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[Feature] Add segformer decode head and related train config (#599) * [Feature]Segformer re-implementation * Using act_cfg and norm_cfg to control activation and normalization * Split this PR into several little PRs * Fix lint error * Remove SegFormerHead * [Feature] Add segformer decode head and related train config * Add ade20K trainval support for segformer 1. Add related train and val configs; 2. Add AlignedResize; * Set arg: find_unused_parameters = True * parameters init refactor * 1. Refactor segformer backbone parameters init; 2. Remove rebundant functions and unit tests; * Remove rebundant codes * Replace Linear Layer to 1X1 Conv * Use nn.ModuleList to refactor segformer head. * Remove local to_xtuple * 1. Remove rebundant codes; 2. Modify module name; * Refactor the backbone of segformer using mmcv.cnn.bricks.transformer.py * Fix some code logic bugs. * Add mit_convert.py to match pretrain keys of segformer. * Resolve some comments. * 1. Add some assert to ensure right params; 2. Support flexible peconv position; * Add pe_index assert and fix unit test. * 1. Add doc string for MixVisionTransformer; 2. Add some unit tests for MixVisionTransformer; * Use hw_shape to pass shape of feature map. * 1. Fix doc string of MixVisionTransformer; 2. Simplify MixFFN; 3. Modify H, W to hw_shape; * Add more unit tests. * Add doc string for shape convertion functions. * Add some unit tests to improve code coverage. * Fix Segformer backbone pretrain weights match bug. * Modify configs of segformer. * resolve the shape convertion functions doc string. * Add pad_to_patch_size arg. * Support progressive test with fewer memory cost. * Modify default value of pad_to_patch_size arg. * Temp code * Using processor to refactor evaluation workflow. * refactor eval hook. * Fix process bar. * Fix middle save argument. * Modify some variable name of dataset evaluate api. * Modify some viriable name of eval hook. * Fix some priority bugs of eval hook. * Fix some bugs about model loading and eval hook. * Add ade20k 640x640 dataset. * Fix related segformer configs. * Depreciated efficient_test. * Fix training progress blocked by eval hook. * Depreciated old test api. * Modify error patch size. * Fix pretrain of mit_b0 * Fix the test api error. * Modify dataset base config. * Fix test api error. * Modify outer api. * Build a sampler test api. * TODO: Refactor format_results. * Modify variable names. * Fix num_classes bug. * Fix sampler index bug. * Fix grammaly bug. * Add part of benchmark results. * Support batch sampler. * More readable test api. * Remove some command arg and fix eval hook bug. * Support format-only arg. * Modify format_results of datasets. * Modify tool which use test apis. * Update readme. * Update readme of segformer. * Updata readme of segformer. * Update segformer readme and fix segformer mit_b4. * Update readme of segformer. * Clean AlignedResize related config. * Clean code from pr #709 * Clean code from pr #709 * Add 512x512 segformer_mit-b5. * Fix lint. * Fix some segformer head bugs. * Add segformer unit tests. * Replace AlignedResize to ResizeToMultiple. * Modify readme of segformer. * Fix bug of ResizeToMultiple. * Add ResizeToMultiple unit tests. * Resolve conflict. * Simplify the implementation of ResizeToMultiple. * Update test results. * Fix multi-scale test error when resize_ratio=1.75 and input size=640x640. * Update segformer results. * Update Segformer results. * Fix some url bugs and pipelines bug. * Move ckpt convertion to tools. * Add segformer official pretrain weights usage. * Clean redundant codes. * Remove redundant codes. * Unfied format. * Add description for segformer converter. * Update workers.
2021-08-13 13:31:19 +08:00
# SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
## Introduction
<!-- [ALGORITHM] -->
```latex
@article{xie2021segformer,
title={SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers},
author={Xie, Enze and Wang, Wenhai and Yu, Zhiding and Anandkumar, Anima and Alvarez, Jose M and Luo, Ping},
journal={arXiv preprint arXiv:2105.15203},
year={2021}
}
```
## Results and models
### ADE20k
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------: | -------------- | ---: | ------------- | ------ | -------- |
|Segformer | MIT-B0 | 512x512 | 160000 | 2.1 | 51.32 | 37.41 | 38.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b0_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20210726_101530-8ffa8fda.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20210726_101530.log.json) |
|Segformer | MIT-B1 | 512x512 | 160000 | 2.6 | 47.66 | 40.97 | 42.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b1_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20210726_112106-d70e859d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20210726_112106.log.json) |
|Segformer | MIT-B2 | 512x512 | 160000 | 3.6 | 30.88 | 45.58 | 47.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b2_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20210726_112103-cbd414ac.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20210726_112103.log.json) |
|Segformer | MIT-B3 | 512x512 | 160000 | 4.8 | 22.11 | 47.82 | 48.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b3_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20210726_081410-962b98d2.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20210726_081410.log.json) |
|Segformer | MIT-B4 | 512x512 | 160000 | 6.1 | 15.45 | 48.46 | 49.76 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b4_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20210728_183055-7f509d7d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20210728_183055.log.json) |
|Segformer | MIT-B5 | 512x512 | 160000 | 7.2 | 11.89 | 49.13 | 50.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b5_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_512x512_160k_ade20k/segformer_mit-b5_512x512_160k_ade20k_20210726_145235-94cedf59.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_512x512_160k_ade20k/segformer_mit-b5_512x512_160k_ade20k_20210726_145235.log.json) |
|Segformer | MIT-B5 | 640x640 | 160000 | 11.5 | 11.30 | 49.62 | 50.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b5_640x640_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20210801_121243-41d2845b.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20210801_121243.log.json) |
Evaluation with AlignedResize:
[Tools] Add vit/swin/mit convert weight scripts (#783) * init scripts * update markdown * update markdown * add docs * delete mit converter and use torch load function * rename segformer readme * update doc * modify doc * 更新中文文档 * Update useful_tools.md * Update useful_tools.md * modify doc * update segformer.yml
2021-08-18 09:42:42 +08:00
| Method | Backbone | Crop Size | Lr schd | mIoU | mIoU(ms+flip) |
| ------ | -------- | --------- | ------: | ---: | ------------- |
|Segformer | MIT-B0 | 512x512 | 160000 | 38.1 | 38.57 |
|Segformer | MIT-B1 | 512x512 | 160000 | 41.64 | 42.76 |
|Segformer | MIT-B2 | 512x512 | 160000 | 46.53 | 47.49 |
|Segformer | MIT-B3 | 512x512 | 160000 | 48.46 | 49.14 |
|Segformer | MIT-B4 | 512x512 | 160000 | 49.34 | 50.29 |
|Segformer | MIT-B5 | 512x512 | 160000 | 50.08 | 50.72 |
|Segformer | MIT-B5 | 640x640 | 160000 | 50.58 | 50.8 |
[Feature] Add segformer decode head and related train config (#599) * [Feature]Segformer re-implementation * Using act_cfg and norm_cfg to control activation and normalization * Split this PR into several little PRs * Fix lint error * Remove SegFormerHead * [Feature] Add segformer decode head and related train config * Add ade20K trainval support for segformer 1. Add related train and val configs; 2. Add AlignedResize; * Set arg: find_unused_parameters = True * parameters init refactor * 1. Refactor segformer backbone parameters init; 2. Remove rebundant functions and unit tests; * Remove rebundant codes * Replace Linear Layer to 1X1 Conv * Use nn.ModuleList to refactor segformer head. * Remove local to_xtuple * 1. Remove rebundant codes; 2. Modify module name; * Refactor the backbone of segformer using mmcv.cnn.bricks.transformer.py * Fix some code logic bugs. * Add mit_convert.py to match pretrain keys of segformer. * Resolve some comments. * 1. Add some assert to ensure right params; 2. Support flexible peconv position; * Add pe_index assert and fix unit test. * 1. Add doc string for MixVisionTransformer; 2. Add some unit tests for MixVisionTransformer; * Use hw_shape to pass shape of feature map. * 1. Fix doc string of MixVisionTransformer; 2. Simplify MixFFN; 3. Modify H, W to hw_shape; * Add more unit tests. * Add doc string for shape convertion functions. * Add some unit tests to improve code coverage. * Fix Segformer backbone pretrain weights match bug. * Modify configs of segformer. * resolve the shape convertion functions doc string. * Add pad_to_patch_size arg. * Support progressive test with fewer memory cost. * Modify default value of pad_to_patch_size arg. * Temp code * Using processor to refactor evaluation workflow. * refactor eval hook. * Fix process bar. * Fix middle save argument. * Modify some variable name of dataset evaluate api. * Modify some viriable name of eval hook. * Fix some priority bugs of eval hook. * Fix some bugs about model loading and eval hook. * Add ade20k 640x640 dataset. * Fix related segformer configs. * Depreciated efficient_test. * Fix training progress blocked by eval hook. * Depreciated old test api. * Modify error patch size. * Fix pretrain of mit_b0 * Fix the test api error. * Modify dataset base config. * Fix test api error. * Modify outer api. * Build a sampler test api. * TODO: Refactor format_results. * Modify variable names. * Fix num_classes bug. * Fix sampler index bug. * Fix grammaly bug. * Add part of benchmark results. * Support batch sampler. * More readable test api. * Remove some command arg and fix eval hook bug. * Support format-only arg. * Modify format_results of datasets. * Modify tool which use test apis. * Update readme. * Update readme of segformer. * Updata readme of segformer. * Update segformer readme and fix segformer mit_b4. * Update readme of segformer. * Clean AlignedResize related config. * Clean code from pr #709 * Clean code from pr #709 * Add 512x512 segformer_mit-b5. * Fix lint. * Fix some segformer head bugs. * Add segformer unit tests. * Replace AlignedResize to ResizeToMultiple. * Modify readme of segformer. * Fix bug of ResizeToMultiple. * Add ResizeToMultiple unit tests. * Resolve conflict. * Simplify the implementation of ResizeToMultiple. * Update test results. * Fix multi-scale test error when resize_ratio=1.75 and input size=640x640. * Update segformer results. * Update Segformer results. * Fix some url bugs and pipelines bug. * Move ckpt convertion to tools. * Add segformer official pretrain weights usage. * Clean redundant codes. * Remove redundant codes. * Unfied format. * Add description for segformer converter. * Update workers.
2021-08-13 13:31:19 +08:00
We replace `AlignedResize` in original implementatiuon to `Resize + ResizeToMultiple`. If you want to test by
using `AlignedResize`, you can change the dataset pipeline like this:
```python
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(2048, 512),
# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
# resize image to multiple of 32, improve SegFormer by 0.5-1.0 mIoU.
dict(type='ResizeToMultiple', size_divisor=32),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
```
## How to use segformer official pretrain weights
We convert the backbone weights from the official repo (https://github.com/NVlabs/SegFormer) with `tools/model_converters/mit_convert.py`.
You may follow below steps to start segformer training preparation:
1. Download segformer pretrain weights (Suggest put in `pretrain/`);
2. Run convert script to convert official pretrain weights: `python tools/model_converters/mit_convert.py pretrain/mit_b0.pth pretrain/mit_b0.pth`;
3. Modify `pretrained` of segformer model config, for example, `pretrained` of `segformer_mit-b0_512x512_160k_ade20k.py` is set to `pretrain/mit_b0.pth`;