添加openmmlab测试用例

README.md

@@ -66,6 +66,9 @@ DCU深度学习样例
 | :----------: | :----------: | :----------: | :----------: | :----------: | :----------: |
 | PyTorch      | v1.10 | Yes | FP32/FP16 | Yes | [FastPitch](http://10.0.100.3/dcutoolkit/deeplearing/dlexamples_new/-/tree/main/PyTorch/Speech/FastPitch) |
 | PyTorch      | v1.10 | Yes | FP32/FP16 | Yes | [Tacotron2](http://10.0.100.3/dcutoolkit/deeplearing/dlexamples_new/-/tree/main/PyTorch/Speech/Tacotron2) |
+| PyTorch      | v1.10 | Yes | FP32/FP16 | Yes | [FastSpeech2](http://10.0.100.3/dcutoolkit/deeplearing/dlexamples_new/-/tree/main/PyTorch/Speech/FastSpeech2) |
+| PyTorch      | v1.10 | Yes | FP32/FP16 | Yes | [ECAPA-TDNN](http://10.0.100.3/dcutoolkit/deeplearing/dlexamples_new/-/tree/main/PyTorch/Speech/ECAPA-TDNN) |
+
 
 
 <br>
@@ -79,3 +82,4 @@ DCU深度学习样例
 | Tensorflow      | v1.15 | Yes | FP32/FP16 | Yes | [Vae-Cf](http://10.0.100.3/dcutoolkit/deeplearing/dlexamples_new/-/tree/add_Recommendation/PyTorch/Recommendation/VAE-CF) |
 | Tensorflow      | v2.7 | Yes | FP32/FP16 | Yes | [WideAndDeep]() |
 
+

openmmlab_test/.vscode/limfxbasicinfo.json

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openmmlab_test/mmclassification-speed-benchmark/.github/CONTRIBUTING.md

+# Contributing to OpenMMLab
+
+All kinds of contributions are welcome, including but not limited to the following.
+
+- Fixes (typo, bugs)
+- New features and components
+
+## Workflow
+
+1. fork and pull the latest OpenMMLab repository (mmclassification)
+3. checkout a new branch (do not use master branch for PRs)
+4. commit your changes
+5. create a PR
+
+Note: If you plan to add some new features that involve large changes, it is encouraged to open an issue for discussion first.
+
+## Code style
+
+### Python
+
+We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style.
+
+We use the following tools for linting and formatting:
+
+- [flake8](http://flake8.pycqa.org/en/latest/): A wrapper around some linter tools.
+- [yapf](https://github.com/google/yapf): A formatter for Python files.
+- [isort](https://github.com/timothycrosley/isort): A Python utility to sort imports.
+- [markdownlint](https://github.com/markdownlint/markdownlint): A linter to check markdown files and flag style issues.
+- [docformatter](https://github.com/myint/docformatter): A formatter to format docstring.
+
+Style configurations of yapf and isort can be found in [setup.cfg](./setup.cfg).
+
+We use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`, `markdown files`,
+fixes `end-of-files`, `double-quoted-strings`, `python-encoding-pragma`, `mixed-line-ending`, sorts `requirments.txt` automatically on every commit.
+The config for a pre-commit hook is stored in [.pre-commit-config](./.pre-commit-config.yaml).
+
+After you clone the repository, you will need to install initialize pre-commit hook.
+
+```shell
+pip install -U pre-commit
+```
+
+From the repository folder
+
+```shell
+pre-commit install
+```
+
+Try the following steps to install ruby when you encounter an issue on installing markdownlint
+
+```shell
+# install rvm
+curl -L https://get.rvm.io | bash -s -- --autolibs=read-fail
+[[ -s "$HOME/.rvm/scripts/rvm" ]] && source "$HOME/.rvm/scripts/rvm"
+rvm autolibs disable
+
+# install ruby
+rvm install 2.7.1
+```
+
+Or refer to [this repo](https://github.com/innerlee/setup) and take [`zzruby.sh`](https://github.com/innerlee/setup/blob/master/zzruby.sh) according its instruction.
+
+After this on every commit check code linters and formatter will be enforced.
+
+>Before you create a PR, make sure that your code lints and is formatted by yapf.
+
+### C++ and CUDA
+
+We follow the [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html).

openmmlab_test/mmclassification-speed-benchmark/.github/workflows/build.yml

+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+
+name: build
+
+on: [push, pull_request]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python 3.7
+        uses: actions/setup-python@v1
+        with:
+          python-version: 3.7
+      - name: Install pre-commit hook
+        run: |
+          pip install pre-commit
+          pre-commit install
+      - name: Linting
+        run: pre-commit run --all-files
+
+  build:
+    runs-on: ubuntu-latest
+    env:
+      UBUNTU_VERSION: ubuntu1804
+    strategy:
+      matrix:
+        python-version: [3.7]
+        torch: [1.3.0, 1.5.0, 1.6.0, 1.7.0, 1.8.0]
+        include:
+          - torch: 1.3.0
+            torchvision: 0.4.2
+          - torch: 1.5.0
+            torchvision: 0.6.0
+          - torch: 1.6.0
+            torchvision: 0.7.0
+          - torch: 1.6.0
+            torchvision: 0.7.0
+            python-version: 3.6
+          - torch: 1.6.0
+            torchvision: 0.7.0
+            python-version: 3.8
+          - torch: 1.7.0
+            torchvision: 0.8.1
+          - torch: 1.7.0
+            torchvision: 0.8.1
+            python-version: 3.6
+          - torch: 1.7.0
+            torchvision: 0.8.1
+            python-version: 3.8
+          - torch: 1.8.0
+            torchvision: 0.9.0
+          - torch: 1.8.0
+            torchvision: 0.9.0
+            python-version: 3.6
+          - torch: 1.8.0
+            torchvision: 0.9.0
+            python-version: 3.8
+
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install Pillow
+        run: pip install Pillow==6.2.2
+        if: ${{matrix.torchvision < 0.5}}
+      - name: Install PyTorch
+        run: pip install --use-deprecated=legacy-resolver torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu  -f https://download.pytorch.org/whl/torch_stable.html
+      - name: Install MMCV
+        run: |
+          pip install --use-deprecated=legacy-resolver mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/torch${{matrix.torch}}/index.html
+          python -c 'import mmcv; print(mmcv.__version__)'
+      - name: Install mmcls dependencies
+        run: |
+          pip install -r requirements.txt
+      - name: Build and install
+        run: |
+          rm -rf .eggs
+          pip install -e . -U
+      - name: Run unittests and generate coverage report
+        run: |
+          coverage run --branch --source mmcls -m pytest tests/
+          coverage xml
+          coverage report -m --omit="mmcls/utils/*","mmcls/apis/*"
+      # Only upload coverage report for python3.7 && pytorch1.5
+      - name: Upload coverage to Codecov
+        if: ${{matrix.torch == '1.5.0' && matrix.python-version == '3.7'}}
+        uses: codecov/codecov-action@v1.0.10
+        with:
+          file: ./coverage.xml
+          flags: unittests
+          env_vars: OS,PYTHON
+          name: codecov-umbrella
+          fail_ci_if_error: false

openmmlab_test/mmclassification-speed-benchmark/.github/workflows/deploy.yml

+name: deploy
+
+on: push
+
+jobs:
+  build-n-publish:
+    runs-on: ubuntu-latest
+    if: startsWith(github.event.ref, 'refs/tags')
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python 3.7
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.7
+      - name: Build MMClassification
+        run: |
+          pip install wheel
+          python setup.py sdist bdist_wheel
+      - name: Publish distribution to PyPI
+        run: |
+          pip install twine
+          twine upload dist/* -u __token__ -p ${{ secrets.pypi_password }}

openmmlab_test/mmclassification-speed-benchmark/.gitignore

+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+**/*.pyc
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# custom
+data
+.vscode
+.idea
+*.pkl
+*.pkl.json
+*.log.json
+work_dirs/
+
+# Pytorch
+*.pth

openmmlab_test/mmclassification-speed-benchmark/.pre-commit-config.yaml

+exclude: ^tests/data/
+repos:
+  - repo: https://gitlab.com/pycqa/flake8.git
+    rev: 3.8.3
+    hooks:
+      - id: flake8
+  - repo: https://github.com/asottile/seed-isort-config
+    rev: v2.2.0
+    hooks:
+      - id: seed-isort-config
+  - repo: https://github.com/timothycrosley/isort
+    rev: 4.3.21
+    hooks:
+      - id: isort
+  - repo: https://github.com/pre-commit/mirrors-yapf
+    rev: v0.30.0
+    hooks:
+      - id: yapf
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v3.1.0
+    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/jumanjihouse/pre-commit-hooks
+    rev: 2.1.4
+    hooks:
+      - id: markdownlint
+        args: ["-r", "~MD002,~MD013,~MD029,~MD033,~MD034",
+              "-t", "allow_different_nesting"]
+  - repo: https://github.com/myint/docformatter
+    rev: v1.3.1
+    hooks:
+      - id: docformatter
+        args: ["--in-place", "--wrap-descriptions", "79"]
+  # - repo: local
+  #   hooks:
+  #     - id: clang-format
+  #       name: clang-format
+  #       description: Format files with ClangFormat
+  #       entry: clang-format -style=google -i
+  #       language: system
+  #       files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|cuh|proto)$

openmmlab_test/mmclassification-speed-benchmark/.readthedocs.yml

+version: 2
+
+python:
+    version: 3.7
+    install:
+      - requirements: requirements/docs.txt
+      - requirements: requirements/readthedocs.txt

openmmlab_test/mmclassification-speed-benchmark/LICENSE

+Copyright 2020 MMClassification Authors. All rights reserved.
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+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

openmmlab_test/mmclassification-speed-benchmark/MANIFEST.in

+include mmcls/model_zoo.yml
+recursive-include mmcls/configs *.py *.yml
+recursive-include mmcls/tools *.sh *.py

openmmlab_test/mmclassification-speed-benchmark/README.md

+<div align="center">
+  <img src="resources/mmcls-logo.png" width="600"/>
+</div>
+
+[![Build Status](https://github.com/open-mmlab/mmclassification/workflows/build/badge.svg)](https://github.com/open-mmlab/mmclassification/actions)
+[![Documentation Status](https://readthedocs.org/projects/mmclassification/badge/?version=latest)](https://mmclassification.readthedocs.io/en/latest/?badge=latest)
+[![codecov](https://codecov.io/gh/open-mmlab/mmclassification/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmclassification)
+[![license](https://img.shields.io/github/license/open-mmlab/mmclassification.svg)](https://github.com/open-mmlab/mmclassification/blob/master/LICENSE)
+
+## Introduction
+
+English | [简体中文](/README_zh-CN.md) | [模型的测试方法及测试步骤](train.md)
+
+MMClassification is an open source image classification toolbox based on PyTorch. It is
+a part of the [OpenMMLab](https://openmmlab.com/) project.
+
+Documentation: https://mmclassification.readthedocs.io/en/latest/
+
+![demo](https://user-images.githubusercontent.com/9102141/87268895-3e0d0780-c4fe-11ea-849e-6140b7e0d4de.gif)
+
+### Major features
+
+- Various backbones and pretrained models
+- Bag of training tricks
+- Large-scale training configs
+- High efficiency and extensibility
+
+## License
+
+This project is released under the [Apache 2.0 license](LICENSE).
+
+## Changelog
+
+v0.12.0 was released in 3/6/2021.
+Please refer to [changelog.md](docs/changelog.md) for details and release history.
+
+## Benchmark and model zoo
+
+Results and models are available in the [model zoo](docs/model_zoo.md).
+
+Supported backbones:
+
+- [x] ResNet
+- [x] ResNeXt
+- [x] SE-ResNet
+- [x] SE-ResNeXt
+- [x] RegNet
+- [x] ShuffleNetV1
+- [x] ShuffleNetV2
+- [x] MobileNetV2
+- [x] MobileNetV3
+
+## Installation
+
+Please refer to [install.md](docs/install.md) for installation and dataset preparation.
+
+## Getting Started
+
+Please see [getting_started.md](docs/getting_started.md) for the basic usage of MMClassification. There are also tutorials for [finetuning models](docs/tutorials/finetune.md), [adding new dataset](docs/tutorials/new_dataset.md), [designing data pipeline](docs/tutorials/data_pipeline.md), and [adding new modules](docs/tutorials/new_modules.md).
+
+## Citation
+
+If you find this project useful in your research, please consider cite:
+
+```BibTeX
+@misc{2020mmclassification,
+    title={OpenMMLab's Image Classification Toolbox and Benchmark},
+    author={MMClassification Contributors},
+    howpublished = {\url{https://github.com/open-mmlab/mmclassification}},
+    year={2020}
+}
+```
+
+## Contributing
+
+We appreciate all contributions to improve MMClassification.
+Please refer to [CONTRUBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
+
+## Acknowledgement
+
+MMClassification is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks.
+We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new classifiers.
+
+## Projects in OpenMMLab
+
+- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
+- [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's next-generation platform for general 3D object detection.
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation 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.
+- [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): OpenMMLab toolbox for text detection, recognition and understanding.
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMlab toolkit for generative models.
+
+

openmmlab_test/mmclassification-speed-benchmark/README_zh-CN.md

+<div align="center">
+  <img src="resources/mmcls-logo.png" width="600"/>
+</div>
+
+[English](/README.md) | 简体中文
+
+[![Build Status](https://github.com/open-mmlab/mmclassification/workflows/build/badge.svg)](https://github.com/open-mmlab/mmclassification/actions)
+[![Documentation Status](https://readthedocs.org/projects/mmclassification/badge/?version=latest)](https://mmclassification.readthedocs.io/en/latest/?badge=latest)
+[![codecov](https://codecov.io/gh/open-mmlab/mmclassification/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmclassification)
+[![license](https://img.shields.io/github/license/open-mmlab/mmclassification.svg)](https://github.com/open-mmlab/mmclassification/blob/master/LICENSE)
+
+## Introduction
+
+MMClassification 是一款基于 PyTorch 的开源图像分类工具箱，是 [OpenMMLab](https://openmmlab.com/) 项目的成员之一
+
+参考文档：https://mmclassification.readthedocs.io/en/latest/
+
+![demo](https://user-images.githubusercontent.com/9102141/87268895-3e0d0780-c4fe-11ea-849e-6140b7e0d4de.gif)
+
+### 主要特性
+
+- 支持多样的主干网络与预训练模型
+- 支持配置多种训练技巧
+- 大量的训练配置文件
+- 高效率和高可扩展性
+
+## 许可证
+
+该项目开源自 [Apache 2.0 license](LICENSE).
+
+## 更新日志
+
+2021/6/3 发布了 v0.12.0 版本
+
+发布历史和更新细节请参考 [更新日志](docs/changelog.md)
+
+## 基准测试及模型库
+
+相关结果和模型可在 [model zoo](docs/model_zoo.md) 中获得
+
+支持的主干网络：
+
+- [x] ResNet
+- [x] ResNeXt
+- [x] SE-ResNet
+- [x] SE-ResNeXt
+- [x] RegNet
+- [x] ShuffleNetV1
+- [x] ShuffleNetV2
+- [x] MobileNetV2
+- [x] MobileNetV3
+
+## 安装
+
+请参考 [安装指南](docs_zh-CN/install.md) 进行安装
+
+## 基础教程
+
+请参考 [基础教程](docs_zh-CN/getting_started.md) 来了解 MMClassification 的基本使用。其中还包含了 [如何微调模型](docs_zh-CN/tutorials/finetune.md), [如何增加新数据集](docs_zh-CN/tutorials/new_dataset.md), [如何设计数据处理流程](docs_zh-CN/tutorials/data_pipeline.md), 以及 [如何增加新模块](docs_zh-CN/tutorials/new_modules.md) 等指南。
+
+## 参与贡献
+
+我们非常欢迎任何有助于提升 MMClassification 的贡献，请参考 [贡献指南](.github/CONTRIBUTING.md) 来了解如何参与贡献。
+
+## 致谢
+
+MMClassification 是一款由不同学校和公司共同贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。
+
+我们希望该工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现现有算法并开发自己的新模型，从而不断为开源社区提供贡献。
+
+## OpenMMLab 的其他项目
+
+- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
+- [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
+- [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 生成模型工具箱
+
+## 欢迎加入 OpenMMLab 社区
+
+扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，加入 OpenMMLab 团队的 [官方交流 QQ 群](https://jq.qq.com/?_wv=1027&k=aCvMxdr3)
+
+<div align="center">
+<img src="/docs/imgs/zhihu_qrcode.jpg" height="400" />  <img src="/docs/imgs/qq_group_qrcode.jpg" height="400" />
+</div>
+
+我们会在 OpenMMLab 社区为大家
+
+- 📢 分享 AI 框架的前沿核心技术
+- 💻 解读 PyTorch 常用模块源码
+- 📰 发布 OpenMMLab 的相关新闻
+- 🚀 介绍 OpenMMLab 开发的前沿算法
+- 🏃 获取更高效的问题答疑和意见反馈
+- 🔥 提供与各行各业开发者充分交流的平台
+
+干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/datasets/cifar100_bs16.py

+# dataset settings
+dataset_type = 'CIFAR100'
+img_norm_cfg = dict(
+    mean=[129.304, 124.070, 112.434],
+    std=[68.170, 65.392, 70.418],
+    to_rgb=False)
+train_pipeline = [
+    dict(type='RandomCrop', size=32, padding=4),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=16,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_prefix='data/cifar100',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_prefix='data/cifar100',
+        pipeline=test_pipeline,
+        test_mode=True),
+    test=dict(
+        type=dataset_type,
+        data_prefix='data/cifar100',
+        pipeline=test_pipeline,
+        test_mode=True))

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/datasets/cifar10_bs16.py

+# dataset settings
+dataset_type = 'CIFAR10'
+img_norm_cfg = dict(
+    mean=[125.307, 122.961, 113.8575],
+    std=[51.5865, 50.847, 51.255],
+    to_rgb=False)
+train_pipeline = [
+    dict(type='RandomCrop', size=32, padding=4),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=16,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type, data_prefix='data/cifar10',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_prefix='data/cifar10',
+        pipeline=test_pipeline,
+        test_mode=True),
+    test=dict(
+        type=dataset_type,
+        data_prefix='data/cifar10',
+        pipeline=test_pipeline,
+        test_mode=True))

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/datasets/imagenet_bs32.py

+# dataset settings
+dataset_type = 'ImageNet'
+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='RandomResizedCrop', size=224),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(256, -1)),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=32,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline),
+    test=dict(
+        # replace `data/val` with `data/test` for standard test
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='accuracy')

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/datasets/imagenet_bs32_pil_resize.py

+# dataset settings
+dataset_type = 'ImageNet'
+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='RandomResizedCrop', size=224, backend='pillow'),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(256, -1), backend='pillow'),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=32,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline),
+    test=dict(
+        # replace `data/val` with `data/test` for standard test
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='accuracy')

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/datasets/imagenet_bs64.py

+# dataset settings
+dataset_type = 'ImageNet'
+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='RandomResizedCrop', size=224),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(256, -1)),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=64,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline),
+    test=dict(
+        # replace `data/val` with `data/test` for standard test
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='accuracy')

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/datasets/imagenet_bs64_pil_resize.py

+# dataset settings
+dataset_type = 'ImageNet'
+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='RandomResizedCrop', size=224, backend='pillow'),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(256, -1), backend='pillow'),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=64,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline),
+    test=dict(
+        # replace `data/val` with `data/test` for standard test
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='accuracy')

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/datasets/voc_bs16.py

+# dataset settings
+dataset_type = 'VOC'
+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='RandomResizedCrop', size=224),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(256, -1)),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=16,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_prefix='data/VOCdevkit/VOC2007/',
+        ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_prefix='data/VOCdevkit/VOC2007/',
+        ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        data_prefix='data/VOCdevkit/VOC2007/',
+        ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',
+        pipeline=test_pipeline))
+evaluation = dict(
+    interval=1, metric=['mAP', 'CP', 'OP', 'CR', 'OR', 'CF1', 'OF1'])