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publaynet_x101.py
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# model settings
model = dict(
type='VSR',
pretrained=None,
chargrid=dict(
type='ChargridEmbedding',
vocab_size=3500,
embedding_dim=64,
),
sentencegrid=dict(
type='SentencegridEmbedding',
auto_model_path="/path/to/Davar-Lab-OCR/demo/text_layout/VSR/common/bert-base-uncased/",
embedding_dim=64,
),
# multimodal merge
multimodal_merge=dict(
with_img_feat=False,
multimodal_feat_merge=dict(
type='VSRFeatureMerge',
merge_type='Weighted',
visual_dim=[256, 512, 1024, 2048],
semantic_dim=[256, 512, 1024, 2048],
with_extra_fc=False,
),
),
# semantic branch
backbone_semantic=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch'),
# visual branch
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.02, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0],
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='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
roi_head=dict(
type='CascadeRoIHeadWGCN',
num_stages=2,
stage_loss_weights=[1, 0.5],
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=[
dict(
type='Shared2FCBBoxHeadWGCN',
in_channels=256,
fc_out_channels=1024,
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=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0),
),
dict(
type='Shared2FCBBoxHeadWGCN',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=6,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0),
relation_module=dict(
type='GCNHead',
in_channels=1024,
pos_embedding=dict(
type='PositionEmbedding2D',
max_position_embeddings=64,
embedding_dim=1024,
width_embedding=True,
height_embedding=True,
),
gcn_module=dict(
type='BertEncoder',
config=dict(
hidden_size=1024,
num_hidden_layers=2,
num_attention_heads=16,
intermediate_size=1024, # 4 x hidden_size
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
layer_norm_eps=1e-12,
output_attentions=False,
output_hidden_states=False,
is_decoder=False, )),
with_line_cls=False,
with_short_cut=True),
),
],
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
mask_head=dict(
type='FCNMaskHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=6,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
# model training and testing settings
train_cfg = dict(
# box level or layout level
box_level=False,
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=0,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=2000,
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=False,
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),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.6,
neg_iou_thr=0.6,
min_pos_iou=0.6,
match_low_quality=False,
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=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
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=True,
with_poly_mask=False,
text_profile=dict(
text_max_length=400,
sensitive='lower',
filtered=False
),
with_cbbox=True,
with_cattribute=False,
with_ctexts=True,
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),
# CharTokenizer for chargrid
dict(type='CharTokenize', vocab="/path/to/Davar-Lab-OCR/demo/text_layout/datalist/PubLayNet/char_vocab.txt", targets=['gt_ctexts']),
dict(type='MMLAFormatBundle'),
dict(type='DavarCollect', keys=['img', 'gt_bboxes', 'gt_texts', 'gt_bboxes_2', 'gt_labels_2', 'gt_masks_2',
'gt_ctexts', 'gt_cbboxes']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='MMLALoadAnnotations',
with_bbox=True,
with_text=True,
with_label=True,
with_care=True,
with_poly_mask=False,
text_profile=dict(
text_max_length=400,
sensitive='lower',
filtered=False
),
with_cbbox=True,
with_cattribute=False,
with_ctexts=True,
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='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='CharTokenize', vocab="/path/to/Davar-Lab-OCR/demo/text_layout/datalist/PubLayNet/char_vocab.txt",
targets=['gt_ctexts']),
dict(type='MMLAFormatBundle'),
dict(type='DavarCollect', keys=['img', 'gt_bboxes', 'gt_texts', 'gt_ctexts', 'gt_cbboxes']),
]
)
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=['/path/to/Davar-Lab-OCR/demo/text_layout/datalist/PubLayNet/Datalist/datalist_train.json'],
img_prefix='/path/to/Davar-Lab-OCR/demo/text_layout/datalist/PubLayNet/Images/train/',
pipeline=train_pipeline,
ann_prefix='/path/to/Davar-Lab-OCR/demo/text_layout/datalist/PubLayNet/Annos/train/',
classes=('others', 'text', 'title', 'list', 'table', 'figure')),
val=dict(
type=dataset_type,
ann_file=['/path/to/Davar-Lab-OCR/demo/text_layout/datalist/PubLayNet/Datalist/datalist_val.json'],
img_prefix='/path/to/Davar-Lab-OCR/demo/text_layout/datalist/PubLayNet/Images/dev/',
pipeline=test_pipeline,
ann_prefix='/path/to/Davar-Lab-OCR/demo/text_layout/datalist/PubLayNet/Annos/dev/',
classes=('text', 'title', 'list', 'table', 'figure'),
coco_ann='/path/to/Davar-Lab-OCR/demo/text_layout/datalist/PubLayNet/coco_val.json'),
test=dict(
type=dataset_type,
ann_file=['/path/to/Davar-Lab-OCR/demo/text_layout/datalist/PubLayNet/Datalist/datalist_val.json'],
img_prefix='/path/to/Davar-Lab-OCR/demo/text_layout/datalist/PubLayNet/Images/dev/',
pipeline=test_pipeline,
ann_prefix='/path/to/Davar-Lab-OCR/demo/text_layout/datalist/PubLayNet/Annos/dev/',
classes=('text', 'title', 'list', 'table', 'figure'),
coco_ann='/path/to/Davar-Lab-OCR/demo/text_layout/datalist/PubLayNet/coco_val.json')
)
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[3, 4, 5])
runner = dict(type='EpochBasedRunner', max_epochs=6)
checkpoint_config = dict(type='DavarCheckpointHook', interval=1, save_mode='lightweight', metric='bbox_mAP')
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir='/path/to/Davar-Lab-OCR/demo/text_layout/VSR/PubLayNet/log/publaynet_x101/'
load_from = '/path/to/Davar-Lab-OCR/demo/text_layout/VSR/common/mask_rcnn_x101_64x4d_fpn_1x_coco_20200201-9352eb0d_with_semantic.pth'
resume_from = None
workflow = [('train', 1)]
evaluation = dict(save_best='bbox_mAP', interval=1, metric='bbox', rule="greater",)