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update ckpt path

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configs/riformer/README.md
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@ -9,7 +9,7 @@
RIFormer is a way to keep a vision backbone effective while removing token mixers in its basic building blocks. Equipped with our proposed optimization strategy, we are able to build an extremely simple vision backbone with encouraging performance, while enjoying the high efficiency during inference. RIFormer shares nearly the same macro and micro design as MetaFormer, but safely removing all token mixers. The quantitative results show that our networks outperform many prevailing backbones with faster inference speed on ImageNet-1K. RIFormer is a way to keep a vision backbone effective while removing token mixers in its basic building blocks. Equipped with our proposed optimization strategy, we are able to build an extremely simple vision backbone with encouraging performance, while enjoying the high efficiency during inference. RIFormer shares nearly the same macro and micro design as MetaFormer, but safely removing all token mixers. The quantitative results show that our networks outperform many prevailing backbones with faster inference speed on ImageNet-1K.
<div align=center> <div align=center>
<img src="https://user-images.githubusercontent.com/48375204/223930120-dc075c8e-0513-42eb-9830-469a45c1d941.png" width="60%"/> <img src="https://user-images.githubusercontent.com/48375204/223930120-dc075c8e-0513-42eb-9830-469a45c1d941.png" width="65%"/>
</div> </div>
## Abstract ## Abstract
@ -36,12 +36,12 @@ Use `classifier.backbone.switch_to_deploy()` interface to switch the RIFormer mo
```python ```python
>>> import torch >>> import torch
>>> from mmcls.apis import init_model, inference_model >>> from mmpretrain import get_model, inference_model
>>> >>>
>>> model = init_model('configs/riformer/riformer-s12_8xb128_in1k.py', '/home/PJLAB/wangjiahao/project/RepIndentityFormer/mmcls_pretrained/224/repidentityformer-s12.pth.tar') >>> model = get_model("riformer-s12_in1k", pretrained=True)
>>> results = inference_model(model, 'demo/demo.JPEG') >>> results = inference_model(model, 'demo/demo.JPEG')
>>> print( (results['pred_class'], results['pred_score']) ) >>> print( (results['pred_class'], results['pred_score']) )
('sea snake' 0.7827475666999817) ('sea snake', 0.7827484011650085)
>>> >>>
>>> # switch to deploy mode >>> # switch to deploy mode
>>> model.backbone.switch_to_deploy() >>> model.backbone.switch_to_deploy()
@ -55,7 +55,7 @@ Use `classifier.backbone.switch_to_deploy()` interface to switch the RIFormer mo
```python ```python
>>> import torch >>> import torch
>>> >>>
>>> model = init_model('configs/riformer/riformer-s12_8xb128_in1k.py', '/home/PJLAB/wangjiahao/project/RepIndentityFormer/mmcls_pretrained/224/repidentityformer-s12.pth.tar') >>> model = get_model("riformer-s12_in1k", pretrained=True)
>>> model.eval() >>> model.eval()
>>> inputs = torch.rand(1, 3, 224, 224).to(model.data_preprocessor.device) >>> inputs = torch.rand(1, 3, 224, 224).to(model.data_preprocessor.device)
>>> # To get classification scores. >>> # To get classification scores.
@ -77,63 +77,37 @@ torch.Size([1, 1000])
**Test Command** **Test Command**
Place the ImageNet dataset to the `data/imagenet/` directory, or prepare datasets according to the [docs](https://mmclassification.readthedocs.io/en/1.x/user_guides/dataset_prepare.html#prepare-dataset). Place the ImageNet dataset to the `data/imagenet/` directory, or prepare datasets according to the [docs](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html#prepare-dataset).
*224×224* *224×224*
Download Checkpoint: Download Checkpoint:
```shell ```shell
wget /home/PJLAB/wangjiahao/project/RepIndentityFormer/mmcls_pretrained/224/repidentityformer-s12.pth.tar wget https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s12_32xb128_in1k_20230406-6741ce71.pth
``` ```
Test use unfused model: Test use unfused model:
```shell ```shell
python tools/test.py configs/riformer/riformer-s12_8xb128_in1k.py /home/PJLAB/wangjiahao/project/RepIndentityFormer/mmcls_pretrained/224/repidentityformer-s12.pth.tar python tools/test.py configs/riformer/riformer-s12_8xb128_in1k.py riformer-s12_32xb128_in1k_20230406-6741ce71.pth
``` ```
Reparameterize checkpoint: Reparameterize checkpoint:
```shell ```shell
python tools/model_converters/reparameterize_model.py configs/riformer/riformer-s12_8xb128_in1k.py /home/PJLAB/wangjiahao/project/RepIndentityFormer/mmcls_pretrained/224/repidentityformer-s12.pth.tar /home/PJLAB/wangjiahao/project/RepIndentityFormer/mmcls_pretrained_deploy/224/repidentityformer-s12.pth.tar python tools/model_converters/reparameterize_model.py configs/riformer/riformer-s12_8xb128_in1k.py riformer-s12_32xb128_in1k_20230406-6741ce71.pth riformer-s12_deploy.pth
``` ```
Test use fused model: Test use fused model:
```shell ```shell
python tools/test.py configs/riformer/deploy/riformer-s12-deploy_8xb128_in1k.py /home/PJLAB/wangjiahao/project/RepIndentityFormer/mmcls_pretrained_deploy/224/repidentityformer-s12.pth.tar python tools/test.py configs/riformer/deploy/riformer-s12-deploy_8xb128_in1k.py riformer-s12_deploy.pth
```
*384×384*
Download Checkpoint:
```shell
wget /home/PJLAB/wangjiahao/project/RepIndentityFormer/mmcls_pretrained/384/repidentityformer-s12.pth.tar
```
Test use unfused model:
```shell
python tools/test.py configs/riformer/riformer-s12_8xb128_in1k_384.py /home/PJLAB/wangjiahao/project/RepIndentityFormer/mmcls_pretrained/384/repidentityformer-s12.pth.tar
```
Reparameterize checkpoint:
```shell
python tools/model_converters/reparameterize_model.py configs/riformer/riformer-s12_8xb128_in1k_384.py /home/PJLAB/wangjiahao/project/RepIndentityFormer/mmcls_pretrained/384/repidentityformer-s12.pth.tar /home/PJLAB/wangjiahao/project/RepIndentityFormer/mmcls_pretrained_deploy/384/repidentityformer-s12.pth.tar
```
Test use fused model:
```shell
python tools/test.py configs/riformer/deploy/riformer-s12-deploy_8xb128_in1k_384.py /home/PJLAB/wangjiahao/project/RepIndentityFormer/mmcls_pretrained_deploy/384/repidentityformer-s12.pth.tar
``` ```
<!-- [TABS-END] --> <!-- [TABS-END] -->
For more configurable parameters, please refer to the [API](https://mmclassification.readthedocs.io/en/1.x/api/generated/mmcls.models.backbones.RIFormer.html#mmcls.models.backbones.RIFormer). For more configurable parameters, please refer to the [API](https://mmpretrain.readthedocs.io/en/latest/api/generated/mmpretrain.models.backbones.RIFormer.html#mmpretrain.models.backbones.RIFormer).
<details> <details>
@ -153,23 +127,23 @@ For example:
```shell ```shell
# download the weight # download the weight
wget /home/PJLAB/wangjiahao/project/RepIndentityFormer/mmcls_pretrained/224/repidentityformer-s12.pth.tar wget https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s12_32xb128_in1k_20230406-6741ce71.pth
# reparameterize unfused weight to fused weight # reparameterize unfused weight to fused weight
python tools/model_converters/reparameterize_model.py configs/riformer/riformer-s12_8xb128_in1k.py /home/PJLAB/wangjiahao/project/RepIndentityFormer/mmcls_pretrained/224/repidentityformer-s12.pth.tar /home/PJLAB/wangjiahao/project/RepIndentityFormer/mmcls_pretrained_deploy/224/repidentityformer-s12.pth.tar python tools/model_converters/reparameterize_model.py configs/riformer/riformer-s12_8xb128_in1k.py riformer-s12_32xb128_in1k_20230406-6741ce71.pth riformer-s12_deploy.pth
``` ```
To use reparameterized weights, you can use the deploy model config file such as the [s12_deploy example](./riformer-s12-deploy_8xb128_in1k.py): To use reparameterized weights, you can use the deploy model config file such as the [s12_deploy example](./deploy/riformer-s12-deploy_8xb128_in1k.py):
```text ```text
# in riformer-s12-deploy_8xb128_in1k.py # in riformer-s12-deploy_8xb128_in1k.py
_base_ = '../riformer-s12-deploy_8xb128_in1k.py' # basic s12 config _base_ = '../deploy/riformer-s12-deploy_8xb128_in1k.py' # basic s12 config
model = dict(backbone=dict(deploy=True)) # switch model into deploy mode model = dict(backbone=dict(deploy=True)) # switch model into deploy mode
``` ```
```shell ```shell
python tools/test.py configs/riformer/deploy/riformer-s12-deploy_8xb128_in1k.py /home/PJLAB/wangjiahao/project/RepIndentityFormer/mmcls_pretrained_deploy/224/repidentityformer-s12.pth.tar python tools/test.py configs/riformer/deploy/riformer-s12-deploy_8xb128_in1k.py riformer-s12_deploy.pth
``` ```
</br> </br>
@ -180,18 +154,18 @@ python tools/test.py configs/riformer/deploy/riformer-s12-deploy_8xb128_in1k.py
### ImageNet-1k ### ImageNet-1k
| Model | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | | Model | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
| :----------: | :--------: | :-------: | :------: | :-------: | :-------: | :------------------------------------------: | :-------------------------------------------------------------------------------------------------: | | :-------------------: | :--------: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------: | :---------------------------------------------------------------------------------------: |
| RIFormer-S12 | 224x224 | 11.92 | 1.82 | 76.90 | 93.06 | [config](./riformer-s12_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s12_8xb128_in1k_20230406-6741ce71.pth) | | riformer-s12_in1k | 224x224 | 11.92 | 1.82 | 76.90 | 93.06 | [config](./riformer-s12_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s12_32xb128_in1k_20230406-6741ce71.pth) |
| RIFormer-S24 | 224x224 | 21.39 | 3.41 | 80.28 | 94.80 | [config](./riformer-s24_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s24_8xb128_in1k_20230406-fdab072a.pth) | | riformer-s24_in1k | 224x224 | 21.39 | 3.41 | 80.28 | 94.80 | [config](./riformer-s24_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s24_32xb128_in1k_20230406-fdab072a.pth) |
| RIFormer-S36 | 224x224 | 30.86 | 5.00 | 81.29 | 95.41 | [config](./riformer-s36_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s36_8xb128_in1k_20230406-fdfcd3b0.pth) | | riformer-s36_in1k | 224x224 | 30.86 | 5.00 | 81.29 | 95.41 | [config](./riformer-s36_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s36_32xb128_in1k_20230406-fdfcd3b0.pth) |
| RIFormer-M36 | 224x224 | 56.17 | 8.80 | 82.57 | 95.99 | [config](./riformer-m36_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m36_8xb128_in1k_20230406-2fcb9d9b.pth) | | riformer-m36_in1k | 224x224 | 56.17 | 8.80 | 82.57 | 95.99 | [config](./riformer-m36_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m36_32xb128_in1k_20230406-2fcb9d9b.pth) |
| RIFormer-M48 | 224x224 | 73.47 | 11.59 | 82.75 | 96.11 | [config](./riformer-m48_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m48_8xb128_in1k_20230406-2b9d1abf.pth) | | riformer-m48_in1k | 224x224 | 73.47 | 11.59 | 82.75 | 96.11 | [config](./riformer-m48_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m48_32xb128_in1k_20230406-2b9d1abf.pth) |
| RIFormer-S12 | 384x384 | 11.92 | 5.36 | 78.29 | 93.93 | [config](./riformer-s12_8xb128_in1k_384.py) | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s12_8xb128_in1k-384px_20230406-145eda4c.pth) | | riformer-s12_384_in1k | 384x384 | 11.92 | 5.36 | 78.29 | 93.93 | [config](./riformer-s12_8xb128_in1k_384px.py) | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s12_32xb128_in1k-384px_20230406-145eda4c.pth) |
| RIFormer-S24 | 384x384 | 21.39 | 10.03 | 81.36 | 95.40 | [config](./riformer-s24_8xb128_in1k_384.py) | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s24_8xb128_in1k-384px_20230406-bafae7ab.pth) | | riformer-s24_384_in1k | 384x384 | 21.39 | 10.03 | 81.36 | 95.40 | [config](./riformer-s24_8xb128_in1k_384px.py) | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s24_32xb128_in1k-384px_20230406-bafae7ab.pth) |
| RIFormer-S36 | 384x384 | 30.86 | 14.70 | 82.22 | 95.95 | [config](./riformer-s36_8xb64_in1k_384.py) | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s36_8xb128_in1k-384px_20230406-017ed3c4.pth) | | riformer-s36_384_in1k | 384x384 | 30.86 | 14.70 | 82.22 | 95.95 | [config](./riformer-s36_8xb64_in1k_384px.py) | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s36_32xb128_in1k-384px_20230406-017ed3c4.pth) |
| RIFormer-M36 | 384x384 | 56.17 | 25.87 | 83.39 | 96.40 | [config](./riformer-m36_8xb64_in1k_384.py) | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m36_8xb128_in1k-384px_20230406-66a6f764.pth) | | riformer-m36_384_in1k | 384x384 | 56.17 | 25.87 | 83.39 | 96.40 | [config](./riformer-m36_8xb64_in1k_384px.py) | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m36_32xb128_in1k-384px_20230406-66a6f764.pth) |
| RIFormer-M48 | 384x384 | 73.47 | 34.06 | 83.70 | 96.60 | [config](./riformer-m48_8xb64_in1k_384.py) | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m48_8xb128_in1k-384px_20230406-2e874826.pth) | | riformer-m48_384_in1k | 384x384 | 73.47 | 34.06 | 83.70 | 96.60 | [config](./riformer-m48_8xb64_in1k_384px.py) | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m48_32xb128_in1k-384px_20230406-2e874826.pth) |
The config files of these models are only for inference. The config files of these models are only for inference.
@ -199,7 +173,7 @@ The config files of these models are only for inference.
```bibtex ```bibtex
@inproceedings{wang2023riformer, @inproceedings{wang2023riformer,
title={MetaFormer is Actually What You Need for Vision}, title={RIFormer: Keep Your Vision Backbone Effective But Removing Token Mixer},
author={Wang, Jiahao and Zhang, Songyang and Liu, Yong and Wu, Taiqiang and Yang, Yujiu and Liu, Xihui and Chen, Kai and Luo, Ping and Lin, Dahua}, author={Wang, Jiahao and Zhang, Songyang and Liu, Yong and Wu, Taiqiang and Yang, Yujiu and Liu, Xihui and Chen, Kai and Luo, Ping and Lin, Dahua},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2023} year={2023}

3
configs/riformer/deploy/riformer-m36-deploy_32xb128_in1k.py
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@ -1,3 +0,0 @@
_base_ = '../riformer-m36_32xb128_in1k.py'
model = dict(backbone=dict(deploy=True))

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configs/riformer/deploy/riformer-m36-deploy_32xb128_in1k_384.py
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@ -1,3 +0,0 @@
_base_ = '../riformer-m36_32xb128_in1k_384.py'
model = dict(backbone=dict(deploy=True))

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configs/riformer/deploy/riformer-m36-deploy_8xb128_in1k.py 100644
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@ -0,0 +1,3 @@
_base_ = '../riformer-m36_8xb128_in1k.py'
model = dict(backbone=dict(deploy=True))

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configs/riformer/deploy/riformer-m36-deploy_8xb64_in1k_384px.py 100644
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@ -0,0 +1,3 @@
_base_ = '../riformer-m36_8xb64_in1k_384px.py'
model = dict(backbone=dict(deploy=True))

3
configs/riformer/deploy/riformer-m48-deploy_32xb128_in1k.py
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@ -1,3 +0,0 @@
_base_ = '../riformer-m48_32xb128_in1k.py'
model = dict(backbone=dict(deploy=True))

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configs/riformer/deploy/riformer-m48-deploy_32xb128_in1k_384.py
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@ -1,3 +0,0 @@
_base_ = '../riformer-m48_32xb128_in1k_384.py'
model = dict(backbone=dict(deploy=True))

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configs/riformer/deploy/riformer-m48-deploy_8xb64_in1k.py 100644
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@ -0,0 +1,3 @@
_base_ = '../riformer-m48_8xb64_in1k.py'
model = dict(backbone=dict(deploy=True))

3
configs/riformer/deploy/riformer-m48-deploy_8xb64_in1k_384px.py 100644
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@ -0,0 +1,3 @@
_base_ = '../riformer-m48_8xb64_in1k_384px.py'
model = dict(backbone=dict(deploy=True))

3
configs/riformer/deploy/riformer-s12-deploy_32xb128_in1k.py
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@ -1,3 +0,0 @@
_base_ = '../riformer-s12_32xb128_in1k.py'
model = dict(backbone=dict(deploy=True))

3
configs/riformer/deploy/riformer-s12-deploy_32xb128_in1k_384.py
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@ -1,3 +0,0 @@
_base_ = '../riformer-s12_32xb128_in1k_384.py'
model = dict(backbone=dict(deploy=True))

3
configs/riformer/deploy/riformer-s12-deploy_8xb128_in1k.py 100644
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@ -0,0 +1,3 @@
_base_ = '../riformer-s12_8xb128_in1k.py'
model = dict(backbone=dict(deploy=True))

3
configs/riformer/deploy/riformer-s12-deploy_8xb128_in1k_384px.py 100644
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@ -0,0 +1,3 @@
_base_ = '../riformer-s12_8xb128_in1k_384px.py'
model = dict(backbone=dict(deploy=True))

3
configs/riformer/deploy/riformer-s24-deploy_32xb128_in1k.py
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@ -1,3 +0,0 @@
_base_ = '../riformer-s24_32xb128_in1k.py'
model = dict(backbone=dict(deploy=True))

3
configs/riformer/deploy/riformer-s24-deploy_32xb128_in1k_384.py
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@ -1,3 +0,0 @@
_base_ = '../riformer-s24_32xb128_in1k_384.py'
model = dict(backbone=dict(deploy=True))

3
configs/riformer/deploy/riformer-s24-deploy_8xb128_in1k.py 100644
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@ -0,0 +1,3 @@
_base_ = '../riformer-s24_8xb128_in1k.py'
model = dict(backbone=dict(deploy=True))

3
configs/riformer/deploy/riformer-s24-deploy_8xb128_in1k_384px.py 100644
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@ -0,0 +1,3 @@
_base_ = '../riformer-s24_8xb128_in1k_384px.py'
model = dict(backbone=dict(deploy=True))

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configs/riformer/deploy/riformer-s36-deploy_32xb128_in1k.py
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@ -1,3 +0,0 @@
_base_ = '../riformer-s36_32xb128_in1k.py'
model = dict(backbone=dict(deploy=True))

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configs/riformer/deploy/riformer-s36-deploy_32xb128_in1k_384.py
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@ -1,3 +0,0 @@
_base_ = '../riformer-s36_32xb128_in1k_384.py'
model = dict(backbone=dict(deploy=True))

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configs/riformer/deploy/riformer-s36-deploy_8xb128_in1k.py 100644
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@ -0,0 +1,3 @@
_base_ = '../riformer-s36_8xb128_in1k.py'
model = dict(backbone=dict(deploy=True))

3
configs/riformer/deploy/riformer-s36-deploy_8xb64_in1k_384px.py 100644
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@ -0,0 +1,3 @@
_base_ = '../riformer-s36_8xb64_in1k_384px.py'
model = dict(backbone=dict(deploy=True))

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configs/riformer/metafile.yml
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@ -12,7 +12,7 @@ Collections:
README: configs/riformer/README.md README: configs/riformer/README.md
Code: Code:
Version: v1.0.rc6 Version: v1.0.rc6
URL: URL: null
Models: Models:
- Name: riformer-s12_in1k - Name: riformer-s12_in1k
@ -28,15 +28,12 @@ Models:
Task: Image Classification Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s12_32xb128_in1k_20230406-6741ce71.pth Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s12_32xb128_in1k_20230406-6741ce71.pth
Config: configs/riformer/riformer-s12_8xb128_in1k.py Config: configs/riformer/riformer-s12_8xb128_in1k.py
Converted From:
Weights:
Code:
- Name: riformer-s24_in1k - Name: riformer-s24_in1k
Metadata: Metadata:
Training Data: ImageNet-1k Training Data: ImageNet-1k
FLOPs: 3412000000 FLOPs: 3412000000
Parameters: 21389000 Parameters: 21389000
In Collection: PoolFormer In Collection: RIFormer
Results: Results:
- Dataset: ImageNet-1k - Dataset: ImageNet-1k
Metrics: Metrics:
@ -45,14 +42,11 @@ Models:
Task: Image Classification Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s24_32xb128_in1k_20230406-fdab072a.pth Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s24_32xb128_in1k_20230406-fdab072a.pth
Config: configs/riformer/riformer-s24_8xb128_in1k.py Config: configs/riformer/riformer-s24_8xb128_in1k.py
Converted From:
Weights:
Code:
- Name: riformer-s36_in1k - Name: riformer-s36_in1k
Metadata: Metadata:
FLOPs: 5003000000 FLOPs: 5003000000
Parameters: 30863000 Parameters: 30863000
In Collection: PoolFormer In Collection: RIFormer
Results: Results:
- Dataset: ImageNet-1k - Dataset: ImageNet-1k
Metrics: Metrics:
@ -61,15 +55,12 @@ Models:
Task: Image Classification Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s36_32xb128_in1k_20230406-fdfcd3b0.pth Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s36_32xb128_in1k_20230406-fdfcd3b0.pth
Config: configs/riformer/riformer-s36_8xb128_in1k.py Config: configs/riformer/riformer-s36_8xb128_in1k.py
Converted From:
Weights:
Code:
- Name: riformer-m36_in1k - Name: riformer-m36_in1k
Metadata: Metadata:
Training Data: ImageNet-1k Training Data: ImageNet-1k
FLOPs: 8801000000 FLOPs: 8801000000
Parameters: 56173000 Parameters: 56173000
In Collection: PoolFormer In Collection: RIFormer
Results: Results:
- Dataset: ImageNet-1k - Dataset: ImageNet-1k
Metrics: Metrics:
@ -78,14 +69,11 @@ Models:
Task: Image Classification Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m36_32xb128_in1k_20230406-2fcb9d9b.pth Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m36_32xb128_in1k_20230406-2fcb9d9b.pth
Config: configs/riformer/riformer-m36_8xb128_in1k.py Config: configs/riformer/riformer-m36_8xb128_in1k.py
Converted From:
Weights:
Code:
- Name: riformer-m48_in1k - Name: riformer-m48_in1k
Metadata: Metadata:
FLOPs: 11590000000 FLOPs: 11590000000
Parameters: 73473000 Parameters: 73473000
In Collection: PoolFormer In Collection: RIFormer
Results: Results:
- Dataset: ImageNet-1k - Dataset: ImageNet-1k
Metrics: Metrics:
@ -94,9 +82,6 @@ Models:
Task: Image Classification Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m48_32xb128_in1k_20230406-2b9d1abf.pth Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m48_32xb128_in1k_20230406-2b9d1abf.pth
Config: configs/riformer/riformer-m48_8xb64_in1k.py Config: configs/riformer/riformer-m48_8xb64_in1k.py
Converted From:
Weights:
Code:
- Name: riformer-s12_384_in1k - Name: riformer-s12_384_in1k
Metadata: Metadata:
FLOPs: 5355000000 FLOPs: 5355000000
@ -109,16 +94,13 @@ Models:
Top 5 Accuracy: 93.93 Top 5 Accuracy: 93.93
Task: Image Classification Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s12_32xb128_in1k-384px_20230406-145eda4c.pth Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s12_32xb128_in1k-384px_20230406-145eda4c.pth
Config: configs/riformer/riformer-s12_8xb128_in1k_384.py Config: configs/riformer/riformer-s12_8xb128_in1k_384px.py
Converted From:
Weights:
Code:
- Name: riformer-s24_384_in1k - Name: riformer-s24_384_in1k
Metadata: Metadata:
Training Data: ImageNet-1k Training Data: ImageNet-1k
FLOPs: 10028000000 FLOPs: 10028000000
Parameters: 21389000 Parameters: 21389000
In Collection: PoolFormer In Collection: RIFormer
Results: Results:
- Dataset: ImageNet-1k - Dataset: ImageNet-1k
Metrics: Metrics:
@ -126,15 +108,12 @@ Models:
Top 5 Accuracy: 95.40 Top 5 Accuracy: 95.40
Task: Image Classification Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s24_32xb128_in1k-384px_20230406-bafae7ab.pth Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s24_32xb128_in1k-384px_20230406-bafae7ab.pth
Config: configs/riformer/riformer-s24_8xb128_in1k_384.py Config: configs/riformer/riformer-s24_8xb128_in1k_384px.py
Converted From:
Weights:
Code:
- Name: riformer-s36_384_in1k - Name: riformer-s36_384_in1k
Metadata: Metadata:
FLOPs: 14702000000 FLOPs: 14702000000
Parameters: 30863000 Parameters: 30863000
In Collection: PoolFormer In Collection: RIFormer
Results: Results:
- Dataset: ImageNet-1k - Dataset: ImageNet-1k
Metrics: Metrics:
@ -142,16 +121,13 @@ Models:
Top 5 Accuracy: 95.95 Top 5 Accuracy: 95.95
Task: Image Classification Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s36_32xb128_in1k-384px_20230406-017ed3c4.pth Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s36_32xb128_in1k-384px_20230406-017ed3c4.pth
Config: configs/riformer/riformer-s36_8xb64_in1k_384.py Config: configs/riformer/riformer-s36_8xb64_in1k_384px.py
Converted From:
Weights:
Code:
- Name: riformer-m36_384_in1k - Name: riformer-m36_384_in1k
Metadata: Metadata:
Training Data: ImageNet-1k Training Data: ImageNet-1k
FLOPs: 25865000000 FLOPs: 25865000000
Parameters: 56173000 Parameters: 56173000
In Collection: PoolFormer In Collection: RIFormer
Results: Results:
- Dataset: ImageNet-1k - Dataset: ImageNet-1k
Metrics: Metrics:
@ -159,15 +135,12 @@ Models:
Top 5 Accuracy: 96.40 Top 5 Accuracy: 96.40
Task: Image Classification Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m36_32xb128_in1k-384px_20230406-66a6f764.pth Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m36_32xb128_in1k-384px_20230406-66a6f764.pth
Config: configs/riformer/riformer-m36_8xb64_in1k_384.py Config: configs/riformer/riformer-m36_8xb64_in1k_384px.py
Converted From:
Weights:
Code:
- Name: riformer-m48_384_in1k - Name: riformer-m48_384_in1k
Metadata: Metadata:
FLOPs: 34060000000 FLOPs: 34060000000
Parameters: 73473000 Parameters: 73473000
In Collection: PoolFormer In Collection: RIFormer
Results: Results:
- Dataset: ImageNet-1k - Dataset: ImageNet-1k
Metrics: Metrics:
@ -175,7 +148,4 @@ Models:
Top 5 Accuracy: 96.60 Top 5 Accuracy: 96.60
Task: Image Classification Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m48_32xb128_in1k-384px_20230406-2e874826.pth Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m48_32xb128_in1k-384px_20230406-2e874826.pth
Config: configs/riformer/riformer-m48_8xb64_in1k_384.py Config: configs/riformer/riformer-m48_8xb64_in1k_384px.py
Converted From:
Weights:
Code:

0
configs/riformer/riformer-m36_8xb64_in1k_384.py → configs/riformer/riformer-m36_8xb64_in1k_384px.py
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configs/riformer/riformer-m48_8xb64_in1k_384.py → configs/riformer/riformer-m48_8xb64_in1k_384px.py
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configs/riformer/riformer-s12_8xb128_in1k_384.py → configs/riformer/riformer-s12_8xb128_in1k_384px.py
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configs/riformer/riformer-s24_8xb128_in1k_384.py → configs/riformer/riformer-s24_8xb128_in1k_384px.py
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configs/riformer/riformer-s36_8xb64_in1k_384.py → configs/riformer/riformer-s36_8xb64_in1k_384px.py
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4
mmpretrain/models/backbones/riformer.py
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@ -110,10 +110,6 @@ class RIFormerBlock(BaseModule):
beta_affn = token_mixer.affine.bias beta_affn = token_mixer.affine.bias
gamma_ln = norm.weight gamma_ln = norm.weight
beta_ln = norm.bias beta_ln = norm.bias
print('gamma_affn:', gamma_affn.shape)
print('beta_affn:', beta_affn.shape)
print('gamma_ln:', gamma_ln.shape)
print('beta_ln:', beta_ln.shape)
return (gamma_ln * gamma_affn), (beta_ln * gamma_affn + beta_affn) return (gamma_ln * gamma_affn), (beta_ln * gamma_affn + beta_affn)
def get_equivalent_scale_bias(self): def get_equivalent_scale_bias(self):