Bump version to v0.1.1

Bump version to v0.1.1

Author: ZwwWayne

.github/workflows/build.yml

@@ -198,7 +198,7 @@ jobs:
       - name: Build and install
         run: pip install -e .
       - name: Run unittests
-        run: coverage run --branch --source mmrotate -m pytest tests -sv
+        run: coverage run --branch --source mmrotate -m pytest tests
       - name: Generate coverage report
         run: |
           coverage xml

README.md

@@ -62,7 +62,18 @@ https://user-images.githubusercontent.com/10410257/154433305-416d129b-60c8-44c7-
 
 </details>
 
+## Changelog
 
+**0.1.1** was released in 14/3/2022:
+
+- Add [colab tutorial](demo/MMRotate_Tutorial.ipynb) for beginners (#66)
+- Support [huge image inference](deom/huge_image_demo.py) (#34)
+- Support HRSC Dataset (#96)
+- Support mixed precision training (#72)
+- Add inference speed statistics [tool](tools/analysis_tools/benchmark.py) (#86)
+- Add confusion matrix analysis [tool](tools/analysis_tools/confusion_matrix.py) (#93)
+
+Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
 
 ## Installation
 
@@ -71,6 +82,7 @@ Please refer to [install.md](docs/en/install.md) for installation guide.
 ## Get Started
 
 Please see [get_started.md](docs/en/get_started.md) for the basic usage of MMRotate.
+We provide [colab tutorial](demo/MMRotate_Tutorial.ipynb) for beginners.
 There are also tutorials:
 
 * [learn the basics](docs/en/intro.md)
@@ -145,21 +157,21 @@ This project is released under the [Apache 2.0 license](LICENSE).
 ## Projects in OpenMMLab
 
 * [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
-* [MIM](https://github.com/open-mmlab/mim): MIM Installs OpenMMLab Packages.
+* [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
 * [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
 * [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
-* [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab next-generation platform for general 3D object detection.
+* [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
+* [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
 * [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
-* [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab next-generation action understanding toolbox and benchmark.
-* [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
+* [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
 * [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
-* [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
-* [MMOCR](https://github.com/open-mmlab/mmocr): A comprehensive toolbox for text detection, recognition and understanding.
-* [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab next-generation toolbox for generative models.
-* [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
-* [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
 * [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
 * [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
 * [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
+* [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
+* [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
+* [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
+* [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
+* [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
+* [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
 * [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.
-* [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.

README_zh-CN.md

@@ -59,13 +59,27 @@ https://user-images.githubusercontent.com/10410257/154433305-416d129b-60c8-44c7-
 
 </details>
 
+## 更新日志
+
+最新的 **0.1.1** 版本已经在 2022.03.14 发布:
+
+- 为初学者添加了 [Colab 教程](demo/MMRotate_Tutorial.ipynb)
+- 支持了[大图推理](deom/huge_image_demo.py)
+- 支持了 HRSC 遥感数据集
+- 支持了混合精度训练
+- 添加了推理速度[统计工具](tools/analysis_tools/benchmark.py)
+- 添加了混淆矩阵[分析工具](tools/analysis_tools/confusion_matrix.py).
+
+如果想了解更多版本更新细节和历史信息，请阅读[更新日志](docs/en/changelog.md)。
+
 ## 安装
 
 请参考 [安装指南](docs/zh_cn/install.md) 进行安装。
 
 ## 教程
 
 请参考 [get_started.md](docs/zh_cn/get_started.md) 了解 MMRotate 的基本使用。
+我们为初学者提供了 [colab 教程](demo/MMRotate_Tutorial.ipynb)。
 MMRotate 也提供了其他更详细的教程:
 
 * [学习基础知识](docs/zh_cn/intro.md)
@@ -141,23 +155,23 @@ MMRotate 是一款由不同学校和公司共同贡献的开源项目。我们
 
 * [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
 * [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
-* [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱与测试基准
-* [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 检测工具箱与测试基准
-* [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用3D目标检测平台
-* [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱与测试基准
-* [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱与测试基准
-* [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
-* [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱与测试基准
-* [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
+* [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
+* [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
+* [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
+* [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
+* [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
 * [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
-* [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 新一代生成模型工具箱
-* [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
-* [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
+* [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
 * [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
 * [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
 * [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
+* [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
+* [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
+* [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
+* [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
+* [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
+* [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
 * [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
-* [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
 
 ## 欢迎加入 OpenMMLab 社区
 

configs/_base_/datasets/hrsc.py

@@ -0,0 +1,53 @@
+# dataset settings
+dataset_type = 'HRSCDataset'
+data_root = 'data/hrsc/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='RResize', img_scale=(800, 800)),
+    dict(type='RRandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(800, 800),
+        flip=False,
+        transforms=[
+            dict(type='RResize'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        classwise=False,
+        ann_file=data_root + 'ImageSets/trainval.txt',
+        ann_subdir=data_root + 'FullDataSet/Annotations/',
+        img_subdir=data_root + 'FullDataSet/AllImages/',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        classwise=False,
+        ann_file=data_root + 'ImageSets/trainval.txt',
+        ann_subdir=data_root + 'FullDataSet/Annotations/',
+        img_subdir=data_root + 'FullDataSet/AllImages/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        classwise=False,
+        ann_file=data_root + 'ImageSets/test.txt',
+        ann_subdir=data_root + 'FullDataSet/Annotations/',
+        img_subdir=data_root + 'FullDataSet/AllImages/',
+        pipeline=test_pipeline))

configs/cfa/README.md

@@ -1,22 +1,25 @@
-# [Beyond Bounding-Box: Convex-hull Feature Adaptation for Oriented and Densely Packed Object Detection.](https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Beyond_Bounding-Box_Convex-Hull_Feature_Adaptation_for_Oriented_and_Densely_Packed_CVPR_2021_paper.pdf)
+# CFA
+> [Beyond Bounding-Box: Convex-hull Feature Adaptation for Oriented and Densely Packed Object Detection.](https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Beyond_Bounding-Box_Convex-Hull_Feature_Adaptation_for_Oriented_and_Densely_Packed_CVPR_2021_paper.pdf)
 
 <!-- [ALGORITHM] -->
+
 ## Abstract
 
-![illustration](https://raw.githubusercontent.com/zytx121/image-host/main/imgs/cfa.png)
+<div align=center>
+<img src="https://raw.githubusercontent.com/zytx121/image-host/main/imgs/cfa.png" width="800"/>
+</div>
 
 Detecting oriented and densely packed objects remains challenging for spatial feature aliasing caused by the intersection of reception fields between objects. In this paper, we propose a convex-hull feature adaptation (CFA) approach for configuring convolutional features in accordance with oriented and densely packed object layouts. CFA is rooted in convex-hull feature representation, which defines a set of dynamically predicted feature points guided by the convex intersection over union (CIoU) to bound the extent of objects. CFA pursues optimal feature assignment by constructing convex-hull sets and dynamically splitting positive or negative convex-hulls. By simultaneously considering overlapping convex-hulls and objects and penalizing convex-hulls shared by multiple objects, CFA alleviates spatial feature aliasing towards optimal feature adaptation. Experiments on DOTA and SKU110KR datasets show that CFA significantly outperforms the baseline approach, achieving new state-of-the-art detection performance.
 
 ## Results and models
 
-### DOTA1.0
+DOTA1.0
 
-#### RepPoints
 |    Backbone   |    mAP   | Angle | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
 |:------------:|:----------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:-------------:|
-| ResNet50 (1024,1024,200) | 59.44 | oc | 1x | 3.45 | 15.9 | - | 2 | [rotated_reppoints_r50_fpn_1x_dota_oc](../rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc.py) |  [model](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc/rotated_reppoints_r50_fpn_1x_dota_oc-d38ce217.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc/rotated_reppoints_r50_fpn_1x_dota_oc_20220205_145010.log.json)
-| ResNet50 (1024,1024,200) | 69.63 | le135 | 1x | 3.45 | 15.7 | - | 2 | [cfa_r50_fpn_1x_dota_le135](./cfa_r50_fpn_1x_dota_le135.py) |  [model](https://download.openmmlab.com/mmrotate/v0.1.0/cfa/cfa_r50_fpn_1x_dota_le135/cfa_r50_fpn_1x_dota_le135-aed1cbc6.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/cfa/cfa_r50_fpn_1x_dota_le135/cfa_r50_fpn_1x_dota_le135_20220205_144859.log.json)
-| ResNet50 (1024,1024,200) | 73.45 | oc | 40e | 3.45 | 15.7 | - | 2 | [cfa_r50_fpn_40e_dota_oc](./cfa_r50_fpn_40e_dota_oc.py) |  [model](https://download.openmmlab.com/mmrotate/v0.1.0/cfa/cfa_r50_fpn_40e_dota_oc/cfa_r50_fpn_40e_dota_oc-2f387232.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/cfa/cfa_r50_fpn_40e_dota_oc/cfa_r50_fpn_40e_dota_oc_20220209_171237.log.json)
+| ResNet50 (1024,1024,200) | 59.44 | oc | 1x | 3.45 | 15.6 | - | 2 | [rotated_reppoints_r50_fpn_1x_dota_oc](../rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc.py) |  [model](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc/rotated_reppoints_r50_fpn_1x_dota_oc-d38ce217.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc/rotated_reppoints_r50_fpn_1x_dota_oc_20220205_145010.log.json)
+| ResNet50 (1024,1024,200) | 69.63 | le135 | 1x | 3.45 | 16.1 | - | 2 | [cfa_r50_fpn_1x_dota_le135](./cfa_r50_fpn_1x_dota_le135.py) |  [model](https://download.openmmlab.com/mmrotate/v0.1.0/cfa/cfa_r50_fpn_1x_dota_le135/cfa_r50_fpn_1x_dota_le135-aed1cbc6.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/cfa/cfa_r50_fpn_1x_dota_le135/cfa_r50_fpn_1x_dota_le135_20220205_144859.log.json)
+| ResNet50 (1024,1024,200) | 73.45 | oc | 40e | 3.45 | 16.1 | - | 2 | [cfa_r50_fpn_40e_dota_oc](./cfa_r50_fpn_40e_dota_oc.py) |  [model](https://download.openmmlab.com/mmrotate/v0.1.0/cfa/cfa_r50_fpn_40e_dota_oc/cfa_r50_fpn_40e_dota_oc-2f387232.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/cfa/cfa_r50_fpn_40e_dota_oc/cfa_r50_fpn_40e_dota_oc_20220209_171237.log.json)
 
 
 ## Citation

configs/g_reppoints/README.md

@@ -1,4 +1,5 @@
-# G-Rep: Gaussian Representation for Arbitrary-Oriented Object Detection.
+# G-Rep
+> > G-Rep: Gaussian Representation for Arbitrary-Oriented Object Detection.
 
 <!-- [ALGORITHM] -->
 ## Abstract
@@ -7,13 +8,12 @@ Core code will release later.
 
 ## Results and models
 
-### DOTA1.0
+DOTA1.0
 
-#### RepPoints
 |    Backbone   |    mAP   | Angle | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
 |:------------:|:----------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:-------------:|
-| ResNet50 (1024,1024,200) | 59.44 | oc | 1x | 3.45 | 15.9 | - | 2 | [rotated_reppoints_r50_fpn_1x_dota_oc](../rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc.py) |  [model](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc/rotated_reppoints_r50_fpn_1x_dota_oc-d38ce217.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc/rotated_reppoints_r50_fpn_1x_dota_oc_20220205_145010.log.json)
-| ResNet50 (1024,1024,200) | 69.49 | le135 | 1x | 4.05 | 10.5 | - | 2 | [g_reppoints_r50_fpn_1x_dota_le135](./g_reppoints_r50_fpn_1x_dota_le135.py) |  [model](https://download.openmmlab.com/mmrotate/v0.1.0/g_reppoints/g_reppoints_r50_fpn_1x_dota_le135/g_reppoints_r50_fpn_1x_dota_le135-b840eed7.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/g_reppoints/g_reppoints_r50_fpn_1x_dota_le135/g_reppoints_r50_fpn_1x_dota_le135_20220202_233631.log.json)
+| ResNet50 (1024,1024,200) | 59.44 | oc | 1x | 3.45 | 15.6 | - | 2 | [rotated_reppoints_r50_fpn_1x_dota_oc](../rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc.py) |  [model](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc/rotated_reppoints_r50_fpn_1x_dota_oc-d38ce217.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc/rotated_reppoints_r50_fpn_1x_dota_oc_20220205_145010.log.json)
+| ResNet50 (1024,1024,200) | 69.49 | le135 | 1x | 4.05 | 8.6 | - | 2 | [g_reppoints_r50_fpn_1x_dota_le135](./g_reppoints_r50_fpn_1x_dota_le135.py) |  [model](https://download.openmmlab.com/mmrotate/v0.1.0/g_reppoints/g_reppoints_r50_fpn_1x_dota_le135/g_reppoints_r50_fpn_1x_dota_le135-b840eed7.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/g_reppoints/g_reppoints_r50_fpn_1x_dota_le135/g_reppoints_r50_fpn_1x_dota_le135_20220202_233631.log.json)
 
 
 ## Citation