deit3-small-p16_8xb256_in1k-224px_gcr8.py

_base_ = ['../_base_/default_runtime.py']

# from ../_base_/models/deit3/deit3-small-p16-224.py
model = dict(
    type='ImageClassifier',
    backbone=dict(
        type='DeiT3',
        arch='s',
        img_size=224,
        patch_size=16,
        drop_path_rate=0.05),
    neck=None,
    head=dict(
        type='GrassmannClsHead',
        gamma=25.0,
        dims=[8] * 1000,
        num_classes=1000,
        in_channels=384,
        loss=dict(type='CrossEntropyLoss', loss_weight=1.0, use_sigmoid=False),
        init_cfg=None,
    ),
    init_cfg=[
        dict(type='TruncNormal', layer='Linear', std=.02),
        dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
    ],
    train_cfg=dict(augments=[
        dict(type='Mixup', alpha=0.8),
        dict(type='CutMix', alpha=1.0)
    ]))

# from ../_base_/datasets/imagenet_bs256_deit3.py
# dataset settings
dataset_type = 'ImageNet'
data_preprocessor = dict(
    num_classes=1000,
    # RGB format normalization parameters
    mean=[123.675, 116.28, 103.53],
    std=[58.395, 57.12, 57.375],
    # convert image from BGR to RGB
    to_rgb=True,
)

bgr_mean = data_preprocessor['mean'][::-1]
bgr_std = data_preprocessor['std'][::-1]

train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='RandomResizedCrop',
        scale=224,
        backend='pillow',
        interpolation='bicubic'),
    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
    dict(type='AutoAugment', policies='3-Augment'),
    dict(type='ColorJitter', brightness=0.3, contrast=0.3, saturation=0.3),
    dict(type='PackInputs'),
]

test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='ResizeEdge',
        scale=224,
        edge='short',
        backend='pillow',
        interpolation='bicubic'),
    dict(type='CenterCrop', crop_size=224),
    dict(type='PackInputs')
]

train_dataloader = dict(
    batch_size=256,
    num_workers=5,
    dataset=dict(
        type=dataset_type,
        data_root='data/imagenet',
        ann_file='meta/train.txt',
        data_prefix='train',
        pipeline=train_pipeline),
    sampler=dict(type='RepeatAugSampler', shuffle=True),
    persistent_workers=True,
)

val_dataloader = dict(
    batch_size=256,
    num_workers=5,
    dataset=dict(
        type=dataset_type,
        data_root='data/imagenet',
        ann_file='meta/val.txt',
        data_prefix='val',
        pipeline=test_pipeline),
    sampler=dict(type='DefaultSampler', shuffle=False),
    persistent_workers=True,
)
val_evaluator = dict(type='Accuracy', topk=(1, 5))

# If you want standard test, please manually configure the test dataset
test_dataloader = val_dataloader
test_evaluator = val_evaluator

# from ../_base_/schedules/imagenet_bs2048_deit3.py
lr = 0.004
# optimizer
optim_wrapper = dict(
    optimizer=dict(
        type='RLambOptimizer',
        lr=lr,
        weight_decay=0.05,
        max_grad_norm=1.0,
        eps=1e-8,
        momentum=0.9,
        lr_factor=0.3 / lr),
    # specific to vit pretrain
    paramwise_cfg=dict(
        norm_decay_mult=0.0,
        bias_decay_mult=0.0,
        flat_decay_mult=0.0,
        custom_keys={
            '.cls_token': dict(decay_mult=0.0),
            '.pos_embed': dict(decay_mult=0.0),
        }),
)

# learning policy
param_scheduler = [
    # warm up learning rate scheduler
    dict(
        type='LinearLR',
        start_factor=1e-6 / lr,
        by_epoch=True,
        begin=0,
        end=5,
        # update by iter
        convert_to_iter_based=False),
    # main learning rate scheduler
    dict(type='CosineAnnealingLR', by_epoch=True, begin=5)
]

# train, val, test setting
train_cfg = dict(by_epoch=True, max_epochs=800, val_interval=1)
val_cfg = dict()
test_cfg = dict()

# NOTE: `auto_scale_lr` is for automatically scaling LR,
# based on the actual training batch size.
auto_scale_lr = dict(base_batch_size=2048)