiler(config=config)
envs = args.dist_env
training_role = envs["training_role"]
t.transpile(
envs["trainer_id"],
program=train_prog,
pservers=envs["pserver_endpoints"],
trainers=envs["num_trainers"],
sync_mode=not args.async_mode,
startup_program=startup_prog)
if training_role == "PSERVER":
pserver_program = t.get_pserver_program(envs["current_endpoint"])
pserver_startup_program = t.get_startup_program(
envs["current_endpoint"],
pserver_program,
startup_program=startup_prog)
return pserver_program, pserver_startup_program
elif training_role == "TRAINER":
train_program = t.get_trainer_program()
return train_program, startup_prog
else:
raise ValueError(
'PADDLE_TRAINING_ROLE environment variable must be either TRAINER or PSERVER'
)
def nccl2_prepare_paddle(trainer_id, startup_prog, main_prog):
config = fluid.DistributeTranspilerConfig()
config.mode = "nccl2"
t = fluid.DistributeTranspiler(config=config)
t.transpile(
trainer_id,
trainers=os.environ.get('PADDLE_TRAINER_ENDPOINTS'),
current_endpoint=os.environ.get('PADDLE_CURRENT_ENDPOINT'),
startup_program=startup_prog,
program=main_prog)
def prepare_for_multi_process(exe, build_strategy, train_prog):
# prepare for multi-process
trainer_id = int(os.environ.get('PADDLE_TRAINER_ID', 0))
num_trainers = int(os.environ.get('PADDLE_TRAINERS_NUM', 1))
if num_trainers < 2: return
build_strategy.num_trainers = num_trainers
build_strategy.trainer_id = trainer_id
# NOTE(zcd): use multi processes to train the model,
# and each process use one GPU card.
startup_prog = fluid.Program()
nccl2_prepare_paddle(trainer_id, startup_prog, train_prog)
# the startup_prog are run two times, but it doesn't matter.
exe.run(startup_prog)
contrib/LaneNet/utils/generate_tusimple_dataset.py 0 → 100644
浏览文件 @ 1942c445
"""
generate tusimple training dataset
"""
import argparse
import glob
import json
import os
import os.path as ops
import shutil
import cv2
import numpy as np
def init_args():
parser = argparse.ArgumentParser()
parser.add_argument('--src_dir', type=str, help='The origin path of unzipped tusimple dataset')
return parser.parse_args()
def process_json_file(json_file_path, src_dir, ori_dst_dir, binary_dst_dir, instance_dst_dir):
assert ops.exists(json_file_path), '{:s} not exist'.format(json_file_path)
image_nums = len(os.listdir(os.path.join(src_dir, ori_dst_dir)))
with open(json_file_path, 'r') as file:
for line_index, line in enumerate(file):
info_dict = json.loads(line)
image_dir = ops.split(info_dict['raw_file'])[0]
image_dir_split = image_dir.split('/')[1:]
image_dir_split.append(ops.split(info_dict['raw_file'])[1])
image_name = '_'.join(image_dir_split)
image_path = ops.join(src_dir, info_dict['raw_file'])
assert ops.exists(image_path), '{:s} not exist'.format(image_path)
h_samples = info_dict['h_samples']
lanes = info_dict['lanes']
image_name_new = '{:s}.png'.format('{:d}'.format(line_index + image_nums).zfill(4))
src_image = cv2.imread(image_path, cv2.IMREAD_COLOR)
dst_binary_image = np.zeros([src_image.shape[0], src_image.shape[1]], np.uint8)
dst_instance_image = np.zeros([src_image.shape[0], src_image.shape[1]], np.uint8)
for lane_index, lane in enumerate(lanes):
assert len(h_samples) == len(lane)
lane_x = []
lane_y = []
for index in range(len(lane)):
if lane[index] == -2:
continue
else:
ptx = lane[index]
pty = h_samples[index]
lane_x.append(ptx)
lane_y.append(pty)
if not lane_x:
continue
lane_pts = np.vstack((lane_x, lane_y)).transpose()
lane_pts = np.array([lane_pts], np.int64)
cv2.polylines(dst_binary_image, lane_pts, isClosed=False,
color=255, thickness=5)
cv2.polylines(dst_instance_image, lane_pts, isClosed=False,
color=lane_index * 50 + 20, thickness=5)
dst_binary_image_path = ops.join(src_dir, binary_dst_dir, image_name_new)
dst_instance_image_path = ops.join(src_dir, instance_dst_dir, image_name_new)
dst_rgb_image_path = ops.join(src_dir, ori_dst_dir, image_name_new)
cv2.imwrite(dst_binary_image_path, dst_binary_image)
cv2.imwrite(dst_instance_image_path, dst_instance_image)
cv2.imwrite(dst_rgb_image_path, src_image)
print('Process {:s} success'.format(image_name))
def gen_sample(src_dir, b_gt_image_dir, i_gt_image_dir, image_dir, phase='train', split=False):
label_list = []
with open('{:s}/{}ing/{}.txt'.format(src_dir, phase, phase), 'w') as file:
for image_name in os.listdir(b_gt_image_dir):
if not image_name.endswith('.png'):
continue
binary_gt_image_path = ops.join(b_gt_image_dir, image_name)
instance_gt_image_path = ops.join(i_gt_image_dir, image_name)
image_path = ops.join(image_dir, image_name)
assert ops.exists(image_path), '{:s} not exist'.format(image_path)
assert ops.exists(instance_gt_image_path), '{:s} not exist'.format(instance_gt_image_path)
b_gt_image = cv2.imread(binary_gt_image_path, cv2.IMREAD_COLOR)
i_gt_image = cv2.imread(instance_gt_image_path, cv2.IMREAD_COLOR)
image = cv2.imread(image_path, cv2.IMREAD_COLOR)
if b_gt_image is None or image is None or i_gt_image is None:
print('image: {:s} corrupt'.format(image_name))
continue
else:
info = '{:s} {:s} {:s}'.format(image_path, binary_gt_image_path, instance_gt_image_path)
file.write(info + '\n')
label_list.append(info)
if phase == 'train' and split:
np.random.RandomState(0).shuffle(label_list)
val_list_len = len(label_list) // 10
val_label_list = label_list[:val_list_len]
train_label_list = label_list[val_list_len:]
with open('{:s}/{}ing/train_part.txt'.format(src_dir, phase, phase), 'w') as file:
for info in train_label_list:
file.write(info + '\n')
with open('{:s}/{}ing/val_part.txt'.format(src_dir, phase, phase), 'w') as file:
for info in val_label_list:
file.write(info + '\n')
return
def process_tusimple_dataset(src_dir):
traing_folder_path = ops.join(src_dir, 'training')
testing_folder_path = ops.join(src_dir, 'testing')
os.makedirs(traing_folder_path, exist_ok=True)
os.makedirs(testing_folder_path, exist_ok=True)
for json_label_path in glob.glob('{:s}/label*.json'.format(src_dir)):
json_label_name = ops.split(json_label_path)[1]
shutil.copyfile(json_label_path, ops.join(traing_folder_path, json_label_name))
for json_label_path in glob.glob('{:s}/test_label.json'.format(src_dir)):
json_label_name = ops.split(json_label_path)[1]
shutil.copyfile(json_label_path, ops.join(testing_folder_path, json_label_name))
train_gt_image_dir = ops.join('training', 'gt_image')
train_gt_binary_dir = ops.join('training', 'gt_binary_image')
train_gt_instance_dir = ops.join('training', 'gt_instance_image')
test_gt_image_dir = ops.join('testing', 'gt_image')
test_gt_binary_dir = ops.join('testing', 'gt_binary_image')
test_gt_instance_dir = ops.join('testing', 'gt_instance_image')
os.makedirs(os.path.join(src_dir, train_gt_image_dir), exist_ok=True)
os.makedirs(os.path.join(src_dir, train_gt_binary_dir), exist_ok=True)
os.makedirs(os.path.join(src_dir, train_gt_instance_dir), exist_ok=True)
os.makedirs(os.path.join(src_dir, test_gt_image_dir), exist_ok=True)
os.makedirs(os.path.join(src_dir, test_gt_binary_dir), exist_ok=True)
os.makedirs(os.path.join(src_dir, test_gt_instance_dir), exist_ok=True)
for json_label_path in glob.glob('{:s}/*.json'.format(traing_folder_path)):
process_json_file(json_label_path, src_dir, train_gt_image_dir, train_gt_binary_dir, train_gt_instance_dir)
gen_sample(src_dir, train_gt_binary_dir, train_gt_instance_dir, train_gt_image_dir, 'train', True)
if __name__ == '__main__':
args = init_args()
process_tusimple_dataset(args.src_dir)
contrib/LaneNet/utils/lanenet_postprocess.py 0 → 100644
浏览文件 @ 1942c445
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# this code heavily base on https://github.com/MaybeShewill-CV/lanenet-lane-detection/blob/master/lanenet_model/lanenet_postprocess.py
"""
LaneNet model post process
"""
import os.path as ops
import math
import cv2
import time
import numpy as np
from sklearn.cluster import DBSCAN
from sklearn.preprocessing import StandardScaler
def _morphological_process(image, kernel_size=5):
"""
morphological process to fill the hole in the binary segmentation result
:param image:
:param kernel_size:
:return:
"""
if len(image.shape) == 3:
raise ValueError('Binary segmentation result image should be a single channel image')
if image.dtype is not np.uint8:
image = np.array(image, np.uint8)
kernel = cv2.getStructuringElement(shape=cv2.MORPH_ELLIPSE, ksize=(kernel_size, kernel_size))
# close operation fille hole
closing = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel, iterations=1)
return closing
def _connect_components_analysis(image):
"""
connect components analysis to remove the small components
:param image:
:return:
"""
if len(image.shape) == 3:
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
else:
gray_image = image
return cv2.connectedComponentsWithStats(gray_image, connectivity=8, ltype=cv2.CV_32S)
class _LaneFeat(object):
"""
"""
def __init__(self, feat, coord, class_id=-1):
"""
lane feat object
:param feat: lane embeddng feats [feature_1, feature_2, ...]
:param coord: lane coordinates [x, y]
:param class_id: lane class id
"""
self._feat = feat
self._coord = coord
self._class_id = class_id
@property
def feat(self):
return self._feat
@feat.setter
def feat(self, value):
if not isinstance(value, np.ndarray):
value = np.array(value, dtype=np.float64)
if value.dtype != np.float32:
value = np.array(value, dtype=np.float64)
self._feat = value
@property
def coord(self):
return self._coord
@coord.setter
def coord(self, value):
if not isinstance(value, np.ndarray):
value = np.array(value)
if value.dtype != np.int32:
value = np.array(value, dtype=np.int32)
self._coord = value
@property
def class_id(self):
return self._class_id
@class_id.setter
def class_id(self, value):
if not isinstance(value, np.int64):
raise ValueError('Class id must be integer')
self._class_id = value
class _LaneNetCluster(object):
"""
Instance segmentation result cluster
"""
def __init__(self):
"""
"""
self._color_map = [np.array([255, 0, 0]),
np.array([0, 255, 0]),
np.array([0, 0, 255]),
np.array([125, 125, 0]),
np.array([0, 125, 125]),
np.array([125, 0, 125]),
np.array([50, 100, 50]),
np.array([100, 50, 100])]
@staticmethod
def _embedding_feats_dbscan_cluster(embedding_image_feats):
"""
dbscan cluster
"""
db = DBSCAN(eps=0.4, min_samples=500)
try:
features = StandardScaler().fit_transform(embedding_image_feats)
db.fit(features)
except Exception as err:
print(err)
ret = {
'origin_features': None,
'cluster_nums': 0,
'db_labels': None,
'unique_labels': None,
'cluster_center': None
}
return ret
db_labels = db.labels_
unique_labels = np.unique(db_labels)
num_clusters = len(unique_labels)
cluster_centers = db.components_
ret = {
'origin_features': features,
'cluster_nums': num_clusters,
'db_labels': db_labels,
'unique_labels': unique_labels,
'cluster_center': cluster_centers
}
return ret
@staticmethod
def _get_lane_embedding_feats(binary_seg_ret, instance_seg_ret):
"""
get lane embedding features according the binary seg result
"""
idx = np.where(binary_seg_ret == 255)
lane_embedding_feats = instance_seg_ret[idx]
lane_coordinate = np.vstack((idx[1], idx[0])).transpose()
assert lane_embedding_feats.shape[0] == lane_coordinate.shape[0]
ret = {
'lane_embedding_feats': lane_embedding_feats,
'lane_coordinates': lane_coordinate
}
return ret
def apply_lane_feats_cluster(self, binary_seg_result, instance_seg_result):
"""
:param binary_seg_result:
:param instance_seg_result:
:return:
"""
# get embedding feats and coords
get_lane_embedding_feats_result = self._get_lane_embedding_feats(
binary_seg_ret=binary_seg_result,
instance_seg_ret=instance_seg_result
)
# dbscan cluster
dbscan_cluster_result = self._embedding_feats_dbscan_cluster(
embedding_image_feats=get_lane_embedding_feats_result['lane_embedding_feats']
)
mask = np.zeros(shape=[binary_seg_result.shape[0], binary_seg_result.shape[1], 3], dtype=np.uint8)
db_labels = dbscan_cluster_result['db_labels']
unique_labels = dbscan_cluster_result['unique_labels']
coord = get_lane_embedding_feats_result['lane_coordinates']
if db_labels is None:
return None, None
lane_coords = []
for index, label in enumerate(unique_labels.tolist()):
if label == -1:
continue
idx = np.where(db_labels == label)
pix_coord_idx = tuple((coord[idx][:, 1], coord[idx][:, 0]))
mask[pix_coord_idx] = self._color_map[index]
lane_coords.append(coord[idx])
return mask, lane_coords
class LaneNetPostProcessor(object):
"""
lanenet post process for lane generation
"""
def __init__(self, ipm_remap_file_path='./utils/tusimple_ipm_remap.yml'):
"""
convert front car view to bird view
"""
assert ops.exists(ipm_remap_file_path), '{:s} not exist'.format(ipm_remap_file_path)
self._cluster = _LaneNetCluster()
self._ipm_remap_file_path = ipm_remap_file_path
remap_file_load_ret = self._load_remap_matrix()
self._remap_to_ipm_x = remap_file_load_ret['remap_to_ipm_x']
self._remap_to_ipm_y = remap_file_load_ret['remap_to_ipm_y']
self._color_map = [np.array([255, 0, 0]),
np.array([0, 255, 0]),
np.array([0, 0, 255]),
np.array([125, 125, 0]),
np.array([0, 125, 125]),
np.array([125, 0, 125]),
np.array([50, 100, 50]),
np.array([100, 50, 100])]
def _load_remap_matrix(self):
fs = cv2.FileStorage(self._ipm_remap_file_path, cv2.FILE_STORAGE_READ)
remap_to_ipm_x = fs.getNode('remap_ipm_x').mat()
remap_to_ipm_y = fs.getNode('remap_ipm_y').mat()
ret = {
'remap_to_ipm_x': remap_to_ipm_x,
'remap_to_ipm_y': remap_to_ipm_y,
}
fs.release()
return ret
def postprocess(self, binary_seg_result, instance_seg_result=None,
min_area_threshold=100, source_image=None,
data_source='tusimple'):
# convert binary_seg_result
binary_seg_result = np.array(binary_seg_result * 255, dtype=np.uint8)
# apply image morphology operation to fill in the hold and reduce the small area
morphological_ret = _morphological_process(binary_seg_result, kernel_size=5)
connect_components_analysis_ret = _connect_components_analysis(image=morphological_ret)
labels = connect_components_analysis_ret[1]
stats = connect_components_analysis_ret[2]
for index, stat in enumerate(stats):
if stat[4] <= min_area_threshold:
idx = np.where(labels == index)
morphological_ret[idx] = 0
# apply embedding features cluster
mask_image, lane_coords = self._cluster.apply_lane_feats_cluster(
binary_seg_result=morphological_ret,
instance_seg_result=instance_seg_result
)
if mask_image is None:
return {
'mask_image': None,
'fit_params': None,
'source_image': None,
}
# lane line fit
fit_params = []
src_lane_pts = []
for lane_index, coords in enumerate(lane_coords):
if data_source == 'tusimple':
tmp_mask = np.zeros(shape=(720, 1280), dtype=np.uint8)
tmp_mask[tuple((np.int_(coords[:, 1] * 720 / 256), np.int_(coords[:, 0] * 1280 / 512)))] = 255
else:
raise ValueError('Wrong data source now only support tusimple')
tmp_ipm_mask = cv2.remap(
tmp_mask,
self._remap_to_ipm_x,
self._remap_to_ipm_y,
interpolation=cv2.INTER_NEAREST
)
nonzero_y = np.array(tmp_ipm_mask.nonzero()[0])
nonzero_x = np.array(tmp_ipm_mask.nonzero()[1])
fit_param = np.polyfit(nonzero_y, nonzero_x, 2)
fit_params.append(fit_param)
[ipm_image_height, ipm_image_width] = tmp_ipm_mask.shape
plot_y = np.linspace(10, ipm_image_height, ipm_image_height - 10)
fit_x = fit_param[0] * plot_y ** 2 + fit_param[1] * plot_y + fit_param[2]
lane_pts = []
for index in range(0, plot_y.shape[0], 5):
src_x = self._remap_to_ipm_x[
int(plot_y[index]), int(np.clip(fit_x[index], 0, ipm_image_width - 1))]
if src_x <= 0:
continue
src_y = self._remap_to_ipm_y[
int(plot_y[index]), int(np.clip(fit_x[index], 0, ipm_image_width - 1))]
src_y = src_y if src_y > 0 else 0
lane_pts.append([src_x, src_y])
src_lane_pts.append(lane_pts)
# tusimple test data sample point along y axis every 10 pixels
source_image_width = source_image.shape[1]
for index, single_lane_pts in enumerate(src_lane_pts):
single_lane_pt_x = np.array(single_lane_pts, dtype=np.float32)[:, 0]
single_lane_pt_y = np.array(single_lane_pts, dtype=np.float32)[:, 1]
if data_source == 'tusimple':
start_plot_y = 240
end_plot_y = 720
else:
raise ValueError('Wrong data source now only support tusimple')
step = int(math.floor((end_plot_y - start_plot_y) / 10))
for plot_y in np.linspace(start_plot_y, end_plot_y, step):
diff = single_lane_pt_y - plot_y
fake_diff_bigger_than_zero = diff.copy()
fake_diff_smaller_than_zero = diff.copy()
fake_diff_bigger_than_zero[np.where(diff <= 0)] = float('inf')
fake_diff_smaller_than_zero[np.where(diff > 0)] = float('-inf')
idx_low = np.argmax(fake_diff_smaller_than_zero)
idx_high = np.argmin(fake_diff_bigger_than_zero)
previous_src_pt_x = single_lane_pt_x[idx_low]
previous_src_pt_y = single_lane_pt_y[idx_low]
last_src_pt_x = single_lane_pt_x[idx_high]
last_src_pt_y = single_lane_pt_y[idx_high]
if previous_src_pt_y < start_plot_y or last_src_pt_y < start_plot_y or \
fake_diff_smaller_than_zero[idx_low] == float('-inf') or \
fake_diff_bigger_than_zero[idx_high] == float('inf'):
continue
interpolation_src_pt_x = (abs(previous_src_pt_y - plot_y) * previous_src_pt_x +
abs(last_src_pt_y - plot_y) * last_src_pt_x) / \
(abs(previous_src_pt_y - plot_y) + abs(last_src_pt_y - plot_y))
interpolation_src_pt_y = (abs(previous_src_pt_y - plot_y) * previous_src_pt_y +
abs(last_src_pt_y - plot_y) * last_src_pt_y) / \
(abs(previous_src_pt_y - plot_y) + abs(last_src_pt_y - plot_y))
if interpolation_src_pt_x > source_image_width or interpolation_src_pt_x < 10:
continue
lane_color = self._color_map[index].tolist()
cv2.circle(source_image, (int(interpolation_src_pt_x),
int(interpolation_src_pt_y)), 5, lane_color, -1)
ret = {
'mask_image': mask_image,
'fit_params': fit_params,
'source_image': source_image,
}
return ret
contrib/LaneNet/vis.py 0 → 100644
浏览文件 @ 1942c445
# coding: utf8
# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
# GPU memory garbage collection optimization flags
os.environ['FLAGS_eager_delete_tensor_gb'] = "0.0"
import sys
cur_path = os.path.abspath(os.path.dirname(__file__))
root_path = os.path.split(os.path.split(cur_path)[0])[0]
SEG_PATH = os.path.join(cur_path, "../../../")
sys.path.append(SEG_PATH)
sys.path.append(root_path)
import matplotlib
matplotlib.use('Agg')
import time
import argparse
import pprint
import cv2
import numpy as np
import paddle.fluid as fluid
from utils.config import cfg
from reader import LaneNetDataset
from models.model_builder import build_model
from models.model_builder import ModelPhase
from utils import lanenet_postprocess
import matplotlib.pyplot as plt
def parse_args():
parser = argparse.ArgumentParser(description='PaddeSeg visualization tools')
parser.add_argument(
'--cfg',
dest='cfg_file',
help='Config file for training (and optionally testing)',
default=None,
type=str)
parser.add_argument(
'--use_gpu', dest='use_gpu', help='Use gpu or cpu', action='store_true')
parser.add_argument(
'--vis_dir',
dest='vis_dir',
help='visual save dir',
type=str,
default='visual')
parser.add_argument(
'--also_save_raw_results',
dest='also_save_raw_results',
help='whether to save raw result',
action='store_true')
parser.add_argument(
'--local_test',
dest='local_test',
help='if in local test mode, only visualize 5 images for testing',
action='store_true')
parser.add_argument(
'opts',
help='See config.py for all options',
default=None,
nargs=argparse.REMAINDER)
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
return parser.parse_args()
def makedirs(directory):
if not os.path.exists(directory):
os.makedirs(directory)
def to_png_fn(fn, name=""):
"""
Append png as filename postfix
"""
directory, filename = os.path.split(fn)
basename, ext = os.path.splitext(filename)
return basename + name + ".png"
def minmax_scale(input_arr):
min_val = np.min(input_arr)
max_val = np.max(input_arr)
output_arr = (input_arr - min_val) * 255.0 / (max_val - min_val)
return output_arr
def visualize(cfg,
vis_file_list=None,
use_gpu=False,
vis_dir="visual",
also_save_raw_results=False,
ckpt_dir=None,
log_writer=None,
local_test=False,
**kwargs):
if vis_file_list is None:
vis_file_list = cfg.DATASET.TEST_FILE_LIST
dataset = LaneNetDataset(
file_list=vis_file_list,
mode=ModelPhase.VISUAL,
shuffle=True,
data_dir=cfg.DATASET.DATA_DIR)
startup_prog = fluid.Program()
test_prog = fluid.Program()
pred, logit = build_model(test_prog, startup_prog, phase=ModelPhase.VISUAL)
# Clone forward graph
test_prog = test_prog.clone(for_test=True)
# Get device environment
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
ckpt_dir = cfg.TEST.TEST_MODEL if not ckpt_dir else ckpt_dir
fluid.io.load_params(exe, ckpt_dir, main_program=test_prog)
save_dir = os.path.join(vis_dir, 'visual_results')
makedirs(save_dir)
if also_save_raw_results:
raw_save_dir = os.path.join(vis_dir, 'raw_results')
makedirs(raw_save_dir)
fetch_list = [pred.name, logit.name]
test_reader = dataset.batch(dataset.generator, batch_size=1, is_test=True)
postprocessor = lanenet_postprocess.LaneNetPostProcessor()
for imgs, grts, grts_instance, img_names, valid_shapes, org_imgs in test_reader:
segLogits, emLogits = exe.run(
program=test_prog,
feed={'image': imgs},
fetch_list=fetch_list,
return_numpy=True)
num_imgs = segLogits.shape[0]
for i in range(num_imgs):
gt_image = org_imgs[i]
binary_seg_image, instance_seg_image = segLogits[i].squeeze(-1), emLogits[i].transpose((1,2,0))
postprocess_result = postprocessor.postprocess(
binary_seg_result=binary_seg_image,
instance_seg_result=instance_seg_image,
source_image=gt_image
)
pred_binary_fn = os.path.join(save_dir, to_png_fn(img_names[i], name='_pred_binary'))
pred_lane_fn = os.path.join(save_dir, to_png_fn(img_names[i], name='_pred_lane'))
pred_instance_fn = os.path.join(save_dir, to_png_fn(img_names[i], name='_pred_instance'))
dirname = os.path.dirname(pred_binary_fn)
makedirs(dirname)
mask_image = postprocess_result['mask_image']
for i in range(4):
instance_seg_image[:, :, i] = minmax_scale(instance_seg_image[:, :, i])
embedding_image = np.array(instance_seg_image).astype(np.uint8)
plt.figure('mask_image')
plt.imshow(mask_image[:, :, (2, 1, 0)])
plt.figure('src_image')
plt.imshow(gt_image[:, :, (2, 1, 0)])
plt.figure('instance_image')
plt.imshow(embedding_image[:, :, (2, 1, 0)])
plt.figure('binary_image')
plt.imshow(binary_seg_image * 255, cmap='gray')
plt.show()
cv2.imwrite(pred_binary_fn, np.array(binary_seg_image * 255).astype(np.uint8))
cv2.imwrite(pred_lane_fn, postprocess_result['source_image'])
cv2.imwrite(pred_instance_fn, mask_image)
print(pred_lane_fn, 'saved!')
if __name__ == '__main__':
args = parse_args()
if args.cfg_file is not None:
cfg.update_from_file(args.cfg_file)
if args.opts:
cfg.update_from_list(args.opts)
cfg.check_and_infer()
print(pprint.pformat(cfg))
visualize(cfg, **args.__dict__)
dataset/download_mini_mechanical_industry_meter.py → contrib/MechanicalIndustryMeter/download_mini_mechanical_industry_meter.py
浏览文件 @ 1942c445
...@@ -16,7 +16,7 @@ import sys ...@@ -16,7 +16,7 @@ import sys
import os import os
LOCAL_PATH = os.path.dirname(os.path.abspath(__file__)) LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
TEST_PATH = os.path.join(LOCAL_PATH, "..", "test") TEST_PATH = os.path.join(LOCAL_PATH, "..", "..", "test")
sys.path.append(TEST_PATH) sys.path.append(TEST_PATH)
from test_utils import download_file_and_uncompress from test_utils import download_file_and_uncompress
......
contrib/MechanicalIndustryMeter/download_unet_mechanical_industry_meter.py 0 → 100644
浏览文件 @ 1942c445
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import os
LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
TEST_PATH = os.path.join(LOCAL_PATH, "..", "..", "test")
sys.path.append(TEST_PATH)
from test_utils import download_file_and_uncompress
if __name__ == "__main__":
download_file_and_uncompress(
url='https://paddleseg.bj.bcebos.com/models/unet_mechanical_industry_meter.tar',
savepath=LOCAL_PATH,
extrapath=LOCAL_PATH)
print("Pretrained Model download success!")
configs/unet_mechanical_meter.yaml → contrib/MechanicalIndustryMeter/unet_mechanical_meter.yaml
浏览文件 @ 1942c445
...@@ -21,14 +21,14 @@ DATALOADER: ...@@ -21,14 +21,14 @@ DATALOADER:
BUF_SIZE: 256 BUF_SIZE: 256
NUM_WORKERS: 4 NUM_WORKERS: 4
DATASET: DATASET:
DATA_DIR: "./dataset/mini_mechanical_industry_meter_data/" DATA_DIR: "./contrib/MechanicalIndustryMeter/mini_mechanical_industry_meter_data/"
IMAGE_TYPE: "rgb" # choice rgb or rgba IMAGE_TYPE: "rgb" # choice rgb or rgba
NUM_CLASSES: 5 NUM_CLASSES: 5
TEST_FILE_LIST: "./dataset/mini_mechanical_industry_meter_data/val_mini.txt" TEST_FILE_LIST: "./contrib/MechanicalIndustryMeter/mini_mechanical_industry_meter_data/val_mini.txt"
TEST_TOTAL_IMAGES: 8 TEST_TOTAL_IMAGES: 8
TRAIN_FILE_LIST: "./dataset/mini_mechanical_industry_meter_data/train_mini.txt" TRAIN_FILE_LIST: "./contrib/MechanicalIndustryMeter/mini_mechanical_industry_meter_data/train_mini.txt"
TRAIN_TOTAL_IMAGES: 64 TRAIN_TOTAL_IMAGES: 64
VAL_FILE_LIST: "./dataset/mini_mechanical_industry_meter_data/val_mini.txt" VAL_FILE_LIST: "./contrib/MechanicalIndustryMeter/mini_mechanical_industry_meter_data/val_mini.txt"
VAL_TOTAL_IMAGES: 8 VAL_TOTAL_IMAGES: 8
SEPARATOR: "|" SEPARATOR: "|"
IGNORE_INDEX: 255 IGNORE_INDEX: 255
......
contrib/README.md
浏览文件 @ 1942c445
# PaddleSeg 特色垂类分割模型 # PaddleSeg 特色垂类分割模型
提供基于PaddlePaddle最新的分割特色模型 提供基于PaddlePaddle最新的分割特色模型:
## Augmented Context Embedding with Edge Perceiving (ACE2P) - [人像分割](#人像分割)
- [人体解析](#人体解析)
- [车道线分割](#车道线分割)
- [工业用表分割](#工业用表分割)
- [在线体验](#在线体验)
## 人像分割
### 1. 模型概述 **Note:** 本章节所有命令均在`contrib/HumanSeg`目录下执行。
CVPR 19 Look into Person (LIP) 单人人像分割比赛冠军模型，详见[ACE2P](./ACE2P)
### 2. 模型下载 ```
cd contrib/HumanSeg
```
点击[链接](https://paddleseg.bj.bcebos.com/models/ACE2P.tgz)，下载, 在contrib/ACE2P下解压, `tar -xzf ACE2P.tgz` ### 1. 模型结构
### 3. 数据下载 DeepLabv3+ backbone为Xception65
前往LIP数据集官网: http://47.100.21.47:9999/overview.php 或点击 [Baidu_Drive](https://pan.baidu.com/s/1nvqmZBN#list/path=%2Fsharelink2787269280-523292635003760%2FLIP%2FLIP&parentPath=%2Fsharelink2787269280-523292635003760), ### 2. 下载模型和数据
执行以下命令下载并解压模型和数据集：
加载Testing_images.zip, 解压到contrib/ACE2P/data文件夹下 ```
python download_HumanSeg.py
```
或点击[链接](https://paddleseg.bj.bcebos.com/models/HumanSeg.tgz)进行手动下载，并解压到contrib/HumanSeg文件夹下
### 4. 运行
**NOTE:** 运行该模型需要2G左右显存 ### 3. 运行
使用GPU预测 使用GPU预测：
``` ```
python -u infer.py --example ACE2P --use_gpu python -u infer.py --example HumanSeg --use_gpu
``` ```
使用CPU预测： 使用CPU预测：
``` ```
python -u infer.py --example ACE2P python -u infer.py --example HumanSeg
``` ```
## 人像分割 (HumanSeg)
预测结果存放在contrib/HumanSeg/HumanSeg/result目录下。
### 1. 模型结构 ### 4. 预测结果示例：
DeepLabv3+ backbone为Xception65 原图：
![](HumanSeg/imgs/Human.jpg)
预测结果：
![](HumanSeg/imgs/HumanSeg.jpg)
### 2. 下载模型和数据
点击[链接](https://paddleseg.bj.bcebos.com/models/HumanSeg.tgz)，下载解压到contrib文件夹下
### 3. 运行 ## 人体解析
![](ACE2P/imgs/result.jpg)
人体解析(Human Parsing)是细粒度的语义分割任务，旨在识别像素级别的人类图像的组成部分（例如，身体部位和服装）。本章节使用冠军模型Augmented Context Embedding with Edge Perceiving (ACE2P)进行预测分割。
**Note:** 本章节所有命令均在`contrib/ACE2P`目录下执行。
使用GPU预测：
``` ```
python -u infer.py --example HumanSeg --use_gpu cd contrib/ACE2P
``` ```
### 1. 模型概述
Augmented Context Embedding with Edge Perceiving (ACE2P)通过融合底层特征、全局上下文信息和边缘细节，端到端训练学习人体解析任务。以ACE2P单人人体解析网络为基础的解决方案在CVPR2019第三届Look into Person (LIP)挑战赛中赢得了全部三个人体解析任务的第一名。详情请参见[ACE2P](./ACE2P)
### 2. 模型下载
执行以下命令下载并解压ACE2P预测模型：
使用CPU预测：
``` ```
python -u infer.py --example HumanSeg python download_ACE2P.py
``` ```
或点击[链接](https://paddleseg.bj.bcebos.com/models/ACE2P.tgz)进行手动下载, 并在contrib/ACE2P下解压。
### 4. 预测结果示例： ### 3. 数据下载
测试图片共10000张，
点击 [Baidu_Drive](https://pan.baidu.com/s/1nvqmZBN#list/path=%2Fsharelink2787269280-523292635003760%2FLIP%2FLIP&parentPath=%2Fsharelink2787269280-523292635003760)
下载Testing_images.zip，或前往LIP数据集官网进行下载。
下载后解压到contrib/ACE2P/data文件夹下
### 4. 运行
使用GPU预测
```
python -u infer.py --example ACE2P --use_gpu
```
使用CPU预测：
```
python -u infer.py --example ACE2P
```
原图：![](imgs/Human.jpg) **NOTE:** 运行该模型需要2G左右显存。由于数据图片较多，预测过程将比较耗时。
#### 5. 预测结果示例：
原图：
![](ACE2P/imgs/117676_2149260.jpg)
预测结果：
预测结果：![](imgs/HumanSeg.jpg) ![](ACE2P/imgs/117676_2149260.png)
### 备注
## 车道线分割 (RoadLine) 1. 数据及模型路径等详细配置见ACE2P/HumanSeg/RoadLine下的config.py文件
2. ACE2P模型需预留2G显存，若显存超可调小FLAGS_fraction_of_gpu_memory_to_use
## 车道线分割
**Note:** 本章节所有命令均在`contrib/RoadLine`目录下执行。
```
cd contrib/RoadLine
```
### 1. 模型结构 ### 1. 模型结构
...@@ -75,7 +142,15 @@ Deeplabv3+ backbone为MobileNetv2 ...@@ -75,7 +142,15 @@ Deeplabv3+ backbone为MobileNetv2
### 2. 下载模型和数据 ### 2. 下载模型和数据
点击[链接](https://paddleseg.bj.bcebos.com/inference_model/RoadLine.tgz)，下载解压在contrib文件夹下
执行以下命令下载并解压模型和数据集：
```
python download_RoadLine.py
```
或点击[链接](https://paddleseg.bj.bcebos.com/inference_model/RoadLine.tgz)进行手动下载，并解压到contrib/RoadLine文件夹下
### 3. 运行 ### 3. 运行
...@@ -92,45 +167,84 @@ python -u infer.py --example RoadLine --use_gpu ...@@ -92,45 +167,84 @@ python -u infer.py --example RoadLine --use_gpu
python -u infer.py --example RoadLine python -u infer.py --example RoadLine
``` ```
预测结果存放在contrib/RoadLine/RoadLine/result目录下。
#### 4. 预测结果示例： #### 4. 预测结果示例：
原图：![](imgs/RoadLine.jpg) 原图：
![](RoadLine/imgs/RoadLine.jpg)
预测结果：![](imgs/RoadLine.png) 预测结果：
![](RoadLine/imgs/RoadLine.png)
## 工业用表分割 ## 工业用表分割
**Note:** 本章节所有命令均在`PaddleSeg`目录下执行。
### 1. 模型结构 ### 1. 模型结构
unet unet
### 2. 数据准备 ### 2. 数据准备
cd到PaddleSeg/dataset文件夹下，执行download_mini_mechanical_industry_meter.py 执行以下命令下载并解压数据集，数据集将存放在contrib/MechanicalIndustryMeter文件夹下：
```
python ./contrib/MechanicalIndustryMeter/download_mini_mechanical_industry_meter.py
```
### 3. 下载预训练模型
```
python ./pretrained_model/download_model.py unet_bn_coco
```
### 4. 训练与评估
### 3. 训练与评估 ```
export CUDA_VISIBLE_DEVICES=0
python ./pdseg/train.py --log_steps 10 --cfg contrib/MechanicalIndustryMeter/unet_mechanical_meter.yaml --use_gpu --do_eval --use_mpio
```
### 5. 可视化
我们已提供了一个训练好的模型，执行以下命令进行下载，下载后将存放在./contrib/MechanicalIndustryMeter/文件夹下。
``` ```
CUDA_VISIBLE_DEVICES=0 python ./pdseg/train.py --log_steps 10 --cfg configs/unet_mechanical_meter.yaml --use_gpu --do_eval --use_mpio python ./contrib/MechanicalIndustryMeter/download_unet_mechanical_industry_meter.py
``` ```
### 4. 可视化 使用该模型进行预测可视化：
我们提供了一个训练好的模型，点击[链接](https://paddleseg.bj.bcebos.com/models/unet_mechanical_industry_meter.tar)，下载后放在PaddleSeg/pretrained_model下
``` ```
CUDA_VISIBLE_DEVICES=0 python ./pdseg/vis.py --cfg configs/unet_mechanical_meter.yaml --use_gpu --vis_dir vis_meter \ python ./pdseg/vis.py --cfg contrib/MechanicalIndustryMeter/unet_mechanical_meter.yaml --use_gpu --vis_dir vis_meter \
TEST.TEST_MODEL "./pretrained_model/unet_gongyeyongbiao/" TEST.TEST_MODEL "./contrib/MechanicalIndustryMeter/unet_mechanical_industry_meter/"
``` ```
可视化结果会保存在vis_meter文件夹下 可视化结果会保存在./vis_meter文件夹下。
### 5. 可视化结果示例： ### 6. 可视化结果示例：
原图：![](imgs/1560143028.5_IMG_3091.JPG) 原图：
![](MechanicalIndustryMeter/imgs/1560143028.5_IMG_3091.JPG)
预测结果：![](imgs/1560143028.5_IMG_3091.png) 预测结果：
# 备注 ![](MechanicalIndustryMeter/imgs/1560143028.5_IMG_3091.png)
## 在线体验
PaddleSeg在AI Studio平台上提供了在线体验的教程，欢迎体验：
|教程|链接|
|-|-|
|工业质检|[点击体验](https://aistudio.baidu.com/aistudio/projectdetail/184392)|
|人像分割|[点击体验](https://aistudio.baidu.com/aistudio/projectdetail/188833)|
|特色垂类模型|[点击体验](https://aistudio.baidu.com/aistudio/projectdetail/226710)|
1. 数据及模型路径等详细配置见ACE2P/HumanSeg/RoadLine下的config.py文件
2. ACE2P模型需预留2G显存，若显存超可调小FLAGS_fraction_of_gpu_memory_to_use
contrib/RealTimeHumanSeg/cpp/CMakeLists.txt 0 → 100644
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cmake_minimum_required(VERSION 3.0)
project(PaddleMaskDetector CXX C)
option(WITH_MKL "Compile demo with MKL/OpenBlas support,defaultuseMKL." ON)
option(WITH_GPU "Compile demo with GPU/CPU, default use CPU." ON)
option(WITH_STATIC_LIB "Compile demo with static/shared library, default use static." ON)
option(USE_TENSORRT "Compile demo with TensorRT." OFF)
SET(PADDLE_DIR "" CACHE PATH "Location of libraries")
SET(OPENCV_DIR "" CACHE PATH "Location of libraries")
SET(CUDA_LIB "" CACHE PATH "Location of libraries")
macro(safe_set_static_flag)
foreach(flag_var
CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE
CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO)
if(${flag_var} MATCHES "/MD")
string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}")
endif(${flag_var} MATCHES "/MD")
endforeach(flag_var)
endmacro()
if (WITH_MKL)
ADD_DEFINITIONS(-DUSE_MKL)
endif()
if (NOT DEFINED PADDLE_DIR OR ${PADDLE_DIR} STREQUAL "")
message(FATAL_ERROR "please set PADDLE_DIR with -DPADDLE_DIR=/path/paddle_influence_dir")
endif()
if (NOT DEFINED OPENCV_DIR OR ${OPENCV_DIR} STREQUAL "")
message(FATAL_ERROR "please set OPENCV_DIR with -DOPENCV_DIR=/path/opencv")
endif()
include_directories("${CMAKE_SOURCE_DIR}/")
include_directories("${PADDLE_DIR}/")
include_directories("${PADDLE_DIR}/third_party/install/protobuf/include")
include_directories("${PADDLE_DIR}/third_party/install/glog/include")
include_directories("${PADDLE_DIR}/third_party/install/gflags/include")
include_directories("${PADDLE_DIR}/third_party/install/xxhash/include")
if (EXISTS "${PADDLE_DIR}/third_party/install/snappy/include")
include_directories("${PADDLE_DIR}/third_party/install/snappy/include")
endif()
if(EXISTS "${PADDLE_DIR}/third_party/install/snappystream/include")
include_directories("${PADDLE_DIR}/third_party/install/snappystream/include")
endif()
include_directories("${PADDLE_DIR}/third_party/install/zlib/include")
include_directories("${PADDLE_DIR}/third_party/boost")
include_directories("${PADDLE_DIR}/third_party/eigen3")
if (EXISTS "${PADDLE_DIR}/third_party/install/snappy/lib")
link_directories("${PADDLE_DIR}/third_party/install/snappy/lib")
endif()
if(EXISTS "${PADDLE_DIR}/third_party/install/snappystream/lib")
link_directories("${PADDLE_DIR}/third_party/install/snappystream/lib")
endif()
link_directories("${PADDLE_DIR}/third_party/install/zlib/lib")
link_directories("${PADDLE_DIR}/third_party/install/protobuf/lib")
link_directories("${PADDLE_DIR}/third_party/install/glog/lib")
link_directories("${PADDLE_DIR}/third_party/install/gflags/lib")
link_directories("${PADDLE_DIR}/third_party/install/xxhash/lib")
link_directories("${PADDLE_DIR}/paddle/lib/")
link_directories("${CMAKE_CURRENT_BINARY_DIR}")
if (WIN32)
include_directories("${PADDLE_DIR}/paddle/fluid/inference")
include_directories("${PADDLE_DIR}/paddle/include")
link_directories("${PADDLE_DIR}/paddle/fluid/inference")
include_directories("${OPENCV_DIR}/build/include")
include_directories("${OPENCV_DIR}/opencv/build/include")
link_directories("${OPENCV_DIR}/build/x64/vc14/lib")
else ()
find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/share/OpenCV NO_DEFAULT_PATH)
include_directories("${PADDLE_DIR}/paddle/include")
link_directories("${PADDLE_DIR}/paddle/lib")
include_directories(${OpenCV_INCLUDE_DIRS})
endif ()
if (WIN32)
add_definitions("/DGOOGLE_GLOG_DLL_DECL=")
set(CMAKE_C_FLAGS_DEBUG "${CMAKE_C_FLAGS_DEBUG} /bigobj /MTd")
set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} /bigobj /MT")
set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} /bigobj /MTd")
set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /bigobj /MT")
if (WITH_STATIC_LIB)
safe_set_static_flag()
add_definitions(-DSTATIC_LIB)
endif()
else()
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -o2 -fopenmp -std=c++11")
set(CMAKE_STATIC_LIBRARY_PREFIX "")
endif()
# TODO let users define cuda lib path
if (WITH_GPU)
if (NOT DEFINED CUDA_LIB OR ${CUDA_LIB} STREQUAL "")
message(FATAL_ERROR "please set CUDA_LIB with -DCUDA_LIB=/path/cuda-8.0/lib64")
endif()
if (NOT WIN32)
if (NOT DEFINED CUDNN_LIB)
message(FATAL_ERROR "please set CUDNN_LIB with -DCUDNN_LIB=/path/cudnn_v7.4/cuda/lib64")
endif()
endif(NOT WIN32)
endif()
if (NOT WIN32)
if (USE_TENSORRT AND WITH_GPU)
include_directories("${PADDLE_DIR}/third_party/install/tensorrt/include")
link_directories("${PADDLE_DIR}/third_party/install/tensorrt/lib")
endif()
endif(NOT WIN32)
if (NOT WIN32)
set(NGRAPH_PATH "${PADDLE_DIR}/third_party/install/ngraph")
if(EXISTS ${NGRAPH_PATH})
include(GNUInstallDirs)
include_directories("${NGRAPH_PATH}/include")
link_directories("${NGRAPH_PATH}/${CMAKE_INSTALL_LIBDIR}")
set(NGRAPH_LIB ${NGRAPH_PATH}/${CMAKE_INSTALL_LIBDIR}/libngraph${CMAKE_SHARED_LIBRARY_SUFFIX})
endif()
endif()
if(WITH_MKL)
include_directories("${PADDLE_DIR}/third_party/install/mklml/include")
if (WIN32)
set(MATH_LIB ${PADDLE_DIR}/third_party/install/mklml/lib/mklml.lib
${PADDLE_DIR}/third_party/install/mklml/lib/libiomp5md.lib)
else ()
set(MATH_LIB ${PADDLE_DIR}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX}
${PADDLE_DIR}/third_party/install/mklml/lib/libiomp5${CMAKE_SHARED_LIBRARY_SUFFIX})
execute_process(COMMAND cp -r ${PADDLE_DIR}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX} /usr/lib)
endif ()
set(MKLDNN_PATH "${PADDLE_DIR}/third_party/install/mkldnn")
if(EXISTS ${MKLDNN_PATH})
include_directories("${MKLDNN_PATH}/include")
if (WIN32)
set(MKLDNN_LIB ${MKLDNN_PATH}/lib/mkldnn.lib)
else ()
set(MKLDNN_LIB ${MKLDNN_PATH}/lib/libmkldnn.so.0)
endif ()
endif()
else()
set(MATH_LIB ${PADDLE_DIR}/third_party/install/openblas/lib/libopenblas${CMAKE_STATIC_LIBRARY_SUFFIX})
endif()
if (WIN32)
if(EXISTS "${PADDLE_DIR}/paddle/fluid/inference/libpaddle_fluid${CMAKE_STATIC_LIBRARY_SUFFIX}")
set(DEPS
${PADDLE_DIR}/paddle/fluid/inference/libpaddle_fluid${CMAKE_STATIC_LIBRARY_SUFFIX})
else()
set(DEPS
${PADDLE_DIR}/paddle/lib/libpaddle_fluid${CMAKE_STATIC_LIBRARY_SUFFIX})
endif()
endif()
if(WITH_STATIC_LIB)
set(DEPS
${PADDLE_DIR}/paddle/lib/libpaddle_fluid${CMAKE_STATIC_LIBRARY_SUFFIX})
else()
set(DEPS
${PADDLE_DIR}/paddle/lib/libpaddle_fluid${CMAKE_SHARED_LIBRARY_SUFFIX})
endif()
if (NOT WIN32)
set(DEPS ${DEPS}
${MATH_LIB} ${MKLDNN_LIB}
glog gflags protobuf z xxhash
)
if(EXISTS "${PADDLE_DIR}/third_party/install/snappystream/lib")
set(DEPS ${DEPS} snappystream)
endif()
if (EXISTS "${PADDLE_DIR}/third_party/install/snappy/lib")
set(DEPS ${DEPS} snappy)
endif()
else()
set(DEPS ${DEPS}
${MATH_LIB} ${MKLDNN_LIB}
opencv_world346 glog gflags_static libprotobuf zlibstatic xxhash)
set(DEPS ${DEPS} libcmt shlwapi)
if (EXISTS "${PADDLE_DIR}/third_party/install/snappy/lib")
set(DEPS ${DEPS} snappy)
endif()
if(EXISTS "${PADDLE_DIR}/third_party/install/snappystream/lib")
set(DEPS ${DEPS} snappystream)
endif()
endif(NOT WIN32)
if(WITH_GPU)
if(NOT WIN32)
if (USE_TENSORRT)
set(DEPS ${DEPS} ${PADDLE_DIR}/third_party/install/tensorrt/lib/libnvinfer${CMAKE_STATIC_LIBRARY_SUFFIX})
set(DEPS ${DEPS} ${PADDLE_DIR}/third_party/install/tensorrt/lib/libnvinfer_plugin${CMAKE_STATIC_LIBRARY_SUFFIX})
endif()
set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX})
set(DEPS ${DEPS} ${CUDNN_LIB}/libcudnn${CMAKE_SHARED_LIBRARY_SUFFIX})
else()
set(DEPS ${DEPS} ${CUDA_LIB}/cudart${CMAKE_STATIC_LIBRARY_SUFFIX} )
set(DEPS ${DEPS} ${CUDA_LIB}/cublas${CMAKE_STATIC_LIBRARY_SUFFIX} )
set(DEPS ${DEPS} ${CUDA_LIB}/cudnn${CMAKE_STATIC_LIBRARY_SUFFIX})
endif()
endif()
if (NOT WIN32)
set(EXTERNAL_LIB "-ldl -lrt -lgomp -lz -lm -lpthread")
set(DEPS ${DEPS} ${EXTERNAL_LIB} ${OpenCV_LIBS})
endif()
add_executable(main main.cc humanseg.cc humanseg_postprocess.cc)
target_link_libraries(main ${DEPS})
if (WIN32)
add_custom_command(TARGET main POST_BUILD
COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/third_party/install/mklml/lib/mklml.dll ./mklml.dll
COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/third_party/install/mklml/lib/libiomp5md.dll ./libiomp5md.dll
COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/third_party/install/mkldnn/lib/mkldnn.dll ./mkldnn.dll
COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/third_party/install/mklml/lib/mklml.dll ./release/mklml.dll
COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/third_party/install/mklml/lib/libiomp5md.dll ./release/libiomp5md.dll
COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/third_party/install/mkldnn/lib/mkldnn.dll ./release/mkldnn.dll
)
endif()
contrib/RealTimeHumanSeg/cpp/CMakeSettings.json 0 → 100644
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{
"configurations": [
{
"name": "x64-Release",
"generator": "Ninja",
"configurationType": "RelWithDebInfo",
"inheritEnvironments": [ "msvc_x64_x64" ],
"buildRoot": "${projectDir}\\out\\build\\${name}",
"installRoot": "${projectDir}\\out\\install\\${name}",
"cmakeCommandArgs": "",
"buildCommandArgs": "-v",
"ctestCommandArgs": "",
"variables": [
{
"name": "CUDA_LIB",
"value": "D:/projects/packages/cuda10_0/lib64",
"type": "PATH"
},
{
"name": "CUDNN_LIB",
"value": "D:/projects/packages/cuda10_0/lib64",
"type": "PATH"
},
{
"name": "OPENCV_DIR",
"value": "D:/projects/packages/opencv3_4_6",
"type": "PATH"
},
{
"name": "PADDLE_DIR",
"value": "D:/projects/packages/fluid_inference1_6_1",
"type": "PATH"
},
{
"name": "CMAKE_BUILD_TYPE",
"value": "Release",
"type": "STRING"
}
]
}
]
}
\ No newline at end of file
contrib/RealTimeHumanSeg/cpp/README.md 0 → 100644
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# 视频实时图像分割模型C++预测部署
本文档主要介绍实时图像分割模型如何在`Windows`和`Linux`上完成基于`C++`的预测部署。
## C++预测部署编译
### 1. 下载模型
点击右边下载：[模型下载地址](https://paddleseg.bj.bcebos.com/deploy/models/humanseg_paddleseg_int8.zip)
模型文件路径将做为预测时的输入参数，请解压到合适的目录位置。
### 2. 编译
本项目支持在Windows和Linux上编译并部署C++项目，不同平台的编译请参考：
- [Linux 编译](./docs/linux_build.md)
- [Windows 使用 Visual Studio 2019编译](./docs/windows_build.md)
contrib/RealTimeHumanSeg/cpp/docs/linux_build.md 0 → 100644
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# 视频实时人像分割模型Linux平台C++预测部署
## 1. 系统和软件依赖
### 1.1 操作系统及硬件要求
- Ubuntu 14.04 或者 16.04 (其它平台未测试)
- GCC版本4.8.5 ~ 4.9.2
- 支持Intel MKL-DNN的CPU
- NOTE: 如需在Nvidia GPU运行，请自行安装CUDA 9.0 / 10.0 + CUDNN 7.3+ (不支持9.1/10.1版本的CUDA)
### 1.2 下载PaddlePaddle C++预测库
PaddlePaddle C++ 预测库主要分为CPU版本和GPU版本。
其中，GPU 版本支持`CUDA 10.0` 和 `CUDA 9.0`:
以下为各版本C++预测库的下载链接：
| 版本 | 链接 |
| ---- | ---- |
| CPU+MKL版 | [fluid_inference.tgz](https://paddle-inference-lib.bj.bcebos.com/1.6.3-cpu-avx-mkl/fluid_inference.tgz) |
| CUDA9.0+MKL 版 | [fluid_inference.tgz](https://paddle-inference-lib.bj.bcebos.com/1.6.3-gpu-cuda9-cudnn7-avx-mkl/fluid_inference.tgz) |
| CUDA10.0+MKL 版 | [fluid_inference.tgz](https://paddle-inference-lib.bj.bcebos.com/1.6.3-gpu-cuda10-cudnn7-avx-mkl/fluid_inference.tgz) |
更多可用预测库版本，请点击以下链接下载:[C++预测库下载列表](https://paddlepaddle.org.cn/documentation/docs/zh/advanced_usage/deploy/inference/build_and_install_lib_cn.html)
下载并解压, 解压后的 `fluid_inference`目录包含的内容：
```
fluid_inference
├── paddle # paddle核心库和头文件
|
├── third_party # 第三方依赖库和头文件
|
└── version.txt # 版本和编译信息
```
**注意:** 请把解压后的目录放到合适的路径，**该目录路径后续会作为编译依赖**使用。
## 2. 编译与运行
### 2.1 配置编译脚本
打开文件`linux_build.sh`, 看到以下内容:
```shell
# 是否使用GPU
WITH_GPU=OFF
# Paddle 预测库路径
PADDLE_DIR=/PATH/TO/fluid_inference/
# CUDA库路径, 仅 WITH_GPU=ON 时设置
CUDA_LIB=/PATH/TO/CUDA_LIB64/
# CUDNN库路径，仅 WITH_GPU=ON 且 CUDA_LIB有效时设置
CUDNN_LIB=/PATH/TO/CUDNN_LIB64/
# OpenCV 库路径, 无须设置
OPENCV_DIR=/PATH/TO/opencv3gcc4.8/
cd build
cmake .. \
-DWITH_GPU=${WITH_GPU} \
-DPADDLE_DIR=${PADDLE_DIR} \
-DCUDA_LIB=${CUDA_LIB} \
-DCUDNN_LIB=${CUDNN_LIB} \
-DOPENCV_DIR=${OPENCV_DIR} \
-DWITH_STATIC_LIB=OFF
make -j4
```
把上述参数根据实际情况做修改后，运行脚本编译程序：
```shell
sh linux_build.sh
```
### 2.2. 运行和可视化
可执行文件有 **2** 个参数，第一个是前面导出的`inference_model`路径，第二个是需要预测的视频路径。
示例:
```shell
./build/main ./models /PATH/TO/TEST_VIDEO
```
点击下载[测试视频](https://paddleseg.bj.bcebos.com/deploy/data/test.avi)
预测的结果保存在视频文件`result.avi`中。
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# 视频实时人像分割模型Windows平台C++预测部署
## 1. 系统和软件依赖
### 1.1 基础依赖
- Windows 10 / Windows Server 2016+ (其它平台未测试)
- Visual Studio 2019 (社区版或专业版均可)
- CUDA 9.0 / 10.0 + CUDNN 7.3+ (不支持9.1/10.1版本的CUDA)
### 1.2 下载OpenCV并设置环境变量
- 在OpenCV官网下载适用于Windows平台的3.4.6版本: [点击下载](https://sourceforge.net/projects/opencvlibrary/files/3.4.6/opencv-3.4.6-vc14_vc15.exe/download)
- 运行下载的可执行文件，将OpenCV解压至合适目录，这里以解压到`D:\projects\opencv`为例
- 把OpenCV动态库加入到系统环境变量
- 此电脑(我的电脑)->属性->高级系统设置->环境变量
- 在系统变量中找到Path（如没有，自行创建），并双击编辑
- 新建，将opencv路径填入并保存，如D:\projects\opencv\build\x64\vc14\bin
**注意:** `OpenCV`的解压目录后续将做为编译配置项使用，所以请放置合适的目录中。
### 1.3 下载PaddlePaddle C++ 预测库
`PaddlePaddle` **C++ 预测库** 主要分为`CPU`和`GPU`版本， 其中`GPU版本`提供`CUDA 9.0` 和 `CUDA 10.0` 支持。
常用的版本如下：
| 版本 | 链接 |
| ---- | ---- |
| CPU+MKL版 | [fluid_inference_install_dir.zip](https://paddle-wheel.bj.bcebos.com/1.6.3/win-infer/mkl/cpu/fluid_inference_install_dir.zip) |
| CUDA9.0+MKL 版 | [fluid_inference_install_dir.zip](https://paddle-wheel.bj.bcebos.com/1.6.3/win-infer/mkl/post97/fluid_inference_install_dir.zip) |
| CUDA10.0+MKL 版 | [fluid_inference_install_dir.zip](https://paddle-wheel.bj.bcebos.com/1.6.3/win-infer/mkl/post107/fluid_inference_install_dir.zip) |
更多不同平台的可用预测库版本，请[点击查看](https://paddlepaddle.org.cn/documentation/docs/zh/advanced_usage/deploy/inference/windows_cpp_inference.html) 选择适合你的版本。
下载并解压, 解压后的 `fluid_inference`目录包含的内容：
```
fluid_inference_install_dir
├── paddle # paddle核心库和头文件
|
├── third_party # 第三方依赖库和头文件
|
└── version.txt # 版本和编译信息
```
**注意：** 这里的`fluid_inference_install_dir` 目录所在路径，将用于后面的编译参数设置，请放置在合适的位置。
## 2. Visual Studio 2019 编译
- 2.1 打开Visual Studio 2019 Community，点击`继续但无需代码`， 如下图：
![step2.1](https://paddleseg.bj.bcebos.com/inference/vs2019_step1.png)
- 2.2 点击 `文件`->`打开`->`CMake`, 如下图：
![step2.2](https://paddleseg.bj.bcebos.com/inference/vs2019_step2.png)
- 2.3 选择本项目根目录`CMakeList.txt`文件打开， 如下图：
![step2.3](https://paddleseg.bj.bcebos.com/deploy/docs/vs2019_step2.3.png)
- 2.4 点击：`项目`->`PaddleMaskDetector的CMake设置`
![step2.4](https://paddleseg.bj.bcebos.com/deploy/docs/vs2019_step2.4.png)
- 2.5 点击浏览设置`OPENCV_DIR`, `CUDA_LIB` 和 `PADDLE_DIR` 3个编译依赖库的位置, 设置完成后点击`保存并生成CMake缓存并加载变量`
![step2.5](https://paddleseg.bj.bcebos.com/inference/vs2019_step5.png)
- 2.6 点击`生成`->`全部生成` 编译项目
![step2.6](https://paddleseg.bj.bcebos.com/inference/vs2019_step6.png)
## 3. 运行程序
成功编译后， 产出的可执行文件在项目子目录`out\build\x64-Release`目录， 按以下步骤运行代码：
- 打开`cmd`切换至该目录
- 运行以下命令传入模型路径与测试视频
```shell
main.exe ./models/ ./data/test.avi
```
第一个参数即人像分割预测模型的路径，第二个参数即要预测的视频。
点击下载[测试视频](https://paddleseg.bj.bcebos.com/deploy/data/test.avi)
运行后，预测结果保存在文件`result.avi`中。
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// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <opencv2/core/core.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/optflow.hpp>
int ThresholdMask(const cv::Mat &fg_cfd,
const float fg_thres,
const float bg_thres,
cv::Mat& fg_mask);
cv::Mat MergeSegMat(const cv::Mat& seg_mat,
const cv::Mat& ori_frame);
int MergeProcess(const uchar *im_buff,
const float *im_scoremap_buff,
const int height,
const int width,
uchar *result_buff);
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...@@ -99,4 +99,4 @@ cd /d D:\projects\PaddleSeg\deploy\cpp\out\build\x64-Release ...@@ -99,4 +99,4 @@ cd /d D:\projects\PaddleSeg\deploy\cpp\out\build\x64-Release
demo.exe --conf=/path/to/your/conf --input_dir=/path/to/your/input/data/directory demo.exe --conf=/path/to/your/conf --input_dir=/path/to/your/input/data/directory
``` ```
更详细说明请参考ReadMe文档： [预测和可视化部分](../ReadMe.md) 更详细说明请参考ReadMe文档： [预测和可视化部分](../README.md)
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## This file must *NOT* be checked into Version Control Systems,
# as it contains information specific to your local configuration.
#
# Location of the SDK. This is only used by Gradle.
# For customization when using a Version Control System, please read the
# header note.
#Mon Nov 25 17:01:52 CST 2019
sdk.dir=/Users/chenlingchi/Library/Android/sdk
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