diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 6c881969b76d907ca804b0e73a0dc913c56d2bee..377affbea0fc47f883f49bdafb540232dea06365 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -23,6 +23,7 @@ files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto|py)$ exclude: (?!.*third_party)^.*$ +- repo: local hooks: - id: clang-format-with-version-check name: clang-format @@ -31,6 +32,7 @@ language: system files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto)$ +- repo: local hooks: - id: cpplint-cpp-source name: cpplint diff --git a/deploy/cpp/include/paddlex/transforms.h b/deploy/cpp/include/paddlex/transforms.h index df8cff59afacbe313f1eec5bc9835a736442583e..f8265db447f693d084c5a789504bc4b0ccc14d28 100644 --- a/deploy/cpp/include/paddlex/transforms.h +++ b/deploy/cpp/include/paddlex/transforms.h @@ -83,7 +83,7 @@ class ResizeByShort : public Transform { } else { max_size_ = -1; } - }; + } virtual bool Run(cv::Mat* im, ImageBlob* data); private: @@ -96,7 +96,7 @@ class ResizeByLong : public Transform { public: virtual void Init(const YAML::Node& item) { long_size_ = item["long_size"].as(); - }; + } virtual bool Run(cv::Mat* im, ImageBlob* data); private: @@ -167,9 +167,6 @@ class Padding : public Transform { height_ = item["target_size"].as>()[1]; } } - if (item["im_padding_value"].IsDefined()) { - value_ = item["im_padding_value"].as>(); - } } virtual bool Run(cv::Mat* im, ImageBlob* data); @@ -177,7 +174,6 @@ class Padding : public Transform { int coarsest_stride_ = -1; int width_ = 0; int height_ = 0; - std::vector value_; }; class Transforms { diff --git a/docs/apis/transforms/cls_transforms.md b/docs/apis/transforms/cls_transforms.md index 9b762a79606f43d6672eeb0ea6d413621ff069bd..7d124b9bed4445eb7a216587cde8a35532f54a48 100755 --- a/docs/apis/transforms/cls_transforms.md +++ b/docs/apis/transforms/cls_transforms.md @@ -122,3 +122,64 @@ paddlex.cls.transforms.RandomDistort(brightness_range=0.9, brightness_prob=0.5, * **saturation_prob** (float): 随机调整饱和度的概率。默认为0.5。 * **hue_range** (int): 色调因子的范围。默认为18。 * **hue_prob** (float): 随机调整色调的概率。默认为0.5。 + +## ComposedClsTransforms类 +```python +paddlex.cls.transforms.ComposedClsTransforms(mode, crop_size=[224, 224], mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) +``` +分类模型中已经组合好的数据处理流程，开发者可以直接使用ComposedClsTransforms，简化手动组合transforms的过程, 该类中已经包含了[RandomCrop](#RandomCrop)和[RandomHorizontalFlip](#RandomHorizontalFlip)两种数据增强方式，你仍可以通过[add_augmenters函数接口](#add_augmenters)添加新的数据增强方式。 +ComposedClsTransforms共包括以下几个步骤： +> 训练阶段： +> > 1. 随机从图像中crop一块子图，并resize成crop_size大小 +> > 2. 将1的输出按0.5的概率随机进行水平翻转 +> > 3. 将图像进行归一化 +> 验证/预测阶段： +> > 1. 将图像按比例Resize，使得最小边长度为crop_size[0] * 1.14 +> > 2. 从图像中心crop出一个大小为crop_size的图像 +> > 3. 将图像进行归一化 + +### 参数 +* **mode** (str): Transforms所处的阶段，包括`train', 'eval'或'test' +* **crop_size** (int|list): 输入到模型里的图像大小，默认为[224, 224]（与原图大小无关，根据上述几个步骤，会将原图处理成该图大小输入给模型训练) +* **mean** (list): 图像均值, 默认为[0.485, 0.456, 0.406]。 +* **std** (list): 图像方差，默认为[0.229, 0.224, 0.225]。 + +### 添加数据增强方式 +```python +ComposedClsTransforms.add_augmenters(augmenters) +``` +> **参数** +> * **augmenters**(list): 数据增强方式列表 + +#### 使用示例 +``` +import paddlex as pdx +from paddlex.cls import transforms +train_transforms = transforms.ComposedClsTransforms(mode='train', crop_size=[320, 320]) +eval_transforms = transforms.ComposedClsTransforms(mode='eval', crop_size=[320, 320]) + +# 添加数据增强 +import imgaug.augmenters as iaa +train_transforms.add_augmenters([ + transforms.RandomDistort(), + iaa.blur.GaussianBlur(sigma=(0.0, 3.0)) +]) +``` +上面代码等价于 +``` +import paddlex as pdx +from paddlex.cls import transforms +train_transforms = transforms.Composed([ + transforms.RandomDistort(), + iaa.blur.GaussianBlur(sigma=(0.0, 3.0)), + # 上面两个为通过add_augmenters额外添加的数据增强方式 + transforms.RandomCrop(crop_size=320), + transforms.RandomHorizontalFlip(prob=0.5), + transforms.Normalize() +]) +eval_transforms = transforms.Composed([ + transforms.ResizeByShort(short_size=int(320*1.14)), + transforms.CenterCrop(crop_size=320), + transforms.Normalize() +]) +``` diff --git a/docs/apis/transforms/det_transforms.md b/docs/apis/transforms/det_transforms.md index 0ee6e57ee778769c0e363eaee9050b36d5f6eb5a..6d9c32815465ff02995dc2b1f80ff68d6bc08edb 100755 --- a/docs/apis/transforms/det_transforms.md +++ b/docs/apis/transforms/det_transforms.md @@ -167,3 +167,133 @@ paddlex.det.transforms.RandomCrop(aspect_ratio=[.5, 2.], thresholds=[.0, .1, .3, * **num_attempts** (int): 在放弃寻找有效裁剪区域前尝试的次数。默认值为50。 * **allow_no_crop** (bool): 是否允许未进行裁剪。默认值为True。 * **cover_all_box** (bool): 是否要求所有的真实标注框都必须在裁剪区域内。默认值为False。 + +## ComposedRCNNTransforms类 +```python +paddlex.det.transforms.ComposedRCNNTransforms(mode, min_max_size=[224, 224], mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) +``` +目标检测FasterRCNN和实例分割MaskRCNN模型中已经组合好的数据处理流程，开发者可以直接使用ComposedRCNNTransforms，简化手动组合transforms的过程, 该类中已经包含了[RandomHorizontalFlip](#RandomHorizontalFlip)数据增强方式，你仍可以通过[add_augmenters函数接口](#add_augmenters)添加新的数据增强方式。 +ComposedRCNNTransforms共包括以下几个步骤： +> 训练阶段： +> > 1. 随机以0.5的概率将图像水平翻转 +> > 2. 将图像进行归一化 +> > 3. 图像采用[ResizeByShort](#ResizeByShort)方式，根据min_max_size参数，进行缩入 +> > 4. 使用[Padding](#Padding)将图像的长和宽分别Padding成32的倍数 +> 验证/预测阶段： +> > 1. 将图像进行归一化 +> > 2. 图像采用[ResizeByShort](#ResizeByShort)方式，根据min_max_size参数，进行缩入 +> > 3. 使用[Padding](#Padding)将图像的长和宽分别Padding成32的倍数 + +### 参数 +* **mode** (str): Transforms所处的阶段，包括`train', 'eval'或'test' +* **min_max_size** (list): 输入模型中图像的最短边长度和最长边长度，参考[ResizeByShort](#ResizeByShort)（与原图大小无关，根据上述几个步骤，会将原图处理成相应大小输入给模型训练)，默认[800, 1333] +* **mean** (list): 图像均值, 默认为[0.485, 0.456, 0.406]。 +* **std** (list): 图像方差，默认为[0.229, 0.224, 0.225]。 + +### 添加数据增强方式 +```python +ComposedRCNNTransforms.add_augmenters(augmenters) +``` +> **参数** +> * **augmenters**(list): 数据增强方式列表 + +#### 使用示例 +``` +import paddlex as pdx +from paddlex.det import transforms +train_transforms = transforms.ComposedRCNNTransforms(mode='train', min_max_size=[800, 1333]) +eval_transforms = transforms.ComposedRCNNTransforms(mode='eval', min_max_size=[800, 1333]) + +# 添加数据增强 +import imgaug.augmenters as iaa +train_transforms.add_augmenters([ + transforms.RandomDistort(), + iaa.blur.GaussianBlur(sigma=(0.0, 3.0)) +]) +``` +上面代码等价于 +``` +import paddlex as pdx +from paddlex.det import transforms +train_transforms = transforms.Composed([ + transforms.RandomDistort(), + iaa.blur.GaussianBlur(sigma=(0.0, 3.0)), + # 上面两个为通过add_augmenters额外添加的数据增强方式 + transforms.RandomHorizontalFlip(prob=0.5), + transforms.Normalize(), + transforms.ResizeByShort(short_size=800, max_size=1333), + transforms.Padding(coarsest_stride=32) +]) +eval_transforms = transforms.Composed([ + transforms.Normalize(), + transforms.ResizeByShort(short_size=800, max_size=1333), + transforms.Padding(coarsest_stride=32) +]) +``` + + +## ComposedYOLOTransforms类 +```python +paddlex.det.transforms.ComposedYOLOTransforms(mode, shape=[608, 608], mixup_epoch=250, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) +``` +目标检测YOLOv3模型中已经组合好的数据处理流程，开发者可以直接使用ComposedYOLOTransforms，简化手动组合transforms的过程, 该类中已经包含了[MixupImage](#MixupImage)、[RandomDistort](#RandomDistort)、[RandomExpand](#RandomExpand)、[RandomCrop](#RandomCrop)、[RandomHorizontalFlip](#RandomHorizontalFlip)5种数据增强方式，你仍可以通过[add_augmenters函数接口](#add_augmenters)添加新的数据增强方式。 +ComposedYOLOTransforms共包括以下几个步骤： +> 训练阶段： +> > 1. 在前mixup_epoch轮迭代中，使用MixupImage策略 +> > 2. 对图像进行随机扰动，包括亮度，对比度，饱和度和色调 +> > 3. 随机扩充图像 +> > 4. 随机裁剪图像 +> > 5. 将4步骤的输出图像Resize成shape参数的大小 +> > 6. 随机0.5的概率水平翻转图像 +> > 7. 图像归一化 +> 验证/预测阶段： +> > 1. 将图像Resize成shape参数大小 +> > 2. 图像归一化 + +### 参数 +* **mode** (str): Transforms所处的阶段，包括`train', 'eval'或'test' +* **shape** (list): 输入模型中图像的大小（与原图大小无关，根据上述几个步骤，会将原图处理成相应大小输入给模型训练)， 默认[608, 608] +* **mixup_epoch**(int): 模型训练过程中，在前mixup_epoch轮迭代中，使用mixup策略，如果为-1，则不使用mixup策略， 默认250。 +* **mean** (list): 图像均值, 默认为[0.485, 0.456, 0.406]。 +* **std** (list): 图像方差，默认为[0.229, 0.224, 0.225]。 + +### 添加数据增强方式 +```python +ComposedYOLOTransforms.add_augmenters(augmenters) +``` +> **参数** +> * **augmenters**(list): 数据增强方式列表 + +#### 使用示例 +``` +import paddlex as pdx +from paddlex.det import transforms +train_transforms = transforms.ComposedYOLOTransforms(mode='train', shape=[480, 480]) +eval_transforms = transforms.ComposedYOLOTransforms(mode='eval', shape=[480, 480]) + +# 添加数据增强 +import imgaug.augmenters as iaa +train_transforms.add_augmenters([ + iaa.blur.GaussianBlur(sigma=(0.0, 3.0)) +]) +``` +上面代码等价于 +``` +import paddlex as pdx +from paddlex.det import transforms +train_transforms = transforms.Composed([ + iaa.blur.GaussianBlur(sigma=(0.0, 3.0)), + # 上面为通过add_augmenters额外添加的数据增强方式 + transforms.MixupImage(mixup_epoch=250), + transforms.RandomDistort(), + transforms.RandomExpand(), + transforms.RandomCrop(), + transforms.Resize(target_size=480, interp='RANDOM'), + transforms.RandomHorizontalFlip(prob=0.5), + transforms.Normalize() +]) +eval_transforms = transforms.Composed([ + transforms.Resize(target_size=480, interp='CUBIC'), + transforms.Normalize() +]) +``` diff --git a/docs/apis/transforms/seg_transforms.md b/docs/apis/transforms/seg_transforms.md index d95d8a4d9a35723b0f489fa972ed28fcadd1d211..1fb2b561e4818edad72fd97f43029de079b355b3 100755 --- a/docs/apis/transforms/seg_transforms.md +++ b/docs/apis/transforms/seg_transforms.md @@ -166,3 +166,63 @@ paddlex.seg.transforms.RandomDistort(brightness_range=0.5, brightness_prob=0.5, * **saturation_prob** (float): 随机调整饱和度的概率。默认为0.5。 * **hue_range** (int): 色调因子的范围。默认为18。 * **hue_prob** (float): 随机调整色调的概率。默认为0.5。 + +## ComposedSegTransforms类 +```python +paddlex.det.transforms.ComposedSegTransforms(mode, train_crop_shape=[769, 769], mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) +``` +语义分割DeepLab和UNet模型中已经组合好的数据处理流程，开发者可以直接使用ComposedSegTransforms，简化手动组合transforms的过程, 该类中已经包含了[RandomHorizontalFlip](#RandomHorizontalFlip)、[ResizeStepScaling](#ResizeStepScaling)、[RandomPaddingCrop](#RandomPaddingCrop)3种数据增强方式，你仍可以通过[add_augmenters函数接口](#add_augmenters)添加新的数据增强方式。 +ComposedSegTransforms共包括以下几个步骤： + > 训练阶段： +> > 1. 随机对图像以0.5的概率水平翻转 +> > 2. 按不同的比例随机Resize原图 +> > 3. 从原图中随机crop出大小为train_crop_size大小的子图，如若crop出来的图小于train_crop_size，则会将图padding到对应大小 +> > 4. 图像归一化 + > 预测阶段： +> > 1. 图像归一化 + + +### 参数 +* **mode** (str): Transforms所处的阶段，包括`train', 'eval'或'test' +* **train_crop_size** (list): 训练过程中随机Crop和Resize后（验证或预测过程中不需配置该参数，自动使用原图大小），输入到模型中图像的大小（与原图大小无关，根据上述几个步骤，会将原图处理成相应大小输入给模型训练)， 默认[769, 769] +* **mean** (list): 图像均值, 默认为[0.485, 0.456, 0.406]。 +* **std** (list): 图像方差，默认为[0.229, 0.224, 0.225]。 + +### 添加数据增强方式 +```python +ComposedSegTransforms.add_augmenters(augmenters) +``` +> **参数** +> * **augmenters**(list): 数据增强方式列表 + +#### 使用示例 +``` +import paddlex as pdx +from paddlex.seg import transforms +train_transforms = transforms.ComposedSegTransforms(mode='train', train_crop_size=[512, 512]) +eval_transforms = transforms.ComposedYOLOTransforms(mode='eval') + +# 添加数据增强 +import imgaug.augmenters as iaa +train_transforms.add_augmenters([ + transforms.RandomDistort(), + iaa.blur.GaussianBlur(sigma=(0.0, 3.0)) +]) +``` +上面代码等价于 +``` +import paddlex as pdx +from paddlex.det import transforms +train_transforms = transforms.Composed([ + transforms.RandomDistort(), + iaa.blur.GaussianBlur(sigma=(0.0, 3.0)), + # 上面2行为通过add_augmenters额外添加的数据增强方式 + transforms.RandomHorizontalFlip(prob=0.5), + transforms.ResizeStepScaling(), + transforms.PaddingCrop(crop_size=[512, 512]), + transforms.Normalize() +]) +eval_transforms = transforms.Composed([ + transforms.Normalize() +]) +``` diff --git a/docs/paddlex_gui/download.md b/docs/paddlex_gui/download.md index 102326977c57ba65c614abca52f65d8a63c80259..77bb9962b37498ec3279a51cdc1faa34da1f498b 100644 --- a/docs/paddlex_gui/download.md +++ b/docs/paddlex_gui/download.md @@ -1 +1,28 @@ -# PaddleX GUI下载安装 +## PaddleX GUI安装 + + PaddleX GUI是提升项目开发效率的核心模块，开发者可快速完成深度学习模型全流程开发。我们诚挚地邀请您前往 [官网](https://www.paddlepaddle.org.cn/paddle/paddleX)下载试用PaddleX GUI可视化前端，并获得您宝贵的意见或开源项目贡献。 + + + +#### 安装推荐环境 + +* **操作系统**： + * Windows7/8/10（推荐Windows 10）； + * Mac OS 10.13+； + * Ubuntu 18.04+； + +***注：处理器需为x86_64架构，支持MKL。*** + +* **训练硬件**： + * **GPU**（仅Windows及Linux系统）： + 推荐使用支持CUDA的NVIDIA显卡，例如：GTX 1070+以上性能的显卡； + Windows系统X86_64驱动版本>=411.31； + Linux系统X86_64驱动版本>=410.48； + 显存8G以上； + * **CPU**： + PaddleX当前支持您用本地CPU进行训练，但推荐使用GPU以获得更好的开发体验。 + * **内存**：建议8G以上 + * **硬盘空间**：建议SSD剩余空间1T以上（非必须） + +***注：PaddleX在Windows及Mac OS系统只支持单卡模型。Windows系统暂不支持NCCL。*** + diff --git a/docs/paddlex_gui/how_to_use.md b/docs/paddlex_gui/how_to_use.md index 1e9a3eeb69d276596636e814f48fad4fe9f3e9d0..32740c114242ccc2c6b7ecacc3088ba163fe7a3c 100644 --- a/docs/paddlex_gui/how_to_use.md +++ b/docs/paddlex_gui/how_to_use.md @@ -1 +1,148 @@ -# PaddleX GUI如何训练模型 +# PaddleX GUI使用文档 + +飞桨全流程开发工具，集飞桨核心框架、模型库、工具及组件等深度学习开发全流程所需能力于一身，易用易集成，是开发者快速入门深度学习、提升深度学习项目开发效率的最佳辅助工具。 + +PaddleX GUI是一个应用PaddleX实现的一个图形化开发客户端产品，它使得开发者通过键入式输入即可完成深度学习模型全流程开发，可大幅度提升项目开发效率。飞桨团队期待各位开发者基于PaddleX，实现出各种符合自己产业实际需求的产品。 + +我们诚挚地邀请您前往 [官网](https://www.paddlepaddle.org.cn/paddlex)下载试用PaddleX GUI，并获得您宝贵的意见或开源项目贡献。 + + + +## 目录 + +* **产品特性** +* **PaddleX GUI可视化前端** +* **FAQ** + + + +## 产品特性 + +\- **全流程打通** + +将深度学习开发全流程打通，并提供可视化开发界面， 省去了对各环节API的熟悉过程及重复的代码开发，极大地提升了开发效率。 + +\- **易用易集成** + +提供功能最全、最灵活的Python API开发模式，完全开源开放，易于集成和二次开发。键入式输入的图形化开发界面，使得非专业算法人员也可快速进行业务POC。 + +\- **融合产业实践经验** + +融合飞桨产业落地经验，精选高质量的视觉模型方案，开放实际的案例教学，手把手带您实现产业需求落地。 + +\- **教程与服务** + +从数据集准备到上线部署，为您提供业务开发全流程的文档说明及技术服务。开发者可以通过QQ群、微信群、GitHub社区等多种形式与飞桨团队及同业合作伙伴交流沟通。 + + + +## PaddleX GUI 可视化前端 + +**第一步：准备数据** + +在开始模型训练前，您需要根据不同的任务类型，将数据标注为相应的格式。目前PaddleX支持【图像分类】、【目标检测】、【语义分割】、【实例分割】四种任务类型。不同类型任务的数据处理方式可查看[数据标注方式](https://paddlex.readthedocs.io/zh_CN/latest/appendix/datasets.html)。 + + + +**第二步：导入我的数据集** + +①数据标注完成后，您需要根据不同的任务，将数据和标注文件，按照客户端提示更名并保存到正确的文件中。 + +②在客户端新建数据集，选择与数据集匹配的任务类型，并选择数据集对应的路径，将数据集导入。 + +![](images/datasets1.jpg) + +③选定导入数据集后，客户端会自动校验数据及标注文件是否合规，校验成功后，您可根据实际需求，将数据集按比例划分为训练集、验证集、测试集。 + +④您可在「数据分析」模块按规则预览您标注的数据集，双击单张图片可放大查看。 + +![](images/dataset2.jpg) + +**第三步：创建项目** + +① 在完成数据导入后，您可以点击「新建项目」创建一个项目。 + +② 您可根据实际任务需求选择项目的任务类型，需要注意项目所采用的数据集也带有任务类型属性，两者需要进行匹配。 + +![](images/project3.jpg) + + + +**第四步：项目开发** + +① **数据选择**：项目创建完成后，您需要选择已载入客户端并校验后的数据集，并点击下一步，进入参数配置页面。 + +![](images/project1.jpg) + +② **参数配置**：主要分为**模型参数**、**训练参数**、**优化策略**三部分。您可根据实际需求选择模型结构、骨架网络及对应的训练参数、优化策略，使得任务效果最佳。 + +![](images/project2.jpg) + +参数配置完成后，点击启动训练，模型开始训练并进行效果评估。 + +③ **训练可视化**：在训练过程中，您可通过VisualDL查看模型训练过程参数变化、日志详情，及当前最优的训练集和验证集训练指标。模型在训练过程中通过点击"中止训练"随时中止训练过程。 + +![](images/visualization1.jpg) + +模型训练结束后，可选择进入『模型剪裁分析』或者直接进入『模型评估』。 + +![](images/visualization2.jpg) + +④ **模型裁剪**：如果开发者希望减少模型的体积、计算量，提升模型在设备上的预测性能，可以采用PaddleX提供的模型裁剪策略。裁剪过程将对模型各卷积层的敏感度信息进行分析，根据各参数对模型效果的影响进行不同比例的裁剪，再进行精调训练获得最终裁剪后的模型。 + +![](images/visualization3.jpg) + +⑤ **模型评估**：在模型评估页面，您可查看训练后的模型效果。评估方法包括混淆矩阵、精度、召回率等。 + +![](images/visualization4.jpg) + +您还可以选择『数据集切分』时留出的『测试数据集』或从本地文件夹中导入一张/多张图片，将训练后的模型进行测试。根据测试结果，您可决定是否将训练完成的模型保存为预训练模型并进入模型发布页面，或返回先前步骤调整参数配置重新进行训练。 + +![](images/visualization5.jpg) + + + +**第五步：模型发布** + +当模型效果满意后，您可根据实际的生产环境需求，选择将模型发布为需要的版本。 + +![](images/publish.jpg) + + + +## FAQ + +1. **为什么训练速度这么慢？** + + PaddleX完全采用您本地的硬件进行计算，深度学习任务确实对算力要求较高，为了使您能快速体验应用PaddleX进行开发，我们适配了CPU硬件，但强烈建议您使用GPU以提升训练速度和开发体验。 + + + +2. **我可以在服务器或云平台上部署PaddleX么？** + + PaddleX GUI是一个适配本地单机安装的客户端，无法在服务器上直接进行部署，您可以直接使用PaddleX API，或采用飞桨核心框架进行服务器上的部署。如果您希望使用公有算力，强烈建议您尝试飞桨产品系列中的 [EasyDL](https://ai.baidu.com/easydl/) 或 [AI Studio](https://aistudio.baidu.com/aistudio/index)进行开发。 + + + +3. **PaddleX支持EasyData标注的数据吗？** + + 支持，PaddleX可顺畅读取EasyData标注的数据。但当前版本的PaddleX GUI暂时无法支持直接导入EasyData数据格式，您可以参照文档，将[数据集进行转换](https://paddlex.readthedocs.io/zh_CN/latest/appendix/how_to_convert_dataset.html)再导入PaddleX GUI进行后续开发。 + 同时，我们也在紧密开发PaddleX GUI可直接导入EasyData数据格式的功能。 + + + +4. **为什么模型裁剪分析耗时这么长？** + + 模型裁剪分析过程是对模型各卷积层的敏感度信息进行分析，根据各参数对模型效果的影响进行不同比例的裁剪。此过程需要重复多次直至FLOPS满足要求，最后再进行精调训练获得最终裁剪后的模型，因此耗时较长。有关模型裁剪的原理，可参见文档[剪裁原理介绍](https://paddlepaddle.github.io/PaddleSlim/algo/algo.html#2-%E5%8D%B7%E7%A7%AF%E6%A0%B8%E5%89%AA%E8%A3%81%E5%8E%9F%E7%90%86) + + + +5. **如何调用后端代码？** + + PaddleX 团队为您整理了相关的API接口文档，方便您学习和使用。具体请参见[PaddleX API说明文档](https://paddlex.readthedocs.io/zh_CN/latest/apis/index.html) + + + +**如果您有更多问题或建议，欢迎以issue的形式，或加入PaddleX官方QQ群（1045148026）直接反馈您的问题和需求** + +![](images/QR.jpg) diff --git a/docs/paddlex_gui/images/QR.jpg b/docs/paddlex_gui/images/QR.jpg new file mode 100644 index 0000000000000000000000000000000000000000..99da2ac887ea9c29e1ee18a79f71bb302422a029 Binary files /dev/null and b/docs/paddlex_gui/images/QR.jpg differ diff --git a/docs/paddlex_gui/images/ReadMe b/docs/paddlex_gui/images/ReadMe new file mode 100644 index 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0000000000000000000000000000000000000000..1900143bceb3435da8ffa04a7fed7b0205e04477 --- /dev/null +++ b/new_tutorials/train/README.md @@ -0,0 +1,18 @@ +# 使用教程——训练模型 + +本目录下整理了使用PaddleX训练模型的示例代码，代码中均提供了示例数据的自动下载，并均使用单张GPU卡进行训练。 + +|代码 | 模型任务 | 数据 | +|------|--------|---------| +|classification/mobilenetv2.py | 图像分类MobileNetV2 | 蔬菜分类 | +|classification/resnet50.py | 图像分类ResNet50 | 蔬菜分类 | +|detection/faster_rcnn_r50_fpn.py | 目标检测FasterRCNN | 昆虫检测 | +|detection/mask_rcnn_f50_fpn.py | 实例分割MaskRCNN | 垃圾分拣 | +|segmentation/deeplabv3p.py | 语义分割DeepLabV3| 视盘分割 | +|segmentation/unet.py | 语义分割UNet | 视盘分割 | + +## 开始训练 +在安装PaddleX后，使用如下命令开始训练 +``` +python classification/mobilenetv2.py +``` diff --git a/new_tutorials/train/classification/mobilenetv2.py b/new_tutorials/train/classification/mobilenetv2.py new file mode 100644 index 0000000000000000000000000000000000000000..3f637125b760de6d992d6a062e4d456bf5038426 --- /dev/null +++ b/new_tutorials/train/classification/mobilenetv2.py @@ -0,0 +1,51 @@ +import os +# 选择使用0号卡 +os.environ['CUDA_VISIBLE_DEVICES'] = '0' + +from paddlex.cls import transforms +import paddlex as pdx + +# 下载和解压蔬菜分类数据集 +veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz' +pdx.utils.download_and_decompress(veg_dataset, path='./') + +# 定义训练和验证时的transforms +train_transforms = transforms.Compose([ + transforms.RandomCrop(crop_size=224), + transforms.RandomHorizontalFlip(), + transforms.Normalize() +]) +eval_transforms = transforms.Compose([ + transforms.ResizeByShort(short_size=256), + transforms.CenterCrop(crop_size=224), + transforms.Normalize() +]) + +# 定义训练和验证所用的数据集 +train_dataset = pdx.datasets.ImageNet( + data_dir='vegetables_cls', + file_list='vegetables_cls/train_list.txt', + label_list='vegetables_cls/labels.txt', + transforms=train_transforms, + shuffle=True) +eval_dataset = pdx.datasets.ImageNet( + data_dir='vegetables_cls', + file_list='vegetables_cls/val_list.txt', + label_list='vegetables_cls/labels.txt', + transforms=eval_transforms) + +# 初始化模型，并进行训练 +# 可使用VisualDL查看训练指标 +# VisualDL启动方式: visualdl --logdir output/mobilenetv2/vdl_log --port 8001 +# 浏览器打开 https://0.0.0.0:8001即可 +# 其中0.0.0.0为本机访问，如为远程服务, 改成相应机器IP +model = pdx.cls.MobileNetV2(num_classes=len(train_dataset.labels)) +model.train( + num_epochs=10, + train_dataset=train_dataset, + train_batch_size=32, + eval_dataset=eval_dataset, + lr_decay_epochs=[4, 6, 8], + learning_rate=0.025, + save_dir='output/mobilenetv2', + use_vdl=True) diff --git a/new_tutorials/train/classification/resnet50.py b/new_tutorials/train/classification/resnet50.py new file mode 100644 index 0000000000000000000000000000000000000000..2e5a9b4820c7e66a83abaca0b13e057b15ceb830 --- /dev/null +++ b/new_tutorials/train/classification/resnet50.py @@ -0,0 +1,58 @@ +import os +# 选择使用0号卡 +os.environ['CUDA_VISIBLE_DEVICES'] = '0' + +import paddle.fluid as fluid +from paddlex.cls import transforms +import paddlex as pdx + +# 下载和解压蔬菜分类数据集 +veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz' +pdx.utils.download_and_decompress(veg_dataset, path='./') + +# 定义训练和验证时的transforms +train_transforms = transforms.Compose( + [transforms.RandomCrop(crop_size=224), + transforms.Normalize()]) +eval_transforms = transforms.Compose([ + transforms.ResizeByShort(short_size=256), + transforms.CenterCrop(crop_size=224), + transforms.Normalize() +]) + +# 定义训练和验证所用的数据集 +train_dataset = pdx.datasets.ImageNet( + data_dir='vegetables_cls', + file_list='vegetables_cls/train_list.txt', + label_list='vegetables_cls/labels.txt', + transforms=train_transforms, + shuffle=True) +eval_dataset = pdx.datasets.ImageNet( + data_dir='vegetables_cls', + file_list='vegetables_cls/val_list.txt', + label_list='vegetables_cls/labels.txt', + transforms=eval_transforms) + +# PaddleX支持自定义构建优化器 +step_each_epoch = train_dataset.num_samples // 32 +learning_rate = fluid.layers.cosine_decay( + learning_rate=0.025, step_each_epoch=step_each_epoch, epochs=10) +optimizer = fluid.optimizer.Momentum( + learning_rate=learning_rate, + momentum=0.9, + regularization=fluid.regularizer.L2Decay(4e-5)) + +# 初始化模型，并进行训练 +# 可使用VisualDL查看训练指标 +# VisualDL启动方式: visualdl --logdir output/resnet50/vdl_log --port 8001 +# 浏览器打开 https://0.0.0.0:8001即可 +# 其中0.0.0.0为本机访问，如为远程服务, 改成相应机器IP +model = pdx.cls.ResNet50(num_classes=len(train_dataset.labels)) +model.train( + num_epochs=10, + train_dataset=train_dataset, + train_batch_size=32, + eval_dataset=eval_dataset, + optimizer=optimizer, + save_dir='output/resnet50', + use_vdl=True) diff --git a/new_tutorials/train/detection/faster_rcnn_r50_fpn.py b/new_tutorials/train/detection/faster_rcnn_r50_fpn.py new file mode 100644 index 0000000000000000000000000000000000000000..cbe6dabe535b5972418349ac31576b344652e69d --- /dev/null +++ b/new_tutorials/train/detection/faster_rcnn_r50_fpn.py @@ -0,0 +1,55 @@ +import os +# 选择使用0号卡 +os.environ['CUDA_VISIBLE_DEVICES'] = '0' + +from paddlex.det import transforms +import paddlex as pdx + +# 下载和解压昆虫检测数据集 +insect_dataset = 'https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz' +pdx.utils.download_and_decompress(insect_dataset, path='./') + +# 定义训练和验证时的transforms +train_transforms = transforms.Compose([ + transforms.RandomHorizontalFlip(), + transforms.Normalize(), + transforms.ResizeByShort(short_size=800, max_size=1333), + transforms.Padding(coarsest_stride=32) +]) + +eval_transforms = transforms.Compose([ + transforms.Normalize(), + transforms.ResizeByShort(short_size=800, max_size=1333), + transforms.Padding(coarsest_stride=32), +]) + +# 定义训练和验证所用的数据集 +train_dataset = pdx.datasets.VOCDetection( + data_dir='insect_det', + file_list='insect_det/train_list.txt', + label_list='insect_det/labels.txt', + transforms=train_transforms, + shuffle=True) +eval_dataset = pdx.datasets.VOCDetection( + data_dir='insect_det', + file_list='insect_det/val_list.txt', + label_list='insect_det/labels.txt', + transforms=eval_transforms) + +# 初始化模型，并进行训练 +# 可使用VisualDL查看训练指标 +# VisualDL启动方式: visualdl --logdir output/faster_rcnn_r50_fpn/vdl_log --port 8001 +# 浏览器打开 https://0.0.0.0:8001即可 +# 其中0.0.0.0为本机访问，如为远程服务, 改成相应机器IP +# num_classes 需要设置为包含背景类的类别数，即: 目标类别数量 + 1 +num_classes = len(train_dataset.labels) + 1 +model = pdx.det.FasterRCNN(num_classes=num_classes) +model.train( + num_epochs=12, + train_dataset=train_dataset, + train_batch_size=2, + eval_dataset=eval_dataset, + learning_rate=0.0025, + lr_decay_epochs=[8, 11], + save_dir='output/faster_rcnn_r50_fpn', + use_vdl=True) diff --git a/new_tutorials/train/detection/mask_rcnn_r50_fpn.py b/new_tutorials/train/detection/mask_rcnn_r50_fpn.py new file mode 100644 index 0000000000000000000000000000000000000000..15a6b840528fe7948c80f4cf605498cf55b5c918 --- /dev/null +++ b/new_tutorials/train/detection/mask_rcnn_r50_fpn.py @@ -0,0 +1,54 @@ +import os +# 选择使用0号卡 +os.environ['CUDA_VISIBLE_DEVICES'] = '0' + +from paddlex.det import transforms +import paddlex as pdx + +# 下载和解压小度熊分拣数据集 +xiaoduxiong_dataset = 'https://bj.bcebos.com/paddlex/datasets/xiaoduxiong_ins_det.tar.gz' +pdx.utils.download_and_decompress(xiaoduxiong_dataset, path='./') + +# 定义训练和验证时的transforms +train_transforms = transforms.Compose([ + transforms.RandomHorizontalFlip(), + transforms.Normalize(), + transforms.ResizeByShort(short_size=800, max_size=1333), + transforms.Padding(coarsest_stride=32) +]) + +eval_transforms = transforms.Compose([ + transforms.Normalize(), + transforms.ResizeByShort(short_size=800, max_size=1333), + transforms.Padding(coarsest_stride=32) +]) + +# 定义训练和验证所用的数据集 +train_dataset = pdx.datasets.CocoDetection( + data_dir='xiaoduxiong_ins_det/JPEGImages', + ann_file='xiaoduxiong_ins_det/train.json', + transforms=train_transforms, + shuffle=True) +eval_dataset = pdx.datasets.CocoDetection( + data_dir='xiaoduxiong_ins_det/JPEGImages', + ann_file='xiaoduxiong_ins_det/val.json', + transforms=eval_transforms) + +# 初始化模型，并进行训练 +# 可使用VisualDL查看训练指标 +# VisualDL启动方式: visualdl --logdir output/mask_rcnn_r50_fpn/vdl_log --port 8001 +# 浏览器打开 https://0.0.0.0:8001即可 +# 其中0.0.0.0为本机访问，如为远程服务, 改成相应机器IP +# num_classes 需要设置为包含背景类的类别数，即: 目标类别数量 + 1 +num_classes = len(train_dataset.labels) + 1 +model = pdx.det.MaskRCNN(num_classes=num_classes) +model.train( + num_epochs=12, + train_dataset=train_dataset, + train_batch_size=1, + eval_dataset=eval_dataset, + learning_rate=0.00125, + warmup_steps=10, + lr_decay_epochs=[8, 11], + save_dir='output/mask_rcnn_r50_fpn', + use_vdl=True) diff --git a/new_tutorials/train/detection/yolov3_darknet53.py b/new_tutorials/train/detection/yolov3_darknet53.py new file mode 100644 index 0000000000000000000000000000000000000000..c38656b04e9a35cd033dc583811c58aa8baafba2 --- /dev/null +++ b/new_tutorials/train/detection/yolov3_darknet53.py @@ -0,0 +1,56 @@ +import os +# 选择使用0号卡 +os.environ['CUDA_VISIBLE_DEVICES'] = '0' + +from paddlex.det import transforms +import paddlex as pdx + +# 下载和解压昆虫检测数据集 +insect_dataset = 'https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz' +pdx.utils.download_and_decompress(insect_dataset, path='./') + +# 定义训练和验证时的transforms +train_transforms = transforms.Compose([ + transforms.MixupImage(mixup_epoch=250), + transforms.RandomDistort(), + transforms.RandomExpand(), + transforms.RandomCrop(), + transforms.Resize(target_size=608, interp='RANDOM'), + transforms.RandomHorizontalFlip(), + transforms.Normalize(), +]) + +eval_transforms = transforms.Compose([ + transforms.Resize(target_size=608, interp='CUBIC'), + transforms.Normalize(), +]) + +# 定义训练和验证所用的数据集 +train_dataset = pdx.datasets.VOCDetection( + data_dir='insect_det', + file_list='insect_det/train_list.txt', + label_list='insect_det/labels.txt', + transforms=train_transforms, + shuffle=True) +eval_dataset = pdx.datasets.VOCDetection( + data_dir='insect_det', + file_list='insect_det/val_list.txt', + label_list='insect_det/labels.txt', + transforms=eval_transforms) + +# 初始化模型，并进行训练 +# 可使用VisualDL查看训练指标 +# VisualDL启动方式: visualdl --logdir output/yolov3_darknet/vdl_log --port 8001 +# 浏览器打开 https://0.0.0.0:8001即可 +# 其中0.0.0.0为本机访问，如为远程服务, 改成相应机器IP +num_classes = len(train_dataset.labels) +model = pdx.det.YOLOv3(num_classes=num_classes, backbone='DarkNet53') +model.train( + num_epochs=270, + train_dataset=train_dataset, + train_batch_size=8, + eval_dataset=eval_dataset, + learning_rate=0.000125, + lr_decay_epochs=[210, 240], + save_dir='output/yolov3_darknet53', + use_vdl=True) diff --git a/new_tutorials/train/segmentation/deeplabv3p.py b/new_tutorials/train/segmentation/deeplabv3p.py new file mode 100644 index 0000000000000000000000000000000000000000..346a229a358a76830112acfd596740c070822874 --- /dev/null +++ b/new_tutorials/train/segmentation/deeplabv3p.py @@ -0,0 +1,50 @@ +import os +# 选择使用0号卡 +os.environ['CUDA_VISIBLE_DEVICES'] = '0' + +import paddlex as pdx +from paddlex.seg import transforms + +# 下载和解压视盘分割数据集 +optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz' +pdx.utils.download_and_decompress(optic_dataset, path='./') + +# 定义训练和验证时的transforms +train_transforms = transforms.Compose([ + transforms.RandomHorizontalFlip(), + transforms.Resize(target_size=512), + transforms.RandomPaddingCrop(crop_size=500), + transforms.Normalize() +]) + +eval_transforms = transforms.Compose( + [transforms.Resize(512), transforms.Normalize()]) + +# 定义训练和验证所用的数据集 +train_dataset = pdx.datasets.SegDataset( + data_dir='optic_disc_seg', + file_list='optic_disc_seg/train_list.txt', + label_list='optic_disc_seg/labels.txt', + transforms=train_transforms, + shuffle=True) +eval_dataset = pdx.datasets.SegDataset( + data_dir='optic_disc_seg', + file_list='optic_disc_seg/val_list.txt', + label_list='optic_disc_seg/labels.txt', + transforms=eval_transforms) + +# 初始化模型，并进行训练 +# 可使用VisualDL查看训练指标 +# VisualDL启动方式: visualdl --logdir output/deeplab/vdl_log --port 8001 +# 浏览器打开 https://0.0.0.0:8001即可 +# 其中0.0.0.0为本机访问，如为远程服务, 改成相应机器IP +num_classes = len(train_dataset.labels) +model = pdx.seg.DeepLabv3p(num_classes=num_classes) +model.train( + num_epochs=40, + train_dataset=train_dataset, + train_batch_size=4, + eval_dataset=eval_dataset, + learning_rate=0.01, + save_dir='output/deeplab', + use_vdl=True) diff --git a/new_tutorials/train/segmentation/hrnet.py b/new_tutorials/train/segmentation/hrnet.py new file mode 100644 index 0000000000000000000000000000000000000000..f887b78c3ae16ae66235f1965ada8bd2355d62c6 --- /dev/null +++ b/new_tutorials/train/segmentation/hrnet.py @@ -0,0 +1,50 @@ +import os +# 选择使用0号卡 +os.environ['CUDA_VISIBLE_DEVICES'] = '0' + +import paddlex as pdx +from paddlex.seg import transforms + +# 下载和解压视盘分割数据集 +optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz' +pdx.utils.download_and_decompress(optic_dataset, path='./') + +# 定义训练和验证时的transforms +train_transforms = transforms.Compose([ + transforms.RandomHorizontalFlip(), transforms.ResizeRangeScaling(), + transforms.RandomPaddingCrop(crop_size=512), transforms.Normalize() +]) + +eval_transforms = transforms.Compose([ + transforms.ResizeByLong(long_size=512), + transforms.Padding(target_size=512), transforms.Normalize() +]) + +# 定义训练和验证所用的数据集 +train_dataset = pdx.datasets.SegDataset( + data_dir='optic_disc_seg', + file_list='optic_disc_seg/train_list.txt', + label_list='optic_disc_seg/labels.txt', + transforms=train_transforms, + shuffle=True) +eval_dataset = pdx.datasets.SegDataset( + data_dir='optic_disc_seg', + file_list='optic_disc_seg/val_list.txt', + label_list='optic_disc_seg/labels.txt', + transforms=eval_transforms) + +# 初始化模型，并进行训练 +# 可使用VisualDL查看训练指标 +# VisualDL启动方式: visualdl --logdir output/unet/vdl_log --port 8001 +# 浏览器打开 https://0.0.0.0:8001即可 +# 其中0.0.0.0为本机访问，如为远程服务, 改成相应机器IP +num_classes = len(train_dataset.labels) +model = pdx.seg.HRNet(num_classes=num_classes) +model.train( + num_epochs=20, + train_dataset=train_dataset, + train_batch_size=4, + eval_dataset=eval_dataset, + learning_rate=0.01, + save_dir='output/hrnet', + use_vdl=True) diff --git a/new_tutorials/train/segmentation/unet.py b/new_tutorials/train/segmentation/unet.py new file mode 100644 index 0000000000000000000000000000000000000000..a683af98322eacb9d0775b3a5256d900f5743bb2 --- /dev/null +++ b/new_tutorials/train/segmentation/unet.py @@ -0,0 +1,53 @@ +import os +# 选择使用0号卡 +os.environ['CUDA_VISIBLE_DEVICES'] = '0' + +import paddlex as pdx +from paddlex.seg import transforms + +# 下载和解压视盘分割数据集 +optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz' +pdx.utils.download_and_decompress(optic_dataset, path='./') + +# 定义训练和验证时的transforms +train_transforms = transforms.Compose([ + transforms.RandomHorizontalFlip(), + transforms.ResizeRangeScaling(), + transforms.RandomPaddingCrop(crop_size=512), + transforms.Normalize() +]) + +eval_transforms = transforms.Compose([ + transforms.ResizeByLong(long_size=512), + transforms.Padding(target_size=512), + transforms.Normalize() +]) + +# 定义训练和验证所用的数据集 +train_dataset = pdx.datasets.SegDataset( + data_dir='optic_disc_seg', + file_list='optic_disc_seg/train_list.txt', + label_list='optic_disc_seg/labels.txt', + transforms=train_transforms, + shuffle=True) +eval_dataset = pdx.datasets.SegDataset( + data_dir='optic_disc_seg', + file_list='optic_disc_seg/val_list.txt', + label_list='optic_disc_seg/labels.txt', + transforms=eval_transforms) + +# 初始化模型，并进行训练 +# 可使用VisualDL查看训练指标 +# VisualDL启动方式: visualdl --logdir output/unet/vdl_log --port 8001 +# 浏览器打开 https://0.0.0.0:8001即可 +# 其中0.0.0.0为本机访问，如为远程服务, 改成相应机器IP +num_classes = len(train_dataset.labels) +model = pdx.seg.UNet(num_classes=num_classes) +model.train( + num_epochs=20, + train_dataset=train_dataset, + train_batch_size=4, + eval_dataset=eval_dataset, + learning_rate=0.01, + save_dir='output/unet', + use_vdl=True) diff --git a/paddlex/__init__.py b/paddlex/__init__.py index 972210bdb80c445e59d4a8ed10418ee988bd353c..d1656161a0d764c0a7fbd125f246d0e43125bcda 100644 --- a/paddlex/__init__.py +++ b/paddlex/__init__.py @@ -53,4 +53,4 @@ log_level = 2 from . import interpret -__version__ = '1.0.4' +__version__ = '1.0.5' diff --git a/paddlex/cv/models/classifier.py b/paddlex/cv/models/classifier.py index 3b90fdcca16deba85656ff2b478129ce52ae795a..ab746ddf7ef7af2baf3da951879f2fdab5b4b8e4 100644 --- a/paddlex/cv/models/classifier.py +++ b/paddlex/cv/models/classifier.py @@ -264,7 +264,8 @@ class BaseClassifier(BaseAPI): im = self.test_transforms(img_file) result = self.exe.run(self.test_prog, feed={'image': im}, - fetch_list=list(self.test_outputs.values())) + fetch_list=list(self.test_outputs.values()), + use_program_cache=True) pred_label = np.argsort(result[0][0])[::-1][:true_topk] res = [{ 'category_id': l, diff --git a/paddlex/cv/models/deeplabv3p.py b/paddlex/cv/models/deeplabv3p.py index 3127bd8549ae221f7f7604613bba2e1437b93605..df8fac603de78097ca24d0410712a68c98f37eae 100644 --- a/paddlex/cv/models/deeplabv3p.py +++ b/paddlex/cv/models/deeplabv3p.py @@ -398,7 +398,8 @@ class DeepLabv3p(BaseAPI): im = np.expand_dims(im, axis=0) result = self.exe.run(self.test_prog, feed={'image': im}, - fetch_list=list(self.test_outputs.values())) + fetch_list=list(self.test_outputs.values()), + use_program_cache=True) pred = result[0] pred = np.squeeze(pred).astype('uint8') logit = result[1] diff --git a/paddlex/cv/models/faster_rcnn.py b/paddlex/cv/models/faster_rcnn.py index 2c2acdd149d1157edfa5a485108698808b4a9c84..29588a6f5f6de6d232b6e6d7d887a211a755a15b 100644 --- a/paddlex/cv/models/faster_rcnn.py +++ b/paddlex/cv/models/faster_rcnn.py @@ -389,7 +389,8 @@ class FasterRCNN(BaseAPI): 'im_shape': im_shape }, fetch_list=list(self.test_outputs.values()), - return_numpy=False) + return_numpy=False, + use_program_cache=True) res = { k: (np.array(v), v.recursive_sequence_lengths()) for k, v in zip(list(self.test_outputs.keys()), outputs) diff --git a/paddlex/cv/models/mask_rcnn.py b/paddlex/cv/models/mask_rcnn.py index dab9c8c532eed5d5a0fc9842ae9d33be7101c202..53396589e7b3cbd16a838659ea9ff3649aa0aefb 100644 --- a/paddlex/cv/models/mask_rcnn.py +++ b/paddlex/cv/models/mask_rcnn.py @@ -357,7 +357,8 @@ class MaskRCNN(FasterRCNN): 'im_shape': im_shape }, fetch_list=list(self.test_outputs.values()), - return_numpy=False) + return_numpy=False, + use_program_cache=True) res = { k: (np.array(v), v.recursive_sequence_lengths()) for k, v in zip(list(self.test_outputs.keys()), outputs) diff --git a/paddlex/cv/models/slim/prune.py b/paddlex/cv/models/slim/prune.py index 810679d3d7cf70a14922a594af3468294f12d29c..41bc2d62e152aaa362490e527655bab4bbcca0f6 100644 --- a/paddlex/cv/models/slim/prune.py +++ b/paddlex/cv/models/slim/prune.py @@ -66,16 +66,15 @@ def sensitivity(program, progress = "%.2f%%" % (progress * 100) logging.info( "Total evaluate iters={}, current={}, progress={}, eta={}". - format( - total_evaluate_iters, current_iter, progress, - seconds_to_hms( - int(cost * (total_evaluate_iters - current_iter)))), + format(total_evaluate_iters, current_iter, progress, + seconds_to_hms( + int(cost * (total_evaluate_iters - current_iter)))), use_color=True) current_iter += 1 pruner = Pruner() - logging.info("sensitive - param: {}; ratios: {}".format( - name, ratio)) + logging.info("sensitive - param: {}; ratios: {}".format(name, + ratio)) pruned_program, param_backup, _ = pruner.prune( program=graph.program, scope=scope, @@ -87,8 +86,8 @@ def sensitivity(program, param_backup=True) pruned_metric = eval_func(pruned_program) loss = (baseline - pruned_metric) / baseline - logging.info("pruned param: {}; {}; loss={}".format( - name, ratio, loss)) + logging.info("pruned param: {}; {}; loss={}".format(name, ratio, + loss)) sensitivities[name][ratio] = loss @@ -221,6 +220,9 @@ def cal_params_sensitivities(model, save_file, eval_dataset, batch_size=8): 其中``weight_0``是卷积Kernel名；``sensitivities['weight_0']``是一个字典，key是裁剪率，value是敏感度。 """ + if os.path.exists(save_file): + os.remove(save_file) + prune_names = get_prune_params(model) def eval_for_prune(program): @@ -264,8 +266,8 @@ def get_params_ratios(sensitivities_file, eval_metric_loss=0.05): if not osp.exists(sensitivities_file): raise Exception('The sensitivities file is not exists!') sensitivitives = paddleslim.prune.load_sensitivities(sensitivities_file) - params_ratios = paddleslim.prune.get_ratios_by_loss( - sensitivitives, eval_metric_loss) + params_ratios = paddleslim.prune.get_ratios_by_loss(sensitivitives, + eval_metric_loss) return params_ratios diff --git a/paddlex/cv/models/yolo_v3.py b/paddlex/cv/models/yolo_v3.py index 9646c81272e22bccf1390f4a738d13c41cf5a445..ac8f1f3d0c42a1f0314c22d0f6ca5fdbcbf30fb6 100644 --- a/paddlex/cv/models/yolo_v3.py +++ b/paddlex/cv/models/yolo_v3.py @@ -363,7 +363,8 @@ class YOLOv3(BaseAPI): feed={'image': im, 'im_size': im_size}, fetch_list=list(self.test_outputs.values()), - return_numpy=False) + return_numpy=False, + use_program_cache=True) res = { k: (np.array(v), v.recursive_sequence_lengths()) for k, v in zip(list(self.test_outputs.keys()), outputs) diff --git a/paddlex/cv/transforms/cls_transforms.py b/paddlex/cv/transforms/cls_transforms.py index 6dc4ea7b95d876ae896c77395ab155bec1727a8a..dbcd34222daf71c05c8f26a2a38c94faacb526f2 100644 --- a/paddlex/cv/transforms/cls_transforms.py +++ b/paddlex/cv/transforms/cls_transforms.py @@ -18,6 +18,7 @@ import random import os.path as osp import numpy as np from PIL import Image, ImageEnhance +import paddlex.utils.logging as logging class ClsTransform: @@ -96,7 +97,11 @@ class Compose(ClsTransform): if not isinstance(augmenters, list): raise Exception( "augmenters should be list type in func add_augmenters()") - self.transforms = augmenters + self.transforms.transforms + transform_names = [type(x).__name__ for x in self.transforms] + for aug in augmenters: + if type(aug).__name__ in transform_names: + logging.error("{} is already in ComposedTransforms, need to remove it from add_augmenters().".format(type(aug).__name__)) + self.transforms = augmenters + self.transforms class RandomCrop(ClsTransform): diff --git a/paddlex/cv/transforms/det_transforms.py b/paddlex/cv/transforms/det_transforms.py index 19db33173b87b7cc20b87054cfbc1241176abc58..6ec3570c192fd3fa6bc174a9f601b250c3b2b651 100644 --- a/paddlex/cv/transforms/det_transforms.py +++ b/paddlex/cv/transforms/det_transforms.py @@ -27,6 +27,7 @@ from PIL import Image, ImageEnhance from .imgaug_support import execute_imgaug from .ops import * from .box_utils import * +import paddlex.utils.logging as logging class DetTransform: @@ -156,7 +157,11 @@ class Compose(DetTransform): if not isinstance(augmenters, list): raise Exception( "augmenters should be list type in func add_augmenters()") - self.transforms = augmenters + self.transforms.transforms + transform_names = [type(x).__name__ for x in self.transforms] + for aug in augmenters: + if type(aug).__name__ in transform_names: + logging.error("{} is already in ComposedTransforms, need to remove it from add_augmenters().".format(type(aug).__name__)) + self.transforms = augmenters + self.transforms class ResizeByShort(DetTransform): diff --git a/paddlex/cv/transforms/seg_transforms.py b/paddlex/cv/transforms/seg_transforms.py index d3c67648d500d915315c5607cfc5c2f5538a9090..9ea1c3bdc2159dbc1f33ac5f15dc710e12ccb83c 100644 --- a/paddlex/cv/transforms/seg_transforms.py +++ b/paddlex/cv/transforms/seg_transforms.py @@ -21,6 +21,7 @@ import numpy as np from PIL import Image import cv2 from collections import OrderedDict +import paddlex.utils.logging as logging class SegTransform: @@ -112,7 +113,11 @@ class Compose(SegTransform): if not isinstance(augmenters, list): raise Exception( "augmenters should be list type in func add_augmenters()") - self.transforms = augmenters + self.transforms.transforms + transform_names = [type(x).__name__ for x in self.transforms] + for aug in augmenters: + if type(aug).__name__ in transform_names: + logging.error("{} is already in ComposedTransforms, need to remove it from add_augmenters().".format(type(aug).__name__)) + self.transforms = augmenters + self.transforms class RandomHorizontalFlip(SegTransform): @@ -1127,6 +1132,6 @@ class ComposedSegTransforms(Compose): ] else: # 验证/预测时的transforms - transforms = [Resize(512), Normalize(mean=mean, std=std)] + transforms = [Normalize(mean=mean, std=std)] super(ComposedSegTransforms, self).__init__(transforms) diff --git a/paddlex/slim.py b/paddlex/slim.py index 57fc104d75307ac13ead57d12717490eb8154acf..407119dc624b9d74807cb9215e00eb3144b7093f 100644 --- a/paddlex/slim.py +++ b/paddlex/slim.py @@ -31,4 +31,4 @@ def export_quant_model(model, batch_size=batch_size, batch_num=batch_num, save_dir=save_dir, - cache_dir='./temp') + cache_dir=cache_dir) diff --git a/paddlex/utils/logging.py b/paddlex/utils/logging.py index e5deb7388459f1052fd90d758d09aad759592ec8..c118a28782528b727bf9af4591d07714cddae6ae 100644 --- a/paddlex/utils/logging.py +++ b/paddlex/utils/logging.py @@ -47,9 +47,10 @@ def info(message="", use_color=False): log(level=2, message=message, use_color=use_color) -def warning(message="", use_color=False): +def warning(message="", use_color=True): log(level=1, message=message, use_color=use_color) -def error(message="", use_color=False): +def error(message="", use_color=True): log(level=0, message=message, use_color=use_color) + sys.exit(-1) diff --git a/setup.py b/setup.py index bba199719ce65075f8a61b965bca49f026406c91..db62ca5e9e8107f2f32e804a0e92fb48766d3c27 100644 --- a/setup.py +++ b/setup.py @@ -19,7 +19,7 @@ long_description = "PaddleX. A end-to-end deeplearning model development toolkit setuptools.setup( name="paddlex", - version='1.0.4', + version='1.0.5', author="paddlex", author_email="paddlex@baidu.com", description=long_description,