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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_6e.py', | |
] | |
model = dict( | |
backbone=dict( | |
type='VisionTransformer', | |
img_size=(32, 128), | |
patch_size=4, | |
embed_dim=768, | |
depth=12, | |
num_heads=12, | |
mlp_ratio=4.0, | |
qkv_bias=True, | |
pretrained=None), | |
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=['CUTE80', 'IIIT5K', 'SVT', 'SVTP', 'IC13', 'IC15']) | |
test_evaluator = dict(dataset_prefixes=[ | |
'artistic', 'multi-oriented', 'contextless', 'curve', 'incomplete', | |
'incomplete-ori', 'multi-words', 'salient', 'general' | |
]) | |