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| # Copyright (c) OpenMMLab. All rights reserved. | |
| # This is a BETA new format config file, and the usage may change recently. | |
| from mmengine.config import read_base | |
| with read_base(): | |
| from .._base_.datasets.imagenet_bs256_beitv2 import * | |
| from .._base_.default_runtime import * | |
| from mmengine.model import ConstantInit, PretrainedInit, TruncNormalInit | |
| from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR | |
| from mmengine.runner import EpochBasedTrainLoop | |
| from torch.optim import AdamW | |
| from mmpretrain.models import (VQKD, BEiT, BEiTPretrainViT, BEiTV2Head, | |
| BEiTV2Neck, CrossEntropyLoss) | |
| vqkd_encoder = dict( | |
| arch='base', | |
| img_size=224, | |
| patch_size=16, | |
| in_channels=3, | |
| out_indices=-1, | |
| drop_rate=0., | |
| drop_path_rate=0., | |
| norm_cfg=dict(type='LN', eps=1e-6), | |
| final_norm=True, | |
| out_type='featmap', | |
| with_cls_token=True, | |
| frozen_stages=-1, | |
| use_abs_pos_emb=True, | |
| use_rel_pos_bias=False, | |
| use_shared_rel_pos_bias=False, | |
| layer_scale_init_value=0., | |
| interpolate_mode='bicubic', | |
| patch_cfg=dict(), | |
| layer_cfgs=dict(), | |
| init_cfg=None) | |
| layer_scale_init_value = 0.1 | |
| drop_path_rate = 0.1 # 0. for 300 epochs and 0.1 for 1600 epochs. | |
| model = dict( | |
| type=BEiT, | |
| backbone=dict( | |
| type=BEiTPretrainViT, | |
| arch='base', | |
| patch_size=16, | |
| out_indices=[-4, -1], | |
| drop_path_rate=drop_path_rate, | |
| final_norm=False, | |
| out_type='raw', | |
| layer_scale_init_value=layer_scale_init_value, | |
| init_cfg=[ | |
| dict(type=TruncNormalInit, std=0.02, layer='Linear'), | |
| dict(type=TruncNormalInit, std=0.02, layer='Conv2d'), | |
| dict(type=ConstantInit, layer='LayerNorm', val=1.0, bias=0.0) | |
| ]), | |
| neck=dict( | |
| type=BEiTV2Neck, | |
| num_layers=2, | |
| early_layers=9, | |
| backbone_arch='base', | |
| drop_path_rate=drop_path_rate, | |
| layer_scale_init_value=layer_scale_init_value, | |
| ), | |
| head=dict( | |
| type=BEiTV2Head, | |
| embed_dims=768, | |
| num_embed=8192, | |
| loss=dict(type=CrossEntropyLoss)), | |
| target_generator=dict( | |
| type=VQKD, | |
| encoder_config=vqkd_encoder, | |
| init_cfg=dict( | |
| type=PretrainedInit, | |
| checkpoint= # noqa | |
| 'https://download.openmmlab.com/mmselfsup/1.x/target_generator_ckpt/vqkd_encoder.pth' # noqa | |
| ))) | |
| # optimizer wrapper | |
| optim_wrapper = dict( | |
| type=AmpOptimWrapper, | |
| loss_scale='dynamic', | |
| # betas: (0.9, 0.98) for 300 epochs and (0.9, 0.999) for 1600 epochs. | |
| optimizer=dict( | |
| type=AdamW, lr=1.5e-3, betas=(0.9, 0.999), weight_decay=0.05), | |
| clip_grad=dict(max_norm=3.0), | |
| paramwise_cfg=dict( | |
| custom_keys={ | |
| # the following configurations are designed for BEiT | |
| '.ln': dict(decay_mult=0.0), | |
| '.bias': dict(decay_mult=0.0), | |
| 'q_bias': dict(decay_mult=0.0), | |
| 'v_bias': dict(decay_mult=0.0), | |
| '.cls_token': dict(decay_mult=0.0), | |
| '.pos_embed': dict(decay_mult=0.0), | |
| '.gamma': dict(decay_mult=0.0), | |
| })) | |
| # learning rate scheduler | |
| param_scheduler = [ | |
| dict( | |
| type=LinearLR, | |
| start_factor=1e-4, | |
| by_epoch=True, | |
| begin=0, | |
| end=10, | |
| convert_to_iter_based=True), | |
| dict( | |
| type=CosineAnnealingLR, | |
| eta_min=1e-5, | |
| by_epoch=True, | |
| begin=10, | |
| end=1600, | |
| convert_to_iter_based=True) | |
| ] | |
| # runtime settings | |
| train_cfg = dict(type=EpochBasedTrainLoop, max_epochs=1600) | |
| default_hooks = dict( | |
| # only keeps the latest 3 checkpoints | |
| checkpoint=dict(type=CheckpointHook, interval=1, max_keep_ckpts=3)) | |
| randomness = dict(seed=0, diff_rank_seed=True) | |
| find_unused_parameters = True | |
| # NOTE: `auto_scale_lr` is for automatically scaling LR | |
| # based on the actual training batch size. | |
| auto_scale_lr = dict(base_batch_size=2048) | |