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default_scope = 'mmdet'
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=100),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(
type='CheckpointHook', interval=1, max_keep_ckpts=5, save_best='auto'),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(type='DetVisualizationHook'))
env_cfg = dict(
cudnn_benchmark=False,
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
dist_cfg=dict(backend='nccl'))
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='DetLocalVisualizer',
vis_backends=[dict(type='LocalVisBackend')],
name='visualizer',
save_dir='./')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = '/home/erik/Riksarkivet/Projects/HTR_Pipeline/models/checkpoints/rtmdet_lines_pr_2/epoch_11.pth'
resume = True
train_cfg = dict(
type='EpochBasedTrainLoop',
max_epochs=12,
val_interval=12,
dynamic_intervals=[(10, 1)])
val_cfg = dict(type='ValLoop')
test_cfg = dict(
type='TestLoop',
pipeline=[
dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
dict(type='Resize', scale=(640, 640), keep_ratio=True),
dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
])
param_scheduler = [
dict(
type='LinearLR', start_factor=1e-05, by_epoch=False, begin=0,
end=1000),
dict(
type='CosineAnnealingLR',
eta_min=1.25e-05,
begin=6,
end=12,
T_max=6,
by_epoch=True,
convert_to_iter_based=True)
]
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=0.00025, weight_decay=0.05),
paramwise_cfg=dict(
norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
auto_scale_lr = dict(enable=False, base_batch_size=16)
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
file_client_args = dict(backend='disk')
train_pipeline = [
dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
poly2mask=False),
dict(type='CachedMosaic', img_scale=(640, 640), pad_val=114.0),
dict(
type='RandomResize',
scale=(1280, 1280),
ratio_range=(0.1, 2.0),
keep_ratio=True),
dict(
type='RandomCrop',
crop_size=(640, 640),
recompute_bbox=True,
allow_negative_crop=True),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', prob=0.5),
dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
dict(
type='CachedMixUp',
img_scale=(640, 640),
ratio_range=(1.0, 1.0),
max_cached_images=20,
pad_val=(114, 114, 114)),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
dict(type='Resize', scale=(640, 640), keep_ratio=True),
dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
tta_model = dict(
type='DetTTAModel',
tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.6), max_per_img=100))
img_scales = [(640, 640), (320, 320), (960, 960)]
tta_pipeline = [
dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
dict(
type='TestTimeAug',
transforms=[[{
'type': 'Resize',
'scale': (640, 640),
'keep_ratio': True
}, {
'type': 'Resize',
'scale': (320, 320),
'keep_ratio': True
}, {
'type': 'Resize',
'scale': (960, 960),
'keep_ratio': True
}],
[{
'type': 'RandomFlip',
'prob': 1.0
}, {
'type': 'RandomFlip',
'prob': 0.0
}],
[{
'type': 'Pad',
'size': (960, 960),
'pad_val': {
'img': (114, 114, 114)
}
}],
[{
'type':
'PackDetInputs',
'meta_keys':
('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'flip', 'flip_direction')
}]])
]
model = dict(
type='RTMDet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False,
batch_augments=None),
backbone=dict(
type='CSPNeXt',
arch='P5',
expand_ratio=0.5,
deepen_factor=0.67,
widen_factor=0.75,
channel_attention=True,
norm_cfg=dict(type='SyncBN'),
act_cfg=dict(type='SiLU', inplace=True)),
neck=dict(
type='CSPNeXtPAFPN',
in_channels=[192, 384, 768],
out_channels=192,
num_csp_blocks=2,
expand_ratio=0.5,
norm_cfg=dict(type='SyncBN'),
act_cfg=dict(type='SiLU', inplace=True)),
bbox_head=dict(
type='RTMDetInsSepBNHead',
num_classes=80,
in_channels=192,
stacked_convs=2,
share_conv=True,
pred_kernel_size=1,
feat_channels=192,
act_cfg=dict(type='SiLU', inplace=True),
norm_cfg=dict(type='SyncBN', requires_grad=True),
anchor_generator=dict(
type='MlvlPointGenerator', offset=0, strides=[8, 16, 32]),
bbox_coder=dict(type='DistancePointBBoxCoder'),
loss_cls=dict(
type='QualityFocalLoss',
use_sigmoid=True,
beta=2.0,
loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
loss_mask=dict(
type='DiceLoss', loss_weight=2.0, eps=5e-06, reduction='mean')),
train_cfg=dict(
assigner=dict(type='DynamicSoftLabelAssigner', topk=13),
allowed_border=-1,
pos_weight=-1,
debug=False),
test_cfg=dict(
nms_pre=400,
min_bbox_size=0,
score_thr=0.4,
nms=dict(type='nms', iou_threshold=0.6),
max_per_img=50,
mask_thr_binary=0.5))
train_pipeline_stage2 = [
dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
poly2mask=False),
dict(
type='RandomResize',
scale=(640, 640),
ratio_range=(0.1, 2.0),
keep_ratio=True),
dict(
type='RandomCrop',
crop_size=(640, 640),
recompute_bbox=True,
allow_negative_crop=True),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', prob=0.5),
dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
dict(type='PackDetInputs')
]
train_dataloader = dict(
batch_size=2,
num_workers=1,
batch_sampler=None,
pin_memory=True,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='ConcatDataset',
datasets=[
dict(
type='CocoDataset',
metainfo=dict(classes='text_line', palette=[(220, 20, 60)]),
data_prefix=dict(
img=
'/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/'
),
ann_file=
'/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/gt_files/coco_lines2.json',
pipeline=[
dict(
type='LoadImageFromFile',
file_client_args=dict(backend='disk')),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
poly2mask=False),
dict(
type='CachedMosaic',
img_scale=(640, 640),
pad_val=114.0),
dict(
type='RandomResize',
scale=(1280, 1280),
ratio_range=(0.1, 2.0),
keep_ratio=True),
dict(
type='RandomCrop',
crop_size=(640, 640),
recompute_bbox=True,
allow_negative_crop=True),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', prob=0.5),
dict(
type='Pad',
size=(640, 640),
pad_val=dict(img=(114, 114, 114))),
dict(
type='CachedMixUp',
img_scale=(640, 640),
ratio_range=(1.0, 1.0),
max_cached_images=20,
pad_val=(114, 114, 114)),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
dict(type='PackDetInputs')
])
]))
val_dataloader = dict(
batch_size=1,
num_workers=10,
dataset=dict(
pipeline=[
dict(
type='LoadImageFromFile',
file_client_args=dict(backend='disk')),
dict(type='Resize', scale=(640, 640), keep_ratio=True),
dict(
type='Pad', size=(640, 640),
pad_val=dict(img=(114, 114, 114))),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
],
type='CocoDataset',
metainfo=dict(classes='text_line', palette=[(220, 20, 60)]),
data_prefix=dict(
img=
'/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/'
),
ann_file=
'/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/gt_files/coco_regions2.json',
test_mode=True),
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False))
test_dataloader = dict(
batch_size=1,
num_workers=10,
dataset=dict(
pipeline=[
dict(
type='LoadImageFromFile',
file_client_args=dict(backend='disk')),
dict(type='Resize', scale=(640, 640), keep_ratio=True),
dict(
type='Pad', size=(640, 640),
pad_val=dict(img=(114, 114, 114))),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
],
type='CocoDataset',
metainfo=dict(classes='text_line', palette=[(220, 20, 60)]),
data_prefix=dict(
img=
'/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/'
),
ann_file=
'/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/gt_files/coco_regions2.json',
test_mode=True),
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False))
max_epochs = 12
stage2_num_epochs = 2
base_lr = 0.00025
interval = 12
val_evaluator = dict(
proposal_nums=(100, 1, 10),
metric=['bbox', 'segm'],
type='CocoMetric',
ann_file=
'/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/gt_files/coco_lines2.json'
)
test_evaluator = dict(
proposal_nums=(100, 1, 10),
metric=['bbox', 'segm'],
type='CocoMetric',
ann_file=
'/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/gt_files/coco_lines2.json'
)
custom_hooks = [
dict(
type='EMAHook',
ema_type='ExpMomentumEMA',
momentum=0.0002,
update_buffers=True,
priority=49),
dict(
type='PipelineSwitchHook',
switch_epoch=10,
switch_pipeline=[
dict(
type='LoadImageFromFile',
file_client_args=dict(backend='disk')),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
poly2mask=False),
dict(
type='RandomResize',
scale=(640, 640),
ratio_range=(0.1, 2.0),
keep_ratio=True),
dict(
type='RandomCrop',
crop_size=(640, 640),
recompute_bbox=True,
allow_negative_crop=True),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', prob=0.5),
dict(
type='Pad', size=(640, 640),
pad_val=dict(img=(114, 114, 114))),
dict(type='PackDetInputs')
])
]
work_dir = '/home/erik/Riksarkivet/Projects/HTR_Pipeline/models/checkpoints/rtmdet_lines_pr_2'
train_batch_size_per_gpu = 2
val_batch_size_per_gpu = 1
train_num_workers = 1
num_classes = 1
metainfo = dict(classes='text_line', palette=[(220, 20, 60)])
icdar_2019 = dict(
type='CocoDataset',
metainfo=dict(classes='text_line', palette=[(220, 20, 60)]),
data_prefix=dict(
img=
'/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/ICDAR-2019/clean/'
),
ann_file=
'/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/ICDAR-2019/clean/gt_files/coco_regions2.json',
pipeline=[
dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
poly2mask=False),
dict(type='CachedMosaic', img_scale=(640, 640), pad_val=114.0),
dict(
type='RandomResize',
scale=(1280, 1280),
ratio_range=(0.1, 2.0),
keep_ratio=True),
dict(
type='RandomCrop',
crop_size=(640, 640),
recompute_bbox=True,
allow_negative_crop=True),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', prob=0.5),
dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
dict(
type='CachedMixUp',
img_scale=(640, 640),
ratio_range=(1.0, 1.0),
max_cached_images=20,
pad_val=(114, 114, 114)),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
dict(type='PackDetInputs')
])
icdar_2019_test = dict(
type='CocoDataset',
metainfo=dict(classes='text_line', palette=[(220, 20, 60)]),
data_prefix=dict(
img=
'/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/ICDAR-2019/clean/'
),
ann_file=
'/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/ICDAR-2019/clean/gt_files/coco_lines.json',
test_mode=True,
pipeline=[
dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
dict(type='Resize', scale=(640, 640), keep_ratio=True),
dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
])
police_records = dict(
type='CocoDataset',
metainfo=dict(classes='text_line', palette=[(220, 20, 60)]),
data_prefix=dict(
img=
'/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/'
),
ann_file=
'/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/gt_files/coco_lines2.json',
pipeline=[
dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
poly2mask=False),
dict(type='CachedMosaic', img_scale=(640, 640), pad_val=114.0),
dict(
type='RandomResize',
scale=(1280, 1280),
ratio_range=(0.1, 2.0),
keep_ratio=True),
dict(
type='RandomCrop',
crop_size=(640, 640),
recompute_bbox=True,
allow_negative_crop=True),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', prob=0.5),
dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
dict(
type='CachedMixUp',
img_scale=(640, 640),
ratio_range=(1.0, 1.0),
max_cached_images=20,
pad_val=(114, 114, 114)),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
dict(type='PackDetInputs')
])
train_list = [
dict(
type='CocoDataset',
metainfo=dict(classes='text_line', palette=[(220, 20, 60)]),
data_prefix=dict(
img=
'/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/'
),
ann_file=
'/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/gt_files/coco_lines2.json',
pipeline=[
dict(
type='LoadImageFromFile',
file_client_args=dict(backend='disk')),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
poly2mask=False),
dict(type='CachedMosaic', img_scale=(640, 640), pad_val=114.0),
dict(
type='RandomResize',
scale=(1280, 1280),
ratio_range=(0.1, 2.0),
keep_ratio=True),
dict(
type='RandomCrop',
crop_size=(640, 640),
recompute_bbox=True,
allow_negative_crop=True),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', prob=0.5),
dict(
type='Pad', size=(640, 640),
pad_val=dict(img=(114, 114, 114))),
dict(
type='CachedMixUp',
img_scale=(640, 640),
ratio_range=(1.0, 1.0),
max_cached_images=20,
pad_val=(114, 114, 114)),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
dict(type='PackDetInputs')
])
]
test_list = [
dict(
type='CocoDataset',
metainfo=dict(classes='text_line', palette=[(220, 20, 60)]),
data_prefix=dict(
img=
'/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/ICDAR-2019/clean/'
),
ann_file=
'/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/ICDAR-2019/clean/gt_files/coco_lines.json',
test_mode=True,
pipeline=[
dict(
type='LoadImageFromFile',
file_client_args=dict(backend='disk')),
dict(type='Resize', scale=(640, 640), keep_ratio=True),
dict(
type='Pad', size=(640, 640),
pad_val=dict(img=(114, 114, 114))),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
])
]
pipeline = [
dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
dict(type='Resize', scale=(640, 640), keep_ratio=True),
dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
launcher = 'pytorch'
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