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tps_resnet_bilstm_attn.py
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tps_resnet_bilstm_attn.py
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###############################################################################
# 1. deploy
size = (32, 100)
mean, std = 0.5, 0.5
sensitive = False
character = 'abcdefghijklmnopqrstuvwxyz0123456789'
batch_max_length = 25
F = 20
hidden_dim = 256
norm_cfg = dict(type='BN')
num_class = len(character) + 2
num_steps = batch_max_length + 1
deploy = dict(
transform=[
dict(type='Sensitive', sensitive=sensitive, need_character=character),
dict(type='ToGray'),
dict(type='Resize', size=size),
dict(type='Normalize', mean=mean, std=std),
dict(type='ToTensor'),
],
converter=dict(
type='AttnConverter',
character=character,
batch_max_length=batch_max_length,
),
model=dict(
type='GModel',
need_text=True,
body=dict(
type='GBody',
pipelines=[
dict(
type='RectificatorComponent',
from_layer='input',
to_layer='rect',
arch=dict(
type='TPS_STN',
F=F,
input_size=size,
output_size=size,
stn=dict(
feature_extractor=dict(
encoder=dict(
backbone=dict(
type='GVGG',
layers=[
('conv', dict(type='ConvModule', in_channels=1, out_channels=64,
kernel_size=3, stride=1, padding=1, norm_cfg=norm_cfg)),
('pool', dict(type='MaxPool2d', kernel_size=2, stride=2)),
('conv', dict(type='ConvModule', in_channels=64, out_channels=128,
kernel_size=3, stride=1, padding=1, norm_cfg=norm_cfg)),
('pool', dict(type='MaxPool2d', kernel_size=2, stride=2)),
('conv', dict(type='ConvModule', in_channels=128, out_channels=256,
kernel_size=3, stride=1, padding=1, norm_cfg=norm_cfg)),
('pool', dict(type='MaxPool2d', kernel_size=2, stride=2)),
('conv', dict(type='ConvModule', in_channels=256, out_channels=512,
kernel_size=3, stride=1, padding=1, norm_cfg=norm_cfg)),
],
),
),
collect=dict(type='CollectBlock', from_layer='c3')
),
pool=dict(type='AdaptiveAvgPool2d', output_size=1),
head=[
dict(type='FCModule', in_channels=512, out_channels=256),
dict(type='FCModule', in_channels=256, out_channels=F * 2, activation=None)
],
),
),
),
dict(
type='FeatureExtractorComponent',
from_layer='rect',
to_layer='cnn_feat',
arch=dict(
encoder=dict(
backbone=dict(
type='GResNet',
layers=[
('conv', dict(type='ConvModule', in_channels=1, out_channels=32, kernel_size=3,
stride=1, padding=1, norm_cfg=norm_cfg)),
('conv', dict(type='ConvModule', in_channels=32, out_channels=64, kernel_size=3,
stride=1, padding=1, norm_cfg=norm_cfg)),
('pool', dict(type='MaxPool2d', kernel_size=2, stride=2, padding=0)),
('block', dict(block_name='BasicBlock', planes=128, blocks=1, stride=1)),
('conv', dict(type='ConvModule', in_channels=128, out_channels=128, kernel_size=3,
stride=1, padding=1, norm_cfg=norm_cfg)),
('pool', dict(type='MaxPool2d', kernel_size=2, stride=2, padding=0)),
('block', dict(block_name='BasicBlock', planes=256, blocks=2, stride=1)),
('conv', dict(type='ConvModule', in_channels=256, out_channels=256, kernel_size=3,
stride=1, padding=1, norm_cfg=norm_cfg)),
('pool', dict(type='MaxPool2d', kernel_size=2, stride=(2, 1), padding=(0, 1))),
('block', dict(block_name='BasicBlock', planes=512, blocks=5, stride=1)),
('conv', dict(type='ConvModule', in_channels=512, out_channels=512, kernel_size=3,
stride=1, padding=1, norm_cfg=norm_cfg)),
('block', dict(block_name='BasicBlock', planes=512, blocks=3, stride=1)),
('conv', dict(type='ConvModule', in_channels=512, out_channels=512, kernel_size=2,
stride=(2, 1), padding=(0, 1), norm_cfg=norm_cfg)),
('conv', dict(type='ConvModule', in_channels=512, out_channels=512, kernel_size=2,
stride=1, padding=0, norm_cfg=norm_cfg)),
],
),
),
collect=dict(type='CollectBlock', from_layer='c4'),
),
),
dict(
type='SequenceEncoderComponent',
from_layer='cnn_feat',
to_layer='rnn_feat',
arch=dict(
type='RNN',
input_pool=dict(type='AdaptiveAvgPool2d', output_size=(1, None)),
layers=[
('rnn',
dict(type='LSTM', input_size=512, hidden_size=256, bidirectional=True, batch_first=True)),
('fc', dict(type='Linear', in_features=512, out_features=256)),
('rnn',
dict(type='LSTM', input_size=256, hidden_size=256, bidirectional=True, batch_first=True)),
('fc', dict(type='Linear', in_features=512, out_features=256)),
],
),
),
],
),
head=dict(
type='AttHead',
num_class=num_class,
num_steps=num_steps,
cell=dict(
type='LSTMCell',
input_size=256 + num_class,
hidden_size=256,
),
input_attention_block=dict(
type='CellAttentionBlock',
feat=dict(
from_layer='rnn_feat',
type='ConvModule',
in_channels=256,
out_channels=256,
kernel_size=1,
bias=False,
activation=None,
),
hidden=dict(
type='ConvModule',
in_channels=256,
out_channels=256,
kernel_size=1,
activation=None,
),
fusion_method='add',
post=dict(
type='ConvModule',
in_channels=256,
out_channels=1,
kernel_size=1,
bias=False,
activation='tanh',
order=('act', 'conv', 'norm'),
),
post_activation='softmax',
),
generator=dict(
type='Linear',
in_features=256,
out_features=num_class,
),
),
),
)
###############################################################################
# 2.common
common = dict(
seed=1111,
logger=dict(
handlers=(
dict(type='StreamHandler', level='INFO'),
dict(type='FileHandler', level='INFO'),
),
),
cudnn_deterministic=False,
cudnn_benchmark=True,
metric=dict(type='Accuracy'),
)
###############################################################################
dataset_params = dict(
batch_max_length=batch_max_length,
data_filter=True,
character=character,
)
test_dataset_params = dict(
batch_max_length=batch_max_length,
data_filter=False,
character=character,
)
data_root = '../../../../dataset/str/data/data_lmdb_release/'
###############################################################################
# 3. test
batch_size = 192
# data
test_root = data_root + 'evaluation/'
test_folder_names = ['IC15_2077']
# test_folder_names = ['CUTE80', 'IC03_867', 'IC13_1015', 'IC15_2077',
# 'IIIT5k_3000', 'SVT', 'SVTP']
test_dataset = [dict(type='LmdbDataset', root=test_root + f_name,
**test_dataset_params) for f_name in test_folder_names]
test = dict(
data=dict(
dataloader=dict(
type='DataLoader',
batch_size=batch_size,
num_workers=4,
shuffle=False,
),
dataset=test_dataset,
transform=deploy['transform'],
),
postprocess_cfg=dict(
sensitive=sensitive,
character=character,
),
)
###############################################################################
# 4. train
# work directory
root_workdir = 'workdir'
# data
train_root = data_root + 'training/'
# MJ dataset
train_root_mj = train_root + 'MJ/'
mj_folder_names = ['/MJ_test', 'MJ_valid', 'MJ_train']
# ST dataset
train_root_st = train_root + 'ST/'
train_dataset_mj = [dict(type='LmdbDataset', root=train_root_mj + folder_name)
for folder_name in mj_folder_names]
train_dataset_st = [dict(type='LmdbDataset', root=train_root_st)]
# valid
valid_root = data_root + 'validation/'
valid_dataset = dict(type='LmdbDataset', root=valid_root, **dataset_params)
# train transforms
train_transforms = [
dict(type='Sensitive', sensitive=sensitive, need_character=character),
dict(type='ToGray'),
dict(type='Resize', size=size),
dict(type='Normalize', mean=mean, std=std),
dict(type='ToTensor'),
]
max_iterations = 300000
milestones = [150000, 250000]
train = dict(
data=dict(
train=dict(
dataloader=dict(
type='DataLoader',
batch_size=batch_size,
num_workers=4,
),
sampler=dict(
type='BalanceSampler',
batch_size=batch_size,
shuffle=True,
oversample=True,
),
dataset=dict(
type='ConcatDatasets',
datasets=[
dict(
type='ConcatDatasets',
datasets=train_dataset_mj,
),
dict(
type='ConcatDatasets',
datasets=train_dataset_st,
)
],
batch_ratio=[0.5, 0.5],
**dataset_params,
),
transform=train_transforms,
),
val=dict(
dataloader=dict(
type='DataLoader',
batch_size=batch_size,
num_workers=4,
shuffle=False,
),
dataset=valid_dataset,
transform=deploy['transform'],
),
),
optimizer=dict(type='Adadelta', lr=1.0, rho=0.95, eps=1e-8),
criterion=dict(type='CrossEntropyLoss', ignore_index=0),
lr_scheduler=dict(type='StepLR',
milestones=milestones,
),
max_iterations=max_iterations,
log_interval=10,
trainval_ratio=2000,
snapshot_interval=20000,
save_best=True,
resume=None,
)