Merge branch 'add_Recommendation' into 'main'

添加openmmlab测试用例

See merge request dcutoolkit/deeplearing/dlexamples_new!32

openmmlab_test/mmclassification-speed-benchmark/configs/vision_transformer/vit_large_patch16_224_finetune_imagenet.py

+# Refer to pytorch-image-models
+_base_ = [
+    '../_base_/models/vit_large_patch16_224_finetune.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256_epochstep.py',
+    '../_base_/default_runtime.py'
+]
+
+img_norm_cfg = dict(
+    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)

openmmlab_test/mmclassification-speed-benchmark/configs/vision_transformer/vit_large_patch16_384_finetune_imagenet.py

+# Refer to pytorch-image-models
+_base_ = [
+    '../_base_/models/vit_large_patch16_384_finetune.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256_epochstep.py',
+    '../_base_/default_runtime.py'
+]
+
+img_norm_cfg = dict(
+    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(384, -1), backend='pillow'),
+    dict(type='CenterCrop', crop_size=384),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+
+data = dict(test=dict(pipeline=test_pipeline))

openmmlab_test/mmclassification-speed-benchmark/configs/vision_transformer/vit_large_patch32_384_finetune_imagenet.py

+# Refer to pytorch-image-models
+_base_ = [
+    '../_base_/models/vit_large_patch32_384_finetune.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256_epochstep.py',
+    '../_base_/default_runtime.py'
+]
+
+img_norm_cfg = dict(
+    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(384, -1), backend='pillow'),
+    dict(type='CenterCrop', crop_size=384),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+
+data = dict(test=dict(pipeline=test_pipeline))

openmmlab_test/mmclassification-speed-benchmark/demo/image_demo.py

+from argparse import ArgumentParser
+
+from mmcls.apis import inference_model, init_model, show_result_pyplot
+
+
+def main():
+    parser = ArgumentParser()
+    parser.add_argument('img', help='Image file')
+    parser.add_argument('config', help='Config file')
+    parser.add_argument('checkpoint', help='Checkpoint file')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+    args = parser.parse_args()
+
+    # build the model from a config file and a checkpoint file
+    model = init_model(args.config, args.checkpoint, device=args.device)
+    # test a single image
+    result = inference_model(model, args.img)
+    # show the results
+    show_result_pyplot(model, args.img, result)
+
+
+if __name__ == '__main__':
+    main()

openmmlab_test/mmclassification-speed-benchmark/docker/serve/Dockerfile

+ARG PYTORCH="1.6.0"
+ARG CUDA="10.1"
+ARG CUDNN="7"
+FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
+
+ARG MMCV="1.3.1"
+ARG MMCLS="0.12.0"
+
+ENV PYTHONUNBUFFERED TRUE
+
+RUN apt-get update && \
+    DEBIAN_FRONTEND=noninteractive apt-get install --no-install-recommends -y \
+    ca-certificates \
+    g++ \
+    openjdk-11-jre-headless \
+    # MMDet Requirements
+    ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 \
+    && rm -rf /var/lib/apt/lists/*
+
+ENV PATH="/opt/conda/bin:$PATH"
+RUN export FORCE_CUDA=1
+
+# TORCHSEVER
+RUN pip install torchserve torch-model-archiver
+
+# MMLAB
+RUN pip install mmcv-full==${MMCV} -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html
+RUN pip install mmcls==${MMCLS}
+
+RUN useradd -m model-server \
+    && mkdir -p /home/model-server/tmp
+
+COPY entrypoint.sh /usr/local/bin/entrypoint.sh
+
+RUN chmod +x /usr/local/bin/entrypoint.sh \
+    && chown -R model-server /home/model-server
+
+COPY config.properties /home/model-server/config.properties
+RUN mkdir /home/model-server/model-store && chown -R model-server /home/model-server/model-store
+
+EXPOSE 8080 8081 8082
+
+USER model-server
+WORKDIR /home/model-server
+ENV TEMP=/home/model-server/tmp
+ENTRYPOINT ["/usr/local/bin/entrypoint.sh"]
+CMD ["serve"]

openmmlab_test/mmclassification-speed-benchmark/docker/serve/config.properties

+inference_address=http://0.0.0.0:8080
+management_address=http://0.0.0.0:8081
+metrics_address=http://0.0.0.0:8082
+model_store=/home/model-server/model-store
+load_models=all

openmmlab_test/mmclassification-speed-benchmark/docker/serve/entrypoint.sh

+#!/bin/bash
+set -e
+
+if [[ "$1" = "serve" ]]; then
+    shift 1
+    torchserve --start --ts-config /home/model-server/config.properties
+else
+    eval "$@"
+fi
+
+# prevent docker exit
+tail -f /dev/null

openmmlab_test/mmclassification-speed-benchmark/docs/Makefile

+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

openmmlab_test/mmclassification-speed-benchmark/docs/changelog.md

+## Changelog
+
+### v0.12.0(3/6/2021)
+
+- Finish adding Chinese tutorials and build Chinese documentation on readthedocs.
+- Update ResNeXt checkpoints and ResNet checkpoints on CIFAR.
+
+#### New Features
+
+- Improve and add Chinese translation of `data_pipeline.md` and `new_modules.md`. (#265)
+- Build Chinese translation on readthedocs. (#267)
+- Add an argument efficientnet_style to `RandomResizedCrop` and `CenterCrop`. (#268)
+
+#### Improvements
+
+- Only allow directory operation when rank==0 when testing. (#258)
+- Fix typo in `base_head`. (#274)
+- Update ResNeXt checkpoints. (#283)
+
+#### Bug Fixes
+
+- Add attribute `data.test` in MNIST configs. (#264)
+- Download CIFAR/MNIST dataset only on rank 0. (#273)
+- Fix MMCV version compatibility. (#276)
+- Fix CIFAR color channels bug and update checkpoints in model zoo. (#280)
+
+### v0.11.1(21/5/2021)
+
+- Refine `new_dataset.md` and add Chinese translation of `finture.md`, `new_dataset.md`.
+
+#### New Features
+
+- Add `dim` argument for `GlobalAveragePooling`. (#236)
+- Add random noise to `RandAugment` magnitude. (#240)
+- Refine `new_dataset.md` and add Chinese translation of `finture.md`, `new_dataset.md`. (#243)
+
+#### Improvements
+
+- Refactor arguments passing for Heads. (#239)
+- Allow more flexible `magnitude_range` in `RandAugment`. (#249)
+- Inherits MMCV registry so that in the future OpenMMLab repos like MMDet and MMSeg could directly use the backbones supported in MMCls. (#252)
+
+#### Bug Fixes
+
+- Fix typo in `analyze_results.py`. (#237)
+- Fix typo in unittests. (#238)
+- Check if specified tmpdir exists when testing to avoid deleting existing data. (#242 & #258)
+- Add missing config files in `MANIFEST.in`. (#250 & #255)
+- Use temporary directory under shared directory to collect results to avoid unavailability of temporary directory for multi-node testing. (#251)
+
+### v0.11.0(1/5/2021)
+
+- Support cutmix trick.
+- Support random augmentation.
+- Add `tools/deployment/test.py` as a ONNX runtime test tool.
+- Support ViT backbone and add training configs for ViT on ImageNet.
+- Add Chinese `README.md` and some Chinese tutorials.
+
+#### New Features
+
+- Support cutmix trick. (#198)
+- Add `simplify` option in `pytorch2onnx.py`. (#200)
+- Support random augmentation. (#201)
+- Add config and checkpoint for training ResNet on CIFAR-100. (#208)
+- Add `tools/deployment/test.py` as a ONNX runtime test tool. (#212)
+- Support ViT backbone and add training configs for ViT on ImageNet. (#214)
+- Add finetuning configs for ViT on ImageNet. (#217)
+- Add `device` option to support training on CPU. (#219)
+- Add Chinese `README.md` and some Chinese tutorials. (#221)
+- Add `metafile.yml` in configs to support interaction with paper with code(PWC) and MMCLI. (#225)
+- Upload configs and converted checkpoints for ViT fintuning on ImageNet. (#230)
+
+#### Improvements
+
+- Fix `LabelSmoothLoss` so that label smoothing and mixup could be enabled at the same time. (#203)
+- Add `cal_acc` option in `ClsHead`. (#206)
+- Check `CLASSES` in checkpoint to avoid unexpected key error. (#207)
+- Check mmcv version when importing mmcls to ensure compatibility. (#209)
+- Update `CONTRIBUTING.md` to align with that in MMCV. (#210)
+- Change tags to html comments in configs README.md. (#226)
+- Clean codes in ViT backbone. (#227)
+- Reformat `pytorch2onnx.md` tutorial. (#229)
+- Update `setup.py` to support MMCLI. (#232)
+
+#### Bug Fixes
+
+- Fix missing `cutmix_prob` in ViT configs. (#220)
+- Fix backend for resize in ResNeXt configs. (#222)
+
+### v0.10.0(1/4/2021)
+
+- Support AutoAugmentation
+- Add tutorials for installation and usage.
+
+#### New Features
+
+- Add `Rotate` pipeline for data augmentation. (#167)
+- Add `Invert` pipeline for data augmentation. (#168)
+- Add `Color` pipeline for data augmentation. (#171)
+- Add `Solarize` and `Posterize` pipeline for data augmentation. (#172)
+- Support fp16 training. (#178)
+- Add tutorials for installation and basic usage of MMClassification.(#176)
+- Support `AutoAugmentation`, `AutoContrast`, `Equalize`, `Contrast`, `Brightness` and `Sharpness` pipelines for data augmentation. (#179)
+
+#### Improvements
+
+- Support dynamic shape export to onnx. (#175)
+- Release training configs and update model zoo for fp16 (#184)
+- Use MMCV's EvalHook in MMClassification (#182)
+
+#### Bug Fixes
+
+- Fix wrong naming in vgg config (#181)
+
+### v0.9.0(1/3/2021)
+
+- Implement mixup trick.
+- Add a new tool to create TensorRT engine from ONNX, run inference and verify outputs in Python.
+
+#### New Features
+
+- Implement mixup and provide configs of training ResNet50 using mixup. (#160)
+- Add `Shear` pipeline for data augmentation. (#163)
+- Add `Translate` pipeline for data augmentation. (#165)
+- Add `tools/onnx2tensorrt.py` as a tool to create TensorRT engine from ONNX, run inference and verify outputs in Python. (#153)
+
+#### Improvements
+
+- Add `--eval-options` in `tools/test.py` to support eval options override, matching the behavior of other open-mmlab projects. (#158)
+- Support showing and saving painted results in `mmcls.apis.test` and `tools/test.py`, matching the behavior of other open-mmlab projects. (#162)
+
+#### Bug Fixes
+
+- Fix configs for VGG, replace checkpoints converted from other repos with the ones trained by ourselves and upload the missing logs in the model zoo. (#161)
+
+### v0.8.0(31/1/2021)
+
+- Support multi-label task.
+- Support more flexible metrics settings.
+- Fix bugs.
+
+#### New Features
+
+- Add evaluation metrics: mAP, CP, CR, CF1, OP, OR, OF1 for multi-label task. (#123)
+- Add BCE loss for multi-label task. (#130)
+- Add focal loss for multi-label task. (#131)
+- Support PASCAL VOC 2007 dataset for multi-label task. (#134)
+- Add asymmetric loss for multi-label task. (#132)
+- Add analyze_results.py to select images for success/fail demonstration. (#142)
+- Support new metric that calculates the total number of occurrences of each label. (#143)
+- Support class-wise evaluation results. (#143)
+- Add thresholds in eval_metrics. (#146)
+- Add heads and a baseline config for multilabel task. (#145)
+
+#### Improvements
+
+- Remove the models with 0 checkpoint and ignore the repeated papers when counting papers to gain more accurate model statistics. (#135)
+- Add tags in README.md. (#137)
+- Fix optional issues in docstring. (#138)
+- Update stat.py to classify papers. (#139)
+- Fix mismatched columns in README.md. (#150)
+- Fix test.py to support more evaluation metrics. (#155)
+
+#### Bug Fixes
+
+- Fix bug in VGG weight_init. (#140)
+- Fix bug in 2 ResNet configs in which outdated heads were used. (#147)
+- Fix bug of misordered height and width in `RandomCrop` and `RandomResizedCrop`. (#151)
+- Fix missing `meta_keys` in `Collect`. (#149 & #152)
+
+### v0.7.0(31/12/2020)
+
+- Add more evaluation metrics.
+- Fix bugs.
+
+#### New Features
+
+- Remove installation of MMCV from requirements. (#90)
+- Add 3 evaluation metrics: precision, recall and F-1 score. (#93)
+- Allow config override during testing and inference with `--options`. (#91 & #96)
+
+#### Improvements
+
+- Use `build_runner` to make runners more flexible. (#54)
+- Support to get category ids in `BaseDataset`. (#72)
+- Allow `CLASSES` override during `BaseDateset` initialization. (#85)
+- Allow input image as ndarray during inference. (#87)
+- Optimize MNIST config. (#98)
+- Add config links in model zoo documentation. (#99)
+- Use functions from MMCV to collect environment. (#103)
+- Refactor config files so that they are now categorized by methods. (#116)
+- Add README in config directory. (#117)
+- Add model statistics. (#119)
+- Refactor documentation in consistency with other MM repositories. (#126)
+
+#### Bug Fixes
+
+- Add missing `CLASSES` argument to dataset wrappers. (#66)
+- Fix slurm evaluation error during training. (#69)
+- Resolve error caused by shape in `Accuracy`. (#104)
+- Fix bug caused by extremely insufficient data in distributed sampler.(#108)
+- Fix bug in `gpu_ids` in distributed training. (#107)
+- Fix bug caused by extremely insufficient data in collect results during testing (#114)
+
+### v0.6.0(11/10/2020)
+
+- Support new method: ResNeSt and VGG.
+- Support new dataset: CIFAR10.
+- Provide new tools to do model inference, model conversion from pytorch to onnx.
+
+#### New Features
+
+- Add model inference. (#16)
+- Add pytorch2onnx. (#20)
+- Add PIL backend for transform `Resize`. (#21)
+- Add ResNeSt. (#25)
+- Add VGG and its pretained models. (#27)
+- Add CIFAR10 configs and models. (#38)
+- Add albumentations transforms. (#45)
+- Visualize results on image demo. (#58)
+
+#### Improvements
+
+- Replace urlretrieve with urlopen in dataset.utils. (#13)
+- Resize image according to its short edge. (#22)
+- Update ShuffleNet config. (#31)
+- Update pre-trained models for shufflenet_v2, shufflenet_v1, se-resnet50, se-resnet101. (#33)
+
+#### Bug Fixes
+
+- Fix init_weights in `shufflenet_v2.py`. (#29)
+- Fix the parameter `size` in test_pipeline. (#30)
+- Fix the parameter in cosine lr schedule. (#32)
+- Fix the convert tools for mobilenet_v2. (#34)
+- Fix crash in CenterCrop transform when image is greyscale (#40)
+- Fix outdated configs. (#53)

openmmlab_test/mmclassification-speed-benchmark/docs/conf.py

+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+import os
+import subprocess
+import sys
+
+sys.path.insert(0, os.path.abspath('..'))
+
+# -- Project information -----------------------------------------------------
+
+project = 'MMClassification'
+copyright = '2020, OpenMMLab'
+author = 'MMClassification Authors'
+version_file = '../mmcls/version.py'
+
+
+def get_version():
+    with open(version_file, 'r') as f:
+        exec(compile(f.read(), version_file, 'exec'))
+    return locals()['__version__']
+
+
+# The full version, including alpha/beta/rc tags
+release = get_version()
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    'sphinx.ext.autodoc',
+    'sphinx.ext.napoleon',
+    'sphinx.ext.viewcode',
+    'recommonmark',
+    'sphinx_markdown_tables',
+]
+
+autodoc_mock_imports = ['mmcls.version']
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+
+# -- Options for HTML output -------------------------------------------------
+source_suffix = {
+    '.rst': 'restructuredtext',
+    '.md': 'markdown',
+}
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = 'sphinx_rtd_theme'
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+
+master_doc = 'index'
+
+
+def builder_inited_handler(app):
+    subprocess.run(['./stat.py'])
+
+
+def setup(app):
+    app.connect('builder-inited', builder_inited_handler)

openmmlab_test/mmclassification-speed-benchmark/docs/getting_started.md

+# Getting Started
+
+This page provides basic tutorials about the usage of MMClassification.
+
+## Prepare datasets
+
+It is recommended to symlink the dataset root to `$MMCLASSIFICATION/data`.
+If your folder structure is different, you may need to change the corresponding paths in config files.
+
+```
+mmclassification
+├── mmcls
+├── tools
+├── configs
+├── docs
+├── data
+│   ├── imagenet
+│   │   ├── meta
+│   │   ├── train
+│   │   ├── val
+│   ├── cifar
+│   │   ├── cifar-10-batches-py
+│   ├── mnist
+│   │   ├── train-images-idx3-ubyte
+│   │   ├── train-labels-idx1-ubyte
+│   │   ├── t10k-images-idx3-ubyte
+│   │   ├── t10k-labels-idx1-ubyte
+
+```
+
+For ImageNet, it has multiple versions, but the most commonly used one is [ILSVRC 2012](http://www.image-net.org/challenges/LSVRC/2012/). It can be accessed with the following steps.
+
+1. Register an account and login to the [download page](http://www.image-net.org/download-images).
+2. Find download links for ILSVRC2012 and download the following two files
+    - ILSVRC2012_img_train.tar (~138GB)
+    - ILSVRC2012_img_val.tar (~6.3GB)
+3. Untar the downloaded files
+4. Download meta data using this [script](https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh)
+
+For MNIST, CIFAR10 and CIFAR100, the datasets will be downloaded and unzipped automatically if they are not found.
+
+For using custom datasets, please refer to [Tutorials 2: Adding New Dataset](tutorials/new_dataset.md).
+
+## Inference with pretrained models
+
+We provide scripts to inference a single image, inference a dataset and test a dataset (e.g., ImageNet).
+
+### Inference a single image
+
+```shell
+python demo/image_demo.py ${IMAGE_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE}
+```
+
+### Inference and test a dataset
+
+- single GPU
+- single node multiple GPU
+- multiple node
+
+You can use the following commands to infer a dataset.
+
+```shell
+# single-gpu
+python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--metrics ${METRICS}] [--out ${RESULT_FILE}]
+
+# multi-gpu
+./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--metrics ${METRICS}] [--out ${RESULT_FILE}]
+
+# multi-node in slurm environment
+python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--metrics ${METRICS}] [--out ${RESULT_FILE}] --launcher slurm
+```
+
+Optional arguments:
+
+- `RESULT_FILE`: Filename of the output results. If not specified, the results will not be saved to a file. Support formats include json, yaml and pickle.
+- `METRICS`：Items to be evaluated on the results, like accuracy, precision, recall, etc.
+
+Examples:
+
+Assume that you have already downloaded the checkpoints to the directory `checkpoints/`.
+Infer ResNet-50 on ImageNet validation set to get predicted labels and their corresponding predicted scores.
+
+```shell
+python tools/test.py configs/imagenet/resnet50_batch256.py checkpoints/xxx.pth --out result.pkl
+```
+
+## Train a model
+
+MMClassification implements distributed training and non-distributed training,
+which uses `MMDistributedDataParallel` and `MMDataParallel` respectively.
+
+All outputs (log files and checkpoints) will be saved to the working directory,
+which is specified by `work_dir` in the config file.
+
+By default we evaluate the model on the validation set after each epoch, you can change the evaluation interval by adding the interval argument in the training config.
+
+```python
+evaluation = dict(interval=12)  # This evaluate the model per 12 epoch.
+```
+
+### Train with a single GPU
+
+```shell
+python tools/train.py ${CONFIG_FILE} [optional arguments]
+```
+
+If you want to specify the working directory in the command, you can add an argument `--work_dir ${YOUR_WORK_DIR}`.
+
+### Train with multiple GPUs
+
+```shell
+./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]
+```
+
+Optional arguments are:
+
+- `--no-validate` (**not suggested**): By default, the codebase will perform evaluation at every k (default value is 1) epochs during the training. To disable this behavior, use `--no-validate`.
+- `--work-dir ${WORK_DIR}`: Override the working directory specified in the config file.
+- `--resume-from ${CHECKPOINT_FILE}`: Resume from a previous checkpoint file.
+
+Difference between `resume-from` and `load-from`:
+`resume-from` loads both the model weights and optimizer status, and the epoch is also inherited from the specified checkpoint. It is usually used for resuming the training process that is interrupted accidentally.
+`load-from` only loads the model weights and the training epoch starts from 0. It is usually used for finetuning.
+
+### Train with multiple machines
+
+If you run MMClassification on a cluster managed with [slurm](https://slurm.schedmd.com/), you can use the script `slurm_train.sh`. (This script also supports single machine training.)
+
+```shell
+[GPUS=${GPUS}] ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR}
+```
+
+You can check [slurm_train.sh](https://github.com/open-mmlab/mmclassification/blob/master/tools/slurm_train.sh) for full arguments and environment variables.
+
+If you have just multiple machines connected with ethernet, you can refer to
+PyTorch [launch utility](https://pytorch.org/docs/stable/distributed_deprecated.html#launch-utility).
+Usually it is slow if you do not have high speed networking like InfiniBand.
+
+### Launch multiple jobs on a single machine
+
+If you launch multiple jobs on a single machine, e.g., 2 jobs of 4-GPU training on a machine with 8 GPUs,
+you need to specify different ports (29500 by default) for each job to avoid communication conflict.
+
+If you use `dist_train.sh` to launch training jobs, you can set the port in commands.
+
+```shell
+CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 ./tools/dist_train.sh ${CONFIG_FILE} 4
+CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 ./tools/dist_train.sh ${CONFIG_FILE} 4
+```
+
+If you use launch training jobs with Slurm, you need to modify the config files (usually the 6th line from the bottom in config files) to set different communication ports.
+
+In `config1.py`,
+
+```python
+dist_params = dict(backend='nccl', port=29500)
+```
+
+In `config2.py`,
+
+```python
+dist_params = dict(backend='nccl', port=29501)
+```
+
+Then you can launch two jobs with `config1.py` ang `config2.py`.
+
+```shell
+CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py ${WORK_DIR}
+CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py ${WORK_DIR}
+```
+
+## Useful tools
+
+We provide lots of useful tools under `tools/` directory.
+
+### Get the FLOPs and params (experimental)
+
+We provide a script adapted from [flops-counter.pytorch](https://github.com/sovrasov/flops-counter.pytorch) to compute the FLOPs and params of a given model.
+
+```shell
+python tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}]
+```
+
+You will get the result like this.
+
+```
+==============================
+Input shape: (3, 224, 224)
+Flops: 4.12 GFLOPs
+Params: 25.56 M
+==============================
+```
+
+**Note**: This tool is still experimental and we do not guarantee that the number is correct. You may well use the result for simple comparisons, but double check it before you adopt it in technical reports or papers.
+
+(1) FLOPs are related to the input shape while parameters are not. The default input shape is (1, 3, 224, 224).
+(2) Some operators are not counted into FLOPs like GN and custom operators. Refer to [`mmcv.cnn.get_model_complexity_info()`](https://github.com/open-mmlab/mmcv/blob/master/mmcv/cnn/utils/flops_counter.py) for details.
+
+### Publish a model
+
+Before you upload a model to AWS, you may want to
+(1) convert model weights to CPU tensors
+(2) delete the optimizer states
+(3) compute the hash of the checkpoint file and append the hash id to the filename.
+
+```shell
+python tools/publish_model.py ${INPUT_FILENAME} ${OUTPUT_FILENAME}
+```
+
+E.g.,
+
+```shell
+python tools/publish_model.py work_dirs/resnet50/latest.pth imagenet_resnet50_20200708.pth
+```
+
+The final output filename will be `imagenet_resnet50_20200708-{hash id}.pth`.
+
+## Tutorials
+
+Currently, we provide five tutorials for users.
+
+- [finetune models](tutorials/finetune.md)
+- [add new dataset](tutorials/new_dataset.md)
+- [design data pipeline](tutorials/data_pipeline.md)
+- [add new modules](tutorials/new_modules.md).

openmmlab_test/mmclassification-speed-benchmark/docs/index.rst

+Welcome to MMClassification's documentation!
+==========================================
+
+You can switch between Chinese and English documents in the lower-left corner of the layout.
+
+您可以在页面左下角切换中英文文档。
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Get Started
+
+   install.md
+   getting_started.md
+
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Model zoo
+
+   modelzoo_statistics.md
+
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Tutorials
+
+   tutorials/finetune.md
+   tutorials/new_dataset.md
+   tutorials/data_pipeline.md
+   tutorials/new_modules.md
+
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Useful Tools and Scripts
+
+   tutorials/pytorch2onnx.md
+   tutorials/onnx2tensorrt.md
+
+
+.. toctree::
+   :caption: Language Switch
+
+   switch_language.md
+
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`search`

openmmlab_test/mmclassification-speed-benchmark/docs/install.md

+## Installation
+
+### Requirements
+
+- Python 3.6+
+- PyTorch 1.3+
+- [MMCV](https://github.com/open-mmlab/mmcv)
+
+The compatible MMClassification and MMCV versions are as below. Please install the correct version of MMCV to avoid installation issues.
+
+| MMClassification version |    MMCV version     |
+|:-------------------:|:-------------------:|
+| master              | mmcv>=1.3.1, <=1.5.0 |
+| 0.12.0              | mmcv>=1.3.1, <=1.5.0 |
+| 0.11.1              | mmcv>=1.3.1, <=1.5.0 |
+| 0.11.0              | mmcv>=1.3.0         |
+| 0.10.0              | mmcv>=1.3.0         |
+| 0.9.0               | mmcv>=1.1.4         |
+| 0.8.0               | mmcv>=1.1.4         |
+| 0.7.0               | mmcv>=1.1.4         |
+| 0.6.0               | mmcv>=1.1.4         |
+
+### Install MMClassification
+
+a. Create a conda virtual environment and activate it.
+
+```shell
+conda create -n open-mmlab python=3.7 -y
+conda activate open-mmlab
+```
+
+b. Install PyTorch and torchvision following the [official instructions](https://pytorch.org/), e.g.,
+
+```shell
+conda install pytorch torchvision -c pytorch
+```
+
+Note: Make sure that your compilation CUDA version and runtime CUDA version match.
+You can check the supported CUDA version for precompiled packages on the [PyTorch website](https://pytorch.org/).
+
+`E.g.1` If you have CUDA 10.1 installed under `/usr/local/cuda` and would like to install
+PyTorch 1.5, you need to install the prebuilt PyTorch with CUDA 10.1.
+
+```shell
+conda install pytorch cudatoolkit=10.1 torchvision -c pytorch
+```
+
+`E.g.2` If you have CUDA 9.2 installed under `/usr/local/cuda` and would like to install
+PyTorch 1.3.1., you need to install the prebuilt PyTorch with CUDA 9.2.
+
+```shell
+conda install pytorch=1.3.1 cudatoolkit=9.2 torchvision=0.4.2 -c pytorch
+```
+
+If you build PyTorch from source instead of installing the prebuilt pacakge,
+you can use more CUDA versions such as 9.0.
+
+c. Clone the mmclassification repository.
+
+```shell
+git clone https://github.com/open-mmlab/mmclassification.git
+cd mmclassification
+```
+
+d. Install build requirements and then install mmclassification.
+
+```shell
+pip install -e .  # or "python setup.py develop"
+```
+
+Note:
+
+1. Following the above instructions, mmclassification is installed on `dev` mode, any local modifications made to the code will take effect without the need to reinstall it (unless you submit some commits and want to update the version number).
+
+2. If you would like to use `opencv-python-headless` instead of `opencv-python`,
+
+you can install it before installing [mmcv](https://github.com/open-mmlab/mmcv).
+
+### Using multiple MMClassification versions
+
+The train and test scripts already modify the `PYTHONPATH` to ensure the script use the MMClassification in the current directory.
+
+To use the default MMClassification installed in the environment rather than that you are working with, you can remove the following line in those scripts
+
+```shell
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH
+```

openmmlab_test/mmclassification-speed-benchmark/docs/model_zoo.md

+# Model Zoo
+
+## ImageNet
+
+ImageNet has multiple versions, but the most commonly used one is [ILSVRC 2012](http://www.image-net.org/challenges/LSVRC/2012/).
+The ResNet family models below are trained by standard data augmentations, i.e., RandomResizedCrop, RandomHorizontalFlip and Normalize.
+
+|         Model         | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
+|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:|
+| VGG-11 | 132.86 | 7.63 | 68.75 | 88.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.log.json) |
+| VGG-13 | 133.05 | 11.34 | 70.02 | 89.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.log.json) |
+| VGG-16 | 138.36 | 15.5 | 71.62 | 90.49 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.log.json) |
+| VGG-19 | 143.67 | 19.67 | 72.41 | 90.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.log.json)|
+| VGG-11-BN | 132.87 | 7.64 | 70.75 | 90.12 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11bn_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.log.json) |
+| VGG-13-BN | 133.05 | 11.36 | 72.15 | 90.71 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13bn_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.log.json) |
+| VGG-16-BN | 138.37 | 15.53 | 73.72 | 91.68 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.log.json) |
+| VGG-19-BN | 143.68 | 19.7 | 74.70 | 92.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19bn_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.log.json)|
+| ResNet-18             | 11.69     | 1.82     | 70.07 | 89.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_batch256_imagenet_20200708-34ab8f90.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_batch256_imagenet_20200708-34ab8f90.log.json) |
+| ResNet-34             | 21.8      | 3.68     | 73.85 | 91.53 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_batch256_imagenet_20200708-32ffb4f7.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_batch256_imagenet_20200708-32ffb4f7.log.json) |
+| ResNet-50             | 25.56     | 4.12     | 76.55 | 93.15 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.log.json) |
+| ResNet-101            | 44.55     | 7.85     | 78.18 | 94.03 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_batch256_imagenet_20200708-753f3608.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_batch256_imagenet_20200708-753f3608.log.json) |
+| ResNet-152            | 60.19     | 11.58    | 78.63 | 94.16 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_batch256_imagenet_20200708-ec25b1f9.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_batch256_imagenet_20200708-ec25b1f9.log.json) |
+| ResNeSt-50*           | 27.48     | 5.41     | 81.13 | 95.59 |  | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest50_imagenet_converted-1ebf0afe.pth) | [log]() |
+| ResNeSt-101*          | 48.28     | 10.27    | 82.32 | 96.24 |  | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest101_imagenet_converted-032caa52.pth) | [log]() |
+| ResNeSt-200*          | 70.2      | 17.53    | 82.41 | 96.22 |  | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest200_imagenet_converted-581a60f2.pth) | [log]() |
+| ResNeSt-269*          | 110.93    | 22.58    | 82.70 | 96.28 |  | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest269_imagenet_converted-59930960.pth) | [log]() |
+| ResNetV1D-50          | 25.58     | 4.36     | 77.54  | 93.57 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d50_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.log.json) |
+| ResNetV1D-101         | 44.57     | 8.09     | 78.93 | 94.48 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d101_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.log.json) |
+| ResNetV1D-152         | 60.21     | 11.82    | 79.41 | 94.7 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d152_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.log.json) |
+| ResNeXt-32x4d-50      | 25.03     | 4.27     | 77.90 | 93.66 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext50_32x4d_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.log.json) |
+| ResNeXt-32x4d-101     | 44.18     | 8.03     | 78.71  | 94.12 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext101_32x4d_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.log.json) |
+| ResNeXt-32x8d-101     | 88.79     | 16.5     | 79.23 | 94.58 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext101_32x8d_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.log.json) |
+| ResNeXt-32x4d-152     | 59.95     | 11.8     | 78.93 | 94.41 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext152_32x4d_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.log.json) |
+| SE-ResNet-50          | 28.09     | 4.13     | 77.74 | 93.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet50_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth) | [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200708-657b3c36.log.json) |
+| SE-ResNet-101         | 49.33     | 7.86     | 78.26 | 94.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet101_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200804-ba5b51d4.pth) | [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200708-038a4d04.log.json) |
+| ShuffleNetV1 1.0x (group=3)   | 1.87      | 0.146    | 68.13 | 87.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v1/shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.pth) | [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.log.json) |
+| ShuffleNetV2 1.0x     | 2.28      | 0.149    | 69.55 | 88.92 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v2/shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200812-5bf4721e.pth) | [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200804-8860eec9.log.json) |
+| MobileNet V2          | 3.5       | 0.319    | 71.86 | 90.42 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v2/mobilenet_v2_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth) | [log](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.log.json) |
+| ViT-B/16*             | 86.86     | 33.03    | 84.20 | 97.18 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit_base_patch16_384_finetune_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/vit_base_patch16_384.pth) | [log]() |
+| ViT-B/32*             | 88.3      | 8.56     | 81.73 | 96.13 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit_base_patch32_384_finetune_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/vit_base_patch32_384.pth) | [log]() |
+| ViT-L/16*             | 304.72    | 116.68   | 85.08 | 97.38 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit_large_patch16_384_finetune_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/vit_large_patch16_384.pth)  | [log]() |
+| ViT-L/32*             | 306.63    | 29.66    | 81.52 | 96.06 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit_large_patch32_384_finetune_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/vit_large_patch32_384.pth)  | [log]() |
+
+Models with * are converted from other repos, others are trained by ourselves.
+
+## CIFAR10
+
+|         Model         | Params(M) | Flops(G) | Top-1 (%) | Config | Download |
+|:---------------------:|:---------:|:--------:|:---------:|:--------:|:--------:|
+| ResNet-18-b16x8 | 11.17 | 0.56 | 94.82 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_b16x8_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20210528-bd6371c8.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20210528-bd6371c8.log.json) |
+| ResNet-34-b16x8 | 21.28 | 1.16 | 95.34 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_b16x8_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20210528-a8aa36a6.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20210528-a8aa36a6.log.json) |
+| ResNet-50-b16x8 | 23.52 | 1.31 | 95.55 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_b16x8_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.log.json) |
+| ResNet-101-b16x8 | 42.51 | 2.52 | 95.58 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_b16x8_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20210528-2d29e936.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20210528-2d29e936.log.json) |
+| ResNet-152-b16x8 | 58.16 | 3.74 | 95.76 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_b16x8_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20210528-3e8e9178.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20210528-3e8e9178.log.json) |

openmmlab_test/mmclassification-speed-benchmark/docs/stat.py

+#!/usr/bin/env python
+import functools as func
+import glob
+import os.path as osp
+import re
+
+import numpy as np
+
+url_prefix = 'https://github.com/open-mmlab/mmclassification/blob/master/'
+
+files = sorted(glob.glob('../configs/*/README.md'))
+
+stats = []
+titles = []
+num_ckpts = 0
+num_configs = 0
+
+for f in files:
+    url = osp.dirname(f.replace('../', url_prefix))
+
+    with open(f, 'r') as content_file:
+        content = content_file.read()
+
+    title = content.split('\n')[0].replace('# ', '').strip()
+
+    ckpts = set(x.lower().strip()
+                for x in re.findall(r'\[model\]\((https?.*)\)', content))
+
+    if len(ckpts) == 0:
+        continue
+
+    _papertype = [x for x in re.findall(r'\[([A-Z]+)\]', content)]
+    assert len(_papertype) > 0
+    papertype = _papertype[0]
+
+    paper = set([(papertype, title)])
+
+    num_ckpts += len(ckpts)
+    titles.append(title)
+
+    statsmsg = f"""
+\t* [{papertype}] [{title}]({url}) ({len(ckpts)} ckpts)
+"""
+    stats.append((paper, ckpts, statsmsg))
+
+allpapers = func.reduce(lambda a, b: a.union(b), [p for p, _, _ in stats])
+msglist = '\n'.join(x for _, _, x in stats)
+
+papertypes, papercounts = np.unique([t for t, _ in allpapers],
+                                    return_counts=True)
+countstr = '\n'.join(
+    [f'   - {t}: {c}' for t, c in zip(papertypes, papercounts)])
+
+modelzoo = f"""
+# Model Zoo Statistics
+
+* Number of papers: {len(set(titles))}
+{countstr}
+
+* Number of checkpoints: {num_ckpts}
+{msglist}
+"""
+
+with open('modelzoo_statistics.md', 'w') as f:
+    f.write(modelzoo)

openmmlab_test/mmclassification-speed-benchmark/docs/switch_language.md

+## <a href='https://mmclassification.readthedocs.io/en/latest/'>English</a>
+
+## <a href='https://mmclassification.readthedocs.io/zh_CN/latest/'>简体中文</a>