Release v0.2.0

.dev_scripts/gather_models.py

@@ -0,0 +1,265 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import glob
+import json
+import os.path as osp
+import shutil
+import subprocess
+from collections import OrderedDict
+
+import mmcv
+import torch
+import yaml
+
+
+def ordered_yaml_dump(data, stream=None, Dumper=yaml.SafeDumper, **kwds):
+
+    class OrderedDumper(Dumper):
+        pass
+
+    def _dict_representer(dumper, data):
+        return dumper.represent_mapping(
+            yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG, data.items())
+
+    OrderedDumper.add_representer(OrderedDict, _dict_representer)
+    return yaml.dump(data, stream, OrderedDumper, **kwds)
+
+
+def process_checkpoint(in_file, out_file):
+    checkpoint = torch.load(in_file, map_location='cpu')
+    # remove optimizer for smaller file size
+    if 'optimizer' in checkpoint:
+        del checkpoint['optimizer']
+
+    # remove ema state_dict
+    for key in list(checkpoint['state_dict']):
+        if key.startswith('ema_'):
+            checkpoint['state_dict'].pop(key)
+
+    # if it is necessary to remove some sensitive data in checkpoint['meta'],
+    # add the code here.
+    if torch.__version__ >= '1.6':
+        torch.save(checkpoint, out_file, _use_new_zipfile_serialization=False)
+    else:
+        torch.save(checkpoint, out_file)
+    sha = subprocess.check_output(['sha256sum', out_file]).decode()
+    final_file = out_file.rstrip('.pth') + '-{}.pth'.format(sha[:8])
+    subprocess.Popen(['mv', out_file, final_file])
+    return final_file
+
+
+def get_final_epoch(config):
+    cfg = mmcv.Config.fromfile('./configs/' + config)
+    return cfg.runner.max_epochs
+
+
+def get_best_epoch(exp_dir):
+    best_epoch_full_path = list(
+        sorted(glob.glob(osp.join(exp_dir, 'best_*.pth'))))[-1]
+    best_epoch_model_path = best_epoch_full_path.split('/')[-1]
+    best_epoch = best_epoch_model_path.split('_')[-1].split('.')[0]
+    return best_epoch_model_path, int(best_epoch)
+
+
+def get_real_epoch(config):
+    cfg = mmcv.Config.fromfile('./configs/' + config)
+    epoch = cfg.runner.max_epochs
+    if cfg.data.train.type == 'RepeatDataset':
+        epoch *= cfg.data.train.times
+    return epoch
+
+
+def get_final_results(log_json_path, epoch, results_lut):
+    result_dict = dict()
+    with open(log_json_path, 'r') as f:
+        for line in f.readlines():
+            log_line = json.loads(line)
+            if 'mode' not in log_line.keys():
+                continue
+
+            if log_line['mode'] == 'train' and log_line['epoch'] == epoch:
+                result_dict['memory'] = log_line['memory']
+
+            if log_line['mode'] == 'val' and log_line['epoch'] == epoch:
+                result_dict.update({
+                    key: log_line[key]
+                    for key in results_lut if key in log_line
+                })
+                return result_dict
+
+
+def get_dataset_name(config):
+    # If there are more dataset, add here.
+    name_map = dict(
+        HRSCDataset='HRSC 2016', SARDataset='SAR', DOTADataset='DOTA v1.0')
+    cfg = mmcv.Config.fromfile('./configs/' + config)
+    return name_map[cfg.dataset_type]
+
+
+def convert_model_info_to_pwc(model_infos):
+    pwc_files = {}
+    for model in model_infos:
+        cfg_folder_name = osp.split(model['config'])[-2]
+        pwc_model_info = OrderedDict()
+        pwc_model_info['Name'] = osp.split(model['config'])[-1].split('.')[0]
+        pwc_model_info['In Collection'] = 'Please fill in Collection name'
+        pwc_model_info['Config'] = osp.join('configs', model['config'])
+
+        # get metadata
+        memory = round(model['results']['memory'] / 1024, 1)
+        epochs = get_real_epoch(model['config'])
+        meta_data = OrderedDict()
+        meta_data['Training Memory (GB)'] = memory
+        meta_data['Epochs'] = epochs
+        pwc_model_info['Metadata'] = meta_data
+
+        # get dataset name
+        dataset_name = get_dataset_name(model['config'])
+
+        # get results
+        results = []
+        # if there are more metrics, add here.
+        if 'mAP' in model['results']:
+            metric = round(model['results']['mAP'] * 100, 1)
+            results.append(
+                OrderedDict(
+                    Task='Object Detection',
+                    Dataset=dataset_name,
+                    Metrics={'box AP': metric}))
+        pwc_model_info['Results'] = results
+
+        link_string = 'https://download.openmmlab.com/mmrotate/v0.1.0/'
+        link_string += '{}/{}'.format(model['config'].rstrip('.py'),
+                                      osp.split(model['model_path'])[-1])
+        pwc_model_info['Weights'] = link_string
+        if cfg_folder_name in pwc_files:
+            pwc_files[cfg_folder_name].append(pwc_model_info)
+        else:
+            pwc_files[cfg_folder_name] = [pwc_model_info]
+    return pwc_files
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Gather benchmarked models')
+    parser.add_argument(
+        'root',
+        type=str,
+        help='root path of benchmarked models to be gathered')
+    parser.add_argument(
+        'out', type=str, help='output path of gathered models to be stored')
+    parser.add_argument(
+        '--best',
+        action='store_true',
+        help='whether to gather the best model.')
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    models_root = args.root
+    models_out = args.out
+    mmcv.mkdir_or_exist(models_out)
+
+    # find all models in the root directory to be gathered
+    raw_configs = list(mmcv.scandir('./configs', '.py', recursive=True))
+
+    # filter configs that is not trained in the experiments dir
+    used_configs = []
+    for raw_config in raw_configs:
+        if osp.exists(osp.join(models_root, raw_config)):
+            used_configs.append(raw_config)
+    print(f'Find {len(used_configs)} models to be gathered')
+
+    # find final_ckpt and log file for trained each config
+    # and parse the best performance
+    model_infos = []
+    for used_config in used_configs:
+        exp_dir = osp.join(models_root, used_config)
+        # check whether the exps is finished
+        if args.best is True:
+            final_model, final_epoch = get_best_epoch(exp_dir)
+        else:
+            final_epoch = get_final_epoch(used_config)
+            final_model = 'epoch_{}.pth'.format(final_epoch)
+
+        model_path = osp.join(exp_dir, final_model)
+        # skip if the model is still training
+        if not osp.exists(model_path):
+            continue
+
+        # get the latest logs
+        log_json_path = list(
+            sorted(glob.glob(osp.join(exp_dir, '*.log.json'))))[-1]
+        log_txt_path = list(sorted(glob.glob(osp.join(exp_dir, '*.log'))))[-1]
+        cfg = mmcv.Config.fromfile('./configs/' + used_config)
+        results_lut = cfg.evaluation.metric
+        if not isinstance(results_lut, list):
+            results_lut = [results_lut]
+        model_performance = get_final_results(log_json_path, final_epoch,
+                                              results_lut)
+
+        if model_performance is None:
+            continue
+
+        model_time = osp.split(log_txt_path)[-1].split('.')[0]
+        model_infos.append(
+            dict(
+                config=used_config,
+                results=model_performance,
+                epochs=final_epoch,
+                model_time=model_time,
+                final_model=final_model,
+                log_json_path=osp.split(log_json_path)[-1]))
+
+    # publish model for each checkpoint
+    publish_model_infos = []
+    for model in model_infos:
+        model_publish_dir = osp.join(models_out, model['config'].rstrip('.py'))
+        mmcv.mkdir_or_exist(model_publish_dir)
+
+        model_name = osp.split(model['config'])[-1].split('.')[0]
+
+        model_name += '_' + model['model_time']
+        publish_model_path = osp.join(model_publish_dir, model_name)
+        trained_model_path = osp.join(models_root, model['config'],
+                                      model['final_model'])
+
+        # convert model
+        final_model_path = process_checkpoint(trained_model_path,
+                                              publish_model_path)
+
+        # copy log
+        shutil.copy(
+            osp.join(models_root, model['config'], model['log_json_path']),
+            osp.join(model_publish_dir, f'{model_name}.log.json'))
+        shutil.copy(
+            osp.join(models_root, model['config'],
+                     model['log_json_path'].rstrip('.json')),
+            osp.join(model_publish_dir, f'{model_name}.log'))
+
+        # copy config to guarantee reproducibility
+        config_path = model['config']
+        config_path = osp.join(
+            'configs',
+            config_path) if 'configs' not in config_path else config_path
+        target_config_path = osp.split(config_path)[-1]
+        shutil.copy(config_path, osp.join(model_publish_dir,
+                                          target_config_path))
+
+        model['model_path'] = final_model_path
+        publish_model_infos.append(model)
+
+    models = dict(models=publish_model_infos)
+    print(f'Totally gathered {len(publish_model_infos)} models')
+    mmcv.dump(models, osp.join(models_out, 'model_info.json'))
+
+    pwc_files = convert_model_info_to_pwc(publish_model_infos)
+    for name in pwc_files:
+        with open(osp.join(models_out, name + '_metafile.yml'), 'w') as f:
+            ordered_yaml_dump(pwc_files[name], f, encoding='utf-8')
+
+
+if __name__ == '__main__':
+    main()

.gitignore

@@ -66,7 +66,9 @@ instance/
 .scrapy
 
 # Sphinx documentation
-docs/_build/
+docs/en/_build/
+docs/zh_cn/_build/
+src
 
 # PyBuilder
 target/

README.md

@@ -64,14 +64,10 @@ https://user-images.githubusercontent.com/10410257/154433305-416d129b-60c8-44c7-
 
 ## Changelog
 
-**0.1.1** was released in 14/3/2022:
+**0.2.0** was released in 30/3/2022:
 
-- 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)
+- Support Circular Smooth Label (CSL, ECCV'20) (#153)
+- Add [browse_dataset](tools/misc/browse_dataset.py) tool (#98)
 
 Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
 
@@ -104,6 +100,7 @@ A summary can be found in the [Model Zoo](docs/en/model_zoo.md) page.
 * [x] [Rotated RepPoints-OBB](configs/rotated_reppoints/README.md) (ICCV'2019)
 * [x] [RoI Transformer](configs/roi_trans/README.md) (CVPR'2019)
 * [x] [Gliding Vertex](configs/gliding_vertex/README.md) (TPAMI'2020)
+* [x] [CSL](configs/csl/README.md) (ECCV'2020)
 * [x] [R<sup>3</sup>Det](configs/r3det/README.md) (AAAI'2021)
 * [x] [S<sup>2</sup>A-Net](configs/s2anet/README.md) (TGRS'2021)
 * [x] [ReDet](configs/redet/README.md) (CVPR'2021)

README_zh-CN.md

@@ -61,14 +61,10 @@ https://user-images.githubusercontent.com/10410257/154433305-416d129b-60c8-44c7-
 
 ## 更新日志
 
-最新的 **0.1.1** 版本已经在 2022.03.14 发布:
+最新的 **0.2.0** 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).
+- 支持了 Circular Sommth Label (CSL, ECCV'20) 模型 (#153)
+- 增加了[数据集浏览工具](tools/misc/browse_dataset.py) (#98)
 
 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)。
 
@@ -100,6 +96,7 @@ MMRotate 也提供了其他更详细的教程:
 * [x] [Rotated RepPoints-OBB](configs/rotated_reppoints/README.md) (ICCV'2019)
 * [x] [RoI Transformer](configs/roi_trans/README.md) (CVPR'2019)
 * [x] [Gliding Vertex](configs/gliding_vertex/README.md) (TPAMI'2020)
+* [x] [CSL](configs/csl/README.md) (ECCV'2020)
 * [x] [R<sup>3</sup>Det](configs/r3det/README.md) (AAAI'2021)
 * [x] [S<sup>2</sup>A-Net](configs/s2anet/README.md) (TGRS'2021)
 * [x] [ReDet](configs/redet/README.md) (CVPR'2021)

configs/csl/README.md

@@ -0,0 +1,43 @@
+# CSL
+> [Arbitrary-Oriented Object Detection with Circular Smooth Label](https://link.springer.com/chapter/10.1007/978-3-030-58598-3_40)
+
+<!-- [ALGORITHM] -->
+## Abstract
+
+<div align=center>
+<img src="https://raw.githubusercontent.com/zytx121/image-host/main/imgs/csl.jpg" width="800"/>
+</div>
+
+Arbitrary-oriented object detection has recently attracted increasing attention in vision for their importance
+in aerial imagery, scene text, and face etc. In this paper, we show that existing regression-based rotation detectors
+suffer the problem of discontinuous boundaries, which is directly caused by angular periodicity or corner ordering.
+By a careful study, we find the root cause is that the ideal predictions are beyond the defined range. We design a
+new rotation detection baseline, to address the boundary problem by transforming angular prediction from a regression
+problem to a classification task with little accuracy loss, whereby high-precision angle classification is devised in
+contrast to previous works using coarse-granularity in rotation detection. We also propose a circular smooth label (CSL)
+technique to handle the periodicity of the angle and increase the error tolerance to adjacent angles. We further
+introduce four window functions in CSL and explore the effect of different window radius sizes on detection performance.
+Extensive experiments and visual analysis on two large-scale public datasets for aerial images i.e. DOTA, HRSC2016,
+as well as scene text dataset ICDAR2015 and MLT, show the effectiveness of our approach.
+
+## Results and models
+
+DOTA1.0
+
+|    Backbone   |    mAP   | Angle | Window func. | Omega | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
+|:------------:|:----------:|:-----------:|:-----------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:-------------:|
+| ResNet50 (1024,1024,200) | 68.42 | le90 | - | - | 1x | 3.38 | 17.8 | - | 2 | [rotated_retinanet_obb_r50_fpn_1x_dota_le90](./rotated_retinanet_obb_r50_fpn_1x_dota_le90.py) |  [model](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_retinanet/rotated_retinanet_obb_r50_fpn_1x_dota_le90/rotated_retinanet_obb_r50_fpn_1x_dota_le90-c0097bc4.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_retinanet/rotated_retinanet_obb_r50_fpn_1x_dota_le90/rotated_retinanet_obb_r50_fpn_1x_dota_le90_20220128_130740.log.json)
+| ResNet50 (1024,1024,200) | 68.79 | le90 | - | - | 1x | 2.36 | 25.9 | - | 2 | [rotated_retinanet_obb_r50_fpn_fp16_1x_dota_le90](./rotated_retinanet_obb_r50_fpn_fp16_1x_dota_le90.py) |  [model](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_retinanet/rotated_retinanet_obb_r50_fpn_fp16_1x_dota_le90/rotated_retinanet_obb_r50_fpn_fp16_1x_dota_le90-01de71b5.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_retinanet/rotated_retinanet_obb_r50_fpn_fp16_1x_dota_le90/rotated_retinanet_obb_r50_fpn_fp16_1x_dota_le90_20220303_183714.log.json)
+| ResNet50 (1024,1024,200) | 69.51 | le90 | Gaussian | 4 | 1x | 2.60 | 24.0 | - | 2 | [rotated_retinanet_obb_csl_gaussian_r50_fpn_fp16_1x_dota_le90](./rotated_retinanet_obb_csl_gaussian_r50_fpn_fp16_1x_dota_le90.py) |  [model](https://download.openmmlab.com/mmrotate/v0.1.0/csl/rotated_retinanet_obb_csl_gaussian_r50_fpn_fp16_1x_dota_le90/rotated_retinanet_obb_csl_gaussian_r50_fpn_fp16_1x_dota_le90-b4271aed.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/csl/rotated_retinanet_obb_csl_gaussian_r50_fpn_fp16_1x_dota_le90/rotated_retinanet_obb_csl_gaussian_r50_fpn_fp16_1x_dota_le90_20220321_010033.log.json)
+
+
+## Citation
+```
+@inproceedings{yang2020arbitrary,
+    title={Arbitrary-Oriented Object Detection with Circular Smooth Label},
+    author={Yang, Xue and Yan, Junchi},
+    booktitle={European Conference on Computer Vision},
+    pages={677--694},
+    year={2020}
+}
+```