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publaynet_chargrid.py
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publaynet_chargrid.py
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# model settings
model = dict(
type='ChargridNetLayout',
use_chargrid=True, # for testing raw image input, set to False
is_cat=True, # for testing raw image input, set to False
pretrained=None,
embedding=dict(
type='Embedding',
vocab_size=96,
embedding_dim=64,
),
backbone=dict(
type='ChargridEncoder',
input_channels=99, # for testing raw image input, set to 3
out_indices=(0, 1, 2, 4),
base_channels=64),
neck=dict(
type='ChargridDecoder',
base_channels=64),
rpn_head=dict(
type='RPNHead',
in_channels=64,
feat_channels=64,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.1, 0.2, 0.4, 0.8, 1.6, 3.2],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
roi_head=dict(
type='StandardRoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=64,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='ChargridRegHead',
in_channels=64,
conv_out_channels=64,
roi_feat_size=7,
num_classes=6,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=64,
featmap_strides=[4, 8, 16, 32]),
mask_head=dict(
type='FCNMaskHead',
num_convs=3,
in_channels=64,
conv_out_channels=64,
num_classes=6,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
# model training and testing settings
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.5,
nms=dict(type='nms', iou_threshold=0.3),
max_per_img=100,
mask_thr_binary=0.5)
)
)
# dataset settings
dataset_type = 'PublaynetDataset'
data_root = ''
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='MMLALoadAnnotations',
with_care=True,
with_bbox=True,
with_text=True,
with_label=False,
with_poly_mask=False,
text_profile=dict(
text_max_length=400,
sensitive='lower',
filtered=False
),
with_cbbox=False,
with_cattribute=False,
with_ctexts=False,
with_bbox_2=True,
with_poly_mask_2=True,
with_label_2=True,
# custom_classes=[1, 2, 3],
# custom_classes_2=[1,2],
),
dict(type='DavarResize', img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768), (1333, 800)],
multiscale_mode='value',
keep_ratio=True),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ChargridDataGeneration', with_label=False, poly_shape=False),
dict(type='ChargridFormatBundle'),
dict(type='DavarCollect', keys=['img', 'chargrid_map', 'gt_bboxes_2', 'gt_labels_2', 'gt_masks_2']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='MMLALoadAnnotations',
with_bbox=True,
with_text=True,
with_label=False,
with_care=True,
with_poly_mask=False,
text_profile=dict(
text_max_length=400,
sensitive='lower',
filtered=False
),
with_cbbox=False,
with_cattribute=False,
with_ctexts=False,
with_bbox_2=False,
with_poly_mask_2=False,
with_label_2=False,
# custom_classes=[1, 2, 3],
# custom_classes_2=[1,2],
),
dict(
type='MultiScaleFlipAug',
img_scale=(1300, 800),
flip=False,
transforms=[
dict(type='DavarResize', keep_ratio=True),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
# CharTokenizer for chargrid
dict(
type='ChargridDataGeneration',
# visualize=True,
# vis_save_dir='/path/to/demo/text_ie/chargrid/vis/publaynet/chargrid',
with_label=False,
poly_shape=False),
dict(type='ChargridFormatBundle'),
dict(type='DavarCollect', keys=['img', 'chargrid_map']),
]
)
]
data = dict(
samples_per_gpu=4,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file='/path/to/demo/text_ie/datalist/PubLayNet/sampled_datalist_train_new.json',
img_prefix='/path/to/PubLayNet/',
ann_prefix='/path/to/PubLayNet/Annos/train/',
pipeline=train_pipeline,
classes=('others', 'text', 'title', 'list', 'table', 'figure')),
val=dict(
type=dataset_type,
ann_file='/path/to/demo/text_ie/datalist/PubLayNet/sampled_datalist_val_new.json',
img_prefix='/path/to/PubLayNet/',
ann_prefix='/path/to/PubLayNet/Annos/dev/',
pipeline=test_pipeline,
classes=('text', 'title', 'list', 'table', 'figure'),
coco_ann='/path/to/PubLayNet/Datalist/coco_val.json'),
test=dict(
type=dataset_type,
ann_file='/data1/repo/demo/text_ie/datalist/PubLayNet/sampled_datalist_val_new.json',
img_prefix='/path/to/PubLayNet/',
ann_prefix='/path/to/PubLayNet/Annos/dev/',
pipeline=test_pipeline,
classes=('text', 'title', 'list', 'table', 'figure'),
coco_ann='/path/to/PubLayNet/Datalist/coco_val.json')
)
# optimizer
find_unused_parameters = True
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[15, 30, 40])
runner = dict(type='EpochBasedRunner', max_epochs=50)
checkpoint_config = dict(type='DavarCheckpointHook', interval=1, save_mode='lightweight', metric='bbox_mAP')
# yapf:disable
log_config = dict(
interval=40,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir='/path/to/demo/text_ie/chargrid/log/publaynet_chargrid/'
load_from = None
resume_from = None
workflow = [('train', 1)]
evaluation = dict(save_best='bbox_mAP', interval=1, metric='bbox', rule="greater",)