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simpleaicv_pytorch_training_examples's Introduction

My ZhiHu column

https://www.zhihu.com/column/c_1249719688055193600

Introduction

This repository provides simple training and testing examples for the following tasks:

Image classification task
Object detection task
Semantic segmentation task
Knowledge distillation task
Contrastive learning task
Masked image modeling task
image inpainting task
diffusion model task

Image classification task

support dataset:

CIFAR100
ImageNet1K(ILSVRC2012)
ImageNet21K(Winter 2021 release)

support network:

ResNet
DarkNet
RepVGG
RegNetX
ViT

Object detection task

support dataset:

VOC2007 and VOC2012
COCO2017
Objects365(v2,2020)

support network:

RetinaNet
FCOS
CenterNet
TTFNet
DETR
DINO-DETR

Semantic segmentation task

support dataset:

ADE20K

support network:

DeepLabv3+
U2Net

Knowledge distillation task

support dataset:

ImageNet1K(ILSVRC2012)

support network:

KD loss(for ResNet)
DML loss(for ResNet)

Contrastive learning task

support dataset:

ImageNet1K(ILSVRC2012)

support network:

DINO(for ResNet)

Masked image modeling task

support dataset:

ImageNet1K(ILSVRC2012)

support network:

MAE(for ViT)

Image inpainting task

support dataset:

CelebA-HQ
Places365-standard
Places365-challenge

support network:

AOT-GAN

diffusion model task

support dataset:

CIFAR10
CIFAR100
CelebA-HQ
FFHQ

support Sampling Method:

DDPM
DDIM
PLMS

All task training results

See all task training results in results.md.

Environments

1、This repository only supports running on ubuntu(verison>=18.04 LTS).

2、This repository only support one machine one gpu/one machine multi gpus mode with pytorch DDP training.

3、Please make sure your Python environment version>=3.7.

4、Please make sure your pytorch version>=1.10.

5、If you want to use torch.complie() function,please make sure your pytorch version>=2.0.

Use pip or conda to install those Packages in your Python environment:

torch
torchvision
pillow
numpy
Cython
pycocotools
opencv-python
scipy
eniops
scikit-image
tqdm
onnx
onnx-simplifier
thop==0.0.31.post2005241907
gradio==3.32.0
yapf

If you want to use dino-detr model,install MultiScaleDeformableAttention Packge in your Python environment:

cd to simpleAICV/detection/compile_multiscale_deformable_attention,then run commands:

chmod +x make.sh
./make.sh

Download my pretrained models and experiments records

You can download all my pretrained models and all my experiments records/checkpoints from huggingface or Baidu-Netdisk.

If you only want to download all my pretrained models(model.state_dict()),you can download pretrained_models folder.

# huggingface
https://huggingface.co/zgcr654321/classification_training/tree/main
https://huggingface.co/zgcr654321/contrastive_learning_training/tree/main
https://huggingface.co/zgcr654321/detection_training/tree/main
https://huggingface.co/zgcr654321/image_inpainting_training/tree/main
https://huggingface.co/zgcr654321/diffusion_model_training/tree/main
https://huggingface.co/zgcr654321/distillation_training/tree/main
https://huggingface.co/zgcr654321/masked_image_modeling_training/tree/main
https://huggingface.co/zgcr654321/semantic_segmentation_training/tree/main
https://huggingface.co/zgcr654321/pretrained_models/tree/main

# Baidu-Netdisk
链接：https://pan.baidu.com/s/1yhEwaZhrb2NZRpJ5eEqHBw 
提取码：rgdo

Prepare datasets

CIFAR10

Make sure the folder architecture as follows:

CIFAR10
|
|-----batches.meta  unzip from cifar-10-python.tar.gz
|-----data_batch_1  unzip from cifar-10-python.tar.gz
|-----data_batch_2  unzip from cifar-10-python.tar.gz
|-----data_batch_3  unzip from cifar-10-python.tar.gz
|-----data_batch_4  unzip from cifar-10-python.tar.gz
|-----data_batch_5  unzip from cifar-10-python.tar.gz
|-----readme.html   unzip from cifar-10-python.tar.gz
|-----test_batch    unzip from cifar-10-python.tar.gz

CIFAR100

Make sure the folder architecture as follows:

CIFAR100
|
|-----train unzip from cifar-100-python.tar.gz
|-----test  unzip from cifar-100-python.tar.gz
|-----meta  unzip from cifar-100-python.tar.gz

ImageNet 1K(ILSVRC2012)

Make sure the folder architecture as follows:

ILSVRC2012
|
|-----train----1000 sub classes folders
|-----val------1000 sub classes folders
Please make sure the same class has same class folder name in train and val folders.

ImageNet 21K(Winter 2021 release)

Make sure the folder architecture as follows:

ImageNet21K
|
|-----train-----------10450 sub classes folders
|-----val-------------10450 sub classes folders
|-----small_classes---10450 sub classes folders
|-----imagenet21k_miil_tree.pth
Please make sure the same class has same class folder name in train and val folders.

VOC2007 and VOC2012

Make sure the folder architecture as follows:

VOCdataset
|                 |----Annotations
|                 |----ImageSets
|----VOC2007------|----JPEGImages
|                 |----SegmentationClass
|                 |----SegmentationObject
|        
|                 |----Annotations
|                 |----ImageSets
|----VOC2012------|----JPEGImages
|                 |----SegmentationClass
|                 |----SegmentationObject

COCO2017

Make sure the folder architecture as follows:

COCO2017
|                |----captions_train2017.json
|                |----captions_val2017.json
|--annotations---|----instances_train2017.json
|                |----instances_val2017.json
|                |----person_keypoints_train2017.json
|                |----person_keypoints_val2017.json
|                 
|                |----train2017
|----images------|----val2017

Objects365(v2,2020)

Make sure the folder architecture as follows:

objects365_2020
|
|                |----zhiyuan_objv2_train.json
|--annotations---|----zhiyuan_objv2_val.json
|                |----sample_2020.json
|                 
|                |----train all train patch folders
|----images------|----val   all val patch folders
                 |----test  all test patch folders

ADE20K

Make sure the folder architecture as follows:

ADE20K
|                 |----training
|---images--------|----validation
|                 |----testing
|        
|                 |----training
|---annotations---|----validation

CelebA-HQ

Make sure the folder architecture as follows:

CelebA-HQ
|                 |----female
|---train---------|----male
|        
|                 |----female
|---val-----------|----male

FFHQ

Make sure the folder architecture as follows:

FFHQ
|
|---images
|---ffhq-dataset-v1.json
|---ffhq-dataset-v2.json

Places365-standard/challenge

Make sure the folder architecture as follows:

Places365-standard/challenge
|
|                            |---train_large all sub folders
|---high_resolution_images---|---val_large   all images
|                            |---test_large  all images

How to train and test model

If you want to train or test model,you need enter a training experiment folder directory,then run train.sh or test.sh.

For example,you can enter classification_training/imagenet/resnet50.

If you want to restart train this model,please delete checkpoints and log folders first,then run train.sh:

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.run --nproc_per_node=2 --master_addr 127.0.1.0 --master_port 10000 ../../../tools/train_classification_model.py --work-dir ./

if you want to test this model,you need have a pretrained model first,modify trained_model_path in test_config.py,then run test.sh:

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.run --nproc_per_node=2 --master_addr 127.0.1.0 --master_port 10000 ../../../tools/test_classification_model.py --work-dir ./

CUDA_VISIBLE_DEVICES is used to specify the gpu ids for this training.Please make sure the number of nproc_per_node equal to the number of using gpu cards.Make sure master_addr/master_port are unique for each training.

All checkpoints/log are saved in training/testing experiment folder directory.

Also, You can modify super parameters in train_config.py/test_config.py.

Citation

If you find my work useful in your research, please consider citing:

@inproceedings{zgcr,
 title={SimpleAICV-pytorch-training-examples},
 author={zgcr},
 year={2023}
}

simpleaicv_pytorch_training_examples's People

Contributors

zgcr avatar

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