[Docs] Use PyTorch style docs theme (#457)
* Change docs theme to pytorch-theme * Update docs rst * Fix docs title level. * Add static resources * Fix lintpull/471/head
Ma Zerun
GitHub
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0b20aa6873
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.header-logo {
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background-image: url("../image/mmcls-logo.png");
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background-size: 204px 40px;
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height: 40px;
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width: 204px;
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}
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After Width: | Height: | Size: 32 KiB |
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API Reference
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=============
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mmcls.apis
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-------------
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.. automodule:: mmcls.apis
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@ -1,19 +1,19 @@
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## Changelog
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# Changelog
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### v0.15.0(31/8/2021)
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## v0.15.0(31/8/2021)
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#### Highlights
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### Highlights
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- Support `hparams` argument in `AutoAugment` and `RandAugment` to provide hyperparameters for sub-policies.
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- Support custom squeeze channels in `SELayer`.
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- Support classwise weight in losses.
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#### New Features
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### New Features
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- Add `hparams` argument in `AutoAugment` and `RandAugment` and some other improvement. ([#398](https://github.com/open-mmlab/mmclassification/pull/398))
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- Support classwise weight in losses. ([#388](https://github.com/open-mmlab/mmclassification/pull/388))
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- Enhence `SELayer` to support custom squeeze channels. ([#417](https://github.com/open-mmlab/mmclassification/pull/417))
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#### Code Refactor
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### Code Refactor
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- Better result visualization. ([#419](https://github.com/open-mmlab/mmclassification/pull/419))
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- Use `post_process` function to handle pred result processing. ([#390](https://github.com/open-mmlab/mmclassification/pull/390))
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@ -22,7 +22,7 @@
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- Avoid unnecessary listdir when building ImageNet. ([#396](https://github.com/open-mmlab/mmclassification/pull/396))
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- Use dynamic mmcv download link in TorchServe dockerfile. ([#387](https://github.com/open-mmlab/mmclassification/pull/387))
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#### Docs Improvement
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### Docs Improvement
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- Add readme of some algorithms and update meta yml. ([#418](https://github.com/open-mmlab/mmclassification/pull/418))
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- Add Copyright information. ([#413](https://github.com/open-mmlab/mmclassification/pull/413))
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@ -30,21 +30,21 @@
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- Update QQ group QR code. ([#393](https://github.com/open-mmlab/mmclassification/pull/393))
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- Add PR template and modify issue template. ([#380](https://github.com/open-mmlab/mmclassification/pull/380))
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### v0.14.0(4/8/2021)
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## v0.14.0(4/8/2021)
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#### Highlights
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### Highlights
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- Add transformer-in-transformer backbone and pretrain checkpoints, refers to [the paper](https://arxiv.org/abs/2103.00112).
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- Add Chinese colab tutorial.
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- Provide dockerfile to build mmcls dev docker image.
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#### New Features
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### New Features
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- Add transformer in transformer backbone and pretrain checkpoints. ([#339](https://github.com/open-mmlab/mmclassification/pull/339))
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- Support mim, welcome to use mim to manage your mmcls project. ([#376](https://github.com/open-mmlab/mmclassification/pull/376))
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- Add Dockerfile. ([#365](https://github.com/open-mmlab/mmclassification/pull/365))
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- Add ResNeSt configs. ([#332](https://github.com/open-mmlab/mmclassification/pull/332))
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#### Improvements
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### Improvements
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- Use the `presistent_works` option if available, to accelerate training. ([#349](https://github.com/open-mmlab/mmclassification/pull/349))
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- Add Chinese ipynb tutorial. ([#306](https://github.com/open-mmlab/mmclassification/pull/306))
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@ -52,7 +52,7 @@
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- Support to test mmdet inference with mmcls backbone. ([#343](https://github.com/open-mmlab/mmclassification/pull/343))
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- Use zero as default value of `thrs` in metrics. ([#341](https://github.com/open-mmlab/mmclassification/pull/341))
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#### Bug Fixes
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### Bug Fixes
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- Fix ImageNet dataset annotation file parse bug. ([#370](https://github.com/open-mmlab/mmclassification/pull/370))
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- Fix docstring typo and init bug in ShuffleNetV1. ([#374](https://github.com/open-mmlab/mmclassification/pull/374))
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@ -63,11 +63,11 @@
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- Fix broken `_base_` link in a resnet config. ([#361](https://github.com/open-mmlab/mmclassification/pull/361))
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- Fix vgg-19 model link missing. ([#363](https://github.com/open-mmlab/mmclassification/pull/363))
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### v0.13.0(3/7/2021)
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## v0.13.0(3/7/2021)
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- Support Swin-Transformer backbone and add training configs for Swin-Transformer on ImageNet.
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#### New Features
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### New Features
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- Support Swin-Transformer backbone and add training configs for Swin-Transformer on ImageNet. (#271)
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- Add pretained model of RegNetX. (#269)
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@ -76,7 +76,7 @@
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- Dump config before training. (#282)
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- Add torchscript and torchserve deployment tools. (#279, #284)
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#### Improvements
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### Improvements
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- Improve test tools and add some new tools. (#322)
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- Correct MobilenetV3 backbone structure and add pretained models. (#291)
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@ -85,7 +85,7 @@
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- Refactor weights initialization method. (#270, #318, #319)
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- Refactor `LabelSmoothLoss` to support multiple calculation formulas. (#285)
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#### Bug Fixes
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### Bug Fixes
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- Fix bug for CPU training. (#286)
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- Fix missing test data when `num_imgs` can not be evenly divided by `num_gpus`. (#299)
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@ -93,47 +93,47 @@
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- Fix `magnitude_std` bug in `RandAugment`. (#309)
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- Fix bug when `samples_per_gpu` is 1. (#311)
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### v0.12.0(3/6/2021)
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## v0.12.0(3/6/2021)
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- Finish adding Chinese tutorials and build Chinese documentation on readthedocs.
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- Update ResNeXt checkpoints and ResNet checkpoints on CIFAR.
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#### New Features
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### New Features
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- Improve and add Chinese translation of `data_pipeline.md` and `new_modules.md`. (#265)
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- Build Chinese translation on readthedocs. (#267)
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- Add an argument efficientnet_style to `RandomResizedCrop` and `CenterCrop`. (#268)
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#### Improvements
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### Improvements
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- Only allow directory operation when rank==0 when testing. (#258)
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- Fix typo in `base_head`. (#274)
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- Update ResNeXt checkpoints. (#283)
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#### Bug Fixes
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### Bug Fixes
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- Add attribute `data.test` in MNIST configs. (#264)
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- Download CIFAR/MNIST dataset only on rank 0. (#273)
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- Fix MMCV version compatibility. (#276)
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- Fix CIFAR color channels bug and update checkpoints in model zoo. (#280)
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### v0.11.1(21/5/2021)
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## v0.11.1(21/5/2021)
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- Refine `new_dataset.md` and add Chinese translation of `finture.md`, `new_dataset.md`.
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#### New Features
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### New Features
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- Add `dim` argument for `GlobalAveragePooling`. (#236)
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- Add random noise to `RandAugment` magnitude. (#240)
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- Refine `new_dataset.md` and add Chinese translation of `finture.md`, `new_dataset.md`. (#243)
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#### Improvements
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### Improvements
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- Refactor arguments passing for Heads. (#239)
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- Allow more flexible `magnitude_range` in `RandAugment`. (#249)
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- Inherits MMCV registry so that in the future OpenMMLab repos like MMDet and MMSeg could directly use the backbones supported in MMCls. (#252)
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#### Bug Fixes
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### Bug Fixes
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- Fix typo in `analyze_results.py`. (#237)
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- Fix typo in unittests. (#238)
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@ -141,7 +141,7 @@
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- Add missing config files in `MANIFEST.in`. (#250 & #255)
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- Use temporary directory under shared directory to collect results to avoid unavailability of temporary directory for multi-node testing. (#251)
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### v0.11.0(1/5/2021)
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## v0.11.0(1/5/2021)
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- Support cutmix trick.
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- Support random augmentation.
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@ -149,7 +149,7 @@
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- Support ViT backbone and add training configs for ViT on ImageNet.
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- Add Chinese `README.md` and some Chinese tutorials.
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#### New Features
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### New Features
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- Support cutmix trick. (#198)
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- Add `simplify` option in `pytorch2onnx.py`. (#200)
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- Add `metafile.yml` in configs to support interaction with paper with code(PWC) and MMCLI. (#225)
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- Upload configs and converted checkpoints for ViT fintuning on ImageNet. (#230)
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#### Improvements
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### Improvements
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- Fix `LabelSmoothLoss` so that label smoothing and mixup could be enabled at the same time. (#203)
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- Add `cal_acc` option in `ClsHead`. (#206)
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- Reformat `pytorch2onnx.md` tutorial. (#229)
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- Update `setup.py` to support MMCLI. (#232)
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#### Bug Fixes
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### Bug Fixes
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- Fix missing `cutmix_prob` in ViT configs. (#220)
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- Fix backend for resize in ResNeXt configs. (#222)
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### v0.10.0(1/4/2021)
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## v0.10.0(1/4/2021)
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- Support AutoAugmentation
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- Add tutorials for installation and usage.
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#### New Features
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### New Features
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- Add `Rotate` pipeline for data augmentation. (#167)
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- Add `Invert` pipeline for data augmentation. (#168)
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- Add tutorials for installation and basic usage of MMClassification.(#176)
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- Support `AutoAugmentation`, `AutoContrast`, `Equalize`, `Contrast`, `Brightness` and `Sharpness` pipelines for data augmentation. (#179)
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#### Improvements
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### Improvements
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- Support dynamic shape export to onnx. (#175)
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- Release training configs and update model zoo for fp16 (#184)
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- Use MMCV's EvalHook in MMClassification (#182)
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#### Bug Fixes
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### Bug Fixes
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- Fix wrong naming in vgg config (#181)
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### v0.9.0(1/3/2021)
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## v0.9.0(1/3/2021)
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- Implement mixup trick.
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- Add a new tool to create TensorRT engine from ONNX, run inference and verify outputs in Python.
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#### New Features
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### New Features
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- Implement mixup and provide configs of training ResNet50 using mixup. (#160)
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- Add `Shear` pipeline for data augmentation. (#163)
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- Add `Translate` pipeline for data augmentation. (#165)
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- Add `tools/onnx2tensorrt.py` as a tool to create TensorRT engine from ONNX, run inference and verify outputs in Python. (#153)
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#### Improvements
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### Improvements
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- Add `--eval-options` in `tools/test.py` to support eval options override, matching the behavior of other open-mmlab projects. (#158)
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- Support showing and saving painted results in `mmcls.apis.test` and `tools/test.py`, matching the behavior of other open-mmlab projects. (#162)
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#### Bug Fixes
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### Bug Fixes
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- 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)
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### v0.8.0(31/1/2021)
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## v0.8.0(31/1/2021)
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- Support multi-label task.
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- Support more flexible metrics settings.
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- Fix bugs.
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#### New Features
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### New Features
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- Add evaluation metrics: mAP, CP, CR, CF1, OP, OR, OF1 for multi-label task. (#123)
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- Add BCE loss for multi-label task. (#130)
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- Add thresholds in eval_metrics. (#146)
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- Add heads and a baseline config for multilabel task. (#145)
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#### Improvements
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### Improvements
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- Remove the models with 0 checkpoint and ignore the repeated papers when counting papers to gain more accurate model statistics. (#135)
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- Add tags in README.md. (#137)
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- Fix mismatched columns in README.md. (#150)
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- Fix test.py to support more evaluation metrics. (#155)
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#### Bug Fixes
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### Bug Fixes
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- Fix bug in VGG weight_init. (#140)
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- Fix bug in 2 ResNet configs in which outdated heads were used. (#147)
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- Fix bug of misordered height and width in `RandomCrop` and `RandomResizedCrop`. (#151)
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- Fix missing `meta_keys` in `Collect`. (#149 & #152)
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### v0.7.0(31/12/2020)
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## v0.7.0(31/12/2020)
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- Add more evaluation metrics.
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- Fix bugs.
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#### New Features
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### New Features
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- Remove installation of MMCV from requirements. (#90)
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- Add 3 evaluation metrics: precision, recall and F-1 score. (#93)
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- Allow config override during testing and inference with `--options`. (#91 & #96)
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#### Improvements
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### Improvements
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- Use `build_runner` to make runners more flexible. (#54)
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- Support to get category ids in `BaseDataset`. (#72)
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- Add model statistics. (#119)
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- Refactor documentation in consistency with other MM repositories. (#126)
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#### Bug Fixes
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### Bug Fixes
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- Add missing `CLASSES` argument to dataset wrappers. (#66)
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- Fix slurm evaluation error during training. (#69)
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@ -295,13 +295,13 @@
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- Fix bug in `gpu_ids` in distributed training. (#107)
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- Fix bug caused by extremely insufficient data in collect results during testing (#114)
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### v0.6.0(11/10/2020)
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## v0.6.0(11/10/2020)
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- Support new method: ResNeSt and VGG.
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- Support new dataset: CIFAR10.
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- Provide new tools to do model inference, model conversion from pytorch to onnx.
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#### New Features
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### New Features
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- Add model inference. (#16)
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- Add pytorch2onnx. (#20)
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- Add albumentations transforms. (#45)
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- Visualize results on image demo. (#58)
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#### Improvements
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### Improvements
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- Replace urlretrieve with urlopen in dataset.utils. (#13)
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- Resize image according to its short edge. (#22)
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- Update ShuffleNet config. (#31)
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- Update pre-trained models for shufflenet_v2, shufflenet_v1, se-resnet50, se-resnet101. (#33)
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#### Bug Fixes
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### Bug Fixes
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- Fix init_weights in `shufflenet_v2.py`. (#29)
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- Fix the parameter `size` in test_pipeline. (#30)
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174
docs/conf.py
174
docs/conf.py
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@ -14,6 +14,11 @@ import os
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import subprocess
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import sys
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import pytorch_sphinx_theme
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from m2r import MdInclude
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from recommonmark.transform import AutoStructify
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from sphinx.builders.html import StandaloneHTMLBuilder
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sys.path.insert(0, os.path.abspath('..'))
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# -- Project information -----------------------------------------------------
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'sphinx.ext.autodoc',
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'sphinx.ext.napoleon',
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'sphinx.ext.viewcode',
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'recommonmark',
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'sphinx_markdown_tables',
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'myst_parser',
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'sphinx_copybutton',
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]
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autodoc_mock_imports = ['matplotlib', 'mmcls.version', 'mmcv.ops']
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# The theme to use for HTML and HTML Help pages. See the documentation for
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# a list of builtin themes.
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#
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html_theme = 'sphinx_rtd_theme'
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html_theme = 'pytorch_sphinx_theme'
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html_theme_path = [pytorch_sphinx_theme.get_html_theme_path()]
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# Theme options are theme-specific and customize the look and feel of a theme
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# further. For a list of options available for each theme, see the
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# documentation.
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#
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html_theme_options = {
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# 'logo_url': 'https://mmocr.readthedocs.io/en/latest/',
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'menu': [
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{
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'name': 'GitHub',
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'url': 'https://github.com/open-mmlab/mmcv'
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},
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{
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'name':
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'Projects',
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'children': [
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{
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'name': 'MMAction2',
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'url': 'https://github.com/open-mmlab/mmaction2',
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},
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{
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'name': 'MMClassification',
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'url': 'https://github.com/open-mmlab/mmclassification',
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},
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{
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'name': 'MMDetection',
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'url': 'https://github.com/open-mmlab/mmdetection',
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},
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{
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'name': 'MMDetection3D',
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'url': 'https://github.com/open-mmlab/mmdetection3d',
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},
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{
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'name': 'MMEditing',
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'url': 'https://github.com/open-mmlab/mmediting',
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},
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{
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'name': 'MMGeneration',
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'url': 'https://github.com/open-mmlab/mmgeneration',
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},
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{
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'name': 'MMOCR',
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'url': 'https://github.com/open-mmlab/mmocr',
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},
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{
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'name': 'MMPose',
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||||
'url': 'https://github.com/open-mmlab/mmpose',
|
||||
},
|
||||
{
|
||||
'name': 'MMSegmentation',
|
||||
'url': 'https://github.com/open-mmlab/mmsegmentation',
|
||||
},
|
||||
{
|
||||
'name': 'MMTracking',
|
||||
'url': 'https://github.com/open-mmlab/mmtracking',
|
||||
},
|
||||
]
|
||||
},
|
||||
{
|
||||
'name':
|
||||
'OpenMMLab',
|
||||
'children': [
|
||||
{
|
||||
'name': 'Homepage',
|
||||
'url': 'https://openmmlab.com/'
|
||||
},
|
||||
{
|
||||
'name': 'GitHub',
|
||||
'url': 'https://github.com/open-mmlab/'
|
||||
},
|
||||
{
|
||||
'name': 'Twitter',
|
||||
'url': 'https://twitter.com/OpenMMLab'
|
||||
},
|
||||
{
|
||||
'name': 'Zhihu',
|
||||
'url': 'https://zhihu.com/people/openmmlab'
|
||||
},
|
||||
]
|
||||
},
|
||||
]
|
||||
}
|
||||
|
||||
# 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']
|
||||
html_static_path = ['_static']
|
||||
html_css_files = ['css/readthedocs.css']
|
||||
|
||||
master_doc = 'index'
|
||||
|
||||
# -- Options for HTMLHelp output ---------------------------------------------
|
||||
|
||||
# Output file base name for HTML help builder.
|
||||
htmlhelp_basename = 'mmclsdoc'
|
||||
|
||||
# -- Options for LaTeX output ------------------------------------------------
|
||||
|
||||
latex_elements = {
|
||||
# The paper size ('letterpaper' or 'a4paper').
|
||||
#
|
||||
# 'papersize': 'letterpaper',
|
||||
|
||||
# The font size ('10pt', '11pt' or '12pt').
|
||||
#
|
||||
# 'pointsize': '10pt',
|
||||
|
||||
# Additional stuff for the LaTeX preamble.
|
||||
#
|
||||
# 'preamble': '',
|
||||
'preamble':
|
||||
r'''
|
||||
|
|
@ -96,10 +202,72 @@ latex_elements = {
|
|||
''',
|
||||
}
|
||||
|
||||
# Grouping the document tree into LaTeX files. List of tuples
|
||||
# (source start file, target name, title,
|
||||
# author, documentclass [howto, manual, or own class]).
|
||||
latex_documents = [
|
||||
(master_doc, 'mmcls.tex', 'MMClassification Documentation',
|
||||
'MMClassification Contributors', 'manual'),
|
||||
]
|
||||
|
||||
# -- Options for manual page output ------------------------------------------
|
||||
|
||||
# One entry per manual page. List of tuples
|
||||
# (source start file, name, description, authors, manual section).
|
||||
man_pages = [(master_doc, 'mmcls', 'MMClassification Documentation', [author],
|
||||
1)]
|
||||
|
||||
# -- Options for Texinfo output ----------------------------------------------
|
||||
|
||||
# Grouping the document tree into Texinfo files. List of tuples
|
||||
# (source start file, target name, title, author,
|
||||
# dir menu entry, description, category)
|
||||
texinfo_documents = [
|
||||
(master_doc, 'mmcls', 'MMClassification Documentation', author, 'mmcls',
|
||||
'One line description of project.', 'Miscellaneous'),
|
||||
]
|
||||
|
||||
# -- Options for Epub output -------------------------------------------------
|
||||
|
||||
# Bibliographic Dublin Core info.
|
||||
epub_title = project
|
||||
|
||||
# The unique identifier of the text. This can be a ISBN number
|
||||
# or the project homepage.
|
||||
#
|
||||
# epub_identifier = ''
|
||||
|
||||
# A unique identification for the text.
|
||||
#
|
||||
# epub_uid = ''
|
||||
|
||||
# A list of files that should not be packed into the epub file.
|
||||
epub_exclude_files = ['search.html']
|
||||
|
||||
# set priority when building html
|
||||
StandaloneHTMLBuilder.supported_image_types = [
|
||||
'image/svg+xml', 'image/gif', 'image/png', 'image/jpeg'
|
||||
]
|
||||
|
||||
# -- Extension configuration -------------------------------------------------
|
||||
# Ignore >>> when copying code
|
||||
copybutton_prompt_text = r'>>> |\.\.\. '
|
||||
copybutton_prompt_is_regexp = True
|
||||
|
||||
|
||||
def builder_inited_handler(app):
|
||||
subprocess.run(['./stat.py'])
|
||||
|
||||
|
||||
def setup(app):
|
||||
app.add_config_value('no_underscore_emphasis', False, 'env')
|
||||
app.add_config_value('m2r_parse_relative_links', False, 'env')
|
||||
app.add_config_value('m2r_anonymous_references', False, 'env')
|
||||
app.add_config_value('m2r_disable_inline_math', False, 'env')
|
||||
app.add_directive('mdinclude', MdInclude)
|
||||
app.add_config_value('recommonmark_config', {
|
||||
'auto_toc_tree_section': 'Contents',
|
||||
'enable_eval_rst': True,
|
||||
}, True)
|
||||
app.add_transform(AutoStructify)
|
||||
app.connect('builder-inited', builder_inited_handler)
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ You can switch between Chinese and English documents in the lower-left corner of
|
|||
您可以在页面左下角切换中英文文档。
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:maxdepth: 1
|
||||
:caption: Get Started
|
||||
|
||||
install.md
|
||||
|
|
@ -14,14 +14,14 @@ You can switch between Chinese and English documents in the lower-left corner of
|
|||
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:maxdepth: 1
|
||||
:caption: Model zoo
|
||||
|
||||
modelzoo_statistics.md
|
||||
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:maxdepth: 1
|
||||
:caption: Tutorials
|
||||
|
||||
tutorials/finetune.md
|
||||
|
|
@ -31,7 +31,7 @@ You can switch between Chinese and English documents in the lower-left corner of
|
|||
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:maxdepth: 1
|
||||
:caption: Useful Tools and Scripts
|
||||
|
||||
tools/pytorch2onnx.md
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
## Installation
|
||||
# Installation
|
||||
|
||||
### Requirements
|
||||
## Requirements
|
||||
|
||||
- Python 3.6+
|
||||
- PyTorch 1.3+
|
||||
|
|
@ -27,7 +27,7 @@ Note: Since the `master` branch is under frequent development, the `mmcv`
|
|||
version dependency may be inaccurate. If you encounter problems when using
|
||||
the `master` branch, please try to update `mmcv` to the latest version.
|
||||
|
||||
### Install MMClassification
|
||||
## Install MMClassification
|
||||
|
||||
a. Create a conda virtual environment and activate it.
|
||||
|
||||
|
|
@ -64,7 +64,7 @@ you can use more CUDA versions such as 9.0.
|
|||
|
||||
c. Install MMClassification repository.
|
||||
|
||||
#### Release version
|
||||
### Release version
|
||||
|
||||
We recommend you to install MMClassification with [MIM](https://github.com/open-mmlab/mim).
|
||||
|
||||
|
|
@ -82,7 +82,7 @@ Or, you can install MMClassification with pip:
|
|||
pip install mmcls
|
||||
```
|
||||
|
||||
#### Develop version
|
||||
### Develop version
|
||||
|
||||
First, clone the MMClassification repository.
|
||||
|
||||
|
|
@ -105,9 +105,9 @@ Note:
|
|||
|
||||
you can install it before installing [mmcv](https://github.com/open-mmlab/mmcv).
|
||||
|
||||
#### Another option: Docker Image
|
||||
### Another option: Docker Image
|
||||
|
||||
We provide a [Dockerfile](/docker/Dockerfile) to build an image.
|
||||
We provide a [Dockerfile](https://github.com/open-mmlab/mmclassification/blob/master/docker/Dockerfile) to build an image.
|
||||
|
||||
```shell
|
||||
# build an image with PyTorch 1.6.0, CUDA 10.1, CUDNN 7.
|
||||
|
|
@ -122,7 +122,7 @@ Run a container built from mmcls image with command:
|
|||
docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/workspace/mmclassification/data mmcls:torch1.6.0-cuda10.1-cudnn7 /bin/bash
|
||||
```
|
||||
|
||||
### Using multiple MMClassification versions
|
||||
## Using multiple MMClassification versions
|
||||
|
||||
The train and test scripts already modify the `PYTHONPATH` to ensure the script use the MMClassification in the current directory.
|
||||
|
||||
|
|
|
|||
|
|
@ -1,3 +1,3 @@
|
|||
## <a href='https://mmclassification.readthedocs.io/en/latest/'>English</a>
|
||||
# <a href='https://mmclassification.readthedocs.io/en/latest/'>English</a>
|
||||
|
||||
## <a href='https://mmclassification.readthedocs.io/zh_CN/latest/'>简体中文</a>
|
||||
# <a href='https://mmclassification.readthedocs.io/zh_CN/latest/'>简体中文</a>
|
||||
|
|
|
|||
|
|
@ -0,0 +1,6 @@
|
|||
.header-logo {
|
||||
background-image: url("../image/mmcls-logo.png");
|
||||
background-size: 204px 40px;
|
||||
height: 40px;
|
||||
width: 204px;
|
||||
}
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 32 KiB |
|
|
@ -1,6 +1,3 @@
|
|||
API Reference
|
||||
=============
|
||||
|
||||
mmcls.apis
|
||||
-------------
|
||||
.. automodule:: mmcls.apis
|
||||
|
|
|
|||
|
|
@ -14,6 +14,11 @@ import os
|
|||
import subprocess
|
||||
import sys
|
||||
|
||||
import pytorch_sphinx_theme
|
||||
from m2r import MdInclude
|
||||
from recommonmark.transform import AutoStructify
|
||||
from sphinx.builders.html import StandaloneHTMLBuilder
|
||||
|
||||
sys.path.insert(0, os.path.abspath('..'))
|
||||
|
||||
# -- Project information -----------------------------------------------------
|
||||
|
|
@ -42,8 +47,9 @@ extensions = [
|
|||
'sphinx.ext.autodoc',
|
||||
'sphinx.ext.napoleon',
|
||||
'sphinx.ext.viewcode',
|
||||
'recommonmark',
|
||||
'sphinx_markdown_tables',
|
||||
'myst_parser',
|
||||
'sphinx_copybutton',
|
||||
]
|
||||
|
||||
autodoc_mock_imports = ['matplotlib', 'mmcls.version', 'mmcv.ops']
|
||||
|
|
@ -65,21 +71,192 @@ source_suffix = {
|
|||
# The theme to use for HTML and HTML Help pages. See the documentation for
|
||||
# a list of builtin themes.
|
||||
#
|
||||
html_theme = 'sphinx_rtd_theme'
|
||||
html_theme = 'pytorch_sphinx_theme'
|
||||
html_theme_path = [pytorch_sphinx_theme.get_html_theme_path()]
|
||||
|
||||
# Theme options are theme-specific and customize the look and feel of a theme
|
||||
# further. For a list of options available for each theme, see the
|
||||
# documentation.
|
||||
#
|
||||
html_theme_options = {
|
||||
# 'logo_url': 'https://mmocr.readthedocs.io/en/latest/',
|
||||
'menu': [
|
||||
{
|
||||
'name': 'GitHub',
|
||||
'url': 'https://github.com/open-mmlab/mmcv'
|
||||
},
|
||||
{
|
||||
'name':
|
||||
'算法库',
|
||||
'children': [
|
||||
{
|
||||
'name': 'MMAction2',
|
||||
'url': 'https://github.com/open-mmlab/mmaction2',
|
||||
},
|
||||
{
|
||||
'name': 'MMClassification',
|
||||
'url': 'https://github.com/open-mmlab/mmclassification',
|
||||
},
|
||||
{
|
||||
'name': 'MMDetection',
|
||||
'url': 'https://github.com/open-mmlab/mmdetection',
|
||||
},
|
||||
{
|
||||
'name': 'MMDetection3D',
|
||||
'url': 'https://github.com/open-mmlab/mmdetection3d',
|
||||
},
|
||||
{
|
||||
'name': 'MMEditing',
|
||||
'url': 'https://github.com/open-mmlab/mmediting',
|
||||
},
|
||||
{
|
||||
'name': 'MMGeneration',
|
||||
'url': 'https://github.com/open-mmlab/mmgeneration',
|
||||
},
|
||||
{
|
||||
'name': 'MMOCR',
|
||||
'url': 'https://github.com/open-mmlab/mmocr',
|
||||
},
|
||||
{
|
||||
'name': 'MMPose',
|
||||
'url': 'https://github.com/open-mmlab/mmpose',
|
||||
},
|
||||
{
|
||||
'name': 'MMSegmentation',
|
||||
'url': 'https://github.com/open-mmlab/mmsegmentation',
|
||||
},
|
||||
{
|
||||
'name': 'MMTracking',
|
||||
'url': 'https://github.com/open-mmlab/mmtracking',
|
||||
},
|
||||
]
|
||||
},
|
||||
{
|
||||
'name':
|
||||
'OpenMMLab',
|
||||
'children': [
|
||||
{
|
||||
'name': '官网',
|
||||
'url': 'https://openmmlab.com/'
|
||||
},
|
||||
{
|
||||
'name': 'GitHub',
|
||||
'url': 'https://github.com/open-mmlab/'
|
||||
},
|
||||
{
|
||||
'name': '推特',
|
||||
'url': 'https://twitter.com/OpenMMLab'
|
||||
},
|
||||
{
|
||||
'name': '知乎',
|
||||
'url': 'https://zhihu.com/people/openmmlab'
|
||||
},
|
||||
]
|
||||
},
|
||||
]
|
||||
}
|
||||
|
||||
# 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']
|
||||
html_static_path = ['_static']
|
||||
html_css_files = ['css/readthedocs.css']
|
||||
|
||||
language = 'zh_CN'
|
||||
|
||||
master_doc = 'index'
|
||||
|
||||
# -- Options for HTMLHelp output ---------------------------------------------
|
||||
|
||||
# Output file base name for HTML help builder.
|
||||
htmlhelp_basename = 'mmclsdoc'
|
||||
|
||||
# -- Options for LaTeX output ------------------------------------------------
|
||||
|
||||
latex_elements = {
|
||||
# The paper size ('letterpaper' or 'a4paper').
|
||||
#
|
||||
# 'papersize': 'letterpaper',
|
||||
|
||||
# The font size ('10pt', '11pt' or '12pt').
|
||||
#
|
||||
# 'pointsize': '10pt',
|
||||
|
||||
# Additional stuff for the LaTeX preamble.
|
||||
#
|
||||
# 'preamble': '',
|
||||
|
||||
# Latex figure (float) alignment
|
||||
#
|
||||
# 'figure_align': 'htbp',
|
||||
}
|
||||
|
||||
# Grouping the document tree into LaTeX files. List of tuples
|
||||
# (source start file, target name, title,
|
||||
# author, documentclass [howto, manual, or own class]).
|
||||
latex_documents = [
|
||||
(master_doc, 'mmcls.tex', 'MMClassification Documentation',
|
||||
'MMClassification Contributors', 'manual'),
|
||||
]
|
||||
|
||||
# -- Options for manual page output ------------------------------------------
|
||||
|
||||
# One entry per manual page. List of tuples
|
||||
# (source start file, name, description, authors, manual section).
|
||||
man_pages = [(master_doc, 'mmcls', 'MMClassification Documentation', [author],
|
||||
1)]
|
||||
|
||||
# -- Options for Texinfo output ----------------------------------------------
|
||||
|
||||
# Grouping the document tree into Texinfo files. List of tuples
|
||||
# (source start file, target name, title, author,
|
||||
# dir menu entry, description, category)
|
||||
texinfo_documents = [
|
||||
(master_doc, 'mmcls', 'MMClassification Documentation', author, 'mmcls',
|
||||
'One line description of project.', 'Miscellaneous'),
|
||||
]
|
||||
|
||||
# -- Options for Epub output -------------------------------------------------
|
||||
|
||||
# Bibliographic Dublin Core info.
|
||||
epub_title = project
|
||||
|
||||
# The unique identifier of the text. This can be a ISBN number
|
||||
# or the project homepage.
|
||||
#
|
||||
# epub_identifier = ''
|
||||
|
||||
# A unique identification for the text.
|
||||
#
|
||||
# epub_uid = ''
|
||||
|
||||
# A list of files that should not be packed into the epub file.
|
||||
epub_exclude_files = ['search.html']
|
||||
|
||||
# set priority when building html
|
||||
StandaloneHTMLBuilder.supported_image_types = [
|
||||
'image/svg+xml', 'image/gif', 'image/png', 'image/jpeg'
|
||||
]
|
||||
|
||||
# -- Extension configuration -------------------------------------------------
|
||||
# Ignore >>> when copying code
|
||||
copybutton_prompt_text = r'>>> |\.\.\. '
|
||||
copybutton_prompt_is_regexp = True
|
||||
|
||||
|
||||
def builder_inited_handler(app):
|
||||
subprocess.run(['./stat.py'])
|
||||
|
||||
|
||||
def setup(app):
|
||||
app.add_config_value('no_underscore_emphasis', False, 'env')
|
||||
app.add_config_value('m2r_parse_relative_links', False, 'env')
|
||||
app.add_config_value('m2r_anonymous_references', False, 'env')
|
||||
app.add_config_value('m2r_disable_inline_math', False, 'env')
|
||||
app.add_directive('mdinclude', MdInclude)
|
||||
app.add_config_value('recommonmark_config', {
|
||||
'auto_toc_tree_section': 'Contents',
|
||||
'enable_eval_rst': True,
|
||||
}, True)
|
||||
app.add_transform(AutoStructify)
|
||||
app.connect('builder-inited', builder_inited_handler)
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ You can switch between Chinese and English documents in the lower-left corner of
|
|||
您可以在页面左下角切换中英文文档。
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:maxdepth: 1
|
||||
:caption: 开始你的第一步
|
||||
|
||||
install.md
|
||||
|
|
@ -14,14 +14,14 @@ You can switch between Chinese and English documents in the lower-left corner of
|
|||
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:maxdepth: 1
|
||||
:caption: 模型库
|
||||
|
||||
modelzoo_statistics.md
|
||||
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:maxdepth: 1
|
||||
:caption: 教程
|
||||
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||||
tutorials/finetune.md
|
||||
|
|
@ -31,7 +31,7 @@ You can switch between Chinese and English documents in the lower-left corner of
|
|||
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:maxdepth: 1
|
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:caption: 实用工具
|
||||
|
||||
tools/pytorch2onnx.md
|
||||
|
|
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|||
|
|
@ -1,6 +1,6 @@
|
|||
## 安装
|
||||
# 安装
|
||||
|
||||
### 安装依赖包
|
||||
## 安装依赖包
|
||||
|
||||
- Python 3.6+
|
||||
- PyTorch 1.3+
|
||||
|
|
@ -26,7 +26,7 @@ MMClassification 和 MMCV 的适配关系如下，请安装正确版本的 MMCV
|
|||
提示：由于 `master` 分支处于频繁开发中，`mmcv` 版本依赖可能不准确。如果您在使用
|
||||
`master` 分支时遇到问题，请尝试更新 `mmcv` 到最新版。
|
||||
|
||||
### 安装 MMClassification 步骤
|
||||
## 安装 MMClassification 步骤
|
||||
|
||||
a. 创建 conda 虚拟环境，并激活
|
||||
|
||||
|
|
@ -62,7 +62,7 @@ conda install pytorch=1.3.1 cudatoolkit=9.2 torchvision=0.4.2 -c pytorch
|
|||
|
||||
c. 安装 MMClassification 库
|
||||
|
||||
#### 稳定版本
|
||||
### 稳定版本
|
||||
|
||||
我们推荐使用 [MIM](https://github.com/open-mmlab/mim) 进行 MMClassification 的安装。
|
||||
|
||||
|
|
@ -79,7 +79,7 @@ MIM 工具可以自动安装 OpenMMLab 旗下的各个项目及其依赖，同
|
|||
pip install mmcls
|
||||
```
|
||||
|
||||
#### 开发版本
|
||||
### 开发版本
|
||||
|
||||
首先，克隆最新的 MMClassification 仓库：
|
||||
|
||||
|
|
@ -100,9 +100,9 @@ pip install -e . # 或者 "python setup.py develop"
|
|||
|
||||
2. 如果希望使用 `opencv-python-headless` 而不是 `opencv-python`，可以在安装 [mmcv](https://github.com/open-mmlab/mmcv) 之前提前安装。
|
||||
|
||||
#### 利用 Docker 镜像安装 MMClassification
|
||||
### 利用 Docker 镜像安装 MMClassification
|
||||
|
||||
MMClassification 提供 [Dockerfile](/docker/Dockerfile) ，可以通过以下命令创建 docker 镜像。
|
||||
MMClassification 提供 [Dockerfile](https://github.com/open-mmlab/mmclassification/blob/master/docker/Dockerfile) ，可以通过以下命令创建 docker 镜像。
|
||||
|
||||
```shell
|
||||
# 创建基于 PyTorch 1.6.0, CUDA 10.1, CUDNN 7 的镜像。
|
||||
|
|
@ -117,7 +117,7 @@ docker build -f ./docker/Dockerfile --rm -t mmcls:torch1.6.0-cuda10.1-cudnn7 .
|
|||
docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/workspace/mmclassification/data mmcls:torch1.6.0-cuda10.1-cudnn7 /bin/bash
|
||||
```
|
||||
|
||||
### 在多个 MMClassification 版本下进行开发
|
||||
## 在多个 MMClassification 版本下进行开发
|
||||
|
||||
MMClassification 的训练和测试脚本已经修改了 `PYTHONPATH` 变量，以确保其能够运行当前目录下的 MMClassification。
|
||||
|
||||
|
|
|
|||
|
|
@ -1,3 +1,3 @@
|
|||
## <a href='https://mmclassification.readthedocs.io/en/latest/'>English</a>
|
||||
# <a href='https://mmclassification.readthedocs.io/en/latest/'>English</a>
|
||||
|
||||
## <a href='https://mmclassification.readthedocs.io/zh_CN/latest/'>简体中文</a>
|
||||
# <a href='https://mmclassification.readthedocs.io/zh_CN/latest/'>简体中文</a>
|
||||
|
|
|
|||
|
|
@ -1,5 +1,7 @@
|
|||
docutils==0.16.0
|
||||
recommonmark
|
||||
m2r
|
||||
myst-parser
|
||||
-e git+https://github.com/open-mmlab/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
|
||||
sphinx==4.0.2
|
||||
sphinx-copybutton
|
||||
sphinx_markdown_tables
|
||||
sphinx_rtd_theme==0.5.2
|
||||
|
|
|
|||
|
|
@ -14,6 +14,6 @@ line_length = 79
|
|||
multi_line_output = 0
|
||||
known_standard_library = pkg_resources,setuptools
|
||||
known_first_party = mmcls
|
||||
known_third_party = PIL,matplotlib,mmcv,mmdet,numpy,onnxruntime,packaging,pytest,seaborn,torch,torchvision,ts
|
||||
known_third_party = PIL,m2r,matplotlib,mmcv,mmdet,numpy,onnxruntime,packaging,pytest,pytorch_sphinx_theme,recommonmark,seaborn,sphinx,torch,torchvision,ts
|
||||
no_lines_before = STDLIB,LOCALFOLDER
|
||||
default_section = THIRDPARTY
|
||||
|
|
|
|||
Loading…
Reference in New Issue