From ee31e8197f729201a1cbd31dc116ed1eb0bc21ea Mon Sep 17 00:00:00 2001 From: FlyingQianMM <245467267@qq.com> Date: Tue, 1 Sep 2020 17:27:40 +0800 Subject: [PATCH] delete thread_num in overlap_tile_predict --- docs/apis/models/semantic_segmentation.md | 31 +++++ docs/examples/index.rst | 1 + .../multi-channel_remote_sensing/README.md | 2 +- docs/examples/remote_sensing.md | 82 ++++++++++++++ docs/examples/remote_sensing/index.rst | 5 - .../multi-channel_remote_sensing/README.md | 2 +- examples/remote_sensing/README.md | 88 +++++++++++++++ examples/remote_sensing/eval.py | 44 ++++++++ .../remote_sensing/images/overlap_tile.png | Bin 0 -> 443023 bytes .../images/visualize_compare.jpg | Bin 0 -> 211177 bytes examples/remote_sensing/predict.py | 18 +++ examples/remote_sensing/prepara_data.py | 95 ++++++++++++++++ examples/remote_sensing/train.py | 55 +++++++++ paddlex/cv/datasets/dataset.py | 5 +- paddlex/cv/models/deeplabv3p.py | 106 +++++++++++++++++- paddlex/cv/transforms/seg_transforms.py | 31 +++-- paddlex/deploy.py | 2 +- tutorials/train/object_detection/ppyolo.py | 2 +- 18 files changed, 539 insertions(+), 30 deletions(-) create mode 100644 docs/examples/remote_sensing.md delete mode 100755 docs/examples/remote_sensing/index.rst create mode 100644 examples/remote_sensing/README.md create mode 100644 examples/remote_sensing/eval.py create mode 100644 examples/remote_sensing/images/overlap_tile.png create mode 100644 examples/remote_sensing/images/visualize_compare.jpg create mode 100644 examples/remote_sensing/predict.py create mode 100644 examples/remote_sensing/prepara_data.py create mode 100644 examples/remote_sensing/train.py diff --git a/docs/apis/models/semantic_segmentation.md b/docs/apis/models/semantic_segmentation.md index ab62bd5..b46a627 100755 --- a/docs/apis/models/semantic_segmentation.md +++ b/docs/apis/models/semantic_segmentation.md @@ -110,6 +110,34 @@ batch_predict(self, img_file_list, transforms=None): > > - **dict**: 每个元素都为列表,表示各图像的预测结果。各图像的预测结果用字典表示,包含关键字'label_map'和'score_map', 'label_map'存储预测结果灰度图,像素值表示对应的类别,'score_map'存储各类别的概率,shape=(h, w, num_classes)。 +### overlap_tile_predict + +``` +overlap_tile_predict(self, img_file, tile_size=[512, 512], pad_size=[64, 64], batch_size=32, transforms=None) +``` + +> DeepLabv3p模型的滑动预测接口, 支持有重叠和无重叠两种方式。 + +> **无重叠的滑动窗口预测**:在输入图片上以固定大小的窗口滑动,分别对每个窗口下的图像进行预测,最后将各窗口的预测结果拼接成输入图片的预测结果。**使用时需要把参数`pad_size`设置为`[0, 0]`**。 + +> **有重叠的滑动窗口预测**:在Unet论文中,作者提出一种有重叠的滑动窗口预测策略(Overlap-tile strategy)来消除拼接处的裂痕感。对各滑动窗口预测时,会向四周扩展一定的面积,对扩展后的窗口进行预测,例如下图中的蓝色部分区域,到拼接时只取各窗口中间部分的预测结果,例如下图中的黄色部分区域。位于输入图像边缘处的窗口,其扩展面积下的像素则通过将边缘部分像素镜像填补得到。 + +![](../../../examples/remote_sensing/images/overlap_tile.png) + +> 需要注意的是,只有在训练过程中定义了eval_dataset,模型在保存时才会将预测时的图像处理流程保存在`DeepLabv3p.test_transforms`和`DeepLabv3p.eval_transforms`中。如未在训练时定义eval_dataset,那在调用预测`overlap_tile_predict`接口时,用户需要再重新定义test_transforms传入给`overlap_tile_predict`接口。 + +> **参数** +> > +> > - **img_file** (str|np.ndarray): 预测图像路径或numpy数组(HWC排列,BGR格式)。 +> > - **tile_size** (list|tuple): 滑动窗口的大小,该区域内用于拼接预测结果,格式为(W,H)。默认值为[512, 512]。 +> > - **pad_size** (list|tuple): 滑动窗口向四周扩展的大小,扩展区域内不用于拼接预测结果,格式为(W,H)。默认值为[64, 64]。 +> > - **batch_size** (int):对窗口进行批量预测时的批量大小。默认值为32。 +> > - **transforms** (paddlex.seg.transforms): 数据预处理操作。 + +> **返回值** +> > +> > - **dict**: 包含关键字'label_map'和'score_map', 'label_map'存储预测结果灰度图,像素值表示对应的类别,'score_map'存储各类别的概率,shape=(h, w, num_classes)。 + ## paddlex.seg.UNet @@ -133,6 +161,7 @@ paddlex.seg.UNet(num_classes=2, upsample_mode='bilinear', use_bce_loss=False, us > - evaluate 评估接口说明同 [DeepLabv3p模型evaluate接口](#evaluate) > - predict 预测接口说明同 [DeepLabv3p模型predict接口](#predict) > - batch_predict 批量预测接口说明同 [DeepLabv3p模型predict接口](#batch-predict) +> - overlap_tile_predict 滑动窗口预测接口同 [DeepLabv3p模型poverlap_tile_predict接口](#overlap-tile-predict) ## paddlex.seg.HRNet @@ -156,6 +185,7 @@ paddlex.seg.HRNet(num_classes=2, width=18, use_bce_loss=False, use_dice_loss=Fal > - evaluate 评估接口说明同 [DeepLabv3p模型evaluate接口](#evaluate) > - predict 预测接口说明同 [DeepLabv3p模型predict接口](#predict) > - batch_predict 批量预测接口说明同 [DeepLabv3p模型predict接口](#batch-predict) +> - overlap_tile_predict 滑动窗预测接口同 [DeepLabv3p模型poverlap_tile_predict接口](#overlap-tile-predict) ## paddlex.seg.FastSCNN @@ -179,3 +209,4 @@ paddlex.seg.FastSCNN(num_classes=2, use_bce_loss=False, use_dice_loss=False, cla > - evaluate 评估接口说明同 [DeepLabv3p模型evaluate接口](#evaluate) > - predict 预测接口说明同 [DeepLabv3p模型predict接口](#predict) > - batch_predict 批量预测接口说明同 [DeepLabv3p模型predict接口](#batch-predict) +> - overlap_tile_predict 滑动窗预测接口同 [DeepLabv3p模型poverlap_tile_predict接口](#overlap-tile-predict) diff --git a/docs/examples/index.rst b/docs/examples/index.rst index 8d7e68e..f232951 100755 --- a/docs/examples/index.rst +++ b/docs/examples/index.rst @@ -13,3 +13,4 @@ PaddleX精选飞桨视觉开发套件在产业实践中的成熟模型结构, meter_reader.md human_segmentation.md multi-channel_remote_sensing/README.md + remote_sensing.md diff --git a/docs/examples/multi-channel_remote_sensing/README.md b/docs/examples/multi-channel_remote_sensing/README.md index 1a46a61..cfa91aa 100644 --- a/docs/examples/multi-channel_remote_sensing/README.md +++ b/docs/examples/multi-channel_remote_sensing/README.md @@ -7,7 +7,7 @@ ## 前置依赖 * Paddle paddle >= 1.8.4 * Python >= 3.5 -* PaddleX >= 1.1.0 +* PaddleX >= 1.1.4 安装的相关问题参考[PaddleX安装](../../install.md) diff --git a/docs/examples/remote_sensing.md b/docs/examples/remote_sensing.md new file mode 100644 index 0000000..26c13b0 --- /dev/null +++ b/docs/examples/remote_sensing.md @@ -0,0 +1,82 @@ +# RGB遥感影像分割 + +本案例基于PaddleX实现遥感影像分割,提供滑动窗口预测方式,以避免在直接对大尺寸图片进行预测时显存不足的发生。此外,滑动窗口之间的重叠程度可配置,以此消除最终预测结果中各窗口拼接处的裂痕感。 + +## 前置依赖 + +* Paddle paddle >= 1.8.4 +* Python >= 3.5 +* PaddleX >= 1.1.4 + +安装的相关问题参考[PaddleX安装](../install.md) + +下载PaddleX源码: + +``` +git clone https://github.com/PaddlePaddle/PaddleX +``` + +该案例所有脚本均位于`PaddleX/examples/remote_sensing/`,进入该目录: + +``` +cd PaddleX/examples/remote_sensing/ +``` + +## 数据准备 + +本案例使用2015 CCF大数据比赛提供的高清遥感影像,包含5张带标注的RGB图像,图像尺寸最大有7969 × 7939、最小有4011 × 2470。该数据集共标注了5类物体,分别是背景(标记为0)、植被(标记为1)、建筑(标记为2)、水体(标记为3)、道路 (标记为4)。 + +本案例将前4张图片划分入训练集,第5张图片作为验证集。为增加训练时的批量大小,以滑动窗口为(1024,1024)、步长为(512, 512)对前4张图片进行切分,加上原本的4张大尺寸图片,训练集一共有688张图片。在训练过程中直接对大图片进行验证会导致显存不足,为避免此类问题的出现,针对验证集以滑动窗口为(769, 769)、步长为(769,769)对第5张图片进行切分,得到40张子图片。 + +运行以下脚本,下载原始数据集,并完成数据集的切分: + +``` +python3 prepare_data.py +``` + +## 模型训练 + +分割模型选择Backbone为MobileNetv3_large_ssld的Deeplabv3模型,该模型兼备高性能高精度的优点。运行以下脚本,进行模型训练: +``` +python3 train.py +``` + +也可以跳过模型训练步骤,直接下载预训练模型进行后续的模型预测和评估: +``` +wget https://bj.bcebos.com/paddlex/examples/remote_sensing/models/ccf_remote_model.tar.gz +tar -xvf ccf_remote_model.tar.gz +``` + +## 模型预测 + +直接对大尺寸图片进行预测会导致显存不足,为避免此类问题的出现,本案例提供了滑动窗口预测接口,支持有重叠和无重叠两种方式。 + +* 无重叠的滑动窗口预测 + +在输入图片上以固定大小的窗口滑动,分别对每个窗口下的图像进行预测,最后将各窗口的预测结果拼接成输入图片的预测结果。由于每个窗口边缘部分的预测效果会比中间部分的差,因此每个窗口拼接处可能会有明显的裂痕感。 + +该预测方式的API接口详见[overlap_tile_predict](https://paddlex.readthedocs.io/zh_CN/develop/apis/models/semantic_segmentation.html#overlap-tile-predict),**使用时需要把参数`pad_size`设置为`[0, 0]`**。 + +* 有重叠的滑动窗口预测 + +在Unet论文中,作者提出一种有重叠的滑动窗口预测策略(Overlap-tile strategy)来消除拼接处的裂痕感。对各滑动窗口预测时,会向四周扩展一定的面积,对扩展后的窗口进行预测,例如下图中的蓝色部分区域,到拼接时只取各窗口中间部分的预测结果,例如下图中的黄色部分区域。位于输入图像边缘处的窗口,其扩展面积下的像素则通过将边缘部分像素镜像填补得到。 + +该预测方式的API接口说明详见[overlap_tile_predict](https://paddlex.readthedocs.io/zh_CN/develop/apis/models/semantic_segmentation.html#overlap-tile-predict)。 + +![](../../examples/remote_sensing/images/overlap_tile.png) + +相比无重叠的滑动窗口预测,有重叠的滑动窗口预测策略将本案例的模型精度miou从80.58%提升至81.52%,并且将预测可视化结果中裂痕感显著消除,可见下图中两种预测方式的效果对比。 + +![](../../examples/remote_sensing/images/visualize_compare.jpg) + +运行以下脚本使用有重叠的滑动窗口进行预测: +``` +python3 predict.py +``` + +## 模型评估 + +在训练过程中,每隔10个迭代轮数会评估一次模型在验证集的精度。由于已事先将原始大尺寸图片切分成小块,此时相当于使用无重叠的大图切小图预测方式,最优模型精度miou为80.58%。运行以下脚本,将采用有重叠的大图切小图的预测方式,重新评估原始大尺寸图片的模型精度,此时miou为81.52%。 +``` +python3 eval.py +``` diff --git a/docs/examples/remote_sensing/index.rst b/docs/examples/remote_sensing/index.rst deleted file mode 100755 index dc37565..0000000 --- a/docs/examples/remote_sensing/index.rst +++ /dev/null @@ -1,5 +0,0 @@ -遥感分割案例 -======================================= - - -这里面写遥感分割案例,可根据需求拆分为多个文档 diff --git a/examples/multi-channel_remote_sensing/README.md b/examples/multi-channel_remote_sensing/README.md index 8554e3d..63ec786 100644 --- a/examples/multi-channel_remote_sensing/README.md +++ b/examples/multi-channel_remote_sensing/README.md @@ -14,7 +14,7 @@ * Paddle paddle >= 1.8.4 * Python >= 3.5 -* PaddleX >= 1.1.0 +* PaddleX >= 1.1.4 安装的相关问题参考[PaddleX安装](../../docs/install.md) diff --git a/examples/remote_sensing/README.md b/examples/remote_sensing/README.md new file mode 100644 index 0000000..2663754 --- /dev/null +++ b/examples/remote_sensing/README.md @@ -0,0 +1,88 @@ +# RGB遥感影像分割 + +本案例基于PaddleX实现遥感影像分割,提供滑动窗口预测方式,以避免在直接对大尺寸图片进行预测时显存不足的发生。此外,滑动窗口之间的重叠程度可配置,以此消除最终预测结果中各窗口拼接处的裂痕感。 + +## 目录 +* [数据准备](#1) +* [模型训练](#2) +* [模型预测](#3) +* [模型评估](#4) + +#### 前置依赖 + +* Paddle paddle >= 1.8.4 +* Python >= 3.5 +* PaddleX >= 1.1.4 + +安装的相关问题参考[PaddleX安装](../install.md) + +下载PaddleX源码: + +``` +git clone https://github.com/PaddlePaddle/PaddleX +``` + +该案例所有脚本均位于`PaddleX/examples/remote_sensing/`,进入该目录: + +``` +cd PaddleX/examples/remote_sensing/ +``` + +##

数据准备

+ +本案例使用2015 CCF大数据比赛提供的高清遥感影像,包含5张带标注的RGB图像,图像尺寸最大有7969 × 7939、最小有4011 × 2470。该数据集共标注了5类物体,分别是背景(标记为0)、植被(标记为1)、建筑(标记为2)、水体(标记为3)、道路 (标记为4)。 + +本案例将前4张图片划分入训练集,第5张图片作为验证集。为增加训练时的批量大小,以滑动窗口为(1024,1024)、步长为(512, 512)对前4张图片进行切分,加上原本的4张大尺寸图片,训练集一共有688张图片。在训练过程中直接对大图片进行验证会导致显存不足,为避免此类问题的出现,针对验证集以滑动窗口为(769, 769)、步长为(769,769)对第5张图片进行切分,得到40张子图片。 + +运行以下脚本,下载原始数据集,并完成数据集的切分: + +``` +python3 prepare_data.py +``` + +##

模型训练

+ +分割模型选择Backbone为MobileNetv3_large_ssld的Deeplabv3模型,该模型兼备高性能高精度的优点。运行以下脚本,进行模型训练: +``` +python3 train.py +``` + +也可以跳过模型训练步骤,直接下载预训练模型进行后续的模型预测和评估: +``` +wget https://bj.bcebos.com/paddlex/examples/remote_sensing/models/ccf_remote_model.tar.gz +tar -xvf ccf_remote_model.tar.gz +``` + +##

模型预测

+ +直接对大尺寸图片进行预测会导致显存不足,为避免此类问题的出现,本案例提供了滑动窗口预测接口,支持有重叠和无重叠两种方式。 + +* 无重叠的滑动窗口预测 + +在输入图片上以固定大小的窗口滑动,分别对每个窗口下的图像进行预测,最后将各窗口的预测结果拼接成输入图片的预测结果。由于每个窗口边缘部分的预测效果会比中间部分的差,因此每个窗口拼接处可能会有明显的裂痕感。 + +该预测方式的API接口详见[overlap_tile_predict](https://paddlex.readthedocs.io/zh_CN/develop/apis/models/semantic_segmentation.html#overlap-tile-predict),**使用时需要把参数`pad_size`设置为`[0, 0]`**。 + +* 有重叠的滑动窗口预测 + +在Unet论文中,作者提出一种有重叠的滑动窗口预测策略(Overlap-tile strategy)来消除拼接处的裂痕感。对各滑动窗口预测时,会向四周扩展一定的面积,对扩展后的窗口进行预测,例如下图中的蓝色部分区域,到拼接时只取各窗口中间部分的预测结果,例如下图中的黄色部分区域。位于输入图像边缘处的窗口,其扩展面积下的像素则通过将边缘部分像素镜像填补得到。 + +该预测方式的API接口说明详见[overlap_tile_predict](https://paddlex.readthedocs.io/zh_CN/develop/apis/models/semantic_segmentation.html#overlap-tile-predict)。 + +![](images/overlap_tile.png) + +相比无重叠的滑动窗口预测,有重叠的滑动窗口预测策略将本案例的模型精度miou从80.58%提升至81.52%,并且将预测可视化结果中裂痕感显著消除,可见下图中两种预测方式的效果对比。 + +![](images/visualize_compare.jpg) + +运行以下脚本使用有重叠的滑动窗口进行预测: +``` +python3 predict.py +``` + +##

模型评估

+ +在训练过程中,每隔10个迭代轮数会评估一次模型在验证集的精度。由于已事先将原始大尺寸图片切分成小块,此时相当于使用无重叠的滑动窗口预测方式,最优模型精度miou为80.58%。运行以下脚本,将采用有重叠的滑动窗口预测方式,重新评估原始大尺寸图片的模型精度,此时miou为81.52%。 +``` +python3 eval.py +``` diff --git a/examples/remote_sensing/eval.py b/examples/remote_sensing/eval.py new file mode 100644 index 0000000..5404945 --- /dev/null +++ b/examples/remote_sensing/eval.py @@ -0,0 +1,44 @@ +# 环境变量配置,用于控制是否使用GPU +# 说明文档:https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html#gpu +import os +os.environ['CUDA_VISIBLE_DEVICES'] = '0' + +import numpy as np +import cv2 +from PIL import Image +from collections import OrderedDict + +import paddlex as pdx +import paddlex.utils.logging as logging +from paddlex.cv.models.utils.seg_eval import ConfusionMatrix + + +def update_confusion_matrix(confusion_matrix, predction, label): + pred = predction["label_map"] + pred = pred[np.newaxis, :, :, np.newaxis] + pred = pred.astype(np.int64) + label = label[np.newaxis, np.newaxis, :, :] + mask = label != model.ignore_index + confusion_matrix.calculate(pred=pred, label=label, ignore=mask) + + +model_dir = 'output/deeplabv3p_mobilenetv3_large_ssld/best_model' +img_file = "dataset/JPEGImages/5.png" +label_file = "dataset/Annotations/5_class.png" + +model = pdx.load_model(model_dir) + +conf_mat = ConfusionMatrix(model.num_classes, streaming=True) + +# API说明:https://paddlex.readthedocs.io/zh_CN/develop/apis/models/semantic_segmentation.html#overlap-tile-predict +overlap_tile_predict = model.overlap_tile_predict( + img_file=img_file, tile_size=(769, 769), pad_size=[64, 64], batch_size=32) + +label = np.asarray(Image.open(label_file)) +update_confusion_matrix(conf_mat, overlap_tile_predict, label) + +category_iou, miou = conf_mat.mean_iou() +category_acc, macc = conf_mat.accuracy() +logging.info( + "miou={:.6f} category_iou={} macc={:.6f} category_acc={} kappa={:.6f}". + format(miou, category_iou, macc, category_acc, conf_mat.kappa())) diff --git a/examples/remote_sensing/images/overlap_tile.png b/examples/remote_sensing/images/overlap_tile.png new file mode 100644 index 0000000000000000000000000000000000000000..60347caeb41b0807ad1cec84fac690e7318d20e1 GIT binary patch literal 443023 zcma&N1yt1C+CEGQh{S*b(jkmU!$WtMfQr(cB3(nbz>uSoN-2_(Lku9@4bnry(A{0% zpgtbYf35SbZ>=E~FnjO2uKT*~{Uhv|syyCZ^1CP~D0qqrGA~e2FhfvK(BiT0Ab)aY zsbY+Ra__N~wDdDYX=&PLPWBd7Hs&ZO3Sn`YSX%0xB&oU|-o5*Q_C*1w7Kgr80go-v z|7D|`0vZlu$Xi+yd6EZuxKBv(SHFSNtx?UO#*MJY@Qvoc@Dg?so6j|hXshcdGZ!;w zbIj8kHE&j2#7mTyIh4_l&{22=IV{BZ(c7NuqCsQ8!26O@ z7*HC`hIz>;g1wI9WGlkFNeP>nWBuevzm}&I(Vg>V_e{;%?>@&NQEDAeet2UkcwMn* z)*aHu8LIR^p~-YfuzMq(mrW`5iTp-H|_LM zGS|il2;$<$Io#hkect^1mL4U``;Qn2$=h9+C+WE;mmn&on z32)n;_;5y0u01nItdQu4CU1G~5qRogZfJ8i;p^#EBi0nY z3>pdcou{-xpYCJ@Jf*eSD%=S%?23QZfJ*cZ^|ZnG2lp_8;sAYMzrZu>>p(C;M)OtR z{Q~cI1Y3q71zp+b3YulhES_co*@P?p*zfS_&BG1S=Ld)yiC+V*{}Cc}>J52ff6>|Y zlBNV84Q_04FnK&c!ryS^KqEpVhW=S-A+>Uhu*m-UZT4HfCWat^(`ODQXwkCWNHn`%Z$mrV< zbS4m`bpyMx{F1x7oyhZ^s4oL154=l^DCgA{9PhNpYO1WiTo_&&7Kh*2KgIOeZ*BU- zlu8G%z}`;`aHv@38K*B?P{=BcDJ4ePuQjYHuQ7jo?DE!eKB?d}1xlSSIy$<_tPLOq zg>n(7DLaa7nTPyjmXa$y~l8d_)u``{Dh|N9X`LZZnb+6lUqJ_eUKX8RTbF zf`Q30eGIl8wu^Lw!8MX%U&)kY%bzn0-=zxnOb>XCK@L%sqne0oQAJT3Dk|K22c) zRF(*Qhq{mj90Ah))|ENs-J?_X8hr66!&VxE=&2@Jl#xO)(483m7U%7~CKoB3FM-8D z4v7wT3R6^@yy)L|(CQG2&P^yYSrAzgB@prxodDDbC&7@g+DAl+X=ZBsjIuET86q!m ztf;IA)&z1>h853q(!ZIcBl3y9eMqN&!Tcik*?57|(@g!Pk4iM!K{krVO!6@viw_sg z7q=GW_RXDuRgiQ=Xx8Zq1oOw(v)J-jovw#nqFuVLUcKUZ#WAw?O=`sBoAs-cyi|3c zob#}6vgKb(a$<70*I%rwuH&rRuIrLVS;*pk&it|eTG?u(OuY=N4D!Z>SDqI}u4Y@P zlc|%UQ$4}rTJk;9^Yi)rBdMdiM?vceBh|beI~x^9rtJfd+vg_qaW-1$N@^VQFDM%;Gv7V@RJ; 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说明文档:https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html#gpu +import os +os.environ['CUDA_VISIBLE_DEVICES'] = '0' + +import paddlex as pdx + +model_dir = 'output/deeplabv3p_mobilenetv3_large_ssld/best_model' +img_file = "dataset/JPEGImages/5.png" +save_dir = 'output/deeplabv3p_mobilenetv3_large_ssld/' + +model = pdx.load_model('output/deeplabv3p_mobilenetv3_large_ssld/best_model') + +# API说明:https://paddlex.readthedocs.io/zh_CN/develop/apis/models/semantic_segmentation.html#overlap-tile-predict +pred = model.overlap_tile_predict( + img_file=img_file, tile_size=(769, 769), pad_size=[64, 64], batch_size=32) + +pdx.seg.visualize(img_file, pred, weight=0., save_dir=save_dir) diff --git a/examples/remote_sensing/prepara_data.py b/examples/remote_sensing/prepara_data.py new file mode 100644 index 0000000..6951bf8 --- /dev/null +++ b/examples/remote_sensing/prepara_data.py @@ -0,0 +1,95 @@ +import os +import os.path as osp +import numpy as np +import cv2 +import shutil +from PIL import Image +import paddlex as pdx + +# 定义训练集切分时的滑动窗口大小和步长,格式为(W, H) +train_tile_size = (1024, 1024) +train_stride = (512, 512) +# 定义验证集切分时的滑动窗口大小和步长,格式(W, H) +val_tile_size = (769, 769) +val_stride = (769, 769) + +# 下载并解压2015 CCF大数据比赛提供的高清遥感影像 +ccf_remote_dataset = 'https://bj.bcebos.com/paddlex/examples/remote_sensing/datasets/ccf_remote_dataset.tar.gz' +pdx.utils.download_and_decompress(ccf_remote_dataset, path='./') + +if not osp.exists('./dataset/JPEGImages'): + os.makedirs('./dataset/JPEGImages') +if not osp.exists('./dataset/Annotations'): + os.makedirs('./dataset/Annotations') + +# 将前4张图片划分入训练集,并切分成小块之后加入到训练集中 +# 并生成train_list.txt +for train_id in range(1, 5): + shutil.copyfile("ccf_remote_dataset/{}.png".format(train_id), + "./dataset/JPEGImages/{}.png".format(train_id)) + shutil.copyfile("ccf_remote_dataset/{}_class.png".format(train_id), + "./dataset/Annotations/{}_class.png".format(train_id)) + mode = 'w' if train_id == 1 else 'a' + with open('./dataset/train_list.txt', mode) as f: + f.write("JPEGImages/{}.png Annotations/{}_class.png\n".format( + train_id, train_id)) + +for train_id in range(1, 5): + image = cv2.imread('ccf_remote_dataset/{}.png'.format(train_id)) + label = Image.open('ccf_remote_dataset/{}_class.png'.format(train_id)) + H, W, C = image.shape + train_tile_id = 1 + for h in range(0, H, train_stride[1]): + for w in range(0, W, train_stride[0]): + left = w + upper = h + right = min(w + train_tile_size[0] * 2, W) + lower = min(h + train_tile_size[1] * 2, H) + tile_image = image[upper:lower, left:right, :] + cv2.imwrite("./dataset/JPEGImages/{}_{}.png".format( + train_id, train_tile_id), tile_image) + cut_label = label.crop((left, upper, right, lower)) + cut_label.save("./dataset/Annotations/{}_class_{}.png".format( + train_id, train_tile_id)) + with open('./dataset/train_list.txt', 'a') as f: + f.write("JPEGImages/{}_{}.png Annotations/{}_class_{}.png\n". + format(train_id, train_tile_id, train_id, + train_tile_id)) + train_tile_id += 1 + +# 将第5张图片切分成小块之后加入到验证集中 +val_id = 5 +val_tile_id = 1 +shutil.copyfile("ccf_remote_dataset/{}.png".format(val_id), + "./dataset/JPEGImages/{}.png".format(val_id)) +shutil.copyfile("ccf_remote_dataset/{}_class.png".format(val_id), + "./dataset/Annotations/{}_class.png".format(val_id)) +image = cv2.imread('ccf_remote_dataset/{}.png'.format(val_id)) +label = Image.open('ccf_remote_dataset/{}_class.png'.format(val_id)) +H, W, C = image.shape +for h in range(0, H, val_stride[1]): + for w in range(0, W, val_stride[0]): + left = w + upper = h + right = min(w + val_tile_size[0], W) + lower = min(h + val_tile_size[1], H) + cut_image = image[upper:lower, left:right, :] + cv2.imwrite("./dataset/JPEGImages/{}_{}.png".format( + val_id, val_tile_id), cut_image) + cut_label = label.crop((left, upper, right, lower)) + cut_label.save("./dataset/Annotations/{}_class_{}.png".format( + val_id, val_tile_id)) + mode = 'w' if val_tile_id == 1 else 'a' + with open('./dataset/val_list.txt', mode) as f: + f.write("JPEGImages/{}_{}.png Annotations/{}_class_{}.png\n". + format(val_id, val_tile_id, val_id, val_tile_id)) + val_tile_id += 1 + +# 生成labels.txt +label_list = ['background', 'vegetation', 'building', 'water', 'road'] +for i, label in enumerate(label_list): + mode = 'w' if i == 0 else 'a' + with open('./dataset/labels.txt', 'a') as f: + name = "{}\n".format(label) if i < len( + label_list) - 1 else "{}".format(label) + f.write(name) diff --git a/examples/remote_sensing/train.py b/examples/remote_sensing/train.py new file mode 100644 index 0000000..6a7ff12 --- /dev/null +++ b/examples/remote_sensing/train.py @@ -0,0 +1,55 @@ +# 环境变量配置,用于控制是否使用GPU +# 说明文档:https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html#gpu +import os +os.environ['CUDA_VISIBLE_DEVICES'] = '0' + +import paddlex as pdx +from paddlex.seg import transforms + +# 定义训练和验证时的transforms +# API说明 https://paddlex.readthedocs.io/zh_CN/develop/apis/transforms/seg_transforms.html +train_transforms = transforms.Compose([ + transforms.RandomPaddingCrop(crop_size=769), + transforms.RandomHorizontalFlip(), transforms.RandomVerticalFlip(), + transforms.Normalize() +]) + +eval_transforms = transforms.Compose( + [transforms.Padding(target_size=769), transforms.Normalize()]) + +# 定义训练和验证所用的数据集 +# API说明:https://paddlex.readthedocs.io/zh_CN/develop/apis/datasets.html#paddlex-datasets-segdataset +train_dataset = pdx.datasets.SegDataset( + data_dir='dataset', + file_list='dataset/train_list.txt', + label_list='dataset/labels.txt', + transforms=train_transforms, + shuffle=True) +eval_dataset = pdx.datasets.SegDataset( + data_dir='dataset', + file_list='dataset/val_list.txt', + label_list='dataset/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,参考https://paddlex.readthedocs.io/zh_CN/develop/train/visualdl.html +num_classes = len(train_dataset.labels) + +# API说明:https://paddlex.readthedocs.io/zh_CN/develop/apis/models/semantic_segmentation.html#paddlex-seg-deeplabv3p +model = pdx.seg.DeepLabv3p( + num_classes=num_classes, + backbone='MobileNetV3_large_x1_0_ssld', + pooling_crop_size=(769, 769)) + +# API说明:https://paddlex.readthedocs.io/zh_CN/develop/apis/models/semantic_segmentation.html#train +# 各参数介绍与调整说明:https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html +model.train( + num_epochs=400, + train_dataset=train_dataset, + train_batch_size=16, + eval_dataset=eval_dataset, + learning_rate=0.01, + save_interval_epochs=10, + pretrain_weights='CITYSCAPES', + save_dir='output/deeplabv3p_mobilenetv3_large_ssld', + use_vdl=True) diff --git a/paddlex/cv/datasets/dataset.py b/paddlex/cv/datasets/dataset.py index 82a29f5..bedbc5a 100644 --- a/paddlex/cv/datasets/dataset.py +++ b/paddlex/cv/datasets/dataset.py @@ -239,9 +239,8 @@ def generate_minibatch(batch_data, label_padding_value=255, mapper=None): _, label_h, label_w = data[1].shape padding_label[:, :label_h, :label_w] = data[1] padding_batch.append((padding_im, padding_label)) - elif len(data[1]) == 0 or isinstance( - data[1][0], - tuple) and data[1][0][0] in ['resize', 'padding']: + elif len(data[1]) == 0 or isinstance(data[1][0], tuple) and data[ + 1][0][0] in ['origin_shape', 'resize', 'padding']: # padding the image and insert 'padding' into `im_info` # of segmentation during the infering phase if len(data[1]) == 0 or 'padding' not in [ diff --git a/paddlex/cv/models/deeplabv3p.py b/paddlex/cv/models/deeplabv3p.py index 49a6a1d..8f7341c 100644 --- a/paddlex/cv/models/deeplabv3p.py +++ b/paddlex/cv/models/deeplabv3p.py @@ -24,6 +24,7 @@ import paddlex.utils.logging as logging import paddlex from paddlex.cv.transforms import arrange_transforms from paddlex.cv.datasets import generate_minibatch +from paddlex.cv.transforms.seg_transforms import Compose from collections import OrderedDict from .base import BaseAPI from .utils.seg_eval import ConfusionMatrix @@ -448,7 +449,11 @@ class DeepLabv3p(BaseAPI): return metrics @staticmethod - def _preprocess(images, transforms, model_type, class_name, thread_pool=None): + def _preprocess(images, + transforms, + model_type, + class_name, + thread_pool=None): arrange_transforms( model_type=model_type, class_name=class_name, @@ -554,3 +559,102 @@ class DeepLabv3p(BaseAPI): preds = DeepLabv3p._postprocess(result, im_info) return preds + + def overlap_tile_predict(self, + img_file, + tile_size=[512, 512], + pad_size=[64, 64], + batch_size=32, + transforms=None): + """有重叠的大图切小图预测。 + Args: + img_file(str|np.ndarray): 预测图像路径,或者是解码后的排列格式为(H, W, C)且类型为float32且为BGR格式的数组。 + tile_size(list|tuple): 滑动窗口的大小,该区域内用于拼接预测结果,格式为(W,H)。默认值为[512, 512]。 + pad_size(list|tuple): 滑动窗口向四周扩展的大小,扩展区域内不用于拼接预测结果,格式为(W,H)。默认值为[64,64]。 + batch_size(int):对窗口进行批量预测时的批量大小。默认值为32 + transforms(paddlex.cv.transforms): 数据预处理操作。 + + + Returns: + dict: 包含关键字'label_map'和'score_map', 'label_map'存储预测结果灰度图, + 像素值表示对应的类别,'score_map'存储各类别的概率,shape=(h, w, num_classes) + """ + + if transforms is None and not hasattr(self, 'test_transforms'): + raise Exception("transforms need to be defined, now is None.") + + if isinstance(img_file, str): + image, _ = Compose.decode_image(img_file, None) + elif isinstance(img_file, np.ndarray): + image = img_file.copy() + else: + raise Exception("im_file must be list/tuple") + + height, width, channel = image.shape + image_tile_list = list() + + # Padding along the left and right sides + if pad_size[0] > 0: + left_pad = cv2.flip(image[0:height, 0:pad_size[0], :], 1) + right_pad = cv2.flip(image[0:height, -pad_size[0]:width, :], 1) + padding_image = cv2.hconcat([left_pad, image]) + padding_image = cv2.hconcat([padding_image, right_pad]) + else: + import copy + padding_image = copy.deepcopy(image) + + # Padding along the upper and lower sides + padding_height, padding_width, _ = padding_image.shape + if pad_size[1] > 0: + upper_pad = cv2.flip( + padding_image[0:pad_size[1], 0:padding_width, :], 0) + lower_pad = cv2.flip( + padding_image[-pad_size[1]:padding_height, 0:padding_width, :], + 0) + padding_image = cv2.vconcat([upper_pad, padding_image]) + padding_image = cv2.vconcat([padding_image, lower_pad]) + + # crop the padding image into tile pieces + padding_height, padding_width, _ = padding_image.shape + + for h_id in range(0, height // tile_size[1] + 1): + for w_id in range(0, width // tile_size[0] + 1): + left = w_id * tile_size[0] + upper = h_id * tile_size[1] + right = min(left + tile_size[0] + pad_size[0] * 2, + padding_width) + lower = min(upper + tile_size[1] + pad_size[1] * 2, + padding_height) + image_tile = padding_image[upper:lower, left:right, :] + image_tile_list.append(image_tile) + + # predict + label_map = np.zeros((height, width), dtype=np.uint8) + score_map = np.zeros( + (height, width, self.num_classes), dtype=np.float32) + num_tiles = len(image_tile_list) + for i in range(0, num_tiles, batch_size): + begin = i + end = min(i + batch_size, num_tiles) + res = self.batch_predict( + img_file_list=image_tile_list[begin:end], + transforms=transforms) + for j in range(begin, end): + h_id = j // (width // tile_size[0] + 1) + w_id = j % (width // tile_size[0] + 1) + left = w_id * tile_size[0] + upper = h_id * tile_size[1] + right = min((w_id + 1) * tile_size[0], width) + lower = min((h_id + 1) * tile_size[1], height) + tile_label_map = res[j - begin]["label_map"] + tile_score_map = res[j - begin]["score_map"] + tile_upper = pad_size[1] + tile_lower = tile_label_map.shape[0] - pad_size[1] + tile_left = pad_size[0] + tile_right = tile_label_map.shape[1] - pad_size[0] + label_map[upper:lower, left:right] = \ + tile_label_map[tile_upper:tile_lower, tile_left:tile_right] + score_map[upper:lower, left:right, :] = \ + tile_score_map[tile_upper:tile_lower, tile_left:tile_right, :] + result = {"label_map": label_map, "score_map": score_map} + return result diff --git a/paddlex/cv/transforms/seg_transforms.py b/paddlex/cv/transforms/seg_transforms.py index c482930..a59e405 100644 --- a/paddlex/cv/transforms/seg_transforms.py +++ b/paddlex/cv/transforms/seg_transforms.py @@ -723,28 +723,25 @@ class Padding(SegTransform): target_width = self.target_size[0] pad_height = target_height - im_height pad_width = target_width - im_width - if pad_height < 0 or pad_width < 0: - raise ValueError( - 'the size of image should be less than target_size, but the size of image ({}, {}), is larger than target_size ({}, {})' - .format(im_width, im_height, target_width, target_height)) - else: - im = cv2.copyMakeBorder( - im, + pad_height = max(pad_height, 0) + pad_width = max(pad_width, 0) + im = cv2.copyMakeBorder( + im, + 0, + pad_height, + 0, + pad_width, + cv2.BORDER_CONSTANT, + value=self.im_padding_value) + if label is not None: + label = cv2.copyMakeBorder( + label, 0, pad_height, 0, pad_width, cv2.BORDER_CONSTANT, - value=self.im_padding_value) - if label is not None: - label = cv2.copyMakeBorder( - label, - 0, - pad_height, - 0, - pad_width, - cv2.BORDER_CONSTANT, - value=self.label_padding_value) + value=self.label_padding_value) if label is None: return (im, im_info) else: diff --git a/paddlex/deploy.py b/paddlex/deploy.py index e7a9264..747cf16 100644 --- a/paddlex/deploy.py +++ b/paddlex/deploy.py @@ -94,7 +94,7 @@ class Predictor: use_gpu=True, gpu_id=0, use_mkl=False, - mkl_thread_num=psutil.cpu_count(), + mkl_thread_num=mp.cpu_count(), use_trt=False, use_glog=False, memory_optimize=True): diff --git a/tutorials/train/object_detection/ppyolo.py b/tutorials/train/object_detection/ppyolo.py index 63b47a9..7f1d4e3 100644 --- a/tutorials/train/object_detection/ppyolo.py +++ b/tutorials/train/object_detection/ppyolo.py @@ -52,7 +52,7 @@ model.train( train_dataset=train_dataset, train_batch_size=8, eval_dataset=eval_dataset, - learning_rate=0.000125, + learning_rate=0.0005, lr_decay_epochs=[210, 240], save_dir='output/ppyolo', use_vdl=True) -- GitLab