关于Detectron库预训练模型的权重转换

关于Detectron库预训练模型的权重转换

最近在调试代码的过程中涉及到detectron库的使用，在模型训练前，主干网络的部分需要加载预训练模型，但是原始的预训练模型在detron库中的代码是不能直接使用的，需要通过转换工具对模型的键值对做一下转换才可以使用，具体的使用流程见：MaskFormer/MODEL_ZOO.md at main · facebookresearch/MaskFormer (github.com)

常见的resnet系列的模型就不在这里举例了，以swin-transformer为例，说明如何使用转换工具。

convert-pretrained-swin-model-to-d2.py文件是转换swin_transformer模型的代码，具体的代码内容如下：

#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved

import pickle as pkl
import sys

import torch

"""
Usage:
  # download pretrained swin model:
  wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth
  # run the conversion
  ./convert-pretrained-model-to-d2.py swin_tiny_patch4_window7_224.pth swin_tiny_patch4_window7_224.pkl
  # Then, use swin_tiny_patch4_window7_224.pkl with the following changes in config:
MODEL:
  WEIGHTS: "/path/to/swin_tiny_patch4_window7_224.pkl"
INPUT:
  FORMAT: "RGB"
"""

if __name__ == "__main__":
    input = sys.argv[1]

    obj = torch.load(input, map_location="cpu")["model"]

    res = {
    
    "model": obj, "__author__": "third_party", "matching_heuristics": True}

    with open(sys.argv[2], "wb") as f:
        pkl.dump(res, f)

使用方式如下（以ubuntu系统为例）：

pip install timm

wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth
python tools/convert-pretrained-swin-model-to-d2.py swin_tiny_patch4_window7_224.pth swin_tiny_patch4_window7_224.pkl

wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth
python tools/convert-pretrained-swin-model-to-d2.py swin_small_patch4_window7_224.pth swin_small_patch4_window7_224.pkl

wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth
python tools/convert-pretrained-swin-model-to-d2.py swin_base_patch4_window12_384_22k.pth swin_base_patch4_window12_384_22k.pkl

wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth
python tools/convert-pretrained-swin-model-to-d2.py swin_large_patch4_window12_384_22k.pth swin_large_patch4_window12_384_22k.pkl

转换之后的结果如下图，即pickle格式的新文件。

附swin transformer系列模型下载地址
microsoft/Swin-Transformer: This is an official implementation for “Swin Transformer: Hierarchical Vision Transformer using Shifted Windows”. (github.com)

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/baidu/config/log
Swin-S	ImageNet-1K	224x224	83.2	96.2	50M	8.7G	437	-	github/baidu/config/log
Swin-B	ImageNet-1K	224x224	83.5	96.5	88M	15.4G	278	-	github/baidu/config/log
Swin-B	ImageNet-1K	384x384	84.5	97.0	88M	47.1G	85	-	github/baidu/config
Swin-T	ImageNet-22K	224x224	80.9	96.0	28M	4.5G	755	github/baidu/config	github/baidu/config
Swin-S	ImageNet-22K	224x224	83.2	97.0	50M	8.7G	437	github/baidu/config	github/baidu/config
Swin-B	ImageNet-22K	224x224	85.2	97.5	88M	15.4G	278	github/baidu/config	github/baidu/config
Swin-B	ImageNet-22K	384x384	86.4	98.0	88M	47.1G	85	github/baidu	github/baidu/config
Swin-L	ImageNet-22K	224x224	86.3	97.9	197M	34.5G	141	github/baidu/config	github/baidu/config
Swin-L	ImageNet-22K	384x384	87.3	98.2	197M	103.9G	42	github/baidu	github/baidu/config

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/baidu/config
SwinV2-S	ImageNet-1K	256x256	8x8	83.7	96.6	50M	11.5G	327	-	github/baidu/config
SwinV2-B	ImageNet-1K	256x256	8x8	84.2	96.9	88M	20.3G	217	-	github/baidu/config
SwinV2-T	ImageNet-1K	256x256	16x16	82.8	96.2	28M	6.6G	437	-	github/baidu/config
SwinV2-S	ImageNet-1K	256x256	16x16	84.1	96.8	50M	12.6G	257	-	github/baidu/config
SwinV2-B	ImageNet-1K	256x256	16x16	84.6	97.0	88M	21.8G	174	-	github/baidu/config
SwinV2-B*	ImageNet-22K	256x256	16x16	86.2	97.9	88M	21.8G	174	github/baidu/config	github/baidu/config
SwinV2-B*	ImageNet-22K	384x384	24x24	87.1	98.2	88M	54.7G	57	github/baidu/config	github/baidu/config
SwinV2-L*	ImageNet-22K	256x256	16x16	86.9	98.0	197M	47.5G	95	github/baidu/config	github/baidu/config
SwinV2-L*	ImageNet-22K	384x384	24x24	87.6	98.3	197M	115.4G	33	github/baidu/config	github/baidu/config

Note:

• SwinV2-B* (SwinV2-L*) 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* (384x384) achieves 78.08 acc@1 on ImageNet-1K-V2 while SwinV2-L* (384x384) achieves 78.31.