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# RepViT
RepViT在iPhone 12上以1ms的延迟实现了超过80%的top-1准确率，本算法基于RepViT进一步优化后准确率超过82%。
## 论文
`RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/pdf/2307.09283
## 模型结构
RepViT以MobileNetV3-L为base进行算法优化，从一个标准的轻量级CNN开始，结合轻量级vit的架构设计，使得既具备CNN小计算量又具备vit架构的优势，本算法进一步解决原算法在空间、全局注意力机制等方面的不足，获得vit在attention方面的优势。
<div align=center>
<img src="./doc/structure.png"/>
</div>
## 算法原理
RepViT与其它图像分类算法一样，将图片数据送入模型后，依次通过预处理、特征提取，然后经过全连接层预测出图片类别。
<div align=center>
<img src="./doc/algorithm.png"/>
</div>
## 环境配置
```
mv repvit-optimize_pytorch RepViT # 去框架名后缀
```
### Docker（方法一）
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk24.04-py310
# <your IMAGE ID>为以上拉取的docker的镜像ID替换，本镜像为：c85ed27005f2
docker run -it --shm-size=32G -v $PWD/RepViT:/home/RepViT -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name repvit <your IMAGE ID> bash
cd /home/RepViT
pip install -r requirements.txt # requirements.txt
```
### Dockerfile（方法二）
```
cd RepViT/docker
docker build --no-cache -t repvit:latest .
docker run --shm-size=32G --name repvit -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video -v $PWD/../../RepViT:/home/RepViT -it repvit bash
# 若遇到Dockerfile启动的方式安装环境需要长时间等待，可注释掉里面的pip安装，启动容器后再安装python库：pip install -r requirements.txt。
```
### Anaconda（方法三）
1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装：
- https://developer.hpccube.com/tool/
```
DTK驱动:dtk24.04
python:python3.10
torch:2.1.0
torchvision:0.16.0
onnxruntime:1.15.0
```
`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应。`
2、其它非特殊库参照requirements.txt安装
```
pip install -r requirements.txt # requirements.txt
```
## 数据集
`cifar100`
- http://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz
项目中已提供[`cifar-100-python`](./cifar100.zip)，解压即可使用，训练数据目录结构如下：
```
cifar100/cifar-100-python
├── train
├── test
├── meta
└── file.txt~
```
## 训练
### 单机多卡
```
# 以数据集CIFAR为基础进行算法效果提升
# 关闭wandb用wandb disabled
python -m torch.distributed.launch --nproc_per_node=4 --master_port 12356 --use_env main.py --model repvit_m0_9 --data-path ./cifar100 --data-set CIFAR --epochs 300 --dist-eval --distillation-type none
```
更多资料可参考源项目的[`README_origin`](./README_origin.md)
## result
### 精度
数据集：cifar100，max epoch为300，推理框架：pytorch，device：DCU K100。
| 算法 | top-1 acc | params |
|:---------:|:---------:|:-------:|
| RepViT | 81.84% | 4757060 |
| RepViT-optimize | 82.91% | 4843172 |
## 应用场景
### 算法类别
`图像分类`
### 热点应用行业
`制造,电商,医疗,能源,教育`
## 源码仓库及问题反馈
- http://developer.hpccube.com/codes/modelzoo/repvit-optimize_pytorch.git
## 参考资料
- https://github.com/THU-MIG/RepViT.git
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# [RepViT-SAM: Towards Real-Time Segmenting Anything](https://arxiv.org/abs/2312.05760)
# [RepViT: Revisiting Mobile CNN From ViT Perspective](https://arxiv.org/abs/2307.09283)
Official PyTorch implementation of **RepViT-SAM** and **RepViT**. CVPR 2024.
<p align="center">
<img src="sam/figures/comparison.png" width=80%> <br>
Models are deployed on iPhone 12 with Core ML Tools to get latency.
</p>
<p align="center">
<img src="figures/latency.png" width=70%> <br>
Models are trained on ImageNet-1K and deployed on iPhone 12 with Core ML Tools to get latency.
</p>
[RepViT-SAM: Towards Real-Time Segmenting Anything](https://arxiv.org/abs/2312.05760).\
Ao Wang, Hui Chen, Zijia Lin, Jungong Han, and Guiguang Ding\
[[`arXiv`](https://arxiv.org/abs/2312.05760)] [[`Project Page`](https://jameslahm.github.io/repvit-sam)]
<details>
<summary>
<font size="+1">Abstract</font>
</summary>
Segment Anything Model (SAM) has shown impressive zero-shot transfer performance for various computer vision tasks recently. However, its heavy computation costs remain daunting for practical applications. MobileSAM proposes to replace the heavyweight image encoder in SAM with TinyViT by employing distillation, which results in a significant reduction in computational requirements. However, its deployment on resource-constrained mobile devices still encounters challenges due to the substantial memory and computational overhead caused by self-attention mechanisms. Recently, RepViT achieves the state-of-the-art performance and latency trade-off on mobile devices by incorporating efficient architectural designs of ViTs into CNNs. Here, to achieve real-time segmenting anything on mobile devices, following, we replace the heavyweight image encoder in SAM with RepViT model, ending up with the RepViT-SAM model. Extensive experiments show that RepViT-SAM can enjoy significantly better zero-shot transfer capability than MobileSAM, along with nearly $10\times$ faster inference speed.
</details>
<br/>
[RepViT: Revisiting Mobile CNN From ViT Perspective](https://arxiv.org/abs/2307.09283).\
Ao Wang, Hui Chen, Zijia Lin, Jungong Han, and Guiguang Ding\
[[`arXiv`](https://arxiv.org/abs/2307.09283)]
<details>
<summary>
<font size="+1">Abstract</font>
</summary>
Recently, lightweight Vision Transformers (ViTs) demonstrate superior performance and lower latency compared with lightweight Convolutional Neural Networks (CNNs) on resource-constrained mobile devices. This improvement is usually attributed to the multi-head self-attention module, which enables the model to learn global representations. However, the architectural disparities between lightweight ViTs and lightweight CNNs have not been adequately examined. In this study, we revisit the efficient design of lightweight CNNs and emphasize their potential for mobile devices. We incrementally enhance the mobile-friendliness of a standard lightweight CNN, specifically MobileNetV3, by integrating the efficient architectural choices of lightweight ViTs. This ends up with a new family of pure lightweight CNNs, namely RepViT. Extensive experiments show that RepViT outperforms existing state-of-the-art lightweight ViTs and exhibits favorable latency in various vision tasks. On ImageNet, RepViT achieves over 80\% top-1 accuracy with 1ms latency on an iPhone 12, which is the first time for a lightweight model, to the best of our knowledge. Our largest model, RepViT-M2.3, obtains 83.7\% accuracy with only 2.3ms latency.
</details>
<br/>
<br/>
**UPDATES** 🔥
- 2023/12/17: [Grounding-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main) supports RepViT-SAM with [Grounded-RepViT-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main/EfficientSAM#run-grounded-repvit-sam-demo). Thanks!
- 2023/12/11: RepViT-SAM has been released. Please refer to [RepViT-SAM](./sam/README.md).
- 2023/12/11: RepViT-M0.6 has been released, achieving 74.1% with ~0.6ms latency. Its checkpoint is [here](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m0_6_distill_300e.pth)
- 2023/09/28: RepViT-M0.9/1.0/1.1/1.5/2.3 models have been released.
- 2023/07/27: RepViT models have been integrated into timm. See https://github.com/huggingface/pytorch-image-models/pull/1876.
<br/>
## Classification on ImageNet-1K
### Models
| Model | Top-1 (300 / 450)| #params | MACs | Latency | Ckpt | Core ML | Log |
|:---------------|:----:|:---:|:--:|:--:|:--:|:--:|:--:|
| M0.9 | 78.7 / 79.1 | 5.1M | 0.8G | 0.9ms | [300e](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m0_9_distill_300e.pth) / [450e](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m0_9_distill_450e.pth) | [300e](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m0_9_distiall_300e_224.mlmodel) / [450e](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m0_9_distiall_450e_224.mlmodel) | [300e](./logs/repvit_m0_9_distill_300e.txt) / [450e](./logs/repvit_m0_9_distill_450e.txt) |
| M1.0 | 80.0 / 80.3 | 6.8M | 1.1G | 1.0ms | [300e](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m1_0_distill_300e.pth) / [450e](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m1_0_distill_450e.pth) | [300e](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m1_0_distiall_300e_224.mlmodel) / [450e](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m1_0_distiall_450e_224.mlmodel) | [300e](./logs/repvit_m1_0_distill_300e.txt) / [450e](./logs/repvit_m1_0_distill_450e.txt) |
| M1.1 | 80.7 / 81.2 | 8.2M | 1.3G | 1.1ms | [300e](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m1_1_distill_300e.pth) / [450e](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m1_1_distill_450e.pth) | [300e](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m1_1_distiall_300e_224.mlmodel) / [450e](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m1_1_distiall_450e_224.mlmodel) | [300e](./logs/repvit_m1_1_distill_300e.txt) / [450e](./logs/repvit_m1_1_distill_450e.txt) |
| M1.5 | 82.3 / 82.5 | 14.0M | 2.3G | 1.5ms | [300e](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m1_5_distill_300e.pth) / [450e](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m1_5_distill_450e.pth) | [300e](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m1_5_distiall_300e_224.mlmodel) / [450e](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m1_5_distiall_450e_224.mlmodel) | [300e](./logs/repvit_m1_5_distill_300e.txt) / [450e](./logs/repvit_m1_5_distill_450e.txt) |
| M2.3 | 83.3 / 83.7 | 22.9M | 4.5G | 2.3ms | [300e](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m2_3_distill_300e.pth) / [450e](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m2_3_distill_450e.pth) | [300e](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m2_3_distiall_300e_224.mlmodel) / [450e](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m2_3_distiall_450e_224.mlmodel) | [300e](./logs/repvit_m2_3_distill_300e.txt) / [450e](./logs/repvit_m2_3_distill_450e.txt) |
Tips: Convert a training-time RepViT into the inference-time structure
```
from timm.models import create_model
import utils
model = create_model('repvit_m0_9')
utils.replace_batchnorm(model)
```
## Latency Measurement
The latency reported in RepViT for iPhone 12 (iOS 16) uses the benchmark tool from [XCode 14](https://developer.apple.com/videos/play/wwdc2022/10027/).
For example, here is a latency measurement of RepViT-M0.9:
![](./figures/repvit_m0_9_latency.png)
Tips: export the model to Core ML model
```
python export_coreml.py --model repvit_m0_9 --ckpt pretrain/repvit_m0_9_distill_300e.pth
```
Tips: measure the throughput on GPU
```
python speed_gpu.py --model repvit_m0_9
```
## ImageNet
### Prerequisites
`conda` virtual environment is recommended.
```
conda create -n repvit python=3.8
pip install -r requirements.txt
```
### Data preparation
Download and extract ImageNet train and val images from http://image-net.org/. The training and validation data are expected to be in the `train` folder and `val` folder respectively:
```
|-- /path/to/imagenet/
|-- train
|-- val
```
### Training
To train RepViT-M0.9 on an 8-GPU machine:
```
python -m torch.distributed.launch --nproc_per_node=8 --master_port 12346 --use_env main.py --model repvit_m0_9 --data-path ~/imagenet --dist-eval
```
Tips: specify your data path and model name!
### Testing
For example, to test RepViT-M0.9:
```
python main.py --eval --model repvit_m0_9 --resume pretrain/repvit_m0_9_distill_300e.pth --data-path ~/imagenet
```
## Downstream Tasks
[Object Detection and Instance Segmentation](detection/README.md)<br>
[Semantic Segmentation](segmentation/README.md)
## Acknowledgement
Classification (ImageNet) code base is partly built with [LeViT](https://github.com/facebookresearch/LeViT), [PoolFormer](https://github.com/sail-sg/poolformer) and [EfficientFormer](https://github.com/snap-research/EfficientFormer).
The detection and segmentation pipeline is from [MMCV](https://github.com/open-mmlab/mmcv) ([MMDetection](https://github.com/open-mmlab/mmdetection) and [MMSegmentation](https://github.com/open-mmlab/mmsegmentation)).
Thanks for the great implementations!
## Citation
If our code or models help your work, please cite our paper:
```BibTeX
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Jungong Han and Guiguang Ding},
year={2023},
eprint={2307.09283},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@misc{wang2023repvitsam,
title={RepViT-SAM: Towards Real-Time Segmenting Anything},
author={Ao Wang and Hui Chen and Zijia Lin and Jungong Han and Guiguang Ding},
year={2023},
eprint={2312.05760},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
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from timm.models import create_model
import utils
model = create_model('checkpoints/repvit_m0_9/2024_05_21_18_42_00/checkpoint_best.pth')
utils.replace_batchnorm(model)
data/datasets.py 0 → 100644
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'''
Build trainining/testing datasets
'''
import os
import json
from torchvision import datasets, transforms
from torchvision.datasets.folder import ImageFolder, default_loader
import torch
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.data import create_transform
try:
from timm.data import TimmDatasetTar
except ImportError:
# for higher version of timm
from timm.data import ImageDataset as TimmDatasetTar
class INatDataset(ImageFolder):
def __init__(self, root, train=True, year=2018, transform=None, target_transform=None,
category='name', loader=default_loader):
self.transform = transform
self.loader = loader
self.target_transform = target_transform
self.year = year
# assert category in ['kingdom','phylum','class','order','supercategory','family','genus','name']
path_json = os.path.join(
root, f'{"train" if train else "val"}{year}.json')
with open(path_json) as json_file:
data = json.load(json_file)
with open(os.path.join(root, 'categories.json')) as json_file:
data_catg = json.load(json_file)
path_json_for_targeter = os.path.join(root, f"train{year}.json")
with open(path_json_for_targeter) as json_file:
data_for_targeter = json.load(json_file)
targeter = {}
indexer = 0
for elem in data_for_targeter['annotations']:
king = []
king.append(data_catg[int(elem['category_id'])][category])
if king[0] not in targeter.keys():
targeter[king[0]] = indexer
indexer += 1
self.nb_classes = len(targeter)
self.samples = []
for elem in data['images']:
cut = elem['file_name'].split('/')
target_current = int(cut[2])
path_current = os.path.join(root, cut[0], cut[2], cut[3])
categors = data_catg[target_current]
target_current_true = targeter[categors[category]]
self.samples.append((path_current, target_current_true))
# __getitem__ and __len__ inherited from ImageFolder
def build_dataset(is_train, args):
transform = build_transform(is_train, args)
if args.data_set == 'CIFAR':
dataset = datasets.CIFAR100(
args.data_path, train=is_train, transform=transform, download=True)
nb_classes = 100
elif args.data_set == 'IMNET':
prefix = 'train' if is_train else 'val'
data_dir = os.path.join(args.data_path, f'{prefix}.tar')
if os.path.exists(data_dir):
dataset = TimmDatasetTar(data_dir, transform=transform)
else:
root = os.path.join(args.data_path, 'train' if is_train else 'val')
dataset = datasets.ImageFolder(root, transform=transform)
nb_classes = 1000
elif args.data_set == 'IMNETEE':
root = os.path.join(args.data_path, 'train' if is_train else 'val')
dataset = datasets.ImageFolder(root, transform=transform)
nb_classes = 10
elif args.data_set == 'FLOWERS':
root = os.path.join(args.data_path, 'train' if is_train else 'test')
dataset = datasets.ImageFolder(root, transform=transform)
if is_train:
dataset = torch.utils.data.ConcatDataset(
[dataset for _ in range(100)])
nb_classes = 102
elif args.data_set == 'INAT':
dataset = INatDataset(args.data_path, train=is_train, year=2018,
category=args.inat_category, transform=transform)
nb_classes = dataset.nb_classes
elif args.data_set == 'INAT19':
dataset = INatDataset(args.data_path, train=is_train, year=2019,
category=args.inat_category, transform=transform)
nb_classes = dataset.nb_classes
return dataset, nb_classes
def build_transform(is_train, args):
resize_im = args.input_size > 32
if is_train:
# this should always dispatch to transforms_imagenet_train
transform = create_transform(
input_size=args.input_size,
is_training=True,
color_jitter=args.color_jitter,
auto_augment=args.aa,
interpolation=args.train_interpolation,
re_prob=args.reprob,
re_mode=args.remode,
re_count=args.recount,
)
if not resize_im:
# replace RandomResizedCropAndInterpolation with
# RandomCrop
transform.transforms[0] = transforms.RandomCrop(
args.input_size, padding=4)
return transform
t = []
if args.finetune:
t.append(
transforms.Resize((args.input_size, args.input_size),
interpolation=3)
)
else:
if resize_im:
size = int((256 / 224) * args.input_size)
t.append(
# to maintain same ratio w.r.t. 224 images
transforms.Resize(size, interpolation=3),
)
t.append(transforms.CenterCrop(args.input_size))
t.append(transforms.ToTensor())
t.append(transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD))
return transforms.Compose(t)
data/samplers.py 0 → 100644
View file @ c218d1c5
'''
Build samplers for data loading
'''
import torch
import torch.distributed as dist
import math
class RASampler(torch.utils.data.Sampler):
"""Sampler that restricts data loading to a subset of the dataset for distributed,
with repeated augmentation.
It ensures that different each augmented version of a sample will be visible to a
different process (GPU)
Heavily based on torch.utils.data.DistributedSampler
"""
def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True):
if num_replicas is None:
if not dist.is_available():
raise RuntimeError(
"Requires distributed package to be available")
num_replicas = dist.get_world_size()
if rank is None:
if not dist.is_available():
raise RuntimeError(
"Requires distributed package to be available")
rank = dist.get_rank()
self.dataset = dataset
self.num_replicas = num_replicas
self.rank = rank
self.epoch = 0
self.num_samples = int(
math.ceil(len(self.dataset) * 3.0 / self.num_replicas))
self.total_size = self.num_samples * self.num_replicas
# self.num_selected_samples = int(math.ceil(len(self.dataset) / self.num_replicas))
self.num_selected_samples = int(math.floor(
len(self.dataset) // 256 * 256 / self.num_replicas))
self.shuffle = shuffle
def __iter__(self):
# deterministically shuffle based on epoch
g = torch.Generator()
g.manual_seed(self.epoch)
if self.shuffle:
indices = torch.randperm(len(self.dataset), generator=g).tolist()
else:
indices = list(range(len(self.dataset)))
# add extra samples to make it evenly divisible
indices = [ele for ele in indices for i in range(3)]
indices += indices[:(self.total_size - len(indices))]
assert len(indices) == self.total_size
# subsample
indices = indices[self.rank:self.total_size:self.num_replicas]
assert len(indices) == self.num_samples
return iter(indices[:self.num_selected_samples])
def __len__(self):
return self.num_selected_samples
def set_epoch(self, epoch):
self.epoch = epoch
data/threeaugment.py 0 → 100644
View file @ c218d1c5
"""
3Augment implementation from (https://github.com/facebookresearch/deit/blob/main/augment.py)
Data-augmentation (DA) based on dino DA (https://github.com/facebookresearch/dino)
and timm DA(https://github.com/rwightman/pytorch-image-models)
Can be called by adding "--ThreeAugment" to the command line
"""
import torch
from torchvision import transforms
from timm.data.transforms import str_to_pil_interp, RandomResizedCropAndInterpolation, ToNumpy, ToTensor
import numpy as np
from torchvision import datasets, transforms
import random
from PIL import ImageFilter, ImageOps
import torchvision.transforms.functional as TF
class GaussianBlur(object):
"""
Apply Gaussian Blur to the PIL image.
"""
def __init__(self, p=0.1, radius_min=0.1, radius_max=2.):
self.prob = p
self.radius_min = radius_min
self.radius_max = radius_max
def __call__(self, img):
do_it = random.random() <= self.prob
if not do_it:
return img
img = img.filter(
ImageFilter.GaussianBlur(
radius=random.uniform(self.radius_min, self.radius_max)
)
)
return img
class Solarization(object):
"""
Apply Solarization to the PIL image.
"""
def __init__(self, p=0.2):
self.p = p
def __call__(self, img):
if random.random() < self.p:
return ImageOps.solarize(img)
else:
return img
class gray_scale(object):
"""
Apply Solarization to the PIL image.
"""
def __init__(self, p=0.2):
self.p = p
self.transf = transforms.Grayscale(3)
def __call__(self, img):
if random.random() < self.p:
return self.transf(img)
else:
return img
class horizontal_flip(object):
"""
Apply Solarization to the PIL image.
"""
def __init__(self, p=0.2,activate_pred=False):
self.p = p
self.transf = transforms.RandomHorizontalFlip(p=1.0)
def __call__(self, img):
if random.random() < self.p:
return self.transf(img)
else:
return img
def new_data_aug_generator(args = None):
img_size = args.input_size
remove_random_resized_crop = False
mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
primary_tfl = []
scale=(0.08, 1.0)
interpolation='bicubic'
if remove_random_resized_crop:
primary_tfl = [
transforms.Resize(img_size, interpolation=3),
transforms.RandomCrop(img_size, padding=4,padding_mode='reflect'),
transforms.RandomHorizontalFlip()
]
else:
primary_tfl = [
RandomResizedCropAndInterpolation(
img_size, scale=scale, interpolation=interpolation),
transforms.RandomHorizontalFlip()
]
secondary_tfl = [transforms.RandomChoice([gray_scale(p=1.0),
Solarization(p=1.0),
GaussianBlur(p=1.0)])]
if args.color_jitter is not None and not args.color_jitter==0:
secondary_tfl.append(transforms.ColorJitter(args.color_jitter, args.color_jitter, args.color_jitter))
final_tfl = [
transforms.ToTensor(),
transforms.Normalize(
mean=torch.tensor(mean),
std=torch.tensor(std))
]
return transforms.Compose(primary_tfl+secondary_tfl+final_tfl)
detection/.gitignore 0 → 100644
View file @ c218d1c5
work_dirs
pretrain
data
det_pretrain
\ No newline at end of file
detection/README.md 0 → 100644
View file @ c218d1c5
# Object Detection and Instance Segmentation
Detection and instance segmentation on MS COCO 2017 is implemented based on [MMDetection](https://github.com/open-mmlab/mmdetection).
## Models
| Model | $AP^b$ | $AP_{50}^b$ | $AP_{75}^b$ | $AP^m$ | $AP_{50}^m$ | $AP_{75}^m$ | Latency | Ckpt | Log |
|:---------------|:----:|:---:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| RepViT-M1.1 | 39.8 | 61.9 | 43.5 | 37.2 | 58.8 | 40.1 | 4.9ms | [M1.1](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m1_1_coco.pth) | [M1.1](./logs/repvit_m1_1_coco.json) |
| RepViT-M1.5 | 41.6 | 63.2 | 45.3 | 38.6 | 60.5 | 41.5 | 6.4ms | [M1.5](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m1_5_coco.pth) | [M1.5](./logs/repvit_m1_5_coco.json) |
| RepViT-M2.3 | 44.6 | 66.1 | 48.8 | 40.8 | 63.6 | 43.9 | 9.9ms | [M2.3](https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m2_3_coco.pth) | [M2.3](./logs/repvit_m2_3_coco.json) |
## Installation
Install [mmcv-full](https://github.com/open-mmlab/mmcv) and [MMDetection v2.28.2](https://github.com/open-mmlab/mmdetection/tree/v2.28.2),
Later versions should work as well.
The easiest way is to install via [MIM](https://github.com/open-mmlab/mim)
```
pip install -U openmim
mim install mmcv-full==1.7.1
mim install mmdet==2.28.2
```
## Data preparation
Prepare COCO 2017 dataset according to the [instructions in MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/1_exist_data_model.md#test-existing-models-on-standard-datasets).
The dataset should be organized as
```
detection
├── data
│ ├── coco
│ │ ├── annotations
│ │ ├── train2017
│ │ ├── val2017
│ │ ├── test2017
```
## Testing
We provide a multi-GPU testing script, specify config file, checkpoint, and number of GPUs to use:
```
./dist_test.sh config_file path/to/checkpoint #GPUs --eval bbox segm
```
For example, to test RepViT-M1.1 on COCO 2017 on an 8-GPU machine,
```
./dist_test.sh configs/mask_rcnn_repvit_m1_1_fpn_1x_coco.py path/to/repvit_m1_1_coco.pth 8 --eval bbox segm
```
## Training
Download ImageNet-1K pretrained weights into `./pretrain`
We provide PyTorch distributed data parallel (DDP) training script `dist_train.sh`, for example, to train RepViT-M1.1 on an 8-GPU machine:
```
./dist_train.sh configs/mask_rcnn_repvit_m1_1_fpn_1x_coco.py 8
```
Tips: specify configs and #GPUs!
detection/checkpoint.py 0 → 100644
View file @ c218d1c5
# Copyright (c) OpenMMLab. All rights reserved.
import io
import os
import os.path as osp
import pkgutil
import re
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from torch.utils import model_zoo
import mmcv
from mmcv.parallel import is_module_wrapper
from mmcv.runner.dist_utils import get_dist_info
ENV_MMCV_HOME = 'MMCV_HOME'
ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'
DEFAULT_CACHE_DIR = '~/.cache'
def load_state_dict(module, state_dict, strict=False, logger=None):
"""Load state_dict to a module.
This method is modified from :meth:`torch.nn.Module.load_state_dict`.
Default value for ``strict`` is set to ``False`` and the message for
param mismatch will be shown even if strict is False.
Args:
module (Module): Module that receives the state_dict.
state_dict (OrderedDict): Weights.
strict (bool): whether to strictly enforce that the keys
in :attr:`state_dict` match the keys returned by this module's
:meth:`~torch.nn.Module.state_dict` function. Default: ``False``.
logger (:obj:`logging.Logger`, optional): Logger to log the error
message. If not specified, print function will be used.
"""
unexpected_keys = []
all_missing_keys = []
err_msg = []
metadata = getattr(state_dict, '_metadata', None)
state_dict = state_dict.copy()
if metadata is not None:
state_dict._metadata = metadata
# use _load_from_state_dict to enable checkpoint version control
def load(module, prefix=''):
# recursively check parallel module in case that the model has a
# complicated structure, e.g., nn.Module(nn.Module(DDP))
if is_module_wrapper(module):
module = module.module
local_metadata = {} if metadata is None else metadata.get(
prefix[:-1], {})
module._load_from_state_dict(state_dict, prefix, local_metadata, True,
all_missing_keys, unexpected_keys,
err_msg)
for name, child in module._modules.items():
if child is not None:
load(child, prefix + name + '.')
load(module)
load = None # break load->load reference cycle
# ignore "num_batches_tracked" of BN layers
missing_keys = [
key for key in all_missing_keys if 'num_batches_tracked' not in key
]
if unexpected_keys:
err_msg.append('unexpected key in source '
f'state_dict: {", ".join(unexpected_keys)}\n')
if missing_keys:
err_msg.append(
f'missing keys in source state_dict: {", ".join(missing_keys)}\n')
rank, _ = get_dist_info()
if len(err_msg) > 0 and rank == 0:
err_msg.insert(
0, 'The model and loaded state dict do not match exactly\n')
err_msg = '\n'.join(err_msg)
if strict:
raise RuntimeError(err_msg)
elif logger is not None:
logger.warning(err_msg)
else:
print(err_msg)
class CheckpointLoader:
"""A general checkpoint loader to manage all schemes."""
_schemes = {}
@classmethod
def _register_scheme(cls, prefixes, loader, force=False):
if isinstance(prefixes, str):
prefixes = [prefixes]
else:
assert isinstance(prefixes, (list, tuple))
for prefix in prefixes:
if (prefix not in cls._schemes) or force:
cls._schemes[prefix] = loader
else:
raise KeyError(
f'{prefix} is already registered as a loader backend, '
'add "force=True" if you want to override it')
# sort, longer prefixes take priority
cls._schemes = OrderedDict(
sorted(cls._schemes.items(), key=lambda t: t[0], reverse=True))
@classmethod
def register_scheme(cls, prefixes, loader=None, force=False):
"""Register a loader to CheckpointLoader.
This method can be used as a normal class method or a decorator.
Args:
prefixes (str or list[str] or tuple[str]):
The prefix of the registered loader.
loader (function, optional): The loader function to be registered.
When this method is used as a decorator, loader is None.
Defaults to None.
force (bool, optional): Whether to override the loader
if the prefix has already been registered. Defaults to False.
"""
if loader is not None:
cls._register_scheme(prefixes, loader, force=force)
return
def _register(loader_cls):
cls._register_scheme(prefixes, loader_cls, force=force)
return loader_cls
return _register
@classmethod
def _get_checkpoint_loader(cls, path):
"""Finds a loader that supports the given path. Falls back to the local
loader if no other loader is found.
Args:
path (str): checkpoint path
Returns:
loader (function): checkpoint loader
"""
for p in cls._schemes:
if path.startswith(p):
return cls._schemes[p]
@classmethod
def load_checkpoint(cls, filename, map_location=None, logger=None):
"""load checkpoint through URL scheme path.
Args:
filename (str): checkpoint file name with given prefix
map_location (str, optional): Same as :func:`torch.load`.
Default: None
logger (:mod:`logging.Logger`, optional): The logger for message.
Default: None
Returns:
dict or OrderedDict: The loaded checkpoint.
"""
checkpoint_loader = cls._get_checkpoint_loader(filename)
class_name = checkpoint_loader.__name__
mmcv.print_log(
f'load checkpoint from {class_name[10:]} path: {filename}', logger)
return checkpoint_loader(filename, map_location)
def _load_checkpoint(filename, map_location=None, logger=None):
"""Load checkpoint from somewhere (modelzoo, file, url).
Args:
filename (str): Accept local filepath, URL, ``torchvision://xxx``,
``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
details.
map_location (str, optional): Same as :func:`torch.load`.
Default: None.
logger (:mod:`logging.Logger`, optional): The logger for error message.
Default: None
Returns:
dict or OrderedDict: The loaded checkpoint. It can be either an
OrderedDict storing model weights or a dict containing other
information, which depends on the checkpoint.
"""
return CheckpointLoader.load_checkpoint(filename, map_location, logger)
def load_checkpoint(model,
filename,
map_location=None,
strict=False,
logger=None,
revise_keys=[(r'^module\.', '')]):
"""Load checkpoint from a file or URI.
Args:
model (Module): Module to load checkpoint.
filename (str): Accept local filepath, URL, ``torchvision://xxx``,
``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
details.
map_location (str): Same as :func:`torch.load`.
strict (bool): Whether to allow different params for the model and
checkpoint.
logger (:mod:`logging.Logger` or None): The logger for error message.
revise_keys (list): A list of customized keywords to modify the
state_dict in checkpoint. Each item is a (pattern, replacement)
pair of the regular expression operations. Default: strip
the prefix 'module.' by [(r'^module\\.', '')].
Returns:
dict or OrderedDict: The loaded checkpoint.
"""
checkpoint = _load_checkpoint(filename, map_location, logger)
# OrderedDict is a subclass of dict
if not isinstance(checkpoint, dict):
raise RuntimeError(
f'No state_dict found in checkpoint file {filename}')
# get state_dict from checkpoint
import pdb; pdb.set_trace()
if 'state_dict' in checkpoint:
state_dict = checkpoint['state_dict']
elif 'model' in checkpoint:
state_dict = checkpoint['model']
else:
state_dict = checkpoint
# strip prefix of state_dict
metadata = getattr(state_dict, '_metadata', OrderedDict())
for p, r in revise_keys:
state_dict = OrderedDict(
{re.sub(p, r, k): v
for k, v in state_dict.items()})
# Keep metadata in state_dict
state_dict._metadata = metadata
# load state_dict
load_state_dict(model, state_dict, strict, logger)
return checkpoint
detection/configs/_base_/datasets/cityscapes_detection.py 0 → 100644
View file @ c218d1c5
# dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize', img_scale=[(2048, 800), (2048, 1024)], keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(2048, 1024),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=1,
workers_per_gpu=2,
train=dict(
type='RepeatDataset',
times=8,
dataset=dict(
type=dataset_type,
ann_file=data_root +
'annotations/instancesonly_filtered_gtFine_train.json',
img_prefix=data_root + 'leftImg8bit/train/',
pipeline=train_pipeline)),
val=dict(
type=dataset_type,
ann_file=data_root +
'annotations/instancesonly_filtered_gtFine_val.json',
img_prefix=data_root + 'leftImg8bit/val/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root +
'annotations/instancesonly_filtered_gtFine_test.json',
img_prefix=data_root + 'leftImg8bit/test/',
pipeline=test_pipeline))
evaluation = dict(interval=1, metric='bbox')
detection/configs/_base_/datasets/cityscapes_instance.py 0 → 100644
View file @ c218d1c5
# dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='Resize', img_scale=[(2048, 800), (2048, 1024)], keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(2048, 1024),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=1,
workers_per_gpu=2,
train=dict(
type='RepeatDataset',
times=8,
dataset=dict(
type=dataset_type,
ann_file=data_root +
'annotations/instancesonly_filtered_gtFine_train.json',
img_prefix=data_root + 'leftImg8bit/train/',
pipeline=train_pipeline)),
val=dict(
type=dataset_type,
ann_file=data_root +
'annotations/instancesonly_filtered_gtFine_val.json',
img_prefix=data_root + 'leftImg8bit/val/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root +
'annotations/instancesonly_filtered_gtFine_test.json',
img_prefix=data_root + 'leftImg8bit/test/',
pipeline=test_pipeline))
evaluation = dict(metric=['bbox', 'segm'])
detection/configs/_base_/datasets/coco_detection.py 0 → 100644
View file @ c218d1c5
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
pipeline=train_pipeline),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline))
evaluation = dict(interval=1, metric='bbox')
detection/configs/_base_/datasets/coco_instance.py 0 → 100644
View file @ c218d1c5
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
pipeline=train_pipeline),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline))
evaluation = dict(metric=['bbox', 'segm'])
detection/configs/_base_/datasets/coco_instance_semantic.py 0 → 100644
View file @ c218d1c5
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='SegRescale', scale_factor=1 / 8),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
seg_prefix=data_root + 'stuffthingmaps/train2017/',
pipeline=train_pipeline),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline))
evaluation = dict(metric=['bbox', 'segm'])
detection/configs/_base_/datasets/deepfashion.py 0 → 100644
View file @ c218d1c5
# dataset settings
dataset_type = 'DeepFashionDataset'
data_root = 'data/DeepFashion/In-shop/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='Resize', img_scale=(750, 1101), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(750, 1101),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
imgs_per_gpu=2,
workers_per_gpu=1,
train=dict(
type=dataset_type,
ann_file=data_root + 'annotations/DeepFashion_segmentation_query.json',
img_prefix=data_root + 'Img/',
pipeline=train_pipeline,
data_root=data_root),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/DeepFashion_segmentation_query.json',
img_prefix=data_root + 'Img/',
pipeline=test_pipeline,
data_root=data_root),
test=dict(
type=dataset_type,
ann_file=data_root +
'annotations/DeepFashion_segmentation_gallery.json',
img_prefix=data_root + 'Img/',
pipeline=test_pipeline,
data_root=data_root))
evaluation = dict(interval=5, metric=['bbox', 'segm'])
detection/configs/_base_/datasets/lvis_v0.5_instance.py 0 → 100644
View file @ c218d1c5
# dataset settings
_base_ = 'coco_instance.py'
dataset_type = 'LVISV05Dataset'
data_root = 'data/lvis_v0.5/'
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
_delete_=True,
type='ClassBalancedDataset',
oversample_thr=1e-3,
dataset=dict(
type=dataset_type,
ann_file=data_root + 'annotations/lvis_v0.5_train.json',
img_prefix=data_root + 'train2017/')),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/lvis_v0.5_val.json',
img_prefix=data_root + 'val2017/'),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/lvis_v0.5_val.json',
img_prefix=data_root + 'val2017/'))
evaluation = dict(metric=['bbox', 'segm'])
detection/configs/_base_/datasets/lvis_v1_instance.py 0 → 100644
View file @ c218d1c5
# dataset settings
_base_ = 'coco_instance.py'
dataset_type = 'LVISV1Dataset'
data_root = 'data/lvis_v1/'
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
_delete_=True,
type='ClassBalancedDataset',
oversample_thr=1e-3,
dataset=dict(
type=dataset_type,
ann_file=data_root + 'annotations/lvis_v1_train.json',
img_prefix=data_root)),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/lvis_v1_val.json',
img_prefix=data_root),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/lvis_v1_val.json',
img_prefix=data_root))
evaluation = dict(metric=['bbox', 'segm'])
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