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| # training schedule for 1x | |
| _base_ = [ | |
| '_base_marec_vit_s.py', | |
| '../_base_/datasets/union14m_train.py', | |
| '../_base_/datasets/union14m_benchmark.py', | |
| '../_base_/default_runtime.py', | |
| '../_base_/schedules/schedule_adamw_cos_10e.py', | |
| ] | |
| _base_.model.pop('backbone') | |
| model = dict( | |
| backbone=dict( | |
| type='VisionTransformer_LoRA', | |
| vit_config=dict( | |
| img_size=(32, 128), | |
| patch_size=4, | |
| embed_dim=768, | |
| depth=12, | |
| num_heads=12, | |
| mlp_ratio=4.0, | |
| qkv_bias=True, | |
| pretrained= # noqa | |
| '../mae/mae_pretrained/vit_base/vit_base_checkpoint-19.pth'), | |
| rank=4), | |
| decoder=dict( | |
| type='MAERecDecoder', | |
| n_layers=6, | |
| d_embedding=768, | |
| n_head=8, | |
| d_model=768, | |
| d_inner=3072, | |
| d_k=96, | |
| d_v=96)) | |
| # dataset settings | |
| train_list = [ | |
| _base_.union14m_challenging, _base_.union14m_hard, _base_.union14m_medium, | |
| _base_.union14m_normal, _base_.union14m_easy | |
| ] | |
| val_list = [_base_.union14m_val] | |
| test_list = [ | |
| _base_.union14m_benchmark_artistic, | |
| _base_.union14m_benchmark_multi_oriented, | |
| _base_.union14m_benchmark_contextless, | |
| _base_.union14m_benchmark_curve, | |
| _base_.union14m_benchmark_incomplete, | |
| _base_.union14m_benchmark_incomplete_ori, | |
| _base_.union14m_benchmark_multi_words, | |
| _base_.union14m_benchmark_salient, | |
| _base_.union14m_benchmark_general, | |
| ] | |
| default_hooks = dict(logger=dict(type='LoggerHook', interval=50)) | |
| auto_scale_lr = dict(base_batch_size=512) | |
| train_dataset = dict( | |
| type='ConcatDataset', datasets=train_list, pipeline=_base_.train_pipeline) | |
| test_dataset = dict( | |
| type='ConcatDataset', datasets=test_list, pipeline=_base_.test_pipeline) | |
| val_dataset = dict( | |
| type='ConcatDataset', datasets=val_list, pipeline=_base_.test_pipeline) | |
| train_dataloader = dict( | |
| batch_size=64, | |
| num_workers=12, | |
| persistent_workers=True, | |
| pin_memory=True, | |
| sampler=dict(type='DefaultSampler', shuffle=True), | |
| dataset=train_dataset) | |
| test_dataloader = dict( | |
| batch_size=128, | |
| num_workers=4, | |
| persistent_workers=True, | |
| pin_memory=True, | |
| drop_last=False, | |
| sampler=dict(type='DefaultSampler', shuffle=False), | |
| dataset=test_dataset) | |
| val_dataloader = dict( | |
| batch_size=128, | |
| num_workers=4, | |
| persistent_workers=True, | |
| pin_memory=True, | |
| drop_last=False, | |
| sampler=dict(type='DefaultSampler', shuffle=False), | |
| dataset=val_dataset) | |
| val_evaluator = dict( | |
| dataset_prefixes=['CUTE80', 'IIIT5K', 'SVT', 'SVTP', 'IC13', 'IC15']) | |
| test_evaluator = dict(dataset_prefixes=[ | |
| 'artistic', 'multi-oriented', 'contextless', 'curve', 'incomplete', | |
| 'incomplete-ori', 'multi-words', 'salient', 'general' | |
| ]) | |