[Feature]: Add pretraining for FGIA (#607)
* [Feature]: Add pretraining for FGIA * [Fix]: Add requirements and title * [Refactor]: Move readme to root folder * [Feature]: Add cls link * [Fix]: Fix typopull/630/head
Yuan Liu
GitHub
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# Solution of FGIA ACCV 2022 (1st Place)
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## Requirements
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```shell
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PyTorch 1.11.0
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torchvision 0.12.0
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CUDA 11.3
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MMEngine >= 0.1.0
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MMCV >= 2.0.0rc0
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MMClassification >= 1.0.0rc1
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```
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## Preparing the dataset
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First you should refactor the folder of your dataset in the following format:
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```text
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mmselfsup
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|── data
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| |── WebiNat5000
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| | |── meta
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| | | |── train.txt
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| | |── train
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| | |── testa
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| | |── testb
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```
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The `train`, `testa`, and `testb` folders contain the same content with
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those provided by the official website of the competition.
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## Start pre-training
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First, you should install all these requirements, following this [page](https://mmselfsup.readthedocs.io/en/dev-1.x/get_started.html).
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Then change your current directory to the root of MMSelfSup
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```shell
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cd $MMSelfSup
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```
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Then you have the following two choices to start pre-training
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### Slurm
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If you have a cluster managed by Slurm, you can use the following command:
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```shell
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## we use 16 NVIDIA 80G A100 GPUs for pre-training
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GPUS_PER_NODE=8 GPUS=16 SRUN_ARGS=${SRUN_ARGS} bash tools/slurm_train.sh ${PARTITION} ${JOB_NAME} projects/fgia_accv2022_1st/config/mae_vit-large-p16_8xb512-amp-coslr-1600e_in1k.py [optional arguments]
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```
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### Pytorch
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Or you can use the following two commands to start distributed training on two separate nodes:
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```shell
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# node 1
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NNODES=2 NODE_RANK=0 PORT=${MASTER_PORT} MASTER_ADDR=${MASTER_ADDR} bash tools/dist_train.sh projects/fgia_accv2022_1st/config/mae_vit-large-p16_8xb512-amp-coslr-1600e_in1k.py 8
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```
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```shell
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# node 2
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NNODES=2 NODE_RANK=1 PORT=${MASTER_PORT} MASTER_ADDR=${MASTER_ADDR} bash tools/dist_train.sh projects/fgia_accv2022_1st/config/mae_vit-large-p16_8xb512-amp-coslr-1600e_in1k.py 8
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```
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All these logs and checkpoints will be saved under the folder `work_dirs`in the root.
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Then you can use the pre-trained weights to initialize the model for downstream fine-tuning, following this [project](https://github.com/open-mmlab/mmclassification/tree/dev-1.x/projects/projects/fgia_accv2022_1st) in MMClassification.
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model = dict(
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type='MAE',
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data_preprocessor=dict(
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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bgr_to_rgb=True),
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backbone=dict(type='MAEViT', arch='l', patch_size=16, mask_ratio=0.75),
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neck=dict(
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type='MAEPretrainDecoder',
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patch_size=16,
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in_chans=3,
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embed_dim=1024,
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decoder_embed_dim=512,
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decoder_depth=8,
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decoder_num_heads=16,
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mlp_ratio=4.0),
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head=dict(
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type='MAEPretrainHead',
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norm_pix=True,
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patch_size=16,
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loss=dict(type='MAEReconstructionLoss')),
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init_cfg=dict(
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type='Pretrained',
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checkpoint= # noqa: E251
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'https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-large-p16_8xb512-fp16-coslr-1600e_in1k/mae_vit-large-p16_8xb512-fp16-coslr-1600e_in1k_20220825-cc7e98c9.pth' # noqa
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))
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custom_imports = dict(imports='mmcls.datasets', allow_failed_imports=False)
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dataset_type = 'mmcls.ImageNet'
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data_root = 'data/WebiNat5000/'
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file_client_args = dict(backend='disk')
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train_pipeline = [
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dict(type='LoadImageFromFile', file_client_args=file_client_args),
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dict(
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type='RandomResizedCrop',
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size=224,
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scale=(0.2, 1.0),
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backend='pillow',
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interpolation='bicubic'),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PackSelfSupInputs', meta_keys=['img_path'])
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]
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train_dataloader = dict(
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batch_size=256,
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num_workers=16,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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collate_fn=dict(type='default_collate'),
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pin_memory=True,
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dataset=dict(
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type='mmcls.ImageNet',
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data_root='data/WebiNat5000/',
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ann_file='data/WebiNat5000/meta/train.txt',
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data_prefix=dict(img_path='train/'),
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pipeline=[
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dict(type='LoadImageFromFile', file_client_args=file_client_args),
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dict(
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type='RandomResizedCrop',
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size=224,
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scale=(0.2, 1.0),
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backend='pillow',
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interpolation='bicubic'),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PackSelfSupInputs', meta_keys=['img_path'])
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]))
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optimizer = dict(type='AdamW', lr=0.0024, betas=(0.9, 0.95), weight_decay=0.05)
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optim_wrapper = dict(
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type='AmpOptimWrapper',
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optimizer=dict(
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type='AdamW', lr=0.0024, betas=(0.9, 0.95), weight_decay=0.05),
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paramwise_cfg=dict(
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custom_keys=dict(
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ln=dict(decay_mult=0.0),
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bias=dict(decay_mult=0.0),
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pos_embed=dict(decay_mult=0.0),
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mask_token=dict(decay_mult=0.0),
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cls_token=dict(decay_mult=0.0))),
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loss_scale='dynamic')
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param_scheduler = [
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dict(
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type='LinearLR',
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start_factor=0.0001,
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by_epoch=True,
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begin=0,
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end=40,
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convert_to_iter_based=True),
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dict(
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type='CosineAnnealingLR',
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T_max=1560,
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by_epoch=True,
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begin=40,
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end=1600,
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convert_to_iter_based=True)
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]
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train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=1600)
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default_scope = 'mmselfsup'
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default_hooks = dict(
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runtime_info=dict(type='RuntimeInfoHook'),
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timer=dict(type='IterTimerHook'),
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logger=dict(type='LoggerHook', interval=100),
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param_scheduler=dict(type='ParamSchedulerHook'),
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checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=1),
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sampler_seed=dict(type='DistSamplerSeedHook'))
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env_cfg = dict(
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cudnn_benchmark=False,
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mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
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dist_cfg=dict(backend='nccl'))
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log_processor = dict(
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window_size=10,
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custom_cfg=[dict(data_src='', method='mean', windows_size='global')])
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vis_backends = [dict(type='LocalVisBackend')]
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visualizer = dict(
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type='SelfSupVisualizer',
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vis_backends=[dict(type='LocalVisBackend')],
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name='visualizer')
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log_level = 'INFO'
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load_from = None
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resume = False
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randomness = dict(seed=0, diff_rank_seed=True)
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launcher = 'slurm'
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work_dir = './work_dirs/selfsup/mae_vit-large-p16_8xb512-amp-coslr-1600e_in1k'
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