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Repository: microsoft/Swin-Transformer
Branch: main
Commit: f82860bfb522
Files: 86
Total size: 392.8 KB
Directory structure:
gitextract_gk47a2w1/
├── .gitignore
├── CODE_OF_CONDUCT.md
├── LICENSE
├── MODELHUB.md
├── README.md
├── SECURITY.md
├── SUPPORT.md
├── config.py
├── configs/
│ ├── simmim/
│ │ ├── simmim_finetune__swin_base__img224_window7__800ep.yaml
│ │ ├── simmim_finetune__swinv2_base__img224_window14__800ep.yaml
│ │ ├── simmim_pretrain__swin_base__img192_window6__800ep.yaml
│ │ └── simmim_pretrain__swinv2_base__img192_window12__800ep.yaml
│ ├── swin/
│ │ ├── swin_base_patch4_window12_384_22kto1k_finetune.yaml
│ │ ├── swin_base_patch4_window12_384_finetune.yaml
│ │ ├── swin_base_patch4_window7_224.yaml
│ │ ├── swin_base_patch4_window7_224_22k.yaml
│ │ ├── swin_base_patch4_window7_224_22kto1k_finetune.yaml
│ │ ├── swin_large_patch4_window12_384_22kto1k_finetune.yaml
│ │ ├── swin_large_patch4_window7_224_22k.yaml
│ │ ├── swin_large_patch4_window7_224_22kto1k_finetune.yaml
│ │ ├── swin_small_patch4_window7_224.yaml
│ │ ├── swin_small_patch4_window7_224_22k.yaml
│ │ ├── swin_small_patch4_window7_224_22kto1k_finetune.yaml
│ │ ├── swin_tiny_c24_patch4_window8_256.yaml
│ │ ├── swin_tiny_patch4_window7_224.yaml
│ │ ├── swin_tiny_patch4_window7_224_22k.yaml
│ │ └── swin_tiny_patch4_window7_224_22kto1k_finetune.yaml
│ ├── swinmlp/
│ │ ├── swin_mlp_base_patch4_window7_224.yaml
│ │ ├── swin_mlp_tiny_c12_patch4_window8_256.yaml
│ │ ├── swin_mlp_tiny_c24_patch4_window8_256.yaml
│ │ └── swin_mlp_tiny_c6_patch4_window8_256.yaml
│ ├── swinmoe/
│ │ ├── swin_moe_base_patch4_window12_192_16expert_32gpu_22k.yaml
│ │ ├── swin_moe_base_patch4_window12_192_32expert_32gpu_22k.yaml
│ │ ├── swin_moe_base_patch4_window12_192_8expert_32gpu_22k.yaml
│ │ ├── swin_moe_base_patch4_window12_192_cosine_router_32expert_32gpu_22k.yaml
│ │ ├── swin_moe_base_patch4_window12_192_densebaseline_22k.yaml
│ │ ├── swin_moe_small_patch4_window12_192_16expert_32gpu_22k.yaml
│ │ ├── swin_moe_small_patch4_window12_192_32expert_32gpu_22k.yaml
│ │ ├── swin_moe_small_patch4_window12_192_64expert_64gpu_22k.yaml
│ │ ├── swin_moe_small_patch4_window12_192_8expert_32gpu_22k.yaml
│ │ ├── swin_moe_small_patch4_window12_192_cosine_router_32expert_32gpu_22k.yaml
│ │ └── swin_moe_small_patch4_window12_192_densebaseline_22k.yaml
│ └── swinv2/
│ ├── swinv2_base_patch4_window12_192_22k.yaml
│ ├── swinv2_base_patch4_window12to16_192to256_22kto1k_ft.yaml
│ ├── swinv2_base_patch4_window12to24_192to384_22kto1k_ft.yaml
│ ├── swinv2_base_patch4_window16_256.yaml
│ ├── swinv2_base_patch4_window8_256.yaml
│ ├── swinv2_large_patch4_window12_192_22k.yaml
│ ├── swinv2_large_patch4_window12to16_192to256_22kto1k_ft.yaml
│ ├── swinv2_large_patch4_window12to24_192to384_22kto1k_ft.yaml
│ ├── swinv2_small_patch4_window16_256.yaml
│ ├── swinv2_small_patch4_window8_256.yaml
│ ├── swinv2_tiny_patch4_window16_256.yaml
│ └── swinv2_tiny_patch4_window8_256.yaml
├── data/
│ ├── __init__.py
│ ├── build.py
│ ├── cached_image_folder.py
│ ├── data_simmim_ft.py
│ ├── data_simmim_pt.py
│ ├── imagenet22k_dataset.py
│ ├── map22kto1k.txt
│ ├── samplers.py
│ └── zipreader.py
├── get_started.md
├── kernels/
│ └── window_process/
│ ├── setup.py
│ ├── swin_window_process.cpp
│ ├── swin_window_process_kernel.cu
│ ├── unit_test.py
│ └── window_process.py
├── logger.py
├── lr_scheduler.py
├── main.py
├── main_moe.py
├── main_simmim_ft.py
├── main_simmim_pt.py
├── models/
│ ├── __init__.py
│ ├── build.py
│ ├── simmim.py
│ ├── swin_mlp.py
│ ├── swin_transformer.py
│ ├── swin_transformer_moe.py
│ └── swin_transformer_v2.py
├── optimizer.py
├── utils.py
├── utils_moe.py
└── utils_simmim.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
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================================================
FILE: CODE_OF_CONDUCT.md
================================================
# Microsoft Open Source Code of Conduct
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
Resources:
- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/)
- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
- Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) Microsoft Corporation.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE
================================================
FILE: MODELHUB.md
================================================
Access code for `baidu` is `swin`.
## ImageNet-1K and ImageNet-22K Pretrained Swin-V1 Models
| name | pretrain | resolution |acc@1 | acc@5 | #params | FLOPs | FPS| 22K model | 1K model |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |:---: |:---: |
| Swin-T | ImageNet-1K | 224x224 | 81.2 | 95.5 | 28M | 4.5G | 755 | - | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth)/[baidu](https://pan.baidu.com/s/156nWJy4Q28rDlrX-rRbI3w)/[config](configs/swin/swin_tiny_patch4_window7_224.yaml)/[log](https://github.com/SwinTransformer/storage/files/7745562/log_swin_tiny_patch4_window7_224.txt) |
| Swin-S | ImageNet-1K | 224x224 | 83.2 | 96.2 | 50M | 8.7G | 437 | - | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth)/[baidu](https://pan.baidu.com/s/1KFjpj3Efey3LmtE1QqPeQg)/[config](configs/swin/swin_small_patch4_window7_224.yaml)/[log](https://github.com/SwinTransformer/storage/files/7745563/log_swin_small_patch4_window7_224.txt) |
| Swin-B | ImageNet-1K | 224x224 | 83.5 | 96.5 | 88M | 15.4G | 278 | - | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224.pth)/[baidu](https://pan.baidu.com/s/16bqCTEc70nC_isSsgBSaqQ)/[config](configs/swin/swin_base_patch4_window7_224.yaml)/[log](https://github.com/SwinTransformer/storage/files/7745564/log_swin_base_patch4_window7_224.txt) |
| Swin-B | ImageNet-1K | 384x384 | 84.5 | 97.0 | 88M | 47.1G | 85 | - | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384.pth)/[baidu](https://pan.baidu.com/s/1xT1cu740-ejW7htUdVLnmw)/[config](configs/swin/swin_base_patch4_window12_384_finetune.yaml) |
| Swin-T | ImageNet-22K | 224x224 | 80.9 | 96.0 | 28M | 4.5G | 755 | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_tiny_patch4_window7_224_22k.pth)/[baidu](https://pan.baidu.com/s/1vct0VYwwQQ8PYkBjwSSBZQ?pwd=swin)/[config](configs/swin/swin_tiny_patch4_window7_224_22k.yaml) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_tiny_patch4_window7_224_22kto1k_finetune.pth)/[baidu](https://pan.baidu.com/s/1K0OO-nGZDPkR8fm_r83e8Q?pwd=swin)/[config](configs/swin/swin_tiny_patch4_window7_224_22kto1k_finetune.yaml) |
| Swin-S | ImageNet-22K | 224x224 | 83.2 | 97.0 | 50M | 8.7G | 437 | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_small_patch4_window7_224_22k.pth)/[baidu](https://pan.baidu.com/s/11NC1xdT5BAGBgazdTme5Sg?pwd=swin)/[config](configs/swin/swin_small_patch4_window7_224_22k.yaml) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_small_patch4_window7_224_22kto1k_finetune.pth)/[baidu](https://pan.baidu.com/s/10RFVfjQJhwPfeHrmxQUaLw?pwd=swin)/[config](configs/swin/swin_small_patch4_window7_224_22kto1k_finetune.yaml) |
| Swin-B | ImageNet-22K | 224x224 | 85.2 | 97.5 | 88M | 15.4G | 278 | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth)/[baidu](https://pan.baidu.com/s/1y1Ec3UlrKSI8IMtEs-oBXA)/[config](configs/swin/swin_base_patch4_window7_224_22k.yaml) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22kto1k.pth)/[baidu](https://pan.baidu.com/s/1n_wNkcbRxVXit8r_KrfAVg)/[config](configs/swin/swin_base_patch4_window7_224_22kto1k_finetune.yaml) |
| Swin-B | ImageNet-22K | 384x384 | 86.4 | 98.0 | 88M | 47.1G | 85 | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth)/[baidu](https://pan.baidu.com/s/1vwJxnJcVqcLZAw9HaqiR6g) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22kto1k.pth)/[baidu](https://pan.baidu.com/s/1caKTSdoLJYoi4WBcnmWuWg)/[config](configs/swin/swin_base_patch4_window12_384_22kto1k_finetune.yaml) |
| Swin-L | ImageNet-22K | 224x224 | 86.3 | 97.9 | 197M | 34.5G | 141 | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22k.pth)/[baidu](https://pan.baidu.com/s/1pws3rOTFuOebBYP3h6Kx8w)/[config](configs/swin/swin_large_patch4_window7_224_22k.yaml) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22kto1k.pth)/[baidu](https://pan.baidu.com/s/1NkQApMWUhxBGjk1ne6VqBQ)/[config](configs/swin/swin_large_patch4_window7_224_22kto1k_finetune.yaml) |
| Swin-L | ImageNet-22K | 384x384 | 87.3 | 98.2 | 197M | 103.9G | 42 | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth)/[baidu](https://pan.baidu.com/s/1sl7o_bJA143OD7UqSLAMoA) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22kto1k.pth)/[baidu](https://pan.baidu.com/s/1X0FLHQyPOC6Kmv2CmgxJvA)/[config](configs/swin/swin_large_patch4_window12_384_22kto1k_finetune.yaml) |
## ImageNet-1K and ImageNet-22K Pretrained Swin-V2 Models
| name | pretrain | resolution | window |acc@1 | acc@5 | #params | FLOPs | FPS |22K model | 1K model |
|:---------------------:| :---: | :---: | :---: | :---: | :---: | :---: | :---: |:---:|:---: |:---: |
| SwinV2-T | ImageNet-1K | 256x256 | 8x8 | 81.8 | 95.9 | 28M | 5.9G | 572 | - | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window8_256.pth)/[baidu](https://pan.baidu.com/s/1RzLkAH_5OtfRCJe6Vlg6rg?pwd=swin)/[config](configs/swinv2/swinv2_tiny_patch4_window8_256.yaml) |
| SwinV2-S | ImageNet-1K | 256x256 | 8x8 | 83.7 | 96.6 | 50M | 11.5G | 327 | - | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_small_patch4_window8_256.pth)/[baidu](https://pan.baidu.com/s/195PdA41szEduW3jEtRSa4Q?pwd=swin)/[config](configs/swinv2/swinv2_small_patch4_window8_256.yaml) |
| SwinV2-B | ImageNet-1K | 256x256 | 8x8 | 84.2 | 96.9 | 88M | 20.3G | 217 | - | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window8_256.pth)/[baidu](https://pan.baidu.com/s/18AfMSz3dPyzIvP1dKuERvQ?pwd=swin)/[config](configs/swinv2/swinv2_base_patch4_window8_256.yaml) |
| SwinV2-T | ImageNet-1K | 256x256 | 16x16 | 82.8 | 96.2 | 28M | 6.6G | 437 | - | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window16_256.pth)/[baidu](https://pan.baidu.com/s/1dyK3cK9Xipmv6RnTtrPocw?pwd=swin)/[config](configs/swinv2/swinv2_tiny_patch4_window16_256.yaml) |
| SwinV2-S | ImageNet-1K | 256x256 | 16x16 | 84.1 | 96.8 | 50M | 12.6G | 257 | - | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_small_patch4_window16_256.pth)/[baidu](https://pan.baidu.com/s/1ZIPiSfWNKTPp821Ka-Mifw?pwd=swin)/[config](configs/swinv2/swinv2_small_patch4_window16_256.yaml) |
| SwinV2-B | ImageNet-1K | 256x256 | 16x16 | 84.6 | 97.0 | 88M | 21.8G | 174 | - | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window16_256.pth)/[baidu](https://pan.baidu.com/s/1dlDQGn8BXCmnh7wQSM5Nhw?pwd=swin)/[config](configs/swinv2/swinv2_base_patch4_window16_256.yaml) |
| SwinV2-B<sup>\*</sup> | ImageNet-22K | 256x256 | 16x16 | 86.2 | 97.9 | 88M | 21.8G | 174 | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12_192_22k.pth)/[baidu](https://pan.baidu.com/s/1Xc2rsSsRQz_sy5mjgfxrMQ?pwd=swin)/[config](configs/swinv2/swinv2_base_patch4_window12_192_22k.yaml) | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to16_192to256_22kto1k_ft.pth)/[baidu](https://pan.baidu.com/s/1sgstld4MgGsZxhUAW7MlmQ?pwd=swin)/[config](configs/swinv2/swinv2_base_patch4_window12to16_192to256_22kto1k_ft.yaml) |
| SwinV2-B<sup>\*</sup> | ImageNet-22K | 384x384 | 24x24 | 87.1 | 98.2 | 88M | 54.7G | 57 | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12_192_22k.pth)/[baidu](https://pan.baidu.com/s/1Xc2rsSsRQz_sy5mjgfxrMQ?pwd=swin)/[config](configs/swinv2/swinv2_base_patch4_window12_192_22k.yaml) | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to24_192to384_22kto1k_ft.pth)/[baidu](https://pan.baidu.com/s/17u3sEQaUYlvfL195rrORzQ?pwd=swin)/[config](configs/swinv2/swinv2_base_patch4_window12to24_192to384_22kto1k_ft.yaml) |
| SwinV2-L<sup>\*</sup> | ImageNet-22K | 256x256 | 16x16 | 86.9 | 98.0 | 197M | 47.5G | 95 | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12_192_22k.pth)/[baidu](https://pan.baidu.com/s/11PhCV7qAGXtZ8dXNgyiGOw?pwd=swin)/[config](configs/swinv2/swinv2_large_patch4_window12_192_22k.yaml) | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to16_192to256_22kto1k_ft.pth)/[baidu](https://pan.baidu.com/s/1pqp31N80qIWjFPbudzB6Bw?pwd=swin)/[config](configs/swinv2/swinv2_large_patch4_window12to16_192to256_22kto1k_ft.yaml) |
| SwinV2-L<sup>\*</sup> | ImageNet-22K | 384x384 | 24x24 | 87.6 | 98.3 | 197M | 115.4G | 33 | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12_192_22k.pth)/[baidu](https://pan.baidu.com/s/11PhCV7qAGXtZ8dXNgyiGOw?pwd=swin)/[config](configs/swinv2/swinv2_large_patch4_window12_192_22k.yaml) | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to24_192to384_22kto1k_ft.pth)/[baidu](https://pan.baidu.com/s/13URdNkygr3Xn0N3e6IwjgA?pwd=swin)/[config](configs/swinv2/swinv2_large_patch4_window12to24_192to384_22kto1k_ft.yaml) |
Note:
- SwinV2-B<sup>\*</sup> (SwinV2-L<sup>\*</sup>) with input resolution of 256x256 and 384x384 both fine-tuned from the
same pre-training model using a smaller input resolution of 192x192.
- SwinV2-B<sup>\*</sup> (384x384) achieves 78.08 acc@1 on ImageNet-1K-V2 while SwinV2-L<sup>\*</sup> (384x384) achieves
78.31.
## ImageNet-1K Pretrained Swin MLP Models
| name | pretrain | resolution |acc@1 | acc@5 | #params | FLOPs | FPS | 1K model |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| [Mixer-B/16](https://arxiv.org/pdf/2105.01601.pdf) | ImageNet-1K | 224x224 | 76.4 | - | 59M | 12.7G | - | [official repo](https://github.com/google-research/vision_transformer) |
| [ResMLP-S24](https://arxiv.org/abs/2105.03404) | ImageNet-1K | 224x224 | 79.4 | - | 30M | 6.0G | 715 | [timm](https://github.com/rwightman/pytorch-image-models) |
| [ResMLP-B24](https://arxiv.org/abs/2105.03404) | ImageNet-1K | 224x224 | 81.0 | - | 116M | 23.0G | 231 | [timm](https://github.com/rwightman/pytorch-image-models) |
| Swin-T/C24 | ImageNet-1K | 256x256 | 81.6 | 95.7 | 28M | 5.9G | 563 | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.5/swin_tiny_c24_patch4_window8_256.pth)/[baidu](https://pan.baidu.com/s/17k-7l6Sxt7uZ7IV0f26GNQ)/[config](configs/swin/swin_tiny_c24_patch4_window8_256.yaml) |
| SwinMLP-T/C24 | ImageNet-1K | 256x256 | 79.4 | 94.6 | 20M | 4.0G | 807 | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.5/swin_mlp_tiny_c24_patch4_window8_256.pth)/[baidu](https://pan.baidu.com/s/1Sa4vP5R0M2RjfIe9HIga-Q)/[config](configs/swin/swin_mlp_tiny_c24_patch4_window8_256.yaml) |
| SwinMLP-T/C12 | ImageNet-1K | 256x256 | 79.6 | 94.7 | 21M | 4.0G | 792 | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.5/swin_mlp_tiny_c12_patch4_window8_256.pth)/[baidu](https://pan.baidu.com/s/1mM9J2_DEVZHUB5ASIpFl0w)/[config](configs/swin/swin_mlp_tiny_c12_patch4_window8_256.yaml) |
| SwinMLP-T/C6 | ImageNet-1K | 256x256 | 79.7 | 94.9 | 23M | 4.0G | 766 | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.5/swin_mlp_tiny_c6_patch4_window8_256.pth)/[baidu](https://pan.baidu.com/s/1hUTYVT2W1CsjICw-3W-Vjg)/[config](configs/swin/swin_mlp_tiny_c6_patch4_window8_256.yaml) |
| SwinMLP-B | ImageNet-1K | 224x224 | 81.3 | 95.3 | 61M | 10.4G | 409 | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.5/swin_mlp_base_patch4_window7_224.pth)/[baidu](https://pan.baidu.com/s/1zww3dnbX3GxNiGfb-GwyUg)/[config](configs/swin/swin_mlp_base_patch4_window7_224.yaml) |
Note: C24 means each head has 24 channels.
## ImageNet-22K Pretrained Swin-MoE Models
| name | #experts | k | router | resolution | window | IN-22K acc@1 | IN-1K/ft acc@1 | IN-1K/5-shot acc@1 | 22K model |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| Swin-MoE-S | 1 (dense) | - | - | 192x192 | 8x8 | 35.5| 83.5 | 70.3 | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.2/swin_moe_small_patch4_window12_192_densebaseline_22k.zip)/[baidu](https://pan.baidu.com/s/1O1m9jT2pGoago_RiRX914w?pwd=swin)/[config](configs/swinmoe/swin_moe_small_patch4_window12_192_densebaseline_22k.yaml) |
| Swin-MoE-S | 8 | 1 | Linear | 192x192 | 8x8 | 36.8 | 84.5 | 75.2 | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.2/swin_moe_small_patch4_window12_192_8expert_32gpu_22k.zip)/[baidu](https://pan.baidu.com/s/198IlYUrWOxEUp7wNdoJT5Q?pwd=swin)/[config](configs/swinmoe/swin_moe_small_patch4_window12_192_8expert_32gpu_22k.yaml) |
| Swin-MoE-S | 16 | 1 | Linear |192x192 | 8x8 | 37.6 | 84.9 | 76.5 | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.2/swin_moe_small_patch4_window12_192_16expert_32gpu_22k.zip)/[baidu](https://pan.baidu.com/s/1vRQweedtT42VwMTqe9-r2A?pwd=swin)/[config](configs/swinmoe/swin_moe_small_patch4_window12_192_16expert_32gpu_22k.yaml) |
| Swin-MoE-S | 32 | 1 | Linear | 192x192 | 8x8 | 37.4 | 84.7 | 75.9 | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.2/swin_moe_small_patch4_window12_192_32expert_32gpu_22k.zip)/[baidu](https://pan.baidu.com/s/1i7rImt5pwO8gJC-PRRuZwQ?pwd=swin)/[config](configs/swinmoe/swin_moe_small_patch4_window12_192_32expert_32gpu_22k.yaml) |
| Swin-MoE-S | 32 | 1 | Cosine | 192x192 | 8x8 | 37.2 | 84.3 | 75.2 | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.2/swin_moe_small_patch4_window12_192_cosine_router_32expert_32gpu_22k.zip)/[baidu](https://pan.baidu.com/s/1Yghr_12ntSrv01I9yatPDQ?pwd=swin)/[config](configs/swinmoe/swin_moe_small_patch4_window12_192_cosine_router_32expert_32gpu_22k.yaml) |
| Swin-MoE-S | 64 | 1 | Linear | 192x192 | 8x8 | 37.8 | 84.7 | 75.7 | - |
| Swin-MoE-S | 128 | 1 | Linear | 192x192 | 8x8 | 37.4 | 84.5 | 75.4 | - |
| Swin-MoE-B | 1 (dense) | - | - | 192x192 | 8x8 | 37.3 | 85.1 | 75.9 | [config](configs/swinmoe/swin_moe_base_patch4_window12_192_densebaseline_22k.yaml) |
| Swin-MoE-B | 8 | 1 | Linear | 192x192 | 8x8 | 38.1 | 85.3 | 77.2 | [config](configs/swinmoe/swin_moe_base_patch4_window12_192_8expert_32gpu_22k.yaml) |
| Swin-MoE-B | 16 | 1 | Linear | 192x192 | 8x8 | 38.7 | 85.5 | 78.2 | [config](configs/swinmoe/swin_moe_base_patch4_window12_192_16expert_32gpu_22k.yaml) |
| Swin-MoE-B | 32 | 1 | Linear | 192x192 | 8x8 | 38.6 | 85.5 | 77.9 | [config](configs/swinmoe/swin_moe_base_patch4_window12_192_32expert_32gpu_22k.yaml) |
| Swin-MoE-B | 32 | 1 | Cosine | 192x192 | 8x8 | 38.5 | 85.3 | 77.3 | [config](configs/swinmoe/swin_moe_base_patch4_window12_192_cosine_router_32expert_32gpu_22k.yaml) |
| Swin-MoE-B | 32 | 2 | Linear | 192x192 | 8x8 | 38.6 | 85.5 | 78.7 | - |
## SimMIM Pretrained Swin-V2 Models
> Please note that all SimMIM pretrained Swin-V2 models will be stored in the Huggingface repository starting July 2024. For more details, refer to the [huggingface repository](https://huggingface.co/zdaxie/SimMIM).
- **Model size** only includes the backbone weights and excludes weights in the decoders/classification heads.
- **Batch size** for all models is set to 2048.
- **Validation loss** is calculated on the ImageNet-1K validation set.
- **Fine-tuned acc@1** refers to the top-1 accuracy on the ImageNet-1K validation set after fine-tuning.
| name | model size | pre-train dataset | pre-train iterations | validation loss | fine-tuned acc@1 | pre-trained model | fine-tuned model |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| SwinV2-Small | 49M | ImageNet-1K 10% | 125k | 0.4820 | 82.69 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_small_1kper10_125k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_small_1kper10_125k.pth?download=true) |
| SwinV2-Small | 49M | ImageNet-1K 10% | 250k | 0.4961 | 83.11 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_small_1kper10_250k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_small_1kper10_250k.pth?download=true) |
| SwinV2-Small | 49M | ImageNet-1K 10% | 500k | 0.5115 | 83.17 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_small_1kper10_500k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_small_1kper10_500k.pth?download=true) |
| SwinV2-Small | 49M | ImageNet-1K 20% | 125k | 0.4751 | 83.05 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_small_1kper20_125k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_small_1kper20_125k.pth?download=true) |
| SwinV2-Small | 49M | ImageNet-1K 20% | 250k | 0.4722 | 83.56 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_small_1kper20_250k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_small_1kper20_250k.pth?download=true) |
| SwinV2-Small | 49M | ImageNet-1K 20% | 500k | 0.4734 | 83.75 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_small_1kper20_500k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_small_1kper20_500k.pth?download=true) |
| SwinV2-Small | 49M | ImageNet-1K 50% | 125k | 0.4732 | 83.04 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_small_1kper50_125k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_small_1kper50_125k.pth?download=true) |
| SwinV2-Small | 49M | ImageNet-1K 50% | 250k | 0.4681 | 83.67 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_small_1kper50_250k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_small_1kper50_250k.pth?download=true) |
| SwinV2-Small | 49M | ImageNet-1K 50% | 500k | 0.4646 | 83.96 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_small_1kper50_500k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_small_1kper50_500k.pth?download=true) |
| SwinV2-Small | 49M | ImageNet-1K | 125k | 0.4728 | 82.92 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_small_1k_125k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_small_1k_125k.pth?download=true) |
| SwinV2-Small | 49M | ImageNet-1K | 250k | 0.4674 | 83.66 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_small_1k_250k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_small_1k_250k.pth?download=true) |
| SwinV2-Small | 49M | ImageNet-1K | 500k | 0.4641 | 84.08 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_small_1k_500k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_small_1k_500k.pth?download=true) |
| SwinV2-Base | 87M | ImageNet-1K 10% | 125k | 0.4822 | 83.33 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_base_1kper10_125k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_base_1kper10_125k.pth?download=true) |
| SwinV2-Base | 87M | ImageNet-1K 10% | 250k | 0.4997 | 83.60 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_base_1kper10_250k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_base_1kper10_250k.pth?download=true) |
| SwinV2-Base | 87M | ImageNet-1K 10% | 500k | 0.5112 | 83.41 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_base_1kper10_500k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_base_1kper10_500k.pth?download=true) |
| SwinV2-Base | 87M | ImageNet-1K 20% | 125k | 0.4703 | 83.86 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_base_1kper20_125k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_base_1kper20_125k.pth?download=true) |
| SwinV2-Base | 87M | ImageNet-1K 20% | 250k | 0.4679 | 84.37 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_base_1kper20_250k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_base_1kper20_250k.pth?download=true) |
| SwinV2-Base | 87M | ImageNet-1K 20% | 500k | 0.4711 | 84.61 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_base_1kper20_500k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_base_1kper20_500k.pth?download=true) |
| SwinV2-Base | 87M | ImageNet-1K 50% | 125k | 0.4683 | 84.04 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_base_1kper50_125k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_base_1kper50_125k.pth?download=true) |
| SwinV2-Base | 87M | ImageNet-1K 50% | 250k | 0.4633 | 84.57 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_base_1kper50_250k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_base_1kper50_250k.pth?download=true) |
| SwinV2-Base | 87M | ImageNet-1K 50% | 500k | 0.4598 | 84.95 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_base_1kper50_500k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_base_1kper50_500k.pth?download=true) |
| SwinV2-Base | 87M | ImageNet-1K | 125k | 0.4680 | 84.13 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_base_1k_125k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_base_1k_125k.pth?download=true) |
| SwinV2-Base | 87M | ImageNet-1K | 250k | 0.4626 | 84.65 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_base_1k_250k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_base_1k_250k.pth?download=true) |
| SwinV2-Base | 87M | ImageNet-1K | 500k | 0.4588 | 85.04 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_base_1k_500k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_base_1k_500k.pth?download=true) |
| SwinV2-Base | 87M | ImageNet-22K | 125k | 0.4695 | 84.11 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_base_22k_125k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_base_22k_125k.pth?download=true) |
| SwinV2-Base | 87M | ImageNet-22K | 250k | 0.4649 | 84.57 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_base_22k_250k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_base_22k_250k.pth?download=true) |
| SwinV2-Base | 87M | ImageNet-22K | 500k | 0.4614 | 85.11 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_base_22k_500k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_base_22k_500k.pth?download=true) |
| SwinV2-Large | 195M | ImageNet-1K 10% | 125k | 0.4995 | 83.69 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_large_1kper10_125k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_large_1kper10_125k.pth?download=true) |
| SwinV2-Large | 195M | ImageNet-1K 10% | 250k | 0.5140 | 83.66 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_large_1kper10_250k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_large_1kper10_250k.pth?download=true) |
| SwinV2-Large | 195M | ImageNet-1K 10% | 500k | 0.5150 | 83.50 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_large_1kper10_500k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_large_1kper10_500k.pth?download=true) |
| SwinV2-Large | 195M | ImageNet-1K 20% | 125k | 0.4675 | 84.38 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_large_1kper20_125k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_large_1kper20_125k.pth?download=true) |
| SwinV2-Large | 195M | ImageNet-1K 20% | 250k | 0.4746 | 84.71 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_large_1kper20_250k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_large_1kper20_250k.pth?download=true) |
| SwinV2-Large | 195M | ImageNet-1K 20% | 500k | 0.4960 | 84.59 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_large_1kper20_500k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_large_1kper20_500k.pth?download=true) |
| SwinV2-Large | 195M | ImageNet-1K 50% | 125k | 0.4622 | 84.78 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_large_1kper50_125k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_large_1kper50_125k.pth?download=true) |
| SwinV2-Large | 195M | ImageNet-1K 50% | 250k | 0.4566 | 85.38 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_large_1kper50_250k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_large_1kper50_250k.pth?download=true) |
| SwinV2-Large | 195M | ImageNet-1K 50% | 500k | 0.4530 | 85.80 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_large_1kper50_500k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_large_1kper50_500k.pth?download=true) |
| SwinV2-Large | 195M | ImageNet-1K | 125k | 0.4611 | 84.98 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_large_1k_125k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_large_1k_125k.pth?download=true) |
| SwinV2-Large | 195M | ImageNet-1K | 250k | 0.4552 | 85.45 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_large_1k_250k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_large_1k_250k.pth?download=true) |
| SwinV2-Large | 195M | ImageNet-1K | 500k | 0.4507 | 85.91 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_large_1k_500k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_large_1k_500k.pth?download=true) |
| SwinV2-Large | 195M | ImageNet-22K | 125k | 0.4649 | 84.61 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_large_22k_125k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_large_22k_125k.pth?download=true) |
| SwinV2-Large | 195M | ImageNet-22K | 250k | 0.4586 | 85.39 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_large_22k_250k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_large_22k_250k.pth?download=true) |
| SwinV2-Large | 195M | ImageNet-22K | 500k | 0.4536 | 85.81 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_large_22k_500k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_large_22k_500k.pth?download=true) |
| SwinV2-Huge | 655M | ImageNet-1K 20% | 125k | 0.4789 | 84.35 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_huge_1kper20_125k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_huge_1kper20_125k.pth?download=true) |
| SwinV2-Huge | 655M | ImageNet-1K 20% | 250k | 0.5038 | 84.16 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_huge_1kper20_250k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_huge_1kper20_250k.pth?download=true) |
| SwinV2-Huge | 655M | ImageNet-1K 20% | 500k | 0.5071 | 83.44 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_huge_1kper20_500k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_huge_1kper20_500k.pth?download=true) |
| SwinV2-Huge | 655M | ImageNet-1K 50% | 125k | 0.4549 | 85.09 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_huge_1kper50_125k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_huge_1kper50_125k.pth?download=true) |
| SwinV2-Huge | 655M | ImageNet-1K 50% | 250k | 0.4511 | 85.64 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_huge_1kper50_250k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_huge_1kper50_250k.pth?download=true) |
| SwinV2-Huge | 655M | ImageNet-1K 50% | 500k | 0.4559 | 85.69 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_huge_1kper50_500k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_huge_1kper50_500k.pth?download=true) |
| SwinV2-Huge | 655M | ImageNet-1K | 125k | 0.4531 | 85.23 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_huge_1k_125k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_huge_1k_125k.pth?download=true) |
| SwinV2-Huge | 655M | ImageNet-1K | 250k | 0.4464 | 85.90 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_huge_1k_250k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_huge_1k_250k.pth?download=true) |
| SwinV2-Huge | 655M | ImageNet-1K | 500k | 0.4416 | 86.34 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_huge_1k_500k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_huge_1k_500k.pth?download=true) |
| SwinV2-Huge | 655M | ImageNet-22K | 125k | 0.4564 | 85.14 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_huge_22k_125k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_huge_22k_125k.pth?download=true) |
| SwinV2-Huge | 655M | ImageNet-22K | 250k | 0.4499 | 85.86 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_huge_22k_250k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_huge_22k_250k.pth?download=true) |
| SwinV2-Huge | 655M | ImageNet-22K | 500k | 0.4444 | 86.27 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_huge_22k_500k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_huge_22k_500k.pth?download=true) |
| SwinV2-giant | 1.06B | ImageNet-1K 50% | 125k | 0.4534 | 85.44 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_giant_1kper50_125k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_giant_1kper50_125k.pth?download=true) |
| SwinV2-giant | 1.06B | ImageNet-1K 50% | 250k | 0.4515 | 85.76 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_giant_1kper50_250k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_giant_1kper50_250k.pth?download=true) |
| SwinV2-giant | 1.06B | ImageNet-1K 50% | 500k | 0.4719 | 85.51 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_giant_1kper50_500k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_giant_1kper50_500k.pth?download=true) |
| SwinV2-giant | 1.06B | ImageNet-1K | 125k | 0.4513 | 85.57 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_giant_1k_125k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_giant_1k_125k.pth?download=true) |
| SwinV2-giant | 1.06B | ImageNet-1K | 250k | 0.4442 | 86.12 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_giant_1k_250k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_giant_1k_250k.pth?download=true) |
| SwinV2-giant | 1.06B | ImageNet-1K | 500k | 0.4395 | 86.46 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_giant_1k_500k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_giant_1k_500k.pth?download=true) |
| SwinV2-giant | 1.06B | ImageNet-22K | 125k | 0.4544 | 85.39 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_giant_22k_125k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_giant_22k_125k.pth?download=true) |
| SwinV2-giant | 1.06B | ImageNet-22K | 250k | 0.4475 | 85.96 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_giant_22k_250k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_giant_22k_250k.pth?download=true) |
| SwinV2-giant | 1.06B | ImageNet-22K | 500k | 0.4416 | 86.53 | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_pretrain_models/swinv2_giant_22k_500k.pth?download=true) | [huggingface](https://huggingface.co/zdaxie/SimMIM/resolve/main/simmim_swinv2_finetune_models/finetune_swinv2_giant_22k_500k.pth?download=true) |
## SimMIM Pretrained Swin-V1 Models
**ImageNet-1K Pre-trained and Fine-tuned Models**
| name | pre-train epochs | pre-train resolution | fine-tune resolution | acc@1 | pre-trained model | fine-tuned model |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| Swin-Base | 100 | 192x192 | 192x192 | 82.8 | [google](https://drive.google.com/file/d/1Wcbr66JL26FF30Kip9fZa_0lXrDAKP-d/view?usp=sharing)/[config](configs/swin_base__100ep/simmim_pretrain__swin_base__img192_window6__100ep.yaml) | [google](https://drive.google.com/file/d/1RsgHfjB4B1ZYblXEQVT-FPX3WSvBrxcs/view?usp=sharing)/[config](configs/swin_base__100ep/simmim_finetune__swin_base__img192_window6__100ep.yaml) |
| Swin-Base | 100 | 192x192 | 224x224 | 83.5 | [google](https://drive.google.com/file/d/1Wcbr66JL26FF30Kip9fZa_0lXrDAKP-d/view?usp=sharing)/[config](configs/swin_base__100ep/simmim_pretrain__swin_base__img192_window6__100ep.yaml) | [google](https://drive.google.com/file/d/1mb43BkW56F5smwiX-g7QUUD7f1Rftq8u/view?usp=sharing)/[config](configs/swin_base__100ep/simmim_finetune__swin_base__img224_window7__100ep.yaml) |
| Swin-Base | 800 | 192x192 | 224x224 | 84.0 | [google](https://drive.google.com/file/d/15zENvGjHlM71uKQ3d2FbljWPubtrPtjl/view?usp=sharing)/[config](configs/swin_base__800ep/simmim_pretrain__swin_base__img192_window6__800ep.yaml) | [google](https://drive.google.com/file/d/1xEKyfMTsdh6TfnYhk5vbw0Yz7a-viZ0w/view?usp=sharing)/[config](configs/swin_base__800ep/simmim_finetune__swin_base__img224_window7__800ep.yaml) |
| Swin-Large | 800 | 192x192 | 224x224 | 85.4 | [google](https://drive.google.com/file/d/1qDxrTl2YUDB0505_4QrU5LU2R1kKmcBP/view?usp=sharing)/[config](configs/swin_large__800ep/simmim_pretrain__swin_large__img192_window12__800ep.yaml) | [google](https://drive.google.com/file/d/1mf0ZpXttEvFsH87Www4oQ-t8Kwr0x485/view?usp=sharing)/[config](configs/swin_large__800ep/simmim_finetune__swin_large__img224_window14__800ep.yaml) |
| SwinV2-Huge | 800 | 192x192 | 224x224 | 85.7 | / | / |
| SwinV2-Huge | 800 | 192x192 | 512x512 | 87.1 | / | / |
================================================
FILE: README.md
================================================
# Swin Transformer
[](https://paperswithcode.com/sota/object-detection-on-coco?p=swin-transformer-v2-scaling-up-capacity-and)
[](https://paperswithcode.com/sota/instance-segmentation-on-coco?p=swin-transformer-v2-scaling-up-capacity-and)
[](https://paperswithcode.com/sota/semantic-segmentation-on-ade20k?p=swin-transformer-v2-scaling-up-capacity-and)
[](https://paperswithcode.com/sota/action-classification-on-kinetics-400?p=swin-transformer-v2-scaling-up-capacity-and)
This repo is the official implementation of ["Swin Transformer: Hierarchical Vision Transformer using Shifted Windows"](https://arxiv.org/pdf/2103.14030.pdf) as well as the follow-ups. It currently includes code and models for the following tasks:
> **Image Classification**: Included in this repo. See [get_started.md](get_started.md) for a quick start.
> **Object Detection and Instance Segmentation**: See [Swin Transformer for Object Detection](https://github.com/SwinTransformer/Swin-Transformer-Object-Detection).
> **Semantic Segmentation**: See [Swin Transformer for Semantic Segmentation](https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation).
> **Video Action Recognition**: See [Video Swin Transformer](https://github.com/SwinTransformer/Video-Swin-Transformer).
> **Semi-Supervised Object Detection**: See [Soft Teacher](https://github.com/microsoft/SoftTeacher).
> **SSL: Contrasitive Learning**: See [Transformer-SSL](https://github.com/SwinTransformer/Transformer-SSL).
> **SSL: Masked Image Modeling**: See [get_started.md#simmim-support](https://github.com/microsoft/Swin-Transformer/blob/main/get_started.md#simmim-support).
> **Mixture-of-Experts**: See [get_started](get_started.md#mixture-of-experts-support) for more instructions.
> **Feature-Distillation**: See [Feature-Distillation](https://github.com/SwinTransformer/Feature-Distillation).
## Updates
***12/29/2022***
1. **Nvidia**'s [FasterTransformer](https://github.com/NVIDIA/FasterTransformer/blob/main/docs/swin_guide.md) now supports Swin Transformer V2 inference, which have significant speed improvements on `T4 and A100 GPUs`.
***11/30/2022***
1. Models and codes of **Feature Distillation** are released. Please refer to [Feature-Distillation](https://github.com/SwinTransformer/Feature-Distillation) for details, and the checkpoints (FD-EsViT-Swin-B, FD-DeiT-ViT-B, FD-DINO-ViT-B, FD-CLIP-ViT-B, FD-CLIP-ViT-L).
***09/24/2022***
1. Merged [SimMIM](https://github.com/microsoft/SimMIM), which is a **Masked Image Modeling** based pre-training approach applicable to Swin and SwinV2 (and also applicable for ViT and ResNet). Please refer to [get started with SimMIM](get_started.md#simmim-support) to play with SimMIM pre-training.
2. Released a series of Swin and SwinV2 models pre-trained using the SimMIM approach (see [MODELHUB for SimMIM](MODELHUB.md#simmim-pretrained-swin-v2-models)), with model size ranging from SwinV2-Small-50M to SwinV2-giant-1B, data size ranging from ImageNet-1K-10% to ImageNet-22K, and iterations from 125k to 500k. You may leverage these models to study the properties of MIM methods. Please look into the [data scaling](https://arxiv.org/abs/2206.04664) paper for more details.
***07/09/2022***
`News`:
1. SwinV2-G achieves `61.4 mIoU` on ADE20K semantic segmentation (+1.5 mIoU over the previous SwinV2-G model), using an additional [feature distillation (FD)](https://github.com/SwinTransformer/Feature-Distillation) approach, **setting a new recrod** on this benchmark. FD is an approach that can generally improve the fine-tuning performance of various pre-trained models, including DeiT, DINO, and CLIP. Particularly, it improves CLIP pre-trained ViT-L by +1.6% to reach `89.0%` on ImageNet-1K image classification, which is **the most accurate ViT-L model**.
2. Merged a PR from **Nvidia** that links to faster Swin Transformer inference that have significant speed improvements on `T4 and A100 GPUs`.
3. Merged a PR from **Nvidia** that enables an option to use `pure FP16 (Apex O2)` in training, while almost maintaining the accuracy.
***06/03/2022***
1. Added **Swin-MoE**, the Mixture-of-Experts variant of Swin Transformer implemented using [Tutel](https://github.com/microsoft/tutel) (an optimized Mixture-of-Experts implementation). **Swin-MoE** is introduced in the [TuTel](https://arxiv.org/abs/2206.03382) paper.
***05/12/2022***
1. Pretrained models of [Swin Transformer V2](https://arxiv.org/abs/2111.09883) on ImageNet-1K and ImageNet-22K are released.
2. ImageNet-22K pretrained models for Swin-V1-Tiny and Swin-V2-Small are released.
***03/02/2022***
1. Swin Transformer V2 and SimMIM got accepted by CVPR 2022. [SimMIM](https://github.com/microsoft/SimMIM) is a self-supervised pre-training approach based on masked image modeling, a key technique that works out the 3-billion-parameter Swin V2 model using `40x less labelled data` than that of previous billion-scale models based on JFT-3B.
***02/09/2022***
1. Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo [](https://huggingface.co/spaces/akhaliq/Swin-Transformer)
***10/12/2021***
1. Swin Transformer received ICCV 2021 best paper award (Marr Prize).
***08/09/2021***
1. [Soft Teacher](https://arxiv.org/pdf/2106.09018v2.pdf) will appear at ICCV2021. The code will be released at [GitHub Repo](https://github.com/microsoft/SoftTeacher). `Soft Teacher` is an end-to-end semi-supervisd object detection method, achieving a new record on the COCO test-dev: `61.3 box AP` and `53.0 mask AP`.
***07/03/2021***
1. Add **Swin MLP**, which is an adaption of `Swin Transformer` by replacing all multi-head self-attention (MHSA) blocks by MLP layers (more precisely it is a group linear layer). The shifted window configuration can also significantly improve the performance of vanilla MLP architectures.
***06/25/2021***
1. [Video Swin Transformer](https://arxiv.org/abs/2106.13230) is released at [Video-Swin-Transformer](https://github.com/SwinTransformer/Video-Swin-Transformer).
`Video Swin Transformer` achieves state-of-the-art accuracy on a broad range of video recognition benchmarks, including action recognition (`84.9` top-1 accuracy on Kinetics-400 and `86.1` top-1 accuracy on Kinetics-600 with `~20x` less pre-training data and `~3x` smaller model size) and temporal modeling (`69.6` top-1 accuracy on Something-Something v2).
***05/12/2021***
1. Used as a backbone for `Self-Supervised Learning`: [Transformer-SSL](https://github.com/SwinTransformer/Transformer-SSL)
Using Swin-Transformer as the backbone for self-supervised learning enables us to evaluate the transferring performance of the learnt representations on down-stream tasks, which is missing in previous works due to the use of ViT/DeiT, which has not been well tamed for down-stream tasks.
***04/12/2021***
Initial commits:
1. Pretrained models on ImageNet-1K ([Swin-T-IN1K](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth), [Swin-S-IN1K](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth), [Swin-B-IN1K](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224.pth)) and ImageNet-22K ([Swin-B-IN22K](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth), [Swin-L-IN22K](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22k.pth)) are provided.
2. The supported code and models for ImageNet-1K image classification, COCO object detection and ADE20K semantic segmentation are provided.
3. The cuda kernel implementation for the [local relation layer](https://arxiv.org/pdf/1904.11491.pdf) is provided in branch [LR-Net](https://github.com/microsoft/Swin-Transformer/tree/LR-Net).
## Introduction
**Swin Transformer** (the name `Swin` stands for **S**hifted **win**dow) is initially described in [arxiv](https://arxiv.org/abs/2103.14030), which capably serves as a
general-purpose backbone for computer vision. It is basically a hierarchical Transformer whose representation is
computed with shifted windows. The shifted windowing scheme brings greater efficiency by limiting self-attention
computation to non-overlapping local windows while also allowing for cross-window connection.
Swin Transformer achieves strong performance on COCO object detection (`58.7 box AP` and `51.1 mask AP` on test-dev) and
ADE20K semantic segmentation (`53.5 mIoU` on val), surpassing previous models by a large margin.
## Main Results on ImageNet with Pretrained Models
**ImageNet-1K and ImageNet-22K Pretrained Swin-V1 Models**
| name | pretrain | resolution |acc@1 | acc@5 | #params | FLOPs | FPS| 22K model | 1K model |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |:---: |:---: |
| Swin-T | ImageNet-1K | 224x224 | 81.2 | 95.5 | 28M | 4.5G | 755 | - | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth)/[baidu](https://pan.baidu.com/s/156nWJy4Q28rDlrX-rRbI3w)/[config](configs/swin/swin_tiny_patch4_window7_224.yaml)/[log](https://github.com/SwinTransformer/storage/files/7745562/log_swin_tiny_patch4_window7_224.txt) |
| Swin-S | ImageNet-1K | 224x224 | 83.2 | 96.2 | 50M | 8.7G | 437 | - | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth)/[baidu](https://pan.baidu.com/s/1KFjpj3Efey3LmtE1QqPeQg)/[config](configs/swin/swin_small_patch4_window7_224.yaml)/[log](https://github.com/SwinTransformer/storage/files/7745563/log_swin_small_patch4_window7_224.txt) |
| Swin-B | ImageNet-1K | 224x224 | 83.5 | 96.5 | 88M | 15.4G | 278 | - | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224.pth)/[baidu](https://pan.baidu.com/s/16bqCTEc70nC_isSsgBSaqQ)/[config](configs/swin/swin_base_patch4_window7_224.yaml)/[log](https://github.com/SwinTransformer/storage/files/7745564/log_swin_base_patch4_window7_224.txt) |
| Swin-B | ImageNet-1K | 384x384 | 84.5 | 97.0 | 88M | 47.1G | 85 | - | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384.pth)/[baidu](https://pan.baidu.com/s/1xT1cu740-ejW7htUdVLnmw)/[config](configs/swin/swin_base_patch4_window12_384_finetune.yaml) |
| Swin-T | ImageNet-22K | 224x224 | 80.9 | 96.0 | 28M | 4.5G | 755 | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_tiny_patch4_window7_224_22k.pth)/[baidu](https://pan.baidu.com/s/1vct0VYwwQQ8PYkBjwSSBZQ?pwd=swin)/[config](configs/swin/swin_tiny_patch4_window7_224_22k.yaml) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_tiny_patch4_window7_224_22kto1k_finetune.pth)/[baidu](https://pan.baidu.com/s/1K0OO-nGZDPkR8fm_r83e8Q?pwd=swin)/[config](configs/swin/swin_tiny_patch4_window7_224_22kto1k_finetune.yaml) |
| Swin-S | ImageNet-22K | 224x224 | 83.2 | 97.0 | 50M | 8.7G | 437 | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_small_patch4_window7_224_22k.pth)/[baidu](https://pan.baidu.com/s/11NC1xdT5BAGBgazdTme5Sg?pwd=swin)/[config](configs/swin/swin_small_patch4_window7_224_22k.yaml) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_small_patch4_window7_224_22kto1k_finetune.pth)/[baidu](https://pan.baidu.com/s/10RFVfjQJhwPfeHrmxQUaLw?pwd=swin)/[config](configs/swin/swin_small_patch4_window7_224_22kto1k_finetune.yaml) |
| Swin-B | ImageNet-22K | 224x224 | 85.2 | 97.5 | 88M | 15.4G | 278 | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth)/[baidu](https://pan.baidu.com/s/1y1Ec3UlrKSI8IMtEs-oBXA)/[config](configs/swin/swin_base_patch4_window7_224_22k.yaml) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22kto1k.pth)/[baidu](https://pan.baidu.com/s/1n_wNkcbRxVXit8r_KrfAVg)/[config](configs/swin/swin_base_patch4_window7_224_22kto1k_finetune.yaml) |
| Swin-B | ImageNet-22K | 384x384 | 86.4 | 98.0 | 88M | 47.1G | 85 | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth)/[baidu](https://pan.baidu.com/s/1vwJxnJcVqcLZAw9HaqiR6g) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22kto1k.pth)/[baidu](https://pan.baidu.com/s/1caKTSdoLJYoi4WBcnmWuWg)/[config](configs/swin/swin_base_patch4_window12_384_22kto1k_finetune.yaml) |
| Swin-L | ImageNet-22K | 224x224 | 86.3 | 97.9 | 197M | 34.5G | 141 | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22k.pth)/[baidu](https://pan.baidu.com/s/1pws3rOTFuOebBYP3h6Kx8w)/[config](configs/swin/swin_large_patch4_window7_224_22k.yaml) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22kto1k.pth)/[baidu](https://pan.baidu.com/s/1NkQApMWUhxBGjk1ne6VqBQ)/[config](configs/swin/swin_large_patch4_window7_224_22kto1k_finetune.yaml) |
| Swin-L | ImageNet-22K | 384x384 | 87.3 | 98.2 | 197M | 103.9G | 42 | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth)/[baidu](https://pan.baidu.com/s/1sl7o_bJA143OD7UqSLAMoA) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22kto1k.pth)/[baidu](https://pan.baidu.com/s/1X0FLHQyPOC6Kmv2CmgxJvA)/[config](configs/swin/swin_large_patch4_window12_384_22kto1k_finetune.yaml) |
**ImageNet-1K and ImageNet-22K Pretrained Swin-V2 Models**
| name | pretrain | resolution | window |acc@1 | acc@5 | #params | FLOPs | FPS |22K model | 1K model |
|:---------------------:| :---: | :---: | :---: | :---: | :---: | :---: | :---: |:---:|:---: |:---: |
| SwinV2-T | ImageNet-1K | 256x256 | 8x8 | 81.8 | 95.9 | 28M | 5.9G | 572 | - | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window8_256.pth)/[baidu](https://pan.baidu.com/s/1RzLkAH_5OtfRCJe6Vlg6rg?pwd=swin)/[config](configs/swinv2/swinv2_tiny_patch4_window8_256.yaml) |
| SwinV2-S | ImageNet-1K | 256x256 | 8x8 | 83.7 | 96.6 | 50M | 11.5G | 327 | - | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_small_patch4_window8_256.pth)/[baidu](https://pan.baidu.com/s/195PdA41szEduW3jEtRSa4Q?pwd=swin)/[config](configs/swinv2/swinv2_small_patch4_window8_256.yaml) |
| SwinV2-B | ImageNet-1K | 256x256 | 8x8 | 84.2 | 96.9 | 88M | 20.3G | 217 | - | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window8_256.pth)/[baidu](https://pan.baidu.com/s/18AfMSz3dPyzIvP1dKuERvQ?pwd=swin)/[config](configs/swinv2/swinv2_base_patch4_window8_256.yaml) |
| SwinV2-T | ImageNet-1K | 256x256 | 16x16 | 82.8 | 96.2 | 28M | 6.6G | 437 | - | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window16_256.pth)/[baidu](https://pan.baidu.com/s/1dyK3cK9Xipmv6RnTtrPocw?pwd=swin)/[config](configs/swinv2/swinv2_tiny_patch4_window16_256.yaml) |
| SwinV2-S | ImageNet-1K | 256x256 | 16x16 | 84.1 | 96.8 | 50M | 12.6G | 257 | - | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_small_patch4_window16_256.pth)/[baidu](https://pan.baidu.com/s/1ZIPiSfWNKTPp821Ka-Mifw?pwd=swin)/[config](configs/swinv2/swinv2_small_patch4_window16_256.yaml) |
| SwinV2-B | ImageNet-1K | 256x256 | 16x16 | 84.6 | 97.0 | 88M | 21.8G | 174 | - | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window16_256.pth)/[baidu](https://pan.baidu.com/s/1dlDQGn8BXCmnh7wQSM5Nhw?pwd=swin)/[config](configs/swinv2/swinv2_base_patch4_window16_256.yaml) |
| SwinV2-B<sup>\*</sup> | ImageNet-22K | 256x256 | 16x16 | 86.2 | 97.9 | 88M | 21.8G | 174 | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12_192_22k.pth)/[baidu](https://pan.baidu.com/s/1Xc2rsSsRQz_sy5mjgfxrMQ?pwd=swin)/[config](configs/swinv2/swinv2_base_patch4_window12_192_22k.yaml) | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to16_192to256_22kto1k_ft.pth)/[baidu](https://pan.baidu.com/s/1sgstld4MgGsZxhUAW7MlmQ?pwd=swin)/[config](configs/swinv2/swinv2_base_patch4_window12to16_192to256_22kto1k_ft.yaml) |
| SwinV2-B<sup>\*</sup> | ImageNet-22K | 384x384 | 24x24 | 87.1 | 98.2 | 88M | 54.7G | 57 | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12_192_22k.pth)/[baidu](https://pan.baidu.com/s/1Xc2rsSsRQz_sy5mjgfxrMQ?pwd=swin)/[config](configs/swinv2/swinv2_base_patch4_window12_192_22k.yaml) | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to24_192to384_22kto1k_ft.pth)/[baidu](https://pan.baidu.com/s/17u3sEQaUYlvfL195rrORzQ?pwd=swin)/[config](configs/swinv2/swinv2_base_patch4_window12to24_192to384_22kto1k_ft.yaml) |
| SwinV2-L<sup>\*</sup> | ImageNet-22K | 256x256 | 16x16 | 86.9 | 98.0 | 197M | 47.5G | 95 | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12_192_22k.pth)/[baidu](https://pan.baidu.com/s/11PhCV7qAGXtZ8dXNgyiGOw?pwd=swin)/[config](configs/swinv2/swinv2_large_patch4_window12_192_22k.yaml) | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to16_192to256_22kto1k_ft.pth)/[baidu](https://pan.baidu.com/s/1pqp31N80qIWjFPbudzB6Bw?pwd=swin)/[config](configs/swinv2/swinv2_large_patch4_window12to16_192to256_22kto1k_ft.yaml) |
| SwinV2-L<sup>\*</sup> | ImageNet-22K | 384x384 | 24x24 | 87.6 | 98.3 | 197M | 115.4G | 33 | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12_192_22k.pth)/[baidu](https://pan.baidu.com/s/11PhCV7qAGXtZ8dXNgyiGOw?pwd=swin)/[config](configs/swinv2/swinv2_large_patch4_window12_192_22k.yaml) | [github](https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to24_192to384_22kto1k_ft.pth)/[baidu](https://pan.baidu.com/s/13URdNkygr3Xn0N3e6IwjgA?pwd=swin)/[config](configs/swinv2/swinv2_large_patch4_window12to24_192to384_22kto1k_ft.yaml) |
Note:
- SwinV2-B<sup>\*</sup> (SwinV2-L<sup>\*</sup>) with input resolution of 256x256 and 384x384 both fine-tuned from the same pre-training model using a smaller input resolution of 192x192.
- SwinV2-B<sup>\*</sup> (384x384) achieves 78.08 acc@1 on ImageNet-1K-V2 while SwinV2-L<sup>\*</sup> (384x384) achieves 78.31.
**ImageNet-1K Pretrained Swin MLP Models**
| name | pretrain | resolution |acc@1 | acc@5 | #params | FLOPs | FPS | 1K model |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| [Mixer-B/16](https://arxiv.org/pdf/2105.01601.pdf) | ImageNet-1K | 224x224 | 76.4 | - | 59M | 12.7G | - | [official repo](https://github.com/google-research/vision_transformer) |
| [ResMLP-S24](https://arxiv.org/abs/2105.03404) | ImageNet-1K | 224x224 | 79.4 | - | 30M | 6.0G | 715 | [timm](https://github.com/rwightman/pytorch-image-models) |
| [ResMLP-B24](https://arxiv.org/abs/2105.03404) | ImageNet-1K | 224x224 | 81.0 | - | 116M | 23.0G | 231 | [timm](https://github.com/rwightman/pytorch-image-models) |
| Swin-T/C24 | ImageNet-1K | 256x256 | 81.6 | 95.7 | 28M | 5.9G | 563 | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.5/swin_tiny_c24_patch4_window8_256.pth)/[baidu](https://pan.baidu.com/s/17k-7l6Sxt7uZ7IV0f26GNQ)/[config](configs/swin/swin_tiny_c24_patch4_window8_256.yaml) |
| SwinMLP-T/C24 | ImageNet-1K | 256x256 | 79.4 | 94.6 | 20M | 4.0G | 807 | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.5/swin_mlp_tiny_c24_patch4_window8_256.pth)/[baidu](https://pan.baidu.com/s/1Sa4vP5R0M2RjfIe9HIga-Q)/[config](configs/swin/swin_mlp_tiny_c24_patch4_window8_256.yaml) |
| SwinMLP-T/C12 | ImageNet-1K | 256x256 | 79.6 | 94.7 | 21M | 4.0G | 792 | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.5/swin_mlp_tiny_c12_patch4_window8_256.pth)/[baidu](https://pan.baidu.com/s/1mM9J2_DEVZHUB5ASIpFl0w)/[config](configs/swin/swin_mlp_tiny_c12_patch4_window8_256.yaml) |
| SwinMLP-T/C6 | ImageNet-1K | 256x256 | 79.7 | 94.9 | 23M | 4.0G | 766 | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.5/swin_mlp_tiny_c6_patch4_window8_256.pth)/[baidu](https://pan.baidu.com/s/1hUTYVT2W1CsjICw-3W-Vjg)/[config](configs/swin/swin_mlp_tiny_c6_patch4_window8_256.yaml) |
| SwinMLP-B | ImageNet-1K | 224x224 | 81.3 | 95.3 | 61M | 10.4G | 409 | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.5/swin_mlp_base_patch4_window7_224.pth)/[baidu](https://pan.baidu.com/s/1zww3dnbX3GxNiGfb-GwyUg)/[config](configs/swin/swin_mlp_base_patch4_window7_224.yaml) |
Note: access code for `baidu` is `swin`. C24 means each head has 24 channels.
**ImageNet-22K Pretrained Swin-MoE Models**
- Please refer to [get_started](get_started.md#mixture-of-experts-support) for instructions on running Swin-MoE.
- Pretrained models for Swin-MoE can be found in [MODEL HUB](MODELHUB.md#imagenet-22k-pretrained-swin-moe-models)
## Main Results on Downstream Tasks
**COCO Object Detection (2017 val)**
| Backbone | Method | pretrain | Lr Schd | box mAP | mask mAP | #params | FLOPs |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| Swin-T | Mask R-CNN | ImageNet-1K | 3x | 46.0 | 41.6 | 48M | 267G |
| Swin-S | Mask R-CNN | ImageNet-1K | 3x | 48.5 | 43.3 | 69M | 359G |
| Swin-T | Cascade Mask R-CNN | ImageNet-1K | 3x | 50.4 | 43.7 | 86M | 745G |
| Swin-S | Cascade Mask R-CNN | ImageNet-1K | 3x | 51.9 | 45.0 | 107M | 838G |
| Swin-B | Cascade Mask R-CNN | ImageNet-1K | 3x | 51.9 | 45.0 | 145M | 982G |
| Swin-T | RepPoints V2 | ImageNet-1K | 3x | 50.0 | - | 45M | 283G |
| Swin-T | Mask RepPoints V2 | ImageNet-1K | 3x | 50.3 | 43.6 | 47M | 292G |
| Swin-B | HTC++ | ImageNet-22K | 6x | 56.4 | 49.1 | 160M | 1043G |
| Swin-L | HTC++ | ImageNet-22K | 3x | 57.1 | 49.5 | 284M | 1470G |
| Swin-L | HTC++<sup>*</sup> | ImageNet-22K | 3x | 58.0 | 50.4 | 284M | - |
Note: <sup>*</sup> indicates multi-scale testing.
**ADE20K Semantic Segmentation (val)**
| Backbone | Method | pretrain | Crop Size | Lr Schd | mIoU | mIoU (ms+flip) | #params | FLOPs |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| Swin-T | UPerNet | ImageNet-1K | 512x512 | 160K | 44.51 | 45.81 | 60M | 945G |
| Swin-S | UperNet | ImageNet-1K | 512x512 | 160K | 47.64 | 49.47 | 81M | 1038G |
| Swin-B | UperNet | ImageNet-1K | 512x512 | 160K | 48.13 | 49.72 | 121M | 1188G |
| Swin-B | UPerNet | ImageNet-22K | 640x640 | 160K | 50.04 | 51.66 | 121M | 1841G |
| Swin-L | UperNet | ImageNet-22K | 640x640 | 160K | 52.05 | 53.53 | 234M | 3230G |
## Citing Swin Transformer
```
@inproceedings{liu2021Swin,
title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
year={2021}
}
```
## Citing Local Relation Networks (the first full-attention visual backbone)
```
@inproceedings{hu2019local,
title={Local Relation Networks for Image Recognition},
author={Hu, Han and Zhang, Zheng and Xie, Zhenda and Lin, Stephen},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
pages={3464--3473},
year={2019}
}
```
## Citing Swin Transformer V2
```
@inproceedings{liu2021swinv2,
title={Swin Transformer V2: Scaling Up Capacity and Resolution},
author={Ze Liu and Han Hu and Yutong Lin and Zhuliang Yao and Zhenda Xie and Yixuan Wei and Jia Ning and Yue Cao and Zheng Zhang and Li Dong and Furu Wei and Baining Guo},
booktitle={International Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2022}
}
```
## Citing SimMIM (a self-supervised approach that enables SwinV2-G)
```
@inproceedings{xie2021simmim,
title={SimMIM: A Simple Framework for Masked Image Modeling},
author={Xie, Zhenda and Zhang, Zheng and Cao, Yue and Lin, Yutong and Bao, Jianmin and Yao, Zhuliang and Dai, Qi and Hu, Han},
booktitle={International Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2022}
}
```
## Citing SimMIM-data-scaling
```
@article{xie2022data,
title={On Data Scaling in Masked Image Modeling},
author={Xie, Zhenda and Zhang, Zheng and Cao, Yue and Lin, Yutong and Wei, Yixuan and Dai, Qi and Hu, Han},
journal={arXiv preprint arXiv:2206.04664},
year={2022}
}
```
## Citing Swin-MoE
```
@misc{hwang2022tutel,
title={Tutel: Adaptive Mixture-of-Experts at Scale},
author={Changho Hwang and Wei Cui and Yifan Xiong and Ziyue Yang and Ze Liu and Han Hu and Zilong Wang and Rafael Salas and Jithin Jose and Prabhat Ram and Joe Chau and Peng Cheng and Fan Yang and Mao Yang and Yongqiang Xiong},
year={2022},
eprint={2206.03382},
archivePrefix={arXiv}
}
```
## Getting Started
- For **Image Classification**, please see [get_started.md](get_started.md) for detailed instructions.
- For **Object Detection and Instance Segmentation**, please see [Swin Transformer for Object Detection](https://github.com/SwinTransformer/Swin-Transformer-Object-Detection).
- For **Semantic Segmentation**, please see [Swin Transformer for Semantic Segmentation](https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation).
- For **Self-Supervised Learning**, please see [Transformer-SSL](https://github.com/SwinTransformer/Transformer-SSL).
- For **Video Recognition**, please see [Video Swin Transformer](https://github.com/SwinTransformer/Video-Swin-Transformer).
## Third-party Usage and Experiments
***In this pargraph, we cross link third-party repositories which use Swin and report results. You can let us know by raising an issue***
(`Note please report accuracy numbers and provide trained models in your new repository to facilitate others to get sense of correctness and model behavior`)
[12/29/2022] Swin Transformers (V2) inference implemented in FasterTransformer: [FasterTransformer](https://github.com/NVIDIA/FasterTransformer/blob/main/docs/swin_guide.md)
[06/30/2022] Swin Transformers (V1) inference implemented in FasterTransformer: [FasterTransformer](https://github.com/NVIDIA/FasterTransformer/blob/main/docs/swin_guide.md)
[05/12/2022] Swin Transformers (V1) implemented in TensorFlow with the pre-trained parameters ported into them. Find the implementation,
TensorFlow weights, code example here in [this repository](https://github.com/sayakpaul/swin-transformers-tf/).
[04/06/2022] Swin Transformer for Audio Classification: [Hierarchical Token Semantic Audio Transformer](https://github.com/RetroCirce/HTS-Audio-Transformer).
[12/21/2021] Swin Transformer for StyleGAN: [StyleSwin](https://github.com/microsoft/StyleSwin)
[12/13/2021] Swin Transformer for Face Recognition: [FaceX-Zoo](https://github.com/JDAI-CV/FaceX-Zoo)
[08/29/2021] Swin Transformer for Image Restoration: [SwinIR](https://github.com/JingyunLiang/SwinIR)
[08/12/2021] Swin Transformer for person reID: [https://github.com/layumi/Person_reID_baseline_pytorch](https://github.com/layumi/Person_reID_baseline_pytorch)
[06/29/2021] Swin-Transformer in PaddleClas and inference based on whl package: [https://github.com/PaddlePaddle/PaddleClas](https://github.com/PaddlePaddle/PaddleClas)
[04/14/2021] Swin for RetinaNet in Detectron: https://github.com/xiaohu2015/SwinT_detectron2.
[04/16/2021] Included in a famous model zoo: https://github.com/rwightman/pytorch-image-models.
[04/20/2021] Swin-Transformer classifier inference using TorchServe: https://github.com/kamalkraj/Swin-Transformer-Serve
## Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
## Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must follow
[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
================================================
FILE: SECURITY.md
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## Reporting Security Issues
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## Policy
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================================================
FILE: SUPPORT.md
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# TODO: The maintainer of this repo has not yet edited this file
**REPO OWNER**: Do you want Customer Service & Support (CSS) support for this product/project?
- **No CSS support:** Fill out this template with information about how to file issues and get help.
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# Support
## How to file issues and get help
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================================================
FILE: config.py
================================================
# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------'
import os
import torch
import yaml
from yacs.config import CfgNode as CN
# pytorch major version (1.x or 2.x)
PYTORCH_MAJOR_VERSION = int(torch.__version__.split('.')[0])
_C = CN()
# Base config files
_C.BASE = ['']
# -----------------------------------------------------------------------------
# Data settings
# -----------------------------------------------------------------------------
_C.DATA = CN()
# Batch size for a single GPU, could be overwritten by command line argument
_C.DATA.BATCH_SIZE = 128
# Path to dataset, could be overwritten by command line argument
_C.DATA.DATA_PATH = ''
# Dataset name
_C.DATA.DATASET = 'imagenet'
# Input image size
_C.DATA.IMG_SIZE = 224
# Interpolation to resize image (random, bilinear, bicubic)
_C.DATA.INTERPOLATION = 'bicubic'
# Use zipped dataset instead of folder dataset
# could be overwritten by command line argument
_C.DATA.ZIP_MODE = False
# Cache Data in Memory, could be overwritten by command line argument
_C.DATA.CACHE_MODE = 'part'
# Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.
_C.DATA.PIN_MEMORY = True
# Number of data loading threads
_C.DATA.NUM_WORKERS = 8
# [SimMIM] Mask patch size for MaskGenerator
_C.DATA.MASK_PATCH_SIZE = 32
# [SimMIM] Mask ratio for MaskGenerator
_C.DATA.MASK_RATIO = 0.6
# -----------------------------------------------------------------------------
# Model settings
# -----------------------------------------------------------------------------
_C.MODEL = CN()
# Model type
_C.MODEL.TYPE = 'swin'
# Model name
_C.MODEL.NAME = 'swin_tiny_patch4_window7_224'
# Pretrained weight from checkpoint, could be imagenet22k pretrained weight
# could be overwritten by command line argument
_C.MODEL.PRETRAINED = ''
# Checkpoint to resume, could be overwritten by command line argument
_C.MODEL.RESUME = ''
# Number of classes, overwritten in data preparation
_C.MODEL.NUM_CLASSES = 1000
# Dropout rate
_C.MODEL.DROP_RATE = 0.0
# Drop path rate
_C.MODEL.DROP_PATH_RATE = 0.1
# Label Smoothing
_C.MODEL.LABEL_SMOOTHING = 0.1
# Swin Transformer parameters
_C.MODEL.SWIN = CN()
_C.MODEL.SWIN.PATCH_SIZE = 4
_C.MODEL.SWIN.IN_CHANS = 3
_C.MODEL.SWIN.EMBED_DIM = 96
_C.MODEL.SWIN.DEPTHS = [2, 2, 6, 2]
_C.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24]
_C.MODEL.SWIN.WINDOW_SIZE = 7
_C.MODEL.SWIN.MLP_RATIO = 4.
_C.MODEL.SWIN.QKV_BIAS = True
_C.MODEL.SWIN.QK_SCALE = None
_C.MODEL.SWIN.APE = False
_C.MODEL.SWIN.PATCH_NORM = True
# Swin Transformer V2 parameters
_C.MODEL.SWINV2 = CN()
_C.MODEL.SWINV2.PATCH_SIZE = 4
_C.MODEL.SWINV2.IN_CHANS = 3
_C.MODEL.SWINV2.EMBED_DIM = 96
_C.MODEL.SWINV2.DEPTHS = [2, 2, 6, 2]
_C.MODEL.SWINV2.NUM_HEADS = [3, 6, 12, 24]
_C.MODEL.SWINV2.WINDOW_SIZE = 7
_C.MODEL.SWINV2.MLP_RATIO = 4.
_C.MODEL.SWINV2.QKV_BIAS = True
_C.MODEL.SWINV2.APE = False
_C.MODEL.SWINV2.PATCH_NORM = True
_C.MODEL.SWINV2.PRETRAINED_WINDOW_SIZES = [0, 0, 0, 0]
# Swin Transformer MoE parameters
_C.MODEL.SWIN_MOE = CN()
_C.MODEL.SWIN_MOE.PATCH_SIZE = 4
_C.MODEL.SWIN_MOE.IN_CHANS = 3
_C.MODEL.SWIN_MOE.EMBED_DIM = 96
_C.MODEL.SWIN_MOE.DEPTHS = [2, 2, 6, 2]
_C.MODEL.SWIN_MOE.NUM_HEADS = [3, 6, 12, 24]
_C.MODEL.SWIN_MOE.WINDOW_SIZE = 7
_C.MODEL.SWIN_MOE.MLP_RATIO = 4.
_C.MODEL.SWIN_MOE.QKV_BIAS = True
_C.MODEL.SWIN_MOE.QK_SCALE = None
_C.MODEL.SWIN_MOE.APE = False
_C.MODEL.SWIN_MOE.PATCH_NORM = True
_C.MODEL.SWIN_MOE.MLP_FC2_BIAS = True
_C.MODEL.SWIN_MOE.INIT_STD = 0.02
_C.MODEL.SWIN_MOE.PRETRAINED_WINDOW_SIZES = [0, 0, 0, 0]
_C.MODEL.SWIN_MOE.MOE_BLOCKS = [[-1], [-1], [-1], [-1]]
_C.MODEL.SWIN_MOE.NUM_LOCAL_EXPERTS = 1
_C.MODEL.SWIN_MOE.TOP_VALUE = 1
_C.MODEL.SWIN_MOE.CAPACITY_FACTOR = 1.25
_C.MODEL.SWIN_MOE.COSINE_ROUTER = False
_C.MODEL.SWIN_MOE.NORMALIZE_GATE = False
_C.MODEL.SWIN_MOE.USE_BPR = True
_C.MODEL.SWIN_MOE.IS_GSHARD_LOSS = False
_C.MODEL.SWIN_MOE.GATE_NOISE = 1.0
_C.MODEL.SWIN_MOE.COSINE_ROUTER_DIM = 256
_C.MODEL.SWIN_MOE.COSINE_ROUTER_INIT_T = 0.5
_C.MODEL.SWIN_MOE.MOE_DROP = 0.0
_C.MODEL.SWIN_MOE.AUX_LOSS_WEIGHT = 0.01
# Swin MLP parameters
_C.MODEL.SWIN_MLP = CN()
_C.MODEL.SWIN_MLP.PATCH_SIZE = 4
_C.MODEL.SWIN_MLP.IN_CHANS = 3
_C.MODEL.SWIN_MLP.EMBED_DIM = 96
_C.MODEL.SWIN_MLP.DEPTHS = [2, 2, 6, 2]
_C.MODEL.SWIN_MLP.NUM_HEADS = [3, 6, 12, 24]
_C.MODEL.SWIN_MLP.WINDOW_SIZE = 7
_C.MODEL.SWIN_MLP.MLP_RATIO = 4.
_C.MODEL.SWIN_MLP.APE = False
_C.MODEL.SWIN_MLP.PATCH_NORM = True
# [SimMIM] Norm target during training
_C.MODEL.SIMMIM = CN()
_C.MODEL.SIMMIM.NORM_TARGET = CN()
_C.MODEL.SIMMIM.NORM_TARGET.ENABLE = False
_C.MODEL.SIMMIM.NORM_TARGET.PATCH_SIZE = 47
# -----------------------------------------------------------------------------
# Training settings
# -----------------------------------------------------------------------------
_C.TRAIN = CN()
_C.TRAIN.START_EPOCH = 0
_C.TRAIN.EPOCHS = 300
_C.TRAIN.WARMUP_EPOCHS = 20
_C.TRAIN.WEIGHT_DECAY = 0.05
_C.TRAIN.BASE_LR = 5e-4
_C.TRAIN.WARMUP_LR = 5e-7
_C.TRAIN.MIN_LR = 5e-6
# Clip gradient norm
_C.TRAIN.CLIP_GRAD = 5.0
# Auto resume from latest checkpoint
_C.TRAIN.AUTO_RESUME = True
# Gradient accumulation steps
# could be overwritten by command line argument
_C.TRAIN.ACCUMULATION_STEPS = 1
# Whether to use gradient checkpointing to save memory
# could be overwritten by command line argument
_C.TRAIN.USE_CHECKPOINT = False
# LR scheduler
_C.TRAIN.LR_SCHEDULER = CN()
_C.TRAIN.LR_SCHEDULER.NAME = 'cosine'
# Epoch interval to decay LR, used in StepLRScheduler
_C.TRAIN.LR_SCHEDULER.DECAY_EPOCHS = 30
# LR decay rate, used in StepLRScheduler
_C.TRAIN.LR_SCHEDULER.DECAY_RATE = 0.1
# warmup_prefix used in CosineLRScheduler
_C.TRAIN.LR_SCHEDULER.WARMUP_PREFIX = True
# [SimMIM] Gamma / Multi steps value, used in MultiStepLRScheduler
_C.TRAIN.LR_SCHEDULER.GAMMA = 0.1
_C.TRAIN.LR_SCHEDULER.MULTISTEPS = []
# Optimizer
_C.TRAIN.OPTIMIZER = CN()
_C.TRAIN.OPTIMIZER.NAME = 'adamw'
# Optimizer Epsilon
_C.TRAIN.OPTIMIZER.EPS = 1e-8
# Optimizer Betas
_C.TRAIN.OPTIMIZER.BETAS = (0.9, 0.999)
# SGD momentum
_C.TRAIN.OPTIMIZER.MOMENTUM = 0.9
# [SimMIM] Layer decay for fine-tuning
_C.TRAIN.LAYER_DECAY = 1.0
# MoE
_C.TRAIN.MOE = CN()
# Only save model on master device
_C.TRAIN.MOE.SAVE_MASTER = False
# -----------------------------------------------------------------------------
# Augmentation settings
# -----------------------------------------------------------------------------
_C.AUG = CN()
# Color jitter factor
_C.AUG.COLOR_JITTER = 0.4
# Use AutoAugment policy. "v0" or "original"
_C.AUG.AUTO_AUGMENT = 'rand-m9-mstd0.5-inc1'
# Random erase prob
_C.AUG.REPROB = 0.25
# Random erase mode
_C.AUG.REMODE = 'pixel'
# Random erase count
_C.AUG.RECOUNT = 1
# Mixup alpha, mixup enabled if > 0
_C.AUG.MIXUP = 0.8
# Cutmix alpha, cutmix enabled if > 0
_C.AUG.CUTMIX = 1.0
# Cutmix min/max ratio, overrides alpha and enables cutmix if set
_C.AUG.CUTMIX_MINMAX = None
# Probability of performing mixup or cutmix when either/both is enabled
_C.AUG.MIXUP_PROB = 1.0
# Probability of switching to cutmix when both mixup and cutmix enabled
_C.AUG.MIXUP_SWITCH_PROB = 0.5
# How to apply mixup/cutmix params. Per "batch", "pair", or "elem"
_C.AUG.MIXUP_MODE = 'batch'
# -----------------------------------------------------------------------------
# Testing settings
# -----------------------------------------------------------------------------
_C.TEST = CN()
# Whether to use center crop when testing
_C.TEST.CROP = True
# Whether to use SequentialSampler as validation sampler
_C.TEST.SEQUENTIAL = False
_C.TEST.SHUFFLE = False
# -----------------------------------------------------------------------------
# Misc
# -----------------------------------------------------------------------------
# [SimMIM] Whether to enable pytorch amp, overwritten by command line argument
_C.ENABLE_AMP = False
# Enable Pytorch automatic mixed precision (amp).
_C.AMP_ENABLE = True
# [Deprecated] Mixed precision opt level of apex, if O0, no apex amp is used ('O0', 'O1', 'O2')
_C.AMP_OPT_LEVEL = ''
# Path to output folder, overwritten by command line argument
_C.OUTPUT = ''
# Tag of experiment, overwritten by command line argument
_C.TAG = 'default'
# Frequency to save checkpoint
_C.SAVE_FREQ = 1
# Frequency to logging info
_C.PRINT_FREQ = 10
# Fixed random seed
_C.SEED = 0
# Perform evaluation only, overwritten by command line argument
_C.EVAL_MODE = False
# Test throughput only, overwritten by command line argument
_C.THROUGHPUT_MODE = False
# local rank for DistributedDataParallel, given by command line argument
_C.LOCAL_RANK = 0
# for acceleration
_C.FUSED_WINDOW_PROCESS = False
_C.FUSED_LAYERNORM = False
def _update_config_from_file(config, cfg_file):
config.defrost()
with open(cfg_file, 'r') as f:
yaml_cfg = yaml.load(f, Loader=yaml.FullLoader)
for cfg in yaml_cfg.setdefault('BASE', ['']):
if cfg:
_update_config_from_file(
config, os.path.join(os.path.dirname(cfg_file), cfg)
)
print('=> merge config from {}'.format(cfg_file))
config.merge_from_file(cfg_file)
config.freeze()
def update_config(config, args):
_update_config_from_file(config, args.cfg)
config.defrost()
if args.opts:
config.merge_from_list(args.opts)
def _check_args(name):
if hasattr(args, name) and eval(f'args.{name}'):
return True
return False
# merge from specific arguments
if _check_args('batch_size'):
config.DATA.BATCH_SIZE = args.batch_size
if _check_args('data_path'):
config.DATA.DATA_PATH = args.data_path
if _check_args('zip'):
config.DATA.ZIP_MODE = True
if _check_args('cache_mode'):
config.DATA.CACHE_MODE = args.cache_mode
if _check_args('pretrained'):
config.MODEL.PRETRAINED = args.pretrained
if _check_args('resume'):
config.MODEL.RESUME = args.resume
if _check_args('accumulation_steps'):
config.TRAIN.ACCUMULATION_STEPS = args.accumulation_steps
if _check_args('use_checkpoint'):
config.TRAIN.USE_CHECKPOINT = True
if _check_args('amp_opt_level'):
print("[warning] Apex amp has been deprecated, please use pytorch amp instead!")
if args.amp_opt_level == 'O0':
config.AMP_ENABLE = False
if _check_args('disable_amp'):
config.AMP_ENABLE = False
if _check_args('output'):
config.OUTPUT = args.output
if _check_args('tag'):
config.TAG = args.tag
if _check_args('eval'):
config.EVAL_MODE = True
if _check_args('throughput'):
config.THROUGHPUT_MODE = True
# [SimMIM]
if _check_args('enable_amp'):
config.ENABLE_AMP = args.enable_amp
# for acceleration
if _check_args('fused_window_process'):
config.FUSED_WINDOW_PROCESS = True
if _check_args('fused_layernorm'):
config.FUSED_LAYERNORM = True
## Overwrite optimizer if not None, currently we use it for [fused_adam, fused_lamb]
if _check_args('optim'):
config.TRAIN.OPTIMIZER.NAME = args.optim
# set local rank for distributed training
if PYTORCH_MAJOR_VERSION == 1:
config.LOCAL_RANK = args.local_rank
else:
config.LOCAL_RANK = int(os.environ['LOCAL_RANK'])
# output folder
config.OUTPUT = os.path.join(config.OUTPUT, config.MODEL.NAME, config.TAG)
config.freeze()
def get_config(args):
"""Get a yacs CfgNode object with default values."""
# Return a clone so that the defaults will not be altered
# This is for the "local variable" use pattern
config = _C.clone()
update_config(config, args)
return config
================================================
FILE: configs/simmim/simmim_finetune__swin_base__img224_window7__800ep.yaml
================================================
MODEL:
TYPE: swin
NAME: simmim_finetune
DROP_PATH_RATE: 0.1
SWIN:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 7
DATA:
IMG_SIZE: 224
TRAIN:
EPOCHS: 100
WARMUP_EPOCHS: 20
BASE_LR: 1.25e-3
WARMUP_LR: 2.5e-7
MIN_LR: 2.5e-7
WEIGHT_DECAY: 0.05
LAYER_DECAY: 0.8
PRINT_FREQ: 100
SAVE_FREQ: 5
TAG: simmim_finetune__swin_base__img224_window7__800ep
================================================
FILE: configs/simmim/simmim_finetune__swinv2_base__img224_window14__800ep.yaml
================================================
MODEL:
TYPE: swinv2
NAME: simmim_finetune
DROP_PATH_RATE: 0.1
SWINV2:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 14
PRETRAINED_WINDOW_SIZES: [ 12, 12, 12, 6 ]
DATA:
IMG_SIZE: 224
TRAIN:
EPOCHS: 100
WARMUP_EPOCHS: 20
BASE_LR: 1.25e-3
WARMUP_LR: 2.5e-7
MIN_LR: 2.5e-7
WEIGHT_DECAY: 0.05
LAYER_DECAY: 0.75
PRINT_FREQ: 100
SAVE_FREQ: 5
TAG: simmim_finetune__swinv2_base__img224_window14__800ep
================================================
FILE: configs/simmim/simmim_pretrain__swin_base__img192_window6__800ep.yaml
================================================
MODEL:
TYPE: swin
NAME: simmim_pretrain
DROP_PATH_RATE: 0.0
SWIN:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 6
DATA:
IMG_SIZE: 192
MASK_PATCH_SIZE: 32
MASK_RATIO: 0.6
TRAIN:
EPOCHS: 800
WARMUP_EPOCHS: 10
BASE_LR: 1e-4
WARMUP_LR: 5e-7
WEIGHT_DECAY: 0.05
LR_SCHEDULER:
NAME: 'multistep'
GAMMA: 0.1
MULTISTEPS: [700,]
PRINT_FREQ: 100
SAVE_FREQ: 5
TAG: simmim_pretrain__swin_base__img192_window6__800ep
================================================
FILE: configs/simmim/simmim_pretrain__swinv2_base__img192_window12__800ep.yaml
================================================
MODEL:
TYPE: swinv2
NAME: simmim_pretrain
DROP_PATH_RATE: 0.1
SIMMIM:
NORM_TARGET:
ENABLE: True
PATCH_SIZE: 47
SWINV2:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 12
DATA:
IMG_SIZE: 192
MASK_PATCH_SIZE: 32
MASK_RATIO: 0.6
TRAIN:
EPOCHS: 800
WARMUP_EPOCHS: 10
BASE_LR: 1e-4
WARMUP_LR: 5e-7
WEIGHT_DECAY: 0.05
LR_SCHEDULER:
NAME: 'multistep'
GAMMA: 0.1
MULTISTEPS: [700,]
PRINT_FREQ: 100
SAVE_FREQ: 5
TAG: simmim_pretrain__swinv2_base__img192_window12__800ep
================================================
FILE: configs/swin/swin_base_patch4_window12_384_22kto1k_finetune.yaml
================================================
DATA:
IMG_SIZE: 384
MODEL:
TYPE: swin
NAME: swin_base_patch4_window12_384_22kto1k_finetune
DROP_PATH_RATE: 0.2
SWIN:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 12
TRAIN:
EPOCHS: 30
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 1e-8
BASE_LR: 2e-05
WARMUP_LR: 2e-08
MIN_LR: 2e-07
TEST:
CROP: False
================================================
FILE: configs/swin/swin_base_patch4_window12_384_finetune.yaml
================================================
DATA:
IMG_SIZE: 384
MODEL:
TYPE: swin
NAME: swin_base_patch4_window12_384_finetune
DROP_PATH_RATE: 0.5
SWIN:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 12
TRAIN:
EPOCHS: 30
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 1e-8
BASE_LR: 2e-05
WARMUP_LR: 2e-08
MIN_LR: 2e-07
TEST:
CROP: False
================================================
FILE: configs/swin/swin_base_patch4_window7_224.yaml
================================================
MODEL:
TYPE: swin
NAME: swin_base_patch4_window7_224
DROP_PATH_RATE: 0.5
SWIN:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 7
================================================
FILE: configs/swin/swin_base_patch4_window7_224_22k.yaml
================================================
DATA:
DATASET: imagenet22K
MODEL:
TYPE: swin
NAME: swin_base_patch4_window7_224_22k
DROP_PATH_RATE: 0.2
SWIN:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 7
TRAIN:
EPOCHS: 90
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 0.05
BASE_LR: 1.25e-4 # 4096 batch-size
WARMUP_LR: 1.25e-7
MIN_LR: 1.25e-6
================================================
FILE: configs/swin/swin_base_patch4_window7_224_22kto1k_finetune.yaml
================================================
MODEL:
TYPE: swin
NAME: swin_base_patch4_window7_224_22kto1k_finetune
DROP_PATH_RATE: 0.2
SWIN:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 7
TRAIN:
EPOCHS: 30
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 1e-8
BASE_LR: 2e-05
WARMUP_LR: 2e-08
MIN_LR: 2e-07
================================================
FILE: configs/swin/swin_large_patch4_window12_384_22kto1k_finetune.yaml
================================================
DATA:
IMG_SIZE: 384
MODEL:
TYPE: swin
NAME: swin_large_patch4_window12_384_22kto1k_finetune
DROP_PATH_RATE: 0.2
SWIN:
EMBED_DIM: 192
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 6, 12, 24, 48 ]
WINDOW_SIZE: 12
TRAIN:
EPOCHS: 30
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 1e-8
BASE_LR: 2e-05
WARMUP_LR: 2e-08
MIN_LR: 2e-07
TEST:
CROP: False
================================================
FILE: configs/swin/swin_large_patch4_window7_224_22k.yaml
================================================
DATA:
DATASET: imagenet22K
MODEL:
TYPE: swin
NAME: swin_large_patch4_window7_224_22k
DROP_PATH_RATE: 0.2
SWIN:
EMBED_DIM: 192
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 6, 12, 24, 48 ]
WINDOW_SIZE: 7
TRAIN:
EPOCHS: 90
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 0.05
BASE_LR: 1.25e-4 # 4096 batch-size
WARMUP_LR: 1.25e-7
MIN_LR: 1.25e-6
================================================
FILE: configs/swin/swin_large_patch4_window7_224_22kto1k_finetune.yaml
================================================
MODEL:
TYPE: swin
NAME: swin_large_patch4_window7_224_22kto1k_finetune
DROP_PATH_RATE: 0.2
SWIN:
EMBED_DIM: 192
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 6, 12, 24, 48 ]
WINDOW_SIZE: 7
TRAIN:
EPOCHS: 30
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 1e-8
BASE_LR: 2e-05
WARMUP_LR: 2e-08
MIN_LR: 2e-07
================================================
FILE: configs/swin/swin_small_patch4_window7_224.yaml
================================================
MODEL:
TYPE: swin
NAME: swin_small_patch4_window7_224
DROP_PATH_RATE: 0.3
SWIN:
EMBED_DIM: 96
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 3, 6, 12, 24 ]
WINDOW_SIZE: 7
================================================
FILE: configs/swin/swin_small_patch4_window7_224_22k.yaml
================================================
DATA:
DATASET: imagenet22K
MODEL:
TYPE: swin
NAME: swin_small_patch4_window7_224_22k
DROP_PATH_RATE: 0.2
SWIN:
EMBED_DIM: 96
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 3, 6, 12, 24 ]
WINDOW_SIZE: 7
TRAIN:
EPOCHS: 90
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 0.05
BASE_LR: 1.25e-4 # 4096 batch-size
WARMUP_LR: 1.25e-7
MIN_LR: 1.25e-6
================================================
FILE: configs/swin/swin_small_patch4_window7_224_22kto1k_finetune.yaml
================================================
MODEL:
TYPE: swin
NAME: swin_small_patch4_window7_224_22kto1k_finetune
DROP_PATH_RATE: 0.2
SWIN:
EMBED_DIM: 96
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 3, 6, 12, 24 ]
WINDOW_SIZE: 7
TRAIN:
EPOCHS: 30
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 1e-8
BASE_LR: 2e-05
WARMUP_LR: 2e-08
MIN_LR: 2e-07
================================================
FILE: configs/swin/swin_tiny_c24_patch4_window8_256.yaml
================================================
DATA:
IMG_SIZE: 256
MODEL:
TYPE: swin
NAME: swin_tiny_c24_patch4_window8_256
DROP_PATH_RATE: 0.2
SWIN:
EMBED_DIM: 96
DEPTHS: [ 2, 2, 6, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 8
================================================
FILE: configs/swin/swin_tiny_patch4_window7_224.yaml
================================================
MODEL:
TYPE: swin
NAME: swin_tiny_patch4_window7_224
DROP_PATH_RATE: 0.2
SWIN:
EMBED_DIM: 96
DEPTHS: [ 2, 2, 6, 2 ]
NUM_HEADS: [ 3, 6, 12, 24 ]
WINDOW_SIZE: 7
================================================
FILE: configs/swin/swin_tiny_patch4_window7_224_22k.yaml
================================================
DATA:
DATASET: imagenet22K
MODEL:
TYPE: swin
NAME: swin_tiny_patch4_window7_224_22k
DROP_PATH_RATE: 0.1
SWIN:
EMBED_DIM: 96
DEPTHS: [ 2, 2, 6, 2 ]
NUM_HEADS: [ 3, 6, 12, 24 ]
WINDOW_SIZE: 7
TRAIN:
EPOCHS: 90
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 0.05
BASE_LR: 1.25e-4 # 4096 batch-size
WARMUP_LR: 1.25e-7
MIN_LR: 1.25e-6
================================================
FILE: configs/swin/swin_tiny_patch4_window7_224_22kto1k_finetune.yaml
================================================
MODEL:
TYPE: swin
NAME: swin_tiny_patch4_window7_224_22kto1k_finetune
DROP_PATH_RATE: 0.1
SWIN:
EMBED_DIM: 96
DEPTHS: [ 2, 2, 6, 2 ]
NUM_HEADS: [ 3, 6, 12, 24 ]
WINDOW_SIZE: 7
TRAIN:
EPOCHS: 30
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 1e-8
BASE_LR: 2e-05
WARMUP_LR: 2e-08
MIN_LR: 2e-07
================================================
FILE: configs/swinmlp/swin_mlp_base_patch4_window7_224.yaml
================================================
MODEL:
TYPE: swin_mlp
NAME: swin_mlp_base_patch4_window7_224
DROP_PATH_RATE: 0.5
SWIN_MLP:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 7
================================================
FILE: configs/swinmlp/swin_mlp_tiny_c12_patch4_window8_256.yaml
================================================
DATA:
IMG_SIZE: 256
MODEL:
TYPE: swin_mlp
NAME: swin_mlp_tiny_c12_patch4_window8_256
DROP_PATH_RATE: 0.2
SWIN_MLP:
EMBED_DIM: 96
DEPTHS: [ 2, 2, 6, 2 ]
NUM_HEADS: [ 8, 16, 32, 64 ]
WINDOW_SIZE: 8
================================================
FILE: configs/swinmlp/swin_mlp_tiny_c24_patch4_window8_256.yaml
================================================
DATA:
IMG_SIZE: 256
MODEL:
TYPE: swin_mlp
NAME: swin_mlp_tiny_c24_patch4_window8_256
DROP_PATH_RATE: 0.2
SWIN_MLP:
EMBED_DIM: 96
DEPTHS: [ 2, 2, 6, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 8
================================================
FILE: configs/swinmlp/swin_mlp_tiny_c6_patch4_window8_256.yaml
================================================
DATA:
IMG_SIZE: 256
MODEL:
TYPE: swin_mlp
NAME: swin_mlp_tiny_c6_patch4_window8_256
DROP_PATH_RATE: 0.2
SWIN_MLP:
EMBED_DIM: 96
DEPTHS: [ 2, 2, 6, 2 ]
NUM_HEADS: [ 16, 32, 64, 128 ]
WINDOW_SIZE: 8
================================================
FILE: configs/swinmoe/swin_moe_base_patch4_window12_192_16expert_32gpu_22k.yaml
================================================
DATA:
DATASET: imagenet22K
IMG_SIZE: 192
MODEL:
TYPE: swin_moe
NAME: swin_moe_base_patch4_window12_192_16expert_32gpu_22k
DROP_PATH_RATE: 0.3
SWIN_MOE:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 12
MLP_FC2_BIAS: False
INIT_STD: 0.005
MOE_BLOCKS: [ [ -1 ], [ -1 ], [ 1, 3, 5, 7, 9, 11, 13, 15, 17 ], [ 1 ] ]
NUM_LOCAL_EXPERTS: -2
TOP_VALUE: 1
CAPACITY_FACTOR: 1.25
IS_GSHARD_LOSS: False
MOE_DROP: 0.1
AUX_LOSS_WEIGHT: 0.01
TRAIN:
EPOCHS: 90
WARMUP_EPOCHS: 10
WEIGHT_DECAY: 0.1
BASE_LR: 1.25e-4 # 4096 batch-size
WARMUP_LR: 1.25e-7
MIN_LR: 1.25e-6
CLIP_GRAD: 3.0
TEST:
SHUFFLE: True
================================================
FILE: configs/swinmoe/swin_moe_base_patch4_window12_192_32expert_32gpu_22k.yaml
================================================
DATA:
DATASET: imagenet22K
IMG_SIZE: 192
MODEL:
TYPE: swin_moe
NAME: swin_moe_base_patch4_window12_192_32expert_32gpu_22k
DROP_PATH_RATE: 0.3
SWIN_MOE:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 12
MLP_FC2_BIAS: False
INIT_STD: 0.005
MOE_BLOCKS: [ [ -1 ], [ -1 ], [ 1, 3, 5, 7, 9, 11, 13, 15, 17 ], [ 1 ] ]
NUM_LOCAL_EXPERTS: 1
TOP_VALUE: 1
CAPACITY_FACTOR: 1.25
IS_GSHARD_LOSS: False
MOE_DROP: 0.1
AUX_LOSS_WEIGHT: 0.01
TRAIN:
EPOCHS: 90
WARMUP_EPOCHS: 10
WEIGHT_DECAY: 0.1
BASE_LR: 1.25e-4 # 4096 batch-size
WARMUP_LR: 1.25e-7
MIN_LR: 1.25e-6
CLIP_GRAD: 3.0
TEST:
SHUFFLE: True
================================================
FILE: configs/swinmoe/swin_moe_base_patch4_window12_192_8expert_32gpu_22k.yaml
================================================
DATA:
DATASET: imagenet22K
IMG_SIZE: 192
MODEL:
TYPE: swin_moe
NAME: swin_moe_base_patch4_window12_192_8expert_32gpu_22k
DROP_PATH_RATE: 0.3
SWIN_MOE:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 12
MLP_FC2_BIAS: False
INIT_STD: 0.005
MOE_BLOCKS: [ [ -1 ], [ -1 ], [ 1, 3, 5, 7, 9, 11, 13, 15, 17 ], [ 1 ] ]
NUM_LOCAL_EXPERTS: -4
TOP_VALUE: 1
CAPACITY_FACTOR: 1.25
IS_GSHARD_LOSS: False
MOE_DROP: 0.1
AUX_LOSS_WEIGHT: 0.01
TRAIN:
EPOCHS: 90
WARMUP_EPOCHS: 10
WEIGHT_DECAY: 0.1
BASE_LR: 1.25e-4 # 4096 batch-size
WARMUP_LR: 1.25e-7
MIN_LR: 1.25e-6
CLIP_GRAD: 3.0
TEST:
SHUFFLE: True
================================================
FILE: configs/swinmoe/swin_moe_base_patch4_window12_192_cosine_router_32expert_32gpu_22k.yaml
================================================
DATA:
DATASET: imagenet22K
IMG_SIZE: 192
MODEL:
TYPE: swin_moe
NAME: swin_moe_base_patch4_window12_192_cosine_router_32expert_32gpu_22k
DROP_PATH_RATE: 0.3
SWIN_MOE:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 12
MLP_FC2_BIAS: False
INIT_STD: 0.005
MOE_BLOCKS: [ [ -1 ], [ -1 ], [ 1, 3, 5, 7, 9, 11, 13, 15, 17 ], [ 1 ] ]
NUM_LOCAL_EXPERTS: 1
TOP_VALUE: 1
CAPACITY_FACTOR: 1.25
COSINE_ROUTER: True
IS_GSHARD_LOSS: False
MOE_DROP: 0.1
AUX_LOSS_WEIGHT: 0.01
TRAIN:
EPOCHS: 90
WARMUP_EPOCHS: 10
WEIGHT_DECAY: 0.1
BASE_LR: 1.25e-4 # 4096 batch-size
WARMUP_LR: 1.25e-7
MIN_LR: 1.25e-6
CLIP_GRAD: 3.0
TEST:
SHUFFLE: True
================================================
FILE: configs/swinmoe/swin_moe_base_patch4_window12_192_densebaseline_22k.yaml
================================================
DATA:
DATASET: imagenet22K
IMG_SIZE: 192
MODEL:
TYPE: swin_moe
NAME: swin_moe_base_patch4_window12_192_densebaseline_22k
DROP_PATH_RATE: 0.2
SWIN_MOE:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 12
MLP_FC2_BIAS: False
MOE_BLOCKS: [ [ -1 ], [ -1 ], [ -1 ], [ -1 ] ]
TRAIN:
EPOCHS: 90
WARMUP_EPOCHS: 10
WEIGHT_DECAY: 0.1
BASE_LR: 1.25e-4 # 4096 batch-size
WARMUP_LR: 1.25e-7
MIN_LR: 1.25e-6
CLIP_GRAD: 3.0
MOE:
SAVE_MASTER: True
TEST:
SHUFFLE: True
================================================
FILE: configs/swinmoe/swin_moe_small_patch4_window12_192_16expert_32gpu_22k.yaml
================================================
DATA:
DATASET: imagenet22K
IMG_SIZE: 192
MODEL:
TYPE: swin_moe
NAME: swin_moe_small_patch4_window12_192_16expert_32gpu_22k
DROP_PATH_RATE: 0.2
SWIN_MOE:
EMBED_DIM: 96
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 3, 6, 12, 24 ]
WINDOW_SIZE: 12
MLP_FC2_BIAS: False
INIT_STD: 0.005
MOE_BLOCKS: [ [ -1 ], [ -1 ], [ 1, 3, 5, 7, 9, 11, 13, 15, 17 ], [ 1 ] ]
NUM_LOCAL_EXPERTS: -2
TOP_VALUE: 1
CAPACITY_FACTOR: 1.25
IS_GSHARD_LOSS: False
MOE_DROP: 0.1
AUX_LOSS_WEIGHT: 0.01
TRAIN:
EPOCHS: 90
WARMUP_EPOCHS: 10
WEIGHT_DECAY: 0.1
BASE_LR: 1.25e-4 # 4096 batch-size
WARMUP_LR: 1.25e-7
MIN_LR: 1.25e-6
CLIP_GRAD: 3.0
TEST:
SHUFFLE: True
================================================
FILE: configs/swinmoe/swin_moe_small_patch4_window12_192_32expert_32gpu_22k.yaml
================================================
DATA:
DATASET: imagenet22K
IMG_SIZE: 192
MODEL:
TYPE: swin_moe
NAME: swin_moe_small_patch4_window12_192_32expert_32gpu_22k
DROP_PATH_RATE: 0.2
SWIN_MOE:
EMBED_DIM: 96
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 3, 6, 12, 24 ]
WINDOW_SIZE: 12
MLP_FC2_BIAS: False
INIT_STD: 0.005
MOE_BLOCKS: [ [ -1 ], [ -1 ], [ 1, 3, 5, 7, 9, 11, 13, 15, 17 ], [ 1 ] ]
NUM_LOCAL_EXPERTS: 1
TOP_VALUE: 1
CAPACITY_FACTOR: 1.25
IS_GSHARD_LOSS: False
MOE_DROP: 0.1
AUX_LOSS_WEIGHT: 0.01
TRAIN:
EPOCHS: 90
WARMUP_EPOCHS: 10
WEIGHT_DECAY: 0.1
BASE_LR: 1.25e-4 # 4096 batch-size
WARMUP_LR: 1.25e-7
MIN_LR: 1.25e-6
CLIP_GRAD: 3.0
TEST:
SHUFFLE: True
================================================
FILE: configs/swinmoe/swin_moe_small_patch4_window12_192_64expert_64gpu_22k.yaml
================================================
DATA:
DATASET: imagenet22K
IMG_SIZE: 192
MODEL:
TYPE: swin_moe
NAME: swin_moe_small_patch4_window12_192_64expert_64gpu_22k
DROP_PATH_RATE: 0.2
SWIN_MOE:
EMBED_DIM: 96
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 3, 6, 12, 24 ]
WINDOW_SIZE: 12
MLP_FC2_BIAS: False
INIT_STD: 0.005
MOE_BLOCKS: [ [ -1 ], [ -1 ], [ 1, 3, 5, 7, 9, 11, 13, 15, 17 ], [ 1 ] ]
NUM_LOCAL_EXPERTS: 1
TOP_VALUE: 1
CAPACITY_FACTOR: 1.25
IS_GSHARD_LOSS: False
MOE_DROP: 0.1
AUX_LOSS_WEIGHT: 0.01
TRAIN:
EPOCHS: 90
WARMUP_EPOCHS: 10
WEIGHT_DECAY: 0.1
BASE_LR: 1.25e-4 # 4096 batch-size
WARMUP_LR: 1.25e-7
MIN_LR: 1.25e-6
CLIP_GRAD: 3.0
TEST:
SHUFFLE: True
================================================
FILE: configs/swinmoe/swin_moe_small_patch4_window12_192_8expert_32gpu_22k.yaml
================================================
DATA:
DATASET: imagenet22K
IMG_SIZE: 192
MODEL:
TYPE: swin_moe
NAME: swin_moe_small_patch4_window12_192_8expert_32gpu_22k
DROP_PATH_RATE: 0.2
SWIN_MOE:
EMBED_DIM: 96
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 3, 6, 12, 24 ]
WINDOW_SIZE: 12
MLP_FC2_BIAS: False
INIT_STD: 0.005
MOE_BLOCKS: [ [ -1 ], [ -1 ], [ 1, 3, 5, 7, 9, 11, 13, 15, 17 ], [ 1 ] ]
NUM_LOCAL_EXPERTS: -4
TOP_VALUE: 1
CAPACITY_FACTOR: 1.25
IS_GSHARD_LOSS: False
MOE_DROP: 0.1
AUX_LOSS_WEIGHT: 0.01
TRAIN:
EPOCHS: 90
WARMUP_EPOCHS: 10
WEIGHT_DECAY: 0.1
BASE_LR: 1.25e-4 # 4096 batch-size
WARMUP_LR: 1.25e-7
MIN_LR: 1.25e-6
CLIP_GRAD: 3.0
TEST:
SHUFFLE: True
================================================
FILE: configs/swinmoe/swin_moe_small_patch4_window12_192_cosine_router_32expert_32gpu_22k.yaml
================================================
DATA:
DATASET: imagenet22K
IMG_SIZE: 192
MODEL:
TYPE: swin_moe
NAME: swin_moe_small_patch4_window12_192_cosine_router_32expert_32gpu_22k
DROP_PATH_RATE: 0.2
SWIN_MOE:
EMBED_DIM: 96
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 3, 6, 12, 24 ]
WINDOW_SIZE: 12
MLP_FC2_BIAS: False
INIT_STD: 0.005
MOE_BLOCKS: [ [ -1 ], [ -1 ], [ 1, 3, 5, 7, 9, 11, 13, 15, 17 ], [ 1 ] ]
NUM_LOCAL_EXPERTS: 1
TOP_VALUE: 1
CAPACITY_FACTOR: 1.25
COSINE_ROUTER: True
IS_GSHARD_LOSS: False
MOE_DROP: 0.1
AUX_LOSS_WEIGHT: 0.01
TRAIN:
EPOCHS: 90
WARMUP_EPOCHS: 10
WEIGHT_DECAY: 0.1
BASE_LR: 1.25e-4 # 4096 batch-size
WARMUP_LR: 1.25e-7
MIN_LR: 1.25e-6
CLIP_GRAD: 3.0
TEST:
SHUFFLE: True
================================================
FILE: configs/swinmoe/swin_moe_small_patch4_window12_192_densebaseline_22k.yaml
================================================
DATA:
DATASET: imagenet22K
IMG_SIZE: 192
MODEL:
TYPE: swin_moe
NAME: swin_moe_small_patch4_window12_192_densebaseline_22k
DROP_PATH_RATE: 0.2
SWIN_MOE:
EMBED_DIM: 96
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 3, 6, 12, 24 ]
WINDOW_SIZE: 12
MLP_FC2_BIAS: False
MOE_BLOCKS: [ [ -1 ], [ -1 ], [ -1 ], [ -1 ] ]
TRAIN:
EPOCHS: 90
WARMUP_EPOCHS: 10
WEIGHT_DECAY: 0.1
BASE_LR: 1.25e-4 # 4096 batch-size
WARMUP_LR: 1.25e-7
MIN_LR: 1.25e-6
CLIP_GRAD: 3.0
MOE:
SAVE_MASTER: True
TEST:
SHUFFLE: True
================================================
FILE: configs/swinv2/swinv2_base_patch4_window12_192_22k.yaml
================================================
DATA:
DATASET: imagenet22K
IMG_SIZE: 192
MODEL:
TYPE: swinv2
NAME: swinv2_base_patch4_window12_192_22k
DROP_PATH_RATE: 0.2
SWINV2:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 12
TRAIN:
EPOCHS: 90
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 0.1
BASE_LR: 1.25e-4 # 4096 batch-size
WARMUP_LR: 1.25e-7
MIN_LR: 1.25e-6
================================================
FILE: configs/swinv2/swinv2_base_patch4_window12to16_192to256_22kto1k_ft.yaml
================================================
DATA:
IMG_SIZE: 256
MODEL:
TYPE: swinv2
NAME: swinv2_base_patch4_window12to16_192to256_22kto1k_ft
DROP_PATH_RATE: 0.2
SWINV2:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 16
PRETRAINED_WINDOW_SIZES: [ 12, 12, 12, 6 ]
TRAIN:
EPOCHS: 30
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 1e-8
BASE_LR: 2e-05
WARMUP_LR: 2e-08
MIN_LR: 2e-07
================================================
FILE: configs/swinv2/swinv2_base_patch4_window12to24_192to384_22kto1k_ft.yaml
================================================
DATA:
IMG_SIZE: 384
MODEL:
TYPE: swinv2
NAME: swinv2_base_patch4_window12to24_192to384_22kto1k_ft
DROP_PATH_RATE: 0.2
SWINV2:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 24
PRETRAINED_WINDOW_SIZES: [ 12, 12, 12, 6 ]
TRAIN:
EPOCHS: 30
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 1e-8
BASE_LR: 2e-05
WARMUP_LR: 2e-08
MIN_LR: 2e-07
TEST:
CROP: False
================================================
FILE: configs/swinv2/swinv2_base_patch4_window16_256.yaml
================================================
DATA:
IMG_SIZE: 256
MODEL:
TYPE: swinv2
NAME: swinv2_base_patch4_window16_256
DROP_PATH_RATE: 0.5
SWINV2:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 16
================================================
FILE: configs/swinv2/swinv2_base_patch4_window8_256.yaml
================================================
DATA:
IMG_SIZE: 256
MODEL:
TYPE: swinv2
NAME: swinv2_base_patch4_window8_256
DROP_PATH_RATE: 0.5
SWINV2:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 8
================================================
FILE: configs/swinv2/swinv2_large_patch4_window12_192_22k.yaml
================================================
DATA:
DATASET: imagenet22K
IMG_SIZE: 192
MODEL:
TYPE: swinv2
NAME: swinv2_large_patch4_window12_192_22k
DROP_PATH_RATE: 0.2
SWINV2:
EMBED_DIM: 192
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 6, 12, 24, 48 ]
WINDOW_SIZE: 12
TRAIN:
EPOCHS: 90
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 0.1
BASE_LR: 1.25e-4 # 4096 batch-size
WARMUP_LR: 1.25e-7
MIN_LR: 1.25e-6
================================================
FILE: configs/swinv2/swinv2_large_patch4_window12to16_192to256_22kto1k_ft.yaml
================================================
DATA:
IMG_SIZE: 256
MODEL:
TYPE: swinv2
NAME: swinv2_base_patch4_window12to16_192to256_22kto1k_ft
DROP_PATH_RATE: 0.2
SWINV2:
EMBED_DIM: 192
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 6, 12, 24, 48 ]
WINDOW_SIZE: 16
PRETRAINED_WINDOW_SIZES: [ 12, 12, 12, 6 ]
TRAIN:
EPOCHS: 30
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 1e-8
BASE_LR: 2e-05
WARMUP_LR: 2e-08
MIN_LR: 2e-07
================================================
FILE: configs/swinv2/swinv2_large_patch4_window12to24_192to384_22kto1k_ft.yaml
================================================
DATA:
IMG_SIZE: 384
MODEL:
TYPE: swinv2
NAME: swinv2_large_patch4_window12to24_192to384_22kto1k_ft
DROP_PATH_RATE: 0.2
SWINV2:
EMBED_DIM: 192
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 6, 12, 24, 48 ]
WINDOW_SIZE: 24
PRETRAINED_WINDOW_SIZES: [ 12, 12, 12, 6 ]
TRAIN:
EPOCHS: 30
WARMUP_EPOCHS: 5
WEIGHT_DECAY: 1e-8
BASE_LR: 2e-05
WARMUP_LR: 2e-08
MIN_LR: 2e-07
TEST:
CROP: False
================================================
FILE: configs/swinv2/swinv2_small_patch4_window16_256.yaml
================================================
DATA:
IMG_SIZE: 256
MODEL:
TYPE: swinv2
NAME: swinv2_small_patch4_window16_256
DROP_PATH_RATE: 0.3
SWINV2:
EMBED_DIM: 96
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 3, 6, 12, 24 ]
WINDOW_SIZE: 16
================================================
FILE: configs/swinv2/swinv2_small_patch4_window8_256.yaml
================================================
DATA:
IMG_SIZE: 256
MODEL:
TYPE: swinv2
NAME: swinv2_small_patch4_window8_256
DROP_PATH_RATE: 0.3
SWINV2:
EMBED_DIM: 96
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 3, 6, 12, 24 ]
WINDOW_SIZE: 8
================================================
FILE: configs/swinv2/swinv2_tiny_patch4_window16_256.yaml
================================================
DATA:
IMG_SIZE: 256
MODEL:
TYPE: swinv2
NAME: swinv2_tiny_patch4_window16_256
DROP_PATH_RATE: 0.2
SWINV2:
EMBED_DIM: 96
DEPTHS: [ 2, 2, 6, 2 ]
NUM_HEADS: [ 3, 6, 12, 24 ]
WINDOW_SIZE: 16
================================================
FILE: configs/swinv2/swinv2_tiny_patch4_window8_256.yaml
================================================
DATA:
IMG_SIZE: 256
MODEL:
TYPE: swinv2
NAME: swinv2_tiny_patch4_window8_256
DROP_PATH_RATE: 0.2
SWINV2:
EMBED_DIM: 96
DEPTHS: [ 2, 2, 6, 2 ]
NUM_HEADS: [ 3, 6, 12, 24 ]
WINDOW_SIZE: 8
================================================
FILE: data/__init__.py
================================================
from .build import build_loader as _build_loader
from .data_simmim_pt import build_loader_simmim
from .data_simmim_ft import build_loader_finetune
def build_loader(config, simmim=False, is_pretrain=False):
if not simmim:
return _build_loader(config)
if is_pretrain:
return build_loader_simmim(config)
else:
return build_loader_finetune(config)
================================================
FILE: data/build.py
================================================
# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import os
import torch
import numpy as np
import torch.distributed as dist
from torchvision import datasets, transforms
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.data import Mixup
from timm.data import create_transform
from .cached_image_folder import CachedImageFolder
from .imagenet22k_dataset import IN22KDATASET
from .samplers import SubsetRandomSampler
try:
from torchvision.transforms import InterpolationMode
def _pil_interp(method):
if method == 'bicubic':
return InterpolationMode.BICUBIC
elif method == 'lanczos':
return InterpolationMode.LANCZOS
elif method == 'hamming':
return InterpolationMode.HAMMING
else:
# default bilinear, do we want to allow nearest?
return InterpolationMode.BILINEAR
import timm.data.transforms as timm_transforms
timm_transforms._pil_interp = _pil_interp
except:
from timm.data.transforms import _pil_interp
def build_loader(config):
config.defrost()
dataset_train, config.MODEL.NUM_CLASSES = build_dataset(is_train=True, config=config)
config.freeze()
print(f"local rank {config.LOCAL_RANK} / global rank {dist.get_rank()} successfully build train dataset")
dataset_val, _ = build_dataset(is_train=False, config=config)
print(f"local rank {config.LOCAL_RANK} / global rank {dist.get_rank()} successfully build val dataset")
num_tasks = dist.get_world_size()
global_rank = dist.get_rank()
if config.DATA.ZIP_MODE and config.DATA.CACHE_MODE == 'part':
indices = np.arange(dist.get_rank(), len(dataset_train), dist.get_world_size())
sampler_train = SubsetRandomSampler(indices)
else:
sampler_train = torch.utils.data.DistributedSampler(
dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
)
if config.TEST.SEQUENTIAL:
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
else:
sampler_val = torch.utils.data.distributed.DistributedSampler(
dataset_val, shuffle=config.TEST.SHUFFLE
)
data_loader_train = torch.utils.data.DataLoader(
dataset_train, sampler=sampler_train,
batch_size=config.DATA.BATCH_SIZE,
num_workers=config.DATA.NUM_WORKERS,
pin_memory=config.DATA.PIN_MEMORY,
drop_last=True,
)
data_loader_val = torch.utils.data.DataLoader(
dataset_val, sampler=sampler_val,
batch_size=config.DATA.BATCH_SIZE,
shuffle=False,
num_workers=config.DATA.NUM_WORKERS,
pin_memory=config.DATA.PIN_MEMORY,
drop_last=False
)
# setup mixup / cutmix
mixup_fn = None
mixup_active = config.AUG.MIXUP > 0 or config.AUG.CUTMIX > 0. or config.AUG.CUTMIX_MINMAX is not None
if mixup_active:
mixup_fn = Mixup(
mixup_alpha=config.AUG.MIXUP, cutmix_alpha=config.AUG.CUTMIX, cutmix_minmax=config.AUG.CUTMIX_MINMAX,
prob=config.AUG.MIXUP_PROB, switch_prob=config.AUG.MIXUP_SWITCH_PROB, mode=config.AUG.MIXUP_MODE,
label_smoothing=config.MODEL.LABEL_SMOOTHING, num_classes=config.MODEL.NUM_CLASSES)
return dataset_train, dataset_val, data_loader_train, data_loader_val, mixup_fn
def build_dataset(is_train, config):
transform = build_transform(is_train, config)
if config.DATA.DATASET == 'imagenet':
prefix = 'train' if is_train else 'val'
if config.DATA.ZIP_MODE:
ann_file = prefix + "_map.txt"
prefix = prefix + ".zip@/"
dataset = CachedImageFolder(config.DATA.DATA_PATH, ann_file, prefix, transform,
cache_mode=config.DATA.CACHE_MODE if is_train else 'part')
else:
root = os.path.join(config.DATA.DATA_PATH, prefix)
dataset = datasets.ImageFolder(root, transform=transform)
nb_classes = 1000
elif config.DATA.DATASET == 'imagenet22K':
prefix = 'ILSVRC2011fall_whole'
if is_train:
ann_file = prefix + "_map_train.txt"
else:
ann_file = prefix + "_map_val.txt"
dataset = IN22KDATASET(config.DATA.DATA_PATH, ann_file, transform)
nb_classes = 21841
else:
raise NotImplementedError("We only support ImageNet Now.")
return dataset, nb_classes
def build_transform(is_train, config):
resize_im = config.DATA.IMG_SIZE > 32
if is_train:
# this should always dispatch to transforms_imagenet_train
transform = create_transform(
input_size=config.DATA.IMG_SIZE,
is_training=True,
color_jitter=config.AUG.COLOR_JITTER if config.AUG.COLOR_JITTER > 0 else None,
auto_augment=config.AUG.AUTO_AUGMENT if config.AUG.AUTO_AUGMENT != 'none' else None,
re_prob=config.AUG.REPROB,
re_mode=config.AUG.REMODE,
re_count=config.AUG.RECOUNT,
interpolation=config.DATA.INTERPOLATION,
)
if not resize_im:
# replace RandomResizedCropAndInterpolation with
# RandomCrop
transform.transforms[0] = transforms.RandomCrop(config.DATA.IMG_SIZE, padding=4)
return transform
t = []
if resize_im:
if config.TEST.CROP:
size = int((256 / 224) * config.DATA.IMG_SIZE)
t.append(
transforms.Resize(size, interpolation=_pil_interp(config.DATA.INTERPOLATION)),
# to maintain same ratio w.r.t. 224 images
)
t.append(transforms.CenterCrop(config.DATA.IMG_SIZE))
else:
t.append(
transforms.Resize((config.DATA.IMG_SIZE, config.DATA.IMG_SIZE),
interpolation=_pil_interp(config.DATA.INTERPOLATION))
)
t.append(transforms.ToTensor())
t.append(transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD))
return transforms.Compose(t)
================================================
FILE: data/cached_image_folder.py
================================================
# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import io
import os
import time
import torch.distributed as dist
import torch.utils.data as data
from PIL import Image
from .zipreader import is_zip_path, ZipReader
def has_file_allowed_extension(filename, extensions):
"""Checks if a file is an allowed extension.
Args:
filename (string): path to a file
Returns:
bool: True if the filename ends with a known image extension
"""
filename_lower = filename.lower()
return any(filename_lower.endswith(ext) for ext in extensions)
def find_classes(dir):
classes = [d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d))]
classes.sort()
class_to_idx = {classes[i]: i for i in range(len(classes))}
return classes, class_to_idx
def make_dataset(dir, class_to_idx, extensions):
images = []
dir = os.path.expanduser(dir)
for target in sorted(os.listdir(dir)):
d = os.path.join(dir, target)
if not os.path.isdir(d):
continue
for root, _, fnames in sorted(os.walk(d)):
for fname in sorted(fnames):
if has_file_allowed_extension(fname, extensions):
path = os.path.join(root, fname)
item = (path, class_to_idx[target])
images.append(item)
return images
def make_dataset_with_ann(ann_file, img_prefix, extensions):
images = []
with open(ann_file, "r") as f:
contents = f.readlines()
for line_str in contents:
path_contents = [c for c in line_str.split('\t')]
im_file_name = path_contents[0]
class_index = int(path_contents[1])
assert str.lower(os.path.splitext(im_file_name)[-1]) in extensions
item = (os.path.join(img_prefix, im_file_name), class_index)
images.append(item)
return images
class DatasetFolder(data.Dataset):
"""A generic data loader where the samples are arranged in this way: ::
root/class_x/xxx.ext
root/class_x/xxy.ext
root/class_x/xxz.ext
root/class_y/123.ext
root/class_y/nsdf3.ext
root/class_y/asd932_.ext
Args:
root (string): Root directory path.
loader (callable): A function to load a sample given its path.
extensions (list[string]): A list of allowed extensions.
transform (callable, optional): A function/transform that takes in
a sample and returns a transformed version.
E.g, ``transforms.RandomCrop`` for images.
target_transform (callable, optional): A function/transform that takes
in the target and transforms it.
Attributes:
samples (list): List of (sample path, class_index) tuples
"""
def __init__(self, root, loader, extensions, ann_file='', img_prefix='', transform=None, target_transform=None,
cache_mode="no"):
# image folder mode
if ann_file == '':
_, class_to_idx = find_classes(root)
samples = make_dataset(root, class_to_idx, extensions)
# zip mode
else:
samples = make_dataset_with_ann(os.path.join(root, ann_file),
os.path.join(root, img_prefix),
extensions)
if len(samples) == 0:
raise (RuntimeError("Found 0 files in subfolders of: " + root + "\n" +
"Supported extensions are: " + ",".join(extensions)))
self.root = root
self.loader = loader
self.extensions = extensions
self.samples = samples
self.labels = [y_1k for _, y_1k in samples]
self.classes = list(set(self.labels))
self.transform = transform
self.target_transform = target_transform
self.cache_mode = cache_mode
if self.cache_mode != "no":
self.init_cache()
def init_cache(self):
assert self.cache_mode in ["part", "full"]
n_sample = len(self.samples)
global_rank = dist.get_rank()
world_size = dist.get_world_size()
samples_bytes = [None for _ in range(n_sample)]
start_time = time.time()
for index in range(n_sample):
if index % (n_sample // 10) == 0:
t = time.time() - start_time
print(f'global_rank {dist.get_rank()} cached {index}/{n_sample} takes {t:.2f}s per block')
start_time = time.time()
path, target = self.samples[index]
if self.cache_mode == "full":
samples_bytes[index] = (ZipReader.read(path), target)
elif self.cache_mode == "part" and index % world_size == global_rank:
samples_bytes[index] = (ZipReader.read(path), target)
else:
samples_bytes[index] = (path, target)
self.samples = samples_bytes
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (sample, target) where target is class_index of the target class.
"""
path, target = self.samples[index]
sample = self.loader(path)
if self.transform is not None:
sample = self.transform(sample)
if self.target_transform is not None:
target = self.target_transform(target)
return sample, target
def __len__(self):
return len(self.samples)
def __repr__(self):
fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
fmt_str += ' Number of datapoints: {}\n'.format(self.__len__())
fmt_str += ' Root Location: {}\n'.format(self.root)
tmp = ' Transforms (if any): '
fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
tmp = ' Target Transforms (if any): '
fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
return fmt_str
IMG_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif']
def pil_loader(path):
# open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
if isinstance(path, bytes):
img = Image.open(io.BytesIO(path))
elif is_zip_path(path):
data = ZipReader.read(path)
img = Image.open(io.BytesIO(data))
else:
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
return img.convert('RGB')
def accimage_loader(path):
import accimage
try:
return accimage.Image(path)
except IOError:
# Potentially a decoding problem, fall back to PIL.Image
return pil_loader(path)
def default_img_loader(path):
from torchvision import get_image_backend
if get_image_backend() == 'accimage':
return accimage_loader(path)
else:
return pil_loader(path)
class CachedImageFolder(DatasetFolder):
"""A generic data loader where the images are arranged in this way: ::
root/dog/xxx.png
root/dog/xxy.png
root/dog/xxz.png
root/cat/123.png
root/cat/nsdf3.png
root/cat/asd932_.png
Args:
root (string): Root directory path.
transform (callable, optional): A function/transform that takes in an PIL image
and returns a transformed version. E.g, ``transforms.RandomCrop``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
loader (callable, optional): A function to load an image given its path.
Attributes:
imgs (list): List of (image path, class_index) tuples
"""
def __init__(self, root, ann_file='', img_prefix='', transform=None, target_transform=None,
loader=default_img_loader, cache_mode="no"):
super(CachedImageFolder, self).__init__(root, loader, IMG_EXTENSIONS,
ann_file=ann_file, img_prefix=img_prefix,
transform=transform, target_transform=target_transform,
cache_mode=cache_mode)
self.imgs = self.samples
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is class_index of the target class.
"""
path, target = self.samples[index]
image = self.loader(path)
if self.transform is not None:
img = self.transform(image)
else:
img = image
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
================================================
FILE: data/data_simmim_ft.py
================================================
# --------------------------------------------------------
# SimMIM
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Zhenda Xie
# --------------------------------------------------------
import os
import torch.distributed as dist
from torch.utils.data import DataLoader, DistributedSampler
from torchvision import datasets, transforms
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.data import Mixup
from timm.data import create_transform
from timm.data.transforms import _pil_interp
def build_loader_finetune(config):
config.defrost()
dataset_train, config.MODEL.NUM_CLASSES = build_dataset(is_train=True, config=config)
config.freeze()
dataset_val, _ = build_dataset(is_train=False, config=config)
num_tasks = dist.get_world_size()
global_rank = dist.get_rank()
sampler_train = DistributedSampler(
dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
)
sampler_val = DistributedSampler(
dataset_val, num_replicas=num_tasks, rank=global_rank, shuffle=False
)
data_loader_train = DataLoader(
dataset_train, sampler=sampler_train,
batch_size=config.DATA.BATCH_SIZE,
num_workers=config.DATA.NUM_WORKERS,
pin_memory=config.DATA.PIN_MEMORY,
drop_last=True,
)
data_loader_val = DataLoader(
dataset_val, sampler=sampler_val,
batch_size=config.DATA.BATCH_SIZE,
num_workers=config.DATA.NUM_WORKERS,
pin_memory=config.DATA.PIN_MEMORY,
drop_last=False,
)
# setup mixup / cutmix
mixup_fn = None
mixup_active = config.AUG.MIXUP > 0 or config.AUG.CUTMIX > 0. or config.AUG.CUTMIX_MINMAX is not None
if mixup_active:
mixup_fn = Mixup(
mixup_alpha=config.AUG.MIXUP, cutmix_alpha=config.AUG.CUTMIX, cutmix_minmax=config.AUG.CUTMIX_MINMAX,
prob=config.AUG.MIXUP_PROB, switch_prob=config.AUG.MIXUP_SWITCH_PROB, mode=config.AUG.MIXUP_MODE,
label_smoothing=config.MODEL.LABEL_SMOOTHING, num_classes=config.MODEL.NUM_CLASSES)
return dataset_train, dataset_val, data_loader_train, data_loader_val, mixup_fn
def build_dataset(is_train, config):
transform = build_transform(is_train, config)
if config.DATA.DATASET == 'imagenet':
prefix = 'train' if is_train else 'val'
root = os.path.join(config.DATA.DATA_PATH, prefix)
dataset = datasets.ImageFolder(root, transform=transform)
nb_classes = 1000
else:
raise NotImplementedError("We only support ImageNet Now.")
return dataset, nb_classes
def build_transform(is_train, config):
resize_im = config.DATA.IMG_SIZE > 32
if is_train:
# this should always dispatch to transforms_imagenet_train
transform = create_transform(
input_size=config.DATA.IMG_SIZE,
is_training=True,
color_jitter=config.AUG.COLOR_JITTER if config.AUG.COLOR_JITTER > 0 else None,
auto_augment=config.AUG.AUTO_AUGMENT if config.AUG.AUTO_AUGMENT != 'none' else None,
re_prob=config.AUG.REPROB,
re_mode=config.AUG.REMODE,
re_count=config.AUG.RECOUNT,
interpolation=config.DATA.INTERPOLATION,
)
if not resize_im:
# replace RandomResizedCropAndInterpolation with
# RandomCrop
transform.transforms[0] = transforms.RandomCrop(config.DATA.IMG_SIZE, padding=4)
return transform
t = []
if resize_im:
if config.TEST.CROP:
size = int((256 / 224) * config.DATA.IMG_SIZE)
t.append(
transforms.Resize(size, interpolation=_pil_interp(config.DATA.INTERPOLATION)),
# to maintain same ratio w.r.t. 224 images
)
t.append(transforms.CenterCrop(config.DATA.IMG_SIZE))
else:
t.append(
transforms.Resize((config.DATA.IMG_SIZE, config.DATA.IMG_SIZE),
interpolation=_pil_interp(config.DATA.INTERPOLATION))
)
t.append(transforms.ToTensor())
t.append(transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD))
return transforms.Compose(t)
================================================
FILE: data/data_simmim_pt.py
================================================
# --------------------------------------------------------
# SimMIM
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Zhenda Xie
# --------------------------------------------------------
import math
import random
import numpy as np
import torch
import torch.distributed as dist
import torchvision.transforms as T
from torch.utils.data import DataLoader, DistributedSampler
from torch.utils.data._utils.collate import default_collate
from torchvision.datasets import ImageFolder
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
class MaskGenerator:
def __init__(self, input_size=192, mask_patch_size=32, model_patch_size=4, mask_ratio=0.6):
self.input_size = input_size
self.mask_patch_size = mask_patch_size
self.model_patch_size = model_patch_size
self.mask_ratio = mask_ratio
assert self.input_size % self.mask_patch_size == 0
assert self.mask_patch_size % self.model_patch_size == 0
self.rand_size = self.input_size // self.mask_patch_size
self.scale = self.mask_patch_size // self.model_patch_size
self.token_count = self.rand_size ** 2
self.mask_count = int(np.ceil(self.token_count * self.mask_ratio))
def __call__(self):
mask_idx = np.random.permutation(self.token_count)[:self.mask_count]
mask = np.zeros(self.token_count, dtype=int)
mask[mask_idx] = 1
mask = mask.reshape((self.rand_size, self.rand_size))
mask = mask.repeat(self.scale, axis=0).repeat(self.scale, axis=1)
return mask
class SimMIMTransform:
def __init__(self, config):
self.transform_img = T.Compose([
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
T.RandomResizedCrop(config.DATA.IMG_SIZE, scale=(0.67, 1.), ratio=(3. / 4., 4. / 3.)),
T.RandomHorizontalFlip(),
T.ToTensor(),
T.Normalize(mean=torch.tensor(IMAGENET_DEFAULT_MEAN),std=torch.tensor(IMAGENET_DEFAULT_STD)),
])
if config.MODEL.TYPE in ['swin', 'swinv2']:
model_patch_size=config.MODEL.SWIN.PATCH_SIZE
else:
raise NotImplementedError
self.mask_generator = MaskGenerator(
input_size=config.DATA.IMG_SIZE,
mask_patch_size=config.DATA.MASK_PATCH_SIZE,
model_patch_size=model_patch_size,
mask_ratio=config.DATA.MASK_RATIO,
)
def __call__(self, img):
img = self.transform_img(img)
mask = self.mask_generator()
return img, mask
def collate_fn(batch):
if not isinstance(batch[0][0], tuple):
return default_collate(batch)
else:
batch_num = len(batch)
ret = []
for item_idx in range(len(batch[0][0])):
if batch[0][0][item_idx] is None:
ret.append(None)
else:
ret.append(default_collate([batch[i][0][item_idx] for i in range(batch_num)]))
ret.append(default_collate([batch[i][1] for i in range(batch_num)]))
return ret
def build_loader_simmim(config):
transform = SimMIMTransform(config)
dataset = ImageFolder(config.DATA.DATA_PATH, transform)
sampler = DistributedSampler(dataset, num_replicas=dist.get_world_size(), rank=dist.get_rank(), shuffle=True)
dataloader = DataLoader(dataset, config.DATA.BATCH_SIZE, sampler=sampler, num_workers=config.DATA.NUM_WORKERS, pin_memory=True, drop_last=True, collate_fn=collate_fn)
return dataloader
================================================
FILE: data/imagenet22k_dataset.py
================================================
import os
import json
import torch.utils.data as data
import numpy as np
from PIL import Image
import warnings
warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
class IN22KDATASET(data.Dataset):
def __init__(self, root, ann_file='', transform=None, target_transform=None):
super(IN22KDATASET, self).__init__()
self.data_path = root
self.ann_path = os.path.join(self.data_path, ann_file)
self.transform = transform
self.target_transform = target_transform
# id & label: https://github.com/google-research/big_transfer/issues/7
# total: 21843; only 21841 class have images: map 21841->9205; 21842->15027
self.database = json.load(open(self.ann_path))
def _load_image(self, path):
try:
im = Image.open(path)
except:
print("ERROR IMG LOADED: ", path)
random_img = np.random.rand(224, 224, 3) * 255
im = Image.fromarray(np.uint8(random_img))
return im
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is class_index of the target class.
"""
idb = self.database[index]
# images
images = self._load_image(self.data_path + '/' + idb[0]).convert('RGB')
if self.transform is not None:
images = self.transform(images)
# target
target = int(idb[1])
if self.target_transform is not None:
target = self.target_transform(target)
return images, target
def __len__(self):
return len(self.database)
================================================
FILE: data/map22kto1k.txt
================================================
359
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================================================
FILE: data/samplers.py
================================================
# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import torch
class SubsetRandomSampler(torch.utils.data.Sampler):
r"""Samples elements randomly from a given list of indices, without replacement.
Arguments:
indices (sequence): a sequence of indices
"""
def __init__(self, indices):
self.epoch = 0
self.indices = indices
def __iter__(self):
return (self.indices[i] for i in torch.randperm(len(self.indices)))
def __len__(self):
return len(self.indices)
def set_epoch(self, epoch):
self.epoch = epoch
================================================
FILE: data/zipreader.py
================================================
# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import os
import zipfile
import io
import numpy as np
from PIL import Image
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
def is_zip_path(img_or_path):
"""judge if this is a zip path"""
return '.zip@' in img_or_path
class ZipReader(object):
"""A class to read zipped files"""
zip_bank = dict()
def __init__(self):
super(ZipReader, self).__init__()
@staticmethod
def get_zipfile(path):
zip_bank = ZipReader.zip_bank
if path not in zip_bank:
zfile = zipfile.ZipFile(path, 'r')
zip_bank[path] = zfile
return zip_bank[path]
@staticmethod
def split_zip_style_path(path):
pos_at = path.index('@')
assert pos_at != -1, "character '@' is not found from the given path '%s'" % path
zip_path = path[0: pos_at]
folder_path = path[pos_at + 1:]
folder_path = str.strip(folder_path, '/')
return zip_path, folder_path
@staticmethod
def list_folder(path):
zip_path, folder_path = ZipReader.split_zip_style_path(path)
zfile = ZipReader.get_zipfile(zip_path)
folder_list = []
for file_foler_name in zfile.namelist():
file_foler_name = str.strip(file_foler_name, '/')
if file_foler_name.startswith(folder_path) and \
len(os.path.splitext(file_foler_name)[-1]) == 0 and \
file_foler_name != folder_path:
if len(folder_path) == 0:
folder_list.append(file_foler_name)
else:
folder_list.append(file_foler_name[len(folder_path) + 1:])
return folder_list
@staticmethod
def list_files(path, extension=None):
if extension is None:
extension = ['.*']
zip_path, folder_path = ZipReader.split_zip_style_path(path)
zfile = ZipReader.get_zipfile(zip_path)
file_lists = []
for file_foler_name in zfile.namelist():
file_foler_name = str.strip(file_foler_name, '/')
if file_foler_name.startswith(folder_path) and \
str.lower(os.path.splitext(file_foler_name)[-1]) in extension:
if len(folder_path) == 0:
file_lists.append(file_foler_name)
else:
file_lists.append(file_foler_name[len(folder_path) + 1:])
return file_lists
@staticmethod
def read(path):
zip_path, path_img = ZipReader.split_zip_style_path(path)
zfile = ZipReader.get_zipfile(zip_path)
data = zfile.read(path_img)
return data
@staticmethod
def imread(path):
zip_path, path_img = ZipReader.split_zip_style_path(path)
zfile = ZipReader.get_zipfile(zip_path)
data = zfile.read(path_img)
try:
im = Image.open(io.BytesIO(data))
except:
print("ERROR IMG LOADED: ", path_img)
random_img = np.random.rand(224, 224, 3) * 255
im = Image.fromarray(np.uint8(random_img))
return im
================================================
FILE: get_started.md
================================================
# Swin Transformer for Image Classification
This folder contains the implementation of the Swin Transformer for image classification.
## Model Zoo
Please refer to [MODEL HUB](MODELHUB.md#imagenet-22k-pretrained-swin-moe-models) for more pre-trained models.
## Usage
### Install
We recommend using the pytorch docker `nvcr>=21.05` by
nvidia: https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch.
- Clone this repo:
```bash
git clone https://github.com/microsoft/Swin-Transformer.git
cd Swin-Transformer
```
- Create a conda virtual environment and activate it:
```bash
conda create -n swin python=3.7 -y
conda activate swin
```
- Install `CUDA>=10.2` with `cudnn>=7` following
the [official installation instructions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
- Install `PyTorch>=1.8.0` and `torchvision>=0.9.0` with `CUDA>=10.2`:
```bash
conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=10.2 -c pytorch
```
- Install `timm==0.4.12`:
```bash
pip install timm==0.4.12
```
- Install other requirements:
```bash
pip install opencv-python==4.4.0.46 termcolor==1.1.0 yacs==0.1.8 pyyaml scipy
```
- Install fused window process for acceleration, activated by passing `--fused_window_process` in the running script
```bash
cd kernels/window_process
python setup.py install #--user
```
### Data preparation
We use standard ImageNet dataset, you can download it from http://image-net.org/. We provide the following two ways to
load data:
- For standard folder dataset, move validation images to labeled sub-folders. The file structure should look like:
```bash
$ tree data
imagenet
├── train
│ ├── class1
│ │ ├── img1.jpeg
│ │ ├── img2.jpeg
│ │ └── ...
│ ├── class2
│ │ ├── img3.jpeg
│ │ └── ...
│ └── ...
└── val
├── class1
│ ├── img4.jpeg
│ ├── img5.jpeg
│ └── ...
├── class2
│ ├── img6.jpeg
│ └── ...
└── ...
```
- To boost the slow speed when reading images from massive small files, we also support zipped ImageNet, which includes
four files:
- `train.zip`, `val.zip`: which store the zipped folder for train and validate splits.
- `train_map.txt`, `val_map.txt`: which store the relative path in the corresponding zip file and ground truth
label. Make sure the data folder looks like this:
```bash
$ tree data
data
└── ImageNet-Zip
├── train_map.txt
├── train.zip
├── val_map.txt
└── val.zip
$ head -n 5 data/ImageNet-Zip/val_map.txt
ILSVRC2012_val_00000001.JPEG 65
ILSVRC2012_val_00000002.JPEG 970
ILSVRC2012_val_00000003.JPEG 230
ILSVRC2012_val_00000004.JPEG 809
ILSVRC2012_val_00000005.JPEG 516
$ head -n 5 data/ImageNet-Zip/train_map.txt
n01440764/n01440764_10026.JPEG 0
n01440764/n01440764_10027.JPEG 0
n01440764/n01440764_10029.JPEG 0
n01440764/n01440764_10040.JPEG 0
n01440764/n01440764_10042.JPEG 0
```
- For ImageNet-22K dataset, make a folder named `fall11_whole` and move all images to labeled sub-folders in this
folder. Then download the train-val split
file ([ILSVRC2011fall_whole_map_train.txt](https://github.com/SwinTransformer/storage/releases/download/v2.0.1/ILSVRC2011fall_whole_map_train.txt)
& [ILSVRC2011fall_whole_map_val.txt](https://github.com/SwinTransformer/storage/releases/download/v2.0.1/ILSVRC2011fall_whole_map_val.txt))
, and put them in the parent directory of `fall11_whole`. The file structure should look like:
```bash
$ tree imagenet22k/
imagenet22k/
├── ILSVRC2011fall_whole_map_train.txt
├── ILSVRC2011fall_whole_map_val.txt
└── fall11_whole
├── n00004475
├── n00005787
├── n00006024
├── n00006484
└── ...
```
### Evaluation
To evaluate a pre-trained `Swin Transformer` on ImageNet val, run:
```bash
python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --master_port 12345 main.py --eval \
--cfg <config-file> --resume <checkpoint> --data-path <imagenet-path>
```
For example, to evaluate the `Swin-B` with a single GPU:
```bash
python -m torch.distributed.launch --nproc_per_node 1 --master_port 12345 main.py --eval \
--cfg configs/swin/swin_base_patch4_window7_224.yaml --resume swin_base_patch4_window7_224.pth --data-path <imagenet-path>
```
### Training from scratch on ImageNet-1K
To train a `Swin Transformer` on ImageNet from scratch, run:
```bash
python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --master_port 12345 main.py \
--cfg <config-file> --data-path <imagenet-path> [--batch-size <batch-size-per-gpu> --output <output-directory> --tag <job-tag>]
```
**Notes**:
- To use zipped ImageNet instead of folder dataset, add `--zip` to the parameters.
- To cache the dataset in the memory instead of reading from files every time, add `--cache-mode part`, which will
shard the dataset into non-overlapping pieces for different GPUs and only load the corresponding one for each GPU.
- When GPU memory is not enough, you can try the following suggestions:
- Use gradient accumulation by adding `--accumulation-steps <steps>`, set appropriate `<steps>` according to your need.
- Use gradient checkpointing by adding `--use-checkpoint`, e.g., it saves about 60% memory when training `Swin-B`.
Please refer to [this page](https://pytorch.org/docs/stable/checkpoint.html) for more details.
- We recommend using multi-node with more GPUs for training very large models, a tutorial can be found
in [this page](https://pytorch.org/tutorials/intermediate/dist_tuto.html).
- To change config options in general, you can use `--opts KEY1 VALUE1 KEY2 VALUE2`, e.g.,
`--opts TRAIN.EPOCHS 100 TRAIN.WARMUP_EPOCHS 5` will change total epochs to 100 and warm-up epochs to 5.
- For additional options, see [config](config.py) and run `python main.py --help` to get detailed message.
For example, to train `Swin Transformer` with 8 GPU on a single node for 300 epochs, run:
`Swin-T`:
```bash
python -m torch.distributed.launch --nproc_per_node 8 --master_port 12345 main.py \
--cfg configs/swin/swin_tiny_patch4_window7_224.yaml --data-path <imagenet-path> --batch-size 128
```
`Swin-S`:
```bash
python -m torch.distributed.launch --nproc_per_node 8 --master_port 12345 main.py \
--cfg configs/swin/swin_small_patch4_window7_224.yaml --data-path <imagenet-path> --batch-size 128
```
`Swin-B`:
```bash
python -m torch.distributed.launch --nproc_per_node 8 --master_port 12345 main.py \
--cfg configs/swin/swin_base_patch4_window7_224.yaml --data-path <imagenet-path> --batch-size 64 \
--accumulation-steps 2 [--use-checkpoint]
```
### Pre-training on ImageNet-22K
For example, to pre-train a `Swin-B` model on ImageNet-22K:
```bash
python -m torch.distributed.launch --nproc_per_node 8 --master_port 12345 main.py \
--cfg configs/swin/swin_base_patch4_window7_224_22k.yaml --data-path <imagenet22k-path> --batch-size 64 \
--accumulation-steps 8 [--use-checkpoint]
```
### Fine-tuning on higher resolution
For example, to fine-tune a `Swin-B` model pre-trained on 224x224 resolution to 384x384 resolution:
```bashs
python -m torch.distributed.launch --nproc_per_node 8 --master_port 12345 main.py \
--cfg configs/swin/swin_base_patch4_window12_384_finetune.yaml --pretrained swin_base_patch4_window7_224.pth \
--data-path <imagenet-path> --batch-size 64 --accumulation-steps 2 [--use-checkpoint]
```
### Fine-tuning from a ImageNet-22K(21K) pre-trained model
For example, to fine-tune a `Swin-B` model pre-trained on ImageNet-22K(21K):
```bashs
python -m torch.distributed.launch --nproc_per_node 8 --master_port 12345 main.py \
--cfg configs/swin/swin_base_patch4_window7_224_22kto1k_finetune.yaml --pretrained swin_base_patch4_window7_224_22k.pth \
--data-path <imagenet-path> --batch-size 64 --accumulation-steps 2 [--use-checkpoint]
```
### Throughput
To measure the throughput, run:
```bash
python -m torch.distributed.launch --nproc_per_node 1 --master_port 12345 main.py \
--cfg <config-file> --data-path <imagenet-path> --batch-size 64 --throughput --disable_amp
```
## Mixture-of-Experts Support
### Install [Tutel](https://github.com/microsoft/tutel)
```bash
python3 -m pip uninstall tutel -y
python3 -m pip install --user --upgrade git+https://github.com/microsoft/tutel@main
```
### Training Swin-MoE
For example, to train a `Swin-MoE-S` model with 32 experts on ImageNet-22K with 32 GPUs (4 nodes):
```bash
python -m torch.distributed.launch --nproc_per_node 8 --nnode=4 \
--node_rank=<node-rank> --master_addr=<master-ip> --master_port 12345 main_moe.py \
--cfg configs/swinmoe/swin_moe_small_patch4_window12_192_32expert_32gpu_22k.yaml --data-path <imagenet22k-path> --batch-size 128
```
### Evaluating Swin-MoE
To evaluate a `Swin-MoE-S` with 32 experts on ImageNet-22K with 32 GPUs (4 nodes):
1. Download the zip file [swin_moe_small_patch4_window12_192_32expert_32gpu_22k.zip](https://github.com/SwinTransformer/storage/releases/download/v2.0.2/swin_moe_small_patch4_window12_192_32expert_32gpu_22k.zip) which contains the pre-trained models for each rank, and unzip them to the folder "swin_moe_small_patch4_window12_192_32expert_32gpu_22k".
2. Run the following evaluation command, note the checkpoint path should not contain the ".rank\<x\>" suffix.
```bash
python -m torch.distributed.launch --nproc_per_node 8 --nnode=4 \
--node_rank=<node-rank> --master_addr=<master-ip> --master_port 12345 main_moe.py \
--cfg configs/swinmoe/swin_moe_small_patch4_window12_192_32expert_32gpu_22k.yaml --data-path <imagenet22k-path> --batch-size 128 \
--resume swin_moe_small_patch4_window12_192_32expert_32gpu_22k/swin_moe_small_patch4_window12_192_32expert_32gpu_22k.pth
```
More Swin-MoE models can be found in [MODEL HUB](MODELHUB.md#imagenet-22k-pretrained-swin-moe-models)
## SimMIM Support
### Evaluating provided models
To evaluate a provided model on ImageNet validation set, run:
```bash
python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> main_simmim_ft.py \
--eval --cfg <config-file> --resume <checkpoint> --data-path <imagenet-path>
```
For example, to evaluate the `Swin Base` model on a single GPU, run:
```bash
python -m torch.distributed.launch --nproc_per_node 1 main_simmim_ft.py \
--eval --cfg configs/simmim/simmim_finetune__swin_base__img224_window7__800ep.yaml --resume simmim_finetune__swin_base__img224_window7__800ep.pth --data-path <imagenet-path>
```
### Pre-training with SimMIM
To pre-train models with `SimMIM`, run:
```bash
python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> main_simmim_pt.py \
--cfg <config-file> --data-path <imagenet-path>/train [--batch-size <batch-size-per-gpu> --output <output-directory> --tag <job-tag>]
```
For example, to pre-train `Swin Base` for 800 epochs on one DGX-2 server, run:
```bash
python -m torch.distributed.launch --nproc_per_node 16 main_simmim_pt.py \
--cfg configs/simmim/simmim_pretrain__swin_base__img192_window6__800ep.yaml --batch-size 128 --data-path <imagenet-path>/train [--output <output-directory> --tag <job-tag>]
```
### Fine-tuning pre-trained models
To fine-tune models pre-trained by `SimMIM`, run:
```bash
python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> main_simmim_ft.py \
--cfg <config-file> --data-path <imagenet-path> --pretrained <pretrained-ckpt> [--batch-size <batch-size-per-gpu> --output <output-directory> --tag <job-tag>]
```
For example, to fine-tune `Swin Base` pre-trained by `SimMIM` on one DGX-2 server, run:
```bash
python -m torch.distributed.launch --nproc_per_node 16 main_simmim_ft.py \
--cfg configs/simmim/simmim_finetune__swin_base__img224_window7__800ep.yaml --batch-size 128 --data-path <imagenet-path> --pretrained <pretrained-ckpt> [--output <output-directory> --tag <job-tag>]
```
================================================
FILE: kernels/window_process/setup.py
================================================
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
setup(name='swin_window_process',
ext_modules=[
CUDAExtension('swin_window_process', [
'swin_window_process.cpp',
'swin_window_process_kernel.cu',
])
],
cmdclass={'build_ext': BuildExtension})
================================================
FILE: kernels/window_process/swin_window_process.cpp
================================================
/*
* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include <torch/torch.h>
#include <torch/extension.h>
at::Tensor roll_and_window_partition_forward_cuda(
at::Tensor & input,
//at::Tensor & output,
const int B,
const int H,
const int W,
const int C,
const int shift_size,
const int window_size);
at::Tensor roll_and_window_partition_backward_cuda(
at::Tensor & grad_in,
//at::Tensor & grad_out,
const int B,
const int H,
const int W,
const int C,
const int shift_size,
const int window_size);
at::Tensor window_merge_and_roll_forward_cuda(
at::Tensor & input,
//at::Tensor & output,
const int B,
const int H,
const int W,
const int C,
const int shift_size,
const int window_size);
at::Tensor window_merge_and_roll_backward_cuda(
at::Tensor & grad_in,
//at::Tensor & grad_out,
const int B,
const int H,
const int W,
const int C,
const int shift_size,
const int window_size);
#define CHECK_CUDA(x) AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor")
#define CHECK_CONTIGUOUS(x) AT_ASSERTM(x.is_contiguous(), #x " must be contiguous")
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
at::Tensor roll_and_window_partition_forward(
at::Tensor & input,
//at::Tensor & output,
const int B,
const int H,
const int W,
const int C,
const int shift_size,
const int window_size){
CHECK_INPUT(input);
return roll_and_window_partition_forward_cuda(input, B, H, W, C, shift_size, window_size);
}
at::Tensor roll_and_window_partition_backward(
at::Tensor & grad_in,
//at::Tensor & grad_out,
const int B,
const int H,
const int W,
const int C,
const int shift_size,
const int window_size){
CHECK_INPUT(grad_in);
return roll_and_window_partition_backward_cuda(grad_in, B, H, W, C, shift_size, window_size);
}
at::Tensor window_merge_and_roll_forward(
at::Tensor & input,
//at::Tensor & output,
const int B,
const int H,
const int W,
const int C,
const int shift_size,
const int window_size){
CHECK_INPUT(input);
return window_merge_and_roll_forward_cuda(input, B, H, W, C, shift_size, window_size);
}
at::Tensor window_merge_and_roll_backward(
at::Tensor & grad_in,
//at::Tensor & grad_out,
const int B,
const int H,
const int W,
const int C,
const int shift_size,
const int window_size){
CHECK_INPUT(grad_in);
return window_merge_and_roll_backward_cuda(grad_in, B, H, W, C, shift_size, window_size);
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("roll_and_window_partition_forward", &roll_and_window_partition_forward, "torch.roll and window_partition.");
m.def("roll_and_window_partition_backward", &roll_and_window_partition_backward, "torch.roll and window_partition.");
m.def("window_merge_and_roll_forward", &window_merge_and_roll_forward, "window merge and torch.roll.");
m.def("window_merge_and_roll_backward", &window_merge_and_roll_backward, "window merge and torch.roll.");
}
================================================
FILE: kernels/window_process/swin_window_process_kernel.cu
================================================
/*
* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include <ATen/ATen.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <torch/extension.h>
#include <stdio.h>
int best_block_dim(int feat_dim){
int best_dim;
if (feat_dim < 384){
best_dim = 64;
}
else{
if (feat_dim < 1024){
best_dim = 128;
}
else{
best_dim = 256;
}
}
return best_dim;
}
template <typename T>
__global__ void roll_and_window_partition_forward_cuda_kernel(
T* input,
T* output,
const int B,
const int H,
const int W,
const int C,
const int shift_size,
const int window_size,
const int nH,
const int nW){
// start
//bool qual = threadIdx.x < C;
int index = threadIdx.x;
int offset;
for (int i = index; i < C; i += blockDim.x) {
offset = ((blockIdx.z * gridDim.y + blockIdx.y) * gridDim.x + blockIdx.x) * C + i; // C = blocksize
int input_offset = blockIdx.z / (nH * nW) * H * W * C +
(blockIdx.z % (nH * nW) / nW * window_size + blockIdx.y - shift_size + H) % H * W * C +
(blockIdx.z % nW * window_size + blockIdx.x - shift_size + W) % W * C +
i;
output[offset] = (T)(__ldg(input + input_offset));
}
}
template <typename T>
__global__ void roll_and_window_partition_backward_cuda_kernel(
T* grad_in,
T* grad_out,
const int B,
const int H,
const int W,
const int C,
const int shift_size,
const int window_size,
const int nH,
const int nW){
// start
int index = threadIdx.x;
int offset;
for (int i = index; i < C; i += blockDim.x) {
offset = ((blockIdx.z * gridDim.y + blockIdx.y) * gridDim.x + blockIdx.x) * C + i; // C = blocksize
int input_offset =
(blockIdx.z * nH * nW + (blockIdx.y + shift_size + H) % H / window_size * nW + (blockIdx.x + shift_size + W) % W / window_size) * window_size * window_size * C +
(blockIdx.y + shift_size + H ) % H % window_size * window_size * C +
(blockIdx.x + shift_size + W ) % W % window_size * C +
i;
grad_out[offset] = (T)(__ldg(grad_in + input_offset));
}
}
template <typename T>
__global__ void window_merge_and_roll_forward_cuda_kernel(
T* input,
T* output,
const int B,
const int H,
const int W,
const int C,
const int shift_size,
const int window_size,
const int nH,
const int nW){
// start
int index = threadIdx.x;
int offset;
for (int i = index; i < C; i += blockDim.x) {
offset = ((blockIdx.z * gridDim.y + blockIdx.y) * gridDim.x + blockIdx.x) * C + i; // C = blocksize
int input_offset =
(blockIdx.z * nH * nW + (blockIdx.y - shift_size + H) % H / window_size * nH + (blockIdx.x - shift_size + W) % W / window_size) * window_size * window_size * C +
(blockIdx.y - shift_size + H) % window_size * window_size * C +
(blockIdx.x - shift_size + W) % window_size * C +
i;
output[offset] = (T)(__ldg(input + input_offset));
}
}
template <typename T>
__global__ void window_merge_and_roll_backward_cuda_kernel(
T* grad_in,
T* grad_out,
const int B,
const int H,
const int W,
const int C,
const int shift_size,
const int window_size,
const int nH,
const int nW){
// start
int index = threadIdx.x;
int offset;
for (int i = index; i < C; i += blockDim.x) {
offset = ((blockIdx.z * gridDim.y + blockIdx.y) * gridDim.x + blockIdx.x) * C + i; // C = blocksize
int input_offset =
(blockIdx.z / (nH * nW)) * H * W * C +
(blockIdx.z % (nH * nW) / nW * window_size + blockIdx.y + shift_size + H) % H * W * C +
(blockIdx.z % nW * window_size + blockIdx.x + shift_size + W) % W * C +
i;
grad_out[offset] = (T)(__ldg(grad_in + input_offset));
}
}
// input: [B, H, W, C]
// output: [B*nH*nW, window_size, window_size, C]
at::Tensor roll_and_window_partition_forward_cuda(
at::Tensor & input,
//at::Tensor & output,
const int B,
const int H,
const int W,
const int C,
const int shift_size,
const int window_size){
int nH = H / window_size;
int nW = W / window_size;
dim3 grid(window_size, window_size, B * nH * nW);
//dim3 block((C + 31) / 32 * 32);
int blocknum = best_block_dim(C);
dim3 block(blocknum);
at::Tensor output;
if (input.scalar_type() == torch::kFloat16){
output = torch::empty({B*nH*nW, window_size, window_size, C}, torch::dtype(torch::kFloat16).device(torch::kCUDA).requires_grad(true));
}
else{
output = torch::empty({B*nH*nW, window_size, window_size, C}, torch::dtype(torch::kFloat32).device(torch::kCUDA).requires_grad(true));
}
AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.type(), "roll_and_window_partition_forward_cuda_kernel", ([&] {
roll_and_window_partition_forward_cuda_kernel<scalar_t><<<grid, block, 0>>>(
input.data<scalar_t>(),
output.data<scalar_t>(),
B,
H,
W,
C,
shift_size,
window_size,
nH,
nW);
}));
return output;
}
// grad_in: [B*nH*nW, window_size, window_size, C]
// grad_out: [B, H, W, C]
at::Tensor roll_and_window_partition_backward_cuda(
at::Tensor & grad_in,
const int B,
const int H,
const int W,
const int C,
const int shift_size,
const int window_size){
int nH = H / window_size;
int nW = W / window_size;
dim3 grid(W, H, B);
//dim3 block((C + 31) / 32 * 32);
int blocknum = best_block_dim(C);
dim3 block(blocknum);
at::Tensor grad_out;
if (grad_in.scalar_type() == torch::kFloat16){
grad_out = torch::empty({B, H, W, C}, torch::dtype(torch::kFloat16).device(torch::kCUDA).requires_grad(false));
}
else{
grad_out = torch::empty({B, H, W, C}, torch::dtype(torch::kFloat32).device(torch::kCUDA).requires_grad(false));
}
AT_DISPATCH_FLOATING_TYPES_AND_HALF(grad_in.type(), "roll_and_window_partition_backward_cuda_kernel", ([&] {
roll_and_window_partition_backward_cuda_kernel<scalar_t><<<grid, block, 0>>>(
grad_in.data<scalar_t>(),
grad_out.data<scalar_t>(),
B,
H,
W,
C,
shift_size,
window_size,
nH,
nW);
}));
return grad_out;
}
// input: [B*nH*nW, window_size, window_size, C]
// output: [B, H, W, C]
at::Tensor window_merge_and_roll_forward_cuda(
at::Tensor & input,
//at::Tensor & output,
const int B,
const int H,
const int W,
const int C,
const int shift_size,
const int window_size){
int nH = H / window_size;
int nW = W / window_size;
dim3 grid(W, H, B);
//dim3 block((C + 31) / 32 * 32);
int blocknum = best_block_dim(C);
dim3 block(blocknum);
//generate output tensor inside
at::Tensor output;
if (input.scalar_type() == torch::kFloat16){
output = torch::empty({B, H, W, C}, torch::dtype(torch::kFloat16).device(torch::kCUDA).requires_grad(true));
}
else{
output = torch::empty({B, H, W, C}, torch::dtype(torch::kFloat32).device(torch::kCUDA).requires_grad(true));
}
AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.type(), "window_merge_and_roll_forward_cuda_kernel", ([&] {
window_merge_and_roll_forward_cuda_kernel<scalar_t><<<grid, block, 0>>>(
input.data<scalar_t>(),
output.data<scalar_t>(),
B,
H,
W,
C,
shift_size,
window_size,
nH,
nW);
}));
return output;
}
at::Tensor window_merge_and_roll_backward_cuda(
at::Tensor & grad_in,
const int B,
const int H,
const int W,
const int C,
const int shift_size,
const int window_size){
int nH = H / window_size;
int nW = W / window_size;
dim3 grid(window_size, window_size, B * nH * nW);
//dim3 block((C + 31) / 32 * 32);
int blocknum = best_block_dim(C);
dim3 block(blocknum);
at::Tensor grad_out;
if (grad_in.scalar_type() == torch::kFloat16){
grad_out = torch::empty({B*nH*nW, window_size, window_size, C}, torch::dtype(torch::kFloat16).device(torch::kCUDA).requires_grad(false));
}
else{
grad_out = torch::empty({B*nH*nW, window_size, window_size, C}, torch::dtype(torch::kFloat32).device(torch::kCUDA).requires_grad(false));
}
AT_DISPATCH_FLOATING_TYPES_AND_HALF(grad_in.type(), "window_merge_and_roll_backward_cuda_kernel", ([&] {
window_merge_and_roll_backward_cuda_kernel<scalar_t><<<grid, block, 0>>>(
grad_in.data<scalar_t>(),
grad_out.data<scalar_t>(),
B,
H,
W,
C,
shift_size,
window_size,
nH,
nW);
}));
return grad_out;
}
================================================
FILE: kernels/window_process/unit_test.py
================================================
# --------------------------------------------------------
# Fused kernel for window process for SwinTransformer
# Copyright (c) 2022 Nvidia
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
import torch
import swin_window_process
import random
import time
import unittest
class WindowProcess(torch.autograd.Function):
@staticmethod
def forward(ctx, input, B, H, W, C, shift_size, window_size):
output = swin_window_process.roll_and_window_partition_forward(input, B, H, W, C, shift_size, window_size)
ctx.B = B
ctx.H = H
ctx.W = W
ctx.C = C
ctx.shift_size = shift_size
ctx.window_size = window_size
return output
@staticmethod
def backward(ctx, grad_in):
B = ctx.B
H = ctx.H
W = ctx.W
C = ctx.C
shift_size = ctx.shift_size
window_size = ctx.window_size
grad_out = swin_window_process.roll_and_window_partition_backward(grad_in, B, H, W, C, shift_size, window_size)
return grad_out, None, None, None, None, None, None, None
class WindowProcessReverse(torch.autograd.Function):
@staticmethod
def forward(ctx, input, B, H, W, C, shift_size, window_size):
output = swin_window_process.window_merge_and_roll_forward(input, B, H, W, C, shift_size, window_size)
ctx.B = B
ctx.H = H
ctx.W = W
ctx.C = C
ctx.shift_size = shift_size
ctx.window_size = window_size
return output
@staticmethod
def backward(ctx, grad_in):
B = ctx.B
H = ctx.H
W = ctx.W
C = ctx.C
shift_size = ctx.shift_size
window_size = ctx.window_size
grad_out = swin_window_process.window_merge_and_roll_backward(grad_in, B, H, W, C, shift_size, window_size)
return grad_out, None, None, None, None, None, None, None
def window_partition(x, window_size):
"""
Args:
x: (B, H, W, C)
window_size (int): window size
Returns:
windows: (num_windows*B, window_size, window_size, C)
"""
B, H, W, C = x.shape
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
return windows
def window_reverse(windows, window_size, H, W):
"""
Args:
windows: (num_windows*B, window_size, window_size, C)
window_size (int): Window size
H (int): Height of image
W (int): Width of image
Returns:
x: (B, H, W, C)
"""
B = int(windows.shape[0] / (H * W / window_size / window_size))
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
return x
def pyt_forward(x, shift_size, window_size):
# x in shape(B, H, W, C)
# cyclic shift
if shift_size > 0:
shifted_x = torch.roll(x, shifts=(-shift_size, -shift_size), dims=(1, 2))
else:
shifted_x = x
# partition windows
x_windows = window_partition(shifted_x, window_size)
return x_windows
def reverse_pyt_forward(attn_windows, shift_size, window_size, H, W):
# x in shape(B*nH*nW, window_size, window_size, C)
shifted_x = window_reverse(attn_windows, window_size, H, W)
if shift_size > 0:
x = torch.roll(shifted_x, shifts=(shift_size, shift_size), dims=(1, 2))
else:
x = shifted_x
return x
def copy_one_tensor(input, requires_grad=True):
input1 = input.clone().detach().requires_grad_(requires_grad).cuda()
return input1
class Test_WindowProcess(unittest.TestCase):
def setUp(self):
self.B = 192
self.H = 56
self.W = 56
self.C = 96
self.shift_size = 2
self.window_size = 7
self.nH = self.H // self.window_size
self.nW = self.W // self.window_size
def test_roll_and_window_partition_forward(self, dtype=torch.float32):
input = torch.randn((self.B, self.H, self.W, self.C), dtype=dtype, requires_grad=True).cuda()
input1 = copy_one_tensor(input, True)
input2 = copy_one_tensor(input, True)
with torch.no_grad():
# ori
expected = pyt_forward(input1, self.shift_size, self.window_size)
# fused kernel
fused_output = WindowProcess.apply(input2, self.B, self.H, self.W, self.C, -self.shift_size, self.window_size)
self.assertTrue(torch.equal(expected, fused_output))
#self.assertTrue(torch.allclose(expected, fused_output, rtol=1e-05, atol=1e-08))
def test_roll_and_window_partition_backward(self, dtype=torch.float32):
input = torch.randn((self.B, self.H, self.W, self.C), dtype=dtype, requires_grad=True).cuda()
d_loss_tensor = torch.randn((self.B*self.nW*self.nH, self.window_size, self.window_size, self.C), dtype=dtype).cuda()
input1 = copy_one_tensor(input, True)
input2 = copy_one_tensor(input, True)
# ori
expected = pyt_forward(input1, self.shift_size, self.window_size)
expected.backward(d_loss_tensor)
# fused kernel
fused_output = WindowProcess.apply(input2, self.B, self.H, self.W, self.C, -self.shift_size, self.window_size)
fused_output.backward(d_loss_tensor)
self.assertTrue(torch.equal(expected, fused_output))
#self.assertTrue(torch.allclose(expected, fused_output, rtol=1e-05, atol=1e-08))
def test_window_merge_and_roll_forward(self, dtype=torch.float32):
input = torch.randn((self.B*self.nH*self.nW, self.window_size, self.window_size, self.C), dtype=dtype, requires_grad=True).cuda()
input1 = copy_one_tensor(input, True)
input2 = copy_one_tensor(input, True)
with torch.no_grad():
# ori
expected = reverse_pyt_forward(input1, self.shift_size, self.window_size, self.H, self.W)
# fused kernel
fused_output = WindowProcessReverse.apply(input2, self.B, self.H, self.W, self.C, self.shift_size, self.window_size)
self.assertTrue(torch.equal(expected, fused_output))
#self.assertTrue(torch.allclose(expected, fused_output, rtol=1e-05, atol=1e-08))
def test_window_merge_and_roll_backward(self, dtype=torch.float32):
input = torch.randn((self.B*self.nH*self.nW, self.window_size, self.window_size, self.C), dtype=dtype, requires_grad=True).cuda()
d_loss_tensor = torch.randn((self.B, self.H, self.W, self.C), dtype=dtype, requires_grad=True).cuda()
input1 = copy_one_tensor(input, True)
input2 = copy_one_tensor(input, True)
# ori
expected = reverse_pyt_forward(input1, self.shift_size, self.window_size, self.H, self.W)
expected.backward(d_loss_tensor)
# fused kernel
fused_output = WindowProcessReverse.apply(input2, self.B, self.H, self.W, self.C, self.shift_size, self.window_size)
fused_output.backward(d_loss_tensor)
self.assertTrue(torch.equal(expected, fused_output))
#self.assertTrue(torch.allclose(expected, fused_output, rtol=1e-05, atol=1e-08))
def test_forward_backward_speed(self, dtype=torch.float32, times=1000):
input = torch.randn((self.B*self.nH*self.nW, self.window_size, self.window_size, self.C), dtype=dtype, requires_grad=True).cuda()
d_loss_tensor = torch.randn((self.B, self.H, self.W, self.C), dtype=dtype, requires_grad=True).cuda()
input1 = copy_one_tensor(input, True)
input2 = copy_one_tensor(input, True)
# SwinTransformer official
def run_pyt(t=1000):
for _ in range(t):
expected = reverse_pyt_forward(input1, self.shift_size, self.window_size, self.H, self.W)
expected.backward(d_loss_tensor)
# my op
def run_fusedop(t=1000):
for _ in range(t):
fused_output = WindowProcessReverse.apply(input2, self.B, self.H, self.W, self.C, self.shift_size, self.window_size)
fused_output.backward(d_loss_tensor)
torch.cuda.synchronize()
t1 = time.time()
run_pyt(t=times)
torch.cuda.synchronize()
t2 = time.time()
run_fusedop(t=times)
torch.cuda.synchronize()
t3 = time.time()
self.assertTrue((t3 - t2) < (t2 - t1))
print('Run {} times'.format(times))
print('Original time cost: {}'.format(t2 - t1))
print('Fused op time cost: {}'.format(t3 - t2))
def test_roll_and_window_partition_forward_fp16(self, dtype=torch.float16):
self.test_roll_and_window_partition_forward(dtype=dtype)
def test_roll_and_window_partition_backward_fp16(self, dtype=torch.float16):
self.test_roll_and_window_partition_backward(dtype=dtype)
def test_window_merge_and_roll_forward_fp16(self, dtype=torch.float16):
self.test_window_merge_and_roll_forward(dtype=dtype)
def test_window_merge_and_roll_backward_fp16(self, dtype=torch.float16):
self.test_window_merge_and_roll_backward(dtype=dtype)
def test_forward_backward_speed_fp16(self, dtype=torch.float16, times=1000):
self.test_forward_backward_speed(dtype=dtype, times=times)
if __name__ == '__main__':
print('Pass only two tensors are exactly the same (using torch.equal).\n')
torch.manual_seed(0)
unittest.main(verbosity=2)
================================================
FILE: kernels/window_process/window_process.py
================================================
# --------------------------------------------------------
# Fused kernel for window process for SwinTransformer
# Copyright (c) 2022 Nvidia
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
import torch
import swin_window_process
class WindowProcess(torch.autograd.Function):
@staticmethod
def forward(ctx, input, B, H, W, C, shift_size, window_size):
output = swin_window_process.roll_and_window_partition_forward(input, B, H, W, C, shift_size, window_size)
ctx.B = B
ctx.H = H
ctx.W = W
ctx.C = C
ctx.shift_size = shift_size
ctx.window_size = window_size
return output
@staticmethod
def backward(ctx, grad_in):
B = ctx.B
H = ctx.H
W = ctx.W
C = ctx.C
shift_size = ctx.shift_size
window_size = ctx.window_size
grad_out = swin_window_process.roll_and_window_partition_backward(grad_in, B, H, W, C, shift_size, window_size)
return grad_out, None, None, None, None, None, None, None
class WindowProcessReverse(torch.autograd.Function):
@staticmethod
def forward(ctx, input, B, H, W, C, shift_size, window_size):
output = swin_window_process.window_merge_and_roll_forward(input, B, H, W, C, shift_size, window_size)
ctx.B = B
ctx.H = H
ctx.W = W
ctx.C = C
ctx.shift_size = shift_size
ctx.window_size = window_size
return output
@staticmethod
def backward(ctx, grad_in):
B = ctx.B
H = ctx.H
W = ctx.W
C = ctx.C
shift_size = ctx.shift_size
window_size = ctx.window_size
#grad_out = ctx.saved_tensors[0]
#grad_out = torch.zeros((B, H, W, C), dtype=dtype).cuda()
grad_out = swin_window_process.window_merge_and_roll_backward(grad_in, B, H, W, C, shift_size, window_size)
return grad_out, None, None, None, None, None, None, None
================================================
FILE: logger.py
================================================
# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import os
import sys
import logging
import functools
from termcolor import colored
@functools.lru_cache()
def create_logger(output_dir, dist_rank=0, name=''):
# create logger
logger = logging.getLogger(name)
logger.setLevel(logging.DEBUG)
logger.propagate = False
# create formatter
fmt = '[%(asctime)s %(name)s] (%(filename)s %(lineno)d): %(levelname)s %(message)s'
color_fmt = colored('[%(asctime)s %(name)s]', 'green') + \
colored('(%(filename)s %(lineno)d)', 'yellow') + ': %(levelname)s %(message)s'
# create console handlers for master process
if dist_rank == 0:
console_handler = logging.StreamHandler(sys.stdout)
console_handler.setLevel(logging.DEBUG)
console_handler.setFormatter(
logging.Formatter(fmt=color_fmt, datefmt='%Y-%m-%d %H:%M:%S'))
logger.addHandler(console_handler)
# create file handlers
file_handler = logging.FileHandler(os.path.join(output_dir, f'log_rank{dist_rank}.txt'), mode='a')
file_handler.setLevel(logging.DEBUG)
file_handler.setFormatter(logging.Formatter(fmt=fmt, datefmt='%Y-%m-%d %H:%M:%S'))
logger.addHandler(file_handler)
return logger
================================================
FILE: lr_scheduler.py
================================================
# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import bisect
import torch
from timm.scheduler.cosine_lr import CosineLRScheduler
from timm.scheduler.step_lr import StepLRScheduler
from timm.scheduler.scheduler import Scheduler
def build_scheduler(config, optimizer, n_iter_per_epoch):
num_steps = int(config.TRAIN.EPOCHS * n_iter_per_epoch)
warmup_steps = int(config.TRAIN.WARMUP_EPOCHS * n_iter_per_epoch)
decay_steps = int(config.TRAIN.LR_SCHEDULER.DECAY_EPOCHS * n_iter_per_epoch)
multi_steps = [i * n_iter_per_epoch for i in config.TRAIN.LR_SCHEDULER.MULTISTEPS]
lr_scheduler = None
if config.TRAIN.LR_SCHEDULER.NAME == 'cosine':
lr_scheduler = CosineLRScheduler(
optimizer,
t_initial=(num_steps - warmup_steps) if config.TRAIN.LR_SCHEDULER.WARMUP_PREFIX else num_steps,
t_mul=1.,
lr_min=config.TRAIN.MIN_LR,
warmup_lr_init=config.TRAIN.WARMUP_LR,
warmup_t=warmup_steps,
cycle_limit=1,
t_in_epochs=False,
warmup_prefix=config.TRAIN.LR_SCHEDULER.WARMUP_PREFIX,
)
elif config.TRAIN.LR_SCHEDULER.NAME == 'linear':
lr_scheduler = LinearLRScheduler(
optimizer,
t_initial=num_steps,
lr_min_rate=0.01,
warmup_lr_init=config.TRAIN.WARMUP_LR,
warmup_t=warmup_steps,
t_in_epochs=False,
)
elif config.TRAIN.LR_SCHEDULER.NAME == 'step':
lr_scheduler = StepLRScheduler(
optimizer,
decay_t=decay_steps,
decay_rate=config.TRAIN.LR_SCHEDULER.DECAY_RATE,
warmup_lr_init=config.TRAIN.WARMUP_LR,
warmup_t=warmup_steps,
t_in_epochs=False,
)
elif config.TRAIN.LR_SCHEDULER.NAME == 'multistep':
lr_scheduler = MultiStepLRScheduler(
optimizer,
milestones=multi_steps,
gamma=config.TRAIN.LR_SCHEDULER.GAMMA,
warmup_lr_init=config.TRAIN.WARMUP_LR,
warmup_t=warmup_steps,
t_in_epochs=False,
)
return lr_scheduler
class LinearLRScheduler(Scheduler):
def __init__(self,
optimizer: torch.optim.Optimizer,
t_initial: int,
lr_min_rate: float,
warmup_t=0,
warmup_lr_init=0.,
t_in_epochs=True,
noise_range_t=None,
noise_pct=0.67,
noise_std=1.0,
noise_seed=42,
initialize=True,
) -> None:
super().__init__(
optimizer, param_group_field="lr",
noise_range_t=noise_range_t, noise_pct=noise_pct, noise_std=noise_std, noise_seed=noise_seed,
initialize=initialize)
self.t_initial = t_initial
self.lr_min_rate = lr_min_rate
self.warmup_t = warmup_t
self.warmup_lr_init = warmup_lr_init
self.t_in_epochs = t_in_epochs
if self.warmup_t:
self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values]
super().update_groups(self.warmup_lr_init)
else:
self.warmup_steps = [1 for _ in self.base_values]
def _get_lr(self, t):
if t < self.warmup_t:
lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps]
else:
t = t - self.warmup_t
total_t = self.t_initial - self.warmup_t
lrs = [v - ((v - v * self.lr_min_rate) * (t / total_t)) for v in self.base_values]
return lrs
def get_epoch_values(self, epoch: int):
if self.t_in_epochs:
return self._get_lr(epoch)
else:
return None
def get_update_values(self, num_updates: int):
if not self.t_in_epochs:
return self._get_lr(num_updates)
else:
return None
class MultiStepLRScheduler(Scheduler):
def __init__(self, optimizer: torch.optim.Optimizer, milestones, gamma=0.1, warmup_t=0, warmup_lr_init=0, t_in_epochs=True) -> None:
super().__init__(optimizer, param_group_field="lr")
self.milestones = milestones
self.gamma = gamma
self.warmup_t = warmup_t
self.warmup_lr_init = warmup_lr_init
self.t_in_epochs = t_in_epochs
if self.warmup_t:
self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values]
super().update_groups(self.warmup_lr_init)
else:
self.warmup_steps = [1 for _ in self.base_values]
assert self.warmup_t <= min(self.milestones)
def _get_lr(self, t):
if t < self.warmup_t:
lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps]
else:
lrs = [v * (self.gamma ** bisect.bisect_right(self.milestones, t)) for v in self.base_values]
return lrs
def get_epoch_values(self, epoch: int):
if self.t_in_epochs:
return self._get_lr(epoch)
else:
return None
def get_update_values(self, num_updates: int):
if not self.t_in_epochs:
return self._get_lr(num_updates)
else:
return None
================================================
FILE: main.py
================================================
# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import os
import time
import json
import random
import argparse
import datetime
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.utils import accuracy, AverageMeter
from config import get_config
from models import build_model
from data import build_loader
from lr_scheduler import build_scheduler
from optimizer import build_optimizer
from logger import create_logger
from utils import load_checkpoint, load_pretrained, save_checkpoint, NativeScalerWithGradNormCount, auto_resume_helper, \
reduce_tensor
# pytorch major version (1.x or 2.x)
PYTORCH_MAJOR_VERSION = int(torch.__version__.split('.')[0])
def parse_option():
parser = argparse.ArgumentParser('Swin Transformer training and evaluation script', add_help=False)
parser.add_argument('--cfg', type=str, required=True, metavar="FILE", help='path to config file', )
parser.add_argument(
"--opts",
help="Modify config options by adding 'KEY VALUE' pairs. ",
default=None,
nargs='+',
)
# easy config modification
parser.add_argument('--batch-size', type=int, help="batch size for single GPU")
parser.add_argument('--data-path', type=str, help='path to dataset')
parser.add_argument('--zip', action='store_true', help='use zipped dataset instead of folder dataset')
parser.add_argument('--cache-mode', type=str, de
gitextract_gk47a2w1/ ├── .gitignore ├── CODE_OF_CONDUCT.md ├── LICENSE ├── MODELHUB.md ├── README.md ├── SECURITY.md ├── SUPPORT.md ├── config.py ├── configs/ │ ├── simmim/ │ │ ├── simmim_finetune__swin_base__img224_window7__800ep.yaml │ │ ├── simmim_finetune__swinv2_base__img224_window14__800ep.yaml │ │ ├── simmim_pretrain__swin_base__img192_window6__800ep.yaml │ │ └── simmim_pretrain__swinv2_base__img192_window12__800ep.yaml │ ├── swin/ │ │ ├── swin_base_patch4_window12_384_22kto1k_finetune.yaml │ │ ├── swin_base_patch4_window12_384_finetune.yaml │ │ ├── swin_base_patch4_window7_224.yaml │ │ ├── swin_base_patch4_window7_224_22k.yaml │ │ ├── swin_base_patch4_window7_224_22kto1k_finetune.yaml │ │ ├── swin_large_patch4_window12_384_22kto1k_finetune.yaml │ │ ├── swin_large_patch4_window7_224_22k.yaml │ │ ├── swin_large_patch4_window7_224_22kto1k_finetune.yaml │ │ ├── swin_small_patch4_window7_224.yaml │ │ ├── swin_small_patch4_window7_224_22k.yaml │ │ ├── swin_small_patch4_window7_224_22kto1k_finetune.yaml │ │ ├── swin_tiny_c24_patch4_window8_256.yaml │ │ ├── swin_tiny_patch4_window7_224.yaml │ │ ├── swin_tiny_patch4_window7_224_22k.yaml │ │ └── swin_tiny_patch4_window7_224_22kto1k_finetune.yaml │ ├── swinmlp/ │ │ ├── swin_mlp_base_patch4_window7_224.yaml │ │ ├── swin_mlp_tiny_c12_patch4_window8_256.yaml │ │ ├── swin_mlp_tiny_c24_patch4_window8_256.yaml │ │ └── swin_mlp_tiny_c6_patch4_window8_256.yaml │ ├── swinmoe/ │ │ ├── swin_moe_base_patch4_window12_192_16expert_32gpu_22k.yaml │ │ ├── swin_moe_base_patch4_window12_192_32expert_32gpu_22k.yaml │ │ ├── swin_moe_base_patch4_window12_192_8expert_32gpu_22k.yaml │ │ ├── swin_moe_base_patch4_window12_192_cosine_router_32expert_32gpu_22k.yaml │ │ ├── swin_moe_base_patch4_window12_192_densebaseline_22k.yaml │ │ ├── swin_moe_small_patch4_window12_192_16expert_32gpu_22k.yaml │ │ ├── swin_moe_small_patch4_window12_192_32expert_32gpu_22k.yaml │ │ ├── swin_moe_small_patch4_window12_192_64expert_64gpu_22k.yaml │ │ ├── swin_moe_small_patch4_window12_192_8expert_32gpu_22k.yaml │ │ ├── swin_moe_small_patch4_window12_192_cosine_router_32expert_32gpu_22k.yaml │ │ └── swin_moe_small_patch4_window12_192_densebaseline_22k.yaml │ └── swinv2/ │ ├── swinv2_base_patch4_window12_192_22k.yaml │ ├── swinv2_base_patch4_window12to16_192to256_22kto1k_ft.yaml │ ├── swinv2_base_patch4_window12to24_192to384_22kto1k_ft.yaml │ ├── swinv2_base_patch4_window16_256.yaml │ ├── swinv2_base_patch4_window8_256.yaml │ ├── swinv2_large_patch4_window12_192_22k.yaml │ ├── swinv2_large_patch4_window12to16_192to256_22kto1k_ft.yaml │ ├── swinv2_large_patch4_window12to24_192to384_22kto1k_ft.yaml │ ├── swinv2_small_patch4_window16_256.yaml │ ├── swinv2_small_patch4_window8_256.yaml │ ├── swinv2_tiny_patch4_window16_256.yaml │ └── swinv2_tiny_patch4_window8_256.yaml ├── data/ │ ├── __init__.py │ ├── build.py │ ├── cached_image_folder.py │ ├── data_simmim_ft.py │ ├── data_simmim_pt.py │ ├── imagenet22k_dataset.py │ ├── map22kto1k.txt │ ├── samplers.py │ └── zipreader.py ├── get_started.md ├── kernels/ │ └── window_process/ │ ├── setup.py │ ├── swin_window_process.cpp │ ├── swin_window_process_kernel.cu │ ├── unit_test.py │ └── window_process.py ├── logger.py ├── lr_scheduler.py ├── main.py ├── main_moe.py ├── main_simmim_ft.py ├── main_simmim_pt.py ├── models/ │ ├── __init__.py │ ├── build.py │ ├── simmim.py │ ├── swin_mlp.py │ ├── swin_transformer.py │ ├── swin_transformer_moe.py │ └── swin_transformer_v2.py ├── optimizer.py ├── utils.py ├── utils_moe.py └── utils_simmim.py
SYMBOL INDEX (316 symbols across 28 files)
FILE: config.py
function _update_config_from_file (line 268) | def _update_config_from_file(config, cfg_file):
function update_config (line 283) | def update_config(config, args):
function get_config (line 352) | def get_config(args):
FILE: data/__init__.py
function build_loader (line 6) | def build_loader(config, simmim=False, is_pretrain=False):
FILE: data/build.py
function _pil_interp (line 25) | def _pil_interp(method):
function build_loader (line 44) | def build_loader(config):
function build_dataset (line 98) | def build_dataset(is_train, config):
function build_transform (line 125) | def build_transform(is_train, config):
FILE: data/cached_image_folder.py
function has_file_allowed_extension (line 18) | def has_file_allowed_extension(filename, extensions):
function find_classes (line 29) | def find_classes(dir):
function make_dataset (line 36) | def make_dataset(dir, class_to_idx, extensions):
function make_dataset_with_ann (line 54) | def make_dataset_with_ann(ann_file, img_prefix, extensions):
class DatasetFolder (line 71) | class DatasetFolder(data.Dataset):
method __init__ (line 92) | def __init__(self, root, loader, extensions, ann_file='', img_prefix='...
method init_cache (line 123) | def init_cache(self):
method __getitem__ (line 145) | def __getitem__(self, index):
method __len__ (line 161) | def __len__(self):
method __repr__ (line 164) | def __repr__(self):
function pil_loader (line 178) | def pil_loader(path):
function accimage_loader (line 192) | def accimage_loader(path):
function default_img_loader (line 201) | def default_img_loader(path):
class CachedImageFolder (line 209) | class CachedImageFolder(DatasetFolder):
method __init__ (line 228) | def __init__(self, root, ann_file='', img_prefix='', transform=None, t...
method __getitem__ (line 236) | def __getitem__(self, index):
FILE: data/data_simmim_ft.py
function build_loader_finetune (line 18) | def build_loader_finetune(config):
function build_dataset (line 61) | def build_dataset(is_train, config):
function build_transform (line 75) | def build_transform(is_train, config):
FILE: data/data_simmim_pt.py
class MaskGenerator (line 21) | class MaskGenerator:
method __init__ (line 22) | def __init__(self, input_size=192, mask_patch_size=32, model_patch_siz...
method __call__ (line 37) | def __call__(self):
class SimMIMTransform (line 48) | class SimMIMTransform:
method __init__ (line 49) | def __init__(self, config):
method __call__ (line 70) | def __call__(self, img):
function collate_fn (line 77) | def collate_fn(batch):
function build_loader_simmim (line 92) | def build_loader_simmim(config):
FILE: data/imagenet22k_dataset.py
class IN22KDATASET (line 12) | class IN22KDATASET(data.Dataset):
method __init__ (line 13) | def __init__(self, root, ann_file='', transform=None, target_transform...
method _load_image (line 24) | def _load_image(self, path):
method __getitem__ (line 33) | def __getitem__(self, index):
method __len__ (line 54) | def __len__(self):
FILE: data/samplers.py
class SubsetRandomSampler (line 11) | class SubsetRandomSampler(torch.utils.data.Sampler):
method __init__ (line 18) | def __init__(self, indices):
method __iter__ (line 22) | def __iter__(self):
method __len__ (line 25) | def __len__(self):
method set_epoch (line 28) | def set_epoch(self, epoch):
FILE: data/zipreader.py
function is_zip_path (line 18) | def is_zip_path(img_or_path):
class ZipReader (line 23) | class ZipReader(object):
method __init__ (line 27) | def __init__(self):
method get_zipfile (line 31) | def get_zipfile(path):
method split_zip_style_path (line 39) | def split_zip_style_path(path):
method list_folder (line 49) | def list_folder(path):
method list_files (line 67) | def list_files(path, extension=None):
method read (line 86) | def read(path):
method imread (line 93) | def imread(path):
FILE: kernels/window_process/swin_window_process.cpp
function roll_and_window_partition_forward (line 70) | at::Tensor roll_and_window_partition_forward(
function roll_and_window_partition_backward (line 84) | at::Tensor roll_and_window_partition_backward(
function window_merge_and_roll_forward (line 98) | at::Tensor window_merge_and_roll_forward(
function window_merge_and_roll_backward (line 112) | at::Tensor window_merge_and_roll_backward(
function PYBIND11_MODULE (line 127) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
FILE: kernels/window_process/unit_test.py
class WindowProcess (line 14) | class WindowProcess(torch.autograd.Function):
method forward (line 16) | def forward(ctx, input, B, H, W, C, shift_size, window_size):
method backward (line 28) | def backward(ctx, grad_in):
class WindowProcessReverse (line 40) | class WindowProcessReverse(torch.autograd.Function):
method forward (line 42) | def forward(ctx, input, B, H, W, C, shift_size, window_size):
method backward (line 55) | def backward(ctx, grad_in):
function window_partition (line 67) | def window_partition(x, window_size):
function window_reverse (line 80) | def window_reverse(windows, window_size, H, W):
function pyt_forward (line 96) | def pyt_forward(x, shift_size, window_size):
function reverse_pyt_forward (line 108) | def reverse_pyt_forward(attn_windows, shift_size, window_size, H, W):
function copy_one_tensor (line 118) | def copy_one_tensor(input, requires_grad=True):
class Test_WindowProcess (line 122) | class Test_WindowProcess(unittest.TestCase):
method setUp (line 123) | def setUp(self):
method test_roll_and_window_partition_forward (line 133) | def test_roll_and_window_partition_forward(self, dtype=torch.float32):
method test_roll_and_window_partition_backward (line 148) | def test_roll_and_window_partition_backward(self, dtype=torch.float32):
method test_window_merge_and_roll_forward (line 165) | def test_window_merge_and_roll_forward(self, dtype=torch.float32):
method test_window_merge_and_roll_backward (line 181) | def test_window_merge_and_roll_backward(self, dtype=torch.float32):
method test_forward_backward_speed (line 198) | def test_forward_backward_speed(self, dtype=torch.float32, times=1000):
method test_roll_and_window_partition_forward_fp16 (line 231) | def test_roll_and_window_partition_forward_fp16(self, dtype=torch.floa...
method test_roll_and_window_partition_backward_fp16 (line 234) | def test_roll_and_window_partition_backward_fp16(self, dtype=torch.flo...
method test_window_merge_and_roll_forward_fp16 (line 237) | def test_window_merge_and_roll_forward_fp16(self, dtype=torch.float16):
method test_window_merge_and_roll_backward_fp16 (line 240) | def test_window_merge_and_roll_backward_fp16(self, dtype=torch.float16):
method test_forward_backward_speed_fp16 (line 243) | def test_forward_backward_speed_fp16(self, dtype=torch.float16, times=...
FILE: kernels/window_process/window_process.py
class WindowProcess (line 11) | class WindowProcess(torch.autograd.Function):
method forward (line 13) | def forward(ctx, input, B, H, W, C, shift_size, window_size):
method backward (line 25) | def backward(ctx, grad_in):
class WindowProcessReverse (line 37) | class WindowProcessReverse(torch.autograd.Function):
method forward (line 39) | def forward(ctx, input, B, H, W, C, shift_size, window_size):
method backward (line 52) | def backward(ctx, grad_in):
FILE: logger.py
function create_logger (line 16) | def create_logger(output_dir, dist_rank=0, name=''):
FILE: lr_scheduler.py
function build_scheduler (line 16) | def build_scheduler(config, optimizer, n_iter_per_epoch):
class LinearLRScheduler (line 66) | class LinearLRScheduler(Scheduler):
method __init__ (line 67) | def __init__(self,
method _get_lr (line 96) | def _get_lr(self, t):
method get_epoch_values (line 105) | def get_epoch_values(self, epoch: int):
method get_update_values (line 111) | def get_update_values(self, num_updates: int):
class MultiStepLRScheduler (line 118) | class MultiStepLRScheduler(Scheduler):
method __init__ (line 119) | def __init__(self, optimizer: torch.optim.Optimizer, milestones, gamma...
method _get_lr (line 135) | def _get_lr(self, t):
method get_epoch_values (line 142) | def get_epoch_values(self, epoch: int):
method get_update_values (line 148) | def get_update_values(self, num_updates: int):
FILE: main.py
function parse_option (line 36) | def parse_option():
function main (line 90) | def main(config):
function train_one_epoch (line 174) | def train_one_epoch(config, model, criterion, data_loader, optimizer, ep...
function validate (line 235) | def validate(config, data_loader, model):
function throughput (line 283) | def throughput(data_loader, model, logger):
FILE: main_moe.py
function parse_option (line 40) | def parse_option():
function main (line 86) | def main(config):
function train_one_epoch (line 184) | def train_one_epoch(config, model, criterion, data_loader, optimizer, ep...
function validate (line 252) | def validate(config, data_loader, model):
function throughput (line 302) | def throughput(data_loader, model, logger):
FILE: main_simmim_ft.py
function parse_option (line 36) | def parse_option():
function main (line 76) | def main(config):
function train_one_epoch (line 155) | def train_one_epoch(config, model, criterion, data_loader, optimizer, ep...
function validate (line 230) | def validate(config, data_loader, model):
function throughput (line 277) | def throughput(data_loader, model, logger):
FILE: main_simmim_pt.py
function parse_option (line 33) | def parse_option():
function main (line 70) | def main(config):
function train_one_epoch (line 120) | def train_one_epoch(config, model, data_loader, optimizer, epoch, lr_sch...
FILE: models/build.py
function build_model (line 15) | def build_model(config, is_pretrain=False):
FILE: models/simmim.py
function norm_targets (line 21) | def norm_targets(targets, patch_size):
class SwinTransformerForSimMIM (line 41) | class SwinTransformerForSimMIM(SwinTransformer):
method __init__ (line 42) | def __init__(self, **kwargs):
method forward (line 50) | def forward(self, x, mask):
method no_weight_decay (line 75) | def no_weight_decay(self):
class SwinTransformerV2ForSimMIM (line 79) | class SwinTransformerV2ForSimMIM(SwinTransformerV2):
method __init__ (line 80) | def __init__(self, **kwargs):
method forward (line 88) | def forward(self, x, mask):
method no_weight_decay (line 113) | def no_weight_decay(self):
class SimMIM (line 117) | class SimMIM(nn.Module):
method __init__ (line 118) | def __init__(self, config, encoder, encoder_stride, in_chans, patch_si...
method forward (line 134) | def forward(self, x, mask):
method no_weight_decay (line 149) | def no_weight_decay(self):
method no_weight_decay_keywords (line 155) | def no_weight_decay_keywords(self):
function build_simmim (line 161) | def build_simmim(config):
FILE: models/swin_mlp.py
class Mlp (line 15) | class Mlp(nn.Module):
method __init__ (line 16) | def __init__(self, in_features, hidden_features=None, out_features=Non...
method forward (line 25) | def forward(self, x):
function window_partition (line 34) | def window_partition(x, window_size):
function window_reverse (line 49) | def window_reverse(windows, window_size, H, W):
class SwinMLPBlock (line 66) | class SwinMLPBlock(nn.Module):
method __init__ (line 82) | def __init__(self, dim, input_resolution, num_heads, window_size=7, sh...
method forward (line 113) | def forward(self, x):
method extra_repr (line 162) | def extra_repr(self) -> str:
method flops (line 166) | def flops(self):
class PatchMerging (line 185) | class PatchMerging(nn.Module):
method __init__ (line 194) | def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
method forward (line 201) | def forward(self, x):
method extra_repr (line 224) | def extra_repr(self) -> str:
method flops (line 227) | def flops(self):
class BasicLayer (line 234) | class BasicLayer(nn.Module):
method __init__ (line 251) | def __init__(self, dim, input_resolution, depth, num_heads, window_size,
method forward (line 278) | def forward(self, x):
method extra_repr (line 288) | def extra_repr(self) -> str:
method flops (line 291) | def flops(self):
class PatchEmbed (line 300) | class PatchEmbed(nn.Module):
method __init__ (line 311) | def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=9...
method forward (line 330) | def forward(self, x):
method flops (line 340) | def flops(self):
class SwinMLP (line 348) | class SwinMLP(nn.Module):
method __init__ (line 369) | def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes...
method _init_weights (line 425) | def _init_weights(self, m):
method no_weight_decay (line 435) | def no_weight_decay(self):
method no_weight_decay_keywords (line 439) | def no_weight_decay_keywords(self):
method forward_features (line 442) | def forward_features(self, x):
method forward (line 456) | def forward(self, x):
method flops (line 461) | def flops(self):
FILE: models/swin_transformer.py
class Mlp (line 26) | class Mlp(nn.Module):
method __init__ (line 27) | def __init__(self, in_features, hidden_features=None, out_features=Non...
method forward (line 36) | def forward(self, x):
function window_partition (line 45) | def window_partition(x, window_size):
function window_reverse (line 60) | def window_reverse(windows, window_size, H, W):
class WindowAttention (line 77) | class WindowAttention(nn.Module):
method __init__ (line 91) | def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scal...
method forward (line 125) | def forward(self, x, mask=None):
method extra_repr (line 158) | def extra_repr(self) -> str:
method flops (line 161) | def flops(self, N):
class SwinTransformerBlock (line 175) | class SwinTransformerBlock(nn.Module):
method __init__ (line 195) | def __init__(self, dim, input_resolution, num_heads, window_size=7, sh...
method forward (line 248) | def forward(self, x):
method extra_repr (line 296) | def extra_repr(self) -> str:
method flops (line 300) | def flops(self):
class PatchMerging (line 315) | class PatchMerging(nn.Module):
method __init__ (line 324) | def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
method forward (line 331) | def forward(self, x):
method extra_repr (line 354) | def extra_repr(self) -> str:
method flops (line 357) | def flops(self):
class BasicLayer (line 364) | class BasicLayer(nn.Module):
method __init__ (line 385) | def __init__(self, dim, input_resolution, depth, num_heads, window_size,
method forward (line 415) | def forward(self, x):
method extra_repr (line 425) | def extra_repr(self) -> str:
method flops (line 428) | def flops(self):
class PatchEmbed (line 437) | class PatchEmbed(nn.Module):
method __init__ (line 448) | def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=9...
method forward (line 467) | def forward(self, x):
method flops (line 477) | def flops(self):
class SwinTransformer (line 485) | class SwinTransformer(nn.Module):
method __init__ (line 512) | def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes...
method _init_weights (line 571) | def _init_weights(self, m):
method no_weight_decay (line 581) | def no_weight_decay(self):
method no_weight_decay_keywords (line 585) | def no_weight_decay_keywords(self):
method forward_features (line 588) | def forward_features(self, x):
method forward (line 602) | def forward(self, x):
method flops (line 607) | def flops(self):
FILE: models/swin_transformer_moe.py
class Mlp (line 23) | class Mlp(nn.Module):
method __init__ (line 24) | def __init__(self, in_features, hidden_features=None, out_features=Non...
method forward (line 34) | def forward(self, x):
class MoEMlp (line 43) | class MoEMlp(nn.Module):
method __init__ (line 44) | def __init__(self, in_features, hidden_features, num_local_experts, to...
method forward (line 86) | def forward(self, x):
method extra_repr (line 90) | def extra_repr(self) -> str:
method _init_weights (line 96) | def _init_weights(self):
function window_partition (line 104) | def window_partition(x, window_size):
function window_reverse (line 119) | def window_reverse(windows, window_size, H, W):
class WindowAttention (line 136) | class WindowAttention(nn.Module):
method __init__ (line 151) | def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scal...
method forward (line 205) | def forward(self, x, mask=None):
method extra_repr (line 239) | def extra_repr(self) -> str:
method flops (line 243) | def flops(self, N):
class SwinTransformerBlock (line 257) | class SwinTransformerBlock(nn.Module):
method __init__ (line 292) | def __init__(self, dim, input_resolution, num_heads, window_size=7, sh...
method forward (line 369) | def forward(self, x):
method extra_repr (line 414) | def extra_repr(self) -> str:
method flops (line 418) | def flops(self):
class PatchMerging (line 436) | class PatchMerging(nn.Module):
method __init__ (line 445) | def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
method forward (line 452) | def forward(self, x):
method extra_repr (line 475) | def extra_repr(self) -> str:
method flops (line 478) | def flops(self):
class BasicLayer (line 485) | class BasicLayer(nn.Module):
method __init__ (line 521) | def __init__(self, dim, input_resolution, depth, num_heads, window_size,
method forward (line 569) | def forward(self, x):
method extra_repr (line 587) | def extra_repr(self) -> str:
method flops (line 590) | def flops(self):
class PatchEmbed (line 599) | class PatchEmbed(nn.Module):
method __init__ (line 610) | def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=9...
method forward (line 629) | def forward(self, x):
method flops (line 639) | def flops(self):
class SwinTransformerMoE (line 647) | class SwinTransformerMoE(nn.Module):
method __init__ (line 690) | def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes...
method _init_weights (line 774) | def _init_weights(self, m):
method no_weight_decay (line 786) | def no_weight_decay(self):
method no_weight_decay_keywords (line 790) | def no_weight_decay_keywords(self):
method forward_features (line 794) | def forward_features(self, x):
method forward (line 809) | def forward(self, x):
method add_param_to_skip_allreduce (line 814) | def add_param_to_skip_allreduce(self, param_name):
method flops (line 817) | def flops(self):
FILE: models/swin_transformer_v2.py
class Mlp (line 16) | class Mlp(nn.Module):
method __init__ (line 17) | def __init__(self, in_features, hidden_features=None, out_features=Non...
method forward (line 26) | def forward(self, x):
function window_partition (line 35) | def window_partition(x, window_size):
function window_reverse (line 50) | def window_reverse(windows, window_size, H, W):
class WindowAttention (line 67) | class WindowAttention(nn.Module):
method __init__ (line 81) | def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_dr...
method forward (line 140) | def forward(self, x, mask=None):
method extra_repr (line 181) | def extra_repr(self) -> str:
method flops (line 185) | def flops(self, N):
class SwinTransformerBlock (line 199) | class SwinTransformerBlock(nn.Module):
method __init__ (line 218) | def __init__(self, dim, input_resolution, num_heads, window_size=7, sh...
method forward (line 270) | def forward(self, x):
method extra_repr (line 308) | def extra_repr(self) -> str:
method flops (line 312) | def flops(self):
class PatchMerging (line 327) | class PatchMerging(nn.Module):
method __init__ (line 336) | def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
method forward (line 343) | def forward(self, x):
method extra_repr (line 366) | def extra_repr(self) -> str:
method flops (line 369) | def flops(self):
class BasicLayer (line 376) | class BasicLayer(nn.Module):
method __init__ (line 396) | def __init__(self, dim, input_resolution, depth, num_heads, window_size,
method forward (line 426) | def forward(self, x):
method extra_repr (line 436) | def extra_repr(self) -> str:
method flops (line 439) | def flops(self):
method _init_respostnorm (line 447) | def _init_respostnorm(self):
class PatchEmbed (line 455) | class PatchEmbed(nn.Module):
method __init__ (line 466) | def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=9...
method forward (line 485) | def forward(self, x):
method flops (line 495) | def flops(self):
class SwinTransformerV2 (line 503) | class SwinTransformerV2(nn.Module):
method __init__ (line 529) | def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes...
method _init_weights (line 590) | def _init_weights(self, m):
method no_weight_decay (line 600) | def no_weight_decay(self):
method no_weight_decay_keywords (line 604) | def no_weight_decay_keywords(self):
method forward_features (line 607) | def forward_features(self, x):
method forward (line 621) | def forward(self, x):
method flops (line 626) | def flops(self):
FILE: optimizer.py
function build_optimizer (line 19) | def build_optimizer(config, model, simmim=False, is_pretrain=False):
function set_weight_decay (line 59) | def set_weight_decay(model, skip_list=(), skip_keywords=()):
function check_keywords_in_name (line 76) | def check_keywords_in_name(name, keywords=()):
function get_pretrain_param_groups (line 84) | def get_pretrain_param_groups(model, skip_list=(), skip_keywords=()):
function get_swin_layer (line 104) | def get_swin_layer(name, num_layers, depths):
function get_finetune_param_groups (line 120) | def get_finetune_param_groups(model, lr, weight_decay, get_layer_func, s...
FILE: utils.py
function load_checkpoint (line 18) | def load_checkpoint(config, model, optimizer, lr_scheduler, loss_scaler,...
function load_pretrained (line 45) | def load_pretrained(config, model, logger):
function save_checkpoint (line 135) | def save_checkpoint(config, epoch, model, max_accuracy, optimizer, lr_sc...
function get_grad_norm (line 150) | def get_grad_norm(parameters, norm_type=2):
function auto_resume_helper (line 163) | def auto_resume_helper(output_dir):
function reduce_tensor (line 176) | def reduce_tensor(tensor):
function ampscaler_get_grad_norm (line 183) | def ampscaler_get_grad_norm(parameters, norm_type: float = 2.0) -> torch...
class NativeScalerWithGradNormCount (line 199) | class NativeScalerWithGradNormCount:
method __init__ (line 202) | def __init__(self):
method __call__ (line 205) | def __call__(self, loss, optimizer, clip_grad=None, parameters=None, c...
method state_dict (line 221) | def state_dict(self):
method load_state_dict (line 224) | def load_state_dict(self, state_dict):
FILE: utils_moe.py
function split_moe_model_state_dict (line 13) | def split_moe_model_state_dict(moe_keys, model_state_dict):
function merge_moe_model_state_dict (line 24) | def merge_moe_model_state_dict(moe_model_state_dict, non_moe_model_state...
function load_checkpoint (line 31) | def load_checkpoint(config, model, optimizer, lr_scheduler, loss_scaler,...
function load_pretrained (line 64) | def load_pretrained(config, model, logger):
function save_checkpoint (line 175) | def save_checkpoint(config, epoch, model, max_accuracy, optimizer, lr_sc...
function auto_resume_helper (line 222) | def auto_resume_helper(output_dir, save_master=False):
function hook_scale_grad (line 240) | def hook_scale_grad(scale, tensor):
FILE: utils_simmim.py
function load_checkpoint (line 16) | def load_checkpoint(config, model, optimizer, lr_scheduler, scaler, logg...
function save_checkpoint (line 53) | def save_checkpoint(config, epoch, model, max_accuracy, optimizer, lr_sc...
function get_grad_norm (line 68) | def get_grad_norm(parameters, norm_type=2):
function auto_resume_helper (line 81) | def auto_resume_helper(output_dir, logger):
function reduce_tensor (line 94) | def reduce_tensor(tensor):
function load_pretrained (line 101) | def load_pretrained(config, model, logger):
function remap_pretrained_keys_swin (line 126) | def remap_pretrained_keys_swin(model, checkpoint_model, logger):
Condensed preview — 86 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (419K chars).
[
{
"path": ".gitignore",
"chars": 1868,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# launch bash\n*.sh\n# nsight system report fil"
},
{
"path": "CODE_OF_CONDUCT.md",
"chars": 444,
"preview": "# Microsoft Open Source Code of Conduct\n\nThis project has adopted the [Microsoft Open Source Code of Conduct](https://op"
},
{
"path": "LICENSE",
"chars": 1141,
"preview": " MIT License\n\n Copyright (c) Microsoft Corporation.\n\n Permission is hereby granted, free of charge, to any pers"
},
{
"path": "MODELHUB.md",
"chars": 40458,
"preview": "Access code for `baidu` is `swin`.\n\n## ImageNet-1K and ImageNet-22K Pretrained Swin-V1 Models\n\n| name | pretrain | resol"
},
{
"path": "README.md",
"chars": 30326,
"preview": "# Swin Transformer\n\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/swin-transformer-v2"
},
{
"path": "SECURITY.md",
"chars": 2780,
"preview": "<!-- BEGIN MICROSOFT SECURITY.MD V0.0.5 BLOCK -->\n\n## Security\n\nMicrosoft takes the security of our software products an"
},
{
"path": "SUPPORT.md",
"chars": 1315,
"preview": "# TODO: The maintainer of this repo has not yet edited this file\r\n\r\n**REPO OWNER**: Do you want Customer Service & Suppo"
},
{
"path": "config.py",
"chars": 11787,
"preview": "# --------------------------------------------------------\n# Swin Transformer\n# Copyright (c) 2021 Microsoft\n# Licensed "
},
{
"path": "configs/simmim/simmim_finetune__swin_base__img224_window7__800ep.yaml",
"chars": 414,
"preview": "MODEL:\n TYPE: swin\n NAME: simmim_finetune\n DROP_PATH_RATE: 0.1\n SWIN:\n EMBED_DIM: 128\n DEPTHS: [ 2, 2, 18, 2 ]"
},
{
"path": "configs/simmim/simmim_finetune__swinv2_base__img224_window14__800ep.yaml",
"chars": 470,
"preview": "MODEL:\n TYPE: swinv2\n NAME: simmim_finetune\n DROP_PATH_RATE: 0.1\n SWINV2:\n EMBED_DIM: 128\n DEPTHS: [ 2, 2, 18,"
},
{
"path": "configs/simmim/simmim_pretrain__swin_base__img192_window6__800ep.yaml",
"chars": 489,
"preview": "MODEL:\n TYPE: swin\n NAME: simmim_pretrain\n DROP_PATH_RATE: 0.0\n SWIN:\n EMBED_DIM: 128\n DEPTHS: [ 2, 2, 18, 2 ]"
},
{
"path": "configs/simmim/simmim_pretrain__swinv2_base__img192_window12__800ep.yaml",
"chars": 564,
"preview": "MODEL:\n TYPE: swinv2\n NAME: simmim_pretrain\n DROP_PATH_RATE: 0.1\n SIMMIM:\n NORM_TARGET:\n ENABLE: True\n "
},
{
"path": "configs/swin/swin_base_patch4_window12_384_22kto1k_finetune.yaml",
"chars": 357,
"preview": "DATA:\n IMG_SIZE: 384\nMODEL:\n TYPE: swin\n NAME: swin_base_patch4_window12_384_22kto1k_finetune\n DROP_PATH_RATE: 0.2\n "
},
{
"path": "configs/swin/swin_base_patch4_window12_384_finetune.yaml",
"chars": 349,
"preview": "DATA:\n IMG_SIZE: 384\nMODEL:\n TYPE: swin\n NAME: swin_base_patch4_window12_384_finetune\n DROP_PATH_RATE: 0.5\n SWIN:\n "
},
{
"path": "configs/swin/swin_base_patch4_window7_224.yaml",
"chars": 184,
"preview": "MODEL:\n TYPE: swin\n NAME: swin_base_patch4_window7_224\n DROP_PATH_RATE: 0.5\n SWIN:\n EMBED_DIM: 128\n DEPTHS: [ "
},
{
"path": "configs/swin/swin_base_patch4_window7_224_22k.yaml",
"chars": 353,
"preview": "DATA:\n DATASET: imagenet22K\nMODEL:\n TYPE: swin\n NAME: swin_base_patch4_window7_224_22k\n DROP_PATH_RATE: 0.2\n SWIN:\n"
},
{
"path": "configs/swin/swin_base_patch4_window7_224_22kto1k_finetune.yaml",
"chars": 313,
"preview": "MODEL:\n TYPE: swin\n NAME: swin_base_patch4_window7_224_22kto1k_finetune\n DROP_PATH_RATE: 0.2\n SWIN:\n EMBED_DIM: 1"
},
{
"path": "configs/swin/swin_large_patch4_window12_384_22kto1k_finetune.yaml",
"chars": 359,
"preview": "DATA:\n IMG_SIZE: 384\nMODEL:\n TYPE: swin\n NAME: swin_large_patch4_window12_384_22kto1k_finetune\n DROP_PATH_RATE: 0.2\n"
},
{
"path": "configs/swin/swin_large_patch4_window7_224_22k.yaml",
"chars": 355,
"preview": "DATA:\n DATASET: imagenet22K\nMODEL:\n TYPE: swin\n NAME: swin_large_patch4_window7_224_22k\n DROP_PATH_RATE: 0.2\n SWIN:"
},
{
"path": "configs/swin/swin_large_patch4_window7_224_22kto1k_finetune.yaml",
"chars": 315,
"preview": "MODEL:\n TYPE: swin\n NAME: swin_large_patch4_window7_224_22kto1k_finetune\n DROP_PATH_RATE: 0.2\n SWIN:\n EMBED_DIM: "
},
{
"path": "configs/swin/swin_small_patch4_window7_224.yaml",
"chars": 184,
"preview": "MODEL:\n TYPE: swin\n NAME: swin_small_patch4_window7_224\n DROP_PATH_RATE: 0.3\n SWIN:\n EMBED_DIM: 96\n DEPTHS: [ "
},
{
"path": "configs/swin/swin_small_patch4_window7_224_22k.yaml",
"chars": 353,
"preview": "DATA:\n DATASET: imagenet22K\nMODEL:\n TYPE: swin\n NAME: swin_small_patch4_window7_224_22k\n DROP_PATH_RATE: 0.2\n SWIN:"
},
{
"path": "configs/swin/swin_small_patch4_window7_224_22kto1k_finetune.yaml",
"chars": 313,
"preview": "MODEL:\n TYPE: swin\n NAME: swin_small_patch4_window7_224_22kto1k_finetune\n DROP_PATH_RATE: 0.2\n SWIN:\n EMBED_DIM: "
},
{
"path": "configs/swin/swin_tiny_c24_patch4_window8_256.yaml",
"chars": 208,
"preview": "DATA:\n IMG_SIZE: 256\nMODEL:\n TYPE: swin\n NAME: swin_tiny_c24_patch4_window8_256\n DROP_PATH_RATE: 0.2\n SWIN:\n EMB"
},
{
"path": "configs/swin/swin_tiny_patch4_window7_224.yaml",
"chars": 182,
"preview": "MODEL:\n TYPE: swin\n NAME: swin_tiny_patch4_window7_224\n DROP_PATH_RATE: 0.2\n SWIN:\n EMBED_DIM: 96\n DEPTHS: [ 2"
},
{
"path": "configs/swin/swin_tiny_patch4_window7_224_22k.yaml",
"chars": 351,
"preview": "DATA:\n DATASET: imagenet22K\nMODEL:\n TYPE: swin\n NAME: swin_tiny_patch4_window7_224_22k\n DROP_PATH_RATE: 0.1\n SWIN:\n"
},
{
"path": "configs/swin/swin_tiny_patch4_window7_224_22kto1k_finetune.yaml",
"chars": 311,
"preview": "MODEL:\n TYPE: swin\n NAME: swin_tiny_patch4_window7_224_22kto1k_finetune\n DROP_PATH_RATE: 0.1\n SWIN:\n EMBED_DIM: 9"
},
{
"path": "configs/swinmlp/swin_mlp_base_patch4_window7_224.yaml",
"chars": 197,
"preview": "MODEL:\n TYPE: swin_mlp\n NAME: swin_mlp_base_patch4_window7_224\n DROP_PATH_RATE: 0.5\n SWIN_MLP:\n EMBED_DIM: 128\n "
},
{
"path": "configs/swinmlp/swin_mlp_tiny_c12_patch4_window8_256.yaml",
"chars": 221,
"preview": "DATA:\n IMG_SIZE: 256\nMODEL:\n TYPE: swin_mlp\n NAME: swin_mlp_tiny_c12_patch4_window8_256\n DROP_PATH_RATE: 0.2\n SWIN_"
},
{
"path": "configs/swinmlp/swin_mlp_tiny_c24_patch4_window8_256.yaml",
"chars": 220,
"preview": "DATA:\n IMG_SIZE: 256\nMODEL:\n TYPE: swin_mlp\n NAME: swin_mlp_tiny_c24_patch4_window8_256\n DROP_PATH_RATE: 0.2\n SWIN_"
},
{
"path": "configs/swinmlp/swin_mlp_tiny_c6_patch4_window8_256.yaml",
"chars": 222,
"preview": "DATA:\n IMG_SIZE: 256\nMODEL:\n TYPE: swin_mlp\n NAME: swin_mlp_tiny_c6_patch4_window8_256\n DROP_PATH_RATE: 0.2\n SWIN_M"
},
{
"path": "configs/swinmoe/swin_moe_base_patch4_window12_192_16expert_32gpu_22k.yaml",
"chars": 697,
"preview": "DATA:\n DATASET: imagenet22K\n IMG_SIZE: 192\nMODEL:\n TYPE: swin_moe\n NAME: swin_moe_base_patch4_window12_192_16expert_"
},
{
"path": "configs/swinmoe/swin_moe_base_patch4_window12_192_32expert_32gpu_22k.yaml",
"chars": 696,
"preview": "DATA:\n DATASET: imagenet22K\n IMG_SIZE: 192\nMODEL:\n TYPE: swin_moe\n NAME: swin_moe_base_patch4_window12_192_32expert_"
},
{
"path": "configs/swinmoe/swin_moe_base_patch4_window12_192_8expert_32gpu_22k.yaml",
"chars": 696,
"preview": "DATA:\n DATASET: imagenet22K\n IMG_SIZE: 192\nMODEL:\n TYPE: swin_moe\n NAME: swin_moe_base_patch4_window12_192_8expert_3"
},
{
"path": "configs/swinmoe/swin_moe_base_patch4_window12_192_cosine_router_32expert_32gpu_22k.yaml",
"chars": 734,
"preview": "DATA:\n DATASET: imagenet22K\n IMG_SIZE: 192\nMODEL:\n TYPE: swin_moe\n NAME: swin_moe_base_patch4_window12_192_cosine_ro"
},
{
"path": "configs/swinmoe/swin_moe_base_patch4_window12_192_densebaseline_22k.yaml",
"chars": 540,
"preview": "DATA:\n DATASET: imagenet22K\n IMG_SIZE: 192\nMODEL:\n TYPE: swin_moe\n NAME: swin_moe_base_patch4_window12_192_densebase"
},
{
"path": "configs/swinmoe/swin_moe_small_patch4_window12_192_16expert_32gpu_22k.yaml",
"chars": 697,
"preview": "DATA:\n DATASET: imagenet22K\n IMG_SIZE: 192\nMODEL:\n TYPE: swin_moe\n NAME: swin_moe_small_patch4_window12_192_16expert"
},
{
"path": "configs/swinmoe/swin_moe_small_patch4_window12_192_32expert_32gpu_22k.yaml",
"chars": 696,
"preview": "DATA:\n DATASET: imagenet22K\n IMG_SIZE: 192\nMODEL:\n TYPE: swin_moe\n NAME: swin_moe_small_patch4_window12_192_32expert"
},
{
"path": "configs/swinmoe/swin_moe_small_patch4_window12_192_64expert_64gpu_22k.yaml",
"chars": 696,
"preview": "DATA:\n DATASET: imagenet22K\n IMG_SIZE: 192\nMODEL:\n TYPE: swin_moe\n NAME: swin_moe_small_patch4_window12_192_64expert"
},
{
"path": "configs/swinmoe/swin_moe_small_patch4_window12_192_8expert_32gpu_22k.yaml",
"chars": 696,
"preview": "DATA:\n DATASET: imagenet22K\n IMG_SIZE: 192\nMODEL:\n TYPE: swin_moe\n NAME: swin_moe_small_patch4_window12_192_8expert_"
},
{
"path": "configs/swinmoe/swin_moe_small_patch4_window12_192_cosine_router_32expert_32gpu_22k.yaml",
"chars": 734,
"preview": "DATA:\n DATASET: imagenet22K\n IMG_SIZE: 192\nMODEL:\n TYPE: swin_moe\n NAME: swin_moe_small_patch4_window12_192_cosine_r"
},
{
"path": "configs/swinmoe/swin_moe_small_patch4_window12_192_densebaseline_22k.yaml",
"chars": 540,
"preview": "DATA:\n DATASET: imagenet22K\n IMG_SIZE: 192\nMODEL:\n TYPE: swin_moe\n NAME: swin_moe_small_patch4_window12_192_densebas"
},
{
"path": "configs/swinv2/swinv2_base_patch4_window12_192_22k.yaml",
"chars": 376,
"preview": "DATA:\n DATASET: imagenet22K\n IMG_SIZE: 192\nMODEL:\n TYPE: swinv2\n NAME: swinv2_base_patch4_window12_192_22k\n DROP_PA"
},
{
"path": "configs/swinv2/swinv2_base_patch4_window12to16_192to256_22kto1k_ft.yaml",
"chars": 393,
"preview": "DATA:\n IMG_SIZE: 256\nMODEL:\n TYPE: swinv2\n NAME: swinv2_base_patch4_window12to16_192to256_22kto1k_ft\n DROP_PATH_RATE"
},
{
"path": "configs/swinv2/swinv2_base_patch4_window12to24_192to384_22kto1k_ft.yaml",
"chars": 413,
"preview": "DATA:\n IMG_SIZE: 384\nMODEL:\n TYPE: swinv2\n NAME: swinv2_base_patch4_window12to24_192to384_22kto1k_ft\n DROP_PATH_RATE"
},
{
"path": "configs/swinv2/swinv2_base_patch4_window16_256.yaml",
"chars": 214,
"preview": "DATA:\n IMG_SIZE: 256\nMODEL:\n TYPE: swinv2\n NAME: swinv2_base_patch4_window16_256\n DROP_PATH_RATE: 0.5\n SWINV2:\n "
},
{
"path": "configs/swinv2/swinv2_base_patch4_window8_256.yaml",
"chars": 212,
"preview": "DATA:\n IMG_SIZE: 256\nMODEL:\n TYPE: swinv2\n NAME: swinv2_base_patch4_window8_256\n DROP_PATH_RATE: 0.5\n SWINV2:\n E"
},
{
"path": "configs/swinv2/swinv2_large_patch4_window12_192_22k.yaml",
"chars": 378,
"preview": "DATA:\n DATASET: imagenet22K\n IMG_SIZE: 192\nMODEL:\n TYPE: swinv2\n NAME: swinv2_large_patch4_window12_192_22k\n DROP_P"
},
{
"path": "configs/swinv2/swinv2_large_patch4_window12to16_192to256_22kto1k_ft.yaml",
"chars": 394,
"preview": "DATA:\n IMG_SIZE: 256\nMODEL:\n TYPE: swinv2\n NAME: swinv2_base_patch4_window12to16_192to256_22kto1k_ft\n DROP_PATH_RATE"
},
{
"path": "configs/swinv2/swinv2_large_patch4_window12to24_192to384_22kto1k_ft.yaml",
"chars": 415,
"preview": "DATA:\n IMG_SIZE: 384\nMODEL:\n TYPE: swinv2\n NAME: swinv2_large_patch4_window12to24_192to384_22kto1k_ft\n DROP_PATH_RAT"
},
{
"path": "configs/swinv2/swinv2_small_patch4_window16_256.yaml",
"chars": 214,
"preview": "DATA:\n IMG_SIZE: 256\nMODEL:\n TYPE: swinv2\n NAME: swinv2_small_patch4_window16_256\n DROP_PATH_RATE: 0.3\n SWINV2:\n "
},
{
"path": "configs/swinv2/swinv2_small_patch4_window8_256.yaml",
"chars": 212,
"preview": "DATA:\n IMG_SIZE: 256\nMODEL:\n TYPE: swinv2\n NAME: swinv2_small_patch4_window8_256\n DROP_PATH_RATE: 0.3\n SWINV2:\n "
},
{
"path": "configs/swinv2/swinv2_tiny_patch4_window16_256.yaml",
"chars": 212,
"preview": "DATA:\n IMG_SIZE: 256\nMODEL:\n TYPE: swinv2\n NAME: swinv2_tiny_patch4_window16_256\n DROP_PATH_RATE: 0.2\n SWINV2:\n "
},
{
"path": "configs/swinv2/swinv2_tiny_patch4_window8_256.yaml",
"chars": 210,
"preview": "DATA:\n IMG_SIZE: 256\nMODEL:\n TYPE: swinv2\n NAME: swinv2_tiny_patch4_window8_256\n DROP_PATH_RATE: 0.2\n SWINV2:\n E"
},
{
"path": "data/__init__.py",
"chars": 382,
"preview": "from .build import build_loader as _build_loader\nfrom .data_simmim_pt import build_loader_simmim\nfrom .data_simmim_ft im"
},
{
"path": "data/build.py",
"chars": 6244,
"preview": "# --------------------------------------------------------\n# Swin Transformer\n# Copyright (c) 2021 Microsoft\n# Licensed "
},
{
"path": "data/cached_image_folder.py",
"chars": 9026,
"preview": "# --------------------------------------------------------\n# Swin Transformer\n# Copyright (c) 2021 Microsoft\n# Licensed "
},
{
"path": "data/data_simmim_ft.py",
"chars": 4285,
"preview": "# --------------------------------------------------------\n# SimMIM\n# Copyright (c) 2021 Microsoft\n# Licensed under The "
},
{
"path": "data/data_simmim_pt.py",
"chars": 3639,
"preview": "# --------------------------------------------------------\n# SimMIM\n# Copyright (c) 2021 Microsoft\n# Licensed under The "
},
{
"path": "data/imagenet22k_dataset.py",
"chars": 1681,
"preview": "import os\nimport json\nimport torch.utils.data as data\nimport numpy as np\nfrom PIL import Image\n\nimport warnings\n\nwarning"
},
{
"path": "data/map22kto1k.txt",
"chars": 5191,
"preview": "359\n368\n460\n475\n486\n492\n496\n514\n516\n525\n547\n548\n556\n563\n575\n641\n648\n723\n733\n765\n801\n826\n852\n858\n878\n896\n900\n905\n908\n910\n"
},
{
"path": "data/samplers.py",
"chars": 781,
"preview": "# --------------------------------------------------------\n# Swin Transformer\n# Copyright (c) 2021 Microsoft\n# Licensed "
},
{
"path": "data/zipreader.py",
"chars": 3333,
"preview": "# --------------------------------------------------------\n# Swin Transformer\n# Copyright (c) 2021 Microsoft\n# Licensed "
},
{
"path": "get_started.md",
"chars": 11890,
"preview": "# Swin Transformer for Image Classification\n\nThis folder contains the implementation of the Swin Transformer for image c"
},
{
"path": "kernels/window_process/setup.py",
"chars": 343,
"preview": "from setuptools import setup\nfrom torch.utils.cpp_extension import BuildExtension, CUDAExtension\n\n\nsetup(name='swin_wind"
},
{
"path": "kernels/window_process/swin_window_process.cpp",
"chars": 3704,
"preview": "/*\n * Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.\n *\n * Licensed under the Apache License, Version 2.0"
},
{
"path": "kernels/window_process/swin_window_process_kernel.cu",
"chars": 9693,
"preview": "/*\n * Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.\n *\n * Licensed under the Apache License, Version 2.0"
},
{
"path": "kernels/window_process/unit_test.py",
"chars": 9592,
"preview": "# --------------------------------------------------------\n# Fused kernel for window process for SwinTransformer\n# Copyr"
},
{
"path": "kernels/window_process/window_process.py",
"chars": 2015,
"preview": "# --------------------------------------------------------\n# Fused kernel for window process for SwinTransformer\n# Copyr"
},
{
"path": "logger.py",
"chars": 1451,
"preview": "# --------------------------------------------------------\n# Swin Transformer\n# Copyright (c) 2021 Microsoft\n# Licensed "
},
{
"path": "lr_scheduler.py",
"chars": 5467,
"preview": "# --------------------------------------------------------\n# Swin Transformer\n# Copyright (c) 2021 Microsoft\n# Licensed "
},
{
"path": "main.py",
"chars": 15162,
"preview": "# --------------------------------------------------------\n# Swin Transformer\n# Copyright (c) 2021 Microsoft\n# Licensed "
},
{
"path": "main_moe.py",
"chars": 16503,
"preview": "# --------------------------------------------------------\n# Swin Transformer\n# Copyright (c) 2021 Microsoft\n# Licensed "
},
{
"path": "main_simmim_ft.py",
"chars": 14065,
"preview": "# --------------------------------------------------------\n# SimMIM\n# Copyright (c) 2021 Microsoft\n# Licensed under The "
},
{
"path": "main_simmim_pt.py",
"chars": 9694,
"preview": "# --------------------------------------------------------\n# SimMIM\n# Copyright (c) 2021 Microsoft\n# Licensed under The "
},
{
"path": "models/__init__.py",
"chars": 30,
"preview": "from .build import build_model"
},
{
"path": "models/build.py",
"chars": 7136,
"preview": "# --------------------------------------------------------\n# Swin Transformer\n# Copyright (c) 2021 Microsoft\n# Licensed "
},
{
"path": "models/simmim.py",
"chars": 7119,
"preview": "\n\n# --------------------------------------------------------\n# SimMIM\n# Copyright (c) 2021 Microsoft\n# Licensed under Th"
},
{
"path": "models/swin_mlp.py",
"chars": 18508,
"preview": "# --------------------------------------------------------\n# Swin Transformer\n# Copyright (c) 2021 Microsoft\n# Licensed "
},
{
"path": "models/swin_transformer.py",
"chars": 26070,
"preview": "# --------------------------------------------------------\n# Swin Transformer\n# Copyright (c) 2021 Microsoft\n# Licensed "
},
{
"path": "models/swin_transformer_moe.py",
"chars": 38203,
"preview": "# --------------------------------------------------------\n# Swin Transformer MoE\n# Copyright (c) 2022 Microsoft\n# Licen"
},
{
"path": "models/swin_transformer_v2.py",
"chars": 26870,
"preview": "# --------------------------------------------------------\n# Swin Transformer V2\n# Copyright (c) 2022 Microsoft\n# Licens"
},
{
"path": "optimizer.py",
"chars": 6183,
"preview": "# --------------------------------------------------------\n# Swin Transformer\n# Copyright (c) 2021 Microsoft\n# Licensed "
},
{
"path": "utils.py",
"chars": 9642,
"preview": "# --------------------------------------------------------\n# Swin Transformer\n# Copyright (c) 2021 Microsoft\n# Licensed "
},
{
"path": "utils_moe.py",
"chars": 11397,
"preview": "# --------------------------------------------------------\n# Swin Transformer\n# Copyright (c) 2021 Microsoft\n# Licensed "
},
{
"path": "utils_simmim.py",
"chars": 8415,
"preview": "# --------------------------------------------------------\n# SimMIM\n# Copyright (c) 2021 Microsoft\n# Licensed under The "
}
]
About this extraction
This page contains the full source code of the microsoft/Swin-Transformer GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 86 files (392.8 KB), approximately 117.6k tokens, and a symbol index with 316 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.