update code

.dev_scripts/gather_test_benchmark_metric.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import glob
+import os.path as osp
+
+from mmengine.config import Config
+from mmengine.fileio import dump, load
+from mmengine.utils import mkdir_or_exist
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Gather benchmarked models metric')
+    parser.add_argument('config', help='test config file path')
+    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 metrics to be stored')
+    parser.add_argument(
+        '--not-show', action='store_true', help='not show metrics')
+    parser.add_argument(
+        '--show-all', action='store_true', help='show all model metrics')
+
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    root_path = args.root
+    metrics_out = args.out
+    result_dict = {}
+
+    cfg = Config.fromfile(args.config)
+
+    for model_key in cfg:
+        model_infos = cfg[model_key]
+        if not isinstance(model_infos, list):
+            model_infos = [model_infos]
+        for model_info in model_infos:
+            record_metrics = model_info['metric']
+            config = model_info['config'].strip()
+            fname, _ = osp.splitext(osp.basename(config))
+            metric_json_dir = osp.join(root_path, fname)
+            if osp.exists(metric_json_dir):
+                json_list = glob.glob(osp.join(metric_json_dir, '*.json'))
+                if len(json_list) > 0:
+                    log_json_path = list(sorted(json_list))[-1]
+
+                    metric = load(log_json_path)
+                    if config in metric.get('config', {}):
+
+                        new_metrics = dict()
+                        for record_metric_key in record_metrics:
+                            record_metric_key_bk = record_metric_key
+                            old_metric = record_metrics[record_metric_key]
+                            if record_metric_key == 'AR_1000':
+                                record_metric_key = 'AR@1000'
+                            if record_metric_key not in metric['metric']:
+                                raise KeyError(
+                                    'record_metric_key not exist, please '
+                                    'check your config')
+                            new_metric = round(
+                                metric['metric'][record_metric_key] * 100, 1)
+                            new_metrics[record_metric_key_bk] = new_metric
+
+                        if args.show_all:
+                            result_dict[config] = dict(
+                                before=record_metrics, after=new_metrics)
+                        else:
+                            for record_metric_key in record_metrics:
+                                old_metric = record_metrics[record_metric_key]
+                                new_metric = new_metrics[record_metric_key]
+                                if old_metric != new_metric:
+                                    result_dict[config] = dict(
+                                        before=record_metrics,
+                                        after=new_metrics)
+                                    break
+                    else:
+                        print(f'{config} not included in: {log_json_path}')
+                else:
+                    print(f'{config} not exist file: {metric_json_dir}')
+            else:
+                print(f'{config} not exist dir: {metric_json_dir}')
+
+    if metrics_out:
+        mkdir_or_exist(metrics_out)
+        dump(result_dict, osp.join(metrics_out, 'batch_test_metric_info.json'))
+    if not args.not_show:
+        print('===================================')
+        for config_name, metrics in result_dict.items():
+            print(config_name, metrics)
+        print('===================================')

.dev_scripts/gather_train_benchmark_metric.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import glob
+import os.path as osp
+
+from gather_models import get_final_results
+from mmengine.config import Config
+from mmengine.fileio import dump
+from mmengine.utils import mkdir_or_exist
+
+try:
+    import xlrd
+except ImportError:
+    xlrd = None
+try:
+    import xlutils
+    from xlutils.copy import copy
+except ImportError:
+    xlutils = None
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Gather benchmarked models metric')
+    parser.add_argument(
+        'root',
+        type=str,
+        help='root path of benchmarked models to be gathered')
+    parser.add_argument(
+        'txt_path', type=str, help='txt path output by benchmark_filter')
+    parser.add_argument(
+        '--out', type=str, help='output path of gathered metrics to be stored')
+    parser.add_argument(
+        '--not-show', action='store_true', help='not show metrics')
+    parser.add_argument(
+        '--excel', type=str, help='input path of excel to be recorded')
+    parser.add_argument(
+        '--ncol', type=int, help='Number of column to be modified or appended')
+
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    if args.excel:
+        assert args.ncol, 'Please specify "--excel" and "--ncol" ' \
+                          'at the same time'
+        if xlrd is None:
+            raise RuntimeError(
+                'xlrd is not installed,'
+                'Please use “pip install xlrd==1.2.0” to install')
+        if xlutils is None:
+            raise RuntimeError(
+                'xlutils is not installed,'
+                'Please use “pip install xlutils==2.0.0” to install')
+        readbook = xlrd.open_workbook(args.excel)
+        sheet = readbook.sheet_by_name('Sheet1')
+        sheet_info = {}
+        total_nrows = sheet.nrows
+        for i in range(3, sheet.nrows):
+            sheet_info[sheet.row_values(i)[0]] = i
+        xlrw = copy(readbook)
+        table = xlrw.get_sheet(0)
+
+    root_path = args.root
+    metrics_out = args.out
+
+    result_dict = {}
+    with open(args.txt_path, 'r') as f:
+        model_cfgs = f.readlines()
+        for i, config in enumerate(model_cfgs):
+            config = config.strip()
+            if len(config) == 0:
+                continue
+
+            config_name = osp.split(config)[-1]
+            config_name = osp.splitext(config_name)[0]
+            result_path = osp.join(root_path, config_name)
+            if osp.exists(result_path):
+                # 1 read config
+                cfg = Config.fromfile(config)
+                total_epochs = cfg.runner.max_epochs
+                final_results = cfg.evaluation.metric
+                if not isinstance(final_results, list):
+                    final_results = [final_results]
+                final_results_out = []
+                for key in final_results:
+                    if 'proposal_fast' in key:
+                        final_results_out.append('AR@1000')  # RPN
+                    elif 'mAP' not in key:
+                        final_results_out.append(key + '_mAP')
+
+                # 2 determine whether total_epochs ckpt exists
+                ckpt_path = f'epoch_{total_epochs}.pth'
+                if osp.exists(osp.join(result_path, ckpt_path)):
+                    log_json_path = list(
+                        sorted(glob.glob(osp.join(result_path,
+                                                  '*.log.json'))))[-1]
+
+                    # 3 read metric
+                    model_performance = get_final_results(
+                        log_json_path, total_epochs, final_results_out)
+                    if model_performance is None:
+                        print(f'log file error: {log_json_path}')
+                        continue
+                    for performance in model_performance:
+                        if performance in ['AR@1000', 'bbox_mAP', 'segm_mAP']:
+                            metric = round(
+                                model_performance[performance] * 100, 1)
+                            model_performance[performance] = metric
+                    result_dict[config] = model_performance
+
+                    # update and append excel content
+                    if args.excel:
+                        if 'AR@1000' in model_performance:
+                            metrics = f'{model_performance["AR@1000"]}' \
+                                      f'(AR@1000)'
+                        elif 'segm_mAP' in model_performance:
+                            metrics = f'{model_performance["bbox_mAP"]}/' \
+                                      f'{model_performance["segm_mAP"]}'
+                        else:
+                            metrics = f'{model_performance["bbox_mAP"]}'
+
+                        row_num = sheet_info.get(config, None)
+                        if row_num:
+                            table.write(row_num, args.ncol, metrics)
+                        else:
+                            table.write(total_nrows, 0, config)
+                            table.write(total_nrows, args.ncol, metrics)
+                            total_nrows += 1
+
+                else:
+                    print(f'{config} not exist: {ckpt_path}')
+            else:
+                print(f'not exist: {config}')
+
+        # 4 save or print results
+        if metrics_out:
+            mkdir_or_exist(metrics_out)
+            dump(result_dict, osp.join(metrics_out, 'model_metric_info.json'))
+        if not args.not_show:
+            print('===================================')
+            for config_name, metrics in result_dict.items():
+                print(config_name, metrics)
+            print('===================================')
+        if args.excel:
+            filename, sufflx = osp.splitext(args.excel)
+            xlrw.save(f'{filename}_o{sufflx}')
+            print(f'>>> Output {filename}_o{sufflx}')

.dev_scripts/linter.sh

+yapf -r -i mmdet/ configs/ tests/ tools/
+isort -rc mmdet/ configs/ tests/ tools/
+flake8 .

.dev_scripts/test_init_backbone.py

+# Copyright (c) OpenMMLab. All rights reserved.
+"""Check out backbone whether successfully load pretrained checkpoint."""
+import copy
+import os
+from os.path import dirname, exists, join
+
+import pytest
+from mmengine.config import Config
+from mmengine.runner import CheckpointLoader
+from mmengine.utils import ProgressBar
+
+from mmdet.registry import MODELS
+
+
+def _get_config_directory():
+    """Find the predefined detector config directory."""
+    try:
+        # Assume we are running in the source mmdetection repo
+        repo_dpath = dirname(dirname(__file__))
+    except NameError:
+        # For IPython development when this __file__ is not defined
+        import mmdet
+        repo_dpath = dirname(dirname(mmdet.__file__))
+    config_dpath = join(repo_dpath, 'configs')
+    if not exists(config_dpath):
+        raise Exception('Cannot find config path')
+    return config_dpath
+
+
+def _get_config_module(fname):
+    """Load a configuration as a python module."""
+    config_dpath = _get_config_directory()
+    config_fpath = join(config_dpath, fname)
+    config_mod = Config.fromfile(config_fpath)
+    return config_mod
+
+
+def _get_detector_cfg(fname):
+    """Grab configs necessary to create a detector.
+
+    These are deep copied to allow for safe modification of parameters without
+    influencing other tests.
+    """
+    config = _get_config_module(fname)
+    model = copy.deepcopy(config.model)
+    return model
+
+
+def _traversed_config_file():
+    """We traversed all potential config files under the `config` file. If you
+    need to print details or debug code, you can use this function.
+
+    If the `backbone.init_cfg` is None (do not use `Pretrained` init way), you
+    need add the folder name in `ignores_folder` (if the config files in this
+    folder all set backbone.init_cfg is None) or add config name in
+    `ignores_file` (if the config file set backbone.init_cfg is None)
+    """
+    config_path = _get_config_directory()
+    check_cfg_names = []
+
+    # `base`, `legacy_1.x` and `common` ignored by default.
+    ignores_folder = ['_base_', 'legacy_1.x', 'common']
+    # 'ld' need load teacher model, if want to check 'ld',
+    # please check teacher_config path first.
+    ignores_folder += ['ld']
+    # `selfsup_pretrain` need convert model, if want to check this model,
+    # need to convert the model first.
+    ignores_folder += ['selfsup_pretrain']
+
+    # the `init_cfg` in 'centripetalnet', 'cornernet', 'cityscapes',
+    # 'scratch' is None.
+    # the `init_cfg` in ssdlite(`ssdlite_mobilenetv2_scratch_600e_coco.py`)
+    # is None
+    # Please confirm `bockbone.init_cfg` is None first.
+    ignores_folder += ['centripetalnet', 'cornernet', 'cityscapes', 'scratch']
+    ignores_file = ['ssdlite_mobilenetv2_scratch_600e_coco.py']
+
+    for config_file_name in os.listdir(config_path):
+        if config_file_name not in ignores_folder:
+            config_file = join(config_path, config_file_name)
+            if os.path.isdir(config_file):
+                for config_sub_file in os.listdir(config_file):
+                    if config_sub_file.endswith('py') and \
+                            config_sub_file not in ignores_file:
+                        name = join(config_file, config_sub_file)
+                        check_cfg_names.append(name)
+    return check_cfg_names
+
+
+def _check_backbone(config, print_cfg=True):
+    """Check out backbone whether successfully load pretrained model, by using
+    `backbone.init_cfg`.
+
+    First, using `CheckpointLoader.load_checkpoint` to load the checkpoint
+        without loading models.
+    Then, using `MODELS.build` to build models, and using
+        `model.init_weights()` to initialize the parameters.
+    Finally, assert weights and bias of each layer loaded from pretrained
+        checkpoint are equal to the weights and bias of original checkpoint.
+        For the convenience of comparison, we sum up weights and bias of
+        each loaded layer separately.
+
+    Args:
+        config (str): Config file path.
+        print_cfg (bool): Whether print logger and return the result.
+
+    Returns:
+        results (str or None): If backbone successfully load pretrained
+            checkpoint, return None; else, return config file path.
+    """
+    if print_cfg:
+        print('-' * 15 + 'loading ', config)
+    cfg = Config.fromfile(config)
+    init_cfg = None
+    try:
+        init_cfg = cfg.model.backbone.init_cfg
+        init_flag = True
+    except AttributeError:
+        init_flag = False
+    if init_cfg is None or init_cfg.get('type') != 'Pretrained':
+        init_flag = False
+    if init_flag:
+        checkpoint = CheckpointLoader.load_checkpoint(init_cfg.checkpoint)
+        if 'state_dict' in checkpoint:
+            state_dict = checkpoint['state_dict']
+        else:
+            state_dict = checkpoint
+
+        model = MODELS.build(cfg.model)
+        model.init_weights()
+
+        checkpoint_layers = state_dict.keys()
+        for name, value in model.backbone.state_dict().items():
+            if name in checkpoint_layers:
+                assert value.equal(state_dict[name])
+
+        if print_cfg:
+            print('-' * 10 + 'Successfully load checkpoint' + '-' * 10 +
+                  '\n', )
+            return None
+    else:
+        if print_cfg:
+            print(config + '\n' + '-' * 10 +
+                  'config file do not have init_cfg' + '-' * 10 + '\n')
+            return config
+
+
+@pytest.mark.parametrize('config', _traversed_config_file())
+def test_load_pretrained(config):
+    """Check out backbone whether successfully load pretrained model by using
+    `backbone.init_cfg`.
+
+    Details please refer to `_check_backbone`
+    """
+    _check_backbone(config, print_cfg=False)
+
+
+def _test_load_pretrained():
+    """We traversed all potential config files under the `config` file. If you
+    need to print details or debug code, you can use this function.
+
+    Returns:
+        check_cfg_names (list[str]): Config files that backbone initialized
+        from pretrained checkpoint might be problematic. Need to recheck
+        the config file. The output including the config files that the
+        backbone.init_cfg is None
+    """
+    check_cfg_names = _traversed_config_file()
+    need_check_cfg = []
+
+    prog_bar = ProgressBar(len(check_cfg_names))
+    for config in check_cfg_names:
+        init_cfg_name = _check_backbone(config)
+        if init_cfg_name is not None:
+            need_check_cfg.append(init_cfg_name)
+        prog_bar.update()
+    print('These config files need to be checked again')
+    print(need_check_cfg)

.owners.yml

+assign:
+  strategy:
+    # random
+    daily-shift-based
+  scedule:
+    '*/1 * * * *'
+  assignees:
+    - Czm369
+    - hhaAndroid
+    - jbwang1997
+    - RangiLyu
+    - BIGWangYuDong
+    - chhluo
+    - ZwwWayne

.pre-commit-config-zh-cn.yaml

+exclude: ^tests/data/
+repos:
+  - repo: https://gitee.com/openmmlab/mirrors-flake8
+    rev: 5.0.4
+    hooks:
+      - id: flake8
+  - repo: https://gitee.com/openmmlab/mirrors-isort
+    rev: 5.11.5
+    hooks:
+      - id: isort
+  - repo: https://gitee.com/openmmlab/mirrors-yapf
+    rev: v0.32.0
+    hooks:
+      - id: yapf
+  - repo: https://gitee.com/openmmlab/mirrors-pre-commit-hooks
+    rev: v4.3.0
+    hooks:
+      - id: trailing-whitespace
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: requirements-txt-fixer
+      - id: double-quote-string-fixer
+      - id: check-merge-conflict
+      - id: fix-encoding-pragma
+        args: ["--remove"]
+      - id: mixed-line-ending
+        args: ["--fix=lf"]
+  - repo: https://gitee.com/openmmlab/mirrors-mdformat
+    rev: 0.7.9
+    hooks:
+      - id: mdformat
+        args: ["--number"]
+        additional_dependencies:
+          - mdformat-openmmlab
+          - mdformat_frontmatter
+          - linkify-it-py
+  - repo: https://gitee.com/openmmlab/mirrors-codespell
+    rev: v2.2.1
+    hooks:
+      - id: codespell
+  - repo: https://gitee.com/openmmlab/mirrors-docformatter
+    rev: v1.3.1
+    hooks:
+      - id: docformatter
+        args: ["--in-place", "--wrap-descriptions", "79"]
+  - repo: https://gitee.com/openmmlab/mirrors-pyupgrade
+    rev: v3.0.0
+    hooks:
+      - id: pyupgrade
+        args: ["--py36-plus"]
+  - repo: https://gitee.com/open-mmlab/pre-commit-hooks
+    rev: v0.2.0
+    hooks:
+      - id: check-algo-readme
+      - id: check-copyright
+        args: ["mmdet"]
+#  - repo: https://gitee.com/openmmlab/mirrors-mypy
+#    rev: v0.812
+#    hooks:
+#      - id: mypy
+#        exclude: "docs"

.pre-commit-config.yaml

+repos:
+  - repo: https://github.com/PyCQA/flake8
+    rev: 5.0.4
+    hooks:
+      - id: flake8
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.11.5
+    hooks:
+      - id: isort
+  - repo: https://github.com/pre-commit/mirrors-yapf
+    rev: v0.32.0
+    hooks:
+      - id: yapf
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.3.0
+    hooks:
+      - id: trailing-whitespace
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: requirements-txt-fixer
+      - id: double-quote-string-fixer
+      - id: check-merge-conflict
+      - id: fix-encoding-pragma
+        args: ["--remove"]
+      - id: mixed-line-ending
+        args: ["--fix=lf"]
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.2.1
+    hooks:
+      - id: codespell
+  - repo: https://github.com/executablebooks/mdformat
+    rev: 0.7.9
+    hooks:
+      - id: mdformat
+        args: ["--number"]
+        additional_dependencies:
+          - mdformat-openmmlab
+          - mdformat_frontmatter
+          - linkify-it-py
+  - repo: https://github.com/myint/docformatter
+    rev: v1.3.1
+    hooks:
+      - id: docformatter
+        args: ["--in-place", "--wrap-descriptions", "79"]
+  - repo: https://github.com/open-mmlab/pre-commit-hooks
+    rev: v0.2.0  # Use the ref you want to point at
+    hooks:
+      - id: check-algo-readme
+      - id: check-copyright
+        args: ["mmdet"]  # replace the dir_to_check with your expected directory to check

.readthedocs.yml

+version: 2
+
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.8"
+
+formats:
+  - epub
+
+python:
+  install:
+    - requirements: requirements/docs.txt
+    - requirements: requirements/readthedocs.txt

CITATION.cff

+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+authors:
+  - name: "MMDetection Contributors"
+title: "OpenMMLab Detection Toolbox and Benchmark"
+date-released: 2018-08-22
+url: "https://github.com/open-mmlab/mmdetection"
+license: Apache-2.0

LICENSE

+Copyright 2018-2023 OpenMMLab. All rights reserved.
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
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+   See the License for the specific language governing permissions and
+   limitations under the License.

MANIFEST.in

+include requirements/*.txt
+include mmdet/VERSION
+include mmdet/.mim/model-index.yml
+include mmdet/.mim/dataset-index.yml
+include mmdet/.mim/demo/*/*
+recursive-include mmdet/.mim/configs *.py *.yml
+recursive-include mmdet/.mim/tools *.sh *.py

README.md

 # RTMDet_mmcv
-## 模型介绍
-## 模型结构
\ No newline at end of file
+## 论文
+`RTMDet: An Empirical Study of Designing Real-Time Object Detectors`<br>
+[论文链接](https://arxiv.org/abs/2212.07784)
+## 模型结构
+RTMDet 将 CSPDarkNet 作为基线，并使用同样构建单元组成的 CSPPAFPN 进行多尺度的特征融合，最后将特征输入给不同的检测头，进行目标检测、实例分割和旋转框检测等任务
+![模型结构](./resources/moel_struct.jpg)
+## 算法原理
+宏观架构与yolo系列没有太大的区别，主要增加了构建基本单元的特征提取能力以及弹错模型不同的组件件的计算效率和精度平衡
+对构建单元进行了更新
+![model](./resources/model.jpg)
+![构建单元](./resources/模块改进.jpg)
+## 环境配置
+### Docker（方法一）
+
+```
+docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.13.1-centos7.6-dtk-23.04.1-py38-latest
+docker run -it -v /path/your_code_data/:/path/your_code_data/ --shm-size=32G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
+cd /path/workspace/
+python -v -e .
+pip3 install -r requirements.txt
+```
+### Dockerfile（方法二）
+```
+cd ./docker
+docker build --no-cache -t mmdet:3.0 .
+docker run -it -v /path/your_code_data/:/path/your_code_data/ --shm-size=32G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
+cd /path/workspace/
+python -v -e .
+```
+### Anaconda（方法三）
+1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装： https://developer.hpccube.com/tool/
+```
+DTK软件栈: dtk23.04.1
+python: python3.8
+torch: 1.13.1
+torchvision: 0.14.1
+mmcv: 2.0.0
+
+```
+
+Tips：以上dtk软件栈、python、torch、mmcv等DCU相关工具版本需要严格一一对应
+
+2、其他非特殊库直接按照requirements.txt安装
+
+```
+cd workspace/
+pip install -v -e .
+pip3 install -r requirements.txt
+```
+
+## 数据集
+COCO2017（在网络良好的情况下，如果没有下载数据集，程序会默认在线下载数据集）
+
+[训练数据](http://images.cocodataset.org/zips/train2017.zip)
+
+[验证数据](http://images.cocodataset.org/zips/val2017.zip)
+
+[测试数据](http://images.cocodataset.org/zips/test2017.zip)
+
+[标签数据](https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels.zip)
+
+数据集的目录结构如下：
+
+```
+├── images 
+│   ├── train2017
+│   ├── val2017
+│   ├── test2017
+├── labels
+│   ├── train2017
+│   ├── val2017
+├── annotations
+│   ├── instances_val2017.json
+├── LICENSE
+├── README.txt 
+├── test-dev2017.txt
+├── train2017.txt
+├── val2017.txt
+
+```
+
+## 训练
+```python
+bash ./train.sh
+```
+## result
+![result](./resources/result.jpg)
+## 精度
+|  模型名称  | batchsize | amp混精 |  精度  |
+|:------:|:---------:|:-----:|:----:|
+|rtmdet-s|    256    |  off  | 44.5 |
+|rtmdet-s|    256    |  on   | 44.5 |
+## 应用场景
+### 算法类别
+`目标检测`
+### 热点应用行业
+`金融,交通,教育`
+## 源码仓库及问题反馈
+- https://developer.hpccube.com/codes/modelzoo/imagegenerate_paddle
+## 参考资料
+- https://github.com/PaddlePaddle/PaddleNLP/tree/develop/ppdiffusers
+- https://aistudio.baidu.com/aistudio/projectdetail/4905623
\ No newline at end of file

configs/_base_/datasets/ade20k_instance.py

+# dataset settings
+dataset_type = 'ADE20KInstanceDataset'
+data_root = 'data/ADEChallengeData2016/'
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection/ADEChallengeData2016/'
+
+# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection/',
+#         'data/': 's3://openmmlab/datasets/detection/'
+#     }))
+backend_args = None
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='Resize', scale=(2560, 640), keep_ratio=True),
+    # If you don't have a gt annotation, delete the pipeline
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor'))
+]
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='ade20k_instance_val.json',
+        data_prefix=dict(img='images/validation'),
+        test_mode=True,
+        pipeline=test_pipeline,
+        backend_args=backend_args))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(
+    type='CocoMetric',
+    ann_file=data_root + 'ade20k_instance_val.json',
+    metric=['bbox', 'segm'],
+    format_only=False,
+    backend_args=backend_args)
+test_evaluator = val_evaluator

configs/_base_/datasets/ade20k_panoptic.py

+# dataset settings
+dataset_type = 'ADE20KPanopticDataset'
+data_root = 'data/ADEChallengeData2016/'
+
+backend_args = None
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='Resize', scale=(2560, 640), keep_ratio=True),
+    dict(type='LoadPanopticAnnotations', backend_args=backend_args),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor'))
+]
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=0,
+    persistent_workers=False,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='ade20k_panoptic_val.json',
+        data_prefix=dict(img='images/validation/', seg='ade20k_panoptic_val/'),
+        test_mode=True,
+        pipeline=test_pipeline,
+        backend_args=backend_args))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(
+    type='CocoPanopticMetric',
+    ann_file=data_root + 'ade20k_panoptic_val.json',
+    seg_prefix=data_root + 'ade20k_panoptic_val/',
+    backend_args=backend_args)
+test_evaluator = val_evaluator

configs/_base_/datasets/ade20k_semantic.py

+dataset_type = 'ADE20KSegDataset'
+data_root = 'data/ADEChallengeData2016/'
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection/ADEChallengeData2016/'
+
+# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection/',
+#         'data/': 's3://openmmlab/datasets/detection/'
+#     }))
+backend_args = None
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='Resize', scale=(2048, 512), keep_ratio=True),
+    dict(
+        type='LoadAnnotations',
+        with_bbox=False,
+        with_mask=False,
+        with_seg=True,
+        reduce_zero_label=True),
+    dict(
+        type='PackDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape'))
+]
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            img_path='images/validation',
+            seg_map_path='annotations/validation'),
+        pipeline=test_pipeline))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(type='SemSegMetric', iou_metrics=['mIoU'])
+test_evaluator = val_evaluator

configs/_base_/datasets/cityscapes_detection.py

+# dataset settings
+dataset_type = 'CityscapesDataset'
+data_root = 'data/cityscapes/'
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/segmentation/cityscapes/'
+
+# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/segmentation/',
+#          'data/': 's3://openmmlab/datasets/segmentation/'
+#      }))
+backend_args = None
+
+train_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='RandomResize',
+        scale=[(2048, 800), (2048, 1024)],
+        keep_ratio=True),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PackDetInputs')
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='Resize', scale=(2048, 1024), keep_ratio=True),
+    # If you don't have a gt annotation, delete the pipeline
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor'))
+]
+
+train_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    batch_sampler=dict(type='AspectRatioBatchSampler'),
+    dataset=dict(
+        type='RepeatDataset',
+        times=8,
+        dataset=dict(
+            type=dataset_type,
+            data_root=data_root,
+            ann_file='annotations/instancesonly_filtered_gtFine_train.json',
+            data_prefix=dict(img='leftImg8bit/train/'),
+            filter_cfg=dict(filter_empty_gt=True, min_size=32),
+            pipeline=train_pipeline,
+            backend_args=backend_args)))
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='annotations/instancesonly_filtered_gtFine_val.json',
+        data_prefix=dict(img='leftImg8bit/val/'),
+        test_mode=True,
+        filter_cfg=dict(filter_empty_gt=True, min_size=32),
+        pipeline=test_pipeline,
+        backend_args=backend_args))
+
+test_dataloader = val_dataloader
+
+val_evaluator = dict(
+    type='CocoMetric',
+    ann_file=data_root + 'annotations/instancesonly_filtered_gtFine_val.json',
+    metric='bbox',
+    backend_args=backend_args)
+
+test_evaluator = val_evaluator

configs/_base_/datasets/cityscapes_instance.py

+# dataset settings
+dataset_type = 'CityscapesDataset'
+data_root = 'data/cityscapes/'
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/segmentation/cityscapes/'
+
+# Method 2: Use backend_args, file_client_args in versions before 3.0.0rc6
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/segmentation/',
+#          'data/': 's3://openmmlab/datasets/segmentation/'
+#      }))
+backend_args = None
+
+train_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='RandomResize',
+        scale=[(2048, 800), (2048, 1024)],
+        keep_ratio=True),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PackDetInputs')
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='Resize', scale=(2048, 1024), keep_ratio=True),
+    # If you don't have a gt annotation, delete the pipeline
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor'))
+]
+
+train_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    batch_sampler=dict(type='AspectRatioBatchSampler'),
+    dataset=dict(
+        type='RepeatDataset',
+        times=8,
+        dataset=dict(
+            type=dataset_type,
+            data_root=data_root,
+            ann_file='annotations/instancesonly_filtered_gtFine_train.json',
+            data_prefix=dict(img='leftImg8bit/train/'),
+            filter_cfg=dict(filter_empty_gt=True, min_size=32),
+            pipeline=train_pipeline,
+            backend_args=backend_args)))
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='annotations/instancesonly_filtered_gtFine_val.json',
+        data_prefix=dict(img='leftImg8bit/val/'),
+        test_mode=True,
+        filter_cfg=dict(filter_empty_gt=True, min_size=32),
+        pipeline=test_pipeline,
+        backend_args=backend_args))
+
+test_dataloader = val_dataloader
+
+val_evaluator = [
+    dict(
+        type='CocoMetric',
+        ann_file=data_root +
+        'annotations/instancesonly_filtered_gtFine_val.json',
+        metric=['bbox', 'segm'],
+        backend_args=backend_args),
+    dict(
+        type='CityScapesMetric',
+        seg_prefix=data_root + 'gtFine/val',
+        outfile_prefix='./work_dirs/cityscapes_metric/instance',
+        backend_args=backend_args)
+]
+
+test_evaluator = val_evaluator
+
+# inference on test dataset and
+# format the output results for submission.
+# test_dataloader = dict(
+#     batch_size=1,
+#     num_workers=2,
+#     persistent_workers=True,
+#     drop_last=False,
+#     sampler=dict(type='DefaultSampler', shuffle=False),
+#     dataset=dict(
+#         type=dataset_type,
+#         data_root=data_root,
+#         ann_file='annotations/instancesonly_filtered_gtFine_test.json',
+#         data_prefix=dict(img='leftImg8bit/test/'),
+#         test_mode=True,
+#         filter_cfg=dict(filter_empty_gt=True, min_size=32),
+#         pipeline=test_pipeline))
+# test_evaluator = dict(
+#         type='CityScapesMetric',
+#         format_only=True,
+#         outfile_prefix='./work_dirs/cityscapes_metric/test')

configs/_base_/datasets/coco_caption.py

+# data settings
+
+dataset_type = 'CocoCaptionDataset'
+data_root = 'data/coco/'
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection/coco/'
+
+# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection/',
+#         'data/': 's3://openmmlab/datasets/detection/'
+#     }))
+backend_args = None
+
+test_pipeline = [
+    dict(
+        type='LoadImageFromFile',
+        imdecode_backend='pillow',
+        backend_args=backend_args),
+    dict(
+        type='Resize',
+        scale=(224, 224),
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(type='PackInputs', meta_keys=['image_id']),
+]
+
+# ann_file download from
+# train dataset: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json # noqa
+# val dataset: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json # noqa
+# test dataset: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json # noqa
+# val evaluator: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val_gt.json # noqa
+# test evaluator: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test_gt.json # noqa
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='annotations/coco_karpathy_val.json',
+        pipeline=test_pipeline,
+    ))
+
+val_evaluator = dict(
+    type='COCOCaptionMetric',
+    ann_file=data_root + 'annotations/coco_karpathy_val_gt.json',
+)
+
+# # If you want standard test, please manually configure the test dataset
+test_dataloader = val_dataloader
+test_evaluator = val_evaluator