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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'
])
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