| 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 = './model.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' | |