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epoch_12 (1).pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e356d393c6ed916b2b1ac085b3ef6075e1cbdad0a3148756f03bbcda41f2d658
+size 474957088

rtmdet_m_textregions_2_concat.py

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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 = './epoch_12.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'