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添加mmclassification-0.24.1代码，删除mmclassification-speed-benchmark

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openmmlab_test/mmclassification-0.24.1/docs/en/api/models.utils.rst 0 → 100644
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.. role:: hidden
:class: hidden-section
mmcls.models.utils
===================================
This package includes some helper functions and common components used in various networks.
.. contents:: mmcls.models.utils
:depth: 2
:local:
:backlinks: top
.. currentmodule:: mmcls.models.utils
Common Components
------------------
.. autosummary::
:toctree: generated
:nosignatures:
:template: classtemplate.rst
InvertedResidual
SELayer
ShiftWindowMSA
MultiheadAttention
ConditionalPositionEncoding
Helper Functions
------------------
channel_shuffle
^^^^^^^^^^^^^^^
.. autofunction:: channel_shuffle
make_divisible
^^^^^^^^^^^^^^
.. autofunction:: make_divisible
to_ntuple
^^^^^^^^^^^^^^
.. autofunction:: to_ntuple
.. autofunction:: to_2tuple
.. autofunction:: to_3tuple
.. autofunction:: to_4tuple
is_tracing
^^^^^^^^^^^^^^
.. autofunction:: is_tracing
openmmlab_test/mmclassification-0.24.1/docs/en/api/transforms.rst 0 → 100644
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.. role:: hidden
:class: hidden-section
Data Transformations
***********************************
In MMClassification, the data preparation and the dataset is decomposed. The
datasets only define how to get samples' basic information from the file
system. These basic information includes the ground-truth label and raw images
data / the paths of images.
To prepare the inputs data, we need to do some transformations on these basic
information. These transformations includes loading, preprocessing and
formatting. And a series of data transformations makes up a data pipeline.
Therefore, you can find the a ``pipeline`` argument in the configs of dataset,
for example:
.. code:: python
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='RandomResizedCrop', size=224),
dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='ToTensor', keys=['gt_label']),
dict(type='Collect', keys=['img', 'gt_label'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Resize', size=256),
dict(type='CenterCrop', crop_size=224),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
]
data = dict(
train=dict(..., pipeline=train_pipeline),
val=dict(..., pipeline=test_pipeline),
test=dict(..., pipeline=test_pipeline),
)
Every item of a pipeline list is one of the following data transformations class. And if you want to add a custom data transformation class, the tutorial :doc:`Custom Data Pipelines </tutorials/data_pipeline>` will help you.
.. contents:: mmcls.datasets.pipelines
:depth: 2
:local:
:backlinks: top
.. currentmodule:: mmcls.datasets.pipelines
Loading
=======
LoadImageFromFile
---------------------
.. autoclass:: LoadImageFromFile
Preprocessing and Augmentation
==============================
CenterCrop
---------------------
.. autoclass:: CenterCrop
Lighting
---------------------
.. autoclass:: Lighting
Normalize
---------------------
.. autoclass:: Normalize
Pad
---------------------
.. autoclass:: Pad
Resize
---------------------
.. autoclass:: Resize
RandomCrop
---------------------
.. autoclass:: RandomCrop
RandomErasing
---------------------
.. autoclass:: RandomErasing
RandomFlip
---------------------
.. autoclass:: RandomFlip
RandomGrayscale
---------------------
.. autoclass:: RandomGrayscale
RandomResizedCrop
---------------------
.. autoclass:: RandomResizedCrop
ColorJitter
---------------------
.. autoclass:: ColorJitter
Composed Augmentation
---------------------
Composed augmentation is a kind of methods which compose a series of data
augmentation transformations, such as ``AutoAugment`` and ``RandAugment``.
.. autoclass:: AutoAugment
.. autoclass:: RandAugment
In composed augmentation, we need to specify several data transformations or
several groups of data transformations (The ``policies`` argument) as the
random sampling space. These data transformations are chosen from the below
table. In addition, we provide some preset policies in `this folder`_.
.. _this folder: https://github.com/open-mmlab/mmclassification/tree/master/configs/_base_/datasets/pipelines
.. autosummary::
:toctree: generated
:nosignatures:
:template: classtemplate.rst
AutoContrast
Brightness
ColorTransform
Contrast
Cutout
Equalize
Invert
Posterize
Rotate
Sharpness
Shear
Solarize
SolarizeAdd
Translate
Formatting
==========
Collect
---------------------
.. autoclass:: Collect
ImageToTensor
---------------------
.. autoclass:: ImageToTensor
ToNumpy
---------------------
.. autoclass:: ToNumpy
ToPIL
---------------------
.. autoclass:: ToPIL
ToTensor
---------------------
.. autoclass:: ToTensor
Transpose
---------------------
.. autoclass:: Transpose
openmmlab_test/mmclassification-0.24.1/docs/en/api/utils.rst 0 → 100644
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.. role:: hidden
:class: hidden-section
mmcls.utils
===================================
These are some useful help function in the ``utils`` package.
.. contents:: mmcls.utils
:depth: 1
:local:
:backlinks: top
.. currentmodule:: mmcls.utils
.. autosummary::
:toctree: generated
:nosignatures:
collect_env
get_root_logger
load_json_log
setup_multi_processes
openmmlab_test/mmclassification-0.24.1/docs/en/changelog.md 0 → 100644
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# Changelog
## v0.24.1(31/10/2022)
### New Features
- Support mmcls with NPU backend. ([#1072](https://github.com/open-mmlab/mmclassification/pull/1072))
### Bug Fixes
- Fix performance issue in convnext DDP train. ([#1098](https://github.com/open-mmlab/mmclassification/pull/1098))
## v0.24.0(30/9/2022)
### Highlights
- Support HorNet, EfficientFormerm, SwinTransformer V2 and MViT backbones.
- Support Standford Cars dataset.
### New Features
- Support HorNet Backbone. ([#1013](https://github.com/open-mmlab/mmclassification/pull/1013))
- Support EfficientFormer. ([#954](https://github.com/open-mmlab/mmclassification/pull/954))
- Support Stanford Cars dataset. ([#893](https://github.com/open-mmlab/mmclassification/pull/893))
- Support CSRA head. ([#881](https://github.com/open-mmlab/mmclassification/pull/881))
- Support Swin Transform V2. ([#799](https://github.com/open-mmlab/mmclassification/pull/799))
- Support MViT and add checkpoints. ([#924](https://github.com/open-mmlab/mmclassification/pull/924))
### Improvements
- \[Improve\] replace loop of progressbar in api/test. ([#878](https://github.com/open-mmlab/mmclassification/pull/878))
- \[Enhance\] RepVGG for YOLOX-PAI. ([#1025](https://github.com/open-mmlab/mmclassification/pull/1025))
- \[Enhancement\] Update VAN. ([#1017](https://github.com/open-mmlab/mmclassification/pull/1017))
- \[Refactor\] Re-write `get_sinusoid_encoding` from third-party implementation. ([#965](https://github.com/open-mmlab/mmclassification/pull/965))
- \[Improve\] Upgrade onnxsim to v0.4.0. ([#915](https://github.com/open-mmlab/mmclassification/pull/915))
- \[Improve\] Fixed typo in `RepVGG`. ([#985](https://github.com/open-mmlab/mmclassification/pull/985))
- \[Improve\] Using `train_step` instead of `forward` in PreciseBNHook ([#964](https://github.com/open-mmlab/mmclassification/pull/964))
- \[Improve\] Use `forward_dummy` to calculate FLOPS. ([#953](https://github.com/open-mmlab/mmclassification/pull/953))
### Bug Fixes
- Fix warning with `torch.meshgrid`. ([#860](https://github.com/open-mmlab/mmclassification/pull/860))
- Add matplotlib minimum version requriments. ([#909](https://github.com/open-mmlab/mmclassification/pull/909))
- val loader should not drop last by default. ([#857](https://github.com/open-mmlab/mmclassification/pull/857))
- Fix config.device bug in toturial. ([#1059](https://github.com/open-mmlab/mmclassification/pull/1059))
- Fix attenstion clamp max params ([#1034](https://github.com/open-mmlab/mmclassification/pull/1034))
- Fix device mismatch in Swin-v2. ([#976](https://github.com/open-mmlab/mmclassification/pull/976))
- Fix the output position of Swin-Transformer. ([#947](https://github.com/open-mmlab/mmclassification/pull/947))
### Docs Update
- Fix typo in config.md. ([#827](https://github.com/open-mmlab/mmclassification/pull/827))
- Add version for torchvision to avoide error. ([#903](https://github.com/open-mmlab/mmclassification/pull/903))
- Fixed typo for `--out-dir` option of analyze_results.py. ([#898](https://github.com/open-mmlab/mmclassification/pull/898))
- Refine the docstring of RegNet ([#935](https://github.com/open-mmlab/mmclassification/pull/935))
## v0.23.2(28/7/2022)
### New Features
- Support MPS device. ([#894](https://github.com/open-mmlab/mmclassification/pull/894))
### Bug Fixes
- Fix a bug in Albu which caused crashing. ([#918](https://github.com/open-mmlab/mmclassification/pull/918))
## v0.23.1(2/6/2022)
### New Features
- Dedicated MMClsWandbHook for MMClassification (Weights and Biases Integration) ([#764](https://github.com/open-mmlab/mmclassification/pull/764))
### Improvements
- Use mdformat instead of markdownlint to format markdown. ([#844](https://github.com/open-mmlab/mmclassification/pull/844))
### Bug Fixes
- Fix wrong `--local_rank`.
### Docs Update
- Update install tutorials. ([#854](https://github.com/open-mmlab/mmclassification/pull/854))
- Fix wrong link in README. ([#835](https://github.com/open-mmlab/mmclassification/pull/835))
## v0.23.0(1/5/2022)
### New Features
- Support DenseNet. ([#750](https://github.com/open-mmlab/mmclassification/pull/750))
- Support VAN. ([#739](https://github.com/open-mmlab/mmclassification/pull/739))
### Improvements
- Support training on IPU and add fine-tuning configs of ViT. ([#723](https://github.com/open-mmlab/mmclassification/pull/723))
### Docs Update
- New style API reference, and easier to use! Welcome [view it](https://mmclassification.readthedocs.io/en/master/api/models.html). ([#774](https://github.com/open-mmlab/mmclassification/pull/774))
## v0.22.1(15/4/2022)
### New Features
- \[Feature\] Support resize relative position embedding in `SwinTransformer`. ([#749](https://github.com/open-mmlab/mmclassification/pull/749))
- \[Feature\] Add PoolFormer backbone and checkpoints. ([#746](https://github.com/open-mmlab/mmclassification/pull/746))
### Improvements
- \[Enhance\] Improve CPE performance by reduce memory copy. ([#762](https://github.com/open-mmlab/mmclassification/pull/762))
- \[Enhance\] Add extra dataloader settings in configs. ([#752](https://github.com/open-mmlab/mmclassification/pull/752))
## v0.22.0(30/3/2022)
### Highlights
- Support a series of CSP Network, such as CSP-ResNet, CSP-ResNeXt and CSP-DarkNet.
- A new `CustomDataset` class to help you build dataset of yourself!
- Support ConvMixer, RepMLP and new dataset - CUB dataset.
### New Features
- \[Feature\] Add CSPNet and backbone and checkpoints ([#735](https://github.com/open-mmlab/mmclassification/pull/735))
- \[Feature\] Add `CustomDataset`. ([#738](https://github.com/open-mmlab/mmclassification/pull/738))
- \[Feature\] Add diff seeds to diff ranks. ([#744](https://github.com/open-mmlab/mmclassification/pull/744))
- \[Feature\] Support ConvMixer. ([#716](https://github.com/open-mmlab/mmclassification/pull/716))
- \[Feature\] Our `dist_train` & `dist_test` tools support distributed training on multiple machines. ([#734](https://github.com/open-mmlab/mmclassification/pull/734))
- \[Feature\] Add RepMLP backbone and checkpoints. ([#709](https://github.com/open-mmlab/mmclassification/pull/709))
- \[Feature\] Support CUB dataset. ([#703](https://github.com/open-mmlab/mmclassification/pull/703))
- \[Feature\] Support ResizeMix. ([#676](https://github.com/open-mmlab/mmclassification/pull/676))
### Improvements
- \[Enhance\] Use `--a-b` instead of `--a_b` in arguments. ([#754](https://github.com/open-mmlab/mmclassification/pull/754))
- \[Enhance\] Add `get_cat_ids` and `get_gt_labels` to KFoldDataset. ([#721](https://github.com/open-mmlab/mmclassification/pull/721))
- \[Enhance\] Set torch seed in `worker_init_fn`. ([#733](https://github.com/open-mmlab/mmclassification/pull/733))
### Bug Fixes
- \[Fix\] Fix the discontiguous output feature map of ConvNeXt. ([#743](https://github.com/open-mmlab/mmclassification/pull/743))
### Docs Update
- \[Docs\] Add brief installation steps in README for copy&paste. ([#755](https://github.com/open-mmlab/mmclassification/pull/755))
- \[Docs\] fix logo url link from mmocr to mmcls. ([#732](https://github.com/open-mmlab/mmclassification/pull/732))
## v0.21.0(04/03/2022)
### Highlights
- Support ResNetV1c and Wide-ResNet, and provide pre-trained models.
- Support dynamic input shape for ViT-based algorithms. Now our ViT, DeiT, Swin-Transformer and T2T-ViT support forwarding with any input shape.
- Reproduce training results of DeiT. And our DeiT-T and DeiT-S have higher accuracy comparing with the official weights.
### New Features
- Add ResNetV1c. ([#692](https://github.com/open-mmlab/mmclassification/pull/692))
- Support Wide-ResNet. ([#715](https://github.com/open-mmlab/mmclassification/pull/715))
- Support gem pooling ([#677](https://github.com/open-mmlab/mmclassification/pull/677))
### Improvements
- Reproduce training results of DeiT. ([#711](https://github.com/open-mmlab/mmclassification/pull/711))
- Add ConvNeXt pretrain models on ImageNet-1k. ([#707](https://github.com/open-mmlab/mmclassification/pull/707))
- Support dynamic input shape for ViT-based algorithms. ([#706](https://github.com/open-mmlab/mmclassification/pull/706))
- Add `evaluate` function for ConcatDataset. ([#650](https://github.com/open-mmlab/mmclassification/pull/650))
- Enhance vis-pipeline tool. ([#604](https://github.com/open-mmlab/mmclassification/pull/604))
- Return code 1 if scripts runs failed. ([#694](https://github.com/open-mmlab/mmclassification/pull/694))
- Use PyTorch official `one_hot` to implement `convert_to_one_hot`. ([#696](https://github.com/open-mmlab/mmclassification/pull/696))
- Add a new pre-commit-hook to automatically add a copyright. ([#710](https://github.com/open-mmlab/mmclassification/pull/710))
- Add deprecation message for deploy tools. ([#697](https://github.com/open-mmlab/mmclassification/pull/697))
- Upgrade isort pre-commit hooks. ([#687](https://github.com/open-mmlab/mmclassification/pull/687))
- Use `--gpu-id` instead of `--gpu-ids` in non-distributed multi-gpu training/testing. ([#688](https://github.com/open-mmlab/mmclassification/pull/688))
- Remove deprecation. ([#633](https://github.com/open-mmlab/mmclassification/pull/633))
### Bug Fixes
- Fix Conformer forward with irregular input size. ([#686](https://github.com/open-mmlab/mmclassification/pull/686))
- Add `dist.barrier` to fix a bug in directory checking. ([#666](https://github.com/open-mmlab/mmclassification/pull/666))
## v0.20.1(07/02/2022)
### Bug Fixes
- Fix the MMCV dependency version.
## v0.20.0(30/01/2022)
### Highlights
- Support K-fold cross-validation. The tutorial will be released later.
- Support HRNet, ConvNeXt, Twins and EfficientNet.
- Support model conversion from PyTorch to Core-ML by a tool.
### New Features
- Support K-fold cross-validation. ([#563](https://github.com/open-mmlab/mmclassification/pull/563))
- Support HRNet and add pre-trained models. ([#660](https://github.com/open-mmlab/mmclassification/pull/660))
- Support ConvNeXt and add pre-trained models. ([#670](https://github.com/open-mmlab/mmclassification/pull/670))
- Support Twins and add pre-trained models. ([#642](https://github.com/open-mmlab/mmclassification/pull/642))
- Support EfficientNet and add pre-trained models.([#649](https://github.com/open-mmlab/mmclassification/pull/649))
- Support `features_only` option in `TIMMBackbone`. ([#668](https://github.com/open-mmlab/mmclassification/pull/668))
- Add conversion script from pytorch to Core-ML model. ([#597](https://github.com/open-mmlab/mmclassification/pull/597))
### Improvements
- New-style CPU training and inference. ([#674](https://github.com/open-mmlab/mmclassification/pull/674))
- Add setup multi-processing both in train and test. ([#671](https://github.com/open-mmlab/mmclassification/pull/671))
- Rewrite channel split operation in ShufflenetV2. ([#632](https://github.com/open-mmlab/mmclassification/pull/632))
- Deprecate the support for "python setup.py test". ([#646](https://github.com/open-mmlab/mmclassification/pull/646))
- Support single-label, softmax, custom eps by asymmetric loss. ([#609](https://github.com/open-mmlab/mmclassification/pull/609))
- Save class names in best checkpoint created by evaluation hook. ([#641](https://github.com/open-mmlab/mmclassification/pull/641))
### Bug Fixes
- Fix potential unexcepted behaviors if `metric_options` is not specified in multi-label evaluation. ([#647](https://github.com/open-mmlab/mmclassification/pull/647))
- Fix API changes in `pytorch-grad-cam&gt;=1.3.7`. ([#656](https://github.com/open-mmlab/mmclassification/pull/656))
- Fix bug which breaks `cal_train_time` in `analyze_logs.py`. ([#662](https://github.com/open-mmlab/mmclassification/pull/662))
### Docs Update
- Update README in configs according to OpenMMLab standard. ([#672](https://github.com/open-mmlab/mmclassification/pull/672))
- Update installation guide and README. ([#624](https://github.com/open-mmlab/mmclassification/pull/624))
## v0.19.0(31/12/2021)
### Highlights
- The feature extraction function has been enhanced. See [#593](https://github.com/open-mmlab/mmclassification/pull/593) for more details.
- Provide the high-acc ResNet-50 training settings from [*ResNet strikes back*](https://arxiv.org/abs/2110.00476).
- Reproduce the training accuracy of T2T-ViT & RegNetX, and provide self-training checkpoints.
- Support DeiT & Conformer backbone and checkpoints.
- Provide a CAM visualization tool based on [pytorch-grad-cam](https://github.com/jacobgil/pytorch-grad-cam), and detailed [user guide](https://mmclassification.readthedocs.io/en/latest/tools/visualization.html#class-activation-map-visualization)!
### New Features
- Support Precise BN. ([#401](https://github.com/open-mmlab/mmclassification/pull/401))
- Add CAM visualization tool. ([#577](https://github.com/open-mmlab/mmclassification/pull/577))
- Repeated Aug and Sampler Registry. ([#588](https://github.com/open-mmlab/mmclassification/pull/588))
- Add DeiT backbone and checkpoints. ([#576](https://github.com/open-mmlab/mmclassification/pull/576))
- Support LAMB optimizer. ([#591](https://github.com/open-mmlab/mmclassification/pull/591))
- Implement the conformer backbone. ([#494](https://github.com/open-mmlab/mmclassification/pull/494))
- Add the frozen function for Swin Transformer model. ([#574](https://github.com/open-mmlab/mmclassification/pull/574))
- Support using checkpoint in Swin Transformer to save memory. ([#557](https://github.com/open-mmlab/mmclassification/pull/557))
### Improvements
- \[Reproduction\] Reproduce RegNetX training accuracy. ([#587](https://github.com/open-mmlab/mmclassification/pull/587))
- \[Reproduction\] Reproduce training results of T2T-ViT. ([#610](https://github.com/open-mmlab/mmclassification/pull/610))
- \[Enhance\] Provide high-acc training settings of ResNet. ([#572](https://github.com/open-mmlab/mmclassification/pull/572))
- \[Enhance\] Set a random seed when the user does not set a seed. ([#554](https://github.com/open-mmlab/mmclassification/pull/554))
- \[Enhance\] Added `NumClassCheckHook` and unit tests. ([#559](https://github.com/open-mmlab/mmclassification/pull/559))
- \[Enhance\] Enhance feature extraction function. ([#593](https://github.com/open-mmlab/mmclassification/pull/593))
- \[Enhance\] Improve efficiency of precision, recall, f1_score and support. ([#595](https://github.com/open-mmlab/mmclassification/pull/595))
- \[Enhance\] Improve accuracy calculation performance. ([#592](https://github.com/open-mmlab/mmclassification/pull/592))
- \[Refactor\] Refactor `analysis_log.py`. ([#529](https://github.com/open-mmlab/mmclassification/pull/529))
- \[Refactor\] Use new API of matplotlib to handle blocking input in visualization. ([#568](https://github.com/open-mmlab/mmclassification/pull/568))
- \[CI\] Cancel previous runs that are not completed. ([#583](https://github.com/open-mmlab/mmclassification/pull/583))
- \[CI\] Skip build CI if only configs or docs modification. ([#575](https://github.com/open-mmlab/mmclassification/pull/575))
### Bug Fixes
- Fix test sampler bug. ([#611](https://github.com/open-mmlab/mmclassification/pull/611))
- Try to create a symbolic link, otherwise copy. ([#580](https://github.com/open-mmlab/mmclassification/pull/580))
- Fix a bug for multiple output in swin transformer. ([#571](https://github.com/open-mmlab/mmclassification/pull/571))
### Docs Update
- Update mmcv, torch, cuda version in Dockerfile and docs. ([#594](https://github.com/open-mmlab/mmclassification/pull/594))
- Add analysis&misc docs. ([#525](https://github.com/open-mmlab/mmclassification/pull/525))
- Fix docs build dependency. ([#584](https://github.com/open-mmlab/mmclassification/pull/584))
## v0.18.0(30/11/2021)
### Highlights
- Support MLP-Mixer backbone and provide pre-trained checkpoints.
- Add a tool to visualize the learning rate curve of the training phase. Welcome to use with the [tutorial](https://mmclassification.readthedocs.io/en/latest/tools/visualization.html#learning-rate-schedule-visualization)!
### New Features
- Add MLP Mixer Backbone. ([#528](https://github.com/open-mmlab/mmclassification/pull/528), [#539](https://github.com/open-mmlab/mmclassification/pull/539))
- Support positive weights in BCE. ([#516](https://github.com/open-mmlab/mmclassification/pull/516))
- Add a tool to visualize learning rate in each iterations. ([#498](https://github.com/open-mmlab/mmclassification/pull/498))
### Improvements
- Use CircleCI to do unit tests. ([#567](https://github.com/open-mmlab/mmclassification/pull/567))
- Focal loss for single label tasks. ([#548](https://github.com/open-mmlab/mmclassification/pull/548))
- Remove useless `import_modules_from_string`. ([#544](https://github.com/open-mmlab/mmclassification/pull/544))
- Rename config files according to the config name standard. ([#508](https://github.com/open-mmlab/mmclassification/pull/508))
- Use `reset_classifier` to remove head of timm backbones. ([#534](https://github.com/open-mmlab/mmclassification/pull/534))
- Support passing arguments to loss from head. ([#523](https://github.com/open-mmlab/mmclassification/pull/523))
- Refactor `Resize` transform and add `Pad` transform. ([#506](https://github.com/open-mmlab/mmclassification/pull/506))
- Update mmcv dependency version. ([#509](https://github.com/open-mmlab/mmclassification/pull/509))
### Bug Fixes
- Fix bug when using `ClassBalancedDataset`. ([#555](https://github.com/open-mmlab/mmclassification/pull/555))
- Fix a bug when using iter-based runner with 'val' workflow. ([#542](https://github.com/open-mmlab/mmclassification/pull/542))
- Fix interpolation method checking in `Resize`. ([#547](https://github.com/open-mmlab/mmclassification/pull/547))
- Fix a bug when load checkpoints in mulit-GPUs environment. ([#527](https://github.com/open-mmlab/mmclassification/pull/527))
- Fix an error on indexing scalar metrics in `analyze_result.py`. ([#518](https://github.com/open-mmlab/mmclassification/pull/518))
- Fix wrong condition judgment in `analyze_logs.py` and prevent empty curve. ([#510](https://github.com/open-mmlab/mmclassification/pull/510))
### Docs Update
- Fix vit config and model broken links. ([#564](https://github.com/open-mmlab/mmclassification/pull/564))
- Add abstract and image for every paper. ([#546](https://github.com/open-mmlab/mmclassification/pull/546))
- Add mmflow and mim in banner and readme. ([#543](https://github.com/open-mmlab/mmclassification/pull/543))
- Add schedule and runtime tutorial docs. ([#499](https://github.com/open-mmlab/mmclassification/pull/499))
- Add the top-5 acc in ResNet-CIFAR README. ([#531](https://github.com/open-mmlab/mmclassification/pull/531))
- Fix TOC of `visualization.md` and add example images. ([#513](https://github.com/open-mmlab/mmclassification/pull/513))
- Use docs link of other projects and add MMCV docs. ([#511](https://github.com/open-mmlab/mmclassification/pull/511))
## v0.17.0(29/10/2021)
### Highlights
- Support Tokens-to-Token ViT backbone and Res2Net backbone. Welcome to use!
- Support ImageNet21k dataset.
- Add a pipeline visualization tool. Try it with the [tutorials](https://mmclassification.readthedocs.io/en/latest/tools/visualization.html#pipeline-visualization)!
### New Features
- Add Tokens-to-Token ViT backbone and converted checkpoints. ([#467](https://github.com/open-mmlab/mmclassification/pull/467))
- Add Res2Net backbone and converted weights. ([#465](https://github.com/open-mmlab/mmclassification/pull/465))
- Support ImageNet21k dataset. ([#461](https://github.com/open-mmlab/mmclassification/pull/461))
- Support seesaw loss. ([#500](https://github.com/open-mmlab/mmclassification/pull/500))
- Add a pipeline visualization tool. ([#406](https://github.com/open-mmlab/mmclassification/pull/406))
- Add a tool to find broken files. ([#482](https://github.com/open-mmlab/mmclassification/pull/482))
- Add a tool to test TorchServe. ([#468](https://github.com/open-mmlab/mmclassification/pull/468))
### Improvements
- Refator Vision Transformer. ([#395](https://github.com/open-mmlab/mmclassification/pull/395))
- Use context manager to reuse matplotlib figures. ([#432](https://github.com/open-mmlab/mmclassification/pull/432))
### Bug Fixes
- Remove `DistSamplerSeedHook` if use `IterBasedRunner`. ([#501](https://github.com/open-mmlab/mmclassification/pull/501))
- Set the priority of `EvalHook` to "LOW" to avoid a bug when using `IterBasedRunner`. ([#488](https://github.com/open-mmlab/mmclassification/pull/488))
- Fix a wrong parameter of `get_root_logger` in `apis/train.py`. ([#486](https://github.com/open-mmlab/mmclassification/pull/486))
- Fix version check in dataset builder. ([#474](https://github.com/open-mmlab/mmclassification/pull/474))
### Docs Update
- Add English Colab tutorials and update Chinese Colab tutorials. ([#483](https://github.com/open-mmlab/mmclassification/pull/483), [#497](https://github.com/open-mmlab/mmclassification/pull/497))
- Add tutuorial for config files. ([#487](https://github.com/open-mmlab/mmclassification/pull/487))
- Add model-pages in Model Zoo. ([#480](https://github.com/open-mmlab/mmclassification/pull/480))
- Add code-spell pre-commit hook and fix a large mount of typos. ([#470](https://github.com/open-mmlab/mmclassification/pull/470))
## v0.16.0(30/9/2021)
### Highlights
- We have improved compatibility with downstream repositories like MMDetection and MMSegmentation. We will add some examples about how to use our backbones in MMDetection.
- Add RepVGG backbone and checkpoints. Welcome to use it!
- Add timm backbones wrapper, now you can simply use backbones of pytorch-image-models in MMClassification!
### New Features
- Add RepVGG backbone and checkpoints. ([#414](https://github.com/open-mmlab/mmclassification/pull/414))
- Add timm backbones wrapper. ([#427](https://github.com/open-mmlab/mmclassification/pull/427))
### Improvements
- Fix TnT compatibility and verbose warning. ([#436](https://github.com/open-mmlab/mmclassification/pull/436))
- Support setting `--out-items` in `tools/test.py`. ([#437](https://github.com/open-mmlab/mmclassification/pull/437))
- Add datetime info and saving model using torch\<1.6 format. ([#439](https://github.com/open-mmlab/mmclassification/pull/439))
- Improve downstream repositories compatibility. ([#421](https://github.com/open-mmlab/mmclassification/pull/421))
- Rename the option `--options` to `--cfg-options` in some tools. ([#425](https://github.com/open-mmlab/mmclassification/pull/425))
- Add PyTorch 1.9 and Python 3.9 build workflow, and remove some CI. ([#422](https://github.com/open-mmlab/mmclassification/pull/422))
### Bug Fixes
- Fix format error in `test.py` when metric returns `np.ndarray`. ([#441](https://github.com/open-mmlab/mmclassification/pull/441))
- Fix `publish_model` bug if no parent of `out_file`. ([#463](https://github.com/open-mmlab/mmclassification/pull/463))
- Fix num_classes bug in pytorch2onnx.py. ([#458](https://github.com/open-mmlab/mmclassification/pull/458))
- Fix missing runtime requirement `packaging`. ([#459](https://github.com/open-mmlab/mmclassification/pull/459))
- Fix saving simplified model bug in ONNX export tool. ([#438](https://github.com/open-mmlab/mmclassification/pull/438))
### Docs Update
- Update `getting_started.md` and `install.md`. And rewrite `finetune.md`. ([#466](https://github.com/open-mmlab/mmclassification/pull/466))
- Use PyTorch style docs theme. ([#457](https://github.com/open-mmlab/mmclassification/pull/457))
- Update metafile and Readme. ([#435](https://github.com/open-mmlab/mmclassification/pull/435))
- Add `CITATION.cff`. ([#428](https://github.com/open-mmlab/mmclassification/pull/428))
## v0.15.0(31/8/2021)
### Highlights
- Support `hparams` argument in `AutoAugment` and `RandAugment` to provide hyperparameters for sub-policies.
- Support custom squeeze channels in `SELayer`.
- Support classwise weight in losses.
### New Features
- Add `hparams` argument in `AutoAugment` and `RandAugment` and some other improvement. ([#398](https://github.com/open-mmlab/mmclassification/pull/398))
- Support classwise weight in losses. ([#388](https://github.com/open-mmlab/mmclassification/pull/388))
- Enhance `SELayer` to support custom squeeze channels. ([#417](https://github.com/open-mmlab/mmclassification/pull/417))
### Code Refactor
- Better result visualization. ([#419](https://github.com/open-mmlab/mmclassification/pull/419))
- Use `post_process` function to handle pred result processing. ([#390](https://github.com/open-mmlab/mmclassification/pull/390))
- Update `digit_version` function. ([#402](https://github.com/open-mmlab/mmclassification/pull/402))
- Avoid albumentations to install both opencv and opencv-headless. ([#397](https://github.com/open-mmlab/mmclassification/pull/397))
- Avoid unnecessary listdir when building ImageNet. ([#396](https://github.com/open-mmlab/mmclassification/pull/396))
- Use dynamic mmcv download link in TorchServe dockerfile. ([#387](https://github.com/open-mmlab/mmclassification/pull/387))
### Docs Improvement
- Add readme of some algorithms and update meta yml. ([#418](https://github.com/open-mmlab/mmclassification/pull/418))
- Add Copyright information. ([#413](https://github.com/open-mmlab/mmclassification/pull/413))
- Fix typo 'metirc'. ([#411](https://github.com/open-mmlab/mmclassification/pull/411))
- Update QQ group QR code. ([#393](https://github.com/open-mmlab/mmclassification/pull/393))
- Add PR template and modify issue template. ([#380](https://github.com/open-mmlab/mmclassification/pull/380))
## v0.14.0(4/8/2021)
### Highlights
- Add transformer-in-transformer backbone and pretrain checkpoints, refers to [the paper](https://arxiv.org/abs/2103.00112).
- Add Chinese colab tutorial.
- Provide dockerfile to build mmcls dev docker image.
### New Features
- Add transformer in transformer backbone and pretrain checkpoints. ([#339](https://github.com/open-mmlab/mmclassification/pull/339))
- Support mim, welcome to use mim to manage your mmcls project. ([#376](https://github.com/open-mmlab/mmclassification/pull/376))
- Add Dockerfile. ([#365](https://github.com/open-mmlab/mmclassification/pull/365))
- Add ResNeSt configs. ([#332](https://github.com/open-mmlab/mmclassification/pull/332))
### Improvements
- Use the `presistent_works` option if available, to accelerate training. ([#349](https://github.com/open-mmlab/mmclassification/pull/349))
- Add Chinese ipynb tutorial. ([#306](https://github.com/open-mmlab/mmclassification/pull/306))
- Refactor unit tests. ([#321](https://github.com/open-mmlab/mmclassification/pull/321))
- Support to test mmdet inference with mmcls backbone. ([#343](https://github.com/open-mmlab/mmclassification/pull/343))
- Use zero as default value of `thrs` in metrics. ([#341](https://github.com/open-mmlab/mmclassification/pull/341))
### Bug Fixes
- Fix ImageNet dataset annotation file parse bug. ([#370](https://github.com/open-mmlab/mmclassification/pull/370))
- Fix docstring typo and init bug in ShuffleNetV1. ([#374](https://github.com/open-mmlab/mmclassification/pull/374))
- Use local ATTENTION registry to avoid conflict with other repositories. ([#376](https://github.com/open-mmlab/mmclassification/pull/375))
- Fix swin transformer config bug. ([#355](https://github.com/open-mmlab/mmclassification/pull/355))
- Fix `patch_cfg` argument bug in SwinTransformer. ([#368](https://github.com/open-mmlab/mmclassification/pull/368))
- Fix duplicate `init_weights` call in ViT init function. ([#373](https://github.com/open-mmlab/mmclassification/pull/373))
- Fix broken `_base_` link in a resnet config. ([#361](https://github.com/open-mmlab/mmclassification/pull/361))
- Fix vgg-19 model link missing. ([#363](https://github.com/open-mmlab/mmclassification/pull/363))
## v0.13.0(3/7/2021)
- Support Swin-Transformer backbone and add training configs for Swin-Transformer on ImageNet.
### New Features
- Support Swin-Transformer backbone and add training configs for Swin-Transformer on ImageNet. (#271)
- Add pretained model of RegNetX. (#269)
- Support adding custom hooks in config file. (#305)
- Improve and add Chinese translation of `CONTRIBUTING.md` and all tools tutorials. (#320)
- Dump config before training. (#282)
- Add torchscript and torchserve deployment tools. (#279, #284)
### Improvements
- Improve test tools and add some new tools. (#322)
- Correct MobilenetV3 backbone structure and add pretained models. (#291)
- Refactor `PatchEmbed` and `HybridEmbed` as independent components. (#330)
- Refactor mixup and cutmix as `Augments` to support more functions. (#278)
- Refactor weights initialization method. (#270, #318, #319)
- Refactor `LabelSmoothLoss` to support multiple calculation formulas. (#285)
### Bug Fixes
- Fix bug for CPU training. (#286)
- Fix missing test data when `num_imgs` can not be evenly divided by `num_gpus`. (#299)
- Fix build compatible with pytorch v1.3-1.5. (#301)
- Fix `magnitude_std` bug in `RandAugment`. (#309)
- Fix bug when `samples_per_gpu` is 1. (#311)
## 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-0.24.1/docs/en/compatibility.md 0 → 100644
View file @ 0fd8347d
# Compatibility of MMClassification 0.x
## MMClassification 0.20.1
### MMCV compatibility
In Twins backbone, we use the `PatchEmbed` module of MMCV, and this module is added after MMCV 1.4.2.
Therefore, we need to update the mmcv version to 1.4.2.
openmmlab_test/mmclassification-0.24.1/docs/en/conf.py 0 → 100644
View file @ 0fd8347d
# 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
import pytorch_sphinx_theme
from sphinx.builders.html import StandaloneHTMLBuilder
sys.path.insert(0, os.path.abspath('../../'))
# -- Project information -----------------------------------------------------
project = 'MMClassification'
copyright = '2020, OpenMMLab'
author = 'MMClassification Authors'
# The full version, including alpha/beta/rc tags
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__']
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.autosummary',
'sphinx.ext.intersphinx',
'sphinx.ext.napoleon',
'sphinx.ext.viewcode',
'myst_parser',
'sphinx_copybutton',
]
autodoc_mock_imports = ['mmcv._ext', 'matplotlib']
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
#
source_suffix = {
'.rst': 'restructuredtext',
'.md': 'markdown',
}
language = 'en'
# The master toctree document.
master_doc = 'index'
# 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 -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
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://mmclassification.readthedocs.io/en/latest/',
'menu': [
{
'name': 'GitHub',
'url': 'https://github.com/open-mmlab/mmclassification'
},
{
'name':
'Colab Tutorials',
'children': [
{
'name':
'Train and inference with shell commands',
'url':
'https://colab.research.google.com/github/'
'open-mmlab/mmclassification/blob/master/docs/en/'
'tutorials/MMClassification_tools.ipynb',
},
{
'name':
'Train and inference with Python APIs',
'url':
'https://colab.research.google.com/github/'
'open-mmlab/mmclassification/blob/master/docs/en/'
'tutorials/MMClassification_python.ipynb',
},
]
},
],
# Specify the language of shared menu
'menu_lang':
'en'
}
# 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_css_files = ['css/readthedocs.css']
html_js_files = ['js/custom.js']
# -- 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'''
\hypersetup{unicode=true}
\usepackage{CJKutf8}
\DeclareUnicodeCharacter{00A0}{\nobreakspace}
\DeclareUnicodeCharacter{2203}{\ensuremath{\exists}}
\DeclareUnicodeCharacter{2200}{\ensuremath{\forall}}
\DeclareUnicodeCharacter{2286}{\ensuremath{\subseteq}}
\DeclareUnicodeCharacter{2713}{x}
\DeclareUnicodeCharacter{27FA}{\ensuremath{\Longleftrightarrow}}
\DeclareUnicodeCharacter{221A}{\ensuremath{\sqrt{}}}
\DeclareUnicodeCharacter{221B}{\ensuremath{\sqrt[3]{}}}
\DeclareUnicodeCharacter{2295}{\ensuremath{\oplus}}
\DeclareUnicodeCharacter{2297}{\ensuremath{\otimes}}
\begin{CJK}{UTF8}{gbsn}
\AtEndDocument{\end{CJK}}
''',
}
# 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', author,
'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',
'OpenMMLab image classification toolbox and benchmark.', '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
# Auto-generated header anchors
myst_heading_anchors = 3
# Configuration for intersphinx
intersphinx_mapping = {
'python': ('https://docs.python.org/3', None),
'numpy': ('https://numpy.org/doc/stable', None),
'torch': ('https://pytorch.org/docs/stable/', None),
'mmcv': ('https://mmcv.readthedocs.io/en/master/', None),
}
def builder_inited_handler(app):
subprocess.run(['./stat.py'])
def setup(app):
app.connect('builder-inited', builder_inited_handler)
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# NPU (HUAWEI Ascend)
## Usage
Please install MMCV with NPU device support according to {external+mmcv:doc}`the tutorial <get_started/build>`.
Here we use 8 NPUs on your computer to train the model with the following command:
```shell
bash tools/dist_train.sh configs/cspnet/resnet50_8xb32_in1k.py 8 --device npu
```
Also, you can use only one NPU to trian the model with the following command:
```shell
python tools/train.py configs/cspnet/resnet50_8xb32_in1k.py --device npu
```
## Verified Models
| Model | Top-1 (%) | Top-5 (%) | Config | Download |
| :--------------------------------------------------------: | :-------: | :-------: | :-----------------------------------------------------------: | :-------------------------------------------------------------: |
| [CSPResNeXt50](../papers/cspnet.md) | 77.10 | 93.55 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnext50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/cspresnext50_8xb32_in1k.log.json) |
| [DenseNet121](../papers/densenet.md) | 72.62 | 91.04 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet121_4xb256_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/densenet121_4xb256_in1k.log.json) |
| [EfficientNet-B4(AA + AdvProp)](../papers/efficientnet.md) | 75.55 | 92.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32-01norm_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/efficientnet-b4_8xb32-01norm_in1k.log.json) |
| [HRNet-W18](../papers/hrnet.md) | 77.01 | 93.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/hrnet-w18_4xb32_in1k.log.json) |
| [ResNetV1D-152](../papers/resnet.md) | 77.11 | 94.54 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d152_8xb32_in1k.py) | [model](<>) \| [log](<>) |
| [ResNet-50](../papers/resnet.md) | 76.40 | - | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32_in1k.py) | [model](<>) \| [log](<>) |
| [ResNetXt-32x4d-50](../papers/resnext.md) | 77.55 | 93.75 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext50-32x4d_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/resnext50-32x4d_8xb32_in1k.log.json) |
| [SE-ResNet-50](../papers/seresnet.md) | 77.64 | 93.76 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/seresnet50_8xb32_in1k.log.json) |
| [VGG-11](../papers/vgg.md) | 68.92 | 88.83 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/vgg11_8xb32_in1k.log.json) |
| [ShuffleNetV2 1.0x](../papers/shufflenet_v2.md) | 69.53 | 88.82 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/shufflenet-v2-1x_16xb64_in1k.json) |
**All above models are provided by Huawei Ascend group.**
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[html writers]
table_style: colwidths-auto
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# Frequently Asked Questions
We list some common troubles faced by many users and their corresponding
solutions here. Feel free to enrich the list if you find any frequent issues
and have ways to help others to solve them. If the contents here do not cover
your issue, please create an issue using the
[provided templates](https://github.com/open-mmlab/mmclassification/issues/new/choose)
and make sure you fill in all required information in the template.
## Installation
- Compatibility issue between MMCV and MMClassification; "AssertionError:
MMCV==xxx is used but incompatible. Please install mmcv>=xxx, \<=xxx."
Compatible MMClassification and MMCV versions are shown as below. Please
choose the correct version of MMCV to avoid installation issues.
| MMClassification version | MMCV version |
| :----------------------: | :--------------------: |
| dev | mmcv>=1.7.0, \<1.9.0 |
| 0.24.1 (master) | mmcv>=1.4.2, \<1.9.0 |
| 0.23.2 | mmcv>=1.4.2, \<1.7.0 |
| 0.22.1 | mmcv>=1.4.2, \<1.6.0 |
| 0.21.0 | mmcv>=1.4.2, \<=1.5.0 |
| 0.20.1 | mmcv>=1.4.2, \<=1.5.0 |
| 0.19.0 | mmcv>=1.3.16, \<=1.5.0 |
| 0.18.0 | mmcv>=1.3.16, \<=1.5.0 |
| 0.17.0 | mmcv>=1.3.8, \<=1.5.0 |
| 0.16.0 | mmcv>=1.3.8, \<=1.5.0 |
| 0.15.0 | mmcv>=1.3.8, \<=1.5.0 |
| 0.15.0 | mmcv>=1.3.8, \<=1.5.0 |
| 0.14.0 | mmcv>=1.3.8, \<=1.5.0 |
| 0.13.0 | mmcv>=1.3.8, \<=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 |
```{note}
Since the `dev` branch is under frequent development, the MMCV
version dependency may be inaccurate. If you encounter problems when using
the `dev` branch, please try to update MMCV to the latest version.
```
- Using Albumentations
If you would like to use `albumentations`, we suggest using `pip install -r requirements/albu.txt` or
`pip install -U albumentations --no-binary qudida,albumentations`.
If you simply use `pip install albumentations>=0.3.2`, it will install `opencv-python-headless` simultaneously
(even though you have already installed `opencv-python`). Please refer to the
[official documentation](https://albumentations.ai/docs/getting_started/installation/#note-on-opencv-dependencies)
for details.
## Coding
- Do I need to reinstall mmcls after some code modifications?
If you follow [the best practice](install.md) and install mmcls from source,
any local modifications made to the code will take effect without
reinstallation.
- How to develop with multiple MMClassification versions?
Generally speaking, we recommend to use different virtual environments to
manage MMClassification in different working directories. However, you
can also use the same environment to develop MMClassification in different
folders, like mmcls-0.21, mmcls-0.23. When you run the train or test shell script,
it will adopt the mmcls package in the current folder. And when you run other Python
script, you can also add `` PYTHONPATH=`pwd` `` at the beginning of your command
to use the package in the current folder.
Conversely, to use the default MMClassification installed in the environment
rather than the one you are working with, you can remove the following line
in those shell scripts:
```shell
PYTHONPATH="$(dirname $0)/..":$PYTHONPATH
```
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# 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 [Tutorial 3: Customize 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}
# Example
python demo/image_demo.py demo/demo.JPEG configs/resnet/resnet50_8xb32_in1k.py \
https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth
```
### Inference and test a dataset
- single GPU
- CPU
- 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}]
# CPU: disable GPUs and run single-gpu testing script
export CUDA_VISIBLE_DEVICES=-1
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:
Infer ResNet-50 on ImageNet validation set to get predicted labels and their corresponding predicted scores.
```shell
python tools/test.py configs/resnet/resnet50_8xb16_cifar10.py \
https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.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) # Evaluate the model per 12 epochs.
```
### 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 CPU
The process of training on the CPU is consistent with single GPU training. We just need to disable GPUs before the training process.
```shell
export CUDA_VISIBLE_DEVICES=-1
```
And then run the script [above](#train-with-a-single-gpu).
```{warning}
The process of training on the CPU is consistent with single GPU training. We just need to disable GPUs before the training process.
```
### Train with multiple GPUs in single machine
```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 ${CHECKPOINT_FILE}`: Resume from a previous checkpoint file.
Difference between `resume-from` and `load-from`:
`resume-from` loads both the model weights and optimizer status, and the epoch is also inherited from the specified checkpoint. It is usually used for resuming the training process that is interrupted accidentally.
`load-from` only loads the model weights and the training epoch starts from 0. It is usually used for finetuning.
### Train with multiple machines
If you launch with multiple machines simply connected with ethernet, you can simply run following commands:
On the first machine:
```shell
NNODES=2 NODE_RANK=0 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR sh tools/dist_train.sh $CONFIG $GPUS
```
On the second machine:
```shell
NNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR sh tools/dist_train.sh $CONFIG $GPUS
```
Usually it is slow if you do not have high speed networking like InfiniBand.
If you run MMClassification on a cluster managed with [slurm](https://slurm.schedmd.com/), you can use the script `slurm_train.sh`. (This script also supports single machine training.)
```shell
[GPUS=${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}
```
### Train with IPU
The process of training on the IPU is consistent with single GPU training. We just need to have IPU machine and environment
and add an extra argument `--ipu-replicas ${IPU_NUM}`
## 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/analysis_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
==============================
```
```{warning}
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.
- FLOPs are related to the input shape while parameters are not. The default input shape is (1, 3, 224, 224).
- 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 publish a model, 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/convert_models/publish_model.py ${INPUT_FILENAME} ${OUTPUT_FILENAME}
```
E.g.,
```shell
python tools/convert_models/publish_model.py work_dirs/resnet50/latest.pth imagenet_resnet50.pth
```
The final output filename will be `imagenet_resnet50_{date}-{hash id}.pth`.
## Tutorials
Currently, we provide five tutorials for users.
- [learn about config](tutorials/config.md)
- [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)
- [customize schedule](tutorials/schedule.md)
- [customize runtime settings](tutorials/runtime.md).
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Welcome to MMClassification's documentation!
============================================
You can switch between Chinese and English documentation 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: 1
:caption: Get Started
install.md
getting_started.md
.. toctree::
:maxdepth: 1
:caption: Tutorials
tutorials/config.md
tutorials/finetune.md
tutorials/new_dataset.md
tutorials/data_pipeline.md
tutorials/new_modules.md
tutorials/schedule.md
tutorials/runtime.md
.. toctree::
:maxdepth: 1
:caption: Model zoo
:glob:
modelzoo_statistics.md
model_zoo.md
papers/*
.. toctree::
:maxdepth: 1
:caption: Useful Tools and Scripts
tools/pytorch2onnx.md
tools/onnx2tensorrt.md
tools/pytorch2torchscript.md
tools/model_serving.md
tools/visualization.md
tools/analysis.md
tools/miscellaneous.md
.. toctree::
:maxdepth: 1
:caption: Community
community/CONTRIBUTING.md
.. toctree::
:maxdepth: 1
:caption: API Reference
mmcls.apis <api/apis>
mmcls.core <api/core>
mmcls.models <api/models>
mmcls.models.utils <api/models.utils>
mmcls.datasets <api/datasets>
Data Transformations <api/transforms>
Batch Augmentation <api/models.utils.augment>
mmcls.utils <api/utils>
.. toctree::
:maxdepth: 1
:caption: Notes
changelog.md
compatibility.md
faq.md
.. toctree::
:maxdepth: 1
:caption: Device Support
device/npu.md
.. toctree::
:caption: Language Switch
English <https://mmclassification.readthedocs.io/en/latest/>
简体中文 <https://mmclassification.readthedocs.io/zh_CN/latest/>
Indices and tables
==================
* :ref:`genindex`
* :ref:`search`
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# Prerequisites
In this section we demonstrate how to prepare an environment with PyTorch.
MMClassification works on Linux, Windows and macOS. It requires Python 3.6+, CUDA 9.2+ and PyTorch 1.5+.
```{note}
If you are experienced with PyTorch and have already installed it, just skip this part and jump to the [next section](#installation). Otherwise, you can follow these steps for the preparation.
```
**Step 1.** Download and install Miniconda from the [official website](https://docs.conda.io/en/latest/miniconda.html).
**Step 2.** Create a conda environment and activate it.
```shell
conda create --name openmmlab python=3.8 -y
conda activate openmmlab
```
**Step 3.** Install PyTorch following [official instructions](https://pytorch.org/get-started/locally/), e.g.
On GPU platforms:
```shell
conda install pytorch torchvision -c pytorch
```
```{warning}
This command will automatically install the latest version PyTorch and cudatoolkit, please check whether they matches your environment.
```
On CPU platforms:
```shell
conda install pytorch torchvision cpuonly -c pytorch
```
# Installation
We recommend that users follow our best practices to install MMClassification. However, the whole process is highly customizable. See [Customize Installation](#customize-installation) section for more information.
## Best Practices
**Step 0.** Install [MMCV](https://github.com/open-mmlab/mmcv) using [MIM](https://github.com/open-mmlab/mim).
```shell
pip install -U openmim
mim install mmcv-full
```
**Step 1.** Install MMClassification.
According to your needs, we support two install modes:
- [Install from source (Recommended)](#install-from-source): You want to develop your own image classification task or new features based on MMClassification framework. For example, you want to add new dataset or new models. And you can use all tools we provided.
- [Install as a Python package](#install-as-a-python-package): You just want to call MMClassification's APIs or import MMClassification's modules in your project.
### Install from source
In this case, install mmcls from source:
```shell
git clone https://github.com/open-mmlab/mmclassification.git
cd mmclassification
pip install -v -e .
# "-v" means verbose, or more output
# "-e" means installing a project in editable mode,
# thus any local modifications made to the code will take effect without reinstallation.
```
Optionally, if you want to contribute to MMClassification or experience experimental functions, please checkout to the dev branch:
```shell
git checkout dev
```
### Install as a Python package
Just install with pip.
```shell
pip install mmcls
```
## Verify the installation
To verify whether MMClassification is installed correctly, we provide some sample codes to run an inference demo.
**Step 1.** We need to download config and checkpoint files.
```shell
mim download mmcls --config resnet50_8xb32_in1k --dest .
```
**Step 2.** Verify the inference demo.
Option (a). If you install mmcls from source, just run the following command:
```shell
python demo/image_demo.py demo/demo.JPEG resnet50_8xb32_in1k.py resnet50_8xb32_in1k_20210831-ea4938fc.pth --device cpu
```
You will see the output result dict including `pred_label`, `pred_score` and `pred_class` in your terminal.
And if you have graphical interface (instead of remote terminal etc.), you can enable `--show` option to show
the demo image with these predictions in a window.
Option (b). If you install mmcls as a python package, open you python interpreter and copy&paste the following codes.
```python
from mmcls.apis import init_model, inference_model
config_file = 'resnet50_8xb32_in1k.py'
checkpoint_file = 'resnet50_8xb32_in1k_20210831-ea4938fc.pth'
model = init_model(config_file, checkpoint_file, device='cpu') # or device='cuda:0'
inference_model(model, 'demo/demo.JPEG')
```
You will see a dict printed, including the predicted label, score and category name.
## Customize Installation
### CUDA versions
When installing PyTorch, you need to specify the version of CUDA. If you are
not clear on which to choose, follow our recommendations:
- For Ampere-based NVIDIA GPUs, such as GeForce 30 series and NVIDIA A100, CUDA 11 is a must.
- For older NVIDIA GPUs, CUDA 11 is backward compatible, but CUDA 10.2 offers better compatibility and is more lightweight.
Please make sure the GPU driver satisfies the minimum version requirements. See [this table](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#cuda-major-component-versions__table-cuda-toolkit-driver-versions) for more information.
```{note}
Installing CUDA runtime libraries is enough if you follow our best practices,
because no CUDA code will be compiled locally. However if you hope to compile
MMCV from source or develop other CUDA operators, you need to install the
complete CUDA toolkit from NVIDIA's [website](https://developer.nvidia.com/cuda-downloads),
and its version should match the CUDA version of PyTorch. i.e., the specified
version of cudatoolkit in `conda install` command.
```
### Install MMCV without MIM
MMCV contains C++ and CUDA extensions, thus depending on PyTorch in a complex
way. MIM solves such dependencies automatically and makes the installation
easier. However, it is not a must.
To install MMCV with pip instead of MIM, please follow
[MMCV installation guides](https://mmcv.readthedocs.io/en/latest/get_started/installation.html).
This requires manually specifying a find-url based on PyTorch version and its CUDA version.
For example, the following command install mmcv-full built for PyTorch 1.10.x and CUDA 11.3.
```shell
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html
```
### Install on CPU-only platforms
MMClassification can be built for CPU only environment. In CPU mode you can
train (requires MMCV version >= 1.4.4), test or inference a model.
Some functionalities are gone in this mode, usually GPU-compiled ops. But don't
worry, almost all models in MMClassification don't depends on these ops.
### Install on Google Colab
[Google Colab](https://research.google.com/) usually has PyTorch installed,
thus we only need to install MMCV and MMClassification with the following
commands.
**Step 1.** Install [MMCV](https://github.com/open-mmlab/mmcv) using [MIM](https://github.com/open-mmlab/mim).
```shell
!pip3 install openmim
!mim install mmcv-full
```
**Step 2.** Install MMClassification from the source.
```shell
!git clone https://github.com/open-mmlab/mmclassification.git
%cd mmclassification
!pip install -e .
```
**Step 3.** Verification.
```python
import mmcls
print(mmcls.__version__)
# Example output: 0.23.0 or newer
```
```{note}
Within Jupyter, the exclamation mark `!` is used to call external executables and `%cd` is a [magic command](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-cd) to change the current working directory of Python.
```
### Using MMClassification with Docker
We provide a [Dockerfile](https://github.com/open-mmlab/mmclassification/blob/master/docker/Dockerfile)
to build an image. Ensure that your [docker version](https://docs.docker.com/engine/install/) >=19.03.
```shell
# build an image with PyTorch 1.8.1, CUDA 10.2
# If you prefer other versions, just modified the Dockerfile
docker build -t mmclassification docker/
```
Run it with
```shell
docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmclassification/data mmclassification
```
## Trouble shooting
If you have some issues during the installation, please first view the [FAQ](faq.md) page.
You may [open an issue](https://github.com/open-mmlab/mmclassification/issues/new/choose)
on GitHub if no solution is found.
openmmlab_test/mmclassification-0.24.1/docs/en/model_zoo.md 0 → 100644
View file @ 0fd8347d
# 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_8xb32_in1k.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_8xb32_in1k.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_8xb32_in1k.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_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_batch256_imagenet_20210208-e6920e4a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_batch256_imagenet_20210208-e6920e4a.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_8xb32_in1k.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_8xb32_in1k.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_8xb32_in1k.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_8xb32_in1k.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) |
| RepVGG-A0\* | 9.11（train) \| 8.31 (deploy) | 1.52 (train) \| 1.36 (deploy) | 72.41 | 90.50 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A0_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A0_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_3rdparty_4xb64-coslr-120e_in1k_20210909-883ab98c.pth) |
| RepVGG-A1\* | 14.09 (train) \| 12.79 (deploy) | 2.64 (train) \| 2.37 (deploy) | 74.47 | 91.85 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A1_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A1_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A1_3rdparty_4xb64-coslr-120e_in1k_20210909-24003a24.pth) |
| RepVGG-A2\* | 28.21 (train) \| 25.5 (deploy) | 5.7 (train) \| 5.12 (deploy) | 76.48 | 93.01 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A2_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A2_3rdparty_4xb64-coslr-120e_in1k_20210909-97d7695a.pth) |
| RepVGG-B0\* | 15.82 (train) \| 14.34 (deploy) | 3.42 (train) \| 3.06 (deploy) | 75.14 | 92.42 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B0_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B0_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B0_3rdparty_4xb64-coslr-120e_in1k_20210909-446375f4.pth) |
| RepVGG-B1\* | 57.42 (train) \| 51.83 (deploy) | 13.16 (train) \| 11.82 (deploy) | 78.37 | 94.11 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1_3rdparty_4xb64-coslr-120e_in1k_20210909-750cdf67.pth) |
| RepVGG-B1g2\* | 45.78 (train) \| 41.36 (deploy) | 9.82 (train) \| 8.82 (deploy) | 77.79 | 93.88 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1g2_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1g2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g2_3rdparty_4xb64-coslr-120e_in1k_20210909-344f6422.pth) |
| RepVGG-B1g4\* | 39.97 (train) \| 36.13 (deploy) | 8.15 (train) \| 7.32 (deploy) | 77.58 | 93.84 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1g4_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1g4_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g4_3rdparty_4xb64-coslr-120e_in1k_20210909-d4c1a642.pth) |
| RepVGG-B2\* | 89.02 (train) \| 80.32 (deploy) | 20.46 (train) \| 18.39 (deploy) | 78.78 | 94.42 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B2_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2_3rdparty_4xb64-coslr-120e_in1k_20210909-bd6b937c.pth) |
| RepVGG-B2g4\* | 61.76 (train) \| 55.78 (deploy) | 12.63 (train) \| 11.34 (deploy) | 79.38 | 94.68 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B2g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-7b7955f0.pth) |
| RepVGG-B3\* | 123.09 (train) \| 110.96 (deploy) | 29.17 (train) \| 26.22 (deploy) | 80.52 | 95.26 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B3_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-dda968bf.pth) |
| RepVGG-B3g4\* | 83.83 (train) \| 75.63 (deploy) | 17.9 (train) \| 16.08 (deploy) | 80.22 | 95.10 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B3g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-4e54846a.pth) |
| RepVGG-D2se\* | 133.33 (train) \| 120.39 (deploy) | 36.56 (train) \| 32.85 (deploy) | 81.81 | 95.94 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-D2se_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-cf3139b7.pth) |
| ResNet-18 | 11.69 | 1.82 | 70.07 | 89.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb32_in1k.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_8xb32_in1k.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 (rsb-a1) | 25.56 | 4.12 | 80.12 | 94.78 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.log.json) |
| ResNet-101 | 44.55 | 7.85 | 78.18 | 94.03 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb32_in1k.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_8xb32_in1k.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) |
| Res2Net-50-14w-8s\* | 25.06 | 4.22 | 78.14 | 93.85 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net50-w14-s8_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w14-s8_3rdparty_8xb32_in1k_20210927-bc967bf1.pth) |
| Res2Net-50-26w-8s\* | 48.40 | 8.39 | 79.20 | 94.36 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net50-w26-s8_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w26-s8_3rdparty_8xb32_in1k_20210927-f547a94b.pth) |
| Res2Net-101-26w-4s\* | 45.21 | 8.12 | 79.19 | 94.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net101-w26-s4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net101-w26-s4_3rdparty_8xb32_in1k_20210927-870b6c36.pth) |
| ResNeSt-50\* | 27.48 | 5.41 | 81.13 | 95.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnest/resnest50_32xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest50_imagenet_converted-1ebf0afe.pth) |
| ResNeSt-101\* | 48.28 | 10.27 | 82.32 | 96.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnest/resnest101_32xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest101_imagenet_converted-032caa52.pth) |
| ResNeSt-200\* | 70.2 | 17.53 | 82.41 | 96.22 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnest/resnest200_64xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest200_imagenet_converted-581a60f2.pth) |
| ResNeSt-269\* | 110.93 | 22.58 | 82.70 | 96.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnest/resnest269_64xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest269_imagenet_converted-59930960.pth) |
| ResNetV1D-50 | 25.58 | 4.36 | 77.54 | 93.57 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d50_8xb32_in1k.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_8xb32_in1k.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_8xb32_in1k.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_8xb32_in1k.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_8xb32_in1k.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_8xb32_in1k.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_8xb32_in1k.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_8xb32_in1k.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_8xb32_in1k.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) |
| RegNetX-400MF | 5.16 | 0.41 | 72.56 | 90.78 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-400mf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-400mf_8xb128_in1k_20211213-89bfc226.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-400mf_8xb128_in1k_20211208_143316.log.json) |
| RegNetX-800MF | 7.26 | 0.81 | 74.76 | 92.32 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-800mf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-800mf_8xb128_in1k_20211213-222b0f11.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-800mf_8xb128_in1k_20211207_143037.log.json) |
| RegNetX-1.6GF | 9.19 | 1.63 | 76.84 | 93.31 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-1.6gf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-1.6gf_8xb128_in1k_20211213-d1b89758.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-1.6gf_8xb128_in1k_20211208_143018.log.json) |
| RegNetX-3.2GF | 15.3 | 3.21 | 78.09 | 94.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-3.2gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-3.2gf_8xb64_in1k_20211213-1fdd82ae.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-3.2gf_8xb64_in1k_20211208_142720.log.json) |
| RegNetX-4.0GF | 22.12 | 4.0 | 78.60 | 94.17 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-4.0gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-4.0gf_8xb64_in1k_20211213-efed675c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-4.0gf_8xb64_in1k_20211207_150431.log.json) |
| RegNetX-6.4GF | 26.21 | 6.51 | 79.38 | 94.65 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-6.4gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-6.4gf_8xb64_in1k_20211215-5c6089da.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-6.4gf_8xb64_in1k_20211213_172748.log.json) |
| RegNetX-8.0GF | 39.57 | 8.03 | 79.12 | 94.51 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-8.0gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-8.0gf_8xb64_in1k_20211213-9a9fcc76.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-8.0gf_8xb64_in1k_20211208_103250.log.json) |
| RegNetX-12GF | 46.11 | 12.15 | 79.67 | 95.03 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-12gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-12gf_8xb64_in1k_20211213-5df8c2f8.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-12gf_8xb64_in1k_20211208_143713.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_16xb64_in1k.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_16xb64_in1k.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_8xb32_in1k.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 | 85.43 | 97.77 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p16_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-98e8652b.pth) |
| ViT-B/32\* | 88.3 | 8.56 | 84.01 | 97.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p32_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p32_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-9cea8599.pth) |
| ViT-L/16\* | 304.72 | 116.68 | 85.63 | 97.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-large-p16_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-large-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-b20ba619.pth) |
| Swin-Transformer tiny | 28.29 | 4.36 | 81.18 | 95.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-tiny_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925-66df6be6.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925.log.json) |
| Swin-Transformer small | 49.61 | 8.52 | 83.02 | 96.29 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-small_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_small_224_b16x64_300e_imagenet_20210615_110219-7f9d988b.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_small_224_b16x64_300e_imagenet_20210615_110219.log.json) |
| Swin-Transformer base | 87.77 | 15.14 | 83.36 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_base_224_b16x64_300e_imagenet_20210616_190742-93230b0d.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_base_224_b16x64_300e_imagenet_20210616_190742.log.json) |
| Transformer in Transformer small\* | 23.76 | 3.36 | 81.52 | 95.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/tnt/tnt-s-p16_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/tnt/tnt-small-p16_3rdparty_in1k_20210903-c56ee7df.pth) |
| T2T-ViT_t-14 | 21.47 | 4.34 | 81.83 | 95.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-14_8xb64_in1k_20211220-f7378dd5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-14_8xb64_in1k_20211220-f7378dd5.log.json) |
| T2T-ViT_t-19 | 39.08 | 7.80 | 82.63 | 96.18 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-19_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-19_8xb64_in1k_20211214-7f5e3aaf.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-19_8xb64_in1k_20211214-7f5e3aaf.log.json) |
| T2T-ViT_t-24 | 64.00 | 12.69 | 82.71 | 96.09 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-24_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-24_8xb64_in1k_20211214-b2a68ae3.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-24_8xb64_in1k_20211214-b2a68ae3.log.json) |
| Mixer-B/16\* | 59.88 | 12.61 | 76.68 | 92.25 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mlp_mixer/mlp-mixer-base-p16_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mlp-mixer/mixer-base-p16_3rdparty_64xb64_in1k_20211124-1377e3e0.pth) |
| Mixer-L/16\* | 208.2 | 44.57 | 72.34 | 88.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mlp_mixer/mlp-mixer-large-p16_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mlp-mixer/mixer-large-p16_3rdparty_64xb64_in1k_20211124-5a2519d2.pth) |
| DeiT-tiny | 5.72 | 1.08 | 74.50 | 92.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-tiny_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.log.json) |
| DeiT-tiny distilled\* | 5.72 | 1.08 | 74.51 | 91.90 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-tiny-distilled_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny-distilled_3rdparty_pt-4xb256_in1k_20211216-c429839a.pth) |
| DeiT-small | 22.05 | 4.24 | 80.69 | 95.06 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-small_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.log.json) |
| DeiT-small distilled\* | 22.05 | 4.24 | 81.17 | 95.40 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-small-distilled_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small-distilled_3rdparty_pt-4xb256_in1k_20211216-4de1d725.pth) |
| DeiT-base | 86.57 | 16.86 | 81.76 | 95.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.log.json) |
| DeiT-base distilled\* | 86.57 | 16.86 | 83.33 | 96.49 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base-distilled_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_pt-16xb64_in1k_20211216-42891296.pth) |
| DeiT-base 384px\* | 86.86 | 49.37 | 83.04 | 96.31 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base_ft-16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_ft-16xb32_in1k-384px_20211124-822d02f2.pth) |
| DeiT-base distilled 384px\* | 86.86 | 49.37 | 85.55 | 97.35 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base-distilled_ft-16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_ft-16xb32_in1k-384px_20211216-e48d6000.pth) |
| Conformer-tiny-p16\* | 23.52 | 4.90 | 81.31 | 95.60 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-tiny-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-tiny-p16_3rdparty_8xb128_in1k_20211206-f6860372.pth) |
| Conformer-small-p32\* | 38.85 | 7.09 | 81.96 | 96.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-small-p32_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-small-p32_8xb128_in1k_20211206-947a0816.pth) |
| Conformer-small-p16\* | 37.67 | 10.31 | 83.32 | 96.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-small-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-small-p16_3rdparty_8xb128_in1k_20211206-3065dcf5.pth) |
| Conformer-base-p16\* | 83.29 | 22.89 | 83.82 | 96.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-base-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-base-p16_3rdparty_8xb128_in1k_20211206-bfdf8637.pth) |
| PCPVT-small\* | 24.11 | 3.67 | 81.14 | 95.69 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-small_3rdparty_8xb128_in1k_20220126-ef23c132.pth) |
| PCPVT-base\* | 43.83 | 6.45 | 82.66 | 96.26 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-base_3rdparty_8xb128_in1k_20220126-f8c4b0d5.pth) |
| PCPVT-large\* | 60.99 | 9.51 | 83.09 | 96.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-large_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-large_3rdparty_16xb64_in1k_20220126-c1ef8d80.pth) |
| SVT-small\* | 24.06 | 2.82 | 81.77 | 95.57 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-small_3rdparty_8xb128_in1k_20220126-8fe5205b.pth) |
| SVT-base\* | 56.07 | 8.35 | 83.13 | 96.29 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-base_3rdparty_8xb128_in1k_20220126-e31cc8e9.pth) |
| SVT-large\* | 99.27 | 14.82 | 83.60 | 96.50 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-large_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-large_3rdparty_16xb64_in1k_20220126-4817645f.pth) |
| EfficientNet-B0\* | 5.29 | 0.02 | 76.74 | 93.17 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32_in1k_20220119-a7e2a0b1.pth) |
| EfficientNet-B0 (AA)\* | 5.29 | 0.02 | 77.26 | 93.41 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32-aa_in1k_20220119-8d939117.pth) |
| EfficientNet-B0 (AA + AdvProp)\* | 5.29 | 0.02 | 77.53 | 93.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32-aa-advprop_in1k_20220119-26434485.pth) |
| EfficientNet-B1\* | 7.79 | 0.03 | 78.68 | 94.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32_in1k_20220119-002556d9.pth) |
| EfficientNet-B1 (AA)\* | 7.79 | 0.03 | 79.20 | 94.42 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32-aa_in1k_20220119-619d8ae3.pth) |
| EfficientNet-B1 (AA + AdvProp)\* | 7.79 | 0.03 | 79.52 | 94.43 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32-aa-advprop_in1k_20220119-5715267d.pth) |
| EfficientNet-B2\* | 9.11 | 0.03 | 79.64 | 94.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32_in1k_20220119-ea374a30.pth) |
| EfficientNet-B2 (AA)\* | 9.11 | 0.03 | 80.21 | 94.96 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32-aa_in1k_20220119-dd61e80b.pth) |
| EfficientNet-B2 (AA + AdvProp)\* | 9.11 | 0.03 | 80.45 | 95.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32-aa-advprop_in1k_20220119-1655338a.pth) |
| EfficientNet-B3\* | 12.23 | 0.06 | 81.01 | 95.34 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32_in1k_20220119-4b4d7487.pth) |
| EfficientNet-B3 (AA)\* | 12.23 | 0.06 | 81.58 | 95.67 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa_in1k_20220119-5b4887a0.pth) |
| EfficientNet-B3 (AA + AdvProp)\* | 12.23 | 0.06 | 81.81 | 95.69 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa-advprop_in1k_20220119-53b41118.pth) |
| EfficientNet-B4\* | 19.34 | 0.12 | 82.57 | 96.09 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32_in1k_20220119-81fd4077.pth) |
| EfficientNet-B4 (AA)\* | 19.34 | 0.12 | 82.95 | 96.26 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32-aa_in1k_20220119-45b8bd2b.pth) |
| EfficientNet-B4 (AA + AdvProp)\* | 19.34 | 0.12 | 83.25 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32-aa-advprop_in1k_20220119-38c2238c.pth) |
| EfficientNet-B5\* | 30.39 | 0.24 | 83.18 | 96.47 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32_in1k_20220119-e9814430.pth) |
| EfficientNet-B5 (AA)\* | 30.39 | 0.24 | 83.82 | 96.76 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32-aa_in1k_20220119-2cab8b78.pth) |
| EfficientNet-B5 (AA + AdvProp)\* | 30.39 | 0.24 | 84.21 | 96.98 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32-aa-advprop_in1k_20220119-f57a895a.pth) |
| EfficientNet-B6 (AA)\* | 43.04 | 0.41 | 84.05 | 96.82 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b6_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b6_3rdparty_8xb32-aa_in1k_20220119-45b03310.pth) |
| EfficientNet-B6 (AA + AdvProp)\* | 43.04 | 0.41 | 84.74 | 97.14 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b6_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b6_3rdparty_8xb32-aa-advprop_in1k_20220119-bfe3485e.pth) |
| EfficientNet-B7 (AA)\* | 66.35 | 0.72 | 84.38 | 96.88 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b7_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b7_3rdparty_8xb32-aa_in1k_20220119-bf03951c.pth) |
| EfficientNet-B7 (AA + AdvProp)\* | 66.35 | 0.72 | 85.14 | 97.23 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b7_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b7_3rdparty_8xb32-aa-advprop_in1k_20220119-c6dbff10.pth) |
| EfficientNet-B8 (AA + AdvProp)\* | 87.41 | 1.09 | 85.38 | 97.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b8_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b8_3rdparty_8xb32-aa-advprop_in1k_20220119-297ce1b7.pth) |
| ConvNeXt-T\* | 28.59 | 4.46 | 82.05 | 95.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-tiny_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-tiny_3rdparty_32xb128_in1k_20220124-18abde00.pth) |
| ConvNeXt-S\* | 50.22 | 8.69 | 83.13 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-small_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-small_3rdparty_32xb128_in1k_20220124-d39b5192.pth) |
| ConvNeXt-B\* | 88.59 | 15.36 | 83.85 | 96.74 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-base_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_3rdparty_32xb128_in1k_20220124-d0915162.pth) |
| ConvNeXt-B\* | 88.59 | 15.36 | 85.81 | 97.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-base_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_in21k-pre-3rdparty_32xb128_in1k_20220124-eb2d6ada.pth) |
| ConvNeXt-L\* | 197.77 | 34.37 | 84.30 | 96.89 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-large_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_3rdparty_64xb64_in1k_20220124-f8a0ded0.pth) |
| ConvNeXt-L\* | 197.77 | 34.37 | 86.61 | 98.04 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-large_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_in21k-pre-3rdparty_64xb64_in1k_20220124-2412403d.pth) |
| ConvNeXt-XL\* | 350.20 | 60.93 | 86.97 | 98.20 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-xlarge_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-xlarge_in21k-pre-3rdparty_64xb64_in1k_20220124-76b6863d.pth) |
| HRNet-W18\* | 21.30 | 4.33 | 76.75 | 93.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w18_3rdparty_8xb32_in1k_20220120-0c10b180.pth) |
| HRNet-W30\* | 37.71 | 8.17 | 78.19 | 94.22 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w30_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w30_3rdparty_8xb32_in1k_20220120-8aa3832f.pth) |
| HRNet-W32\* | 41.23 | 8.99 | 78.44 | 94.19 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w32_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w32_3rdparty_8xb32_in1k_20220120-c394f1ab.pth) |
| HRNet-W40\* | 57.55 | 12.77 | 78.94 | 94.47 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w40_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w40_3rdparty_8xb32_in1k_20220120-9a2dbfc5.pth) |
| HRNet-W44\* | 67.06 | 14.96 | 78.88 | 94.37 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w44_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w44_3rdparty_8xb32_in1k_20220120-35d07f73.pth) |
| HRNet-W48\* | 77.47 | 17.36 | 79.32 | 94.52 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w48_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w48_3rdparty_8xb32_in1k_20220120-e555ef50.pth) |
| HRNet-W64\* | 128.06 | 29.00 | 79.46 | 94.65 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w64_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w64_3rdparty_8xb32_in1k_20220120-19126642.pth) |
| HRNet-W18 (ssld)\* | 21.30 | 4.33 | 81.06 | 95.70 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w18_3rdparty_8xb32-ssld_in1k_20220120-455f69ea.pth) |
| HRNet-W48 (ssld)\* | 77.47 | 17.36 | 83.63 | 96.79 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w48_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w48_3rdparty_8xb32-ssld_in1k_20220120-d0459c38.pth) |
| WRN-50\* | 68.88 | 11.44 | 81.45 | 95.53 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/wrn/wide-resnet50_timm_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/wrn/wide-resnet50_3rdparty-timm_8xb32_in1k_20220304-83ae4399.pth) |
| WRN-101\* | 126.89 | 22.81 | 78.84 | 94.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/wrn/wide-resnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/wrn/wide-resnet101_3rdparty_8xb32_in1k_20220304-8d5f9d61.pth) |
| CSPDarkNet50\* | 27.64 | 5.04 | 80.05 | 95.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspdarknet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspdarknet50_3rdparty_8xb32_in1k_20220329-bd275287.pth) |
| CSPResNet50\* | 21.62 | 3.48 | 79.55 | 94.68 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnet50_3rdparty_8xb32_in1k_20220329-dd6dddfb.pth) |
| CSPResNeXt50\* | 20.57 | 3.11 | 79.96 | 94.96 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnext50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnext50_3rdparty_8xb32_in1k_20220329-2cc84d21.pth) |
| DenseNet121\* | 7.98 | 2.88 | 74.96 | 92.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet121_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet121_4xb256_in1k_20220426-07450f99.pth) |
| DenseNet169\* | 14.15 | 3.42 | 76.08 | 93.11 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet169_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet169_4xb256_in1k_20220426-a2889902.pth) |
| DenseNet201\* | 20.01 | 4.37 | 77.32 | 93.64 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet201_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet201_4xb256_in1k_20220426-05cae4ef.pth) |
| DenseNet161\* | 28.68 | 7.82 | 77.61 | 93.83 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet161_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet161_4xb256_in1k_20220426-ee6a80a9.pth) |
| VAN-T\* | 4.11 | 0.88 | 75.41 | 93.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-tiny_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-tiny_8xb128_in1k_20220501-385941af.pth) |
| VAN-S\* | 13.86 | 2.52 | 81.01 | 95.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-small_8xb128_in1k_20220501-17bc91aa.pth) |
| VAN-B\* | 26.58 | 5.03 | 82.80 | 96.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-base_8xb128_in1k_20220501-6a4cc31b.pth) |
| VAN-L\* | 44.77 | 8.99 | 83.86 | 96.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-large_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-large_8xb128_in1k_20220501-f212ba21.pth) |
| MViTv2-tiny\* | 24.17 | 4.70 | 82.33 | 96.15 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-tiny_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-tiny_3rdparty_in1k_20220722-db7beeef.pth) |
| MViTv2-small\* | 34.87 | 7.00 | 83.63 | 96.51 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-small_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-small_3rdparty_in1k_20220722-986bd741.pth) |
| MViTv2-base\* | 51.47 | 10.20 | 84.34 | 96.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-base_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-base_3rdparty_in1k_20220722-9c4f0a17.pth) |
| MViTv2-large\* | 217.99 | 42.10 | 85.25 | 97.14 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-large_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-large_3rdparty_in1k_20220722-2b57b983.pth) |
| EfficientFormer-l1\* | 12.19 | 1.30 | 80.46 | 94.99 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l1_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l1_3rdparty_in1k_20220803-d66e61df.pth) |
| EfficientFormer-l3\* | 31.41 | 3.93 | 82.45 | 96.18 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l3_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l3_3rdparty_in1k_20220803-dde1c8c5.pth) |
| EfficientFormer-l7\* | 82.23 | 10.16 | 83.40 | 96.60 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l7_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l7_3rdparty_in1k_20220803-41a552bb.pth) |
*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_8xb16_cifar10.py) |
| ResNet-34-b16x8 | 21.28 | 1.16 | 95.34 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_8xb16_cifar10.py) |
| ResNet-50-b16x8 | 23.52 | 1.31 | 95.55 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb16_cifar10.py) |
| ResNet-101-b16x8 | 42.51 | 2.52 | 95.58 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb16_cifar10.py) |
| ResNet-152-b16x8 | 58.16 | 3.74 | 95.76 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_8xb16_cifar10.py) |
openmmlab_test/mmclassification-0.24.1/docs/en/stat.py 0 → 100644
View file @ 0fd8347d
#!/usr/bin/env python
import functools as func
import glob
import os
import re
from pathlib import Path
import numpy as np
MMCLS_ROOT = Path(__file__).absolute().parents[1]
url_prefix = 'https://github.com/open-mmlab/mmclassification/blob/master/'
papers_root = Path('papers')
papers_root.mkdir(exist_ok=True)
files = [Path(f) for f in sorted(glob.glob('../../configs/*/README.md'))]
stats = []
titles = []
num_ckpts = 0
num_configs = 0
for f in files:
with open(f, 'r') as content_file:
content = content_file.read()
# Extract checkpoints
ckpts = set(x.lower().strip()
for x in re.findall(r'\[model\]\((https?.*)\)', content))
if len(ckpts) == 0:
continue
num_ckpts += len(ckpts)
# Extract paper title
match_res = list(re.finditer(r'> \[(.*)\]\((.*)\)', content))
if len(match_res) > 0:
title, paperlink = match_res[0].groups()
else:
title = content.split('\n')[0].replace('# ', '').strip()
paperlink = None
titles.append(title)
# Replace paper link to a button
if paperlink is not None:
start = match_res[0].start()
end = match_res[0].end()
# link_button = f'<a href="{paperlink}" class="paperlink">{title}</a>'
link_button = f'[{title}]({paperlink})'
content = content[:start] + link_button + content[end:]
# Extract paper type
_papertype = [x for x in re.findall(r'\[([A-Z]+)\]', content)]
assert len(_papertype) > 0
papertype = _papertype[0]
paper = set([(papertype, title)])
# Write a copy of README
copy = papers_root / (f.parent.name + '.md')
if copy.exists():
os.remove(copy)
def replace_link(matchobj):
# Replace relative link to GitHub link.
name = matchobj.group(1)
link = matchobj.group(2)
if not link.startswith('http') and (f.parent / link).exists():
rel_link = (f.parent / link).absolute().relative_to(MMCLS_ROOT)
link = url_prefix + str(rel_link)
return f'[{name}]({link})'
content = re.sub(r'\[([^\]]+)\]\(([^)]+)\)', replace_link, content)
with open(copy, 'w') as copy_file:
copy_file.write(content)
statsmsg = f"""
\t* [{papertype}] [{title}]({copy}) ({len(ckpts)} ckpts)
"""
stats.append(dict(paper=paper, ckpts=ckpts, statsmsg=statsmsg, copy=copy))
allpapers = func.reduce(lambda a, b: a.union(b),
[stat['paper'] for stat in stats])
msglist = '\n'.join(stat['statsmsg'] for stat 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 Summary
* Number of papers: {len(set(titles))}
{countstr}
* Number of checkpoints: {num_ckpts}
{msglist}
"""
with open('modelzoo_statistics.md', 'w') as f:
f.write(modelzoo)
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# Analysis
<!-- TOC -->
- [Log Analysis](#log-analysis)
- [Plot Curves](#plot-curves)
- [Calculate Training Time](#calculate-training-time)
- [Result Analysis](#result-analysis)
- [Evaluate Results](#evaluate-results)
- [View Typical Results](#view-typical-results)
- [Model Complexity](#model-complexity)
- [FAQs](#faqs)
<!-- TOC -->
## Log Analysis
### Plot Curves
`tools/analysis_tools/analyze_logs.py` plots curves of given keys according to the log files.
<div align=center><img src="../_static/image/tools/analysis/analyze_log.jpg" style=" width: 75%; height: 30%; "></div>
```shell
python tools/analysis_tools/analyze_logs.py plot_curve \
${JSON_LOGS} \
[--keys ${KEYS}] \
[--title ${TITLE}] \
[--legend ${LEGEND}] \
[--backend ${BACKEND}] \
[--style ${STYLE}] \
[--out ${OUT_FILE}] \
[--window-size ${WINDOW_SIZE}]
```
**Description of all arguments**：
- `json_logs` : The paths of the log files, separate multiple files by spaces.
- `--keys` : The fields of the logs to analyze, separate multiple keys by spaces. Defaults to 'loss'.
- `--title` : The title of the figure. Defaults to use the filename.
- `--legend` : The names of legend, the number of which must be equal to `len(${JSON_LOGS}) * len(${KEYS})`. Defaults to use `"${JSON_LOG}-${KEYS}"`.
- `--backend` : The backend of matplotlib. Defaults to auto selected by matplotlib.
- `--style` : The style of the figure. Default to `whitegrid`.
- `--out` : The path of the output picture. If not set, the figure won't be saved.
- `--window-size`: The shape of the display window. The format should be `'W*H'`. Defaults to `'12*7'`.
```{note}
The `--style` option depends on `seaborn` package, please install it before setting it.
```
Examples:
- Plot the loss curve in training.
```shell
python tools/analysis_tools/analyze_logs.py plot_curve your_log_json --keys loss --legend loss
```
- Plot the top-1 accuracy and top-5 accuracy curves, and save the figure to results.jpg.
```shell
python tools/analysis_tools/analyze_logs.py plot_curve your_log_json --keys accuracy_top-1 accuracy_top-5 --legend top1 top5 --out results.jpg
```
- Compare the top-1 accuracy of two log files in the same figure.
```shell
python tools/analysis_tools/analyze_logs.py plot_curve log1.json log2.json --keys accuracy_top-1 --legend exp1 exp2
```
```{note}
The tool will automatically select to find keys in training logs or validation logs according to the keys.
Therefore, if you add a custom evaluation metric, please also add the key to `TEST_METRICS` in this tool.
```
### Calculate Training Time
`tools/analysis_tools/analyze_logs.py` can also calculate the training time according to the log files.
```shell
python tools/analysis_tools/analyze_logs.py cal_train_time \
${JSON_LOGS}
[--include-outliers]
```
**Description of all arguments**:
- `json_logs` : The paths of the log files, separate multiple files by spaces.
- `--include-outliers` : If set, include the first iteration in each epoch (Sometimes the time of first iterations is longer).
Example:
```shell
python tools/analysis_tools/analyze_logs.py cal_train_time work_dirs/some_exp/20200422_153324.log.json
```
The output is expected to be like the below.
```text
-----Analyze train time of work_dirs/some_exp/20200422_153324.log.json-----
slowest epoch 68, average time is 0.3818
fastest epoch 1, average time is 0.3694
time std over epochs is 0.0020
average iter time: 0.3777 s/iter
```
## Result Analysis
With the `--out` argument in `tools/test.py`, we can save the inference results of all samples as a file.
And with this result file, we can do further analysis.
### Evaluate Results
`tools/analysis_tools/eval_metric.py` can evaluate metrics again.
```shell
python tools/analysis_tools/eval_metric.py \
${CONFIG} \
${RESULT} \
[--metrics ${METRICS}] \
[--cfg-options ${CFG_OPTIONS}] \
[--metric-options ${METRIC_OPTIONS}]
```
Description of all arguments:
- `config` : The path of the model config file.
- `result`: The Output result file in json/pickle format from `tools/test.py`.
- `--metrics` : Evaluation metrics, the acceptable values depend on the dataset.
- `--cfg-options`: If specified, the key-value pair config will be merged into the config file, for more details please refer to [Tutorial 1: Learn about Configs](../tutorials/config.md)
- `--metric-options`: If specified, the key-value pair arguments will be passed to the `metric_options` argument of dataset's `evaluate` function.
```{note}
In `tools/test.py`, we support using `--out-items` option to select which kind of results will be saved. Please ensure the result file includes "class_scores" to use this tool.
```
**Examples**:
```shell
python tools/analysis_tools/eval_metric.py configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py your_result.pkl --metrics accuracy --metric-options "topk=(1,5)"
```
### View Typical Results
`tools/analysis_tools/analyze_results.py` can save the images with the highest scores in successful or failed prediction.
```shell
python tools/analysis_tools/analyze_results.py \
${CONFIG} \
${RESULT} \
[--out-dir ${OUT_DIR}] \
[--topk ${TOPK}] \
[--cfg-options ${CFG_OPTIONS}]
```
**Description of all arguments**:
- `config` : The path of the model config file.
- `result`: Output result file in json/pickle format from `tools/test.py`.
- `--out-dir`: Directory to store output files.
- `--topk`: The number of images in successful or failed prediction with the highest `topk` scores to save. If not specified, it will be set to 20.
- `--cfg-options`: If specified, the key-value pair config will be merged into the config file, for more details please refer to [Tutorial 1: Learn about Configs](../tutorials/config.md)
```{note}
In `tools/test.py`, we support using `--out-items` option to select which kind of results will be saved. Please ensure the result file includes "pred_score", "pred_label" and "pred_class" to use this tool.
```
**Examples**:
```shell
python tools/analysis_tools/analyze_results.py \
configs/resnet/resnet50_b32x8_imagenet.py \
result.pkl \
--out-dir results \
--topk 50
```
## Model Complexity
### 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/analysis_tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}]
```
Description of all arguments:
- `config` : The path of the model config file.
- `--shape`: Input size, support single value or double value parameter, such as `--shape 256` or `--shape 224 256`. If not set, default to be `224 224`.
You will get a result like this.
```text
==============================
Input shape: (3, 224, 224)
Flops: 4.12 GFLOPs
Params: 25.56 M
==============================
```
```{warning}
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.
- FLOPs are related to the input shape while parameters are not. The default input shape is (1, 3, 224, 224).
- 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.
```
## FAQs
- None
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# Miscellaneous
<!-- TOC -->
- [Print the entire config](#print-the-entire-config)
- [Verify Dataset](#verify-dataset)
- [FAQs](#faqs)
<!-- TOC -->
## Print the entire config
`tools/misc/print_config.py` prints the whole config verbatim, expanding all its imports.
```shell
python tools/misc/print_config.py ${CONFIG} [--cfg-options ${CFG_OPTIONS}]
```
Description of all arguments:
- `config` : The path of the model config file.
- `--cfg-options`: If specified, the key-value pair config will be merged into the config file, for more details please refer to [Tutorial 1: Learn about Configs](../tutorials/config.md)
**Examples**:
```shell
python tools/misc/print_config.py configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py
```
## Verify Dataset
`tools/misc/verify_dataset.py` can verify dataset, check whether there are broken pictures in the given dataset.
```shell
python tools/print_config.py \
${CONFIG} \
[--out-path ${OUT-PATH}] \
[--phase ${PHASE}] \
[--num-process ${NUM-PROCESS}]
[--cfg-options ${CFG_OPTIONS}]
```
**Description of all arguments**:
- `config` : The path of the model config file.
- `--out-path` : The path to save the verification result, if not set, defaults to 'brokenfiles.log'.
- `--phase` : Phase of dataset to verify, accept "train" "test" and "val", if not set, defaults to "train".
- `--num-process` : number of process to use, if not set, defaults to 1.
- `--cfg-options`: If specified, the key-value pair config will be merged into the config file, for more details please refer to [Tutorial 1: Learn about Configs](../tutorials/config.md)
**Examples**:
```shell
python tools/misc/verify_dataset.py configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py --out-path broken_imgs.log --phase val --num-process 8
```
## FAQs
- None
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# Model Serving
In order to serve an `MMClassification` model with [`TorchServe`](https://pytorch.org/serve/), you can follow the steps:
## 1. Convert model from MMClassification to TorchServe
```shell
python tools/deployment/mmcls2torchserve.py ${CONFIG_FILE} ${CHECKPOINT_FILE} \
--output-folder ${MODEL_STORE} \
--model-name ${MODEL_NAME}
```
```{note}
${MODEL_STORE} needs to be an absolute path to a folder.
```
Example:
```shell
python tools/deployment/mmcls2torchserve.py \
configs/resnet/resnet18_8xb32_in1k.py \
checkpoints/resnet18_8xb32_in1k_20210831-fbbb1da6.pth \
--output-folder ./checkpoints \
--model-name resnet18_in1k
```
## 2. Build `mmcls-serve` docker image
```shell
docker build -t mmcls-serve:latest docker/serve/
```
## 3. Run `mmcls-serve`
Check the official docs for [running TorchServe with docker](https://github.com/pytorch/serve/blob/master/docker/README.md#running-torchserve-in-a-production-docker-environment).
In order to run in GPU, you need to install [nvidia-docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). You can omit the `--gpus` argument in order to run in GPU.
Example:
```shell
docker run --rm \
--cpus 8 \
--gpus device=0 \
-p8080:8080 -p8081:8081 -p8082:8082 \
--mount type=bind,source=`realpath ./checkpoints`,target=/home/model-server/model-store \
mmcls-serve:latest
```
```{note}
`realpath ./checkpoints` points to the absolute path of "./checkpoints", and you can replace it with the absolute path where you store torchserve models.
```
[Read the docs](https://github.com/pytorch/serve/blob/master/docs/rest_api.md) about the Inference (8080), Management (8081) and Metrics (8082) APis
## 4. Test deployment
```shell
curl http://127.0.0.1:8080/predictions/${MODEL_NAME} -T demo/demo.JPEG
```
You should obtain a response similar to:
```json
{
"pred_label": 58,
"pred_score": 0.38102269172668457,
"pred_class": "water snake"
}
```
And you can use `test_torchserver.py` to compare result of TorchServe and PyTorch, and visualize them.
```shell
python tools/deployment/test_torchserver.py ${IMAGE_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} ${MODEL_NAME}
[--inference-addr ${INFERENCE_ADDR}] [--device ${DEVICE}]
```
Example:
```shell
python tools/deployment/test_torchserver.py \
demo/demo.JPEG \
configs/resnet/resnet18_8xb32_in1k.py \
checkpoints/resnet18_8xb32_in1k_20210831-fbbb1da6.pth \
resnet18_in1k
```
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# ONNX to TensorRT (Experimental)
<!-- TOC -->
- [ONNX to TensorRT (Experimental)](#onnx-to-tensorrt-experimental)
- [How to convert models from ONNX to TensorRT](#how-to-convert-models-from-onnx-to-tensorrt)
- [Prerequisite](#prerequisite)
- [Usage](#usage)
- [List of supported models convertible to TensorRT](#list-of-supported-models-convertible-to-tensorrt)
- [Reminders](#reminders)
- [FAQs](#faqs)
<!-- TOC -->
## How to convert models from ONNX to TensorRT
### Prerequisite
1. Please refer to [install.md](https://mmclassification.readthedocs.io/en/latest/install.html#install-mmclassification) for installation of MMClassification from source.
2. Use our tool [pytorch2onnx.md](./pytorch2onnx.md) to convert the model from PyTorch to ONNX.
### Usage
```bash
python tools/deployment/onnx2tensorrt.py \
${MODEL} \
--trt-file ${TRT_FILE} \
--shape ${IMAGE_SHAPE} \
--max-batch-size ${MAX_BATCH_SIZE} \
--workspace-size ${WORKSPACE_SIZE} \
--fp16 \
--show \
--verify \
```
Description of all arguments:
- `model` : The path of an ONNX model file.
- `--trt-file`: The Path of output TensorRT engine file. If not specified, it will be set to `tmp.trt`.
- `--shape`: The height and width of model input. If not specified, it will be set to `224 224`.
- `--max-batch-size`: The max batch size of TensorRT model, should not be less than 1.
- `--fp16`: Enable fp16 mode.
- `--workspace-size` : The required GPU workspace size in GiB to build TensorRT engine. If not specified, it will be set to `1` GiB.
- `--show`: Determines whether to show the outputs of the model. If not specified, it will be set to `False`.
- `--verify`: Determines whether to verify the correctness of models between ONNXRuntime and TensorRT. If not specified, it will be set to `False`.
Example:
```bash
python tools/deployment/onnx2tensorrt.py \
checkpoints/resnet/resnet18_b16x8_cifar10.onnx \
--trt-file checkpoints/resnet/resnet18_b16x8_cifar10.trt \
--shape 224 224 \
--show \
--verify \
```
## List of supported models convertible to TensorRT
The table below lists the models that are guaranteed to be convertible to TensorRT.
| Model | Config | Status |
| :----------: | :-----------------------------------------------------: | :----: |
| MobileNetV2 | `configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py` | Y |
| ResNet | `configs/resnet/resnet18_8xb16_cifar10.py` | Y |
| ResNeXt | `configs/resnext/resnext50-32x4d_8xb32_in1k.py` | Y |
| ShuffleNetV1 | `configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py` | Y |
| ShuffleNetV2 | `configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py` | Y |
Notes:
- *All models above are tested with Pytorch==1.6.0 and TensorRT-7.2.1.6.Ubuntu-16.04.x86_64-gnu.cuda-10.2.cudnn8.0*
## Reminders
- If you meet any problem with the listed models above, please create an issue and it would be taken care of soon. For models not included in the list, we may not provide much help here due to the limited resources. Please try to dig a little deeper and debug by yourself.
## FAQs
- None
openmmlab_test/mmclassification-0.24.1/docs/en/tools/pytorch2onnx.md 0 → 100644
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# Pytorch to ONNX (Experimental)
<!-- TOC -->
- [Pytorch to ONNX (Experimental)](#pytorch-to-onnx-experimental)
- [How to convert models from Pytorch to ONNX](#how-to-convert-models-from-pytorch-to-onnx)
- [Prerequisite](#prerequisite)
- [Usage](#usage)
- [Description of all arguments:](#description-of-all-arguments)
- [How to evaluate ONNX models with ONNX Runtime](#how-to-evaluate-onnx-models-with-onnx-runtime)
- [Prerequisite](#prerequisite-1)
- [Usage](#usage-1)
- [Description of all arguments](#description-of-all-arguments-1)
- [Results and Models](#results-and-models)
- [List of supported models exportable to ONNX](#list-of-supported-models-exportable-to-onnx)
- [Reminders](#reminders)
- [FAQs](#faqs)
<!-- TOC -->
## How to convert models from Pytorch to ONNX
### Prerequisite
1. Please refer to [install](https://mmclassification.readthedocs.io/en/latest/install.html#install-mmclassification) for installation of MMClassification.
2. Install onnx and onnxruntime
```shell
pip install onnx onnxruntime==1.5.1
```
### Usage
```bash
python tools/deployment/pytorch2onnx.py \
${CONFIG_FILE} \
--checkpoint ${CHECKPOINT_FILE} \
--output-file ${OUTPUT_FILE} \
--shape ${IMAGE_SHAPE} \
--opset-version ${OPSET_VERSION} \
--dynamic-export \
--show \
--simplify \
--verify \
```
### Description of all arguments:
- `config` : The path of a model config file.
- `--checkpoint` : The path of a model checkpoint file.
- `--output-file`: The path of output ONNX model. If not specified, it will be set to `tmp.onnx`.
- `--shape`: The height and width of input tensor to the model. If not specified, it will be set to `224 224`.
- `--opset-version` : The opset version of ONNX. If not specified, it will be set to `11`.
- `--dynamic-export` : Determines whether to export ONNX with dynamic input shape and output shapes. If not specified, it will be set to `False`.
- `--show`: Determines whether to print the architecture of the exported model. If not specified, it will be set to `False`.
- `--simplify`: Determines whether to simplify the exported ONNX model. If not specified, it will be set to `False`.
- `--verify`: Determines whether to verify the correctness of an exported model. If not specified, it will be set to `False`.
Example:
```bash
python tools/deployment/pytorch2onnx.py \
configs/resnet/resnet18_8xb16_cifar10.py \
--checkpoint checkpoints/resnet/resnet18_8xb16_cifar10.pth \
--output-file checkpoints/resnet/resnet18_8xb16_cifar10.onnx \
--dynamic-export \
--show \
--simplify \
--verify \
```
## How to evaluate ONNX models with ONNX Runtime
We prepare a tool `tools/deployment/test.py` to evaluate ONNX models with ONNXRuntime or TensorRT.
### Prerequisite
- Install onnx and onnxruntime-gpu
```shell
pip install onnx onnxruntime-gpu
```
### Usage
```bash
python tools/deployment/test.py \
${CONFIG_FILE} \
${ONNX_FILE} \
--backend ${BACKEND} \
--out ${OUTPUT_FILE} \
--metrics ${EVALUATION_METRICS} \
--metric-options ${EVALUATION_OPTIONS} \
--show
--show-dir ${SHOW_DIRECTORY} \
--cfg-options ${CFG_OPTIONS} \
```
### Description of all arguments
- `config`: The path of a model config file.
- `model`: The path of a ONNX model file.
- `--backend`: Backend for input model to run and should be `onnxruntime` or `tensorrt`.
- `--out`: The path of output result file in pickle format.
- `--metrics`: Evaluation metrics, which depends on the dataset, e.g., "accuracy", "precision", "recall", "f1_score", "support" for single label dataset, and "mAP", "CP", "CR", "CF1", "OP", "OR", "OF1" for multi-label dataset.
- `--show`: Determines whether to show classifier outputs. If not specified, it will be set to `False`.
- `--show-dir`: Directory where painted images will be saved
- `--metrics-options`: Custom options for evaluation, the key-value pair in `xxx=yyy` format will be kwargs for `dataset.evaluate()` function
- `--cfg-options`: Override some settings in the used config file, the key-value pair in `xxx=yyy` format will be merged into config file.
### Results and Models
This part selects ImageNet for onnxruntime verification. ImageNet has multiple versions, but the most commonly used one is [ILSVRC 2012](http://www.image-net.org/challenges/LSVRC/2012/).
<table border="1" class="docutils">
<tr>
<th align="center">Model</th>
<th align="center">Config</th>
<th align="center">Metric</th>
<th align="center">PyTorch</th>
<th align="center">ONNXRuntime</th>
<th align="center">TensorRT-fp32</th>
<th align="center">TensorRT-fp16</th>
</tr>
<tr>
<td align="center">ResNet</td>
<td align="center"><code>resnet50_8xb32_in1k.py</code></td>
<td align="center">Top 1 / 5</td>
<td align="center">76.55 / 93.15</td>
<td align="center">76.49 / 93.22</td>
<td align="center">76.49 / 93.22</td>
<td align="center">76.50 / 93.20</td>
</tr>
<tr>
<td align="center">ResNeXt</td>
<td align="center"><code>resnext50-32x4d_8xb32_in1k.py</code></td>
<td align="center">Top 1 / 5</td>
<td align="center">77.90 / 93.66</td>
<td align="center">77.90 / 93.66</td>
<td align="center">77.90 / 93.66</td>
<td align="center">77.89 / 93.65</td>
</tr>
<tr>
<td align="center">SE-ResNet</td>
<td align="center"><code>seresnet50_8xb32_in1k.py</code></td>
<td align="center">Top 1 / 5</td>
<td align="center">77.74 / 93.84</td>
<td align="center">77.74 / 93.84</td>
<td align="center">77.74 / 93.84</td>
<td align="center">77.74 / 93.85</td>
</tr>
<tr>
<td align="center">ShuffleNetV1</td>
<td align="center"><code>shufflenet-v1-1x_16xb64_in1k.py</code></td>
<td align="center">Top 1 / 5</td>
<td align="center">68.13 / 87.81</td>
<td align="center">68.13 / 87.81</td>
<td align="center">68.13 / 87.81</td>
<td align="center">68.10 / 87.80</td>
</tr>
<tr>
<td align="center">ShuffleNetV2</td>
<td align="center"><code>shufflenet-v2-1x_16xb64_in1k.py</code></td>
<td align="center">Top 1 / 5</td>
<td align="center">69.55 / 88.92</td>
<td align="center">69.55 / 88.92</td>
<td align="center">69.55 / 88.92</td>
<td align="center">69.55 / 88.92</td>
</tr>
<tr>
<td align="center">MobileNetV2</td>
<td align="center"><code>mobilenet-v2_8xb32_in1k.py</code></td>
<td align="center">Top 1 / 5</td>
<td align="center">71.86 / 90.42</td>
<td align="center">71.86 / 90.42</td>
<td align="center">71.86 / 90.42</td>
<td align="center">71.88 / 90.40</td>
</tr>
</table>
## List of supported models exportable to ONNX
The table below lists the models that are guaranteed to be exportable to ONNX and runnable in ONNX Runtime.
| Model | Config | Batch Inference | Dynamic Shape | Note |
| :----------: | :-------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------: | :-----------: | ---- |
| MobileNetV2 | [mobilenet-v2_8xb32_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py) | Y | Y | |
| ResNet | [resnet18_8xb16_cifar10.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnet/resnet18_8xb16_cifar10.py) | Y | Y | |
| ResNeXt | [resnext50-32x4d_8xb32_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnext/resnext50-32x4d_8xb32_in1k.py) | Y | Y | |
| SE-ResNet | [seresnet50_8xb32_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/seresnet/seresnet50_8xb32_in1k.py) | Y | Y | |
| ShuffleNetV1 | [shufflenet-v1-1x_16xb64_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py) | Y | Y | |
| ShuffleNetV2 | [shufflenet-v2-1x_16xb64_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py) | Y | Y | |
Notes:
- *All models above are tested with Pytorch==1.6.0*
## Reminders
- If you meet any problem with the listed models above, please create an issue and it would be taken care of soon. For models not included in the list, please try to dig a little deeper and debug a little bit more and hopefully solve them by yourself.
## FAQs
- None
openmmlab_test/mmclassification-0.24.1/docs/en/tools/pytorch2torchscript.md 0 → 100644
View file @ 0fd8347d
# Pytorch to TorchScript (Experimental)
<!-- TOC -->
- [Pytorch to TorchScript (Experimental)](#pytorch-to-torchscript-experimental)
- [How to convert models from Pytorch to TorchScript](#how-to-convert-models-from-pytorch-to-torchscript)
- [Usage](#usage)
- [Description of all arguments](#description-of-all-arguments)
- [Reminders](#reminders)
- [FAQs](#faqs)
<!-- TOC -->
## How to convert models from Pytorch to TorchScript
### Usage
```bash
python tools/deployment/pytorch2torchscript.py \
${CONFIG_FILE} \
--checkpoint ${CHECKPOINT_FILE} \
--output-file ${OUTPUT_FILE} \
--shape ${IMAGE_SHAPE} \
--verify \
```
### Description of all arguments
- `config` : The path of a model config file.
- `--checkpoint` : The path of a model checkpoint file.
- `--output-file`: The path of output TorchScript model. If not specified, it will be set to `tmp.pt`.
- `--shape`: The height and width of input tensor to the model. If not specified, it will be set to `224 224`.
- `--verify`: Determines whether to verify the correctness of an exported model. If not specified, it will be set to `False`.
Example:
```bash
python tools/deployment/pytorch2onnx.py \
configs/resnet/resnet18_8xb16_cifar10.py \
--checkpoint checkpoints/resnet/resnet18_8xb16_cifar10.pth \
--output-file checkpoints/resnet/resnet18_8xb16_cifar10.pt \
--verify \
```
Notes:
- *All models are tested with Pytorch==1.8.1*
## Reminders
- For torch.jit.is_tracing() is only supported after v1.6. For users with pytorch v1.3-v1.5, we suggest early returning tensors manually.
- If you meet any problem with the models in this repo, please create an issue and it would be taken care of soon.
## FAQs
- None
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