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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_bs64_swin_224 import * | |
| from ..._base_.schedules.imagenet_bs1024_adamw_swin import * | |
| from ..._base_.default_runtime import * | |
| from mmengine.hooks import CheckpointHook | |
| from mmengine.model import PretrainedInit, TruncNormalInit | |
| from mmengine.optim import CosineAnnealingLR, LinearLR | |
| from torch.optim import AdamW | |
| from mmpretrain.datasets import LoadImageFromFile, PackInputs, RandomFlip | |
| from mmpretrain.engine.optimizers import \ | |
| LearningRateDecayOptimWrapperConstructor | |
| from mmpretrain.models import (BEiTViT, ImageClassifier, LabelSmoothLoss, | |
| LinearClsHead) | |
| from mmpretrain.models.utils.batch_augments import CutMix, Mixup | |
| data_preprocessor = dict( | |
| num_classes=1000, | |
| mean=[127.5, 127.5, 127.5], | |
| std=[127.5, 127.5, 127.5], | |
| to_rgb=True, | |
| ) | |
| # model settings | |
| model = dict( | |
| type=ImageClassifier, | |
| backbone=dict( | |
| type=BEiTViT, | |
| arch='base', | |
| img_size=224, | |
| patch_size=16, | |
| drop_path_rate=0.1, | |
| out_type='avg_featmap', | |
| use_abs_pos_emb=False, | |
| use_rel_pos_bias=True, | |
| use_shared_rel_pos_bias=False, | |
| init_cfg=dict(type=PretrainedInit, checkpoint='', prefix='backbone.')), | |
| neck=None, | |
| head=dict( | |
| type=LinearClsHead, | |
| num_classes=1000, | |
| in_channels=768, | |
| loss=dict(type=LabelSmoothLoss, label_smooth_val=0.1, mode='original'), | |
| init_cfg=[dict(type=TruncNormalInit, layer='Linear', std=0.02)]), | |
| train_cfg=dict( | |
| augments=[dict(type=Mixup, alpha=0.8), | |
| dict(type=CutMix, alpha=1.0)])) | |
| train_pipeline = [ | |
| dict(type=LoadImageFromFile), | |
| dict( | |
| type=RandomResizedCrop, | |
| scale=224, | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type=RandomFlip, prob=0.5, direction='horizontal'), | |
| dict( | |
| type=RandAugment, | |
| policies='timm_increasing', | |
| num_policies=2, | |
| total_level=10, | |
| magnitude_level=9, | |
| magnitude_std=0.5, | |
| hparams=dict(pad_val=[104, 116, 124], interpolation='bicubic')), | |
| dict( | |
| type=RandomErasing, | |
| erase_prob=0.25, | |
| mode='rand', | |
| min_area_ratio=0.02, | |
| max_area_ratio=0.3333333333333333, | |
| fill_color=[103.53, 116.28, 123.675], | |
| fill_std=[57.375, 57.12, 58.395]), | |
| dict(type=PackInputs) | |
| ] | |
| test_pipeline = [ | |
| dict(type=LoadImageFromFile), | |
| dict( | |
| type=ResizeEdge, | |
| scale=256, | |
| edge='short', | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type=CenterCrop, crop_size=224), | |
| dict(type=PackInputs) | |
| ] | |
| train_dataloader = dict(batch_size=128, dataset=dict(pipeline=train_pipeline)) | |
| val_dataloader = dict(batch_size=128, dataset=dict(pipeline=test_pipeline)) | |
| test_dataloader = val_dataloader | |
| # optimizer wrapper | |
| optim_wrapper = dict( | |
| optimizer=dict(type=AdamW, lr=4e-3, weight_decay=0.05, betas=(0.9, 0.999)), | |
| constructor=LearningRateDecayOptimWrapperConstructor, | |
| paramwise_cfg=dict( | |
| _delete_=True, | |
| layer_decay_rate=0.65, | |
| 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=20, | |
| convert_to_iter_based=True), | |
| dict( | |
| type=CosineAnnealingLR, | |
| by_epoch=True, | |
| begin=20, | |
| end=100, | |
| eta_min=1e-6, | |
| convert_to_iter_based=True) | |
| ] | |
| # runtime settings | |
| default_hooks = dict( | |
| # save checkpoint per epoch. | |
| checkpoint=dict(type=CheckpointHook, interval=1, max_keep_ckpts=2)) | |
| train_cfg = dict(by_epoch=True, max_epochs=100) | |
| randomness = dict(seed=0) | |